[ { "path": ".git-blame-ignore-revs", "content": "# 2026-03-07 14:05:31 -0500 - markiewicz@stanford.edu - sty: Clean up trailing whitespace [ignore-rev]\nb1ce2c83fdc83e94ef772159d298ca7961dfe629\n" }, { "path": ".gitattributes", "content": "src/bids/_version.py export-subst\n" }, { "path": ".github/codespell_exclude_lines.txt", "content": " \"/RADME\", # wrong filename\n \"/CANGES\", # wrong filename\n \"/dataset_descrption.json\", # wrong filename\n \"/dataset_description.jon\", # wrong extension\n \"/participants.sv\", # wrong extension\n \"/participnts.tsv\", # wrong filename\n \"/particpants.json\" # wrong filename\n \"/participants.son\" # wrong extension\n" }, { "path": ".github/codespell_ignore_words.txt", "content": "complies\nnin\nnwe\nte\n" }, { "path": ".github/dependabot.yml", "content": "# https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file\nversion: 2\nupdates:\n - package-ecosystem: \"github-actions\"\n directory: \"/\"\n schedule:\n interval: \"weekly\"\n groups:\n actions-infrastructure:\n patterns:\n - \"actions/*\"\n - package-ecosystem: \"gitsubmodule\"\n directory: \"/\"\n schedule:\n interval: \"weekly\"\n - package-ecosystem: uv\n directory: \"/\"\n schedule:\n interval: weekly\n groups:\n uv:\n patterns:\n - \"*\"\n" }, { "path": ".github/release-drafter.yml", "content": "template: |\n ## Release Notes\n \n ## CHANGES\n $CHANGES\n" }, { "path": ".github/workflows/build-test-deploy.yml", "content": "name: Python\n\non:\n push:\n branches:\n - main\n - maint/*\n tags:\n - '*'\n pull_request: {}\n schedule:\n # 8am EST / 9am EDT Mondays\n - cron: '0 13 * * 1'\n # Allow job to be triggered manually from GitHub interface\n workflow_dispatch:\n\ndefaults:\n run:\n shell: bash\n\n# Force tox and pytest to use color\nenv:\n FORCE_COLOR: true\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\npermissions:\n contents: read\n\njobs:\n build:\n name: Build & verify package\n runs-on: ubuntu-latest\n permissions:\n attestations: write\n id-token: write\n steps:\n - uses: actions/checkout@v6\n with:\n fetch-depth: 0\n - uses: hynek/build-and-inspect-python-package@v2\n with:\n attest-build-provenance-github: ${{ github.event_name != 'pull_request' }}\n\n test:\n # Check each OS, all supported Python, minimum versions and latest releases\n name: Test\n runs-on: ${{ matrix.os }}\n needs: [build]\n strategy:\n matrix:\n os: ['ubuntu-latest', 'macos-latest']\n python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']\n dependencies: ['full']\n source: ['repo']\n include:\n - os: ubuntu-latest\n python-version: '3.10'\n dependencies: min\n source: 'repo'\n - os: ubuntu-latest\n python-version: '3'\n dependencies: full\n source: 'sdist'\n fail-fast: false\n\n env:\n DEPENDS: ${{ matrix.dependencies }}\n\n steps:\n - uses: actions/checkout@v6\n if: matrix.source == 'repo'\n with:\n submodules: recursive\n fetch-depth: 0\n - name: Download packages built by build-and-inspect-python-package\n if: matrix.source == 'sdist'\n uses: actions/download-artifact@v8\n with:\n name: Packages\n path: dist\n - name: Extract sdist\n if: matrix.source == 'sdist'\n run: tar --strip-components=1 -xzf dist/*.tar.gz\n - name: Install the latest version of uv\n uses: astral-sh/setup-uv@v7\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v6\n with:\n python-version: ${{ matrix.python-version }}\n allow-prereleases: true\n - name: Display Python version\n run: python -c \"import sys; print(sys.version)\"\n - name: Install tox\n run: |\n uv tool install tox --with=tox-uv --with=tox-gh-actions\n - name: Show tox config\n run: tox c\n - name: Run tox\n run: tox -v --exit-and-dump-after 1200\n - uses: codecov/codecov-action@v6\n if: ${{ always() }}\n with:\n token: ${{ secrets.CODECOV_TOKEN }}\n\n test-publish:\n name: Push package to test.pypi.org\n if: github.event_name == 'push'\n runs-on: ubuntu-latest\n needs: [build]\n permissions:\n attestations: write\n id-token: write\n\n steps:\n - name: Download packages built by build-and-inspect-python-package\n uses: actions/download-artifact@v8\n with:\n name: Packages\n path: dist\n\n - name: Upload package to PyPI\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n repository-url: https://test.pypi.org/legacy/\n skip-existing: true\n\n publish:\n name: Publish released package to pypi.org\n if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')\n runs-on: ubuntu-latest\n needs: [test, test-publish]\n permissions:\n attestations: write\n id-token: write\n\n steps:\n - name: Download packages built by build-and-inspect-python-package\n uses: actions/download-artifact@v8\n with:\n name: Packages\n path: dist\n\n - name: Upload package to PyPI\n uses: pypa/gh-action-pypi-publish@release/v1\n" }, { "path": ".github/workflows/codespell.yml", "content": "---\nname: Codespell\n\non:\n push:\n branches: [main]\n pull_request:\n branches: [main]\n\njobs:\n codespell:\n name: Check for spelling errors\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v6\n - uses: codespell-project/actions-codespell@master\n" }, { "path": ".github/workflows/docs.yml", "content": "name: Documentation\n\non:\n push:\n branches:\n - main\n - maint/*\n - rel/*\n - docs/*\n tags:\n - '*'\n pull_request:\n workflow_dispatch:\n\ndefaults:\n run:\n shell: bash\n\n# Force tox to use color\nenv:\n FORCE_COLOR: true\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\npermissions:\n contents: write\n\njobs:\n docs:\n runs-on: ubuntu-latest\n strategy:\n matrix:\n include:\n - python-version: '3.11'\n steps:\n - uses: actions/checkout@v6\n with:\n submodules: recursive\n fetch-depth: 0\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v6\n with:\n python-version: ${{ matrix.python-version }}\n - name: Display Python version\n run: python -c \"import sys; print(sys.version)\"\n - name: Build documentation\n run: pipx run tox -e docs\n - name: Upload docs as artifacts\n uses: actions/upload-artifact@v7\n with:\n path: doc/_build/html\n - name: Deploy (on tags)\n if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')\n uses: peaceiris/actions-gh-pages@v4\n with:\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: doc/_build/html\n force_orphan: true\n user_name: \"BIDS Maintenance\"\n user_email: bids-maintenance@gmail.com\n" }, { "path": ".github/workflows/prerelease.yml", "content": "name: Pre-release\n\non:\n push:\n branches:\n - main\n pull_request:\n branches:\n - main\n schedule:\n # 8am EST / 9am EDT Mondays\n - cron: '0 13 * * 1'\n # Allow job to be triggered manually from GitHub interface\n workflow_dispatch:\n\ndefaults:\n run:\n shell: bash\n\n# Force tox and pytest to use color\nenv:\n FORCE_COLOR: true\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\npermissions:\n contents: read\n\njobs:\n test:\n name: Test\n if: |\n github.event_name != 'pull_request'\n || contains(github.event.pull_request.labels.*.name, 'prerelease')\n runs-on: ${{ matrix.os }}\n strategy:\n matrix:\n os: ['ubuntu-latest', 'macos-latest']\n python-version: ['3.12', '3.13', '3.14']\n fail-fast: false\n\n permissions:\n actions: write\n\n env:\n DEPENDS: pre\n\n steps:\n - uses: actions/checkout@v6\n with:\n submodules: recursive\n fetch-depth: 0\n - name: Install the latest version of uv\n uses: astral-sh/setup-uv@v7\n with:\n python-version: ${{ matrix.python-version }}\n - name: Display Python version\n run: python -c \"import sys; print(sys.version)\"\n - name: Install tox\n run: |\n uv tool install tox --with=tox-uv --with=tox-gh-actions\n - name: Show tox config\n run: tox c\n - name: Run tox\n run: tox -v --exit-and-dump-after 1200\n - uses: codecov/codecov-action@v6\n if: ${{ always() }}\n with:\n token: ${{ secrets.CODECOV_TOKEN }}\n" }, { "path": ".gitignore", "content": "historical/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n**/*.egg-info\n.installed.cfg\n*.egg\npip-wheel-metadata/\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\ncov.xml\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n.ipynb\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Pipenv\nPipfile\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n*.DS_Store\n*.orig\n\n# Sphinx\ndoc/_build/\ndoc/auto_examples/\ndoc/gen_modules/\ndoc/generated/\n\n# Pytest\n.pytest_cache/\n\n# Vim\n.*.sw[op]\n\n# Emacs and others\n*~\n.vscode\n\n# Ignore test sql database files\nbidsdb.sqlite\nfmriprep.sqlite\n" }, { "path": ".gitmodules", "content": "[submodule \"bids-examples\"]\n\tpath = bids-examples\n\turl = https://github.com/bids-standard/bids-examples.git\n\tbranch = master\n" }, { "path": ".mailmap", "content": "Alejandro de la Vega \nAlejandro de la Vega \nAlejandro de la Vega \nAlexander Ivanov \nAlexis Thual \nAli Khan \nAli Khan \nAnkur Sinha \nAntonin Rovai <81312414+arovai@users.noreply.github.com>\nAriel Rokem \nBasile Pinsard \nBertrand Thirion \nChristopher J. Markiewicz \nChristopher J. Markiewicz \nChristopher J. Markiewicz \nChris Holdgraf \nChris Holdgraf \nDimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com>\nDylan M. Nielson \nDylan M. Nielson \nEvgenii Kalenkovich \nGal Ben-Zvi \nGregory Kiar \nHans Johnson \nHans Johnson \nIsla Staden \nJames D. Kent \nJames D. Kent \nJohannes Wennberg \nJon Haitz Legarreta \nKrzysztof J. Gorgolewski \nLee S. Tirrell \nLee S. Tirrell \nMainak Jas \nMatteo Visconti di Oleggio Castello \nMathias Goncalves \nMathias Goncalves Mathias Goncalves \nMichael Philipp Notter \nNick Guenther \nOscar Esteban \nPhilip A Cook \nSatrajit Ghosh \nSatrajit Ghosh \nSteven Tilley II \nSteven Tilley II \nTal Yarkoni \nTaylor Salo \nTaylor Salo \nYaroslav O. Halchenko \n" }, { "path": ".pre-commit-config.yaml", "content": "ci:\n autoupdate_schedule: quarterly\n\nrepos:\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v6.0.0\n hooks:\n - id: trailing-whitespace\n exclude: .gitignore\n - id: end-of-file-fixer\n - id: check-yaml\n - id: check-json\n - id: check-toml\n - id: check-added-large-files\n - repo: https://github.com/astral-sh/ruff-pre-commit\n rev: v0.14.10\n hooks:\n - id: ruff-check\n args: [ \"--fix\", \"--show-fixes\"]\n - id: ruff-format\n" }, { "path": ".zenodo.json", "content": "{\n \"title\": \"PyBIDS: Python tools for BIDS datasets\",\n \"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.\",\n \"creators\": [\n {\n \"affiliation\": \"University of Texas at Austin\",\n \"name\": \"Yarkoni, Tal\",\n \"orcid\": \"0000-0002-6558-5113\"\n },\n {\n \"affiliation\": \"Stanford University\",\n \"name\": \"Markiewicz, Christopher J.\",\n \"orcid\": \"0000-0002-6533-164X\"\n },\n {\n \"affiliation\": \"University of Texas at Austin\",\n \"name\": \"de la Vega, Alejandro\",\n \"orcid\": \"0000-0001-9062-3778\"\n },\n {\n \"affiliation\": \"Department of Psychology, Stanford University\",\n \"name\": \"Gorgolewski, Krzysztof J.\",\n \"orcid\": \"0000-0003-3321-7583\"\n },\n {\n \"affiliation\": \"University of Pennsylvania\",\n \"name\": \"Salo, Taylor\",\n \"orcid\": \"0000-0001-9813-3167\"\n },\n {\n \"affiliation\": \"Université catholique de Louvain\",\n \"name\": \"Gau, Remi\",\n \"orcid\": \"0000-0002-1535-9767\"\n },\n {\n \"affiliation\": \"Dartmouth College\",\n \"name\": \"Halchenko, Yaroslav O.\",\n \"orcid\": \"0000-0003-3456-2493\"\n },\n {\n \"affiliation\": \"CEA\",\n \"name\": \"Papadopoulos Orfanos, Dimitri\",\n \"orcid\": \"0000-0002-1242-8990\"\n },\n {\n \"affiliation\": \"Department of Psychology, Stanford University\",\n \"name\": \"Esteban, Oscar\",\n \"orcid\": \"0000-0001-8435-6191\"\n },\n {\n \"affiliation\": \"University of Texas at Austin\",\n \"name\": \"McNamara, Quinten\"\n },\n {\n \"affiliation\": \"University of Texas at Austin\",\n \"name\": \"Kent, James D.\",\n \"orcid\": \"0000-0002-4892-2659\"\n },\n {\n \"affiliation\": \"University of Oregon\",\n \"name\": \"DeStasio, Krista\",\n \"orcid\": \"0000-0002-3959-9060\"\n },\n {\n \"affiliation\": \"McGill University\",\n \"name\": \"Poline, Jean-Baptiste\",\n \"orcid\": \"0000-0002-9794-749X\"\n },\n {\n \"affiliation\": \"University of Iowa\",\n \"name\": \"Johnson, Hans\",\n \"orcid\": \"0000-0001-9513-2660\"\n },\n {\n \"name\": \"Kalenkovich, Evgenii\",\n \"orcid\": \"0000-0002-4606-4179\"\n },\n {\n \"affiliation\": \"Institute for Problems of Information Transmission\",\n \"name\": \"Petrov, Dmitry\"\n },\n {\n \"affiliation\": \"National Institute of Mental Health\",\n \"name\": \"Nielson, Dylan M.\",\n \"orcid\": \"0000-0003-4613-6643\"\n },\n {\n \"affiliation\": \"Department of Psychology, Stanford University\",\n \"name\": \"Goncalves, Mathias\",\n \"orcid\": \"0000-0002-7252-7771\"\n },\n {\n \"affiliation\": \"Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany\",\n \"name\": \"Appelhoff, Stefan\",\n \"orcid\": \"0000-0001-8002-0877\"\n },\n {\n \"affiliation\": \"Western University, London, ON, CA\",\n \"name\": \"Van Dyken, Peter\",\n \"orcid\": \"0000-0001-7357-5815\"\n },\n {\n \"name\": \"Bansal, Shashank\",\n \"affiliation\": \"Stanford University\",\n \"orcid\": \"0000-0002-1252-8772 \"\n },\n {\n \"affiliation\": \"Concordia University\",\n \"name\": \"Hayot-Sasson, Valérie\",\n \"orcid\": \"0000-0002-4830-4535\"\n },\n {\n \"name\": \"Stewart, Ashley\"\n },\n {\n \"affiliation\": \"MRC Cognition and Brain Sciences Unit\",\n \"name\": \"Carlin, Johan\",\n \"orcid\": \"0000-0003-0933-1239\"\n },\n {\n \"affiliation\": \"Montreal Neurological Institute and Hospital\",\n \"name\": \"Kiar, Gregory\",\n \"orcid\": \"0000-0001-8915-496X\"\n },\n {\n \"affiliation\": \"Alan Turing Institute; Department of Psychiatry, University of Cambridge\",\n \"name\": \"Whitaker, Kirstie\",\n \"orcid\": \"0000-0001-8498-4059\"\n },\n {\n \"affiliation\": \"MIT, HMS\",\n \"name\": \"Ghosh, Satrajit\",\n \"orcid\": \"0000-0002-5312-6729\"\n },\n {\n \"affiliation\": \"Psychoinformatics Lab, Institute of Psychology, Otto-von-Guericke University Magdeburg\",\n \"name\": \"Wagner, Adina\",\n \"orcid\": \"0000-0003-2917-3450\"\n },\n {\n \"affiliation\": \"Montreal Neurological Institute and Hospital\",\n \"name\": \"DuPre, Elizabeth\",\n \"orcid\": \"0000-0003-1358-196X\"\n },\n {\n \"name\": \"Khan, Ali\",\n \"orcid\": \"0000-0002-0760-8647\"\n },\n {\n \"name\": \"Janke, Andrew\",\n \"orcid\": \"0000-0002-6314-9027\"\n },\n {\n \"affiliation\": \"University of Pennsylvania\",\n \"name\": \"Cook, Philip A\",\n \"orcid\": \"0000-0002-1985-8475\"\n },\n {\n \"name\": \"Ivanov, Alexander\"\n },\n {\n \"affiliation\": \"Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation; University of Queensland\",\n \"name\": \"Gillman, Ashley\",\n \"orcid\": \"0000-0001-9130-1092\"\n },\n {\n \"affiliation\": \"KTH Royal Institute of Technology\",\n \"name\": \"Wennberg, Johannes\"\n },\n {\n \"affiliation\": \"CorticoMetrics LLC\",\n \"name\": \"Tirrell, Lee S.\",\n \"orcid\": \"0000-0001-9393-8361\"\n },\n {\n \"affiliation\": \"Holland Bloorview Kids Rehabilitation Hospital\",\n \"name\": \"Tilley II, Steven\",\n \"orcid\": \"0000-0003-4853-5082\"\n },\n {\n \"name\": \"Li, Adam\",\n \"affiliation\": \"Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA\",\n \"orcid\": \"0000-0001-8421-365X\"\n },\n {\n \"affiliation\": \"Child Mind Institute\",\n \"name\": \"Erkent, Alp\",\n \"orcid\": \"0000-0002-8820-4782\"\n },\n {\n \"affiliation\": \"University of Montréal, Montréal, Canada\",\n \"name\": \"Pinsard, Basile\",\n \"orcid\": \"0000-0002-4391-3075\"\n },\n {\n \"affiliation\": \"Stanford University\",\n \"name\": \"Mumford, Jeanette\",\n \"orcid\": \"0000-0002-0926-3531\"\n },\n {\n \"affiliation\": \"Université de Sherbrooke\",\n \"name\": \"Legarreta, Jon Haitz\",\n \"orcid\": \"0000-0002-9661-1396\"\n },\n {\n \"affiliation\": \"Charite Universitatsmedizin Berlin, Germany\",\n \"name\": \"Waller, Lea\",\n \"orcid\": \"0000-0002-3239-6957\"\n },\n {\n \"affiliation\": \"Télécom ParisTech, France\",\n \"name\": \"Jas, Mainak\",\n \"orcid\": \"0000-0002-3199-9027\"\n },\n {\n \"affiliation\": \"Otto-von-Guericke-University Magdeburg, Germany\",\n \"name\": \"Hanke, Michael\",\n \"orcid\": \"0000-0001-6398-6370\"\n },\n {\n \"affiliation\": \"Polytechnique Montréal\",\n \"name\": \"Guenther, Nick\"\n },\n {\n \"affiliation\": \"Stanford University\",\n \"name\": \"Poldrack, Russell\",\n \"orcid\": \"0000-0001-6755-0259\"\n },\n {\n \"affiliation\": \"The University of Washington eScience Institute\",\n \"name\": \"Rokem, Ariel\",\n \"orcid\": \"0000-0003-0679-1985\"\n },\n {\n \"affiliation\": \"Ottawa Hospital Research Institute\",\n \"name\": \"Boulay, Chadwick\",\n \"orcid\": \"0000-0003-1747-3931\"\n },\n {\n \"affiliation\": \"CEA, Inria\",\n \"name\": \"Thual, Alexis\"\n },\n {\n \"affiliation\": \"Berkeley Institute for Data Science; University of California at Berkeley\",\n \"name\": \"Holdgraf, Chris\",\n \"orcid\": \"0000-0002-2391-0678\"\n },\n {\n \"affiliation\": \"Queen Mary University London\",\n \"name\": \"Staden, Isla\",\n \"orcid\": \"0000-0002-0795-1154\"\n },\n {\n \"name\": \"Staph, Jason A.\",\n \"orcid\": \"0000-0002-5226-3350\"\n },\n {\n \"affiliation\": \"Brigham and Women's Hospital\",\n \"name\": \"Drew, William\",\n \"orcid\": \"0000-0002-9178-8731\"\n },\n {\n \"affiliation\": \"University College London\",\n \"name\": \"Sinha, Ankur\",\n \"orcid\": \"0000-0001-7568-7167\"\n },\n {\n \"affiliation\": \"National Institutes of Health\",\n \"name\": \"Galassi, Anthony\",\n \"orcid\": \"0000-0001-6550-4574\"\n },\n {\n \"affiliation\": \"Université Libre de Bruxelles - Hôpital Erasme\",\n \"name\": \"Rovai, Antonin\",\n \"orcid\": \"0000-0001-6781-5341\"\n },\n {\n \"affiliation\": \"University of Pennsylvania\",\n \"name\": \"Adebimpe, Azeez\",\n \"orcid\": \"0000-0001-9049-0135\"\n },\n {\n \"affiliation\": \"INRIA\",\n \"name\": \"Thirion, Bertrand\",\n \"orcid\": \"0000-0001-5018-7895\"\n },\n {\n \"affiliation\": \"Univ Rennes, Inria, CNRS, Inserm\",\n \"name\": \"Clénet, Boris\",\n \"orcid\": \"0009-0006-7727-5100\"\n },\n {\n \"affiliation\": \"Stanford University\",\n \"name\": \"Shain, Cory\",\n \"orcid\": \"0000-0002-2704-7197\"\n },\n {\n \"affiliation\": \"Rutgers University\",\n \"name\": \"Kleinschmidt, Dave F.\",\n \"orcid\": \"0000-0002-7442-2762\"\n },\n {\n \"name\": \"Ferdman, Emmanuel\",\n \"orcid\": \"0009-0004-8953-0151\"\n },\n {\n \"affiliation\": \"Centre for Addiction and Mental Health, University of Toronto, Canada\",\n \"name\": \"Dickie, Erin W\",\n \"orcid\": \"0000-0003-3028-9864\"\n },\n {\n \"affiliation\": \"Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich\",\n \"name\": \"Raimondo, Fede\",\n \"orcid\": \"0000-0003-4087-8259\"\n },\n {\n \"affiliation\": \"Sagol School of Neuroscience, Tel Aviv University\",\n \"name\": \"Ben-Zvi, Gal\",\n \"orcid\": \"0000-0002-5655-9423\"\n },\n {\n \"affiliation\": \"NIMH IRP\",\n \"name\": \"Lee, John A.\",\n \"orcid\": \"0000-0001-5884-4247\"\n },\n {\n \"name\": \"Kruper, John\"\n },\n {\n \"affiliation\": \"UC Berkeley\",\n \"name\": \"Visconti di Oleggio Castello, Matteo\",\n \"orcid\": \"0000-0001-7931-5272\"\n },\n {\n \"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\",\n \"name\": \"Notter, Michael Philipp\",\n \"orcid\": \"0000-0002-5866-047X\"\n },\n {\n \"affiliation\": \"Sainte-Anne Hospital Center, Université Paris Descartes\",\n \"name\": \"Roca, Pauline\",\n \"orcid\": \"0000-0003-2089-6636\"\n },\n {\n \"affiliation\": \"Stanford University\",\n \"name\": \"Blair, Ross\",\n \"orcid\": \"0000-0003-3007-1056\"\n },\n {\n \"affiliation\": \"University of Pennsylvania\",\n \"name\": \"Pati, Sarthak\",\n \"orcid\": \"0000-0003-2243-8487\"\n },\n {\n \"name\": \"Sundaravadivelu, Suganya\"\n },\n {\n \"name\": \"Asciutto, Will\"\n },\n {\n \"name\": \"pierre-nedelec\"\n }\n ],\n \"keywords\": [\n \"BIDS\",\n \"brain imaging data structure\",\n \"neuroscience\",\n \"neuroimaging\",\n \"neuroinformatics\",\n \"python\"\n ],\n \"license\": \"mit-license\",\n \"upload_type\": \"software\"\n}\n" }, { "path": "CHANGELOG.rst", "content": "Changelog\n=========\n\nVersion 0.22.0 (April 22, 2026)\n-------------------------------\n\nNew feature release in the 0.22.x series.\n\nThis release introduces schema-based layout configurations::\n\n from bids.layout import BIDSLayout\n\n layout = BIDSLayout('/path/to/dataset', config='bids-schema')\n\nThis will use the version of the schema included in\nthe installed version of bidsschematools_.\nThe schema configuration will be the way to incorporate updates from\nnew versions of BIDS (v1.11+), and will eventually be made the default\nconfiguration.\nPlease try it out and report any issues you encounter\nto help us make the transition as smooth as possible.\nWe are particularly interested in reports from\nusers who supply custom configurations.\n\nThe following dependencies have been updated to new minimum versions:\n\n* NumPy 1.25\n* SciPy 1.11\n* NiBabel 5.1\n\n* ENH: Sort BIDS file name entities (#1220)\n* ENH: Use BIDS schema to \"drive\" pybids #818 (#1163)\n* DOC: Fix documentation deployment (#1224)\n* MNT: Add Python 3.14 tests (#1175)\n* MNT: Skip pre-release checks on unlabeled PRs (#1188)\n* MNT: Format with Ruff (#1231)\n* MNT: Dependabot updates (#1232, #1233, #1234, #1236, #1237, #1238, #1239, #1240, #1242)\n\nVersion 0.21.0 (October 16, 2025)\n---------------------------------\n\nNew feature release in the 0.21.x series.\n\nThis release fixes compatibility with ``univeral_pathlib`` 0.3.\nThe following dependencies have been updated to new minimum versions:\n\n* Python 3.10\n* NumPy 1.24\n* SciPy 1.10\n* NiBabel 5.0\n* Pandas 2.0\n\nMerged pull requests:\n\n* FIX: UPath protocol mismatch (#1183)\n* MNT: Drop Python 3.9 support (#1184)\n\nVersion 0.20.0 (September 24, 2025)\n-----------------------------------\n\nNew feature release in the 0.20.x series.\n\nThis release finalizes some deprecations that were started in the 0.7 and 0.14 series.\nIn particular, passing indexer arguments to ``BIDSLayout`` and disabling\n``absolute_paths`` mode in ``BIDSLayout()`` or ``BIDSLayout.get()`` will now error.\nTo adjust indexing behavior, create an explicit ``BIDSLayoutIndexer()`` and pass\n``BIDSLayout(..., indexer=indexer)``.\nTo access relative paths, use the ``BIDSFile.relative_path`` property on\n``BIDSLayout.get()`` return values.\n\n* FIX: Check for attribute that can be absent before assigning (#1165)\n* FIX: Accommodate deprecations in the upcoming Pandas 3 release (#1173)\n* FIX: Repaired convolution issue that arises when onset values are repeated (#1133)\n* ENH: StatsModels: Add `invalid_inputs` parameter to allow dropping of missing data columns to run nodes (#1145)\n* ENH: Add missing root-level path patterns (#1169)\n* ENH: Parse ``seg-`` entities from dseg or probseg files (#1172)\n* DOC: Update link to derivatives config file (#1132)\n* MNT: Address warnings produced by pybids and dependencies (#1136)\n* MNT: Lock test environment, update with dependabot (#1134)\n\nVersion 0.19.0 (March 05, 2025)\n-------------------------------\n\nNew feature release in the 0.19.x series.\n\nThis release adds plus signs to labels and suffixes, anticipating the\nrelease of BIDS 1.10.1 or 1.11.0 (whichever is next).\nUntil the BIDS specification is updated and the ``bidsschematools``\npackage containing the updated schema is published, files with ``+`` will\nnot validate, but can still be indexed with ``BIDSLayout(..., validate=False)``.\n\nSeveral small fixes resolving issues with BIDS Stats Models are included,\nand complex JSON metadata (lists and objects) will now play more nicely with\ncontexts where they need to be hashed.\n\n* FIX: Catch StopIteration on directory walk (#1124)\n* FIX: Ensure dotfiles are always filtered (#1123)\n* FIX: Copy entities dict before modifying run variable type (#1120)\n* FIX: Allow numpy arrays to treat PaddedInt like int (#1119)\n* FIX: Make lists and dicts hashable (#1112)\n* ENH: Allow plus signs in labels (#1117)\n* MNT: Remove formulaic upper bound (#1121)\n* MNT: Use tox-uv to ease lower bound checking, test on Python 3.13 (#1109)\n\nVersion 0.18.1 (December 05, 2024)\n----------------------------------\n\nBug-fix release in the 0.18.x series.\n\n0.18.0 was missing package data, which went unnoticed because the workflow\nto publish to PyPI was also broken. These have been fixed, and we no longer\nalter global logging state.\n\nWith thanks to Fede Raimondo for the contribution.\n\n* FIX: Install package data (#1108)\n* FIX: Remove calls to root logger basicConfig (#1106)\n* MNT: Upload attested package to (test) PyPI (#1110)\n\nVersion 0.18.0 (November 12, 2024)\n----------------------------------\n\nNew feature release in the 0.18.x series.\n\nNo actual new features, but this version moves test data out of the\ninstalled distribution and ratchets its dependencies on Python and\nthe Scientific Python stack, justifying a minor release.\n\n* FIX: Improve compatibility with cloud paths (#1094)\n* MNT: Transition to src/ layout (#1099)\n* MNT: Update minimum Python (3.9) and dependencies (SPEC 0 + 1yr) (#1092)\n* MNT: Update test for BIDS-MRS (#1089)\n\nVersion 0.17.2 (September 19, 2024)\n-----------------------------------\n\nBug-fix release in the 0.17.x series.\n\n* BUG: symlink dirs treated as files during indexing (#1087)\n\nVersion 0.17.1 (September 06, 2024)\n-----------------------------------\n\nBug-fix release in the 0.17.x series.\n\nMostly quiets down warnings generated by more recent versions of Pandas.\n\n* FIX: Create indices with object dtype (#1084)\n* MNT: Transfer validation tests to bids validator (#1081)\n\nVersion 0.17.0 (July 29, 2024)\n------------------------------\n\nFeature release in the 0.17.x series.\n\nThis release includes experimental support for MRS (BEP22) datasets\nand `universal_pathlib `__.\n\n* ENH: pathlib -> universal_pathlib to enable cloud (and other filesystem) support (#1074)\n* ENH: Add MRS entities, path patterns (#1075)\n* STY: No parentheses after assert (#1065)\n* MNT: Bump bids-examples from `47c4da1` to `401f4cf` (#1073)\n* MNT: Fix typo newly found by codespell (#1066)\n\nVersion 0.16.5 (May 22, 2024)\n-----------------------------\n\nBug-fix release in the 0.16.x series.\n\nThis release includes minor fixes, but reworks the testing\ninfrastructure and resolves Python 3.12 incompatibilities.\n\n* FIX: Handle FileNotFoundError in BIDSLayout init when is_derivative=True and validate=False (#1049)\n* FIX: Add dwi root sbref, missing dwi entities to path patterns (#1059)\n* ENH: Update indexer to treat .zarr as files not dirs (#1046)\n* DOC: Deal with several warnings and errors in the doc build (#1061)\n* TST: Add bids-examples tests to cover ieeg, eeg, dwi (#1060)\n* MNT: Add Python 3.12 support (#1057)\n* MNT: Use tox to simplify testing environment setup (#1055)\n* MNT: Bump codecov/codecov-action from 3 to 4 (#1041)\n* MNT: Bump bids-examples from `1a000d6` to `eff47f1` (#1039)\n* MNT: Bump bids-examples from `b6e5234` to `1a000d6` (#1038)\n* MNT: Bump actions/upload-artifact from 3 to 4 (#1037)\n* MNT: Bump actions/setup-python from 4 to 5 (#1036)\n\nVersion 0.16.4 (November 30, 2023)\n----------------------------------\n\nBug-fix release in the 0.16.x series.\n\nThis release resolves a bug likely introduced in 0.9.0\n(when PyBIDS started using SQL),\nin which boolean metadata fields with values of ``False`` were read in as\n``True``.\n\n* FIX: Ensure values of boolean metadata fields are preserved (#1034)\n* FIX: update config for PET tracer entity (#1018)\n* ENH: Clearer BIDSValidation error message (#1023)\n* STY: pacify codespell (#1029)\n* CI: Build(deps): Bump bids-examples from `064db11` to `b6e5234` (#1031)\n* CI: Build(deps): Bump bids-examples from `d039879` to `064db11` (#1028)\n* CI: Build(deps): Bump bids-examples from `bfcc5c6` to `d039879` (#1027)\n* CI: Bump bids-examples from `502c917` to `bfcc5c6` (#1026)\n* CI: Bump bids-examples from `1c30c6e` to `502c917` (#1025)\n* CI: Bump bids-examples from `e857597` to `1c30c6e` (#1021)\n* CI: Bump bids-examples from `c557d1f` to `e857597` (#1019)\n* CI: Bump bids-examples from `75968b9` to `c557d1f` (#1011)\n* CI: Bump actions/checkout from 3 to 4 (#1024)\n\nVersion 0.16.3 (August 16, 2023)\n--------------------------------\n\nBug-fix release in the 0.16.x series.\n\nThis release resolves a regression introduced in 0.16.2 when JSON sidecar files\ncontain ``null`` values.\n\n* FIX: Drop JSON sidecar fields with null values during metadata indexing (#1016)\n\nVersion 0.16.2 (August 11, 2023)\n--------------------------------\n\nBug-fix and performance release in the 0.16.x series.\nNotably, indexing is now significantly faster (2-2.5x).\n\n* ENH: Speed up indexing by bulk committing to database (#1013)\n* Include indexer in add_derivatives call (#1009)\n* Bump bids-examples from `8550641` to `75968b9` (#1008)\n* Bump bids-examples from `e96b702` to `8550641` (#1007)\n* Bump bids-examples from `d8455af` to `e96b702` (#1005)\n* Bump bids-examples from `bc36231` to `d8455af` (#1003)\n* CI: Use OIDC publishing instead of tokens (#1001)\n\nVersion 0.16.1 (May 01, 2023)\n-----------------------------\n\nBug-fix release in the 0.16.x series.\n\n* FIX: Improve parsing of suffix/extension in non-BIDS files (#997)\n* FIX: Support use of Query enums in filter lists (#994)\n* MNT: Clean up imports in documentation scripts (#980)\n* MNT: update zenodo (#995)\n* CI: Update Python version for doc/sdist/wheel tests (#996)\n\nVersion 0.16.0 (April 26, 2023)\n-------------------------------\n\nNew feature release in the 0.16.x series.\n\n* FIX: Adapt to SQLAlchemy 1.4+ (#985)\n* FIX: Fill na with zero for VIF (#976)\n* ENH: Only warn if missing values not explicitly set (#977)\n* ENH: Create run reports of whole graph / create HTML reports (#918)\n* ENH: Speed up Sparse to Dense BIDSVariable conversion (#969)\n* ENH: Update config to support motion and nirs modalities (#965)\n* REF: Add derivative subdatasets in a less ad hoc fashion (#954)\n* MNT: Update minimum dependencies, add CI job to test them (#987)\n* MNT: Drop jinja2/markupsafe constraints (#986)\n* MNT: Fix ruff issue E712 (#981)\n* MNT: A few Python3 updates (#971)\n* MNT: Drop Python 3.7 EOL (#974)\n* MNT: Fix repeat-word typo (#968)\n* MNT: Update vendored inflect to 5.6.2 (#961)\n* MNT: Fix flake8 issues found by ruff (#955)\n* MNT: Remove (large) unused PNG from doc directory (#962)\n* MNT: http:// → https:// (#960)\n* MNT: Update git submodules (#964)\n* CI: Drop codecov PyPI package and appveyor config (#979)\n* CI: Add Python 3.11 (#975)\n* CI: Replace custom update_submodules action with dependabot action (#967)\n* CI: fix errors caused by changes in examples (#963)\n* CI: Update parameters to gh-action-pypi-publish (#957)\n\nVersion 0.15.6 (February 28, 2023)\n----------------------------------\n\nBug-fix release in 0.15.x series.\n\n* FIX: Exclude non-Hashables from set in .get(return_type='id') (#948)\n* FIX: Broken pattern matching (#936)\n* FIX: Coerce oversampling parameter in convolution to int (#929)\n* FIX: Allowing custom BIDS configuration files for derivative datasets (#922)\n* FIX: Address change in default pandas grouping behavior (#917)\n* ENH: Speed up BIDSLayout.get(return_type='id', ...) (#942)\n* ENH: Update default anat filename patterns in config (#933)\n* DOC: Make clear how to ignore folders (#945)\n* DOC: Fix tutorial link in README (#944)\n* DOC: Mention conda installation in README (#928)\n* MNT: Update git submodules (#946 + #943 + #932)\n* MNT: Move package metadata to pyproject.toml, unvendor versioneer (#907)\n* MNT: Bump formulaic max version constraint to include 0.5.x (#916)\n\nVersion 0.15.5 (November 07, 2022)\n----------------------------------\n\nBug-fix release in 0.15.x series.\n\n* FIX: Use kwargs for DataFrame.pivot() (#913)\n* ENH: Add transformation history to ``BIDSStatsModelsNodeOutput`` (#905)\n* ENH: Update examples, add to sphinx, and convert to .md (#908)\n* ENH: More helpful __repr__ in modeling (#906)\n* MNT: Update git submodules (#911)\n* MNT: Bump peter-evans/create-pull-request from 3 to 4 (#904)\n* CI: Automatically update GitHub Actions in the future (#903)\n\nVersion 0.15.4 (October 08, 2022)\n---------------------------------\n\nBug-fix release in 0.15.x series.\n\n* FIX: Use 'contrasts' in DummyContrasts, not 'condition_list' (#902)\n* FIX: Transformations: sync_kwargs, for non-list arguments, listify by variable length (#901)\n* CI: Update actions versions (#899)\n\nVersion 0.15.3 (September 12, 2022)\n-----------------------------------\n\nBug-fix release in 0.15.x series.\n\n* FIX: Test and update sync_kwargs class variable on transformations (#891)\n* FIX: Filtering of DM columns (#892)\n* ENH: Enable using participants.tsv, scans.tsv & session.tsv values as grouping variables (#894)\n* REF: Rename incoming single contrast column to intercept (#886)\n* DOC: Update references to nistats with references to nilearn in docs (#882)\n* TST: some sort of QA for derivatives' default_path_patterns (#879)\n* MNT: Update git submodules (#887)\n* MNT: Codespell errors (#885)\n* MNT: Update git submodules (#884)\n* MNT: Update git submodules (#883)\n* CI: Update actions versions (#897)\n\nVersion 0.15.2 (July 25, 2022)\n------------------------------\n\nBug-fix release in 0.15.x series.\n\n* FIX: Synchronize kwargs in transformations, e.g., in Assign (#836)\n* FIX: Add HRF derivatives to variable collection when created (#838)\n* FIX: Interpolate derivative pipeline name in error message (#847)\n* FIX: Match only within relative path when indexer is validating (#859)\n* FIX: Rename intercept in DummyContrast (#866)\n* FIX: Add variables to correct Node in load_tsv_variables (#872)\n* FIX: Repair variable IO so scans.tsv is found (#869)\n* FIX: Restore automodel functionality (#853)\n* FIX: Allow ``pybids layout --derivatives`` to be a boolean flag or accept a path (#848)\n* ENH: Add default path patterns to derivatives.json (#605)\n* ENH: Rename statsmodels contrasts if they cannot be uniquely identified downstream (#861)\n* ENH: Make intercept-only first level models throw a NotImplementedError (#854)\n* ENH: Update config to support microscopy, qMRI, PET, ASL (#840)\n* TEST: Add bids-examples submodule and bids_examples pytest fixture (#842)\n* TEST: Add tests for default path patterns (#846)\n* TEST: Update bids-examples and remove expected failing tests (#845)\n* MNT: Update git submodules (#874)\n* MNT: Add workflow top automatically update submodule (#870)\n* MNT: Remove deprecated no dot config (#841)\n* CI: Upgrade several GitHub actions to v3 (#867)\n* CI: Add cron jobs to avoid sneaky failures during low activity periods (#860)\n\nVersion 0.15.1 (April 04, 2022)\n-------------------------------\n\nBug-fix release in the 0.15.x series.\n\n* RF/FIX: Decompose filter construction for special queries and lists (#826)\n* ENH: Relax group_by rules, allowing any entity to be used (#829)\n* MNT: Replace deprecated DataFrame.append call (#833)\n* DOC: Fresh rerun of entire pybids_tutorial.ipynb (#832)\n\nVersion 0.15.0 (March 28, 2022)\n-------------------------------\n\nNew feature release in the 0.15.x series.\n\nDownstream tools should be aware of a potentially breaking, albeit\nlong-demanded, change introduced in #819. Run indices are now stored\nso that the integers that come out retain any zero-padding that was\nfound during parsing.\n\nThis release also introduces the ``bids.ext`` namespace package that\nallows independent packages to install modules in this namespace.\nThis is an infrastructural change that will allow some components to\nbe separately managed and follow a different development pace.\n\n* FIX: Allow grouping by run and session when entities are undefined (#822)\n* FIX: Clarify exception message (#806)\n* FIX: Catch UnicodeDecodeErrors along with JSONDecodeErrors for better reporting (#796)\n* FIX: Accept paths/strings for layout configuration files (#799)\n* FIX: Small typo: repeated word in docstring (#793)\n* ENH: Retain zero-padding in run entities while preserving integer queries and comparisons (#819)\n* ENH: Add bids.ext namespace package for subpackages (#820)\n* ENH: Handle wildcards in model X (#810)\n* ENH: Implement automatic detection of derivative data (#805)\n* ENH: Add new ``Query`` for optional entities (#809)\n* ENH: Add __main__ to allow ``python -m bids`` to run CLI (#794)\n* REF: Improve modularization of bids.reports (#617)\n* DOC: Link from sphinx documentation to notebook tutorials. (#797)\n* MNT: Test on Python 3.10, various CI updates (#824)\n* MNT: Avoid jinja2 v3 until nbconvert handles breakages (#823)\n\nVersion 0.14.1 (March 29, 2022)\n-------------------------------\nBug-fix release in the 0.14.x series.\n\n* RF/FIX: Decompose filter construction for special queries and lists (#826)\n\nIncludes the following back-ports from 0.15.0:\n\n* FIX: Clarify exception message (#806)\n* FIX: Catch UnicodeDecodeErrors along with JSONDecodeErrors for better reporting (#796)\n* FIX: Accept paths/strings for layout configuration files (#799)\n* ENH: Add __main__ to allow ``python -m bids`` to run CLI (#794)\n\nVersion 0.14.0 (November 09, 2021)\n----------------------------------\n\nNew feature release in the 0.14.x series.\n\nThis release includes a significant refactor of BIDS Statistical Models,\nreplacing the ``bids.analysis`` module with ``bids.modeling``.\n\nChanges to ``bids.layout`` are minimal, and we do not anticipate API breakage.\n\n* FIX: LGTM.com warning: Implicit string concatenation in a list (#785)\n* FIX: Take the intersection of variables and Model.X,\n ignoring missing variables (usually contrasts) (#764)\n* FIX: Associate \"is_metadata\" with Tag, not Entity; and only return\n non-metadata entries for core Entities in ``get(return_type='id')`` (#749)\n* FIX: Only include regressors if they are TSV (#752)\n* FIX: ensure force_dense=True runs to_dense only on sparse variables (#745)\n* FIX: get unique, with conflicting meta-data (#748)\n* FIX: Clean up some deprecation and syntax warnings (#738)\n* ENH: Add ``pybids upgrade`` command (#654)\n* ENH: Add Lag transformation (#759)\n* ENH: Use indirect transformations structure (#737)\n* ENH: Add visualization for statsmodel graph (#742)\n* ENH: Permit explicit intercept (1) in Contrasts and DummyContrasts (#743)\n* ENH: Add meta-analysis model type (#731)\n* ENH: Contrast type is now test (#733)\n* REF: Use pathlib.Path internally when possible (#746)\n* REF: Remove group_by from edges and add filter (#734)\n* REF: Improved/refactored StatsModels module (#722)\n* MNT: Make sure codespell skips .git when run locally (#787)\n* MNT: LGTM.com recommendations (#786)\n* MNT: Better codespell configuration (#782)\n* MNT: Constrain formulaic version to 0.2.x . (#784)\n* MNT: Update versioneer: 0.18 → 0.20 (#778)\n* MNT: Add \"codespell\" tool to CI checks to catch typos sooner (#776)\n* MNT: Disable bids-nodot mode (#769)\n* MNT: Send codecov reports again (#766)\n* MNT: Set minimum version to Python 3.6 (#739)\n\nVersion 0.13.2 (August 20, 2021)\n--------------------------------\n\nBug-fix release in the 0.13 series.\n\n* FIX/TEST: gunzip regressors.tsv.gz, allow timeseries.tsv as well (#767)\n* FIX: run is required (#762)\n* MNT: Patch 0.13.x maint branch (#763)\n\nVersion 0.13.1 (May 21, 2021)\n-----------------------------\n\nBug-fix release in the 0.13 series.\n\n* ENH: Improve `get` performance (#723)\n* STY: Fix typos identified by codespell (#720)\n* TEST: dataset-level model spec retrieval (#693)\n\nVersion 0.13 (April 14, 2021)\n-----------------------------\n\nNew feature release in the 0.13 series.\n\n* FIX: Resample to n_vols for sampling_rate == 'TR' (#713)\n* FIX: Lazily load metadata, skip when missing data files are missing sidecars (#711)\n* FIX: Ensure indicator matrix is boolean when indexing in Split transformation (#710)\n* FIX: Correctly pair metadata file when creating association records (#699)\n* FIX: Resolve side-effects of new testfile in #682 (#695)\n* FIX: BIDSLayout -- TypeError: unhashable type: 'dict' (#682)\n* ENH: Add res/den entities to derivatives.json (#709)\n* ENH: Update datatypes (#708)\n* ENH: add more informative validation error message for dataset_description.json (#705)\n* ENH: Add flip, inv, mt, and part entities (#688)\n* CI: Run packaging tests on main repository only (#696)\n* CI: Migrate to GH actions (#691)\n\nVersion 0.12.4 (November 10, 2020)\n----------------------------------\n\nBug-fix release in the 0.12.x series.\n\n* FIX: Do not error when popping missing ``scan_length`` (#679)\n\nVersion 0.12.3 (October 23, 2020)\n---------------------------------\n\n* FIX: Require aligned input for logical operations (#649)\n* ENH: Incremental variable loading in Step setup (#672)\n\nVersion 0.12.2 (October 09, 2020)\n---------------------------------\n\nBug-fix release in 0.12.x series.\n\n* FIX: Support nibabel < 3 when calculating time series length (#669)\n* FIX: Sanitize single derivative Path passed to BIDSLayout (#665)\n* FIX: Force UTF-8 encoding in _index_metadata (#663)\n* FIX: Explicitly convert to HTML when testing tutorial (nbconvert 6.0 breakage) (#664)\n\nVersion 0.12.1 (September 09, 2020)\n-----------------------------------\n\n* FIX: drop obsolete test (#652)\n* FIX: Convolve zero-duration (impulse) events when variable contains multiple events (#645)\n* ENH: Add CLI to PyBIDS (#650)\n* ENH: Add relpath attribute to BIDSFile and associated refactoring (#647)\n* DOC: Add example for using parse_file_entities from bids.layout (#639)\n* MNT: Remove deprecated database_file argument to BIDSLayout (#644)\n\nVersion 0.12.0 (August 04, 2020)\n--------------------------------\nNew feature release for the 0.12.x series.\n\nThis release includes significant speedups for ``BIDSLayout`` creation and improves\nhandling of GIFTI and CIFTI-2 derivative files.\n\n* FIX: Remove need to eager load associations (#642)\n* ENH: Fetch number of time points from NIfTI, GIFTI or CIFTI-2 (#637)\n* ENH: Catch any NIfTI/GIFTI (incl. CIFTI-2) files as BIDSImageFiles (#638)\n\nVersion 0.11.1 (July 02, 2020)\n------------------------------\nBug-fix release in the 0.11.x series\n\nWith thanks to Mathias Goncalves for finding and fixing the issue.\n\n* FIX: Preserve ``get`` logic when using custom config (#636)\n\nVersion 0.11.0 (June 29, 2020)\n------------------------------\nNew feature release in the 0.11.x series.\n\nOne significant change in this PR is to add the configuration option ``extension_initial_dot``.\nSetting to ``True`` will adopt the future default behavior of returning ``extension`` entities with\nan initial dot (``.``).\n\nOther notable changes include a significant refactoring of the Analysis module, and a number of\nsmall improvements to error reporting that should add up to simpler debugging for users and\ndevelopers.\n\n* FIX: Post-fix And and Or transformations with underscore (#628)\n* FIX: made _validate_file work on Windows (#627)\n* FIX: Scale transformation fails if passed constant input. (#614)\n* FIX: Certain queries involving multiple layouts are very slow (#616)\n* FIX: Bug in get() when passing enums as extensions (#612)\n* FIX: Bug in BIDSDataFile.get_df() (#611)\n* FIX: Make entity assertions Python 3.5-safe (#606)\n* FIX: BIDSLayout.build_path to respect absolute_paths. (#580)\n* ENH: Adds OS-level file copying instead of reading/writing via Python (#613)\n* ENH: Add explicit warning message when users pass in filters as a dictionary keyword (#623)\n* ENH: Introduce PyBIDS exceptions (#615)\n* ENH: Add example bids and derivatives dataset_description.json strings to error messages (#620)\n* ENH: Improved handling of invalid filters (#610)\n* ENH: Add method to generate report from list of files (#607)\n* ENH: Pass kwargs from BIDSImageFile.get_image() to nibabel.load (#601)\n* ENH: Model spec module and associated utilities (#548)\n* ENH: Add BIDSMetadata dictionary to report file with missing metadata (#593)\n* RF: Add ``extension_initial_dot`` config option to transition to extension entity with initial dot (#629)\n* MNT: Automatically deploy docs (#598)\n* CI: Drop --pre check for Python 3.5 (#621)\n* CI: Test on Python 3.8 (#594)\n\nVersion 0.10.2 (February 26, 2020)\n----------------------------------\nBug fix release in the 0.10.x series.\n\n* FIX: Add Replace as exception to recursive JSON conversion (#589)\n\nVersion 0.10.1 (February 10, 2020)\n----------------------------------\nBug fix release in the 0.10.x series.\n\nThis release just makes available some of the latest minor fixes and improvements.\n\n* FIX: Replace ``os.path.sep`` with ``fwdslash`` because bids validator hardcodes fwd (#582)\n* FIX: Refactor of ``build_path`` and inner machinery (#574)\n* FIX: Domain entity, and slow ``__repr__`` (#569)\n* FIX: \"strict\" helptext in ``BIDSLayout.write_contents_to_file`` (#566)\n* FIX: typos in helpstrings and comments (#564)\n* FIX: Correct term \"caret\" to \"angle bracket\" in helpstrings (#565)\n* ENH: Extend ``build_path`` to generate lists of files (#576)\n* ENH: Add one parametric test of ``BIDSLayout.build_path`` (#577)\n* ENH: Enable partial metadata indexing (#560)\n* ENH: Upscale to collection sampling rate prior to resampling (#568)\n* ENH: Calculate default sampling rate for ``SparseRunVariable.to_dense`` (#571)\n* MNT: Add ``.vscode`` (for Visual Studio Code) to ``.gitignore`` (#562)\n* MNT: Ignore pip-wheel-metadata (#581)\n* DOC: Remove Python 2 support statement, now that v0.10.0 has dropped it (#561)\n\nVersion 0.10.0 (December 03, 2019)\n----------------------------------\nNew feature release in the 0.10.x series.\n\nThis release removes Python 2 support.\n\n* ENH: Helpful error for db argument mismatch, and add classmethod load_from_db (#547)\n* ENH: Add Resample transformation (#373)\n* ENH: Save BIDSLayout + Derivatives to folder (with init arguments) (#540)\n* ENH: Adds support for NONE and ANY query Enums (#542)\n* ENH: Transformation-related improvements (#541)\n* ENH: FEMA contrasts (#520)\n* STY: Minor PEP8 Fixes (#545)\n* MNT: Various (#543)\n* MNT: Remove Python 2.7 support (#524)\n* CI: Configure Circle Artifact Redirector (#544)\n\nVersion 0.9.5 (November 6, 2019)\n--------------------------------\nBug fix release in the 0.9.x series.\n\nFinal planned release with Python 2 support.\n\n* FIX: Filter before downsampling (#529)\n* FIX: Copy input dict in ``replace_entities``\\ ; Typos in ``default_path_patterns`` (#517)\n* FIX: Use string dtype for all entities when using regex search (#511)\n* FIX: Update Sphinx docs for 2.2 (#507)\n* ENH: Enable automatic derivative database caching (#523)\n* ENH: Raise ValueError if BIDSLayout.build_path fails to match any pattern (#508)\n* RF: Subclass analysis Node from object (#528)\n* DOC: Unify docstring format (#499)\n* DOC: Auto-generate stubs (#513)\n* STY: .sql is a misleading extension to sqlite files (#531)\n* STY: General cleanups (#526, #527)\n\nVersion 0.9.4 (September 20, 2019)\n----------------------------------\nBug fix release in the 0.9.x series.\n\n* FIX: Ignore ``default_ignore`` paths by default (#495)\n* FIX: Filter and sort on scalar metadata in ``get_nodes()`` (#488)\n* FIX: Automatically sanitize dtype of ``get()`` arguments (#492)\n* FIX: Check that ``default_path_patterns`` is not ``None`` before using in ``build_path`` (#485)\n* FIX: Add CBV and phase modalities to func datatype path pattern (#479)\n* FIX: Drop bold suffix constraint from echo entity (#477)\n* ENH: Enforce dtypes on spec-defined columns when reading in DFs (#494)\n* ENH: Validate built paths (#496)\n* ENH: Update contrast enhancing agent query name (ceagent) (#497)\n* DOC: Add citation information to README (#493)\n* DOC: Improve wording in Python notebook example comment (#484)\n* DOC: Finish sentence in Python Notebook example documentation (#483)\n* DOC: Add JOSS Badge (#472)\n* STY: Apply syntax highlight to Python notebook example doc (#482)\n* MAINT: Move setup configuration to setup.cfg (#448)\n* MAINT: Additional Zenodo metadata (#470)\n* MAINT/CI: Use ``extras_require`` to declare dependencies for tests (#471)\n\nVersion 0.9.3 (August 7, 2019)\n------------------------------\nThis version includes a number of minor fixes and improvements, one of which\nbreaks the existing API (by renaming two entities; see #464). Changes\ninclude:\n\n* FIX: Avoid DB collisions for redundant entities (#468)\n* FIX: Minor changes to entity names in core spec (#464)\n* FIX: Make bids.reports work properly with .nii images (#463)\n* CI: Execute notebook in Travis (#461)\n* ENH: More sensible **repr** for Tag model (#467)\n\nVersion 0.9.2 (July 12, 2019)\n-----------------------------\nThis version includes a number of minor fixes and improvements.\nEEG files are better handled, and ``BIDSLayout`` and ``BIDSFile`` play more\nnicely with ``Path``\\ -like objects.\n\nWith thanks to new contributor Cecile Madjar.\n\n* FIX: Instantiate ``ignore``\\ /\\ ``force_index`` after root validation (#457)\n* FIX: Restore ``=None`` query returning files lacking the entity (#458)\n* ENH: Add ``BIDSJSONFile`` (#444)\n* ENH: Add ``BIDSFile.__fspath__`` to work with pathlib (#449)\n* ENH: Add ``eeg`` datatype to layout config (#455)\n* RF: Remove unused kwargs to BIDSFile (#443)\n* DOC: Improve docstring consistency, style (#443)\n* DOC: Address final JOSS review (#453)\n* STY: PEP8 Fixes (#456)\n* MAINT: Set name explicitly in setup.py (#450)\n\nVersion 0.9.1 (May 24, 2019)\n----------------------------\nHotfix release:\n\n* Fixed package deployment issues (#446)\n* Updated author list (#447)\n\nThanks to new contributors Erin Dickie, Chadwick Boulay and Johannes Wennberg.\n\nVersion 0.9.0 (May 21, 2019)\n----------------------------\nVersion 0.9 replaces the native Python backend with a SQLite database managed\nvia SQLAlchemy. The layout module has been refactored (again), but API changes\nare minimal. This release also adds many new features and closes a number of\nopen issues.\n\nAPI CHANGES/DEPRECATIONS:\n\n* The ``extensions`` argument has now been banished forever; instead, use\n ``extension``\\ , which is now defined as a first-class entity. The former will\n continue to work until at least the 0.11 release (closes #404).\n* Relatedly, values for ``extension`` should no longer include a leading ``.``\\ ,\n though this should also continue to work for the time being.\n* The ``BIDSLayout`` init argument ``index_associated`` has been removed as the\n various other filtering/indexing options mean there is longer a good reason for\n users to manipulate this.\n* ``bids.layout.MetadataIndex`` no longer exists. It's unlikely that anyone will\n notice this.\n* ``BIDSLayout.get_metadata()`` no longer takes additional entities as optional\n keyword arguments (they weren't necessary for anything).\n* Direct access to most ``BIDSFile`` properties is discouraged, and in one case\n is broken in 0.9 (for ``.metadata``\\ , which was unavoidable, because it's reserved\n by SQLAlchemy). Instead, users should use getters (\\ ``get_metadata``\\ , ``get_image``\\ ,\n ``get_df``\\ , etc.) whenever possible.\n\nNEW FUNCTIONALITY:\n\n* All file and metadata indexing and querying is now supported by a\n relational (SQLite) database (see #422). While this has few API implications,\n the efficiency of many operations is improved, and complex user-generated\n queries can now be performed via the SQLAlchemy ``.session`` stored in each\n ``BIDSLayout``.\n* Adds ``.save()`` method to the ``BIDSLayout`` that saves the current SQLite DB\n to the specified location. Conversely, passing a filename as ``database_file`` at\n init will use the specified store instead of re-indexing all files. This\n eliminates the need for a pickling strategy (#435).\n* Related to the above, the ``BIDSLayout`` init adds a ``reset_database`` argument\n that forces reindexing even if a ``database_file`` is specified.\n* The ``BIDSLayout`` has a new ``index_metadata`` flag that controls whether or\n not the contents of JSON metadata files are indexed.\n* Added ``metadata`` flag to ``BIDSLayout.to_df()`` that controls whether or not\n metadata columns are included in the returned pandas ``DataFrame`` (#232).\n* Added ``get_entities()`` method to ``BIDSLayout`` that allows retrieval of all\n ``Entity`` instances available within a specified scope (#346).\n* Adds ``drop_invalid_filters`` argument to ``BIDSLayout.get()``\\ , enabling users to\n (optionally) ensure that invalid filters don't clobber all search results\n (#402).\n* ``BIDSFile`` instances now have a ``get_associations()`` method that returns\n associated files (see #431).\n* The ``BIDSFile`` class has been split into a hierarchy, with ``BIDSImageFile``\n and ``BIDSDataFile`` subclasses. The former adds a ``get_image()`` method (returns\n a NiBabel image); the latter adds a ``get_df()`` method (returns a pandas DF).\n All ``BIDSFile`` instances now also have a ``get_entities()`` method.\n\nBUG FIXES AND OTHER MINOR CHANGES:\n\n* Metadata key/value pairs and file entities are now treated identically,\n eliminating a source of ambiguity in search (see #398).\n* Metadata no longer bleeds between raw and derivatives directories unless\n explicitly specified (see #383).\n* ``BIDSLayout.get_collections()`` no longer drops user-added columns (#273).\n* Various minor fixes/improvements/changes to tests.\n* The tutorial Jupyter notebook has been fixed and updated to reflect the\n latest changes.\n\nVersion 0.8.0 (February 15, 2019)\n---------------------------------\nVersion 0.8 refactors much of the layout module. It drops the grabbit\ndependency, overhauls the file indexing process, and features a number of other\nimprovements. However, changes to the public API are very minimal, and in the\nvast majority of cases, 0.8 should be a drop-in replacement for 0.7.*.\n\nAPI-BREAKING CHANGES:\n\n* Changes to (rarely-used) BIDSLayout initialization arguments:\n\n * ``include`` and ``exclude`` have been replaced with ``ignore`` and\n ``force_index``. Paths passed to ``ignore`` will be ignored from indexing;\n paths passed to ``force_index`` will be forcibly indexed even if they are\n otherwise BIDS-non-compliant. ``force_index`` takes precedence over ``ignore``.\n\n* Most querying/selection methods add a new ``scope`` argument that controls\n scope of querying (e.g., ``'raw'``\\ , ``'derivatives'``\\ , ``'all'``\\ , etc.). In some\n cases this replaces the more limited ``derivatives`` argument.\n\n* No more ``domains``\\ : with the grabbit removal (see below), the notion of a\n ``'domain'`` has been removed. This should impact few users, but those who need\n to restrict indexing or querying to specific parts of a BIDS project should be\n able to use the ``scope`` argument more effectively.\n\nOTHER CHANGES:\n\n* FIX: Path indexing issues in ``get_file()`` (#379)\n* FIX: Duplicate file returns under certain conditions (#350)\n* FIX: Pass new variable args as kwargs in split() (#386) @effigies\n* TEST: Update naming conventions for synthetic dataset (#385) @effigies\n* REF: The grabbit package is no longer a dependency; as a result, much of the\n functionality from grabbit has been ported over to pybids.\n* REF: Required functionality from six and inflect is now bundled with pybids\n in ``bids.external``\\ , minimizing external dependencies.\n* REF: Core modules have been reorganized. Key data structures and containers\n (e.g., ``BIDSFile``\\ , ``Entity``\\ , etc.) are now in a new ``bids.layout.core`` module.\n* REF: A new ``Config`` class has been introduced to house the information\n found in ``bids.json`` and other layout configuration files.\n* REF: The file-indexing process has been completely refactored. A new\n hierarchy of ``BIDSNode`` objects has been introduced. While this has no real\n impact on the public API, and isn't really intended for public consumption yet,\n it will in future make it easier for users to work with BIDS projects in a\n tree-like way, while also laying the basis for a more sensible approach to\n reading and accessing associated BIDS data (e.g., .tsv files).\n* MNT: All invocations of ``pd.read_table`` have been replaced with ``read_csv``.\n\nVersion 0.7.1 (February 01, 2019)\n---------------------------------\nThis is a bug fix release in the 0.7 series. The primary API change is improved\nhandling of ``Path`` objects.\n\n* FIX: Path validation (#342)\n* FIX: Ensure consistent entities at all levels (#326)\n* FIX: Edge case where a resampled column was too-long-by-one (#365)\n* FIX: Use BIDS metadata for TR over nii header (#357)\n* FIX: Add check for ``run_info`` to be a list, pass ``run_info`` in correct position. (#353)\n* FIX: If ``sampling_rate`` is ``'auto'``\\ , set to first rate of ``DenseRunVariables`` (#351)\n* FIX: Get the absolute path of the test data directory (#347)\n* FIX: Update reports to be 0.7-compatible (#341)\n* ENH: Rename ``sr`` variable to more intuitive ``interval`` (#366)\n* ENH: Support ``pathlib.Path`` and other ``str``\\ -castable types (#307)\n* MNT: Updates link to derivative config file in notebook (#344)\n* MNT: Add bids-validator dependency (#363)\n* MNT: Require pandas >= 0.23.0 (#348)\n* MNT: Bump grabbit version (#338)\n* CI: Ignore OSX Python 3.5 failures (#372)\n* CI: Build with Python 3.7 on Travis, deploy on 3.6 (#337)\n\nVersion 0.7.0 (January 10, 2019)\n--------------------------------\nThis is a major, API-breaking release. It introduces a large number of new features, bug fixes, and improvements.\n\nAPI-BREAKING CHANGES:\n\n* A number of entities (or keywords) have been renamed to align more closely with the BIDS specification documents:\n * 'type' becomes 'suffix'\n * 'modality' becomes 'datatype'\n * 'acq' is removed (use 'acquisition')\n * 'mod' becomes 'modality'\n* The following directories are no longer indexed by default: derivatives/, code/, stimuli/, models/, sourcedata/. They must be explicitly included using the 'include' initialization argument.\n* The grabbids module has been renamed to layout and BIDSLayout.py and BIDSvalidator.py are now layout.py and validation.py, respectively.\n* The BIDS validator is now enabled by default at layout initialization (i.e., ``validate=True``\\ )\n* The ``exclude`` initialization argument has been removed.\n* ``BIDSLayout.parse_entities`` utility has been removed (use the more flexible ``parse_file_entities``\\ ).\n* 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.\n* 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.\n\nNEW FEATURES:\n\n* 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)\n* A new BIDSFile wrapper provides easy access to ``.metadata`` and ``.image``\n* HRF convolution is now supported via bundling of nistats' hemodynamic_models module; convolution is handled via the ``convolve_HRF`` transformation.\n* Named config paths that customize how projects are processed can be added at run-time (#313)\n* Preliminary support for BIDS-Derivatives RC1 (mainly core keywords)\n\nMINOR IMPROVEMENTS AND BUG FIXES:\n\n* Specifying 'derivatives' in a path specification now automatically includes 'bids' (#246)\n* Zenodo DOIs are now minted with new releases (#308)\n* Variable loading via load_variables can now be done incrementally\n* Expanded and improved path-building via ``layout.build_path()``\n* ``get_collections`` no longer breaks when ``merge=True`` and the list is empty (#202)\n* Layout initialization no longer fails when ``validate=True`` (#222)\n* The auto_contrasts field in the modeling tools now complies with the BIDS-Model spec (#234)\n* Printing a ``BIDSFile`` now provides more useful information, including path (#298)\n* Resample design matrix to 1/TR by default (#309)\n* Fix the Sum transformation\n* Ensure that resampling works properly when a sampling rate is passed to ``get_design_matrix`` (#297)\n* Propagate derivative entities into top-level dynamic getters (#306)\n* Deprecated ``get_header`` call in nibabel removed (#300)\n* Fix bug in entity indexing for ``BIDSVariableCollection`` (#319)\n* Exclude modules with heavy dependencies from root namespace for performance reasons (#321)\n* Fix bug that caused in-place updating of input selectors in ``Analysis`` objects (#323)\n* Add a DropNA transformation (#325)\n* Add a ``get_tr()`` method to ``BIDSLayout`` (#327)\n* Add entity hints when calling ``get()`` with a ``target`` argument (#328)\n* Improved test coverage\n\nVersion 0.6.5 (August 21, 2018)\n-------------------------------\n\n* FIX: Do not drop rows of NaNs (#217) @adelavega\n* FIX: Declare run as having integer type (#236) @effigies\n* ENH: MEG support (#229) @jasmainak\n* REF: rename grabbids to layout, closes #228 (#230) @ltirrell\n* DOC: add .get_collection examples to tutorial (#219) @Shotgunosine\n* DOC: Fix link in README to point to documentation (#223) @KirstieJane\n* DOC: Add binder link for tutorial (#225) @KirstieJane\n* MAINT: Restore \"analysis\" installation extra (#218) @yarikoptic\n* MAINT: Do not import tests in __init__.py (#226) @tyarkoni\n\nVersion 0.5.1 (March 9, 2018)\n-----------------------------\nHotfix release:\n\n* Includes data files omitted from 0.5.0 release.\n* Improves testing of installation.\n\nVersion 0.5.0 (March 6, 2018)\n-----------------------------\nThis is a major release that introduces the following features:\n\n* A new ``bids.variables`` module that adds the following submodules:\n * ``bids.variables.entities.py``\\ : Classes for representing BIDS hierarchies as a graph-like structure.\n * ``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.).\n * ``bids.variables.io.py``\\ : Tools for loading variable data from BIDS projects.\n * ``bids.variables.collections``\\ : Containers that facilitate aggregation and manipulation of ``Variable`` classes.\n* Extensions to the ``BIDSLayout`` class that make it easy to retrieve data/variables from the project (i.e., ``Layout.get_collections``\\ )\n* A new ``auto_model`` utility that generates simple BIDS-Model specifications from BIDS projects (thanks to @Shotgunosine)\n* A new ``reports`` module that generates methods sections from metadata in BIDS projects (thanks to @tsalo)\n* Experimental support for copying/writing out files in a BIDS-compliant way\n* Expand ``bids.json`` config file to include missing entity definitions\n* Ability to parse files without updating the Layout index\n* Updated grabbids module to reflect grabbit changes that now allow many-to-many mapping of configurations to folders\n* Too many other minor improvements and bug fixes to list (when you're very lazy, even a small amount of work is too much)\n\nVersion 0.4.2 (November 16, 2017)\n---------------------------------\nWe did some minor stuff, but we were drunk again and couldn't read our handwriting on the napkin the next morning.\n\nVersion 0.4.1 (November 3, 2017)\n--------------------------------\nWe did some minor stuff, and we didn't think it was important enough to document.\n\nVersion 0.4.0 (November 1, 2017)\n--------------------------------\nWe did some stuff, but other stuff was happening in the news, and we were too distracted to write things down.\n\nVersion 0.3.0 (August 11, 2017)\n-------------------------------\nWe did some stuff, but we were drunk and forgot to write it down.\n\nVersion 0.2.1 (June 8, 2017)\n----------------------------\nHistory as we know it begins here.\n" }, { "path": "LICENSE", "content": "The MIT License (MIT)\n\nCopyright (c) 2015-2016, Ariel Rokem, The University of Washington eScience Institute.\nCopyright (c) 2016--, PyBIDS developers, Planet Earth\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n" }, { "path": "MANIFEST.in", "content": "graft src\ngraft tests\ngraft doc\n\ninclude tox.ini\n# Include uv.lock so that tox can run from sdist\ninclude uv.lock\n\nrecursive-exclude src README.md\n" }, { "path": "README.md", "content": "# PyBIDS\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.01294/status.svg)](https://doi.org/10.21105/joss.01294)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2537312.svg)](https://doi.org/10.5281/zenodo.2537312)\n\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/bids-standard/pybids/main)\n[![PyPI version](https://badge.fury.io/py/pybids.svg)](https://badge.fury.io/py/pybids)\n[![Conda version](https://img.shields.io/conda/vn/conda-forge/pybids)](https://anaconda.org/conda-forge/pybids)\n\nPyBIDS is a Python library to centralize interactions with datasets conforming\nBIDS (Brain Imaging Data Structure) format. For more information about BIDS\nvisit https://bids.neuroimaging.io.\n\nPyBIDS currently supports Python 3 on POSIX operating systems (including Mac OS). Windows is not officially supported, though most PyBIDS functionality will probably work fine.\n\n## Installation\nPyBIDS is most easily installed from pip. To install the latest official release:\n\n> pip install pybids\n\nOr, alternatively via conda:\n\n> conda install -c conda-forge pybids\n\nIf you want to live on the bleeding edge, you can install from main:\n\n> pip install git+https://github.com/bids-standard/pybids.git\n\n### Dependencies\nPyBIDS 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).\n\n## Usage\nGet started by checking out [the documentation](https://bids-standard.github.io/pybids)!\n\nOr 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.\n\n## Development and testing\nPyBIDS uses [tox](https://tox.wiki/) to manage testing environments and ensure that the same\ncommands can easily be run locally and in CI.\n\nInstall tox with:\n\n```\npip install tox\n```\n\nTo run tests with standard, minimal, or pre-release dependencies, use:\n\n```\ntox run -e [full|min|pre]\n```\n\nTo run with a specific version, also specify `py3X`:\n\n```\ntox run -e py311-full\n```\n\nSee `tox` documentation for more detail.\n\n## How to cite\nTo 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):\n\nYarkoni et al., (2019). PyBIDS: Python tools for BIDS datasets. Journal of Open Source Software, 4(40), 1294, https://doi.org/10.21105/joss.01294\n\nYarkoni, 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\n\n## Community guidelines\nBug 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.\n\nPull 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.\n" }, { "path": "codecov.yml", "content": "coverage:\n range: \"0...100\"\n status:\n patch:\n default:\n target: 0\n threshold: 100\n project:\n default:\n target: 0\n threshold: 100\n patch:\n target: 0\n threshold: 100\n unittests:\n target: 0\n threshold: 100\n flags:\n - \"unittests\"\n smoketests:\n target: 0\n threshold: 100\n flags:\n - \"smoketests\"\n ignore: # files and folders that will be removed during processing\n - \"doc\"\n" }, { "path": "doc/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help clean Makefile\n\nclean:\n\trm -rf $(BUILDDIR)/*\n\trm -rf generated/*\n\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "doc/_static/theme_overrides.css", "content": "/* override table width restrictions */\n@media screen and (min-width: 767px) {\n\n .wy-table-responsive table td {\n /* !important prevents the common CSS stylesheets from overriding\n this as on RTD they are loaded after this stylesheet */\n white-space: normal !important;\n }\n\n .wy-table-responsive {\n overflow: visible !important;\n }\n}\n" }, { "path": "doc/_templates/class.rst", "content": ":mod:`{{module}}`.{{objname}}\n{{ underline }}==============\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block methods %}\n {% endblock %}\n\n.. include:: {{module}}.{{objname}}.examples\n\n.. raw:: html\n\n
\n" }, { "path": "doc/_templates/function.rst", "content": ":mod:`{{module}}`.{{objname}}\n{{ underline }}====================\n\n.. currentmodule:: {{ module }}\n\n.. autofunction:: {{ objname }}\n\n.. include:: {{module}}.{{objname}}.examples\n\n.. raw:: html\n\n
\n" }, { "path": "doc/_templates/module.rst", "content": "{{ fullname }}\n{{ underline }}\n\n.. automodule:: {{ fullname }}\n\n {% block functions %}\n {% if functions %}\n .. rubric:: Functions\n\n .. autosummary::\n :toctree:\n {% for item in functions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block classes %}\n {% if classes %}\n .. rubric:: Classes\n\n .. autosummary::\n :toctree:\n {% for item in classes %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block exceptions %}\n {% if exceptions %}\n .. rubric:: Exceptions\n\n .. autosummary::\n :toctree:\n {% for item in exceptions %}\n {{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n" }, { "path": "doc/analysis/index.rst", "content": ".. _analysis:\n\n=====================================================\n``analysis``: Model specification for BIDS datasets\n=====================================================\n\n.. contents:: **Contents**\n :local:\n :depth: 1\n\n|\n\n.. currentmodule:: pybids.modeling\n\n.. _overview:\n\nAn overview of the analysis module\n==================================\n\n.. topic:: **BIDS models**\n\n The `BIDS Models specification`_ is an extension to the BIDS standard for\n describing and organizing general linear models (GLMs) or similar models\n fitted to BIDS datasets.\n\n A GLM can be concisely described with a json file, regardless of the software\n used to fit the model (e.g., `nilearn`_, `AfNI`_, `FSL`_, `SPM`_).\n\nThe PyBIDS analysis module provides high-level model specification functionality\nfor BIDS datasets. It assumes that model information is represented in line with\nthe (upcoming) BIDS-Model specification.\n\nNote that, at present, pybids.modeling does not provide a one-stop model-fitting\nsolution. You will need to call some other package (e.g., `nilearn`_, or a\nnon-Python package like `FSL`_ or `SPM`_ via `Nipype`_) to handle model estimation.\nWhat pybids.modeling will do for you is automatically handle the loading and\ntransformation of all variables, and the construction of design matrices and\ncontrasts.\n\n.. _BIDS Models specification: https://bids-standard.github.io/model-zoo/\n.. _nilearn: https://nilearn.github.io\n.. _AfNI: https://afni.nimh.nih.gov\n.. _FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/\n.. _SPM: https://www.fil.ion.ucl.ac.uk/spm/\n.. _Nipype: https://nipype.readthedocs.io\n\n\nQuickstart\n==========\nA simple example of standard usage follows. We assume that we have a root folder\ncontaining a BIDS-compliant project in ``/bidsproject``, as well as a BIDS-Models\nJSON specification in ``model.json``.\n\n >>> from bids.modeling import BIDSStatsModelsGraph\n >>> # Initialize the BIDSStatsModelsGraph\n >>> analysis = BIDSStatsModelsGraph('/bidsproject', 'model1.json')\n >>> # Setup constructs all the design matrices\n >>> analysis.setup()\n >>> # Sample query: retrieve first-level design matrix for one run\n >>> dm = analysis[0].get_design_matrix(subject='01', run=1, task='taskA')\n >>> # Sample query: retrieve session-level contrast matrix\n >>> cm = analysis[1].get_contrasts(subject='01', session='retest')\n\n\n.. note::\n\n For a more detailed example, please refer to the Tutorial: :doc:`/examples/statsmodels_tutorial`\n" }, { "path": "doc/api.rst", "content": "API Reference\n==============\n\n.. _base_ref:\n\n:mod:`bids.layout`: Querying BIDS datasets\n------------------------------------------\n\n.. autosummary:: bids.layout\n :toctree: generated/\n\n bids.layout.BIDSLayout\n bids.layout.BIDSValidator\n bids.layout.BIDSFile\n bids.layout.BIDSDataFile\n bids.layout.BIDSJSONFile\n bids.layout.Config\n bids.layout.Entity\n bids.layout.Tag\n bids.layout.parse_file_entities\n bids.layout.add_config_paths\n bids.layout.index.BIDSLayoutIndexer\n bids.layout.writing\n\n.. currentmodule:: bids\n\n\n:mod:`bids.modeling`: Model specification for BIDS datasets\n------------------------------------------------------------\n\n.. autosummary:: bids.modeling\n :toctree: generated/\n\n bids.modeling.statsmodels\n bids.modeling.auto_model\n bids.modeling.statsmodels.BIDSStatsModelsGraph\n bids.modeling.statsmodels.BIDSStatsModelsNode\n\n.. currentmodule:: bids\n\n.. _calibration_ref:\n\n\n:mod:`bids.reports`: Data acquisition report generation\n--------------------------------------------------------\n\n.. autosummary:: bids.reports\n :toctree: generated/\n\n bids.reports.BIDSReport\n bids.reports.parsing\n bids.reports.parameters\n bids.reports.utils\n\n.. currentmodule:: bids\n\n\n:mod:`bids.variables`: Variables\n--------------------------------------------------\n\n.. autosummary:: bids.variables\n :toctree: generated/\n\n bids.variables.SimpleVariable\n bids.variables.SparseRunVariable\n bids.variables.DenseRunVariable\n bids.variables.BIDSVariableCollection\n bids.variables.BIDSRunVariableCollection\n bids.variables.merge_collections\n bids.variables.load_variables\n bids.variables.merge_variables\n bids.variables.io\n bids.variables.entities\n bids.variables.collections\n bids.variables.variables\n\n.. currentmodule:: bids\n\n\n:mod:`bids.config`: PyBIDS Configuration utilities\n--------------------------------------------------\n\n.. autosummary:: bids.config\n :toctree: generated/\n\n bids.config.set_option\n bids.config.set_options\n bids.config.get_option\n bids.config.from_file\n bids.config.reset_options\n\n.. currentmodule:: bids\n\n\n:mod:`bids.utils`: Utility functions\n--------------------------------------------------\n\n.. autosummary:: bids.utils\n :toctree: generated/\n\n bids.utils.listify\n bids.utils.matches_entities\n bids.utils.convert_JSON\n bids.utils.make_bidsfile\n\n\n.. currentmodule:: bids" }, { "path": "doc/conf.py", "content": "# Configuration file for the Sphinx documentation builder. # noqa: D100, EXE002\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nfrom datetime import date\n\nimport sphinx_rtd_theme\n\nimport bids\n\nsys.path.insert(0, os.path.abspath('../'))\n\n# -- Project information -----------------------------------------------------\n\nproject = 'PyBIDS'\ncopyright = f'2015-{date.today().year}, Developers of PyBIDS' # noqa: A001, DTZ011\nauthor = 'Developers of PyBIDS'\n\ncurrentdir = os.path.abspath(os.path.dirname(__file__))\ncurrentdir = os.path.abspath(os.path.dirname(__file__))\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.autosectionlabel',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.coverage',\n 'sphinx.ext.mathjax',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.githubpages',\n 'sphinx.ext.napoleon',\n 'numpydoc',\n 'myst_nb',\n]\n\nintersphinx_mapping = {\n 'python': ('https://docs.python.org/3.5', None),\n 'numpy': ('https://docs.scipy.org/doc/numpy', None),\n 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),\n 'matplotlib': ('https://matplotlib.org/', None),\n 'scikit-learn': ('https://scikit-learn.org/0.17', None),\n 'nibabel': ('https://nipy.org/nibabel/', None),\n 'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),\n 'neurosynth': ('https://neurosynth.readthedocs.io/en/latest/', None),\n}\n\nintersphinx_timeout = 5\n\n# If your documentation needs a minimal Sphinx version, state it here.\nneeds_sphinx = '6.2.0'\n\n# Generate stubs\nautosummary_generate = True\nautodoc_default_flags = ['members', 'inherited-members']\nadd_module_names = False\nnumpydoc_class_members_toctree = False\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix of source filenames.\nsource_suffix = ['.rst', '.md']\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The short X.Y version.\nversion = bids.__version__\n# The full version, including alpha/beta/rc tags.\nrelease = version\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = 'sphinx_rtd_theme'\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# If false, no module index is generated.\nhtml_domain_indices = False\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [('index', 'bids', 'bids Documentation', [author], 1)]\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n 'index',\n 'bids',\n 'bids Documentation',\n author,\n 'bids',\n 'One line description of project.',\n 'Miscellaneous',\n ),\n]\n\n# If false, no module index is generated.\ntexinfo_domain_indices = False\n\nnb_custom_formats = {\n '.md': ['jupytext.reads', {'fmt': 'mystnb'}],\n}\n" }, { "path": "doc/examples/index.rst", "content": ".. tutorials:\n\nTutorials\n==========\n\nThe following executable examples are available.\n\nNote 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.\n\n.. toctree::\n :maxdepth: 1\n\n pybids_tutorial.md\n reports_tutorial.md\n statsmodels_tutorial.md" }, { "path": "doc/examples/pybids_tutorial.md", "content": "---\njupytext:\n text_representation:\n extension: .md\n format_name: myst\n format_version: 0.13\n jupytext_version: 1.14.1\nkernelspec:\n display_name: Python 3 (ipykernel)\n language: python\n name: python3\n---\n\n# Introduction to `pybids`\n\n[`pybids`](https://github.com/bids-standard/pybids) is a tool to query, summarize and manipulate data using the BIDS standard. \nIn this tutorial we will use a `pybids` test dataset to illustrate some of the functionality of `pybids.layout`\n\n```{code-cell} ipython3\nfrom bids import BIDSLayout\nfrom bids.tests import get_test_data_path\nimport os\n```\n\n## The `BIDSLayout`\n\nAt 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:\n\n```{code-cell} ipython3\n# Here we're using an example BIDS dataset that's bundled with the pybids tests\ndata_path = os.path.join(get_test_data_path(), '7t_trt')\n\n# Initialize the layout\nlayout = BIDSLayout(data_path)\n\n# Print some basic information about the layout\nlayout\n```\n\n### Querying the `BIDSLayout`\nWhen 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:\n\n```{code-cell} ipython3\nall_files = layout.get()\nprint(\"There are {} files in the layout.\".format(len(all_files)))\nprint(\"\\nThe first 10 files are:\")\nall_files[:10]\n```\n\nThe 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:\n\n```{code-cell} ipython3\nlayout.get(return_type='filename')[:10]\n```\n\nThis time, we get back only the names of the files.\n\n### Filtering files by entities\nThe 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'`:\n\n```{code-cell} ipython3\n# Retrieve filenames of all BOLD runs for subject 01\nlayout.get(subject='01', extension='nii.gz', suffix='bold', return_type='filename')\n```\n\nIf 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:\n\n* `suffix`: The part of a BIDS filename just before the extension (e.g., `'bold'`, `'events'`, `'physio'`, etc.).\n* `subject`: The subject label\n* `session`: The session label\n* `run`: The run index\n* `task`: The task name\n\nNew 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.\n\n### Filtering by metadata\nAll 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.).\n\nFor 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:\n\n```{code-cell} ipython3\n# Retrieve all files where SamplingFrequency (a metadata key) = 100\n# and acquisition = prefrontal, for the first two subjects\nlayout.get(subject=['01', '02'], SamplingFrequency=100, acquisition=\"prefrontal\")\n```\n\nNotice 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).\n\n### Other `return_type` values\nWhile 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:\n\n```{code-cell} ipython3\n# Ask get() to return the ids of subjects that have T1w files\nlayout.get(return_type='id', target='subject', suffix='T1w')\n```\n\nIf 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:\n\n```{code-cell} ipython3\nlayout.get(return_type='dir', target='subject')\n```\n\n### Other `get()` options\nThe `.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:\n* `regex_search`: If you set this to `True`, string filter argument values will be interpreted as regular expressions.\n* `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.\n\n+++\n\n## The `BIDSFile`\nWhen 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`.\n\n```{code-cell} ipython3\n# Pick the 15th file in the dataset\nbf = layout.get()[15]\n\n# Print it\nbf\n```\n\nHere 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!):\n* `.path`: The full path of the associated file\n* `.filename`: The associated file's filename (without directory)\n* `.dirname`: The directory containing the file\n* `.get_entities()`: Returns information about entities associated with this `BIDSFile` (optionally including metadata)\n* `.get_image()`: Returns the file contents as a nibabel image (only works for image files)\n* `.get_df()`: Get file contents as a pandas DataFrame (only works for TSV files)\n* `.get_metadata()`: Returns a dictionary of all metadata found in associated JSON files\n* `.get_associations()`: Returns a list of all files associated with this one in some way\n\nLet's see some of these in action.\n\n```{code-cell} ipython3\n# Print all the entities associated with this file, and their values\nbf.get_entities()\n```\n\n```{code-cell} ipython3\n# Print all the metadata associated with this file\nbf.get_metadata()\n```\n\n```{code-cell} ipython3\n# We can the union of both of the above in one shot like this\nbf.get_entities(metadata='all')\n```\n\nHere 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.\n\n```{code-cell} ipython3\nbf.get_associations()\n```\n\n+++ {\"slideshow\": {\"slide_type\": \"slide\"}}\n\nIn 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`:\n\n```{code-cell} ipython3\n# Use a different test dataset--one that contains physio recording files\ndata_path = os.path.join(get_test_data_path(), 'synthetic')\nlayout2 = BIDSLayout(data_path)\n\n# Get the first physiological recording file\nrecfile = layout2.get(suffix='physio')[0]\n\n# Get contents as a DataFrame and show the first few rows\ndf = recfile.get_df()\ndf.head()\n```\n\nWhile 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.\n\nMind you, if we don't *want* the timing information, we can ignore it:\n\n```{code-cell} ipython3\nrecfile.get_df(include_timing=False).head()\n```\n\n## Other utilities\n\n### Filename parsing\nSay you have a filename, and you want to manually extract BIDS entities from it. The `parse_file_entities` method provides the facility:\n\n```{code-cell} ipython3\npath = \"/a/fake/path/to/a/BIDS/file/sub-01_run-1_T2w.nii.gz\"\nlayout.parse_file_entities(path)\n```\n\nA 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)) - \n\n```{code-cell} ipython3\nfrom bids.layout import parse_file_entities\n\npath = \"/a/fake/path/to/a/BIDS/file/sub-01_run-1_T2w.nii.gz\"\nparse_file_entities(path)\n```\n\n### Path construction\nYou 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!):\n\n```{code-cell} ipython3\nentities = {\n 'subject': '01',\n 'run': 2,\n 'task': 'nback',\n 'suffix': 'bold'\n}\n\nlayout.build_path(entities)\n```\n\nYou 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:\n\n```{code-cell} ipython3\n# Define the pattern to build out of the components passed in the dictionary\npattern = \"sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_{suffix}.nii.gz\",\n\nentities = {\n 'subject': '01',\n 'run': 2,\n 'task': 'nback',\n 'suffix': 'z'\n}\n\n# Notice we pass the new pattern as the second argument\nlayout.build_path(entities, pattern, validate=False)\n```\n\nNote 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).\n\n+++\n\n### Loading derivatives\n\nBy 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):\n\n```{code-cell} ipython3\n# Define paths to root and derivatives folders\nroot = os.path.join(get_test_data_path(), 'synthetic')\nlayout2 = BIDSLayout(root, derivatives=True)\nlayout2\n```\n\nThe `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.\n\nThe following call returns the filenames of all derivatives files.\n\n```{code-cell} ipython3\n# Get all files in derivatives\nlayout2.get(scope='derivatives', return_type='file')\n```\n\n### Exporting a `BIDSLayout` to a pandas `Dataframe`\nIf 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.\n\n```{code-cell} ipython3\n# Convert the layout to a pandas dataframe\ndf = layout.to_df()\ndf.head()\n```\n\nWe 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.\n\n```{code-cell} ipython3\nlayout.to_df(metadata=True).head()\n```\n\n## Retrieving BIDS variables \nBIDS 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.\n\nIn 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). \n\n```{code-cell} ipython3\n# Get subject variables as a dataframe and merge them back in with the layout\nsubj_df = layout.get_collections(level='subject', merge=True).to_df()\nsubj_df.head()\n```\n\n## BIDSValidator\n\n`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/).\n\n```{code-cell} ipython3\nfrom bids import BIDSValidator\n\n# Note that when using the bids validator, the filepath MUST be relative to the top level bids directory\nvalidator = BIDSValidator()\nvalidator.is_bids('/sub-02/ses-01/anat/sub-02_ses-01_T2w.nii.gz')\n```\n\n```{code-cell} ipython3\n# Can decide if a filepath represents a file part of the specification\nvalidator.is_file('/sub-02/ses-01/anat/sub-02_ses-01_T2w.json')\n```\n\n```{code-cell} ipython3\n# Can check if a file is at the top level of the dataset\nvalidator.is_top_level('/dataset_description.json')\n```\n\n```{code-cell} ipython3\n# or subject (or session) level\nvalidator.is_subject_level('/dataset_description.json')\n```\n\n```{code-cell} ipython3\nvalidator.is_session_level('/sub-02/ses-01/sub-02_ses-01_scans.json')\n```\n\n```{code-cell} ipython3\n# Can decide if a filepath represents phenotypic data\nvalidator.is_phenotypic('/sub-02/ses-01/anat/sub-02_ses-01_T2w.nii.gz')\n```\n" }, { "path": "doc/examples/reports_tutorial.md", "content": "---\njupytext:\n text_representation:\n extension: .md\n format_name: myst\n format_version: 0.13\n jupytext_version: 1.14.1\nkernelspec:\n display_name: Python 3\n language: python\n name: python3\n---\n\n# Writing methods reports with `pybids.reports`\n\nThis tutorial \n\n```{code-cell} ipython3\n%load_ext autoreload\n%autoreload 2\n\nfrom bids import BIDSLayout\nfrom bids.reports import BIDSReport\nfrom bids.tests import get_test_data_path\nimport os\n```\n\n```{code-cell} ipython3\ndataset = \"synthetic\"\n\n# Here we're using an example BIDS dataset that's bundled with the pybids tests\ndata_path = os.path.join(get_test_data_path(), dataset)\n\n# Load the BIDS dataset\nlayout = BIDSLayout(data_path)\n```\n\n```{code-cell} ipython3\n# Initialize a report for the dataset\nreport = BIDSReport(layout)\n```\n\n```{code-cell} ipython3\n# Method generate returns a Counter of unique descriptions across subjects\ndescriptions = report.generate()\n```\n\n```{code-cell} ipython3\n# For datasets containing a single study design, all but the most common\n# description most likely reflect random missing data.\npub_description = descriptions.most_common()[0][0]\nprint(pub_description)\n```\n" }, { "path": "doc/examples/statsmodels_tutorial.md", "content": "---\njupytext:\n text_representation:\n extension: .md\n format_name: myst\n format_version: 0.13\n jupytext_version: 1.14.1\nkernelspec:\n display_name: Python 3 (ipykernel)\n language: python\n name: python3\n---\n\n# Executing and inspecting StatsModels with `pybids.modeling`\nA minimalistic tutorial illustrating usage of the tools in the `bids.modeling` module—most notably, `BIDSStatsModelsGraph` and its various components.\n\n```{code-cell} ipython3\n%load_ext autoreload\n%autoreload 2\n\nimport pandas as pd\nimport numpy as np\nfrom bids.modeling import BIDSStatsModelsGraph\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\nfrom os.path import join\nimport json\n```\n\n## Load BIDSLayout\nStandard 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`).\n\n```{code-cell} ipython3\nlayout_path = join(get_test_data_path(), 'ds005')\nlayout = BIDSLayout(layout_path)\njson_file = join(layout_path, 'models', 'ds-005_type-test_model.json')\nspec = json.load(open(json_file, 'r'))\n```\n\n## Initialize the graph\nThe `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.\n\n```{code-cell} ipython3\ngraph = BIDSStatsModelsGraph(layout, spec)\n```\n\n## Load variables from BIDS dataset\nWe 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.\n\n```{code-cell} ipython3\ngraph.load_collections(scan_length=480)\n```\n\n## Access specific nodes\nNow 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).\n\n```{code-cell} ipython3\n# Equivalent to calling graph.get_node('run') in this case.\nroot_node = graph.root_node\n```\n\n## Inspect `BIDSVariableCollection`\n\n+++\n\nWe can take a look at the original variables available for each node prior to running the node (i.e. applying any transformations)\n\n```{code-cell} ipython3\ncolls = root_node.get_collections()\nfirst_sub = colls[0] # Collection for the first subject / run \n```\n\n```{code-cell} ipython3\nfirst_sub.to_df(entities=False)\n```\n\nNote that by default `to_df` combines both sparse and dense variables. \n\nWe can take a look at the individual variable objects as well:\n\n```{code-cell} ipython3\nfirst_sub.variables\n```\n\n## Executing a node\nWe 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`.\n\nNote 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).\n\n```{code-cell} ipython3\n# force_dense controls whether the output for run-level design matrices\n# will be resampled to a uniform \"dense\" representation, or left alone\n# as a sparse representation if possible.\noutputs = root_node.run(group_by=['run', 'subject'], force_dense=False, transformation_history=True)\n```\n\n## Node outputs\n\n+++\n\nThe 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):\n\n```{code-cell} ipython3\nlen(outputs)\n```\n\n```{code-cell} ipython3\n# The first 10 `BIDSStatsModelsNodeOutput` objects\noutputs[:10]\n```\n\nLet'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:\n\n```{code-cell} ipython3\noutputs[0].entities\n```\n\nNext, we can get the design matrix itself via the `.X` attribute:\n\n```{code-cell} ipython3\noutputs[0].X\n```\n\nHere 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`:\n\n```{code-cell} ipython3\noutputs[0].contrasts\n```\n\nEach `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.\n\nOne 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?\n\nThe 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.\n\n```{code-cell} ipython3\noutputs[0].metadata\n```\n\nThere'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.\n\n+++\n\n## Transformation History\n\n+++\n\nTo generate the final design matrix, pybids applies all transformations specified in the model for that given run.\n\nHowever, 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.\n\nOptionally, 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\n\n```{code-cell} ipython3\nts = outputs[0].trans_hist\nts\n```\n\nThe first item (index=0), is the original collection. We can access the `BIDSRunVariableCollection` using the output attribute:\n\n```{code-cell} ipython3\nts[0].output\n```\n\nWe can examine the full collection at each step using either a combined dataframe:\n\n```{code-cell} ipython3\nts[1].output.to_df(entities=False)\n```\n\nOr examine individual variables in their native representation (e.g. `SparseRunVariable` or `DenseRunVariable`)\n\n```{code-cell} ipython3\nts[1].output.variables\n```\n\n```{code-cell} ipython3\nts[1].output.variables['RT'].to_df(entities=False)\n```\n\n## Traversing the graph\nSo 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.\n\n```{code-cell} ipython3\nroot_node.children\n```\n\nIn 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:\n\n```{code-cell} ipython3\nnext_node = root_node.children[0].destination\nnext_node.level, next_node.name\n```\n\nWe assign the first connected node to `next_node`, and print out its `level` and `name` for inspection (both are session).\n\n## Passing in inputs\nArmed 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!\n\nThe 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.)\n\nLet'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`:\n\n```{code-cell} ipython3\nfrom itertools import chain\ncontrasts = list(chain(*[s.contrasts for s in outputs[:-2]]))\nlen(contrasts)\n```\n\nNotice 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.\n\nNow 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`.\n\n```{code-cell} ipython3\nsess_outputs = next_node.run(contrasts, group_by=['subject', 'contrast'])\nlen(sess_outputs)\n```\n\nAgain 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).\n\nThis becomes clearer if we inspect the same attributes we looked at earlier:\n\n```{code-cell} ipython3\nsess_outputs[0].contrasts\n```\n\n```{code-cell} ipython3\n# Concatenate the X and metadata DFs for easy reading.\n# Note that only the first column is actually part of the\n# design matrix; the others are just metadata.\npd.concat([sess_outputs[0].X, sess_outputs[0].metadata], axis=1)\n```\n\nNotice 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']`).\n" }, { "path": "doc/includes/big_toc_css.rst", "content": "..\n File to ..include in a document with a big table of content, to give\n it 'style'\n\n:orphan:\n\n.. raw:: html\n\n \n" }, { "path": "doc/includes/bigger_toc_css.rst", "content": "..\n File to ..include in a document with a very big table of content, to\n give it 'style'\n\n:orphan:\n\n.. raw:: html\n\n \n" }, { "path": "doc/index.rst", "content": ".. bids\n\nWelcome to pybids's documentation!\n====================================\n\n``pybids`` is a set of tools for working with Brain Imaging Data Structure (BIDS) datasets.\nFor more information about BIDS, please visit the `BIDS website`_.\n\n.. _BIDS website: https://bids.neuroimaging.io\n\nContents\n==========\n\n.. toctree::\n :maxdepth: 2\n\n user_guide.rst\n examples/index.rst\n api.rst\n whats_new.rst\n\nCiting pybids\n=============\nTo 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):\n\n 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\n\n 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\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "doc/introduction.rst", "content": ".. _introduction:\n\n=====================================================\nIntroduction: What is pybids?\n=====================================================\n\n.. contents:: **Contents**\n :local:\n :depth: 1\n\nWhat is BIDS and why should you use it?\n========================================\n\n.. topic:: **Why use BIDS?**\n\n The Brain Imaging Data Structure provides a standard for organizing, naming, and\n describing neuroimaging datasets. By organizing your data in this manner, you can\n make sure of a number of tools that are designed to work with BIDS datasets out\n of the box. Moreover, many open datasets are provided in BIDS format, which\n means that being familiar with this structure will make it easier for you to\n analyze public data as well as your own.\n\n For more information on the Brain Imaging Data Structure (BIDS), visit https://bids.neuroimaging.io.\n\nWhat is pybids and why should you use it?\n==========================================\n\n.. topic:: **Why use pybids?**\n\n Pybids makes it easier for neuroimagers who utilize the BIDS standard to query,\n summarize, and manipulate their data. A number of Python packages for analyzing\n neuroimaging data, including `Nipype`_ and `nilearn`_, are optimized to\n work with BIDS datasets.\n\nInstalling pybids\n=================\nPybids is easy to install.\nTo install the most recent release, use pip::\n\n>>> pip install pybids\n\nIf you want the \"bleeding-edge\" version of pybids, you can install directly from\nthe GitHub repository::\n\n>>> pip install git+https://github.com/bids-standard/pybids.git\n\nFinding help\n==============\n\n:Mailing lists and forums:\n\n * Don't hesitate to ask questions about pybids on `NeuroStars\n `_.\n\n * You can find help with neuroimaging in Python (file I/O,\n neuroimaging-specific questions) via the nipy user group:\n https://groups.google.com/forum/?fromgroups#!forum/nipy-user\n\n * If you notice a bug in the pybids code, please `open an issue`_ in the\n pybids repository.\n\n.. _nilearn: https://nilearn.github.io\n.. _Nipype: https://nipype.readthedocs.io\n.. _open an issue: https://github.com/bids-standard/pybids/issues\n" }, { "path": "doc/layout/index.rst", "content": ".. _layout:\n\n=====================================================\n``layout``: Querying BIDS datasets\n=====================================================\n\n.. contents:: **Contents**\n :local:\n :depth: 1\n\n|\n\n.. currentmodule:: pybids.layout\n\n.. _loading_datasets:\n\nLoading BIDS datasets\n===========================================\n\nThe :obj:`bids.layout.BIDSLayout` class requires the path to a valid BIDS dataset::\n\n >>> from os.path import join\n >>> from bids import BIDSLayout\n >>> from bids.tests import get_test_data_path\n >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic'))\n\nThe ``BIDSLayout`` instance is a lightweight container for all of the files in the\nBIDS project directory. It automatically detects any BIDS entities found in the\nfile paths, and allows us to perform simple but relatively powerful queries over\nthe file tree. By default, defined BIDS entities include things like \"subject\",\n\"session\", \"run\", and \"type\".\n\n.. hint::\n\n To exclude folders from indexing (e.g. only index a subset of subjects to save time),\n you can pass a list of folder names, or a regex pattern to the ``ignore`` argument. \n For example, to ignore all subjects except \"25\":\n\n >>> BIDSLayout(bids_dir, ignore=[re.compile(r\"(sub-(?!25)\\d*/)\")])\n\n.. _querying_datasets:\n\nQuerying datasets\n===========================================\n\nPybids layouts can be queried according to a number of parameters, using\ngrabbit's ``get`` functionality.\n\nFor example, if we want to get a list of subjects in the dataset::\n\n >>> layout.get_subjects()\n ['01', '02', '03', '04', '05']\n\nWe can also get a list of all available sessions::\n\n >>> layout.get_sessions()\n ['01', '02']\n\nOr a list of tasks::\n\n >>> layout.get_tasks()\n ['nback', 'rest']\n\n.. extracting_metadata:\n\nExtracting metadata\n====================\n\nA number of ``BIDSLayout`` methods extract metadata associated with files.\n\nFor example, if we want event (task timing) information for a given fMRI scan, we can use ``get_events``::\n\n >>> f = layout.get(task='nback', run=1, extension='nii.gz')[0].filename\n >>> layout.get_events(f)\n\nWe can also extract metadata from the json files associated with a scan file::\n\n >>> f = layout.get(task='nback', run=1, extension='nii.gz')[0].filename\n >>> layout.get_metadata(f)\n\n\n.. note::\n\n For a more detailed set of examples, please refer to the Tutorial: :doc:`/examples/pybids_tutorial`\n" }, { "path": "doc/make.bat", "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=.\r\nset BUILDDIR=_build\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.https://www.sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n" }, { "path": "doc/reports/index.rst", "content": ".. _reports:\n\n=====================================================\n``reports``: Data acquisition report generation\n=====================================================\n\n.. contents:: **Contents**\n :local:\n :depth: 1\n\n|\n\n.. currentmodule:: pybids.reports\n\n.. _initializing_reports:\n\nInitializing reports\n===========================================\n\nThe :obj:`bids.reports.BIDSReport` class requires a :obj:`bids.BIDSLayout` object as an argument::\n\n >>> from os.path import join\n >>> from bids import BIDSLayout\n >>> from bids.reports import BIDSReport\n >>> from bids.tests import get_test_data_path\n >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic'))\n >>> report = BIDSReport(layout)\n\n.. _generating_reports:\n\nGenerating reports\n===========================================\n\nPybids reports are then generated with the ``generate`` method, which returns a\n:obj:`collections.Counter` of reports::\n\n >>> counter = report.generate()\n >>> main_report = counter.most_common()[0][0]\n >>> print(main_report)\n r\"\"\"\n For session 01:\n \tMR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner.\n \tTen runs of N-Back UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms;\n echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm;\n matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during\n which 64 functional volumes were acquired.\n \tFive runs of Rest UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms;\n echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm;\n matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during\n which 64 functional volumes were acquired.\n\n For session 02:\n \tMR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner.\n \tTen runs of N-Back UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms;\n echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm;\n matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during\n which 64 functional volumes were acquired.\n \tFive runs of Rest UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms;\n echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm;\n matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during\n which 64 functional volumes were acquired.\n\n Dicoms were converted to NIfTI-1 format. This section was (in part) generated\n automatically using pybids (0.5).\"\"\"\n\n.. _generating_subreports:\n\nGenerating reports on subsets of the data\n-------------------------------------------\n\nThe ``generate`` method allows for keyword restrictions, just like\n:obj:`bids.BIDSLayout`'s ``get`` method. For example, to\ngenerate a report only for ``nback`` task data in session ``01``::\n\n >>> counter = report.generate(session='01', task='nback')\n >>> main_report = counter.most_common()[0][0]\n >>> print(main_report)\n r\"\"\"\n For session 01:\n MR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner.\n Ten runs of N-Back fMRI data were collected (64 slices; repetition time,\n TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of\n view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was\n 2:40 minutes in length, during which 64 functional volumes were acquired.\n\n Dicoms were converted to NIfTI-1 format. This section was (in part)\n generated automatically using pybids (0.5).\"\"\"\n\n\n.. note::\n\n For a more detailed set of examples, please refer to the Tutorial: :doc:`/examples/reports_tutorial`." }, { "path": "doc/sphinxext/docscrape.py", "content": "# ruff: noqa: D301\n\"\"\"Extract reference documentation from the NumPy source tree.\"\"\"\n\nimport collections\nimport inspect\nimport pydoc\nimport re\nimport textwrap\nfrom warnings import warn\n\n\nclass Reader:\n \"\"\"A line-based string reader.\"\"\"\n\n def __init__(self, data):\n \"\"\"Parameters # noqa: D301\n ----------\n data : str\n String with lines separated by '\\n'.\n\n \"\"\"\n if isinstance(data, list):\n self._str = data\n else:\n self._str = data.split('\\n') # store string as list of lines\n\n self.reset()\n\n def __getitem__(self, n):\n return self._str[n]\n\n def reset(self): # noqa: D102\n self._l = 0 # current line nr\n\n def read(self): # noqa: D102\n if not self.eof():\n out = self[self._l]\n self._l += 1\n return out\n else:\n return ''\n\n def seek_next_non_empty_line(self): # noqa: D102\n for l in self[self._l :]: # noqa: E741\n if l.strip():\n break\n else:\n self._l += 1\n\n def eof(self): # noqa: D102\n return self._l >= len(self._str)\n\n def read_to_condition(self, condition_func): # noqa: D102\n start = self._l\n for line in self[start:]:\n if condition_func(line):\n return self[start : self._l]\n self._l += 1\n if self.eof():\n return self[start : self._l + 1]\n return []\n\n def read_to_next_empty_line(self): # noqa: D102\n self.seek_next_non_empty_line()\n\n def is_empty(line):\n return not line.strip()\n\n return self.read_to_condition(is_empty)\n\n def read_to_next_unindented_line(self): # noqa: D102\n def is_unindented(line):\n return line.strip() and (len(line.lstrip()) == len(line))\n\n return self.read_to_condition(is_unindented)\n\n def peek(self, n=0): # noqa: D102\n if self._l + n < len(self._str):\n return self[self._l + n]\n else:\n return ''\n\n def is_empty(self): # noqa: D102\n return not ''.join(self._str).strip()\n\n\nclass NumpyDocString(collections.abc.Mapping): # noqa: D101\n def __init__(self, docstring, config={}): # noqa: B006\n docstring = textwrap.dedent(docstring).split('\\n')\n\n self._doc = Reader(docstring)\n self._parsed_data = {\n 'Signature': '',\n 'Summary': [''],\n 'Extended Summary': [],\n 'Parameters': [],\n 'Returns': [],\n 'Yields': [],\n 'Raises': [],\n 'Warns': [],\n 'Other Parameters': [],\n 'Attributes': [],\n 'Methods': [],\n 'See Also': [],\n 'Notes': [],\n 'Warnings': [],\n 'References': '',\n 'Examples': '',\n 'index': {},\n }\n\n self._parse()\n\n def __getitem__(self, key):\n return self._parsed_data[key]\n\n def __setitem__(self, key, val):\n if key not in self._parsed_data:\n warn('Unknown section %s' % key) # noqa: B028, UP031\n else:\n self._parsed_data[key] = val\n\n def __iter__(self):\n return iter(self._parsed_data)\n\n def __len__(self):\n return len(self._parsed_data)\n\n def _is_at_section(self):\n self._doc.seek_next_non_empty_line()\n\n if self._doc.eof():\n return False\n\n l1 = self._doc.peek().strip() # e.g. Parameters\n\n if l1.startswith('.. index::'):\n return True\n\n l2 = self._doc.peek(1).strip() # ---------- or ==========\n return l2.startswith('-' * len(l1)) or l2.startswith('=' * len(l1))\n\n def _strip(self, doc):\n i = 0\n j = 0\n for i, line in enumerate(doc): # noqa: B007\n if line.strip():\n break\n\n for j, line in enumerate(doc[::-1]): # noqa: B007\n if line.strip():\n break\n\n return doc[i : len(doc) - j]\n\n def _read_to_next_section(self):\n section = self._doc.read_to_next_empty_line()\n\n while not self._is_at_section() and not self._doc.eof():\n if not self._doc.peek(-1).strip(): # previous line was empty\n section += ['']\n\n section += self._doc.read_to_next_empty_line()\n\n return section\n\n def _read_sections(self):\n while not self._doc.eof():\n data = self._read_to_next_section()\n name = data[0].strip()\n\n if name.startswith('..'): # index section\n yield name, data[1:]\n elif len(data) < 2:\n yield StopIteration\n else:\n yield name, self._strip(data[2:])\n\n def _parse_param_list(self, content):\n r = Reader(content)\n params = []\n while not r.eof():\n header = r.read().strip()\n if ' : ' in header:\n arg_name, arg_type = header.split(' : ')[:2]\n else:\n arg_name, arg_type = header, ''\n\n desc = r.read_to_next_unindented_line()\n desc = dedent_lines(desc)\n\n params.append((arg_name, arg_type, desc))\n\n return params\n\n _name_rgx = re.compile(\n r'^\\s*(:(?P\\w+):`(?P[a-zA-Z0-9_.-]+)`|'\n r' (?P[a-zA-Z0-9_.-]+))\\s*',\n re.X,\n )\n\n def _parse_see_also(self, content):\n \"\"\"func_name : Descriptive text\n continued text\n another_func_name : Descriptive text\n func_name1, func_name2, :meth:`func_name`, func_name3\n\n \"\"\"\n items = []\n\n def parse_item_name(text):\n \"\"\"Match ':role:`name`' or 'name'\"\"\"\n m = self._name_rgx.match(text)\n if m:\n g = m.groups()\n if g[1] is None:\n return g[3], None\n else:\n return g[2], g[1]\n raise ValueError('%s is not a item name' % text) # noqa: UP031\n\n def push_item(name, rest):\n if not name:\n return\n name, role = parse_item_name(name)\n items.append((name, list(rest), role))\n del rest[:]\n\n current_func = None\n rest = []\n\n for line in content:\n if not line.strip():\n continue\n\n m = self._name_rgx.match(line)\n if m and line[m.end() :].strip().startswith(':'):\n push_item(current_func, rest)\n current_func, line = line[: m.end()], line[m.end() :]\n rest = [line.split(':', 1)[1].strip()]\n if not rest[0]:\n rest = []\n elif not line.startswith(' '):\n push_item(current_func, rest)\n current_func = None\n if ',' in line:\n for func in line.split(','):\n if func.strip():\n push_item(func, [])\n elif line.strip():\n current_func = line\n elif current_func is not None:\n rest.append(line.strip())\n push_item(current_func, rest)\n return items\n\n def _parse_index(self, section, content):\n \"\"\".. index: default\n :refguide: something, else, and more\n\n \"\"\"\n\n def strip_each_in(lst):\n return [s.strip() for s in lst]\n\n out = {}\n section = section.split('::')\n if len(section) > 1:\n out['default'] = strip_each_in(section[1].split(','))[0]\n for line in content:\n line = line.split(':')\n if len(line) > 2:\n out[line[1]] = strip_each_in(line[2].split(','))\n return out\n\n def _parse_summary(self):\n \"\"\"Grab signature (if given) and summary\"\"\"\n if self._is_at_section():\n return\n\n # If several signatures present, take the last one\n while True:\n summary = self._doc.read_to_next_empty_line()\n summary_str = ' '.join([s.strip() for s in summary]).strip()\n if re.compile(r'^([\\w., ]+=)?\\s*[\\w\\.]+\\(.*\\)$').match(summary_str):\n self['Signature'] = summary_str\n if not self._is_at_section():\n continue\n break\n\n if summary is not None:\n self['Summary'] = summary\n\n if not self._is_at_section():\n self['Extended Summary'] = self._read_to_next_section()\n\n def _parse(self):\n self._doc.reset()\n self._parse_summary()\n\n sections = list(self._read_sections())\n section_names = set([section for section, content in sections]) # noqa: C403\n\n has_returns = 'Returns' in section_names\n has_yields = 'Yields' in section_names\n # We could do more tests, but we are not. Arbitrarily.\n if has_returns and has_yields:\n msg = 'Docstring contains both a Returns and Yields section.'\n raise ValueError(msg)\n\n for section, content in sections:\n if not section.startswith('..'):\n section = (s.capitalize() for s in section.split(' '))\n section = ' '.join(section)\n if section in (\n 'Parameters',\n 'Returns',\n 'Yields',\n 'Raises',\n 'Warns',\n 'Other Parameters',\n 'Attributes',\n 'Methods',\n ):\n self[section] = self._parse_param_list(content)\n elif section.startswith('.. index::'):\n self['index'] = self._parse_index(section, content)\n elif section == 'See Also':\n self['See Also'] = self._parse_see_also(content)\n else:\n self[section] = content\n\n # string conversion routines\n\n def _str_header(self, name, symbol='-'):\n return [name, len(name) * symbol]\n\n def _str_indent(self, doc, indent=4):\n out = []\n for line in doc:\n out += [' ' * indent + line]\n return out\n\n def _str_signature(self):\n if self['Signature']:\n return [self['Signature'].replace('*', r'\\*')] + ['']\n else:\n return ['']\n\n def _str_summary(self):\n if self['Summary']:\n return self['Summary'] + ['']\n else:\n return []\n\n def _str_extended_summary(self):\n if self['Extended Summary']:\n return self['Extended Summary'] + ['']\n else:\n return []\n\n def _str_param_list(self, name):\n out = []\n if self[name]:\n out += self._str_header(name)\n for param, param_type, desc in self[name]:\n if param_type:\n out += ['%s : %s' % (param, param_type)] # noqa: UP031\n else:\n out += [param]\n out += self._str_indent(desc)\n out += ['']\n return out\n\n def _str_section(self, name):\n out = []\n if self[name]:\n out += self._str_header(name)\n out += self[name]\n out += ['']\n return out\n\n def _str_see_also(self, func_role):\n if not self['See Also']:\n return []\n out = []\n out += self._str_header('See Also')\n last_had_desc = True\n for func, desc, role in self['See Also']:\n if role:\n link = ':%s:`%s`' % (role, func) # noqa: UP031\n elif func_role:\n link = ':%s:`%s`' % (func_role, func) # noqa: UP031\n else:\n link = '`%s`_' % func # noqa: UP031\n if desc or last_had_desc:\n out += ['']\n out += [link]\n else:\n out[-1] += ', %s' % link # noqa: UP031\n if desc:\n out += self._str_indent([' '.join(desc)])\n last_had_desc = True\n else:\n last_had_desc = False\n out += ['']\n return out\n\n def _str_index(self):\n idx = self['index']\n out = []\n out += ['.. index:: %s' % idx.get('default', '')] # noqa: UP031\n for section, references in idx.items():\n if section == 'default':\n continue\n out += [' :%s: %s' % (section, ', '.join(references))] # noqa: UP031\n return out\n\n def __str__(self, func_role=''):\n out = []\n out += self._str_signature()\n out += self._str_summary()\n out += self._str_extended_summary()\n for param_list in (\n 'Parameters',\n 'Returns',\n 'Yields',\n 'Other Parameters',\n 'Raises',\n 'Warns',\n ):\n out += self._str_param_list(param_list)\n out += self._str_section('Warnings')\n out += self._str_see_also(func_role)\n for s in ('Notes', 'References', 'Examples'):\n out += self._str_section(s)\n for param_list in ('Attributes', 'Methods'):\n out += self._str_param_list(param_list)\n out += self._str_index()\n return '\\n'.join(out)\n\n\ndef indent(str, indent=4): # noqa: A002, D103\n indent_str = ' ' * indent\n if str is None:\n return indent_str\n lines = str.split('\\n')\n return '\\n'.join(indent_str + l for l in lines) # noqa: E741\n\n\ndef dedent_lines(lines):\n \"\"\"Deindent a list of lines maximally\"\"\"\n return textwrap.dedent('\\n'.join(lines)).split('\\n')\n\n\ndef header(text, style='-'): # noqa: D103\n return text + '\\n' + style * len(text) + '\\n'\n\n\nclass FunctionDoc(NumpyDocString): # noqa: D101\n def __init__(self, func, role='func', doc=None, config={}): # noqa: B006\n self._f = func\n self._role = role # e.g. \"func\" or \"meth\"\n\n if doc is None:\n if func is None:\n raise ValueError('No function or docstring given')\n doc = inspect.getdoc(func) or ''\n NumpyDocString.__init__(self, doc)\n\n if not self['Signature'] and func is not None:\n func, func_name = self.get_func()\n try:\n # try to read signature\n argspec = inspect.getfullargspec(func)\n argspec = inspect.formatargspec(*argspec)\n argspec = argspec.replace('*', r'\\*')\n signature = '%s%s' % (func_name, argspec) # noqa: UP031\n except TypeError:\n signature = '%s()' % func_name # noqa: UP031\n self['Signature'] = signature\n\n def get_func(self): # noqa: D102\n func_name = getattr(self._f, '__name__', self.__class__.__name__)\n if inspect.isclass(self._f):\n func = getattr(self._f, '__call__', self._f.__init__) # noqa: B004\n else:\n func = self._f\n return func, func_name\n\n def __str__(self):\n out = ''\n\n func, func_name = self.get_func()\n signature = self['Signature'].replace('*', r'\\*') # noqa: F841\n\n roles = {'func': 'function', 'meth': 'method'}\n\n if self._role:\n if self._role not in roles:\n print('Warning: invalid role %s' % self._role) # noqa: UP031\n out += '.. %s:: %s\\n \\n\\n' % (roles.get(self._role, ''), func_name) # noqa: UP031\n\n out += super().__str__(func_role=self._role)\n return out\n\n\nclass ClassDoc(NumpyDocString): # noqa: D101\n extra_public_methods = ['__call__']\n\n def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc, config={}): # noqa: B006\n if not inspect.isclass(cls) and cls is not None:\n raise ValueError('Expected a class or None, but got %r' % cls) # noqa: UP031\n self._cls = cls\n\n self.show_inherited_members = config.get('show_inherited_class_members', True)\n\n if modulename and not modulename.endswith('.'):\n modulename += '.'\n self._mod = modulename\n\n if doc is None:\n if cls is None:\n raise ValueError('No class or documentation string given')\n doc = pydoc.getdoc(cls)\n\n NumpyDocString.__init__(self, doc)\n\n if config.get('show_class_members', True):\n\n def splitlines_x(s):\n if not s:\n return []\n else:\n return s.splitlines()\n\n for field, items in [('Methods', self.methods), ('Attributes', self.properties)]:\n if not self[field]:\n doc_list = []\n for name in sorted(items):\n try:\n doc_item = pydoc.getdoc(getattr(self._cls, name))\n doc_list.append((name, '', splitlines_x(doc_item)))\n except AttributeError:\n pass # method doesn't exist\n self[field] = doc_list\n\n @property\n def methods(self): # noqa: D102\n if self._cls is None:\n return []\n return [\n name\n for name, func in inspect.getmembers(self._cls)\n if (\n (not name.startswith('_') or name in self.extra_public_methods)\n and isinstance(func, collections.abc.Callable)\n and self._is_show_member(name)\n )\n ]\n\n @property\n def properties(self): # noqa: D102\n if self._cls is None:\n return []\n return [\n name\n for name, func in inspect.getmembers(self._cls)\n if (\n not name.startswith('_')\n and (\n func is None or isinstance(func, property) or inspect.isgetsetdescriptor(func)\n )\n and self._is_show_member(name)\n )\n ]\n\n def _is_show_member(self, name):\n if self.show_inherited_members:\n return True # show all class members\n if name not in self._cls.__dict__:\n return False # class member is inherited, we do not show it\n return True\n" }, { "path": "doc/sphinxext/docscrape_sphinx.py", "content": "import inspect # noqa: D100\nimport pydoc\nimport re\nimport textwrap\n\nimport sphinx\nfrom docscrape import ClassDoc, FunctionDoc, NumpyDocString\n\n\nclass SphinxDocString(NumpyDocString): # noqa: D101\n def __init__(self, docstring, config={}): # noqa: B006\n self.use_plots = config.get('use_plots', False)\n NumpyDocString.__init__(self, docstring, config=config)\n\n # string conversion routines\n def _str_header(self, name, symbol='`'):\n return ['.. rubric:: ' + name, '']\n\n def _str_field_list(self, name):\n return [':' + name + ':']\n\n def _str_indent(self, doc, indent=4):\n out = []\n for line in doc:\n out += [' ' * indent + line]\n return out\n\n def _str_signature(self):\n return ['']\n if self['Signature']:\n return ['``%s``' % self['Signature']] + [''] # noqa: UP031\n else:\n return ['']\n\n def _str_summary(self):\n return self['Summary'] + ['']\n\n def _str_extended_summary(self):\n return self['Extended Summary'] + ['']\n\n def _str_param_list(self, name):\n out = []\n if self[name]:\n out += self._str_field_list(name)\n out += ['']\n for param, param_type, desc in self[name]:\n out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) # noqa: UP031\n out += ['']\n out += self._str_indent(desc, 8)\n out += ['']\n return out\n\n @property\n def _obj(self):\n if hasattr(self, '_cls'):\n return self._cls\n elif hasattr(self, '_f'):\n return self._f\n return None\n\n def _str_member_list(self, name):\n \"\"\"Generate a member listing, autosummary:: table where possible,\n and a table where not.\n\n \"\"\"\n out = []\n if self[name]:\n out += ['.. rubric:: %s' % name, ''] # noqa: UP031\n prefix = getattr(self, '_name', '')\n\n if prefix:\n prefix = '~%s.' % prefix # noqa: UP031\n\n autosum = []\n others = []\n for param, param_type, desc in self[name]:\n param = param.strip()\n if not self._obj or hasattr(self._obj, param):\n autosum += [' %s%s' % (prefix, param)] # noqa: UP031\n else:\n others.append((param, param_type, desc))\n\n if autosum:\n out += ['.. autosummary::', ' :toctree:', '']\n out += autosum\n\n if others:\n maxlen_0 = max([len(x[0]) for x in others])\n maxlen_1 = max([len(x[1]) for x in others])\n hdr = '=' * maxlen_0 + ' ' + '=' * maxlen_1 + ' ' + '=' * 10\n fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1) # noqa: UP031\n n_indent = maxlen_0 + maxlen_1 + 4\n out += [hdr]\n for param, param_type, desc in others:\n out += [fmt % (param.strip(), param_type)]\n out += self._str_indent(desc, n_indent)\n out += [hdr]\n out += ['']\n return out\n\n def _str_section(self, name):\n out = []\n if self[name]:\n out += self._str_header(name)\n out += ['']\n content = textwrap.dedent('\\n'.join(self[name])).split('\\n')\n out += content\n out += ['']\n return out\n\n def _str_see_also(self, func_role):\n out = []\n if self['See Also']:\n see_also = super()._str_see_also(func_role)\n out = ['.. seealso::', '']\n out += self._str_indent(see_also[2:])\n return out\n\n def _str_warnings(self):\n out = []\n if self['Warnings']:\n out = ['.. warning::', '']\n out += self._str_indent(self['Warnings'])\n return out\n\n def _str_index(self):\n idx = self['index']\n out = []\n if len(idx) == 0:\n return out\n\n out += ['.. index:: %s' % idx.get('default', '')] # noqa: UP031\n for section, references in idx.iteritems():\n if section == 'default':\n continue\n elif section == 'refguide':\n out += [' single: %s' % (', '.join(references))] # noqa: UP031\n else:\n out += [' %s: %s' % (section, ','.join(references))] # noqa: UP031\n return out\n\n def _str_references(self):\n out = []\n if self['References']:\n out += self._str_header('References')\n if isinstance(self['References'], str):\n self['References'] = [self['References']]\n out.extend(self['References'])\n out += ['']\n # Latex collects all references to a separate bibliography,\n # so we need to insert links to it\n if sphinx.__version__ >= '0.6':\n out += ['.. only:: latex', '']\n else:\n out += ['.. latexonly::', '']\n items = []\n for line in self['References']:\n m = re.match(r'.. \\[([a-z0-9._-]+)\\]', line, re.I)\n if m:\n items.append(m.group(1))\n out += [' ' + ', '.join(['[%s]_' % item for item in items]), ''] # noqa: UP031\n return out\n\n def _str_examples(self):\n examples_str = '\\n'.join(self['Examples'])\n\n if self.use_plots and 'import matplotlib' in examples_str and 'plot::' not in examples_str:\n out = []\n out += self._str_header('Examples')\n out += ['.. plot::', '']\n out += self._str_indent(self['Examples'])\n out += ['']\n return out\n else:\n return self._str_section('Examples')\n\n def __str__(self, indent=0, func_role='obj'):\n out = []\n out += self._str_signature()\n out += self._str_index() + ['']\n out += self._str_summary()\n out += self._str_extended_summary()\n for param_list in ('Parameters', 'Returns', 'Other Parameters', 'Raises', 'Warns'):\n out += self._str_param_list(param_list)\n out += self._str_warnings()\n out += self._str_see_also(func_role)\n out += self._str_section('Notes')\n out += self._str_references()\n out += self._str_examples()\n for param_list in ('Attributes', 'Methods'):\n out += self._str_member_list(param_list)\n out = self._str_indent(out, indent)\n return '\\n'.join(out)\n\n\nclass SphinxFunctionDoc(SphinxDocString, FunctionDoc): # noqa: D101\n def __init__(self, obj, doc=None, config={}): # noqa: B006\n self.use_plots = config.get('use_plots', False)\n FunctionDoc.__init__(self, obj, doc=doc, config=config)\n\n\nclass SphinxClassDoc(SphinxDocString, ClassDoc): # noqa: D101\n def __init__(self, obj, doc=None, func_doc=None, config={}): # noqa: B006\n self.use_plots = config.get('use_plots', False)\n ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config)\n\n\nclass SphinxObjDoc(SphinxDocString): # noqa: D101\n def __init__(self, obj, doc=None, config={}): # noqa: B006\n self._f = obj\n SphinxDocString.__init__(self, doc, config=config)\n\n\ndef get_doc_object(obj, what=None, doc=None, config={}): # noqa: B006, D103\n if what is None:\n if inspect.isclass(obj):\n what = 'class'\n elif inspect.ismodule(obj):\n what = 'module'\n elif callable(obj):\n what = 'function'\n else:\n what = 'object'\n if what == 'class':\n return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc, config=config)\n elif what in ('function', 'method'):\n return SphinxFunctionDoc(obj, doc=doc, config=config)\n else:\n if doc is None:\n doc = pydoc.getdoc(obj)\n return SphinxObjDoc(obj, doc, config=config)\n" }, { "path": "doc/sphinxext/github.py", "content": "\"\"\"Define text roles for GitHub\n\n* ghissue - Issue\n* ghpull - Pull Request\n* ghuser - User\n\nAdapted from bitbucket example here:\nhttps://bitbucket.org/birkenfeld/sphinx-contrib/src/tip/bitbucket/sphinxcontrib/bitbucket.py\n\nAuthors\n-------\n\n* Doug Hellmann\n* Min RK\n\"\"\"\n#\n# Original Copyright (c) 2010 Doug Hellmann. All rights reserved.\n#\n\nfrom docutils import nodes, utils\nfrom docutils.parsers.rst.roles import set_classes\n\n\ndef make_link_node(rawtext, app, type, slug, options): # noqa: A002\n \"\"\"Create a link to a github resource.\n\n :param rawtext: Text being replaced with link node.\n :param app: Sphinx application context\n :param type: Link type (issues, changeset, etc.)\n :param slug: ID of the thing to link to\n :param options: Options dictionary passed to role func.\n \"\"\"\n try:\n base = app.config.github_project_url\n if not base:\n raise AttributeError\n if not base.endswith('/'):\n base += '/'\n except AttributeError as err:\n raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) # noqa: B904, UP031\n\n ref = base + type + '/' + slug + '/'\n set_classes(options)\n prefix = '#'\n if type == 'pull':\n prefix = 'PR ' + prefix\n node = nodes.reference(rawtext, prefix + utils.unescape(slug), refuri=ref, **options)\n return node\n\n\ndef ghissue_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006\n \"\"\"Link to a GitHub issue.\n\n Returns 2 part tuple containing list of nodes to insert into the\n document and a list of system messages. Both are allowed to be\n empty.\n\n :param name: The role name used in the document.\n :param rawtext: The entire markup snippet, with role.\n :param text: The text marked with the role.\n :param lineno: The line number where rawtext appears in the input.\n :param inliner: The inliner instance that called us.\n :param options: Directive options for customization.\n :param content: The directive content for customization.\n \"\"\"\n try:\n issue_num = int(text)\n if issue_num <= 0:\n raise ValueError\n except ValueError:\n msg = inliner.reporter.error(\n 'GitHub issue number must be a number greater than or equal to 1; ' # noqa: UP031\n '\"%s\" is invalid.' % text,\n line=lineno,\n )\n prb = inliner.problematic(rawtext, rawtext, msg)\n return [prb], [msg]\n app = inliner.document.settings.env.app\n # app.info('issue %r' % text)\n if 'pull' in name.lower():\n category = 'pull'\n elif 'issue' in name.lower():\n category = 'issues'\n else:\n msg = inliner.reporter.error(\n 'GitHub roles include \"ghpull\" and \"ghissue\", \"%s\" is invalid.' % name, line=lineno # noqa: UP031\n )\n prb = inliner.problematic(rawtext, rawtext, msg)\n return [prb], [msg]\n node = make_link_node(rawtext, app, category, str(issue_num), options)\n return [node], []\n\n\ndef ghuser_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006\n \"\"\"Link to a GitHub user.\n\n Returns 2 part tuple containing list of nodes to insert into the\n document and a list of system messages. Both are allowed to be\n empty.\n\n :param name: The role name used in the document.\n :param rawtext: The entire markup snippet, with role.\n :param text: The text marked with the role.\n :param lineno: The line number where rawtext appears in the input.\n :param inliner: The inliner instance that called us.\n :param options: Directive options for customization.\n :param content: The directive content for customization.\n \"\"\"\n app = inliner.document.settings.env.app # noqa: F841\n # app.info('user link %r' % text)\n ref = 'https://www.github.com/' + text\n node = nodes.reference(rawtext, text, refuri=ref, **options)\n return [node], []\n\n\ndef ghcommit_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006\n \"\"\"Link to a GitHub commit.\n\n Returns 2 part tuple containing list of nodes to insert into the\n document and a list of system messages. Both are allowed to be\n empty.\n\n :param name: The role name used in the document.\n :param rawtext: The entire markup snippet, with role.\n :param text: The text marked with the role.\n :param lineno: The line number where rawtext appears in the input.\n :param inliner: The inliner instance that called us.\n :param options: Directive options for customization.\n :param content: The directive content for customization.\n \"\"\"\n app = inliner.document.settings.env.app\n # app.info('user link %r' % text)\n try:\n base = app.config.github_project_url\n if not base:\n raise AttributeError\n if not base.endswith('/'):\n base += '/'\n except AttributeError as err:\n raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) # noqa: B904, UP031\n\n ref = base + text\n node = nodes.reference(rawtext, text[:6], refuri=ref, **options)\n return [node], []\n\n\ndef setup(app):\n \"\"\"Install the plugin.\n\n :param app: Sphinx application context.\n \"\"\"\n app.info('Initializing GitHub plugin')\n app.add_role('ghissue', ghissue_role)\n app.add_role('ghpull', ghissue_role)\n app.add_role('ghuser', ghuser_role)\n app.add_role('ghcommit', ghcommit_role)\n app.add_config_value('github_project_url', None, 'env')\n return\n" }, { "path": "doc/sphinxext/math_dollar.py", "content": "import re # noqa: D100\n\n\ndef dollars_to_math(source):\n r\"\"\"Replace dollar signs with backticks.\n\n More precisely, do a regular expression search. Replace a plain\n dollar sign ($) by a backtick (`). Replace an escaped dollar sign\n (\\$) by a dollar sign ($). Don't change a dollar sign preceded or\n followed by a backtick (`$ or $`), because of strings like\n \"``$HOME``\". Don't make any changes on lines starting with\n spaces, because those are indented and hence part of a block of\n code or examples.\n\n This also doesn't replaces dollar signs enclosed in curly braces,\n to avoid nested math environments, such as ::\n\n $f(n) = 0 \\text{ if $n$ is prime}$\n\n Thus the above line would get changed to\n\n `f(n) = 0 \\text{ if $n$ is prime}`\n \"\"\"\n s = '\\n'.join(source)\n if s.find('$') == -1:\n return\n # This searches for \"$blah$\" inside a pair of curly braces --\n # don't change these, since they're probably coming from a nested\n # math environment. So for each match, we replace it with a temporary\n # string, and later on we substitute the original back.\n global _data\n _data = {}\n\n def repl(matchobj):\n global _data\n s = matchobj.group(0)\n t = '___XXX_REPL_%d___' % len(_data) # noqa: UP031\n _data[t] = s\n return t\n\n s = re.sub(r'({[^{}$]*\\$[^{}$]*\\$[^{}]*})', repl, s)\n # matches $...$\n dollars = re.compile(r'(?\n //Function to make the index toctree collapsible\n $(function () {\n $('.toctree-l2')\n .click(function(event){\n if (event.target.tagName.toLowerCase() != \"a\") {\n if ($(this).children('ul').length > 0) {\n $(this).attr('data-content',\n (!$(this).children('ul').is(':hidden')) ? '\\u25ba' : '\\u25bc');\n $(this).children('ul').toggle();\n }\n return true; //Makes links clickable\n }\n })\n .mousedown(function(event){ return false; }) //Firefox highlighting fix\n .children('ul').hide();\n // Initialize the values\n $('li.toctree-l2:not(:has(ul))').attr('data-content', '-');\n $('li.toctree-l2:has(ul)').attr('data-content', '\\u25ba');\n $('li.toctree-l2:has(ul)').css('cursor', 'pointer');\n\n $('.toctree-l2').hover(\n function () {\n if ($(this).children('ul').length > 0) {\n $(this).css('background-color', '#D0D0D0').children('ul').css('background-color', '#F0F0F0');\n $(this).attr('data-content',\n (!$(this).children('ul').is(':hidden')) ? '\\u25bc' : '\\u25ba');\n }\n else {\n $(this).css('background-color', '#F9F9F9');\n }\n },\n function () {\n $(this).css('background-color', 'white').children('ul').css('background-color', 'white');\n if ($(this).children('ul').length > 0) {\n $(this).attr('data-content',\n (!$(this).children('ul').is(':hidden')) ? '\\u25bc' : '\\u25ba');\n }\n }\n );\n });\n\n \n\n \n" }, { "path": "doc/user_guide.rst", "content": "\n.. _user_guide:\n\nUser guide\n==========\n\n.. sidebar:: **Download for offline viewing**\n\n Download the `user guide and examples\n `_.\n\n.. toctree::\n :numbered:\n\n introduction.rst\n layout/index.rst\n analysis/index.rst\n reports/index.rst" }, { "path": "doc/whats_new.rst", "content": ".. include:: ../CHANGELOG.rst\n" }, { "path": "long_description.rst", "content": "PyBIDS\n======\nPyBIDS is a Python module to interface with datasets conforming BIDS.\nSee BIDS paper_ and https://bids.neuroimaging.io website for more information.\n\n.. _paper: https://www.nature.com/articles/sdata201644\n\nLicense\n=======\n``pybids`` is licensed under the terms of the MIT license. See the file\n\"LICENSE\" for information on the history of this software, terms & conditions\nfor usage, and a DISCLAIMER OF ALL WARRANTIES.\n\nAll trademarks referenced herein are property of their respective holders.\n\nCopyright (c) 2016--, PyBIDS developers, Planet Earth\n" }, { "path": "paper/paper.bib", "content": "@article{Gorgolewski2016-sk,\n title = \"A Practical Guide for Improving Transparency and Reproducibility\n in Neuroimaging Research\",\n author = {Gorgolewski, Krzysztof J and\n Poldrack, Russell A},\n abstract = \"Recent years have seen an increase in alarming signals regarding\n the lack of replicability in neuroscience, psychology, and other\n related fields. To avoid a widespread crisis in neuroimaging\n research and consequent loss of credibility in the public eye, we\n need to improve how we do science. This article aims to be a\n practical guide for researchers at any stage of their careers\n that will help them make their research more reproducible and\n transparent while minimizing the additional effort that this\n might require. The guide covers three major topics in open\n science (data, code, and publications) and offers practical\n advice as well as highlighting advantages of adopting more open\n research practices that go beyond improved transparency and\n reproducibility.\",\n journal = \"PLOS Biology\",\n volume = 14,\n number = 7,\n pages = \"e1002506\",\n month = jul,\n year = 2016,\n doi = {10.1371/journal.pbio.1002506},\n language = \"en\"\n}\n\n@article{Gorgolewski2016-nw,\n title = \"The brain imaging data structure, a format for organizing and\n describing outputs of neuroimaging experiments\",\n author = {Gorgolewski, Krzysztof J and\n Auer, Tibor and\n Calhoun, Vince D and\n Craddock, R Cameron and\n Das, Samir and\n Duff, Eugene P and\n Flandin, Guillaume and\n Ghosh, Satrajit S and\n Glatard, Tristan and\n Halchenko, Yaroslav O and\n Handwerker, Daniel A and\n Hanke, Michael and\n Keator, David and\n Li, Xiangrui and\n Michael, Zachary and\n Maumet, Camille and\n Nichols, B Nolan and\n Nichols, Thomas E and\n Pellman, John and\n Poline, Jean-Baptiste and\n Rokem, Ariel and\n Schaefer, Gunnar and\n Sochat, Vanessa and\n Triplett, William and\n Turner, Jessica A and\n Varoquaux, Ga{\\\"e}l and\n Poldrack, Russell A},\n abstract = \"The development of magnetic resonance imaging (MRI) techniques\n has defined modern neuroimaging. Since its inception, tens of\n thousands of studies using techniques such as functional MRI and\n diffusion weighted imaging have allowed for the non-invasive\n study of the brain. Despite the fact that MRI is routinely used\n to obtain data for neuroscience research, there has been no\n widely adopted standard for organizing and describing the data\n collected in an imaging experiment. This renders sharing and\n reusing data (within or between labs) difficult if not impossible\n and unnecessarily complicates the application of automatic\n pipelines and quality assurance protocols. To solve this problem,\n we have developed the Brain Imaging Data Structure (BIDS), a\n standard for organizing and describing MRI datasets. The BIDS\n standard uses file formats compatible with existing software,\n unifies the majority of practices already common in the field,\n and captures the metadata necessary for most common data\n processing operations.\",\n journal = \"Scientific Data\",\n volume = 3,\n pages = \"160044\",\n month = jun,\n year = 2016,\n doi = {10.1038/sdata.2016.44},\n language = \"en\"\n}\n\n@article{Gorgolewski2017-cz,\n title = \"{BIDS} apps: Improving ease of use, accessibility, and\n reproducibility of neuroimaging data analysis methods\",\n author = {Gorgolewski, Krzysztof J and\n Alfaro-Almagro, Fidel and\n Auer, Tibor and\n Bellec, Pierre and\n Capot{\\u a}, Mihai and\n Chakravarty, M Mallar and\n Churchill, Nathan W and\n Cohen, Alexander Li and\n Craddock, R Cameron and\n Devenyi, Gabriel A and\n Eklund, Anders and\n Esteban, Oscar and\n Flandin, Guillaume and\n Ghosh, Satrajit S and\n Guntupalli, J Swaroop and\n Jenkinson, Mark and\n Keshavan, Anisha and\n Kiar, Gregory and\n Liem, Franziskus and\n Raamana, Pradeep Reddy and\n Raffelt, David and\n Steele, Christopher J and\n Quirion, Pierre-Olivier and\n Smith, Robert E and\n Strother, Stephen C and\n Varoquaux, Ga{\\\"e}l and\n Wang, Yida and\n Yarkoni, Tal and\n Poldrack, Russell A},\n abstract = \"The rate of progress in human neurosciences is limited by the\n inability to easily apply a wide range of analysis methods to the\n plethora of different datasets acquired in labs around the world.\n In this work, we introduce a framework for creating, testing,\n versioning and archiving portable applications for analyzing\n neuroimaging data organized and described in compliance with the\n Brain Imaging Data Structure (BIDS). The portability of these\n applications (BIDS Apps) is achieved by using container\n technologies that encapsulate all binary and other dependencies\n in one convenient package. BIDS Apps run on all three major\n operating systems with no need for complex setup and\n configuration and thanks to the comprehensiveness of the BIDS\n standard they require little manual user input. Previous\n containerized data processing solutions were limited to single\n user environments and not compatible with most multi-tenant High\n Performance Computing systems. BIDS Apps overcome this limitation\n by taking advantage of the Singularity container technology. As a\n proof of concept, this work is accompanied by 22 ready to use\n BIDS Apps, packaging a diverse set of commonly used neuroimaging\n algorithms.\",\n journal = \"PLOS Computational Biology\",\n volume = 13,\n number = 3,\n pages = \"e1005209\",\n month = mar,\n year = 2017,\n doi = {10.1371/journal.pcbi.1005209},\n language = \"en\"\n}\n\n@article{Poldrack2017-gz,\n title = \"Scanning the horizon: towards transparent and reproducible\n neuroimaging research\",\n author = {Poldrack, Russell A and\n Baker, Chris I and\n Durnez, Joke and\n Gorgolewski, Krzysztof J and\n Matthews, Paul M and\n Munaf{\\`o}, Marcus R and\n Nichols, Thomas E and\n Poline, Jean-Baptiste and\n Vul, Edward and\n Yarkoni, Tal},\n abstract = \"Functional neuroimaging techniques have transformed our ability\n to probe the neurobiological basis of behaviour and are\n increasingly being applied by the wider neuroscience community.\n However, concerns have recently been raised that the conclusions\n that are drawn from some human neuroimaging studies are either\n spurious or not generalizable. Problems such as low statistical\n power, flexibility in data analysis, software errors and a lack\n of direct replication apply to many fields, but perhaps\n particularly to functional MRI. Here, we discuss these problems,\n outline current and suggested best practices, and describe how we\n think the field should evolve to produce the most meaningful and\n reliable answers to neuroscientific questions.\",\n journal = \"Nature Reviews Neuroscience\",\n volume = 18,\n number = 2,\n pages = \"115--126\",\n month = feb,\n year = 2017,\n doi = {10.1038/nrn.2016.167},\n language = \"en\"\n}\n\n@article{mriqc,\n title={MRIQC: Advancing the automatic prediction of image quality in {MRI} from unseen sites},\n author={Esteban, Oscar and\n Birman, Daniel and\n Schaer, Marie and\n Koyejo, Oluwasanmi O and\n Poldrack, Russell A and\n Gorgolewski, Krzysztof J},\n journal={PLOS One},\n volume={12},\n number={9},\n pages={e0184661},\n year={2017},\n doi={10.1371/journal.pone.0184661},\n publisher={Public Library of Science}\n}\n\n@article{fmriprep,\n title={FMRIPrep: a robust preprocessing pipeline for functional {MRI}},\n author={Esteban, Oscar and\n Markiewicz, Christopher J and\n Blair, Ross W and\n Moodie, Craig A and\n Isik, A Ilkay and\n Erramuzpe, Asier and\n Kent, James D and\n Goncalves, Mathias and\n DuPre, Elizabeth and\n Snyder, Madeleine and\n Oya, Hiroyuki and\n Ghosh, Satrajit S and\n Wright, Jessey and\n Durnez, Joke and\n Poldrack, Russell A and\n Gorgolewski, Krzysztof J},\n journal={Nature Methods},\n volume={16},\n number={1},\n pages={111},\n year={2019},\n doi={10.1038/s41592-018-0235-4},\n publisher={Nature Publishing Group}\n}\n" }, { "path": "paper/paper.md", "content": "---\ntitle: 'PyBIDS: Python tools for BIDS datasets'\ntags:\n - Python\n - BIDS\n - neuroimaging\nauthors:\n - affiliation: 1\n name: Tal Yarkoni\n orcid: 0000-0002-6558-5113\n - affiliation: 2\n name: Christopher J Markiewicz\n orcid: 0000-0002-6533-164X\n - affiliation: 1\n name: Alejandro de la Vega\n orcid: 0000-0001-9062-3778\n - affiliation: 2\n name: Krzysztof J Gorgolewski\n orcid: 0000-0003-3321-7583\n - affiliation: 3\n name: Taylor Salo\n orcid: 0000-0001-9813-3167\n - affiliation: 4\n name: Yaroslav O Halchenko\n orcid: 0000-0003-3456-2493\n - affiliation: 1\n name: Quinten McNamara\n - affiliation: 5\n name: Krista DeStasio\n orcid: 0000-0002-3959-9060\n - affiliation: 6\n name: Jean-Baptiste Poline\n orcid: 0000-0002-9794-749X\n - affiliation: 7\n name: Dmitry Petrov\n - affiliation: 8\n name: Valérie Hayot-Sasson\n orcid: 0000-0002-4830-4535\n - affiliation: 9\n name: Dylan M Nielson\n orcid: 0000-0003-4613-6643\n - affiliation: 10\n name: Johan Carlin\n orcid: 0000-0003-0933-1239\n - affiliation: 11\n name: Gregory Kiar\n orcid: 0000-0001-8915-496X\n - affiliation: 12\n name: Kirstie Whitaker\n orcid: 0000-0001-8498-4059\n - affiliation: 11\n name: Elizabeth DuPre\n orcid: 0000-0003-1358-196X\n - affiliation: 13\n name: Adina Wagner\n orcid: 0000-0003-2917-3450\n - affiliation: 14\n name: Lee S Tirrell\n orcid: 0000-0001-9393-8361\n - affiliation: 15\n name: Mainak Jas\n orcid: 0000-0002-3199-9027\n - affiliation: 13\n name: Michael Hanke\n orcid: 0000-0001-6398-6370\n - affiliation: 2\n name: Russell A Poldrack\n orcid: 0000-0001-6755-0259\n - affiliation: 2\n name: Oscar Esteban\n orcid: 0000-0001-8435-6191\n - affiliation: 16\n name: Stefan Appelhoff\n orcid: 0000-0001-8002-0877\n - affiliation: 17\n name: Chris Holdgraf\n orcid: 0000-0002-2391-0678\n - affiliation: 18\n name: Isla Staden\n orcid: 0000-0002-0795-1154\n - affiliation: 19\n name: Bertrand Thirion\n orcid: 0000-0001-5018-7895\n - affiliation: 20\n name: Dave F Kleinschmidt\n orcid: 0000-0002-7442-2762\n - affiliation: 9\n name: John A Lee\n orcid: 0000-0001-5884-4247\n - affiliation: 17\n name: Matteo Visconti di Oleggio Castello\n orcid: 0000-0001-7931-5272\n - affiliation: 21\n name: Michael P Notter\n orcid: 0000-0002-5866-047X\n - affiliation: 2\n name: Ross Blair\n orcid: 0000-0003-3007-1056\naffiliations:\n- index: 1\n name: University of Texas at Austin\n- index: 2\n name: Stanford University\n- index: 3\n name: Florida International University\n- index: 4\n name: Dartmouth College\n- index: 5\n name: University of Oregon\n- index: 6\n name: McGill University\n- index: 7\n name: University of Southern California\n- index: 8\n name: Concordia University\n- index: 9\n name: National Institute of Mental Health\n- index: 10\n name: MRC Cognition and Brain Sciences Unit\n- index: 11\n name: Montreal Neurological Institute and Hospital\n- index: 12\n name: Alan Turing Institute\n- index: 13\n name: Otto-von-Guericke University Magdeburg\n- index: 14\n name: CorticoMetrics LLC\n- index: 15\n name: Télécom ParisTech, France\n- index: 16\n name: Max Planck Institute for Human Development, Berlin, Germany\n- index: 17\n name: University of California at Berkeley\n- index: 18\n name: Queen Mary University London\n- index: 19\n name: INRIA\n- index: 20\n name: Rutgers University\n- index: 21\n name: University of Lausanne\ndate: 19 February 2019\nbibliography: paper.bib\n---\n\n# Summary\n\nBrain imaging researchers regularly work with large, heterogeneous,\nhigh-dimensional datasets. Historically, researchers have dealt with this\ncomplexity idiosyncratically, with every lab or individual implementing their\nown preprocessing and analysis procedures. The resulting lack of field-wide\nstandards has severely limited reproducibility and data sharing and reuse.\n\nTo address this problem, we and others recently introduced the Brain Imaging\nData Standard (``BIDS``; [@Gorgolewski2016-nw]), a specification meant to\nstandardize the process of representing brain imaging data. BIDS is\ndeliberately designed with adoption in mind; it adheres to a user-focused\nphilosophy that prioritizes common use cases and discourages complexity. By\nsuccessfully encouraging a large and ever-growing subset of the community to\nadopt a common standard for naming and organizing files, BIDS has made it much\neasier for researchers to share, reuse, and process their data\n[@Gorgolewski2017-cz].\n\nThe ability to efficiently develop high-quality spec-compliant applications\nitself depends to a large extent on the availability of good tooling.\nBecause many operations recur widely across diverse contexts—for example,\nalmost every tool designed to work with BIDS datasets involves regular\nfile-filtering operations—there is a strong incentive to develop utility\nlibraries that provide common functionality via a standardized, simple API.\n\n``PyBIDS`` is a Python package that makes it easier to work with BIDS\ndatasets. In principle, its scope includes virtually any functionality that is\nlikely to be of general use when working with BIDS datasets (*i.e.*, that is not\nspecific to one narrow context). At present, its core and most widely used\nmodule supports simple and flexible querying and manipulation of BIDS datasets.\nPyBIDS makes it easy for researchers and developers working in Python to search\nfor BIDS files by keywords and/or metadata; to consolidate and retrieve\nfile-associated metadata spread out across multiple levels of a BIDS hierarchy;\nto construct BIDS-valid path names for new files; and to validate projects\nagainst the BIDS specification, among other applications.\n\nIn addition to this core functionality, PyBIDS also contains an ever-growing\nset of modules that support additional capabilities meant to keep up with the\nevolution and expansion of the BIDS specification itself. Currently, PyBIDS\nincludes tools for (1) reading and manipulating data contained in various\nBIDS-defined files (*e.g.*, physiological recordings, event files, or\nparticipant-level variables); (2) constructing design matrices and contrasts\nthat support the new ``BIDS-StatsModel`` specification (for machine-readable\nrepresentation of fMRI statistical models); and (3) automated generation of\npartial Methods sections for inclusion in publications.\n\nPyBIDS can be easily installed on all platforms via pip (``pip install\npybids``), though currently it is not officially supported on Windows. The\npackage has few dependencies outside of standard Python numerical and image\nanalysis libraries (*i.e.*, numpy, scipy, pandas, and NiBabel). The core API\nis deliberately kept minimalistic: nearly all interactions with PyBIDS\nfunctionality occur through a core ``BIDSLayout`` object initialized by passing\nin a path to a BIDS dataset. For most applications, no custom configuration\nshould be required.\n\nAlthough technically still in alpha release, PyBIDS is already being used both\nas a dependency in dozens of other open-source brain imaging packages --\n*e.g.*, fMRIPrep [@fmriprep], MRIQC [@mriqc], datalad-neuroimaging\n(https://github.com/datalad/datalad-neuroimaging), and fitlins\n(https://github.com/poldracklab/fitlins) -- and directly in many researchers'\ncustom Python workflows. Development is extremely active, with bug fixes and\nnew features continually being added (https://github.com/bids-standard/pybids),\nand major releases occurring approximately every 6 months. As of this writing,\n29 people have contributed code to PyBIDS, and many more have provided feedback\nand testing. The API is relatively stable, and documentation and testing\nstandards follow established norms for open-source scientific software. We\nencourage members of the brain imaging community currently working in Python to\ntry using PyBIDS, and welcome new contributions.\n\n# Acknowledgements\n\nPyBIDS development is partly supported by NIH awards R01MH109682 (PI: Yarkoni),\nR24MH114705 (PI: Poldrack), R01EB020740 (PI: Ghosh), and P41EB019936 (PI:\nKennedy), and NSF award 1429999 (PI: Halchenko).\n\n# References\n" }, { "path": "pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\", \"versioneer[toml]\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"pybids\"\ndescription = \"bids: interface with datasets conforming to BIDS\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\nlicense = { file=\"LICENSE\" }\nauthors = [\n { name = \"PyBIDS Developers\", email = \"bids-discussion@googlegroups.com\" },\n]\nkeywords = [\"BIDS\", \"neuroimaging\"]\nclassifiers = [\n \"Development Status :: 3 - Alpha\",\n \"Environment :: Console\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Topic :: Scientific/Engineering\",\n]\ndependencies = [\n \"numpy >=1.25\", # SPEC 0 + 1yr (3yr)\n \"scipy >=1.11\", # SPEC 0 + 1yr (3yr)\n \"nibabel >=5.1\", # SPEC 0 + 1yr (3yr)\n \"pandas >=2.0\", # SPEC 0 + 1yr (3yr)\n \"formulaic >=0.3\",\n \"sqlalchemy >=1.4.31\",\n \"bids-validator>=1.14.7\", # Keep up-to-date to ensure support for recent modalities\n \"bidsschematools>=1.1.0\", # Required for schema-based configs\n \"num2words >=0.5.10\",\n \"click >=8.0\",\n \"universal_pathlib >=0.2.2\",\n \"frozendict >=2.3\",\n]\ndynamic = [\"version\"]\n\n[project.optional-dependencies]\ndoc = [\n \"sphinx >=6.2\",\n \"numpydoc\",\n \"sphinx_rtd_theme\",\n \"myst_nb\",\n \"jupytext\",\n]\nplotting = [\"graphviz\"]\ntest = [\n \"pytest >= 8\",\n \"pytest-cov >= 7\",\n \"bsmschema >= 0.1\",\n \"coverage[toml] >= 7\",\n \"altair >= 5\",\n \"pytest-xdist >= 3.8\",\n \"s3fs >= 2025\" #for testing remote uri\n]\nmodel_reports = [\n \"jinja2\",\n \"altair\"\n]\ntutorial = [\n \"nbconvert\",\n \"jinja2\",\n \"markupsafe\",\n \"jupyter_client\",\n \"ipykernel\",\n]\n# Aliases\ndocs = [\"pybids[doc]\"]\ntests = [\"pybids[test]\"]\nci_tests = [\"pybids[test]\"]\ndev = [\"pybids[doc,test,plotting]\"]\n\n[project.urls]\nHomepage = \"https://github.com/bids-standard/pybids\"\n\"Bug Tracker\" = \"https://github.com/bids-standard/pybids/issues\"\n\n[project.scripts]\npybids = \"bids.cli:cli\"\n\n[tool.setuptools.package-data]\n\"*\" = [\"data/*\", \"data/*/*\"]\n\"bids.layout.config\" = [\"*.json\"]\n\n[tool.versioneer]\nVCS = \"git\"\nstyle = \"pep440-pre\"\nversionfile_source = \"src/bids/_version.py\"\nversionfile_build = \"bids/_version.py\"\n\n[tool.coverage.run]\nbranch = true\nsource = [\"src/\", \"tests/\"]\nomit = [\n \"*/setup.py\",\n \"*/external/*\",\n \"*/due.py\",\n \"*/*version.py\",\n]\n\n[tool.coverage.paths]\nsource = [\n \"src/bids\",\n \"**/site-packages/bids\",\n]\n\n[tool.coverage.report]\ninclude = [\"src/*\", \"tests/*\"]\n\n" }, { "path": "ruff.toml", "content": "line-length = 99\nextend-exclude = [\n \"_version.py\", # Auto-generated\n \"tests/data\", # Submodules\n \"bids-examples\",\n \".cursor\",\n \".vscode\",\n \".venv\",\n \"paper\",\n \"scripts/\",\n \"tools/\",\n \"*tests/*\",\n \"*.ipynb\",\n \"src/bids/external/\",\n]\n\n[format]\nquote-style = \"single\"\nline-ending = \"lf\"\ndocstring-code-format = true\n\n[lint]\nextend-select = [\n \"F\", # pyflakes\n \"E\", # pycodestyle errors\n \"W\", # pycodestyle warnings\n \"I\", # isort\n \"D\", # pydocstyle\n \"UP\", # pyupgrade\n \"YTT\", # flake8-2020\n \"S\", # bandit\n \"BLE\", # blind-except\n \"B\", # bugbear\n \"A\", # builtins\n \"C4\", # comprehensions\n \"DTZ\", # datetimez\n \"T10\", # debugger\n # \"EM\", # errmsg\n \"EXE\", # executable\n \"ISC\", # implicit-str-concat\n \"ICN\", # import-conventions\n \"PT\", # pytest-style\n \"Q\", # quotes\n]\nignore = [\n \"D105\", # undocumented-magic-method\n \"D107\", # undocumented __init__\n \"D203\", # blank line before class docstring\n \"D213\", # multi-line-summary-first-line\n \"PT022\", # useless-yield-fixture; yield keeps fixtures/sessions alive\n \"Q000\", # double quotes\n \"Q003\", # avoidable-escaped-quote\n # --- Fix one group at a time; remove from ignore when done ---\n # 1. Docstrings (D*)\n \"D205\", # blank line between summary and description\n \"D400\", # ends with period\n \"D401\", # imperative mood\n \"D415\", # ends with period/question/exclamation\n]\n\n[lint.extend-per-file-ignores]\n\"setup.py\" = [\"D\"]\n\"src/bids/external/__init__.py\" = [\"D104\"]\n\"src/bids/modeling/report/__init__.py\" = [\"D104\"]\n\"*/test_*.py\" = [\n \"S101\", # assert\n \"D\",\n]\n" }, { "path": "schema_integration_demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BIDS Schema Integration Demo\\n\",\n \"\\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.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"from bids import BIDSLayout\\n\",\n \"from bids.tests import get_test_data_path\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setup: Using a test dataset\\n\",\n \"\\n\",\n \"We'll use the 7T TRT dataset that comes with PyBIDS for testing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using dataset at: /home/ashley/repos/bids/pybids/tests/data/7t_trt\\n\",\n \"total 76\\n\",\n \"drwxr-xr-x 12 ashley ashley 4096 Aug 21 08:59 .\\n\",\n \"drwxr-xr-x 8 ashley ashley 4096 Aug 21 08:59 ..\\n\",\n \"-rw-r--r-- 1 ashley ashley 0 Aug 21 08:59 README\\n\",\n \"-rw-r--r-- 1 ashley ashley 55 Aug 21 08:59 dataset_description.json\\n\",\n \"-rw-r--r-- 1 ashley ashley 482 Aug 21 08:59 participants.tsv\\n\",\n \"drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-01\\n\",\n \"drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-02\\n\",\n \"drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-03\\n\",\n \"drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-04\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Get path to test dataset\\n\",\n \"data_path = os.path.join(get_test_data_path(), '7t_trt')\\n\",\n \"print(f'Using dataset at: {data_path}')\\n\",\n \"\\n\",\n \"# List the first few files to see the structure\\n\",\n \"!ls -la {data_path} | head -10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 1: Legacy JSON-based Configuration\\n\",\n \"\\n\",\n \"The traditional PyBIDS approach uses JSON configuration files to define entities and their patterns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Legacy layout initialized with 339 files\\n\",\n \"\\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 \"\\n\",\n \"Layout info:\\n\",\n \"BIDS Layout: .../bids/pybids/tests/data/7t_trt | Subjects: 10 | Sessions: 20 | Runs: 20\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Initialize layout with legacy config (this is the default)\\n\",\n \"layout_legacy = BIDSLayout(data_path, config=['bids'])\\n\",\n \"print(f'Legacy layout initialized with {len(layout_legacy.get())} files')\\n\",\n \"print(f'\\\\nAvailable entities: {sorted(layout_legacy.get_entities().keys())}')\\n\",\n \"\\n\",\n \"# Print basic layout information like in the docs tutorial\\n\",\n \"print('\\\\nLayout info:')\\n\",\n \"print(layout_legacy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Found 6 BOLD files for subject 01:\\n\",\n \" sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\\n\",\n \" sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\\n\",\n \" sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz\\n\",\n \"\\n\",\n \"Compare return types:\\n\",\n \"return_types=[, ]\\n\",\n \"return_types=[, ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Query example: Get all BOLD files for subject 01\\n\",\n \"bold_files_legacy = layout_legacy.get(\\n\",\n \" subject='01',\\n\",\n \" suffix='bold',\\n\",\n \" extension='nii.gz',\\n\",\n \" return_type='filename', # Returns filenames instead of BIDSFile objects\\n\",\n \")\\n\",\n \"print(f'Found {len(bold_files_legacy)} BOLD files for subject 01:')\\n\",\n \"for f in bold_files_legacy:\\n\",\n \" print(f' {os.path.basename(f)}')\\n\",\n \"\\n\",\n \"# Show the difference between return types like in docs\\n\",\n \"print('\\\\nCompare return types:')\\n\",\n \"bold_objects = layout_legacy.get(subject='01', suffix='bold', extension='nii.gz')[:2]\\n\",\n \"bold_filenames = layout_legacy.get(\\n\",\n \" subject='01', suffix='bold', extension='nii.gz', return_type='filename'\\n\",\n \")[:2]\\n\",\n \"\\n\",\n \"print(f'return_types={[type(obj) for obj in bold_objects]}')\\n\",\n \"print(f'return_types={[type(fname) for fname in bold_filenames]}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 2: New Schema-based Configuration\\n\",\n \"\\n\",\n \"The new approach uses the BIDS schema directly via `bidsschematools` to understand the dataset structure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Schema-based layout initialized with 339 files\\n\",\n \"\\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 \"\\n\",\n \"Layout info:\\n\",\n \"BIDS Layout: .../bids/pybids/tests/data/7t_trt | Subjects: 10 | Sessions: 20 | Runs: 20\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Initialize layout with schema-based config\\n\",\n \"# This uses Config.load('bids-schema') internally when you pass 'bids-schema' in config\\n\",\n \"layout_schema = BIDSLayout(data_path, config=['bids-schema'])\\n\",\n \"print(f'Schema-based layout initialized with {len(layout_schema.get())} files')\\n\",\n \"print(f'\\\\nAvailable entities: {sorted(layout_schema.get_entities().keys())}')\\n\",\n \"\\n\",\n \"# Print basic layout information\\n\",\n \"print('\\\\nLayout info:')\\n\",\n \"print(layout_schema)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Found 6 BOLD files for subject 01:\\n\",\n \" sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\\n\",\n \" sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\\n\",\n \" sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\\n\",\n \" sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz\\n\",\n \"\\n\",\n \"Compare return types:\\n\",\n \"return_types=[, ]\\n\",\n \"return_types=[, ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Same query with schema-based layout\\n\",\n \"bold_files_schema = layout_schema.get(\\n\",\n \" subject='01', suffix='bold', extension='nii.gz', return_type='filename'\\n\",\n \")\\n\",\n \"print(f'Found {len(bold_files_schema)} BOLD files for subject 01:')\\n\",\n \"for f in bold_files_schema:\\n\",\n \" print(f' {os.path.basename(f)}')\\n\",\n \"\\n\",\n \"# Show the difference between return types like in docs\\n\",\n \"print('\\\\nCompare return types:')\\n\",\n \"bold_objects_schema = layout_schema.get(subject='01', suffix='bold', extension='nii.gz')[:2]\\n\",\n \"bold_filenames_schema = layout_schema.get(\\n\",\n \" subject='01', suffix='bold', extension='nii.gz', return_type='filename'\\n\",\n \")[:2]\\n\",\n \"\\n\",\n \"print(f'return_types={[type(obj) for obj in bold_objects_schema]}')\\n\",\n \"print(f'return_types={[type(fname) for fname in bold_filenames_schema]}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 3: Comparing the Two Approaches\\n\",\n \"\\n\",\n \"Let's verify that both approaches give us the same results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Legacy layout: 339 files\\n\",\n \"Schema layout: 339 files\\n\",\n \"\\n\",\n \"Match: True\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Compare file counts\\n\",\n \"legacy_count = len(layout_legacy.get())\\n\",\n \"schema_count = len(layout_schema.get())\\n\",\n \"\\n\",\n \"print(f'Legacy layout: {legacy_count} files')\\n\",\n \"print(f'Schema layout: {schema_count} files')\\n\",\n \"print(f'\\\\nMatch: {legacy_count == schema_count}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Legacy entities: 44\\n\",\n \"Schema entities: 49\\n\",\n \"\\n\",\n \"Entities only in legacy: ['fmap', 'inv', 'mt', 'proc', 'scans', 'staining']\\n\",\n \"\\n\",\n \"Entities only in schema: ['density', 'description', 'hemisphere', 'inversion', 'label', 'mtransfer', 'processing', 'resolution', 'segmentation', 'split', 'stain']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Compare entities\\n\",\n \"legacy_entities = set(layout_legacy.get_entities().keys())\\n\",\n \"schema_entities = set(layout_schema.get_entities().keys())\\n\",\n \"\\n\",\n \"print(f'Legacy entities: {len(legacy_entities)}')\\n\",\n \"print(f'Schema entities: {len(schema_entities)}')\\n\",\n \"\\n\",\n \"# Check for differences\\n\",\n \"only_legacy = legacy_entities - schema_entities\\n\",\n \"only_schema = schema_entities - legacy_entities\\n\",\n \"\\n\",\n \"if only_legacy:\\n\",\n \" print(f'\\\\nEntities only in legacy: {sorted(only_legacy)}')\\n\",\n \"if only_schema:\\n\",\n \" print(f'\\\\nEntities only in schema: {sorted(only_schema)}')\\n\",\n \"if not only_legacy and not only_schema:\\n\",\n \" print('\\\\nAll entities match!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Query: {'suffix': 'bold', 'extension': 'nii.gz'}\\n\",\n \"Legacy: 60 files\\n\",\n \"Schema: 60 files\\n\",\n \"✓ Results match!\\n\",\n \"\\n\",\n \"Query: {'suffix': 'T1w', 'extension': 'nii.gz'}\\n\",\n \"Legacy: 10 files\\n\",\n \"Schema: 10 files\\n\",\n \"✓ Results match!\\n\",\n \"\\n\",\n \"Query: {'subject': '01', 'suffix': 'scans', 'extension': 'tsv'}\\n\",\n \"Legacy: 2 files\\n\",\n \"Schema: 2 files\\n\",\n \"✓ Results match!\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Compare specific query results\\n\",\n \"def compare_queries(layout1, layout2, name1='Layout 1', name2='Layout 2', **kwargs):\\n\",\n \" \\\"\\\"\\\"Compare results from two layouts for the same query.\\\"\\\"\\\"\\n\",\n \" result1 = set(layout1.get(return_type='filename', **kwargs))\\n\",\n \" result2 = set(layout2.get(return_type='filename', **kwargs))\\n\",\n \"\\n\",\n \" print(f'Query: {kwargs}')\\n\",\n \" print(f'{name1}: {len(result1)} files')\\n\",\n \" print(f'{name2}: {len(result2)} files')\\n\",\n \"\\n\",\n \" if result1 == result2:\\n\",\n \" print('✓ Results match!\\\\n')\\n\",\n \" else:\\n\",\n \" only1 = result1 - result2\\n\",\n \" only2 = result2 - result1\\n\",\n \" if only1:\\n\",\n \" print(f'Only in {name1}: {[os.path.basename(f) for f in only1]}')\\n\",\n \" if only2:\\n\",\n \" print(f'Only in {name2}: {[os.path.basename(f) for f in only2]}')\\n\",\n \" print()\\n\",\n \"\\n\",\n \"\\n\",\n \"# Test various queries\\n\",\n \"compare_queries(\\n\",\n \" layout_legacy, layout_schema, 'Legacy', 'Schema', suffix='bold', extension='nii.gz'\\n\",\n \")\\n\",\n \"\\n\",\n \"compare_queries(layout_legacy, layout_schema, 'Legacy', 'Schema', suffix='T1w', extension='nii.gz')\\n\",\n \"\\n\",\n \"# 1. Test with scans TSV files (these exist)\\n\",\n \"compare_queries(\\n\",\n \" layout_legacy, layout_schema, 'Legacy', 'Schema', subject='01', suffix='scans', extension='tsv'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 5: Performance Comparison\\n\",\n \"\\n\",\n \"Let's compare the initialization time for both approaches.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Legacy initialization: 0.684 seconds\\n\",\n \"Schema initialization: 0.679 seconds\\n\",\n \"\\n\",\n \"Difference: 0.005 seconds\\n\",\n \"Schema is faster by 0.8%\\n\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"\\n\",\n \"# Time legacy initialization\\n\",\n \"start = time.time()\\n\",\n \"layout_legacy_timed = BIDSLayout(data_path, config=['bids'], reset_database=True)\\n\",\n \"legacy_time = time.time() - start\\n\",\n \"\\n\",\n \"# Time schema initialization\\n\",\n \"start = time.time()\\n\",\n \"layout_schema_timed = BIDSLayout(data_path, config=['bids-schema'], reset_database=True)\\n\",\n \"schema_time = time.time() - start\\n\",\n \"\\n\",\n \"print(f'Legacy initialization: {legacy_time:.3f} seconds')\\n\",\n \"print(f'Schema initialization: {schema_time:.3f} seconds')\\n\",\n \"print(f'\\\\nDifference: {abs(legacy_time - schema_time):.3f} seconds')\\n\",\n \"print(\\n\",\n \" f'Schema is {\\\"faster\\\" if schema_time < legacy_time else \\\"slower\\\"} by {abs(1 - schema_time / legacy_time) * 100:.1f}%'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.18\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "setup.cfg", "content": "[codespell]\nskip = ./.git,external,versioneer.py,_version.py\nignore-words = .github/codespell_ignore_words.txt\nexclude-file = .github/codespell_exclude_lines.txt\n" }, { "path": "setup.py", "content": "#!/usr/bin/env python\nimport versioneer\nfrom setuptools import setup\n\nsetup(\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n)\n" }, { "path": "src/bids/__init__.py", "content": "# For backwards compatibility # noqa: D104\nfrom bids_validator import BIDSValidator\n\nfrom .due import Doi, due\nfrom .layout import BIDSLayout, BIDSLayoutIndexer\n\n__all__ = [\n 'modeling',\n 'BIDSLayout',\n 'BIDSLayoutIndexer',\n 'BIDSValidator',\n 'config',\n 'layout',\n 'reports',\n 'utils',\n 'variables',\n]\n\ndue.cite(\n Doi('10.1038/sdata.2016.44'),\n description='Brain Imaging Data Structure',\n tags=['reference-implementation'],\n path='bids',\n)\n\ndel due, Doi\n\nfrom . import _version # noqa: E402\n\n__version__ = _version.get_versions()['version']\n" }, { "path": "src/bids/__main__.py", "content": "from .cli import cli # noqa: D100\n\nif __name__ == '__main__':\n cli()\n" }, { "path": "src/bids/_version.py", "content": "\n# This file helps to compute a version number in source trees obtained from\n# git-archive tarball (such as those provided by githubs download-from-tag\n# feature). Distribution tarballs (built by setup.py sdist) and build\n# directories (produced by setup.py build) will contain a much shorter file\n# that just contains the computed version number.\n\n# This file is released into the public domain.\n# Generated by versioneer-0.29\n# https://github.com/python-versioneer/python-versioneer\n\n\"\"\"Git implementation of _version.py.\"\"\"\n\nimport errno\nimport os\nimport re\nimport subprocess\nimport sys\nfrom typing import Any, Callable, Dict, List, Optional, Tuple\nimport functools\n\n\ndef get_keywords() -> Dict[str, str]:\n \"\"\"Get the keywords needed to look up the version information.\"\"\"\n # these strings will be replaced by git during git-archive.\n # setup.py/versioneer.py will grep for the variable names, so they must\n # each be defined on a line of their own. _version.py will just call\n # get_keywords().\n git_refnames = \"$Format:%d$\"\n git_full = \"$Format:%H$\"\n git_date = \"$Format:%ci$\"\n keywords = {\"refnames\": git_refnames, \"full\": git_full, \"date\": git_date}\n return keywords\n\n\nclass VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n VCS: str\n style: str\n tag_prefix: str\n parentdir_prefix: str\n versionfile_source: str\n verbose: bool\n\n\ndef get_config() -> VersioneerConfig:\n \"\"\"Create, populate and return the VersioneerConfig() object.\"\"\"\n # these strings are filled in when 'setup.py versioneer' creates\n # _version.py\n cfg = VersioneerConfig()\n cfg.VCS = \"git\"\n cfg.style = \"pep440-pre\"\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = \"None\"\n cfg.versionfile_source = \"src/bids/_version.py\"\n cfg.verbose = False\n return cfg\n\n\nclass NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\nLONG_VERSION_PY: Dict[str, str] = {}\nHANDLERS: Dict[str, Dict[str, Callable]] = {}\n\n\ndef register_vcs_handler(vcs: str, method: str) -> Callable: # decorator\n \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n def decorate(f: Callable) -> Callable:\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n return decorate\n\n\ndef run_command(\n commands: List[str],\n args: List[str],\n cwd: Optional[str] = None,\n verbose: bool = False,\n hide_stderr: bool = False,\n env: Optional[Dict[str, str]] = None,\n) -> Tuple[Optional[str], Optional[int]]:\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n process = None\n\n popen_kwargs: Dict[str, Any] = {}\n if sys.platform == \"win32\":\n # This hides the console window if pythonw.exe is used\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n popen_kwargs[\"startupinfo\"] = startupinfo\n\n for command in commands:\n try:\n dispcmd = str([command] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n process = subprocess.Popen([command] + args, cwd=cwd, env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None), **popen_kwargs)\n break\n except OSError as e:\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = process.communicate()[0].strip().decode()\n if process.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, process.returncode\n return stdout, process.returncode\n\n\ndef versions_from_parentdir(\n parentdir_prefix: str,\n root: str,\n verbose: bool,\n) -> Dict[str, Any]:\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for _ in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\"version\": dirname[len(parentdir_prefix):],\n \"full-revisionid\": None,\n \"dirty\": False, \"error\": None, \"date\": None}\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\"Tried directories %s but none started with prefix %s\" %\n (str(rootdirs), parentdir_prefix))\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")\n\n\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs: str) -> Dict[str, str]:\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords: Dict[str, str] = {}\n try:\n with open(versionfile_abs, \"r\") as fobj:\n for line in fobj:\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n except OSError:\n pass\n return keywords\n\n\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(\n keywords: Dict[str, str],\n tag_prefix: str,\n verbose: bool,\n) -> Dict[str, Any]:\n \"\"\"Get version information from git keywords.\"\"\"\n if \"refnames\" not in keywords:\n raise NotThisMethod(\"Short version file found\")\n date = keywords.get(\"date\")\n if date is not None:\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"main\".\n tags = {r for r in refs if re.search(r'\\d', r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix):]\n # Filter out refs that exactly match prefix or that don't start\n # with a number once the prefix is stripped (mostly a concern\n # when prefix is '')\n if not re.match(r'\\d', r):\n continue\n if verbose:\n print(\"picking %s\" % r)\n return {\"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": None,\n \"date\": date}\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": \"no suitable tags\", \"date\": None}\n\n\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(\n tag_prefix: str,\n root: str,\n verbose: bool,\n runner: Callable = run_command\n) -> Dict[str, Any]:\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n # GIT_DIR can interfere with correct operation of Versioneer.\n # It may be intended to be passed to the Versioneer-versioned project,\n # but that should not change where we get our version from.\n env = os.environ.copy()\n env.pop(\"GIT_DIR\", None)\n runner = functools.partial(runner, env=env)\n\n _, rc = runner(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root,\n hide_stderr=not verbose)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = runner(GITS, [\n \"describe\", \"--tags\", \"--dirty\", \"--always\", \"--long\",\n \"--match\", f\"{tag_prefix}[[:digit:]]*\"\n ], cwd=root)\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = runner(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces: Dict[str, Any] = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n branch_name, rc = runner(GITS, [\"rev-parse\", \"--abbrev-ref\", \"HEAD\"],\n cwd=root)\n # --abbrev-ref was added in git-1.6.3\n if rc != 0 or branch_name is None:\n raise NotThisMethod(\"'git rev-parse --abbrev-ref' returned error\")\n branch_name = branch_name.strip()\n\n if branch_name == \"HEAD\":\n # If we aren't exactly on a branch, pick a branch which represents\n # the current commit. If all else fails, we are on a branchless\n # commit.\n branches, rc = runner(GITS, [\"branch\", \"--contains\"], cwd=root)\n # --contains was added in git-1.5.4\n if rc != 0 or branches is None:\n raise NotThisMethod(\"'git branch --contains' returned error\")\n branches = branches.split(\"\\n\")\n\n # Remove the first line if we're running detached\n if \"(\" in branches[0]:\n branches.pop(0)\n\n # Strip off the leading \"* \" from the list of branches.\n branches = [branch[2:] for branch in branches]\n if \"main\" in branches:\n branch_name = \"main\"\n elif not branches:\n branch_name = None\n else:\n # Pick the first branch that is returned. Good or bad.\n branch_name = branches[0]\n\n pieces[\"branch\"] = branch_name\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n if not mo:\n # unparsable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = (\"unable to parse git-describe output: '%s'\"\n % describe_out)\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%s' doesn't start with prefix '%s'\"\n print(fmt % (full_tag, tag_prefix))\n pieces[\"error\"] = (\"tag '%s' doesn't start with prefix '%s'\"\n % (full_tag, tag_prefix))\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix):]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n out, rc = runner(GITS, [\"rev-list\", \"HEAD\", \"--left-right\"], cwd=root)\n pieces[\"distance\"] = len(out.split()) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = runner(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[0].strip()\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces\n\n\ndef plus_or_dot(pieces: Dict[str, Any]) -> str:\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"\n\n\ndef render_pep440(pieces: Dict[str, Any]) -> str:\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_branch(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .\n\n The \".dev0\" means not main branch. Note that .dev0 sorts backwards\n (a feature branch will appear \"older\" than the main branch).\n\n Exceptions:\n 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n if pieces[\"branch\"] != \"main\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0\"\n if pieces[\"branch\"] != \"main\":\n rendered += \".dev0\"\n rendered += \"+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef pep440_split_post(ver: str) -> Tuple[str, Optional[int]]:\n \"\"\"Split pep440 version string at the post-release segment.\n\n Returns the release segments before the post-release and the\n post-release version number (or -1 if no post-release segment is present).\n \"\"\"\n vc = str.split(ver, \".post\")\n return vc[0], int(vc[1] or 0) if len(vc) == 2 else None\n\n\ndef render_pep440_pre(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[.postN.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post0.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n if pieces[\"distance\"]:\n # update the post release segment\n tag_version, post_version = pep440_split_post(pieces[\"closest-tag\"])\n rendered = tag_version\n if post_version is not None:\n rendered += \".post%d.dev%d\" % (post_version + 1, pieces[\"distance\"])\n else:\n rendered += \".post0.dev%d\" % (pieces[\"distance\"])\n else:\n # no commits, use the tag as the version\n rendered = pieces[\"closest-tag\"]\n else:\n # exception #1\n rendered = \"0.post0.dev%d\" % pieces[\"distance\"]\n return rendered\n\n\ndef render_pep440_post(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered\n\n\ndef render_pep440_post_branch(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .\n\n The \".dev0\" means not main branch.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"main\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"main\":\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_old(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered\n\n\ndef render_git_describe(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render_git_describe_long(pieces: Dict[str, Any]) -> str:\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]:\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None}\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-branch\":\n rendered = render_pep440_branch(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-post-branch\":\n rendered = render_pep440_post_branch(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"], \"error\": None,\n \"date\": pieces.get(\"date\")}\n\n\ndef get_versions() -> Dict[str, Any]:\n \"\"\"Get version information or return default if unable to do so.\"\"\"\n # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have\n # __file__, we can work backwards from there to the root. Some\n # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which\n # case we can only use expanded keywords.\n\n cfg = get_config()\n verbose = cfg.verbose\n\n try:\n return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,\n verbose)\n except NotThisMethod:\n pass\n\n try:\n root = os.path.realpath(__file__)\n # versionfile_source is the relative path from the top of the source\n # tree (where the .git directory might live) to this file. Invert\n # this to find the root from __file__.\n for _ in cfg.versionfile_source.split('/'):\n root = os.path.dirname(root)\n except NameError:\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to find root of source tree\",\n \"date\": None}\n\n try:\n pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n return render(pieces, cfg.style)\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n except NotThisMethod:\n pass\n\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to compute version\", \"date\": None}\n" }, { "path": "src/bids/cli.py", "content": "import json # noqa: D100\nimport os\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport click\n\nfrom . import __version__\nfrom .layout import BIDSLayout, BIDSLayoutIndexer\nfrom .modeling.report.base import generate_report\nfrom .utils import validate_multiple as _validate_multiple\n\n# alias -h to trigger help message\nCONTEXT_SETTINGS = {'help_option_names': ['-h', '--help']}\n\n\nclass Either(click.ParamType):\n \"\"\"Click type that will accept any of the types passed at initialization.\n\n Examples\n --------\n >>> Either(click.BOOL, click.STRING).convert(True, None, None)\n True\n >>> Either(click.BOOL, click.STRING).convert('Test', None, None)\n 'Test'\n\n Note that the order of types can affect the interpreted type.\n\n >>> Either(click.BOOL, click.INT).convert('1', None, None)\n True\n >>> Either(click.INT, click.BOOL).convert('1', None, None)\n 1\n\n The motivating use case is True or path:\n\n >>> PathOrTrue = Either(click.BOOL, click.Path(exists=True))\n >>> PathOrTrue.convert(True, None, None)\n True\n >>> cwd = os.getcwd()\n >>> PathOrTrue.convert(cwd, None, None) == cwd\n True\n >>> PathOrTrue.convert('/does/not/exist', None, None) # doctest: +IGNORE_EXCEPTION_DETAIL\n Traceback (most recent call last):\n ...\n BadParameter: '/does/not/exist' does not match expected types:\n boolean: \"'/does/not/exist' is not a valid boolean.\"\n path: \"'/does/not/exist' does not exist.\"\n\n \"\"\"\n\n def __init__(self, *types):\n self.types = types\n\n @property\n def name(self): # noqa: D102\n tpstrings = tuple(tp.name for tp in self.types) # noqa: F841\n return 'any type in {tpstrings}'\n\n def convert(self, value, param, ctx): # noqa: D102\n errors = []\n for tp in self.types:\n try:\n return tp.convert(value, param, ctx)\n except click.BadParameter as err:\n errors.append(f'{tp.name}: {err.message!r}')\n messages = '\\n'.join(errors)\n self.fail(f'{value!r} does not match expected types:\\n{messages}')\n\n\nclass PathOrRegex(click.ParamType):\n \"\"\"A helper Type to parse BIDSLayoutIndexer ignore/force entries\"\"\"\n\n name = 'path or m/regex/'\n\n def convert(self, value, param, ctx): # noqa: D102\n import re\n\n if re.match(r'^m/.*/$', value): # has form \"m//\"\n value = re.compile(value[2:-1])\n return value\n\n\n@click.group(context_settings=CONTEXT_SETTINGS)\n@click.version_option(__version__, prog_name='pybids')\ndef cli():\n \"\"\"Command-line interface for PyBIDS operations\"\"\"\n pass\n\n\n@cli.command(context_settings=CONTEXT_SETTINGS)\n@click.argument('root', type=click.Path(file_okay=False, exists=True))\n@click.argument('db-path', type=click.Path(file_okay=False, resolve_path=True, exists=True))\n@click.option(\n '--derivatives',\n multiple=True,\n default=[False],\n is_flag=False,\n flag_value=True,\n show_default=True,\n type=Either(click.BOOL, click.Path(exists=True)),\n help='Specifies whether and/or which derivatives to index.',\n)\n@click.option(\n '--reset-db',\n default=False,\n show_default=True,\n is_flag=True,\n help='Remove existing database index if present.',\n)\n@click.option(\n '--validate/--no-validate',\n default=True,\n show_default=True,\n help='Check for BIDS compliance when indexing files.',\n)\n@click.option('--config', multiple=True, help='Optional name(s) of configuration file(s) to use.')\n@click.option(\n '--index-metadata/--no-index-metadata',\n default=False,\n show_default=True,\n help='Include metadata when indexing files.',\n)\n@click.option(\n '--ignore',\n multiple=True,\n type=PathOrRegex(),\n help='Path (from root) or regex to exclude from indexing. '\n \"Regex entries need to fitted with leading 'm/' and trailing '/'.\",\n)\n@click.option(\n '--force-index',\n multiple=True,\n type=PathOrRegex(),\n help='Path (from root) or regex to include when indexing. '\n \"Regex entries need to fitted with leading 'm/' and trailing '/'.\",\n)\n@click.option(\n '--config-filename',\n type=click.Path(),\n default='layout_config.json',\n show_default=True,\n help='Name of filename within directories that contains configuration information.',\n)\ndef layout(\n root,\n db_path,\n derivatives,\n reset_db,\n validate,\n config,\n index_metadata,\n ignore,\n force_index,\n config_filename,\n):\n \"\"\"Initialize a BIDSLayout, and create an SQLite database index.\"\"\"\n # ensure empty multiples are set to None\n derivatives = _validate_multiple(derivatives, retval=False)\n config = _validate_multiple(config)\n ignore = _validate_multiple(ignore)\n force_index = _validate_multiple(force_index)\n\n if not (Path(db_path) / 'layout_index.sqlite').exists():\n reset_db = True\n\n layout = BIDSLayout( # noqa: F841\n root,\n database_path=db_path,\n reset_database=reset_db,\n validate=validate,\n config=config,\n indexer=BIDSLayoutIndexer(\n validate=validate,\n index_metadata=index_metadata,\n ignore=ignore,\n force_index=force_index,\n config_filename=config_filename,\n ),\n )\n if reset_db:\n click.echo(f'Successfully generated database index at {db_path}')\n else:\n click.echo(\n f'Previously generated database index found at {db_path}. '\n 'To generate a new index, rerun with ``--reset-db``'\n )\n\n\n@cli.command(context_settings=CONTEXT_SETTINGS)\n@click.argument('root', type=click.Path(file_okay=False, exists=True))\ndef upgrade(root):\n \"\"\"Upgrade common experimental BIDS features to finalized versions.\"\"\"\n click.echo(\n 'WARNING. This upgrade tool is EXPERIMENTAL and MAY damage your '\n 'dataset. Please ensure you have a backup before proceeding.'\n )\n click.confirm('Proceed?', abort=True)\n changes = False\n\n description_path = Path(root) / 'dataset_description.json'\n orig_desc = json.loads(description_path.read_text())\n desc = upgrade_dataset_description(orig_desc)\n\n if desc != orig_desc:\n description_path.write_text(json.dumps(desc))\n changes = True\n\n val = click.prompt('Load dataset and update filenames?', default='Y', type=click.Choice('YN'))\n if val == 'Y':\n upgrade_filenames(root, desc)\n\n if changes:\n click.echo(\n 'Upgrade complete. Please run the bids-validator '\n '(https://bids-standard.github.io/bids-validator) '\n 'to confirm the correctness of the changes.'\n )\n else:\n click.echo('No changes to make!')\n\n\ndef upgrade_dataset_description(description):\n \"\"\"Upgrade dataset_description.json with recommended values\"\"\"\n description = deepcopy(description)\n\n # Give an opportunity to update to latest version\n bidsver = description.get('BIDSVersion')\n if bidsver is None or bidsver < '1.6.0':\n val = click.prompt(f'Update BIDS Version? (current: {bidsver})', default='1.6.0', type=str)\n if val.startswith('1.'):\n description['BIDSVersion'] = val\n else:\n click.echo('Expected version to be 1.x, e.g., 1.6.0. Skipping.')\n\n # Always update DatasetType if missing\n if 'DatasetType' not in description:\n val = click.prompt(\n 'Is this dataset [r]aw or [d]erivative?',\n default='r',\n type=click.Choice(('r', 'd'), case_sensitive=False),\n )\n description['DatasetType'] = 'raw' if val == 'R' else 'derivative'\n\n if description['DatasetType'] == 'derivative':\n if 'PipelineDescription' in description:\n val = click.prompt(\n 'Convert PipelineDescription to GeneratedBy?', default='Y', type=click.Choice('YN')\n )\n if val == 'Y':\n description['GeneratedBy'] = [description.pop('PipelineDescription')]\n\n return description\n\n\ndef upgrade_filenames(root, description): # noqa: D103\n dstype = description['DatasetType']\n layout = BIDSLayout(root, validate=False, config='bids' if dstype == 'raw' else 'derivatives')\n\n # Rename regressors.tsv to timeseries.tsv\n regressors = layout.get(suffix='regressors')\n policy = None\n with click.progressbar(regressors) as bar:\n for bidsfile in bar:\n action = policy\n new_path = bidsfile.path.replace('regressors.', 'timeseries.')\n if action is None:\n action = click.prompt(\n f'Rename {bidsfile.path} to {new_path}? ([y]es/[n]o/[A]ll/[N]one)',\n default='y',\n type=click.Choice('ynAN'),\n show_choices=False,\n )\n if action in 'AN':\n policy = action\n if action in 'yA':\n click.echo(f'Renaming {bidsfile.path} -> {new_path}')\n os.rename(bidsfile.path, new_path)\n else:\n click.echo(f'Not renaming {bidsfile.path}')\n\n\n@cli.command(context_settings=CONTEXT_SETTINGS)\n@click.argument('model', type=click.Path(file_okay=True, exists=True, dir_okay=False))\n@click.argument('root', type=click.Path(file_okay=False, exists=True))\n@click.argument('derivatives', type=click.Path(file_okay=False, exists=True), nargs=-1)\n@click.argument('output_dir', type=click.Path(file_okay=False, exists=False))\n@click.option('--scan_length', type=click.FLOAT)\n@click.option(\n '--sub_label', '-s', multiple=True, help='The label(s) of the subjects(s) to create report for'\n)\n@click.option(\n '--ses_label', '-ss', multiple=True, help='The label(s) of the session(s) to create report for'\n)\n@click.option(\n '--task_label', '-t', multiple=True, help='The label(s) of the task(s) to create report for'\n)\n@click.option(\n '--run_label', '-r', multiple=True, help='The label(s) of the run(s) to create report for'\n)\ndef model_report(\n model, root, derivatives, output_dir, scan_length, sub_label, ses_label, task_label, run_label\n):\n \"\"\"Generate a report of a BIDS StatsModel.\"\"\"\n entities = {'subject': sub_label, 'session': ses_label, 'task': task_label, 'run': run_label}\n\n entities = {key: val for key, val in entities.items() if val}\n generate_report(model, root, derivatives, output_dir, scan_length, **entities)\n" }, { "path": "src/bids/config.py", "content": "\"\"\"Utilities for manipulating package-level settings.\"\"\"\n\nimport json\nimport os\nimport warnings\n\nfrom upath import UPath as Path\n\nfrom .utils import listify\n\n__all__ = ['set_option', 'set_options', 'get_option']\n\n_config_name = 'pybids_config.json'\n\nconf_path = str(Path(__file__).absolute().parent.joinpath('layout', 'config', '{}.json'))\n_default_settings = {\n 'config_paths': {name: conf_path.format(name) for name in ['bids', 'derivatives']},\n # XXX 0.16: Remove\n 'extension_initial_dot': True,\n}\n\n\ndef set_option(key, value):\n \"\"\"Set a package-wide option.\n\n Args:\n key (str): The name of the option to set.\n value (object): The new value of the option.\n\n \"\"\"\n if key not in _settings:\n raise ValueError(\"Invalid pybids setting: '%s'\" % key) # noqa: UP031\n # XXX 0.16: Remove\n elif key == 'extension_initial_dot':\n if value is not True:\n raise ValueError(\n f'Cannot set {key!r} to {value!r} as of pybids 0.14. '\n 'This setting is always True, and will be removed '\n 'entirely in 0.16.'\n )\n warnings.warn( # noqa: B028\n \"Setting 'extension_initial_dot' will be removed in pybids 0.16.\", FutureWarning\n )\n _settings[key] = value\n\n\ndef set_options(**kwargs):\n \"\"\"Set multiple package-wide options.\n\n Args:\n kwargs: Keyword arguments to pass onto set_option().\n\n \"\"\"\n for k, v in kwargs.items():\n set_option(k, v)\n\n\ndef get_option(key):\n \"\"\"Retrieve the current value of a package-wide option.\n\n Args:\n key (str): The name of the option to retrieve.\n\n \"\"\"\n if key not in _settings:\n raise ValueError(\"Invalid pybids setting: '%s'\" % key) # noqa: UP031\n return _settings[key]\n\n\ndef from_file(filenames, error_on_missing=True):\n \"\"\"Load package-wide settings from specified file(s).\n\n Args:\n filenames (str, list): Filename or list of filenames containing JSON\n dictionary of settings.\n error_on_missing (bool): If True, raises an error if a file doesn't\n exist.\n\n \"\"\"\n filenames = listify(filenames)\n for f in filenames:\n if Path(f).exists():\n settings = json.loads(Path(f).read_text(encoding='utf-8'))\n _settings.update(settings)\n elif error_on_missing:\n raise ValueError(\"Config file '%s' does not exist.\" % f) # noqa: UP031\n\n\ndef reset_options(update_from_file=False):\n \"\"\"Reset all options to the package defaults.\n\n Args:\n update_from_file (bool): If True, re-applies any config files found in\n standard locations.\n\n \"\"\"\n global _settings\n _settings = _default_settings.copy()\n if update_from_file:\n _update_from_standard_locations()\n\n\ndef _update_from_standard_locations():\n \"\"\"Check standard locations for config files and update settings if found.\n Order is user's home dir, environment variable ($PYBIDS_CONFIG), and then\n current directory--with later files taking precedence over earlier ones.\n \"\"\"\n locs = [Path.home() / _config_name, Path('.') / _config_name]\n if 'PYBIDS_CONFIG' in os.environ:\n locs.insert(1, os.environ['PYBIDS_CONFIG'])\n from_file(locs, False)\n\n\n_settings = {}\nreset_options(True)\n" }, { "path": "src/bids/conftest.py", "content": "# ruff: noqa: D404\n\"\"\"This module allows you to mock the config file as needed. # noqa: D404\nA default fixture that simply returns a safe-to-modify copy of\nthe default value is provided.\nThis can be overridden by parametrizing over the option you wish to\nmock.\n\ne.g.\n\n>>> @pytest.mark.parametrize('extension_initial_dot', (True, False))\n... def test_fixture(mock_config, extension_initial_dot):\n... import bids\n...\n... assert bids.config.get_option('extension_initial_dot') == extension_initial_dot\n\"\"\"\n\nimport os\nfrom unittest.mock import patch\n\nimport pytest\nfrom upath import UPath as Path\n\n\n@pytest.fixture\ndef config_paths(): # noqa: D103\n import bids.config\n\n return bids.config.get_option('config_paths').copy()\n\n\n@pytest.fixture\ndef extension_initial_dot(): # noqa: D103\n import bids.config\n\n return bids.config.get_option('extension_initial_dot')\n\n\n@pytest.fixture\ndef mock_config(config_paths, extension_initial_dot): # noqa: D103\n import bids.config\n\n with patch.dict('bids.config._settings'):\n bids.config._settings['config_paths'] = config_paths\n bids.config._settings['extension_initial_dot'] = extension_initial_dot\n yield\n\n\n@pytest.fixture(scope='session')\ndef bids_examples(): # noqa: D103\n examples_dir = Path(\n os.getenv(\n 'BIDS_EXAMPLES', Path(__file__).absolute().parent.parent.parent / 'bids-examples'\n )\n )\n\n if not Path.is_dir(examples_dir / 'ds001'):\n pytest.skip(\n f'BIDS examples missing from {examples_dir}. '\n 'Override default location with BIDS_EXAMPLES environment variable.'\n )\n return examples_dir\n\n\n@pytest.fixture(scope='session')\ndef tests_dir(): # noqa: D103\n test_dir = Path(\n os.getenv('PYBIDS_TEST_DATA', Path(__file__).absolute().parent.parent.parent / 'tests')\n )\n\n if not Path.is_dir(test_dir / 'data'):\n pytest.skip(\n f'BIDS examples missing from {test_dir}. '\n 'Override default location with PYBIDS_TEST_DATA environment variable.'\n )\n return test_dir\n" }, { "path": "src/bids/due.py", "content": "# emacs: at the end of the file\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### #\n\"\"\"Stub file for a guaranteed safe import of duecredit constructs: if duecredit\nis not available.\n\nTo use it, place it into your project codebase to be imported, e.g. copy as\n\n cp stub.py /path/tomodule/module/due.py\n\nNote that it might be better to avoid naming it duecredit.py to avoid shadowing\ninstalled duecredit.\n\nThen use in your code as\n\n from .due import due, Doi, BibTeX, Text\n\nSee https://github.com/duecredit/duecredit/blob/master/README.md for examples.\n\nOrigin: Originally a part of the duecredit\nCopyright: 2015-2019 DueCredit developers\nLicense: BSD-2\n\"\"\"\n\n__version__ = '0.0.7'\n\n\nclass InactiveDueCreditCollector:\n \"\"\"Just a stub at the Collector which would not do anything\"\"\"\n\n def _donothing(self, *args, **kwargs):\n \"\"\"Perform no good and no bad\"\"\"\n pass\n\n def dcite(self, *args, **kwargs):\n \"\"\"If I could cite I would\"\"\"\n\n def nondecorating_decorator(func):\n return func\n\n return nondecorating_decorator\n\n active = False\n activate = add = cite = dump = load = _donothing\n\n def __repr__(self):\n return self.__class__.__name__ + '()'\n\n\ndef _donothing_func(*args, **kwargs):\n \"\"\"Perform no good and no bad\"\"\"\n pass\n\n\ntry:\n from duecredit import BibTeX, Doi, Text, Url, due\n\n if 'due' in locals() and not hasattr(due, 'cite'):\n raise RuntimeError('Imported due lacks .cite. DueCredit is now disabled')\nexcept Exception as e: # noqa: BLE001\n if type(e).__name__ not in ('ImportError', 'ModuleNotFoundError'):\n import logging\n\n logging.getLogger('duecredit').error('Failed to import duecredit due to %s' % str(e)) # noqa: UP031\n # Initiate due stub\n due = InactiveDueCreditCollector()\n BibTeX = Doi = Url = Text = _donothing_func\n\n# Emacs mode definitions\n# Local Variables:\n# mode: python\n# py-indent-offset: 4\n# tab-width: 4\n# indent-tabs-mode: nil\n# End:\n" }, { "path": "src/bids/exceptions.py", "content": "\"\"\"Exceptions.\n\nExceptions relating to problems with BIDS itself, should carry BIDS in their\nname. All exceptions should subclass from PyBIDSError\n\"\"\"\n\n\nclass PyBIDSError(Exception):\n \"\"\"Base class. Typically for mix-in.\"\"\"\n\n\nclass ConfigError(ValueError, PyBIDSError):\n \"\"\"Problems with config file.\"\"\"\n\n\nclass NoMatchError(ValueError, PyBIDSError):\n \"\"\"No match found where it is required.\"\"\"\n\n\nclass BIDSEntityError(AttributeError, PyBIDSError):\n \"\"\"An unknown entity.\"\"\"\n\n\nclass TargetError(ValueError, PyBIDSError):\n \"\"\"An unknown target.\"\"\"\n\n\nclass BIDSValidationError(ValueError, PyBIDSError):\n \"\"\"An invalid BIDS dataset.\"\"\"\n\n\nclass BIDSDerivativesValidationError(BIDSValidationError):\n \"\"\"An invalid BIDS derivative dataset.\"\"\"\n\n\nclass BIDSConflictingValuesError(BIDSValidationError):\n \"\"\"A value conflict (e.g. in filename and side-car .json)\"\"\"\n\n\nclass BIDSChildDatasetError(ValueError, PyBIDSError):\n \"\"\"An invalid BIDS dataset.\"\"\"\n" }, { "path": "src/bids/ext/__init__.py", "content": "\"\"\"The PyBIDS extension namespace package\n\n``bids.ext`` is reserved as a namespace for extensions to install into.\nTo write such an extension, the following things are needed:\n\n1) Create a new package with the following structure (assuming setuptools)::\n\n package/\n bids/\n ext/\n __init__.py\n EXTENSION/\n __init__.py\n ...\n setup.cfg\n setup.py\n\n The important things to note are that the ``bids/`` directory must be\n empty apart from ``ext/`` and ``bids/ext/`` must be empty except for\n your extension and an ``__init__.py``.\n\n2) Place the following (and nothing else) in ``__init__.py``::\n\n __path__ = __import__('pkgutil').extend_path(__path__, __name__)\n\n3) Include the following lines in ``setup.cfg``::\n\n [options]\n install_requires =\n pybids >= 0.15\n packages = find_namespace:\n\n [options.packages.find]\n include =\n bids.ext.EXTENSION\n bids.ext.EXTENSION.*\n\n\"\"\"\n\n__path__ = __import__('pkgutil').extend_path(__path__, __name__)\n" }, { "path": "src/bids/external/__init__.py", "content": "\n" }, { "path": "src/bids/external/inflect.py", "content": "\"\"\"\ncorrectly generate plurals, ordinals, indefinite articles;\nconvert numbers to words\n\nCopyright (C) 2010 Paul Dyson\n\nBased upon the Perl module Lingua::EN::Inflect by Damian Conway.\n\nThe original Perl module Lingua::EN::Inflect by Damian Conway is\navailable from http://search.cpan.org/~dconway/\n\nThis module can be downloaded at http://pypi.org/project/inflect\n\nmethods:\n classical inflect\n plural plural_noun plural_verb plural_adj singular_noun no num a an\n compare compare_nouns compare_verbs compare_adjs\n present_participle\n ordinal\n number_to_words\n join\n defnoun defverb defadj defa defan\n\nINFLECTIONS: classical inflect\n plural plural_noun plural_verb plural_adj singular_noun compare\n no num a an present_participle\n\nPLURALS: classical inflect\n plural plural_noun plural_verb plural_adj singular_noun no num\n compare compare_nouns compare_verbs compare_adjs\n\nCOMPARISONS: classical\n compare compare_nouns compare_verbs compare_adjs\n\nARTICLES: classical inflect num a an\n\nNUMERICAL: ordinal number_to_words\n\nUSER_DEFINED: defnoun defverb defadj defa defan\n\nExceptions:\n UnknownClassicalModeError\n BadNumValueError\n BadChunkingOptionError\n NumOutOfRangeError\n BadUserDefinedPatternError\n BadRcFileError\n BadGenderError\n\n\"\"\"\n\nimport ast\nimport re\nimport functools\nimport contextlib\nfrom typing import (\n Dict,\n Union,\n Optional,\n Iterable,\n List,\n Match,\n Tuple,\n Callable,\n Sequence,\n cast,\n)\n\n\nclass UnknownClassicalModeError(Exception):\n pass\n\n\nclass BadNumValueError(Exception):\n pass\n\n\nclass BadChunkingOptionError(Exception):\n pass\n\n\nclass NumOutOfRangeError(Exception):\n pass\n\n\nclass BadUserDefinedPatternError(Exception):\n pass\n\n\nclass BadRcFileError(Exception):\n pass\n\n\nclass BadGenderError(Exception):\n pass\n\n\nSTDOUT_ON = False\n\n\ndef print3(txt: str) -> None:\n if STDOUT_ON:\n print(txt)\n\n\ndef enclose(s: str) -> str:\n return f\"(?:{s})\"\n\n\ndef joinstem(cutpoint: Optional[int] = 0, words: Optional[Iterable[str]] = None) -> str:\n \"\"\"\n join stem of each word in words into a string for regex\n each word is truncated at cutpoint\n cutpoint is usually negative indicating the number of letters to remove\n from the end of each word\n\n e.g.\n joinstem(-2, [\"ephemeris\", \"iris\", \".*itis\"]) returns\n (?:ephemer|ir|.*it)\n\n \"\"\"\n if words is None:\n words = \"\"\n return enclose(\"|\".join(w[:cutpoint] for w in words))\n\n\ndef bysize(words: Iterable[str]) -> Dict[int, set]:\n \"\"\"\n take a list of words and return a dict of sets sorted by word length\n e.g.\n ret[3]=set(['ant', 'cat', 'dog', 'pig'])\n ret[4]=set(['frog', 'goat'])\n ret[5]=set(['horse'])\n ret[8]=set(['elephant'])\n \"\"\"\n ret: Dict[int, set] = {}\n for w in words:\n if len(w) not in ret:\n ret[len(w)] = set()\n ret[len(w)].add(w)\n return ret\n\n\ndef make_pl_si_lists(\n lst: Iterable[str],\n plending: str,\n siendingsize: Optional[int],\n dojoinstem: bool = True,\n):\n \"\"\"\n given a list of singular words: lst\n\n an ending to append to make the plural: plending\n\n the number of characters to remove from the singular\n before appending plending: siendingsize\n\n a flag whether to create a joinstem: dojoinstem\n\n return:\n a list of pluralised words: si_list (called si because this is what you need to\n look for to make the singular)\n\n the pluralised words as a dict of sets sorted by word length: si_bysize\n the singular words as a dict of sets sorted by word length: pl_bysize\n if dojoinstem is True: a regular expression that matches any of the stems: stem\n \"\"\"\n if siendingsize is not None:\n siendingsize = -siendingsize\n si_list = [w[:siendingsize] + plending for w in lst]\n pl_bysize = bysize(lst)\n si_bysize = bysize(si_list)\n if dojoinstem:\n stem = joinstem(siendingsize, lst)\n return si_list, si_bysize, pl_bysize, stem\n else:\n return si_list, si_bysize, pl_bysize\n\n\n# 1. PLURALS\n\npl_sb_irregular_s = {\n \"corpus\": \"corpuses|corpora\",\n \"opus\": \"opuses|opera\",\n \"genus\": \"genera\",\n \"mythos\": \"mythoi\",\n \"penis\": \"penises|penes\",\n \"testis\": \"testes\",\n \"atlas\": \"atlases|atlantes\",\n \"yes\": \"yeses\",\n}\n\npl_sb_irregular = {\n \"child\": \"children\",\n \"chili\": \"chilis|chilies\",\n \"brother\": \"brothers|brethren\",\n \"infinity\": \"infinities|infinity\",\n \"loaf\": \"loaves\",\n \"lore\": \"lores|lore\",\n \"hoof\": \"hoofs|hooves\",\n \"beef\": \"beefs|beeves\",\n \"thief\": \"thiefs|thieves\",\n \"money\": \"monies\",\n \"mongoose\": \"mongooses\",\n \"ox\": \"oxen\",\n \"cow\": \"cows|kine\",\n \"graffito\": \"graffiti\",\n \"octopus\": \"octopuses|octopodes\",\n \"genie\": \"genies|genii\",\n \"ganglion\": \"ganglions|ganglia\",\n \"trilby\": \"trilbys\",\n \"turf\": \"turfs|turves\",\n \"numen\": \"numina\",\n \"atman\": \"atmas\",\n \"occiput\": \"occiputs|occipita\",\n \"sabretooth\": \"sabretooths\",\n \"sabertooth\": \"sabertooths\",\n \"lowlife\": \"lowlifes\",\n \"flatfoot\": \"flatfoots\",\n \"tenderfoot\": \"tenderfoots\",\n \"romany\": \"romanies\",\n \"jerry\": \"jerries\",\n \"mary\": \"maries\",\n \"talouse\": \"talouses\",\n \"rom\": \"roma\",\n \"carmen\": \"carmina\",\n}\n\npl_sb_irregular.update(pl_sb_irregular_s)\n# pl_sb_irregular_keys = enclose('|'.join(pl_sb_irregular.keys()))\n\npl_sb_irregular_caps = {\n \"Romany\": \"Romanies\",\n \"Jerry\": \"Jerrys\",\n \"Mary\": \"Marys\",\n \"Rom\": \"Roma\",\n}\n\npl_sb_irregular_compound = {\"prima donna\": \"prima donnas|prime donne\"}\n\nsi_sb_irregular = {v: k for (k, v) in pl_sb_irregular.items()}\nfor k in list(si_sb_irregular):\n if \"|\" in k:\n k1, k2 = k.split(\"|\")\n si_sb_irregular[k1] = si_sb_irregular[k2] = si_sb_irregular[k]\n del si_sb_irregular[k]\nsi_sb_irregular_caps = {v: k for (k, v) in pl_sb_irregular_caps.items()}\nsi_sb_irregular_compound = {v: k for (k, v) in pl_sb_irregular_compound.items()}\nfor k in list(si_sb_irregular_compound):\n if \"|\" in k:\n k1, k2 = k.split(\"|\")\n si_sb_irregular_compound[k1] = si_sb_irregular_compound[\n k2\n ] = si_sb_irregular_compound[k]\n del si_sb_irregular_compound[k]\n\n# si_sb_irregular_keys = enclose('|'.join(si_sb_irregular.keys()))\n\n# Z's that don't double\n\npl_sb_z_zes_list = (\"quartz\", \"topaz\")\npl_sb_z_zes_bysize = bysize(pl_sb_z_zes_list)\n\npl_sb_ze_zes_list = (\"snooze\",)\npl_sb_ze_zes_bysize = bysize(pl_sb_ze_zes_list)\n\n\n# CLASSICAL \"..is\" -> \"..ides\"\n\npl_sb_C_is_ides_complete = [\n # GENERAL WORDS...\n \"ephemeris\",\n \"iris\",\n \"clitoris\",\n \"chrysalis\",\n \"epididymis\",\n]\n\npl_sb_C_is_ides_endings = [\n # INFLAMATIONS...\n \"itis\"\n]\n\npl_sb_C_is_ides = joinstem(\n -2, pl_sb_C_is_ides_complete + [f\".*{w}\" for w in pl_sb_C_is_ides_endings]\n)\n\npl_sb_C_is_ides_list = pl_sb_C_is_ides_complete + pl_sb_C_is_ides_endings\n\n(\n si_sb_C_is_ides_list,\n si_sb_C_is_ides_bysize,\n pl_sb_C_is_ides_bysize,\n) = make_pl_si_lists(pl_sb_C_is_ides_list, \"ides\", 2, dojoinstem=False)\n\n\n# CLASSICAL \"..a\" -> \"..ata\"\n\npl_sb_C_a_ata_list = (\n \"anathema\",\n \"bema\",\n \"carcinoma\",\n \"charisma\",\n \"diploma\",\n \"dogma\",\n \"drama\",\n \"edema\",\n \"enema\",\n \"enigma\",\n \"lemma\",\n \"lymphoma\",\n \"magma\",\n \"melisma\",\n \"miasma\",\n \"oedema\",\n \"sarcoma\",\n \"schema\",\n \"soma\",\n \"stigma\",\n \"stoma\",\n \"trauma\",\n \"gumma\",\n \"pragma\",\n)\n\n(\n si_sb_C_a_ata_list,\n si_sb_C_a_ata_bysize,\n pl_sb_C_a_ata_bysize,\n pl_sb_C_a_ata,\n) = make_pl_si_lists(pl_sb_C_a_ata_list, \"ata\", 1)\n\n# UNCONDITIONAL \"..a\" -> \"..ae\"\n\npl_sb_U_a_ae_list = (\n \"alumna\",\n \"alga\",\n \"vertebra\",\n \"persona\",\n \"vita\",\n)\n(\n si_sb_U_a_ae_list,\n si_sb_U_a_ae_bysize,\n pl_sb_U_a_ae_bysize,\n pl_sb_U_a_ae,\n) = make_pl_si_lists(pl_sb_U_a_ae_list, \"e\", None)\n\n# CLASSICAL \"..a\" -> \"..ae\"\n\npl_sb_C_a_ae_list = (\n \"amoeba\",\n \"antenna\",\n \"formula\",\n \"hyperbola\",\n \"medusa\",\n \"nebula\",\n \"parabola\",\n \"abscissa\",\n \"hydra\",\n \"nova\",\n \"lacuna\",\n \"aurora\",\n \"umbra\",\n \"flora\",\n \"fauna\",\n)\n(\n si_sb_C_a_ae_list,\n si_sb_C_a_ae_bysize,\n pl_sb_C_a_ae_bysize,\n pl_sb_C_a_ae,\n) = make_pl_si_lists(pl_sb_C_a_ae_list, \"e\", None)\n\n\n# CLASSICAL \"..en\" -> \"..ina\"\n\npl_sb_C_en_ina_list = (\"stamen\", \"foramen\", \"lumen\")\n\n(\n si_sb_C_en_ina_list,\n si_sb_C_en_ina_bysize,\n pl_sb_C_en_ina_bysize,\n pl_sb_C_en_ina,\n) = make_pl_si_lists(pl_sb_C_en_ina_list, \"ina\", 2)\n\n\n# UNCONDITIONAL \"..um\" -> \"..a\"\n\npl_sb_U_um_a_list = (\n \"bacterium\",\n \"agendum\",\n \"desideratum\",\n \"erratum\",\n \"stratum\",\n \"datum\",\n \"ovum\",\n \"extremum\",\n \"candelabrum\",\n)\n(\n si_sb_U_um_a_list,\n si_sb_U_um_a_bysize,\n pl_sb_U_um_a_bysize,\n pl_sb_U_um_a,\n) = make_pl_si_lists(pl_sb_U_um_a_list, \"a\", 2)\n\n# CLASSICAL \"..um\" -> \"..a\"\n\npl_sb_C_um_a_list = (\n \"maximum\",\n \"minimum\",\n \"momentum\",\n \"optimum\",\n \"quantum\",\n \"cranium\",\n \"curriculum\",\n \"dictum\",\n \"phylum\",\n \"aquarium\",\n \"compendium\",\n \"emporium\",\n \"encomium\",\n \"gymnasium\",\n \"honorarium\",\n \"interregnum\",\n \"lustrum\",\n \"memorandum\",\n \"millennium\",\n \"rostrum\",\n \"spectrum\",\n \"speculum\",\n \"stadium\",\n \"trapezium\",\n \"ultimatum\",\n \"medium\",\n \"vacuum\",\n \"velum\",\n \"consortium\",\n \"arboretum\",\n)\n\n(\n si_sb_C_um_a_list,\n si_sb_C_um_a_bysize,\n pl_sb_C_um_a_bysize,\n pl_sb_C_um_a,\n) = make_pl_si_lists(pl_sb_C_um_a_list, \"a\", 2)\n\n\n# UNCONDITIONAL \"..us\" -> \"i\"\n\npl_sb_U_us_i_list = (\n \"alumnus\",\n \"alveolus\",\n \"bacillus\",\n \"bronchus\",\n \"locus\",\n \"nucleus\",\n \"stimulus\",\n \"meniscus\",\n \"sarcophagus\",\n)\n(\n si_sb_U_us_i_list,\n si_sb_U_us_i_bysize,\n pl_sb_U_us_i_bysize,\n pl_sb_U_us_i,\n) = make_pl_si_lists(pl_sb_U_us_i_list, \"i\", 2)\n\n# CLASSICAL \"..us\" -> \"..i\"\n\npl_sb_C_us_i_list = (\n \"focus\",\n \"radius\",\n \"genius\",\n \"incubus\",\n \"succubus\",\n \"nimbus\",\n \"fungus\",\n \"nucleolus\",\n \"stylus\",\n \"torus\",\n \"umbilicus\",\n \"uterus\",\n \"hippopotamus\",\n \"cactus\",\n)\n\n(\n si_sb_C_us_i_list,\n si_sb_C_us_i_bysize,\n pl_sb_C_us_i_bysize,\n pl_sb_C_us_i,\n) = make_pl_si_lists(pl_sb_C_us_i_list, \"i\", 2)\n\n\n# CLASSICAL \"..us\" -> \"..us\" (ASSIMILATED 4TH DECLENSION LATIN NOUNS)\n\npl_sb_C_us_us = (\n \"status\",\n \"apparatus\",\n \"prospectus\",\n \"sinus\",\n \"hiatus\",\n \"impetus\",\n \"plexus\",\n)\npl_sb_C_us_us_bysize = bysize(pl_sb_C_us_us)\n\n# UNCONDITIONAL \"..on\" -> \"a\"\n\npl_sb_U_on_a_list = (\n \"criterion\",\n \"perihelion\",\n \"aphelion\",\n \"phenomenon\",\n \"prolegomenon\",\n \"noumenon\",\n \"organon\",\n \"asyndeton\",\n \"hyperbaton\",\n)\n(\n si_sb_U_on_a_list,\n si_sb_U_on_a_bysize,\n pl_sb_U_on_a_bysize,\n pl_sb_U_on_a,\n) = make_pl_si_lists(pl_sb_U_on_a_list, \"a\", 2)\n\n# CLASSICAL \"..on\" -> \"..a\"\n\npl_sb_C_on_a_list = (\"oxymoron\",)\n\n(\n si_sb_C_on_a_list,\n si_sb_C_on_a_bysize,\n pl_sb_C_on_a_bysize,\n pl_sb_C_on_a,\n) = make_pl_si_lists(pl_sb_C_on_a_list, \"a\", 2)\n\n\n# CLASSICAL \"..o\" -> \"..i\" (BUT NORMALLY -> \"..os\")\n\npl_sb_C_o_i = [\n \"solo\",\n \"soprano\",\n \"basso\",\n \"alto\",\n \"contralto\",\n \"tempo\",\n \"piano\",\n \"virtuoso\",\n] # list not tuple so can concat for pl_sb_U_o_os\n\npl_sb_C_o_i_bysize = bysize(pl_sb_C_o_i)\nsi_sb_C_o_i_bysize = bysize([f\"{w[:-1]}i\" for w in pl_sb_C_o_i])\n\npl_sb_C_o_i_stems = joinstem(-1, pl_sb_C_o_i)\n\n# ALWAYS \"..o\" -> \"..os\"\n\npl_sb_U_o_os_complete = {\"ado\", \"ISO\", \"NATO\", \"NCO\", \"NGO\", \"oto\"}\nsi_sb_U_o_os_complete = {f\"{w}s\" for w in pl_sb_U_o_os_complete}\n\n\npl_sb_U_o_os_endings = [\n \"aficionado\",\n \"aggro\",\n \"albino\",\n \"allegro\",\n \"ammo\",\n \"Antananarivo\",\n \"archipelago\",\n \"armadillo\",\n \"auto\",\n \"avocado\",\n \"Bamako\",\n \"Barquisimeto\",\n \"bimbo\",\n \"bingo\",\n \"Biro\",\n \"bolero\",\n \"Bolzano\",\n \"bongo\",\n \"Boto\",\n \"burro\",\n \"Cairo\",\n \"canto\",\n \"cappuccino\",\n \"casino\",\n \"cello\",\n \"Chicago\",\n \"Chimango\",\n \"cilantro\",\n \"cochito\",\n \"coco\",\n \"Colombo\",\n \"Colorado\",\n \"commando\",\n \"concertino\",\n \"contango\",\n \"credo\",\n \"crescendo\",\n \"cyano\",\n \"demo\",\n \"ditto\",\n \"Draco\",\n \"dynamo\",\n \"embryo\",\n \"Esperanto\",\n \"espresso\",\n \"euro\",\n \"falsetto\",\n \"Faro\",\n \"fiasco\",\n \"Filipino\",\n \"flamenco\",\n \"furioso\",\n \"generalissimo\",\n \"Gestapo\",\n \"ghetto\",\n \"gigolo\",\n \"gizmo\",\n \"Greensboro\",\n \"gringo\",\n \"Guaiabero\",\n \"guano\",\n \"gumbo\",\n \"gyro\",\n \"hairdo\",\n \"hippo\",\n \"Idaho\",\n \"impetigo\",\n \"inferno\",\n \"info\",\n \"intermezzo\",\n \"intertrigo\",\n \"Iquico\",\n \"jumbo\",\n \"junto\",\n \"Kakapo\",\n \"kilo\",\n \"Kinkimavo\",\n \"Kokako\",\n \"Kosovo\",\n \"Lesotho\",\n \"libero\",\n \"libido\",\n \"libretto\",\n \"lido\",\n \"Lilo\",\n \"limbo\",\n \"limo\",\n \"lineno\",\n \"lingo\",\n \"lino\",\n \"livedo\",\n \"loco\",\n \"logo\",\n \"lumbago\",\n \"macho\",\n \"macro\",\n \"mafioso\",\n \"magneto\",\n \"magnifico\",\n \"Majuro\",\n \"Malabo\",\n \"manifesto\",\n \"Maputo\",\n \"Maracaibo\",\n \"medico\",\n \"memo\",\n \"metro\",\n \"Mexico\",\n \"micro\",\n \"Milano\",\n \"Monaco\",\n \"mono\",\n \"Montenegro\",\n \"Morocco\",\n \"Muqdisho\",\n \"myo\",\n \"neutrino\",\n \"Ningbo\",\n \"octavo\",\n \"oregano\",\n \"Orinoco\",\n \"Orlando\",\n \"Oslo\",\n \"panto\",\n \"Paramaribo\",\n \"Pardusco\",\n \"pedalo\",\n \"photo\",\n \"pimento\",\n \"pinto\",\n \"pleco\",\n \"Pluto\",\n \"pogo\",\n \"polo\",\n \"poncho\",\n \"Porto-Novo\",\n \"Porto\",\n \"pro\",\n \"psycho\",\n \"pueblo\",\n \"quarto\",\n \"Quito\",\n \"repo\",\n \"rhino\",\n \"risotto\",\n \"rococo\",\n \"rondo\",\n \"Sacramento\",\n \"saddo\",\n \"sago\",\n \"salvo\",\n \"Santiago\",\n \"Sapporo\",\n \"Sarajevo\",\n \"scherzando\",\n \"scherzo\",\n \"silo\",\n \"sirocco\",\n \"sombrero\",\n \"staccato\",\n \"sterno\",\n \"stucco\",\n \"stylo\",\n \"sumo\",\n \"Taiko\",\n \"techno\",\n \"terrazzo\",\n \"testudo\",\n \"timpano\",\n \"tiro\",\n \"tobacco\",\n \"Togo\",\n \"Tokyo\",\n \"torero\",\n \"Torino\",\n \"Toronto\",\n \"torso\",\n \"tremolo\",\n \"typo\",\n \"tyro\",\n \"ufo\",\n \"UNESCO\",\n \"vaquero\",\n \"vermicello\",\n \"verso\",\n \"vibrato\",\n \"violoncello\",\n \"Virgo\",\n \"weirdo\",\n \"WHO\",\n \"WTO\",\n \"Yamoussoukro\",\n \"yo-yo\",\n \"zero\",\n \"Zibo\",\n] + pl_sb_C_o_i\n\npl_sb_U_o_os_bysize = bysize(pl_sb_U_o_os_endings)\nsi_sb_U_o_os_bysize = bysize([f\"{w}s\" for w in pl_sb_U_o_os_endings])\n\n\n# UNCONDITIONAL \"..ch\" -> \"..chs\"\n\npl_sb_U_ch_chs_list = (\"czech\", \"eunuch\", \"stomach\")\n\n(\n si_sb_U_ch_chs_list,\n si_sb_U_ch_chs_bysize,\n pl_sb_U_ch_chs_bysize,\n pl_sb_U_ch_chs,\n) = make_pl_si_lists(pl_sb_U_ch_chs_list, \"s\", None)\n\n\n# UNCONDITIONAL \"..[ei]x\" -> \"..ices\"\n\npl_sb_U_ex_ices_list = (\"codex\", \"murex\", \"silex\")\n(\n si_sb_U_ex_ices_list,\n si_sb_U_ex_ices_bysize,\n pl_sb_U_ex_ices_bysize,\n pl_sb_U_ex_ices,\n) = make_pl_si_lists(pl_sb_U_ex_ices_list, \"ices\", 2)\n\npl_sb_U_ix_ices_list = (\"radix\", \"helix\")\n(\n si_sb_U_ix_ices_list,\n si_sb_U_ix_ices_bysize,\n pl_sb_U_ix_ices_bysize,\n pl_sb_U_ix_ices,\n) = make_pl_si_lists(pl_sb_U_ix_ices_list, \"ices\", 2)\n\n# CLASSICAL \"..[ei]x\" -> \"..ices\"\n\npl_sb_C_ex_ices_list = (\n \"vortex\",\n \"vertex\",\n \"cortex\",\n \"latex\",\n \"pontifex\",\n \"apex\",\n \"index\",\n \"simplex\",\n)\n\n(\n si_sb_C_ex_ices_list,\n si_sb_C_ex_ices_bysize,\n pl_sb_C_ex_ices_bysize,\n pl_sb_C_ex_ices,\n) = make_pl_si_lists(pl_sb_C_ex_ices_list, \"ices\", 2)\n\n\npl_sb_C_ix_ices_list = (\"appendix\",)\n\n(\n si_sb_C_ix_ices_list,\n si_sb_C_ix_ices_bysize,\n pl_sb_C_ix_ices_bysize,\n pl_sb_C_ix_ices,\n) = make_pl_si_lists(pl_sb_C_ix_ices_list, \"ices\", 2)\n\n\n# ARABIC: \"..\" -> \"..i\"\n\npl_sb_C_i_list = (\"afrit\", \"afreet\", \"efreet\")\n\n(si_sb_C_i_list, si_sb_C_i_bysize, pl_sb_C_i_bysize, pl_sb_C_i) = make_pl_si_lists(\n pl_sb_C_i_list, \"i\", None\n)\n\n\n# HEBREW: \"..\" -> \"..im\"\n\npl_sb_C_im_list = (\"goy\", \"seraph\", \"cherub\")\n\n(si_sb_C_im_list, si_sb_C_im_bysize, pl_sb_C_im_bysize, pl_sb_C_im) = make_pl_si_lists(\n pl_sb_C_im_list, \"im\", None\n)\n\n\n# UNCONDITIONAL \"..man\" -> \"..mans\"\n\npl_sb_U_man_mans_list = \"\"\"\n ataman caiman cayman ceriman\n desman dolman farman harman hetman\n human leman ottoman shaman talisman\n\"\"\".split()\npl_sb_U_man_mans_caps_list = \"\"\"\n Alabaman Bahaman Burman German\n Hiroshiman Liman Nakayaman Norman Oklahoman\n Panaman Roman Selman Sonaman Tacoman Yakiman\n Yokohaman Yuman\n\"\"\".split()\n\n(\n si_sb_U_man_mans_list,\n si_sb_U_man_mans_bysize,\n pl_sb_U_man_mans_bysize,\n) = make_pl_si_lists(pl_sb_U_man_mans_list, \"s\", None, dojoinstem=False)\n(\n si_sb_U_man_mans_caps_list,\n si_sb_U_man_mans_caps_bysize,\n pl_sb_U_man_mans_caps_bysize,\n) = make_pl_si_lists(pl_sb_U_man_mans_caps_list, \"s\", None, dojoinstem=False)\n\n# UNCONDITIONAL \"..louse\" -> \"..lice\"\npl_sb_U_louse_lice_list = (\"booklouse\", \"grapelouse\", \"louse\", \"woodlouse\")\n\n(\n si_sb_U_louse_lice_list,\n si_sb_U_louse_lice_bysize,\n pl_sb_U_louse_lice_bysize,\n) = make_pl_si_lists(pl_sb_U_louse_lice_list, \"lice\", 5, dojoinstem=False)\n\npl_sb_uninflected_s_complete = [\n # PAIRS OR GROUPS SUBSUMED TO A SINGULAR...\n \"breeches\",\n \"britches\",\n \"pajamas\",\n \"pyjamas\",\n \"clippers\",\n \"gallows\",\n \"hijinks\",\n \"headquarters\",\n \"pliers\",\n \"scissors\",\n \"testes\",\n \"herpes\",\n \"pincers\",\n \"shears\",\n \"proceedings\",\n \"trousers\",\n # UNASSIMILATED LATIN 4th DECLENSION\n \"cantus\",\n \"coitus\",\n \"nexus\",\n # RECENT IMPORTS...\n \"contretemps\",\n \"corps\",\n \"debris\",\n \"siemens\",\n # DISEASES\n \"mumps\",\n # MISCELLANEOUS OTHERS...\n \"diabetes\",\n \"jackanapes\",\n \"series\",\n \"species\",\n \"subspecies\",\n \"rabies\",\n \"chassis\",\n \"innings\",\n \"news\",\n \"mews\",\n \"haggis\",\n]\n\npl_sb_uninflected_s_endings = [\n # RECENT IMPORTS...\n \"ois\",\n # DISEASES\n \"measles\",\n]\n\npl_sb_uninflected_s = pl_sb_uninflected_s_complete + [\n f\".*{w}\" for w in pl_sb_uninflected_s_endings\n]\n\npl_sb_uninflected_herd = (\n # DON'T INFLECT IN CLASSICAL MODE, OTHERWISE NORMAL INFLECTION\n \"wildebeest\",\n \"swine\",\n \"eland\",\n \"bison\",\n \"buffalo\",\n \"cattle\",\n \"elk\",\n \"rhinoceros\",\n \"zucchini\",\n \"caribou\",\n \"dace\",\n \"grouse\",\n \"guinea fowl\",\n \"guinea-fowl\",\n \"haddock\",\n \"hake\",\n \"halibut\",\n \"herring\",\n \"mackerel\",\n \"pickerel\",\n \"pike\",\n \"roe\",\n \"seed\",\n \"shad\",\n \"snipe\",\n \"teal\",\n \"turbot\",\n \"water fowl\",\n \"water-fowl\",\n)\n\npl_sb_uninflected_complete = [\n # SOME FISH AND HERD ANIMALS\n \"tuna\",\n \"salmon\",\n \"mackerel\",\n \"trout\",\n \"bream\",\n \"sea-bass\",\n \"sea bass\",\n \"carp\",\n \"cod\",\n \"flounder\",\n \"whiting\",\n \"moose\",\n # OTHER ODDITIES\n \"graffiti\",\n \"djinn\",\n \"samuri\",\n \"offspring\",\n \"pence\",\n \"quid\",\n \"hertz\",\n] + pl_sb_uninflected_s_complete\n# SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE)\n\npl_sb_uninflected_caps = [\n # ALL NATIONALS ENDING IN -ese\n \"Portuguese\",\n \"Amoyese\",\n \"Borghese\",\n \"Congoese\",\n \"Faroese\",\n \"Foochowese\",\n \"Genevese\",\n \"Genoese\",\n \"Gilbertese\",\n \"Hottentotese\",\n \"Kiplingese\",\n \"Kongoese\",\n \"Lucchese\",\n \"Maltese\",\n \"Nankingese\",\n \"Niasese\",\n \"Pekingese\",\n \"Piedmontese\",\n \"Pistoiese\",\n \"Sarawakese\",\n \"Shavese\",\n \"Vermontese\",\n \"Wenchowese\",\n \"Yengeese\",\n]\n\n\npl_sb_uninflected_endings = [\n # UNCOUNTABLE NOUNS\n \"butter\",\n \"cash\",\n \"furniture\",\n \"information\",\n # SOME FISH AND HERD ANIMALS\n \"fish\",\n \"deer\",\n \"sheep\",\n # ALL NATIONALS ENDING IN -ese\n \"nese\",\n \"rese\",\n \"lese\",\n \"mese\",\n # DISEASES\n \"pox\",\n # OTHER ODDITIES\n \"craft\",\n] + pl_sb_uninflected_s_endings\n# SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE)\n\n\npl_sb_uninflected_bysize = bysize(pl_sb_uninflected_endings)\n\n\n# SINGULAR WORDS ENDING IN ...s (ALL INFLECT WITH ...es)\n\npl_sb_singular_s_complete = [\n \"acropolis\",\n \"aegis\",\n \"alias\",\n \"asbestos\",\n \"bathos\",\n \"bias\",\n \"bronchitis\",\n \"bursitis\",\n \"caddis\",\n \"cannabis\",\n \"canvas\",\n \"chaos\",\n \"cosmos\",\n \"dais\",\n \"digitalis\",\n \"epidermis\",\n \"ethos\",\n \"eyas\",\n \"gas\",\n \"glottis\",\n \"hubris\",\n \"ibis\",\n \"lens\",\n \"mantis\",\n \"marquis\",\n \"metropolis\",\n \"pathos\",\n \"pelvis\",\n \"polis\",\n \"rhinoceros\",\n \"sassafras\",\n \"trellis\",\n] + pl_sb_C_is_ides_complete\n\n\npl_sb_singular_s_endings = [\"ss\", \"us\"] + pl_sb_C_is_ides_endings\n\npl_sb_singular_s_bysize = bysize(pl_sb_singular_s_endings)\n\nsi_sb_singular_s_complete = [f\"{w}es\" for w in pl_sb_singular_s_complete]\nsi_sb_singular_s_endings = [f\"{w}es\" for w in pl_sb_singular_s_endings]\nsi_sb_singular_s_bysize = bysize(si_sb_singular_s_endings)\n\npl_sb_singular_s_es = [\"[A-Z].*es\"]\n\npl_sb_singular_s = enclose(\n \"|\".join(\n pl_sb_singular_s_complete\n + [f\".*{w}\" for w in pl_sb_singular_s_endings]\n + pl_sb_singular_s_es\n )\n)\n\n\n# PLURALS ENDING IN uses -> use\n\n\nsi_sb_ois_oi_case = (\"Bolshois\", \"Hanois\")\n\nsi_sb_uses_use_case = (\"Betelgeuses\", \"Duses\", \"Meuses\", \"Syracuses\", \"Toulouses\")\n\nsi_sb_uses_use = (\n \"abuses\",\n \"applauses\",\n \"blouses\",\n \"carouses\",\n \"causes\",\n \"chartreuses\",\n \"clauses\",\n \"contuses\",\n \"douses\",\n \"excuses\",\n \"fuses\",\n \"grouses\",\n \"hypotenuses\",\n \"masseuses\",\n \"menopauses\",\n \"misuses\",\n \"muses\",\n \"overuses\",\n \"pauses\",\n \"peruses\",\n \"profuses\",\n \"recluses\",\n \"reuses\",\n \"ruses\",\n \"souses\",\n \"spouses\",\n \"suffuses\",\n \"transfuses\",\n \"uses\",\n)\n\nsi_sb_ies_ie_case = (\n \"Addies\",\n \"Aggies\",\n \"Allies\",\n \"Amies\",\n \"Angies\",\n \"Annies\",\n \"Annmaries\",\n \"Archies\",\n \"Arties\",\n \"Aussies\",\n \"Barbies\",\n \"Barries\",\n \"Basies\",\n \"Bennies\",\n \"Bernies\",\n \"Berties\",\n \"Bessies\",\n \"Betties\",\n \"Billies\",\n \"Blondies\",\n \"Bobbies\",\n \"Bonnies\",\n \"Bowies\",\n \"Brandies\",\n \"Bries\",\n \"Brownies\",\n \"Callies\",\n \"Carnegies\",\n \"Carries\",\n \"Cassies\",\n \"Charlies\",\n \"Cheries\",\n \"Christies\",\n \"Connies\",\n \"Curies\",\n \"Dannies\",\n \"Debbies\",\n \"Dixies\",\n \"Dollies\",\n \"Donnies\",\n \"Drambuies\",\n \"Eddies\",\n \"Effies\",\n \"Ellies\",\n \"Elsies\",\n \"Eries\",\n \"Ernies\",\n \"Essies\",\n \"Eugenies\",\n \"Fannies\",\n \"Flossies\",\n \"Frankies\",\n \"Freddies\",\n \"Gillespies\",\n \"Goldies\",\n \"Gracies\",\n \"Guthries\",\n \"Hallies\",\n \"Hatties\",\n \"Hetties\",\n \"Hollies\",\n \"Jackies\",\n \"Jamies\",\n \"Janies\",\n \"Jannies\",\n \"Jeanies\",\n \"Jeannies\",\n \"Jennies\",\n \"Jessies\",\n \"Jimmies\",\n \"Jodies\",\n \"Johnies\",\n \"Johnnies\",\n \"Josies\",\n \"Julies\",\n \"Kalgoorlies\",\n \"Kathies\",\n \"Katies\",\n \"Kellies\",\n \"Kewpies\",\n \"Kristies\",\n \"Laramies\",\n \"Lassies\",\n \"Lauries\",\n \"Leslies\",\n \"Lessies\",\n \"Lillies\",\n \"Lizzies\",\n \"Lonnies\",\n \"Lories\",\n \"Lorries\",\n \"Lotties\",\n \"Louies\",\n \"Mackenzies\",\n \"Maggies\",\n \"Maisies\",\n \"Mamies\",\n \"Marcies\",\n \"Margies\",\n \"Maries\",\n \"Marjories\",\n \"Matties\",\n \"McKenzies\",\n \"Melanies\",\n \"Mickies\",\n \"Millies\",\n \"Minnies\",\n \"Mollies\",\n \"Mounties\",\n \"Nannies\",\n \"Natalies\",\n \"Nellies\",\n \"Netties\",\n \"Ollies\",\n \"Ozzies\",\n \"Pearlies\",\n \"Pottawatomies\",\n \"Reggies\",\n \"Richies\",\n \"Rickies\",\n \"Robbies\",\n \"Ronnies\",\n \"Rosalies\",\n \"Rosemaries\",\n \"Rosies\",\n \"Roxies\",\n \"Rushdies\",\n \"Ruthies\",\n \"Sadies\",\n \"Sallies\",\n \"Sammies\",\n \"Scotties\",\n \"Selassies\",\n \"Sherries\",\n \"Sophies\",\n \"Stacies\",\n \"Stefanies\",\n \"Stephanies\",\n \"Stevies\",\n \"Susies\",\n \"Sylvies\",\n \"Tammies\",\n \"Terries\",\n \"Tessies\",\n \"Tommies\",\n \"Tracies\",\n \"Trekkies\",\n \"Valaries\",\n \"Valeries\",\n \"Valkyries\",\n \"Vickies\",\n \"Virgies\",\n \"Willies\",\n \"Winnies\",\n \"Wylies\",\n \"Yorkies\",\n)\n\nsi_sb_ies_ie = (\n \"aeries\",\n \"baggies\",\n \"belies\",\n \"biggies\",\n \"birdies\",\n \"bogies\",\n \"bonnies\",\n \"boogies\",\n \"bookies\",\n \"bourgeoisies\",\n \"brownies\",\n \"budgies\",\n \"caddies\",\n \"calories\",\n \"camaraderies\",\n \"cockamamies\",\n \"collies\",\n \"cookies\",\n \"coolies\",\n \"cooties\",\n \"coteries\",\n \"crappies\",\n \"curies\",\n \"cutesies\",\n \"dogies\",\n \"eyries\",\n \"floozies\",\n \"footsies\",\n \"freebies\",\n \"genies\",\n \"goalies\",\n \"groupies\",\n \"hies\",\n \"jalousies\",\n \"junkies\",\n \"kiddies\",\n \"laddies\",\n \"lassies\",\n \"lies\",\n \"lingeries\",\n \"magpies\",\n \"menageries\",\n \"mommies\",\n \"movies\",\n \"neckties\",\n \"newbies\",\n \"nighties\",\n \"oldies\",\n \"organdies\",\n \"overlies\",\n \"pies\",\n \"pinkies\",\n \"pixies\",\n \"potpies\",\n \"prairies\",\n \"quickies\",\n \"reveries\",\n \"rookies\",\n \"rotisseries\",\n \"softies\",\n \"sorties\",\n \"species\",\n \"stymies\",\n \"sweeties\",\n \"ties\",\n \"underlies\",\n \"unties\",\n \"veggies\",\n \"vies\",\n \"yuppies\",\n \"zombies\",\n)\n\n\nsi_sb_oes_oe_case = (\n \"Chloes\",\n \"Crusoes\",\n \"Defoes\",\n \"Faeroes\",\n \"Ivanhoes\",\n \"Joes\",\n \"McEnroes\",\n \"Moes\",\n \"Monroes\",\n \"Noes\",\n \"Poes\",\n \"Roscoes\",\n \"Tahoes\",\n \"Tippecanoes\",\n \"Zoes\",\n)\n\nsi_sb_oes_oe = (\n \"aloes\",\n \"backhoes\",\n \"canoes\",\n \"does\",\n \"floes\",\n \"foes\",\n \"hoes\",\n \"mistletoes\",\n \"oboes\",\n \"pekoes\",\n \"roes\",\n \"sloes\",\n \"throes\",\n \"tiptoes\",\n \"toes\",\n \"woes\",\n)\n\nsi_sb_z_zes = (\"quartzes\", \"topazes\")\n\nsi_sb_zzes_zz = (\"buzzes\", \"fizzes\", \"frizzes\", \"razzes\")\n\nsi_sb_ches_che_case = (\n \"Andromaches\",\n \"Apaches\",\n \"Blanches\",\n \"Comanches\",\n \"Nietzsches\",\n \"Porsches\",\n \"Roches\",\n)\n\nsi_sb_ches_che = (\n \"aches\",\n \"avalanches\",\n \"backaches\",\n \"bellyaches\",\n \"caches\",\n \"cloches\",\n \"creches\",\n \"douches\",\n \"earaches\",\n \"fiches\",\n \"headaches\",\n \"heartaches\",\n \"microfiches\",\n \"niches\",\n \"pastiches\",\n \"psyches\",\n \"quiches\",\n \"stomachaches\",\n \"toothaches\",\n \"tranches\",\n)\n\nsi_sb_xes_xe = (\"annexes\", \"axes\", \"deluxes\", \"pickaxes\")\n\nsi_sb_sses_sse_case = (\"Hesses\", \"Jesses\", \"Larousses\", \"Matisses\")\nsi_sb_sses_sse = (\n \"bouillabaisses\",\n \"crevasses\",\n \"demitasses\",\n \"impasses\",\n \"mousses\",\n \"posses\",\n)\n\nsi_sb_ves_ve_case = (\n # *[nwl]ives -> [nwl]live\n \"Clives\",\n \"Palmolives\",\n)\nsi_sb_ves_ve = (\n # *[^d]eaves -> eave\n \"interweaves\",\n \"weaves\",\n # *[nwl]ives -> [nwl]live\n \"olives\",\n # *[eoa]lves -> [eoa]lve\n \"bivalves\",\n \"dissolves\",\n \"resolves\",\n \"salves\",\n \"twelves\",\n \"valves\",\n)\n\n\nplverb_special_s = enclose(\n \"|\".join(\n [pl_sb_singular_s]\n + pl_sb_uninflected_s\n + list(pl_sb_irregular_s)\n + [\"(.*[csx])is\", \"(.*)ceps\", \"[A-Z].*s\"]\n )\n)\n\n_pl_sb_postfix_adj_defn = (\n (\"general\", enclose(r\"(?!major|lieutenant|brigadier|adjutant|.*star)\\S+\")),\n (\"martial\", enclose(\"court\")),\n (\"force\", enclose(\"pound\")),\n)\n\npl_sb_postfix_adj: Iterable[str] = (\n enclose(val + f\"(?=(?:-|\\\\s+){key})\") for key, val in _pl_sb_postfix_adj_defn\n)\n\npl_sb_postfix_adj_stems = f\"({'|'.join(pl_sb_postfix_adj)})(.*)\"\n\n\n# PLURAL WORDS ENDING IS es GO TO SINGULAR is\n\nsi_sb_es_is = (\n \"amanuenses\",\n \"amniocenteses\",\n \"analyses\",\n \"antitheses\",\n \"apotheoses\",\n \"arterioscleroses\",\n \"atheroscleroses\",\n \"axes\",\n # 'bases', # bases -> basis\n \"catalyses\",\n \"catharses\",\n \"chasses\",\n \"cirrhoses\",\n \"cocces\",\n \"crises\",\n \"diagnoses\",\n \"dialyses\",\n \"diereses\",\n \"electrolyses\",\n \"emphases\",\n \"exegeses\",\n \"geneses\",\n \"halitoses\",\n \"hydrolyses\",\n \"hypnoses\",\n \"hypotheses\",\n \"hystereses\",\n \"metamorphoses\",\n \"metastases\",\n \"misdiagnoses\",\n \"mitoses\",\n \"mononucleoses\",\n \"narcoses\",\n \"necroses\",\n \"nemeses\",\n \"neuroses\",\n \"oases\",\n \"osmoses\",\n \"osteoporoses\",\n \"paralyses\",\n \"parentheses\",\n \"parthenogeneses\",\n \"periphrases\",\n \"photosyntheses\",\n \"probosces\",\n \"prognoses\",\n \"prophylaxes\",\n \"prostheses\",\n \"preces\",\n \"psoriases\",\n \"psychoanalyses\",\n \"psychokineses\",\n \"psychoses\",\n \"scleroses\",\n \"scolioses\",\n \"sepses\",\n \"silicoses\",\n \"symbioses\",\n \"synopses\",\n \"syntheses\",\n \"taxes\",\n \"telekineses\",\n \"theses\",\n \"thromboses\",\n \"tuberculoses\",\n \"urinalyses\",\n)\n\npl_prep_list = \"\"\"\n about above across after among around at athwart before behind\n below beneath beside besides between betwixt beyond but by\n during except for from in into near of off on onto out over\n since till to under until unto upon with\"\"\".split()\n\npl_prep_list_da = pl_prep_list + [\"de\", \"du\", \"da\"]\n\npl_prep_bysize = bysize(pl_prep_list_da)\n\npl_prep = enclose(\"|\".join(pl_prep_list_da))\n\npl_sb_prep_dual_compound = fr\"(.*?)((?:-|\\s+)(?:{pl_prep})(?:-|\\s+))a(?:-|\\s+)(.*)\"\n\n\nsingular_pronoun_genders = {\n \"neuter\",\n \"feminine\",\n \"masculine\",\n \"gender-neutral\",\n \"feminine or masculine\",\n \"masculine or feminine\",\n}\n\npl_pron_nom = {\n # NOMINATIVE REFLEXIVE\n \"i\": \"we\",\n \"myself\": \"ourselves\",\n \"you\": \"you\",\n \"yourself\": \"yourselves\",\n \"she\": \"they\",\n \"herself\": \"themselves\",\n \"he\": \"they\",\n \"himself\": \"themselves\",\n \"it\": \"they\",\n \"itself\": \"themselves\",\n \"they\": \"they\",\n \"themself\": \"themselves\",\n # POSSESSIVE\n \"mine\": \"ours\",\n \"yours\": \"yours\",\n \"hers\": \"theirs\",\n \"his\": \"theirs\",\n \"its\": \"theirs\",\n \"theirs\": \"theirs\",\n}\n\nsi_pron: Dict[str, Dict[str, Union[str, Dict[str, str]]]] = {\n \"nom\": {v: k for (k, v) in pl_pron_nom.items()}\n}\nsi_pron[\"nom\"][\"we\"] = \"I\"\n\n\npl_pron_acc = {\n # ACCUSATIVE REFLEXIVE\n \"me\": \"us\",\n \"myself\": \"ourselves\",\n \"you\": \"you\",\n \"yourself\": \"yourselves\",\n \"her\": \"them\",\n \"herself\": \"themselves\",\n \"him\": \"them\",\n \"himself\": \"themselves\",\n \"it\": \"them\",\n \"itself\": \"themselves\",\n \"them\": \"them\",\n \"themself\": \"themselves\",\n}\n\npl_pron_acc_keys = enclose(\"|\".join(pl_pron_acc))\npl_pron_acc_keys_bysize = bysize(pl_pron_acc)\n\nsi_pron[\"acc\"] = {v: k for (k, v) in pl_pron_acc.items()}\n\nfor _thecase, _plur, _gend, _sing in (\n (\"nom\", \"they\", \"neuter\", \"it\"),\n (\"nom\", \"they\", \"feminine\", \"she\"),\n (\"nom\", \"they\", \"masculine\", \"he\"),\n (\"nom\", \"they\", \"gender-neutral\", \"they\"),\n (\"nom\", \"they\", \"feminine or masculine\", \"she or he\"),\n (\"nom\", \"they\", \"masculine or feminine\", \"he or she\"),\n (\"nom\", \"themselves\", \"neuter\", \"itself\"),\n (\"nom\", \"themselves\", \"feminine\", \"herself\"),\n (\"nom\", \"themselves\", \"masculine\", \"himself\"),\n (\"nom\", \"themselves\", \"gender-neutral\", \"themself\"),\n (\"nom\", \"themselves\", \"feminine or masculine\", \"herself or himself\"),\n (\"nom\", \"themselves\", \"masculine or feminine\", \"himself or herself\"),\n (\"nom\", \"theirs\", \"neuter\", \"its\"),\n (\"nom\", \"theirs\", \"feminine\", \"hers\"),\n (\"nom\", \"theirs\", \"masculine\", \"his\"),\n (\"nom\", \"theirs\", \"gender-neutral\", \"theirs\"),\n (\"nom\", \"theirs\", \"feminine or masculine\", \"hers or his\"),\n (\"nom\", \"theirs\", \"masculine or feminine\", \"his or hers\"),\n (\"acc\", \"them\", \"neuter\", \"it\"),\n (\"acc\", \"them\", \"feminine\", \"her\"),\n (\"acc\", \"them\", \"masculine\", \"him\"),\n (\"acc\", \"them\", \"gender-neutral\", \"them\"),\n (\"acc\", \"them\", \"feminine or masculine\", \"her or him\"),\n (\"acc\", \"them\", \"masculine or feminine\", \"him or her\"),\n (\"acc\", \"themselves\", \"neuter\", \"itself\"),\n (\"acc\", \"themselves\", \"feminine\", \"herself\"),\n (\"acc\", \"themselves\", \"masculine\", \"himself\"),\n (\"acc\", \"themselves\", \"gender-neutral\", \"themself\"),\n (\"acc\", \"themselves\", \"feminine or masculine\", \"herself or himself\"),\n (\"acc\", \"themselves\", \"masculine or feminine\", \"himself or herself\"),\n):\n try:\n si_pron[_thecase][_plur][_gend] = _sing # type: ignore\n except TypeError:\n si_pron[_thecase][_plur] = {}\n si_pron[_thecase][_plur][_gend] = _sing # type: ignore\n\n\nsi_pron_acc_keys = enclose(\"|\".join(si_pron[\"acc\"]))\nsi_pron_acc_keys_bysize = bysize(si_pron[\"acc\"])\n\n\ndef get_si_pron(thecase, word, gender) -> str:\n try:\n sing = si_pron[thecase][word]\n except KeyError:\n raise # not a pronoun\n try:\n return sing[gender] # has several types due to gender\n except TypeError:\n return cast(str, sing) # answer independent of gender\n\n\n# These dictionaries group verbs by first, second and third person\n# conjugations.\n\nplverb_irregular_pres = {\n \"am\": \"are\",\n \"are\": \"are\",\n \"is\": \"are\",\n \"was\": \"were\",\n \"were\": \"were\",\n \"was\": \"were\",\n \"have\": \"have\",\n \"have\": \"have\",\n \"has\": \"have\",\n \"do\": \"do\",\n \"do\": \"do\",\n \"does\": \"do\",\n}\n\nplverb_ambiguous_pres = {\n \"act\": \"act\",\n \"act\": \"act\",\n \"acts\": \"act\",\n \"blame\": \"blame\",\n \"blame\": \"blame\",\n \"blames\": \"blame\",\n \"can\": \"can\",\n \"can\": \"can\",\n \"can\": \"can\",\n \"must\": \"must\",\n \"must\": \"must\",\n \"must\": \"must\",\n \"fly\": \"fly\",\n \"fly\": \"fly\",\n \"flies\": \"fly\",\n \"copy\": \"copy\",\n \"copy\": \"copy\",\n \"copies\": \"copy\",\n \"drink\": \"drink\",\n \"drink\": \"drink\",\n \"drinks\": \"drink\",\n \"fight\": \"fight\",\n \"fight\": \"fight\",\n \"fights\": \"fight\",\n \"fire\": \"fire\",\n \"fire\": \"fire\",\n \"fires\": \"fire\",\n \"like\": \"like\",\n \"like\": \"like\",\n \"likes\": \"like\",\n \"look\": \"look\",\n \"look\": \"look\",\n \"looks\": \"look\",\n \"make\": \"make\",\n \"make\": \"make\",\n \"makes\": \"make\",\n \"reach\": \"reach\",\n \"reach\": \"reach\",\n \"reaches\": \"reach\",\n \"run\": \"run\",\n \"run\": \"run\",\n \"runs\": \"run\",\n \"sink\": \"sink\",\n \"sink\": \"sink\",\n \"sinks\": \"sink\",\n \"sleep\": \"sleep\",\n \"sleep\": \"sleep\",\n \"sleeps\": \"sleep\",\n \"view\": \"view\",\n \"view\": \"view\",\n \"views\": \"view\",\n}\n\nplverb_ambiguous_pres_keys = re.compile(\n fr\"^({enclose('|'.join(plverb_ambiguous_pres))})((\\s.*)?)$\", re.IGNORECASE\n)\n\n\nplverb_irregular_non_pres = (\n \"did\",\n \"had\",\n \"ate\",\n \"made\",\n \"put\",\n \"spent\",\n \"fought\",\n \"sank\",\n \"gave\",\n \"sought\",\n \"shall\",\n \"could\",\n \"ought\",\n \"should\",\n)\n\nplverb_ambiguous_non_pres = re.compile(\n r\"^((?:thought|saw|bent|will|might|cut))((\\s.*)?)$\", re.IGNORECASE\n)\n\n# \"..oes\" -> \"..oe\" (the rest are \"..oes\" -> \"o\")\n\npl_v_oes_oe = (\"canoes\", \"floes\", \"oboes\", \"roes\", \"throes\", \"woes\")\npl_v_oes_oe_endings_size4 = (\"hoes\", \"toes\")\npl_v_oes_oe_endings_size5 = (\"shoes\",)\n\n\npl_count_zero = (\"0\", \"no\", \"zero\", \"nil\")\n\n\npl_count_one = (\"1\", \"a\", \"an\", \"one\", \"each\", \"every\", \"this\", \"that\")\n\npl_adj_special = {\"a\": \"some\", \"an\": \"some\", \"this\": \"these\", \"that\": \"those\"}\n\npl_adj_special_keys = re.compile(\n fr\"^({enclose('|'.join(pl_adj_special))})$\", re.IGNORECASE\n)\n\npl_adj_poss = {\n \"my\": \"our\",\n \"your\": \"your\",\n \"its\": \"their\",\n \"her\": \"their\",\n \"his\": \"their\",\n \"their\": \"their\",\n}\n\npl_adj_poss_keys = re.compile(fr\"^({enclose('|'.join(pl_adj_poss))})$\", re.IGNORECASE)\n\n\n# 2. INDEFINITE ARTICLES\n\n# THIS PATTERN MATCHES STRINGS OF CAPITALS STARTING WITH A \"VOWEL-SOUND\"\n# CONSONANT FOLLOWED BY ANOTHER CONSONANT, AND WHICH ARE NOT LIKELY\n# TO BE REAL WORDS (OH, ALL RIGHT THEN, IT'S JUST MAGIC!)\n\nA_abbrev = re.compile(\n r\"\"\"\n(?! FJO | [HLMNS]Y. | RY[EO] | SQU\n | ( F[LR]? | [HL] | MN? | N | RH? | S[CHKLMNPTVW]? | X(YL)?) [AEIOU])\n[FHLMNRSX][A-Z]\n\"\"\",\n re.VERBOSE,\n)\n\n# THIS PATTERN CODES THE BEGINNINGS OF ALL ENGLISH WORDS BEGINING WITH A\n# 'y' FOLLOWED BY A CONSONANT. ANY OTHER Y-CONSONANT PREFIX THEREFORE\n# IMPLIES AN ABBREVIATION.\n\nA_y_cons = re.compile(r\"^(y(b[lor]|cl[ea]|fere|gg|p[ios]|rou|tt))\", re.IGNORECASE)\n\n# EXCEPTIONS TO EXCEPTIONS\n\nA_explicit_a = re.compile(r\"^((?:unabomber|unanimous|US))\", re.IGNORECASE)\n\nA_explicit_an = re.compile(\n r\"^((?:euler|hour(?!i)|heir|honest|hono[ur]|mpeg))\", re.IGNORECASE\n)\n\nA_ordinal_an = re.compile(r\"^([aefhilmnorsx]-?th)\", re.IGNORECASE)\n\nA_ordinal_a = re.compile(r\"^([bcdgjkpqtuvwyz]-?th)\", re.IGNORECASE)\n\n\n# NUMERICAL INFLECTIONS\n\nnth = {\n 0: \"th\",\n 1: \"st\",\n 2: \"nd\",\n 3: \"rd\",\n 4: \"th\",\n 5: \"th\",\n 6: \"th\",\n 7: \"th\",\n 8: \"th\",\n 9: \"th\",\n 11: \"th\",\n 12: \"th\",\n 13: \"th\",\n}\nnth_suff = set(nth.values())\n\nordinal = dict(\n ty=\"tieth\",\n one=\"first\",\n two=\"second\",\n three=\"third\",\n five=\"fifth\",\n eight=\"eighth\",\n nine=\"ninth\",\n twelve=\"twelfth\",\n)\n\nordinal_suff = re.compile(fr\"({'|'.join(ordinal)})\\Z\")\n\n\n# NUMBERS\n\nunit = [\"\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\"]\nteen = [\n \"ten\",\n \"eleven\",\n \"twelve\",\n \"thirteen\",\n \"fourteen\",\n \"fifteen\",\n \"sixteen\",\n \"seventeen\",\n \"eighteen\",\n \"nineteen\",\n]\nten = [\n \"\",\n \"\",\n \"twenty\",\n \"thirty\",\n \"forty\",\n \"fifty\",\n \"sixty\",\n \"seventy\",\n \"eighty\",\n \"ninety\",\n]\nmill = [\n \" \",\n \" thousand\",\n \" million\",\n \" billion\",\n \" trillion\",\n \" quadrillion\",\n \" quintillion\",\n \" sextillion\",\n \" septillion\",\n \" octillion\",\n \" nonillion\",\n \" decillion\",\n]\n\n\n# SUPPORT CLASSICAL PLURALIZATIONS\n\ndef_classical = dict(\n all=False, zero=False, herd=False, names=True, persons=False, ancient=False\n)\n\nall_classical = {k: True for k in def_classical}\nno_classical = {k: False for k in def_classical}\n\n\n# Maps strings to built-in constant types\nstring_to_constant = {\"True\": True, \"False\": False, \"None\": None}\n\n\n# Pre-compiled regular expression objects\nDOLLAR_DIGITS = re.compile(r\"\\$(\\d+)\")\nFUNCTION_CALL = re.compile(r\"((\\w+)\\([^)]*\\)*)\", re.IGNORECASE)\nPARTITION_WORD = re.compile(r\"\\A(\\s*)(.+?)(\\s*)\\Z\")\nPL_SB_POSTFIX_ADJ_STEMS_RE = re.compile(\n fr\"^(?:{pl_sb_postfix_adj_stems})$\", re.IGNORECASE\n)\nPL_SB_PREP_DUAL_COMPOUND_RE = re.compile(\n fr\"^(?:{pl_sb_prep_dual_compound})$\", re.IGNORECASE\n)\nDENOMINATOR = re.compile(r\"(?P.+)( (per|a) .+)\")\nPLVERB_SPECIAL_S_RE = re.compile(fr\"^({plverb_special_s})$\")\nWHITESPACE = re.compile(r\"\\s\")\nENDS_WITH_S = re.compile(r\"^(.*[^s])s$\", re.IGNORECASE)\nENDS_WITH_APOSTROPHE_S = re.compile(r\"^(.*)'s?$\")\nINDEFINITE_ARTICLE_TEST = re.compile(r\"\\A(\\s*)(?:an?\\s+)?(.+?)(\\s*)\\Z\", re.IGNORECASE)\nSPECIAL_AN = re.compile(r\"^[aefhilmnorsx]$\", re.IGNORECASE)\nSPECIAL_A = re.compile(r\"^[bcdgjkpqtuvwyz]$\", re.IGNORECASE)\nSPECIAL_ABBREV_AN = re.compile(r\"^[aefhilmnorsx][.-]\", re.IGNORECASE)\nSPECIAL_ABBREV_A = re.compile(r\"^[a-z][.-]\", re.IGNORECASE)\nCONSONANTS = re.compile(r\"^[^aeiouy]\", re.IGNORECASE)\nARTICLE_SPECIAL_EU = re.compile(r\"^e[uw]\", re.IGNORECASE)\nARTICLE_SPECIAL_ONCE = re.compile(r\"^onc?e\\b\", re.IGNORECASE)\nARTICLE_SPECIAL_ONETIME = re.compile(r\"^onetime\\b\", re.IGNORECASE)\nARTICLE_SPECIAL_UNIT = re.compile(r\"^uni([^nmd]|mo)\", re.IGNORECASE)\nARTICLE_SPECIAL_UBA = re.compile(r\"^u[bcfghjkqrst][aeiou]\", re.IGNORECASE)\nARTICLE_SPECIAL_UKR = re.compile(r\"^ukr\", re.IGNORECASE)\nSPECIAL_CAPITALS = re.compile(r\"^U[NK][AIEO]?\")\nVOWELS = re.compile(r\"^[aeiou]\", re.IGNORECASE)\n\nDIGIT_GROUP = re.compile(r\"(\\d)\")\nTWO_DIGITS = re.compile(r\"(\\d)(\\d)\")\nTHREE_DIGITS = re.compile(r\"(\\d)(\\d)(\\d)\")\nTHREE_DIGITS_WORD = re.compile(r\"(\\d)(\\d)(\\d)(?=\\D*\\Z)\")\nTWO_DIGITS_WORD = re.compile(r\"(\\d)(\\d)(?=\\D*\\Z)\")\nONE_DIGIT_WORD = re.compile(r\"(\\d)(?=\\D*\\Z)\")\n\nFOUR_DIGIT_COMMA = re.compile(r\"(\\d)(\\d{3}(?:,|\\Z))\")\nNON_DIGIT = re.compile(r\"\\D\")\nWHITESPACES_COMMA = re.compile(r\"\\s+,\")\nCOMMA_WORD = re.compile(r\", (\\S+)\\s+\\Z\")\nWHITESPACES = re.compile(r\"\\s+\")\n\n\nPRESENT_PARTICIPLE_REPLACEMENTS = (\n (re.compile(r\"ie$\"), r\"y\"),\n (\n re.compile(r\"ue$\"),\n r\"u\",\n ), # TODO: isn't ue$ -> u encompassed in the following rule?\n (re.compile(r\"([auy])e$\"), r\"\\g<1>\"),\n (re.compile(r\"ski$\"), r\"ski\"),\n (re.compile(r\"[^b]i$\"), r\"\"),\n (re.compile(r\"^(are|were)$\"), r\"be\"),\n (re.compile(r\"^(had)$\"), r\"hav\"),\n (re.compile(r\"^(hoe)$\"), r\"\\g<1>\"),\n (re.compile(r\"([^e])e$\"), r\"\\g<1>\"),\n (re.compile(r\"er$\"), r\"er\"),\n (re.compile(r\"([^aeiou][aeiouy]([bdgmnprst]))$\"), r\"\\g<1>\\g<2>\"),\n)\n\nDIGIT = re.compile(r\"\\d\")\n\n\nclass Words(str):\n lowered: str\n split_: List[str]\n first: str\n last: str\n\n def __init__(self, orig) -> None:\n self.lowered = self.lower()\n self.split_ = self.split()\n self.first = self.split_[0]\n self.last = self.split_[-1]\n\n\nclass engine:\n def __init__(self) -> None:\n\n self.classical_dict = def_classical.copy()\n self.persistent_count: Optional[int] = None\n self.mill_count = 0\n self.pl_sb_user_defined: List[str] = []\n self.pl_v_user_defined: List[str] = []\n self.pl_adj_user_defined: List[str] = []\n self.si_sb_user_defined: List[str] = []\n self.A_a_user_defined: List[str] = []\n self.thegender = \"neuter\"\n self.__number_args: Optional[Dict[str, str]] = None\n\n @property\n def _number_args(self):\n return cast(Dict[str, str], self.__number_args)\n\n @_number_args.setter\n def _number_args(self, val):\n self.__number_args = val\n\n deprecated_methods = dict(\n pl=\"plural\",\n plnoun=\"plural_noun\",\n plverb=\"plural_verb\",\n pladj=\"plural_adj\",\n sinoun=\"single_noun\",\n prespart=\"present_participle\",\n numwords=\"number_to_words\",\n plequal=\"compare\",\n plnounequal=\"compare_nouns\",\n plverbequal=\"compare_verbs\",\n pladjequal=\"compare_adjs\",\n wordlist=\"join\",\n )\n\n def __getattr__(self, meth):\n if meth in self.deprecated_methods:\n print3(f\"{meth}() deprecated, use {self.deprecated_methods[meth]}()\")\n raise DeprecationWarning\n raise AttributeError\n\n def defnoun(self, singular: str, plural: str) -> int:\n \"\"\"\n Set the noun plural of singular to plural.\n\n \"\"\"\n self.checkpat(singular)\n self.checkpatplural(plural)\n self.pl_sb_user_defined.extend((singular, plural))\n self.si_sb_user_defined.extend((plural, singular))\n return 1\n\n def defverb(self, s1: str, p1: str, s2: str, p2: str, s3: str, p3: str) -> int:\n \"\"\"\n Set the verb plurals for s1, s2 and s3 to p1, p2 and p3 respectively.\n\n Where 1, 2 and 3 represent the 1st, 2nd and 3rd person forms of the verb.\n\n \"\"\"\n self.checkpat(s1)\n self.checkpat(s2)\n self.checkpat(s3)\n self.checkpatplural(p1)\n self.checkpatplural(p2)\n self.checkpatplural(p3)\n self.pl_v_user_defined.extend((s1, p1, s2, p2, s3, p3))\n return 1\n\n def defadj(self, singular: str, plural: str) -> int:\n \"\"\"\n Set the adjective plural of singular to plural.\n\n \"\"\"\n self.checkpat(singular)\n self.checkpatplural(plural)\n self.pl_adj_user_defined.extend((singular, plural))\n return 1\n\n def defa(self, pattern: str) -> int:\n \"\"\"\n Define the indefinite article as 'a' for words matching pattern.\n\n \"\"\"\n self.checkpat(pattern)\n self.A_a_user_defined.extend((pattern, \"a\"))\n return 1\n\n def defan(self, pattern: str) -> int:\n \"\"\"\n Define the indefinite article as 'an' for words matching pattern.\n\n \"\"\"\n self.checkpat(pattern)\n self.A_a_user_defined.extend((pattern, \"an\"))\n return 1\n\n def checkpat(self, pattern: Optional[str]) -> None:\n \"\"\"\n check for errors in a regex pattern\n \"\"\"\n if pattern is None:\n return\n try:\n re.match(pattern, \"\")\n except re.error:\n print3(f\"\\nBad user-defined singular pattern:\\n\\t{pattern}\\n\")\n raise BadUserDefinedPatternError\n\n def checkpatplural(self, pattern: str) -> None:\n \"\"\"\n check for errors in a regex replace pattern\n \"\"\"\n return\n\n def ud_match(self, word: str, wordlist: List[str]) -> Optional[str]:\n for i in range(len(wordlist) - 2, -2, -2): # backwards through even elements\n mo = re.search(fr\"^{wordlist[i]}$\", word, re.IGNORECASE)\n if mo:\n if wordlist[i + 1] is None:\n return None\n pl = DOLLAR_DIGITS.sub(\n r\"\\\\1\", wordlist[i + 1]\n ) # change $n to \\n for expand\n return mo.expand(pl)\n return None\n\n def classical(self, **kwargs) -> None:\n \"\"\"\n turn classical mode on and off for various categories\n\n turn on all classical modes:\n classical()\n classical(all=True)\n\n turn on or off specific claassical modes:\n e.g.\n classical(herd=True)\n classical(names=False)\n\n By default all classical modes are off except names.\n\n unknown value in args or key in kwargs raises\n exception: UnknownClasicalModeError\n\n \"\"\"\n if not kwargs:\n self.classical_dict = all_classical.copy()\n return\n if \"all\" in kwargs:\n if kwargs[\"all\"]:\n self.classical_dict = all_classical.copy()\n else:\n self.classical_dict = no_classical.copy()\n\n for k, v in kwargs.items():\n if k in def_classical:\n self.classical_dict[k] = v\n else:\n raise UnknownClassicalModeError\n\n def num(\n self, count: Optional[int] = None, show: Optional[int] = None\n ) -> str: # (;$count,$show)\n \"\"\"\n Set the number to be used in other method calls.\n\n Returns count.\n\n Set show to False to return '' instead.\n\n \"\"\"\n if count is not None:\n try:\n self.persistent_count = int(count)\n except ValueError:\n raise BadNumValueError\n if (show is None) or show:\n return str(count)\n else:\n self.persistent_count = None\n return \"\"\n\n def gender(self, gender: str) -> None:\n \"\"\"\n set the gender for the singular of plural pronouns\n\n can be one of:\n 'neuter' ('they' -> 'it')\n 'feminine' ('they' -> 'she')\n 'masculine' ('they' -> 'he')\n 'gender-neutral' ('they' -> 'they')\n 'feminine or masculine' ('they' -> 'she or he')\n 'masculine or feminine' ('they' -> 'he or she')\n \"\"\"\n if gender in singular_pronoun_genders:\n self.thegender = gender\n else:\n raise BadGenderError\n\n def _get_value_from_ast(self, obj):\n \"\"\"\n Return the value of the ast object.\n \"\"\"\n if isinstance(obj, ast.Num):\n return obj.n\n elif isinstance(obj, ast.Str):\n return obj.s\n elif isinstance(obj, ast.List):\n return [self._get_value_from_ast(e) for e in obj.elts]\n elif isinstance(obj, ast.Tuple):\n return tuple([self._get_value_from_ast(e) for e in obj.elts])\n\n # None, True and False are NameConstants in Py3.4 and above.\n elif isinstance(obj, ast.NameConstant):\n return obj.value\n\n # Probably passed a variable name.\n # Or passed a single word without wrapping it in quotes as an argument\n # ex: p.inflect(\"I plural(see)\") instead of p.inflect(\"I plural('see')\")\n raise NameError(f\"name '{obj.id}' is not defined\")\n\n def _string_to_substitute(\n self, mo: Match, methods_dict: Dict[str, Callable]\n ) -> str:\n \"\"\"\n Return the string to be substituted for the match.\n \"\"\"\n matched_text, f_name = mo.groups()\n # matched_text is the complete match string. e.g. plural_noun(cat)\n # f_name is the function name. e.g. plural_noun\n\n # Return matched_text if function name is not in methods_dict\n if f_name not in methods_dict:\n return matched_text\n\n # Parse the matched text\n a_tree = ast.parse(matched_text)\n\n # get the args and kwargs from ast objects\n args_list = [\n self._get_value_from_ast(a)\n for a in a_tree.body[0].value.args # type: ignore[attr-defined]\n ]\n kwargs_list = {\n kw.arg: self._get_value_from_ast(kw.value)\n for kw in a_tree.body[0].value.keywords # type: ignore[attr-defined]\n }\n\n # Call the corresponding function\n return methods_dict[f_name](*args_list, **kwargs_list)\n\n # 0. PERFORM GENERAL INFLECTIONS IN A STRING\n\n def inflect(self, text: str) -> str:\n \"\"\"\n Perform inflections in a string.\n\n e.g. inflect('The plural of cat is plural(cat)') returns\n 'The plural of cat is cats'\n\n can use plural, plural_noun, plural_verb, plural_adj,\n singular_noun, a, an, no, ordinal, number_to_words,\n and prespart\n\n \"\"\"\n save_persistent_count = self.persistent_count\n\n # Dictionary of allowed methods\n methods_dict: Dict[str, Callable] = {\n \"plural\": self.plural,\n \"plural_adj\": self.plural_adj,\n \"plural_noun\": self.plural_noun,\n \"plural_verb\": self.plural_verb,\n \"singular_noun\": self.singular_noun,\n \"a\": self.a,\n \"an\": self.a,\n \"no\": self.no,\n \"ordinal\": self.ordinal,\n \"number_to_words\": self.number_to_words,\n \"present_participle\": self.present_participle,\n \"num\": self.num,\n }\n\n # Regular expression to find Python's function call syntax\n output = FUNCTION_CALL.sub(\n lambda mo: self._string_to_substitute(mo, methods_dict), text\n )\n self.persistent_count = save_persistent_count\n return output\n\n # ## PLURAL SUBROUTINES\n\n def postprocess(self, orig: str, inflected) -> str:\n inflected = str(inflected)\n if \"|\" in inflected:\n word_options = inflected.split(\"|\")\n # When two parts of a noun need to be pluralized\n if len(word_options[0].split(\" \")) == len(word_options[1].split(\" \")):\n result = inflected.split(\"|\")[self.classical_dict[\"all\"]].split(\" \")\n # When only the last part of the noun needs to be pluralized\n else:\n result = inflected.split(\" \")\n for index, word in enumerate(result):\n if \"|\" in word:\n result[index] = word.split(\"|\")[self.classical_dict[\"all\"]]\n else:\n result = inflected.split(\" \")\n\n # Try to fix word wise capitalization\n for index, word in enumerate(orig.split(\" \")):\n if word == \"I\":\n # Is this the only word for exceptions like this\n # Where the original is fully capitalized\n # without 'meaning' capitalization?\n # Also this fails to handle a capitalizaion in context\n continue\n if word.capitalize() == word:\n result[index] = result[index].capitalize()\n if word == word.upper():\n result[index] = result[index].upper()\n return \" \".join(result)\n\n def partition_word(self, text: str) -> Tuple[str, str, str]:\n mo = PARTITION_WORD.search(text)\n if mo:\n return mo.group(1), mo.group(2), mo.group(3)\n else:\n return \"\", \"\", \"\"\n\n def plural(self, text: str, count: Optional[Union[str, int]] = None) -> str:\n \"\"\"\n Return the plural of text.\n\n If count supplied, then return text if count is one of:\n 1, a, an, one, each, every, this, that\n\n otherwise return the plural.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n pre, word, post = self.partition_word(text)\n if not word:\n return text\n plural = self.postprocess(\n word,\n self._pl_special_adjective(word, count)\n or self._pl_special_verb(word, count)\n or self._plnoun(word, count),\n )\n return f\"{pre}{plural}{post}\"\n\n def plural_noun(self, text: str, count: Optional[Union[str, int]] = None) -> str:\n \"\"\"\n Return the plural of text, where text is a noun.\n\n If count supplied, then return text if count is one of:\n 1, a, an, one, each, every, this, that\n\n otherwise return the plural.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n pre, word, post = self.partition_word(text)\n if not word:\n return text\n plural = self.postprocess(word, self._plnoun(word, count))\n return f\"{pre}{plural}{post}\"\n\n def plural_verb(self, text: str, count: Optional[Union[str, int]] = None) -> str:\n \"\"\"\n Return the plural of text, where text is a verb.\n\n If count supplied, then return text if count is one of:\n 1, a, an, one, each, every, this, that\n\n otherwise return the plural.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n pre, word, post = self.partition_word(text)\n if not word:\n return text\n plural = self.postprocess(\n word,\n self._pl_special_verb(word, count) or self._pl_general_verb(word, count),\n )\n return f\"{pre}{plural}{post}\"\n\n def plural_adj(self, text: str, count: str = None) -> str:\n \"\"\"\n Return the plural of text, where text is an adjective.\n\n If count supplied, then return text if count is one of:\n 1, a, an, one, each, every, this, that\n\n otherwise return the plural.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n pre, word, post = self.partition_word(text)\n if not word:\n return text\n plural = self.postprocess(word, self._pl_special_adjective(word, count) or word)\n return f\"{pre}{plural}{post}\"\n\n def compare(self, word1: str, word2: str) -> Union[str, bool]:\n \"\"\"\n compare word1 and word2 for equality regardless of plurality\n\n return values:\n eq - the strings are equal\n p:s - word1 is the plural of word2\n s:p - word2 is the plural of word1\n p:p - word1 and word2 are two different plural forms of the one word\n False - otherwise\n\n \"\"\"\n return (\n self._plequal(word1, word2, self.plural_noun)\n or self._plequal(word1, word2, self.plural_verb)\n or self._plequal(word1, word2, self.plural_adj)\n )\n\n def compare_nouns(self, word1: str, word2: str) -> Union[str, bool]:\n \"\"\"\n compare word1 and word2 for equality regardless of plurality\n word1 and word2 are to be treated as nouns\n\n return values:\n eq - the strings are equal\n p:s - word1 is the plural of word2\n s:p - word2 is the plural of word1\n p:p - word1 and word2 are two different plural forms of the one word\n False - otherwise\n\n \"\"\"\n return self._plequal(word1, word2, self.plural_noun)\n\n def compare_verbs(self, word1: str, word2: str) -> Union[str, bool]:\n \"\"\"\n compare word1 and word2 for equality regardless of plurality\n word1 and word2 are to be treated as verbs\n\n return values:\n eq - the strings are equal\n p:s - word1 is the plural of word2\n s:p - word2 is the plural of word1\n p:p - word1 and word2 are two different plural forms of the one word\n False - otherwise\n\n \"\"\"\n return self._plequal(word1, word2, self.plural_verb)\n\n def compare_adjs(self, word1: str, word2: str) -> Union[str, bool]:\n \"\"\"\n compare word1 and word2 for equality regardless of plurality\n word1 and word2 are to be treated as adjectives\n\n return values:\n eq - the strings are equal\n p:s - word1 is the plural of word2\n s:p - word2 is the plural of word1\n p:p - word1 and word2 are two different plural forms of the one word\n False - otherwise\n\n \"\"\"\n return self._plequal(word1, word2, self.plural_adj)\n\n def singular_noun(\n self,\n text: str,\n count: Optional[Union[int, str]] = None,\n gender: Optional[str] = None,\n ) -> Union[str, bool]:\n \"\"\"\n Return the singular of text, where text is a plural noun.\n\n If count supplied, then return the singular if count is one of:\n 1, a, an, one, each, every, this, that or if count is None\n\n otherwise return text unchanged.\n\n Whitespace at the start and end is preserved.\n\n >>> p = engine()\n >>> p.singular_noun('horses')\n 'horse'\n >>> p.singular_noun('knights')\n 'knight'\n\n Returns False when a singular noun is passed.\n\n >>> p.singular_noun('horse')\n False\n >>> p.singular_noun('knight')\n False\n >>> p.singular_noun('soldier')\n False\n\n \"\"\"\n pre, word, post = self.partition_word(text)\n if not word:\n return text\n sing = self._sinoun(word, count=count, gender=gender)\n if sing is not False:\n plural = self.postprocess(word, sing)\n return f\"{pre}{plural}{post}\"\n return False\n\n def _plequal(self, word1: str, word2: str, pl) -> Union[str, bool]: # noqa: C901\n classval = self.classical_dict.copy()\n self.classical_dict = all_classical.copy()\n if word1 == word2:\n return \"eq\"\n if word1 == pl(word2):\n return \"p:s\"\n if pl(word1) == word2:\n return \"s:p\"\n self.classical_dict = no_classical.copy()\n if word1 == pl(word2):\n return \"p:s\"\n if pl(word1) == word2:\n return \"s:p\"\n self.classical_dict = classval.copy()\n\n if pl == self.plural or pl == self.plural_noun:\n if self._pl_check_plurals_N(word1, word2):\n return \"p:p\"\n if self._pl_check_plurals_N(word2, word1):\n return \"p:p\"\n if pl == self.plural or pl == self.plural_adj:\n if self._pl_check_plurals_adj(word1, word2):\n return \"p:p\"\n return False\n\n def _pl_reg_plurals(self, pair: str, stems: str, end1: str, end2: str) -> bool:\n pattern = fr\"({stems})({end1}\\|\\1{end2}|{end2}\\|\\1{end1})\"\n return bool(re.search(pattern, pair))\n\n def _pl_check_plurals_N(self, word1: str, word2: str) -> bool:\n stem_endings = (\n (pl_sb_C_a_ata, \"as\", \"ata\"),\n (pl_sb_C_is_ides, \"is\", \"ides\"),\n (pl_sb_C_a_ae, \"s\", \"e\"),\n (pl_sb_C_en_ina, \"ens\", \"ina\"),\n (pl_sb_C_um_a, \"ums\", \"a\"),\n (pl_sb_C_us_i, \"uses\", \"i\"),\n (pl_sb_C_on_a, \"ons\", \"a\"),\n (pl_sb_C_o_i_stems, \"os\", \"i\"),\n (pl_sb_C_ex_ices, \"exes\", \"ices\"),\n (pl_sb_C_ix_ices, \"ixes\", \"ices\"),\n (pl_sb_C_i, \"s\", \"i\"),\n (pl_sb_C_im, \"s\", \"im\"),\n (\".*eau\", \"s\", \"x\"),\n (\".*ieu\", \"s\", \"x\"),\n (\".*tri\", \"xes\", \"ces\"),\n (\".{2,}[yia]n\", \"xes\", \"ges\"),\n )\n\n words = map(Words, (word1, word2))\n pair = \"|\".join(word.last for word in words)\n\n return (\n pair in pl_sb_irregular_s.values()\n or pair in pl_sb_irregular.values()\n or pair in pl_sb_irregular_caps.values()\n or any(\n self._pl_reg_plurals(pair, stems, end1, end2)\n for stems, end1, end2 in stem_endings\n )\n )\n\n def _pl_check_plurals_adj(self, word1: str, word2: str) -> bool:\n word1a = word1[: word1.rfind(\"'\")] if word1.endswith((\"'s\", \"'\")) else \"\"\n word2a = word2[: word2.rfind(\"'\")] if word2.endswith((\"'s\", \"'\")) else \"\"\n\n return (\n bool(word1a)\n and bool(word2a)\n and (\n self._pl_check_plurals_N(word1a, word2a)\n or self._pl_check_plurals_N(word2a, word1a)\n )\n )\n\n def get_count(self, count: Optional[Union[str, int]] = None) -> Union[str, int]:\n if count is None and self.persistent_count is not None:\n count = self.persistent_count\n\n if count is not None:\n count = (\n 1\n if (\n (str(count) in pl_count_one)\n or (\n self.classical_dict[\"zero\"]\n and str(count).lower() in pl_count_zero\n )\n )\n else 2\n )\n else:\n count = \"\"\n return count\n\n # @profile\n def _plnoun( # noqa: C901\n self, word: str, count: Optional[Union[str, int]] = None\n ) -> str:\n count = self.get_count(count)\n\n # DEFAULT TO PLURAL\n\n if count == 1:\n return word\n\n # HANDLE USER-DEFINED NOUNS\n\n value = self.ud_match(word, self.pl_sb_user_defined)\n if value is not None:\n return value\n\n # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS\n\n if word == \"\":\n return word\n\n word = Words(word)\n\n if word.last.lower() in pl_sb_uninflected_complete:\n return word\n\n if word in pl_sb_uninflected_caps:\n return word\n\n for k, v in pl_sb_uninflected_bysize.items():\n if word.lowered[-k:] in v:\n return word\n\n if self.classical_dict[\"herd\"] and word.last.lower() in pl_sb_uninflected_herd:\n return word\n\n # HANDLE COMPOUNDS (\"Governor General\", \"mother-in-law\", \"aide-de-camp\", ETC.)\n\n mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word)\n if mo and mo.group(2) != \"\":\n return f\"{self._plnoun(mo.group(1), 2)}{mo.group(2)}\"\n\n if \" a \" in word.lowered or \"-a-\" in word.lowered:\n mo = PL_SB_PREP_DUAL_COMPOUND_RE.search(word)\n if mo and mo.group(2) != \"\" and mo.group(3) != \"\":\n return (\n f\"{self._plnoun(mo.group(1), 2)}\"\n f\"{mo.group(2)}\"\n f\"{self._plnoun(mo.group(3))}\"\n )\n\n if len(word.split_) >= 3:\n for numword in range(1, len(word.split_) - 1):\n if word.split_[numword] in pl_prep_list_da:\n return \" \".join(\n word.split_[: numword - 1]\n + [self._plnoun(word.split_[numword - 1], 2)]\n + word.split_[numword:]\n )\n\n # only pluralize denominators in units\n mo = DENOMINATOR.search(word.lowered)\n if mo:\n index = len(mo.group(\"denominator\"))\n return f\"{self._plnoun(word[:index])}{word[index:]}\"\n\n # handle units given in degrees (only accept if\n # there is no more than one word following)\n # degree Celsius => degrees Celsius but degree\n # fahrenheit hour => degree fahrenheit hours\n if len(word.split_) >= 2 and word.split_[-2] == \"degree\":\n return \" \".join([self._plnoun(word.first)] + word.split_[1:])\n\n with contextlib.suppress(ValueError):\n return self._handle_prepositional_phrase(\n word.lowered,\n functools.partial(self._plnoun, count=2),\n '-',\n )\n\n # HANDLE PRONOUNS\n\n for k, v in pl_pron_acc_keys_bysize.items():\n if word.lowered[-k:] in v: # ends with accusative pronoun\n for pk, pv in pl_prep_bysize.items():\n if word.lowered[:pk] in pv: # starts with a prep\n if word.lowered.split() == [\n word.lowered[:pk],\n word.lowered[-k:],\n ]:\n # only whitespace in between\n return word.lowered[:-k] + pl_pron_acc[word.lowered[-k:]]\n\n try:\n return pl_pron_nom[word.lowered]\n except KeyError:\n pass\n\n try:\n return pl_pron_acc[word.lowered]\n except KeyError:\n pass\n\n # HANDLE ISOLATED IRREGULAR PLURALS\n\n if word.last in pl_sb_irregular_caps:\n llen = len(word.last)\n return f\"{word[:-llen]}{pl_sb_irregular_caps[word.last]}\"\n\n lowered_last = word.last.lower()\n if lowered_last in pl_sb_irregular:\n llen = len(lowered_last)\n return f\"{word[:-llen]}{pl_sb_irregular[lowered_last]}\"\n\n dash_split = word.lowered.split('-')\n if (\" \".join(dash_split[-2:])).lower() in pl_sb_irregular_compound:\n llen = len(\n \" \".join(dash_split[-2:])\n ) # TODO: what if 2 spaces between these words?\n return (\n f\"{word[:-llen]}\"\n f\"{pl_sb_irregular_compound[(' '.join(dash_split[-2:])).lower()]}\"\n )\n\n if word.lowered[-3:] == \"quy\":\n return f\"{word[:-1]}ies\"\n\n if word.lowered[-6:] == \"person\":\n if self.classical_dict[\"persons\"]:\n return f\"{word}s\"\n else:\n return f\"{word[:-4]}ople\"\n\n # HANDLE FAMILIES OF IRREGULAR PLURALS\n\n if word.lowered[-3:] == \"man\":\n for k, v in pl_sb_U_man_mans_bysize.items():\n if word.lowered[-k:] in v:\n return f\"{word}s\"\n for k, v in pl_sb_U_man_mans_caps_bysize.items():\n if word[-k:] in v:\n return f\"{word}s\"\n return f\"{word[:-3]}men\"\n if word.lowered[-5:] == \"mouse\":\n return f\"{word[:-5]}mice\"\n if word.lowered[-5:] == \"louse\":\n v = pl_sb_U_louse_lice_bysize.get(len(word))\n if v and word.lowered in v:\n return f\"{word[:-5]}lice\"\n return f\"{word}s\"\n if word.lowered[-5:] == \"goose\":\n return f\"{word[:-5]}geese\"\n if word.lowered[-5:] == \"tooth\":\n return f\"{word[:-5]}teeth\"\n if word.lowered[-4:] == \"foot\":\n return f\"{word[:-4]}feet\"\n if word.lowered[-4:] == \"taco\":\n return f\"{word[:-5]}tacos\"\n\n if word.lowered == \"die\":\n return \"dice\"\n\n # HANDLE UNASSIMILATED IMPORTS\n\n if word.lowered[-4:] == \"ceps\":\n return word\n if word.lowered[-4:] == \"zoon\":\n return f\"{word[:-2]}a\"\n if word.lowered[-3:] in (\"cis\", \"sis\", \"xis\"):\n return f\"{word[:-2]}es\"\n\n for lastlet, d, numend, post in (\n (\"h\", pl_sb_U_ch_chs_bysize, None, \"s\"),\n (\"x\", pl_sb_U_ex_ices_bysize, -2, \"ices\"),\n (\"x\", pl_sb_U_ix_ices_bysize, -2, \"ices\"),\n (\"m\", pl_sb_U_um_a_bysize, -2, \"a\"),\n (\"s\", pl_sb_U_us_i_bysize, -2, \"i\"),\n (\"n\", pl_sb_U_on_a_bysize, -2, \"a\"),\n (\"a\", pl_sb_U_a_ae_bysize, None, \"e\"),\n ):\n if word.lowered[-1] == lastlet: # this test to add speed\n for k, v in d.items():\n if word.lowered[-k:] in v:\n return word[:numend] + post\n\n # HANDLE INCOMPLETELY ASSIMILATED IMPORTS\n\n if self.classical_dict[\"ancient\"]:\n if word.lowered[-4:] == \"trix\":\n return f\"{word[:-1]}ces\"\n if word.lowered[-3:] in (\"eau\", \"ieu\"):\n return f\"{word}x\"\n if word.lowered[-3:] in (\"ynx\", \"inx\", \"anx\") and len(word) > 4:\n return f\"{word[:-1]}ges\"\n\n for lastlet, d, numend, post in (\n (\"n\", pl_sb_C_en_ina_bysize, -2, \"ina\"),\n (\"x\", pl_sb_C_ex_ices_bysize, -2, \"ices\"),\n (\"x\", pl_sb_C_ix_ices_bysize, -2, \"ices\"),\n (\"m\", pl_sb_C_um_a_bysize, -2, \"a\"),\n (\"s\", pl_sb_C_us_i_bysize, -2, \"i\"),\n (\"s\", pl_sb_C_us_us_bysize, None, \"\"),\n (\"a\", pl_sb_C_a_ae_bysize, None, \"e\"),\n (\"a\", pl_sb_C_a_ata_bysize, None, \"ta\"),\n (\"s\", pl_sb_C_is_ides_bysize, -1, \"des\"),\n (\"o\", pl_sb_C_o_i_bysize, -1, \"i\"),\n (\"n\", pl_sb_C_on_a_bysize, -2, \"a\"),\n ):\n if word.lowered[-1] == lastlet: # this test to add speed\n for k, v in d.items():\n if word.lowered[-k:] in v:\n return word[:numend] + post\n\n for d, numend, post in (\n (pl_sb_C_i_bysize, None, \"i\"),\n (pl_sb_C_im_bysize, None, \"im\"),\n ):\n for k, v in d.items():\n if word.lowered[-k:] in v:\n return word[:numend] + post\n\n # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS\n\n if lowered_last in pl_sb_singular_s_complete:\n return f\"{word}es\"\n\n for k, v in pl_sb_singular_s_bysize.items():\n if word.lowered[-k:] in v:\n return f\"{word}es\"\n\n if word.lowered[-2:] == \"es\" and word[0] == word[0].upper():\n return f\"{word}es\"\n\n if word.lowered[-1] == \"z\":\n for k, v in pl_sb_z_zes_bysize.items():\n if word.lowered[-k:] in v:\n return f\"{word}es\"\n\n if word.lowered[-2:-1] != \"z\":\n return f\"{word}zes\"\n\n if word.lowered[-2:] == \"ze\":\n for k, v in pl_sb_ze_zes_bysize.items():\n if word.lowered[-k:] in v:\n return f\"{word}s\"\n\n if word.lowered[-2:] in (\"ch\", \"sh\", \"zz\", \"ss\") or word.lowered[-1] == \"x\":\n return f\"{word}es\"\n\n # HANDLE ...f -> ...ves\n\n if word.lowered[-3:] in (\"elf\", \"alf\", \"olf\"):\n return f\"{word[:-1]}ves\"\n if word.lowered[-3:] == \"eaf\" and word.lowered[-4:-3] != \"d\":\n return f\"{word[:-1]}ves\"\n if word.lowered[-4:] in (\"nife\", \"life\", \"wife\"):\n return f\"{word[:-2]}ves\"\n if word.lowered[-3:] == \"arf\":\n return f\"{word[:-1]}ves\"\n\n # HANDLE ...y\n\n if word.lowered[-1] == \"y\":\n if word.lowered[-2:-1] in \"aeiou\" or len(word) == 1:\n return f\"{word}s\"\n\n if self.classical_dict[\"names\"]:\n if word.lowered[-1] == \"y\" and word[0] == word[0].upper():\n return f\"{word}s\"\n\n return f\"{word[:-1]}ies\"\n\n # HANDLE ...o\n\n if lowered_last in pl_sb_U_o_os_complete:\n return f\"{word}s\"\n\n for k, v in pl_sb_U_o_os_bysize.items():\n if word.lowered[-k:] in v:\n return f\"{word}s\"\n\n if word.lowered[-2:] in (\"ao\", \"eo\", \"io\", \"oo\", \"uo\"):\n return f\"{word}s\"\n\n if word.lowered[-1] == \"o\":\n return f\"{word}es\"\n\n # OTHERWISE JUST ADD ...s\n\n return f\"{word}s\"\n\n @classmethod\n def _handle_prepositional_phrase(cls, phrase, transform, sep):\n \"\"\"\n Given a word or phrase possibly separated by sep, parse out\n the prepositional phrase and apply the transform to the word\n preceding the prepositional phrase.\n\n Raise ValueError if the pivot is not found or if at least two\n separators are not found.\n\n >>> engine._handle_prepositional_phrase(\"man-of-war\", str.upper, '-')\n 'MAN-of-war'\n >>> engine._handle_prepositional_phrase(\"man of war\", str.upper, ' ')\n 'MAN of war'\n \"\"\"\n parts = phrase.split(sep)\n if len(parts) < 3:\n raise ValueError(\"Cannot handle words with fewer than two separators\")\n\n pivot = cls._find_pivot(parts, pl_prep_list_da)\n\n transformed = transform(parts[pivot - 1]) or parts[pivot - 1]\n return \" \".join(\n parts[: pivot - 1] + [sep.join([transformed, parts[pivot], ''])]\n ) + \" \".join(parts[(pivot + 1) :])\n\n @staticmethod\n def _find_pivot(words, candidates):\n pivots = (\n index for index in range(1, len(words) - 1) if words[index] in candidates\n )\n try:\n return next(pivots)\n except StopIteration:\n raise ValueError(\"No pivot found\")\n\n def _pl_special_verb( # noqa: C901\n self, word: str, count: Optional[Union[str, int]] = None\n ) -> Union[str, bool]:\n if self.classical_dict[\"zero\"] and str(count).lower() in pl_count_zero:\n return False\n count = self.get_count(count)\n\n if count == 1:\n return word\n\n # HANDLE USER-DEFINED VERBS\n\n value = self.ud_match(word, self.pl_v_user_defined)\n if value is not None:\n return value\n\n # HANDLE IRREGULAR PRESENT TENSE (SIMPLE AND COMPOUND)\n\n try:\n words = Words(word)\n except IndexError:\n return False # word is ''\n\n if words.first in plverb_irregular_pres:\n return f\"{plverb_irregular_pres[words.first]}{words[len(words.first) :]}\"\n\n # HANDLE IRREGULAR FUTURE, PRETERITE AND PERFECT TENSES\n\n if words.first in plverb_irregular_non_pres:\n return word\n\n # HANDLE PRESENT NEGATIONS (SIMPLE AND COMPOUND)\n\n if words.first.endswith(\"n't\") and words.first[:-3] in plverb_irregular_pres:\n return (\n f\"{plverb_irregular_pres[words.first[:-3]]}n't\"\n f\"{words[len(words.first) :]}\"\n )\n\n if words.first.endswith(\"n't\"):\n return word\n\n # HANDLE SPECIAL CASES\n\n mo = PLVERB_SPECIAL_S_RE.search(word)\n if mo:\n return False\n if WHITESPACE.search(word):\n return False\n\n if words.lowered == \"quizzes\":\n return \"quiz\"\n\n # HANDLE STANDARD 3RD PERSON (CHOP THE ...(e)s OFF SINGLE WORDS)\n\n if (\n words.lowered[-4:] in (\"ches\", \"shes\", \"zzes\", \"sses\")\n or words.lowered[-3:] == \"xes\"\n ):\n return words[:-2]\n\n if words.lowered[-3:] == \"ies\" and len(words) > 3:\n return words.lowered[:-3] + \"y\"\n\n if (\n words.last.lower() in pl_v_oes_oe\n or words.lowered[-4:] in pl_v_oes_oe_endings_size4\n or words.lowered[-5:] in pl_v_oes_oe_endings_size5\n ):\n return words[:-1]\n\n if words.lowered.endswith(\"oes\") and len(words) > 3:\n return words.lowered[:-2]\n\n mo = ENDS_WITH_S.search(words)\n if mo:\n return mo.group(1)\n\n # OTHERWISE, A REGULAR VERB (HANDLE ELSEWHERE)\n\n return False\n\n def _pl_general_verb(\n self, word: str, count: Optional[Union[str, int]] = None\n ) -> str:\n count = self.get_count(count)\n\n if count == 1:\n return word\n\n # HANDLE AMBIGUOUS PRESENT TENSES (SIMPLE AND COMPOUND)\n\n mo = plverb_ambiguous_pres_keys.search(word)\n if mo:\n return f\"{plverb_ambiguous_pres[mo.group(1).lower()]}{mo.group(2)}\"\n\n # HANDLE AMBIGUOUS PRETERITE AND PERFECT TENSES\n\n mo = plverb_ambiguous_non_pres.search(word)\n if mo:\n return word\n\n # OTHERWISE, 1st OR 2ND PERSON IS UNINFLECTED\n\n return word\n\n def _pl_special_adjective(\n self, word: str, count: Optional[Union[str, int]] = None\n ) -> Union[str, bool]:\n count = self.get_count(count)\n\n if count == 1:\n return word\n\n # HANDLE USER-DEFINED ADJECTIVES\n\n value = self.ud_match(word, self.pl_adj_user_defined)\n if value is not None:\n return value\n\n # HANDLE KNOWN CASES\n\n mo = pl_adj_special_keys.search(word)\n if mo:\n return pl_adj_special[mo.group(1).lower()]\n\n # HANDLE POSSESSIVES\n\n mo = pl_adj_poss_keys.search(word)\n if mo:\n return pl_adj_poss[mo.group(1).lower()]\n\n mo = ENDS_WITH_APOSTROPHE_S.search(word)\n if mo:\n pl = self.plural_noun(mo.group(1))\n trailing_s = \"\" if pl[-1] == \"s\" else \"s\"\n return f\"{pl}'{trailing_s}\"\n\n # OTHERWISE, NO IDEA\n\n return False\n\n # @profile\n def _sinoun( # noqa: C901\n self,\n word: str,\n count: Optional[Union[str, int]] = None,\n gender: Optional[str] = None,\n ) -> Union[str, bool]:\n count = self.get_count(count)\n\n # DEFAULT TO PLURAL\n\n if count == 2:\n return word\n\n # SET THE GENDER\n\n try:\n if gender is None:\n gender = self.thegender\n elif gender not in singular_pronoun_genders:\n raise BadGenderError\n except (TypeError, IndexError):\n raise BadGenderError\n\n # HANDLE USER-DEFINED NOUNS\n\n value = self.ud_match(word, self.si_sb_user_defined)\n if value is not None:\n return value\n\n # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS\n\n if word == \"\":\n return word\n\n if word in si_sb_ois_oi_case:\n return word[:-1]\n\n words = Words(word)\n\n if words.last.lower() in pl_sb_uninflected_complete:\n return word\n\n if word in pl_sb_uninflected_caps:\n return word\n\n for k, v in pl_sb_uninflected_bysize.items():\n if words.lowered[-k:] in v:\n return word\n\n if self.classical_dict[\"herd\"] and words.last.lower() in pl_sb_uninflected_herd:\n return word\n\n if words.last.lower() in pl_sb_C_us_us:\n return word if self.classical_dict[\"ancient\"] else False\n\n # HANDLE COMPOUNDS (\"Governor General\", \"mother-in-law\", \"aide-de-camp\", ETC.)\n\n mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word)\n if mo and mo.group(2) != \"\":\n return f\"{self._sinoun(mo.group(1), 1, gender=gender)}{mo.group(2)}\"\n\n with contextlib.suppress(ValueError):\n return self._handle_prepositional_phrase(\n words.lowered,\n functools.partial(self._sinoun, count=1, gender=gender),\n ' ',\n )\n\n with contextlib.suppress(ValueError):\n return self._handle_prepositional_phrase(\n words.lowered,\n functools.partial(self._sinoun, count=1, gender=gender),\n '-',\n )\n\n # HANDLE PRONOUNS\n\n for k, v in si_pron_acc_keys_bysize.items():\n if words.lowered[-k:] in v: # ends with accusative pronoun\n for pk, pv in pl_prep_bysize.items():\n if words.lowered[:pk] in pv: # starts with a prep\n if words.lowered.split() == [\n words.lowered[:pk],\n words.lowered[-k:],\n ]:\n # only whitespace in between\n return words.lowered[:-k] + get_si_pron(\n \"acc\", words.lowered[-k:], gender\n )\n\n try:\n return get_si_pron(\"nom\", words.lowered, gender)\n except KeyError:\n pass\n\n try:\n return get_si_pron(\"acc\", words.lowered, gender)\n except KeyError:\n pass\n\n # HANDLE ISOLATED IRREGULAR PLURALS\n\n if words.last in si_sb_irregular_caps:\n llen = len(words.last)\n return \"{}{}\".format(word[:-llen], si_sb_irregular_caps[words.last])\n\n if words.last.lower() in si_sb_irregular:\n llen = len(words.last.lower())\n return \"{}{}\".format(word[:-llen], si_sb_irregular[words.last.lower()])\n\n dash_split = words.lowered.split(\"-\")\n if (\" \".join(dash_split[-2:])).lower() in si_sb_irregular_compound:\n llen = len(\n \" \".join(dash_split[-2:])\n ) # TODO: what if 2 spaces between these words?\n return \"{}{}\".format(\n word[:-llen],\n si_sb_irregular_compound[(\" \".join(dash_split[-2:])).lower()],\n )\n\n if words.lowered[-5:] == \"quies\":\n return word[:-3] + \"y\"\n\n if words.lowered[-7:] == \"persons\":\n return word[:-1]\n if words.lowered[-6:] == \"people\":\n return word[:-4] + \"rson\"\n\n # HANDLE FAMILIES OF IRREGULAR PLURALS\n\n if words.lowered[-4:] == \"mans\":\n for k, v in si_sb_U_man_mans_bysize.items():\n if words.lowered[-k:] in v:\n return word[:-1]\n for k, v in si_sb_U_man_mans_caps_bysize.items():\n if word[-k:] in v:\n return word[:-1]\n if words.lowered[-3:] == \"men\":\n return word[:-3] + \"man\"\n if words.lowered[-4:] == \"mice\":\n return word[:-4] + \"mouse\"\n if words.lowered[-4:] == \"lice\":\n v = si_sb_U_louse_lice_bysize.get(len(word))\n if v and words.lowered in v:\n return word[:-4] + \"louse\"\n if words.lowered[-5:] == \"geese\":\n return word[:-5] + \"goose\"\n if words.lowered[-5:] == \"teeth\":\n return word[:-5] + \"tooth\"\n if words.lowered[-4:] == \"feet\":\n return word[:-4] + \"foot\"\n\n if words.lowered == \"dice\":\n return \"die\"\n\n # HANDLE UNASSIMILATED IMPORTS\n\n if words.lowered[-4:] == \"ceps\":\n return word\n if words.lowered[-3:] == \"zoa\":\n return word[:-1] + \"on\"\n\n for lastlet, d, unass_numend, post in (\n (\"s\", si_sb_U_ch_chs_bysize, -1, \"\"),\n (\"s\", si_sb_U_ex_ices_bysize, -4, \"ex\"),\n (\"s\", si_sb_U_ix_ices_bysize, -4, \"ix\"),\n (\"a\", si_sb_U_um_a_bysize, -1, \"um\"),\n (\"i\", si_sb_U_us_i_bysize, -1, \"us\"),\n (\"a\", si_sb_U_on_a_bysize, -1, \"on\"),\n (\"e\", si_sb_U_a_ae_bysize, -1, \"\"),\n ):\n if words.lowered[-1] == lastlet: # this test to add speed\n for k, v in d.items():\n if words.lowered[-k:] in v:\n return word[:unass_numend] + post\n\n # HANDLE INCOMPLETELY ASSIMILATED IMPORTS\n\n if self.classical_dict[\"ancient\"]:\n\n if words.lowered[-6:] == \"trices\":\n return word[:-3] + \"x\"\n if words.lowered[-4:] in (\"eaux\", \"ieux\"):\n return word[:-1]\n if words.lowered[-5:] in (\"ynges\", \"inges\", \"anges\") and len(word) > 6:\n return word[:-3] + \"x\"\n\n for lastlet, d, class_numend, post in (\n (\"a\", si_sb_C_en_ina_bysize, -3, \"en\"),\n (\"s\", si_sb_C_ex_ices_bysize, -4, \"ex\"),\n (\"s\", si_sb_C_ix_ices_bysize, -4, \"ix\"),\n (\"a\", si_sb_C_um_a_bysize, -1, \"um\"),\n (\"i\", si_sb_C_us_i_bysize, -1, \"us\"),\n (\"s\", pl_sb_C_us_us_bysize, None, \"\"),\n (\"e\", si_sb_C_a_ae_bysize, -1, \"\"),\n (\"a\", si_sb_C_a_ata_bysize, -2, \"\"),\n (\"s\", si_sb_C_is_ides_bysize, -3, \"s\"),\n (\"i\", si_sb_C_o_i_bysize, -1, \"o\"),\n (\"a\", si_sb_C_on_a_bysize, -1, \"on\"),\n (\"m\", si_sb_C_im_bysize, -2, \"\"),\n (\"i\", si_sb_C_i_bysize, -1, \"\"),\n ):\n if words.lowered[-1] == lastlet: # this test to add speed\n for k, v in d.items():\n if words.lowered[-k:] in v:\n return word[:class_numend] + post\n\n # HANDLE PLURLS ENDING IN uses -> use\n\n if (\n words.lowered[-6:] == \"houses\"\n or word in si_sb_uses_use_case\n or words.last.lower() in si_sb_uses_use\n ):\n return word[:-1]\n\n # HANDLE PLURLS ENDING IN ies -> ie\n\n if word in si_sb_ies_ie_case or words.last.lower() in si_sb_ies_ie:\n return word[:-1]\n\n # HANDLE PLURLS ENDING IN oes -> oe\n\n if (\n words.lowered[-5:] == \"shoes\"\n or word in si_sb_oes_oe_case\n or words.last.lower() in si_sb_oes_oe\n ):\n return word[:-1]\n\n # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS\n\n if word in si_sb_sses_sse_case or words.last.lower() in si_sb_sses_sse:\n return word[:-1]\n\n if words.last.lower() in si_sb_singular_s_complete:\n return word[:-2]\n\n for k, v in si_sb_singular_s_bysize.items():\n if words.lowered[-k:] in v:\n return word[:-2]\n\n if words.lowered[-4:] == \"eses\" and word[0] == word[0].upper():\n return word[:-2]\n\n if words.last.lower() in si_sb_z_zes:\n return word[:-2]\n\n if words.last.lower() in si_sb_zzes_zz:\n return word[:-2]\n\n if words.lowered[-4:] == \"zzes\":\n return word[:-3]\n\n if word in si_sb_ches_che_case or words.last.lower() in si_sb_ches_che:\n return word[:-1]\n\n if words.lowered[-4:] in (\"ches\", \"shes\"):\n return word[:-2]\n\n if words.last.lower() in si_sb_xes_xe:\n return word[:-1]\n\n if words.lowered[-3:] == \"xes\":\n return word[:-2]\n\n # HANDLE ...f -> ...ves\n\n if word in si_sb_ves_ve_case or words.last.lower() in si_sb_ves_ve:\n return word[:-1]\n\n if words.lowered[-3:] == \"ves\":\n if words.lowered[-5:-3] in (\"el\", \"al\", \"ol\"):\n return word[:-3] + \"f\"\n if words.lowered[-5:-3] == \"ea\" and word[-6:-5] != \"d\":\n return word[:-3] + \"f\"\n if words.lowered[-5:-3] in (\"ni\", \"li\", \"wi\"):\n return word[:-3] + \"fe\"\n if words.lowered[-5:-3] == \"ar\":\n return word[:-3] + \"f\"\n\n # HANDLE ...y\n\n if words.lowered[-2:] == \"ys\":\n if len(words.lowered) > 2 and words.lowered[-3] in \"aeiou\":\n return word[:-1]\n\n if self.classical_dict[\"names\"]:\n if words.lowered[-2:] == \"ys\" and word[0] == word[0].upper():\n return word[:-1]\n\n if words.lowered[-3:] == \"ies\":\n return word[:-3] + \"y\"\n\n # HANDLE ...o\n\n if words.lowered[-2:] == \"os\":\n\n if words.last.lower() in si_sb_U_o_os_complete:\n return word[:-1]\n\n for k, v in si_sb_U_o_os_bysize.items():\n if words.lowered[-k:] in v:\n return word[:-1]\n\n if words.lowered[-3:] in (\"aos\", \"eos\", \"ios\", \"oos\", \"uos\"):\n return word[:-1]\n\n if words.lowered[-3:] == \"oes\":\n return word[:-2]\n\n # UNASSIMILATED IMPORTS FINAL RULE\n\n if word in si_sb_es_is:\n return word[:-2] + \"is\"\n\n # OTHERWISE JUST REMOVE ...s\n\n if words.lowered[-1] == \"s\":\n return word[:-1]\n\n # COULD NOT FIND SINGULAR\n\n return False\n\n # ADJECTIVES\n\n def a(self, text: str, count: int = 1) -> str:\n \"\"\"\n Return the appropriate indefinite article followed by text.\n\n The indefinite article is either 'a' or 'an'.\n\n If count is not one, then return count followed by text\n instead of 'a' or 'an'.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n mo = INDEFINITE_ARTICLE_TEST.search(text)\n if mo:\n word = mo.group(2)\n if not word:\n return text\n pre = mo.group(1)\n post = mo.group(3)\n result = self._indef_article(word, count)\n return f\"{pre}{result}{post}\"\n return \"\"\n\n an = a\n\n def _indef_article(self, word: str, count: int) -> str: # noqa: C901\n mycount = self.get_count(count)\n\n if mycount != 1:\n return f\"{count} {word}\"\n\n # HANDLE USER-DEFINED VARIANTS\n\n value = self.ud_match(word, self.A_a_user_defined)\n if value is not None:\n return f\"{value} {word}\"\n\n for regexen, article in (\n # HANDLE ORDINAL FORMS\n (A_ordinal_a, \"a\"),\n (A_ordinal_an, \"an\"),\n # HANDLE SPECIAL CASES\n (A_explicit_an, \"an\"),\n (SPECIAL_AN, \"an\"),\n (SPECIAL_A, \"a\"),\n # HANDLE ABBREVIATIONS\n (A_abbrev, \"an\"),\n (SPECIAL_ABBREV_AN, \"an\"),\n (SPECIAL_ABBREV_A, \"a\"),\n # HANDLE CONSONANTS\n (CONSONANTS, \"a\"),\n # HANDLE SPECIAL VOWEL-FORMS\n (ARTICLE_SPECIAL_EU, \"a\"),\n (ARTICLE_SPECIAL_ONCE, \"a\"),\n (ARTICLE_SPECIAL_ONETIME, \"a\"),\n (ARTICLE_SPECIAL_UNIT, \"a\"),\n (ARTICLE_SPECIAL_UBA, \"a\"),\n (ARTICLE_SPECIAL_UKR, \"a\"),\n (A_explicit_a, \"a\"),\n # HANDLE SPECIAL CAPITALS\n (SPECIAL_CAPITALS, \"a\"),\n # HANDLE VOWELS\n (VOWELS, \"an\"),\n # HANDLE y...\n # (BEFORE CERTAIN CONSONANTS IMPLIES (UNNATURALIZED) \"i..\" SOUND)\n (A_y_cons, \"an\"),\n ):\n mo = regexen.search(word)\n if mo:\n return f\"{article} {word}\"\n\n # OTHERWISE, GUESS \"a\"\n return f\"a {word}\"\n\n # 2. TRANSLATE ZERO-QUANTIFIED $word TO \"no plural($word)\"\n\n def no(self, text: str, count: Optional[Union[int, str]] = None) -> str:\n \"\"\"\n If count is 0, no, zero or nil, return 'no' followed by the plural\n of text.\n\n If count is one of:\n 1, a, an, one, each, every, this, that\n return count followed by text.\n\n Otherwise return count follow by the plural of text.\n\n In the return value count is always followed by a space.\n\n Whitespace at the start and end is preserved.\n\n \"\"\"\n if count is None and self.persistent_count is not None:\n count = self.persistent_count\n\n if count is None:\n count = 0\n mo = PARTITION_WORD.search(text)\n if mo:\n pre = mo.group(1)\n word = mo.group(2)\n post = mo.group(3)\n else:\n pre = \"\"\n word = \"\"\n post = \"\"\n\n if str(count).lower() in pl_count_zero:\n count = 'no'\n return f\"{pre}{count} {self.plural(word, count)}{post}\"\n\n # PARTICIPLES\n\n def present_participle(self, word: str) -> str:\n \"\"\"\n Return the present participle for word.\n\n word is the 3rd person singular verb.\n\n \"\"\"\n plv = self.plural_verb(word, 2)\n ans = plv\n\n for regexen, repl in PRESENT_PARTICIPLE_REPLACEMENTS:\n ans, num = regexen.subn(repl, plv)\n if num:\n return f\"{ans}ing\"\n return f\"{ans}ing\"\n\n # NUMERICAL INFLECTIONS\n\n def ordinal(self, num: Union[int, str]) -> str: # noqa: C901\n \"\"\"\n Return the ordinal of num.\n\n num can be an integer or text\n\n e.g. ordinal(1) returns '1st'\n ordinal('one') returns 'first'\n\n \"\"\"\n if DIGIT.match(str(num)):\n if isinstance(num, (int, float)):\n n = int(num)\n else:\n if \".\" in str(num):\n try:\n # numbers after decimal,\n # so only need last one for ordinal\n n = int(num[-1])\n\n except ValueError: # ends with '.', so need to use whole string\n n = int(num[:-1])\n else:\n n = int(num)\n try:\n post = nth[n % 100]\n except KeyError:\n post = nth[n % 10]\n return f\"{num}{post}\"\n else:\n # Mad props to Damian Conway (?) whose ordinal()\n # algorithm is type-bendy enough to foil MyPy\n str_num: str = num # type:\tignore[assignment]\n mo = ordinal_suff.search(str_num)\n if mo:\n post = ordinal[mo.group(1)]\n rval = ordinal_suff.sub(post, str_num)\n else:\n rval = f\"{str_num}th\"\n return rval\n\n def millfn(self, ind: int = 0) -> str:\n if ind > len(mill) - 1:\n print3(\"number out of range\")\n raise NumOutOfRangeError\n return mill[ind]\n\n def unitfn(self, units: int, mindex: int = 0) -> str:\n return f\"{unit[units]}{self.millfn(mindex)}\"\n\n def tenfn(self, tens, units, mindex=0) -> str:\n if tens != 1:\n tens_part = ten[tens]\n if tens and units:\n hyphen = \"-\"\n else:\n hyphen = \"\"\n unit_part = unit[units]\n mill_part = self.millfn(mindex)\n return f\"{tens_part}{hyphen}{unit_part}{mill_part}\"\n return f\"{teen[units]}{mill[mindex]}\"\n\n def hundfn(self, hundreds: int, tens: int, units: int, mindex: int) -> str:\n if hundreds:\n andword = f\" {self._number_args['andword']} \" if tens or units else \"\"\n # use unit not unitfn as simpler\n return (\n f\"{unit[hundreds]} hundred{andword}\"\n f\"{self.tenfn(tens, units)}{self.millfn(mindex)}, \"\n )\n if tens or units:\n return f\"{self.tenfn(tens, units)}{self.millfn(mindex)}, \"\n return \"\"\n\n def group1sub(self, mo: Match) -> str:\n units = int(mo.group(1))\n if units == 1:\n return f\" {self._number_args['one']}, \"\n elif units:\n return f\"{unit[units]}, \"\n else:\n return f\" {self._number_args['zero']}, \"\n\n def group1bsub(self, mo: Match) -> str:\n units = int(mo.group(1))\n if units:\n return f\"{unit[units]}, \"\n else:\n return f\" {self._number_args['zero']}, \"\n\n def group2sub(self, mo: Match) -> str:\n tens = int(mo.group(1))\n units = int(mo.group(2))\n if tens:\n return f\"{self.tenfn(tens, units)}, \"\n if units:\n return f\" {self._number_args['zero']} {unit[units]}, \"\n return f\" {self._number_args['zero']} {self._number_args['zero']}, \"\n\n def group3sub(self, mo: Match) -> str:\n hundreds = int(mo.group(1))\n tens = int(mo.group(2))\n units = int(mo.group(3))\n if hundreds == 1:\n hunword = f\" {self._number_args['one']}\"\n elif hundreds:\n hunword = str(unit[hundreds])\n else:\n hunword = f\" {self._number_args['zero']}\"\n if tens:\n tenword = self.tenfn(tens, units)\n elif units:\n tenword = f\" {self._number_args['zero']} {unit[units]}\"\n else:\n tenword = f\" {self._number_args['zero']} {self._number_args['zero']}\"\n return f\"{hunword} {tenword}, \"\n\n def hundsub(self, mo: Match) -> str:\n ret = self.hundfn(\n int(mo.group(1)), int(mo.group(2)), int(mo.group(3)), self.mill_count\n )\n self.mill_count += 1\n return ret\n\n def tensub(self, mo: Match) -> str:\n return f\"{self.tenfn(int(mo.group(1)), int(mo.group(2)), self.mill_count)}, \"\n\n def unitsub(self, mo: Match) -> str:\n return f\"{self.unitfn(int(mo.group(1)), self.mill_count)}, \"\n\n def enword(self, num: str, group: int) -> str:\n # import pdb\n # pdb.set_trace()\n\n if group == 1:\n num = DIGIT_GROUP.sub(self.group1sub, num)\n elif group == 2:\n num = TWO_DIGITS.sub(self.group2sub, num)\n num = DIGIT_GROUP.sub(self.group1bsub, num, 1)\n elif group == 3:\n num = THREE_DIGITS.sub(self.group3sub, num)\n num = TWO_DIGITS.sub(self.group2sub, num, 1)\n num = DIGIT_GROUP.sub(self.group1sub, num, 1)\n elif int(num) == 0:\n num = self._number_args[\"zero\"]\n elif int(num) == 1:\n num = self._number_args[\"one\"]\n else:\n num = num.lstrip().lstrip(\"0\")\n self.mill_count = 0\n # surely there's a better way to do the next bit\n mo = THREE_DIGITS_WORD.search(num)\n while mo:\n num = THREE_DIGITS_WORD.sub(self.hundsub, num, 1)\n mo = THREE_DIGITS_WORD.search(num)\n num = TWO_DIGITS_WORD.sub(self.tensub, num, 1)\n num = ONE_DIGIT_WORD.sub(self.unitsub, num, 1)\n return num\n\n def number_to_words( # noqa: C901\n self,\n num: Union[int, str],\n wantlist: bool = False,\n group: int = 0,\n comma: str = \",\",\n andword: str = \"and\",\n zero: str = \"zero\",\n one: str = \"one\",\n decimal: str = \"point\",\n threshold: Optional[int] = None,\n ) -> Union[str, List[str]]:\n \"\"\"\n Return a number in words.\n\n group = 1, 2 or 3 to group numbers before turning into words\n comma: define comma\n\n andword:\n word for 'and'. Can be set to ''.\n e.g. \"one hundred and one\" vs \"one hundred one\"\n\n zero: word for '0'\n one: word for '1'\n decimal: word for decimal point\n threshold: numbers above threshold not turned into words\n\n parameters not remembered from last call. Departure from Perl version.\n \"\"\"\n self._number_args = {\"andword\": andword, \"zero\": zero, \"one\": one}\n num = str(num)\n\n # Handle \"stylistic\" conversions (up to a given threshold)...\n if threshold is not None and float(num) > threshold:\n spnum = num.split(\".\", 1)\n while comma:\n (spnum[0], n) = FOUR_DIGIT_COMMA.subn(r\"\\1,\\2\", spnum[0])\n if n == 0:\n break\n try:\n return f\"{spnum[0]}.{spnum[1]}\"\n except IndexError:\n return str(spnum[0])\n\n if group < 0 or group > 3:\n raise BadChunkingOptionError\n nowhite = num.lstrip()\n if nowhite[0] == \"+\":\n sign = \"plus\"\n elif nowhite[0] == \"-\":\n sign = \"minus\"\n else:\n sign = \"\"\n\n if num in nth_suff:\n num = zero\n\n myord = num[-2:] in nth_suff\n if myord:\n num = num[:-2]\n finalpoint = False\n if decimal:\n if group != 0:\n chunks = num.split(\".\")\n else:\n chunks = num.split(\".\", 1)\n if chunks[-1] == \"\": # remove blank string if nothing after decimal\n chunks = chunks[:-1]\n finalpoint = True # add 'point' to end of output\n else:\n chunks = [num]\n\n first: Union[int, str, bool] = 1\n loopstart = 0\n\n if chunks[0] == \"\":\n first = 0\n if len(chunks) > 1:\n loopstart = 1\n\n for i in range(loopstart, len(chunks)):\n chunk = chunks[i]\n # remove all non numeric \\D\n chunk = NON_DIGIT.sub(\"\", chunk)\n if chunk == \"\":\n chunk = \"0\"\n\n if group == 0 and (first == 0 or first == \"\"):\n chunk = self.enword(chunk, 1)\n else:\n chunk = self.enword(chunk, group)\n\n if chunk[-2:] == \", \":\n chunk = chunk[:-2]\n chunk = WHITESPACES_COMMA.sub(\",\", chunk)\n\n if group == 0 and first:\n chunk = COMMA_WORD.sub(f\" {andword} \\\\1\", chunk)\n chunk = WHITESPACES.sub(\" \", chunk)\n # chunk = re.sub(r\"(\\A\\s|\\s\\Z)\", self.blankfn, chunk)\n chunk = chunk.strip()\n if first:\n first = \"\"\n chunks[i] = chunk\n\n numchunks = []\n if first != 0:\n numchunks = chunks[0].split(f\"{comma} \")\n\n if myord and numchunks:\n # TODO: can this be just one re as it is in perl?\n mo = ordinal_suff.search(numchunks[-1])\n if mo:\n numchunks[-1] = ordinal_suff.sub(ordinal[mo.group(1)], numchunks[-1])\n else:\n numchunks[-1] += \"th\"\n\n for chunk in chunks[1:]:\n numchunks.append(decimal)\n numchunks.extend(chunk.split(f\"{comma} \"))\n\n if finalpoint:\n numchunks.append(decimal)\n\n # wantlist: Perl list context. can explicitly specify in Python\n if wantlist:\n if sign:\n numchunks = [sign] + numchunks\n return numchunks\n elif group:\n signout = f\"{sign} \" if sign else \"\"\n return f\"{signout}{', '.join(numchunks)}\"\n else:\n signout = f\"{sign} \" if sign else \"\"\n num = f\"{signout}{numchunks.pop(0)}\"\n if decimal is None:\n first = True\n else:\n first = not num.endswith(decimal)\n for nc in numchunks:\n if nc == decimal:\n num += f\" {nc}\"\n first = 0\n elif first:\n num += f\"{comma} {nc}\"\n else:\n num += f\" {nc}\"\n return num\n\n # Join words with commas and a trailing 'and' (when appropriate)...\n\n def join(\n self,\n words: Optional[Sequence[str]],\n sep: Optional[str] = None,\n sep_spaced: bool = True,\n final_sep: Optional[str] = None,\n conj: str = \"and\",\n conj_spaced: bool = True,\n ) -> str:\n \"\"\"\n Join words into a list.\n\n e.g. join(['ant', 'bee', 'fly']) returns 'ant, bee, and fly'\n\n options:\n conj: replacement for 'and'\n sep: separator. default ',', unless ',' is in the list then ';'\n final_sep: final separator. default ',', unless ',' is in the list then ';'\n conj_spaced: boolean. Should conj have spaces around it\n\n \"\"\"\n if not words:\n return \"\"\n if len(words) == 1:\n return words[0]\n\n if conj_spaced:\n if conj == \"\":\n conj = \" \"\n else:\n conj = f\" {conj} \"\n\n if len(words) == 2:\n return f\"{words[0]}{conj}{words[1]}\"\n\n if sep is None:\n if \",\" in \"\".join(words):\n sep = \";\"\n else:\n sep = \",\"\n if final_sep is None:\n final_sep = sep\n\n final_sep = f\"{final_sep}{conj}\"\n\n if sep_spaced:\n sep += \" \"\n\n return f\"{sep.join(words[0:-1])}{final_sep}{words[-1]}\"\n" }, { "path": "src/bids/layout/README.md", "content": "# layout\n\nPyBIDS used to depend on [Grabbit](https://github.com/grabbles/grabbit) and here\nwe described interactions with its Layout. Since then PyBIDS grew into an\nindependent beast and we refer the curious minds to\n[our tutorial](../../examples/pybids_tutorial.md) Jupyter notebook.\n" }, { "path": "src/bids/layout/__init__.py", "content": "# Backwards compatibility # noqa: D104\nfrom bids_validator import BIDSValidator\n\nfrom .index import BIDSLayoutIndexer\nfrom .layout import BIDSLayout, Query\nfrom .models import BIDSDataFile, BIDSFile, BIDSImageFile, BIDSJSONFile, Config, Entity, Tag\nfrom .utils import add_config_paths, parse_file_entities\n\n__all__ = [\n 'BIDSLayout',\n 'BIDSLayoutIndexer',\n 'BIDSValidator',\n 'add_config_paths',\n 'parse_file_entities',\n 'BIDSFile',\n 'BIDSImageFile',\n 'BIDSDataFile',\n 'BIDSJSONFile',\n 'Config',\n 'Entity',\n 'Tag',\n 'Query',\n]\n" }, { "path": "src/bids/layout/config/bids.json", "content": "{\n \"name\": \"bids\",\n \"entities\": [\n {\n \"name\": \"subject\",\n \"pattern\": \"[/\\\\\\\\]+sub-([a-zA-Z0-9+]+)\",\n \"directory\": \"{subject}\"\n },\n {\n \"name\": \"session\",\n \"pattern\": \"[_/\\\\\\\\]+ses-([a-zA-Z0-9+]+)\",\n \"mandatory\": false,\n \"directory\": \"{subject}{session}\"\n },\n {\n \"name\": \"sample\",\n \"pattern\": \"[_/\\\\\\\\]+sample-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"task\",\n \"pattern\": \"[_/\\\\\\\\]+task-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"tracksys\",\n \"pattern\": \"[_/\\\\\\\\]+tracksys-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"acquisition\",\n \"pattern\": \"[_/\\\\\\\\]+acq-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"nucleus\",\n \"pattern\": \"[_/\\\\\\\\]+nuc-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"volume\",\n \"pattern\": \"[_/\\\\\\\\]+voi-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"ceagent\",\n \"pattern\": \"[_/\\\\\\\\]+ce-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"staining\",\n \"pattern\": \"[_/\\\\\\\\]+stain-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"tracer\",\n \"pattern\": \"[_/\\\\\\\\]+trc-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"reconstruction\",\n \"pattern\": \"[_/\\\\\\\\]+rec-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"direction\",\n \"pattern\": \"[_/\\\\\\\\]+dir-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"run\",\n \"pattern\": \"[_/\\\\\\\\]+run-(\\\\d+)\",\n \"dtype\": \"int\"\n },\n {\n \"name\": \"proc\",\n \"pattern\": \"[_/\\\\\\\\]+proc-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"modality\",\n \"pattern\": \"[_/\\\\\\\\]+mod-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"echo\",\n \"pattern\": \"[_/\\\\\\\\]+echo-([0-9]+)\"\n },\n {\n \"name\": \"flip\",\n \"pattern\": \"[_/\\\\\\\\]+flip-([0-9]+)\"\n },\n {\n \"name\": \"inv\",\n \"pattern\": \"[_/\\\\\\\\]+inv-([0-9]+)\"\n },\n {\n \"name\": \"mt\",\n \"pattern\": \"[_/\\\\\\\\]+mt-(on|off)\"\n },\n {\n \"name\": \"part\",\n \"pattern\": \"[_/\\\\\\\\]+part-(imag|mag|phase|real)\"\n },\n {\n \"name\": \"recording\",\n \"pattern\": \"[_/\\\\\\\\]+recording-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"space\",\n \"pattern\": \"[_/\\\\\\\\]+space-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"chunk\",\n \"pattern\": \"[_/\\\\\\\\]+chunk-([0-9]+)\"\n },\n {\n \"name\": \"suffix\",\n \"pattern\": \"(?:^|[_/\\\\\\\\])([a-zA-Z0-9+]+)\\\\.[^/\\\\\\\\]+$\"\n },\n {\n \"name\": \"scans\",\n \"pattern\": \"(.*\\\\_scans.tsv)$\"\n },\n {\n \"name\": \"fmap\",\n \"pattern\": \"(phasediff|magnitude[1-2]|phase[1-2]|fieldmap|epi)\\\\.nii\"\n },\n {\n \"name\": \"datatype\",\n \"pattern\": \"[/\\\\\\\\]+(anat|beh|dwi|eeg|fmap|func|ieeg|meg|micr|motion|mrs|nirs|perf|pet)[/\\\\\\\\]+\"\n },\n {\n \"name\": \"extension\",\n \"pattern\": \"[^./\\\\\\\\](\\\\.[^/\\\\\\\\]+)$\"\n }\n ],\n \"default_path_patterns\": [\n \"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}\",\n \"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}\",\n \"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}\",\n \"sub-{subject}[/ses-{session}]/{datatype|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_part-{part}][_chunk-]_{suffix}{extension<.bval|.bvec|.json|.nii.gz|.nii>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_part-{part}][_chunk-]_{suffix}{extension<.json|.nii.gz|.nii>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_dir-{direction}][_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}]_dir-{direction}[_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{session}]/[{datatype|func}/]sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.tsv.gz|.json>|.tsv.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|meg}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension}\",\n \"sub-{subject}[/ses-{session}]/{datatype|meg}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{session}]/{datatype|meg}/sub-{subject}[_ses-{session}][_acq-{acquisition}]_{suffix}{extension<.json>|.json}\",\n \"sub-{subject}[/ses-{session}]/{datatype|meg}/sub-{subject}[_ses-{session}][_acq-{acquisition}]_{suffix}{extension<.pos>|.pos}\",\n \"sub-{subject}[/ses-{session}]/{datatype|meg}/sub-{subject}[_ses-{session}]_acq-{acquisition}_{suffix|meg}{extension<.fif|.dat>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|motion}/sub-{subject}[_ses-{session}]_task-{task}_tracksys-{tracksys}[_acq-{acquisition}][_run-{run}]_{suffix|motion}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{session}]/{datatype|mrs}/sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}][_nuc-{nucleus}][_voi-{volume}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_inv-{inv}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|eeg}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.edf|.vhdr|.vmrk|.eeg|.set|.fdt|.bdf|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|nirs}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.snirf|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|nirs}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.tsv|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|nirs}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}][_recording-{recording}]_{suffix}{extension<.tsv.gz|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|nirs}/sub-{subject}[_ses-{session}][_acq-{acquisition}]_{suffix}{extension<.tsv|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|nirs}/sub-{subject}[_ses-{session}][_acq-{acquisition}]_{suffix}{extension<.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|ieeg}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.mefd/|.json|.edf|.vhdr|.eeg|.vmrk|.set|.fdt|.nwb|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{session}]/{datatype}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_space-{space}]_{suffix}{extension<.json>|.json}\",\n \"sub-{subject}[/ses-{session}]/{datatype}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_space-{space}]_{suffix}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{session}]/{datatype}/sub-{subject}[_ses-{session}][_acq-{acquisition}]_{suffix|photo}{extension<.jpg>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|eeg}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_run-{run}][_recording-{recording}]_{suffix}{extension<.tsv.gz|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|micr}/sub-{subject}[_ses-{session}]_sample-{sample}[_acq-{acquisition}][_stain-{staining}][_run-{run}][_chunk-{chunk}]_{suffix}{extension<.png|.tif|.ome.tif|.ome.btf|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|micr}/sub-{subject}[_ses-{session}]_sample-{sample}[_acq-{acquisition}]_{suffix|photo}{extension<.jpg|.png|.tif|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_echo-{echo}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_flip-{flip}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_inv-{inv}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_flip-{flip}]_inv-{inv}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_flip-{flip}_mt-{mt}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_mt-{mt}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_flip-{flip}[_inv-{inv}][_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_echo-{echo}_flip-{flip}[_inv-{inv}][_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_flip-{flip}][_inv-{inv}][_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_flip-{flip}_inv-{inv}[_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|pet}/sub-{subject}[_ses-{session}][_task-{task}][_trc-{tracer}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|pet}/sub-{subject}[_ses-{session}][_task-{task}][_trc-{tracer}][_rec-{reconstruction}][_run-{run}]_recording-{recording}_{suffix}{extension<.tsv|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|pet}/sub-{subject}[_ses-{session}]_task-{task}[_trc-{tracer}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.tsv|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|pet}/sub-{subject}[_ses-{session}][_task-{task}][_trc-{tracer}][_rec-{reconstruction}][_run-{run}][_recording-{recording}]_{suffix}{extension<.tsv.gz|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|perf}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|perf}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}]_{suffix}{extension<.tsv|.json>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|perf}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.jpg>}\",\n \"sub-{subject}[/ses-{session}]/{datatype|perf}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_recording{recording}]_{suffix}{extension<.tsv|.json>}\",\n \"[task-{task}_][acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_][part-{part}_][chunk-{chunk}_]{suffix}{extension<.json>|.json}\",\n \"[task-{task}_][acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][mod-{modality}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>}\",\n \"task-{task}[_acq-{acquisition}][_ce-{ceagent}][_dir-{direction}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}_tracksys-{tracksys}[acq-{acquisition}_][run-{run}_]{suffix}{extension<.tsv|.json>}\",\n \"[task-{task}_][acq-{acquisition}_][nuc-{nucleus}_][voi-{volume}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_][inv-{inv}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_][part-{part}_][chunk-_]{suffix}{extension<.bval|.bvec|.json>|.json}\",\n \"[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_][part-{part}_][chunk-_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][dir-{direction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_]dir-{direction}[_run-{run}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.json>}\",\n \"sample-{sample}[_acq-{acquisition}][_stain-{stain}][_run-{run}][_chunk-{chunk}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]echo-{echo}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_]flip-{flip}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]inv-{inv}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_][flip-{flip}_]inv-{inv}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_]flip-{flip}_mt-{mt}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]mt-{mt}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]_flip-{flip}[_inv-{inv}][_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]echo-{echo}_flip-{flip}_[inv-{inv}_][part-{part}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_][flip-{flip}_][inv-{inv}_][part-{part_}]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_][echo-{echo}_]flip-{flip}_inv-{inv}[_part-{part}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[task-{task}_][trc-{tracer}_][rec-{reconstruction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[task-{task}_][trc-{tracer}_][rec-{reconstruction}_][run-{run}_]recording-{recording}_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_trc-{tracer}][_rec-{reconstruction}][_run-{run}]_{suffix}{extension<.json>|.json}\",\n \"[task-{task}][_trc-{tracer}][_rec-{reconstruction}][_run-{run}][_recording-{recording}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_][recording-{recording}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][space-{space}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}\",\n \"sample-{sample}[_acq-{acquisition}]_{suffix|photo}{extension<.json>}\"\n ]\n}\n" }, { "path": "src/bids/layout/config/derivatives.json", "content": "{\n \"name\": \"derivatives\",\n \"entities\": [\n {\n \"name\": \"atlas\",\n \"pattern\": \"atlas-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"roi\",\n \"pattern\": \"roi-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"label\",\n \"pattern\": \"label-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"desc\",\n \"pattern\": \"desc-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"from\",\n \"pattern\": \"(?:^|_)from-([a-zA-Z0-9+]+).*xfm\"\n },\n {\n \"name\": \"to\",\n \"pattern\": \"(?:^|_)to-([a-zA-Z0-9+]+).*xfm\"\n },\n {\n \"name\": \"mode\",\n \"pattern\": \"(?:^|_)mode-([a-zA-Z0-9+]+).*xfm\"\n },\n {\n \"name\": \"hemi\",\n \"pattern\": \"hemi-(L|R)\"\n },\n {\n \"name\": \"segmentation\",\n \"pattern\": \"seg-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"res\",\n \"pattern\": \"res-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"den\",\n \"pattern\": \"den-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"model\",\n \"pattern\": \"model-([a-zA-Z0-9+]+)\"\n },\n {\n \"name\": \"subset\",\n \"pattern\": \"subset-([a-zA-Z0-9+]+)\"\n }\n ],\n\n \"default_path_patterns\": [\n \"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}\",\n \"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}\",\n \"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}\",\n \"sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_space-{space}][_seg-{segmentation}][_res-{res}][_label-{label}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json|.tsv>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}][_space-{space}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}][_space-{space}][_res-{res}][_den-{den}][_label-{label}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_space-{space}][_desc-{desc}]_{suffix}{extension|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_space-{space}][_res-{res}][_den-{den}][_label-{label}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_dir-{direction}][_run-{run}][_space-{space}][_desc-{desc}]_{fmap}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"sub-{subject}[/ses-{session}]/{datatype|fmap}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}]_dir-{direction}[_run-{run}][_space-{space}][_desc-{desc}]_{fmap}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][space-{space}_][res-{res}_][den-{den}_][label-{label}_][desc-{desc}_]{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][mod-{modality}_][space-{space}_][desc-{desc}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}][_space-{space}][_desc-{desc}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}][_space-{space}][_res-{res}][_den-{den}][_label-{label}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"[acq-{acquisition}_][space-{space}_][desc-{desc}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][space-{space}_][res-{res}_][den-{den}_][label-{label}_][desc-{desc}_]{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"[acq-{acquisition}_][dir-{direction}_][run-{run}_][space-{space}_][desc-{desc}_]{fmap}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_]dir-{direction}[_run-{run}][_space-{space}][_desc-{desc}]_{fmap}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}][_desc-{desc}]_{suffix}{extension<.json>|.json}\"\n ]\n}\n" }, { "path": "src/bids/layout/db.py", "content": "\"\"\"Database-related functionality.\"\"\"\n\nimport re\nimport sqlite3\nfrom functools import lru_cache\n\nimport sqlalchemy as sa\nfrom upath import UPath as Path\n\nfrom bids.utils import listify\n\nfrom .models import Base, Config, LayoutInfo\n\n\ndef get_database_file(path): # noqa: D103\n if path is not None:\n path = Path(path)\n database_file = path / 'layout_index.sqlite'\n path.mkdir(exist_ok=True, parents=True)\n else:\n database_file = None\n return database_file\n\n\nclass ConnectionManager: # noqa: D101\n def __init__(self, database_path=None, reset_database=False, config=None, init_args=None):\n self.database_file = get_database_file(database_path)\n\n # Determine if file exists before we create it in _get_engine()\n reset_database = (\n reset_database # manual reset\n or self.database_file is None # in-memory DB\n or not self.database_file.exists() # file hasn't been created yet\n )\n\n self.engine = self._get_engine(self.database_file)\n self.sessionmaker = sa.orm.sessionmaker(bind=self.engine)\n self._session = None\n\n if reset_database:\n self.reset_database(init_args, config)\n\n self._database_reset = reset_database\n\n def _get_engine(self, database_file):\n if database_file is not None:\n # https://docs.sqlalchemy.org/en/13/dialects/sqlite.html\n # When a file-based database is specified, the dialect will use\n # NullPool as the source of connections. This pool closes and\n # discards connections which are returned to the pool immediately.\n # SQLite file-based connections have extremely low overhead, so\n # pooling is not necessary. The scheme also prevents a connection\n # from being used again in a different thread and works best\n # with SQLite's coarse-grained file locking.\n from sqlalchemy.pool import NullPool\n\n engine = sa.create_engine(\n f'sqlite:///{database_file}',\n connect_args={'check_same_thread': False},\n poolclass=NullPool,\n )\n else:\n # https://docs.sqlalchemy.org/en/13/dialects/sqlite.html\n # Using a Memory Database in Multiple Threads\n # To use a :memory: database in a multithreaded scenario, the same\n # connection object must be shared among threads, since the\n # database exists only within the scope of that connection. The\n # StaticPool implementation will maintain a single connection\n # globally, and the check_same_thread flag can be passed to\n # Pysqlite as False. Note that using a :memory: database in\n # multiple threads requires a recent version of SQLite.\n from sqlalchemy.pool import StaticPool\n\n engine = sa.create_engine(\n 'sqlite://', # In memory database\n connect_args={'check_same_thread': False},\n poolclass=StaticPool,\n )\n\n def regexp(expr, item):\n \"\"\"Regex function for SQLite's REGEXP.\"\"\"\n if not isinstance(item, str):\n return False\n reg = re.compile(expr, re.I)\n return reg.search(item) is not None\n\n engine.connect()\n\n # Do not remove this decorator!!! An in-line create_function call will\n # work when using an in-memory SQLite DB, but fails when using a file.\n # For more details, see https://stackoverflow.com/questions/12461814/\n @sa.event.listens_for(engine, 'begin')\n def do_begin(conn):\n conn.connection.create_function('regexp', 2, regexp)\n\n return engine\n\n @classmethod\n def exists(cls, database_path): # noqa: D102\n return get_database_file(database_path).exists()\n\n def reset_database(self, init_args, config): # noqa: D102\n Base.metadata.drop_all(self.engine)\n Base.metadata.create_all(self.engine)\n # Add LayoutInfo record\n if not isinstance(init_args, LayoutInfo):\n layout_info = LayoutInfo(**init_args)\n self.session.add(layout_info)\n # Add config records\n config = [Config.load(c, session=self.session) for c in listify(config)]\n self.session.add_all(config)\n self.session.commit()\n\n def save_database(self, database_path, replace_connection=True):\n \"\"\"Save the current index as a SQLite3 DB at the specified location.\n\n Note: This is only necessary if a database_path was not specified\n at initialization, and the user now wants to save the index.\n If a database_path was specified originally, there is no need to\n re-save using this method.\n\n Parameters\n ----------\n database_path : str\n The path to the desired database folder. By default,\n uses .db_cache. If a relative path is passed, it is assumed to\n be relative to the BIDSLayout root directory.\n replace_connection : bool, optional\n If True, returns a new ConnectionManager that points to the newly\n created database. If False, returns the current instance.\n\n \"\"\"\n database_file = get_database_file(database_path)\n new_db = sqlite3.connect(str(database_file))\n old_db = self.engine.connect().connection\n\n with new_db:\n for line in old_db.iterdump():\n if line not in ('BEGIN;', 'COMMIT;'):\n new_db.execute(line)\n new_db.commit()\n\n if replace_connection:\n return ConnectionManager(database_path, init_args=self.layout_info)\n else:\n return self\n\n @property\n def session(self): # noqa: D102\n if self._session is None:\n self.reset_session()\n return self._session\n\n @property\n @lru_cache # noqa: B019\n def layout_info(self): # noqa: D102\n return self.session.query(LayoutInfo).first()\n\n def reset_session(self):\n \"\"\"Force a new session.\"\"\"\n self._session = self.sessionmaker()\n" }, { "path": "src/bids/layout/index.py", "content": "\"\"\"File-indexing functionality.\"\"\"\n\nimport json\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom functools import cache, partial\n\nfrom bids_validator import BIDSValidator\nfrom upath import UPath as Path\n\nfrom ..exceptions import BIDSConflictingValuesError\nfrom ..utils import listify, make_bidsfile\nfrom .models import Config, Entity, FileAssociation, Tag, _create_tag_dict\nfrom .validation import validate_indexing_args\n\n\ndef _regexfy(patt, root=None):\n if hasattr(patt, 'search'):\n return patt\n\n patt = Path(patt)\n\n if patt.is_absolute():\n patt = str(patt.relative_to(root or '/'))\n\n return re.compile(r'^/' + str(patt) + r'.*')\n\n\ndef _extract_entities(bidsfile, entities):\n match_vals = {}\n for e in entities.values():\n m = e.match_file(bidsfile)\n if m is None and e.mandatory:\n break\n if m is not None:\n match_vals[e.name] = (e, m)\n return match_vals\n\n\ndef _check_path_matches_patterns(path, patterns, root=None):\n \"\"\"Check if the path matches at least one of the provided patterns.\"\"\"\n if not patterns:\n return False\n\n path = path.absolute()\n if root is not None:\n if isinstance(path, Path):\n path = Path('/') / Path(path.path).relative_to(Path(root.path))\n else:\n path = Path('/') / path.relative_to(root)\n\n # Path now can be downcast to str\n path = str(path)\n\n for patt in patterns:\n if patt.search(path):\n return True\n return False\n\n\ndef _validate_path(path, incl_patt=None, excl_patt=None, root=None):\n if _check_path_matches_patterns(path, incl_patt, root=root):\n return True\n\n if _check_path_matches_patterns(path, excl_patt, root=root):\n return False\n\n\nclass BIDSLayoutIndexer:\n \"\"\"Indexer class for BIDSLayout.\n\n Parameters\n ----------\n validate : bool, optional\n If True, all files are checked for BIDS compliance when first indexed,\n and non-compliant files are ignored. This provides a convenient way to\n restrict file indexing to only those files defined in the \"core\" BIDS\n spec, as setting ``validate=True`` will lead noncompliant files\n like ``sub-01/nonbidsfile.txt`` to be ignored.\n ignore : str or SRE_Pattern or list\n Path(s) to exclude from indexing. Each path is either a string or a\n SRE_Pattern object (i.e., compiled regular expression). If a string is\n passed, it must be either an absolute path, or be relative to the BIDS\n project root. If an SRE_Pattern is passed, the contained regular\n expression will be matched against the full (absolute) path of all\n files and directories. By default, indexing ignores all files in\n 'code/', 'stimuli/', 'sourcedata/', 'models/', and any hidden\n files/dirs beginning with '.' at root level.\n force_index : str or SRE_Pattern or list\n Path(s) to forcibly index in the BIDSLayout, even if they would\n otherwise fail validation. See the documentation for the ignore\n argument for input format details. Note that paths in force_index takes\n precedence over those in ignore (i.e., if a file matches both ignore\n and force_index, it *will* be indexed).\n Note: NEVER include 'derivatives' here; use the derivatives argument\n (or :obj:`bids.layout.BIDSLayout.add_derivatives`) for that.\n index_metadata : bool\n If True, all metadata files are indexed. If False, metadata will not be\n available (but indexing will be faster).\n config_filename : str\n Optional name of filename within directories\n that contains configuration information.\n **filters\n keyword arguments passed to the .get() method of a\n :obj:`bids.layout.BIDSLayout` object. These keyword arguments define\n what files get selected for metadata indexing.\n\n \"\"\"\n\n def __init__(\n self,\n validate=False,\n ignore=None,\n force_index=None,\n index_metadata=True,\n config_filename='layout_config.json',\n **filters,\n ):\n self.ignore = ignore\n self.force_index = force_index\n self.index_metadata = index_metadata\n self.config_filename = config_filename\n self.filters = filters\n self.validator = None\n\n if validate:\n self.validator = BIDSValidator(index_associated=True)\n\n # Layout-dependent attributes to be set in __call__()\n self._layout = None\n self._config = None\n self._include_patterns = None\n self._exclude_patterns = None\n\n def __call__(self, layout): # noqa: D102\n self._layout = layout\n self._config = list(layout.config.values())\n\n ignore, force = validate_indexing_args(self.ignore, self.force_index, self._layout._root)\n\n # Do not accept string patterns\n self._include_patterns = [\n _regexfy(patt, root=self._layout._root) for patt in listify(force)\n ]\n self._exclude_patterns = [\n _regexfy(patt, root=self._layout._root) for patt in listify(ignore)\n ]\n\n all_bfs, all_tag_dicts = self._index_dir(self._layout._root, self._config)\n\n self.session.bulk_save_objects(all_bfs)\n self.session.bulk_insert_mappings(Tag, all_tag_dicts)\n self.session.commit()\n\n if self.index_metadata:\n self._index_metadata()\n\n @property\n def session(self): # noqa: D102\n return self._layout.connection_manager.session\n\n def _validate_file(self, f):\n matched_patt = _validate_path(\n f,\n incl_patt=self._include_patterns,\n excl_patt=self._exclude_patterns,\n root=self._layout._root,\n )\n\n if matched_patt is not None:\n return matched_patt\n\n if self.validator is None:\n return True\n\n # BIDS validator expects absolute paths, but really these are relative\n # to the BIDS project root.\n to_check = Path(f.path).relative_to(\n Path(self._layout._root.path)\n ) # use .path then Path() to drop the uri prefix\n # Pretend the path is an absolute path\n to_check = Path('/') / to_check\n # bids-validator works with posix paths only\n to_check = to_check.as_posix()\n return self.validator.is_bids(to_check)\n\n def _index_dir(self, path, config, force=None):\n root_path = Path(self._layout._root.path) # drops the uri prefix if it is there\n abs_path = root_path / Path(path.path).relative_to(root_path)\n\n # Derivative directories must always be added separately\n if root_path.joinpath('derivatives') in abs_path.parents:\n return [], []\n\n config = list(config) # Shallow copy\n\n # Check for additional config file in directory\n layout_file = self.config_filename\n config_file = abs_path / layout_file\n if config_file.exists():\n cfg = Config.load(config_file, session=self.session)\n config.append(cfg)\n\n # Track which entities are valid in filenames for this directory\n config_entities = {}\n for c in config:\n config_entities.update(c.entities)\n\n # Get lists of 1st-level subdirectories and files in the path directory\n try:\n _, dirnames, filenames = next(path.fs.walk(path.path))\n except StopIteration:\n # It's unclear when this gets hit, but tests will occasionally fail here.\n # Hopefully this only occurs when we enter an empty directory,\n # but even if not, there's nothing else to do in this case.\n return [], []\n\n # Symbolic links are returned as filenames even if they point to directories\n # Temporary list to store symbolic links that need to be removed from filenames\n links_to_dirs = []\n\n # Find directories in filenames\n for possible_link in filenames:\n full_path = path / possible_link\n if full_path.is_dir():\n links_to_dirs.append(possible_link)\n\n dirnames.extend(links_to_dirs)\n\n for link in links_to_dirs:\n filenames.remove(link)\n\n # Move any directories suffixed with .zarr to filenames\n zarrnames = [entry for entry in dirnames if Path(entry).suffix == '.zarr']\n dirnames = [entry for entry in dirnames if Path(entry).suffix != '.zarr']\n filenames.extend(zarrnames)\n\n # If layout configuration file exists, delete it\n if self.config_filename in filenames:\n filenames.remove(self.config_filename)\n\n all_bfs = []\n all_tag_dicts = []\n for f in filenames:\n abs_fn = path / f\n # Skip files that fail validation, unless forcibly indexing\n if force or self._validate_file(abs_fn):\n bf, tag_dicts = self._index_file(abs_fn, config_entities)\n all_tag_dicts += tag_dicts\n all_bfs.append(bf)\n\n # Recursively index subdirectories\n for d in dirnames:\n d = path / d\n force = _validate_path(\n d,\n incl_patt=self._include_patterns,\n excl_patt=self._exclude_patterns,\n root=self._layout._root,\n )\n if force is not False:\n dir_bfs, dir_tag_dicts = self._index_dir(d, config, force=force)\n all_bfs += dir_bfs\n all_tag_dicts += dir_tag_dicts\n\n return all_bfs, all_tag_dicts\n\n def _index_file(self, abs_fn, entities):\n \"\"\"Create DB record for file and its tags.\"\"\"\n bf = make_bidsfile(abs_fn)\n\n # Extract entity values\n match_vals = {}\n for e in entities.values():\n m = e.match_file(bf)\n if m is None and e.mandatory:\n break\n if m is not None:\n match_vals[e.name] = (e, m)\n\n # Create Entity <=> BIDSFile mappings\n tag_dicts = [\n _create_tag_dict(bf, ent, val, ent._dtype) for ent, val in match_vals.values()\n ]\n\n return bf, tag_dicts\n\n def _index_metadata(self):\n \"\"\"Index metadata for all files in the BIDS dataset.\"\"\"\n filters = self.filters\n\n if filters:\n # ensure we are returning objects\n filters['return_type'] = 'object'\n\n if filters.get('extension'):\n filters['extension'] = listify(filters['extension'])\n # ensure json files are being indexed\n if '.json' not in filters['extension']:\n filters['extension'].append('.json')\n\n # Process JSON files first if we're indexing metadata\n all_files = self._layout.get(**filters)\n\n # Track ALL entities we've seen in file names or metadatas\n all_entities = {}\n for c in self._config:\n all_entities.update(c.entities)\n\n # If key/value pairs in JSON files duplicate ones extracted from files,\n # we can end up with Tag collisions in the DB. To prevent this, we\n # store all filename/entity pairs and the value, and then check against\n # that before adding each new Tag.\n all_tags = {}\n for t in self.session.query(Tag).all():\n key = f'{t.file_path}_{t.entity_name}'\n all_tags[key] = str(t.value)\n\n # We build up a store of all file data as we iterate files. It looks\n # like: { extension/suffix: dirname: [(entities, payload)]}}.\n # The payload is left empty for non-JSON files.\n file_data = {}\n\n # Memoizing JSON loader\n # Use as a function to allow lazy loading so only read JSON files\n # if they correspond to data files that are indexed\n @cache\n def load_json(path):\n with Path(path).fs.open(Path(path).path, 'r', encoding='utf-8') as handle:\n try:\n return json.load(handle)\n except (UnicodeDecodeError, json.JSONDecodeError) as e:\n raise OSError(\n f'Error occurred while trying to decode JSON from file {path}'\n ) from e\n\n filenames = []\n for bf in all_files:\n if 'suffix' in bf.entities and 'extension' in bf.entities:\n file_ents = bf.entities.copy()\n suffix = file_ents.pop('suffix')\n ext = file_ents.pop('extension')\n key = f'{ext}/{suffix}'\n if key not in file_data:\n file_data[key] = defaultdict(list)\n\n payload = None\n if ext == '.json':\n payload = partial(load_json, bf)\n else:\n filenames.append(bf)\n\n to_store = (file_ents, payload, bf.path)\n file_data[key][bf._dirname].append(to_store)\n\n # To avoid integrity errors, track primary keys we've seen\n seen_assocs = set()\n\n def create_association_pair(src, dst, kind, kind2=None):\n objs = []\n kind2 = kind2 or kind\n pk1 = '#'.join([src, dst, kind])\n if pk1 not in seen_assocs:\n objs.append(FileAssociation(src=src, dst=dst, kind=kind))\n seen_assocs.add(pk1)\n pk2 = '#'.join([dst, src, kind2])\n if pk2 not in seen_assocs:\n objs.append(FileAssociation(src=dst, dst=src, kind=kind2))\n seen_assocs.add(pk2)\n\n return objs\n\n all_objs = []\n all_tag_dicts = []\n for bf in filenames:\n file_ents = bf.entities.copy()\n suffix = file_ents.pop('suffix', None)\n ext = file_ents.pop('extension', None)\n file_ent_keys = set(file_ents.keys())\n\n if suffix is None or ext is None:\n continue\n\n # Extract metadata associated with the file. The idea is\n # that we loop over parent directories, and if we find\n # payloads in the file_data store (indexing by directory\n # and current file suffix), we check to see if the\n # candidate JS file's entities are entirely consumed by\n # the current file. If so, it's a valid candidate, and we\n # add the payload to the stack. Finally, we invert the\n # stack and merge the payloads in order.\n ext_key = f'{ext}/{suffix}'\n json_key = f'.json/{suffix}'\n dirname = bf._dirname\n\n payloads = []\n ancestors = []\n\n while True:\n # Get JSON payloads\n json_data = file_data.get(json_key, {}).get(dirname, [])\n for js_ents, js_md, js_path in json_data:\n js_keys = set(js_ents.keys())\n if js_keys - file_ent_keys:\n continue\n matches = [js_ents[name] == file_ents[name] for name in js_keys]\n if all(matches):\n payloads.append((js_md, js_path))\n\n # Get all files this file inherits from\n candidates = file_data.get(ext_key, {}).get(dirname, [])\n for ents, _, path in candidates:\n keys = set(ents.keys())\n if keys - file_ent_keys:\n continue\n matches = [ents[name] == file_ents[name] for name in keys]\n if all(matches):\n ancestors.append(path)\n\n parent = dirname.parent\n\n if parent == dirname:\n break\n dirname = parent\n\n if not payloads:\n continue\n\n # Missing data files can tolerate absent metadata files,\n # but we will try to load it anyway\n virtual_datafile = not bf._path.exists()\n\n # Create DB records for metadata associations\n js_file = payloads[0][1]\n all_objs += create_association_pair(js_file, bf.path, 'Metadata')\n\n # Consolidate metadata by looping over inherited JSON files\n file_md = {}\n for pl, js_file in payloads[::-1]: # noqa: B007\n try:\n file_md.update(pl())\n except FileNotFoundError:\n if not virtual_datafile:\n raise\n # Drop metadata if any files are missing\n # Otherwise missing overrides could give misleading metadata\n file_md = {}\n break\n\n # Create FileAssociation records for JSON inheritance\n n_pl = len(payloads)\n for i, (pl, js_file) in enumerate(payloads): # noqa: B007\n if (i + 1) < n_pl:\n other = payloads[i + 1][1]\n all_objs += create_association_pair(js_file, other, 'Child', 'Parent')\n\n # Inheritance for current file\n n_pl = len(ancestors)\n for i, src in enumerate(ancestors):\n if (i + 1) < n_pl:\n dst = ancestors[i + 1]\n all_objs += create_association_pair(src, dst, 'Child', 'Parent')\n\n # Files with IntendedFor field always get mapped to targets\n intended = listify(file_md.get('IntendedFor', []))\n for target in intended:\n # IntendedFor paths should use BIDS URIs\n # Previously (now deprecated), paths may be relative to participant sub-dir\n target = _resolve_intent(target, self._layout._root, bf.entities['subject'])\n if target is None:\n warnings.warn(f'Skipping association for {target}', stacklevel=2)\n continue\n all_objs += create_association_pair(\n bf.path, str(target), 'IntendedFor', 'InformedBy'\n )\n\n # Link files to BOLD runs\n if suffix in ['physio', 'stim', 'events', 'sbref']:\n images = self._layout.get(\n extension=['.nii', '.nii.gz'],\n suffix='bold',\n return_type='filename',\n **file_ents,\n )\n for img in images:\n all_objs += create_association_pair(bf.path, img, 'IntendedFor', 'InformedBy')\n\n # Link files to DWI runs\n if suffix == 'sbref' or ext in ['bvec', 'bval']:\n images = self._layout.get(\n extension=['.nii', '.nii.gz'],\n suffix='dwi',\n return_type='filename',\n **file_ents,\n )\n for img in images:\n all_objs += create_association_pair(bf.path, img, 'IntendedFor', 'InformedBy')\n\n # Create Tag <-> Entity mappings, and any newly discovered Entities\n for md_key, md_val in file_md.items():\n # Treat null entries (deserialized to None) as absent\n # Alternative is to cast None to null in layout.models._create_tag_dict\n if md_val is None:\n continue\n tag_string = f'{bf.path}_{md_key}'\n # Skip pairs that were already found in the filenames\n if tag_string in all_tags:\n file_val = all_tags[tag_string]\n if str(md_val) != file_val:\n msg = (\n \"Conflicting values found for entity '{}' in \"\n \"filename {} (value='{}') versus its JSON sidecar \"\n \"(value='{}'). Please reconcile this discrepancy.\"\n )\n raise BIDSConflictingValuesError(\n msg.format(md_key, bf.path, file_val, md_val)\n )\n continue\n if md_key not in all_entities:\n all_entities[md_key] = Entity(md_key)\n self.session.add(all_entities[md_key])\n tag = _create_tag_dict(bf, all_entities[md_key], md_val, is_metadata=True)\n all_tag_dicts.append(tag)\n\n self.session.bulk_save_objects(all_objs)\n self.session.bulk_insert_mappings(Tag, all_tag_dicts)\n self.session.commit()\n\n\ndef _resolve_intent(intent: str, root: Path, subject: str) -> str | None:\n \"\"\"Resolve IntendedFor paths as absolute paths.\n\n Path may be either a BIDS URI (prefix bids:: or bids::) or\n a relative path from subject directory.\n\n Examples\n --------\n >>> _resolve_intent('bids::sub-01/anat/sub-01_T1w.nii.gz', Path(), '01')\n 'sub-01/anat/sub-01_T1w.nii.gz'\n >>> _resolve_intent('anat/sub-01_T1w.nii.gz', Path(), '01')\n 'sub-01/anat/sub-01_T1w.nii.gz'\n >>> _resolve_intent('bids:named_dataset:sub-01/anat/sub-01_T1w.nii.gz', Path(), '01')\n\n \"\"\"\n if not intent.startswith('bids:'):\n return str(root / f'sub-{subject}' / intent)\n if intent.startswith('bids::'):\n return str(root / intent[6:])\n" }, { "path": "src/bids/layout/layout.py", "content": "\"\"\"BIDSLayout class.\"\"\"\n\nimport copy\nimport difflib\nimport enum\nimport json\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom functools import lru_cache, partial\nfrom itertools import chain\n\nimport sqlalchemy as sa\nfrom bids_validator import BIDSValidator\nfrom sqlalchemy.orm import aliased\nfrom sqlalchemy.sql.expression import cast\nfrom upath import UPath as Path\n\nfrom ..exceptions import (\n BIDSDerivativesValidationError,\n BIDSEntityError,\n BIDSValidationError,\n NoMatchError,\n TargetError,\n)\nfrom ..external import inflect\nfrom ..utils import hashablefy, listify, natural_sort\nfrom .db import ConnectionManager\nfrom .index import BIDSLayoutIndexer\nfrom .models import BIDSFile, Config, DerivativeDatasets, Entity, Tag\nfrom .utils import BIDSMetadata, parse_file_entities\nfrom .validation import EXAMPLE_DERIVATIVES_DESCRIPTION, validate_derivative_path, validate_root\nfrom .writing import build_path, write_to_file\n\ntry:\n from operator import call\nexcept ImportError: # PY310\n\n def call(func, *args, **kwargs):\n return func(*args, **kwargs)\n\n\n__all__ = ['BIDSLayout']\n\n\nclass Sentinel:\n def __repr__(self):\n return self.__class__.__name__\n\n\n@call\nclass RemovedOption(Sentinel):\n pass\n\n\nclass BIDSLayout:\n \"\"\"Layout class representing an entire BIDS dataset.\n\n Parameters\n ----------\n root : str\n The root directory of the BIDS dataset.\n validate : bool, optional\n If True, all files are checked for BIDS compliance when first indexed,\n and non-compliant files are ignored. This provides a convenient way to\n restrict file indexing to only those files defined in the \"core\" BIDS\n spec, as setting validate=True will lead files in supplementary folders\n like derivatives/, code/, etc. to be ignored.\n derivatives : bool or str or list, optional\n Specifies whether and/or which\n derivatives to index. If True, all pipelines found in the\n derivatives/ subdirectory will be indexed. If a str or list, gives\n the paths to one or more derivatives directories to index. If False\n or None, the derivatives/ directory is ignored during indexing, and\n derivatives will have to be added manually via add_derivatives().\n Note: derivatives datasets MUST contain a dataset_description.json\n file in order to be indexed.\n config : str or list or None, optional\n Optional name(s) of configuration file(s) to use.\n By default (None), uses 'bids'. Can also be 'bids-schema' to use\n the official BIDS schema instead of JSON config files.\n sources : :obj:`bids.layout.BIDSLayout` or list or None, optional\n Optional BIDSLayout(s) from which the current BIDSLayout is derived.\n config_filename : str\n Optional name of filename within directories\n that contains configuration information.\n regex_search : bool\n Whether to require exact matching (True) or regex\n search (False, default) when comparing the query string to each\n entity in .get() calls. This sets a default for the instance, but\n can be overridden in individual .get() requests.\n database_path : str\n Optional path to directory containing SQLite database file index\n for this BIDS dataset. If a value is passed and the folder\n already exists, indexing is skipped. By default (i.e., if None),\n an in-memory SQLite database is used, and the index will not\n persist unless .save() is explicitly called.\n reset_database : bool\n If True, any existing directory specified in the\n database_path argument is deleted, and the BIDS dataset provided\n in the root argument is reindexed. If False, indexing will be\n skipped and the existing database file will be used. Ignored if\n database_path is not provided.\n indexer: BIDSLayoutIndexer or callable\n An optional BIDSLayoutIndexer instance to use for indexing, or any\n callable that takes a BIDSLayout instance as its only argument. If\n None, a new indexer with default parameters will be implicitly created.\n is_derivative: bool\n Index dataset as a derivative dataset. This can be enabled along with\n validate=False to index derivatives without dataset_description.json. If\n validate=True, the dataset must have a dataset_description.json with\n DatasetType=derivative and GeneratedBy\n\n \"\"\"\n\n def __init__(\n self,\n root=None,\n validate=True,\n absolute_paths=RemovedOption,\n derivatives=False,\n config=None,\n sources=None,\n regex_search=False,\n database_path=None,\n reset_database=False,\n indexer=None,\n is_derivative=False,\n **indexer_kwargs,\n ):\n if absolute_paths is not RemovedOption:\n msg = (\n 'The absolute_paths argument is no longer supported. Use the '\n '`.path` attribute of returned BIDSFile objects for absolute '\n 'paths, and the `.relpath` attribute for relative paths.'\n )\n raise TypeError(msg)\n\n if indexer_kwargs:\n args = ', '.join(f'{key}={val}' for key, val in indexer_kwargs.items())\n msg = (\n 'Passing kwargs to the BIDSLayoutIndexer is no longer supported. '\n f'Pass `indexer=BIDSLayoutIndexer({args})` instead.'\n )\n raise TypeError(msg)\n\n # Load from existing database file\n load_db = (\n database_path is not None\n and reset_database is False\n and ConnectionManager.exists(database_path)\n )\n\n if load_db:\n self.connection_manager = ConnectionManager(database_path)\n info = self.connection_manager.layout_info\n # Overwrite init args with values in DB\n root = info.root\n derivatives = info.derivatives\n config = info.config\n\n # Validate that a valid BIDS project exists at root\n root, description = validate_root(root, validate)\n if any([is_derivative, description and description.get('DatasetType') == 'derivative']):\n try:\n self.source_pipeline = validate_derivative_path(root)\n except (BIDSValidationError, FileNotFoundError) as err:\n if validate:\n raise err\n self.source_pipeline = None\n default_config = ['bids', 'derivatives']\n self.is_derivative = True\n else:\n self.source_pipeline = None\n default_config = ['bids']\n self.is_derivative = False\n\n self._root = root # type: Path\n self.description = description\n self.derivatives = DerivativeDatasets()\n self.sources = sources\n self.regex_search = regex_search\n\n # Initialize a completely new layout and index the dataset\n if not load_db:\n # If config supplied from user or loaded from db, use it. Otherwise, fall\n # back to a default\n config = listify(config if config else default_config)\n\n init_args = dict(root=root, derivatives=derivatives, config=config) # noqa: C408\n\n self.connection_manager = ConnectionManager(\n database_path, reset_database, config, init_args\n )\n\n # Do not overwrite indexer variable, so the same configuration is passed to\n # add_derivatives() below\n _indexer = indexer or BIDSLayoutIndexer(\n validate=validate and not is_derivative,\n )\n _indexer(self)\n\n # Add derivatives if any are found\n if derivatives:\n if derivatives is True:\n derivatives = root / 'derivatives'\n self.add_derivatives(\n derivatives,\n parent_database_path=database_path,\n validate=validate,\n derivatives=None,\n sources=self,\n config=None,\n regex_search=regex_search,\n reset_database=reset_database,\n indexer=indexer,\n )\n\n @property\n def root(self): # noqa: D102\n return str(self._root)\n\n def __getattr__(self, key):\n \"\"\"Dynamically inspect missing methods for get_() calls\n and return a partial function of get() if a match is found.\n \"\"\"\n if key.startswith('get_'):\n ent_name = key.replace('get_', '')\n entities = self.get_entities()\n # Use inflect to check both singular and plural forms\n if ent_name not in entities:\n sing = inflect.engine().singular_noun(ent_name)\n if sing in entities:\n ent_name = sing\n else:\n raise BIDSEntityError(\n f\"'get_{ent_name}' can't be called because '{ent_name}' isn't a \"\n 'recognized entity name.'\n )\n return partial(self.get, return_type='id', target=ent_name)\n # Spit out default message if we get this far\n raise AttributeError(\n '%s object has no attribute named %r' % (self.__class__.__name__, key) # noqa: UP031\n )\n\n def __repr__(self):\n \"\"\"Provide a tidy summary of key properties.\"\"\"\n n_subjects = len(\n [\n s.value\n for s in self.session.query(Tag)\n .filter_by(entity_name='subject')\n .group_by(Tag._value)\n ]\n )\n\n n_sessions = len(\n set( # noqa: C401\n (t.value, t.file.entities.get('subject'))\n for t in self.session.query(Tag).filter_by(entity_name='session')\n if t.file.entities.get('subject')\n )\n )\n\n n_runs = len(\n set( # noqa: C401\n (t.value, t.file.entities.get('subject'))\n for t in self.session.query(Tag).filter_by(entity_name='run')\n if isinstance(t.value, int) and t.file.entities.get('subject')\n )\n )\n\n root = self.root[-30:]\n s = (\n f'BIDS Layout: ...{root} | Subjects: {n_subjects} | Sessions: {n_sessions} | '\n f'Runs: {n_runs}'\n )\n return s\n\n def _in_scope(self, scope):\n \"\"\"Determine whether current BIDSLayout is in the passed scope.\n\n Parameters\n ----------\n scope : str or list\n The intended scope(s). Each value must be one of 'all', 'raw',\n 'derivatives', or a pipeline name.\n\n \"\"\"\n scope = listify(scope)\n\n if 'all' in scope:\n return True\n\n # We assume something is a BIDS-derivatives dataset if it either has a\n # defined pipeline name, or is applying the 'derivatives' rules.\n pl_name = self.source_pipeline\n\n return any(\n [\n 'raw' in scope and not self.is_derivative,\n ('derivatives' in scope or pl_name in scope) and self.is_derivative,\n ]\n )\n\n def _get_layouts_in_scope(self, scope):\n \"\"\"Return all layouts in the passed scope.\"\"\"\n if scope == 'self':\n return [self]\n\n def collect_layouts(layout):\n \"\"\"Recursively build a list of layouts.\"\"\"\n children = list(layout.derivatives.values())\n layouts = [collect_layouts(d) for d in children]\n return [layout] + list(chain(*layouts))\n\n layouts = [l for l in collect_layouts(self) if l._in_scope(scope)] # noqa: E741\n return list(set(layouts))\n\n def _sanitize_query_dtypes(self, entities):\n \"\"\"Automatically convert entity query values to correct dtypes.\"\"\"\n entities = entities.copy()\n names = list(entities.keys())\n ents = {e.name: e for e in self.session.query(Entity).filter(Entity.name.in_(names)).all()}\n # Fail silently because the DB may still know how to reconcile\n # type differences.\n for name, val in entities.items():\n if isinstance(val, enum.Enum):\n continue\n try:\n if isinstance(val, (list, tuple)):\n entities[name] = [\n v if isinstance(v, Query) else ents[name]._astype(v) for v in val\n ]\n else:\n entities[name] = ents[name]._astype(val)\n except Exception: # noqa: BLE001, S110\n pass\n return entities\n\n @property\n def session(self): # noqa: D102\n return self.connection_manager.session\n\n @property\n @lru_cache # noqa: B019\n def config(self): # noqa: D102\n return {c.name: c for c in self.session.query(Config).all()}\n\n @property\n def entities(self):\n \"\"\"Get the entities.\"\"\"\n return self.get_entities()\n\n @property\n def files(self):\n \"\"\"Get the files.\"\"\"\n return self.get_files()\n\n @classmethod\n def load(cls, database_path):\n \"\"\"Load index from database path. Initialization parameters are set to\n those found in database_path JSON sidecar.\n\n Parameters\n ----------\n database_path : str, Path\n The path to the desired database folder. If a relative path is\n passed, it is assumed to be relative to the BIDSLayout root\n directory.\n\n \"\"\"\n return cls(database_path=database_path)\n\n def save(self, database_path, replace_connection=True):\n \"\"\"Save the current index as a SQLite3 DB at the specified location.\n\n Note: This is only necessary if a database_path was not specified\n at initialization, and the user now wants to save the index.\n If a database_path was specified originally, there is no need to\n re-save using this method.\n\n Parameters\n ----------\n database_path : str\n The path to the desired database folder. By default,\n uses .db_cache. If a relative path is passed, it is assumed to\n be relative to the BIDSLayout root directory.\n replace_connection : bool, optional\n If True, the newly created database will\n be used for all subsequent connections. This means that any\n changes to the index made after the .save() call will be\n reflected in the database file. If False, the previous database\n will continue to be used, and any subsequent changes will not\n be reflected in the new file unless save() is explicitly called\n again.\n\n \"\"\"\n database_path = Path(database_path)\n\n self.connection_manager = self.connection_manager.save_database(\n database_path, replace_connection\n )\n\n # Recursively save children\n for pipeline_name, der in self.derivatives.items():\n der.save(database_path / pipeline_name)\n\n def get_entities(self, scope='all', metadata=None):\n \"\"\"Get entities for all layouts in the specified scope.\n\n Parameters\n ----------\n scope : str\n The scope of the search space. Indicates which\n BIDSLayouts' entities to extract.\n See :obj:`bids.layout.BIDSLayout.get` docstring for valid values.\n\n metadata : bool or None\n By default (None), all available entities\n are returned. If True, only entities found in metadata files\n (and not defined for filenames) are returned. If False, only\n entities defined for filenames (and not those found in JSON\n sidecars) are returned.\n\n Returns\n -------\n dict\n Dictionary where keys are entity names and\n values are Entity instances.\n\n \"\"\"\n # TODO: memoize results\n layouts = self._get_layouts_in_scope(scope)\n entities = {}\n for l in layouts: # noqa: E741\n query = l.session.query(Entity)\n if metadata is not None:\n query = query.join(Tag).filter_by(is_metadata=metadata)\n results = query.all()\n entities.update({e.name: e for e in results})\n return entities\n\n def _find_directories_for_entity_using_schema(self, entity_name, results):\n \"\"\"Find directories using schema directory rules.\"\"\"\n import os\n\n from bidsschematools import schema as bst_schema\n\n # Load schema directory rules\n bids_schema = bst_schema.load_schema()\n\n directory_rules = bids_schema.rules.directories.raw\n directories = set()\n\n # Build directory paths using schema rules for each file\n for f in results:\n if entity_name in f.entities:\n # Build path based on schema directory hierarchy\n path_components = self._build_path_from_schema_rules(f.entities, directory_rules)\n if path_components:\n # Convert to full directory path\n directory_path = os.path.join(self.root, *path_components)\n directories.add(directory_path)\n\n return list(directories)\n\n def _build_path_from_schema_rules(self, entities, directory_rules):\n \"\"\"Build directory path components from schema rules and entity values.\"\"\"\n path_components = []\n current_level = 'root'\n\n # Traverse schema directory hierarchy\n while current_level:\n rule = directory_rules.get(current_level)\n if not rule: # pragma: no cover\n # Schema always provides valid rules; current_level only set from schema subdirs\n break\n\n rule_dict = dict(rule)\n rule_entity = rule_dict.get('entity')\n\n # If this rule corresponds to an entity, add directory component\n if rule_entity and rule_entity in entities:\n entity_value = entities[rule_entity]\n if rule_entity == 'subject':\n path_components.append(f'sub-{entity_value}')\n elif rule_entity == 'session':\n path_components.append(f'ses-{entity_value}')\n else: # pragma: no cover\n # BIDS schema only defines subject/session entity directories currently\n path_components.append(str(entity_value))\n elif current_level == 'datatype' and 'datatype' in entities:\n # Special case: datatype rule has no entity field but we use the datatype value\n path_components.append(entities['datatype'])\n\n # Determine next level based on subdirs and available entities\n subdirs = rule_dict.get('subdirs', [])\n next_level = None\n\n # First pass: look for entity-based subdirs across all options\n entity_candidates = []\n rule_candidates = []\n\n for subdir in subdirs:\n if isinstance(subdir, dict) and 'oneOf' in subdir:\n for option in subdir['oneOf']:\n if option in entities:\n entity_candidates.append(option)\n elif option in directory_rules:\n rule_candidates.append(option)\n elif isinstance(subdir, str):\n if subdir in entities:\n entity_candidates.append(subdir)\n elif subdir in directory_rules:\n rule_candidates.append(subdir)\n\n # Prefer entity candidates, fallback to rule candidates\n if entity_candidates:\n next_level = entity_candidates[0] # Take first entity match\n elif rule_candidates:\n next_level = rule_candidates[0] # Take first rule match\n\n current_level = next_level\n\n return path_components\n\n def get_files(self, scope='all'):\n \"\"\"Get BIDSFiles for all layouts in the specified scope.\n\n Parameters\n ----------\n scope : str\n The scope of the search space. Indicates which\n BIDSLayouts' entities to extract.\n See :obj:`bids.layout.BIDSLayout.get` docstring for valid values.\n\n\n Returns\n -------\n A dict, where keys are file paths and values\n are :obj:`bids.layout.BIDSFile` instances.\n\n \"\"\"\n # TODO: memoize results\n layouts = self._get_layouts_in_scope(scope)\n files = {}\n for l in layouts: # noqa: E741\n results = l.session.query(BIDSFile).all()\n files.update({f.path: f for f in results})\n return files\n\n def clone(self):\n \"\"\"Return a deep copy of the current BIDSLayout.\"\"\"\n return copy.deepcopy(self)\n\n def parse_file_entities(\n self, filename, scope='all', entities=None, config=None, include_unmatched=False\n ):\n \"\"\"Parse the passed filename for entity/value pairs.\n\n Parameters\n ----------\n filename : str\n The filename to parse for entity values\n scope : str or list, optional\n The scope of the search space. Indicates which BIDSLayouts'\n entities to extract. See :obj:`bids.layout.BIDSLayout.get`\n docstring for valid values. By default, extracts all entities.\n entities : list or None, optional\n An optional list of Entity instances to use in\n extraction. If passed, the scope and config arguments are\n ignored, and only the Entities in this list are used.\n config : str or :obj:`bids.layout.models.Config` or list or None, optional\n One or more :obj:`bids.layout.models.Config` objects, or paths\n to JSON config files on disk, containing the Entity definitions\n to use in extraction. If passed, scope is ignored.\n include_unmatched : bool, optional\n If True, unmatched entities are included\n in the returned dict, with values set to None. If False\n (default), unmatched entities are ignored.\n\n Returns\n -------\n dict\n Dictionary where keys are Entity names and values are the\n values extracted from the filename.\n\n \"\"\"\n # If either entities or config is specified, just pass through\n if entities is None and config is None:\n layouts = self._get_layouts_in_scope(scope)\n config = chain(*[list(l.config.values()) for l in layouts]) # noqa: E741\n config = list(set(config))\n\n return parse_file_entities(filename, entities, config, include_unmatched)\n\n def _get_derivative_dirs(self, paths):\n for path in paths:\n p = Path(path).absolute()\n base = p.name\n if Path.exists(p / 'dataset_description.json'):\n yield (base, p)\n else:\n yield from (\n (f'{base}/{dd.parent.name}', dd.parent)\n for dd in p.glob('*/dataset_description.json')\n )\n\n def add_derivatives(self, path, parent_database_path=None, **kwargs):\n \"\"\"Add BIDS-Derivatives datasets to tracking.\n\n Parameters\n ----------\n path : str or list\n One or more paths to BIDS-Derivatives datasets.\n Each path can point to either a derivatives/ directory\n containing one or more pipeline directories, or to a single\n pipeline directory (e.g., derivatives/fmriprep).\n parent_database_path : str or Path\n If not None, use the pipeline name from the dataset_description.json\n file as the database folder name to nest within the parent database\n folder name to write out derivative index to.\n kwargs : dict\n Optional keyword arguments to pass on to\n BIDSLayout() when initializing each of the derivative datasets.\n\n Notes\n -----\n Every derivatives directory intended for indexing MUST contain a\n valid dataset_description.json file. See the BIDS-Derivatives\n specification for details.\n\n \"\"\"\n paths = listify(path)\n if parent_database_path:\n parent_database_path = Path(parent_database_path)\n deriv_paths = list(self._get_derivative_dirs(paths))\n if not deriv_paths:\n warnings.warn( # noqa: B028\n 'Derivative indexing was requested, but no valid '\n 'datasets were found in the specified locations '\n f'({paths}). Note that all BIDS-Derivatives datasets must'\n ' meet all the requirements for BIDS-Raw datasets '\n '(a common problem is to fail to include a '\n \"'dataset_description.json' file in derivatives \"\n 'datasets).\\n'\n + \"Example contents of 'dataset_description.json':\\n%s\" # noqa: UP031\n % json.dumps(EXAMPLE_DERIVATIVES_DESCRIPTION)\n )\n\n # Default config and sources values\n kwargs['sources'] = kwargs.get('sources') or self\n\n for name, path in deriv_paths:\n if parent_database_path:\n child_database_path = parent_database_path / name\n kwargs['database_path'] = child_database_path\n if name in self.derivatives:\n raise BIDSDerivativesValidationError(\n f'Pipeline name {name} ({path!s}) has already been added to this '\n 'BIDSLayout. Every added pipeline must have a unique name!'\n )\n self.derivatives[name] = BIDSLayout(path, is_derivative=True, **kwargs)\n\n def to_df(self, metadata=False, **filters):\n \"\"\"Return information for BIDSFiles tracked in Layout as pd.DataFrame.\n\n Parameters\n ----------\n metadata : bool, optional\n If True, includes columns for all metadata fields.\n If False, only filename-based entities are included as columns.\n filters : dict, optional\n Optional keyword arguments passed on to get(). This allows\n one to easily select only a subset of files for export.\n\n Returns\n -------\n :obj:`pandas.DataFrame`\n A pandas DataFrame, where each row is a file, and each column is\n a tracked entity. NaNs are injected whenever a file has no\n value for a given attribute.\n\n \"\"\"\n try:\n import pandas as pd\n except ImportError:\n raise ImportError('Missing dependency: \"pandas\"') # noqa: B904\n\n # TODO: efficiency could probably be improved further by joining the\n # BIDSFile and Tag tables and running a single query. But this would\n # require refactoring the below to use _build_file_query, which will\n # in turn likely require generalizing the latter.\n files = self.get(**filters)\n file_paths = [f.path for f in files]\n query = self.session.query(Tag).filter(Tag.file_path.in_(file_paths))\n\n if not metadata:\n query = query.join(Entity).filter(Tag.is_metadata == False) # noqa: E712\n\n tags = query.all()\n\n tags = [[t.file_path, t.entity_name, t.value] for t in tags]\n data = pd.DataFrame(tags, columns=['path', 'entity', 'value'])\n data = data.pivot(index='path', columns='entity', values='value')\n\n # Add in orphaned files with no Tags. Maybe make this an argument?\n orphans = list(set(file_paths) - set(data.index))\n for o in orphans:\n data.loc[o] = pd.Series(dtype=float)\n\n return data.reset_index()\n\n def get( # noqa: D417\n self,\n return_type='object',\n target=None,\n scope='all',\n regex_search=False,\n absolute_paths=RemovedOption,\n invalid_filters='error',\n **filters,\n ):\n \"\"\"Retrieve files and/or metadata from the current Layout.\n\n Parameters\n ----------\n return_type : str, optional\n Type of result to return. Valid values:\n 'object' (default): return a list of matching BIDSFile objects.\n 'file' or 'filename': return a list of matching filenames.\n 'dir': return a list of directories.\n 'id': return a list of unique IDs. Must be used together\n with a valid target.\n target : str, optional\n Optional name of the target entity to get results for\n (only used if return_type is 'dir' or 'id').\n scope : str or list, optional\n Scope of the search space. If passed, only\n nodes/directories that match the specified scope will be\n searched. Possible values include:\n 'all' (default): search all available directories.\n 'derivatives': search all derivatives directories.\n 'raw': search only BIDS-Raw directories.\n 'self': search only the directly called BIDSLayout.\n : the name of a BIDS-Derivatives pipeline.\n regex_search : bool or None, optional\n Whether to require exact matching\n (False) or regex search (True) when comparing the query string\n to each entity.\n invalid_filters (str): Controls behavior when named filters are\n encountered that don't exist in the database (e.g., in the case of\n a typo like subbject='0.1'). Valid values:\n 'error' (default): Raise an explicit error.\n 'drop': Silently drop invalid filters (equivalent to not having\n passed them as arguments in the first place).\n 'allow': Include the invalid filters in the query, resulting\n in no results being returned.\n filters : dict\n Any optional key/values to filter the entities on.\n Keys are entity names, values are regexes to filter on. For\n example, passing filters={'subject': 'sub-[12]'} would return\n only files that match the first two subjects. In addition to\n ordinary data types, the following enums are defined (in the\n Query class):\n * Query.NONE: The named entity must not be defined.\n * Query.ANY: the named entity must be defined, but can have any\n value.\n\n Returns\n -------\n list of :obj:`bids.layout.BIDSFile` or str\n A list of BIDSFiles (default) or strings (see return_type).\n\n \"\"\"\n if not return_type.startswith(('obj', 'file')) and return_type not in ('id', 'dir'):\n raise ValueError(\n f'Invalid return_type <{return_type}> specified (must be one '\n \"of 'object', 'file', 'filename', 'id', or 'dir').\"\n )\n\n if absolute_paths is not RemovedOption:\n msg = (\n 'The absolute_paths argument is no longer supported. Use the '\n '`.path` attribute of returned BIDSFile objects for absolute '\n 'paths, and the `.relpath` attribute for relative paths.'\n )\n raise TypeError(msg)\n\n layouts = self._get_layouts_in_scope(scope)\n\n entities = self.get_entities()\n\n # error check on users accidentally passing in filters\n if isinstance(filters.get('filters'), dict):\n raise RuntimeError(\n 'You passed in filters as a dictionary named '\n 'filters; please pass the keys in as named '\n 'keywords to the `get()` call. For example: '\n '`layout.get(**filters)`.'\n )\n\n # Ensure leading periods if extensions were passed\n if 'extension' in filters and (\n 'bids' in self.config or any('bids-schema' in c for c in self.config)\n ):\n filters['extension'] = [\n '.' + x.lstrip('.') if isinstance(x, str) else x\n for x in listify(filters['extension'])\n ]\n\n if invalid_filters != 'allow':\n bad_filters = set(filters.keys()) - set(entities.keys())\n if bad_filters:\n if invalid_filters == 'drop':\n for bad_filt in bad_filters:\n filters.pop(bad_filt)\n elif invalid_filters == 'error':\n first_bad = list(bad_filters)[0]\n msg = f\"'{first_bad}' is not a recognized entity. \"\n ents = list(entities.keys())\n suggestions = difflib.get_close_matches(first_bad, ents)\n if suggestions:\n msg += f'Did you mean {suggestions}? '\n raise ValueError(\n msg + \"If you're sure you want to impose \"\n 'this constraint, set '\n \"invalid_filters='allow'.\"\n )\n\n # Provide some suggestions if target is specified and invalid.\n if target is not None and target not in entities:\n potential = list(entities.keys())\n suggestions = difflib.get_close_matches(target, potential)\n if suggestions:\n message = f'Did you mean one of: {suggestions}?'\n else:\n message = f'Valid targets are: {potential}'\n raise TargetError((\"Unknown target '{}'. \" + message).format(target))\n elif target is None and return_type in ['id', 'dir']:\n raise TargetError(\n 'If return_type is \"id\" or \"dir\", a valid target entity must also be specified.'\n )\n\n results = []\n for l in layouts: # noqa: E741\n query = l._build_file_query(filters=filters, regex_search=regex_search)\n # NOTE: The following line, when uncommented, eager loads\n # associations. This was introduced in order to prevent sessions\n # from randomly detaching. It should be fixed by setting\n # expire_on_commit at session creation, but let's leave this here\n # for another release or two to make sure we don't have any further\n # problems.\n # query = query.options(joinedload(BIDSFile.tags)\n # .joinedload(Tag.entity))\n results.extend(query.all())\n\n if return_type.startswith('file'):\n results = natural_sort([f.path for f in results])\n elif return_type in ['id', 'dir']:\n if target is None:\n raise TargetError(\n 'If return_type is \"id\" or \"dir\", a valid '\n 'target entity must also be specified.'\n )\n\n metadata = target not in self.get_entities(metadata=False)\n\n if return_type == 'id':\n ent_iter = (\n hashablefy(res.get_entities(metadata=metadata))\n for res in results\n if target in res.entities\n )\n results = list(dict.fromkeys(ents[target] for ents in ent_iter if target in ents))\n results = natural_sort(list(set(results)))\n elif return_type == 'dir':\n # Check if we're using schema-based config or legacy config\n using_schema = any('bids-schema' in str(c) for c in listify(self.config))\n\n if using_schema:\n # Use schema directory rules for schema-based configs\n directories = self._find_directories_for_entity_using_schema(target, results)\n results = natural_sort(list(set(directories)))\n else:\n # Use original template-based approach for legacy configs\n template = entities[target].directory\n if template is None:\n raise ValueError(\n 'Return type set to directory, but no ' # noqa: UP031\n 'directory template is defined for the '\n 'target entity (\"%s\").' % target\n )\n # Construct regex search pattern from target directory template\n # On Windows, the regex won't compile if, e.g., there is a folder starting with \"U\" on the path. # noqa: E501\n # Converting to a POSIX path with forward slashes solves this.\n template = self._root.as_posix() + template\n to_rep = re.findall(r'{(.*?)\\}', template)\n for ent in to_rep:\n patt = entities[ent].pattern\n template = template.replace('{%s}' % ent, patt) # noqa: UP031\n # Avoid matching subfolders. We are working with POSIX paths here, so we explicitly use \"/\" # noqa: E501\n # as path separator.\n template += r'[^/]*$'\n matches = [\n f.dirname for f in results if re.search(template, f._dirname.as_posix())\n ]\n results = natural_sort(list(set(matches)))\n else:\n results = natural_sort(results, 'path')\n\n return results\n\n def get_file(self, filename, scope='all'):\n \"\"\"Return the BIDSFile object with the specified path.\n\n Parameters\n ----------\n filename : str\n The path of the file to retrieve. Must be either an absolute path,\n or relative to the root of this BIDSLayout.\n scope : str or list, optional\n Scope of the search space. If passed, only BIDSLayouts that match\n the specified scope will be searched. See :obj:`BIDSLayout.get`\n docstring for valid values. Default is 'all'.\n\n Returns\n -------\n :obj:`bids.layout.BIDSFile` or None\n File found, or None if no match was found.\n\n \"\"\"\n filename = self._root.joinpath(filename).absolute()\n for layout in self._get_layouts_in_scope(scope):\n result = layout.session.query(BIDSFile).filter_by(path=str(filename)).first() # noqa: E501\n if result:\n return result\n return None\n\n def _build_file_query(self, **kwargs):\n query = self.session.query(BIDSFile).filter_by(is_dir=False)\n\n filters = kwargs.get('filters')\n\n # Entity filtering\n if filters:\n regex = kwargs.get('regex_search', False)\n\n filters = self._sanitize_query_dtypes(filters)\n\n for name, val in filters.items():\n tag_alias = aliased(Tag)\n\n val_list = list(listify(val)) if val is not None else [None]\n if not val_list:\n continue\n\n if Query.OPTIONAL in val_list:\n continue\n\n none = required = False\n if None in val_list:\n none = True\n val_list.remove(None)\n if Query.NONE in val_list:\n none = True\n val_list.remove(Query.NONE)\n if Query.REQUIRED in val_list:\n required = True\n val_list.remove(Query.REQUIRED)\n\n if none and required:\n # Always true, apply no filter\n continue\n\n # Baseline, use join, start accumulating clauses\n join_method = query.join\n val_clauses = []\n\n # NONE and REQUIRED get special treatment\n if none:\n join_method = query.outerjoin\n val_clauses.append(tag_alias._value.is_(None))\n if required:\n val_clauses.append(tag_alias._value.isnot(None))\n\n # Any remaining values\n if regex:\n val_clauses.extend([tag_alias._value.op('REGEXP')(str(v)) for v in val_list])\n elif val_list:\n _value = tag_alias._value\n if isinstance(val_list[0], int):\n _value = cast(tag_alias._value, sa.Integer)\n\n val_clauses.append(_value.in_(val_list))\n\n # Looking for intersection with list of vals, so use OR\n val_clause = sa.or_(*val_clauses) if len(val_clauses) > 1 else val_clauses[0]\n\n query = join_method(\n tag_alias,\n sa.and_(BIDSFile.path == tag_alias.file_path, tag_alias.entity_name == name),\n )\n\n query = query.filter(val_clause)\n\n query = query.group_by(BIDSFile.path)\n\n return query\n\n def get_collections(\n self,\n level,\n types=None,\n variables=None,\n merge=False,\n sampling_rate=None,\n skip_empty=False,\n **kwargs,\n ):\n \"\"\"Return one or more variable Collections in the BIDS project.\n\n Parameters\n ----------\n level : {'run', 'session', 'subject', 'dataset'}\n The level of analysis to return variables for.\n Must be one of 'run', 'session','subject', or 'dataset'.\n types : str or list\n Types of variables to retrieve. All valid values reflect the\n filename stipulated in the BIDS spec for each kind of variable.\n Valid values include: 'events', 'physio', 'stim', 'scans',\n 'participants', 'sessions', and 'regressors'. Default is None.\n variables : list\n Optional list of variables names to return. If None, all available\n variables are returned.\n merge : bool\n If True, variables are merged across all observations of the\n current level. E.g., if level='subject', variables from all\n subjects will be merged into a single collection. If False, each\n observation is handled separately, and the result is returned\n as a list.\n sampling_rate : int or str\n If level='run', the sampling rate to pass onto the returned\n :obj:`bids.variables.collections.BIDSRunVariableCollection`.\n skip_empty : bool\n Whether or not to skip empty Variables (i.e., where there are no\n rows/records in a file after applying any filtering operations\n like dropping NaNs).\n kwargs\n Optional additional arguments to pass onto\n :obj:`bids.variables.io.load_variables`.\n\n Returns\n -------\n list of :obj:`bids.variables.collections.BIDSVariableCollection`\n or :obj:`bids.variables.collections.BIDSVariableCollection`\n A list if merge=False;\n a single :obj:`bids.variables.collections.BIDSVariableCollection`\n if merge=True.\n\n \"\"\"\n from bids.variables import load_variables\n\n index = load_variables(self, types=types, levels=level, skip_empty=skip_empty, **kwargs)\n return index.get_collections(level, variables, merge, sampling_rate=sampling_rate)\n\n def get_metadata(self, path, include_entities=False, scope='all'):\n \"\"\"Return metadata found in JSON sidecars for the specified file.\n\n Parameters\n ----------\n path : str\n Path to the file to get metadata for.\n include_entities : bool, optional\n If True, all available entities extracted\n from the filename (rather than JSON sidecars) are included in\n the returned metadata dictionary.\n scope : str or list, optional\n The scope of the search space. Each element must\n be one of 'all', 'raw', 'self', 'derivatives', or a\n BIDS-Derivatives pipeline name. Defaults to searching all\n available datasets.\n\n Returns\n -------\n dict\n A dictionary of key/value pairs extracted from all of the\n target file's associated JSON sidecars.\n\n Notes\n -----\n A dictionary containing metadata extracted from all matching .json\n files is returned. In cases where the same key is found in multiple\n files, the values in files closer to the input filename will take\n precedence, per the inheritance rules in the BIDS specification.\n\n \"\"\"\n md = BIDSMetadata(str(path))\n for layout in self._get_layouts_in_scope(scope):\n query = layout.session.query(Tag).join(BIDSFile).filter(BIDSFile.path == str(path))\n\n if not include_entities:\n query = query.join(Entity).filter(Tag.is_metadata == True) # noqa: E712\n\n results = query.all()\n if results:\n md.update({t.entity_name: t.value for t in results})\n return md\n\n return md\n\n def get_dataset_description(self, scope='self', all_=False):\n \"\"\"Return contents of dataset_description.json.\n\n Parameters\n ----------\n scope : str\n The scope of the search space. Only descriptions of\n BIDSLayouts that match the specified scope will be returned.\n See :obj:`bids.layout.BIDSLayout.get` docstring for valid values.\n Defaults to 'self' --i.e., returns the dataset_description.json\n file for only the directly-called BIDSLayout.\n all_ : bool\n If True, returns a list containing descriptions for\n all matching layouts. If False (default), returns for only the\n first matching layout.\n\n Returns\n -------\n dict or list of dict\n a dictionary or list of dictionaries (depending on all_).\n\n \"\"\"\n layouts = self._get_layouts_in_scope(scope)\n if not all_:\n return layouts[0].get_file('dataset_description.json').get_dict()\n return [l.get_file('dataset_description.json').get_dict() for l in layouts] # noqa: E741\n\n def get_nearest( # noqa: D417\n self,\n path,\n return_type='filename',\n strict=True,\n all_=False,\n ignore_strict_entities='extension',\n full_search=False,\n **filters,\n ):\n \"\"\"Walk up file tree from specified path and return nearest matching file(s).\n\n Parameters\n ----------\n path (str): The file to search from.\n return_type (str): What to return; must be one of 'filename'\n (default) or 'tuple'.\n strict (bool): When True, all entities present in both the input\n path and the target file(s) must match perfectly. When False,\n files will be ordered by the number of matching entities, and\n partial matches will be allowed.\n all_ (bool): When True, returns all matching files. When False\n (default), only returns the first match.\n ignore_strict_entities (str, list): Optional entity/entities to\n exclude from strict matching when strict is True. This allows\n one to search, e.g., for files of a different type while\n matching all other entities perfectly by passing\n ignore_strict_entities=['type']. Ignores extension by default.\n full_search (bool): If True, searches all indexed files, even if\n they don't share a common root with the provided path. If\n False, only files that share a common root will be scanned.\n filters : dict\n Optional keywords to pass on to :obj:`bids.layout.BIDSLayout.get`.\n\n \"\"\"\n path = Path(path).absolute()\n\n # Make sure we have a valid suffix\n if not filters.get('suffix'):\n f = self.get_file(path)\n if 'suffix' not in f.entities:\n raise BIDSValidationError(\n \"File '%s' does not have a valid suffix, most \" # noqa: UP031\n 'likely because it is not a valid BIDS file.' % path\n )\n filters['suffix'] = f.entities['suffix']\n\n # Collect matches for all entities\n entities = {}\n for ent in self.get_entities(metadata=False).values():\n m = ent.regex.search(str(path))\n if m:\n entities[ent.name] = ent._astype(m.group(1))\n\n # Remove any entities we want to ignore when strict matching is on\n if strict and ignore_strict_entities is not None:\n for k in listify(ignore_strict_entities):\n entities.pop(k, None)\n\n # Get candidate files\n results = self.get(**filters)\n\n # Make a dictionary of directories --> contained files\n folders = defaultdict(list)\n for f in results:\n folders[f._dirname].append(f)\n\n # Build list of candidate directories to check\n search_paths = []\n while True:\n if path in folders and folders[path]:\n search_paths.append(path)\n parent = path.parent\n if parent == path:\n break\n path = parent\n\n if full_search:\n unchecked = set(folders.keys()) - set(search_paths)\n search_paths.extend(path for path in unchecked if folders[path])\n\n def count_matches(f):\n # Count the number of entities shared with the passed file\n f_ents = f.entities\n keys = set(entities.keys()) & set(f_ents.keys())\n shared = len(keys)\n return [shared, sum([entities[k] == f_ents[k] for k in keys])]\n\n matches = []\n\n for path in search_paths:\n # Sort by number of matching entities. Also store number of\n # common entities, for filtering when strict=True.\n num_ents = [[f] + count_matches(f) for f in folders[path]]\n # Filter out imperfect matches (i.e., where number of common\n # entities does not equal number of matching entities).\n if strict:\n num_ents = [f for f in num_ents if f[1] == f[2]]\n num_ents.sort(key=lambda x: x[2], reverse=True)\n\n if num_ents:\n for f_match in num_ents:\n matches.append(f_match[0])\n\n if not all_:\n break\n\n matches = [match.path if return_type.startswith('file') else match for match in matches]\n return matches if all_ else matches[0] if matches else None\n\n def get_bvec(self, path, **kwargs):\n \"\"\"Get bvec file for passed path.\"\"\"\n result = self.get_nearest(path, extension='.bvec', suffix='dwi', all_=True, **kwargs)\n return listify(result)[0]\n\n def get_bval(self, path, **kwargs):\n \"\"\"Get bval file for passed path.\"\"\"\n result = self.get_nearest(path, suffix='dwi', extension='.bval', all_=True, **kwargs)\n return listify(result)[0]\n\n def get_fieldmap(self, path, return_list=False):\n \"\"\"Get fieldmap(s) for specified path.\"\"\"\n fieldmaps = self._get_fieldmaps(path)\n\n if return_list:\n return fieldmaps\n else:\n if len(fieldmaps) == 1:\n return fieldmaps[0]\n elif len(fieldmaps) > 1:\n raise ValueError(\n 'More than one fieldmap found, but the '\n \"'return_list' argument was set to False. \"\n 'Either ensure that there is only one '\n 'fieldmap for this image, or set the '\n \"'return_list' argument to True and handle \"\n 'the result as a list.'\n )\n else: # len(fieldmaps) == 0\n return None\n\n def _get_fieldmaps(self, path):\n path = str(path)\n sub = self.parse_file_entities(path)['subject']\n fieldmap_set = []\n suffix = '(phase1|phasediff|epi|fieldmap)'\n files = self.get(\n subject=sub, suffix=suffix, regex_search=True, extension=['.nii.gz', '.nii']\n )\n for file in files:\n metadata = self.get_metadata(file.path)\n if metadata and 'IntendedFor' in metadata.keys():\n intended_for = listify(metadata['IntendedFor'])\n # path uses local os separators while _suff read from json likely uses author's os separators, so we # noqa: E501\n # convert _suff to use local separators.\n if any([path.endswith(str(Path(_suff))) for _suff in intended_for]): # noqa: C419\n cur_fieldmap = {}\n if file.entities['suffix'] == 'phasediff':\n cur_fieldmap = {\n 'phasediff': file.path,\n 'magnitude1': file.path.replace('phasediff', 'magnitude1'),\n 'suffix': 'phasediff',\n }\n magnitude2 = file.path.replace('phasediff', 'magnitude2')\n if Path(magnitude2).is_file():\n cur_fieldmap['magnitude2'] = magnitude2\n elif file.entities['suffix'] == 'phase1':\n cur_fieldmap['phase1'] = file.path\n cur_fieldmap['magnitude1'] = file.path.replace('phase1', 'magnitude1')\n cur_fieldmap['phase2'] = file.path.replace('phase1', 'phase2')\n cur_fieldmap['magnitude2'] = file.path.replace('phase1', 'magnitude2')\n cur_fieldmap['suffix'] = 'phase'\n elif file.entities['suffix'] == 'epi':\n cur_fieldmap['epi'] = file.path\n cur_fieldmap['suffix'] = 'epi'\n elif file.entities['suffix'] == 'fieldmap':\n cur_fieldmap['fieldmap'] = file.path\n cur_fieldmap['magnitude'] = file.path.replace('fieldmap', 'magnitude')\n cur_fieldmap['suffix'] = 'fieldmap'\n fieldmap_set.append(cur_fieldmap)\n return fieldmap_set\n\n def get_tr(self, derivatives=False, **filters):\n \"\"\"Return the scanning repetition time (TR) for one or more runs.\n\n Parameters\n ----------\n derivatives : bool\n If True, also checks derivatives images.\n filters : dict\n Optional keywords used to constrain the selected runs.\n Can be any arguments valid for a .get call (e.g., BIDS entities\n or JSON sidecar keys).\n\n Returns\n -------\n float\n A single float.\n\n Notes\n -----\n Raises an exception if more than one unique TR is found.\n\n \"\"\"\n # Constrain search to functional images\n filters.update(suffix='bold', datatype='func')\n scope = 'all' if derivatives else 'raw'\n images = self.get(extension=['.nii', '.nii.gz'], scope=scope, **filters)\n if not images:\n raise NoMatchError('No functional images that match criteria found.')\n\n all_trs = set()\n for img in images:\n md = self.get_metadata(img.path)\n all_trs.add(round(float(md['RepetitionTime']), 5))\n\n if len(all_trs) > 1:\n raise NoMatchError(f'Unique TR cannot be found given filters {filters!r}')\n return all_trs.pop()\n\n def build_path(\n self,\n source,\n path_patterns=None,\n strict=False,\n scope='all',\n validate=True,\n absolute_paths=True,\n ):\n \"\"\"Construct a target filename for a file or dictionary of entities.\n\n Parameters\n ----------\n source : str or :obj:`bids.layout.BIDSFile` or dict\n The source data to use to construct the new file path.\n Must be one of:\n - A BIDSFile object\n - A string giving the path of a BIDSFile contained within the\n current Layout.\n - A dict of entities, with entity names in keys and values in\n values\n path_patterns : list\n Optional path patterns to use to construct\n the new file path. If None, the Layout-defined patterns will\n be used. Entities should be represented by the name\n surrounded by curly braces. Optional portions of the patterns\n should be denoted by square brackets. Entities that require a\n specific value for the pattern to match can pass them inside\n angle brackets. Default values can be assigned by specifying a string\n after the pipe operator. E.g., (e.g., {type|bold} would\n only match the pattern if the entity 'type' was passed and its\n value is \"image\", otherwise the default value \"bold\" will be\n used).\n Example: 'sub-{subject}/[var-{name}/]{id}.csv'\n Result: 'sub-01/var-SES/1045.csv'\n strict : bool, optional\n If True, all entities must be matched inside a\n pattern in order to be a valid match. If False, extra entities\n will be ignored so long as all mandatory entities are found.\n scope : str or list, optional\n The scope of the search space. Indicates which\n BIDSLayouts' path patterns to use. See BIDSLayout docstring\n for valid values. By default, uses all available layouts. If\n two or more values are provided, the order determines the\n precedence of path patterns (i.e., earlier layouts will have\n higher precedence).\n validate : bool, optional\n If True, built path must pass BIDS validator. If\n False, no validation is attempted, and an invalid path may be\n returned (e.g., if an entity value contains a hyphen).\n absolute_paths : bool, optional\n Report either absolute or relative (to the top of the\n dataset) paths.\n\n \"\"\"\n # 'is_file' is a crude check for Path objects\n if isinstance(source, str) or hasattr(source, 'is_file'):\n source = str(source)\n if source not in self.files:\n source = self._root / source\n\n source = self.get_file(source)\n\n if isinstance(source, BIDSFile):\n source = source.entities\n\n if path_patterns is None:\n layouts = self._get_layouts_in_scope(scope)\n path_patterns = []\n seen_configs = set()\n for l in layouts: # noqa: E741\n for c in l.config.values():\n if c in seen_configs:\n continue\n if c.default_path_patterns is not None:\n path_patterns.extend(c.default_path_patterns)\n seen_configs.add(c)\n\n built = build_path(source, path_patterns, strict)\n if built is None:\n raise ValueError(f'Unable to construct build path with source {source}')\n to_check = Path('/').joinpath(built).as_posix()\n\n if validate and not BIDSValidator().is_bids(to_check):\n raise BIDSValidationError(\n f'Built path {built} is not a valid BIDS filename. '\n 'Please make sure all provided entity values are '\n 'spec-compliant.'\n )\n\n if absolute_paths:\n built = self._root / built # type: pathlib.Path\n # convert into a posix path for consistency with `writing.build_path`\n built = built.as_posix() # type: str\n\n return built\n\n def copy_files(\n self,\n files=None,\n path_patterns=None,\n symbolic_links=True,\n root=None,\n conflicts='fail',\n **kwargs,\n ):\n \"\"\"Copy BIDSFile(s) to new locations.\n\n The new locations are defined by each BIDSFile's entities and the\n specified `path_patterns`.\n\n Parameters\n ----------\n files : list\n Optional list of BIDSFile objects to write out. If\n none provided, use files from running a get() query using\n remaining **kwargs.\n path_patterns : str or list\n Write patterns to pass to each file's write_file method.\n symbolic_links : bool\n Whether to copy each file as a symbolic link or a deep copy.\n root : str\n Optional root directory that all patterns are relative\n to. Defaults to dataset root.\n conflicts : str\n Defines the desired action when the output path already exists.\n Must be one of:\n 'fail': raises an exception\n 'skip' does nothing\n 'overwrite': overwrites the existing file\n 'append': adds a suffix to each file copy, starting with 1\n kwargs : dict\n Optional key word arguments to pass into a get() query.\n\n \"\"\"\n root = self.root if root is None else root\n\n _files = self.get(**kwargs)\n if files:\n _files = list(set(files).intersection(_files))\n\n for f in _files:\n f.copy(path_patterns, symbolic_link=symbolic_links, root=root, conflicts=conflicts)\n\n def write_to_file( # noqa: D417\n self,\n entities,\n path_patterns=None,\n contents=None,\n link_to=None,\n copy_from=None,\n content_mode='text',\n conflicts='fail',\n strict=False,\n validate=True,\n ):\n \"\"\"Write data to a file defined by the passed entities and patterns.\n\n Parameters\n ----------\n entities : dict\n A dictionary of entities, with Entity names in\n keys and values for the desired file in values.\n path_patterns : list\n Optional path patterns to use when building\n the filename. If None, the Layout-defined patterns will be\n used.\n contents : object\n Contents to write to the generate file path.\n Can be any object serializable as text or binary data (as\n defined in the content_mode argument).\n link_to : str\n Optional path with which to create a symbolic link\n to. Used as an alternative to and takes priority over the\n contents argument.\n conflicts : str\n Defines the desired action when the output path already exists.\n Must be one of:\n 'fail': raises an exception\n 'skip' does nothing\n 'overwrite': overwrites the existing file\n 'append': adds a suffix to each file copy, starting with 1\n strict : bool\n If True, all entities must be matched inside a\n pattern in order to be a valid match. If False, extra entities\n will be ignored so long as all mandatory entities are found.\n validate : bool\n If True, built path must pass BIDS validator. If\n False, no validation is attempted, and an invalid path may be\n returned (e.g., if an entity value contains a hyphen).\n\n \"\"\"\n path = self.build_path(entities, path_patterns, strict, validate=validate)\n\n if path is None:\n raise ValueError(\n 'Cannot construct any valid filename for '\n 'the passed entities given available path '\n 'patterns.'\n )\n\n write_to_file(\n path,\n contents=contents,\n link_to=link_to,\n copy_from=copy_from,\n content_mode=content_mode,\n conflicts=conflicts,\n root=self.root,\n )\n\n\nclass Query(enum.Enum):\n \"\"\"Enums for use with BIDSLayout.get().\"\"\"\n\n NONE = 1 # Entity must not be present\n REQUIRED = ANY = 2 # Entity must be defined, but with an arbitrary value\n OPTIONAL = 3 # Entity may or may not be defined\n" }, { "path": "src/bids/layout/models.py", "content": "\"\"\"Model classes used in BIDSLayouts.\"\"\"\n\nimport json\nimport os\nimport re\nimport warnings\nfrom collections import UserDict\nfrom copy import deepcopy\nfrom functools import lru_cache\nfrom itertools import chain\nfrom pathlib import Path\n\nfrom bidsschematools import rules\nfrom sqlalchemy import Boolean, Column, ForeignKey, String, Table\nfrom sqlalchemy.ext.associationproxy import association_proxy\nfrom sqlalchemy.orm import backref, object_session, reconstructor, relationship\nfrom sqlalchemy.orm.collections import attribute_mapped_collection\nfrom upath import UPath\n\ntry:\n from sqlalchemy.orm import declarative_base\nexcept ImportError: # sqlalchemy < 1.4\n from sqlalchemy.ext.declarative import declarative_base\n\nfrom ..config import get_option\nfrom ..exceptions import BIDSChildDatasetError\nfrom ..utils import bids_sort, listify\nfrom .utils import BIDSMetadata, PaddedInt\nfrom .writing import build_path, write_to_file\n\nBase = declarative_base()\n\n\nclass LayoutInfo(Base):\n \"\"\"Contains information about a BIDSLayout's initialization parameters.\"\"\"\n\n __tablename__ = 'layout_info'\n\n root = Column(String, primary_key=True)\n absolute_paths = Column(Boolean, default=True) # Removed, but may be in older DBs\n _derivatives = Column(String)\n _config = Column(String)\n\n def __init__(self, **kwargs):\n init_args = self._sanitize_init_args(kwargs)\n raw_cols = ['root']\n json_cols = ['derivatives', 'config']\n all_cols = raw_cols + json_cols\n missing_cols = set(all_cols) - set(init_args.keys())\n if missing_cols:\n raise ValueError(f'Missing mandatory initialization args: {missing_cols}')\n for col in all_cols:\n setattr(self, col, init_args[col])\n if col in json_cols:\n json_data = json.dumps(init_args[col])\n setattr(self, '_' + col, json_data)\n\n @reconstructor\n def _init_on_load(self):\n if self.absolute_paths is False:\n warnings.warn(\n 'PyBIDS database loaded with deprecated `absolute_paths` '\n 'option. Returned paths will be absolute.',\n stacklevel=2,\n )\n for col in ['derivatives', 'config']:\n db_val = getattr(self, '_' + col)\n setattr(self, col, json.loads(db_val))\n\n def _sanitize_init_args(self, kwargs):\n \"\"\"Prepare initialization arguments for serialization\"\"\"\n if 'root' in kwargs:\n kwargs['root'] = str(UPath(kwargs['root']).absolute())\n\n if 'config' in kwargs and isinstance(kwargs['config'], list):\n kwargs['config'] = [\n str(UPath(config).absolute()) if isinstance(config, os.PathLike) else config\n for config in kwargs['config']\n ]\n\n # Get abspaths\n if kwargs.get('derivatives') not in (None, True, False):\n kwargs['derivatives'] = [\n str(UPath(der).absolute()) for der in listify(kwargs['derivatives'])\n ]\n\n if kwargs.pop('absolute_paths', True) is not True:\n warnings.warn(\n 'Deprecated `absolute_paths` option passed. Value must be True.',\n stacklevel=3,\n )\n\n return kwargs\n\n def __repr__(self):\n return f''\n\n\nclass Config(Base):\n \"\"\"Container for BIDS configuration information.\n\n Parameters\n ----------\n name : str\n The name to give the Config (e.g., 'bids').\n entities : list\n A list of dictionaries containing entity configuration\n information.\n default_path_patterns : list\n Optional list of patterns used to build new paths.\n session : :obj:`sqlalchemy.orm.session.Session` or None\n An optional SQLAlchemy session. If passed,\n the session is used to update the database with any newly created\n Entity objects. If None, no database update occurs.\n\n \"\"\"\n\n __tablename__ = 'configs'\n\n name = Column(String, primary_key=True)\n _default_path_patterns = Column(String)\n entities = relationship(\n 'Entity',\n secondary='config_to_entity_map',\n collection_class=attribute_mapped_collection('name'),\n )\n\n def __init__(self, name, entities=None, default_path_patterns=None, session=None):\n self.name = name\n self.default_path_patterns = default_path_patterns\n self._default_path_patterns = json.dumps(default_path_patterns)\n\n if entities:\n for ent in entities:\n if session is not None:\n existing = session.query(Config).filter_by(name=ent['name']).first()\n else:\n existing = None\n ent = existing or Entity(**ent)\n self.entities[ent.name] = ent\n if session is not None:\n session.add_all(list(self.entities.values()))\n session.commit()\n\n @reconstructor\n def _init_on_load(self):\n self.default_path_patterns = json.loads(self._default_path_patterns)\n\n @classmethod\n def load(cls, config, session=None):\n \"\"\"Load a Config instance from the passed configuration data.\n\n Parameters\n ----------\n config : str or dict\n A string or dict containing configuration information.\n Must be one of:\n * A string giving the name of a predefined config file\n (e.g., 'bids' or 'derivatives')\n * A path to a JSON file containing config information\n * A dictionary containing config information\n * 'bids-schema' to load from the official BIDS schema\n * dict with 'schema_version' key to load specific BIDS version\n (e.g., {'schema_version': '1.9.0'})\n session : :obj:`sqlalchemy.orm.session.Session` or None\n An optional SQLAlchemy Session instance.\n If passed, the session is used to check the database for (and\n return) an existing Config with name defined in config['name'].\n\n Returns\n -------\n A Config instance.\n\n \"\"\"\n # Handle schema-based config loading\n if config == 'bids-schema':\n return cls._from_schema(session=session)\n elif isinstance(config, dict) and 'schema_version' in config:\n return cls._from_schema(schema_version=config['schema_version'], session=session)\n\n # Existing JSON/file-based loading\n if isinstance(config, (str, Path, UPath)):\n config_paths = get_option('config_paths')\n if config in config_paths:\n config = config_paths[config]\n if not UPath(config).exists():\n raise ValueError(f'{config} is not a valid path.')\n else:\n with open(config) as f:\n config = json.load(f)\n\n # Return existing Config record if one exists\n if session is not None:\n result = session.query(Config).filter_by(name=config['name']).first()\n if result:\n return result\n\n return Config(session=session, **config)\n\n @classmethod\n def _from_schema(cls, schema_version=None, session=None):\n \"\"\"Load config from BIDS schema.\n\n Parameters\n ----------\n schema_version : str, optional\n Specific BIDS specification version to load (e.g., \"1.9.0\", \"1.8.0\").\n If None, uses the schema version bundled with bidsschematools.\n session : :obj:`sqlalchemy.orm.session.Session` or None\n An optional SQLAlchemy Session instance.\n\n Returns\n -------\n Config\n A Config instance populated with entities and patterns\n extracted from the BIDS schema.\n\n \"\"\"\n from bidsschematools import schema as bidsschema\n from upath import UPath\n\n # Load schema - either default or specific version\n if schema_version is None:\n bids_schema = bidsschema.load_schema()\n else:\n # Load specific schema version from versioned URL\n schema_url = UPath(\n f'https://bids-specification.readthedocs.io/en/v{schema_version}/schema.json'\n )\n try:\n bids_schema = bidsschema.load_schema(schema_url)\n except Exception as e: # noqa: BLE001\n raise ValueError( # noqa: B904\n f'Failed to load BIDS schema version {schema_version}. '\n f'Check that the version exists at {schema_url}. '\n f'Error: {e}'\n )\n\n # Collect ALL patterns from bidsschematools (keep existing collection logic)\n all_patterns = []\n file_sections = {\n 'raw': bids_schema.rules.files.raw,\n 'deriv': bids_schema.rules.files.deriv,\n 'common': bids_schema.rules.files.common,\n }\n\n for section_type, sections in file_sections.items():\n if section_type == 'raw':\n for datatype_name in sections.keys():\n datatype_rules = getattr(sections, datatype_name)\n regex_rules = rules.regexify_filename_rules(\n datatype_rules, bids_schema, level=1\n )\n all_patterns.extend(regex_rules)\n else:\n for subsection_name in sections.keys():\n subsection_rules = getattr(sections, subsection_name)\n regex_rules = rules.regexify_filename_rules(\n subsection_rules, bids_schema, level=1\n )\n all_patterns.extend(regex_rules)\n\n # Store patterns directly - NO PARSING EVER\n config_name = f'bids-schema-{bids_schema.bids_version}'\n\n # Extract entities directly from schema rules\n entity_names = cls._extract_entity_names_from_rules(bids_schema)\n\n # Extract actual values for key entities from schema rules directly\n entity_values = cls._extract_entity_values_from_rules(bids_schema, entity_names)\n\n # Create Entity objects\n entities = cls._create_entities_from_schema(entity_names, entity_values, bids_schema)\n\n config = cls(name=config_name, entities=entities, session=session)\n\n return config\n\n @classmethod\n def _extract_entity_names_from_rules(cls, bids_schema):\n \"\"\"Extract entity names directly from schema rules\"\"\"\n # Get entity names from all rule sections\n entity_names = set()\n file_sections = {\n 'raw': bids_schema.rules.files.raw,\n 'deriv': bids_schema.rules.files.deriv,\n 'common': bids_schema.rules.files.common,\n }\n\n for section_type, sections in file_sections.items(): # noqa: B007\n for section_name in sections.keys():\n section_rules = getattr(sections, section_name)\n for rule_name in section_rules.keys():\n rule = getattr(section_rules, rule_name)\n rule_dict = dict(rule)\n if 'entities' in rule_dict:\n entity_names.update(rule_dict['entities'].keys())\n\n # Add special constructs that appear in patterns but aren't \"entities\"\n entity_names.update(['suffix', 'extension', 'datatype'])\n\n return entity_names\n\n @classmethod\n def _create_entities_from_schema(cls, entity_names, entity_values, bids_schema):\n \"\"\"Create Entity objects directly from schema information.\"\"\"\n entities = []\n\n for entity_name in sorted(entity_names):\n # Get entity info from schema if available\n entity_spec = bids_schema.objects.entities.get(entity_name, {})\n\n # Handle special entities that don't have BIDS prefixes\n special_entities = {'suffix', 'extension', 'datatype'}\n\n if entity_name in special_entities:\n # These entities use explicit value lists from schema rules\n if entity_name in entity_values and entity_values[entity_name]:\n # Filter out problematic values for extension entity\n if entity_name == 'extension':\n # Remove empty string and wildcard patterns\n filtered_values = {\n v\n for v in entity_values[entity_name]\n if v and v not in ('.*', '**', '*')\n }\n values = '|'.join(sorted(filtered_values))\n else:\n values = '|'.join(sorted(entity_values[entity_name]))\n\n if entity_name == 'extension':\n pattern = rf'({values})\\Z' # Extensions are at the end\n elif entity_name == 'datatype':\n pattern = rf'/({values})/' # Datatypes are directory names\n else: # suffix\n pattern = rf'[_/\\\\\\\\]({values})\\.?' # Before extension, with optional dot\n else:\n # Regular BIDS entities - use schema format patterns directly\n bids_prefix = entity_spec.get('name', entity_name)\n format_type = entity_spec.get('format', 'label')\n\n # Get the official value pattern from schema formats\n value_pattern = '[0-9a-zA-Z]+' # Default pattern\n if format_type in bids_schema.objects.formats:\n format_obj = dict(bids_schema.objects.formats[format_type])\n value_pattern = format_obj.get('pattern', value_pattern)\n\n # Determine separator based on entity type\n if entity_name in ['subject', 'session']:\n # Directory-level entities\n pattern = rf'[/\\\\\\\\]+{bids_prefix}-({value_pattern})'\n else:\n # File-level entities\n pattern = rf'[_/\\\\\\\\]+{bids_prefix}-({value_pattern})'\n\n entity_config = {\n 'name': entity_name,\n 'pattern': pattern,\n 'dtype': 'int' if entity_spec.get('format') == 'index' else 'str',\n }\n\n entities.append(entity_config)\n\n return entities\n\n @classmethod\n def _extract_entity_values_from_rules(cls, bids_schema, entity_names):\n \"\"\"Extract entity values directly from schema rules\"\"\"\n entity_values = {name: set() for name in entity_names}\n file_sections = {\n 'raw': bids_schema.rules.files.raw,\n 'deriv': bids_schema.rules.files.deriv,\n 'common': bids_schema.rules.files.common,\n }\n\n for section_type, sections in file_sections.items(): # noqa: B007\n for section_name in sections.keys():\n section_rules = getattr(sections, section_name)\n for rule_name in section_rules.keys():\n rule = getattr(section_rules, rule_name)\n rule_dict = dict(rule)\n\n # Get values for special entities directly from rules\n if 'suffixes' in rule_dict:\n entity_values['suffix'].update(rule_dict['suffixes'])\n if 'extensions' in rule_dict:\n entity_values['extension'].update(rule_dict['extensions'])\n if 'datatypes' in rule_dict:\n entity_values['datatype'].update(rule_dict['datatypes'])\n\n # For regular entities, we don't extract specific values\n # They use generic patterns like [0-9a-zA-Z]+\n\n return entity_values\n\n def __repr__(self):\n return f''\n\n\nclass BIDSFile(Base):\n \"\"\"Represents a single file or directory in a BIDS dataset.\n\n Parameters\n ----------\n filename : str\n The path to the corresponding file.\n\n \"\"\"\n\n __tablename__ = 'files'\n\n path = Column(String, primary_key=True)\n filename = Column(String)\n dirname = Column(String)\n entities = association_proxy('tags', 'value')\n is_dir = Column(Boolean, index=True)\n class_ = Column(String(20))\n\n _associations = relationship(\n 'BIDSFile',\n secondary='associations',\n primaryjoin='FileAssociation.dst == BIDSFile.path',\n secondaryjoin='FileAssociation.src == BIDSFile.path',\n )\n\n __mapper_args__ = {'polymorphic_on': class_, 'polymorphic_identity': 'file'}\n\n def __init__(self, filename):\n self.path = str(filename)\n self.filename = self._path.name\n self.dirname = str(self._path.parent)\n self.is_dir = not self.filename\n\n @property\n def _path(self):\n return UPath(self.path)\n\n @property\n def _dirname(self):\n return UPath(self.dirname)\n\n def __repr__(self):\n return f\"<{self.__class__.__name__} filename='{self.path}'>\"\n\n def __fspath__(self):\n return self.path\n\n @property\n @lru_cache # noqa: B019\n def relpath(self):\n \"\"\"Return path relative to layout root\"\"\"\n root = object_session(self).query(LayoutInfo).first().root\n return str(UPath(self.path).relative_to(root))\n\n def get_associations(self, kind=None, include_parents=False):\n \"\"\"Get associated files, optionally limiting by association kind.\n\n Parameters\n ----------\n kind : str\n The kind of association to return (e.g., \"Child\").\n By default, all associations are returned.\n include_parents : bool\n If True, files related through inheritance\n are included in the returned list. If False, only directly\n associated files are returned. For example, a file's JSON\n sidecar will always be returned, but other JSON files from\n which the sidecar inherits will only be returned if\n include_parents=True.\n\n Returns\n -------\n list\n A list of BIDSFile instances.\n\n \"\"\"\n if kind is None and not include_parents:\n return self._associations\n\n session = object_session(self)\n q = (\n session.query(BIDSFile)\n .join(FileAssociation, BIDSFile.path == FileAssociation.dst)\n .filter_by(src=self.path)\n )\n\n if kind is not None:\n q = q.filter_by(kind=kind)\n\n associations = q.all()\n\n if not include_parents:\n return associations\n\n def collect_associations(results, bidsfile):\n results.append(bidsfile)\n for p in bidsfile.get_associations('Child'):\n results = collect_associations(results, p)\n return results\n\n return list(chain(*[collect_associations([], bf) for bf in associations]))\n\n def get_metadata(self):\n \"\"\"Return all metadata associated with the current file.\"\"\"\n md = BIDSMetadata(self.path)\n md.update(self.get_entities(metadata=True))\n return md\n\n def get_entities(self, metadata=False, values='tags'):\n \"\"\"Return entity information for the current file.\n\n Parameters\n ----------\n metadata : bool or None\n If False (default), only entities defined\n for filenames (and not those found in the JSON sidecar) are\n returned. If True, only entities found in metadata files (and not\n defined for filenames) are returned. If None, all available\n entities are returned.\n values : str\n The kind of object to return in the dict's values.\n Must be one of:\n * 'tags': Returns only the tagged value--e.g., if the key\n is \"subject\", the value might be \"01\".\n * 'objects': Returns the corresponding Entity instance.\n\n Returns\n -------\n dict\n A dict, where keys are entity names and values are Entity\n instances.\n\n \"\"\"\n if metadata is None and values == 'tags':\n return self.entities\n\n session = object_session(self)\n query = session.query(Tag).filter_by(file_path=self.path).join(Entity)\n\n if metadata not in (None, 'all'):\n query = query.filter(Tag.is_metadata == metadata)\n\n results = query.all()\n if values.startswith('obj'):\n return bids_sort({t.entity_name: t.entity for t in results})\n return bids_sort({t.entity_name: t.value for t in results})\n\n def copy(self, path_patterns, symbolic_link=False, root=None, conflicts='fail'):\n \"\"\"Copy the contents of a file to a new location.\n\n Parameters\n ----------\n path_patterns : list\n List of patterns used to construct the new\n filename. See :obj:`build_path` documentation for details.\n symbolic_link : bool\n If True, use a symbolic link to point to the\n existing file. If False, creates a new file.\n root : str\n Optional path to prepend to the constructed filename.\n conflicts : str\n Defines the desired action when the output path already exists.\n Must be one of:\n 'fail': raises an exception\n 'skip' does nothing\n 'overwrite': overwrites the existing file\n 'append': adds a suffix to each file copy, starting with 1\n\n \"\"\"\n new_filename = build_path(self.entities, path_patterns)\n if not new_filename:\n return None\n\n if new_filename[-1] == os.sep:\n new_filename += self.filename\n\n if self._path.is_absolute() or root is None:\n path = self._path\n else:\n path = UPath(root) / self._path\n\n if not path.exists():\n raise ValueError(\"Target filename to copy/symlink (%s) doesn't exist.\" % path) # noqa: UP031\n\n kwargs = dict(path=new_filename, root=root, conflicts=conflicts) # noqa: C408\n if symbolic_link:\n kwargs['link_to'] = path\n else:\n kwargs['copy_from'] = path\n\n write_to_file(**kwargs)\n\n\nclass BIDSDataFile(BIDSFile):\n \"\"\"Represents a single data file in a BIDS dataset.\n\n Derived from `BIDSFile` and provides additional functionality such as\n obtaining pandas DataFrame data representation (via `get_df`).\n \"\"\"\n\n __mapper_args__ = {'polymorphic_identity': 'data_file'}\n\n def get_df(self, include_timing=True, adjust_onset=False, enforce_dtypes=True, **pd_args):\n \"\"\"Return the contents of a tsv file as a pandas DataFrame.\n\n Parameters\n ----------\n include_timing : bool\n If True, adds an \"onset\" column to dense\n timeseries files (e.g., *_physio.tsv.gz).\n adjust_onset : bool\n If True, the onset of each sample in a dense\n timeseries file is shifted to reflect the \"StartTime\" value in\n the JSON sidecar. If False, the first sample starts at 0 secs.\n Ignored if include_timing=False.\n enforce_dtypes : bool\n If True, enforces the data types defined in\n the BIDS spec on core columns (e.g., subject_id and session_id\n must be represented as strings).\n pd_args : dict\n Optional keyword arguments to pass onto pd.read_csv().\n\n Returns\n -------\n :obj:`pandas.DataFrame`\n A pandas DataFrame.\n\n \"\"\"\n import numpy as np\n import pandas as pd\n\n if enforce_dtypes:\n dtype = {'subject_id': str, 'session_id': str, 'participant_id': str}\n else:\n dtype = None\n\n # TODO: memoize this for efficiency. (Note: caching is insufficient,\n # because the dtype enforcement will break if we ignore the value of\n # enforce_dtypes).\n suffix = self.entities['suffix']\n header = None if suffix in {'physio', 'stim'} else 'infer'\n self.data = pd.read_csv(\n self.path, sep='\\t', na_values='n/a', dtype=dtype, header=header, **pd_args\n )\n\n data = self.data.copy()\n\n if self.entities['extension'] == '.tsv.gz':\n md = self.get_metadata()\n # We could potentially include some validation here, but that seems\n # like a job for the BIDS Validator.\n data.columns = md['Columns']\n if include_timing:\n onsets = np.arange(len(data)) / md['SamplingFrequency']\n if adjust_onset:\n onsets += md['StartTime']\n data.insert(0, 'onset', onsets)\n\n return data\n\n\nclass BIDSImageFile(BIDSFile):\n \"\"\"Represents a single neuroimaging data file in a BIDS dataset.\n\n Derived from `BIDSFile` and provides additional functionality such as\n obtaining nibabel's image file representation (via `get_image`).\n \"\"\"\n\n __mapper_args__ = {'polymorphic_identity': 'image_file'}\n\n def get_image(self, **kwargs):\n \"\"\"Return the associated image file (if it exists) as a NiBabel object\n\n Any keyword arguments are passed to ``nibabel.load``.\n \"\"\"\n try:\n import nibabel as nb\n\n return nb.load(self.path, **kwargs)\n except Exception as e:\n raise ValueError(\n f\"'{self.path}' does not appear to be an image format NiBabel can read.\"\n ) from e\n\n\nclass BIDSJSONFile(BIDSFile):\n \"\"\"Represents a single JSON metadata file in a BIDS dataset.\n\n Derived from `BIDSFile` and provides additional functionality for reading\n the contents of JSON files as either dicts or strings.\n \"\"\"\n\n __mapper_args__ = {'polymorphic_identity': 'json_file'}\n\n def get_dict(self):\n \"\"\"Return the contents of the current file as a dictionary.\"\"\"\n d = json.loads(self.get_json())\n if not isinstance(d, dict):\n raise ValueError(\n 'File %s is a json containing %s, not a dict which was expected' # noqa: UP031\n % (self.path, type(d))\n )\n return d\n\n def get_json(self):\n \"\"\"Return the contents of the current file as a JSON string.\"\"\"\n with open(self.path) as f:\n return f.read()\n\n\nclass Entity(Base):\n \"\"\"Represents a single entity defined in the JSON config.\n\n Parameters\n ----------\n name : str\n The name of the entity (e.g., 'subject', 'run', etc.)\n pattern : str\n A regex pattern used to match against file names.\n Must define at least one group, and only the first group is\n kept as the match.\n mandatory : bool\n If True, every File _must_ match this entity.\n directory : str\n Optional pattern defining a directory associated\n with the entity.\n dtype : str\n The optional data type of the Entity values. Must be\n one of 'int', 'float', 'bool', or 'str'. If None, no type\n enforcement will be attempted, which means the dtype of the\n value may be unpredictable.\n\n \"\"\"\n\n __tablename__ = 'entities'\n\n name = Column(String, primary_key=True)\n mandatory = Column(Boolean, default=False)\n pattern = Column(String)\n directory = Column(String, nullable=True)\n _dtype = Column(String, default='str')\n files = association_proxy('tags', 'value')\n\n def __init__(self, name, pattern=None, mandatory=False, directory=None, dtype='str'):\n self.name = name\n self.pattern = pattern\n self.mandatory = mandatory\n self.directory = directory\n\n if not isinstance(dtype, str):\n dtype = dtype.__name__\n self._dtype = dtype\n\n self._init_on_load()\n\n def __repr__(self):\n return f''\n\n @reconstructor\n def _init_on_load(self):\n if self._dtype not in ('str', 'float', 'int', 'bool'):\n raise ValueError(\n f\"Invalid dtype '{self._dtype}'. Must be one of 'int', 'float', 'bool', or 'str'.\"\n )\n if self._dtype == 'int':\n self.dtype = PaddedInt\n else:\n self.dtype = eval(self._dtype) # noqa: S307\n self.regex = re.compile(self.pattern) if self.pattern is not None else None\n\n def __iter__(self):\n yield from self.unique()\n\n def __deepcopy__(self, memo):\n cls = self.__class__\n result = cls.__new__(cls)\n\n # Directly copy the SQLAlchemy connection before any setattr calls,\n # otherwise failures occur sporadically on Python 3.5 when the\n # _sa_instance_state attribute (randomly!) disappears.\n result._sa_instance_state = self._sa_instance_state\n\n memo[id(self)] = result\n\n for k, v in self.__dict__.items():\n if k == '_sa_instance_state':\n continue\n new_val = getattr(self, k) if k == 'regex' else deepcopy(v, memo)\n setattr(result, k, new_val)\n return result\n\n def match_file(self, f):\n \"\"\"Determine whether the passed file matches the Entity.\n\n Parameters\n ----------\n f : BIDSFile\n The BIDSFile instance to match against.\n\n Returns\n -------\n the matched value if a match was found, otherwise None.\n\n \"\"\"\n if self.regex is None:\n return None\n m = self.regex.search(f.path)\n val = m.group(1) if m is not None else None\n\n return self._astype(val)\n\n def unique(self):\n \"\"\"Return all unique values/levels for the current entity.\"\"\"\n return list(set(self.files.values()))\n\n def count(self, files=False):\n \"\"\"Return a count of unique values or files.\n\n Parameters\n ----------\n files : bool\n When True, counts all files mapped to the Entity.\n When False, counts all unique values.\n\n Returns\n -------\n int\n Count of unique values or files.\n\n \"\"\"\n return len(self.files) if files else len(self.unique())\n\n def _astype(self, val):\n if val is not None and self.dtype is not None:\n val = self.dtype(val)\n return val\n\n\ntype_map = {\n 'str': str,\n 'int': PaddedInt,\n 'float': float,\n 'bool': bool,\n 'json': 'json',\n}\n\n\nclass Tag(Base):\n \"\"\"Represents an association between a File and an Entity.\n\n Parameters\n ----------\n file : BIDSFile\n The associated BIDSFile.\n entity : Entity\n The associated Entity.\n value : json-serializable type\n The value to store for this file/entity pair. Must be of type\n str, int, float, bool, or any json-serializable structure.\n dtype : str\n Optional type for the value field. If None, inferred from\n value. If passed, must be one of str, int, float, bool, or json.\n Any other value will be treated as json (and will fail if the\n value can't be serialized to json).\n is_metadata : bool\n Indicates whether or not the Entity is derived\n from JSON sidecars (True) or is a predefined Entity from a\n config (False).\n\n \"\"\"\n\n __tablename__ = 'tags'\n\n file_path = Column(String, ForeignKey('files.path'), primary_key=True)\n entity_name = Column(String, ForeignKey('entities.name'), primary_key=True)\n _value = Column(String, nullable=False)\n _dtype = Column(String, default='str')\n is_metadata = Column(Boolean, default=False)\n\n file = relationship(\n 'BIDSFile',\n backref=backref('tags', collection_class=attribute_mapped_collection('entity_name')),\n )\n entity = relationship(\n 'Entity',\n backref=backref('tags', collection_class=attribute_mapped_collection('file_path')),\n )\n\n def __init__(self, file, entity, value, dtype=None, is_metadata=False):\n data = _create_tag_dict(file, entity, value, dtype, is_metadata)\n\n self.file_path = data['file_path']\n self.entity_name = data['entity_name']\n self._dtype = data['_dtype']\n self._value = data['_value']\n self.is_metadata = data['is_metadata']\n\n self.dtype = type_map[self._dtype]\n if self._dtype != 'json':\n self.value = self.dtype(value)\n else:\n self.value = value\n\n def __repr__(self):\n msg = ''\n return msg.format(self.file_path, self.entity_name, self.value)\n\n @reconstructor\n def _init_on_load(self):\n if self._dtype == 'json':\n self.value = json.loads(self._value)\n self.dtype = 'json'\n elif self._dtype == 'bool':\n self.value = self._value == 'True'\n self.dtype = bool\n else:\n self.dtype = type_map[self._dtype]\n self.value = self.dtype(self._value)\n\n\ndef _create_tag_dict(file, entity, value, dtype=None, is_metadata=False):\n data = {}\n if dtype is None:\n dtype = type(value)\n\n if not isinstance(dtype, str):\n dtype = dtype.__name__\n\n if dtype in ['list', 'dict']:\n _dtype = 'json'\n _value = json.dumps(value)\n else:\n _dtype = dtype\n _value = str(value)\n if _dtype not in ('str', 'float', 'int', 'bool', 'json'):\n raise ValueError(\n f'Passed value has an invalid dtype ({dtype}). Must be one of '\n 'int, float, bool, or str.'\n )\n\n data['is_metadata'] = is_metadata\n data['file_path'] = file.path\n data['entity_name'] = entity.name\n data['_dtype'] = _dtype\n data['_value'] = _value\n\n return data\n\n\nclass FileAssociation(Base): # noqa: D101\n __tablename__ = 'associations'\n\n src = Column(String, ForeignKey('files.path'), primary_key=True)\n dst = Column(String, ForeignKey('files.path'), primary_key=True)\n kind = Column(String, primary_key=True)\n\n\n# Association objects\nconfig_to_entity_map = Table(\n 'config_to_entity_map',\n Base.metadata,\n Column('config', String, ForeignKey('configs.name')),\n Column('entity', String, ForeignKey('entities.name')),\n)\n\n\nclass DerivativeDatasets(UserDict): # noqa: D101\n def __getitem__(self, key):\n try:\n return super().__getitem__(key)\n except KeyError:\n pass\n\n try:\n result = self.get_pipeline(key)\n warnings.warn( # noqa: B028\n 'Directly selecting derivative datasets using '\n 'pipeline name (i.e. dataset.derivatives[] will be '\n 'phased out in an upcoming release. Select instead using the folder '\n 'name of the dataset (i.e. dataset.derivatives[]), or use '\n 'dataset.derivatives.get_pipeline().',\n DeprecationWarning,\n )\n return result\n except KeyError as err:\n raise KeyError(\n f'No datasets found matching {key} either as a pipeline name or as '\n 'a dataset file name.'\n ) from err\n\n def get_pipeline(self, pipeline): # noqa: D102\n matches = {\n (name, dataset)\n for name, dataset in self.data.items()\n if dataset.source_pipeline == pipeline\n }\n if len(matches) > 1:\n datasets = '\\n\\t- '.join(match[0] for match in matches)\n raise BIDSChildDatasetError(\n f'Multiple datasets generated by {pipeline} were found:\\n'\n f'\\t- {datasets}\\n\\n'\n 'Select a specific dataset by using '\n 'dataset.derivatives[].'\n )\n if not matches:\n raise KeyError(f'No match found for {pipeline}')\n return next(iter(matches))[1]\n" }, { "path": "src/bids/layout/tests/__init__.py", "content": "" }, { "path": "src/bids/layout/tests/conftest.py", "content": "import shutil\nfrom os.path import join\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\n\n\n# Fixture uses in the rest of the tests\n@pytest.fixture(scope='session')\ndef layout_7t_trt(tests_dir):\n return BIDSLayout(tests_dir / 'data' / '7t_trt')\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005(tests_dir):\n return BIDSLayout(tests_dir / 'data' / 'ds005')\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005_no_validate(tests_dir):\n return BIDSLayout(tests_dir / 'data' / 'ds005', validate=False)\n\n\n@pytest.fixture(scope='session')\ndef layout_ds117(tests_dir):\n return BIDSLayout(tests_dir / 'data' / 'ds000117')\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005_derivs(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n layout = BIDSLayout(data_dir)\n deriv_dir = join(data_dir, 'derivatives', 'events')\n layout.add_derivatives(deriv_dir)\n return layout\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005_deriv_dummy_vxxx(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n layout = BIDSLayout(data_dir)\n deriv_dir = tests_dir / 'data' / 'ds005_derivs' / 'dummy-vx.x.x'\n layout.add_derivatives(deriv_dir)\n return layout\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005_deriv_both_dummies(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n layout = BIDSLayout(data_dir)\n deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy-vx.x.x'\n deriv_dir2 = tests_dir / 'data' / 'ds005_derivs' / 'dummy'\n layout.add_derivatives([deriv_dir1, deriv_dir2])\n return layout\n\n\n@pytest.fixture(scope='session')\ndef db_dir(tmpdir_factory):\n fn = tmpdir_factory.mktemp('data')\n return fn\n\n\n@pytest.fixture(scope='session', params=[None, 'bidsdb', 'bidsdb'])\ndef layout_ds005_multi_derivs(tests_dir, request, db_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n database_path = str(db_dir / request.param) if request.param else None\n\n layout = BIDSLayout(data_dir, database_path=database_path)\n deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy'\n deriv_dir2 = join(data_dir, 'derivatives', 'events')\n layout.add_derivatives([deriv_dir1, deriv_dir2])\n return layout\n\n\n@pytest.fixture(scope='session')\ndef layout_ds005_deriv_name_collision(tests_dir, request):\n data_dir = tests_dir / 'data' / 'ds005'\n\n layout = BIDSLayout(data_dir)\n deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy'\n deriv_dir2 = join(data_dir, 'derivatives', 'events')\n layout.add_derivatives([deriv_dir1, deriv_dir2])\n return layout\n\n\n@pytest.fixture(scope='session', params=[None, 'bidsdb-synth', 'bidsdb-synth'])\ndef layout_synthetic(tests_dir, request, db_dir):\n path = tests_dir / 'data' / 'synthetic'\n database_path = str(db_dir / request.param) if request.param else None\n return BIDSLayout(path, derivatives=True, database_path=database_path)\n\n\n@pytest.fixture(scope='session')\ndef layout_synthetic_nodb(tests_dir, request, db_dir):\n path = tests_dir / 'data' / 'synthetic'\n return BIDSLayout(path, derivatives=True)\n\n\n@pytest.fixture\ndef temporary_dataset(tmp_path, tests_dir):\n path = tests_dir / 'data' / 'ds005'\n shutil.copytree(path, tmp_path / 'ds005')\n return tmp_path / 'ds005'\n" }, { "path": "src/bids/layout/tests/data/sample_validation_config.json", "content": "{\n \"name\": \"user_sequences\",\n \"sequences\": [\n {\n \"modality\": \"func\",\n \"session\": \"wave1\",\n \"type\": \"bold\",\n \"task\": \"sst\",\n \"runs\": 2\n },\n {\n \"modality\": \"func\",\n \"session\": \"wave1\",\n \"type\": \"bold\",\n \"task\": \"gng\",\n \"runs\": 2\n },\n {\n \"modality\": \"func\",\n \"session\": \"wave1\",\n \"type\": \"bold\",\n \"task\": \"react\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave1\",\n \"type\": \"phasediff\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave1\",\n \"type\": \"magnitude1\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave1\",\n \"type\": \"magnitude2\",\n \"runs\": 2\n },\n {\n \"modality\": \"anat\",\n \"session\": \"wave1\",\n \"type\": \"T1w\",\n \"runs\": 1\n },\n {\n \"modality\": \"func\",\n \"session\": \"wave2\",\n \"type\": \"bold\",\n \"task\": \"sst\",\n \"runs\": 2\n },\n {\n \"modality\": \"func\",\n \"session\": \"wave2\",\n \"type\": \"bold\",\n \"task\": \"gng\",\n \"runs\": 2\n },\n {\n \"modality\": \"func\",\n \"session\": \"wave2\",\n \"type\": \"bold\",\n \"task\": \"react\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave2\",\n \"type\": \"phasediff\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave2\",\n \"type\": \"magnitude1\",\n \"runs\": 2\n },\n {\n \"modality\": \"fmap\",\n \"session\": \"wave2\",\n \"type\": \"magnitude2\",\n \"runs\": 2\n },\n {\n \"modality\": \"anat\",\n \"session\": \"wave2\",\n \"type\": \"T1w\",\n \"runs\": 1\n }\n ]\n}\n" }, { "path": "src/bids/layout/tests/test_db.py", "content": "\"\"\"Test functionality in the db module--mostly related to connection\nmanagement.\"\"\"\n\nfrom bids.layout.db import get_database_file\n\n\ndef test_get_database_file(tmp_path):\n assert get_database_file(None) is None\n new_path = tmp_path / \"a_new_subdir\"\n assert not new_path.exists()\n db_file = get_database_file(new_path)\n assert db_file == new_path / 'layout_index.sqlite'\n assert new_path.exists()\n" }, { "path": "src/bids/layout/tests/test_layout.py", "content": "\"\"\"Tests of functionality in the layout module--mostly related to the\nBIDSLayout class.\"\"\"\n\nimport json\nimport os\nimport re\nimport shutil\nfrom os.path import abspath, basename, join\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom bids.exceptions import (\n BIDSChildDatasetError,\n BIDSDerivativesValidationError,\n BIDSValidationError,\n NoMatchError,\n TargetError,\n)\nfrom bids.layout import BIDSLayout, Query\nfrom bids.layout.index import BIDSLayoutIndexer, _check_path_matches_patterns, _regexfy\nfrom bids.layout.models import Config\nfrom bids.layout.utils import PaddedInt\nfrom bids.utils import bids_sort, natural_sort\n\n\ndef test_layout_init(layout_7t_trt):\n assert isinstance(layout_7t_trt.files, dict)\n\n\n@pytest.mark.parametrize(\n 'index_metadata,filters,result',\n [\n (True, {}, 3.0),\n (False, {}, None),\n (True, {}, 3.0),\n (True, {'task': 'rest'}, 3.0),\n (True, {'task': 'rest', 'extension': ['.nii.gz']}, 3.0),\n (True, {'task': 'rest', 'extension': '.nii.gz'}, 3.0),\n (True, {'task': 'rest', 'extension': ['.nii.gz', '.json'], 'return_type': 'file'}, 3.0),\n ],\n)\ndef test_index_metadata(tests_dir, index_metadata, filters, result, mock_config):\n data_dir = tests_dir / 'data' / '7t_trt'\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(index_metadata=index_metadata, **filters),\n )\n\n sample_file = layout.get(task='rest', extension='.nii.gz', acquisition='fullbrain')\n assert bool(sample_file)\n metadata = sample_file[0].get_metadata()\n assert metadata.get('RepetitionTime') == result\n\n\n@pytest.mark.parametrize('config_type', [str, Path])\ndef test_config_filename(tests_dir, config_type):\n # Use custom config that replaces session with oligarchy\n config_path = tests_dir / 'data' / 'bids_specs_with_oligarchy.json'\n layout = BIDSLayout(tests_dir / 'data' / '7t_trt', config=config_type(config_path))\n # Validate that we are using the desired configuration\n assert 'oligarchy' in layout.get_entities()\n\n\ndef test_layout_repr(layout_7t_trt):\n assert 'Subjects: 10 | Sessions: 20 | Runs: 20' in str(layout_7t_trt)\n\n\ndef test_invalid_dataset_description(tests_dir, tmp_path):\n shutil.copytree(tests_dir / 'data' / '7t_trt', tmp_path / '7t_dset')\n (tmp_path / '7t_dset' / 'dataset_description.json').write_text('I am not a valid json file')\n with pytest.raises(BIDSValidationError) as exc:\n BIDSLayout(tmp_path / '7t_dset')\n\n assert 'is not a valid json file' in str(exc.value)\n\n\ndef test_layout_repr_overshadow_run(tests_dir, tmp_path):\n \"\"\"A test creating a layout to replicate #681.\"\"\"\n shutil.copytree(tests_dir / 'data' / '7t_trt', tmp_path / '7t_trt')\n (tmp_path / '7t_trt' / 'sub-01' / 'ses-1' / 'sub-01_ses-1_scans.json').write_text(\n json.dumps({'run': {'Description': 'metadata to cause #681'}})\n )\n assert 'Subjects: 10 | Sessions: 20 | Runs: 20' in str(BIDSLayout(tmp_path / '7t_trt'))\n\n\n# def test_layout_copy(layout_7t_trt):\n# # Largely a smoke test to guarantee that copy() does not blow\n# # see https://github.com/bids-standard/pybids/pull/400#issuecomment-467961124\n# import copy\n# l = layout_7t_trt\n\n# lcopy = copy.copy(l)\n# assert repr(lcopy) == repr(l)\n# assert str(lcopy) == str(l)\n\n# lcopy = copy.deepcopy(l)\n# assert repr(lcopy) == repr(l)\n# assert str(lcopy) == str(l)\n\n\ndef test_load_description(layout_7t_trt):\n # Should not raise an error\n assert hasattr(layout_7t_trt, 'description')\n assert layout_7t_trt.description['Name'] == '7t_trt'\n assert layout_7t_trt.description['BIDSVersion'] == '1.0.0rc3'\n\n\ndef test_get_file(layout_ds005_derivs):\n layout = layout_ds005_derivs\n\n # relative path in BIDS-Raw\n orig_file = 'sub-13/func/sub-13_task-mixedgamblestask_run-01_bold.nii.gz'\n target = os.path.join(*orig_file.split('/'))\n assert layout.get_file(target)\n assert layout.get_file(target, scope='raw')\n assert not layout.get_file(target, scope='derivatives')\n\n # absolute path in BIDS-Raw\n target = (layout.root + '/' + orig_file).split('/')\n target = os.path.sep + os.path.join(*target)\n assert layout.get_file(target)\n assert layout.get_file(target, scope='raw')\n assert not layout.get_file(target, scope='derivatives')\n\n # relative path in derivatives pipeline\n orig_file = (\n 'derivatives/events/sub-01/func/sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv'\n )\n target = os.path.join(*orig_file.split('/'))\n assert layout.get_file(target)\n assert not layout.get_file(target, scope='raw')\n assert layout.get_file(target, scope='derivatives')\n\n # absolute path in derivatives pipeline\n target = (layout.root + '/' + orig_file).split('/')\n target = os.path.sep + os.path.join(*target)\n assert layout.get_file(target)\n assert not layout.get_file(target, scope='raw')\n assert layout.get_file(target, scope='derivatives')\n assert layout.get_file(target, scope='events')\n\n # No such file\n assert not layout.get_file('bleargh')\n assert not layout.get_file('/absolute/bleargh')\n\n\nclass TestDerivativeAsRoot:\n def test_dataset_without_datasettype_parsed_as_raw(self, tests_dir):\n ds_root = tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_dataset_type'\n unvalidated = BIDSLayout(ds_root, validate=False)\n assert len(unvalidated.get()) == 4\n with pytest.raises(ValueError):\n unvalidated.get(desc='preproc')\n\n validated = BIDSLayout(ds_root)\n assert len(validated.get()) == 1\n\n def test_derivative_indexing_forced_with_is_derivative(self, tests_dir):\n unvalidated = BIDSLayout(\n tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_type_or_description',\n is_derivative=True,\n validate=False,\n )\n assert len(unvalidated.get()) == 4\n assert len(unvalidated.get(desc='preproc')) == 3\n\n def test_forced_derivative_indexing_fails_validation(self, tests_dir):\n with pytest.raises(BIDSDerivativesValidationError):\n BIDSLayout(\n tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_type_or_description',\n is_derivative=True,\n validate=True,\n )\n\n def test_dataset_missing_generatedby_fails_validation(self, tests_dir):\n with pytest.raises(BIDSDerivativesValidationError):\n BIDSLayout(\n tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_pipeline_description'\n )\n\n def test_correctly_formatted_derivative_loads_as_derivative(self, tests_dir):\n layout = BIDSLayout(tests_dir / 'data' / 'ds005_derivs' / 'dummy', validate=False)\n assert len(layout.get()) == 4\n assert len(layout.get(desc='preproc')) == 3\n\n @pytest.mark.parametrize(\n 'dataset_path',\n [\n Path('ds005_derivs', 'dummy'),\n Path('ds005_derivs', 'format_errs', 'no_pipeline_description'),\n ],\n )\n def test_derivative_datasets_load_with_no_validation(self, tests_dir, dataset_path):\n layout = BIDSLayout(tests_dir / 'data' / dataset_path, validate=False)\n assert len(layout.get()) == 4\n assert len(layout.get(desc='preproc')) == 3\n\n\ndef test_get_metadata(layout_7t_trt):\n target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target))\n assert result['RepetitionTime'] == 3.0\n\n\ndef test_get_metadata2(layout_7t_trt):\n target = 'sub-03/ses-1/fmap/sub-03_ses-1_run-1_phasediff.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target))\n assert result['EchoTime1'] == 0.006\n\n\ndef test_get_metadata3(layout_7t_trt):\n target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target))\n assert result['EchoTime'] == 0.020\n\n target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target))\n assert result['EchoTime'] == 0.017\n\n\ndef test_get_metadata4(layout_ds005):\n target = 'sub-03/anat/sub-03_T1w.nii.gz'\n target = target.split('/')\n result = layout_ds005.get_metadata(join(layout_ds005.root, *target))\n assert result == {}\n\n\ndef test_get_metadata_error2(layout_7t_trt):\n \"\"\"Ensure that False boolean fields are retained as False.\"\"\"\n target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n target = target.split('/')\n # Check get_metadata from the BIDSLayout\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target))\n assert result['MTState'] is False\n\n # Check get_metadata from the BIDSFile\n files = layout_7t_trt.get(\n task='rest', acquisition='fullbrain', suffix='bold', extension='.nii.gz'\n )\n result = files[0].get_metadata()\n assert result['MTState'] is False\n\n\ndef test_get_metadata_meg(layout_ds117):\n funcs = [\n 'get_subjects',\n 'get_sessions',\n 'get_tasks',\n 'get_runs',\n 'get_acquisitions',\n 'get_procs',\n ]\n assert all([hasattr(layout_ds117, f) for f in funcs])\n procs = layout_ds117.get_procs()\n assert procs == ['sss']\n target = 'sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif'\n target = target.split('/')\n result = layout_ds117.get_metadata(join(layout_ds117.root, *target))\n metadata_keys = ['MEGChannelCount', 'SoftwareFilters', 'SubjectArtefactDescription']\n assert all([k in result for k in metadata_keys])\n\n\ndef test_get_metadata5(layout_7t_trt):\n target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target), include_entities=True)\n assert result['EchoTime'] == 0.020\n assert result['subject'] == '01'\n assert result['acquisition'] == 'fullbrain'\n\n\ndef test_get_metadata_via_bidsfile(layout_7t_trt):\n \"\"\"Same as test_get_metadata5, but called through BIDSFile.\"\"\"\n target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz'\n target = target.split('/')\n path = join(layout_7t_trt.root, *target)\n result = layout_7t_trt.files[path].get_metadata()\n assert result['EchoTime'] == 0.020\n # include_entities is False when called through a BIDSFile\n assert 'subject' not in result\n\n\ndef test_metadata_equivalence(layout_7t_trt):\n \"\"\"Ensure that JSON metadata is not corrupted by the layout.\"\"\"\n bold = layout_7t_trt.get(\n subject='02', acquisition='fullbrain', suffix='bold', extension='.nii.gz'\n )[0]\n root_metadata_file = layout_7t_trt.get(\n subject=None, acquisition='fullbrain', suffix='bold', extension='.json'\n )[0]\n\n assert bold.get_metadata() == root_metadata_file.get_dict()\n\n physio = layout_7t_trt.get(\n subject='02', acquisition='fullbrain', suffix='physio', extension='.tsv.gz'\n )[0]\n root_metadata_file = layout_7t_trt.get(\n subject=None, acquisition='fullbrain', suffix='physio', extension='.json'\n )[0]\n\n assert physio.get_metadata() == root_metadata_file.get_dict()\n\n # Verify that we cover all common metadata types\n types = {type(val) for val in bold.get_metadata().values()}\n assert types == {str, float, bool, list}\n assert isinstance(physio.get_metadata()['StartTime'], int)\n\n\ndef test_get_metadata_error(layout_7t_trt):\n \"\"\"Same as test_get_metadata5, but called through BIDSFile.\"\"\"\n target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz'\n target = target.split('/')\n path = join(layout_7t_trt.root, *target)\n result = layout_7t_trt.files[path].get_metadata()\n with pytest.raises(KeyError) as err:\n result['Missing']\n assert f\"Metadata term 'Missing' unavailable for file {path}\" in str(err.value)\n\n result = layout_7t_trt.get_metadata(path)\n with pytest.raises(KeyError) as err:\n result['Missing']\n assert f\"Metadata term 'Missing' unavailable for file {path}\" in str(err.value)\n\n\ndef test_get_with_bad_target(layout_7t_trt):\n with pytest.raises(TargetError) as exc:\n layout_7t_trt.get(target='unicorn')\n msg = str(exc.value)\n assert 'subject' in msg and 'reconstruction' in msg and 'proc' in msg\n with pytest.raises(TargetError) as exc:\n layout_7t_trt.get(target='sub')\n msg = str(exc.value)\n assert 'subject' in msg and 'reconstruction' not in msg\n\n\ndef test_get_bvals_bvecs(layout_ds005):\n dwifile = layout_ds005.get(subject='01', datatype='dwi')[0]\n result = layout_ds005.get_bval(dwifile.path)\n assert result == abspath(join(layout_ds005.root, 'dwi.bval'))\n\n result = layout_ds005.get_bvec(dwifile.path)\n assert result == abspath(join(layout_ds005.root, 'dwi.bvec'))\n\n\ndef test_get_subjects(layout_7t_trt):\n result = layout_7t_trt.get_subjects()\n predicted = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']\n assert set(predicted) == set(result)\n\n\ndef test_get_fieldmap(layout_7t_trt):\n target = 'sub-03/ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_fieldmap(join(layout_7t_trt.root, *target))\n assert result['suffix'] == 'phasediff'\n assert result['phasediff'].endswith('sub-03_ses-1_run-1_phasediff.nii.gz')\n\n\ndef test_get_fieldmap2(layout_7t_trt):\n target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n target = target.split('/')\n result = layout_7t_trt.get_fieldmap(join(layout_7t_trt.root, *target))\n assert result['suffix'] == 'phasediff'\n assert result['phasediff'].endswith('sub-03_ses-2_run-2_phasediff.nii.gz')\n\n\ndef test_bids_json(layout_7t_trt):\n res = layout_7t_trt.get(return_type='id', target='run')\n assert set(res) == {1, 2}\n res = layout_7t_trt.get(return_type='id', target='session')\n assert set(res) == {'1', '2'}\n\n\ndef test_get_return_type_dir(layout_7t_trt):\n res = layout_7t_trt.get(target='subject', return_type='dir')\n\n # returned directories should be in sorted order so we can match exactly\n target = [os.path.join(layout_7t_trt.root, f'sub-{i:02d}') for i in range(1, 11)]\n\n assert target == res\n\n\n@pytest.mark.parametrize('acq', [None, Query.NONE])\ndef test_get_val_none(layout_7t_trt, acq):\n t1w_files = layout_7t_trt.get(subject='01', session='1', suffix='T1w')\n assert len(t1w_files) == 1\n assert 'acq' not in t1w_files[0].path\n t1w_files = layout_7t_trt.get(subject='01', session='1', suffix='T1w', acquisition=acq)\n assert len(t1w_files) == 1\n bold_files = layout_7t_trt.get(subject='01', session='1', suffix='bold', acquisition=acq)\n assert len(bold_files) == 0\n\n\ndef test_get_val_enum_any(layout_7t_trt):\n t1w_files = layout_7t_trt.get(\n subject='01', session='1', suffix='T1w', acquisition=Query.ANY, extension=Query.ANY\n )\n assert not t1w_files\n bold_files = layout_7t_trt.get(\n subject='01', session='1', run=1, suffix='bold', acquisition=Query.ANY\n )\n assert len(bold_files) == 2\n\n\ndef test_get_val_enum_any_optional(layout_7t_trt, layout_ds005):\n # layout with sessions\n query = {\n 'subject': '01',\n 'run': 1,\n 'suffix': 'bold',\n }\n bold_files = layout_7t_trt.get(session=Query.OPTIONAL, **query)\n assert len(bold_files) == 3\n\n # layout without sessions\n bold_files = layout_ds005.get(session=Query.REQUIRED, **query)\n assert not bold_files\n bold_files = layout_ds005.get(session=Query.OPTIONAL, **query)\n assert len(bold_files) == 1\n\n\ndef test_get_return_sorted(layout_7t_trt):\n bids_files = layout_7t_trt.get(target='subject')\n paths = [r.path for r in bids_files]\n assert natural_sort(paths) == paths\n\n files = layout_7t_trt.get(target='subject', return_type='file')\n assert files == paths\n\n\ndef test_ignore_files(tests_dir, layout_ds005):\n data_dir = tests_dir / 'data' / 'ds005'\n target1 = join(data_dir, 'models', 'ds-005_type-test_model.json')\n target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json')\n layout1 = BIDSLayout(data_dir, validate=False)\n assert target1 not in layout_ds005.files\n assert target1 not in layout1.files\n assert target2 not in layout1.files\n # now the models/ dir should show up, because passing ignore explicitly\n # overrides the default - but 'model/extras/' should still be ignored\n # because of the regex.\n ignore = [re.compile('xtra'), 'dummy']\n indexer = BIDSLayoutIndexer(validate=False, ignore=ignore)\n layout2 = BIDSLayout(data_dir, indexer=indexer)\n assert target1 in layout2.files\n assert target2 not in layout2.files\n\n\ndef test_force_index(tests_dir, layout_ds005):\n data_dir = tests_dir / 'data' / 'ds005'\n target = join(data_dir, 'models', 'ds-005_type-test_model.json')\n indexer = BIDSLayoutIndexer(force_index=[re.compile('models(/.*)?')])\n model_layout = BIDSLayout(data_dir, validate=True, indexer=indexer)\n assert target not in layout_ds005.files\n assert target in model_layout.files\n assert 'all' not in model_layout.get_subjects()\n for f in model_layout.files.values():\n assert 'derivatives' not in f.path\n\n\ndef test_nested_include_exclude(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n target1 = join(data_dir, 'models', 'ds-005_type-test_model.json')\n target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json')\n\n # Excluding a directory will disallow indexing further, even if forced\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=[os.path.join('models', 'extras')],\n ignore=['models'],\n ),\n )\n assert not layout.get_file(target1)\n assert not layout.get_file(target2)\n\n # To nest patterns, we must allow searching within\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=[os.path.join('models', 'extras')],\n ignore=[re.compile('^/models/.+$')],\n ),\n )\n assert not layout.get_file(target1)\n assert layout.get_file(target2)\n\n # Including a directory will disallow marking ignore subdirectories within\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=['models'],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert layout.get_file(target2)\n\n # To nest a directory exclusion within an inclusion, use regex\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=[re.compile('^/models/?$')],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert not layout.get_file(target2)\n\n # Force file inclusion despite directory-level exclusion\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=['models', target2],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert layout.get_file(target2)\n\n # To nest patterns, we must allow searching within\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n force_index=[os.path.join('models', 'extras')],\n ignore=[re.compile('^/models/.+$')],\n ),\n )\n assert not layout.get_file(target1)\n assert layout.get_file(target2)\n\n # With the validator, these paths will not be valid anyways\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=True,\n force_index=['models'],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert layout.get_file(target2)\n\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=True,\n force_index=[re.compile('^/models/?$')],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert not layout.get_file(target2)\n\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=True,\n force_index=['models', target2],\n ignore=[os.path.join('models', 'extras')],\n ),\n )\n assert layout.get_file(target1)\n assert layout.get_file(target2)\n\n\ndef test_nested_include_exclude_with_regex(tests_dir):\n # ~same as above test, but use regexps instead of strings\n patt1 = re.compile(r'.*dels/?$')\n patt2 = re.compile(r'.*xtra.*')\n data_dir = tests_dir / 'data' / 'ds005'\n target1 = join(data_dir, 'models', 'ds-005_type-test_model.json')\n target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json')\n\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n ignore=[patt2],\n force_index=[patt1],\n ),\n )\n assert layout.get_file(target1)\n assert not layout.get_file(target2)\n\n # If ignore matches a folder, it won't be indexed\n patt1 = re.compile(r'.*dels/.+$')\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=False,\n ignore=[patt1],\n force_index=[patt2],\n ),\n )\n assert not layout.get_file(target1)\n assert layout.get_file(target2)\n\n # With valid_only no path should be indexed under models/\n patt1 = re.compile(r'.*dels/ds-005.*')\n patt2 = re.compile(r'.*xtra.*')\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=True,\n ignore=[patt2],\n force_index=[patt1],\n ),\n )\n assert layout.get_file(target1)\n assert not layout.get_file(target2)\n\n # If ignore matches a folder, it won't be indexed\n patt1 = re.compile(r'.*dels/.+$')\n layout = BIDSLayout(\n data_dir,\n indexer=BIDSLayoutIndexer(\n validate=True,\n ignore=[patt1],\n force_index=[patt2],\n ),\n )\n assert not layout.get_file(target1)\n assert layout.get_file(target2)\n\n\ndef test_layout_with_derivs(tests_dir, layout_ds005_derivs):\n assert layout_ds005_derivs.root == str(tests_dir / 'data' / 'ds005')\n assert isinstance(layout_ds005_derivs.files, dict)\n assert len(layout_ds005_derivs.derivatives) == 1\n deriv = layout_ds005_derivs.derivatives['events']\n assert deriv.files\n assert len(deriv.files) == 2\n event_file = 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv'\n deriv_files = [basename(f) for f in list(deriv.files.keys())]\n assert event_file in deriv_files\n assert 'roi' in deriv.entities\n assert 'subject' in deriv.entities\n\n\ndef test_accessing_deriv_by_pipeline_name_is_deprecated(layout_ds005_deriv_dummy_vxxx):\n with pytest.deprecated_call():\n deriv = layout_ds005_deriv_dummy_vxxx.derivatives['dummy']\n assert deriv.files\n assert len(deriv.files) == 4\n\n\ndef test_cant_access_nonexistant_deriv_by_key(layout_ds005_deriv_dummy_vxxx):\n with pytest.raises(KeyError):\n layout_ds005_deriv_dummy_vxxx.derivatives['foo']\n\n\ndef test_accessing_deriv_by_pipeline_name_via_method(layout_ds005_deriv_dummy_vxxx):\n deriv = layout_ds005_deriv_dummy_vxxx.derivatives.get_pipeline('dummy')\n assert deriv.files\n assert len(deriv.files) == 4\n\n\ndef test_cant_get_nonexistant_deriv_via_method(layout_ds005_deriv_dummy_vxxx):\n with pytest.raises(KeyError):\n layout_ds005_deriv_dummy_vxxx.derivatives.get_pipeline('foo')\n\n\ndef test_cant_get_deriv_with_duplicate_pipeline_via_method(layout_ds005_deriv_both_dummies):\n with pytest.raises(BIDSChildDatasetError):\n layout_ds005_deriv_both_dummies.derivatives.get_pipeline('dummy')\n\n\ndef test_layout_with_multi_derivs(tests_dir, layout_ds005_multi_derivs):\n assert layout_ds005_multi_derivs.root == str(tests_dir / 'data' / 'ds005')\n assert isinstance(layout_ds005_multi_derivs.files, dict)\n assert len(layout_ds005_multi_derivs.derivatives) == 2\n deriv = layout_ds005_multi_derivs.derivatives['events']\n assert deriv.files\n assert len(deriv.files) == 2\n deriv = layout_ds005_multi_derivs.derivatives['dummy']\n assert deriv.files\n assert len(deriv.files) == 4\n assert 'roi' in deriv.entities\n assert 'subject' in deriv.entities\n preproc = layout_ds005_multi_derivs.get(desc='preproc')\n assert len(preproc) == 3\n\n\ndef test_layout_with_conflicting_deriv_folders(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n layout = BIDSLayout(data_dir)\n deriv_dir = tests_dir / 'data' / 'ds005_derivs' / 'dummy'\n with pytest.raises(BIDSDerivativesValidationError):\n layout.add_derivatives([deriv_dir, deriv_dir])\n\n\ndef test_query_derivatives(layout_ds005_derivs):\n result = layout_ds005_derivs.get(suffix='events', return_type='object', extension='.tsv')\n result = [f.filename for f in result]\n assert len(result) == 49\n assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' in result\n result = layout_ds005_derivs.get(\n suffix='events', return_type='object', scope='raw', extension='.tsv'\n )\n assert len(result) == 48\n result = [f.filename for f in result]\n assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' not in result\n result = layout_ds005_derivs.get(\n suffix='events', return_type='object', desc='extra', extension='.tsv'\n )\n assert len(result) == 1\n result = [f.filename for f in result]\n assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' in result\n\n\ndef test_restricted_words_in_path(tests_dir, tmpdir):\n orig_path = tests_dir / 'data' / 'synthetic'\n parent_dir = str(tmpdir / 'derivatives' / 'pipeline')\n os.makedirs(parent_dir)\n new_path = join(parent_dir, 'sourcedata')\n os.symlink(orig_path, new_path)\n orig_layout = BIDSLayout(orig_path)\n new_layout = BIDSLayout(new_path)\n\n orig_files = set(f.replace(str(orig_path), '') for f in orig_layout.files)\n new_files = set(f.replace(new_path, '') for f in new_layout.files)\n assert orig_files == new_files\n\n\ndef test_derivative_getters(tests_dir):\n synth_path = tests_dir / 'data' / 'synthetic'\n bare_layout = BIDSLayout(synth_path, derivatives=False)\n full_layout = BIDSLayout(synth_path, derivatives=True)\n assert bare_layout.get_spaces() == []\n assert set(full_layout.get_spaces()) == {'MNI152NLin2009cAsym', 'T1w'}\n\n\ndef test_get_tr(layout_7t_trt):\n # Bad subject, should fail\n with pytest.raises(NoMatchError) as exc:\n layout_7t_trt.get_tr(subject='zzz')\n assert str(exc.value).startswith('No functional images')\n # There are multiple tasks with different TRs, so this should fail\n with pytest.raises(NoMatchError) as exc:\n layout_7t_trt.get_tr(subject=['01', '02'])\n assert str(exc.value).startswith('Unique TR')\n # This should work\n tr = layout_7t_trt.get_tr(subject=['01', '02'], acquisition='fullbrain')\n assert tr == 3.0\n tr = layout_7t_trt.get_tr(subject=['01', '02'], acquisition='prefrontal')\n assert tr == 4.0\n\n tr = layout_7t_trt.get_RepetitionTime()\n assert sum([t in tr for t in [3.0, 4.0]]) == 2\n\n\ndef test_get_nonhashable_metadata(layout_ds117):\n \"\"\"Test nonhashable metadata values (#683).\"\"\"\n assert layout_ds117.get_IntendedFor(subject=['01'])[0] == (\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-03_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-04_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-06_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-07_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_bold.nii.gz',\n 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-09_bold.nii.gz',\n )\n\n landmarks = layout_ds117.get_AnatomicalLandmarkCoordinates(subject=['01'])[0]\n assert landmarks['Nasion'] == (43, 111, 95)\n assert landmarks['LPA'] == (140, 74, 16)\n assert landmarks['RPA'] == (143, 74, 173)\n\n\ndef test_to_df(layout_ds117):\n # Only filename entities\n df = layout_ds117.to_df()\n n_files = len(layout_ds117.files)\n assert len(df) == n_files\n target = {\n 'datatype',\n 'fmap',\n 'run',\n 'path',\n 'acquisition',\n 'scans',\n 'echo',\n 'session',\n 'subject',\n 'suffix',\n 'task',\n 'proc',\n 'extension',\n }\n assert set(df.columns) == target\n assert set(df['subject'].dropna().unique()) == {'01', '02', 'emptyroom'}\n\n # Include metadata entities\n df = layout_ds117.to_df(metadata=True)\n assert df.shape == (n_files, len(target) + 44)\n assert not ({'InstitutionAddress', 'TriggerChannelCount', 'EchoTime'} - set(df.columns))\n\n\n# XXX 0.14: Add dot to extension (difficult to parametrize with module-scoped fixture)\ndef test_parse_file_entities_from_layout(layout_synthetic):\n layout = layout_synthetic\n filename = '/sub-03_ses-07_run-4_desc-bleargh_sekret.nii.gz'\n\n # Test with entities taken from bids config\n target = {\n 'subject': '03',\n 'session': '07',\n 'run': 4,\n 'suffix': 'sekret',\n 'extension': '.nii.gz',\n }\n assert target == layout.parse_file_entities(filename, config='bids')\n config = Config.load('bids')\n assert target == layout.parse_file_entities(filename, config=[config])\n assert target == layout.parse_file_entities(filename, scope='raw')\n\n # Test with default scope--i.e., everything\n target = {\n 'subject': '03',\n 'session': '07',\n 'run': 4,\n 'suffix': 'sekret',\n 'desc': 'bleargh',\n 'extension': '.nii.gz',\n }\n assert target == layout.parse_file_entities(filename)\n # Test with only the fmriprep pipeline (which includes both configs)\n assert target == layout.parse_file_entities(filename, scope='fmriprep')\n assert target == layout.parse_file_entities(filename, scope='derivatives')\n\n # Test with only the derivative config\n target = {'desc': 'bleargh'}\n assert target == layout.parse_file_entities(filename, config='derivatives')\n\n\ndef test_deriv_indexing(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n deriv_dir = join(data_dir, 'derivatives', 'bbr')\n\n # missing dataset_description.json\n with pytest.warns(UserWarning):\n layout = BIDSLayout(data_dir, derivatives=deriv_dir)\n\n # Should work fine\n deriv_dir = join(data_dir, 'derivatives', 'events')\n layout = BIDSLayout(data_dir, derivatives=deriv_dir)\n assert layout.get(scope='derivatives')\n assert layout.get(scope='events')\n assert not layout.get(scope='nonexistent')\n\n\ndef test_path_arguments(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005'\n deriv_dir = join(data_dir, 'derivatives', 'events')\n\n layout = BIDSLayout(Path(data_dir), derivatives=Path(deriv_dir))\n assert layout.get(scope='derivatives')\n assert layout.get(scope='events')\n assert not layout.get(scope='nonexistent')\n\n layout = BIDSLayout(Path(data_dir), derivatives=[Path(deriv_dir)])\n assert layout.get(scope='derivatives')\n assert layout.get(scope='events')\n assert not layout.get(scope='nonexistent')\n\n\ndef test_layout_in_scope(layout_ds005, layout_ds005_derivs):\n assert layout_ds005._in_scope(['all'])\n assert layout_ds005._in_scope('raw')\n assert layout_ds005._in_scope(['all', 'ignored'])\n assert not layout_ds005._in_scope(['derivatives', 'ignored'])\n\n deriv = layout_ds005_derivs.derivatives['events']\n assert deriv._in_scope('all')\n assert deriv._in_scope(['derivatives'])\n assert deriv._in_scope('events')\n assert not deriv._in_scope('raw')\n\n\ndef test_get_layouts_in_scope(layout_ds005_multi_derivs):\n l = layout_ds005_multi_derivs\n assert len(l._get_layouts_in_scope('all')) == 3\n assert len(l._get_layouts_in_scope('nonexistent')) == 0\n assert len(l._get_layouts_in_scope(['events', 'dummy'])) == 2\n assert len(l._get_layouts_in_scope(['derivatives'])) == 2\n assert len(l._get_layouts_in_scope('raw')) == 1\n self_scope = l._get_layouts_in_scope('self')\n assert len(self_scope) == 1\n assert self_scope == [l]\n\n\ndef test_get_dataset_description(layout_ds005_multi_derivs):\n l = layout_ds005_multi_derivs\n dd = l.get_dataset_description()\n assert isinstance(dd, dict)\n assert dd['Name'] == 'Mixed-gambles task'\n dd = l.get_dataset_description('all', True)\n assert isinstance(dd, list)\n assert len(dd) == 3\n names = {'Mixed-gambles task', 'Mixed-gambles task -- dummy derivative'}\n assert set([d['Name'] for d in dd]) == names\n\n\ndef test_indexed_file_associations(layout_7t_trt):\n img = layout_7t_trt.get(\n subject='01',\n run=1,\n suffix='bold',\n session='1',\n acquisition='fullbrain',\n extension='.nii.gz',\n )[0]\n assocs = img.get_associations()\n assert len(assocs) == 3\n targets = {\n os.path.join(layout_7t_trt.root, 'sub-01/ses-1/fmap/sub-01_ses-1_run-1_phasediff.nii.gz'),\n os.path.join(img.dirname, 'sub-01_ses-1_task-rest_acq-fullbrain_run-1_physio.tsv.gz'),\n os.path.join(img.dirname, 'sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.json'),\n }\n assert set([a.path for a in assocs]) == set(targets)\n\n # Test with parents included\n targets.add(os.path.join(layout_7t_trt.root, 'task-rest_acq-fullbrain_bold.json'))\n assocs = img.get_associations(include_parents=True)\n assert len(assocs) == 4\n assert set([a.path for a in assocs]) == set(targets)\n\n # Get the root-level JSON and check that its associations are correct\n js = [a for a in assocs if a.path.endswith('json')][1]\n assert len(js.get_associations()) == 40\n assert len(js.get_associations('Parent')) == 1\n assert len(js.get_associations('Metadata')) == 39\n assert not js.get_associations('InformedBy')\n\n\ndef test_layout_save(tests_dir, tmp_path, layout_7t_trt):\n layout_7t_trt.save(str(tmp_path / 'f.sqlite'), replace_connection=False)\n data_dir = tests_dir / 'data' / '7t_trt'\n layout = BIDSLayout(data_dir, database_path=str(tmp_path / 'f.sqlite'))\n oldfies = set(layout_7t_trt.get(suffix='events', return_type='file'))\n newfies = set(layout.get(suffix='events', return_type='file'))\n assert oldfies == newfies\n\n\ndef test_indexing_tag_conflict(tests_dir):\n data_dir = tests_dir / 'data' / 'ds005_conflict'\n with pytest.raises(BIDSValidationError) as exc:\n layout = BIDSLayout(data_dir)\n assert str(exc.value).startswith('Conflicting values found')\n assert 'run' in str(exc.value)\n\n\ndef test_get_with_wrong_dtypes(layout_7t_trt):\n \"\"\"Test automatic dtype sanitization.\"\"\"\n l = layout_7t_trt\n assert (\n l.get(run=1)\n == l.get(run='1')\n == l.get(run=np.int64(1))\n == l.get(run=[1, '15'])\n == l.get(run='01')\n )\n assert not l.get(run='not_numeric')\n assert l.get(session=1) == l.get(session='1')\n\n\ndef test_get_with_regex_search(layout_7t_trt):\n \"\"\"Tests that regex-based searching works.\"\"\"\n l = layout_7t_trt\n\n # subject matches both '10' and '01'\n results = l.get(\n subject='1',\n session='1',\n task='rest',\n suffix='bold',\n acquisition='fron.al',\n extension='.nii.gz',\n regex_search=True,\n )\n assert len(results) == 2\n\n # subject matches '10'\n results = l.get(\n subject='^1',\n session='1',\n task='rest',\n suffix='bold',\n acquisition='fron.al',\n extension='.nii.gz',\n regex_search=True,\n return_type='filename',\n )\n assert len(results) == 1\n assert results[0].endswith('sub-10_ses-1_task-rest_acq-prefrontal_bold.nii.gz')\n\n\ndef test_get_with_regex_search_bad_dtype(layout_7t_trt):\n \"\"\"Tests that passing in a non-string dtype for an entity doesn't crash\n regexp-based searching (i.e., that implicit conversion is done\n appropriately).\"\"\"\n l = layout_7t_trt\n results = l.get(\n subject='1',\n run=1,\n task='rest',\n suffix='bold',\n acquisition='fullbrain',\n extension='.nii.gz',\n regex_search=True,\n )\n # Two runs (1 per session) for each of subjects '10' and '01'\n assert len(results) == 4\n\n\ndef test_get_with_invalid_filters(layout_ds005):\n l = layout_ds005\n # Raise error with suggestions\n with pytest.raises(ValueError, match='session'):\n l.get(subject='12', ses=True, invalid_filters='error')\n with pytest.raises(ValueError, match='session'):\n l.get(subject='12', ses=True)\n # Silently drop amazing\n res_without = l.get(subject='12', suffix='bold')\n res_drop = l.get(subject='12', suffix='bold', amazing='!!!', invalid_filters='drop')\n assert res_without == res_drop\n assert len(res_drop) == 3\n # Retain amazing, producing empty set\n allow_res = l.get(subject='12', amazing=True, invalid_filters='allow')\n assert allow_res == []\n\n # assert warning when filters are passed in\n filters = {'subject': '1'}\n with pytest.raises(RuntimeError, match='You passed in filters as a dictionary'):\n l.get(filters=filters)\n # Correct call:\n l.get(**filters)\n\n\ndef test_get_with_query_constants_in_match_list(layout_ds005):\n l = layout_ds005\n get1 = l.get(subject='12', run=1, suffix='bold')\n get_none = l.get(subject='12', run=None, suffix='bold')\n get_any = l.get(subject='12', run=Query.ANY, suffix='bold')\n get1_and_none = l.get(subject='12', run=[None, 1], suffix='bold')\n get1_and_any = l.get(subject='12', run=[Query.ANY, 1], suffix='bold')\n get_none_and_any = l.get(subject='12', run=[Query.ANY, Query.NONE], suffix='bold')\n assert set(get1_and_none) == set(get1) | set(get_none)\n assert set(get1_and_any) == set(get1) | set(get_any)\n assert set(get_none_and_any) == set(get_none) | set(get_any)\n\n\ndef test_get_non_run_entity_with_query_constants_in_match_list(layout_ds005):\n l = layout_ds005\n get1 = l.get(subject='01', acquisition='MPRAGE', suffix='T1w')\n get_none = l.get(subject='01', acquisition=None, suffix='T1w')\n get_any = l.get(subject='01', acquisition=Query.ANY, suffix='T1w')\n get1_and_none = l.get(subject='01', acquisition=[None, 'MPRAGE'], suffix='T1w')\n get1_and_any = l.get(subject='01', acquisition=[Query.ANY, 'MPRAGE'], suffix='T1w')\n get_none_and_any = l.get(subject='01', acquisition=[Query.ANY, Query.NONE], suffix='T1w')\n assert set(get1_and_none) == set(get1) | set(get_none)\n assert set(get1_and_any) == set(get1) | set(get_any)\n assert set(get_none_and_any) == set(get_none) | set(get_any)\n\n\ndef test_query_constants_work_on_extension(layout_ds005_no_validate):\n l = layout_ds005_no_validate\n get_both = l.get(subject='11', datatype='dwi', extension=Query.OPTIONAL)\n get_ext = l.get(subject='11', datatype='dwi', extension=Query.REQUIRED)\n get_no_ext = l.get(subject='11', datatype='dwi', extension=Query.NONE)\n assert len(get_both) == 2\n assert len(get_ext) == 1\n assert len(get_no_ext) == 1\n assert 'extension' in get_ext[0].get_entities()\n assert 'extension' not in get_no_ext[0].get_entities()\n\n\ndef test_load_layout(layout_synthetic_nodb, db_dir):\n db_path = str(db_dir / 'tmp_db')\n layout_synthetic_nodb.save(db_path)\n reloaded = BIDSLayout.load(db_path)\n assert sorted(layout_synthetic_nodb.get(return_type='file')) == sorted(\n reloaded.get(return_type='file')\n )\n cm1 = layout_synthetic_nodb.connection_manager\n cm2 = reloaded.connection_manager\n for attr in ['root', 'config', 'derivatives']:\n assert getattr(cm1.layout_info, attr) == getattr(cm2.layout_info, attr)\n\n\ndef test_load_layout_config_not_overwritten(layout_synthetic_nodb, tmpdir):\n modified_dataset_path = tmpdir / 'modified'\n shutil.copytree(layout_synthetic_nodb.root, modified_dataset_path)\n\n # Save index\n db_path = str(tmpdir / 'tmp_db')\n BIDSLayout(modified_dataset_path).save(db_path)\n\n # Update dataset_description.json\n dataset_description = modified_dataset_path / 'dataset_description.json'\n with dataset_description.open('r') as f:\n description = json.load(f)\n description['DatasetType'] = 'derivative'\n description['GeneratedBy'] = [{'Name': 'foo'}]\n with dataset_description.open('w') as f:\n json.dump(description, f)\n\n # Reload\n db_layout = BIDSLayout(modified_dataset_path, database_path=db_path)\n fresh_layout = BIDSLayout(modified_dataset_path, validate=False)\n cm1 = db_layout.connection_manager\n cm2 = fresh_layout.connection_manager\n for attr in ['root', 'derivatives']:\n assert getattr(cm1.layout_info, attr) == getattr(cm2.layout_info, attr)\n\n assert cm1.layout_info.config != cm2.layout_info.config\n\n\ndef test_padded_run_roundtrip(layout_ds005):\n for run in (1, '1', '01'):\n res = layout_ds005.get(subject='01', task='mixedgamblestask', run=run, extension='.nii.gz')\n assert len(res) == 1\n boldfile = res[0]\n ents = boldfile.get_entities()\n assert isinstance(ents['run'], PaddedInt)\n assert ents['run'] == 1\n newpath = layout_ds005.build_path(ents, absolute_paths=False)\n assert newpath == 'sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz'\n\n\n@pytest.mark.parametrize(\n 'fname',\n [\n 'sub-01/anat/sub-01_T1w.nii.gz',\n '.datalad',\n 'code',\n 'sub-01/.datalad',\n ],\n)\ndef test_indexer_patterns(fname):\n root = Path('/home/user/.cache/data/')\n path = root / fname\n\n assert bool(\n _check_path_matches_patterns(\n path,\n [_regexfy('code')],\n root=root,\n )\n ) is (fname == 'code')\n\n assert bool(\n _check_path_matches_patterns(\n path,\n [_regexfy('code', root=root)],\n root=root,\n )\n ) is (fname == 'code')\n\n assert (\n _check_path_matches_patterns(\n path,\n [re.compile(r'/\\.')],\n root=None,\n )\n is True\n )\n\n assert _check_path_matches_patterns(\n path,\n [re.compile(r'/\\.')],\n root=root,\n ) is ('.datalad' in fname)\n\n\ndef test_symlinks_in_path(tests_dir, tmp_path):\n src_ds = os.path.abspath(tests_dir / 'data' / '7t_trt')\n src_sub = join(src_ds, 'sub-04')\n\n # This will be a normal directory with symlink contents\n ds_with_links = Path(tmp_path / '7t_trt').absolute()\n\n os.makedirs(ds_with_links, exist_ok=False)\n\n link_ds_description = ds_with_links / 'dataset_description.json'\n link_sub = ds_with_links / 'sub-04'\n\n os.symlink(join(src_ds, 'dataset_description.json'), link_ds_description)\n os.symlink(src_sub, link_sub)\n\n assert 'Subjects: 1 | Sessions: 2 | Runs: 2' in str(BIDSLayout(tmp_path / '7t_trt'))\n\n\ndef test_ignore_dotfiles(temporary_dataset):\n arbitrary_dotfile = temporary_dataset / '.dotfile'\n ds_store = temporary_dataset / 'sub-01' / '.DS_Store'\n osx_companion_file = temporary_dataset / '._task-mixedgamblestask_bold.json'\n\n arbitrary_dotfile.touch()\n ds_store.touch()\n osx_companion_file.touch()\n\n # Default behavior\n layout = BIDSLayout(temporary_dataset, validate=False)\n assert str(temporary_dataset / 'dataset_description.json') in layout.files\n assert str(arbitrary_dotfile) not in layout.files\n assert str(ds_store) not in layout.files\n assert str(osx_companion_file) not in layout.files\n\n # Explicit ignores do not disable dotfile filtering\n indexer = BIDSLayoutIndexer(ignore=['some_ignore'])\n layout = BIDSLayout(temporary_dataset, validate=False, indexer=indexer)\n assert str(temporary_dataset / 'dataset_description.json') in layout.files\n assert str(arbitrary_dotfile) not in layout.files\n assert str(ds_store) not in layout.files\n assert str(osx_companion_file) not in layout.files\n\n\ndef test_empty_directory(temporary_dataset):\n anat = temporary_dataset / 'sub-01' / 'anat'\n shutil.rmtree(anat)\n anat.mkdir()\n\n layout = BIDSLayout(temporary_dataset)\n\n assert layout.get(subject='01', datatype='anat') == []\n\n\n@pytest.mark.parametrize(\n 'intent',\n [\n 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz',\n 'bids:mydataset:sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz',\n 'func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz',\n [\n 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz',\n 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-02_bold.nii.gz',\n ],\n ],\n)\ndef test_intended_for(temporary_dataset, intent):\n fmap_dir = temporary_dataset / 'sub-01' / 'fmap'\n fmap_dir.mkdir()\n\n # Create a fieldmap + populate IntendedFor\n fmap = fmap_dir / 'sub-01_fieldmap.nii.gz'\n fmap.touch()\n fmap_metadata = fmap.parent / fmap.name.replace('.nii.gz', '.json')\n fmap_metadata.write_text(json.dumps({'IntendedFor': intent}))\n\n layout = BIDSLayout(temporary_dataset)\n assert layout.get_IntendedFor(subject='01')\n\n\ndef test_get_return_type_dir_with_schema_datatype(layout_7t_trt):\n \"\"\"Test return_type='dir' with schema config for datatype entity (covers line 463).\"\"\"\n # Create a schema-based layout\n layout = BIDSLayout(layout_7t_trt.root, config='bids-schema', derivatives=False)\n\n # Query for datatype directories - this should trigger the code path\n # that handles non-subject/non-session entities (line 463)\n dirs = layout.get(target='datatype', return_type='dir')\n\n # Should return actual datatype directories\n assert len(dirs) > 0\n # Check that these are actual directories in the dataset\n for d in dirs:\n assert os.path.exists(d)\n\n\ndef test_get_return_type_dir_with_legacy_config_no_template():\n \"\"\"Test return_type='dir' with legacy config missing directory template (covers line 842).\"\"\"\n # Create a minimal config without directory templates\n config_dict = {\n 'name': 'test_config',\n 'entities': [\n {\n 'name': 'subject',\n 'pattern': '[/\\\\\\\\]sub-([a-zA-Z0-9]+)',\n 'directory': '/sub-{subject}/', # This has a template\n },\n {\n 'name': 'task',\n 'pattern': '_task-([a-zA-Z0-9]+)',\n # No directory template - this should trigger the error\n },\n ],\n }\n\n # Create temporary test dataset\n import tempfile\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Create minimal BIDS structure\n Path(tmpdir).joinpath('dataset_description.json').write_text(\n '{\"Name\": \"test\", \"BIDSVersion\": \"1.6.0\"}'\n )\n sub_dir = Path(tmpdir) / 'sub-01'\n sub_dir.mkdir()\n test_file = sub_dir / 'sub-01_task-rest_bold.nii.gz'\n test_file.touch()\n\n # Load with our custom config\n layout = BIDSLayout(tmpdir, config=config_dict, validate=False)\n\n # This should raise ValueError when trying to get directories for 'task'\n # because task entity has no directory template\n with pytest.raises(ValueError, match='Return type set to directory'):\n layout.get(target='task', return_type='dir')\n\n\ndef test_bids_sort(layout_7t_trt):\n files = layout_7t_trt.get(task='rest', extension='.nii.gz')\n assert len(files) > 0\n import copy\n import random\n\n from bidsschematools.schema import load_schema\n\n # we apply bids_sort at the model level, but just to be extra sure\n # we sort here one more time\n first_file_ents_sorted = bids_sort(files[0].get_entities())\n schema_order = list(load_schema().rules.entities) + ['suffix', 'extension', 'datatype']\n\n # collect keys from file entity then unsort them\n sorted_keys = list(first_file_ents_sorted.keys())\n unsorted_keys = copy.copy(sorted_keys)\n while unsorted_keys == sorted_keys:\n random.shuffle(unsorted_keys)\n\n first_file_ents_unsorted = {}\n for key in unsorted_keys:\n first_file_ents_unsorted[key] = first_file_ents_sorted[key]\n\n # check order of sorted entities against schema\n for i, entity in enumerate(sorted_keys):\n for j in sorted_keys[i + 1 :]:\n if entity in schema_order and j in schema_order: # pragma: no branch\n assert schema_order.index(entity) < schema_order.index(j)\n\n assert list(first_file_ents_unsorted.keys()) != sorted_keys\n assert list(bids_sort(first_file_ents_unsorted).keys()) == sorted_keys\n" }, { "path": "src/bids/layout/tests/test_layout_on_examples.py", "content": "\"\"\"Tests runs layout on bids examples and make sure all files are caught\"\"\"\n\n\"\"\" TODO\n- add more 'vanilla' datasets\n- missing files in micr?\n\"\"\"\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\n\n# Values for the number of file got from a:\n#\n# find ds_name -type f | wc -l\n#\n\n\n@pytest.mark.parametrize(\n 'dataset, nb_files',\n [\n ('qmri_irt1', 17),\n ('qmri_mese', 74),\n ('qmri_mp2rage', 18),\n ('qmri_mp2rageme', 29),\n ('qmri_mtsat', 26),\n ('qmri_sa2rage', 10),\n ('qmri_vfa', 19),\n ('qmri_mpm', 126),\n ('qmri_qsm', 10),\n ],\n)\ndef test_layout_on_examples_with_derivatives(dataset, nb_files, bids_examples):\n layout = BIDSLayout(bids_examples / dataset, derivatives=True)\n files = layout.get()\n assert len(files) == nb_files\n\n\n@pytest.mark.parametrize(\n 'dataset, nb_files',\n [\n ('micr_SEM', 16),\n ('micr_SPIM', 26),\n ('asl001', 8),\n ('asl002', 10),\n ('asl003', 10),\n ('asl004', 12),\n ('asl005', 10),\n ('ds001', 135), # with anat and func data\n ('eeg_cbm', 104),\n ('ds000246', 19), # with meg data (excludes all files in .ds folders)\n ('genetics_ukbb', 96), # with dwi data\n ('ieeg_visual_multimodal', 148), # also with fmap data; ignore stimmuli folder\n ('pet001', 12),\n ('pet002', 20),\n ('pet003', 9),\n ('pet004', 10),\n ('pet005', 14),\n ('qmri_megre', 19),\n ('qmri_tb1tfl', 6),\n ('motion_dualtask', 645),\n ('fnirs_tapping', 39),\n ('mrs_fmrs', 169),\n ('mrs_biggaba', 172),\n ('mrs_2dmrsi', 67),\n ],\n)\ndef test_layout_on_examples_no_derivatives(dataset, nb_files, bids_examples):\n layout = BIDSLayout(bids_examples / dataset)\n files = layout.get()\n assert len(files) == nb_files\n" }, { "path": "src/bids/layout/tests/test_models.py", "content": "\"\"\"Tests of functionality in the models module.\"\"\"\n\nimport copy\nimport json\nimport os\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom bids.layout.models import (\n Base,\n BIDSFile,\n BIDSImageFile,\n Config,\n Entity,\n FileAssociation,\n LayoutInfo,\n Tag,\n)\nfrom bids.layout.utils import PaddedInt\nfrom bids.tests import get_test_data_path\n\n\ndef create_session():\n engine = create_engine('sqlite://')\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n return Session()\n\n\n@pytest.fixture\ndef sample_bidsfile(tmpdir):\n testfile = 'sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n fn = tmpdir.mkdir('tmp').join(testfile)\n fn.write('###')\n return BIDSFile(os.path.join(str(fn)))\n\n\n@pytest.fixture(scope='module')\ndef subject_entity():\n return Entity(\n 'subject',\n r'[/\\\\\\\\]sub-([a-zA-Z0-9]+)',\n mandatory=False,\n directory='{subject}',\n dtype='str',\n )\n\n\ndef test_layoutinfo_init():\n args = dict(\n root='/made/up/path',\n validate=True,\n index_metadata=False,\n derivatives=True,\n ignore=['code/', 'blergh/'],\n force_index=None,\n )\n with pytest.raises(ValueError) as exc:\n LayoutInfo(**args)\n assert str(exc.value).startswith('Missing mandatory')\n args['config'] = ['bids', 'derivatives']\n info = LayoutInfo(**args)\n assert info.derivatives is True\n assert info._derivatives == 'true'\n\n\ndef test_entity_initialization():\n e = Entity('avaricious', r'aardvark-(\\d+)')\n assert e.name == 'avaricious'\n assert e.pattern == r'aardvark-(\\d+)'\n assert not e.mandatory\n assert e.directory is None\n assert e.files == {}\n\n\ndef test_entity_init_all_args(subject_entity):\n ent = subject_entity\n assert ent.name == 'subject'\n assert ent.pattern == r'[/\\\\\\\\]sub-([a-zA-Z0-9]+)'\n assert ent.mandatory is False\n assert ent.directory == '{subject}'\n\n\ndef test_entity_init_with_bad_dtype():\n with pytest.raises(ValueError) as exc:\n ent = Entity('test', dtype='superfloat')\n assert str(exc.value).startswith('Invalid dtype')\n\n\ndef test_entity_matches(tmpdir):\n filename = 'aardvark-4-reporting-for-duty.txt'\n tmpdir.mkdir('tmp').join(filename).write('###')\n f = BIDSFile(os.path.join(str(tmpdir), filename))\n e = Entity('avaricious', r'aardvark-(\\d+)')\n result = e.match_file(f)\n assert result == '4'\n\n\ndef test_entity_deepcopy(subject_entity):\n e = subject_entity\n clone = copy.deepcopy(subject_entity)\n for attr in ['name', 'pattern', 'mandatory', 'directory', 'regex']:\n assert getattr(e, attr) == getattr(clone, attr)\n assert e != clone\n\n\ndef test_file_associations():\n session = create_session()\n img = BIDSFile('sub-03/func/sub-03_task-rest_run-2_bold.nii.gz')\n md1 = BIDSFile('sub-03/func/sub-03_task-rest_run-2_bold.json')\n md2 = BIDSFile('task-rest_run-2_bold.json')\n assocs = [\n FileAssociation(src=md1.path, dst=img.path, kind='MetadataFor'),\n FileAssociation(src=img.path, dst=md1.path, kind='MetadataIn'),\n FileAssociation(src=md1.path, dst=md2.path, kind='Child'),\n FileAssociation(src=md2.path, dst=md1.path, kind='Parent'),\n FileAssociation(src=md2.path, dst=img.path, kind='Informs'),\n ]\n session.add_all([img, md1, md2] + assocs)\n session.commit()\n assert img._associations == [md1, md2] == img.get_associations()\n assert md2._associations == [md1]\n assert img.get_associations(kind='MetadataFor') == []\n assert img.get_associations(kind='MetadataIn') == [md1]\n results = img.get_associations(kind='MetadataIn', include_parents=True)\n assert set(results) == {md1, md2}\n\n\ndef test_tag_init(sample_bidsfile, subject_entity):\n f, e = sample_bidsfile, subject_entity\n tag = Tag(f, e, 'zzz')\n rep = str(tag)\n assert rep.startswith(' 0\n\n # Check that standard entities are present (using full names per PyBIDS convention)\n entity_names = {e.name for e in config.entities.values()}\n expected_entities = {'subject', 'session', 'task', 'run'}\n assert expected_entities.issubset(entity_names)\n\n # Check that PyBIDS-specific entities are present\n pybids_entities = {'extension', 'suffix', 'datatype'}\n assert pybids_entities.issubset(entity_names)\n\n def test_schema_entity_patterns(self):\n \"\"\"Test that entity patterns are correctly generated.\"\"\"\n config = Config.load('bids-schema')\n\n # Test subject entity (directory-based)\n if 'subject' in config.entities:\n subject_entity = config.entities['subject']\n assert subject_entity.regex is not None\n # Should match /sub-01/ or similar\n match = subject_entity.regex.search('/sub-01/')\n assert match is not None\n assert match.group(1) == '01'\n\n # Test task entity (file-based)\n if 'task' in config.entities:\n task_entity = config.entities['task']\n assert task_entity.regex is not None\n # Should match _task-rest_ or similar\n match = task_entity.regex.search('_task-rest_')\n assert match is not None\n assert match.group(1) == 'rest'\n\n def test_schema_version_tracking(self):\n \"\"\"Test that schema version is tracked in config name.\"\"\"\n config = Config.load('bids-schema')\n\n # Config name should include version\n assert 'bids-schema-' in config.name\n # Should have some version number after the dash\n version_part = config.name.split('bids-schema-')[1]\n assert len(version_part) > 0\n\n def test_schema_version_parameter(self):\n \"\"\"Test loading specific schema version.\"\"\"\n # Test loading BIDS v1.9.0 schema\n config = Config.load({'schema_version': '1.9.0'})\n\n assert config.name.startswith('bids-schema-')\n assert len(config.entities) > 0\n # Should include the version in the name\n assert '1.9.0' in config.name\n\n def test_invalid_schema_version(self):\n \"\"\"Test that loading invalid schema version raises appropriate error.\"\"\"\n # Test with a non-existent version\n with pytest.raises(ValueError, match='Failed to load BIDS schema version'):\n Config.load({'schema_version': '99.99.99'})\n\n def test_entity_generation(self):\n \"\"\"Test entity pattern generation using real schema.\"\"\"\n config = Config.load('bids-schema')\n\n # Test that entities were generated\n assert len(config.entities) > 0\n\n # Test specific entity properties we care about (using full names)\n assert 'subject' in config.entities\n assert 'task' in config.entities\n assert 'run' in config.entities\n\n # Test that patterns are valid regex\n for entity_name, entity in config.entities.items():\n assert entity.regex is not None, f'Entity {entity_name} missing regex'\n # Test that regex compiles and works\n assert hasattr(entity.regex, 'search'), f'Entity {entity_name} regex invalid'\n\n # Test specific entity patterns work correctly\n subject_entity = config.entities['subject']\n assert 'sub-' in subject_entity.pattern\n\n # Test subject pattern matches expected format\n match = subject_entity.regex.search('/sub-01/')\n assert match is not None\n assert match.group(1) == '01'\n\n # Test task pattern matches expected format\n task_entity = config.entities['task']\n match = task_entity.regex.search('_task-rest_')\n assert match is not None\n assert match.group(1) == 'rest'\n\n # Test run pattern matches expected format\n run_entity = config.entities['run']\n match = run_entity.regex.search('_run-02_')\n assert match is not None\n assert match.group(1) == '02'\n\n def test_schema_directory_query(self):\n \"\"\"Test directory queries with schema-based config.\"\"\"\n import os\n from pathlib import Path\n\n from bids import BIDSLayout\n\n # Use the 7t_trt test dataset\n data_dir = Path(__file__).parent.parent.parent.parent.parent / 'tests' / 'data' / '7t_trt'\n if not data_dir.exists():\n pytest.skip('Test dataset not available')\n\n # Create layout with schema config\n layout = BIDSLayout(data_dir, config='bids-schema', derivatives=False)\n\n # Test directory query for subjects\n subject_dirs = layout.get(target='subject', return_type='dir')\n assert len(subject_dirs) > 0\n assert all(os.path.exists(d) for d in subject_dirs)\n # Should include subject directories\n assert any('sub-' in os.path.basename(d) for d in subject_dirs)\n" }, { "path": "src/bids/layout/tests/test_schema_pattern_validation.py", "content": "\"\"\"Tests to verify schema patterns match actual schema content.\"\"\"\n\nfrom bidsschematools import schema as bst_schema\n\nfrom bids.layout.models import Config\n\n\nclass TestSchemaAccuracy:\n \"\"\"Test that patterns accurately reflect BIDS schema content.\"\"\"\n\n def test_extension_patterns_match_schema(self):\n \"\"\"Test that extension patterns match schema extensions.\"\"\"\n config = Config.load('bids-schema')\n\n # Load schema directly\n bids_schema = bst_schema.load_schema()\n\n # Get extension entity\n extension_entity = config.entities['extension']\n\n # Test that schema extensions are matched (excluding wildcards)\n schema_extensions = [\n ext['value']\n for ext in bids_schema.objects.extensions.values()\n if ext.get('value') and ext['value'].startswith('.') and ext['value'] not in ('.*',)\n ]\n\n # Collect all failures\n matched_extensions = []\n unmatched_extensions = []\n\n test_files = [f'test{ext}' for ext in schema_extensions]\n for ext, test_file in zip(schema_extensions, test_files):\n match = extension_entity.regex.search(test_file)\n if match is not None:\n matched_extensions.append(ext)\n else:\n unmatched_extensions.append(ext)\n\n # Report all differences at once\n coverage = (\n len(matched_extensions) / len(schema_extensions) * 100 if schema_extensions else 0\n )\n\n error_msg = []\n error_msg.append(\n f'Extension coverage: {coverage:.1f}% ({len(matched_extensions)}/{len(schema_extensions)})'\n )\n if unmatched_extensions:\n error_msg.append(\n f'Unmatched extensions ({len(unmatched_extensions)}): {unmatched_extensions[:20]}'\n )\n if len(unmatched_extensions) > 20:\n error_msg.append(f'... and {len(unmatched_extensions) - 20} more')\n if matched_extensions:\n error_msg.append(\n f'Matched extensions ({len(matched_extensions)}): {matched_extensions[:10]}'\n )\n if len(matched_extensions) > 10:\n error_msg.append(f'... and {len(matched_extensions) - 10} more')\n\n assert len(unmatched_extensions) == 0, '; '.join(error_msg)\n\n def test_suffix_patterns_match_schema(self):\n \"\"\"Test that suffix patterns match schema suffixes.\"\"\"\n config = Config.load('bids-schema')\n\n # Load schema directly\n bids_schema = bst_schema.load_schema()\n\n # Get suffix entity\n suffix_entity = config.entities['suffix']\n\n # Test that schema suffixes are matched using actual suffix values, not keys\n schema_suffix_values = [\n obj['value'] for obj in bids_schema.objects.suffixes.values() if obj.get('value')\n ]\n\n # Collect all failures\n matched_suffixes = []\n unmatched_suffixes = []\n\n # Test all suffixes using their actual values\n test_files = [f'_{suffix}.nii.gz' for suffix in schema_suffix_values]\n for suffix, test_file in zip(schema_suffix_values, test_files):\n match = suffix_entity.regex.search(test_file)\n if match is not None:\n matched_suffixes.append(suffix)\n else:\n unmatched_suffixes.append(suffix)\n\n # Report all differences at once\n coverage = (\n len(matched_suffixes) / len(schema_suffix_values) * 100 if schema_suffix_values else 0\n )\n\n error_msg = []\n error_msg.append(\n f'Suffix coverage: {coverage:.1f}% ({len(matched_suffixes)}/{len(schema_suffix_values)})'\n )\n if unmatched_suffixes:\n error_msg.append(\n f'Unmatched suffixes ({len(unmatched_suffixes)}): {unmatched_suffixes[:20]}'\n )\n if len(unmatched_suffixes) > 20:\n error_msg.append(f'... and {len(unmatched_suffixes) - 20} more')\n if matched_suffixes:\n error_msg.append(\n f'Matched suffixes ({len(matched_suffixes)}): {matched_suffixes[:10]}'\n )\n if len(matched_suffixes) > 10:\n error_msg.append(f'... and {len(matched_suffixes) - 10} more')\n\n assert len(unmatched_suffixes) == 0, '; '.join(error_msg)\n\n def test_datatype_patterns_match_schema(self):\n \"\"\"Test that datatype patterns match schema datatypes.\"\"\"\n config = Config.load('bids-schema')\n\n # Load schema directly\n bids_schema = bst_schema.load_schema()\n\n # Get datatype entity\n datatype_entity = config.entities['datatype']\n\n # Test that schema datatypes are matched\n schema_datatypes = list(bids_schema.objects.datatypes.keys())\n\n # Collect all failures\n matched_datatypes = []\n unmatched_datatypes = []\n\n test_paths = [f'/{dtype}/' for dtype in schema_datatypes]\n for dtype, test_path in zip(schema_datatypes, test_paths):\n match = datatype_entity.regex.search(test_path)\n if match is not None:\n matched_datatypes.append(dtype)\n else:\n unmatched_datatypes.append(dtype)\n\n # Report all differences at once\n coverage = len(matched_datatypes) / len(schema_datatypes) * 100 if schema_datatypes else 0\n\n error_msg = []\n error_msg.append(\n f'Datatype coverage: {coverage:.1f}% ({len(matched_datatypes)}/{len(schema_datatypes)})'\n )\n if unmatched_datatypes:\n error_msg.append(\n f'Unmatched datatypes ({len(unmatched_datatypes)}): {unmatched_datatypes}'\n )\n if matched_datatypes:\n error_msg.append(f'Matched datatypes ({len(matched_datatypes)}): {matched_datatypes}')\n\n assert len(unmatched_datatypes) == 0, '; '.join(error_msg)\n\n def test_entity_format_patterns_match_schema(self):\n \"\"\"Test that entity patterns use actual schema format patterns.\"\"\"\n config = Config.load('bids-schema')\n\n # Load schema directly\n bids_schema = bst_schema.load_schema()\n\n # Test a few key entities\n test_entities = [\n ('subject', 'label'),\n ('run', 'index'),\n ('echo', 'index'),\n ]\n\n for entity_name, expected_format in test_entities:\n if entity_name in config.entities:\n entity = config.entities[entity_name]\n\n # Get expected pattern from schema\n format_obj = bids_schema.objects.formats[expected_format]\n expected_pattern = format_obj['pattern']\n\n # Check that our entity pattern contains the schema pattern\n assert expected_pattern in entity.pattern, (\n f\"Entity {entity_name} pattern doesn't contain schema format pattern\"\n )\n\n def test_no_hardcoded_values(self):\n \"\"\"Test that we don't have hardcoded schema values anymore.\"\"\"\n config = Config.load('bids-schema')\n\n # Load schema directly to get actual counts\n bids_schema = bst_schema.load_schema()\n\n # We should have more entities than before because we're not missing any\n assert len(config.entities) >= len(bids_schema.objects.entities)\n\n # Extension pattern should include schema extensions\n extension_entity = config.entities['extension']\n schema_extension_count = len(\n [\n ext\n for ext in bids_schema.objects.extensions.values()\n if ext.get('value') and ext['value'].startswith('.')\n ]\n )\n\n # Pattern should include many extensions (not just a generic catch-all)\n assert len(extension_entity.pattern) > 100, 'Extension pattern seems too simple'\n\n # Suffix pattern should include schema suffixes\n suffix_entity = config.entities['suffix']\n schema_suffix_count = len(bids_schema.objects.suffixes)\n\n # Pattern should include many suffixes\n assert len(suffix_entity.pattern) > 200, 'Suffix pattern seems too simple'\n\n print(f'Schema extensions used: {schema_extension_count}')\n print(f'Schema suffixes used: {schema_suffix_count}')\n print(f'Schema datatypes used: {len(bids_schema.objects.datatypes)}')\n print(f'Schema entities used: {len(bids_schema.objects.entities)}')\n" }, { "path": "src/bids/layout/tests/test_schema_version_differences.py", "content": "\"\"\"Tests for differences between BIDS schema versions.\"\"\"\n\nimport json\nimport tempfile\nfrom pathlib import Path\n\nfrom bids import BIDSLayout\nfrom bids.layout.models import Config\n\n\nclass TestSchemaVersionDifferences:\n \"\"\"Test that different schema versions handle different features correctly.\"\"\"\n\n def test_entity_parsing_version_differences(self):\n \"\"\"Test that tracksys entity (added in v1.9.0) works in newer schemas but fails in v1.8.0.\"\"\"\n\n with tempfile.TemporaryDirectory() as temp_dir:\n dataset_dir = Path(temp_dir) / 'test_dataset'\n dataset_dir.mkdir()\n\n # Create minimal dataset with motion file using tracksys entity\n with open(dataset_dir / 'dataset_description.json', 'w') as f:\n json.dump({'Name': 'Test', 'BIDSVersion': '1.10.1', 'Authors': ['Test']}, f)\n\n motion_dir = dataset_dir / 'sub-01' / 'motion'\n motion_dir.mkdir(parents=True)\n (motion_dir / 'sub-01_tracksys-imu_motion.tsv').touch()\n\n # Test tracksys query across schema versions\n def test_tracksys_query(config):\n layout = BIDSLayout(dataset_dir, config=[config])\n try:\n return len(layout.get(tracksys='imu', return_type='filename')) > 0\n except Exception:\n return False\n\n # tracksys entity was added in v1.9.0 for motion datatype\n assert test_tracksys_query('bids-schema'), 'Current schema should support tracksys'\n assert test_tracksys_query({'schema_version': '1.9.0'}), (\n 'v1.9.0 should support tracksys (motion added)'\n )\n assert not test_tracksys_query({'schema_version': '1.8.0'}), (\n 'v1.8.0 should NOT support tracksys'\n )\n\n def test_motion_datatype_evolution(self):\n \"\"\"Test that motion datatype (BEP029) support was added in v1.9.0.\"\"\"\n\n # Motion datatype was added in v1.9.0 according to changelog:\n # v1.9.0: [ENH] Extend BIDS for Motion data (BEP029) #981\n\n config_v190 = Config.load({'schema_version': '1.9.0'})\n config_v180 = Config.load({'schema_version': '1.8.0'})\n\n entities_v190 = {e.name for e in config_v190.entities.values()}\n entities_v180 = {e.name for e in config_v180.entities.values()}\n\n # Motion-specific entity 'tracksys' should be in v1.9.0+ but not v1.8.0\n assert 'tracksys' in entities_v190, (\n 'tracksys entity should exist in v1.9.0 (motion datatype was added)'\n )\n assert 'tracksys' not in entities_v180, (\n 'tracksys entity should NOT exist in v1.8.0 (before motion datatype)'\n )\n\n print('✓ Motion datatype evolution verified:')\n print(f' v1.8.0 (before motion): tracksys = {\"tracksys\" in entities_v180}')\n print(f' v1.9.0 (motion added): tracksys = {\"tracksys\" in entities_v190}')\n\n # Test specific motion-related entities that were added\n motion_entities = {\n 'tracksys'\n } # Could expand this list as more motion entities are identified\n\n for entity in motion_entities:\n assert entity in entities_v190, f\"Motion entity '{entity}' should exist in v1.9.0+\"\n assert entity not in entities_v180, (\n f\"Motion entity '{entity}' should NOT exist in v1.8.0\"\n )\n\n def test_schema_version_metadata_differences(self):\n \"\"\"Test that schema versions have different BIDS version numbers.\"\"\"\n\n # Load different schema versions\n config_current = Config.load('bids-schema')\n config_v190 = Config.load({'schema_version': '1.9.0'})\n config_v180 = Config.load({'schema_version': '1.8.0'})\n\n # Check that config names reflect the versions\n assert 'bids-schema-' in config_current.name\n assert '1.9.0' in config_v190.name\n assert '1.8.0' in config_v180.name\n\n print(f'Current config: {config_current.name}')\n print(f'v1.9.0 config: {config_v190.name}')\n print(f'v1.8.0 config: {config_v180.name}')\n\n # Verify they're different configurations\n assert config_current.name != config_v190.name\n assert config_v190.name != config_v180.name\n" }, { "path": "src/bids/layout/tests/test_schema_vs_json_config.py", "content": "\"\"\"Tests to verify schema-driven config matches old JSON config behavior.\"\"\"\n\nimport re\n\nfrom bids.layout.models import Config\n\n\nclass TestConfigCompatibility:\n \"\"\"Test that new schema-driven config matches old JSON config behavior.\"\"\"\n\n def test_core_entities_present_in_both_configs(self):\n \"\"\"Test that essential BIDS entities are present in both configs.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Test that both configs support the fundamental BIDS entities\n core_entities = ['subject', 'session', 'task', 'run', 'acquisition']\n\n # Check core entities exist (allowing for naming differences)\n old_has_core = all(entity in old_config.entities for entity in core_entities)\n new_has_core = all(entity in new_config.entities for entity in core_entities)\n\n assert old_has_core and new_has_core, 'Both configs should support core BIDS entities'\n\n def test_schema_entities_are_superset_of_valid_old_entities(self):\n \"\"\"Test that schema includes all valid entities from old config.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Map old config entities to their schema equivalents\n entity_mapping = {\n 'inv': 'inversion',\n 'mt': 'mtransfer',\n 'proc': 'processing',\n # 'fmap', 'scans', 'staining' are invalid - exclude them\n }\n\n valid_old_entities = set(old_config.entities.keys()) - {'fmap', 'scans', 'staining'}\n missing_from_new = []\n\n for old_entity in valid_old_entities:\n expected_new_entity = entity_mapping.get(old_entity, old_entity)\n if expected_new_entity not in new_config.entities:\n missing_from_new.append(f'{old_entity} -> {expected_new_entity}')\n\n assert len(missing_from_new) == 0, f'Schema missing valid entities: {missing_from_new}'\n\n def test_new_suffixes_compatible_with_old_pattern(self):\n \"\"\"Test that all new schema suffixes would be accepted by old generic pattern.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Get old pattern (generic) and new suffixes (explicit)\n old_pattern = old_config.entities['suffix'].regex\n new_suffixes = self._extract_suffixes_from_pattern(new_config.entities['suffix'].pattern)\n\n # Test that old pattern would accept all new suffixes\n incompatible_suffixes = []\n for suffix in new_suffixes:\n test_filename = f'sub-01_task-test_{suffix}.nii.gz'\n if not old_pattern.search(test_filename):\n incompatible_suffixes.append(suffix)\n\n assert len(incompatible_suffixes) == 0, (\n f'Old pattern would reject these valid schema suffixes: {incompatible_suffixes}'\n )\n\n def test_new_extensions_compatible_with_old_pattern(self):\n \"\"\"Test that all new schema extensions would be accepted by old generic pattern.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Get old pattern (generic) and new extensions (explicit)\n old_pattern = old_config.entities['extension'].regex\n new_extensions = self._extract_extensions_from_pattern(\n new_config.entities['extension'].pattern\n )\n\n # Test that old pattern would accept all new extensions\n incompatible_extensions = []\n directory_extensions = []\n\n for extension in new_extensions:\n test_filename = f'sub-01_task-test_bold{extension}'\n if not old_pattern.search(test_filename):\n if extension.endswith('/'):\n # Directory-style extensions are a new BIDS feature not supported by old pattern\n directory_extensions.append(extension)\n else:\n incompatible_extensions.append(extension)\n\n # Core file extensions should be compatible\n assert len(incompatible_extensions) == 0, (\n f'Old pattern would reject these valid schema file extensions: {incompatible_extensions}'\n )\n\n # Report directory extensions as expected difference\n if directory_extensions:\n print(\n f'Note: {len(directory_extensions)} directory-style extensions are new BIDS features: {directory_extensions}'\n )\n\n # Core extensions should be present\n core_extensions = {'.nii.gz', '.nii', '.tsv', '.json'}\n assert core_extensions.issubset(new_extensions), 'Missing core extensions from new config'\n\n def test_datatype_coverage_matches(self):\n \"\"\"Test that datatype patterns cover the same datatypes.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Extract datatypes from patterns\n old_datatypes = self._extract_datatypes_from_pattern(\n old_config.entities['datatype'].pattern\n )\n new_datatypes = self._extract_datatypes_from_pattern(\n new_config.entities['datatype'].pattern\n )\n\n missing = old_datatypes - new_datatypes\n extra = new_datatypes - old_datatypes\n\n error_msg = []\n if missing:\n error_msg.append(f'Missing datatypes: {sorted(missing)}')\n if extra:\n error_msg.append(f'Extra datatypes: {sorted(extra)}')\n\n # Core datatypes should be preserved\n core_datatypes = {'anat', 'func', 'dwi', 'fmap'}\n assert core_datatypes.issubset(new_datatypes), (\n f'Missing core datatypes; {\"; \".join(error_msg)}'\n )\n\n # New should have at least as many as old (schema grows)\n assert len(new_datatypes) >= len(old_datatypes), (\n f'Fewer datatypes than old config; {\"; \".join(error_msg)}'\n )\n\n def test_functional_filename_parsing_compatibility(self):\n \"\"\"Test that both configs can parse BIDS filenames and extract core entities.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Test core entity extraction (skip extension due to pattern differences)\n test_cases = [\n {\n 'filename': '/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz',\n 'core_entities': {'subject': '01', 'task': 'rest', 'suffix': 'bold'},\n },\n {\n 'filename': '/dataset/sub-02/ses-pre/anat/sub-02_ses-pre_T1w.nii.gz',\n 'core_entities': {'subject': '02', 'session': 'pre', 'suffix': 'T1w'},\n },\n ]\n\n for test_case in test_cases:\n filename = test_case['filename']\n expected = test_case['core_entities']\n\n old_entities = self._parse_filename_entities(filename, old_config)\n new_entities = self._parse_filename_entities(filename, new_config)\n\n # Test that both configs extract the core BIDS entities\n for key, expected_value in expected.items():\n old_extracted = old_entities.get(key)\n new_extracted = new_entities.get(key)\n\n # Both should extract the same core information\n assert old_extracted == expected_value, (\n f'Old config failed to extract {key}={expected_value} from {filename}, got {old_extracted}'\n )\n assert new_extracted == expected_value, (\n f'New config failed to extract {key}={expected_value} from {filename}, got {new_extracted}'\n )\n\n # Test that new config has valid patterns\n assert len(new_config.entities) > 20, 'New config should have many entities'\n assert 'subject' in new_config.entities, 'New config missing subject entity'\n assert 'suffix' in new_config.entities, 'New config missing suffix entity'\n\n def test_new_config_parses_schema_compliant_filenames(self):\n \"\"\"Test that new config correctly parses filenames with schema entities.\"\"\"\n new_config = Config.load('bids-schema')\n\n # Test files that use entities only available in schema (not old config)\n schema_specific_cases = [\n '/dataset/sub-01/anat/sub-01_hemi-L_T1w.nii.gz', # hemisphere entity\n '/dataset/sub-01/anat/sub-01_desc-preproc_T1w.nii.gz', # description entity\n '/dataset/sub-01/anat/sub-01_res-native_T1w.nii.gz', # resolution entity\n ]\n\n for filename in schema_specific_cases:\n entities = self._parse_filename_entities(filename, new_config)\n\n # Should extract subject and suffix at minimum\n assert 'subject' in entities, f'Failed to extract subject from {filename}'\n assert 'suffix' in entities, f'Failed to extract suffix from {filename}'\n\n # Should extract the schema-specific entity\n if 'hemi-' in filename:\n assert 'hemisphere' in entities, f'Failed to extract hemisphere from {filename}'\n if 'desc-' in filename:\n assert 'description' in entities, f'Failed to extract description from {filename}'\n if 'res-' in filename:\n assert 'resolution' in entities, f'Failed to extract resolution from {filename}'\n\n def test_pattern_structure_similarity(self):\n \"\"\"Test that pattern structures are similar between old and new.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Test key entities have reasonable pattern structures\n key_entities = ['subject', 'session', 'task', 'run', 'suffix', 'extension']\n\n for entity_name in key_entities:\n if entity_name in old_config.entities and entity_name in new_config.entities:\n old_pattern = old_config.entities[entity_name].pattern\n new_pattern = new_config.entities[entity_name].pattern\n\n # Both should be non-empty\n assert len(old_pattern) > 0, f'Old {entity_name} pattern is empty'\n assert len(new_pattern) > 0, f'New {entity_name} pattern is empty'\n\n # Both should contain capturing groups\n old_groups = len(re.findall(r'\\([^)]*\\)', old_pattern))\n new_groups = len(re.findall(r'\\([^)]*\\)', new_pattern))\n\n assert old_groups > 0, f'Old {entity_name} pattern has no groups'\n assert new_groups > 0, f'New {entity_name} pattern has no groups'\n\n def test_config_names_and_metadata(self):\n \"\"\"Test config metadata is reasonable.\"\"\"\n old_config = Config.load('bids')\n new_config = Config.load('bids-schema')\n\n # Old config should have simple name\n assert old_config.name == 'bids'\n\n # New config should include version info\n assert new_config.name.startswith('bids-schema-')\n assert len(new_config.name) > len('bids-schema-')\n\n # Both should have reasonable entity counts\n assert len(old_config.entities) > 10, 'Old config has too few entities'\n assert len(new_config.entities) > 10, 'New config has too few entities'\n\n # Report entity count ratio for analysis\n ratio = len(new_config.entities) / len(old_config.entities)\n print(f'Entity count ratio (new/old): {ratio:.2f}')\n\n # Helper methods\n def _extract_suffixes_from_pattern(self, pattern):\n \"\"\"Extract suffixes from a pattern like [_/\\\\]+(suffix1|suffix2|...)\"\"\"\n suffixes = set()\n\n # Look for alternation groups\n matches = re.findall(r'\\(([^)]+)\\)', pattern)\n for match in matches:\n if '|' in match:\n suffixes.update(match.split('|'))\n else:\n suffixes.add(match)\n\n return suffixes\n\n def _extract_extensions_from_pattern(self, pattern):\n \"\"\"Extract extensions from a pattern.\"\"\"\n extensions = set()\n\n # Look for patterns with dots\n matches = re.findall(r'\\\\?\\.\\w+(?:\\\\?\\.\\w+)*', pattern)\n for match in matches:\n # Clean up regex escaping\n clean_ext = match.replace('\\\\', '')\n extensions.add(clean_ext)\n\n # Also look for alternation groups\n alt_matches = re.findall(r'\\(([^)]+)\\)', pattern)\n for match in alt_matches:\n if '|' in match and '.' in match:\n for item in match.split('|'):\n if '.' in item:\n clean_item = item.replace('\\\\', '')\n extensions.add(clean_item)\n\n return extensions\n\n def _extract_datatypes_from_pattern(self, pattern):\n \"\"\"Extract datatypes from a pattern like [/\\\\]+(datatype1|datatype2|...)\"\"\"\n datatypes = set()\n\n # Look for alternation groups\n matches = re.findall(r'\\(([^)]+)\\)', pattern)\n for match in matches:\n if '|' in match:\n datatypes.update(match.split('|'))\n else:\n datatypes.add(match)\n\n return datatypes\n\n def _parse_filename_entities(self, filename, config):\n \"\"\"Parse entities from filename using config patterns.\"\"\"\n entities = {}\n\n for entity_name, entity in config.entities.items():\n if hasattr(entity, 'regex') and entity.regex:\n match = entity.regex.search(filename)\n if match and len(match.groups()) > 0:\n entities[entity_name] = match.group(1)\n\n return entities\n\n def _map_entity_name_old_to_new(self, new_entity_name):\n \"\"\"Map new entity names to old entity names for compatibility testing.\"\"\"\n # Old config sometimes used different names\n name_mapping = {\n 'subject': 'subject',\n 'session': 'session',\n 'task': 'task',\n 'run': 'run',\n 'acquisition': 'acquisition',\n 'suffix': 'suffix',\n 'extension': 'extension',\n # Add other mappings as needed\n }\n return name_mapping.get(new_entity_name, new_entity_name)\n" }, { "path": "src/bids/layout/tests/test_utils.py", "content": "\"\"\"Test miscellaneous utilities.\"\"\"\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport bids\nfrom bids.exceptions import ConfigError\n\nfrom ..models import Config, Entity\nfrom ..utils import BIDSMetadata, PaddedInt, add_config_paths, parse_file_entities\n\n\ndef test_bidsmetadata_class():\n md = BIDSMetadata('fakefile')\n with pytest.raises(KeyError) as err:\n md['Missing']\n assert \"Metadata term 'Missing' unavailable for file fakefile.\" in str(err)\n md['Missing'] = 1\n assert md['Missing'] == 1\n\n\ndef test_parse_file_entities(mock_config):\n filename = '/sub-03_ses-07_run-4_desc-bleargh_sekret.nii.gz'\n\n # Test with entities taken from bids config\n target = {\n 'subject': '03',\n 'session': '07',\n 'run': 4,\n 'suffix': 'sekret',\n 'extension': '.nii.gz',\n }\n assert target == parse_file_entities(filename, config='bids')\n config = Config.load('bids')\n assert target == parse_file_entities(filename, config=[config])\n\n # Test with entities taken from bids and derivatives config\n target = {\n 'subject': '03',\n 'session': '07',\n 'run': 4,\n 'suffix': 'sekret',\n 'desc': 'bleargh',\n 'extension': '.nii.gz',\n }\n assert target == parse_file_entities(filename)\n assert target == parse_file_entities(filename, config=['bids', 'derivatives'])\n\n # Test with list of Entities\n entities = [\n Entity('subject', '[/\\\\\\\\]sub-([a-zA-Z0-9]+)'),\n Entity('run', '[_/\\\\\\\\]run-0*(\\\\d+)', dtype=int),\n Entity('suffix', '[._]*([a-zA-Z0-9]*?)\\\\.[^/\\\\\\\\]+$'),\n Entity('desc', 'desc-([a-zA-Z0-9]+)'),\n ]\n # Leave out session to distinguish from previous test target\n target = {'subject': '03', 'run': 4, 'suffix': 'sekret', 'desc': 'bleargh'}\n assert target == parse_file_entities(filename, entities=entities)\n\n\n@pytest.mark.parametrize(\n 'filename, target',\n [\n ('/path/to/sub-01.ext', {'subject': '01', 'extension': '.ext'}),\n ('/path/to/stub.ext', {'suffix': 'stub', 'extension': '.ext'}),\n ('/path/to/.dotfile', {}),\n ('/path/to/stub', {}),\n ],\n)\ndef test_parse_degenerate_files(mock_config, filename, target):\n assert parse_file_entities(filename, config='bids') == target\n\n\ndef test_add_config_paths():\n bids_dir = os.path.dirname(bids.__file__)\n bids_json = os.path.join(bids_dir, 'layout', 'config', 'bids.json')\n with pytest.raises(ConfigError) as exc:\n add_config_paths(test_config1='nonexistentpath.json')\n assert str(exc.value).startswith('Configuration file')\n with pytest.raises(ConfigError) as exc:\n add_config_paths(bids=bids_json)\n assert str(exc.value).startswith(\"Configuration 'bids' already\")\n add_config_paths(dummy=bids_json)\n config = Config.load('dummy')\n assert 'subject' in config.entities\n\n\ndef test_PaddedInt_array_comparisons():\n # Array comparisons should work, not raise exceptions\n arr = np.array([5, 5, 5])\n assert np.all(arr == PaddedInt(5))\n assert np.all(arr == PaddedInt('05'))\n assert np.all(PaddedInt(5) == arr)\n assert np.all(PaddedInt('05') == arr)\n\n # If the value gets put into an array, it should be considered an int\n # We lose the padding, but it's unlikely we would try to recover it\n # from an array.\n assert np.array([PaddedInt(5)]).dtype == np.int64\n\n # Verify that we do get some False results\n assert np.array_equal(np.array([4, 5, 6]) == PaddedInt(5), [False, True, False])\n\n\ndef test_PaddedInt_dataframe_behavior():\n # Verify that pandas dataframes are not more special than numpy arrays,\n # as far as PaddedInt is concerned\n df = pd.DataFrame({'a': [5, 5, 5]})\n pidf = pd.DataFrame({'a': [PaddedInt(5)] * 3})\n assert np.all(df['a'] == PaddedInt(5))\n assert np.all(df == pidf)\n\n assert pidf['a'].dtype is np.dtype('int64')\n" }, { "path": "src/bids/layout/tests/test_validation.py", "content": "\"\"\"Tests of BIDSValidator functionality.\"\"\"\n\nfrom os.path import join\n\nimport pytest\nfrom bids_validator import BIDSValidator\n\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\n\n\n# Fixture uses in the rest of the tests\n@pytest.fixture\ndef testvalidator():\n return BIDSValidator()\n\n\ndef test_layout_with_validation():\n data_dir = join(get_test_data_path(), '7t_trt')\n layout1 = BIDSLayout(data_dir, validate=True)\n layout2 = BIDSLayout(data_dir, validate=False)\n assert len(layout1.files) < len(layout2.files)\n # Not a valid BIDS file\n badfile = join(data_dir, 'test.bval')\n assert badfile not in layout1.files\n assert badfile in layout2.files\n" }, { "path": "src/bids/layout/tests/test_writing.py", "content": "\"\"\"Tests related to file-writing functionality.\"\"\"\n\nimport os\nimport shutil\nfrom os.path import dirname, exists, islink, join\n\nimport pytest\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom bids import BIDSLayout\nfrom bids.layout.models import Base, BIDSFile, Entity, Tag\nfrom bids.layout.writing import _PATTERN_FIND, build_path\nfrom bids.tests import get_test_data_path\n\n\n@pytest.fixture\ndef writable_file(tmpdir):\n engine = create_engine('sqlite://')\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n\n testfile = 'sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz'\n fn = tmpdir.mkdir('tmp').join(testfile)\n fn.write('###')\n bf = BIDSFile(os.path.join(str(fn)))\n\n tag_dict = {'task': 'rest', 'run': 2, 'subject': '3'}\n ents = {name: Entity(name) for name in tag_dict.keys()}\n tags = [Tag(bf, ents[k], value=v) for k, v in tag_dict.items()]\n\n session.add_all(list(ents.values()) + tags + [bf])\n session.commit()\n yield bf\n\n\n@pytest.fixture(scope='module')\ndef tmp_bids(tmpdir_factory):\n tmp_bids = tmpdir_factory.mktemp('tmp_bids')\n yield tmp_bids\n shutil.rmtree(str(tmp_bids))\n # Ugly hack\n try:\n shutil.rmtree(join(get_test_data_path(), '7t_trt', 'sub-Bob'))\n except Exception:\n pass\n\n\n@pytest.fixture(scope='module')\ndef layout(tmp_bids):\n orig_dir = join(get_test_data_path(), '7t_trt')\n # return BIDSLayout(data_dir, absolute_paths=False)\n new_dir = join(str(tmp_bids), 'bids')\n os.symlink(orig_dir, new_dir)\n return BIDSLayout(new_dir)\n\n\nclass TestWritableFile:\n def test_parse_pattern_re(self):\n \"\"\"Unit tests on the strict entity pattern finder regex.\"\"\"\n assert _PATTERN_FIND.findall('{extension|nii.gz}') == [\n ('{extension|nii.gz}', 'extension', 'nii|nii.gz|json', 'nii.gz')\n ]\n assert _PATTERN_FIND.findall('{extension<.nii|.nii.gz|.json>|.nii.gz}') == [\n (\n '{extension<.nii|.nii.gz|.json>|.nii.gz}',\n 'extension',\n '.nii|.nii.gz|.json',\n '.nii.gz',\n )\n ]\n assert _PATTERN_FIND.findall('{extension|json}') == [\n ('{extension|json}', 'extension', 'json|jsld', 'json')\n ]\n assert _PATTERN_FIND.findall('{task}/r-{run}.nii.gz') == [\n ('{task}', 'task', 'func|rest', ''),\n ('{run}', 'run', '', ''),\n ]\n\n pattern = \"\"\"\\\nsub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]\\\n[_space-{space}]_{suffix}.\\\n{extension|nii.gz}\"\"\"\n assert sorted(_PATTERN_FIND.findall(pattern)) == [\n ('{acquisition}', 'acquisition', '', ''),\n ('{ceagent}', 'ceagent', '', ''),\n ('{extension|nii.gz}', 'extension', 'nii|nii.gz|json', 'nii.gz'),\n ('{reconstruction}', 'reconstruction', '', ''),\n ('{session}', 'session', '', ''),\n ('{session}', 'session', '', ''),\n ('{space}', 'space', '', ''),\n ('{subject}', 'subject', '', ''),\n ('{subject}', 'subject', '', ''),\n (\n '{suffix}',\n 'suffix',\n 'T1w|T2w|T1rho|T1map|T2map|T2star|FLAIR|FLASH|PDmap|PD|PDT2|inplaneT[12]|angio',\n '',\n ),\n ]\n\n def test_build_path(self, writable_file):\n # Single simple pattern\n with pytest.raises(TypeError):\n build_path(writable_file.entities)\n pat = join(writable_file.dirname, '{task}/sub-{subject}/run-{run}.nii.gz')\n target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz')\n assert build_path(writable_file.entities, pat) == target\n\n # Multiple simple patterns\n pats = [\n '{session}/{task}/r-{run}.nii.gz',\n 't-{task}/{subject}-{run}.nii.gz',\n '{subject}/{task}.nii.gz',\n ]\n pats = [join(writable_file.dirname, p) for p in pats]\n target = join(writable_file.dirname, 't-rest/3-2.nii.gz')\n assert build_path(writable_file.entities, pats) == target\n\n # Pattern with optional entity\n pats = ['[{session}/]{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz']\n pats = [join(writable_file.dirname, p) for p in pats]\n target = join(writable_file.dirname, 'rest/r-2.nii.gz')\n assert build_path(writable_file.entities, pats) == target\n\n # Pattern with conditional values\n pats = ['{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz']\n pats = [join(writable_file.dirname, p) for p in pats]\n target = join(writable_file.dirname, 't-rest/3-2.nii.gz')\n assert build_path(writable_file.entities, pats) == target\n\n # Pattern with valid conditional values\n pats = ['{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz']\n pats = [join(writable_file.dirname, p) for p in pats]\n target = join(writable_file.dirname, 'rest/r-2.nii.gz')\n assert build_path(writable_file.entities, pats) == target\n\n # Pattern with optional entity with conditional values\n pats = ['[{task}/]r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz']\n pats = [join(writable_file.dirname, p) for p in pats]\n target = join(writable_file.dirname, 'r-2.nii.gz')\n assert build_path(writable_file.entities, pats) == target\n\n # Pattern with default value\n pats = ['ses-{session|A}/r-{run}.nii.gz']\n assert build_path({'run': 3}, pats) == 'ses-A/r-3.nii.gz'\n\n # Pattern with both valid and default values\n pats = ['ses-{session|D}/r-{run}.nii.gz']\n assert build_path({'run': 3}, pats) == 'ses-D/r-3.nii.gz'\n pats = ['ses-{session|D}/r-{run}.nii.gz']\n assert build_path({'session': 'B', 'run': 3}, pats) == 'ses-B/r-3.nii.gz'\n\n # Test extensions with dot and warning is issued\n pats = ['ses-{session|D}/r-{run}.{extension}']\n with pytest.warns(UserWarning) as record:\n assert (\n build_path({'session': 'B', 'run': 3, 'extension': '.nii'}, pats)\n == 'ses-B/r-3.nii'\n )\n assert 'defines an invalid default value' in record[0].message.args[0]\n\n # Test expansion of optional characters\n pats = ['ses-{session<[ABCD]>|D}/r-{run}.{extension}']\n assert build_path({'session': 'B', 'run': 3, 'extension': '.nii'}, pats) == 'ses-B/r-3.nii'\n\n # Test default-only patterns are correctly overridden by setting entity\n entities = {\n 'subject': '01',\n 'extension': 'bvec',\n 'suffix': 'T1rho',\n }\n pats = (\n 'sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}]'\n '[_acq-{acquisition}]_{suffix|dwi}.{extension|nii.gz}'\n )\n assert build_path(entities, pats) == 'sub-01/dwi/sub-01_T1rho.bvec'\n assert build_path(entities, pats, strict=True) == 'sub-01/dwi/sub-01_T1rho.bvec'\n\n # Test multiple paths\n pats = ['ses-{session}/r-{run}.{extension|nii.gz}']\n assert sorted(\n build_path(\n {'session': ['A', 'B'], 'run': [1, 2], 'extension': ['.nii.gz', 'json']}, pats\n )\n ) == [\n 'ses-A/r-1.json',\n 'ses-A/r-1.nii.gz',\n 'ses-A/r-2.json',\n 'ses-A/r-2.nii.gz',\n 'ses-B/r-1.json',\n 'ses-B/r-1.nii.gz',\n 'ses-B/r-2.json',\n 'ses-B/r-2.nii.gz',\n ]\n\n def test_strict_build_path(self):\n # Test with strict matching--should fail\n pats = ['[{session}/]{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz']\n entities = {'subject': 1, 'task': 'A', 'run': 2}\n assert build_path(entities, pats, True)\n entities = {'subject': 1, 'task': 'A', 'age': 22}\n assert not build_path(entities, pats, True)\n\n def test_build_file(self, writable_file, tmp_bids, caplog):\n # Simple write out\n new_dir = join(writable_file.dirname, 'rest')\n pat = join(writable_file.dirname, '{task}/sub-{subject}/run-{run}.nii.gz')\n target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz')\n writable_file.copy(pat)\n assert exists(target)\n\n # Conflict handling\n with pytest.raises(ValueError):\n writable_file.copy(pat)\n with pytest.raises(ValueError):\n writable_file.copy(pat, conflicts='fail')\n with pytest.warns(UserWarning) as record:\n writable_file.copy(pat, conflicts='skip')\n log_message = record[0].message.args[0]\n assert log_message == f'A file at path {target} already exists, skipping writing file.'\n writable_file.copy(pat, conflicts='append')\n append_target = join(writable_file.dirname, 'rest/sub-3/run-2_1.nii.gz')\n assert exists(append_target)\n writable_file.copy(pat, conflicts='overwrite')\n assert exists(target)\n shutil.rmtree(new_dir)\n\n # Symbolic linking\n writable_file.copy(pat, symbolic_link=True)\n assert islink(target)\n shutil.rmtree(new_dir)\n\n # Using different root\n root = str(tmp_bids.mkdir('tmp2'))\n pat = join(root, '{task}/sub-{subject}/run-{run}.nii.gz')\n target = join(root, 'rest/sub-3/run-2.nii.gz')\n writable_file.copy(pat, root=root)\n assert exists(target)\n\n # Copy into directory functionality\n pat = join(writable_file.dirname, '{task}/')\n writable_file.copy(pat)\n target = join(writable_file.dirname, 'rest', writable_file.filename)\n assert exists(target)\n shutil.rmtree(new_dir)\n\n\nclass TestWritableLayout:\n def test_write_files(self, tmp_bids, layout):\n tmpdir = str(tmp_bids)\n pat = join(\n str(tmpdir),\n 'sub-{subject<02>}'\n '/ses-{session}'\n '/r-{run}'\n '/suffix-{suffix}'\n '/acq-{acquisition}'\n '/task-{task}.nii.gz',\n )\n layout.copy_files(path_patterns=pat)\n example_file = join(\n str(tmpdir), 'sub-02/ses-2/r-1/suffix-bold/acq-fullbrain/task-rest.nii.gz'\n )\n example_file2 = join(\n str(tmpdir), 'sub-01/ses-2/r-1/suffix-bold/acq-fullbrain/task-rest.nii.gz'\n )\n\n assert exists(example_file)\n assert not exists(example_file2)\n\n pat = join(\n str(tmpdir),\n 'sub-{subject<01>}/ses-{session}/r-{run}/suffix-{suffix}/task-{task}.nii.gz',\n )\n example_file = join(str(tmpdir), 'sub-01/ses-2/r-1/suffix-bold/task-rest.nii.gz')\n # Should fail without the 'overwrite' because there are multiple\n # files that produce the same path.\n with pytest.raises(ValueError):\n layout.copy_files(path_patterns=pat)\n try:\n os.remove(example_file)\n except OSError:\n pass\n layout.copy_files(path_patterns=pat, conflicts='overwrite')\n assert exists(example_file)\n\n def test_write_to_file(self, tmp_bids, layout):\n contents = 'test'\n entities = {'subject': 'Bob', 'session': '01'}\n pat = join('sub-{subject}/ses-{session}/desc.txt')\n layout.write_to_file(entities, path_patterns=pat, contents=contents, validate=False)\n target = join(str(tmp_bids), 'bids', 'sub-Bob/ses-01/desc.txt')\n assert exists(target)\n with open(target) as f:\n written = f.read()\n assert written == contents\n assert target not in layout.files\n\n def test_write_to_file_defaults(self, tmp_bids, layout):\n contents = 'test'\n entities = {\n 'subject': 'Bob',\n 'session': '01',\n 'run': '1',\n 'suffix': 'bold',\n 'task': 'test',\n 'acquisition': 'test',\n 'bval': 0,\n }\n layout.write_to_file(entities, contents=contents)\n target = join(\n str(tmp_bids),\n 'bids',\n 'sub-Bob',\n 'ses-01',\n 'func',\n 'sub-Bob_ses-01_task-test_acq-test_run-1_bold.nii.gz',\n )\n assert exists(target)\n with open(target) as f:\n written = f.read()\n assert written == contents\n\n def test_build_file_from_layout(self, tmpdir, layout):\n entities = {'subject': 'Bob', 'session': '01', 'run': '1'}\n pat = join(str(tmpdir), 'sub-{subject}/ses-{session}/r-{run}.nii.gz')\n path = layout.build_path(entities, path_patterns=pat, validate=False)\n assert path == join(str(tmpdir), 'sub-Bob/ses-01/r-1.nii.gz')\n\n data_dir = join(dirname(__file__), 'data', '7t_trt')\n filename = 'sub-04_ses-1_task-rest_acq-fullbrain_run-1_physio.tsv.gz'\n file = join('sub-04', 'ses-1', 'func', filename)\n path = layout.build_path(file, path_patterns=pat, validate=False)\n assert path.endswith('sub-04/ses-1/r-1.nii.gz')\n" }, { "path": "src/bids/layout/utils.py", "content": "\"\"\"Miscellaneous layout-related utilities.\"\"\"\n\nfrom upath import UPath as Path\n\nfrom .. import config as cf\nfrom ..exceptions import ConfigError\nfrom ..utils import listify, make_bidsfile\n\n\nclass BIDSMetadata(dict):\n \"\"\"Metadata dictionary that reports the associated file on lookup failures.\"\"\"\n\n def __init__(self, source_file):\n self._source_file = source_file\n super().__init__()\n\n def __getitem__(self, key):\n try:\n return super().__getitem__(key)\n except KeyError:\n raise KeyError(f'Metadata term {key!r} unavailable for file {self._source_file}.') # noqa: B904\n\n\nclass PaddedInt(int):\n \"\"\"Integer type that preserves zero-padding\n\n Acts like an int in almost all ways except that string formatting\n will keep the original zero-padding. Numeric format specifiers\n\n >>> PaddedInt(1)\n 1\n >>> p2 = PaddedInt('02')\n >>> p2\n 02\n >>> str(p2)\n '02'\n >>> p2 == 2\n True\n >>> p2 in range(3)\n True\n >>> f'{p2}'\n '02'\n >>> f'{p2:s}'\n '02'\n >>> f'{p2!s}'\n '02'\n >>> f'{p2!r}'\n '02'\n >>> f'{p2:d}'\n '2'\n >>> f'{p2:03d}'\n '002'\n >>> f'{p2:f}'\n '2.000000'\n >>> {2: 'val'}.get(p2)\n 'val'\n >>> {p2: 'val'}.get(2)\n 'val'\n\n Note that arithmetic will break the padding.\n\n >>> str(p2 + 1)\n '3'\n \"\"\"\n\n def __init__(self, val):\n self.sval = str(val)\n\n def __eq__(self, val):\n try:\n return val == self.sval or super().__eq__(val)\n except ValueError:\n # `or` triggers `__bool__`. If this fails, it is almost\n # certainly an array type. Do not attempt string comparisons\n # and allow val to determine the return type\n return val == self\n\n def __str__(self):\n return self.sval\n\n def __repr__(self):\n return self.sval\n\n def __format__(self, format_spec):\n \"\"\"Format a padded integer\n\n If a format spec can be used on a string, apply it to the zero-padded string.\n Otherwise format as an integer.\n \"\"\"\n try:\n return format(self.sval, format_spec)\n except ValueError:\n return super().__format__(format_spec)\n\n def __hash__(self):\n return super().__hash__()\n\n\ndef parse_file_entities(filename, entities=None, config=None, include_unmatched=False):\n \"\"\"Parse the passed filename for entity/value pairs.\n\n Parameters\n ----------\n filename : str\n The filename to parse for entity values\n entities : list or None, optional\n An optional list of Entity instances to use in extraction.\n If passed, the config argument is ignored. Default is None.\n config : str or :obj:`bids.layout.models.Config` or list or None, optional\n One or more :obj:`bids.layout.models.Config` objects or names of\n configurations to use in matching. Each element must be a\n :obj:`bids.layout.models.Config` object, or a valid\n :obj:`bids.layout.models.Config` name (e.g., 'bids' or 'derivatives').\n If None, all available configs are used. Default is None.\n include_unmatched : bool, optional\n If True, unmatched entities are included in the returned dict,\n with values set to None.\n If False (default), unmatched entities are ignored.\n\n Returns\n -------\n dict\n Keys are Entity names and values are the values from the filename.\n\n \"\"\"\n # Load Configs if needed\n if entities is None:\n if config is None:\n config = ['bids', 'derivatives']\n\n from .models import Config\n\n config = [Config.load(c) if not isinstance(c, Config) else c for c in listify(config)]\n\n # Consolidate entities from all Configs into a single dict\n entities = {}\n for c in config:\n entities.update(c.entities)\n entities = entities.values()\n\n # Extract matches\n bf = make_bidsfile(filename)\n ent_vals = {}\n for ent in entities:\n match = ent.match_file(bf)\n if match is not None or include_unmatched:\n ent_vals[ent.name] = match\n\n return ent_vals\n\n\ndef add_config_paths(**kwargs):\n \"\"\"Add to the pool of available configuration files for BIDSLayout.\n\n Parameters\n ----------\n kwargs : dict\n Dictionary specifying where to find additional config files.\n Keys are names, values are paths to the corresponding .json file.\n\n Examples\n --------\n > add_config_paths(my_config='/path/to/config')\n > layout = BIDSLayout('/path/to/bids', config=['bids', 'my_config'])\n\n \"\"\"\n for k, path in kwargs.items():\n if not Path(path).exists():\n raise ConfigError(f'Configuration file \"{k}\" does not exist')\n if k in cf.get_option('config_paths'):\n raise ConfigError(f'Configuration {k!r} already exists')\n\n kwargs.update(**cf.get_option('config_paths'))\n cf.set_option('config_paths', kwargs)\n" }, { "path": "src/bids/layout/validation.py", "content": "\"\"\"Functionality related to validation of BIDSLayouts and BIDS projects.\"\"\"\n\nimport json\nimport re\nimport warnings\n\nfrom upath import UPath as Path\n\nfrom ..exceptions import BIDSDerivativesValidationError, BIDSValidationError\nfrom ..utils import listify\n\nMANDATORY_BIDS_FIELDS = {\n 'Name': {'Name': 'Example dataset'},\n 'BIDSVersion': {'BIDSVersion': '1.0.2'},\n}\n\n\nMANDATORY_DERIVATIVES_FIELDS = {\n **MANDATORY_BIDS_FIELDS,\n 'GeneratedBy': {'GeneratedBy': [{'Name': 'Example pipeline'}]},\n}\n\nEXAMPLE_BIDS_DESCRIPTION = {k: val[k] for val in MANDATORY_BIDS_FIELDS.values() for k in val}\n\n\nEXAMPLE_DERIVATIVES_DESCRIPTION = {\n k: val[k] for val in MANDATORY_DERIVATIVES_FIELDS.values() for k in val\n}\n\n\nDEFAULT_LOCATIONS_TO_IGNORE = {\n re.compile(r'^/(code|models|sourcedata|stimuli)'),\n}\n\nALWAYS_IGNORE = (\n re.compile(r'/\\.'), # dotfiles should never be indexed\n)\n\n\ndef validate_root(root, validate): # noqa: D103\n # Validate root argument and make sure it contains mandatory info\n try:\n root = Path(root)\n except TypeError:\n raise TypeError( # noqa: B904\n 'root argument must be a pathlib.Path (or a type that '\n 'supports casting to pathlib.Path, such as '\n 'string) specifying the directory '\n 'containing the BIDS dataset.'\n )\n\n root = root.absolute()\n if not root.exists():\n raise ValueError('BIDS root does not exist: %s' % root) # noqa: UP031\n\n target = root / 'dataset_description.json'\n if not target.exists():\n if validate:\n raise BIDSValidationError(\n \"'dataset_description.json' is missing from project root.\" # noqa: UP031\n ' Every valid BIDS dataset must have this file.'\n \"\\nExample contents of 'dataset_description.json': \\n%s\"\n % json.dumps(EXAMPLE_BIDS_DESCRIPTION)\n )\n else:\n description = None\n else:\n err = None\n try:\n with target.fs.open(target.path, 'r', encoding='utf-8') as desc_fd:\n description = json.load(desc_fd)\n except (UnicodeDecodeError, json.JSONDecodeError) as e:\n description = None\n err = e\n if validate:\n if description is None:\n raise BIDSValidationError(\n \"'dataset_description.json' is not a valid json file.\"\n \" There is likely a typo in your 'dataset_description.json' at \"\n f'{target.resolve()}. '\n \"\\nExample contents of 'dataset_description.json': \\n%s\"\n % json.dumps(EXAMPLE_BIDS_DESCRIPTION)\n ) from err\n\n for k in MANDATORY_BIDS_FIELDS:\n if k not in description:\n raise BIDSValidationError(\n 'Mandatory %r field missing from '\n \"'dataset_description.json' at \"\n f'{target.resolve()}. '\n '\\nExample: %s' % (k, MANDATORY_BIDS_FIELDS[k])\n )\n\n return root, description\n\n\ndef validate_derivative_path(path, **kwargs): # noqa: D103\n # Collect all paths that contain a dataset_description.json\n dd = Path(path) / 'dataset_description.json'\n description = json.loads(dd.read_text(encoding='utf-8'))\n pipeline_names = [\n pipeline['Name'] for pipeline in description.get('GeneratedBy', []) if 'Name' in pipeline\n ]\n if pipeline_names:\n pipeline_name = pipeline_names[0]\n elif 'PipelineDescription' in description:\n warnings.warn( # noqa: B028\n 'The PipelineDescription field was superseded '\n 'by GeneratedBy in BIDS 1.4.0. You can use '\n '``pybids upgrade`` to update your derivative '\n 'dataset.'\n )\n pipeline_name = description['PipelineDescription'].get('Name')\n else:\n pipeline_name = None\n if pipeline_name is None:\n raise BIDSDerivativesValidationError(\n 'Every valid BIDS-derivatives dataset must '\n 'have a GeneratedBy.Name field set '\n \"inside 'dataset_description.json', \"\n f'here {dd.resolve()}. '\n f'\\nExample: {MANDATORY_DERIVATIVES_FIELDS[\"GeneratedBy\"]}'\n )\n return pipeline_name\n\n\ndef _sort_patterns(patterns, root):\n \"\"\"Return sorted patterns, from more specific to more general.\"\"\"\n regexes = [patt for patt in patterns if hasattr(patt, 'search')]\n\n paths = [\n str((root / patt).absolute()) for patt in listify(patterns) if not hasattr(patt, 'search')\n ]\n # Sort patterns from general to specific\n paths.sort(key=len)\n\n # Combine and return (note path patterns are reversed, specific first)\n return [Path(p) for p in reversed(paths)] + regexes\n\n\ndef validate_indexing_args(ignore, force_index, root): # noqa: D103\n if ignore is None:\n ignore = DEFAULT_LOCATIONS_TO_IGNORE - set(force_index or [])\n\n ignore = list(ignore)\n\n ignore.extend(ALWAYS_IGNORE)\n\n # root has already been validated to be a directory\n ignore = _sort_patterns(ignore, root)\n force_index = _sort_patterns(force_index or [], root)\n\n # Derivatives get special handling; they shouldn't be indexed normally\n for entry in force_index:\n condi = isinstance(entry, str) and str(entry.resolve()).startswith('derivatives')\n if condi:\n msg = (\n \"Do not pass 'derivatives' in the force_index \"\n 'list. To index derivatives, either set '\n 'derivatives=True, or use add_derivatives().'\n )\n raise ValueError(msg)\n\n return ignore, force_index\n" }, { "path": "src/bids/layout/writing.py", "content": "\"\"\"Contains helper functions that involve writing operations.\"\"\"\n\nimport re\nimport shutil\nimport sys\nimport warnings\nfrom itertools import product\nfrom string import Formatter\n\nfrom upath import UPath as Path\n\nfrom ..utils import listify\n\n__all__ = ['build_path', 'write_to_file']\n\n_PATTERN_FIND = re.compile(r'({([\\w\\d]*?)(?:<([^>]+)>)?(?:\\|((?:\\.?[\\w])+))?\\})')\n\n\ndef build_path(entities, path_patterns, strict=False):\n \"\"\"Constructs a path given a set of entities and a list of potential\n filename patterns to use.\n\n Parameters\n ----------\n entities : :obj:`dict`\n A dictionary mapping entity names to entity values.\n Entities with ``None`` or empty-string value will be removed.\n Otherwise, entities will be cast to string values, therefore\n if any format is expected (e.g., zero-padded integers), the\n value should be formatted.\n path_patterns : :obj:`str` or :obj:`list`\n One or more filename patterns to write\n the file to. Entities should be represented by the name\n surrounded by curly braces. Optional portions of the patterns\n should be denoted by square brackets. Entities that require a\n specific value for the pattern to match can pass them inside\n angle brackets. Default values can be assigned by specifying a string after\n the pipe operator. E.g., (e.g., {type|bold} would only match\n the pattern if the entity 'type' was passed and its value is\n \"image\", otherwise the default value \"bold\" will be used).\n strict : :obj:`bool`\n If True, all passed entities must be matched inside a\n pattern in order to be a valid match. If False, extra entities will\n be ignored so long as all mandatory entities are found.\n\n Returns\n -------\n A constructed path for this file based on the provided patterns, or\n ``None`` if no path was built given the combination of entities and patterns.\n\n Examples\n --------\n >>> entities = {\n ... 'extension': '.nii',\n ... 'space': 'MNI',\n ... 'subject': '001',\n ... 'suffix': 'inplaneT2',\n ... }\n >>> patterns = [\n ... 'sub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}]'\n ... '[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]_'\n ... '{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}',\n ... 'sub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}]'\n ... '[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]'\n ... '[_space-{space}][_desc-{desc}]_{suffix}'\n ... '{extension<.nii|.nii.gz|.json>|.nii.gz}',\n ... ]\n >>> build_path(entities, patterns)\n 'sub-001/anat/sub-001_inplaneT2.nii'\n\n >>> build_path(entities, patterns, strict=True)\n 'sub-001/anat/sub-001_space-MNI_inplaneT2.nii'\n\n >>> entities['space'] = None\n >>> build_path(entities, patterns, strict=True)\n 'sub-001/anat/sub-001_inplaneT2.nii'\n\n >>> # If some entity is set to None, they are dropped\n >>> entities['extension'] = None\n >>> build_path(entities, patterns, strict=True)\n 'sub-001/anat/sub-001_inplaneT2.nii.gz'\n\n >>> # If some entity is set to empty-string, they are dropped\n >>> entities['extension'] = ''\n >>> build_path(entities, patterns, strict=True)\n 'sub-001/anat/sub-001_inplaneT2.nii.gz'\n\n >>> # If some selector is not in the pattern, skip it...\n >>> entities['datatype'] = 'anat'\n >>> build_path(entities, patterns)\n 'sub-001/anat/sub-001_inplaneT2.nii.gz'\n\n >>> # ... unless the pattern should be strictly matched\n >>> entities['datatype'] = 'anat'\n >>> build_path(entities, patterns, strict=True) is None\n True\n\n >>> # If the value of an entity is not valid, do not match the pattern\n >>> entities['suffix'] = 'bold'\n >>> build_path(entities, patterns) is None\n True\n\n >>> entities = {\n ... 'extension': '.bvec',\n ... 'subject': '001',\n ... }\n >>> patterns = (\n ... 'sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}]'\n ... '[_acq-{acquisition}]_{suffix|dwi}{extension<.bval|.bvec|.json|.nii.gz|.nii>|.nii.gz}'\n ... )\n >>> build_path(entities, patterns, strict=True)\n 'sub-001/dwi/sub-001_dwi.bvec'\n\n >>> # Lists of entities are expanded\n >>> entities = {\n ... 'extension': '.bvec',\n ... 'subject': ['%02d' % i for i in range(1, 4)],\n ... }\n >>> build_path(entities, patterns, strict=True)\n ['sub-01/dwi/sub-01_dwi.bvec', 'sub-02/dwi/sub-02_dwi.bvec', 'sub-03/dwi/sub-03_dwi.bvec']\n\n \"\"\"\n path_patterns = listify(path_patterns)\n\n # Drop None and empty-strings, keep zeros, and listify\n entities = {k: listify(v) for k, v in entities.items() if v or v == 0}\n\n # Loop over available patherns, return first one that matches all\n for pattern in path_patterns:\n entities_matched = list(_PATTERN_FIND.findall(pattern))\n defined = [e[1] for e in entities_matched]\n\n # If strict, all entities must be contained in the pattern\n if strict:\n if set(entities.keys()) - set(defined):\n continue\n\n # Iterate through the provided path patterns\n new_path = pattern\n\n # Expand options within valid values and\n # check whether entities provided have acceptable value\n tmp_entities = entities.copy() # Do not modify the original query\n\n # Accept extensions with and without leading dot\n if 'extension' in tmp_entities:\n exts = [e.lstrip('.') for e in tmp_entities['extension']]\n # Does this pattern place a dot before the extension, or expect it inside?\n if re.search(r'\\.\\{extension', pattern):\n tmp_entities['extension'] = exts\n else:\n tmp_entities['extension'] = ['.' + e for e in exts]\n\n for fmt, name, valid, defval in entities_matched:\n valid_expanded = [v for val in valid.split('|') if val for v in _expand_options(val)]\n if valid_expanded and defval and defval not in valid_expanded:\n warnings.warn(\n f'Pattern {fmt!r} is inconsistent as it defines an invalid default value.',\n stacklevel=2,\n )\n\n if (\n valid_expanded\n and name in tmp_entities\n and set(tmp_entities[name]) - set(valid_expanded)\n ):\n continue\n\n if defval and name not in tmp_entities:\n tmp_entities[name] = [defval]\n\n # At this point, valid & default values are checked & set - simplify pattern\n new_path = new_path.replace(fmt, '{%s}' % name) # noqa: UP031\n\n optional_patterns = re.findall(r'(\\[.*?\\])', new_path)\n # Optional patterns with selector are cast to mandatory or removed\n for op in optional_patterns:\n for ent_name in {k for k, v in entities.items() if v is not None}:\n if ('{%s}' % ent_name) in op: # noqa: UP031\n new_path = new_path.replace(op, op[1:-1])\n continue\n\n # Surviving optional patterns are removed\n new_path = new_path.replace(op, '')\n\n # Replace entities\n fields = {pat[1] for pat in Formatter().parse(new_path) if pat[1] and not pat[1].isdigit()}\n if fields - set(tmp_entities.keys()):\n continue\n\n tmp_entities = {k: v for k, v in tmp_entities.items() if k in fields}\n\n new_path = [new_path.format(**e) for e in _expand_entities(tmp_entities)]\n\n if new_path:\n if len(new_path) == 1:\n new_path = new_path[0]\n return new_path\n\n return None\n\n\ndef write_to_file(\n path,\n contents=None,\n link_to=None,\n copy_from=None,\n content_mode='text',\n root=None,\n conflicts='fail',\n):\n \"\"\"Writes provided contents to a new path, or copies from an old path.\n\n Parameters\n ----------\n path : str\n Destination path of the desired contents.\n contents : str\n Raw text or binary encoded string of contents to write\n to the new path.\n link_to : str\n Optional path with which to create a symbolic link to.\n Used as an alternative to, and takes priority over, the contents\n argument.\n copy_from : str\n Optional filename to copy to new location. Used an alternative to, and\n takes priority over, the contents argument.\n content_mode : {'text', 'binary'}\n Either 'text' or 'binary' to indicate the writing\n mode for the new file. Only relevant if contents is provided.\n root : str\n Optional root directory that all patterns are relative\n to. Defaults to current working directory.\n conflicts : {'fail', 'skip', 'overwrite', 'append'}\n One of 'fail', 'skip', 'overwrite', or 'append'\n that defines the desired action when the output path already\n exists. 'fail' raises an exception; 'skip' does nothing;\n 'overwrite' overwrites the existing file; 'append' adds a suffix\n to each file copy, starting with 1. Default is 'fail'.\n\n \"\"\"\n path = Path(path)\n\n if root is None and not path.is_absolute():\n root = Path.cwd()\n\n if root:\n path = root / path\n\n if path.exists() or path.is_symlink():\n if conflicts == 'fail':\n msg = 'A file at path {} already exists.'\n raise ValueError(msg.format(path))\n elif conflicts == 'skip':\n msg = 'A file at path {} already exists, skipping writing file.'\n warnings.warn(msg.format(path)) # noqa: B028\n return\n elif conflicts == 'overwrite':\n if path.is_dir():\n warnings.warn( # noqa: B028\n 'New path is a directory, not going to overwrite it, skipping instead.'\n )\n return\n path.unlink()\n elif conflicts == 'append':\n i = 1\n while i < sys.maxsize:\n suffixes = ''.join(path.suffixes)\n appended_filename = path.with_name(\n path.name.rstrip(suffixes) + '_%d' % i + suffixes # noqa: UP031\n )\n if not appended_filename.exists() and not appended_filename.is_symlink():\n path = appended_filename\n break\n i += 1\n else:\n raise ValueError('Did not provide a valid conflicts parameter')\n\n if not path.parent.exists():\n path.parent.mkdir(parents=True)\n\n if link_to is not None:\n path.symlink_to(link_to)\n elif copy_from is not None:\n if not Path(copy_from).exists():\n raise ValueError(f\"Source file '{copy_from}' does not exist.\")\n shutil.copy(copy_from, path)\n\n elif contents:\n mode = 'wb' if content_mode == 'binary' else 'w'\n with path.open(mode) as f:\n f.write(contents)\n else:\n raise ValueError('One of contents, copy_from or link_to must be provided.')\n\n\ndef _expand_options(value):\n \"\"\"Expand optional substrings of valid entity values.\n\n Examples\n --------\n >>> _expand_options('[Jj]son[12]')\n ['Json1', 'Json2', 'json1', 'json2']\n\n >>> _expand_options('json')\n ['json']\n\n \"\"\"\n expand_patterns = re.findall(r'\\[(.*?)\\]', value)\n if not expand_patterns:\n return [value]\n\n value = re.sub(r'\\[(.*?)\\]', '%s', value)\n return [value % _r for _r in product(*expand_patterns)]\n\n\ndef _expand_entities(entities):\n \"\"\"Generate multiple replacement queries based on all combinations of values.\n\n Examples\n --------\n >>> entities = {'subject': ['01', '02'], 'session': ['1', '2'], 'task': ['rest', 'finger']}\n >>> out = _expand_entities(entities)\n >>> len(out)\n 8\n\n >>> {'subject': '01', 'session': '1', 'task': 'rest'} in out\n True\n\n >>> {'subject': '02', 'session': '1', 'task': 'rest'} in out\n True\n\n >>> {'subject': '01', 'session': '2', 'task': 'rest'} in out\n True\n\n >>> {'subject': '02', 'session': '2', 'task': 'rest'} in out\n True\n\n >>> {'subject': '01', 'session': '1', 'task': 'finger'} in out\n True\n\n >>> {'subject': '02', 'session': '1', 'task': 'finger'} in out\n True\n\n >>> {'subject': '01', 'session': '2', 'task': 'finger'} in out\n True\n\n >>> {'subject': '02', 'session': '2', 'task': 'finger'} in out\n True\n\n \"\"\"\n keys = list(entities.keys())\n values = list(product(*[entities[k] for k in keys]))\n return [{k: v for k, v in zip(keys, combs)} for combs in values] # noqa: B905, C416\n" }, { "path": "src/bids/modeling/README.md", "content": "## PyBIDS: Modeling\n\nThe 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.\n\nNote 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.\n" }, { "path": "src/bids/modeling/__init__.py", "content": "from .auto_model import auto_model # noqa: D104\nfrom .statsmodels import (\n BIDSStatsModelsEdge,\n BIDSStatsModelsGraph,\n BIDSStatsModelsNode,\n BIDSStatsModelsNodeOutput,\n)\nfrom .transformations import TransformerManager\n\n__all__ = [\n 'BIDSStatsModelsGraph',\n 'BIDSStatsModelsNode',\n 'BIDSStatsModelsNodeOutput',\n 'BIDSStatsModelsEdge',\n 'auto_model',\n 'TransformerManager',\n]\n" }, { "path": "src/bids/modeling/auto_model.py", "content": "from collections import OrderedDict # noqa: D100\n\nimport numpy as np\n\nfrom bids.variables import load_variables\n\n\ndef _make_passthrough_contrast(level, model_type='glm', test='t'):\n gb_dict = dict( # noqa: C408\n Subject=['subject', 'contrast'], Session=['session', 'contrast'], Dataset=['contrast']\n )\n block = dict( # noqa: C408\n Level=level,\n Name=level,\n GroupBy=gb_dict[level],\n Model={'Type': model_type, 'X': [1]},\n DummyContrasts={'Test': test},\n )\n return block\n\n\ndef auto_model(layout, scan_length=None, one_vs_rest=False):\n \"\"\"Create a simple default model for each of the tasks in a BIDSLayout.\n Contrasts each trial type against all other trial types and trial types\n at the run level and then uses dummy contrasts at each other level\n present to aggregate these results up.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n A BIDSLayout instance\n scan_length : int\n Scan length for loading event variables in cases\n where the scan length can not be read from the nifti.\n Primarily for testing.\n one_vs_rest : bool\n Set to True if you would like to autogenerate\n contrasts of each trial type against everyother trialtype.\n\n Returns\n -------\n list\n list of model dictionaries for each task\n\n \"\"\"\n base_name = layout._root.name\n tasks = layout.entities['task'].unique()\n task_models = []\n\n for task_name in tasks:\n # Populate model meta-data\n model = OrderedDict()\n model['Name'] = '_'.join([base_name, task_name])\n model['Description'] = 'Autogenerated model for the %s task from %s' % ( # noqa: UP031\n task_name,\n base_name,\n )\n model['BIDSModelVersion'] = '1.0.0'\n model['Input'] = {'task': [task_name]}\n nodes = []\n\n # Make run level block\n transformations = dict( # noqa: C408\n Transformer='pybids-transforms-v1',\n Instructions=[dict(Name='Factor', Input='trial_type')], # noqa: C408\n )\n run = dict( # noqa: C408\n Level='Run', Name='Run', GroupBy=['run', 'subject'], Transformations=transformations\n )\n\n # Get trial types\n run_nodes = load_variables(layout, task=task_name, levels=['run'], scan_length=scan_length)\n\n evs = []\n for n in run_nodes.nodes:\n evs.extend(n.variables['trial_type'].values.values)\n trial_types = np.unique(evs)\n trial_type_factors = ['trial_type.' + tt for tt in trial_types]\n\n run_model = dict(Type='glm', X=trial_type_factors) # noqa: C408\n # Add HRF\n run_model['HRF'] = dict( # noqa: C408\n Variables=trial_type_factors,\n Model='DoubleGamma',\n Parameters=dict( # noqa: C408\n PeakDelay=3,\n PeakDispersion=6,\n UndershootDelay=10,\n UndershootDispersion=12,\n PeakUndershootRatio=0.2,\n ),\n )\n run['Model'] = run_model\n\n if one_vs_rest:\n # If there are multiple trial types, build contrasts\n contrasts = []\n for tt in trial_types:\n cdict = OrderedDict()\n if len(trial_types) > 1:\n cdict['Name'] = 'run_' + tt + '_vs_others'\n else:\n cdict['Name'] = 'run_' + tt\n cdict['ConditionList'] = trial_type_factors\n\n # Calculate weights for contrast\n weights = np.ones(len(trial_types))\n try:\n weights[trial_types != tt] = -1.0 / (len(trial_types) - 1)\n except ZeroDivisionError:\n pass\n cdict['Weights'] = list(weights)\n\n cdict['Test'] = 't'\n contrasts.append(cdict)\n\n run['Contrasts'] = contrasts\n nodes.append(run)\n\n if one_vs_rest:\n # if there are multiple sessions/subjects, t-test run level contrasts at\n # session/subject level\n if len(layout.get_sessions()) > 1:\n nodes.append(_make_passthrough_contrast('Session', 'meta'))\n\n if len(layout.get_subjects()) > 1:\n nodes.append(_make_passthrough_contrast('Subject', 'meta'))\n\n nodes.append(_make_passthrough_contrast('Dataset'))\n\n model['Nodes'] = nodes\n task_models.append(model)\n\n return task_models\n" }, { "path": "src/bids/modeling/hrf.py", "content": "# ruff: noqa: D404\n\"\"\"This module is for hemodynamic response function (hrf) specification. # noqa: D404\nHere we provide for SPM, Glover hrfs and finite timpulse response (FIR) models.\nThis module closely follows SPM implementation\n\nAuthor: Bertrand Thirion, 2011--2015\n\"\"\"\n\nimport warnings\n\nimport numpy as np\n\n\ndef _gamma_difference_hrf(\n tr,\n oversampling=50,\n time_length=32.0,\n onset=0.0,\n delay=6,\n undershoot=16.0,\n dispersion=1.0,\n u_dispersion=1.0,\n ratio=0.167,\n):\n \"\"\"Compute an hrf as the difference of two gamma functions\n\n Parameters\n ----------\n tr : float\n scan repeat time, in seconds\n oversampling : int, optional (default=16)\n temporal oversampling factor\n time_length : float, optional (default=32)\n hrf kernel length, in seconds\n onset: float\n onset time of the hrf\n delay: float, optional\n delay parameter of the hrf (in s.)\n undershoot: float, optional\n undershoot parameter of the hrf (in s.)\n dispersion : float, optional\n dispersion parameter for the first gamma function\n u_dispersion : float, optional\n dispersion parameter for the second gamma function\n ratio : float, optional\n ratio of the two gamma components\n\n Returns\n -------\n hrf : array of shape(length / tr * oversampling, dtype=float)\n hrf sampling on the oversampled time grid\n\n \"\"\"\n from scipy.stats import gamma\n\n dt = tr / oversampling\n time_stamps = np.linspace(0, time_length, np.rint(float(time_length) / dt).astype(int))\n time_stamps -= onset\n hrf = gamma.pdf(time_stamps, delay / dispersion, dt / dispersion) - ratio * gamma.pdf(\n time_stamps, undershoot / u_dispersion, dt / u_dispersion\n )\n hrf /= hrf.sum()\n return hrf\n\n\ndef spm_hrf(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the SPM hrf model\n\n Parameters\n ----------\n tr : float\n scan repeat time, in seconds\n oversampling : int, optional\n temporal oversampling factor\n time_length : float, optional\n hrf kernel length, in seconds\n onset : float, optional\n hrf onset time, in seconds\n\n Returns\n -------\n hrf: array of shape(length / tr * oversampling, dtype=float)\n hrf sampling on the oversampled time grid\n\n \"\"\"\n return _gamma_difference_hrf(tr, oversampling, time_length, onset)\n\n\ndef glover_hrf(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the Glover hrf model\n\n Parameters\n ----------\n tr : float\n scan repeat time, in seconds\n oversampling : int, optional\n temporal oversampling factor\n time_length : float, optional\n hrf kernel length, in seconds\n onset : float, optional\n onset of the response\n\n Returns\n -------\n hrf: array of shape(length / tr * oversampling, dtype=float)\n hrf sampling on the oversampled time grid\n\n \"\"\"\n return _gamma_difference_hrf(\n tr,\n oversampling,\n time_length,\n onset,\n delay=6,\n undershoot=12.0,\n dispersion=0.9,\n u_dispersion=0.9,\n ratio=0.35,\n )\n\n\ndef spm_time_derivative(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the SPM time derivative hrf (dhrf) model\n\n Parameters\n ----------\n tr: float\n scan repeat time, in seconds\n oversampling: int, optional\n temporal oversampling factor, optional\n time_length: float, optional\n hrf kernel length, in seconds\n onset: float, optional\n onset of the response in seconds\n\n Returns\n -------\n dhrf: array of shape(length / tr, dtype=float)\n dhrf sampling on the provided grid\n\n \"\"\"\n do = 0.1\n dhrf = (\n 1.0\n / do\n * (\n spm_hrf(tr, oversampling, time_length, onset)\n - spm_hrf(tr, oversampling, time_length, onset + do)\n )\n )\n return dhrf\n\n\ndef glover_time_derivative(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the Glover time derivative hrf (dhrf) model\n\n Parameters\n ----------\n tr: float\n scan repeat time, in seconds\n oversampling: int,\n temporal oversampling factor, optional\n time_length: float,\n hrf kernel length, in seconds\n onset: float,\n onset of the response\n\n Returns\n -------\n dhrf: array of shape(length / tr), dtype=float\n dhrf sampling on the provided grid\n\n \"\"\"\n do = 0.1\n dhrf = (\n 1.0\n / do\n * (\n glover_hrf(tr, oversampling, time_length, onset)\n - glover_hrf(tr, oversampling, time_length, onset + do)\n )\n )\n return dhrf\n\n\ndef spm_dispersion_derivative(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the SPM dispersion derivative hrf model\n\n Parameters\n ----------\n tr: float\n scan repeat time, in seconds\n oversampling: int, optional\n temporal oversampling factor in seconds\n time_length: float, optional\n hrf kernel length, in seconds\n onset : float, optional\n onset of the response in seconds\n\n Returns\n -------\n dhrf: array of shape(length / tr * oversampling), dtype=float\n dhrf sampling on the oversampled time grid\n\n \"\"\"\n dd = 0.01\n dhrf = (\n 1.0\n / dd\n * (\n -_gamma_difference_hrf(tr, oversampling, time_length, onset, dispersion=1.0 + dd)\n + _gamma_difference_hrf(tr, oversampling, time_length, onset)\n )\n )\n return dhrf\n\n\ndef glover_dispersion_derivative(tr, oversampling=50, time_length=32.0, onset=0.0):\n \"\"\"Implementation of the Glover dispersion derivative hrf model\n\n Parameters\n ----------\n tr: float\n scan repeat time, in seconds\n oversampling: int, optional\n temporal oversampling factor in seconds\n time_length: float, optional\n hrf kernel length, in seconds\n onset : float, optional\n onset of the response in seconds\n\n Returns\n -------\n dhrf: array of shape(length / tr * oversampling), dtype=float\n dhrf sampling on the oversampled time grid\n\n \"\"\"\n dd = 0.01\n dhrf = (\n 1.0\n / dd\n * (\n -_gamma_difference_hrf(\n tr,\n oversampling,\n time_length,\n onset,\n delay=6,\n undershoot=12.0,\n dispersion=0.9 + dd,\n ratio=0.35,\n )\n + _gamma_difference_hrf(\n tr,\n oversampling,\n time_length,\n onset,\n delay=6,\n undershoot=12.0,\n dispersion=0.9,\n ratio=0.35,\n )\n )\n )\n return dhrf\n\n\ndef _sample_condition(exp_condition, frame_times, oversampling=50, min_onset=-24):\n \"\"\"Make a possibly oversampled event regressor from condition information.\n\n Parameters\n ----------\n exp_condition : arraylike of shape (3, n_events)\n yields description of events for this condition as a\n (onsets, durations, amplitudes) triplet\n frame_times : array of shape(n_scans)\n sample time points\n oversampling : int, optional\n factor for oversampling event regressor\n min_onset : float, optional\n minimal onset relative to frame_times[0] (in seconds)\n events that start before frame_times[0] + min_onset are not considered\n\n Returns\n -------\n regressor: array of shape(over_sampling * n_scans)\n possibly oversampled event regressor\n hr_frame_times : array of shape(over_sampling * n_scans)\n time points used for regressor sampling\n\n \"\"\"\n # Find the high-resolution frame_times\n n = frame_times.size\n min_onset = float(min_onset)\n n_hr = (\n (n - 1)\n * 1.0\n / (frame_times.max() - frame_times.min())\n * (frame_times.max() * (1 + 1.0 / (n - 1)) - frame_times.min() - min_onset)\n * oversampling\n ) + 1\n\n hr_frame_times = np.linspace(\n frame_times.min() + min_onset,\n frame_times.max() * (1 + 1.0 / (n - 1)),\n np.rint(n_hr).astype(int),\n )\n\n # Get the condition information\n onsets, durations, values = tuple(map(np.asanyarray, exp_condition))\n if (onsets < frame_times[0] + min_onset).any():\n warnings.warn( # noqa: B028\n (\n 'Some stimulus onsets are earlier than %s in the'\n ' experiment and are thus not considered in the model'\n % (frame_times[0] + min_onset)\n ),\n UserWarning,\n )\n\n # Set up the regressor timecourse\n tmax = len(hr_frame_times)\n regressor = np.zeros_like(hr_frame_times).astype(float)\n t_onset = np.minimum(np.searchsorted(hr_frame_times, onsets), tmax - 1)\n for t, v in zip(t_onset, values): # noqa: B905\n regressor[t] += v\n t_offset = np.minimum(np.searchsorted(hr_frame_times, onsets + durations), tmax - 1)\n\n # Handle the case where duration is 0 by offsetting at t + 1\n for i, t in enumerate(t_offset):\n if t < (tmax - 1) and t == t_onset[i]:\n t_offset[i] += 1\n\n for t, v in zip(t_offset, values): # noqa: B905\n regressor[t] -= v\n regressor = np.cumsum(regressor)\n\n return regressor, hr_frame_times\n\n\ndef _resample_regressor(hr_regressor, hr_frame_times, frame_times):\n \"\"\"This function sub-samples the regressors at frame times # noqa: D404\n\n Parameters\n ----------\n hr_regressor : array of shape(n_samples),\n the regressor time course sampled at high temporal resolution\n hr_frame_times : array of shape(n_samples),\n the corresponding time stamps\n frame_times: array of shape(n_scans),\n the desired time stamps\n\n Returns\n -------\n regressor: array of shape(n_scans)\n the resampled regressor\n\n \"\"\"\n from scipy.interpolate import interp1d\n\n f = interp1d(hr_frame_times, hr_regressor)\n return f(frame_times).T\n\n\ndef _orthogonalize(X):\n \"\"\"Orthogonalize every column of design `X` w.r.t preceding columns\n\n Parameters\n ----------\n X : array of shape(n, p)\n the data to be orthogonalized\n\n Returns\n -------\n X : array of shape(n, p)\n the data after orthogonalization\n\n Notes\n -----\n X is changed in place. The columns are not normalized.\n\n \"\"\"\n if X.size == X.shape[0]:\n return X\n from numpy.linalg import pinv\n\n for i in range(1, X.shape[1]):\n X[:, i] -= np.dot(np.dot(X[:, i], X[:, :i]), pinv(X[:, :i]))\n # X[:, i] /= norm(X[:, i])\n return X\n\n\ndef _regressor_names(con_name, hrf_model, fir_delays=None):\n \"\"\"Return a list of regressor names, computed from con-name and hrf type\n\n Parameters\n ----------\n con_name: string\n identifier of the condition\n hrf_model: string or None,\n hrf model chosen\n fir_delays: 1D array_like, optional,\n Delays used in case of an FIR model\n\n Returns\n -------\n names: list of strings,\n regressor names\n\n \"\"\"\n if hrf_model in ['glover', 'spm', None]:\n return [con_name]\n elif hrf_model in ['glover + derivative', 'spm + derivative']:\n return [con_name, con_name + '_derivative']\n elif hrf_model in ['spm + derivative + dispersion', 'glover + derivative + dispersion']:\n return [con_name, con_name + '_derivative', con_name + '_dispersion']\n elif hrf_model == 'fir':\n return [con_name + '_delay_%d' % i for i in fir_delays] # noqa: UP031\n\n\ndef _hrf_kernel(hrf_model, tr, oversampling=50, fir_delays=None):\n \"\"\"Given the specification of the hemodynamic model and time parameters,\n return the list of matching kernels\n\n Parameters\n ----------\n hrf_model : string or None,\n identifier of the hrf model\n tr : float\n the repetition time in seconds\n oversampling : int, optional\n temporal oversampling factor to have a smooth hrf\n fir_delays : list of floats,\n list of delays for finite impulse response models\n\n Returns\n -------\n hkernel : list of arrays\n samples of the hrf (the number depends on the hrf_model used)\n\n \"\"\"\n acceptable_hrfs = [\n 'spm',\n 'spm + derivative',\n 'spm + derivative + dispersion',\n 'fir',\n 'glover',\n 'glover + derivative',\n 'glover + derivative + dispersion',\n None,\n ]\n if hrf_model == 'spm':\n hkernel = [spm_hrf(tr, oversampling)]\n elif hrf_model == 'spm + derivative':\n hkernel = [spm_hrf(tr, oversampling), spm_time_derivative(tr, oversampling)]\n elif hrf_model == 'spm + derivative + dispersion':\n hkernel = [\n spm_hrf(tr, oversampling),\n spm_time_derivative(tr, oversampling),\n spm_dispersion_derivative(tr, oversampling),\n ]\n elif hrf_model == 'glover':\n hkernel = [glover_hrf(tr, oversampling)]\n elif hrf_model == 'glover + derivative':\n hkernel = [glover_hrf(tr, oversampling), glover_time_derivative(tr, oversampling)]\n elif hrf_model == 'glover + derivative + dispersion':\n hkernel = [\n glover_hrf(tr, oversampling),\n glover_time_derivative(tr, oversampling),\n glover_dispersion_derivative(tr, oversampling),\n ]\n elif hrf_model == 'fir':\n hkernel = [\n np.hstack((np.zeros(f * oversampling), np.ones(oversampling))) for f in fir_delays\n ]\n elif hrf_model is None:\n hkernel = [np.hstack((1, np.zeros(oversampling - 1)))]\n else:\n raise ValueError(f'\"{hrf_model}\" is not a known hrf model. Use one of {acceptable_hrfs}')\n return hkernel\n\n\ndef compute_regressor(\n exp_condition,\n hrf_model,\n frame_times,\n con_id='cond',\n oversampling=50,\n fir_delays=None,\n min_onset=-24,\n):\n \"\"\"Convolve regressors with hrf model (the main function of the module)\n\n Parameters\n ----------\n exp_condition : array-like of shape (3, n_events)\n yields description of events for this condition as a\n (onsets, durations, amplitudes) triplet\n hrf_model : {'spm', 'spm + derivative', 'spm + derivative + dispersion',\n 'glover', 'glover + derivative', 'fir', None}\n Name of the hrf model to be used\n frame_times : array of shape (n_scans)\n the desired sampling times\n con_id : string\n optional identifier of the condition\n oversampling : int, optional\n oversampling factor to perform the convolution\n fir_delays : 1D-array-like, optional\n delays (in seconds) used in case of a finite impulse response model\n min_onset : float, optional\n minimal onset relative to frame_times[0] (in seconds)\n events that start before frame_times[0] + min_onset are not considered\n\n Returns\n -------\n computed_regressors: array of shape(n_scans, n_reg)\n computed regressors sampled at frame times\n reg_names: list of strings\n corresponding regressor names\n\n Notes\n -----\n The different hemodynamic models can be understood as follows:\n 'spm': this is the hrf model used in SPM\n 'spm + derivative': SPM model plus its time derivative (2 regressors)\n 'spm + time + dispersion': idem, plus dispersion derivative (3 regressors)\n 'glover': this one corresponds to the Glover hrf\n 'glover + derivative': the Glover hrf + time derivative (2 regressors)\n 'glover + derivative + dispersion': idem + dispersion derivative\n (3 regressors)\n 'fir': finite impulse response basis, a set of delayed dirac models\n with arbitrary length. This one currently assumes regularly spaced\n frame times (i.e. fixed time of repetition).\n It is expected that spm standard and Glover model would not yield\n large differences in most cases.\n\n In case of glover and spm models, the derived regressors are\n orthogonalized wrt the main one.\n\n \"\"\"\n # this is the average tr in this session, not necessarily the true tr\n tr = float(frame_times.max()) / (np.size(frame_times) - 1)\n\n # 1. create the high temporal resolution regressor\n hr_regressor, hr_frame_times = _sample_condition(\n exp_condition, frame_times, oversampling, min_onset\n )\n\n # 2. create the hrf model(s)\n hkernel = _hrf_kernel(hrf_model, tr, oversampling, fir_delays)\n\n # 3. convolve the regressor and hrf, and downsample the regressor\n conv_reg = np.array([np.convolve(hr_regressor, h)[: hr_regressor.size] for h in hkernel])\n\n # 4. temporally resample the regressors\n computed_regressors = _resample_regressor(conv_reg, hr_frame_times, frame_times)\n\n # 5. orthogonalize the regressors\n if hrf_model != 'fir':\n computed_regressors = _orthogonalize(computed_regressors)\n\n # 6 generate regressor names\n reg_names = _regressor_names(con_id, hrf_model, fir_delays=fir_delays)\n return computed_regressors, reg_names\n" }, { "path": "src/bids/modeling/model_spec.py", "content": "from abc import ABCMeta, abstractmethod # noqa: D100\n\nimport numpy as np\nimport pandas as pd\nfrom formulaic import model_matrix\n\nfrom bids.utils import convert_JSON\n\n\nclass ModelSpec(metaclass=ABCMeta):\n \"\"\"Base class for all ModelSpec classes.\"\"\"\n\n @abstractmethod\n def from_df(self, df, model, metadata=None):\n \"\"\"Initialize from a pandas DataFrame.\"\"\"\n pass\n\n\nclass GLMMSpec(ModelSpec):\n \"\"\"Generalized Linear Mixed Model specification.\n s\n\n Parameters\n ----------\n terms : list of Term\n A list of Term instances to include in the GLMMSpec instance.\n X: pd.DataFrame\n A pandas DataFrame containing the fixed effect design matrix\n (i.e., the X matrix in the typical mixed effect formulation). Each\n column will be internally converted to a separate Term instance.\n Z: pd.DataFrame\n A pandas DataFrame containing the random effect/grouping matrix\n (i.e., the Z matrix in the typical mixed effect formulation). Columns\n that share variance components are identified by the groups argument.\n groups: NDArray\n A binary 2d array with dimension k x v, where k is the number of\n columns in Z and v is the number of distinct variance components. A\n value of 1 indicates that the i'th of k rows is a level in the j's of\n v variance components. If Z is passed and groups is None, it is assumed\n that all columns in Z share the same single variance.\n sigma: NDArray\n An optional 2d array giving the covariance matrix for the variance\n components defined in the groups argument. Has dimension v x v, where v\n is the number of columns in groups.\n family: str\n The name of the family to use for the error distribution. By default,\n gaussian.\n link: str\n The name of the link function to use. Default depends on family. In the\n case of a gaussian (default family), an identity link is used.\n priors: dict\n Optional specification of default priors to use for new terms.\n\n \"\"\"\n\n def __init__(\n self,\n terms=None,\n X=None,\n Z=None,\n groups=None,\n sigma=None,\n family=None,\n link=None,\n priors=None,\n ):\n self.terms = {}\n self.family = family\n self.link = link\n self.sigma = sigma\n\n if priors is not None:\n self.set_priors(priors)\n\n if terms is not None:\n for t in terms:\n self.add_term(t)\n if X is not None:\n self.build_fixed_terms(X)\n if Z is not None:\n self.build_variance_components(Z, groups, sigma)\n\n def __repr__(self):\n return f\"<{self.__class__.__name__}{[term.name for term in self.fixed_terms]}'>\"\n\n def set_priors(self, fixed=None, random=None): # noqa: D102\n raise NotImplementedError(\"Custom prior use hasn't been implemented yet.\")\n\n def build_fixed_terms(self, X):\n \"\"\"Build one or more fixed terms from the columns of a pandas DF.\n\n Parameters\n ----------\n X : pd.DataFrame\n A pandas DataFrame containing variables to convert to Term\n instances. Each column is converted to a different (fixed) Term,\n with the name taken from the column name.\n\n \"\"\"\n for col in X.columns:\n data = X.loc[:, col].values\n cat = data.dtype.name in ('str', 'category', 'object')\n # TODO: get default prior\n t = Term(col, data, categorical=cat)\n self.add_term(t)\n\n def build_variance_components(self, Z, groups=None, sigma=None, names=None):\n \"\"\"Build one or more variance components from the columns of a binary\n grouping matrix and variance specification.\n\n Arguments:\n Z (DataFrame, NDArray): A binary 2D array or pandas DataFrame. Each\n column represents a column/predictor, each row represents an\n observation.\n groups (2DArray): A 2D binary array that maps the columns of Z\n onto variance components. Has dimension n_rows(Z) x k,\n where k is the number of distinct variance components. If None,\n a single group over all columns of Z is assumed.\n sigma (2DArray): A k x k 2D covariance matrix specifying the\n covariances between variance components. Currently unused.\n names (list): Optional list specifying the names of the groups.\n\n \"\"\"\n if sigma is not None:\n raise NotImplementedError('Covariance specification is currently not supported.')\n\n if groups is None:\n groups = np.ones((Z.shape[1], 1))\n n_grps = groups.shape[1]\n\n if names is None:\n names = getattr(groups, 'columns', [f'VC{i}' for i in range(n_grps)])\n\n # Work with array instead of DF\n if hasattr(groups, 'values'):\n groups = groups.values\n\n for i in range(n_grps):\n z_grp = Z[:, groups[:, i].astype(bool)]\n # TODO: select default prior\n vc = VarComp(names[i], z_grp)\n self.add_term(vc)\n\n def add_term(self, term):\n \"\"\"Add a new Term to the instance.\n\n Parameters\n ----------\n term : Term\n A Term instance to add to the current instance.\n\n \"\"\"\n if term.name in self.terms:\n raise ValueError(f'Term with name {term.name} already exists!')\n self.terms[term.name] = term\n\n @property\n def X(self):\n \"\"\"Return X design matrix (i.e., fixed component of model).\"\"\"\n if not self.fixed_terms:\n return None\n names, cols = zip(*[(c.name, c.values) for c in self.fixed_terms]) # noqa: B905\n return pd.DataFrame(np.c_[cols], columns=names)\n\n @property\n def Z(self):\n \"\"\"Return Z design matrix (i.e., random effects/variance components).\"\"\"\n if not self.variance_components:\n return None\n names, cols = [], []\n for c in self.variance_components:\n cols.append(c.values)\n names.extend([f'{c.name}.{i}' for i in range(c.values.shape[1])])\n return pd.DataFrame(np.concatenate(cols, axis=1), columns=names)\n\n @property\n def fixed_terms(self):\n \"\"\"Return a list of all available fixed effects.\"\"\"\n return [t for t in self.terms.values() if not isinstance(t, VarComp)]\n\n @property\n def variance_components(self):\n \"\"\"Return a list of all available variance components.\"\"\"\n return [t for t in self.terms.values() if isinstance(t, VarComp)]\n\n @classmethod\n def from_df(cls, df, model, metadata=None, formula=None):\n \"\"\"Initialize a GLMMSpec instance from a BIDSVariableCollection and\n a BIDS-StatsModels JSON spec.\n\n Parameters\n ----------\n df : DataFrame\n A pandas DataFrame containing predictor information (i.e., the\n fixed component of the design matrix).\n model : dict\n The \"Model\" section from a BIDS-StatsModel specification.\n metadata: DataFrame\n Optional DataFrame containing additional columns that are not part\n of the design matrix but may have downstream informational use\n and/or contain variables needed to define random effects. Rows must\n map 1-to-1 with those in `df`.\n formula: str\n Optional Wilkinson (R-style) formula specifying the fixed (X) part\n of the design matrix. All variables referenced in the formula must\n be present as columns in `df`. Output names will follow the\n conventions specified in the `formulaic` documentation. Note that\n only the right-hand part of the formula should be passed (i.e.,\n pass \"X1 * X2\", not \"y ~ X1 * X2\"). If provided, will take\n precedence over any formula found in the `model`.\n\n Returns\n -------\n A GLMMSpec instance.\n\n \"\"\"\n kwargs = {}\n\n # Fixed terms\n model = convert_JSON(model)\n\n formula = formula or model.get('formula')\n if formula is not None:\n df = model_matrix(formula, df)\n\n kwargs['X'] = df\n\n # Variance components\n vcs = model.get('variance_components', [])\n Z_list = []\n\n if vcs:\n # VCs can be defined by variables in either the fixed predictor\n # DF or the supplementary metadata DF, so concatenate them.\n all_vars = [df, metadata] if metadata is not None else [df]\n all_vars = pd.concat(all_vars, axis=1)\n\n for vc in vcs:\n # Levels can either be defined by the levels of a single\n # categorical (\"LevelsFrom\") or by a set of binary variables.\n if 'levels_from' in vc:\n data = all_vars[vc['levels_from']].values\n Z_list.append(pd.get_dummies(data).values)\n else:\n df = all_vars.loc[:, vc['levels']]\n Z_list.append(df.values)\n\n Z = np.concatenate(Z_list, axis=1)\n groups = np.zeros((Z.shape[1], len(Z_list)))\n c = 0\n for i, vc in enumerate(Z_list):\n n = vc.shape[1]\n groups[c : (c + n), i] = 1\n c += n\n groups = pd.DataFrame(groups, columns=[vc['name'] for vc in vcs])\n\n kwargs['Z'] = Z\n kwargs['groups'] = groups\n\n error = model.get('error')\n if error:\n kwargs['family'] = error.get('family')\n kwargs['link'] = error.get('link')\n\n return GLMMSpec(**kwargs)\n\n\nclass MetaAnalysisSpec(GLMMSpec): # noqa: D101\n pass\n\n\nclass Term:\n \"\"\"Represents a model term.\n\n Parameters\n ----------\n name : str\n The name of the term.\n values : iterable\n A 1d array or other iterable containing the predictor values.\n categorical : bool\n Indicates whether or not the Term represents a categorical variable.\n prior : dict\n Optional specification of the prior distribution for the Term.\n metadata : dict\n Arbitrary metadata to store internally.\n\n \"\"\"\n\n def __init__(self, name, values, categorical=False, prior=None, metadata=None):\n self.name = name\n self.values = values\n self.categorical = categorical\n self.prior = prior\n self.metadata = metadata or {}\n\n\nclass VarComp(Term):\n \"\"\"Represents a variance component/random effect.\n\n Parameters\n ----------\n name : str\n The name of the variance component.\n values : iterable\n A 2d binary array identifying the observations that belong to the\n levels of the variance component. Has dimension n x k, where n is the\n number of observed rows in the dataset and k is the number of levels\n in the factor.\n prior : dict\n Optional specification of the prior distribution for the VarComp.\n metadata : dict\n Arbitrary metadata to store internally.\n\n \"\"\"\n\n def __init__(self, name, values, prior=None, metadata=None):\n super().__init__(name, values, categorical=True, prior=prior, metadata=metadata)\n self.index_vec = self.dummies_to_vec(values)\n\n @staticmethod\n def dummies_to_vec(dummies):\n \"\"\"Convert dummy-coded columns to a single integer index.\n\n Parameters\n ----------\n dummies : NDArray\n 2d binary array to recode as a single vector.\n\n Notes\n -----\n Used for the sake of computational efficiency (i.e., to avoid lots of\n large matrix multiplications in the backends), invert the dummy-coding\n process and represent full-rank dummies as a vector of indices into the\n coefficients.\n\n \"\"\"\n vec = np.zeros(len(dummies), dtype=int)\n for i in range(dummies.shape[1]):\n vec[(dummies[:, i] == 1)] = i + 1\n return vec\n\n\nclass Prior:\n \"\"\"Abstract specification of a term prior.\n\n Parameters\n ----------\n name : str\n Name of prior distribution (e.g., Normal, Bernoulli, etc.)\n kwargs: dict\n Optional keywords specifying the parameters of the named distribution.\n\n Notes\n -----\n At present there's no controlled vocabulary of supported prior names and\n arguments, but users implementing new Bayesian estimators are encouraged to\n use the names used in PyMC3 (e.g., 'Normal', parameterized with mu and\n sd arguments).\n\n \"\"\"\n\n def __init__(self, name, **kwargs):\n self.name = name\n self.kwargs = kwargs\n" }, { "path": "src/bids/modeling/report/__init__.py", "content": "\n" }, { "path": "src/bids/modeling/report/base.py", "content": "import datetime # noqa: D100\nimport os\nfrom pathlib import Path\n\nfrom bids import __version__ as bids_version\nfrom bids.layout import BIDSLayout\nfrom bids.modeling import BIDSStatsModelsGraph\n\nfrom .utils import deroot, displayify, generate_contrast_matrix, snake_to_camel, to_alphanum\n\nPATH_PATTERNS = [\n 'reports/[sub-{subject}/][ses-{session}/][level-{level}_][sub-{subject}_][ses-{session}_]'\n '[run-{run}_]model-{model}.html'\n]\n\n\ndef _build_node_dict(node, all_entities):\n report_node = {\n 'name': node.name,\n 'analyses': [],\n 'group_by': node.group_by,\n 'model': node.model,\n 'level': node.level,\n 'transformations': node.transformations,\n }\n for out in node.outputs_:\n analysis_dict = {'entities': {}, 'contrasts': []}\n\n for contrast_info in out.contrasts:\n cents = contrast_info.entities.copy()\n cents['level'] = out.node.level\n cents['name'] = out.node.name\n\n for key in ('datatype', 'desc', 'suffix', 'extension'):\n cents.pop(key, None)\n for key in all_entities:\n cents.pop(key, None)\n\n for k, v in cents.items():\n if k in ('name', 'contrast'):\n cents.update({k: to_alphanum(str(v))})\n\n analysis_dict['entities'] = {\n key: val\n for key, val in cents.items()\n if key in ('subject', 'session', 'task', 'run') and val\n }\n\n analysis_dict['contrasts'].append(\n {\n 'name': displayify(contrast_info.name),\n }\n )\n\n ents = out.entities.copy()\n # Space doesn't apply to design/contrast matrices, or resolution\n for k in ['space', 'res']:\n ents.pop(k, None)\n\n analysis_dict['X'] = out.X\n\n # If reports were run-level\n if out.report_ is not None:\n analysis_dict['design_matrix_plot'] = out.report_['design_matrix_plot'].to_json()\n analysis_dict['design_matrix_corrplot'] = out.report_[\n 'design_matrix_corrplot'\n ].to_json()\n analysis_dict['VIF'] = out.report_['VIF']\n if hasattr(out, 'trans_hist'):\n analysis_dict['trans_hist'] = out.trans_hist\n\n analysis_dict['contrast_matrix'] = generate_contrast_matrix(out.contrasts, out.X.columns)\n report_node['analyses'].append(analysis_dict)\n\n return report_node\n\n\ndef _build_report_dict(graph):\n report = {\n 'dataset': {\n 'name': graph.layout.description['Name'],\n },\n 'model': graph.model,\n 'nodes': [],\n 'version': bids_version,\n 'timestamp': datetime.datetime.now(), # noqa: DTZ005\n 'graph_plot': graph.write_graph(format='svg', pipe=True),\n }\n\n if 'DatasetDOI' in graph.layout.description:\n report['dataset']['doi'] = graph.layout.description['DatasetDOI']\n\n all_entities = graph.layout.get_entities(metadata=True)\n for name, node in graph.nodes.items(): # noqa: B007\n report['nodes'].append(_build_node_dict(node, all_entities))\n\n # Get subjects hackily\n report['subjects'] = sorted(\n {analysis_dict['entities']['subject'] for analysis_dict in report['nodes'][0]['analyses']}\n )\n\n return report\n\n\ndef _write_report(report_dict, out_dir, template_path=None):\n try:\n import jinja2\n except ImportError:\n raise ImportError( # noqa: B904\n 'Jinja2 must be installed to generate reports. '\n 'You can install it with pip install jinja2.'\n )\n\n if template_path is None:\n loader = jinja2.PackageLoader('bids', 'modeling/report')\n template_file = 'report_template.jinja'\n else:\n loader = jinja2.FileSystemLoader(os.path.dirname(template_path))\n template_file = os.path.basename(template_path)\n\n tpl = jinja2.Environment(loader=loader).get_template(template_file) # noqa: S701\n\n model = snake_to_camel(report_dict['model']['name'])\n out_path = Path(out_dir)\n out_path.mkdir(parents=True, exist_ok=True)\n\n report_dict = deroot(report_dict, str(out_path))\n\n html = tpl.render(report_dict)\n Path.write_text(out_path / f'{model}_report.html', html)\n\n\ndef generate_report(model, dataset_path, derivatives, output_dir, scan_length=None, **entities):\n \"\"\"Generate a report for a model.\n\n Parameters\n ----------\n model : str\n Path to BIDS-StatsModels model.\n dataset_path : str\n Path to BIDS dataset.\n derivatives : str or list\n Path to BIDS derivatives.\n output_dir : str\n Path to output directory.\n scan_length : float (optional)\n Length of scan in seconds. If not provided, will be read from\n dataset (requires image files to be present).\n entities : dict (optional)\n A dictionary of BIDS entities to filter the layout on.\n\n \"\"\"\n # Initialize BIDSLayout\n layout = BIDSLayout(dataset_path, derivatives=derivatives)\n\n # Build BIDSStatsModelsGraph instance\n graph = BIDSStatsModelsGraph(layout, model)\n\n graph.load_collections(scan_length=scan_length, **entities)\n\n # Run entire graph\n graph.run_graph(\n scan_length=scan_length, entities=entities, transformation_history=True, node_reports=True\n )\n\n report_dict = _build_report_dict(graph)\n\n _write_report(report_dict, output_dir)\n" }, { "path": "src/bids/modeling/report/report_template.jinja", "content": "\n\n\n\n \n \n PyBIDS StatsModel Report\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n
\n
\n

Stats Model Report

\n
\n
\n Dataset\n :\n
\n
\n {{ dataset.name }}{% if dataset.doi %} (doi:\n {{ dataset.doi }}\n ){% endif %}\n
\n
\n
\n
\n
\n Model\n :\n
\n
\n {{ model.name }}\n
\n
\n
\n
\n
\n Participants ({{ subjects|count }})\n :\n
\n
\n {{ subjects|join(', ') }}\n
\n
\n
\n
\n\n
\n Specification\n 
\n                        {{ model|tojson(indent=2) }}\n
\n
\n\n
\n Graph\n
\n {{ graph_plot }}\n
\n
\n
\n
\n

Node Reports

\n
\n {% for node in nodes %}\n {{ node.name }}\n {% endfor %}\n
\n\n
\n {% for node in nodes %}\n {% set node_loop = loop %}\n
\n
\n
\n
\n
\n Level\n
\n
{{ node.level }}
\n
\n
\n
\n
\n Group By\n
\n
{{ node.group_by | join(\", \") }}
\n
\n
\n
\n
\n Model\n
\n
X: {{ node.model['x'] | join(\", \") }}
\n
\n
\n {% if node.model['type'] %}\n
\n
\n
Type: {{ node.model['type'] }}
\n
\n
\n {% endif %}\n
\n {% if loop.first %}\n
\n A report was generated for each node instance. All but the first are collapsed, but each should be inspected.\n
\n {% endif %}\n\n {% for analysis in node.analyses %}\n {% set analysis_loop = loop %}\n
\n \n {{ analysis.entities.items()|map('join', ': ')|map('capitalize')|join(', ')}}\n \n
\n
\n
Design Matrix
\n {% if 'design_matrix_plot' in analysis %}\n
\n \n
\n Design Matrix Table\n
\n {% endif %}\n
{{ analysis.X.to_html(border=0, bold_rows=True, classes=[\"display\", \"compact\"], table_id=\"{}{}_dmraw\".format(node.name,loop.index)) | safe }}
\n \n {% if 'design_matrix_plot' in analysis %}\n
\n
\n {% endif %}\n
\n {% if 'trans_hist' in analysis %}\n
\n
Transformation History
\n {% for trans in analysis.trans_hist %}\n {% if loop.first %}\n

Each transformation is applied to the design matrix in the order shown. The final design matrix is used in the model estimation.

\n
\n Original Variables\n {% else %}\n
\n {{ trans.index }}. {{ trans.transformation_name}}\n
    \n
  • \n Input Columns:\n {{ trans.input_cols|join(', ') }}\n
    • \n
  • \n Arguments:\n {{ trans.transformation_kwargs.items()|map('join', ': ')|map('capitalize')|join(', ')}}\n
    • \n
\n {% endif %}\n {% for var in trans.output.variables|sort() %}\n
\n {{ trans.output.variables[var].to_df(entities=False, condition=False).to_html(index=False, border=0, classes=[\"display\", \"compact\"], table_id=\"{}{}{}{}_thvar\".format(node.name, analysis_loop.index, trans.index, var.replace('.', '_')))}}\n \n
\n {% endfor %}\n
\n {% endfor %}\n
\n {% endif %}\n {% if 'design_matrix_corrplot' in analysis %}\n
\n
Correlation Matrix
\n
\n
\n
\n {% if node_loop.first %}\n

\n Pairwise correlation between regressors.\n Very high or low correlations among variables of interest or between variables of interest and nuisance regressors can indicate deficiency in the design. High correlations among nuisance regressors will generally have little effect on the model. It is recommended to interpret these correlations in the context of the VIF for each contrast.\n

\n {% endif %}\n
\n
\n \n
\n {% endif %}\n
\n

Contrasts

\n {% if node_loop.first %}\n A contrast matrix was generated for each {{ node.name }}. Except in rare cases, these should be identical.\n {% endif %}\n
{{ analysis.contrast_matrix.style.to_html(border=0) | safe }}
\n
\n {% if 'VIF' in analysis %}\n
\n
Variance Inflation Factor
\n
\n
\n
{{ analysis.VIF.style.hide(axis='index').to_html(border=0) | safe }}
\n
\n
\n {% if node_loop.first %}\n

\n Estimated VIF for each contrast. \n This is done by extending the effective regressor definition from\n Smith et al (2007) Meaningful design and contrast estimability (NeuroImage).\n 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. VIF values below 5 are generally considered acceptable, but values above 10 are problematic. VIF can be used to determine if high pass filter range is appropriate. If the VIF for a contrast is high, it may be necessary to increase the high pass filter cutoff. If the VIF for a contrast is low, it may be possible to decrease the high pass filter cutoff.\n

\n {% endif %}\n
\n
\n
\n {% endif %}\n
\n
\n {% endfor %}\n
\n
\n {% endfor %}\n
\n
\n\n
\n

About

\n
    \n
  • PyBIDS version: {{ version }}
    • \n
  • Date processed: {{ timestamp }}
    • \n
\n
\n
\n \n\n" }, { "path": "src/bids/modeling/report/utils.py", "content": "import re # noqa: D100\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.linalg import null_space\n\n\ndef node_report(node_output):\n \"\"\"Generate a report for a single node.\"\"\"\n # If only intercept model, don't run reports\n if node_output.node.level != 'run':\n return {}\n\n _report = {\n 'VIF': get_all_contrast_vif(node_output),\n }\n try:\n from .viz import plot_corr_matrix, plot_design_matrix\n except ImportError:\n warnings.warn( # noqa: B028\n 'altair failed to import, and is required for StatsModel report plots.', ImportWarning\n )\n else:\n _report['design_matrix_plot'] = plot_design_matrix(node_output.X, timecourse=True)\n\n _report['design_matrix_corrplot'] = plot_corr_matrix(node_output.X)\n\n return _report\n\n\ndef est_vif(desmat): # noqa: D417\n \"\"\"General variance inflation factor estimation. Calculates VIF for all\n regressors in the design matrix.\n\n Parameters\n ----------\n desmat (DataFrame): design matrix. Intercept not required.\n\n Returns\n -------\n vif_data (DataFrame): Variance inflation factor for each regressor in the design matrix\n generally goal is VIF<5\n\n \"\"\"\n from statsmodels.stats.outliers_influence import variance_inflation_factor\n\n desmat_with_intercept = desmat.copy()\n desmat_with_intercept['intercept'] = 1\n vif_data = pd.DataFrame()\n vif_data['regressor'] = desmat_with_intercept.columns.drop('intercept')\n vif_data['VIF'] = [\n variance_inflation_factor(desmat_with_intercept.values, i)\n for i in range(len(desmat_with_intercept.columns))\n if desmat_with_intercept.columns[i] != 'intercept'\n ]\n return vif_data\n\n\ndef get_eff_reg_vif(desmat, contrast_def): # noqa: D417\n \"\"\"The goal of this function is to estimate a variance inflation factor for a contrast.\n This is done by extending the effective regressor definition from Smith et al (2007)\n Meaningful design and contrast estimability (NeuroImage). Regressors involved\n in the contrast estimate are rotated to span the same space as the original space\n consisting of the effective regressor and and an orthogonal basis. The rest of the\n regressors are unchanged.\n\n Parameters\n ----------\n desmat (DataFrame): design matrix. Assumed to be a pandas dataframe with column\n headings which are used define the contrast of interest\n contrast_def (array or Series): a single contrast defined as a vector\n\n Returns\n -------\n vif (float): a single VIF for the contrast of interest\n\n \"\"\"\n des_nuisance_regs = desmat[desmat.columns[contrast_def == 0]]\n des_contrast_regs = desmat[desmat.columns[contrast_def != 0]]\n\n con = np.atleast_2d(contrast_def[contrast_def != 0])\n con2_t = null_space(con)\n con_t = np.transpose(con)\n x = des_contrast_regs.copy().values\n q = np.linalg.pinv(np.transpose(x) @ x)\n f1 = np.linalg.pinv(con @ q @ con_t)\n pc = con_t @ f1 @ con @ q\n con3_t = con2_t - pc @ con2_t\n f3 = np.linalg.pinv(np.transpose(con3_t) @ q @ con3_t)\n eff_reg = x @ q @ np.transpose(con) @ f1\n eff_reg = pd.DataFrame(eff_reg, columns=[0])\n\n other_reg = x @ q @ con3_t @ f3\n other_reg_names = [f'orth_proj{val}' for val in range(other_reg.shape[1])]\n other_reg = pd.DataFrame(other_reg, columns=other_reg_names)\n\n des_for_vif = pd.concat([eff_reg, other_reg, des_nuisance_regs], axis=1)\n vif_dat = est_vif(des_for_vif)\n vif_output = vif_dat[vif_dat.regressor == 0].VIF.values[0]\n return vif_output\n\n\ndef generate_contrast_matrix(contrasts, cols): # noqa: D417\n \"\"\"Generate a contrast matrix from a list of contrast definitions.\n\n Parameters\n ----------\n contrasts : list of ContrastInfo objects\n cols : list of design matrix columns\n\n Returns\n -------\n contrast_matrix : 2D array\n\n \"\"\"\n rows = []\n ix = []\n for con in contrasts:\n vec = cols.map(dict(zip(con.conditions, con.weights))).fillna(0) # noqa: B905\n rows.append(vec.tolist())\n ix.append(con.name)\n\n df = pd.DataFrame(rows, columns=cols.tolist(), index=ix)\n return df\n\n\ndef get_all_contrast_vif(node_output): # noqa: D417\n \"\"\"Calculates the VIF for multiple contrasts\n\n Parameters\n ----------\n node_output (BIDSStatsModelNodeOutput): Node output to compute VIF for.\n\n Returns\n -------\n vif_contrasts (DataFrame): Data frame containing the VIFs for all contrasts.\n\n \"\"\"\n vif_contrasts = {'contrast': [], 'VIF': []}\n\n con_matrix = generate_contrast_matrix(node_output.contrasts, node_output.X.columns)\n for name, weights in con_matrix.iterrows():\n # Transform weights to vector matching X's columns\n vif_out = get_eff_reg_vif(node_output.X, weights)\n vif_contrasts['contrast'].append(name)\n vif_contrasts['VIF'].append(vif_out)\n vif_contrasts = pd.DataFrame(vif_contrasts)\n return vif_contrasts\n\n\ndef deroot(val, root): # noqa: D103\n if isinstance(val, str):\n if val.startswith(root):\n idx = len(root)\n if val[idx] == '/':\n idx += 1\n val = val[idx:]\n elif isinstance(val, list):\n val = [deroot(elem, root) for elem in val]\n elif isinstance(val, dict):\n val = {key: deroot(value, root) for key, value in val.items()}\n\n return val\n\n\ndef snake_to_camel(string): # noqa: D103\n words = string.split('_')\n return words[0] + ''.join(word.title() for word in words[1:])\n\n\ndef displayify(contrast_name): # noqa: D103\n for match, repl in (('_gt_', ' > '), ('_lt_', ' < '), ('_vs_', ' vs. ')):\n contrast_name = contrast_name.replace(match, repl)\n return contrast_name\n\n\ndef to_alphanum(string):\n \"\"\"Convert string to alphanumeric\n\n Replaces all other characters with underscores and then converts to camelCase\n\n Examples\n --------\n >>> to_alphanum('abc123')\n 'abc123'\n >>> to_alphanum('a four word phrase')\n 'aFourWordPhrase'\n >>> to_alphanum('hyphen-separated')\n 'hyphenSeparated'\n >>> to_alphanum('object.attribute')\n 'objectAttribute'\n >>> to_alphanum('array[index]')\n 'arrayIndex'\n >>> to_alphanum('array[0]')\n 'array0'\n >>> to_alphanum('snake_case')\n 'snakeCase'\n\n \"\"\"\n return snake_to_camel(re.sub('[^a-zA-Z0-9]', '_', string))\n" }, { "path": "src/bids/modeling/report/viz.py", "content": "import altair as alt # noqa: D100\nimport numpy as np\n\n\ndef plot_corr_matrix(dm_wide): # noqa: D103\n dm_corr = dm_wide.corr()\n dm_corr = dm_corr.where(np.triu(np.ones(dm_corr.shape)).astype(np.bool)).reset_index()\n dm_corr_long = dm_corr.melt(\n var_name='index_2', value_vars=dm_wide.columns, id_vars='index', value_name='r'\n )\n\n corr_mat = (\n alt.Chart(dm_corr_long)\n .mark_rect()\n .encode(\n alt.X('index', sort=None, axis=alt.Axis(title=None)),\n alt.Y('index_2', sort=None, axis=alt.Axis(title=None)),\n tooltip=['r'],\n fill=alt.Color(\n 'r:Q',\n sort='descending',\n legend=alt.Legend(title='r'),\n scale=alt.Scale(scheme='redyellowblue', domain=[-1, 1]),\n ),\n )\n .properties(width=375, height=350)\n .configure_scale(bandPaddingInner=0.015)\n .configure_view(strokeWidth=0)\n .interactive()\n )\n\n return corr_mat\n\n\nalt.data_transformers.enable('default', max_rows=None)\n\n\ndef melt_dm(dm): # noqa: D103\n dm = dm.reset_index().rename(columns={'index': 'scan_number'})\n return dm.melt('scan_number', var_name='regressor', value_name='value')\n\n\ndef plot_design_matrix(dm_wide, scale=False, timecourse=True): # noqa: D103\n if scale:\n dm_wide = (dm_wide - dm_wide.mean()) / (dm_wide.max() - dm_wide.min())\n dm = melt_dm(dm_wide)\n\n time_labels = list(range(0, dm.scan_number.max(), 50))\n\n base_color = alt.Color(\n 'value:Q', sort='ascending', legend=None, scale=alt.Scale(scheme='viridis')\n )\n\n heat = (\n alt.Chart(dm)\n .mark_rect()\n .encode(\n alt.Y(\n 'scan_number:O',\n axis=alt.Axis(title='Time (TRs)', ticks=False, values=time_labels, labels=True),\n ),\n alt.X(\n 'regressor:N', sort=None, axis=alt.Axis(labelAngle=-45, title=None, ticks=False)\n ),\n fill=base_color,\n stroke=base_color,\n )\n .properties(\n width=750,\n height=450,\n )\n )\n\n selection = alt.selection_multi(fields=['regressor'])\n color = alt.Color('regressor:N', legend=None)\n\n if timecourse:\n line = (\n alt.Chart(dm, title='Timecourse (shift-click legend to select)')\n .mark_line(clip=True)\n .encode(\n alt.X(\n 'scan_number',\n axis=alt.Axis(title='Time (TRs)', values=time_labels, ticks=False),\n scale=alt.Scale(domain=(0, int(dm.scan_number.max()))),\n ),\n y=alt.Y('value:Q', axis=alt.Axis(title='Amplitude (scaled for visualization)')),\n color=color,\n )\n .transform_filter(selection)\n .properties(width=650, height=225)\n )\n\n legend = (\n alt.Chart(dm)\n .mark_square(size=200)\n .encode(y=alt.Y('regressor:N', axis=alt.Axis(orient='right')), color=color)\n .add_selection(selection)\n )\n\n resolve = alt.Resolve(scale=alt.LegendResolveMap(color=alt.ResolveMode('independent')))\n if timecourse:\n plt = alt.vconcat(heat, line | legend, resolve=resolve)\n else:\n plt = alt.vconcat(heat, legend, resolve=resolve)\n\n return plt.configure_scale(bandPaddingInner=0.0)\n" }, { "path": "src/bids/modeling/statsmodels.py", "content": "\"\"\"BIDS-StatsModels functionality.\"\"\"\n\nimport fnmatch\nimport itertools\nimport json\nimport re\nimport warnings\nfrom collections import Counter, OrderedDict, defaultdict, namedtuple\nfrom functools import reduce\n\nimport numpy as np\nimport pandas as pd\n\nfrom bids.layout import BIDSLayout\nfrom bids.modeling import transformations as tm\nfrom bids.utils import convert_JSON, listify, matches_entities\nfrom bids.variables import BIDSVariableCollection, merge_collections\n\nfrom .model_spec import GLMMSpec, MetaAnalysisSpec\nfrom .report.utils import node_report, snake_to_camel\n\n\ndef validate_model(model, *, stacklevel=2): # noqa: D417\n \"\"\"Validate a BIDS-StatsModel structure.\n\n Parameters\n ----------\n model : dict\n A full BIDS-StatsModels specification document loaded from JSON.\n\n Returns\n -------\n True if the model passes validation. Raises an exception otherwise.\n\n \"\"\"\n # Identify non-unique names\n names = Counter([n['name'] for n in model['nodes']])\n duplicates = [n for n, count in names.items() if count > 1]\n if duplicates:\n raise ValueError(\n f\"Non-unique node names found: '{duplicates}'. Please ensure\"\n ' all nodes in the model have unique names.'\n )\n\n def capitalize(obj):\n if isinstance(obj, list):\n return [capitalize(i) for i in obj]\n if isinstance(obj, dict):\n return {snake_to_camel(k).capitalize(): capitalize(v) for k, v in obj.items()}\n return obj\n\n if 'edges' in model:\n for edge in model['edges']:\n if edge['source'] not in names:\n raise ValueError(\"Missing source node: '{}'\".format(edge['source']))\n if edge['destination'] not in names:\n raise ValueError(\"Missing destination node: '{}'\".format(edge['destination']))\n\n # XXX: May 2021: Helping old models to work. This shouldn't last more than 2 years.\n for node in model['nodes']:\n messages = []\n if 'type' in node.get('dummy_contrasts', {}):\n node['dummy_contrasts']['test'] = node['dummy_contrasts'].pop('type')\n messages.append('\"DummyContrasts\": Contrast \"Type\" is now \"Test\".')\n for contrast in node.get('contrasts', []):\n if 'type' in contrast:\n contrast['test'] = contrast.pop('type')\n messages.append('Contrast {contrast[\"name\"]}]: Contrast \"Type\" is now \"Test\".')\n if isinstance(node.get('transformations'), list):\n transformations = {\n 'transformer': 'pybids-transforms-v1',\n 'instructions': node['transformations'],\n }\n node['transformations'] = transformations\n messages.append(\n 'Transformations are now a dictionary with \"Transformer\" and \"Instructions\" keys.'\n )\n if messages:\n new_text = json.dumps(capitalize(node), indent=2)\n notes = '\\n '.join(messages)\n warnings.warn(\n f'[Node \"{node[\"name\"]}\"] notes:\\n {notes}\\n'\n f'[Node \"{node[\"name\"]}\"] reformatted:\\n{new_text}\\n',\n stacklevel=stacklevel,\n )\n return True\n\n\nBIDSStatsModelsEdge = namedtuple('BIDSStatsModelsEdge', ('source', 'destination', 'filter'))\n\n\nContrastInfo = namedtuple('ContrastInfo', ('name', 'conditions', 'weights', 'test', 'entities'))\n\n\nclass BIDSStatsModelsGraph:\n \"\"\"Rooted graph structure representing the contents of a BIDS-StatsModel file.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout` or str\n A BIDSLayout instance or path to pass on\n to the BIDSLayout initializer.\n model : str or dict\n A BIDS model specification. Can either be a\n string giving the path of the JSON model spec, or an already-loaded\n dict containing the model info.\n\n \"\"\"\n\n def __init__(self, layout, model):\n if not isinstance(layout, BIDSLayout):\n layout = BIDSLayout(layout)\n self.layout = layout\n self.model = self._load_model(model)\n self.nodes = self._load_nodes(self.model)\n self.edges = self._load_edges(self.model, self.nodes)\n self._root_node = self.model.get('root', list(self.nodes.values())[0])\n\n def __repr__(self):\n return f\"<{self.__class__.__name__}[{{name='{self.model['name']}', description='{self.model['description']}', ... }}]>\" # noqa: E501\n\n def __getitem__(self, key):\n \"\"\"Alias for get_node(key).\"\"\"\n return self.get_node(key)\n\n @property\n def root_node(self):\n \"\"\"Returns the graph's root node.\"\"\"\n return self._root_node\n\n @staticmethod\n def _load_model(model, validate=True):\n # Load model info from JSON and do some validation\n if isinstance(model, str):\n with open(model, encoding='utf-8') as fobj:\n model = json.load(fobj)\n # Convert JSON from CamelCase to snake_case keys\n model = convert_JSON(model)\n if validate:\n validate_model(model, stacklevel=4) # Warn the caller of __init__\n return model\n\n @staticmethod\n def _load_nodes(model):\n nodes = OrderedDict()\n for node_args in model['nodes']:\n node = BIDSStatsModelsNode(**node_args)\n nodes[node.name] = node\n return nodes\n\n @staticmethod\n def _load_edges(model, nodes):\n \"\"\"Build edges between nodes.\"\"\"\n edges = model.get('edges', [])\n # assume we have an optional top-level 'Pipeline' field\n if not edges or model.get('pipeline', False):\n node_vals = list(nodes.values())\n for i in range(1, len(node_vals)):\n edges.append(\n {\n 'source': node_vals[i - 1].name,\n 'destination': node_vals[i].name,\n }\n )\n\n for edge in edges:\n src_node, dst_node = nodes[edge['source']], nodes[edge['destination']]\n edge = BIDSStatsModelsEdge(src_node, dst_node, edge.get('filter', {}))\n src_node.add_child(edge)\n dst_node.add_parent(edge)\n\n return edges\n\n def get_node(self, name):\n \"\"\"Return the named node.\n\n Parameters\n ----------\n name : str\n The name of the node to retrieve (as defined in the BIDS-StatsModel\n document).\n\n Returns\n -------\n A BIDSStatsModelsNode instance.\n\n \"\"\"\n if name not in self.nodes:\n raise KeyError(f'There is no node with the name \"{name}\".')\n return self.nodes[name]\n\n def load_collections(self, nodes=None, drop_na=False, **kwargs): # noqa: D417\n \"\"\"Load collections in all nodes.\n\n Parameters\n ----------\n nodes : list\n Optional list of nodes to set up. Each element must be a string\n giving the (unique) name of the node, Nodes that do not match\n will be silently skipped.\n drop_na : bool\n Boolean indicating whether or not to automatically\n drop events that have a n/a amplitude when reading in data\n from event files.\n\n \"\"\"\n # Use inputs from BIDS-StatsModel document, and update with kwargs\n selectors = self.model.get('input', {}).copy()\n selectors.update(kwargs)\n\n for node in self.nodes.values():\n # Skip any nodes whose names don't match list\n if nodes is not None and node.name not in nodes:\n continue\n\n # Currently allowing scan_length to be passed as a hack; remove for\n # levels above run.\n node_kwargs = selectors.copy()\n if node.level != 'run':\n node_kwargs.pop('scan_length', None)\n\n collections = self.layout.get_collections(node.level, drop_na=drop_na, **node_kwargs)\n node.add_collections(collections)\n\n def write_graph(self, dotfilename='graph.dot', format='png', pipe=False): # noqa: A002, D417\n \"\"\"Generates a graphviz dot file and a png file\n\n Parameters\n ----------\n format: 'png', 'svg'\n\n \"\"\"\n from graphviz import Digraph\n\n dot = Digraph(\n 'structs',\n filename=dotfilename,\n node_attr={'shape': 'record'},\n comment=self.model['name'],\n format=format,\n )\n\n for node, nobj in self.nodes.items():\n dot.node(node, f' name: {nobj.name}| level: {nobj.level}')\n\n for edge in self.edges:\n dot.edge(edge['source'], edge['destination'])\n\n if pipe:\n dot = dot.pipe(encoding='utf-8')\n else:\n dot.render()\n\n return dot\n\n def run_graph(self, entities=None, **kwargs):\n \"\"\"Run the entire graph recursively.\n\n Parameters\n ----------\n entities : dict (optional)\n Optional dictionary of BIDS entities to use when loading collections.\n kwargs : dict\n Optional dictionary of keyword arguments to pass to\n BIDSStatsModelsNode.run()\n\n \"\"\"\n if entities is None:\n entities = {}\n\n _run_node_recursive(self.root_node, filters=entities, **kwargs)\n\n\ndef _run_node_recursive(node, inputs=None, filters=None, **kwargs):\n \"\"\"Run a node recursively, storing outputs in place as\n BIDSStatsModelsNode.outputs_.\n\n \"\"\"\n if filters == None: # noqa: E711\n filters = {}\n\n # Run node\n run_kwargs = {**filters, **kwargs}\n node.outputs_ = node.run(inputs, group_by=node.group_by, **run_kwargs)\n\n # Inputs to next node\n contrasts = list(itertools.chain(*[s.contrasts for s in node.outputs_]))\n\n for edge in node.children:\n _run_node_recursive(edge.destination, contrasts, filters=edge.filter, **kwargs)\n\n\nclass BIDSStatsModelsNode:\n \"\"\"Represents a single node in a BIDS-StatsModel graph.\n\n Parameters\n ----------\n level : str\n The BIDS keyword to use as the grouping variable; must be one of\n ['run', 'session', 'subject', or 'dataset'].\n name : str\n Name to assign to the node.\n model : dict\n The 'model' part of the BIDS-StatsModels specification.\n transformations : dict\n Optional dictionary specifying transformations to apply. Dictionary\n must include \"transformer\" and \"instructions\" keys. \"transformer\"\n indicates the specification to follow. \"instructions\" is a list of\n instructions matching that specification.\n contrasts : list\n List of contrasts to apply to the parameter estimates generated when\n the model is fit.\n dummy_contrasts : dict\n Optional dictionary specifying which conditions to create indicator\n contrasts for. Dictionary may include a \"test\" key ('t'),\n and optionally a subset of \"conditions\".\n group_by: [str]\n Optional list of strings giving the names of entities that define the\n grouping structure for all variables. The current node will be executed\n separately for each unique combination of levels specified in group_by.\n For example, if group_by=['contrast', 'subject'], and there are 2\n contrasts and 3 subjects, then there will be 6 separate iterations, and\n the returned list will have 6 elements. Any value passed here will be\n overridden if one is passed when run() is called on a node.\n\n \"\"\"\n\n def __init__(\n self,\n level,\n name,\n model,\n group_by,\n transformations=None,\n contrasts=None,\n dummy_contrasts=False,\n ):\n self.level = level.lower()\n self.name = name\n self.model = model\n if transformations is None:\n transformations = {'transformer': 'pybids-transforms-v1', 'instructions': []}\n self.transformations = transformations\n self.contrasts = contrasts or []\n self.dummy_contrasts = dummy_contrasts\n self._collections = []\n self._group_data = []\n self.children = []\n self.parents = []\n if group_by is None:\n raise ValueError(f'group_by is not defined for Node: {name}')\n self.group_by = group_by\n\n # Check for intercept only run level model and throw an error\n try:\n if (self.level == 'run') and (self.model['x'] == [1]):\n raise NotImplementedError(\n 'Run level intercept only models are not currently supported.'\n 'If this is a feature you need, please leave a comment at'\n 'https://github.com/bids-standard/pybids/issues/852.'\n )\n except KeyError:\n # We talked about X being required, I don't know if we want to throw an error over that requirement here # noqa: E501\n # though.\n pass\n\n def __repr__(self):\n return f'<{self.__class__.__name__}(level={self.level}, name={self.name})>'\n\n @staticmethod\n def _build_groups(objects, group_by):\n \"\"\"Group list of objects into bins defined by specified entities.\n\n Parameters\n ----------\n objects : list\n List of objects containing an .entities dictionary as an attribute.\n group_by : list of str\n List of strings indicating which entities to group on.\n\n Returns\n -------\n A dictionary, where the keys are tuples of tuples and the values are lists\n containing subsets of the original object list. Each outer tuple of the keys\n represents a particular entity/value combination; each inner tuple has two\n elements, where the first is the name of the entity and the second is the value.\n\n Notes\n -----\n * The passed objects can be of any type, but all elements in the list must\n have an implemented .entities dictionary.\n * Objects that do not have defined values for all of the requested\n `entities` will be silently ignored.\n\n Examples\n --------\n >>> bidsfile = namedtuple('bidsfile', ('entities',))\n >>> bidsfile1 = bidsfile(entities={'subject': '01'})\n >>> bidsfile2 = bidsfile(entities={'subject': '02'})\n >>> groups = {(('subject', '01'),): [bidsfile1], (('subject', '02'),): [bidsfile2]}\n >>> BIDSStatsModelsNode._build_groups([bidsfile1, bidsfile2], ['subject']) == groups\n True\n\n \"\"\"\n if not group_by:\n return {(): objects}\n\n groups = defaultdict(list)\n\n # Get unique values in each grouping variable and construct indexing DF\n entities = [obj.entities for obj in objects]\n df = pd.DataFrame.from_records(entities)\n\n # Separate BIDSVariableCollections\n collections = [obj.to_df() for obj in objects if type(obj) == BIDSVariableCollection] # noqa: E721\n if collections:\n metadata_vars = reduce(pd.DataFrame.merge, collections)\n on_vars = list({'subject', 'session', 'run'} & set(metadata_vars.columns))\n df = df.merge(metadata_vars, how='left', on=on_vars)\n\n # Single-run tasks and single-session subjects may not have entities\n dummy_groups = {'run', 'session'} - set(df.columns)\n group_by = set(group_by) - dummy_groups\n\n # Verify all columns in group_by exist and raise sensible error if not\n missing_vars = list(group_by - set(df.columns))\n if missing_vars:\n raise ValueError(\n f'group_by contains variable(s) {missing_vars} that could not '\n 'be found in the entity index.'\n )\n\n # Restrict DF to only grouping columns\n df = df.loc[:, list(group_by)]\n\n unique_vals = {col: df[col].dropna().unique().tolist() for col in group_by}\n\n # Note: we can't just naively bucket objects based on the values of the\n # grouping entities, because an object may have undefined values for\n # one or more grouping variables. So we \"fill in\" the missing values by\n # Looping over objects. For each one, identify all grouping variables\n # with missing values. Loop over the cartesian product of the unique\n # values of the missing entities, combining the permutation values with\n # the base (present) grouping entities. Sort on this to produce the\n # group key.\n for i, row in df.iterrows():\n defined = df.columns[df.iloc[i].notnull()].tolist()\n base_ents = [(k, row[k]) for k in defined]\n missing = df.columns[df.iloc[i].isnull()].tolist()\n\n records = []\n\n if missing:\n product = itertools.product(*[unique_vals[col] for col in missing])\n for perm in product:\n fill_ents = [(k, v) for (k, v) in dict(zip(missing, perm)).items()] # noqa: B905\n records.append(base_ents + fill_ents)\n else:\n records.append(base_ents)\n\n for rec in records:\n grp_key = tuple(sorted(rec))\n groups[grp_key].append(objects[i])\n\n return groups\n\n def run(\n self,\n inputs=None,\n group_by=None,\n force_dense=True,\n sampling_rate='TR',\n invalid_inputs='error',\n invalid_contrasts='drop',\n missing_values=None,\n transformation_history=False,\n node_reports=False,\n **filters,\n ):\n \"\"\"Execute node with provided inputs.\n\n Parameters\n ----------\n inputs: [[ContrastInfo]]\n List of list of ContrastInfo tuples passed from the previous node.\n Each element in the outer list maps to the output of a unit at the\n previous level; each element in the inner list is a ContrastInfo\n tuple. E.g., if contrast information is being passed from run-level\n to subject-level, each outer element is a run.\n group_by: [str]\n Optional list of strings giving the names of entities that define\n the grouping structure for all variables. The current node will be\n executed separately for each unique combination of levels specified\n in group_by. For example, if group_by=['contrast', 'subject'], and\n there are 2 contrasts and 3 subjects, then there will be 6 separate\n iterations, and the returned list will have 6 elements. If None is\n passed, the value set at node initialization (if any) will be used.\n force_dense: bool\n If True, the returned design matrices contained in ModelSpec\n instances will represent time in a dense (i.e., uniform temporal\n sampling) format. If False, the returned format will be sparse if\n all available variables are sparse, and dense otherwise. Ignored\n if none of the variables at this node are run-level.\n sampling_rate: str, float\n The sampling rate to use for timeseries resampling when working\n with run-level variables and returning dense output. Ignored if\n there are no run-level variables, or if force_dense is False and\n all available variables are sparse.\n invalid_inputs: str\n Indicates how to handle invalid inputs--i.e., ones where the `X`\n specification contains variables that aren't found at run-time.\n Valid values:\n * 'drop': Drop invalid contrasts, retain the rest.\n * 'error' (default): Raise an error.\n invalid_contrasts: str\n Indicates how to handle invalid contrasts--i.e., ones where the\n specification contains variables that aren't found at run-time.\n Valid values:\n * 'drop' (default): Drop invalid contrasts, retain the rest.\n * 'ignore': Keep invalid contrasts despite the missing variables.\n * 'error': Raise an error.\n missing_values: str\n Indicates how to handle missing values in the data. Valid values:\n * 'fill' (default): Fill missing values with 0.\n * 'ignore': Keep missing values.\n * 'error': Raise an error.\n Defaults to 'fill' if None, but raises warning if not explicitly set.\n transformation_history: bool\n If True, the returned ModelSpec instances will include a history of\n variable collections after each transformation.\n node_reports: bool\n If True, report measures and plots will be included in the output\n filters: dict\n Optional keyword arguments used to constrain the subset of the data\n that's processed. E.g., passing subject='01' will process and\n return data only for files associated with subject '01'.\n\n Returns\n -------\n A list of BIDSStatsModelsNodeOutput instances.\n\n \"\"\"\n inputs = inputs or []\n collections = self._collections\n group_by = listify(group_by or self.group_by)\n\n # Filter inputs and collections if needed\n if filters:\n inputs = [i for i in inputs if matches_entities(i, filters)]\n collections = [c for c in collections if matches_entities(c, filters)]\n\n # group all collections and inputs\n all_objects = inputs + collections\n groups = self._build_groups(all_objects, group_by)\n\n results = []\n\n for grp_ents, grp_objs in list(groups.items()):\n # split group's objects into inputs and collections\n grp_inputs, grp_colls = [], []\n for obj in grp_objs:\n if isinstance(obj, BIDSVariableCollection):\n grp_colls.append(obj)\n else:\n grp_inputs.append(obj)\n\n node_output = BIDSStatsModelsNodeOutput(\n node=self,\n entities=dict(grp_ents),\n collections=grp_colls,\n inputs=grp_inputs,\n force_dense=force_dense,\n sampling_rate=sampling_rate,\n invalid_inputs=invalid_inputs,\n invalid_contrasts=invalid_contrasts,\n missing_values=missing_values,\n transformation_history=transformation_history,\n node_reports=node_reports,\n )\n results.append(node_output)\n\n return results\n\n def add_child(self, edge):\n \"\"\"Add an edge to a child node.\n\n Parameters\n ----------\n edge : BIDSStatsModelsEdge\n An edge to add to the list of children.\n\n \"\"\"\n self.children.append(edge)\n\n def add_parent(self, edge):\n \"\"\"Add an edge to a parent node.\n\n Parameters\n ----------\n edge : BIDSStatsModelsEdge\n An edge to add to the list of children.\n\n \"\"\"\n self.parents.append(edge)\n\n def add_collections(self, collections):\n \"\"\"Add BIDSVariableCollections (i.e., predictors) to the current node.\n\n Parameters\n ----------\n collections : [BIDSVariableCollection]\n List of BIDSVariableCollection objects to add to current node.\n\n Notes\n -----\n No checking for redundancy is performed, so if load_collections() is\n invoked multiple times with overlapping selectors, redundant predictors\n are likely to be stored internally.\n\n \"\"\"\n self._collections.extend(collections)\n\n def get_collections(self, **filters):\n \"\"\"Returns BIDSVariableCollections at the current node.\n\n Parameters\n ----------\n filters : dict\n Optional keyword filters used to constrain which of the available\n collections get returned (e.g., passing subject=['01', '02'] will\n return collections for only subjects '01' and '02').\n\n Returns\n -------\n list of BIDSVariableCollection instances\n One instance per unit of the current analysis level (e.g., if\n level='run', each element in the list represents the collection\n for a single run).\n\n \"\"\"\n # Keeps only collections that match target entities, and also removes\n # those keys from the kwargs dict.\n return [c for c in self._collections if matches_entities(c, filters)]\n\n\ndef expand_wildcards(selectors, pool): # noqa: D103\n out = list(selectors)\n for spec in selectors:\n if re.search(r'[\\*\\?\\[\\]]', spec):\n idx = out.index(spec)\n out[idx : idx + 1] = fnmatch.filter(pool, spec)\n return out\n\n\nclass BIDSStatsModelsNodeOutput:\n \"\"\"Represents a single node in a BIDSStatsModelsGraph.\n\n Parameters\n ----------\n node : BIDSStatsModelsNode\n The node that generated the current instance.\n entities : dict\n Dictionary of entities/metadata applicable to the current node output.\n collections : [BIDSVariableCollection]\n List of BIDSVariableCollection instances needed too generate outputs.\n inputs : [ContrastInfo]\n List of ContrastInfo instances used to generate outputs.\n force_dense: bool\n If True, the returned design matrices contained in ModelSpec instances\n will represent time in a dense (i.e., uniform temporal sampling) format.\n If False, the returned format will be sparse if all available variables\n are sparse, and dense otherwise. Ignored if none of the variables at\n this node are run-level.\n sampling_rate: str, float\n The sampling rate to use for timeseries resampling when working with\n run-level variables and returning dense output. Ignored if there are no\n run-level variables, or if force_dense is False and all available\n variables are sparse.\n invalid_inputs: str\n Indicates how to handle invalid inputs--i.e., ones where the `X`\n specification contains variables that aren't found at run-time.\n Valid values:\n * 'drop': Drop invalid contrasts, retain the rest.\n * 'error' (default): Raise an error.\n invalid_contrasts: str\n Indicates how to handle invalid contrasts--i.e., ones where the\n specification contains variables that aren't found at run-time.\n Valid values:\n * 'drop' (default): Drop invalid contrasts, retain the rest.\n * 'ignore': Keep invalid contrasts despite the missing variables.\n * 'error': Raise an error.\n missing_values: str\n Indicates how to handle missing values in the data. Valid values:\n * 'fill' (default): Fill missing values with 0.\n * 'ignore': Keep missing values.\n * 'error': Raise an error.\n Defaults to 'fill' if None, but raises warning if not explicitly set.\n transformation_history: bool\n If True, the returned ModelSpec instances will include a history of\n variable collections after each transformation.\n node_reports: bool\n If True, a report will be generated for each node output.\n\n \"\"\"\n\n def __init__(\n self,\n node,\n entities={}, # noqa: B006\n collections=None,\n inputs=None,\n force_dense=True,\n sampling_rate='TR',\n invalid_inputs='error',\n invalid_contrasts='drop',\n missing_values=None,\n *,\n transformation_history=False,\n node_reports=False,\n ):\n \"\"\"Initialize a new BIDSStatsModelsNodeOutput instance.\n Applies the node's model to the specified collections and inputs, including\n applying transformations and generating final model specs and design matrices (X).\n \"\"\"\n collections = collections or []\n inputs = inputs or []\n\n self.node = node\n self.entities = entities\n self.force_dense = force_dense\n self.sampling_rate = sampling_rate\n self.invalid_inputs = invalid_inputs\n self.invalid_contrasts = invalid_contrasts\n self.coll_hist = None\n\n # Apply transformations and convert collections to single DF\n dfs = self._collections_to_dfs(collections, collection_history=transformation_history)\n\n if inputs:\n dfs.append(self._inputs_to_df(inputs))\n\n df = reduce(pd.DataFrame.merge, dfs)\n\n # If dummy coded condition columns needed, generate and concat\n var_names = list(self.node.model['x'])\n if inputs:\n dummy_df = pd.get_dummies(df['contrast'])\n dummies_needed = set(var_names).intersection(dummy_df)\n if dummies_needed:\n df = pd.concat([df, dummy_df[list(dummies_needed)]], axis=1)\n\n # If a single incoming contrast, keep track of name\n if 'contrast' in df.columns and df['contrast'].nunique() == 1:\n unique_in_contrast = df['contrast'].unique()[0]\n else:\n unique_in_contrast = None\n\n # Handle the special 1 construct.\n # Add column of 1's to the design matrix called \"intercept\"\n if 1 in var_names:\n if 'intercept' in var_names:\n raise ValueError(\"Cannot define both '1' and 'intercept' in 'X'\")\n\n var_names = ['intercept' if i == 1 else i for i in var_names]\n if 'intercept' not in df.columns:\n df.insert(0, 'intercept', 1)\n\n var_names = expand_wildcards(var_names, df.columns)\n\n # Verify all X names are actually present\n missing = list(set(var_names) - set(df.columns))\n if missing:\n if self.invalid_inputs == 'drop':\n # warn caller of BIDSStatsModelsNode.run()\n warnings.warn(\n f'X specification includes variable(s) {missing}, '\n 'but these were not found in data matrix. '\n 'These will be dropped.',\n stacklevel=3,\n )\n var_names = [v for v in var_names if v not in missing]\n elif self.invalid_inputs == 'error':\n raise ValueError(\n f'X specification includes variable(s) {missing}, but '\n 'these were not found in data matrix.'\n )\n else:\n raise ValueError(\n f\"invalid_inputs must be one of ['drop', 'error'], got {self.invalid_inputs}\"\n )\n\n # separate the design columns from the entity columns\n self.data = df.loc[:, var_names]\n self.metadata = df.loc[:, df.columns.difference(var_names)]\n\n # replace missing values with 0 and warn user which columns had missing values\n if missing_values != 'ignore':\n missing_cols = self.data.columns[self.data.isnull().any()]\n if len(missing_cols) > 0:\n base_message = f'NaNs detected in columns: {missing_cols}'\n\n if missing_values in ['fill', None]:\n self.data.fillna(0, inplace=True)\n if missing_values is None:\n base_message += (\n ' were replaced with 0. Consider '\n ' handling missing values using transformations.'\n )\n # warn caller of BIDSStatsModelsNode.run()\n warnings.warn(base_message, stacklevel=3)\n elif missing_values == 'error':\n base_message += '. Explicitly replace missing values using transformations.'\n raise ValueError(base_message)\n\n # create ModelSpec and build contrasts\n kind = node.model.get('type', 'glm').lower()\n SpecCls = {\n 'glm': GLMMSpec,\n 'meta': MetaAnalysisSpec,\n }[kind]\n self.model_spec = SpecCls.from_df(self.data, node.model, self.metadata)\n self.contrasts = self._build_contrasts(unique_in_contrast)\n\n self.report_ = node_report(self) if node_reports else None\n\n def _collections_to_dfs(self, collections, *, collection_history=False):\n \"\"\"Merges collections and converts them to a pandas DataFrame.\"\"\"\n if not collections:\n return []\n\n # group all collections by level\n coll_levels = defaultdict(list)\n [coll_levels[coll.level].append(coll) for coll in collections]\n\n var_names = list(set(self.node.model['x']) - {1})\n\n grp_dfs = []\n trans_hist = []\n # merge all collections at each level and export to a DataFrame\n for level, colls in coll_levels.items():\n # Note: we currently merge _before_ selecting variables. Selecting\n # variables first could be done by passing `variables=all_vars` as\n # an argument on the next line), but we can't do this right now\n # because we can't guarantee that all the variables named in `X`\n # in the model section already exist; some might be created by the\n # transformations.\n coll = merge_collections(colls)\n\n # apply transformations\n transformations = self.node.transformations\n if transformations:\n transformer = tm.TransformerManager(\n transformations['transformer'], keep_history=collection_history\n )\n coll = transformer.transform(coll.clone(), transformations['instructions'])\n\n if hasattr(transformer, 'history_'):\n trans_hist += transformer.history_\n\n # Take the intersection of variables and Model.X (var_names), ignoring missing\n # variables (usually contrasts)\n coll.variables = {\n v: coll.variables[v]\n for v in expand_wildcards(var_names, coll.variables)\n if v in coll.variables\n }\n if not coll.variables:\n continue\n\n # run collections need to be handled separately because to_df()\n # takes extra arguments related to handling of time\n if level == 'run':\n if self.force_dense:\n coll = coll.to_dense(sampling_rate=self.sampling_rate)\n coll = coll.to_df(sampling_rate=self.sampling_rate)\n else:\n coll = coll.to_df()\n grp_dfs.append(coll)\n\n if collection_history:\n self.trans_hist = trans_hist\n\n return grp_dfs\n\n def _inputs_to_df(self, inputs):\n # Convert the inputs to a DataFrame and add to list. Each row is\n # an input; each column is an entity\n rows = [{**con.entities, 'contrast': con.name} for con in inputs]\n input_df = pd.DataFrame.from_records(rows)\n return input_df\n\n def _build_contrasts(self, unique_in_contrast=None):\n \"\"\"Contrast list of ContrastInfo objects based on current state.\n\n Parameters\n ----------\n unique_in_contrast : string\n Name of unique incoming contrast inputs (i.e. if there is only 1)\n\n \"\"\"\n in_contrasts = self.node.contrasts.copy()\n col_names = set(self.X.columns)\n\n # Create dummy contrasts as regular contrasts\n dummies = self.node.dummy_contrasts\n if dummies:\n if {'conditions', 'condition_list'} & set(dummies):\n # warn caller of BIDSStatsModelsNode.run()\n warnings.warn(\n \"Use 'Contrasts' not 'Conditions' or 'ConditionList' to specify\"\n \"DummyContrasts. Renaming to 'Contrasts' for now.\",\n stacklevel=4,\n )\n dummies['contrasts'] = dummies.pop('conditions', None) or dummies.pop(\n 'condition_list', None\n )\n\n if 'contrasts' in dummies:\n conditions = set(dummies['contrasts'])\n else:\n conditions = col_names\n\n for col_name in conditions:\n if col_name == 'intercept':\n col_name = 1\n\n in_contrasts.insert(\n 0,\n {\n 'name': col_name,\n 'condition_list': [col_name],\n 'weights': [1],\n 'test': dummies.get('test'),\n },\n )\n\n # Process all contrasts, starting with dummy contrasts\n # Dummy contrasts are replaced if a contrast is defined with same name\n contrasts = {}\n for con in in_contrasts:\n condition_list = list(con['condition_list'])\n\n # Rename special 1 construct\n condition_list = ['intercept' if i == 1 else i for i in condition_list]\n\n name = con['name']\n\n # Rename contrast name\n if name == 1:\n name = unique_in_contrast or 'intercept'\n else:\n # If Node has single contrast input, as is grouped by contrast\n # Rename contrast to append incoming contrast name\n if unique_in_contrast:\n name = f'{unique_in_contrast}_{name}'\n\n missing_vars = set(condition_list) - col_names\n if missing_vars:\n if self.invalid_contrasts == 'error':\n msg = (\n \"Variable(s) '{}' specified in condition list for \"\n \"contrast '{}', but not found in available data.\".format(\n missing_vars, con['name']\n )\n )\n raise ValueError(msg)\n elif self.invalid_contrasts == 'drop':\n continue\n weights = np.atleast_2d(con['weights']) # noqa: F841\n\n # Add contrast name to entities; can be used in grouping downstream\n entities = {**self.entities, 'contrast': name}\n ci = ContrastInfo(name, condition_list, con['weights'], con.get('test'), entities)\n contrasts[name] = ci\n\n return list(contrasts.values())\n\n @property\n def X(self):\n \"\"\"Return design matrix via the current ModelSpec.\"\"\"\n return self.model_spec.X\n\n def __repr__(self):\n return f'<{self.__class__.__name__}(name={self.node.name}, entities={self.entities})>'\n" }, { "path": "src/bids/modeling/tests/__init__.py", "content": "" }, { "path": "src/bids/modeling/tests/test_automodel.py", "content": "from itertools import chain\nfrom os.path import join\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.modeling import BIDSStatsModelsGraph\nfrom bids.modeling.auto_model import auto_model\nfrom bids.tests import get_test_data_path\n\n\n@pytest.fixture\ndef model():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n\n models = auto_model(layout, scan_length=480, one_vs_rest=True)\n\n return models[0]\n\n\ndef test_automodel_valid(model):\n try:\n from bsmschema.models import BIDSStatsModel\n except ImportError:\n pytest.skip('Needs bsmschema, available for Python 3.8+')\n BIDSStatsModel.model_validate(model)\n\n\ndef test_automodel_runs(model):\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n\n # Test to make sure an analaysis can be setup from the generated model\n graph = BIDSStatsModelsGraph(layout, model)\n graph.load_collections(scan_length=480, subject=['01', '02'])\n outputs = graph['Run'].run()\n assert len(outputs) == 6\n cis = list(chain(*[op.contrasts for op in outputs]))\n assert len(cis) == 6\n outputs = graph['Subject'].run(cis)\n # 2 subjects x 1 contrast\n assert len(outputs) == 2\n cis = list(chain(*[op.contrasts for op in outputs]))\n assert len(cis) == 2\n\n\ndef test_auto_model_graph(model):\n assert model['Name'] == 'ds005_mixedgamblestask'\n\n # run level\n block = model['Nodes'][0]\n assert block['Name'] == 'Run'\n assert block['Level'] == 'Run'\n assert block['Model']['Type'] == 'glm'\n assert block['Transformations']['Transformer'] == 'pybids-transforms-v1'\n assert block['Transformations']['Instructions'][0]['Name'] == 'Factor'\n assert block['Contrasts'][0]['Name'] == 'run_parametric gain'\n assert block['Contrasts'][0]['Weights'] == [1]\n assert block['Contrasts'][0]['Test'] == 't'\n\n # subject level\n block = model['Nodes'][1]\n assert block['Name'] == 'Subject'\n assert block['Level'] == 'Subject'\n assert block['Model']['Type'] == 'meta'\n assert block['Model']['X'][0] == 1\n assert block['DummyContrasts'] == {'Test': 't'}\n\n # dataset level\n block = model['Nodes'][2]\n assert block['Name'] == 'Dataset'\n assert block['Level'] == 'Dataset'\n assert block['Model']['Type'] == 'glm'\n assert block['Model']['X'][0] == 1\n assert block['DummyContrasts'] == {'Test': 't'}\n" }, { "path": "src/bids/modeling/tests/test_hrf.py", "content": "import numpy as np\n\nfrom ..hrf import compute_regressor\n\n\ndef test_duplicate_onsets():\n onset = [\n 16,\n 24,\n 48,\n 79,\n 80,\n 112,\n 127,\n 144,\n 176,\n 176,\n 208,\n 216,\n 240,\n 272,\n 272,\n 304,\n 329,\n ]\n duration = [16, 1, 16, 1, 16, 16, 1, 16, 1, 16, 16, 1, 16, 1, 16, 16, 1]\n\n trial_types = {\n \"Face\": [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0],\n \"Hand\": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n \"Swallow\": [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1],\n }\n\n model = \"spm\"\n resample_frames = np.arange(0, 340.0, 0.1)\n\n for name, trial_type in trial_types.items():\n vals = np.vstack([onset, duration, trial_type])\n regressor, _ = compute_regressor(\n vals, model, resample_frames, fir_delays=None, min_onset=0, oversampling=1\n )\n\n assert np.all((regressor > -0.2) & (regressor < 1.2))\n" }, { "path": "src/bids/modeling/tests/test_model_spec.py", "content": "import json\nfrom os.path import join\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom bids.modeling.model_spec import GLMMSpec, Term, VarComp\nfrom bids.tests import get_test_data_path\nfrom bids.variables import BIDSVariableCollection\n\n\n@pytest.fixture(scope='module')\ndef collection():\n \"\"\"Create a collection at dataset level.\"\"\"\n df = pd.DataFrame(\n {\n 'subject': np.repeat(np.arange(10), 4),\n 'run': np.tile(np.arange(4), 10),\n 'age': np.repeat(np.random.normal(40, 10, 10).astype(int), 4),\n }\n )\n return BIDSVariableCollection.from_df(df[['age']], entities=df[['subject', 'run']])\n\n\ndef test_df_to_model_spec(collection):\n layout_path = join(get_test_data_path(), 'ds005')\n json_file = join(layout_path, 'models', 'ds-005_type-mfx_model.json')\n spec = json.load(open(json_file))\n df = collection.to_df()\n data = df[['age']]\n metadata = df[['subject', 'run']]\n md = GLMMSpec.from_df(data, spec['Nodes'][1]['Model'], metadata)\n\n assert len(md.terms) == 2\n assert md.terms['age'].values.shape == (40,)\n assert md.terms['SubjectSlopes'].values.shape == (40, 10)\n index_vec = md.terms['SubjectSlopes'].index_vec\n assert index_vec.shape == (40,)\n assert np.array_equal(np.sort(np.unique(index_vec)), np.arange(10) + 1)\n assert md.Z.shape == (40, 10)\n assert md.Z.columns[0] == 'SubjectSlopes.0'\n\n\ndef test_glmmspec_empty_init():\n md = GLMMSpec()\n assert md.terms == {}\n assert md.X is None\n assert md.Z is None\n assert md.family is None\n assert md.family is None\n assert md.sigma is None\n\n\ndef test_fixed_term_init():\n t = Term('dummy', np.random.normal(size=20), categorical=True)\n assert t.name == 'dummy'\n assert t.values.shape == (20,)\n assert t.categorical\n\n\ndef test_var_comp_init():\n Z = np.repeat(np.eye(20), 5, axis=0)\n t = VarComp('sigma', Z)\n assert t.name == 'sigma'\n assert t.values.shape == (100, 20)\n assert t.categorical\n" }, { "path": "src/bids/modeling/tests/test_statsmodels.py", "content": "from itertools import chain\nfrom os import path\nfrom os.path import join\n\nfrom nibabel.optpkg import optional_package\n\ngraphviz, has_graphviz, _ = optional_package('graphviz')\n\nimport json\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.layout.utils import PaddedInt\nfrom bids.modeling import BIDSStatsModelsGraph\nfrom bids.modeling.statsmodels import ContrastInfo, expand_wildcards\nfrom bids.tests import get_test_data_path\nfrom bids.variables import BIDSVariableCollection\n\n\n@pytest.fixture\ndef graph():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')\n graph = BIDSStatsModelsGraph(layout, json_file)\n graph.load_collections(scan_length=480, subject=['01', '02'])\n return graph\n\n\n@pytest.fixture\ndef graph_intercept():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path, derivatives=join(layout_path, 'derivatives', 'events'))\n json_file = join(layout_path, 'models', 'ds-005_type-test_intercept.json')\n graph = BIDSStatsModelsGraph(layout, json_file)\n graph.load_collections(scan_length=480, subject=['01', '02'])\n return graph\n\n\n@pytest.fixture\ndef graph_nodummy():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n json_file = join(layout_path, 'models', 'ds-005_type-testnodummy_model.json')\n graph = BIDSStatsModelsGraph(layout, json_file)\n graph.load_collections(scan_length=480, subject=['01', '02'])\n return graph\n\n\ndef test_load_collections_types(graph):\n # Very narrow regression test that ensures that load_collections does not\n # modify the types of entities in-place\n layout = graph.layout\n bold = layout.get(suffix='bold', extension='nii.gz')[0]\n assert isinstance(bold.entities['run'], PaddedInt)\n\n\n@pytest.mark.skipif(not has_graphviz, reason='Test requires graphviz')\ndef test_write_graph(graph, tmp_path):\n from graphviz import Digraph\n\n dot = graph.write_graph(tmp_path / 'graph.dot')\n assert isinstance(dot, Digraph)\n assert path.exists(tmp_path / 'graph.dot')\n assert path.exists(tmp_path / 'graph.dot.png')\n\n\ndef test_repr(graph):\n assert (\n graph.__repr__()\n == \"\"\n )\n node = graph.nodes['run']\n assert node.__repr__() == ''\n assert (\n node.run()[0].__repr__()\n == \"\"\n )\n\n\ndef test_manual_intercept(graph_intercept):\n # Test that a automatic intercept (1) is correct\n # Intercept could should be all 1s\n run = graph_intercept['run']\n outputs = run.run(subject='01', run=1)\n assert outputs[0].X.intercept.min() == 1.0\n\n # Defining both 1 and 'intercept' raises an error\n run.model['x'] = [1, 'intercept']\n with pytest.raises(ValueError, match=\"Cannot define both '1' and 'intercept' in 'X'\"):\n run.run(subject='01', run=1)\n\n # \"intercept\" variable from event files is loaded correctly (should not be all 1s)\n run = graph_intercept.nodes['run']\n run.model['x'] = ['intercept']\n outputs = run.run(subject='01', run=1)\n assert outputs[0].X.intercept.min() != 1.0\n\n\ndef test_first_level_sparse_design_matrix(graph):\n outputs = graph['run'].run(subject=['01'], force_dense=False)\n assert len(outputs) == 3\n df = outputs[0].X\n assert df.shape == (86, 3)\n assert set(df.columns) == {'RT', 'gain', 'RT:gain'}\n metadata = outputs[0].metadata\n assert metadata.shape == (86, 7)\n assert set(metadata.columns) == {\n 'onset',\n 'duration',\n 'subject',\n 'run',\n 'task',\n 'datatype',\n 'suffix',\n }\n\n\ndef test_incremental_data_loading():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')\n graph = BIDSStatsModelsGraph(layout, json_file)\n graph.load_collections(scan_length=480, subject=['01'], run=[1])\n graph.load_collections(scan_length=480, subject=['02'], run=[2])\n assert len(graph['run'].get_collections()) == 2\n\n\ndef test_step_get_collections(graph):\n collections = graph['run'].get_collections(subject='01')\n assert len(collections) == 3\n assert isinstance(collections[0], BIDSVariableCollection)\n\n\ndef test_contrast_info(graph):\n outputs = graph['run'].run(subject='01')\n contrast_lists = [op.contrasts for op in outputs]\n assert len(contrast_lists) == 3\n for cl in contrast_lists:\n assert len(cl) == 3\n cl = [c for c in cl if c.test == 't']\n assert set([c.name for c in cl]) == {'RT', 'RT:gain', 'gain'}\n assert set([c.test for c in cl]) == {'t'}\n assert len(cl[1].conditions) == 1\n assert cl[1].conditions[0] in {'RT', 'gain', 'RT:gain'}\n assert cl[1].weights == [1]\n assert isinstance(cl[0], ContrastInfo)\n assert cl[0]._fields == ('name', 'conditions', 'weights', 'test', 'entities')\n\n\ndef test_contrast_dummy_vs_explicit(graph, graph_nodummy):\n # Check that ContrastInfo from model w/ DummyContrasts\n # and explicit Contrasts are identical\n outputs = graph['run'].run(subject='01', run=1)\n outputs_nodummy = graph_nodummy['run'].run(subject='01', run=1)\n\n for con in outputs[0].contrasts:\n match = [c for c in outputs_nodummy[0].contrasts if c.name == con.name][0]\n\n assert con.conditions == match.conditions\n assert con.weights == match.weights\n assert con.test == match.test\n\n cis = list(chain(*[op.contrasts for op in outputs]))\n outputs_sub = graph['participant'].run(cis, group_by=['subject', 'contrast'])\n output = [o for o in outputs_sub if o.entities['contrast'] == 'RT'][0]\n\n cis = list(chain(*[op.contrasts for op in outputs_nodummy]))\n outputs_nodummy_sub = graph_nodummy['participant'].run(cis, group_by=['subject', 'contrast'])\n output_nodummy = [o for o in outputs_nodummy_sub if o.entities['contrast'] == 'RT'][0]\n\n for con in output.contrasts:\n match = [c for c in output_nodummy.contrasts if c.name == con.name][0]\n\n assert con.conditions == match.conditions\n assert con.weights == match.weights\n assert con.test == match.test\n\n\ndef test_get_run_level_model_spec(graph):\n outputs = graph['run'].run(subject='01', run=1)\n assert len(outputs) == 1\n model_spec = outputs[0].model_spec\n assert model_spec.__class__.__name__ == 'GLMMSpec'\n # Note: this implicitly also tests use of formulas, because one is defined\n # in the model for the interaction of RT and gain.\n assert model_spec.X.shape == (240, 3)\n assert model_spec.Z is None\n assert {'RT', 'gain', 'RT:gain'} == set(model_spec.terms.keys())\n\n\ndef test_entire_graph_smoketest(graph):\n # Smoke test of entire graph; should hit almost all major pieces.\n # We do the following:\n # At run level, construct a design matrix containing gain, RT, and gain * RT.\n # At subject level, aggregate within runs, do nothing else.\n # At dataset level, do one-sample t-tests separately for each gender,\n # but also two-sample t-tests comparing males and females.\n # Note that there are only 2 subjects in the graph.\n # Note also that there is only one session (with no session label), which\n # should have no effect as a grouping variable\n outputs = graph['run'].run()\n # 2 subjects x 3 runs\n assert len(outputs) == 6\n cis = list(chain(*[op.contrasts for op in outputs]))\n assert len(cis) == 18\n outputs = graph['participant'].run(cis)\n # 2 subjects x 3 contrasts)\n assert len(outputs) == 6\n # * 2 participant level contrasts = 12\n cis = list(chain(*[op.contrasts for op in outputs]))\n assert len(cis) == 12\n\n # Test output names for single subject\n out_contrasts = [c.entities['contrast'] for c in cis if c.entities['subject'] == '01']\n\n expected_outs = ['gain', 'gain_neg', 'RT', 'RT_neg', 'RT:gain', 'RT:gain_neg']\n\n assert set(out_contrasts) == set(expected_outs)\n\n # Construct new ContrastInfo objects with name updated to reflect last\n # contrast. This would normally be done by the handling tool (e.g., fitlins)\n inputs = []\n for op in outputs:\n fields = dict(op.contrasts[0]._asdict())\n contrast_name = op.metadata['contrast'].iloc[0]\n fields['name'] = contrast_name\n fields['entities']['contrast'] = contrast_name\n inputs.append(ContrastInfo(**fields))\n\n # GROUP DIFFERENCE NODE\n outputs = graph['group-diff'].run(inputs)\n # 3 contrasts\n assert len(outputs) == 3\n cis = list(chain(*[op.contrasts for op in outputs]))\n # 3 contrasts x 2 subjects\n assert len(cis) == 6\n model_spec = outputs[0].model_spec\n assert model_spec.__class__.__name__ == 'GLMMSpec'\n assert model_spec.X.shape == (2, 2)\n assert model_spec.Z is None\n assert len(model_spec.terms) == 2\n assert not set(model_spec.terms.keys()) - {'intercept', 'sex'}\n\n # BY-GROUP NODE\n outputs = graph['by-group'].run(inputs)\n # 3 contrasts\n assert len(outputs) == 6\n cis = list(chain(*[op.contrasts for op in outputs]))\n # two groups x 3 contrasts\n assert len(cis) == 6\n model_spec = outputs[0].model_spec\n assert model_spec.__class__.__name__ == 'GLMMSpec'\n assert model_spec.X.shape == (1, 1)\n assert model_spec.Z is None\n assert not set(model_spec.terms.keys()) - {'intercept'}\n\n # explicit-contrast NODE\n outputs = graph['explicit-contrast'].run(inputs)\n # 1 group x 1 contrast\n assert len(outputs) == 1\n assert len(outputs[0].contrasts) == 1\n assert outputs[0].X['gain'].sum() == 2\n model_spec = outputs[0].model_spec\n assert model_spec.__class__.__name__ == 'GLMMSpec'\n assert model_spec.X.shape == (6, 1)\n assert not set(model_spec.terms.keys()) - {'gain'}\n\n contrast = outputs[0].contrasts[0]\n assert contrast.name == 'gain'\n\n\ndef test_expand_wildcards():\n # No wildcards == no modification\n assert expand_wildcards(['a', 'b'], ['a', 'c']) == ['a', 'b']\n # No matches == removal\n assert expand_wildcards(['a', 'b*'], ['a', 'c']) == ['a']\n # Matches expand in-place\n assert expand_wildcards(['a*', 'b'], ['a', 'c']) == ['a', 'b']\n assert expand_wildcards(['a*', 'b'], ['a0', 'c', 'a1', 'a2']) == ['a0', 'a1', 'a2', 'b']\n # Some examples\n assert expand_wildcards(['trial_type.*'], ['trial_type.A', 'trial_type.B']) == [\n 'trial_type.A',\n 'trial_type.B',\n ]\n assert expand_wildcards(\n ['non_steady_state*'], ['non_steady_state00', 'non_steady_state01']\n ) == ['non_steady_state00', 'non_steady_state01']\n\n\ndef test_interceptonly_runlevel_error():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n json_file = join(layout_path, 'models', 'ds-005_type-interceptonlyrunlevel_model.json')\n with pytest.raises(NotImplementedError):\n graph = BIDSStatsModelsGraph(layout, json_file)\n\n\ndef test_missing_value_fill():\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path, derivatives=join(layout_path, 'derivatives', 'fmriprep'))\n json_file = join(layout_path, 'models', 'ds-005_type-test_model.json')\n\n model = json.load(open(json_file))\n # Add 'global_signal_derivative1' to the model\n model['Nodes'][0]['Model'] = {'X': ['RT', 'gain', 'global_signal_derivative1']}\n\n graph = BIDSStatsModelsGraph(layout, model)\n graph.load_collections(scan_length=480, subject=['01', '02'])\n\n # Check that missing values are filled in\n # Assert that a warnings is raised\n with pytest.warns(UserWarning):\n graph.run_graph()\n\n # Assert that there are no missing values\n outputs = graph.nodes['run'].outputs_\n assert not outputs[0].model_spec.X.isna().any().any()\n\n # Check that missing_values='error' raises an error\n with pytest.raises(ValueError):\n graph.run_graph(missing_values='error')\n\n # Check that missing_values='ignore' passes through missing values\n graph.run_graph(missing_values='ignore')\n\n outputs = graph.nodes['run'].outputs_\n assert outputs[0].model_spec.X.isna().any().any()\n" }, { "path": "src/bids/modeling/tests/test_transformations.py", "content": "# from bids.modeling.variables import load_variables\nimport math\nfrom os.path import join, sep\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.modeling import transformations as transform\nfrom bids.tests import get_test_data_path\nfrom bids.variables import DenseRunVariable, SparseRunVariable\nfrom bids.variables.collections import BIDSRunVariableCollection\nfrom bids.variables.entities import RunInfo\n\ntry:\n from unittest import mock\nexcept ImportError:\n from unittest import mock\n\n\n# Sub-select collection for faster testing, without sacrificing anything\n# in the tests\nSUBJECTS = ['01', '02']\nNRUNS = 3\nSCAN_LENGTH = 480\n\ncached_collections = {}\n\n\n@pytest.fixture\ndef collection():\n if 'ds005' not in cached_collections:\n layout_path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(layout_path)\n cached_collections['ds005'] = layout.get_collections(\n 'run',\n types=['events'],\n scan_length=SCAN_LENGTH,\n merge=True,\n sampling_rate=10,\n subject=SUBJECTS,\n )\n # Always return a clone!\n return cached_collections['ds005'].clone()\n\n\n@pytest.fixture\ndef sparse_run_variable_with_missing_values():\n data = pd.DataFrame(\n {'onset': [2, 5, 11, 17], 'duration': [1.2, 1.6, 0.8, 2], 'amplitude': [1, 1, np.nan, 1]}\n )\n run_info = [RunInfo({'subject': '01'}, 20, 2, 'dummy.nii.gz', 10)]\n var = SparseRunVariable(name='var', data=data, run_info=run_info, source='events')\n return BIDSRunVariableCollection([var])\n\n\ndef test_convolve_multi(collection):\n # Just tests that we can convolve multiple arguments with one model\n output_names = ['unique_name', 'another_unique_name']\n transform.Convolve(collection, ['parametric gain', 'loss'], output=output_names, model='spm')\n\n assert set(output_names).issubset(collection.variables)\n\n\ndef test_convolve(collection):\n rt = collection.variables['RT']\n transform.Convolve(collection, ['RT'], output=['reaction_time'])\n rt_conv = collection.variables['reaction_time']\n\n assert rt_conv.values.shape[0] == rt.get_duration() * collection.sampling_rate\n\n transform.ToDense(collection, ['RT'], output=['rt_dense'])\n transform.Convolve(collection, 'rt_dense', derivative=True)\n\n # test the derivative exists\n assert collection.variables.get('rt_dense_derivative')\n\n dense_conv = collection.variables['reaction_time']\n\n assert (\n dense_conv.values.shape[0]\n == collection.variables['rt_dense_derivative'].values.shape[0]\n == rt.get_duration() * collection.sampling_rate\n )\n\n\ndef test_convolve_oversampling(collection):\n # Prepare a dense version of RT\n transform.ToDense(collection, ['RT'], output=['rt_dense'])\n\n # Test adaptive oversampling computation\n # Events are 3s duration events every 4s, so resolution demanded by the data is 1Hz\n # To resolve 1Hz frequencies, we must sample at >=2Hz\n args = (mock.ANY, 'spm', mock.ANY)\n kwargs = dict(fir_delays=None, min_onset=0)\n mock_return = (np.array([[0, 0]]), ['cond'])\n with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked:\n mocked.return_value = mock_return\n # Sampling rate is 10Hz, no oversampling needed\n transform.Convolve(collection, 'RT', output='rt_mock')\n mocked.assert_called_with(*args, oversampling=1.0, **kwargs)\n\n with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked:\n mocked.return_value = mock_return\n # Sampling rate is 10Hz, no oversampling needed\n transform.Convolve(collection, 'rt_dense', output='rt_mock')\n mocked.assert_called_with(*args, oversampling=1.0, **kwargs)\n\n with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked:\n mocked.return_value = mock_return\n # Slow sampling rate, oversample (4x) to 2Hz\n collection.sampling_rate = 0.5\n transform.Convolve(collection, 'RT', output='rt_mock')\n mocked.assert_called_with(*args, oversampling=4.0, **kwargs)\n\n with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked:\n mocked.return_value = mock_return\n # Dense variable is already sampled at 10Hz, no oversampling needed\n collection.sampling_rate = 0.5\n transform.Convolve(collection, 'rt_dense', output='rt_mock')\n mocked.assert_called_with(*args, oversampling=1.0, **kwargs)\n\n with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked:\n mocked.return_value = mock_return\n # Onset requires 10Hz resolution, oversample (2x) to 20Hz\n collection.sampling_rate = 10\n collection['RT'].onset[0] += 0.1\n transform.Convolve(collection, 'RT', output='rt_mock')\n mocked.assert_called_with(*args, oversampling=2.0, **kwargs)\n\n\ndef test_convolve_impulse():\n # Smoke test impulse convolution\n data = pd.DataFrame({'onset': [10, 20], 'duration': [0, 0], 'amplitude': [1, 1]})\n run_info = [RunInfo({'subject': '01'}, 25, 2, 'dummy.nii.gz', 10)]\n var = SparseRunVariable(name='var', data=data, run_info=run_info, source='events')\n coll = BIDSRunVariableCollection([var])\n transform.ToDense(coll, 'var', output='var_dense')\n transform.Convolve(coll, 'var', output='var_hrf')\n transform.Convolve(coll, 'var_dense', output='var_dense_hrf')\n\n\ndef test_rename(collection):\n dense_rt = collection.variables['RT'].to_dense(collection.sampling_rate)\n assert len(dense_rt.values) == math.ceil(\n len(SUBJECTS) * NRUNS * SCAN_LENGTH * collection.sampling_rate\n )\n transform.Rename(collection, ['RT'], output=['reaction_time'])\n assert 'reaction_time' in collection.variables\n assert 'RT' not in collection.variables\n col = collection.variables['reaction_time']\n assert col.name == 'reaction_time'\n assert col.onset.max() == 476\n\n\ndef test_product(collection):\n c = collection\n transform.Product(collection, variables=['parametric gain', 'gain'], output='prod')\n res = c['prod'].values\n assert (res == c['parametric gain'].values * c['gain'].values).all()\n\n\ndef test_sum(collection):\n c = collection\n transform.Sum(collection, variables=['parametric gain', 'gain'], output='sum')\n res = c['sum'].values\n target = c['parametric gain'].values + c['gain'].values\n assert np.array_equal(res, target)\n transform.Sum(collection, variables=['parametric gain', 'gain'], output='sum', weights=[2, 2])\n assert np.array_equal(c['sum'].values, target * 2)\n with pytest.raises(ValueError):\n transform.Sum(\n collection, variables=['parametric gain', 'gain'], output='sum', weights=[1, 1, 1]\n )\n\n\ndef test_scale(collection, sparse_run_variable_with_missing_values):\n transform.Scale(\n collection,\n variables=['RT', 'parametric gain'],\n output=['RT_Z', 'gain_Z'],\n groupby=['run', 'subject'],\n rescale=True,\n )\n groupby = collection['RT'].get_grouper(['run', 'subject'])\n z1 = collection['RT_Z'].values\n z2 = (\n collection['RT']\n .values.groupby(groupby, group_keys=False)\n .apply(lambda x: (x - x.mean()) / x.std())\n )\n assert np.allclose(z1, z2)\n\n # Test constant input\n coll = sparse_run_variable_with_missing_values\n coll['var'].values.fillna(1)\n with pytest.raises(ValueError, match='Cannot scale.*1.0'):\n transform.Scale(coll, 'var').values\n with pytest.raises(ValueError, match='Cannot scale.*1.0'):\n transform.Scale(coll, 'var', replace_na='before').values\n transform.Scale(coll, 'var', replace_na='after', output='zero')\n assert coll['zero'].values.unique() == 0\n\n\ndef test_demean(collection):\n transform.Demean(collection, variables=['RT'], output=['RT_dm'])\n m1 = collection['RT_dm'].values\n m2 = collection['RT'].values\n m2 -= m2.values.mean()\n assert np.allclose(m1, m2)\n\n\ndef test_orthogonalize_dense(collection):\n transform.Factor(collection, 'trial_type', sep=sep)\n\n sampling_rate = collection.sampling_rate\n # Store pre-orth variables needed for tests\n pg_pre = collection['trial_type/parametric gain'].to_dense(sampling_rate)\n rt = collection['RT'].to_dense(sampling_rate)\n\n # Orthogonalize and store result\n with pytest.warns(UserWarning, match='Found a mix of dense and sparse.*'):\n transform.Orthogonalize(\n collection,\n variables='trial_type/parametric gain',\n other='RT',\n dense=True,\n groupby=['run', 'subject'],\n )\n pg_post = collection['trial_type/parametric gain']\n\n # Verify that the to_dense() calls result in identical indexing\n ent_cols = ['subject', 'run']\n assert pg_pre.to_df()[ent_cols].equals(rt.to_df()[ent_cols])\n assert pg_post.to_df()[ent_cols].equals(rt.to_df()[ent_cols])\n\n vals = np.c_[rt.values, pg_pre.values, pg_post.values]\n df = pd.DataFrame(vals, columns=['rt', 'pre', 'post'])\n groupby = rt.get_grouper(['run', 'subject'])\n pre_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 1])\n post_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 2])\n assert (pre_r > 0.2).any()\n assert (post_r < 0.0001).all()\n\n\ndef test_orthogonalize_sparse(collection):\n pg_pre = collection['parametric gain'].values\n rt = collection['RT'].values\n transform.Orthogonalize(\n collection, variables='parametric gain', other='RT', groupby=['run', 'subject']\n )\n pg_post = collection['parametric gain'].values\n vals = np.c_[rt.values, pg_pre.values, pg_post.values]\n df = pd.DataFrame(vals, columns=['rt', 'pre', 'post'])\n groupby = collection['RT'].get_grouper(['run', 'subject'])\n pre_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 1])\n post_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 2])\n assert (pre_r > 0.2).any()\n assert (post_r < 0.0001).all()\n\n\ndef test_split(collection):\n orig = collection['RT'].clone(name='RT_2')\n collection['RT_2'] = orig.clone()\n collection['RT_3'] = collection['RT'].clone(name='RT_3').to_dense(collection.sampling_rate)\n\n rt_pre_onsets = collection['RT'].onset\n rt_pre_values = collection['RT'].values.values\n\n # Grouping SparseEventVariable by one column\n transform.Split(collection, ['RT'], ['respcat'])\n\n # Verify names\n assert (\n 'RT.respcat[0]' in collection.variables.keys()\n and 'RT.respcat[-1]' in collection.variables.keys()\n )\n\n # Verify values\n rt_post_onsets = np.r_[\n collection['RT.respcat[0]'].onset,\n collection['RT.respcat[-1]'].onset,\n collection['RT.respcat[1]'].onset,\n ]\n assert np.array_equal(np.sort(rt_pre_onsets), np.sort(rt_post_onsets))\n\n rt_post_values = np.r_[\n collection['RT.respcat[0]'].values.values,\n collection['RT.respcat[-1]'].values.values,\n collection['RT.respcat[1]'].values.values,\n ]\n assert np.array_equal(np.sort(rt_pre_values), np.sort(rt_post_values))\n\n # Grouping SparseEventVariable by multiple columns\n transform.Split(collection, variables=['RT_2'], by=['loss', 'respcat'])\n assert (\n 'RT_2.loss[13].respcat[-1]' in collection.variables.keys()\n and 'RT_2.loss[13].respcat[1]' in collection.variables.keys()\n )\n\n # Grouping by DenseEventVariable\n transform.Split(collection, variables='RT_3', by='respcat')\n targets = ['RT_3.respcat[-1]', 'RT_3.respcat[0]', 'RT_3.respcat[1]']\n assert not set(targets) - set(collection.variables.keys())\n assert collection['respcat'].values.nunique() == 3\n n_dense = len(collection['RT_3'].values)\n assert len(collection['RT_3.respcat[-1]'].values) == n_dense\n\n # Grouping by entities in the index\n collection['RT_4'] = orig.clone(name='RT_4')\n transform.Split(collection, variables=['RT_4'], by=['respcat', 'run'])\n assert 'RT_4.respcat[-1].run[3]' in collection.variables.keys()\n\n\ndef test_resample_dense(collection):\n new_sampling_rate = 50\n old_sampling_rate = collection.sampling_rate\n upsampling = float(new_sampling_rate) / old_sampling_rate\n\n collection['RT'] = collection['RT'].to_dense(old_sampling_rate)\n old_rt = collection['RT'].clone()\n collection.resample(new_sampling_rate, in_place=True)\n assert math.floor(len(old_rt.values) * upsampling) == len(collection['RT'].values)\n # Should work after explicitly converting categoricals\n transform.Factor(collection, 'trial_type')\n\n new_sampling_rate2 = 5\n upsampling2 = float(new_sampling_rate2) / old_sampling_rate\n\n collection.resample(new_sampling_rate2, force_dense=True, in_place=True)\n assert len(old_rt.values) == math.ceil(\n float(len(collection['parametric gain'].values) / upsampling2)\n )\n\n\ndef test_threshold(collection):\n old_pg = collection['parametric gain']\n orig_vals = old_pg.values\n\n collection['pg'] = old_pg.clone(name='pg')\n transform.Threshold(collection, 'pg', threshold=0.2, binarize=True)\n assert collection.variables['pg'].values.sum() == (orig_vals >= 0.2).sum()\n\n collection['pg'] = old_pg.clone(name='pg')\n transform.Threshold(collection, 'pg', threshold=0.2, binarize=False)\n assert collection.variables['pg'].values.sum() != (orig_vals >= 0.2).sum()\n coll_sum = (collection.variables['pg'].values >= 0.2).sum()\n assert coll_sum == (orig_vals >= 0.2).sum()\n\n collection['pg'] = old_pg.clone(name='pg')\n transform.Threshold(collection, 'pg', threshold=-0.1, binarize=True, signed=False, above=False)\n n = np.logical_and(orig_vals <= 0.1, orig_vals >= -0.1).sum()\n assert collection.variables['pg'].values.sum() == n\n\n\ndef test_assign(collection):\n transform.Assign(\n collection, 'parametric gain', target='RT', target_attr='onset', output='test1'\n )\n t1 = collection['test1']\n pg = collection['parametric gain']\n rt = collection['RT']\n assert np.array_equal(t1.onset, pg.values.values)\n assert np.array_equal(t1.duration, rt.duration)\n assert np.array_equal(t1.values.values, rt.values.values)\n\n transform.Assign(\n collection,\n 'RT',\n target='parametric gain',\n input_attr='onset',\n target_attr='amplitude',\n output='test2',\n )\n t2 = collection['test2']\n assert np.array_equal(t2.values.values, rt.onset)\n assert np.array_equal(t2.onset, pg.onset)\n assert np.array_equal(t2.duration, pg.duration)\n\n\ndef test_assign_multiple(collection):\n # test kwarg distribution\n transform.Assign(\n collection,\n ['RT', 'respcat'],\n target=['gain', 'loss'],\n input_attr=['amplitude', 'amplitude'],\n target_attr=['duration', 'amplitude'],\n output=['gain_rt', 'loss_cat'],\n )\n rt = collection['RT']\n gain_rt = collection['gain_rt']\n loss_cat = collection['loss_cat']\n rc = collection['respcat']\n\n assert np.array_equal(gain_rt.duration, rt.values.values)\n assert np.array_equal(loss_cat.values.values, rc.values.values)\n\n\ndef test_copy(collection):\n transform.Copy(collection, 'RT', output='RT_copy')\n assert 'RT_copy' in collection.variables.keys()\n assert np.array_equal(collection['RT'].values.values, collection['RT_copy'].values.values)\n\n\ndef test_expand_variable_names(collection):\n # Should fail because two output values are required following expansion\n with pytest.raises(Exception):\n transform.Copy(collection, '*resp*')\n\n transform.Copy(collection, '*resp*', output_suffix='_copy')\n assert 'respnum_copy' in collection.variables.keys()\n assert 'respcat_copy' in collection.variables.keys()\n assert np.array_equal(\n collection['respcat'].values.values, collection['respcat_copy'].values.values\n )\n assert np.array_equal(\n collection['respnum'].values.values, collection['respnum_copy'].values.values\n )\n\n\ndef test_factor(collection):\n # Full-rank dummy-coding, only one unique value in variable\n trial_type = collection.variables['trial_type'].clone()\n coll = collection.clone()\n transform.Factor(coll, 'trial_type', sep='@')\n assert 'trial_type@parametric gain' in coll.variables.keys()\n pg = coll.variables['trial_type@parametric gain']\n assert pg.values.unique() == [1]\n assert pg.values.shape == trial_type.values.shape\n\n # Reduced-rank dummy-coding, only one unique value in variable\n coll = collection.clone()\n transform.Factor(coll, 'trial_type', constraint='mean_zero')\n assert 'trial_type.parametric gain' in coll.variables.keys()\n pg = coll.variables['trial_type.parametric gain']\n assert pg.values.unique() == [1]\n assert pg.values.shape == trial_type.values.shape\n\n # full-rank dummy-coding, multiple values\n coll = collection.clone()\n transform.Factor(coll, 'respnum')\n targets = set(['respnum.%d' % d for d in range(0, 5)])\n assert not targets - set(coll.variables.keys())\n assert all([set(coll.variables[t].values.unique()) == {0.0, 1.0} for t in targets])\n data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True)\n assert (data.sum(axis=1) == 1).all()\n\n # reduced-rank dummy-coding, multiple values\n coll = collection.clone()\n transform.Factor(coll, 'respnum', constraint='drop_one')\n targets = set(['respnum.%d' % d for d in range(1, 5)])\n assert not targets - set(coll.variables.keys())\n assert 'respnum.0' not in coll.variables.keys()\n assert all([set(coll.variables[t].values.unique()) == {0.0, 1.0} for t in targets])\n data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True)\n assert set(np.unique(data.sum(axis=1).values.ravel())) == {0.0, 1.0}\n\n # Effect coding, multiple values\n coll = collection.clone()\n transform.Factor(coll, 'respnum', constraint='mean_zero')\n targets = set(['respnum.%d' % d for d in range(1, 5)])\n assert not targets - set(coll.variables.keys())\n assert 'respnum.0' not in coll.variables.keys()\n assert all([set(coll.variables[t].values.unique()) == {-0.25, 0.0, 1.0} for t in targets])\n data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True)\n assert set(np.unique(data.sum(axis=1).values.ravel())) == {-1.0, 1.0}\n\n\ndef test_filter(collection):\n orig = collection['parametric gain'].clone()\n q = 'parametric gain > 0'\n transform.Filter(collection, 'parametric gain', query=q)\n assert len(orig.values) == 2 * len(collection['parametric gain'].values)\n assert np.all(collection['parametric gain'].values > 0)\n\n orig = collection['RT'].clone()\n q = 'parametric gain > 0.1'\n with pytest.warns(UserWarning, match='Forcing all sparse variables to dense.*'):\n transform.Filter(collection, 'RT', query=q, by='parametric gain')\n assert len(orig.values) != len(collection['RT'].values)\n assert len(collection['RT'].values) == 96 * len(SUBJECTS)\n\n\ndef test_replace(collection):\n orig = collection['parametric gain'].clone()\n # Values\n replace_dict = {0.0335: 2.0, -0.139: 2.0}\n transform.Replace(collection, 'parametric gain', replace_dict)\n target = set(orig.values.unique()) - {0.0335, -0.139} | {2.0}\n assert set(collection['parametric gain'].values.unique()) == target\n # Durations\n replace_dict = {3: 2}\n transform.Replace(collection, 'parametric gain', replace_dict, 'duration')\n target = set(np.unique(orig.duration)) - {3} | {2.0}\n assert set(np.unique(collection['parametric gain'].duration)) == target\n # Onsets\n replace_dict = {4.0: 3.0, 476.0: 475.5}\n transform.Replace(collection, 'parametric gain', replace_dict, 'onset')\n target = set(np.unique(orig.onset)) - {4.0, 476.0} | {3.0, 475.5}\n assert set(np.unique(collection['parametric gain'].onset)) == target\n\n\ndef test_select(collection):\n coll = collection.clone()\n keep = ['RT', 'parametric gain', 'respcat']\n transform.Select(coll, keep)\n assert set(coll.variables.keys()) == set(keep)\n\n\ndef test_delete(collection):\n coll = collection.clone()\n all_cols = set(coll.variables.keys())\n drop = ['RT', 'parametric gain', 'respcat']\n transform.Delete(coll, drop)\n assert all_cols - set(coll.variables.keys()) == set(drop)\n\n\ndef test_and(collection):\n coll = collection.clone()\n transform.Factor(coll, 'respnum')\n names = ['respnum.%d' % d for d in range(0, 5)]\n\n coll.variables['respnum.0'].onset += 1\n\n # Should fail because I misaligned variable\n with pytest.raises(ValueError):\n transform.And(coll, names, output='misaligned')\n\n # Should pass because dense is set to True and will align\n transform.And(coll, names, output='misaligned', dense=True)\n\n\ndef test_or(collection):\n coll = collection.clone()\n transform.Factor(coll, 'respnum')\n names = ['respnum.%d' % d for d in range(0, 5)]\n transform.Or(coll, names, output='disjunction')\n assert (coll.variables['disjunction'].values == 1).all()\n\n coll['copy'] = coll.variables['respnum.0'].clone(name='copy')\n transform.Or(coll, ['respnum.0', 'copy'], output='or')\n assert coll.variables['or'].values.astype(float).equals(coll.variables['respnum.0'].values)\n\n\ndef test_not(collection):\n coll = collection.clone()\n pre_rt = coll.variables['RT'].values.values\n transform.Not(coll, 'RT')\n post_rt = coll.variables['RT'].values.values\n assert (post_rt == ~pre_rt.astype(bool)).all()\n\n\ndef test_dropna(sparse_run_variable_with_missing_values):\n var = sparse_run_variable_with_missing_values.variables['var']\n coll = sparse_run_variable_with_missing_values.clone()\n transform.DropNA(coll, 'var')\n post_trans = coll.variables['var']\n assert len(var.values) > len(post_trans.values)\n assert np.array_equal(post_trans.values, [1, 1, 1])\n assert np.array_equal(post_trans.onset, [2, 5, 17])\n assert np.array_equal(post_trans.duration, [1.2, 1.6, 2])\n assert len(post_trans.index) == 3\n\n\ndef test_group(collection):\n coll = collection.clone()\n with pytest.raises(ValueError):\n # Can't use an existing variable name as the group name\n transform.Group(coll, ['gain', 'loss'], name='gain')\n\n transform.Group(coll, ['gain', 'loss'], name='outcome_vars')\n assert coll.groups == {'outcome_vars': ['gain', 'loss']}\n\n # Checks that variable groups are replaced properly\n transform.Rename(coll, ['outcome_vars'], output=['gain_renamed', 'loss_renamed'])\n assert 'gain_renamed' in coll.variables\n assert 'loss_renamed' in coll.variables\n assert 'gain' not in coll.variables\n assert 'loss' not in coll.variables\n\n\ndef test_resample(collection):\n coll = collection.clone()\n\n transform.ToDense(coll, 'parametric gain', output='pg_dense')\n pg = coll.variables['pg_dense']\n old_shape = pg.values.shape\n try:\n trapezoid = np.trapezoid\n except AttributeError: # NP1.26\n trapezoid = np.trapz\n old_auc = trapezoid(np.abs(pg.values.values.squeeze()), dx=0.1)\n transform.Resample(coll, 'pg_dense', 1)\n pg = coll.variables['pg_dense']\n new_shape = pg.values.shape\n # Spacing (dx) is 10* larger when downsampled from 10hz to 1hz\n new_auc = trapezoid(np.abs(pg.values.values.squeeze()), dx=1)\n\n # Shape from 10hz to 1hz\n assert new_shape[0] == old_shape[0] / 10\n\n # Assert that the auc is more or less the same (not exact, rounding error)\n # Values are around 0.25\n assert np.allclose(old_auc, new_auc, rtol=0.05)\n\n\ndef test_Lag():\n var = DenseRunVariable(\n name='rot_x',\n values=np.arange(5.0, 20.0),\n run_info=RunInfo({}, 15, 1, 'none', 15),\n source='regressors',\n sampling_rate=1,\n )\n coll = BIDSRunVariableCollection([var], sampling_rate=1)\n\n # Forward shift\n transform.Lag(coll, 'rot_x', output='d_rot_x')\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[0, 0], 5.0)\n assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0))\n\n # Backward shift\n transform.Lag(coll, 'rot_x', output='d_rot_x', shift=-1)\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[-1, 0], 19.0)\n assert np.allclose(d_rot_x[:-1, 0], np.arange(6.0, 20.0))\n\n # Half shift; don't know why you'd want to do it, but you can\n transform.Lag(coll, 'rot_x', output='half_shift', shift=0.5, order=1)\n half_shift = coll['half_shift'].values.values\n assert np.isclose(half_shift[0, 0], 5.0)\n assert np.allclose(half_shift[1:, 0], np.arange(5.5, 19.5))\n\n # Constant mode\n transform.Lag(coll, 'rot_x', output='d_rot_x', mode='constant')\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[0, 0], 0.0)\n assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0))\n\n # Reflect mode\n transform.Lag(coll, 'rot_x', output='d_rot_x', mode='reflect')\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[0, 0], 5.0)\n assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0))\n\n # Forward shift -> Backward difference\n transform.Lag(coll, 'rot_x', output='d_rot_x', difference=True)\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[0, 0], 0.0)\n assert np.allclose(d_rot_x[1:, 0], 1.0)\n\n # Backward shift -> Forward difference\n transform.Lag(coll, 'rot_x', output='d_rot_x', shift=-1, difference=True)\n d_rot_x = coll['d_rot_x'].values.values\n assert np.isclose(d_rot_x[-1, 0], 0.0)\n assert np.allclose(d_rot_x[:-1, 0], 1.0)\n" }, { "path": "src/bids/modeling/transformations/__init__.py", "content": "from .base import TransformerManager # noqa: D104\nfrom .compute import (\n And_,\n Convolve,\n Demean,\n Lag,\n Not,\n Or_,\n Orthogonalize,\n Product,\n Scale,\n Sum,\n Threshold,\n)\nfrom .munge import (\n Assign,\n Copy,\n Delete,\n DropNA,\n Factor,\n Filter,\n Group,\n Rename,\n Replace,\n Resample,\n Select,\n Split,\n ToDense,\n)\n\nAnd = And_\nOr = Or_\n\n__all__ = [\n 'And',\n 'And_',\n 'Assign',\n 'Convolve',\n 'Copy',\n 'Demean',\n 'Delete',\n 'DropNA',\n 'Factor',\n 'Filter',\n 'Group',\n 'Lag',\n 'Not',\n 'Or',\n 'Or_',\n 'Orthogonalize',\n 'Product',\n 'Rename',\n 'Replace',\n 'Resample',\n 'Scale',\n 'Select',\n 'Split',\n 'Sum',\n 'Threshold',\n 'ToDense',\n 'TransformerManager',\n]\n" }, { "path": "src/bids/modeling/transformations/base.py", "content": "\"\"\"Base Transformation class and associated utilities.\"\"\"\n\nimport inspect\nimport itertools\nimport re\nimport warnings\nfrom abc import ABCMeta, abstractmethod\nfrom copy import deepcopy\nfrom dataclasses import dataclass\n\nimport numpy as np\nimport pandas as pd\n\nfrom bids.modeling import transformations as pbt\nfrom bids.utils import convert_JSON, listify\nfrom bids.variables import SparseRunVariable\nfrom bids.variables.collections import BIDSVariableCollection\n\n\nclass Transformation(metaclass=ABCMeta): # noqa: D101\n ### Class-level settings ###\n # The following settings govern the way Transformations are applied to the\n # data. The default settings can be overridden within subclasses.\n\n # List all argument names that specify variables used by Transformation.\n # This is necessary in order to ensure that all and only variables touched\n # by the transformation are cloned before any manipulation occurs.\n # variables in 'variables' are always cloned, so only additional arguments\n # should be specified here.\n _variables_used = ()\n\n # What data type to pass onto the core _transform() logic. Must be one\n # of 'variable' (the entire BIDSVariable object), 'pandas' (the extracted\n # pandas DF stored in .values), or 'numpy' (just the numpy array inside\n # the .values property of the pandas DF). To minimize overhead and\n # simplify code, it is recommended to avoid using 'variable' if possible.\n _input_type = 'pandas'\n\n # The data type the internal _transform() method is expected to return.\n # Must be one of 'variable', 'pandas', 'numpy', or 'none'. In the last\n # case, all desired changes must be made in-place within _transform(), as\n # no further changes will be committed.\n _return_type = 'pandas'\n\n # Indicates if variables must be aligned with one another\n # (i.e., onsets and durations match perfectly) before processing.\n # If True, will require all variables to be aligned (throw exception).\n # If 'force', will auto-align variables by densification.\n # Defaults to None.\n _aligned_required = None\n\n # (Optional) If alignment is required,sets which variables must be aligned.\n # If None, all variables must be aligned. It can also be a a string\n # indicating which column contains the names of columns to be aligned,\n # or a tuple which explicitly names the columns to be aligned.\n _aligned_variables = None\n\n # Boolean indicating whether the Transformation should be applied to each\n # variable in the input list in turn. When True (default), Transformation\n # is applied once per element in the variable list, with all arguments\n # being passed repeatedly. When False, all data (i.e., variables or their\n # pandas DFs or ndarrays, as specified in _input_type) are passed to the\n # Transformation simultaneously.\n _loopable = True\n\n # Boolean indicating whether the Transformation can handle groupby\n # operations. When True, a 'groupby' argument is made implicitly available,\n # and if passed, the Transformation will be applied separately to each\n # subset of the data, as defined by the variables named in groupby. When\n # False, the Transformations does not allow grouping, and will raise an\n # exception if groupby is passed. Transformations should set this to False\n # if the groupby argument cannot possibly change the returned result.\n _groupable = True\n\n # Boolean indicating whether the output argument is mandatory. When False\n # (default), transformations will be applied in-place unless output is set.\n # When True, the user must explicitly specify the output, or an exception\n # is raised.\n _output_required = False\n\n # An implicit 'dense' argument is always available, and indicates whether\n # or not to operate on dense variables. When True, the arguments listed in\n # _densify control which variables will be densified. Defaults to variables\n # named in the 'variables' argument. Note that if this value is overridden,\n # 'variables' will need to be explicitly included (i.e., the subclass's\n # _densify tuple replaces the base class rather than appending to it).\n _densify = ('variables',)\n\n # Allow categorical variables in the input arguments? When None (default),\n # any categorical variables encountered as inputs will raise an exception.\n # Otherwise, a tuple giving the names of the arguments whose variables will\n # be passed through as-is even if categorical.\n _allow_categorical = None\n\n # Boolean indicating whether to treat each key word argument as a one-to-one\n # mapping with each variable or to treat the key word argument as applying to\n # every input variable.\n _sync_kwargs = True\n\n def __new__(cls, collection, variables, *args, **kwargs): # noqa: D102\n t = super().__new__(cls)\n t._setup(collection, variables, *args, **kwargs)\n return t.transform()\n\n def _setup(self, collection, variables, *args, **kwargs):\n \"\"\"Replaces __init__ to set instance attributes because on Python\n >= 3.3, we can't override both new and init.\n \"\"\"\n self.collection = collection\n self.variables = listify(variables)\n self.groupby = kwargs.pop('groupby', None)\n self.output = listify(kwargs.pop('output', None))\n self.output_prefix = kwargs.pop('output_prefix', None)\n self.output_suffix = kwargs.pop('output_suffix', None)\n self.dense = kwargs.pop('dense', False)\n\n # Convert any args to named keyword arguments in order to make sure\n # that operations like densification, alignment, etc. correctly detect\n # all named arguments.\n if args:\n arg_spec = inspect.getfullargspec(self._transform)\n for i, arg_val in enumerate(args):\n # Skip first two argnames--they're always 'self' and\n # 'variables'\n kwargs[arg_spec.args[2 + i]] = arg_val\n\n self.kwargs = kwargs\n # listify kwargs if synced\n if self._sync_kwargs:\n for k, v in self.kwargs.items():\n if not isinstance(v, list):\n self.kwargs[k] = [v] * len(self.variables)\n elif not len(self.kwargs[k]) == len(self.variables):\n raise ValueError(f'Length of {k} must match length of variables')\n\n # Expand any detected variable group names or wild cards\n self._expand_variable_groups()\n self._expand_variable_names()\n\n def _expand_variable_groups(self):\n \"\"\"Replace any detected variable groups with the associated lists of\n variable names.\n \"\"\"\n groups = self.collection.groups\n variables = [groups[v] if v in groups else [v] for v in self.variables]\n self.variables = list(itertools.chain(*variables))\n\n def _expand_variable_names(self):\n \"\"\"Filter all available arguments against collection's variables using\n unix-style pattern matching.\n \"\"\"\n\n def _replace_arg_values(values):\n is_iter = isinstance(values, (list, tuple))\n values = listify(values)\n result = []\n # Only try to match strings containing a relevant special character\n for v in values:\n if isinstance(v, str) and re.search(r'[\\*\\?\\[\\]]', v):\n result.append(self.collection.match_variables(v))\n else:\n result.append([v])\n\n result = list(itertools.chain(*result))\n # Don't return a list unless we have to\n if is_iter or len(result) > 1:\n return result\n return result[0]\n\n # 'variables' is stored separately, so handle it separately\n self.variables = _replace_arg_values(self.variables)\n\n for k, arg in self.kwargs.items():\n self.kwargs[k] = _replace_arg_values(arg)\n\n def _clone_variables(self):\n \"\"\"Deep copy all variables the transformation touches. This prevents us\n from unnecessarily overwriting existing variables.\n \"\"\"\n # Always clone the target variables\n self._variables = {v: self.collection[v].clone() for v in self.variables}\n\n if not self._variables_used:\n return\n\n # Loop over argument names and clone all variable names in each one\n for var in self._variables_used:\n for v in listify(self.kwargs.get(var, [])):\n # Kludge: we need to allow entity variables to be passed as\n # names even though they don't exist as separate variables\n if v not in self.collection.variables and v in [\n 'task',\n 'run',\n 'session',\n 'subject',\n ]:\n continue\n self._variables[v] = deepcopy(self.collection[v])\n\n def _check_categorical_variables(self):\n \"\"\"Convert categorical variables to dummy-coded indicators.\"\"\"\n # Collect variable names to pass through\n pass_thru = []\n if self._allow_categorical is not None:\n for arg in self._allow_categorical:\n keys = self.variables if arg == 'variables' else self.kwargs.get(arg, [])\n pass_thru.extend(listify(keys))\n pass_thru = list(set(pass_thru))\n\n for name, col in self._variables.items():\n if name not in pass_thru:\n if col.values.values.dtype.kind not in 'bifc':\n msg = (\n \"The %s transformation does not allow variable '%s'\"\n ' to be categorical. Either pass a different '\n 'variable or explicitly convert to a set of binary '\n \"indicators via the 'factor' transformation.\"\n )\n raise ValueError(msg % (self.__class__.__name__, name))\n\n def _densify_variables(self):\n variables = []\n\n for var in self._densify:\n if var == 'variables':\n variables.extend(self.variables)\n else:\n variables.extend(listify(self.kwargs.get(var, [])))\n\n for v in variables:\n var = self._variables[v]\n if isinstance(var, SparseRunVariable):\n sr = self.collection.sampling_rate\n self._variables[v] = var.to_dense(sr)\n\n def transform(self): # noqa: D102\n output_passed = not (\n self.output is None and self.output_prefix is None and self.output_suffix is None\n )\n\n if not output_passed and self._output_required:\n raise ValueError(\n \"Transformation '%s' requires output names to be \" # noqa: UP031\n \"provided. Please set at least one of 'output',\"\n \"'output_prefix', or 'output_suffix'.\" % self.__class__.__name__\n )\n\n kwargs = self.kwargs\n\n # Deep copy all variables we expect to touch\n self._clone_variables()\n\n # Make sure we don't have categorical variables we can't handle\n self._check_categorical_variables()\n\n # Densify variables if needed\n if self.dense:\n self._densify_variables()\n\n # Set variables we plan to operate on directly\n variables = [self._variables[c] for c in self.variables]\n\n # Align variables if needed\n self._align_variables(variables)\n\n # Pass desired type--variable, DataFrame, or NDArray\n def select_type(col):\n return {'variable': col, 'pandas': col.values, 'numpy': col.values.values}[\n self._input_type\n ]\n\n data = [select_type(c) for c in variables]\n\n if not self._loopable:\n variables = [variables]\n\n i_kwargs = kwargs\n for i, col in enumerate(variables):\n if self._sync_kwargs:\n i_kwargs = {k: v[i] for k, v in kwargs.items()}\n # If we still have a list, pass all variables in one block\n if isinstance(col, (list, tuple)):\n result = self._transform(data, **i_kwargs)\n if self._return_type not in ['none', None]:\n col = col[0].clone(data=result, name=self.output[0])\n # Otherwise loop over variables individually\n else:\n if self._groupable and self.groupby is not None:\n result = col.apply(self._transform, groupby=self.groupby, **i_kwargs)\n else:\n result = self._transform(data[i], **i_kwargs)\n\n if self._return_type in ['none', None]:\n continue\n elif self._return_type == 'numpy':\n col.values = pd.DataFrame(result)\n elif self._return_type == 'pandas':\n col.values = result\n elif self._return_type == 'variable':\n col = result\n\n # Overwrite existing variable\n if not output_passed:\n # If multiple variables were returned, add each one separately\n if isinstance(result, (list, tuple)):\n for r in result:\n self.collection[r.name] = r\n else:\n self.collection[col.name] = col\n\n # Set as a new variable\n else:\n # Either assign new name in order, or reuse existing one\n if self.output is not None:\n n_vars = len(self.variables)\n n_output = len(self.output)\n if n_vars == n_output or not self._loopable:\n _output = self.output[i]\n elif n_output == 1:\n _output = str(self.output) + '_' + col.name\n else:\n msg = (\n 'Number of output variable names in provided '\n 'list ({}) does not match the number of variables'\n ' produced by the transformation ({}).'\n )\n raise ValueError(msg.format(n_output, n_vars))\n else:\n _output = col.name\n\n # Add prefix and suffix if provided\n if self.output_prefix is not None:\n _output = self.output_prefix + _output\n if self.output_suffix is not None:\n _output += self.output_suffix\n\n # If multiple variables were returned, add each one separately\n if isinstance(result, (list, tuple)):\n # rename first output\n result[0].name = _output\n self.collection[_output] = result[0]\n\n for r in result[1:]:\n self.collection[r.name] = r\n else:\n col.name = _output\n self.collection[_output] = col\n\n @abstractmethod\n def _transform(self, **kwargs):\n pass\n\n def _preprocess(self, col):\n return col\n\n def _postprocess(self, col):\n return col\n\n def _align_variables(self, variables):\n \"\"\"Checks whether the specified variables have aligned indexes. This\n implies either that all variables are dense, or that all variables are\n sparse and have exactly the same onsets and durations. If variables are\n not aligned and force = True, all variables will be forced to dense\n format in order to ensure alignment.\n \"\"\"\n if self._aligned_required is None or self._aligned_required == 'none':\n return\n\n def _align(variables):\n # If any variable is dense, all variables must be dense\n sparse = [c for c in variables if isinstance(c, SparseRunVariable)]\n if len(sparse) < len(variables):\n if sparse:\n msg = (\n 'Found a mix of dense and sparse variables. May '\n 'cause problems for some transformations.'\n )\n # This gets called deep in Transformations.__new__()\n warnings.warn(msg, stacklevel=5)\n # If all are sparse, durations, onsets, and index must match\n # perfectly for all\n else:\n\n def get_col_data(col):\n return np.c_[col.values.index, col.duration, col.onset]\n\n def compare_variables(a, b):\n return len(a) == len(b) and np.allclose(a, b)\n\n # Compare 1st col with each of the others\n fc = get_col_data(variables[0])\n if not all([compare_variables(fc, get_col_data(c)) for c in variables[1:]]): # noqa: C419\n if self._aligned_required == 'force_dense':\n msg = (\n 'Forcing all sparse variables to dense in '\n 'order to ensure proper alignment.'\n )\n sr = self.collection.sampling_rate\n variables = [c.to_dense(sr) for c in variables]\n warnings.warn(msg, stacklevel=5)\n else:\n raise ValueError(\n 'Misaligned sparse variables found.'\n 'To force variables into alignment by densifying,'\n 'set dense=True in the Transformation arguments'\n )\n\n _aligned_variables = True if not self._aligned_variables else self._aligned_variables\n _aligned_variables = [\n listify(self.kwargs[v]) for v in listify(_aligned_variables) if v in self.kwargs\n ]\n _aligned_variables = list(itertools.chain(*_aligned_variables))\n _aligned_variables = [self.collection[c] for c in _aligned_variables if c]\n\n if _aligned_variables and self._loopable:\n for c in variables:\n # TODO: should clone all variables in align_variables before\n # alignment to prevent conversion to dense in any given\n # iteration having side effects. This could be an issue if,\n # e.g., some vars in 'variables' are dense and some are sparse.\n _align([c] + _aligned_variables)\n else:\n _align(listify(variables) + _aligned_variables)\n\n\n@dataclass\nclass TransformationOutput: # noqa: D101\n index: int\n output: BIDSVariableCollection\n transformation_name: str\n transformation_kwargs: dict\n input_cols: list\n level: str\n\n\nclass TransformerManager:\n \"\"\"Handles registration and application of transformations to\n BIDSVariableCollections.\n\n Parameters\n ----------\n default: object\n A module or other object containing default transformations as\n attributes. Any named transformation not explicitly registered on\n the TransformerManager instance is expected to be found here.\n If None, the PyBIDS transformations module is used.\n keep_history: bool\n Whether to keep snapshots variable after a transformation is applied.\n If True, a list of collections will be stored in the ``history_`` attribute.\n\n \"\"\"\n\n def __init__(self, default=None, keep_history=True):\n self.transformations = {}\n if default in (None, 'pybids-transforms-v1'):\n # Default to PyBIDS transformations\n default = pbt\n self.default = default\n self.keep_history = keep_history\n\n def _sanitize_name(self, name):\n \"\"\"Replace any invalid/reserved transformation names with acceptable\n equivalents.\n\n Parameters\n ----------\n name: str\n The name of the transformation to sanitize.\n\n \"\"\"\n if name in ('And', 'Or'):\n name += '_'\n return name\n\n def register(self, name, func):\n \"\"\"Register a new transformation handler.\n\n Parameters\n ----------\n name : str\n The name of the transformation to handle.\n func : callable\n The callable to invoke when the named transformation is applied.\n\n \"\"\"\n name = self._sanitize_name(name)\n self.transformations[name] = func\n\n def transform(self, collection, transformations):\n \"\"\"Apply all transformations to the variables in the collection.\n\n Parameters\n ----------\n collection: BIDSVariableCollection\n The BIDSVariableCollection containing variables to transform.\n transformations : list\n List of transformations to apply.\n\n \"\"\"\n if self.keep_history:\n self.history_ = [\n TransformationOutput(\n index=0,\n output=collection.clone(),\n transformation_name=None,\n transformation_kwargs=None,\n input_cols=None,\n level=None,\n )\n ]\n\n for ix, t in enumerate(transformations):\n t = convert_JSON(t) # make sure all keys are snake case\n kwargs = dict(t)\n name = self._sanitize_name(kwargs.pop('name'))\n cols = kwargs.pop('input', None)\n\n # Check registered transformations; fall back on default module\n func = self.transformations.get(name, None)\n if func is None:\n if not hasattr(self.default, name):\n raise ValueError(\n \"No transformation '%s' found: either \" # noqa: UP031\n 'explicitly register a handler, or pass a'\n ' default module that supports it.' % name\n )\n func = getattr(self.default, name)\n\n # Apply the transformation\n func(collection, cols, **kwargs)\n\n # Take snapshot of collection after transformation\n if self.keep_history:\n self.history_.append(\n TransformationOutput(\n index=ix + 1,\n output=collection.clone(),\n transformation_name=name,\n transformation_kwargs=kwargs,\n input_cols=cols,\n level=collection.level,\n )\n )\n\n return collection\n" }, { "path": "src/bids/modeling/transformations/compute.py", "content": "\"\"\"Transformations that primarily involve numerical computation on variables.\"\"\"\n\nimport math\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import ndimage as ndi\n\nfrom bids.modeling import hrf\nfrom bids.utils import listify\nfrom bids.variables import DenseRunVariable, SparseRunVariable\n\nfrom .base import Transformation\n\n\ndef _fractional_gcd(vals, res=0.001):\n from functools import reduce\n from math import gcd\n\n return reduce(gcd, (int(np.round(val / res)) for val in vals)) * res\n\n\nclass Convolve(Transformation):\n \"\"\"Convolve the input variable with an HRF.\n\n Parameters\n ----------\n var : Variable\n The variable to convolve.\n model : str\n The name of the HRF model to apply. Must be one of 'spm',\n 'glover', or 'fir'.\n derivative : bool\n Whether or not to include the temporal derivative.\n dispersion : bool\n Whether or not to include the dispersion derivative.\n fir_delays : iterable\n A list or iterable of delays to use if model is\n 'fir' (ignored otherwise). Spacing between delays must be fixed.\n\n Notes\n -----\n Uses the HRF convolution functions implemented in nilearn.\n\n \"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _sync_kwargs = False\n\n def _transform(self, var, model='spm', derivative=False, dispersion=False, fir_delays=None):\n model = model.lower()\n\n df = var.to_df(entities=False)\n\n if isinstance(var, SparseRunVariable):\n sampling_rate = self.collection.sampling_rate\n dur = var.get_duration()\n resample_frames = np.linspace(\n 0, dur, int(math.ceil(dur * sampling_rate)), endpoint=False\n )\n safety = 2 # Double frequency to resolve events\n else:\n resample_frames = df['onset'].values\n sampling_rate = var.sampling_rate\n safety = 1 # Maximum signal resolution is already 0.5 * SR\n\n vals = df[['onset', 'duration', 'amplitude']].values.T\n\n if model in ['spm', 'glover']:\n if derivative:\n model += ' + derivative'\n if dispersion:\n model += ' + dispersion'\n elif model != 'fir':\n raise ValueError(\"Model must be one of 'spm', 'glover', or 'fir'.\")\n\n # Sampling at >100Hz will never be useful, but can be wildly expensive\n max_freq, min_interval = 100, 0.01\n # Sampling at <1Hz can degrade signals\n min_freq, max_interval = 1, 1 # noqa: F841\n\n # Given the sampling rate, determine an oversampling factor to ensure that\n # events can be modeled with reasonable precision\n unique_onsets = np.unique(df.onset)\n unique_durations = np.unique(df.duration)\n # Align existing data ticks with, event onsets and offsets, up to ms resolution\n # Note that GCD ignores zeros, so 0 onsets and impulse responses (0 durations) do\n # not harm this.\n required_resolution = _fractional_gcd(\n np.concatenate((unique_onsets, unique_durations)), res=min_interval\n )\n # Bound the effective sampling rate between min_freq and max_freq\n effective_sr = max(min_freq, min(safety / required_resolution, max_freq))\n convolved = hrf.compute_regressor(\n vals,\n model,\n resample_frames,\n fir_delays=fir_delays,\n min_onset=0,\n oversampling=int(np.ceil(effective_sr / sampling_rate)),\n )\n\n results = []\n arr, names = convolved\n for conv, name in zip(np.split(arr, arr.shape[1], axis=1), names): # noqa: B905\n new_name = '_'.join([var.name, name.split('_')[-1]]) if '_' in name else var.name\n results.append(\n DenseRunVariable(\n name=new_name,\n values=conv,\n run_info=var.run_info,\n source=var.source,\n sampling_rate=sampling_rate,\n )\n )\n return results\n\n\nclass Demean(Transformation): # noqa: D101\n def _transform(self, data):\n return data - data.mean()\n\n\nclass Orthogonalize(Transformation): # noqa: D101\n _variables_used = ('variables', 'other')\n _densify = ('variables', 'other')\n _aligned_required = 'force_dense'\n _aligned_variables = 'other'\n _sync_kwargs = False\n\n def _transform(self, var, other):\n other = listify(other)\n\n # Set up X matrix and slice into it based on target variable indices\n X = np.array([self._variables[c].values.values.squeeze() for c in other]).T\n X = X[var.index, :]\n assert len(X) == len(var) # noqa: S101\n y = var.values\n _aX = np.c_[np.ones(len(y)), X]\n coefs, resids, rank, s = np.linalg.lstsq(_aX, y, rcond=None)\n result = pd.DataFrame(y - X.dot(coefs[1:]), index=var.index)\n return result\n\n\nclass Product(Transformation): # noqa: D101\n _loopable = False\n _groupable = False\n _aligned_required = True\n _output_required = True\n\n def _transform(self, data):\n data = pd.concat(data, axis=1, sort=True)\n return data.product(axis=1)\n\n\nclass Scale(Transformation):\n \"\"\"Scale a variable.\n\n Parameters\n ----------\n data : :obj:`pandas.Series` or :obj:`pandas.DataFrame`\n The variables to scale.\n demean : bool\n If True, demean each column.\n rescale : bool\n If True, divide variables by their standard deviation.\n replace_na : str\n Whether/when to replace missing values with 0. If\n None, no replacement is performed. If 'before', missing values are\n replaced with 0's before scaling. If 'after', missing values are\n replaced with 0 after scaling.\n\n Notes\n -----\n If a constant column is passed in, and replace_na is None or 'before', an\n exception will be raised.\n\n \"\"\"\n\n def _transform(self, data, demean=True, rescale=True, replace_na=None):\n if data.nunique() == 1 and replace_na in {None, 'before'}:\n val = data.unique()[0]\n raise ValueError(\n f'Cannot scale a column with constant value ({val})! '\n \"If you want a constant column of 0's returned, \"\n \"set replace_na to 'after'.\"\n )\n if replace_na == 'before':\n data = data.fillna(0.0)\n if demean:\n data -= data.mean()\n if rescale:\n data /= data.std()\n if replace_na == 'after':\n data = data.fillna(0.0)\n return data\n\n\nclass Sum(Transformation): # noqa: D101\n _loopable = False\n _groupable = False\n _aligned_required = True\n _output_required = True\n _sync_kwargs = False\n\n def _transform(self, data, weights=None):\n data = pd.concat(data, axis=1, sort=True)\n if weights is None:\n weights = np.ones(data.shape[1])\n else:\n weights = np.array(weights)\n if len(weights.ravel()) != data.shape[1]:\n raise ValueError(\n 'If weights are passed to sum(), the number '\n 'of elements must equal number of variables'\n ' being summed.'\n )\n return (data * weights).sum(axis=1)\n\n\nclass Threshold(Transformation):\n \"\"\"Threshold and/or binarize a variable.\n\n Parameters\n ----------\n data :obj:`pandas.Series` or :obj:`pandas.DataFrame`\n The pandas structure to threshold.\n threshold : float\n The value to binarize around (values above will\n be assigned 1, values below will be assigned 0).\n binarize : bool\n If True, binarizes all non-zero values (i.e., every\n non-zero value will be set to 1).\n above : bool\n Specifies which values to retain with respect to the\n cut-off. If True, all value above the threshold will be kept; if\n False, all values below the threshold will be kept. Defaults to\n True.\n signed : bool\n Specifies whether to treat the threshold as signed\n (default) or unsigned. For example, when passing above=True and\n threshold=3, if signed=True, all and only values above +3 would be\n retained. If signed=False, all absolute values > 3 would be retained\n (i.e.,values in the range -3 < X < 3 would be set to 0).\n\n \"\"\"\n\n _groupable = False\n\n def _transform(self, data, threshold=0.0, binarize=False, above=True, signed=True):\n if not signed:\n threshold = np.abs(threshold)\n data = data.abs()\n keep = data >= threshold if above else data <= threshold\n data[~keep] = 0\n if binarize:\n data[keep] = 1\n return data\n\n\nclass And_(Transformation):\n \"\"\"Logical AND on two or more variables.\n\n Parameters\n ----------\n dfs : list of :obj:`pandas.DataFrame`\n variables to enter into the conjunction.\n\n \"\"\"\n\n _loopable = False\n _groupable = False\n _output_required = True\n _aligned_required = True\n _sync_kwargs = False\n\n def _transform(self, dfs):\n df = pd.concat(dfs, axis=1, sort=True)\n return df.all(axis=1).astype(int)\n\n\nclass Not(Transformation):\n \"\"\"Logical negation of a variable.\n\n Parameters\n ----------\n var : :obj:`pandas.Series`\n Variable to negate. Must be convertible to bool.\n\n \"\"\"\n\n _loopable = True\n _groupable = False\n sync_kwargs = False\n\n def _transform(self, var):\n return ~var.astype(bool)\n\n\nclass Or_(Transformation):\n \"\"\"Logical OR (inclusive) on two or more variables.\n\n Parameters\n ----------\n dfs : list of :obj:`pandas.DataFrame`\n variables to enter into the disjunction.\n\n \"\"\"\n\n _loopable = False\n _groupable = False\n _output_required = True\n _aligned_required = True\n sync_kwargs = False\n\n def _transform(self, dfs):\n df = pd.concat(dfs, axis=1, sort=True)\n return df.any(axis=1).astype(int)\n\n\nclass Lag(Transformation):\n \"\"\"Lag variable\n\n Returns a variable that is lagged by a specified number of time points.\n Spline interpolation of the requested ``order`` is used for non-integer\n shifts.\n Points outside the input are filled according to the given ``mode``.\n Negative shifts move values toward the beginning of the sequence.\n If ``difference`` is ``True``, the backward difference is calculated for\n positive shifts, and the forward difference is calculated for negative\n shifts.\n\n Additional ``mode``s may be defined if there is need for them.\n The ``scipy.ndimage.shift`` method provides the reference implementation\n for all current modes. \"Constant\" is equivalent to the shift parameter\n \"cval\".\n\n\n Parameters\n ----------\n var : :obj:`numpy.ndarray`\n variable to lag\n shift : float, optional\n number of places to shift the values (default: 1)\n order : int, optional\n order of spline interpolation, from 0-5 (default: 3)\n mode : string\n the `mode` parameter determines how the input array is extended\n beyond its boundaries. Default is 'nearest'.\n The following values are accepted:\n\n \"nearest\" (a a a a | a b c d | d d d d)\n The input is extended by replicating the boundary values\n \"reflect\" (d c b a | a b c d | d c b a)\n The input is extended by reflecting the array over the edge\n \"constant\" (k k k k | a b c d | k k k k)\n The input is extended by filling all values beyond the edge\n with the same constant value, defined by the ``constant`` parameter\n constant : float, optional\n value to fill past edges of input if ``mode`` is ``\"constant\"``.\n (default: 0)\n difference : boolean, optional\n Calculate the backward (if shift is positive) or forward (if shift is\n negative) difference.\n For the forward difference dx of an array x, dx[i] = x[i+1] - x[i].\n For the backward difference dx of an array x, dx[i] = x[i] - x[i-1].\n (default: ``False``)\n\n \"\"\"\n\n _input_type = 'numpy'\n _return_type = 'numpy'\n\n def _transform(self, var, shift=1, order=3, mode='nearest', constant=0.0, difference=False):\n var = var.flatten()\n shifted = ndi.shift(var, shift=shift, order=order, mode=mode, cval=constant)\n if not difference:\n return shifted\n elif shift >= 0:\n return var - shifted\n else:\n return shifted - var\n" }, { "path": "src/bids/modeling/transformations/munge.py", "content": "\"\"\"Transformations that primarily involve manipulating/munging variables into\nother formats or shapes.\n\"\"\"\n\nimport re\n\n# Might not be needed since python 3.7\n# See https://github.com/bids-standard/pybids/issues/423 for more info\nfrom collections import OrderedDict as odict\n\nimport numpy as np\nimport pandas as pd\nfrom formulaic import model_matrix\n\nfrom bids.utils import listify\nfrom bids.variables import DenseRunVariable, SimpleVariable\n\nfrom .base import Transformation\n\n\nclass Assign(Transformation):\n \"\"\"Assign one variable's amplitude, duration, or onset attribute to\n another.\n \"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _allow_categorical = ('variables', 'target')\n\n def _transform(self, input, target, input_attr='amplitude', target_attr='amplitude'): # noqa: A002\n # assign only makes sense for sparse variables; dense variables don't\n # have durations or onsets, and amplitudes can be copied by cloning\n if isinstance(input, DenseRunVariable):\n raise ValueError(\n \"The 'assign' transformation can only be applied\" # noqa: UP031\n ' to sparsely-coded event types. The input '\n 'variable (%s) is dense.' % input.name\n )\n\n target = self.collection.variables[target].clone()\n if isinstance(target, DenseRunVariable):\n raise ValueError(\n \"The 'assign' transformation can only be applied\" # noqa: UP031\n ' to sparsely-coded event types. The target '\n 'variable (%s) is dense.' % target.name\n )\n\n # Ensure attributes are valid\n valid_attrs = ['amplitude', 'duration', 'onset']\n if input_attr not in valid_attrs:\n raise ValueError('Valid values for input_attr are: %s.' % valid_attrs) # noqa: UP031\n if target_attr not in valid_attrs:\n raise ValueError('Valid values for target_attr are: %s.' % valid_attrs) # noqa: UP031\n\n # variables must have same number of events, but do *not* need to have\n # aligned onsets.\n l_s, l_t = len(input.values), len(target.values)\n if l_s != l_t:\n raise ValueError(\n 'Input and target variables do not contain the ' # noqa: UP031\n 'same number of events (%d vs. %d).' % (l_s, l_t)\n )\n\n if input_attr.startswith('amplitude'):\n vals = input.values.values\n else:\n vals = getattr(input, input_attr)\n\n if target_attr.startswith('amplitude'):\n target.values[:] = vals\n else:\n setattr(target, target_attr, vals)\n\n return target\n\n\nclass Copy(Transformation):\n \"\"\"Copy/clone a variable.\n\n Parameters\n ----------\n col : str\n Name of variable to copy.\n\n \"\"\"\n\n _groupable = False\n _output_required = True\n _allow_categorical = ('variables',)\n\n def _transform(self, col):\n # We don't have to do anything else here b/c it's handled in base.\n return col\n\n\nclass Delete(Transformation):\n \"\"\"Delete variables from the namespace.\n\n Parameters\n ----------\n variables : list or str\n Name(s) of variables to delete.\n\n \"\"\"\n\n _groupable = False\n _loopable = False\n _input_type = 'variable'\n _return_type = 'none'\n _allow_categorical = ('variables',)\n\n def _transform(self, variables):\n variables = set([v.name for v in variables]) # noqa: C403\n self.collection.variables = {\n k: v for k, v in self.collection.variables.items() if k not in variables\n }\n\n\nclass DropNA(Transformation): # noqa: D101\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _allow_categorical = ('variables',)\n\n def _transform(self, var):\n # Identify non-NA rows\n valid = var.values.notna().values\n var.select_rows(valid)\n return var\n\n\nclass Factor(Transformation): # noqa: D101\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _allow_categorical = ('variables',)\n\n def _transform(self, var, constraint='none', ref_level=None, sep='.'):\n result = []\n data = var.to_df()\n orig_name = var.name\n variableClass = var.__class__\n\n levels = np.sort(data['amplitude'].unique())\n new_cols = pd.get_dummies(data['amplitude'], drop_first=False, dtype=float)[levels]\n\n if len(levels) > 1 and constraint in ('drop_one', 'mean_zero'):\n if ref_level is None:\n ref_level = levels[0]\n new_cols = new_cols.drop(ref_level, axis=1)\n\n if constraint == 'mean_zero':\n ref_inds = data['amplitude'] == ref_level\n new_cols.loc[ref_inds, :] = -1.0 / (len(levels) - 1)\n\n for lev in levels:\n if ref_level is not None and lev == ref_level:\n continue\n name = ''.join([var.name, sep, str(lev)])\n lev_data = data.copy()\n lev_data['amplitude'] = new_cols[lev]\n args = [name, lev_data, var.source]\n if hasattr(var, 'run_info'):\n args.insert(2, var.run_info)\n new_col = variableClass(*args)\n result.append(new_col)\n\n # Remove existing variable. TODO: allow user to leave original in?\n self.collection.variables.pop(orig_name)\n\n return result\n\n\nclass Filter(Transformation):\n \"\"\"Filter (remove) the values given a query to satisfy\"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _aligned_required = 'force_dense'\n _aligned_variables = 'by'\n _allow_categorical = ('variables', 'by')\n\n def _transform(self, var, query, by=None):\n if by is None:\n by = []\n\n names = [var.name] + listify(by)\n\n # assure ordered dict so we have consistent (if not correct) operation,\n # because later we ask for name_map.values\n # pandas .query can't handle non-identifiers in variable names, so we\n # need to replace them in both the variable names and the query string.\n name_map = odict((n, re.sub('[^a-zA-Z0-9_]+', '_', n)) for n in names)\n for k, v in name_map.items():\n query = query.replace(k, v)\n\n data = pd.concat([self.collection[n].values for n in names], axis=1, sort=True)\n # Make sure we can use integer index\n data = data.reset_index(drop=True)\n data.columns = list(name_map.values())\n data = data.query(query)\n\n # Truncate target variable to retained rows\n var.select_rows(data.index.values)\n\n return var\n\n\nclass Group(Transformation):\n \"\"\"Groups a list of variables.\"\"\"\n\n _groupable = False\n _loopable = False\n _input_type = 'variable'\n _return_type = 'none'\n\n def _transform(self, variables, name):\n if name in self.variables:\n raise ValueError(\n f\"Variable group name '{name}' conflicts with an existing variable name!\"\n )\n self.collection.groups[name] = [v.name for v in variables]\n\n\nclass Rename(Transformation):\n \"\"\"Rename a variable.\n\n Parameters\n ----------\n var : str\n Name of existing variable to rename.\n\n \"\"\"\n\n _groupable = False\n _output_required = True\n _input_type = 'variable'\n _allow_categorical = ('variables',)\n\n def _transform(self, var):\n # Rename happens automatically in the base class, so all we need to\n # do is unset the original variable in the collection.\n self.collection.variables.pop(var.name)\n return var.values\n\n\nclass Replace(Transformation):\n \"\"\"Replace values in the values, onset, or duration attributes.\"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _allow_categorical = ('variables',)\n\n def _transform(self, var, replace, attribute='value'):\n if attribute == 'value':\n var.values = var.values.replace(replace)\n elif attribute == 'onset':\n var.onset = pd.Series(var.onset).replace(replace).values\n elif attribute == 'duration':\n var.duration = pd.Series(var.duration).replace(replace).values\n else:\n raise ValueError(\"Invalid attribute. Must be one of 'value','onset', or 'duration'.\")\n\n return var\n\n\nclass Select(Transformation):\n \"\"\"Select variables to retain.\n\n Parameters\n ----------\n variables : list or str\n Name(s) of variables to retain. All variables\n not in the list will be dropped from the collection.\n\n \"\"\"\n\n _groupable = False\n _loopable = False\n _input_type = 'variable'\n _return_type = 'none'\n _allow_categorical = ('variables',)\n\n def _transform(self, variables):\n self.collection.variables = {v.name: v for v in variables}\n\n\nclass Split(Transformation):\n \"\"\"Split a single variable into N variables as defined by the levels of one\n or more other variables.\n\n Parameters\n ----------\n by : str or list\n Name(s) of variable(s) to split on.\n\n \"\"\"\n\n _variables_used = ('variables', 'by')\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n _allow_categorical = ('by',)\n _densify = ('variables', 'by')\n _sync_kwargs = False\n\n def _transform(self, var, by):\n if not isinstance(var, SimpleVariable):\n self._densify_variables()\n\n # Set up all the splitting variables as a DF. Note that variables in\n # 'by' can be either regular variables, or entities in the index--so\n # we need to check both places.\n all_variables = self._variables\n by_variables = [\n all_variables[v].values if v in all_variables else var.index[v].reset_index(drop=True)\n for v in listify(by)\n ]\n group_data = pd.concat(by_variables, axis=1, sort=True)\n group_data.columns = listify(by)\n\n # Use formulaic to create splitting design matrix\n group_data = group_data.astype(str)\n formula = '0+' + ':'.join(listify(by))\n dm = model_matrix(formula, data=group_data)\n dm.columns = [col.replace(':', '.') for col in dm.columns]\n # formulaic output naming convention differs from patsy\n dm.columns = [col.replace('[T.', '[') for col in dm.columns]\n\n return var.split(dm)\n\n\nclass ToDense(Transformation):\n \"\"\"Convert variable to dense representation.\"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n\n def _transform(self, var, sampling_rate=10):\n return var.to_dense(sampling_rate=sampling_rate)\n\n\nclass Resample(Transformation):\n \"\"\"Frequency concersion and resampling of variable\"\"\"\n\n _groupable = False\n _input_type = 'variable'\n _return_type = 'variable'\n\n def _transform(self, var, sampling_rate=10):\n return var.resample(sampling_rate=sampling_rate)\n" }, { "path": "src/bids/reports/README.md", "content": "## PyBIDS: Reports\n\nThe PyBIDS reports module generates publication-quality data acquisition descriptions from BIDS datasets.\n\nNOTE: The reports module is experimental and currently under active development, and as such should be used with caution.\nPlease remember to verify any generated report before putting it to use.\n\nAdditionally, only MRI datatypes (func, anat, fmap, and dwi) are currently supported.\n\n### Quickstart\n\nA simple example of standard usage follows. We assume that we have a root folder containing a BIDS-compliant project in `/bidsproject`.\n\n```python\nfrom bids.layout import BIDSLayout\nfrom bids.reports import BIDSReport\n\n# Load the BIDS dataset\nlayout = BIDSLayout('/bidsproject')\n\n# Initialize a report for the dataset\nreport = BIDSReport(layout)\n\n# Method generate returns a Counter of unique descriptions across subjects\ndescriptions = report.generate()\n\n# For datasets containing a single study design, all but the most common\n# description most likely reflect random missing data.\npub_description = descriptions.most_common()[0][0]\n```\n" }, { "path": "src/bids/reports/__init__.py", "content": "from .report import BIDSReport # noqa: D104\n\n__all__ = ['BIDSReport']\n" }, { "path": "src/bids/reports/config/converters.json", "content": "{\n \"dir\": {\n \"i\": \"left to right\",\n \"i-\": \"right to left\",\n \"j\": \"posterior to anterior\",\n \"j-\": \"anterior to posterior\",\n \"k\": \"inferior to superior\",\n \"k-\": \"superior to inferior\"\n },\n \"seq\": {\n \"EP\": \"echo planar\",\n \"GR\": \"gradient recalled\",\n \"IR\": \"inversion recovery\",\n \"RM\": \"research mode\",\n \"SE\": \"spin echo\"\n },\n \"seqvar\": {\n \"MP\": \"MAG prepared\",\n \"MTC\": \"magnetization transfer contrast\",\n \"NONE\": \"no sequence variant\",\n \"OSP\": \"oversampling phase\",\n \"SK\": \"segmented k-space\",\n \"SP\": \"spoiled\",\n \"SS\": \"steady state\",\n \"TRSS\": \"time reversed steady state\"\n }\n}\n" }, { "path": "src/bids/reports/conftest.py", "content": "import json # noqa: D100\nimport warnings\nfrom importlib.resources import files\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\n\n\n@pytest.fixture(scope='session')\ndef testlayout(tests_dir):\n \"\"\"A BIDSLayout for testing.\"\"\"\n return BIDSLayout(tests_dir / 'data' / 'synthetic')\n\n\n@pytest.fixture(scope='session')\ndef config_file(): # noqa: D103\n # PY39: config isn't a module, so can't be used in files()\n # This is relaxed in more recent Pythons\n return files('bids.reports') / 'config' / 'converters.json'\n\n\n@pytest.fixture(scope='session')\ndef testconfig(config_file): # noqa: D103\n return json.loads(config_file.read_text())\n\n\n@pytest.fixture(autouse=True)\ndef _unsupported_files():\n with warnings.catch_warnings():\n warnings.filterwarnings(\n 'ignore',\n message=r'.*magnitude.* not yet supported.*',\n category=UserWarning,\n append=True,\n )\n yield\n" }, { "path": "src/bids/reports/parameters.py", "content": "\"\"\"Functions for building strings for individual parameters.\"\"\"\n\nimport logging\nimport math\nimport os\nimport os.path as op\n\nimport nibabel as nib\nfrom num2words import num2words\n\nfrom .utils import list_to_str, num_to_str, remove_duplicates\n\nLOGGER = logging.getLogger('pybids.reports.parsing')\n\n\ndef describe_slice_timing(img, metadata: dict) -> str:\n \"\"\"Generate description of slice timing from metadata.\"\"\"\n if 'SliceTiming' in metadata:\n slice_order = ' in {0} order'.format(get_slice_info(metadata['SliceTiming'])) # noqa: UP030\n n_slices = len(metadata['SliceTiming'])\n else:\n slice_order = ''\n n_slices = img.shape[2]\n\n return f'{n_slices} slices{slice_order}'\n\n\ndef describe_repetition_time(metadata: dict):\n \"\"\"Generate description of repetition time from metadata.\"\"\"\n tr = metadata['RepetitionTime'] * 1000\n tr = num_to_str(tr)\n return f'repetition time, TR={tr}ms'\n\n\ndef describe_func_duration(n_vols: int, tr) -> str:\n \"\"\"Generate description of functional run length from repetition time and number of volumes.\"\"\"\n run_secs = math.ceil(n_vols * tr)\n mins, secs = divmod(run_secs, 60)\n return f'{int(mins)}:{int(secs):02.0f}'\n\n\ndef describe_duration(files) -> str:\n \"\"\"Generate general description of scan length from files.\"\"\"\n first_file = files[0]\n metadata = first_file.get_metadata()\n\n tr = metadata['RepetitionTime']\n imgs = [nib.load(f) for f in files]\n n_vols = [img.shape[3] for img in imgs]\n\n if len(set(n_vols)) > 1:\n min_vols = min(n_vols)\n max_vols = max(n_vols)\n min_dur = describe_func_duration(min_vols, tr)\n max_dur = describe_func_duration(max_vols, tr)\n dur_str = f'{min_dur}-{max_dur}'\n n_vols = f'{min_vols}-{max_vols}'\n\n else:\n n_vols = n_vols[0]\n dur_str = describe_func_duration(n_vols, tr)\n\n dur_str = f'Run duration was {dur_str} minutes, during which {n_vols} volumes were acquired.'\n return dur_str\n\n\ndef describe_multiband_factor(metadata) -> str:\n \"\"\"Generate description of the multi-band acceleration applied, if used.\"\"\"\n return (\n 'MB factor={}'.format(metadata['MultibandAccelerationFactor'])\n if metadata.get('MultibandAccelerationFactor', 1) > 1\n else ''\n )\n\n\ndef describe_echo_times(files):\n \"\"\"Generate description of echo times from metadata field.\n\n Parameters\n ----------\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to file collection.\n\n Returns\n -------\n te_str : str\n Description of echo times.\n me_str : str\n Whether the data are multi-echo or single-echo.\n\n \"\"\"\n echo_times = [f.get_metadata()['EchoTime'] for f in files]\n echo_times = sorted(list(set(echo_times))) # noqa: C414\n if len(echo_times) > 1:\n te = [num_to_str(t * 1000) for t in echo_times]\n te = list_to_str(te)\n me_str = 'multi-echo'\n else:\n te = num_to_str(echo_times[0] * 1000)\n me_str = 'single-echo'\n te_str = f'echo time, TE={te}ms'\n return te_str, me_str\n\n\ndef describe_echo_times_fmap(files):\n \"\"\"Generate description of echo times from metadata field for fmaps\n\n Parameters\n ----------\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to file collection.\n\n Returns\n -------\n te_str : str\n Description of echo times.\n\n \"\"\"\n # TODO handle all types of fieldmaps\n\n echo_times1 = [f.get_metadata()['EchoTime1'] for f in files]\n echo_times2 = [f.get_metadata()['EchoTime2'] for f in files]\n echo_times1 = sorted(list(set(echo_times1))) # noqa: C414\n echo_times2 = sorted(list(set(echo_times2))) # noqa: C414\n if len(echo_times1) <= 1 and len(echo_times2) <= 1:\n # if that's not the case we should probably throw a warning\n # because we should expect the same echo times for all values\n te1 = num_to_str(echo_times1[0] * 1000)\n te2 = num_to_str(echo_times2[0] * 1000)\n return f'echo time 1 / 2, TE1/2={te1}{te2}ms'\n\n\ndef describe_image_size(img):\n \"\"\"Generate description imaging data sizes, including FOV, voxel size, and matrix size.\n\n Parameters\n ----------\n img : nibabel.nifti1.Nifti1Image\n Image object from which to determine sizes.\n\n Returns\n -------\n fov_str\n matrixsize_str\n voxelsize_str\n\n \"\"\"\n vs_str, ms_str, fov_str = get_size_str(img)\n\n fov_str = f'field of view, FOV={fov_str}mm'\n voxelsize_str = f'voxel size={vs_str}mm'\n matrixsize_str = f'matrix size={ms_str}'\n\n return fov_str, matrixsize_str, voxelsize_str\n\n\ndef describe_inplane_accel(metadata: dict) -> str:\n \"\"\"Generate description of in-plane acceleration factor, if any.\"\"\"\n return (\n 'in-plane acceleration factor={}'.format(metadata['ParallelReductionFactorInPlane'])\n if metadata.get('ParallelReductionFactorInPlane', 1) > 1\n else ''\n )\n\n\ndef describe_flip_angle(metadata: dict) -> str:\n \"\"\"Generate description of flip angle.\"\"\"\n return 'flip angle, FA={}'.format(metadata.get('FlipAngle', 'UNKNOWN'))\n\n\ndef describe_dmri_directions(img):\n \"\"\"Generate description of diffusion directions.\"\"\"\n return f'{img.shape[3]} diffusion directions'\n\n\ndef describe_bvals(bval_file) -> str:\n \"\"\"Generate description of dMRI b-values.\"\"\"\n # Parse bval file\n with open(bval_file) as file_object:\n raw_bvals = file_object.read().splitlines()\n # Flatten list of space-separated values\n bvals = [item for sublist in [line.split(' ') for line in raw_bvals] for item in sublist]\n bvals = sorted([int(v) for v in set(bvals)])\n bvals = [num_to_str(v) for v in bvals]\n bval_str = list_to_str(bvals)\n bval_str = f'b-values of {bval_str} acquired'\n return bval_str\n\n\ndef describe_pe_direction(metadata: dict, config: dict) -> str:\n \"\"\"Generate description of phase encoding direction.\"\"\"\n dir_str = config['dir'][metadata['PhaseEncodingDirection']]\n dir_str = f'phase encoding: {dir_str}'\n return dir_str\n\n\ndef describe_intendedfor_targets(metadata: dict, layout) -> str:\n \"\"\"Generate description of intended for targets.\"\"\"\n if 'IntendedFor' in metadata.keys():\n scans = metadata['IntendedFor']\n run_dict = {}\n\n for scan in scans:\n fn = op.basename(scan)\n if_file = [f for f in layout.get(extension=['.nii', '.nii.gz']) if fn in f.path][0]\n run_num = int(if_file.entities['run'])\n target_type = if_file.entities['suffix'].upper()\n\n if target_type == 'BOLD':\n iff_meta = layout.get_metadata(if_file.path)\n task = iff_meta.get('TaskName', if_file.entities['task'])\n target_type_str = f'{task} {target_type} scan'\n else:\n target_type_str = f'{target_type} scan'\n\n if target_type_str not in run_dict.keys():\n run_dict[target_type_str] = []\n\n run_dict[target_type_str].append(run_num)\n\n for scan in run_dict.keys():\n run_dict[scan] = [num2words(r, ordinal=True) for r in sorted(run_dict[scan])]\n\n out_list = []\n\n for scan in run_dict.keys():\n if len(run_dict[scan]) > 1:\n s = 's'\n else:\n s = ''\n\n run_str = list_to_str(run_dict[scan])\n string = f'{run_str} run{s} of the {scan}'\n out_list.append(string)\n\n for_str = f' for the {list_to_str(out_list)}'\n\n else:\n for_str = ''\n\n return for_str\n\n\ndef get_slice_info(slice_times) -> str:\n \"\"\"Extract slice order from slice timing info.\n\n TODO: Be more specific with slice orders.\n Currently anything where there's some kind of skipping is interpreted as\n interleaved of some kind.\n\n Parameters\n ----------\n slice_times : array-like\n A list of slice times in seconds or milliseconds or whatever.\n\n Returns\n -------\n slice_order_name : :obj:`str`\n The name of the slice order sequence.\n\n \"\"\"\n # Slice order\n slice_times = remove_duplicates(slice_times)\n slice_order = sorted(range(len(slice_times)), key=lambda k: slice_times[k])\n\n if slice_order == list(range(len(slice_order))):\n slice_order_name = 'sequential ascending'\n\n elif slice_order == list(reversed(range(len(slice_order)))):\n slice_order_name = 'sequential descending'\n\n elif slice_order[0] < slice_order[1]:\n # We're allowing some wiggle room on interleaved.\n slice_order_name = 'interleaved ascending'\n\n elif slice_order[0] > slice_order[1]:\n slice_order_name = 'interleaved descending'\n\n else:\n slice_order = [str(s) for s in slice_order]\n raise Exception('Unknown slice order: [{0}]'.format(', '.join(slice_order))) # noqa: UP030\n\n return slice_order_name\n\n\ndef describe_sequence(metadata: dict, config: dict):\n \"\"\"Extract and reformat imaging sequence(s) and variant(s) into pretty strings.\n\n Parameters\n ----------\n metadata : :obj:`dict`\n The metadata for the scan.\n config : :obj:`dict`\n A dictionary with relevant information regarding sequences, sequence\n variants, phase encoding directions, and task names.\n\n Returns\n -------\n seqs : :obj:`str`\n Sequence names.\n variants : :obj:`str`\n Sequence variant names.\n\n \"\"\"\n seq_abbrs = metadata.get('ScanningSequence', '').split('_')\n seqs = [config['seq'].get(seq, seq) for seq in seq_abbrs]\n seqs = list_to_str(seqs)\n if seq_abbrs[0]:\n seqs += f' ({os.path.sep.join(seq_abbrs)})'\n\n variants = [\n config['seqvar'].get(var, var) for var in metadata.get('SequenceVariant', '').split('_')\n ]\n variants = list_to_str(variants)\n\n return seqs, variants\n\n\ndef get_size_str(img):\n \"\"\"Extract and reformat voxel size, matrix size, FOV, and number of slices into strings.\n\n Parameters\n ----------\n img : :obj:`nibabel.Nifti1Image`\n Image from scan from which to derive parameters.\n\n Returns\n -------\n voxel_size : :obj:`str`\n Voxel size string (e.g., '2x2x2')\n matrix_size : :obj:`str`\n Matrix size string (e.g., '128x128')\n fov : :obj:`str`\n Field of view string (e.g., '256x256')\n\n \"\"\"\n n_x, n_y = img.shape[:2]\n import numpy as np\n\n matrix_size = f'{num_to_str(n_x)}x{num_to_str(n_y)}'\n\n voxel_dims = np.array(img.header.get_zooms()[:3])\n\n voxel_size = 'x'.join([num_to_str(s) for s in voxel_dims])\n\n fov = [n_x, n_y] * voxel_dims[:2]\n fov = 'x'.join([num_to_str(s) for s in fov])\n\n return voxel_size, matrix_size, fov\n" }, { "path": "src/bids/reports/parsing.py", "content": "\"\"\"Parsing functions for generating BIDSReports.\"\"\"\n\nimport logging\nimport warnings\n\nimport nibabel as nib\nfrom num2words import num2words\n\nfrom .. import __version__\nfrom ..utils import collect_associated_files\nfrom . import parameters\n\nLOGGER = logging.getLogger('pybids.reports.parsing')\n\n\ndef func_info(layout, files, config):\n \"\"\"Generate a paragraph describing T2*-weighted functional scans.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to DWI scan.\n config : :obj:`dict`\n A dictionary with relevant information regarding sequences, sequence\n variants, phase encoding directions, and task names.\n\n Returns\n -------\n desc : :obj:`str`\n A description of the scan's acquisition information.\n\n \"\"\"\n first_file = files[0]\n metadata = first_file.get_metadata()\n img = nib.load(first_file.path)\n\n # General info\n task_name = first_file.get_entities()['task'] + ' task'\n task_name = metadata.get('TaskName', task_name)\n seqs, variants = parameters.describe_sequence(metadata, config)\n all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414\n n_runs = len(all_runs)\n if n_runs == 1:\n run_str = f'{num2words(n_runs).title()} run'\n else:\n run_str = f'{num2words(n_runs).title()} runs'\n dur_str = parameters.describe_duration(files)\n\n # Parameters\n slice_str = parameters.describe_slice_timing(img, metadata)\n tr_str = parameters.describe_repetition_time(metadata)\n te_str, me_str = parameters.describe_echo_times(files)\n fa_str = parameters.describe_flip_angle(metadata)\n fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img)\n mb_str = parameters.describe_multiband_factor(metadata)\n inplaneaccel_str = parameters.describe_inplane_accel(metadata)\n\n parameters_str = [\n slice_str,\n tr_str,\n te_str,\n fa_str,\n fov_str,\n matrixsize_str,\n voxelsize_str,\n mb_str,\n inplaneaccel_str,\n ]\n parameters_str = [d for d in parameters_str if len(d)]\n parameters_str = '; '.join(parameters_str)\n\n desc = (\n f'{run_str} of {task_name} {variants} {seqs} {me_str} fMRI data were '\n f'collected ({parameters_str}). {dur_str}'\n )\n return desc\n\n\ndef anat_info(layout, files, config):\n \"\"\"Generate a paragraph describing T1- and T2-weighted structural scans.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to DWI scan.\n config : :obj:`dict`\n A dictionary with relevant information regarding sequences, sequence\n variants, phase encoding directions, and task names.\n\n Returns\n -------\n desc : :obj:`str`\n A description of the scan's acquisition information.\n\n \"\"\"\n first_file = files[0]\n metadata = first_file.get_metadata()\n img = nib.load(first_file.path)\n\n # General info\n seqs, variants = parameters.describe_sequence(metadata, config)\n all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414\n n_runs = len(all_runs)\n if n_runs == 1:\n run_str = f'{num2words(n_runs).title()} run'\n else:\n run_str = f'{num2words(n_runs).title()} runs'\n scan_type = first_file.get_entities()['suffix'].replace('w', '-weighted')\n\n # Parameters\n slice_str = parameters.describe_slice_timing(img, metadata)\n tr_str = parameters.describe_repetition_time(metadata)\n te_str, me_str = parameters.describe_echo_times(files)\n fa_str = parameters.describe_flip_angle(metadata)\n fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img)\n\n parameters_str = [\n slice_str,\n tr_str,\n te_str,\n fa_str,\n fov_str,\n matrixsize_str,\n voxelsize_str,\n ]\n parameters_str = [d for d in parameters_str if len(d)]\n parameters_str = '; '.join(parameters_str)\n\n desc = (\n f'{run_str} of {scan_type} {variants} {seqs} {me_str} structural MRI '\n f'data were collected ({parameters_str}).'\n )\n return desc\n\n\ndef dwi_info(layout, files, config):\n \"\"\"Generate a paragraph describing DWI scan acquisition information.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to DWI scan.\n config : :obj:`dict`\n A dictionary with relevant information regarding sequences, sequence\n variants, phase encoding directions, and task names.\n\n Returns\n -------\n desc : :obj:`str`\n A description of the DWI scan's acquisition information.\n\n \"\"\"\n first_file = files[0]\n metadata = first_file.get_metadata()\n img = nib.load(first_file.path)\n bval_file = first_file.path.replace('.nii.gz', '.bval').replace('.nii', '.bval')\n\n # General info\n seqs, variants = parameters.describe_sequence(metadata, config)\n all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414\n n_runs = len(all_runs)\n if n_runs == 1:\n run_str = f'{num2words(n_runs).title()} run'\n else:\n run_str = f'{num2words(n_runs).title()} runs'\n\n # Parameters\n tr_str = parameters.describe_repetition_time(metadata)\n te_str, me_str = parameters.describe_echo_times(files)\n fa_str = parameters.describe_flip_angle(metadata)\n fov_str, voxelsize_str, matrixsize_str = parameters.describe_image_size(img)\n bval_str = parameters.describe_bvals(bval_file)\n nvec_str = parameters.describe_dmri_directions(img)\n mb_str = parameters.describe_multiband_factor(metadata)\n\n parameters_str = [\n tr_str,\n te_str,\n fa_str,\n fov_str,\n matrixsize_str,\n voxelsize_str,\n bval_str,\n nvec_str,\n mb_str,\n ]\n parameters_str = [d for d in parameters_str if len(d)]\n parameters_str = '; '.join(parameters_str)\n\n desc = (\n f'{run_str} of {variants} {seqs} diffusion-weighted (dMRI) data were '\n f'collected ({parameters_str}).'\n )\n return desc\n\n\ndef fmap_info(layout, files, config):\n \"\"\"Generate a paragraph describing field map acquisition information.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to field map scan.\n config : :obj:`dict`\n A dictionary with relevant information regarding sequences, sequence\n variants, phase encoding directions, and task names.\n\n Returns\n -------\n desc : :obj:`str`\n A description of the field map's acquisition information.\n\n \"\"\"\n first_file = files[0]\n metadata = first_file.get_metadata()\n img = nib.load(first_file.path)\n\n # General info\n seqs, variants = parameters.describe_sequence(metadata, config)\n\n # Parameters\n dir_str = parameters.describe_pe_direction(metadata, config)\n slice_str = parameters.describe_slice_timing(img, metadata)\n tr_str = parameters.describe_repetition_time(metadata)\n te_str = parameters.describe_echo_times_fmap(files)\n fa_str = parameters.describe_flip_angle(metadata)\n fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img)\n mb_str = parameters.describe_multiband_factor(metadata)\n\n parameters_str = [\n dir_str,\n slice_str,\n tr_str,\n te_str,\n fa_str,\n fov_str,\n matrixsize_str,\n voxelsize_str,\n mb_str,\n ]\n parameters_str = [d for d in parameters_str if len(d)]\n parameters_str = '; '.join(parameters_str)\n\n for_str = parameters.describe_intendedfor_targets(metadata, layout)\n\n desc = f'A {variants} {seqs} field map ({parameters_str}) was acquired{for_str}.'\n return desc\n\n\ndef general_acquisition_info(metadata):\n \"\"\"General sentence on data acquisition.\n\n This should be the first sentence in the MRI data acquisition section.\n\n Parameters\n ----------\n metadata : :obj:`dict`\n The metadata for the dataset.\n\n Returns\n -------\n out_str : :obj:`str`\n Output string with scanner information.\n\n \"\"\"\n out_str = 'MR data were acquired using a {tesla}-Tesla {manu} {model} MRI scanner.'.format(\n tesla=metadata.get('MagneticFieldStrength', 'UNKNOWN'),\n manu=metadata.get('Manufacturer', 'MANUFACTURER'),\n model=metadata.get('ManufacturersModelName', 'MODEL'),\n )\n return out_str\n\n\ndef final_paragraph(metadata):\n \"\"\"Describe dicom-to-nifti conversion process and methods generation.\n\n Parameters\n ----------\n metadata : :obj:`dict`\n The metadata for the scan.\n\n Returns\n -------\n desc : :obj:`str`\n Output string with scanner information.\n\n \"\"\"\n if 'ConversionSoftware' in metadata.keys():\n soft = metadata['ConversionSoftware']\n vers = metadata['ConversionSoftwareVersion']\n software_str = f' using {soft} ({vers})'\n else:\n software_str = ''\n desc = (\n f'Dicoms were converted to NIfTI-1 format{software_str}. '\n 'This section was (in part) generated automatically using pybids '\n f'({__version__}).'\n )\n return desc\n\n\ndef parse_files(layout, data_files, sub, config):\n \"\"\"Loop through files in a BIDSLayout and generate appropriate descriptions.\n\n Then, compile all of the descriptions into a list.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n data_files : :obj:`list` of :obj:`bids.layout.models.BIDSFile`\n List of nifti files in layout corresponding to subject/session combo.\n sub : :obj:`str`\n Subject ID.\n config : :obj:`dict`\n Configuration info for methods generation.\n\n \"\"\"\n return _parse_files(layout, data_files, sub, config)\n\n\n# Indirection to allow warnings to be raised in user-written code\ndef _parse_files(layout, data_files, sub, config, *, stacklevel=3):\n # Group files into individual runs\n data_files = collect_associated_files(layout, data_files, extra_entities=['run'])\n\n description_list = []\n # Assume all data have same basic info\n description_list.append(general_acquisition_info(data_files[0][0].get_metadata()))\n\n for group in data_files:\n if group[0].entities['datatype'] == 'func':\n group_description = func_info(layout, group, config)\n\n elif (group[0].entities['datatype'] == 'anat') and group[0].entities['suffix'].endswith(\n 'w'\n ):\n group_description = anat_info(layout, group, config)\n\n elif group[0].entities['datatype'] == 'dwi':\n group_description = dwi_info(layout, group, config)\n\n elif (group[0].entities['datatype'] == 'fmap') and group[0].entities[\n 'suffix'\n ] == 'phasediff':\n group_description = fmap_info(layout, group, config)\n\n elif group[0].entities['datatype'] in ['eeg', 'meg', 'beh', 'perf']:\n warnings.warn(\n group[0].entities['datatype'] + ' not yet supported.', stacklevel=stacklevel\n )\n continue\n\n else:\n warnings.warn(group[0].filename + ' not yet supported.', stacklevel=stacklevel)\n continue\n\n description_list.append(group_description)\n\n return description_list\n" }, { "path": "src/bids/reports/report.py", "content": "\"\"\"Generate publication-quality data acquisition methods section from BIDS dataset.\"\"\"\n\nimport json\nimport os.path as op\nfrom collections import Counter\n\nfrom bids.reports import parsing, utils\n\n\nclass BIDSReport:\n \"\"\"Generate publication-quality data acquisition section from BIDS dataset.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n config : :obj:`str` or :obj:`dict`, optional\n Configuration info for methods generation. Can be a path to a file\n (str), a dictionary, or None. If None, loads and uses default\n configuration information.\n Keys in the dictionary include:\n\n 'dir':\n a dictionary for converting encoding direction strings\n (e.g., j-) to descriptions (e.g., anterior to\n posterior)\n 'seq':\n a dictionary of sequence abbreviations (e.g., EP) and\n corresponding names (e.g., echo planar)\n 'seqvar':\n a dictionary of sequence variant abbreviations\n (e.g., SP) and corresponding names (e.g., spoiled)\n\n Warnings\n --------\n pybids' automatic report generation is experimental and currently under\n active development, and as such should be used with caution.\n Please remember to verify any generated report before putting it to use.\n\n Additionally, only MRI datatypes (func, anat, fmap, and dwi) are currently\n supported.\n\n \"\"\"\n\n def __init__(self, layout, config=None):\n self.layout = layout\n if config is None:\n config = op.join(\n op.dirname(op.abspath(__file__)),\n 'config',\n 'converters.json',\n )\n\n if isinstance(config, str):\n with open(config) as fobj:\n config = json.load(fobj)\n\n if not isinstance(config, dict):\n raise ValueError(\n 'Input config must be None, dict, or path to json file containing dict.'\n )\n\n self.config = config\n\n def generate_from_files(self, files):\n r\"\"\"Generate a methods section from a list of files.\n\n Parameters\n ----------\n files : list of BIDSImageFile objects\n List of files from which to generate methods description.\n\n Returns\n -------\n counter : :obj:`collections.Counter`\n A dictionary of unique descriptions across subjects in the file list,\n along with the number of times each pattern occurred. In cases\n where all subjects underwent the same protocol, the most common\n pattern is most likely the most complete. In cases where the\n file list contains multiple protocols, each pattern will need to be\n inspected manually.\n\n Examples\n --------\n >>> from os.path import join\n >>> from bids.layout import BIDSLayout\n >>> from bids.reports import BIDSReport\n >>> from bids.tests import get_test_data_path\n >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic'))\n >>> report = BIDSReport(layout)\n >>> files = layout.get(session='01', extension=['.nii.gz', '.nii'])\n >>> counter = report.generate_from_files(files)\n Number of patterns detected: 1\n Remember to double-check everything and to replace with a degree symbol.\n\n >>> counter.most_common()[0][0] # doctest: +ELLIPSIS\n 'In session 01, MR data were...'\n\n \"\"\"\n descriptions = []\n\n subjects = sorted(list(set([f.get_entities().get('subject') for f in files]))) # noqa: C403, C414\n sessions = sorted(list(set([f.get_entities().get('session') for f in files]))) # noqa: C403, C414\n for sub in subjects:\n subject_files = [f for f in files if f.get_entities().get('subject') == sub]\n description_list = []\n for ses in sessions:\n data_files = [f for f in subject_files if f.get_entities().get('session') == ses]\n\n if data_files:\n ses_description = parsing._parse_files(\n self.layout,\n data_files,\n sub,\n self.config,\n )\n ses_description[0] = f'In session {ses}, ' + ses_description[0]\n description_list += ses_description\n metadata = self.layout.get_metadata(data_files[0].path)\n else:\n raise Exception(f'No imaging files for subject {sub}')\n\n # Assume all data were converted the same way and use the last nifti\n # file's json for conversion information.\n if 'metadata' not in vars():\n raise Exception('No valid jsons found. Cannot generate final paragraph.')\n\n description = '\\n\\t'.join(description_list)\n description += f'\\n\\n{parsing.final_paragraph(metadata)}'\n descriptions.append(description)\n counter = Counter(descriptions)\n print(f'Number of patterns detected: {len(counter.keys())}')\n print(utils.reminder())\n return counter\n\n def generate(self, **kwargs):\n r\"\"\"Generate the methods section.\n\n Parameters\n ----------\n kwargs : dict\n Keyword arguments passed to BIDSLayout to select subsets of the\n dataset.\n\n Returns\n -------\n counter : :obj:`collections.Counter`\n A dictionary of unique descriptions across subjects in the dataset,\n along with the number of times each pattern occurred. In cases\n where all subjects underwent the same protocol, the most common\n pattern is most likely the most complete. In cases where the\n dataset contains multiple protocols, each pattern will need to be\n inspected manually.\n\n Examples\n --------\n >>> from os.path import join\n >>> from bids.layout import BIDSLayout\n >>> from bids.reports import BIDSReport\n >>> from bids.tests import get_test_data_path\n >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic'))\n >>> report = BIDSReport(layout)\n >>> counter = report.generate(session='01')\n Number of patterns detected: 1\n Remember to double-check everything and to replace with a degree symbol.\n\n >>> counter.most_common()[0][0] # doctest: +ELLIPSIS\n 'In session 01, MR data were...'\n\n \"\"\"\n descriptions = []\n\n subjects = self.layout.get_subjects(**kwargs)\n kwargs = {k: v for k, v in kwargs.items() if k != 'subject'}\n for sub in subjects:\n descriptions.append(self._report_subject(subject=sub, **kwargs))\n counter = Counter(descriptions)\n print(f'Number of patterns detected: {len(counter.keys())}')\n print(utils.reminder())\n return counter\n\n def _report_subject(self, subject, **kwargs): # noqa: D417\n \"\"\"Write a report for a single subject.\n\n Parameters\n ----------\n subject : :obj:`str`\n Subject ID.\n\n Attributes\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n Layout object for a BIDS dataset.\n config : :obj:`dict`\n Configuration info for methods generation.\n\n Returns\n -------\n description : :obj:`str`\n A publication-ready report of the dataset's data acquisition\n information. Each scan type is given its own paragraph.\n\n \"\"\"\n description_list = []\n # Remove session from kwargs if provided, else set session as all available\n sessions = kwargs.pop('session', self.layout.get_sessions(subject=subject, **kwargs))\n if not sessions:\n sessions = [None]\n elif not isinstance(sessions, list):\n sessions = [sessions]\n\n for ses in sessions:\n data_files = self.layout.get(\n subject=subject,\n extension=['.nii', '.nii.gz'],\n **kwargs,\n )\n\n if data_files:\n ses_description = parsing._parse_files(\n self.layout,\n data_files,\n subject,\n self.config,\n )\n ses_description[0] = f'In session {ses}, ' + ses_description[0]\n description_list += ses_description\n metadata = self.layout.get_metadata(data_files[0].path)\n else:\n raise Exception(f'No imaging files for subject {subject}')\n\n # Assume all data were converted the same way and use the first nifti\n # file's json for conversion information.\n description = '\\n\\t'.join(description_list)\n description += f'\\n\\n{parsing.final_paragraph(metadata)}'\n return description\n" }, { "path": "src/bids/reports/tests/__init__.py", "content": "" }, { "path": "src/bids/reports/tests/test_parameters.py", "content": "import nibabel as nib\nimport pytest\n\nfrom bids.reports import parameters\n\n\n@pytest.fixture(scope='module')\ndef testimg(testlayout):\n func_files = testlayout.get(\n subject='01',\n session='01',\n task='nback',\n run='01',\n extension=['.nii.gz'],\n )\n return nib.load(func_files[0].path)\n\n\n@pytest.fixture(scope='module')\ndef testdiffimg(testlayout):\n dwi_files = testlayout.get(\n subject='01',\n session='01',\n datatype='dwi',\n extension=['.nii.gz'],\n )\n return nib.load(dwi_files[0].path)\n\n\n@pytest.fixture\ndef testmeta():\n return {\n 'RepetitionTime': 2.0,\n 'MultibandAccelerationFactor': 2,\n 'ParallelReductionFactorInPlane': 2,\n 'FlipAngle': 90,\n 'PhaseEncodingDirection': 'i',\n 'SliceTiming': [0, 1, 2, 3],\n }\n\n\n@pytest.fixture\ndef testmeta_light():\n return {'RepetitionTime': 2.0}\n\n\n@pytest.mark.parametrize(\n 'ScanningSequence, expected_seq',\n [\n ('EP', 'echo planar (EP)'),\n ('GR', 'gradient recalled (GR)'),\n ('IR', 'inversion recovery (IR)'),\n ('RM', 'research mode (RM)'),\n ('SE', 'spin echo (SE)'),\n ('SE_EP', 'spin echo and echo planar (SE/EP)'),\n ],\n)\n@pytest.mark.parametrize(\n 'SequenceVariant, expected_var',\n [\n ('MP', 'MAG prepared'),\n ('MTC', 'magnetization transfer contrast'),\n ('NONE', 'no sequence variant'),\n ('OSP', 'oversampling phase'),\n ('SK', 'segmented k-space'),\n ('SS', 'steady state'),\n ('TRSS', 'time reversed steady state'),\n ('MP_SS', 'MAG prepared and steady state'),\n ],\n)\ndef test_describe_sequence(\n ScanningSequence, expected_seq, SequenceVariant, expected_var, testconfig\n):\n \"\"\"test for sequence and variant type description\"\"\"\n metadata = {\n 'SequenceVariant': SequenceVariant,\n 'ScanningSequence': ScanningSequence,\n }\n seqs, variants = parameters.describe_sequence(metadata, testconfig)\n\n assert seqs == expected_seq\n assert variants == expected_var\n\n\n@pytest.mark.parametrize(\n 'pe_direction, expected',\n [\n ('i', 'left to right'),\n ('i-', 'right to left'),\n ('j', 'posterior to anterior'),\n ('j-', 'anterior to posterior'),\n ('k', 'inferior to superior'),\n ('k-', 'superior to inferior'),\n ],\n)\ndef test_describe_pe_direction(pe_direction, expected, testconfig):\n \"\"\"test for phase encoding direction description\"\"\"\n metadata = {'PhaseEncodingDirection': pe_direction}\n dir_str = parameters.describe_pe_direction(metadata, testconfig)\n assert dir_str == 'phase encoding: ' + expected\n\n\ndef test_describe_bvals_smoke(testlayout):\n \"\"\"Smoke test for parsing _dwi.bval\n\n It should return a str description when provided valid inputs.\n \"\"\"\n bval_file = testlayout.get(\n subject='01',\n session='01',\n suffix='dwi',\n extension=['.bval'],\n )\n\n bval_str = parameters.describe_bvals(bval_file[0])\n assert isinstance(bval_str, str)\n\n\ndef test_describe_echo_times_smoke(testlayout):\n \"\"\"Smoke test for parsing echo time\n It should return a str description when provided valid inputs.\n \"\"\"\n anat_file = testlayout.get(\n subject='01',\n session='01',\n suffix='T1w',\n extension=['.nii.gz'],\n )\n\n te_str, me_str = parameters.describe_echo_times(anat_file)\n assert isinstance(te_str, str)\n\n\ndef test_describe_echo_times_fmap(testlayout):\n \"\"\"Smoke test for parsing echo time\n\n It should return a str description when provided valid inputs.\n \"\"\"\n fmap_file = testlayout.get(\n subject='01',\n session='01',\n suffix='phasediff',\n extension=['.nii.gz'],\n )\n\n te_str = parameters.describe_echo_times_fmap(fmap_file)\n assert isinstance(te_str, str)\n\n\ndef test_describe_repetition_time_smoke(testmeta):\n # when\n tr_str = parameters.describe_repetition_time(testmeta)\n # then\n expected = 'repetition time, TR=2000ms'\n assert tr_str == expected\n\n\ndef test_describe_func_duration_smoke():\n # given\n n_vols = 100\n tr = 2.5\n # when\n duration = parameters.describe_func_duration(n_vols, tr)\n # then\n expected = '4:10'\n assert duration == expected\n\n\ndef test_describe_multiband_factor_smoke(testmeta, testmeta_light):\n # when\n mb_str = parameters.describe_multiband_factor(testmeta)\n # then\n expected = 'MB factor=2'\n assert mb_str == expected\n\n # when\n mb_str = parameters.describe_multiband_factor(testmeta_light)\n # then\n expected = ''\n assert mb_str == expected\n\n\ndef test_describe_inplane_accel_smoke(testmeta, testmeta_light):\n # when\n mb_str = parameters.describe_inplane_accel(testmeta)\n # then\n expected = 'in-plane acceleration factor=2'\n assert mb_str == expected\n\n # when\n mb_str = parameters.describe_inplane_accel(testmeta_light)\n # then\n expected = ''\n assert mb_str == expected\n\n\ndef test_describe_flip_angle_smoke(testmeta):\n # when\n mb_str = parameters.describe_flip_angle(testmeta)\n # then\n expected = 'flip angle, FA=90'\n assert mb_str == expected\n\n\n@pytest.mark.parametrize(\n 'slice_times, expected',\n [\n ((1, 2, 3, 4), 'sequential ascending'),\n ([4, 3, 2, 1], 'sequential descending'),\n ([1, 3, 2, 4], 'interleaved ascending'),\n ([4, 2, 3, 1], 'interleaved descending'),\n ],\n)\ndef test_get_slice_info(slice_times, expected):\n slice_order_name = parameters.get_slice_info(slice_times)\n assert slice_order_name == expected\n\n\ndef test_describe_slice_timing(testimg, testmeta, testmeta_light):\n slice_str = parameters.describe_slice_timing(testimg, testmeta)\n expected = '4 slices in sequential ascending order'\n assert slice_str == expected\n\n slice_str = parameters.describe_slice_timing(testimg, testmeta_light)\n expected = '64 slices'\n assert slice_str == expected\n\n\ndef test_get_size_str(testimg):\n voxel_size, matrix_size, fov = parameters.get_size_str(testimg)\n expected_vox = '2x2x2'\n expected_mat = '64x64'\n expected_fov = '128x128'\n assert voxel_size == expected_vox\n assert matrix_size == expected_mat\n assert fov == expected_fov\n\n\ndef test_describe_image_size(testimg):\n fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(testimg)\n expected_vox = 'voxel size=2x2x2mm'\n expected_mat = 'matrix size=64x64'\n expected_fov = 'field of view, FOV=128x128mm'\n assert voxelsize_str == expected_vox\n assert matrixsize_str == expected_mat\n assert fov_str == expected_fov\n\n\ndef test_describe_dmri_directions(testdiffimg):\n dif_dir_str = parameters.describe_dmri_directions(testdiffimg)\n expected = '64 diffusion directions'\n assert dif_dir_str == expected\n\n\ndef test_describe_intendedfor_targets(testmeta_light, testlayout):\n for_str = parameters.describe_intendedfor_targets(testmeta_light, testlayout)\n assert for_str == ''\n\n fmap_files = testlayout.get(\n subject='01',\n session='01',\n suffix='phasediff',\n extension=['.nii.gz'],\n )\n metadata = fmap_files[0].get_metadata()\n for_str = parameters.describe_intendedfor_targets(metadata, testlayout)\n assert for_str == ' for the first and second runs of the N-Back BOLD scan'\n" }, { "path": "src/bids/reports/tests/test_parsing.py", "content": "\"\"\"Tests for bids.reports.parsing.\"\"\"\n\nimport pytest\n\nfrom bids.reports import parsing\n\n\n@pytest.fixture\ndef testmeta():\n metadata = {'RepetitionTime': 2.0}\n return metadata\n\n\ndef test_anat_info_smoke(testlayout, testconfig):\n \"\"\"Smoke test for parsing.anat_info.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n anat_files = testlayout.get(\n subject='01',\n session='01',\n suffix='T1w',\n extension=['.nii.gz'],\n )\n\n desc = parsing.anat_info(testlayout, anat_files, testconfig)\n assert isinstance(desc, str)\n\n\ndef test_dwi_info_smoke(testlayout, testconfig):\n \"\"\"Smoke test for parsing.dwi_info.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n dwi_files = testlayout.get(\n subject='01',\n session='01',\n datatype='dwi',\n extension=['.nii.gz'],\n )\n\n desc = parsing.dwi_info(testlayout, dwi_files, testconfig)\n assert isinstance(desc, str)\n\n\ndef test_fmap_info_smoke(testlayout, testconfig):\n \"\"\"Smoke test for parsing.fmap_info.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n fmap_files = testlayout.get(\n subject='01',\n session='01',\n datatype='fmap',\n suffix='phasediff',\n extension=['.nii.gz'],\n )\n\n desc = parsing.fmap_info(testlayout, fmap_files, testconfig)\n assert isinstance(desc, str)\n\n\ndef test_func_info_smoke(testlayout, testconfig):\n \"\"\"Smoke test for parsing.func_info.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n func_files = testlayout.get(\n subject='01',\n session='01',\n task='nback',\n run='01',\n extension=['.nii.gz'],\n )\n\n desc = parsing.func_info(testlayout, func_files, testconfig)\n assert isinstance(desc, str)\n\n\ndef test_general_acquisition_info_smoke(testmeta):\n \"\"\"Smoke test for parsing.general_acquisition_info.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n desc = parsing.general_acquisition_info(testmeta)\n assert isinstance(desc, str)\n\n\ndef test_final_paragraph_smoke(testmeta):\n \"\"\"Smoke test for parsing.final_paragraph.\n\n It should return a str description when provided valid inputs.\n \"\"\"\n desc = parsing.final_paragraph(testmeta)\n assert isinstance(desc, str)\n\n\ndef test_parse_files_smoke(testlayout, testconfig):\n \"\"\"Smoke test for parsing.parse_files.\n\n It should return a list of string descriptions when provided valid inputs,\n with each string containing the description for a single nifti file\n (except functional data, which is combined within task, across runs).\n \"\"\"\n subject = '01'\n niftis = testlayout.get(subject=subject, extension=['.nii', '.nii.gz'])\n desc = parsing.parse_files(testlayout, niftis, subject, testconfig)\n assert isinstance(desc, list)\n assert isinstance(desc[0], str)\n" }, { "path": "src/bids/reports/tests/test_report.py", "content": "\"\"\"Tests for bids.reports.report.\"\"\"\nfrom collections import Counter\n\nfrom bids.reports import BIDSReport\n\n\ndef test_report_init(testlayout):\n \"\"\"Report initialization should return a BIDSReport object.\"\"\"\n report = BIDSReport(testlayout)\n assert isinstance(report, BIDSReport)\n\n\ndef test_report_gen(testlayout):\n \"\"\"Report generation should return a counter of unique descriptions in the dataset.\"\"\"\n report = BIDSReport(testlayout)\n descriptions = report.generate()\n assert isinstance(descriptions, Counter)\n\n\ndef test_report_gen_from_files(testlayout):\n \"\"\"Report generation from file list should return a counter of unique\n descriptions in the dataset.\n \"\"\"\n report = BIDSReport(testlayout)\n files = testlayout.get(extension=[\".nii.gz\", \".nii\"])\n descriptions = report.generate_from_files(files)\n assert isinstance(descriptions, Counter)\n\n\ndef test_report_subject(testlayout):\n \"\"\"Generating a report for one subject should only return one subject's\n description (i.e., one pattern with a count of one).\n \"\"\"\n report = BIDSReport(testlayout)\n descriptions = report.generate(subject=\"01\")\n assert sum(descriptions.values()) == 1\n\n\ndef test_report_session(testlayout):\n \"\"\"Generating a report for one session should mean that no other sessions\n appear in any of the unique descriptions.\n \"\"\"\n report = BIDSReport(testlayout)\n descriptions = report.generate(session=\"01\")\n assert \"session 02\" not in \" \".join(descriptions.keys())\n\n\ndef test_report_file_config(testlayout, config_file):\n \"\"\"Report initialization should take in a config file and use that if provided.\"\"\"\n report = BIDSReport(testlayout, config=str(config_file))\n descriptions = report.generate()\n assert isinstance(descriptions, Counter)\n\n\ndef test_report_dict_config(testlayout, testconfig):\n \"\"\"Report initialization should take in a config dict and use that if provided.\"\"\"\n report = BIDSReport(testlayout, config=testconfig)\n descriptions = report.generate()\n assert isinstance(descriptions, Counter)\n" }, { "path": "src/bids/reports/utils.py", "content": "\"\"\"Generate publication-quality data acquisition methods section from BIDS dataset.\n\nUtilities to generate the MRI data acquisition portion of a\nmethods section from a BIDS dataset.\n\"\"\"\n\nimport logging\n\nLOGGER = logging.getLogger('pybids.reports.utils')\n\n\ndef reminder():\n \"\"\"Remind users about things they need to do after generating the report.\"\"\"\n return 'Remember to double-check everything and to replace with a degree symbol.'\n\n\ndef remove_duplicates(seq):\n \"\"\"Return unique elements from list while preserving order.\n\n From https://stackoverflow.com/a/480227/2589328\n \"\"\"\n seen = set()\n seen_add = seen.add\n return [x for x in seq if not (x in seen or seen_add(x))]\n\n\ndef num_to_str(num):\n \"\"\"Convert an int or float to a nice string.\n\n E.g.,\n 21 -> '21'\n 2.500 -> '2.5'\n 3. -> '3'\n \"\"\"\n return f'{num:0.02f}'.rstrip('0').rstrip('.')\n\n\ndef list_to_str(lst):\n \"\"\"Turn a list into a comma- and/or and-separated string.\n\n Parameters\n ----------\n lst : :obj:`list`\n A list of strings to join into a single string.\n\n Returns\n -------\n str_ : :obj:`str`\n A string with commas and/or ands separating the elements from ``lst``.\n\n \"\"\"\n if len(lst) == 1:\n str_ = lst[0]\n elif len(lst) == 2:\n str_ = ' and '.join(lst)\n elif len(lst) > 2:\n str_ = ', '.join(lst[:-1])\n str_ += f', and {lst[-1]}'\n else:\n raise ValueError('List of length 0 provided.')\n return str_\n" }, { "path": "src/bids/tests/__init__.py", "content": "from .utils import get_test_data_path\n\n__all__ = [\n 'get_test_data_path',\n]\n" }, { "path": "src/bids/tests/data/images/4d.bval", "content": "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\n" }, { "path": "src/bids/tests/test_cli.py", "content": "import os\n\nimport pytest\nfrom click.testing import CliRunner\n\nfrom bids.cli import cli\nfrom bids.tests import get_test_data_path\nfrom bids.utils import validate_multiple\n\n\n@pytest.fixture\ndef runner():\n return CliRunner()\n\n\ndef test_cli_entrypoint(runner):\n res = runner.invoke(cli, catch_exceptions=False)\n if res.exit_code == 2: # click 8.2+\n help_text = res.stderr\n elif res.exit_code == 0:\n help_text = res.stdout\n assert 'Command-line interface for PyBIDS operations' in help_text\n assert runner.invoke(cli, ['-h'], catch_exceptions=False).stdout == help_text\n # verify versioning\n assert runner.invoke(cli, ['--version'], catch_exceptions=False).stdout.startswith('pybids')\n\n\ndef test_validate_multiple():\n assert validate_multiple(()) is None\n assert validate_multiple((), retval=False) is False\n assert validate_multiple(('bids',)) == 'bids'\n assert validate_multiple((1, 2)) == (1, 2)\n with pytest.raises(AssertionError):\n validate_multiple('not a tuple')\n\n\ndef test_layout(runner, tmp_path):\n def is_success(res):\n return res.stdout.startswith('Successfully generated database index')\n\n res = runner.invoke(cli, ['layout', '--help'])\n assert 'Initialize a BIDSLayout' in res.stdout\n\n bids_dir = os.path.join(get_test_data_path(), 'ds005')\n db0 = tmp_path / 'db0'\n db0.mkdir()\n res = runner.invoke(cli, ['layout', bids_dir, str(db0)], catch_exceptions=False)\n assert is_success(res)\n # rerunning targeting the save directory should not generate a new index\n res = runner.invoke(cli, ['layout', bids_dir, str(db0)], catch_exceptions=False)\n assert not is_success(res)\n # but forcing it should\n res = runner.invoke(cli, ['layout', bids_dir, str(db0), '--reset-db'], catch_exceptions=False)\n assert is_success(res)\n\n db1 = tmp_path / 'db1'\n db1.mkdir()\n # throw the kitchen sink at it\n res = runner.invoke(\n cli,\n [\n 'layout',\n bids_dir,\n str(db1),\n '--validate',\n '--no-index-metadata',\n '--ignore',\n 'derivatives',\n '--ignore',\n 'sourcedata',\n '--ignore',\n r'm/^\\./',\n '--force-index',\n 'test',\n '--config',\n 'bids',\n ],\n catch_exceptions=False,\n )\n assert is_success(res)\n" }, { "path": "src/bids/tests/test_config.py", "content": "import json\nimport os\nimport tempfile\nimport warnings\n\nimport pytest\n\nimport bids\nfrom bids.config import reset_options\nfrom bids.tests import get_test_data_path\n\n\ndef test_load_from_standard_paths():\n # Verify defaults\n reset_options(False)\n assert bids.config._settings == bids.config._default_settings\n\n env_config = {'loop_preproc': True}\n cwd_config = {'loop_preproc': True}\n\n handle, f = tempfile.mkstemp(suffix='.json')\n json.dump(env_config, open(f, 'w'))\n os.environ['PYBIDS_CONFIG'] = f\n target = 'pybids_config.json'\n if os.path.exists(target):\n pytest.skip(\n 'Cannot test bids config because the default config file'\n ' (pybids_config.json) already exists in the current '\n 'working directory. Skipping test to avoid overwriting.'\n )\n json.dump(cwd_config, open(target, 'w'))\n reset_options(True)\n os.unlink(target)\n opts = bids.config._settings\n\n # assert opts['loop_preproc']\n\n reset_options(False)\n\n\ndef test_set_option():\n reset_options(False)\n opts = bids.config._settings\n\n # bids.config.set_options(loop_preproc=False)\n # assert opts['loop_preproc'] == False\n\n with pytest.raises(ValueError):\n bids.config.set_option('bad_key', False)\n\n reset_options(False)\n\n\n# def test_get_option():\n# reset_options(False)\n# assert not bids.config.get_option('loop_preproc')\n\n\ndef test_extension_initial_dot(mock_config):\n ds117 = os.path.join(get_test_data_path(), 'ds000117')\n\n # Setting False is disabled\n with pytest.raises(ValueError, match='Cannot set'):\n bids.config.set_option('extension_initial_dot', False)\n with pytest.raises(ValueError, match='Cannot set'):\n bids.config.set_option('extension_initial_dot', None)\n\n # Setting True warns\n with pytest.warns(FutureWarning, match='will be removed'):\n bids.config.set_option('extension_initial_dot', True)\n\n # No warnings on layout construction\n with warnings.catch_warnings():\n warnings.simplefilter('error')\n layout = bids.BIDSLayout(ds117)\n assert layout.get(extension='nii.gz')[0].entities['extension'] == '.nii.gz'\n" }, { "path": "src/bids/tests/utils.py", "content": "\"\"\"Test-related utilities\"\"\"\n\nfrom pathlib import Path\n\n\ndef get_test_data_path():\n return str(Path(__file__).parent.parent.parent.parent / 'tests' / 'data')\n" }, { "path": "src/bids/utils.py", "content": "\"\"\"Utility functions.\"\"\"\n\nimport os\nimport re\nfrom functools import cache\nfrom pathlib import Path # noqa: F811\n\nfrom frozendict import frozendict as _frozendict\nfrom upath import UPath as Path # noqa: F811\n\n\n# Monkeypatch to print out frozendicts *as if* they were dictionaries.\nclass frozendict(_frozendict):\n \"\"\"A hashable dictionary type.\"\"\"\n\n def __repr__(self):\n \"\"\"Override frozendict representation.\"\"\"\n return repr({k: v for k, v in self.items()}) # noqa: C416\n\n\ndef listify(obj):\n \"\"\"Wraps all non-list or tuple objects in a list; provides a simple way\n to accept flexible arguments.\n \"\"\"\n return obj if isinstance(obj, (list, tuple, type(None))) else [obj]\n\n\ndef hashablefy(obj):\n \"\"\"Make dictionaries and lists hashable or raise.\"\"\"\n if isinstance(obj, list):\n return tuple([hashablefy(o) for o in obj])\n\n if isinstance(obj, dict):\n return frozendict({k: hashablefy(v) for k, v in obj.items()})\n return obj\n\n\ndef matches_entities(obj, entities, strict=False):\n \"\"\"Checks whether an object's entities match the input.\"\"\"\n if strict and set(obj.entities.keys()) != set(entities.keys()):\n return False\n\n comm_ents = list(set(obj.entities.keys()) & set(entities.keys()))\n for k in comm_ents:\n current = obj.entities[k]\n target = entities[k]\n if isinstance(target, (list, tuple)):\n if current not in target:\n return False\n elif current != target:\n return False\n return True\n\n\ndef natural_sort(l, field=None): # noqa: E741\n \"\"\"Based on snippet found at https://stackoverflow.com/a/4836734/2445984\"\"\"\n convert = lambda text: int(text) if text.isdigit() else text.lower() # noqa: E731\n\n def alphanum_key(key):\n if field is not None:\n key = getattr(key, field)\n if not isinstance(key, str):\n key = str(key)\n return [convert(c) for c in re.split('([0-9]+)', key)]\n\n return sorted(l, key=alphanum_key)\n\n\ndef convert_JSON(j):\n \"\"\"Recursively convert CamelCase keys to snake_case.\n From: https://stackoverflow.com/questions/17156078/\n converting-identifier-naming-between-camelcase-and-\n underscores-during-json-seria\n \"\"\"\n\n def camel_to_snake(s):\n a = re.compile('((?<=[a-z0-9])[A-Z]|(?!^)[A-Z](?=[a-z]))')\n return a.sub(r'_\\1', s).lower()\n\n def convertArray(a):\n newArr = []\n for i in a:\n if isinstance(i, list):\n newArr.append(convertArray(i))\n elif isinstance(i, dict):\n newArr.append(convert_JSON(i))\n else:\n newArr.append(i)\n return newArr\n\n out = {}\n for k, value in j.items():\n newK = camel_to_snake(k)\n\n # Replace transformation uses a dict, so skip lower-casing\n if isinstance(value, dict) and k != 'Replace':\n out[newK] = convert_JSON(value)\n elif isinstance(value, list):\n out[newK] = convertArray(value)\n else:\n out[newK] = value\n\n return out\n\n\ndef splitext(path):\n \"\"\"Splitext for paths with directories that may contain dots.\n From https://stackoverflow.com/questions/5930036/separating-file-extensions-using-python-os-path-module\n \"\"\"\n li = []\n path_without_extensions = os.path.join(\n os.path.dirname(path), os.path.basename(path).split(os.extsep)[0]\n )\n extensions = os.path.basename(path).split(os.extsep)[1:]\n li.append(path_without_extensions)\n # li.append(extensions) if you want extensions in another list inside the list that is returned. # noqa: E501\n li.extend(extensions)\n return li\n\n\ndef make_bidsfile(filename):\n \"\"\"Create a BIDSFile instance of the appropriate class.\"\"\"\n from .layout import models\n\n # Extract all extensions from filename (a.tar.gz -> .tar.gz, not just .gz)\n ext = ''.join(Path(filename).suffixes)\n\n if ext.endswith(('.nii', '.nii.gz', '.gii')):\n cls = 'BIDSImageFile'\n elif ext in ['.tsv', '.tsv.gz']:\n cls = 'BIDSDataFile'\n elif ext == '.json':\n cls = 'BIDSJSONFile'\n else:\n cls = 'BIDSFile'\n\n Cls = getattr(models, cls)\n return Cls(filename)\n\n\ndef collect_associated_files(layout, files, extra_entities=()): # noqa: D417\n \"\"\"Collect and group BIDSFiles with multiple files per acquisition.\n\n Parameters\n ----------\n layout\n files : list of BIDSFile\n extra_entities\n\n Returns\n -------\n collected_files : list of list of BIDSFile\n\n \"\"\"\n MULTICONTRAST_ENTITIES = ['echo', 'part', 'ch', 'direction']\n MULTICONTRAST_SUFFIXES = [\n ('bold', 'phase'),\n ('phase1', 'phase2', 'phasediff', 'magnitude1', 'magnitude2'),\n ]\n if len(extra_entities):\n MULTICONTRAST_ENTITIES += extra_entities\n\n collected_files = []\n for f in files:\n if len(collected_files) and any(f in filegroup for filegroup in collected_files):\n continue\n ents = f.get_entities()\n ents = {k: v for k, v in ents.items() if k not in MULTICONTRAST_ENTITIES}\n\n # Group files with differing multi-contrast entity values, but same\n # everything else.\n all_suffixes = ents['suffix']\n for mcs in MULTICONTRAST_SUFFIXES:\n if ents['suffix'] in mcs:\n all_suffixes = mcs\n break\n ents.pop('suffix')\n associated_files = layout.get(suffix=all_suffixes, **ents)\n collected_files.append(associated_files)\n return collected_files\n\n\ndef validate_multiple(val, retval=None):\n \"\"\"Any click.Option with the multiple flag will return an empty tuple if not set.\n\n This helper method converts empty tuples to a desired return value (default: None).\n This helper method selects the first item in single-item tuples.\n \"\"\"\n assert isinstance(val, tuple) # noqa: S101\n\n if val == tuple(): # noqa: C408\n return retval\n if len(val) == 1:\n return val[0]\n return val\n\n\n@cache\ndef entity_indices(schema_spec=None): # noqa: D103\n from collections import defaultdict\n\n from bidsschematools.schema import load_schema\n\n entities = load_schema(schema_spec).rules.entities + ['suffix', 'extension', 'datatype']\n\n return defaultdict(lambda e=entities: len(e), {elem: idx for idx, elem in enumerate(entities)})\n\n\ndef bids_sort(unsorted: dict, schema_spec=None):\n \"\"\"Sorts filename entity dictionaries according to their order as defined in\n schema.rules.entities as well as suffix, extension. Lastly, appends datatype\n to the end of the sort to accommodate pybids datastructures.\n\n Parameters\n ----------\n unsorted: dict\n A dictionary containing bids file entities and their values.\n schema_spec: str\n Path or version of schema to use, defaults to the version bundled\n with bidsschematools.\n\n Returns\n -------\n sorted_bids: dict\n\n \"\"\"\n indices = entity_indices(schema_spec)\n\n return {k: unsorted[k] for k in sorted(unsorted, key=indices.__getitem__)}\n" }, { "path": "src/bids/variables/__init__.py", "content": "# ruff: noqa: D104\nfrom .collections import BIDSRunVariableCollection, BIDSVariableCollection, merge_collections\nfrom .io import load_variables\nfrom .variables import DenseRunVariable, SimpleVariable, SparseRunVariable, merge_variables\n\n__all__ = [\n 'SimpleVariable',\n 'SparseRunVariable',\n 'DenseRunVariable',\n 'BIDSVariableCollection',\n 'BIDSRunVariableCollection',\n 'merge_collections',\n 'load_variables',\n 'merge_variables',\n]\n" }, { "path": "src/bids/variables/collections.py", "content": "\"\"\"Classes and functions related to the management of sets of BIDSVariables.\"\"\"\n\nimport fnmatch\nimport re\nimport warnings\nfrom collections import OrderedDict\nfrom copy import copy\nfrom functools import cache\nfrom itertools import chain\n\nimport numpy as np\nimport pandas as pd\nfrom pandas.api.types import is_numeric_dtype\n\nfrom bids.utils import listify\n\nfrom .variables import (\n BIDSVariable,\n DenseRunVariable,\n SimpleVariable,\n SparseRunVariable,\n merge_variables,\n)\n\n\ndef _pandas_3_0():\n \"\"\"Silence pandas warnings and opt in to future behavior.\n\n This sets pandas behavior to 3.0+ defaults.\n Prior to pandas 3.0, the fillna() and replace() methods would convert\n object columns to float64 if the resulting series was all float64.\n In 3.0+, you need to use infer_objects() to do this.\n\n This also opts-in to copy-on-write, which previously required `copy=False`\n to be set.\n \"\"\"\n if args := _pandas_3_0_options():\n return pd.option_context(*args)\n\n import contextlib\n\n return contextlib.nullcontext()\n\n\n@cache\ndef _pandas_3_0_options():\n options = [\n ('future.no_silent_downcasting', True),\n ('mode.copy_on_write', True),\n ]\n\n args = []\n for option in options:\n try:\n pd.get_option(option[0])\n except KeyError:\n continue\n args.extend(option)\n return args\n\n\nclass BIDSVariableCollection:\n \"\"\"A container for one or more variables extracted from variable files\n at a single level of analysis.\n\n Parameters\n ----------\n variables : list\n A list of BIDSVariables or SimpleVariables.\n name : str\n Optional name to assign to the collection.\n\n Notes\n -----\n Variables in the list must all share the same analysis level, which\n must be one of 'session', 'subject', or 'dataset' level. For\n run-level Variables, use the BIDSRunVariableCollection.\n\n \"\"\"\n\n def __init__(self, variables, name=None):\n self.name = name\n\n if not variables:\n raise ValueError('No variables were provided')\n SOURCE_TO_LEVEL = {\n 'events': 'run',\n 'physio': 'run',\n 'stim': 'run',\n 'regressors': 'run',\n 'scans': 'session',\n 'sessions': 'subject',\n 'participants': 'dataset',\n }\n var_levels = set( # noqa: C403\n [\n SOURCE_TO_LEVEL[v.source] if v.source in SOURCE_TO_LEVEL else v.source\n for v in variables\n ]\n )\n\n # TODO: relax this requirement & allow implicit merging between levels\n if len(var_levels) > 1:\n raise ValueError(\n 'A Collection cannot be initialized from ' # noqa: UP031\n 'variables at more than one level of analysis. '\n 'Levels found in input variables: %s' % var_levels\n )\n elif not var_levels:\n raise ValueError(\n 'None of the provided variables matched any of the known levels, which are: %s' # noqa: UP031\n % (', '.join(sorted(SOURCE_TO_LEVEL.values())))\n )\n\n self.level = list(var_levels)[0]\n variables = self.merge_variables(variables)\n self.variables = {v.name: v for v in variables}\n self._index_entities()\n\n # Container for variable groups (see BIDS-StatsModels spec)--maps from\n # group names to lists of variables.\n self.groups = {}\n\n @staticmethod\n def merge_variables(variables, **kwargs): # noqa: D417\n \"\"\"Concatenates Variables along row axis.\n\n Parameters\n ----------\n variables : list\n List of Variables to merge. Variables can have\n different names (and all Variables that share a name will be\n concatenated together).\n\n Returns\n -------\n list\n A list of Variables.\n\n \"\"\"\n var_dict = OrderedDict()\n for v in variables:\n if v.name not in var_dict:\n var_dict[v.name] = []\n var_dict[v.name].append(v)\n return [merge_variables(vars_, **kwargs) for vars_ in list(var_dict.values())]\n\n def to_df(self, variables=None, format='wide', fillna=np.nan, entities=True, timing=True): # noqa: A002\n \"\"\"Merge BIDVariables in the collection into a single pandas DataFrame.\n\n Parameters\n ----------\n variables : list of str or BIDSVariable\n Optional list of variables or variable names to retain. If strings\n are passed, each one gives the name of a variable in the current\n collection. If BIDSVariables are passed, they will be used as-is.\n If None, all variables are returned. Strings and BIDSVariables\n cannot be mixed in the list.\n format : {'wide', 'long'}\n Whether to return a DataFrame in 'wide' or 'long' format. In 'wide'\n format, each row is defined by a unique entity combination, and\n each variable is in a separate column. In 'long' format, each row\n is a unique combination of entities and variable names, and a\n single 'amplitude' column provides the value.\n fillna : value\n Replace missing values with the specified value.\n entities : bool\n Whether or not to include a column for each entity.\n timing : bool\n Whether or not to include onset and duration columns.\n\n Returns\n -------\n :obj:`pandas.DataFrame`\n A pandas DataFrame.\n\n \"\"\"\n if variables is None:\n variables = list(self.variables.keys())\n\n # Can receive already-selected Variables from sub-classes\n if not isinstance(variables[0], BIDSVariable):\n variables = [v for v in self.variables.values() if v.name in variables]\n\n # Convert all variables to separate DFs.\n # Note: bad things can happen if we pass the conditions, entities, and\n # timing flags through to the individual variables and then do\n # concat/reshaping operations. So instead, we set them all to True\n # temporarily, do what we need to, then drop them later if needed.\n dfs = [v.to_df(True, True, timing=True) for v in variables]\n\n # Always concatenate along row axis (for format='wide', we'll pivot).\n df = pd.concat(dfs, axis=0, sort=True)\n\n all_cols = set(df.columns)\n ent_cols = list(all_cols - {'condition', 'amplitude', 'onset', 'duration'})\n\n if format == 'long':\n with _pandas_3_0():\n df = df.reset_index(drop=True).fillna(fillna).infer_objects()\n else:\n # Rows in wide format can only be defined by combinations of level entities\n # plus (for run-level variables) onset and duration.\n valid_vars = {'run', 'session', 'subject', 'dataset', 'onset', 'duration'}\n idx_cols = list(valid_vars & all_cols)\n\n with _pandas_3_0():\n df['amplitude'] = df['amplitude'].fillna('n/a')\n wide_df = df.pivot_table(\n index=idx_cols, columns='condition', values='amplitude', aggfunc='first'\n )\n\n select_cols = list(set(ent_cols) - set(idx_cols))\n\n if entities and select_cols:\n ent_df = df.groupby(idx_cols)[select_cols].first()\n df = pd.concat([wide_df, ent_df], axis=1)\n else:\n df = wide_df\n\n with _pandas_3_0():\n df = df.reset_index().replace('n/a', fillna).infer_objects()\n df.columns.name = None\n\n # Drop any columns we don't want\n if not timing:\n df.drop(['onset', 'duration'], axis=1, inplace=True)\n if not entities:\n df.drop(ent_cols, axis=1, inplace=True, errors='ignore')\n\n return df\n\n @classmethod\n def from_df(cls, data, entities=None, source='contrast'): # noqa: D417\n \"\"\"Create a Collection from a pandas DataFrame.\n\n Parameters\n ----------\n df : :obj:`pandas.DataFrame`\n The DataFrame to convert to a Collection. Each\n column will be converted to a SimpleVariable.\n entities : :obj:`pandas.DataFrame`\n An optional second DataFrame containing\n entity information.\n source : str\n The value to set as the source for all Variables.\n\n Returns\n -------\n BIDSVariableCollection\n\n \"\"\"\n variables = []\n for col in data.columns:\n _data = pd.DataFrame(data[col].values, columns=['amplitude'])\n if entities is not None:\n _data = pd.concat([_data, entities], axis=1, sort=True)\n variables.append(SimpleVariable(name=col, data=_data, source=source))\n return BIDSVariableCollection(variables)\n\n def clone(self):\n \"\"\"Returns a copy of the current instance.\"\"\"\n # We can't simply deepcopy, because variables have non-serializable\n # attributes. So we shallow copy then explicitly clone collections and\n # more complex objects.\n clone = copy(self)\n clone.entities = self.entities.copy()\n clone.variables = {k: v.clone() for (k, v) in self.variables.items()}\n return clone\n\n def _index_entities(self):\n \"\"\"Sets current instance's entities based on the existing index.\n\n Notes\n -----\n Only entity key/value pairs common to all rows in all contained\n Variables are returned. E.g., if a Collection contains Variables\n extracted from runs 1, 2 and 3 from subject '01', the returned dict\n will be {'subject': '01'}; the runs will be excluded as they vary\n across the Collection contents.\n\n \"\"\"\n all_ents = pd.DataFrame.from_records([v.entities for v in self.variables.values()])\n constant = all_ents.apply(lambda x: x.nunique() == 1)\n if constant.empty:\n self.entities = {}\n else:\n keep = all_ents.columns[constant]\n ents = {k: all_ents[k].dropna().iloc[0] for k in keep}\n self.entities = {k: v for k, v in ents.items() if pd.notnull(v)}\n\n def __getitem__(self, var):\n if var in self.variables:\n return self.variables[var]\n keys = list(self.variables.keys())\n raise ValueError(\n f\"No variable named '{var}' found in this collection. Available names are {keys}.\"\n )\n\n def __setitem__(self, var, obj):\n # Ensure name matches collection key, but raise warning if needed.\n if obj.name != var:\n warnings.warn(\n f'The provided key to use in the collection ({var!r}) '\n \"does not match the passed Column object's existing \"\n f'name ({obj.name!r}). The Column name will be set to match '\n 'the provided key.',\n stacklevel=2,\n )\n obj.name = var\n self.variables[var] = obj\n\n def match_variables(self, pattern, return_type='name', match_type='unix'):\n \"\"\"Return columns whose names match the provided pattern.\n\n Parameters\n ----------\n pattern : str, list\n One or more regex patterns to match all variable names against.\n return_type : {'name', 'variable'}\n What to return. Must be one of:\n 'name': Returns a list of names of matching variables.\n 'variable': Returns a list of Variable objects whose names\n match.\n match_type : str\n Matching approach to use. Either 'regex' (full-blown regular\n expression matching) or 'unix' (unix-style pattern matching\n via the fnmatch module).\n\n Returns\n -------\n A list of all matching variables or variable names\n\n \"\"\"\n pattern = listify(pattern)\n results = []\n for patt in pattern:\n if match_type.lower().startswith('re'):\n patt = re.compile(patt)\n vars_ = [v for v in self.variables.keys() if patt.search(v)]\n else:\n vars_ = fnmatch.filter(list(self.variables.keys()), patt)\n if return_type.startswith('var'):\n vars_ = [self.variables[v] for v in vars_]\n results.extend(vars_)\n return results\n\n def __repr__(self):\n return f'<{self.__class__.__name__}{sorted(list(self.variables.keys()))}>' # noqa: C414\n\n\nclass BIDSRunVariableCollection(BIDSVariableCollection):\n \"\"\"A container for one or more RunVariables--i.e., Variables that have a\n temporal dimension.\n\n Parameters\n ----------\n variables : list\n A list of SparseRunVariable and/or DenseRunVariable.\n sampling_rate : float\n Sampling rate (in Hz) to use when working with\n dense representations of variables. If None, defaults to 10.\n\n Notes\n -----\n Variables in the list must all be at the 'run' level. For other\n levels (session, subject, or dataset), use the\n BIDSVariableCollection.\n\n \"\"\"\n\n def __init__(self, variables, sampling_rate=None):\n # Don't put the default value in signature because None is passed from\n # several places and we don't want multiple conflicting defaults.\n if sampling_rate:\n if isinstance(sampling_rate, str):\n raise ValueError('Sampling rate must be numeric.')\n self.sampling_rate = sampling_rate or 10\n super().__init__(variables)\n\n def get_dense_variables(self, variables=None):\n \"\"\"Returns a list of all stored DenseRunVariables.\"\"\"\n if variables is None:\n variables = set(self.variables.keys())\n return [\n v\n for v in self.variables.values()\n if isinstance(v, DenseRunVariable) and v.name in variables\n ]\n\n def get_sparse_variables(self, variables=None):\n \"\"\"Returns a list of all stored SparseRunVariables.\"\"\"\n if variables is None:\n variables = set(self.variables.keys())\n return [\n v\n for v in self.variables.values()\n if isinstance(v, SparseRunVariable) and v.name in variables\n ]\n\n def all_dense(self): # noqa: D102\n return len(self.get_dense_variables()) == len(self.variables)\n\n def all_sparse(self): # noqa: D102\n return len(self.get_sparse_variables()) == len(self.variables)\n\n def _get_sampling_rate(self, sampling_rate):\n \"\"\"Parse sampling rate argument and return appropriate value.\"\"\"\n if sampling_rate is None:\n return self.sampling_rate\n\n if isinstance(sampling_rate, (float, int)):\n return sampling_rate\n\n if sampling_rate == 'TR':\n trs = {var.run_info[0].tr for var in self.variables.values()}\n if not trs:\n raise ValueError(\n 'Repetition time unavailable; specify '\n 'sampling_rate in Hz explicitly or set to'\n \" 'highest'.\"\n )\n elif len(trs) > 1:\n raise ValueError(\n 'Non-unique Repetition times found '\n f'({trs!r}); specify sampling_rate explicitly'\n )\n return 1.0 / trs.pop()\n\n if sampling_rate.lower() == 'highest':\n dense_vars = self.get_dense_variables()\n # If no dense variables are available, fall back on instance SR\n if not dense_vars:\n return self.sampling_rate\n var_srs = [v.sampling_rate for v in dense_vars]\n if len(var_srs) == 1:\n return var_srs[0]\n return max(*var_srs)\n\n raise ValueError(\n f\"Invalid sampling_rate value '{sampling_rate}' provided. Must be \"\n \"a float, None, 'TR', or 'highest'.\"\n )\n\n def _densify_and_resample(\n self,\n sampling_rate=None,\n variables=None,\n resample_dense=False,\n force_dense=False,\n in_place=False,\n kind='linear',\n ):\n sr = self._get_sampling_rate(sampling_rate)\n\n _dense, _sparse = [], []\n\n # Filter variables and sort by class\n for name, var in self.variables.items():\n if variables is not None and name not in variables:\n continue\n if isinstance(var, DenseRunVariable):\n _dense.append(var)\n else:\n _sparse.append(var)\n\n _variables = {}\n\n if force_dense:\n for v in _sparse:\n if is_numeric_dtype(v.values):\n _variables[v.name] = v.to_dense(sr)\n\n if resample_dense:\n # Propagate 'TR' if exact match to TR is required\n sr_arg = sampling_rate if sampling_rate == 'TR' else sr\n for v in _dense:\n _variables[v.name] = v.resample(sr_arg, kind=kind)\n\n for v in _dense:\n if v.name not in _variables:\n _variables[v.name] = v\n\n coll = self if in_place else self.clone()\n\n if in_place:\n coll.variables.update(_variables)\n else:\n coll.variables = _variables\n\n coll.sampling_rate = sr\n return coll\n\n def to_dense( # noqa: D417\n self,\n sampling_rate=None,\n variables=None,\n in_place=False,\n resample_dense=False,\n kind='linear',\n ):\n \"\"\"Convert all contained SparseRunVariables to DenseRunVariables.\n\n Parameters\n ----------\n sampling_rate : None, {'TR', 'highest'}, float\n Sampling rate to use when densifying sparse variables. If None,\n uses the currently stored instance value. If 'TR', the repetition\n time is used, if available, to select the sampling rate (1/TR).\n If 'highest', all variables are resampled to the highest sampling\n rate of any of the existing dense variables. The sampling rate may\n also be specified explicitly in Hz as a float.\n variables : list\n Optional list of names of Variables to resample. If None, all\n variables are resampled.\n in_place : bool\n When True, all variables are overwritten in-place.\n When False, returns resampled versions of all variables.\n kind : str\n Argument to pass to scipy's interp1d; indicates the kind of\n interpolation approach to use. See interp1d docs for valid values.\n\n Returns\n -------\n A BIDSVariableCollection (if in_place is False).\n\n Notes\n -----\n Categorical variables are ignored.\n\n \"\"\"\n return self._densify_and_resample(\n sampling_rate,\n variables,\n resample_dense=resample_dense,\n in_place=in_place,\n kind=kind,\n force_dense=True,\n )\n\n def resample(\n self,\n sampling_rate=None,\n variables=None,\n force_dense=False,\n in_place=False,\n kind='linear',\n ):\n \"\"\"Resample all dense variables (and optionally, sparse ones) to the\n specified sampling rate.\n\n Parameters\n ----------\n sampling_rate : int or float\n Target sampling rate (in Hz). If None, uses the instance value.\n variables : list\n Optional list of names of Variables to resample. If None, all\n variables are resampled.\n force_dense : bool\n if True, all sparse variables will be forced to dense.\n in_place : bool\n When True, all variables are overwritten in-place.\n When False, returns resampled versions of all variables.\n kind : str\n Argument to pass to scipy's interp1d; indicates the kind of\n interpolation approach to use. See interp1d docs for valid values.\n\n Returns\n -------\n A BIDSVariableCollection (if in_place is False).\n\n \"\"\"\n return self._densify_and_resample(\n sampling_rate,\n variables,\n force_dense=force_dense,\n in_place=in_place,\n kind=kind,\n resample_dense=True,\n )\n\n def to_df(\n self,\n variables=None,\n format='wide', # noqa: A002\n fillna=np.nan,\n sampling_rate='highest',\n include_sparse=True,\n include_dense=True,\n entities=True,\n timing=True,\n ):\n \"\"\"Merge variables into a single pandas DataFrame.\n\n Parameters\n ----------\n variables : list\n Optional list of variable names to retain;\n if None, all variables are written out.\n format : str\n Whether to return a DataFrame in 'wide' or 'long'\n format. In 'wide' format, each row is defined by a unique\n onset/duration, and each variable is in a separate column. In\n 'long' format, each row is a unique combination of onset,\n duration, and variable name, and a single 'amplitude' column\n provides the value.\n fillna : value\n Replace missing values with the specified value.\n sampling_rate : float\n Specifies the sampling rate to use for all variables in the event\n that resampling needs to be performed (i.e., if some variables are\n sparse, or if dense variables have different sampling rates). Must\n be one of 'TR', 'highest', None, or a float (specifying the rate in\n Hz). If None, uses the instance sampling rate (10 Hz by default).\n include_sparse : bool\n Whether or not to include sparse variables in the output.\n include_dense : bool\n Whether or not to include dense variables in the output.\n entities : bool\n Whether or not to include a column for each entity.\n timing : bool\n Whether or not to include onset and duration columns.\n\n Returns\n -------\n :obj:`pandas.DataFrame`\n A pandas DataFrame.\n\n Notes\n -----\n The precise format of the resulting DataFrame depends on the variables\n contained in the current instance. If all variables are sparse, the\n output will also be sparse--i.e., the events in the DataFrame may have\n non-uniform timing. If at least one dense variable is present, and the\n user has not explicitly excluded dense variables (by setting\n include_dense=False), all selected variables will be implicitly\n converted to dense using the specified `sampling_rate` (if provided).\n To avoid unexpected behavior, we recommend converting mixed collections\n to all-dense form explicitly via the `to_dense()` or `resample()`\n methods before calling `to_df()`.\n\n \"\"\"\n if not include_sparse and not include_dense:\n raise ValueError(\n \"You can't exclude both dense and sparse variables! That leaves nothing!\"\n )\n\n _vars = []\n\n if include_sparse:\n _vars += self.get_sparse_variables(variables)\n\n if include_dense:\n _vars += self.get_dense_variables(variables)\n\n if not _vars:\n raise ValueError('No variables were selected for output.')\n\n # If all variables are sparse/simple, we can pass them as-is. Otherwise\n # we first force all variables to dense via .resample().\n if all(isinstance(v, SimpleVariable) for v in _vars):\n variables = _vars\n else:\n sampling_rate = sampling_rate or self.sampling_rate\n var_names = [v.name for v in _vars]\n collection = self.resample(\n sampling_rate, variables=var_names, force_dense=include_sparse\n )\n variables = list(collection.variables.values())\n\n return super().to_df(variables, format, fillna, entities=entities, timing=timing)\n\n\ndef merge_collections(collections, sampling_rate='highest', output_level=None, variables=None):\n \"\"\"Merge two or more collections at the same level of analysis.\n\n Parameters\n ----------\n collections : list\n List of Collections to merge.\n sampling_rate : int or str\n Sampling rate to use if it becomes necessary\n to resample DenseRunVariables. Either an integer or 'highest' (see\n merge_variables docstring for further explanation).\n output_level : str, optional\n Assign a new level (e.g., 'run', 'subject', etc.) to the merged\n collection. If None, the current level is retained.\n variables : list\n Optional list of names of variables to keep. If None, all are retained.\n\n Returns\n -------\n BIDSVariableCollection or BIDSRunVariableCollection\n Result type depends on the type of the input collections.\n\n \"\"\"\n collections = listify(collections)\n if len(collections) == 1 and variables is None:\n return collections[0]\n\n levels = set([c.level for c in collections]) # noqa: C403\n if len(levels) > 1:\n raise ValueError(\n \"At the moment, it's only possible to merge \" # noqa: UP031\n 'Collections at the same level of analysis. You '\n 'passed collections at levels: %s.' % levels\n )\n\n cls = collections[0].__class__\n\n # Flatten all variables from all collections into a single list\n keep_vars = list(chain(*[c.variables.values() for c in collections]))\n if variables is not None:\n keep_vars = [var for var in keep_vars if var.name in variables]\n variables = keep_vars\n\n # merge_variables will automatically merge all variables that share name\n variables = cls.merge_variables(variables, sampling_rate=sampling_rate)\n\n if isinstance(collections[0], BIDSRunVariableCollection):\n if sampling_rate == 'highest':\n rates = [var.sampling_rate for var in variables if isinstance(var, DenseRunVariable)]\n\n # TODO: this looks like it takes first, not highest... verify\n sampling_rate = rates[0] if rates else None\n\n return cls(variables, sampling_rate)\n\n # For non-run collections, we may need to set a different output level\n coll = cls(variables)\n if output_level is not None:\n coll.level = output_level\n return coll\n" }, { "path": "src/bids/variables/entities.py", "content": "\"\"\"Data classes for internal BIDS data hierarchy.\"\"\"\n\nfrom collections import namedtuple\nfrom itertools import chain\n\nimport pandas as pd\n\nfrom bids.utils import matches_entities\n\nfrom . import collections as clc\n\nBASE_ENTITIES = ['subject', 'session', 'task', 'run']\n\n\nclass Node:\n \"\"\"Base class for objects that represent a single object in the BIDS\n hierarchy.\n\n Parameters\n ----------\n id : int or str\n A value uniquely identifying this node. Typically the\n entity value extracted from the filename via layout.\n\n \"\"\"\n\n def __init__(self, level, entities):\n self.level = level.lower()\n self.entities = entities\n if self.entities is not None:\n self.base_ents = {e: val for e, val in entities.items() if e in BASE_ENTITIES}\n else:\n self.base_ents = None\n self.variables = {}\n\n def add_variable(self, variable):\n \"\"\"Adds a BIDSVariable to the current Node's list.\n\n Parameters\n ----------\n variable : BIDSVariable\n The Variable to add to the list.\n\n \"\"\"\n if self.base_ents is not None:\n for e, val in self.base_ents.items():\n if e in variable.entities and variable.entities[e] != val:\n raise ValueError('Variable and node entity mismatch.')\n self.variables[variable.name] = variable\n\n\nclass RunNode(Node):\n \"\"\"Represents a single Run in a BIDS project.\n\n Parameters\n ----------\n id : int\n The index of the run.\n entities : dict\n Dictionary of entities for this Node.\n image_file : str\n The full path to the corresponding nifti image.\n duration : float\n Duration of the run, in seconds.\n repetition_time : float\n TR for the run.\n task : str\n The task name for this run.\n\n \"\"\"\n\n def __init__(self, entities, image_file, duration, repetition_time, n_vols):\n self.image_file = image_file\n self.duration = duration\n self.repetition_time = repetition_time\n self.n_vols = n_vols\n super().__init__('run', entities)\n\n def get_info(self): # noqa: D102\n # Note: do not remove the dict() call! self.entities is a SQLAlchemy\n # association_proxy mapping, and without the conversion, the connection\n # to the DB persists, causing problems on Python 3.5 if we try to clone\n # a RunInfo or any containing object.\n entities = dict(self.entities)\n return RunInfo(entities, self.duration, self.repetition_time, self.image_file, self.n_vols)\n\n\n# Stores key information for each Run.\nRunInfo_ = namedtuple('RunInfo', ['entities', 'duration', 'tr', 'image', 'n_vols'])\n\n\n# Wrap with class to provide docstring\nclass RunInfo(RunInfo_):\n \"\"\"A namedtuple storing run-related information.\n\n Properties include 'entities', 'duration', 'tr', and 'image', 'n_vols'.\n \"\"\"\n\n pass\n\n\nclass NodeIndex:\n \"\"\"Represents the top level in a BIDS hierarchy.\"\"\"\n\n def __init__(self):\n super().__init__()\n self.index = pd.DataFrame()\n self.nodes = []\n\n def get_collections(self, unit, names=None, merge=False, sampling_rate=None, **entities):\n \"\"\"Retrieve variable data for a specified level in the Dataset.\n\n Parameters\n ----------\n unit : str\n The unit of analysis to return variables for. Must be\n one of 'run', 'session', 'subject', or 'dataset'.\n names : list\n Optional list of variables names to return. If\n None, all available variables are returned.\n merge : bool\n If True, variables are merged across all observations\n of the current unit. E.g., if unit='subject' and return_type=\n 'collection', variables from all subjects will be merged into a\n single collection. If False, each observation is handled\n separately, and the result is returned as a list.\n sampling_rate : int or str\n If unit='run', the sampling rate to\n pass onto the returned BIDSRunVariableCollection.\n entities : dict\n Optional constraints used to limit what gets returned.\n\n Returns\n -------\n A list of BIDSVariableCollections if merge=False; a single\n BIDSVariableCollection if merge=True.\n\n \"\"\"\n nodes = self.get_nodes(unit, entities)\n var_sets = []\n\n for n in nodes:\n var_set = list(n.variables.values())\n var_set = [v for v in var_set if matches_entities(v, entities)]\n if names is not None:\n var_set = [v for v in var_set if v.name in names]\n # Additional filtering on Variables past run level, because their\n # contents are extracted from TSV files containing rows from\n # multiple observations\n if unit != 'run':\n var_set = [v.filter(entities) for v in var_set]\n var_sets.append(var_set)\n\n if merge:\n var_sets = [list(chain(*var_sets))]\n\n results = []\n for vs in var_sets:\n if not vs:\n continue\n if unit == 'run':\n vs = clc.BIDSRunVariableCollection(vs, sampling_rate)\n else:\n vs = clc.BIDSVariableCollection(vs)\n results.append(vs)\n\n if merge:\n return results[0] if results else None\n\n return results\n\n def get_nodes(self, level=None, entities=None, strict=False):\n \"\"\"Retrieves all nodes that match the specified criteria.\n\n Parameters\n ----------\n level : str\n The level of analysis of nodes to return.\n entities : dict\n Entities to filter on. All nodes must have\n matching values on all defined keys to be included.\n strict : bool\n If True, an exception will be raised if the entities\n dict contains any keys that aren't contained in the current\n index.\n\n Returns\n -------\n A list of Node instances.\n\n \"\"\"\n entities = {} if entities is None else entities.copy()\n\n if level is not None:\n entities['level'] = level\n\n if entities is None:\n return self.nodes\n\n match_ents = set(entities.keys())\n common_cols = list(match_ents & set(self.index.columns))\n\n if strict and match_ents - common_cols:\n raise ValueError('Invalid entities: ', match_ents - common_cols)\n\n if not common_cols:\n return self.nodes\n\n # Construct query string that handles both single values and iterables\n query = []\n for col in common_cols:\n oper = 'in' if isinstance(entities[col], (list, tuple)) else '=='\n q = f'{col} {oper} {repr(entities[col])}'\n query.append(q)\n query = ' and '.join(query)\n\n rows = self.index.query(query)\n if rows.empty:\n return []\n\n # Sort and return\n sort_cols = [\n 'subject',\n 'session',\n 'task',\n 'run',\n 'node_index',\n 'suffix',\n 'level',\n 'datatype',\n ]\n sort_cols = [sc for sc in sort_cols if sc in set(rows.columns)]\n rows = rows.sort_values(sort_cols)\n inds = rows['node_index'].astype(int)\n return [self.nodes[i] for i in inds]\n\n def create_node(self, level, entities, *args, **kwargs):\n \"\"\"Creates a new child Node.\n\n Parameters\n ----------\n level : str\n The level of analysis of the new Node.\n entities : dict\n Dictionary of entities belonging to Node.\n args, kwargs : dict\n Optional positional or named arguments to pass on to\n class-specific initializers. These arguments are only used if\n a Node that matches the passed entities doesn't already exist,\n and a new one must be created.\n\n Returns\n -------\n A Node instance.\n\n \"\"\"\n if level == 'run':\n node = RunNode(entities, *args, **kwargs)\n else:\n node = Node(level, entities)\n\n entities = dict(entities, node_index=len(self.nodes), level=level)\n self.nodes.append(node)\n # Because \"entities\" may have non-scalar values (such as `SliceTiming`)\n # we need to first create a Series to avoid expansion\n # From here we can concatenate\n node_row = pd.DataFrame(pd.Series(entities)).T\n self.index = pd.concat([self.index, node_row], ignore_index=True)\n return node\n\n def get_or_create_node(self, level, entities, *args, **kwargs):\n \"\"\"Retrieves a child Node based on the specified criteria, creating a\n new Node if necessary.\n\n Parameters\n ----------\n level : str\n The level of analysis of the Node.\n entities : dict\n Dictionary of entities to include in newly-created\n Nodes or filter existing ones.\n args, kwargs : dict\n Optional positional or named arguments to pass on to\n class-specific initializers. These arguments are only used if\n a Node that matches the passed entities doesn't already exist,\n and a new one must be created.\n\n Returns\n -------\n A Node instance.\n\n \"\"\"\n result = self.get_nodes(level, entities)\n\n if result:\n if len(result) > 1:\n raise ValueError(\n \"More than one matching Node found! If you're\"\n ' expecting more than one Node, use '\n 'get_nodes() instead of get_or_create_node().'\n )\n return result[0]\n\n return self.create_node(level, entities, *args, **kwargs)\n" }, { "path": "src/bids/variables/io.py", "content": "\"\"\"Tools for reading/writing BIDS data files.\"\"\"\n\nimport json\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nfrom bids.utils import listify\n\nfrom .entities import NodeIndex\nfrom .variables import DenseRunVariable, SimpleVariable, SparseRunVariable\n\nBASE_ENTITIES = ['subject', 'session', 'task', 'run']\nALL_ENTITIES = BASE_ENTITIES + ['datatype', 'suffix', 'acquisition']\n\n\ndef load_variables( # noqa: D417\n layout,\n types=None,\n levels=None,\n skip_empty=True,\n dataset=None,\n scope='all',\n regex_search=None,\n **kwargs,\n):\n \"\"\"A convenience wrapper for one or more load_*_variables() calls.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n BIDSLayout containing variable files.\n types : str or list\n Types of variables to retrieve. All valid values\n reflect the filename stipulated in the BIDS spec for each kind of\n variable. Valid values include: 'events', 'physio', 'stim',\n 'scans', 'participants', 'sessions', and 'regressors'.\n levels : str or list\n Optional level(s) of variables to load. Valid\n values are 'run', 'session', 'subject', or 'dataset'. This is\n simply a shorthand way to specify types--e.g., 'run' will be\n converted to types=['events', 'physio', 'stim', 'regressors'].\n skip_empty : bool\n Whether or not to skip empty Variables (i.e.,\n where there are no rows/records in a file after applying any\n filtering operations like dropping NaNs).\n dataset : NodeIndex\n An existing NodeIndex container to store the\n loaded data in. Can be used to iteratively construct a dataset\n that contains otherwise heterogeneous sets of variables. If None,\n a new NodeIndex is used.\n scope : str or list\n The scope of the space to search for variables. See\n docstring for BIDSLayout for details and valid predefined values.\n kwargs : dict\n Optional keyword arguments to pass onto the individual\n load_*_variables() calls.\n\n Returns\n -------\n A NodeIndex instance.\n\n Examples\n --------\n >>> load_variables(layout, ['events', 'physio'], subject='01') # doctest: +SKIP\n # returns all variables stored in _events.tsv and _physio.tsv.gz files\n # for runs that belong to subject with id '01'.\n\n \"\"\"\n TYPES = ['events', 'physio', 'stim', 'scans', 'participants', 'sessions', 'regressors']\n\n types = listify(types)\n\n if types is None:\n if levels is not None:\n types = []\n lev_map = {\n 'run': ['events', 'physio', 'stim', 'regressors'],\n 'session': ['scans'],\n 'subject': ['sessions', 'scans'],\n 'dataset': ['participants'],\n }\n [types.extend(lev_map[l.lower()]) for l in listify(levels)] # noqa: E741\n else:\n types = TYPES\n\n bad_types = set(types) - set(TYPES)\n if bad_types:\n raise ValueError('Invalid variable types: %s' % bad_types) # noqa: UP031\n\n dataset = dataset or NodeIndex()\n\n run_types = list({'events', 'physio', 'stim', 'regressors'} - set(types))\n type_flags = dict.fromkeys(run_types, False)\n if len(type_flags) < 4:\n _kwargs = kwargs.copy()\n _kwargs.update(type_flags)\n dataset = _load_time_variables(layout, dataset, scope=scope, **_kwargs)\n\n for t in {'scans', 'sessions', 'participants'} & set(types):\n kwargs.pop('suffix', None) # suffix is always one of values above\n dataset = _load_tsv_variables(\n layout, t, dataset, scope=scope, regex_search=regex_search, **kwargs\n )\n\n return dataset\n\n\ndef _get_nvols(img_f):\n import nibabel as nb\n\n img = nb.load(img_f)\n nvols = 0\n if isinstance(img, nb.Nifti1Pair):\n nvols = img.shape[3]\n elif isinstance(img, nb.Cifti2Image):\n for ax in map(img.header.get_axis, range(len(img.header.matrix))):\n if isinstance(ax, nb.cifti2.SeriesAxis):\n nvols = ax.size\n break\n else:\n raise ValueError('No series axis found in %s' % img_f) # noqa: UP031\n elif isinstance(img, nb.GiftiImage):\n nvols = len(img.get_arrays_from_intent('time series'))\n else:\n raise ValueError('Unknown image type %s: %s' % img.__class__, img_f) # noqa: UP031\n\n return nvols\n\n\ndef _load_time_variables( # noqa: D417\n layout,\n dataset=None,\n columns=None,\n scan_length=None,\n drop_na=True,\n events=True,\n physio=True,\n stim=True,\n regressors=True,\n skip_empty=True,\n scope='all',\n **selectors,\n):\n \"\"\"Loads all variables found in *_events.tsv files and returns them as a\n BIDSVariableCollection.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n A BIDSLayout to scan.\n dataset : NodeIndex\n A BIDS NodeIndex container. If None, a new one is\n initialized.\n columns : list\n Optional list of names specifying which columns in the\n event files to read. By default, reads all columns found.\n scan_length : float\n Optional duration of runs (in seconds). By\n default, this will be extracted from the BOLD image. However, in\n cases where the user doesn't have access to the images (e.g.,\n because only file handles are locally available), a fixed duration\n can be manually specified as a fallback.\n drop_na : bool\n If True, removes all events where amplitude is n/a. If\n False, leaves n/a values intact. Note that in the latter case,\n transformations that requires numeric values may fail.\n events : bool\n If True, extracts variables from events.tsv files.\n physio : bool\n If True, extracts variables from _physio files.\n stim : bool\n If True, extracts variables from _stim files.\n skip_empty : bool\n Whether or not to skip empty Variables (i.e.,\n where there are no rows/records in a file, or all onsets,\n durations, and amplitudes are 0).\n scope : str or list\n The scope of the space to search for variables. See\n docstring for BIDSLayout for details and valid predefined values.\n selectors : dict\n Optional keyword arguments passed on to the\n BIDSLayout instance's get() method; can be used to constrain\n which data are loaded.\n\n Returns\n -------\n A NodeIndex instance.\n\n \"\"\"\n # Extract any non-keyword arguments\n selectors = selectors.copy()\n\n if dataset is None:\n dataset = NodeIndex()\n\n selectors['datatype'] = 'func'\n selectors['suffix'] = 'bold'\n exts = selectors.pop('extension', ['.nii', '.nii.gz', '.func.gii', '.dtseries.nii'])\n images = layout.get(return_type='object', scope=scope, extension=exts, **selectors)\n\n if not images:\n raise ValueError('No functional images that match criteria found.')\n\n # Main loop over images\n for img_obj in images:\n img_f = img_obj.path\n\n # Run is not mandatory, but we need it to behave like an int and not\n # a PaddedInt later when we call `dataset.get_nodes('run', select_on)`\n # Without this, we would see \"run ==02\" in the query, which breaks\n # pandas.\n entities = img_obj.entities.copy()\n if 'run' in entities:\n entities['run'] = int(entities['run'])\n\n tr = img_obj.get_metadata()['RepetitionTime']\n\n # Get duration of run: first try to get it directly from the image\n # header; if that fails, look for a scan_length argument.\n try:\n nvols = _get_nvols(img_f)\n duration = nvols * tr\n except Exception as e:\n if scan_length is not None:\n duration = scan_length\n nvols = int(np.rint(scan_length / tr))\n else:\n msg = (\n 'Unable to extract scan duration from one or more '\n 'BOLD runs, and no scan_length argument was provided '\n 'as a fallback. Please check that the image files are '\n 'available, or manually specify the scan duration.'\n )\n raise ValueError(msg) from e\n\n # We don't want to pass all the image file's entities onto get_node(),\n # as there can be unhashable nested slice timing values, and this also\n # slows down querying unnecessarily. Instead, pick out files only based\n # on the core BIDS entities and any entities explicitly passed as\n # selectors.\n # TODO: one downside of this approach is the stripped entities also\n # won't be returned in the resulting node due to the way things are\n # implemented. Consider adding a flag to control this.\n select_on = {k: v for (k, v) in entities.items() if k in BASE_ENTITIES or k in selectors}\n\n # If a matching node already exists, return it\n result = dataset.get_nodes('run', select_on)\n\n if result:\n if len(result) > 1:\n raise ValueError(\n 'More than one existing Node matches the '\n 'specified entities! You may need to pass '\n 'additional selectors to narrow the search.'\n )\n run = result[0]\n\n else:\n # Otherwise create a new node and use that.\n # We first convert any entity values that are currently collections to\n # JSON strings to prevent nasty hashing problems downstream. Note that\n # isinstance() isn't as foolproof as actually trying to hash the\n # value, but the latter is likely to be slower, and since values are\n # coming from JSON or filenames, there's no real chance of encountering\n # anything but a list or dict.\n entities = {\n k: (json.dumps(v) if isinstance(v, (list, dict)) else v)\n for (k, v) in entities.items()\n }\n\n run = dataset.create_node(\n 'run',\n entities,\n image_file=img_f,\n duration=duration,\n repetition_time=tr,\n n_vols=nvols,\n )\n run_info = run.get_info()\n\n # Process event files\n if events:\n dfs = layout.get_nearest(\n img_f,\n extension='.tsv',\n suffix='events',\n all_=True,\n full_search=True,\n ignore_strict_entities=['suffix', 'extension'],\n )\n for _data in dfs:\n _data = pd.read_csv(_data, sep='\\t')\n if 'amplitude' in _data.columns:\n if (\n _data['amplitude'].astype(int) == 1\n ).all() and 'trial_type' in _data.columns:\n msg = (\n \"Column 'amplitude' with constant value 1 \"\n 'is unnecessary in event files; ignoring it.'\n )\n _data = _data.drop('amplitude', axis=1)\n else:\n msg = \"Column name 'amplitude' is reserved; renaming it to 'amplitude_'.\"\n _data = _data.rename(columns={'amplitude': 'amplitude_'})\n warnings.warn(msg) # noqa: B028\n\n # Pandas already converts 'n/a' to NaN. Leaving this comment\n # because we used to do it manually here.\n # We also converted to numeric, but this is now irrelevant.\n\n _cols = columns or list(set(_data.columns.tolist()) - {'onset', 'duration'})\n\n # Construct a DataFrame for each extra column\n for col in _cols:\n df = _data[['onset', 'duration']].copy()\n df['amplitude'] = _data[col].values\n\n # Add in all of the run's entities as new columns for\n # index\n for entity, value in entities.items():\n if entity in ALL_ENTITIES:\n df[entity] = value\n\n if drop_na:\n df = df.dropna(subset=['amplitude'])\n\n if df.empty:\n continue\n\n var = SparseRunVariable(name=col, data=df, run_info=run_info, source='events')\n run.add_variable(var)\n\n # Process confound files\n if regressors:\n sub_ents = {k: v for k, v in entities.items() if k in BASE_ENTITIES}\n confound_files = layout.get(\n suffix=['regressors', 'timeseries'], scope=scope, extension='.tsv', **sub_ents\n )\n for cf in confound_files:\n _data = pd.read_csv(cf.path, sep='\\t', na_values='n/a')\n if columns is not None:\n conf_cols = list(set(_data.columns) & set(columns))\n _data = _data.loc[:, conf_cols]\n for col in _data.columns:\n sr = 1.0 / run.repetition_time\n var = DenseRunVariable(\n name=col,\n values=_data[[col]],\n run_info=run_info,\n source='regressors',\n sampling_rate=sr,\n )\n run.add_variable(var)\n\n # Process recordinging files\n rec_types = []\n if physio:\n rec_types.append('physio')\n if stim:\n rec_types.append('stim')\n\n if rec_types:\n rec_files = layout.get_nearest(\n img_f,\n extension='.tsv.gz',\n all_=True,\n suffix=rec_types,\n ignore_strict_entities=['suffix', 'extension'],\n full_search=True,\n )\n for rf in rec_files:\n metadata = layout.get_metadata(rf)\n if not metadata:\n raise ValueError(\"No .json sidecar found for '%s'.\" % rf) # noqa: UP031\n data = pd.read_csv(rf, sep='\\t')\n freq = metadata['SamplingFrequency']\n st = metadata['StartTime']\n rf_cols = metadata['Columns']\n data.columns = rf_cols\n\n # Filter columns if user passed names\n if columns is not None:\n rf_cols = list(set(rf_cols) & set(columns))\n data = data.loc[:, rf_cols]\n\n n_cols = len(rf_cols)\n if not n_cols:\n continue\n\n # Keep only in-scan samples\n if st < 0:\n start_ind = np.floor(-st * freq)\n values = data.values[start_ind:, :]\n else:\n values = data.values\n\n if st > 0:\n n_pad = int(freq * st)\n pad = np.zeros((n_pad, n_cols))\n values = np.r_[pad, values]\n\n n_rows = int(run.duration * freq)\n if len(values) > n_rows:\n values = values[:n_rows, :]\n elif len(values) < n_rows:\n pad = np.zeros((n_rows - len(values), n_cols))\n values = np.r_[values, pad]\n\n df = pd.DataFrame(values, columns=rf_cols)\n source = 'physio' if '_physio.tsv' in rf else 'stim'\n for col in df.columns:\n var = DenseRunVariable(\n name=col,\n values=df[[col]],\n run_info=run_info,\n source=source,\n sampling_rate=freq,\n )\n run.add_variable(var)\n return dataset\n\n\ndef _load_tsv_variables( # noqa: D417\n layout,\n suffix,\n dataset=None,\n columns=None,\n prepend_type=False,\n scope='all',\n regex_search=None,\n **selectors,\n):\n \"\"\"Reads variables from scans.tsv, sessions.tsv, and participants.tsv.\n\n Parameters\n ----------\n layout : :obj:`bids.layout.BIDSLayout`\n The BIDSLayout to use.\n suffix : str\n The suffix of file to read from. Must be one of 'scans',\n 'sessions', or 'participants'.\n dataset : NodeIndex\n A BIDS NodeIndex container. If None, a new one is\n initialized.\n columns : list\n Optional list of names specifying which columns in the\n files to return. If None, all columns are returned.\n prepend_type : bool\n If True, variable names are prepended with the\n type name (e.g., 'age' becomes 'participants.age').\n scope : str or list\n The scope of the space to search for variables. See\n docstring for BIDSLayout for details and valid predefined values.\n selectors : dict\n Optional keyword arguments passed onto the\n BIDSLayout instance's get() method; can be used to constrain\n which data are loaded.\n\n Returns\n -------\n A NodeIndex instance.\n\n \"\"\"\n if regex_search is None:\n regex_search = layout.regex_search\n\n # Sanitize the selectors: only keep entities at current level or above\n valid_entities_map = {\n 'scans': ['subject', 'session'],\n 'sessions': ['subject'],\n 'participants': [],\n }\n valid_entities = valid_entities_map[suffix]\n layout_kwargs = {k: v for k, v in selectors.items() if k in valid_entities}\n\n if dataset is None:\n dataset = NodeIndex()\n\n files = layout.get(extension='.tsv', suffix=suffix, scope=scope, **layout_kwargs)\n\n for f in files:\n _data = f.get_df(include_timing=False)\n\n # Entities can be defined either within the first column of the .tsv\n # file (for entities that vary by row), or from the full file path\n # (for entities constant over all rows in the file). We extract both\n # and store them in the main DataFrame alongside other variables (as\n # they'll be extracted when the BIDSVariable is initialized anyway).\n for ent_name, ent_val in f.entities.items():\n if ent_name in ALL_ENTITIES:\n _data[ent_name] = ent_val\n\n # Handling is a bit more convoluted for scans.tsv, because the first\n # column contains the run filename, which we also need to parse.\n if suffix == 'scans':\n # Suffix is guaranteed to be present in each filename, so drop the\n # constant column with value 'scans' to make way for it and prevent\n # two 'suffix' columns.\n _data.drop(columns=['suffix'], inplace=True)\n\n image = _data['filename']\n _data = _data.drop('filename', axis=1)\n dn = f._dirname\n paths = [str(dn / p) for p in image.values]\n ent_recs = [dict(layout.files[p].entities) for p in paths if p in layout.files]\n ent_cols = pd.DataFrame.from_records(ent_recs)\n\n # Remove entity columns found in both DFs\n dupes = list(set(ent_cols.columns) & set(_data.columns))\n to_drop = ['extension'] + dupes\n ent_cols.drop(columns=to_drop, inplace=True)\n\n _data = pd.concat([_data, ent_cols], axis=1, sort=True)\n\n # The BIDS spec requires ID columns to be named 'session_id', 'run_id',\n # etc., and IDs begin with entity prefixes (e.g., 'sub-01'). To ensure\n # consistent internal handling, we strip these suffixes and prefixes.\n elif suffix == 'sessions':\n _data = _data.rename(columns={'session_id': 'session'})\n _data['session'] = _data['session'].str.replace('ses-', '')\n elif suffix == 'participants':\n _data = _data.rename(columns={'participant_id': 'subject'})\n _data['subject'] = _data['subject'].str.replace('sub-', '')\n\n def make_patt(x, regex_search=False):\n patt = '%s' % x # noqa: UP031\n if isinstance(x, (int, float)):\n # allow for leading zeros if a number was specified\n # regardless of regex_search\n patt = '0*' + patt\n if not regex_search:\n patt = '^%s$' % patt # noqa: UP031\n return patt\n\n # Filter rows on all selectors\n comm_cols = list(set(_data.columns) & set(selectors.keys()))\n for col in comm_cols:\n vals = listify(selectors.get(col))\n if regex_search and any(isinstance(val, str) for val in vals):\n patt = '|'.join(make_patt(x, regex_search=True) for x in vals)\n _data = _data[_data[col].str.contains(patt)]\n else:\n _data = _data[_data[col].isin(vals)]\n\n level = {'scans': 'session', 'sessions': 'subject', 'participants': 'dataset'}[suffix]\n\n node = dataset.get_or_create_node(level, f.entities)\n\n ent_cols = list(set(ALL_ENTITIES) & set(_data.columns))\n amp_cols = list(set(_data.columns) - set(ent_cols))\n\n if columns is not None:\n amp_cols = list(set(amp_cols) & set(columns))\n\n for col_name in amp_cols:\n # Rename columns: values must be in 'amplitude'\n df = _data.loc[:, [col_name] + ent_cols]\n df.columns = ['amplitude'] + ent_cols\n\n if prepend_type:\n col_name = '%s.%s' % (suffix, col_name) # noqa: UP031\n\n node.add_variable(SimpleVariable(name=col_name, data=df, source=suffix))\n\n return dataset\n" }, { "path": "src/bids/variables/tests/__init__.py", "content": "" }, { "path": "src/bids/variables/tests/test_collections.py", "content": "from os.path import join\n\nimport numpy as np\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\nfrom bids.variables import DenseRunVariable, SparseRunVariable, merge_collections\nfrom bids.variables.entities import RunInfo\n\n\n@pytest.fixture(scope='module')\ndef run_coll():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n # Limit to a few subjects to reduce test running time\n return layout.get_collections(\n 'run', types=['events'], merge=True, scan_length=480, subject=['01', '02', '04']\n )\n\n\n@pytest.fixture(scope='module')\ndef run_coll_bad_length():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n # Limit to a few subjects to reduce test running time\n return layout.get_collections(\n 'run', types=['events'], merge=True, scan_length=480.1, subject=['01', '02', '04']\n )\n\n\n@pytest.fixture(scope='module')\ndef run_coll_list():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n return layout.get_collections(\n 'run', types=['events'], merge=False, scan_length=480, subject=['01', '02', '04']\n )\n\n\n@pytest.fixture(scope='module')\ndef run_coll_derivs():\n path = join(get_test_data_path(), 'ds005')\n deriv_path = join(get_test_data_path(), 'ds005', 'derivatives', 'fmriprep')\n layout = BIDSLayout(path, derivatives=deriv_path)\n # Limit to a few subjects to reduce test running time\n return layout.get_collections(\n 'run', types=['events', 'regressors'], merge=False, scan_length=480, subject=['01', '02']\n )\n\n\ndef test_run_variable_collection_init(run_coll):\n assert isinstance(run_coll.variables, dict)\n assert run_coll.sampling_rate == 10\n assert (\n run_coll.__repr__()\n == \"\"\n )\n\n\ndef test_run_variable_collection_sparse_variable_accessors(run_coll):\n coll = run_coll.clone()\n assert coll.get_sparse_variables()\n assert coll.all_sparse()\n coll.variables['RT'] = coll.variables['RT'].to_dense(1)\n assert not coll.all_sparse()\n assert len(coll.get_sparse_variables()) + 1 == len(coll.variables)\n\n\ndef test_run_variable_collection_dense_variable_accessors(run_coll):\n coll = run_coll.clone()\n coll.variables['RT'] = coll.variables['RT'].to_dense(1)\n assert not coll.all_dense()\n assert len(coll.get_dense_variables()) == 1\n for k, v in coll.variables.items():\n if k == 'RT':\n continue\n coll.variables[k] = v.to_dense(1)\n assert coll.all_dense()\n\n\ndef test_run_variable_collection_get_sampling_rate(run_coll):\n coll = run_coll.clone()\n assert coll._get_sampling_rate(None) == 10\n coll.variables['RT'].run_info[0] = RunInfo({}, 200, 10, None, 20)\n with pytest.raises(ValueError) as exc:\n coll._get_sampling_rate('TR')\n assert str(exc.value).startswith('Non-unique')\n assert coll._get_sampling_rate('highest') == 10\n coll.variables['RT1'] = coll.variables['RT'].to_dense(5.0)\n coll.variables['RT2'] = coll.variables['RT'].to_dense(12.0)\n assert coll._get_sampling_rate('highest') == 12.0\n assert coll._get_sampling_rate(20) == 20\n with pytest.raises(ValueError) as exc:\n coll._get_sampling_rate('BLARGH')\n assert str(exc.value).startswith('Invalid')\n\n\ndef test_resample_run_variable_collection(run_coll):\n run_coll = run_coll.clone()\n resampled = run_coll.resample()\n assert not resampled.variables # Empty because all variables are sparse\n\n resampled = run_coll.resample(force_dense=True).variables\n assert len(resampled) == 7\n assert all([isinstance(v, DenseRunVariable) for v in resampled.values()])\n assert len(set([v.sampling_rate for v in resampled.values()])) == 1\n targ_len = 480 * 3 * 3 * 10\n assert all([len(v.values) == targ_len for v in resampled.values()])\n\n sr = 20\n resampled = run_coll.resample(sr, force_dense=True).variables\n targ_len = 480 * 3 * 3 * sr\n assert all([len(v.values) == targ_len for v in resampled.values()])\n\n run_coll.resample(sr, force_dense=True, in_place=True)\n assert len(run_coll.variables) == 8\n vars_ = run_coll.variables.values()\n vars_ = [v for v in vars_ if v.name != 'trial_type']\n assert all([len(v.values) == targ_len for v in vars_])\n assert all([v.sampling_rate == sr for v in vars_])\n assert all([isinstance(v, DenseRunVariable) for v in vars_])\n\n\ndef test_run_variable_collection_to_df_all_sparse_vars(run_coll):\n run_coll = run_coll.clone()\n\n timing_cols = {'onset', 'duration'}\n entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'}\n cond_names = {\n 'PTval',\n 'RT',\n 'gain',\n 'loss',\n 'parametric gain',\n 'respcat',\n 'respnum',\n 'trial_type',\n }\n condition = {'condition'}\n ampl = {'amplitude'}\n\n # Fails because all vars are sparse\n with pytest.raises(ValueError):\n df = run_coll.to_df(include_sparse=False)\n\n # Fails because no such variables exist\n with pytest.raises(ValueError):\n df = run_coll.to_df(variables=['rubadubdub', '999'])\n\n # Wide format\n df = run_coll.to_df()\n events_per_sub = 256\n assert df.shape == (events_per_sub * 3, 15)\n assert set(df.columns) == timing_cols.union(entity_cols, cond_names)\n\n # Wide format, selecting variables by name\n df = run_coll.to_df(format='wide', variables=['RT', 'PTval'])\n assert df.shape == (events_per_sub * 3, 9)\n assert set(df.columns) == timing_cols.union(entity_cols, {'RT', 'PTval'})\n\n # Long format\n df = run_coll.to_df(format='long')\n assert df.shape == (events_per_sub * 3 * 8, 9)\n assert set(df.columns) == timing_cols.union(entity_cols, condition, ampl)\n\n # Long format, selecting variables by name\n df = run_coll.to_df(format='long', variables=['RT', 'PTval'])\n assert df.shape == (events_per_sub * 3 * 2, 9)\n assert set(df.columns) == timing_cols.union(entity_cols, condition, ampl)\n\n # Wide format without entity columns\n df = run_coll.to_df(format='wide', entities=False)\n assert df.shape == (events_per_sub * 3, 10)\n assert set(df.columns) == timing_cols | cond_names\n\n # Wide format without timing columns\n df = run_coll.to_df(format='wide', timing=False)\n assert df.shape == (events_per_sub * 3, 13)\n assert set(df.columns) == entity_cols | cond_names\n\n\ndef test_run_variable_collection_to_df_all_dense_vars(run_coll):\n timing_cols = {'onset', 'duration'}\n entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'}\n cond_names = {\n 'PTval',\n 'RT',\n 'gain',\n 'loss',\n 'parametric gain',\n 'respcat',\n 'respnum',\n 'trial_type',\n }\n md_names = {'TaskName', 'RepetitionTime', 'extension', 'SliceTiming'}\n condition = {'condition'}\n ampl = {'amplitude'}\n\n # First few tests assume uniform sampling rate for all columns\n unif_coll = run_coll.to_dense(sampling_rate=10)\n\n # Fails because all vars are dense\n with pytest.raises(ValueError):\n unif_coll.to_df(include_dense=False)\n\n # Fails because no such variables exist\n with pytest.raises(ValueError):\n df = unif_coll.to_df(variables=['rubadubdub', '999'])\n\n # Wide format\n df = unif_coll.to_df()\n rows_per_var = 3 * 3 * 480 * 10 # subjects x runs x time x sampling rate\n assert df.shape == (rows_per_var, 18)\n cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'}\n assert set(df.columns) == cols\n\n # Wide format with variable selection\n df = unif_coll.to_df(variables=['RT', 'gain'])\n assert df.shape == (rows_per_var, 13)\n cols = (timing_cols | entity_cols | {'RT', 'gain'} | md_names) - {'trial_type'}\n assert set(df.columns) == cols\n\n # Long format\n df = unif_coll.to_df(format='long')\n assert df.shape == (rows_per_var * 7, 13)\n cols = timing_cols | entity_cols | condition | ampl | md_names\n assert set(df.columns) == cols\n\n # Long format with variable selection\n df = unif_coll.to_df(format='long', variables=['RT', 'gain'])\n assert df.shape == (rows_per_var * 2, 13)\n cols = timing_cols | entity_cols | condition | ampl | md_names\n assert set(df.columns) == cols\n\n # Test resampling to TR\n df = unif_coll.to_df(sampling_rate='TR')\n n_rows = int(480 * 3 * 3 / 2)\n assert df.shape == (n_rows, 18)\n cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'}\n assert set(df.columns) == cols\n\n # Test resampling to highest when variable sampling rates diverge\n unif_coll.resample(variables='RT', sampling_rate=12, in_place=True)\n df = unif_coll.to_df(sampling_rate='highest')\n n_rows = int(rows_per_var * 12 / 10)\n assert df.shape == (n_rows, 18)\n\n\ndef test_run_variable_collection_bad_length_to_df_all_dense_vars(run_coll_bad_length):\n timing_cols = {'onset', 'duration'}\n entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'}\n cond_names = {\n 'PTval',\n 'RT',\n 'gain',\n 'loss',\n 'parametric gain',\n 'respcat',\n 'respnum',\n 'trial_type',\n }\n md_names = {'TaskName', 'RepetitionTime', 'extension', 'SliceTiming'}\n condition = {'condition'}\n ampl = {'amplitude'}\n\n unif_coll = run_coll_bad_length.to_dense(sampling_rate=10)\n\n df = unif_coll.to_df()\n rows_per_var = np.round(3 * 3 * 480.1 * 10) # subjects x runs x time x sampling rate\n\n # Test resampling without setting sample rate (default sr == 10)\n df = unif_coll.to_df(format='long')\n assert df.shape == (rows_per_var * 7, 13)\n cols = timing_cols | entity_cols | condition | ampl | md_names\n assert set(df.columns) == cols\n\n # Test resampling to TR\n df = unif_coll.to_df(sampling_rate='TR')\n n_rows = int(480 * 3 * 3 / 2) # (Note number of volumes is 480, not 480.1)\n assert df.shape == (n_rows, 18)\n cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'}\n assert set(df.columns) == cols\n\n\ndef test_run_variable_collection_to_df_mixed_vars(run_coll):\n coll = run_coll.clone()\n coll.to_dense(10, variables=['RT', 'loss', 'gain'], in_place=True)\n\n # Only sparse variables\n df = coll.to_df(include_dense=False)\n rows = 3 * 256\n assert df.shape == (rows, 12)\n assert 'RT' not in df.columns\n assert 'respcat' in df.columns\n\n # Only dense variables\n df = coll.to_df(include_sparse=False)\n rows = 3 * 3 * 480 * 10\n assert df.shape == (rows, 14)\n assert 'RT' in df.columns\n assert 'respcat' not in df.columns\n\n # Everything\n df = coll.to_df(sampling_rate=5)\n rows = 3 * 3 * 480 * 5\n assert df.shape == (rows, 18)\n assert not {'RT', 'respcat'} - set(df.columns)\n\n\ndef test_merge_collections(run_coll, run_coll_list):\n df1 = run_coll.to_df().sort_values(['subject', 'run', 'onset'])\n rcl = [c.clone() for c in run_coll_list]\n coll = merge_collections(rcl)\n df2 = coll.to_df().sort_values(['subject', 'run', 'onset'])\n assert df1.equals(df2)\n\n\ndef test_get_collection_entities(run_coll_list):\n coll = run_coll_list[0]\n ents = coll.entities\n assert not ({'run', 'task', 'subject', 'suffix', 'datatype'} - set(ents.keys()))\n\n merged = merge_collections(run_coll_list[:3])\n ents = merged.entities\n assert not ({'task', 'subject', 'suffix', 'datatype'} - set(ents.keys()))\n assert ents['subject'] == '01'\n\n merged = merge_collections(run_coll_list[3:6])\n ents = merged.entities\n assert not ({'task', 'subject', 'suffix', 'datatype'} - set(ents.keys()))\n assert ents['subject'] == '02'\n\n\ndef test_match_variables(run_coll):\n matches = run_coll.match_variables('^.{1,2}a', match_type='regex')\n assert set(matches) == {'gain', 'parametric gain'}\n assert not run_coll.match_variables('.{1,3}a')\n matches = run_coll.match_variables('^.{1,2}a', match_type='regex', return_type='variable')\n assert len(matches) == 2\n assert all([isinstance(m, SparseRunVariable) for m in matches])\n matches = run_coll.match_variables('*gain')\n assert set(matches) == {'gain', 'parametric gain'}\n\n\ndef test_n_variables(run_coll_derivs):\n # Test that variables are ingested into correct collection\n # non_steady_state_outlier00 should only exist for sub-01 run-01\n match = [\n c\n for c in run_coll_derivs\n if c.entities.get('subject', None) == '01' and c.entities['run'] == 1\n ]\n assert 'non_steady_state_outlier00' in match[0].variables.keys()\n\n # Find collections with 'non_steady_state_outlier00'\n matches = [c for c in run_coll_derivs if 'non_steady_state_outlier00' in c.variables.keys()]\n assert len(matches) == 1\n\n # Test that collections have not incorrectly merged multiple subjects\n for c in run_coll_derivs:\n assert 'subject' in c.entities\n" }, { "path": "src/bids/variables/tests/test_entities.py", "content": "from os.path import join\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\nfrom bids.variables import BIDSRunVariableCollection, load_variables\nfrom bids.variables.entities import Node, NodeIndex, RunNode\n\n\n@pytest.fixture(scope='module')\ndef layout1():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n return layout\n\n\n@pytest.fixture(scope='module')\ndef layout2():\n path = join(get_test_data_path(), '7t_trt')\n layout = BIDSLayout(path)\n return layout\n\n\ndef test_run(layout1):\n img = layout1.get(\n subject='01', task='mixedgamblestask', suffix='bold', run=1, return_type='obj'\n )[0]\n run = RunNode(None, img.filename, 480, 2, 480 / 2)\n assert run.image_file == img.filename\n assert run.duration == 480\n assert run.repetition_time == 2\n assert run.n_vols == 480 / 2\n\n\ndef test_get_or_create_node(layout1):\n img = layout1.get(\n subject='01', task='mixedgamblestask', suffix='bold', run=1, return_type='obj'\n )[0]\n index = NodeIndex()\n\n entities = {'subject': '01', 'session': 1}\n sess = index.get_or_create_node('session', entities)\n assert sess.__class__ == Node\n\n sess2 = index.get_or_create_node('session', entities)\n assert sess2 == sess\n\n run = index.get_or_create_node(\n 'run',\n img.entities,\n image_file=img.filename,\n duration=480,\n repetition_time=2,\n n_vols=480 / 2,\n )\n assert run.__class__ == RunNode\n assert run.duration == 480\n assert run.n_vols == 480 / 2\n\n\ndef test_get_nodes(layout1):\n index = load_variables(layout1, scan_length=480)\n # scans.tsv\n nodes = index.get_nodes('session')\n assert len(nodes) == 2\n # participants.tsv\n nodes = index.get_nodes('dataset')\n assert len(nodes) == 1\n assert all([isinstance(n, Node) for n in nodes])\n nodes = index.get_nodes('run', {'subject': ['01', '02', '03']})\n assert len(nodes) == 9\n assert all([isinstance(n, RunNode) for n in nodes])\n\n\ndef test_get_collections_merged(layout1):\n dataset = load_variables(layout1, scan_length=480)\n collection = dataset.get_collections('run', merge=True)\n assert isinstance(collection, BIDSRunVariableCollection)\n assert len(collection.variables) == 8\n vals = collection.variables['RT'].values\n ents = collection.variables['RT'].index\n assert len(ents) == len(vals) == 4096\n assert set(ents.columns) == {'task', 'run', 'subject', 'suffix', 'datatype'}\n\n\ndef test_get_collections_unmerged(layout2):\n dataset = load_variables(layout2, types=['sessions'], scan_length=480)\n colls = dataset.get_collections('subject', merge=False)\n assert len(colls) == 10\n assert len(colls[0].variables) == 94\n assert colls[0]['panas_at_ease'].values.shape == (2,)\n" }, { "path": "src/bids/variables/tests/test_io.py", "content": "from os.path import join\n\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\nfrom bids.variables import DenseRunVariable, SimpleVariable, SparseRunVariable, load_variables\nfrom bids.variables.entities import Node, NodeIndex, RunNode\n\n\n@pytest.fixture\ndef layout1():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n return layout\n\n\n@pytest.fixture(scope='module', params=['events', 'preproc'])\ndef synthetic(request):\n root = join(get_test_data_path(), 'synthetic')\n if request.param == 'preproc':\n layout = BIDSLayout(root, derivatives=True)\n dataset = load_variables(layout, skip_empty=True, desc='preproc', space='T1w')\n else:\n layout = BIDSLayout(root)\n dataset = load_variables(layout, skip_empty=True)\n return request.param, dataset\n\n\ndef test_load_events(layout1):\n index = load_variables(layout1, types='events', scan_length=480)\n runs = index.get_nodes(level='run', entities={'subject': '01'})\n assert len(runs) == 3\n assert isinstance(runs[0], RunNode)\n variables = runs[0].variables\n assert len(variables) == 8\n targ_cols = {'parametric gain', 'PTval', 'trial_type', 'respnum'}\n assert not (targ_cols - set(variables.keys()))\n assert isinstance(variables['parametric gain'], SparseRunVariable)\n assert variables['parametric gain'].index.shape == (86, 5)\n assert variables['parametric gain'].source == 'events'\n\n\ndef test_load_participants(layout1):\n index = load_variables(layout1, types='participants')\n assert isinstance(index, NodeIndex)\n dataset = index.get_nodes(level='dataset')[0]\n assert isinstance(dataset, Node)\n assert len(dataset.variables) == 2\n assert {'age', 'sex'} == set(dataset.variables.keys())\n age = dataset.variables['age']\n assert isinstance(age, SimpleVariable)\n assert age.index.shape == (16, 2)\n assert age.values.shape == (16,)\n\n index = load_variables(layout1, types='participants', subject=['^1.*'], regex_search=True)\n age = index.get_nodes(level='dataset')[0].variables['age']\n assert age.index.shape == (7, 2)\n assert age.values.shape == (7,)\n\n\ndef test_load_synthetic_dataset(synthetic):\n param, index = synthetic\n # Runs\n runs = index.get_nodes('run')\n assert len(runs) == 5 * 2 * 3\n runs = index.get_nodes('run', {'task': 'nback'})\n assert len(runs) == 5 * 2 * 2\n variables = runs[0].variables\n\n if param == 'preproc':\n # non-exhaustive\n match = ['Cosine01', 'stdDVARS', 'RotZ', 'FramewiseDisplacement']\n sum_dense = 56\n else:\n match = ['trial_type', 'weight', 'respiratory', 'cardiac', 'stimA', 'stimB']\n sum_dense = 4\n\n for v in match:\n assert v in variables.keys()\n\n assert sum([isinstance(v, DenseRunVariable) for v in variables.values()]) == sum_dense\n assert all([len(r.variables['weight'].values) == 42 for r in runs])\n\n # Sessions\n sessions = index.get_nodes('session')\n assert len(sessions) == 5 * 2\n target_cols = {'acq_time', 'TaskName', 'RepetitionTime', 'EchoTime', 'FlipAngle'}\n assert set(sessions[0].variables.keys()) == target_cols\n data = sessions[0].variables['acq_time'].filter({'task': 'nback'})\n assert len(data.values) == 2\n\n # Subjects\n subs = index.get_nodes('subject')\n assert len(subs) == 5\n assert set(subs[0].variables.keys()) == {'systolic_blood_pressure'}\n" }, { "path": "src/bids/variables/tests/test_variables.py", "content": "import json\nimport os\nimport uuid\nfrom os.path import join\n\nimport nibabel as nb\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom bids.layout import BIDSLayout\nfrom bids.tests import get_test_data_path\nfrom bids.variables import DenseRunVariable, SimpleVariable, load_variables, merge_variables\nfrom bids.variables.entities import RunInfo\n\n\ndef generate_DEV(name='test', sr=20, duration=480):\n n = duration * sr\n values = np.random.normal(size=n)\n ent_names = ['task', 'run', 'session', 'subject']\n entities = {e: uuid.uuid4().hex for e in ent_names}\n image = uuid.uuid4().hex + '.nii.gz'\n run_info = (RunInfo(entities, duration, 2, image, duration / 2),)\n return DenseRunVariable(\n name='test', values=values, run_info=run_info, source='dummy', sampling_rate=sr\n )\n\n\n@pytest.fixture\ndef layout1():\n path = join(get_test_data_path(), 'ds005')\n layout = BIDSLayout(path)\n return layout\n\n\n@pytest.fixture(scope='module')\ndef layout2():\n path = join(get_test_data_path(), '7t_trt')\n layout = BIDSLayout(path)\n return layout\n\n\ndef test_dense_event_variable_init():\n dev = generate_DEV()\n assert dev.sampling_rate == 20\n assert dev.run_info[0].duration == 480\n assert dev.source == 'dummy'\n assert len(dev.values) == len(dev.index)\n assert dev.__repr__() == \"\"\n\n\ndef test_dense_event_variable_resample():\n dev = generate_DEV()\n dev2 = dev.clone().resample(sampling_rate=40)\n assert len(dev2.values) == len(dev2.index)\n assert len(dev2.values) == 2 * len(dev.values)\n\n\ndef test_merge_wrapper():\n dev = generate_DEV()\n data = pd.DataFrame({'amplitude': [4, 3, 2, 5]})\n sev = SimpleVariable('simple', data, 'dummy')\n # Should break if asked to merge different classes\n with pytest.raises(ValueError, match='Variables of different classes'):\n merge_variables([dev, sev])\n\n\ndef test_sparse_run_variable_to_dense(layout1):\n index = load_variables(layout1, types='events', scan_length=480)\n runs = index.get_nodes('run', {'subject': ['01', '02']})\n\n for _, run in enumerate(runs):\n var = run.variables['RT']\n dense = var.to_dense(20)\n\n # All sparse values must occur in the dense variable\n dense_vals = set(np.unique(dense.values.values))\n sparse_vals = set(np.unique(var.values.values)) | {0}\n assert not (sparse_vals - dense_vals)\n\n assert len(dense.values) > len(var.values)\n assert isinstance(dense, DenseRunVariable)\n assert dense.values.shape == (9600, 1)\n assert len(dense.run_info) == len(var.run_info)\n assert dense.source == 'events'\n\n\ndef test_sparse_run_variable_to_dense_default_sr(layout1):\n index = load_variables(layout1, types='events', scan_length=480)\n runs = index.get_nodes('run', {'subject': ['01', '02']})\n\n for i, run in enumerate(runs):\n var = run.variables['RT']\n dense = var.to_dense()\n\n # Check that a sensible sampling rate was found\n assert np.allclose(dense.sampling_rate, 1)\n\n # Check that all unique values are identical\n sparse_vals = set(np.unique(var.values.values)) | {0}\n dense_vals = set(np.unique(dense.values.values))\n assert sparse_vals == dense_vals\n\n assert len(dense.values) > len(var.values)\n assert isinstance(dense, DenseRunVariable)\n assert dense.values.shape == (480, 1)\n assert len(dense.run_info) == len(var.run_info)\n assert dense.source == 'events'\n\n\ndef test_merge_densified_variables(layout1):\n SR = 10\n dataset = load_variables(layout1, types='events', scan_length=480)\n runs = dataset.get_nodes('run')\n vars_ = [r.variables['RT'].to_dense(SR) for r in runs]\n dense = merge_variables(vars_)\n assert isinstance(dense, DenseRunVariable)\n n_rows = 480 * SR\n assert dense.values.shape == (len(runs) * n_rows, 1)\n for i in range(len(runs)):\n onset = i * n_rows\n offset = onset + n_rows\n run_vals = vars_[i].values\n dense_vals = dense.values.iloc[onset:offset].reset_index(drop=True)\n assert dense_vals.equals(run_vals)\n\n\ndef test_densify_merged_variables(layout1):\n SR = 10\n dataset = load_variables(layout1, types='events', scan_length=480)\n runs = dataset.get_nodes('run')\n vars_ = [r.variables['RT'] for r in runs]\n var = merge_variables(vars_)\n dense = var.to_dense(SR)\n assert isinstance(dense, DenseRunVariable)\n n_rows = 480 * SR\n assert dense.values.shape == (len(runs) * n_rows, 1)\n for i in range(len(runs)):\n onset = i * n_rows\n offset = onset + n_rows\n # resampled values from split vs. merged will be trivially different\n # at boundaries due to interpolation, so leave a buffer\n run_vals = vars_[i].to_dense(SR).values.values.ravel()\n dense_vals = dense.values.values.ravel()[onset:offset]\n assert run_vals.shape == dense_vals.shape\n assert np.array_equal(run_vals, dense_vals)\n\n\ndef test_merge_simple_variables(layout2):\n index = load_variables(layout2, types='sessions')\n subjects = index.get_nodes('subject')\n variables = [s.variables['panas_sad'] for s in subjects]\n n_rows = sum([len(c.values) for c in variables])\n merged = merge_variables(variables)\n assert len(merged.values) == n_rows\n assert set(merged.index.columns) == set(variables[0].index.columns)\n assert variables[3].values.iloc[1] == merged.values.iloc[7]\n\n\ndef test_merge_sparse_run_variables(layout1):\n dataset = load_variables(layout1, types='events', scan_length=480)\n runs = dataset.get_nodes('run')\n variables = [r.variables['RT'] for r in runs]\n n_rows = sum([len(c.values) for c in variables])\n merged = merge_variables(variables)\n assert len(merged.values) == n_rows\n assert set(merged.index.columns) == set(variables[0].index.columns)\n\n\ndef test_merge_dense_run_variables(layout2):\n variables = [generate_DEV() for i in range(20)]\n variables += [generate_DEV(duration=400) for i in range(8)]\n n_rows = sum([len(c.values) for c in variables])\n merged = merge_variables(variables)\n assert len(merged.values) == n_rows\n assert set(merged.index.columns) == set(variables[0].index.columns)\n\n\ndef test_simple_variable_to_df(layout1):\n pass\n\n\ndef test_sparse_run_variable_to_df(layout1):\n pass\n\n\ndef test_dense_run_variable_to_df(layout2):\n pass\n\n\ndef test_filter_simple_variable(layout2):\n dataset = load_variables(layout2, types=['scans'])\n sessions = dataset.get_nodes('session')\n variables = [s.variables['surroundings'] for s in sessions]\n merged = merge_variables(variables)\n assert merged.to_df().shape == (60, 9)\n filt = merged.filter({'acquisition': 'fullbrain'})\n assert filt.to_df().shape == (40, 9)\n flt1 = merged.filter({'acquisition': 'fullbrain', 'subject': ['01', '02']}).to_df()\n assert flt1.shape == (8, 9)\n query = 'acquisition==\"fullbrain\" and subject in [\"01\", \"02\"]'\n flt2 = merged.filter(query=query)\n flt2 = flt2.to_df()\n assert flt1.equals(flt2)\n assert merged.filter({'nonexistent': 2}, strict=True) is None\n merged.filter({'acquisition': 'fullbrain'}, inplace=True)\n assert merged.to_df().shape == (40, 9)\n\n\n@pytest.mark.parametrize('TR, nvols', [(2.00000, 251), (2.000001, 251)])\ndef test_resampling_edge_case(tmpdir, TR, nvols):\n tmpdir.chdir()\n os.makedirs('sub-01/func')\n with open('sub-01/func/sub-01_task-task_events.tsv', 'w') as fobj:\n fobj.write('onset\\tduration\\tval\\n1\\t0.1\\t1\\n')\n with open('sub-01/func/sub-01_task-task_bold.json', 'w') as fobj:\n json.dump({'RepetitionTime': TR}, fobj)\n\n dataobj = np.zeros((5, 5, 5, nvols), dtype=np.int16)\n affine = np.diag((2.5, 2.5, 2.5, 1))\n img = nb.Nifti1Image(dataobj, affine)\n img.header.set_zooms((2.5, 2.5, 2.5, TR))\n img.to_filename('sub-01/func/sub-01_task-task_bold.nii.gz')\n\n layout = BIDSLayout('.', validate=False)\n coll = load_variables(layout).get_collections('run')[0]\n dense_var = coll.variables['val'].to_dense(coll.sampling_rate)\n regressor = dense_var.resample(1.0 / TR).values\n assert regressor.shape == (nvols, 1)\n\n\ndef test_downsampling(tmpdir):\n tmpdir.chdir()\n os.makedirs('sub-01/func')\n import numpy as np\n\n TR, newTR, nvols, newvols = 2.00000, 6.0, 90, 30\n Fs = 1 / TR\n t = np.linspace(0, int(nvols / Fs), nvols, endpoint=False)\n values = np.sin(0.025 * 2 * np.pi * t) + np.cos(0.1166 * 2 * np.pi * t)\n with open('sub-01/func/sub-01_task-task_events.tsv', 'w') as fobj:\n fobj.write('onset\\tduration\\tval\\n')\n for idx, val in enumerate(values):\n fobj.write('%f\\t%f\\t%f\\n' % (idx * TR, TR, val))\n with open('sub-01/func/sub-01_task-task_bold.json', 'w') as fobj:\n json.dump({'RepetitionTime': TR}, fobj)\n\n dataobj = np.zeros((5, 5, 5, nvols), dtype=np.int16)\n affine = np.diag((2.5, 2.5, 2.5, 1))\n img = nb.Nifti1Image(dataobj, affine)\n img.header.set_zooms((2.5, 2.5, 2.5, TR))\n img.to_filename('sub-01/func/sub-01_task-task_bold.nii.gz')\n\n layout = BIDSLayout('.', validate=False)\n coll = load_variables(layout).get_collections('run')[0]\n dense_var = coll.variables['val'].to_dense(1.0 / TR)\n regressor = dense_var.resample(1.0 / newTR).values\n assert regressor.shape == (newvols, 1)\n # This checks that the filtering has happened. If it has not, then\n # this value for this frequency bin will be an alias and have a\n # very different amplitude\n assert np.allclose(np.abs(np.fft.fft(regressor.values.ravel()))[9], 0.46298273)\n # This checks that the signal (0.025 Hz) within the new Nyquist\n # rate actually gets passed through.\n assert np.allclose(np.abs(np.fft.fft(regressor.values.ravel()))[4], 8.88189504)\n" }, { "path": "src/bids/variables/variables.py", "content": "\"\"\"Classes for representing BIDS variables.\"\"\"\n\nimport math\nimport warnings\nfrom abc import ABCMeta, abstractmethod\nfrom copy import deepcopy\nfrom functools import reduce\nfrom itertools import chain\n\nimport numpy as np\nimport pandas as pd\n\nfrom bids.utils import listify\n\n\nclass BIDSVariable(metaclass=ABCMeta):\n \"\"\"Base representation of a column in a BIDS project.\"\"\"\n\n # Columns that define special properties (e.g., onset, duration). These\n # will be stored separately from the main data object, and are accessible\n # as properties on the BIDSVariable instance.\n _property_columns = set()\n\n def __init__(self, name, values, source):\n self.name = name\n self.values = values\n self.source = source\n self.entities = self._extract_entities()\n\n def __repr__(self):\n return f\"<{self.__class__.__name__}(name='{self.name}', source='{self.source}')>\"\n\n def clone(self, data=None, **kwargs):\n \"\"\"Clone (deep copy) the current column, optionally replacing its\n data and/or any other attributes.\n\n Parameters\n ----------\n data : :obj:`pandas.DataFrame` or array_like\n Optional new data to substitute into\n the cloned column. Must have same dimensionality as the\n original.\n kwargs : dict\n Optional keyword arguments containing new attribute\n values to set in the copy. E.g., passing `name='my_name'`\n would set the `.name` attribute on the cloned instance to the\n passed value.\n\n \"\"\"\n result = deepcopy(self)\n if data is not None:\n if data.shape != self.values.shape:\n # If data can be re-shaped safely, do so\n if data.squeeze().shape == self.values.squeeze().shape:\n data = data.values.reshape(self.values.shape)\n else:\n raise ValueError(\n 'Replacement data has shape %s; must have' # noqa: UP031\n ' same shape as existing data %s.' % (data.shape, self.values.shape)\n )\n result.values = pd.DataFrame(data)\n\n if kwargs:\n for k, v in kwargs.items():\n setattr(result, k, v)\n\n # Need to update name on Series as well\n # result.values.name = kwargs.get('name', self.name)\n return result\n\n def filter(self, filters=None, query=None, strict=False, inplace=False):\n \"\"\"Returns a copy of the current Variable with only rows that match\n the filters retained.\n\n Parameters\n ----------\n filters : dict\n Dictionary of filters to apply. Keys can be either\n 'amplitude' or any named entity. Values must be single values\n or lists.\n query : str\n Optional query string to pass to df.query(). Will not\n be validated in any way, so must have valid column names. Takes\n precedence over filters in the event that both are passed.\n strict : bool\n By default, keys in 'filters' that cannot be found\n in the Variable will be silently ignored. If strict=True, None\n will be returned in such cases.\n inplace : bool\n If True, filtering is performed in place. If False,\n a filtered copy of the Variable is returned.\n\n Returns\n -------\n BIDSVariable or None if no rows are left after filtering.\n\n \"\"\"\n if filters is None and query is None:\n raise ValueError(\"Either the 'filters' or the 'query' argument must be provided!\")\n\n if filters is not None and query is None:\n query = []\n for name, val in filters.items():\n if name != 'amplitude' and name not in self.index.columns:\n if strict:\n return None\n continue\n oper = 'in' if isinstance(val, (list, tuple)) else '=='\n q = f'{name} {oper} {repr(val)}'\n query.append(q)\n query = ' and '.join(query)\n\n var = self if inplace else self.clone()\n\n if query:\n inds = self.to_df().query(query).index\n\n var.values = var.values.loc[inds]\n var.index = var.index.loc[inds]\n if hasattr(self, '_build_entity_index'):\n var._build_entity_index()\n\n if not inplace:\n return var\n\n @classmethod\n def merge(cls, variables, name=None, **kwargs):\n \"\"\"Merge/concatenate a list of variables along the row axis.\n\n Parameters\n ----------\n variables : list\n A list of Variables to merge.\n name : str\n Optional name to assign to the output Variable. By\n default, uses the same name as the input variables.\n kwargs : dict\n Optional keyword arguments to pass onto the class-specific\n merge() call. See merge_variables docstring for details.\n\n Returns\n -------\n A single BIDSVariable of the same class as the input variables.\n\n See Also\n --------\n merge_variables\n\n \"\"\"\n variables = listify(variables)\n if len(variables) == 1:\n return variables[0]\n\n var_names = set([v.name for v in variables]) # noqa: C403\n if len(var_names) > 1:\n raise ValueError(\n 'Columns with different names cannot be merged. ' # noqa: UP031\n 'Column names provided: %s' % var_names\n )\n\n if name is None:\n name = variables[0].name\n\n return cls._merge(variables, name, **kwargs)\n\n @classmethod\n @abstractmethod\n def _merge(cls, variables, name, **kwargs):\n pass\n\n def get_grouper(self, groupby='run'):\n \"\"\"Return a list suitable for use in groupby calls.\n\n Parameters\n ----------\n groupby : str or list\n Name(s) of column(s) defining the grouper\n object. Anything that would be valid inside a .groupby() call\n on a pandas structure.\n\n Returns\n -------\n list\n A list defining the groups.\n\n \"\"\"\n grouper = self.index.loc[:, groupby]\n return grouper.apply(lambda x: '@@@'.join(x.astype(str).values), axis=1)\n\n def apply(self, func, groupby='run', *args, **kwargs):\n \"\"\"Applies the passed function to the groups defined by the groupby\n argument. Works identically to the standard pandas df.groupby() call.\n\n Parameters\n ----------\n func : callable\n The function to apply to each group.\n groupby : str or list\n Name(s) of column(s) defining the grouping.\n args, kwargs : dict\n Optional positional and keyword arguments to pass\n onto the function call.\n\n \"\"\"\n grouper = self.get_grouper(groupby)\n return self.values.groupby(grouper, group_keys=False).apply(func, *args, **kwargs)\n\n def to_df(self, condition=True, entities=True, **kwargs): # noqa: D417\n \"\"\"Convert to a DataFrame, with columns for name and entities.\n\n Parameters\n ----------\n condition : bool\n If True, adds a column for condition name, and\n names the amplitude column 'amplitude'. If False, returns just\n onset, duration, and amplitude, and gives the amplitude column\n the current column name.\n entities : bool\n If True, adds extra columns for all entities.\n\n \"\"\"\n amp = 'amplitude' if condition else self.name\n data = pd.DataFrame({amp: self.values.values.ravel()})\n\n for sc in self._property_columns:\n data[sc] = getattr(self, sc)\n\n if condition:\n data['condition'] = self.name\n\n if entities:\n ent_data = self.index.reset_index(drop=True)\n data = pd.concat([data, ent_data], axis=1, sort=True)\n\n return data.reset_index(drop=True)\n\n def _extract_entities(self):\n \"\"\"Returns a dict of all non-varying entities for the current Variable.\n\n Notes\n -----\n Only entity key/value pairs common to all rows in the Variable\n are returned. E.g., if a Variable contains events extracted from\n runs 1, 2 and 3 from subject '01', the returned dict will be\n {'subject': '01'}; the runs will be excluded as they vary across\n the Variable contents.\n\n \"\"\"\n\n def is_unique(s):\n a = s.to_numpy()\n return (a[0] == a).all()\n\n constant = self.index.apply(is_unique)\n if constant.empty:\n return {}\n else:\n keep = self.index.columns[constant]\n first_row_ix = self.index.index[0]\n res = {}\n for k in keep:\n v = self.index.loc[first_row_ix, k]\n if pd.isna(v): # Only drop NaNs if we get that on first try\n v = self.index[k].dropna().iloc[0]\n res[k] = v\n return res\n\n\nclass SimpleVariable(BIDSVariable):\n \"\"\"Represents a simple design matrix column that has no timing\n information.\n\n Parameters\n ----------\n name : str\n Name of the column.\n data : :obj:`pandas.DataFrame`\n A pandas DataFrame minimally containing a column\n named 'amplitude' as well as any identifying entities.\n source : str\n The type of BIDS variable file the data were extracted\n from. Must be one of: 'events', 'physio', 'stim', 'regressors',\n 'scans', 'sessions', 'participants', or 'beh'.\n kwargs : dict\n Optional keyword arguments passed onto superclass.\n\n \"\"\"\n\n _entity_columns = {'condition', 'amplitude'}\n\n def __init__(self, name, data, source, **kwargs):\n ent_cols = list(set(data.columns) - self._entity_columns)\n self.index = data.loc[:, ent_cols]\n\n values = data['amplitude'].reset_index(drop=True)\n values.name = name\n\n super().__init__(name, values, source)\n\n def split(self, grouper):\n \"\"\"Split the current SparseRunVariable into multiple columns.\n\n Parameters\n ----------\n grouper : :obj:`pandas.DataFrame`\n Binary DF specifying the design matrix to use for splitting. Number\n of rows must match current ``SparseRunVariable``;\n a new ``SparseRunVariable`` will be generated for each column in\n the grouper.\n\n Returns\n -------\n A list of SparseRunVariables, one per column in the grouper DF.\n\n \"\"\"\n data = self.to_df(condition=True, entities=True)\n data = data.drop('condition', axis=1)\n\n subsets = []\n for i, col_name in enumerate(grouper.columns): # noqa: B007\n col_data = data.loc[grouper[col_name].astype(bool), :]\n name = f'{self.name}.{col_name}'\n col = self.__class__(\n name=name,\n data=col_data,\n source=self.source,\n run_info=getattr(self, 'run_info', None),\n )\n subsets.append(col)\n return subsets\n\n @classmethod\n def _merge(cls, variables, name, **kwargs):\n dfs = [v.to_df() for v in variables]\n data = pd.concat(dfs, axis=0, sort=True).reset_index(drop=True)\n data = data.rename(columns={name: 'amplitude'})\n return cls(name, data, source=variables[0].source, **kwargs)\n\n def select_rows(self, rows):\n \"\"\"Truncate internal arrays to keep only the specified rows.\n\n Parameters\n ----------\n rows : array_like\n An integer or boolean array identifying the indices\n of rows to keep.\n\n \"\"\"\n self.values = self.values.iloc[rows]\n self.index = self.index.iloc[rows, :]\n for prop in self._property_columns:\n vals = getattr(self, prop)[rows]\n setattr(self, prop, vals)\n\n\nclass SparseRunVariable(SimpleVariable):\n \"\"\"A sparse representation of a single column of events.\n\n Parameters\n ----------\n name : str\n Name of the column.\n data : :obj:`pandas.DataFrame`\n A pandas DataFrame minimally containing the columns\n 'onset', 'duration', and 'amplitude'.\n run_info : list\n A list of RunInfo objects carrying information about\n all runs represented in the Variable.\n source : str\n The type of BIDS variable file the data were extracted\n from. Must be one of: 'events', 'physio', 'stim', 'regressors',\n 'scans', 'sessions', 'participants', or 'beh'.\n kwargs : dict\n Optional keyword arguments passed onto superclass.\n\n \"\"\"\n\n _property_columns = {'onset', 'duration'}\n\n def __init__(self, name, data, run_info, source, **kwargs):\n if hasattr(run_info, 'duration'):\n run_info = [run_info]\n if not isinstance(run_info, list):\n raise TypeError('We expect a list of run_info, got %s' % repr(run_info)) # noqa: UP031\n self.run_info = run_info\n for sc in self._property_columns:\n arr = data.pop(sc).values\n arr.flags['WRITEABLE'] = True\n setattr(self, sc, arr)\n super().__init__(name, data, source, **kwargs)\n\n def get_duration(self):\n \"\"\"Return the total duration of the Variable's run(s).\"\"\"\n return sum([r.duration for r in self.run_info])\n\n def to_dense(self, sampling_rate=None):\n \"\"\"Convert the current sparse column to a dense representation.\n\n If sampling_rate is not provided, the largest interval able to\n faithfully represent all onsets and durations will be determined.\n The sampling rate is the reciprocal of that interval.\n\n Parameters\n ----------\n sampling_rate : float or None\n Sampling rate (in Hz) to use when constructing the DenseRunVariable\n\n Returns\n -------\n DenseRunVariable\n\n \"\"\"\n # Cast onsets and durations to milliseconds\n onsets = np.round(self.onset * 1000).astype(int)\n durations = np.round(self.duration * 1000).astype(int)\n gcd = np.gcd.reduce(np.r_[onsets, durations])\n bin_sr = 1000.0 / gcd\n\n # never use a computed SR smaller than the requested one, because\n # when events are widely-spaced and timing is very regular, this can\n # result in a nasty loss of precision in the resampling step.\n if sampling_rate is not None:\n bin_sr = max(bin_sr, sampling_rate)\n else:\n sampling_rate = bin_sr\n\n duration = math.ceil( # Round up to nearest second\n round(bin_sr * self.get_duration(), 3) # Cut off at millisecond precision\n )\n ts = pd.Series(0, index=np.arange(duration), dtype=self.values.dtype)\n\n onsets = np.round(self.onset * bin_sr).astype(int)\n durations = np.round(self.duration * bin_sr).astype(int)\n\n run_i, start, last_ind = 0, 0, 0\n for i, val in enumerate(self.values.values):\n if onsets[i] < last_ind:\n start += self.run_info[run_i].duration * bin_sr\n run_i += 1\n _onset = int(start + onsets[i])\n _offset = int(_onset + durations[i])\n if _onset >= duration:\n warnings.warn(\n 'The onset time of a variable seems to exceed '\n 'the runs duration, hence runs are incremented '\n 'by one internally.',\n stacklevel=2,\n )\n ts[_onset:_offset] = val\n last_ind = onsets[i]\n\n if bin_sr != sampling_rate:\n # Resample the time series to the requested sampling rate\n num = int(math.ceil(duration * sampling_rate / bin_sr))\n ts = _resample(ts, sampling_rate, bin_sr, num)\n\n run_info = list(self.run_info)\n dense_var = DenseRunVariable(\n name=self.name,\n values=ts,\n run_info=run_info,\n source=self.source,\n sampling_rate=sampling_rate,\n )\n\n return dense_var\n\n def _extract_entities(self):\n # Get all entities common to all runs. The super method already does\n # this for entities that show up in filenames, so we just add the\n # ones that show up in the RunInfo tuples, as those include metadata.\n ent_items = [run.entities.items() for run in self.run_info]\n entities = reduce(lambda x, y: x & y, ent_items, ent_items[0])\n base_ents = super()._extract_entities()\n return dict(entities, **base_ents)\n\n @classmethod\n def _merge(cls, variables, name, **kwargs):\n run_info = list(chain(*[v.run_info for v in variables]))\n return super()._merge(variables, name, run_info=run_info, **kwargs)\n\n\nclass DenseRunVariable(BIDSVariable):\n \"\"\"A dense representation of a single column.\n\n Parameters\n ----------\n name : :obj:`str`\n The name of the column.\n values : :obj:`numpy.ndarray`\n The values/amplitudes to store.\n run_info : :obj:`list`\n A list of RunInfo objects carrying information about all runs\n represented in the Variable.\n source : {'events', 'physio', 'stim', 'regressors', 'scans', 'sessions', 'participants', 'beh'}\n The type of BIDS variable file the data were extracted from.\n sampling_rate : :obj:`float`\n Mandatory sampling rate (in Hz) to use. Must match the sampling rate used\n to generate the values.\n\n \"\"\"\n\n def __init__(self, name, values, run_info, source, sampling_rate):\n values = pd.DataFrame(values)\n\n if not isinstance(sampling_rate, (float, int)):\n raise TypeError(\n 'sampling_rate must be a float or integer, not %s' % type(sampling_rate) # noqa: UP031\n )\n\n if hasattr(run_info, 'duration'):\n run_info = [run_info]\n self.run_info = run_info\n self.sampling_rate = sampling_rate\n self.index = self._build_entity_index(run_info, sampling_rate)\n\n super().__init__(name, values, source)\n\n def split(self, grouper):\n \"\"\"Split the current DenseRunVariable into multiple columns.\n\n Parameters\n ----------\n grouper : :obj:`pandas.DataFrame`\n Binary DF specifying the design matrix to use for splitting. Number\n of rows must match current ``DenseRunVariable``; a new ``DenseRunVariable``\n will be generated for each column in the grouper.\n\n Returns\n -------\n A list of DenseRunVariables, one per unique value in the grouper.\n\n \"\"\"\n values = grouper.values * self.values.values\n df = pd.DataFrame(values, columns=grouper.columns)\n return [\n DenseRunVariable(\n name='%s.%s' % (self.name, name), # noqa: UP031\n values=df[name].values,\n run_info=self.run_info,\n source=self.source,\n sampling_rate=self.sampling_rate,\n )\n for i, name in enumerate(df.columns)\n ]\n\n def _build_entity_index(self, run_info, sampling_rate, match_vol=False):\n \"\"\"Build the entity index from run information.\"\"\"\n interval = int(round(1000.0 / sampling_rate))\n\n def _create_index(all_keys, all_reps, all_ents):\n all_keys = np.array(sorted(all_keys))\n df = pd.DataFrame(\n np.zeros((sum(all_reps), len(all_keys)), dtype=object), columns=all_keys\n )\n\n prev_ix = 0\n for i, reps in enumerate(all_reps):\n for k, v in all_ents[i].items():\n col_ix = np.where(all_keys == k)[0][0]\n df.iloc[prev_ix : prev_ix + reps, col_ix] = v\n prev_ix = reps\n\n return df\n\n all_reps = []\n all_ents = []\n all_keys = set()\n for run in run_info:\n if match_vol:\n # If TR, fix reps to n_vols to ensure match\n all_reps.append(run.n_vols)\n else:\n all_reps.append(int(math.ceil(run.duration * sampling_rate)))\n\n all_ents.append(run.entities)\n all_keys.update(run.entities.keys())\n\n self.timestamps = pd.date_range(0, periods=sum(all_reps), freq='%sms' % interval) # noqa: UP031\n\n return _create_index(all_keys, all_reps, all_ents)\n\n def resample(self, sampling_rate, inplace=False, kind='linear'):\n \"\"\"Resample the Variable to the specified sampling rate.\n\n Parameters\n ----------\n sampling_rate : :obj:`int`, :obj:`float`\n Target sampling rate (in Hz).\n inplace : :obj:`bool`, optional\n If True, performs resampling in-place. If False, returns a resampled\n copy of the current Variable. Default is False.\n kind : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'}\n Argument to pass to :obj:`scipy.interpolate.interp1d`; indicates\n the kind of interpolation approach to use. See interp1d docs for\n valid values. Default is 'linear'.\n\n \"\"\"\n if not inplace:\n var = self.clone()\n var.resample(sampling_rate, True, kind)\n return var\n\n match_vol = False\n if sampling_rate == 'TR':\n match_vol = True\n sampling_rate = 1.0 / self.run_info[0].tr\n\n if sampling_rate == self.sampling_rate:\n return\n\n self.index = self._build_entity_index(self.run_info, sampling_rate, match_vol)\n\n self.values = pd.DataFrame(\n _resample(\n self.values.values.ravel(),\n sampling_rate,\n self.sampling_rate,\n len(self.index),\n kind=kind,\n )\n )\n assert len(self.values) == len(self.index) # noqa: S101\n\n self.sampling_rate = sampling_rate\n\n def to_df(self, condition=True, entities=True, timing=True, sampling_rate=None): # noqa: D417\n \"\"\"Convert to a DataFrame, with columns for name and entities.\n\n Parameters\n ----------\n condition : :obj:`bool`\n If True, adds a column for condition name, and names the amplitude\n column 'amplitude'. If False, returns just onset, duration, and\n amplitude, and gives the amplitude column the current column name.\n entities : :obj:`bool`\n If True, adds extra columns for all entities.\n timing : :obj:`bool`\n If True, includes onset and duration columns (even though events are\n sampled uniformly). If False, omits them.\n\n \"\"\"\n if sampling_rate not in (None, self.sampling_rate):\n return self.resample(sampling_rate).to_df(condition, entities)\n\n df = super().to_df(condition, entities)\n\n if timing:\n df['onset'] = self.timestamps.values.astype(float) / 1e9\n df['duration'] = 1.0 / self.sampling_rate\n\n return df\n\n @classmethod\n def _merge(cls, variables, name, sampling_rate=None, **kwargs):\n if not isinstance(sampling_rate, int):\n rates = set([v.sampling_rate for v in variables]) # noqa: C403\n if len(rates) == 1:\n sampling_rate = list(rates)[0]\n else:\n if sampling_rate == 'auto':\n sampling_rate = max(rates)\n else:\n msg = (\n 'Cannot merge DenseRunVariables (%s) with different' # noqa: UP031\n ' sampling rates (%s). Either specify an integer '\n 'sampling rate to use for all variables, or set '\n \"sampling_rate='highest' to use the highest sampling\"\n ' rate found.' % (name, rates)\n )\n raise ValueError(msg)\n\n variables = [v.resample(sampling_rate) for v in variables]\n values = pd.concat([v.values for v in variables], axis=0, sort=True)\n run_info = list(chain(*[v.run_info for v in variables]))\n source = variables[0].source\n return DenseRunVariable(\n name=name, values=values, run_info=run_info, source=source, sampling_rate=sampling_rate\n )\n\n\ndef merge_variables(variables, **kwargs):\n \"\"\"Merge/concatenate a list of variables along the row axis.\n\n Parameters\n ----------\n variables : :obj:`list`\n A list of Variables to merge.\n kwargs\n Optional keyword arguments to pass onto the class-specific merge() call.\n\n Possible args:\n\n sampling_rate (int, str):\n The sampling rate to use if resampling\n of DenseRunVariables is necessary for harmonization. If\n 'highest', the highest sampling rate found will be used. This\n argument is only used when passing DenseRunVariables in the\n variables list.\n\n Returns\n -------\n A single BIDSVariable of the same class as the input variables.\n\n Notes\n -----\n - Currently, this function only support homogeneously-typed lists. In\n future, it may be extended to support implicit conversion.\n - Variables in the list must all share the same name (i.e., it is not\n possible to merge two different variables into a single variable.)\n\n \"\"\"\n classes = set([v.__class__ for v in variables]) # noqa: C403\n if len(classes) > 1:\n raise ValueError(\n 'Variables of different classes cannot be merged. ' # noqa: UP031\n 'Variables passed are of classes: %s' % classes\n )\n\n sources = set([v.source for v in variables]) # noqa: C403\n if len(sources) > 1:\n raise ValueError(\n 'Variables extracted from different types of files ' # noqa: UP031\n 'cannot be merged. Sources found: %s' % sources\n )\n\n return list(classes)[0].merge(variables, **kwargs)\n\n\ndef _resample(y, new_sr, old_sr, new_num, kind='linear'):\n n = len(y)\n x = np.arange(n)\n if new_sr < old_sr:\n # Downsampling, so filter the signal\n from scipy.signal import butter, filtfilt\n\n # cutoff = new Nyqist / old Nyquist\n b, a = butter(5, (new_sr / 2.0) / (old_sr / 2.0), btype='low', output='ba', analog=False)\n y = filtfilt(b, a, y)\n\n from scipy.interpolate import interp1d\n\n f = interp1d(x, y, kind=kind)\n x_new = np.linspace(0, n - 1, num=new_num)\n\n return f(x_new)\n" }, { "path": "tests/data/7t_trt/README", "content": "" }, { "path": "tests/data/7t_trt/dataset_description.json", "content": "{\n \"BIDSVersion\": \"1.0.0rc3\",\n \"Name\": \"7t_trt\"\n}" }, { "path": "tests/data/7t_trt/participants.tsv", "content": "participant_id\tsex\tage_at_first_scan_years\tnumber_of_scans_before\thandedness\nsub-01\tF\t29\t17\t100\nsub-02\tF\t23\t6\t100\nsub-03\tM\t25\t18\t86\nsub-04\tM\t26\t8\t100\nsub-05\tM\t27\t28\t-84\nsub-06\tF\t23\t27\t100\nsub-07\tM\t29\t9\t100\nsub-08\tM\t25\t28\t90\nsub-09\tM\t25\t37\t100\nsub-10\tM\t24\t30\t89\nsub-11\tM\t30\t5\t100\nsub-12\tM\t24\t8\t100\nsub-13\tF\t26\t28\t100\nsub-14\tF\t24\t5\t100\nsub-15\tF\t22\t21\t100\nsub-16\tF\t23\t5\t80\nsub-17\tF\t25\t41\t80\nsub-18\tM\t27\t51\t100\nsub-19\tM\t25\t13\t100\nsub-20\tF\t22\t23\t83\nsub-21\tF\t24\t48\t100\nsub-22\tM\t25\t50\t100\n" }, { "path": "tests/data/7t_trt/sub-01/ses-1/fmap/sub-01_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-01/ses-1/fmap/sub-01_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.json", "content": "{\n \"EchoTime\": 0.020\n}" }, { "path": "tests/data/7t_trt/sub-01/ses-1/sub-01_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t90\t0\t100\t100\t80\t100\t100\t95\t0\t100\t90\t30\nfunc/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t85\t0\t90\t100\t100\t100\t20\t100\t0\t100\t100\t30\nfunc/sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t75\t0\t75\t100\t100\t85\t15\t100\t0\t100\t95\t35\n" }, { "path": "tests/data/7t_trt/sub-01/ses-2/fmap/sub-01_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-01/ses-2/fmap/sub-01_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-01/ses-2/sub-01_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t95\t0\t100\t0\t90\t100\t75\t100\t20\t100\t100\t60\nfunc/sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t70\t25\t100\t0\t100\t100\t80\t75\t0\t100\t100\t65\nfunc/sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t100\t0\t100\t100\t100\t100\t20\t70\t0\t100\t80\t65\n" }, { "path": "tests/data/7t_trt/sub-01/sub-01_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t500.7708333333333\t507.0\tn/a\t96.0\t1.0\t0.0\t1\t6\t4\t6\t2\t4\t4\t2\t5\t4\t6\t3\t5\t6\t6\t2\t1\t5\t2\t2\t4\t2\t7\t4\t1\t5\t1\t2\t4\t4\t1\t7\t1\t7\t2\t1\t5\t6\t1\t4\t2\t6\t2\t1\t1\t4\t2\t6\t1\t1\t1\t4\t5\t1\t5\t6\t1\t4\t6\t3\t5\t8.0\t9.5\t9\t5\t9\t1.0\t7\t0.5\t1\t108\t64\t66\t109\t69\t70\t0\t0\t75\t15\t5\t70\t0\t100\t0\t100\t95\t50\nses-2\t503.3333333333333\t595.0\tn/a\t96.0\t1.0\t0.0\t1\t4\t2\t6\t1\t4\t5\t2\t4\t5\t6\t3\t5\t5\t3\t2\t5\t7\t1\t5\t4\t1\t7\t4\t1\t6\t1\t1\t4\t4\t1\t6\t5\t5\t3\t1\t1\t6\t1\t4\t2\t6\t1\t1\t1\t4\t1\t5\t1\t4\t1\t4\t4\t1\t5\t7\t1\t6\t6\t7\t1\t8.0\t7.5\t7\t9\t9\t2.0\t5\t2.0\t3\t101\t66\t70\t101\t65\t76\t100\t5\t80\t90\t5\t80\t20\t100\t0\t100\t100\t50\n" }, { "path": "tests/data/7t_trt/sub-02/ses-1/fmap/sub-02_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-02_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-02/ses-1/fmap/sub-02_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-02_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-02/ses-1/sub-02_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-02_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t85\t35\t85\t5\t100\t100\t25\t30\t55\t85\t100\t75\nfunc/sub-02_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t100\t0\t100\t5\t100\t100\t30\t100\t70\t90\t100\t85\nfunc/sub-02_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t60\t35\t75\t25\t70\t90\t65\t100\t85\t55\t90\t40\n" }, { "path": "tests/data/7t_trt/sub-02/ses-2/fmap/sub-02_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-02_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-02/ses-2/fmap/sub-02_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-02_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-02/ses-2/sub-02_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-02_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t60\t25\t85\t35\t100\t100\t25\t35\t75\t90\t65\t55\nfunc/sub-02_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t70\t25\t80\t10\t70\t100\t10\t15\t85\t90\t95\t60\nfunc/sub-02_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t65\t35\t85\t10\t60\t95\t0\t10\t60\t85\t80\t60\n" }, { "path": "tests/data/7t_trt/sub-02/sub-02_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t351.7291666666667\t297.6\tn/a\t96.0\t5.0\t0.0\t2\t5\t1\t4\t1\t5\t5\t5\t6\t1\t4\t5\t5\t5\t3\t1\t2\t3\t3\t5\t5\t5\t6\t4\t1\t2\t1\t1\t1\t1\t1\t4\t1\t3\t4\t2\t5\t5\t2\t3\t5\t5\t1\t2\t1\t3\t1\t3\t3\t2\t3\t2\t2\t2\t3\t2\t1\t5\t4\t3\t5\t9.0\t9.0\t7\t8\t2\t1.0\t1\t0.0\t5\t99\t65\t69\t100\t76\t73\t85\t5\t80\t15\t100\t95\t70\t100\t10\t100\t100\t65\nses-2\t366.3157894736842\tn/a\t0.0\t95.0\t0.0\t1.0\t2\t4\t1\t3\t3\t3\t4\t3\t3\t1\t2\t1\t4\t3\t2\t1\t2\t2\t2\t4\t3\t4\t4\t4\t3\t2\t2\t1\t2\t3\t2\t4\t1\t4\t3\t3\t5\t4\t1\t2\t4\t4\t1\t2\t1\t4\t3\t3\t3\t1\t2\t4\t3\t3\t2\t3\t1\t3\t3\t2\t2\t8.0\t8.0\t3\t8\t4\t1.0\t5\t0.0\t5\t108\t75\t98\t106\t67\t85\t55\t25\t75\t55\t95\t100\t60\t100\t60\t80\t60\t15\n" }, { "path": "tests/data/7t_trt/sub-03/ses-1/fmap/sub-03_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-03/ses-1/fmap/sub-03_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-03/ses-1/sub-03_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t80\t30\t50\t70\t40\t100\t15\t60\t100\t0\t65\t90\nfunc/sub-03_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t60\t20\t35\t85\t55\t90\t75\t100\t80\t40\t65\t40\nfunc/sub-03_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t95\t5\t60\t50\t20\t45\t30\t100\t45\t55\t90\t35\n" }, { "path": "tests/data/7t_trt/sub-03/ses-2/fmap/sub-03_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-03/ses-2/fmap/sub-03_ses-2_run-2_phasediff.json", "content": "{\"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\"]}" }, { "path": "tests/data/7t_trt/sub-03/ses-2/sub-03_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-03_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t90\t10\t85\t15\t60\t75\t25\t90\t45\t55\t70\t65\nfunc/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t80\t35\t80\t30\t60\t75\t25\t100\t55\t75\t90\t75\nfunc/sub-03_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t90\t40\t60\t60\t40\t95\t5\t100\t55\t75\t100\t45\n" }, { "path": "tests/data/7t_trt/sub-03/sub-03-test.bval", "content": "" }, { "path": "tests/data/7t_trt/sub-03/sub-03_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t426.71875\t441.0\tn/a\t96.0\t1.0\t0.0\t3\t6\t4\t6\t2\t3\t4\t1\t6\t1\t5\t2\t6\t7\t7\t1\t2\t6\t1\t5\t4\t2\t6\t2\t2\t5\t2\t1\t5\t2\t1\t6\t3\t6\t1\t1\t5\t7\t2\t5\t3\t6\t2\t7\t1\t3\t2\t5\t1\t1\t2\t1\t6\t1\t4\t5\t1\t6\t5\t6\t1\t9.0\t8.5\t8\t8\t3\t3.0\t3\t1.0\t1\t122\t69\t69\t128\t66\t62\t60\t45\t10\t100\t100\t100\t10\t100\t100\t0\t100\t75\nses-2\t396.0104166666667\tn/a\tn/a\t96.0\t0.0\t0.0\t3\t6\t2\t7\t1\t5\t5\t7\t6\t2\t6\t3\t6\t7\t1\t1\t1\t6\t1\t5\t4\t2\t6\t3\t1\t5\t3\t1\t6\t4\t2\t5\t1\t7\t2\t1\t4\t6\t1\t5\t3\t6\t1\t7\t1\t2\t1\t5\t1\t1\t1\t1\t6\t1\t6\t5\t1\t6\t5\t6\t2\t9.0\t8.5\t6\t5\t3\t2.5\t5\t0.2\t1\t112\t72\t65\t139\t69\t55\t75\t20\t70\t30\t85\t60\t5\t90\t75\t40\t40\t65\n" }, { "path": "tests/data/7t_trt/sub-04/ses-1/fmap/sub-04_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-04_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-04/ses-1/fmap/sub-04_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-04_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-04/ses-1/sub-04_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-04_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t60\t35\t75\t25\t45\t35\t20\t50\t80\t40\t80\t40\nfunc/sub-04_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t50\t50\t70\t55\t70\t70\t35\t65\t65\t60\t80\t40\nfunc/sub-04_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t50\t50\t80\t45\t55\t65\t25\t65\t75\t60\t85\t40\n" }, { "path": "tests/data/7t_trt/sub-04/ses-2/fmap/sub-04_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-04_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-04/ses-2/fmap/sub-04_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-04_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-04/ses-2/sub-04_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-04_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t65\t20\t65\t45\t55\t65\t15\t75\t75\t65\t70\t60\nfunc/sub-04_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t70\t30\t80\t40\t60\t55\t10\t60\t65\t65\t75\t60\nfunc/sub-04_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t65\t20\t75\t40\t65\t65\t5\t65\t70\t50\t75\t60\n" }, { "path": "tests/data/7t_trt/sub-04/sub-04_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t417.90625\t443.0\tn/a\t96.0\t1.0\t0.0\t4\t2\t1\t5\t5\t4\t3\t1\t3\t1\t3\t5\t2\t2\t2\t1\t3\t3\t4\t2\t1\t3\t3\t1\t3\t3\t5\t4\t2\t2\t3\t4\t1\t2\t4\t3\t3\t1\t3\t3\t4\t3\t1\t3\t1\t1\t3\t2\t2\t1\t4\t1\t6\t2\t1\t3\t1\t6\t3\t4\t2\t7.0\t7.5\t5\t6\t6\t1.0\t5\t0.1\t1\t130\t74\t64\t110\t60\t68\t65\t20\t90\t30\t75\t45\t0\t100\t50\t85\t80\t40\nses-2\t409.3645833333333\t348.0\tn/a\t96.0\t1.0\t0.0\t4\t5\t1\t4\t3\t5\t2\t1\t5\t1\t2\t5\t1\t2\t3\t1\t1\t5\t2\t5\t1\t1\t4\t1\t4\t3\t2\t1\t2\t1\t2\t3\t1\t3\t2\t2\t5\t2\t2\t1\t2\t3\t1\t3\t1\t4\t1\t2\t2\t1\t2\t1\t5\t2\t1\t4\t1\t5\t3\t3\t2\t7.0\t7.5\t3\t3\t4\t1.0\t5\t0.1\t3\t110\t63\t72\t140\t81\t77\t75\t20\t75\t30\t70\t50\t10\t85\t60\t70\t85\t45\n" }, { "path": "tests/data/7t_trt/sub-05/ses-1/fmap/sub-05_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-05_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-05/ses-1/fmap/sub-05_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-05_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-05/ses-1/sub-05_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-05_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t70\t30\t75\t60\t40\t70\t25\t25\t50\t25\t65\t35\nfunc/sub-05_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t60\t35\t65\t35\t35\t70\t30\t55\t65\t35\t65\t30\nfunc/sub-05_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t70\t25\t80\t25\t30\t75\t25\t35\t70\t30\t70\t35\n" }, { "path": "tests/data/7t_trt/sub-05/ses-2/fmap/sub-05_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-05_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-05/ses-2/fmap/sub-05_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-05_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-05/ses-2/sub-05_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-05_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t75\t25\t75\t30\t35\t80\t15\t35\t70\t35\t80\t30\nfunc/sub-05_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t70\t30\t80\t20\t25\t80\t20\t30\t75\t25\t75\t25\nfunc/sub-05_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t75\t20\t75\t25\t25\t75\t25\t25\t80\t25\t75\t25\n" }, { "path": "tests/data/7t_trt/sub-05/sub-05_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t372.1145833333333\t355.5\tn/a\t96.0\t2.0\t0.0\t5\t6\t1\t6\t1\t3\t2\t3\t4\t1\t6\t3\t5\t6\t4\t1\t2\t6\t1\t2\t2\t2\t5\t2\t5\t5\t3\t2\t1\t4\t1\t5\t2\t5\t2\t4\t3\t5\t1\t5\t2\t3\t1\t6\t1\t3\t1\t4\t2\t1\t2\t1\t5\t1\t4\t5\t1\t6\t4\t6\t2\t7.0\t7.5\t7\t6\t7\t1.5\t5\t0.0\t5\t105\t69\t66\t117\t65\t71\t65\t15\t40\t70\t40\t65\t80\t60\t65\t30\t75\t15\nses-2\t369.5578947368421\t646.0\t0.0\t95.0\t1.0\t1.0\t5\t5\t1\t5\t1\t2\t2\t2\t5\t1\t6\t2\t4\t5\t4\t1\t2\t5\t1\t3\t2\t1\t5\t2\t2\t4\t2\t2\t3\t2\t1\t5\t2\t5\t2\t1\t2\t4\t1\t4\t2\t2\t1\t5\t1\t2\t2\t5\t2\t2\t1\t1\t5\t2\t3\t5\t1\t5\t3\t5\t2\t7.5\t8.0\t7\t4\t7\t1.5\t5\t0.0\t5\t112\t67\t74\t116\t68\t75\t75\t15\t75\t20\t20\t65\t10\t70\t70\t10\t25\t15\n" }, { "path": "tests/data/7t_trt/sub-06/ses-1/fmap/sub-06_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-06_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-06/ses-1/fmap/sub-06_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-06_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-06/ses-1/sub-06_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-06_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t35\t35\t40\t45\t40\t35\t45\t35\t40\t40\t70\t10\nfunc/sub-06_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t40\t45\t50\t45\t40\t60\t45\t40\t40\t50\t70\t40\nfunc/sub-06_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t40\t40\t40\t45\t45\t55\t40\t45\t35\t45\t40\t25\n" }, { "path": "tests/data/7t_trt/sub-06/ses-2/fmap/sub-06_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-06_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-06/ses-2/fmap/sub-06_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-06_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-06/ses-2/sub-06_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-06_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t45\t35\t65\t55\t50\t60\t55\t5\t45\t55\t40\t30\nfunc/sub-06_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t40\t50\t55\t60\t45\t55\t40\t50\t45\t55\t65\t30\nfunc/sub-06_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t45\t55\t45\t45\t40\t60\t40\t35\t55\t60\t60\t40\n" }, { "path": "tests/data/7t_trt/sub-06/sub-06_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t443.84375\t374.0\tn/a\t96.0\t4.0\t0.0\t6\t3\t2\t5\t3\t2\t3\t2\t5\t1\t3\t4\t4\t4\t5\t1\t2\t5\t2\t5\t2\t2\t4\t2\t2\t4\t3\t3\t1\t2\t2\t5\t1\t4\t2\t2\t4\t3\t2\t1\t3\t4\t1\t4\t1\t2\t1\t4\t3\t1\t2\t1\t6\t2\t3\t6\t1\t4\t4\t5\t2\t7.0\t6.0\t4\t8\t4\t4.0\t5\t0.0\t5\t97\t59\t52\t97\t59\t55\t50\t15\t30\t40\t35\t25\t35\t80\t50\t65\t35\t10\nses-2\t447.4895833333333\t632.0\tn/a\t96.0\t1.0\t0.0\t6\t4\t2\t4\t3\t1\t2\t2\t3\t1\t3\t4\t4\t4\t4\t1\t3\t4\t2\t4\t1\t4\t4\t3\t3\t4\t3\t3\t1\t2\t2\t5\t2\t4\t3\t3\t2\t2\t1\t1\t3\t2\t1\t3\t1\t2\t1\t3\t2\t1\t3\t1\t5\t2\t2\t4\t1\t4\t3\t4\t2\t6.5\t5.0\t3\t7\t3\t4.0\t5\t0.0\t5\t98\t59\t54\t99\t58\t54\t30\t55\t65\t60\t20\t45\t40\t20\t30\t50\t15\t30\n" }, { "path": "tests/data/7t_trt/sub-07/ses-1/fmap/sub-07_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-07_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-07/ses-1/fmap/sub-07_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-07_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-07/ses-1/sub-07_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-07_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t85\t10\t60\t75\t20\t65\t5\t30\t95\t15\t65\t35\nfunc/sub-07_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t65\t5\t60\t60\t10\t80\t10\t60\t90\t15\t40\t30\nfunc/sub-07_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t60\t15\t70\t15\t25\t35\t40\t80\t20\t65\t80\t25\n" }, { "path": "tests/data/7t_trt/sub-07/ses-2/fmap/sub-07_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-07_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-07/ses-2/fmap/sub-07_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-07_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-07/ses-2/sub-07_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-07_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t75\t0\t25\t5\t0\t5\t5\t100\t5\t80\t95\t50\nfunc/sub-07_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t95\t0\t75\t20\t35\t95\t0\t85\t75\t25\t30\t10\nfunc/sub-07_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t65\t15\t60\t25\t5\t70\t5\t60\t70\t30\t25\t15\n" }, { "path": "tests/data/7t_trt/sub-07/sub-07_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t395.3541666666667\t377.44444444444446\tn/a\t96.0\t9.0\t0.0\t7\t7\t6\t7\t8\t7\t2\t6\t3\t2\t5\t1\t6\t3\t2\t4\t5\t6\t2\t6\t1\t6\t7\t5\t1\t1\t6\t2\t2\t1\t3\t5\t3\t2\t6\t1\t1\t6\t2\t2\t6\t1\t7\t2\t2\t2\t5\t1\t3\t2\t5\t1\t7\t2\t2\t1\t4\t2\t1\t1\t1\t5.0\t1.0\t4\t6\t1\t5.0\t5\t4.0\t2\t121\t73\t61\t131\t73\t60\t0\t0\t5\t0\t0\t0\t70\t95\t0\t0\t0\t0\nses-2\t407.8\t375.3333333333333\t0.0\t95.0\t3.0\t1.0\t7\t5\t1\t6\t3\t2\t2\t3\t5\t1\t6\t2\t5\t6\t6\t1\t2\t7\t3\t2\t2\t3\t5\t4\t3\t5\t2\t1\t4\t2\t3\t5\t1\t5\t2\t4\t2\t5\t1\t2\t2\t5\t2\t6\t2\t3\t1\t5\t2\t2\t2\t1\t4\t3\t2\t5\t1\t4\t5\t4\t2\t7.0\t8.0\t8\t8\t6\t2.0\t3\t3.0\t2\t107\t61\t53\t115\t60\t54\t90\t0\t0\t80\t5\t0\t0\t90\t25\t0\t100\t15\n" }, { "path": "tests/data/7t_trt/sub-08/ses-1/fmap/sub-08_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-08_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-08/ses-1/fmap/sub-08_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-08_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-08/ses-1/sub-08_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-08_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t55\t45\t35\t45\t15\t55\t0\t40\t95\t55\t65\t65\nfunc/sub-08_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t70\t30\t60\t55\t35\t85\t10\t65\t75\t80\t80\t75\nfunc/sub-08_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t75\t30\t55\t60\t50\t85\t50\t25\t55\t75\t40\t50\n" }, { "path": "tests/data/7t_trt/sub-08/ses-2/fmap/sub-08_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-08_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-08/ses-2/fmap/sub-08_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-08_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-08/ses-2/sub-08_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-08_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t60\t35\t65\t65\t35\t70\t50\t20\t70\t55\t50\t70\nfunc/sub-08_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t65\t30\t75\t60\t30\t65\t65\t30\t80\t60\t60\t60\nfunc/sub-08_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t60\t45\t55\t45\t40\t65\t15\t25\t85\t75\t60\t65\n" }, { "path": "tests/data/7t_trt/sub-08/sub-08_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t393.8958333333333\t332.875\tn/a\t96.0\t8.0\t0.0\t8\t6\t2\t6\t2\t2\t5\t5\t5\t3\t3\t2\t7\t6\t5\t2\t2\t5\t2\t5\t4\t4\t7\t3\t1\t2\t3\t3\t4\t2\t3\t6\t3\t6\t2\t1\t5\t7\t3\t5\t3\t5\t2\t3\t1\t4\t2\t5\t1\t5\t2\t3\t5\t2\t6\t6\t1\t5\t5\t6\t3\t6.5\t5.5\t3\t7\t4\t2.8\t2\t0.8\t2\t129\t69\t56\t126\t77\t63\t75\t15\t70\t65\t40\t100\t40\t15\t80\t80\t75\t90\nses-2\t386.6041666666667\t336.5\tn/a\t96.0\t4.0\t0.0\t8\t6\t3\t6\t3\t3\t3\t3\t4\t2\t4\t1\t6\t5\t4\t3\t2\t4\t3\t5\t2\t4\t5\t4\t1\t3\t3\t1\t4\t3\t3\t5\t3\t6\t2\t1\t5\t6\t1\t4\t4\t4\t3\t3\t1\t5\t3\t3\t2\t3\t2\t4\t4\t1\t5\t5\t2\t5\t5\t4\t2\t6.0\t6.5\t4\t6\t6\t2.5\t3\t0.7\t1\t123\t67\t62\t121\t74\t60\t80\t40\t65\t50\t40\t85\t45\t65\t80\t35\t85\t65\n" }, { "path": "tests/data/7t_trt/sub-09/ses-1/fmap/sub-09_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-09_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-09/ses-1/fmap/sub-09_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-09_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-09/ses-1/sub-09_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-09_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t75\t5\t65\t60\t70\t80\t40\t75\t65\t0\t70\t20\nfunc/sub-09_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t75\t15\t75\t35\t35\t100\t10\t100\t90\t0\t80\t30\nfunc/sub-09_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t80\t15\t90\t20\t60\t80\t5\t100\t100\t0\t80\t25\n" }, { "path": "tests/data/7t_trt/sub-09/ses-2/fmap/sub-09_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-09_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-09/ses-2/fmap/sub-09_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-09_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-09/ses-2/sub-09_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-09_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t65\t25\t75\t65\t70\t70\t30\t70\t90\t15\t80\t15\nfunc/sub-09_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t65\t20\t65\t30\t40\t85\t60\t95\t0\t65\t75\t10\nfunc/sub-09_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t95\t5\t95\t20\t20\t20\t0\t100\t15\t95\t100\t70\n" }, { "path": "tests/data/7t_trt/sub-09/sub-09_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t485.9462365591398\tn/a\t0.0\t93.0\t0.0\t3.0\t9\t7\t2\t6\t2\t4\t3\t7\t7\t3\t3\t1\t6\t6\t5\t1\t1\t6\t1\t5\t7\t2\t7\t2\t3\t5\t2\t2\t3\t1\t2\t5\t1\t6\t2\t3\t3\t7\t1\t5\t2\t4\t1\t6\t1\t2\t1\t6\t1\t2\t1\t1\t4\t1\t7\t6\t1\t4\t5\t6\t2\t6.0\t7.0\t7\t7\t8\t1.5\t5\t0.5\t5\t126\t77\t65\t136\t74\t62\t55\t10\t60\t45\t65\t55\t90\t75\t15\t95\t100\t35\nses-2\t497.29347826086956\tn/a\t0.0\t92.0\t0.0\t4.0\t9\t5\t1\t6\t2\t4\t2\t2\t5\t1\t6\t1\t5\t5\t4\t1\t2\t7\t1\t3\t3\t2\t6\t2\t3\t6\t1\t1\t3\t2\t3\t6\t1\t4\t2\t2\t2\t5\t1\t4\t1\t5\t1\t6\t1\t2\t1\t3\t1\t3\t1\t1\t5\t1\t1\t4\t1\t5\t5\t6\t1\t5.0\t6.0\t7\t4\t8\t1.5\t5\t0.5\t5\t123\t71\t65\t133\t65\t64\t95\t20\t25\t70\t30\t75\t15\t95\t10\t85\t60\t5\n" }, { "path": "tests/data/7t_trt/sub-10/ses-1/fmap/sub-10_ses-1_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-10_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-10/ses-1/fmap/sub-10_ses-1_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-1/func/sub-10_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-10/ses-1/sub-10_ses-1_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-10_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\t55\t60\t20\t60\t40\t65\t65\t30\t90\t55\t20\t15\nfunc/sub-10_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\t50\t35\t40\t60\t75\t55\t20\t35\t80\t25\t55\t35\nfunc/sub-10_ses-1_task-rest_acq-prefrontal_bold.nii.gz\t50\t55\t75\t70\t65\t40\t80\t30\t70\t10\t65\t60\n" }, { "path": "tests/data/7t_trt/sub-10/ses-2/fmap/sub-10_ses-2_run-1_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-10_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-10/ses-2/fmap/sub-10_ses-2_run-2_phasediff.json", "content": "{\"EchoTime2\": 0.00702, \"EchoTime1\": 0.006, \"IntendedFor\": \"ses-2/func/sub-10_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\"}" }, { "path": "tests/data/7t_trt/sub-10/ses-2/sub-10_ses-2_scans.tsv", "content": "filename\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance\timages\twords\tspecific_vague\tintrusive\nfunc/sub-10_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\t65\t55\t65\t75\t70\t60\t70\t20\t60\t40\t35\t30\nfunc/sub-10_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\t95\t30\t70\t80\t85\t95\t60\t55\t100\t30\t75\t60\nfunc/sub-10_ses-2_task-rest_acq-prefrontal_bold.nii.gz\t100\t35\t100\t40\t95\t45\t55\t60\t85\t20\t90\t60\n" }, { "path": "tests/data/7t_trt/sub-10/sub-10_sessions.tsv", "content": "session\tCCPT_avg_succ_RT\tCCPT_avg_FP_RT\tCCPT_avg_FN_RT\tCCPT_succ_count\tCCPT_FP_count\tCCPT_FN_count\tsubject_id\tpanas_cheerful\tpanas_disgusted\tpanas_attentive\tpanas_bashful\tpanas_sluggish\tpanas_daring\tpanas_surprised\tpanas_strong\tpanas_scornful\tpanas_relaxed\tpanas_irritable\tpanas_delighted\tpanas_inspired \tpanas_fearless\tpanas_disgusted_with_self\tpanas_sad\tpanas_calm\tpanas_afraid\tpanas_tired\tpanas_amazed\tpanas_shaky\tpanas_happy\tpanas_timid\tpanas_alone\tpanas_alert\tpanas_upset\tpanas_angry\tpanas_bold\tpanas_blue\tpanas_shy\tpanas_active\tpanas_guilty\tpanas_joyful\tpanas_nervous\tpanas_lonely\tpanas_sleepy\tpanas_excited\tpanas_hostile\tpanas_proud\tpanas_jittery\tpanas_lively\tpanas_ashamed\tpanas_at_ease\tpanas_scared\tpanas_drowsy\tpanas_angry_at_self\tpanas_enthusiastic\tpanas_downhearted\tpanas_sheepish\tpanas_distressed\tpanas_blameworthy\tpanas_determined\tpanas_frightened\tpanas_astonished\tpanas_interested\tpanas_loathing\tpanas_confident\tpanas_energetic\tpanas_concentrating\tpanas_dissatisfied_with_self\thours_of_sleep_ussually\thours_of_sleep_last_night\tvigilance\tquality_of_sleep\tthirst\tliters_of_water_daily\trelative_water_intake\tcaffeine_daily\trelative_caffeine_intake\tsystolic_blood_pressure_left\tdiastolic_blood_pressure_left\tpulse_left\tsystolic_blood_pressure_right\tdiastolic_blood_pressure_right\tpulse_right\tpositive\tnegative\tfuture\tpast\tmyself\tpeople\tsurroundings\tvigilance_nyc-q\timages\twords\tspecific_vague\tintrusive\nses-1\t465.5104166666667\t474.0\tn/a\t96.0\t1.0\t0.0\t10\t5\t1\t4\t1\t4\t1\t1\t1\t1\t4\t3\t5\t5\t2\t1\t3\t4\t1\t4\t1\t1\t6\t1\t4\t3\t3\t2\t1\t3\t1\t4\t1\t6\t3\t4\t4\t6\t1\t3\t3\t4\t1\t4\t1\t3\t3\t4\t1\t1\t2\t1\t3\t1\t1\t4\t1\t3\t4\t4\t4\t7.0\t5.5\t2\t6\t2\t1.25\t5\t0.2\t5\t121\t74\t74\t125\t76\t74\t45\t65\t60\t75\t85\t90\t80\t5\t85\t10\t10\t20\nses-2\t473.4791666666667\t367.0\tn/a\t96.0\t1.0\t0.0\t10\t6\t1\t5\t1\t4\t1\t2\t4\t1\t6\t4\t6\t6\t4\t1\t3\t6\t1\t4\t2\t2\t6\t2\t3\t4\t3\t4\t1\t1\t1\t5\t1\t6\t2\t3\t4\t6\t2\t4\t1\t5\t1\t5\t1\t3\t1\t4\t2\t2\t1\t1\t3\t1\t1\t5\t1\t3\t4\t4\t1\t7.0\t7.0\t2\t5\t4\t1.25\t4\t0.2\t5\t113\t70\t67\t110\t65\t65\t60\t45\t80\t50\t65\t75\t100\t25\t75\t20\t25\t30\n" }, { "path": "tests/data/7t_trt/task-rest_acq-fullbrain_bold.json", "content": "{\n \"CogAtlasID\": \"trm_4c8a834779883\",\n \"EchoTime\": 0.017,\n \"EffectiveEchoSpacing\": 0.0003333262223739227,\n \"MTState\": false,\n \"PhaseEncodingDirection\": \"j-\",\n \"RepetitionTime\": 3.0,\n \"SliceEncodingDirection\": \"k\",\n \"SliceTiming\": [\n 1.508,\n 0.0,\n 1.55,\n 0.043,\n 1.592,\n 0.087,\n 1.635,\n 0.13,\n 1.677,\n 0.173,\n 1.722,\n 0.215,\n 1.765,\n 0.26,\n 1.808,\n 0.302,\n 1.85,\n 0.345,\n 1.893,\n 0.388,\n 1.938,\n 0.43,\n 1.98,\n 0.475,\n 2.022,\n 0.518,\n 2.065,\n 0.56,\n 2.11,\n 0.603,\n 2.152,\n 0.645,\n 2.195,\n 0.69,\n 2.238,\n 0.733,\n 2.28,\n 0.775,\n 2.325,\n 0.818,\n 2.367,\n 0.86,\n 2.41,\n 0.905,\n 2.453,\n 0.948,\n 2.495,\n 0.99,\n 2.54,\n 1.032,\n 2.583,\n 1.075,\n 2.625,\n 1.12,\n 2.668,\n 1.163,\n 2.71,\n 1.205,\n 2.755,\n 1.248,\n 2.798,\n 1.293,\n 2.84,\n 1.335,\n 2.883,\n 1.378,\n 2.925,\n 1.42,\n 2.97,\n 1.462\n ],\n \"TaskName\": \"Rest\"\n}" }, { "path": "tests/data/7t_trt/task-rest_acq-fullbrain_run-1_physio.json", "content": "{\"StartTime\": 0, \"SamplingFrequency\": 100, \"Columns\": [\"cardiac\", \"respiratory\", \"trigger\", \"oxygen saturation\"]}" }, { "path": "tests/data/7t_trt/task-rest_acq-fullbrain_run-2_physio.json", "content": "{\"StartTime\": 0, \"SamplingFrequency\": 100, \"Columns\": [\"cardiac\", \"respiratory\", \"trigger\", \"oxygen saturation\"]}" }, { "path": "tests/data/7t_trt/task-rest_acq-prefrontal_bold.json", "content": "{\n \"CogAtlasID\": \"trm_4c8a834779883\",\n \"EchoTime\": 0.026,\n \"EffectiveEchoSpacing\": 0.00032998944033790924,\n \"PhaseEncodingDirection\": \"j-\",\n \"RepetitionTime\": 4.0,\n \"SliceEncodingDirection\": \"k\",\n \"SliceTiming\": [\n 3.915,\n 3.815,\n 3.715,\n 3.615,\n 3.515,\n 3.413,\n 3.312,\n 3.212,\n 3.112,\n 3.013,\n 2.913,\n 2.81,\n 2.71,\n 2.61,\n 2.51,\n 2.41,\n 2.31,\n 2.21,\n 2.108,\n 2.008,\n 1.908,\n 1.808,\n 1.707,\n 1.608,\n 1.505,\n 1.405,\n 1.305,\n 1.205,\n 1.105,\n 1.005,\n 0.902,\n 0.802,\n 0.703,\n 0.603,\n 0.502,\n 0.402,\n 0.302,\n 0.2,\n 0.1,\n 0.0\n ],\n \"TaskName\": \"Rest\"\n}" }, { "path": "tests/data/7t_trt/task-rest_acq-prefrontal_physio.json", "content": "{\"StartTime\": 0, \"SamplingFrequency\": 100, \"Columns\": [\"cardiac\", \"respiratory\", \"trigger\", \"oxygen saturation\"]}" }, { "path": "tests/data/7t_trt/test.bval", "content": "" }, { "path": "tests/data/bids_specs_with_oligarchy.json", "content": "{\n \"name\": \"bids\",\n \"entities\": [\n {\n \"name\": \"subject\",\n \"pattern\": \"[/\\\\\\\\]+sub-([a-zA-Z0-9]+)\",\n \"directory\": \"{subject}\"\n },\n {\n \"name\": \"oligarchy\",\n \"pattern\": \"[_/\\\\\\\\]+ses-([a-zA-Z0-9]+)\",\n \"mandatory\": false,\n \"directory\": \"{subject}{oligarchy}\"\n },\n {\n \"name\": \"task\",\n \"pattern\": \"[_/\\\\\\\\]+task-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"acquisition\",\n \"pattern\": \"[_/\\\\\\\\]+acq-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"ceagent\",\n \"pattern\": \"[_/\\\\\\\\]+ce-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"reconstruction\",\n \"pattern\": \"[_/\\\\\\\\]+rec-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"direction\",\n \"pattern\": \"[_/\\\\\\\\]+dir-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"run\",\n \"pattern\": \"[_/\\\\\\\\]+run-0*(\\\\d+)\",\n \"dtype\": \"int\"\n },\n {\n \"name\": \"proc\",\n \"pattern\": \"[_/\\\\\\\\]+proc-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"modality\",\n \"pattern\": \"[_/\\\\\\\\]+mod-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"echo\",\n \"pattern\": \"[_/\\\\\\\\]+echo-([0-9]+)\"\n },\n {\n \"name\": \"recording\",\n \"pattern\": \"[_/\\\\\\\\]+recording-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"space\",\n \"pattern\": \"[_/\\\\\\\\]+space-([a-zA-Z0-9]+)\"\n },\n {\n \"name\": \"suffix\",\n \"pattern\": \"[._]*([a-zA-Z0-9]*?)\\\\.[^/\\\\\\\\]+$\"\n },\n {\n \"name\": \"scans\",\n \"pattern\": \"(.*\\\\_scans.tsv)$\"\n },\n {\n \"name\": \"fmap\",\n \"pattern\": \"(phasediff|magnitude[1-2]|phase[1-2]|fieldmap|epi)\\\\.nii\"\n },\n {\n \"name\": \"datatype\",\n \"pattern\": \"[/\\\\\\\\]+(func|anat|fmap|dwi|meg|eeg)[/\\\\\\\\]+\"\n },\n {\n \"name\": \"extension\",\n \"pattern\": \"[._]*[a-zA-Z0-9]*?(\\\\.[^/\\\\\\\\]+)$\"\n }\n ],\n\n \"default_path_patterns\": [\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|anat}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"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}\",\n \"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}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|dwi}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}]_{suffix}{extension<.bval|.bvec|.json|.nii.gz|.nii>|.nii.gz}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|fmap}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_dir-{direction}][_run-{run}]_{fmap}{extension<.nii|.nii.gz|.json>|.nii.gz}\",\n \"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}\",\n \"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}\",\n \"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}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension<.tsv|.json>|.tsv}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}]_{suffix}{extension<.json>|.json}\",\n \"sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}]_{suffix}{extension<.jpg>|.jpg}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_][rec-{reconstruction}_][mod-{modality}_]{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_ce-{ceagent}][_dir-{direction}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_]{suffix}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][dir-{direction}_][run-{run}_]{fmap}{extension<.json>|.json}\",\n \"[acq-{acquisition}_][ce-{ceagent}_]dir-{direction}[_run-{run}]_{fmap}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.json>|.json}\",\n \"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.json>}\"\n ]\n}\n" }, { "path": "tests/data/ds000117/.bidsignore", "content": "run-*_echo-*_FLASH.json\n**/sub-*_ses-mri_run-*_echo-*_FLASH.nii.gz\n" }, { "path": "tests/data/ds000117/CHANGES", "content": "Revision history for OpenfMRI dataset ds000117\n\n1.0.0 2017-11-08\n\n - Converted to BIDS format\n - Changed subject order (see README)\n\n0.1.1 2016-09-17\n\n - 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\n\n0.1.0 2014-11-03\n\n - Initial Release\n" }, { "path": "tests/data/ds000117/README", "content": "This dataset was obtained from the OpenfMRI project (http://www.openfmri.org).\nAccession #: ds000117\nDescription: Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face recognition\n\nPlease cite the following references if you use these data:\n\nA multi-subject, multi-modal human neuroimaging dataset \n\nWakeman, D.G. & Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci. Data 2:150001 doi: 10.1038/sdata.2015.1\n\n\n Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face recognition\n ==================================================================================\n\nPlease note that the subject ordering has changed since version 0.1.x (non-BIDS) of this \ndataset. Additionally, three subjects have been left out. The mapping is as follows:\n0.1.x: 02 03 05 14 08 09 10 11 12 15 16 17 18 23 24 25\ncurrent: 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16\n\nfunc/\n-----\nThe experiment began at the third nifti volume (corresponding to 5 ignored scans: 3 forced siemens dummies (no files) and 2 included in the data).\n\nmeg/\n----\nThree anatomical fiducials were digitized for aligning the MEG with the MRI: the nasion \n(lowest depression between the eyes) and the left and right ears (lowest depression \nbetween the tragus and the helix, above the tragus). This procedure is illustrated here:\nhttp://neuroimage.usc.edu/brainstorm/CoordinateSystems#Subject_Coordinate_System_.28SCS_.2F_CTF.29\nand in task-facerecognition_fidinfo.pdf \n\nThe following triggers are included in the .fif files and are also used in the “trigger” column of the meg and bold events files:\n\nTrigger Label Simplified Label\n\n5 Initial Famous Face FAMOUS\n6 Immediate Repeat Famous Face FAMOUS\n7 Delayed Repeat Famous Face FAMOUS\n13 Initial Unfamiliar Face UNFAMILIAR\n14 Immediate Repeat Unfamiliar Face UNFAMILIAR\n15 Delayed Repeat Unfamiliar Face UNFAMILIAR\n17 Initial Scrambled Face SCRAMBLED\n18 Immediate Repeat Scrambled Face SCRAMBLED\n19 Delayed Repeat Scrambled Face SCRAMBLED\n\nstimuli/meg/\n------------\nThe .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.\n\nstimuli/mri/\n------------\nThe .bmp files correspond to those described in the text.\n\n\nIf you wish to publish any of these data, please acknowledge Daniel Wakeman and Richard Henson. The data has been published so far in:\n\n 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\n\n 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.\n\n 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.\n\t\n\t\n### Comments added by Openfmri Curators ###\n===========================================\n \nGeneral Comments\n----------------\n\n \nDefacing\n--------\nDefacing was performed by the submitter\n \nQuality Control\n---------------\nMriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/\n \nWhere to discuss the dataset\n----------------------------\n1) www.openfmri.org/dataset/ds000117/ See the comments section at the bottom of the dataset\npage.\n2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000117.\n3) Send an email to submissions@openfmri.org. Please include the accession number in\nyour email.\n \nKnown Issues\n------------\nN/A\n \n\n\n\n" }, { "path": "tests/data/ds000117/acq-epi_T1w.json", "content": "{\n \"RepetitionTime\": 2,\n \"EchoTime\": 0.095,\n \"FlipAngle\": 90,\n \"Manufacturer\": \"Siemens\",\n \"ManufacturersModelName\": \"TIM TRIO\",\n \"MagneticFieldStrength\": 3,\n \"PulseSequenceType\": \"EPI\"\n}\n" }, { "path": "tests/data/ds000117/acq-mprage_T1w.json", "content": "{\n \"RepetitionTime\": 2.25,\n \"EchoTime\": 0.00298,\n \"InversionTime\": 0.9,\n \"FlipAngle\": 9,\n \"Manufacturer\": \"Siemens\",\n \"ManufacturersModelName\": \"TIM TRIO\",\n \"MagneticFieldStrength\": 3,\n \"PulseSequenceType\": \"MPRAGE\"\n}\n" }, { "path": "tests/data/ds000117/dataset_description.json", "content": "{\n\t\"BIDSVersion\": \"1.0.2\",\n\t\"License\": \"CC0\",\n\t\"Name\": \"A multi-subject, multi-modal human neuroimaging dataset\",\n\t\"Authors\": [\"Wakeman, DG\", \"Henson, RN\"],\n\t\"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.\",\n\t\"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'\",\n\t\"ReferencesAndLinks\": [\"https://www.ncbi.nlm.nih.gov/pubmed/25977808\", \"https://openfmri.org/dataset/ds000117/\"],\n\t\"Funding\": [\"UK Medical Research Council (MC_A060_5PR10)\",\"Elekta Ltd.\"]\n}\n\n" }, { "path": "tests/data/ds000117/participants.tsv", "content": "participant_id\tage\tsex\tfirst_ses\nsub-01\t31\tM\tmeg\nsub-02\t25\tM\tmeg\nsub-03\t30\tM\tmeg\nsub-04\t26\tF\tmeg\nsub-05\t23\tF\tmeg\nsub-06\t26\tM\tmeg\nsub-07\t31\tF\tmeg\nsub-08\t26\tM\tmeg\nsub-09\t29\tM\tmeg\nsub-10\t23\tM\tmeg\nsub-11\t24\tF\tmeg\nsub-12\t24\tF\tmeg\nsub-13\t25\tF\tmeg\nsub-14\t24\tF\tmri\nsub-15\t30\tM\tmri\nsub-16\t25\tM\tmri\nsub-emptyroom\tn/a\tn/a\tn/a\n" }, { "path": "tests/data/ds000117/run-1_echo-1_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.00185\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-2_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.00415\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-3_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.00645\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-4_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.00875\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-5_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.01105\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-6_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.01335\n}\n" }, { "path": "tests/data/ds000117/run-1_echo-7_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 5,\n \"EchoTime\": 0.01565\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-1_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.00185\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-2_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.00415\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-3_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.00645\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-4_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.00875\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-5_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.01105\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-6_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.01335\n}\n" }, { "path": "tests/data/ds000117/run-2_echo-7_FLASH.json", "content": "{\n \"RepetitionTime\": 0.020,\n \"FlipAngle\": 30,\n \"EchoTime\": 0.01565\n}\n" }, { "path": "tests/data/ds000117/sub-01/ses-meg/beh/sub-01_ses-meg_task-facerecognition_events.tsv", "content": "onset\tduration\tresponse_time\tbutton_press\tstim_file\nn/a\tn/a\t1.553\t3\tmeg/f123.bmp\nn/a\tn/a\t1.586\t1\tmeg/f045.bmp\nn/a\tn/a\t2.139\t1\tmeg/f135.bmp\nn/a\tn/a\t1.571\t1\tmeg/u021.bmp\nn/a\tn/a\t0.955\t3\tmeg/f038.bmp\nn/a\tn/a\t1.485\t1\tmeg/u052.bmp\nn/a\tn/a\t1.386\t3\tmeg/f081.bmp\nn/a\tn/a\t1.474\t2\tmeg/f024.bmp\nn/a\tn/a\t1.054\t1\tmeg/u116.bmp\nn/a\tn/a\t1.077\t1\tmeg/u119.bmp\nn/a\tn/a\t1.707\t2\tmeg/f116.bmp\nn/a\tn/a\t1.861\t1\tmeg/u136.bmp\nn/a\tn/a\t1.57\t1\tmeg/u118.bmp\nn/a\tn/a\t2.379\t2\tmeg/u023.bmp\nn/a\tn/a\t0.97\t3\tmeg/f012.bmp\nn/a\tn/a\t1.355\t1\tmeg/u114.bmp\nn/a\tn/a\t5.396\t2\tmeg/u104.bmp\nn/a\tn/a\t1.027\t3\tmeg/f133.bmp\nn/a\tn/a\t5.954\t2\tmeg/u084.bmp\nn/a\tn/a\t0.786\t3\tmeg/f006.bmp\nn/a\tn/a\t1.14\t1\tmeg/u121.bmp\nn/a\tn/a\t0.822\t3\tmeg/f010.bmp\nn/a\tn/a\t1.47\t1\tmeg/u145.bmp\nn/a\tn/a\t3.731\t2\tmeg/f029.bmp\nn/a\tn/a\t1.425\t1\tmeg/u142.bmp\nn/a\tn/a\t3.303\t2\tmeg/f122.bmp\nn/a\tn/a\t0.703\t3\tmeg/f086.bmp\nn/a\tn/a\t0.771\t1\tmeg/u037.bmp\nn/a\tn/a\t0.669\t3\tmeg/f080.bmp\nn/a\tn/a\t0.803\t3\tmeg/f054.bmp\nn/a\tn/a\t3.018\t1\tmeg/u106.bmp\nn/a\tn/a\t1.614\t2\tmeg/f128.bmp\nn/a\tn/a\t0.717\t3\tmeg/f034.bmp\nn/a\tn/a\t0.914\t1\tmeg/u007.bmp\nn/a\tn/a\t1.638\t1\tmeg/u105.bmp\nn/a\tn/a\t3.619\t2\tmeg/u042.bmp\nn/a\tn/a\t3.838\t3\tmeg/f129.bmp\nn/a\tn/a\t2.559\t1\tmeg/u015.bmp\nn/a\tn/a\t0.981\t1\tmeg/u097.bmp\nn/a\tn/a\t1.703\t1\tmeg/u049.bmp\nn/a\tn/a\t0.811\t1\tmeg/u146.bmp\nn/a\tn/a\t6.148\t1\tmeg/f041.bmp\nn/a\tn/a\t1.579\t1\tmeg/u111.bmp\nn/a\tn/a\t3.988\t2\tmeg/u060.bmp\nn/a\tn/a\t1.038\t3\tmeg/f130.bmp\nn/a\tn/a\t0.757\t1\tmeg/u148.bmp\nn/a\tn/a\t1.608\t1\tmeg/u068.bmp\nn/a\tn/a\t5.134\t2\tmeg/f115.bmp\nn/a\tn/a\t4.361\t2\tmeg/f148.bmp\nn/a\tn/a\t1.341\t3\tmeg/f146.bmp\nn/a\tn/a\t1.07\t3\tmeg/f061.bmp\nn/a\tn/a\t1.067\t1\tmeg/u149.bmp\nn/a\tn/a\t2.426\t2\tmeg/u041.bmp\nn/a\tn/a\t1.663\t1\tmeg/u057.bmp\nn/a\tn/a\t5.083\t1\tmeg/f092.bmp\nn/a\tn/a\t0.879\t3\tmeg/f047.bmp\nn/a\tn/a\t0.925\t1\tmeg/u056.bmp\nn/a\tn/a\t6.927\t2\tmeg/u086.bmp\nn/a\tn/a\t3.622\t1\tmeg/f028.bmp\nn/a\tn/a\t4.36\t2\tmeg/f138.bmp\nn/a\tn/a\t5.451\t2\tmeg/u138.bmp\nn/a\tn/a\t1.712\t1\tmeg/u022.bmp\nn/a\tn/a\t1.147\t1\tmeg/u140.bmp\nn/a\tn/a\t3.469\t3\tmeg/f055.bmp\nn/a\tn/a\t2.293\t3\tmeg/f017.bmp\nn/a\tn/a\t4.887\t1\tmeg/u010.bmp\nn/a\tn/a\t5.98\t2\tmeg/f037.bmp\nn/a\tn/a\t1.74\t3\tmeg/f150.bmp\nn/a\tn/a\t0.812\t1\tmeg/u150.bmp\nn/a\tn/a\t6.501\t2\tmeg/f091.bmp\nn/a\tn/a\t0.662\t1\tmeg/u053.bmp\nn/a\tn/a\t1.277\t3\tmeg/f147.bmp\nn/a\tn/a\t1.09\t1\tmeg/f073.bmp\nn/a\tn/a\t6.526\t3\tmeg/f112.bmp\nn/a\tn/a\t0.662\t1\tmeg/u133.bmp\nn/a\tn/a\t0.965\t3\tmeg/f078.bmp\nn/a\tn/a\t0.895\t3\tmeg/f067.bmp\nn/a\tn/a\t0.708\t3\tmeg/f062.bmp\nn/a\tn/a\t1.937\t1\tmeg/u039.bmp\nn/a\tn/a\t0.73\t1\tmeg/u083.bmp\nn/a\tn/a\t0.975\t3\tmeg/f144.bmp\nn/a\tn/a\t5.64\t3\tmeg/f102.bmp\nn/a\tn/a\t2.03\t1\tmeg/u067.bmp\nn/a\tn/a\t12.115\t1\tmeg/u085.bmp\nn/a\tn/a\t2.111\t1\tmeg/u120.bmp\nn/a\tn/a\t1.937\t3\tmeg/f114.bmp\nn/a\tn/a\t19.897\t2\tmeg/f025.bmp\nn/a\tn/a\t3.078\t1\tmeg/f097.bmp\nn/a\tn/a\t0.775\t3\tmeg/f026.bmp\nn/a\tn/a\t0.93\t3\tmeg/f064.bmp\nn/a\tn/a\t1.453\t1\tmeg/u018.bmp\nn/a\tn/a\t1.091\t1\tmeg/u046.bmp\nn/a\tn/a\t1.023\t1\tmeg/f019.bmp\nn/a\tn/a\t3.95\t1\tmeg/f108.bmp\nn/a\tn/a\t1.298\t1\tmeg/u076.bmp\nn/a\tn/a\t1.038\t1\tmeg/u103.bmp\nn/a\tn/a\t2.636\t1\tmeg/u024.bmp\nn/a\tn/a\t1.128\t3\tmeg/f083.bmp\nn/a\tn/a\t1.044\t3\tmeg/f082.bmp\nn/a\tn/a\t2.673\t3\tmeg/f069.bmp\nn/a\tn/a\t1.067\t3\tmeg/f015.bmp\nn/a\tn/a\t0.713\t3\tmeg/f090.bmp\nn/a\tn/a\t1.029\t1\tmeg/u006.bmp\nn/a\tn/a\t0.709\t3\tmeg/f074.bmp\nn/a\tn/a\t0.914\t1\tmeg/u065.bmp\nn/a\tn/a\t2.271\t2\tmeg/u109.bmp\nn/a\tn/a\t7.777\t2\tmeg/f049.bmp\nn/a\tn/a\t1.237\t3\tmeg/f051.bmp\nn/a\tn/a\t0.707\t3\tmeg/f137.bmp\nn/a\tn/a\t1.368\t3\tmeg/f079.bmp\nn/a\tn/a\t1.226\t1\tmeg/u113.bmp\nn/a\tn/a\t1.129\t1\tmeg/u011.bmp\nn/a\tn/a\t0.797\t1\tmeg/u089.bmp\nn/a\tn/a\t2.766\t2\tmeg/u092.bmp\nn/a\tn/a\t2.22\t1\tmeg/u038.bmp\nn/a\tn/a\t4.241\t1\tmeg/u108.bmp\nn/a\tn/a\t5.442\t3\tmeg/f036.bmp\nn/a\tn/a\t1.735\t1\tmeg/u033.bmp\nn/a\tn/a\t0.633\t1\tmeg/u087.bmp\nn/a\tn/a\t2.234\t3\tmeg/f011.bmp\nn/a\tn/a\t0.934\t1\tmeg/u059.bmp\nn/a\tn/a\t1.337\t3\tmeg/f033.bmp\nn/a\tn/a\t0.707\t1\tmeg/u009.bmp\nn/a\tn/a\t2.736\t1\tmeg/u088.bmp\nn/a\tn/a\t1.432\t1\tmeg/f104.bmp\nn/a\tn/a\t0.647\t3\tmeg/f096.bmp\nn/a\tn/a\t1.134\t3\tmeg/f021.bmp\nn/a\tn/a\t1.594\t1\tmeg/f027.bmp\nn/a\tn/a\t1.242\t3\tmeg/f125.bmp\nn/a\tn/a\t3.424\t1\tmeg/u016.bmp\nn/a\tn/a\t2.451\t2\tmeg/u003.bmp\nn/a\tn/a\t0.695\t1\tmeg/u071.bmp\nn/a\tn/a\t1.084\t1\tmeg/u081.bmp\nn/a\tn/a\t0.97\t1\tmeg/u047.bmp\nn/a\tn/a\t0.979\t1\tmeg/u123.bmp\nn/a\tn/a\t0.637\t1\tmeg/u013.bmp\nn/a\tn/a\t0.981\t1\tmeg/u070.bmp\nn/a\tn/a\t5.23\t1\tmeg/f039.bmp\nn/a\tn/a\t0.972\t3\tmeg/f118.bmp\nn/a\tn/a\t1.174\t1\tmeg/u144.bmp\nn/a\tn/a\t3.919\t2\tmeg/u091.bmp\nn/a\tn/a\t2.341\t3\tmeg/f043.bmp\nn/a\tn/a\t1.093\t1\tmeg/u099.bmp\nn/a\tn/a\t1.93\t1\tmeg/u126.bmp\nn/a\tn/a\t0.804\t1\tmeg/u055.bmp\nn/a\tn/a\t0.708\t3\tmeg/f050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119.742721 -42.419735\nHP26 -4.138379 123.333812 -41.250061\nHP27 -4.645715 122.928649 -41.632541\nHP28 -7.645026 118.241042 -40.623777\nHP29 -11.694361 102.196515 -42.781755\nHP30 -12.633718 100.212246 -44.258635\nHP31 -13.177519 92.282236 -46.680979\nHP32 -16.992813 89.160085 -45.762878\nHP33 -25.728393 82.579762 -44.747125\nHP34 -36.149990 77.769399 -41.163038\nHP35 -50.215941 63.713044 -40.038567\nHP36 -55.565093 54.288596 -38.844958\nHP37 3.858263 124.781758 -43.720849\nHP38 3.120840 124.004394 -49.031839\nHP39 2.221717 116.954237 -53.577725\nHP40 4.720646 104.005307 -56.402568\nHP41 4.414939 103.242159 -62.349916\nHP42 3.405877 98.742664 -68.537757\nHP43 4.935935 96.861959 -72.823852\nHP44 10.066256 112.322211 -25.651310\nHP45 12.344509 109.183222 -28.178196\nHP46 15.425405 103.421032 -33.148963\nHP47 17.228352 94.435781 -41.735440\nHP48 15.648069 92.877239 -51.815856\nHP49 -9.999723 105.133295 -28.481038\nHP50 -11.411687 96.988350 -34.295455\nHP51 -14.067918 90.546906 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44.713113 108.931050\nEEG25 56.384243 36.836743 94.021723\nEEG26 71.645387 34.478907 69.624178\nEEG27 77.943638 27.837217 43.503366\nEEG28 76.682836 24.503380 7.806600\nEEG29 -67.306936 12.964278 11.728967\nEEG30 -72.286591 9.182304 43.818120\nEEG31 -66.543683 13.543010 77.077925\nEEG32 -52.325152 12.786508 100.967228\nEEG33 -28.058508 11.587024 117.958099\nEEG34 0.515388 10.886192 127.589867\nEEG35 31.805173 6.536782 118.437216\nEEG36 56.951597 6.579429 103.760369\nEEG37 76.052949 4.173875 77.149861\nEEG38 82.475826 0.407666 44.392847\nEEG39 76.656207 5.803704 10.339987\nEEG40 -65.812863 -27.363211 11.045937\nEEG41 -70.271567 -19.258797 43.507040\nEEG42 -65.125063 -19.402683 76.445818\nEEG43 -50.571010 -19.684166 100.700520\nEEG44 -26.139095 -20.469844 117.116943\nEEG45 2.228650 -28.723806 122.992389\nEEG46 30.695502 -25.055677 118.207820\nEEG47 55.387076 -27.999461 101.945251\nEEG48 71.168326 -28.828055 76.790884\nEEG49 75.748861 -30.874938 45.843091\nEEG50 75.099401 -36.851078 13.305152\nEEG51 -57.699382 -49.266040 14.818007\nEEG52 -60.904540 -42.891011 47.196265\nEEG53 -57.396710 -44.834912 72.146513\nEEG54 -45.032416 -48.414901 92.535041\nEEG55 -22.031019 -50.848439 106.276602\nEEG56 0.269551 -53.469285 110.015355\nEEG57 25.679089 -50.323054 107.643180\nEEG58 44.429924 -54.198727 94.807386\nEEG59 58.876328 -54.019928 73.102519\nEEG60 63.348949 -54.602191 50.033085\nEEG65 62.841251 -55.753943 17.162310\nEEG66 -47.459051 -63.615277 49.595017\nEEG67 -30.846596 -69.850221 78.941919\nEEG68 -1.357119 -74.640468 86.260043\nEEG69 31.340498 -75.262114 80.961607\nEEG70 45.114044 -75.206041 55.369329\nEEG71 -29.643130 -78.484789 54.704785\nEEG72 -2.303147 -87.170467 55.933770\nEEG73 22.523638 -86.916521 57.126023\nEEG74 2.855967 -83.032936 18.157925\n" }, { "path": "tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_proc-sss_coordsystem.json", "content": "{\n \"MEGCoordinateSystem\": \"Elekta/Neuromag\",\n \"MEGCoordinateUnits\": \"mm\",\n \"MEGCoordinateSystemDescription\": \"VectorView: 102 Magnetometers, 204 Planar Gradiometers\",\n \"EEGCoordinateSystem\": \"Elekta/Neuromag\",\n \"EEGCoordinateUnits\": \"mm\",\n \"EEGCoordinateSystemDescription\": \"70 Easycap sites, roughly corresponding to extended 10-20 system: see http://imaging.mrc-cbu.cam.ac.uk/meg/VectorviewDescription#EEGlabels\",\n \"DigitizedHeadPoints\": \"sub-01_ses-meg_headshape.pos\",\n \"DigitizedHeadPointsCoordinateSystem\": \"Elekta/Neuromag\",\n \"DigitizedHeadPointsCoordinateUnits\": \"mm\",\n \"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.\",\n \"AnatomicalLandmarkCoordinates\": {\n \"LPA\": [-71,0,0],\n \"RPA\": [77.56,0,0],\n \"Nasion\": [0,106.36,0]\n },\n \"AnatomicalLandmarkCoordinateSystem\": \"Elekta/Neuromag\",\n \"AnatomicalLandmarkCoordinateUnits\": \"mm\",\n \"HeadCoilCoordinates\": {\n \"coil1\": [14.38,-86.02,77.84],\n \"coil2\": [20.74,39.33,121.43],\n \"coil3\": [63.03,70.44,43.05],\n \"coil4\": [-68.38,-1.15,87.01000000000001],\n \"coil5\": [-6.2,107.9,59.15]\n },\n \"HeadCoilCoordinateSystem\": \"Elekta/Neuromag\",\n \"HeadCoilCoordinateUnits\": \"mm\",\n \"HeadCoilCoordinateSystemDescription\": \"5 HPI coils used\",\n \"AnatomicalLandmarkCoordinateSystemDescription\": \"For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation\",\n \"FiducialsDescription\": \"For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation\",\n \"IntendedFor\": \"sub-01/ses-mri/anat/sub-01_ses-mri_acq-mprage_T1w.nii.gz\"\n}" }, { "path": "tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-01_proc-sss_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-06_proc-sss_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-01/ses-meg/sub-01_ses-meg_scans.tsv", "content": "filename\tacq_time\nmeg/sub-01_ses-meg_task-facerecognition_run-01_meg.fif\t##-##-##T12:04:14\nmeg/sub-01_ses-meg_task-facerecognition_run-02_meg.fif\t##-##-##T12:17:17\nmeg/sub-01_ses-meg_task-facerecognition_run-03_meg.fif\t##-##-##T12:27:22\nmeg/sub-01_ses-meg_task-facerecognition_run-04_meg.fif\t##-##-##T12:37:33\nmeg/sub-01_ses-meg_task-facerecognition_run-05_meg.fif\t##-##-##T12:47:23\nmeg/sub-01_ses-meg_task-facerecognition_run-06_meg.fif\t##-##-##T12:58:06\n" }, { "path": "tests/data/ds000117/sub-01/ses-mri/anat/sub-01_ses-mri_acq-mprage_T1w.json", "content": "{\n \"AnatomicalMRICoordinateSystem\": \"NIfTI\",\n \"AnatomicalMRICoordinateUnits\": \"voxels\",\n \"AnatomicalLandmarkCoordinates\": {\n \"Nasion\": [43,111,95],\n \"LPA\": [140,74,16],\n \"RPA\": [143,74,173]\n }\n}" }, { "path": "tests/data/ds000117/sub-01/ses-mri/dwi/sub-01_ses-mri_dwi.bval", "content": "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 \n" }, { "path": "tests/data/ds000117/sub-01/ses-mri/dwi/sub-01_ses-mri_dwi.bvec", "content": "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 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"content": "{\n\t\"RepetitionTime\": 8.4,\n\t\"EchoTime\": 0.09,\n\t\"FlipAngle\": 90,\n\t\"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],\n\t\"EffectiveEchoSpacing\": 0.0003927297862,\n\t\"PhaseEncodingDirection\": \"j-\",\n\t\"TotalReadoutTime\": 0.069\n}\n\n" }, { "path": "tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_magnitude1.nii", "content": "" }, { "path": "tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_magnitude2.nii", "content": "" }, { "path": "tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_phasediff.json", "content": "{\n \"RepetitionTime\": 0.4,\n \"EchoTime1\": 0.00519,\n \"EchoTime2\": 0.00765,\n \"FlipAngle\": 60,\n \"IntendedFor\": [\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-03_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-04_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-06_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-07_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_bold.nii.gz\",\n \"ses-mri/func/sub-01_ses-mri_task-facerecognition_run-09_bold.nii.gz\"\n ]\n}" }, { "path": "tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_phasediff.nii", "content": "" }, { "path": "tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-02/ses-meg/beh/sub-02_ses-meg_task-facerecognition_events.tsv", "content": 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t1\tmeg/f045.bmp\nn/a\tn/a\t0.995\t1\tmeg/f145.bmp\nn/a\tn/a\t1.02\t1\tmeg/u034.bmp\nn/a\tn/a\t0.86\t3\tmeg/f137.bmp\nn/a\tn/a\t0.779\t3\tmeg/f014.bmp\n" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_headshape.pos", "content": "HP1 55.147540 80.692977 17.205879\nHP2 52.778859 85.604548 18.001463\nHP3 44.109419 94.402283 20.544779\nHP4 29.976312 103.205472 18.672556\nHP5 19.735051 106.274135 18.359832\nHP6 7.532482 107.924908 15.995979\nHP7 4.277199 107.274145 16.341604\nHP8 -2.740122 106.911391 14.390081\nHP9 -12.668613 106.757343 14.683546\nHP10 -22.152876 104.642212 15.193284\nHP11 -32.551471 102.337301 17.836973\nHP12 -39.153725 98.298967 17.706491\nHP13 -48.049390 93.225747 16.454466\nHP14 -52.363977 85.288733 15.537292\nHP15 -54.657679 80.771506 13.644069\nHP16 -56.387220 76.571465 15.118374\nHP17 63.628130 57.991728 -24.013333\nHP18 61.072923 63.678846 -30.906051\nHP19 51.580694 69.366828 -40.964939\nHP20 38.465183 77.696130 -45.704879\nHP21 23.417629 83.982155 -50.444670\nHP22 15.537076 85.931823 -53.106248\nHP23 0.578686 119.975969 -49.216166\nHP24 4.970736 119.126804 -49.497910\nHP25 13.382346 106.134549 -49.456000\nHP26 16.051166 93.201682 -48.272453\nHP27 15.110678 86.366199 -51.196702\nHP28 -2.145140 118.849009 -48.655152\nHP29 -8.919184 112.238258 -48.958443\nHP30 -3.399826 116.774455 -46.867415\nHP31 -10.367556 102.209613 -47.294702\nHP32 -12.926067 93.115509 -47.284879\nHP33 -13.967781 86.222008 -49.109802\nHP34 -43.626297 66.445574 -47.315709\nHP35 -45.279294 66.798165 -44.742171\nHP36 -53.442158 59.082106 -40.193792\nHP37 -57.119593 51.263243 -37.571661\nHP38 -57.829209 49.455896 -38.515843\nHP39 -58.707021 60.353160 -17.786786\nHP40 1.834636 115.320377 -38.548119\nHP41 2.375286 117.999256 -44.449680\nHP42 3.428198 120.396122 -54.695986\nHP43 4.237128 118.715867 -59.141073\nHP44 4.207544 110.519856 -63.358963\nHP45 3.855203 99.572778 -63.404225\nHP46 3.083070 94.843850 -65.466829\nHP47 3.809020 91.073617 -69.810718\nHP48 -68.135083 0.505328 -1.376949\nHP49 1.131747 102.847844 1.985706\nHP50 73.568404 1.139671 0.838313\nEEG1 -43.344878 -70.988059 24.929738\nEEG2 -1.199658 105.568260 44.282172\nEEG3 41.444339 -74.400991 23.012131\nEEG4 -51.119912 84.095865 42.232238\nEEG5 -31.736657 94.214886 64.089589\nEEG6 1.261111 98.582447 73.774375\nEEG7 34.108348 94.347298 63.838668\nEEG8 51.140502 85.372716 35.745870\nEEG9 -61.411042 59.780777 40.786427\nEEG10 -55.912364 67.960441 63.186474\nEEG11 -44.518959 72.251767 81.666052\nEEG12 -22.402762 75.095594 96.509665\nEEG13 2.931454 75.780779 102.171451\nEEG14 28.309999 73.628634 95.784351\nEEG15 46.812650 70.610464 82.745694\nEEG16 59.314862 68.631142 61.435204\nEEG17 63.901529 56.993157 38.903911\nEEG18 -65.384358 28.724315 12.941687\nEEG19 -67.829810 35.464376 43.905631\nEEG20 -64.800784 40.714145 70.967168\nEEG21 -49.890969 43.535829 95.349774\nEEG22 -27.724134 46.358198 111.411005\nEEG23 3.076066 47.327429 120.179005\nEEG24 33.363525 45.731038 111.422092\nEEG25 55.811346 40.741265 95.176615\nEEG26 70.555784 36.883891 66.226199\nEEG27 72.134227 30.418545 39.604705\nEEG28 67.722008 31.394482 9.971317\nEEG29 -67.608893 9.936094 12.965322\nEEG30 -71.626037 5.502224 43.248177\nEEG31 -70.058070 11.376828 78.466430\nEEG32 -55.782031 13.122320 104.301244\nEEG33 -31.682409 15.259057 121.779293\nEEG34 1.437288 12.252092 131.456017\nEEG35 34.003925 14.404385 121.449552\nEEG36 57.146545 11.093110 104.526281\nEEG37 72.354443 9.184241 76.648727\nEEG38 74.295163 4.480332 45.277804\nEEG39 71.291946 10.489583 13.099283\nEEG40 -66.813529 -29.734476 15.368368\nEEG41 -68.581097 -23.321256 45.485646\nEEG42 -67.441180 -19.132227 78.577697\nEEG43 -53.836778 -16.655296 105.468512\nEEG44 -32.658979 -20.376891 121.411033\nEEG45 0.519983 -24.695426 130.635589\nEEG46 36.205400 -20.341009 122.718550\nEEG47 56.541242 -20.303696 104.552068\nEEG48 69.579877 -20.050853 80.296978\nEEG49 71.221836 -26.686817 45.325786\nEEG50 71.336187 -31.179681 12.693845\nEEG51 -56.041691 -52.096173 20.041374\nEEG52 -59.316333 -47.624990 53.415563\nEEG53 -55.411518 -45.973077 78.787759\nEEG54 -41.910768 -47.469005 99.195912\nEEG55 -28.912831 -49.199164 115.158826\nEEG56 -2.799268 -53.697497 117.383793\nEEG57 30.348305 -45.825724 111.078314\nEEG58 45.439728 -48.907459 96.923694\nEEG59 56.948587 -48.308834 73.998503\nEEG60 60.751729 -51.325262 49.159199\nEEG65 58.626920 -54.260924 17.515516\nEEG66 -43.526452 -67.882642 59.894446\nEEG67 -31.494118 -67.913525 83.181031\nEEG68 1.700925 -70.747599 88.120200\nEEG69 30.231001 -69.561437 82.449622\nEEG70 43.441176 -71.045503 55.840127\nEEG71 -26.563726 -82.068458 61.233371\nEEG72 -0.150288 -87.540075 59.425525\nEEG73 25.532788 -84.128201 59.583589\nEEG74 -0.410772 -91.595180 27.508289\n" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_coordsystem.json", "content": "{\n \"MEGCoordinateSystem\": \"Elekta/Neuromag\",\n \"MEGCoordinateUnits\": \"mm\",\n \"MEGCoordinateSystemDescription\": \"VectorView: 102 Magnetometers, 204 Planar Gradiometers\",\n \"EEGCoordinateSystem\": \"Elekta/Neuromag\",\n \"EEGCoordinateUnits\": \"mm\",\n \"EEGCoordinateSystemDescription\": \"70 Easycap sites, roughly corresponding to extended 10-20 system: see http://imaging.mrc-cbu.cam.ac.uk/meg/VectorviewDescription#EEGlabels\",\n \"DigitizedHeadPoints\": \"sub-02_ses-meg_headshape.pos\",\n \"DigitizedHeadPointsCoordinateSystem\": \"Elekta/Neuromag\",\n \"DigitizedHeadPointsCoordinateUnits\": \"mm\",\n \"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.\",\n \"IntendedFor\": \"sub-02/ses-mri/anat/sub-02_ses-mri_acq-mprage_T1w.nii.gz\",\n \"AnatomicalLandmarkCoordinates\": {\n \"LPA\": [-69.66,0,0],\n \"RPA\": [71.69,0,0],\n \"Nasion\": [0,102.82,0]\n },\n \"AnatomicalLandmarkCoordinateSystem\": \"Elekta/Neuromag\",\n \"AnatomicalLandmarkCoordinateUnits\": \"mm\",\n \"AnatomicalLandmarkCoordinateSystemDescription\": \"For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation\",\n \"HeadCoilCoordinates\": {\n \"coil1\": [71.23249,13.62723,91.294631],\n \"coil2\": [16.225291,-85.930645,74.58901400000001],\n \"coil3\": [-15.709732,-7.188886,132.537663],\n \"coil4\": [-12.606272,86.735755,93.681544],\n \"coil5\": [-71.436286,-2.527088,90.64806299999999]\n },\n \"HeadCoilCoordinateSystem\": \"Elekta/Neuromag\",\n \"HeadCoilCoordinateUnits\": \"mm\",\n \"HeadCoilCoordinateSystemDescription\": \"5 HPI coils used\",\n \"FiducialsDescription\": \"For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation\"\n}" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_events.tsv", "content": "onset\tduration\tonset_sample\tstim_type\ttrigger\tstim_file\n25.1136\t0\t27625\tFamous\t5\tmeg/f074.bmp\n28.1709\t0\t30988\tUnfamiliar\t13\tmeg/u011.bmp\n31.3282\t0\t34461\tScrambled\t17\tmeg/s036.bmp\n34.4355\t0\t37879\tFamous\t5\tmeg/f112.bmp\n37.7764\t0\t41554\tFamous\t6\tmeg/f112.bmp\n40.9673\t0\t45064\tUnfamiliar\t13\tmeg/u066.bmp\n44.2745\t0\t48702\tUnfamiliar\t14\tmeg/u066.bmp\n47.3482\t0\t52083\tUnfamiliar\t13\tmeg/u113.bmp\n50.5055\t0\t55556\tUnfamiliar\t14\tmeg/u113.bmp\n53.58\t0\t58938\tFamous\t7\tmeg/f074.bmp\n56.6536\t0\t62319\tScrambled\t17\tmeg/s100.bmp\n59.7773\t0\t65755\tScrambled\t18\tmeg/s100.bmp\n62.9345\t0\t69228\tUnfamiliar\t15\tmeg/u011.bmp\n66.1582\t0\t72774\tUnfamiliar\t13\tmeg/u069.bmp\n69.3664\t0\t76303\tUnfamiliar\t14\tmeg/u069.bmp\n72.39\t0\t79629\tScrambled\t19\tmeg/s036.bmp\n75.63\t0\t83193\tFamous\t5\tmeg/f039.bmp\n78.9545\t0\t86850\tFamous\t5\tmeg/f020.bmp\n82.1618\t0\t90378\tFamous\t5\tmeg/f101.bmp\n85.37\t0\t93907\tScrambled\t17\tmeg/s058.bmp\n88.46\t0\t97306\tScrambled\t18\tmeg/s058.bmp\n91.8345\t0\t101018\tUnfamiliar\t13\tmeg/u061.bmp\n94.9245\t0\t104417\tUnfamiliar\t14\tmeg/u061.bmp\n98.1155\t0\t107927\tFamous\t5\tmeg/f105.bmp\n101.356\t0\t111492\tFamous\t7\tmeg/f039.bmp\n104.464\t0\t114910\tFamous\t5\tmeg/f138.bmp\n107.505\t0\t118255\tFamous\t7\tmeg/f020.bmp\n110.795\t0\t121874\tFamous\t5\tmeg/f064.bmp\n113.852\t0\t125237\tFamous\t6\tmeg/f064.bmp\n116.926\t0\t128619\tFamous\t7\tmeg/f101.bmp\n120.1\t0\t132110\tUnfamiliar\t13\tmeg/u127.bmp\n123.24\t0\t135564\tScrambled\t17\tmeg/s103.bmp\n126.364\t0\t139000\tUnfamiliar\t13\tmeg/u143.bmp\n129.538\t0\t142492\tFamous\t7\tmeg/f105.bmp\n132.762\t0\t146038\tFamous\t5\tmeg/f119.bmp\n135.903\t0\t149493\tFamous\t6\tmeg/f119.bmp\n139.244\t0\t153168\tFamous\t7\tmeg/f138.bmp\n142.552\t0\t156807\tUnfamiliar\t13\tmeg/u074.bmp\n145.742\t0\t160316\tUnfamiliar\t14\tmeg/u074.bmp\n149.066\t0\t163973\tUnfamiliar\t13\tmeg/u134.bmp\n152.124\t0\t167336\tUnfamiliar\t15\tmeg/u127.bmp\n155.381\t0\t170919\tScrambled\t17\tmeg/s150.bmp\n158.573\t0\t174430\tScrambled\t19\tmeg/s103.bmp\n161.813\t0\t177994\tUnfamiliar\t13\tmeg/u077.bmp\n164.853\t0\t181338\tUnfamiliar\t14\tmeg/u077.bmp\n168.127\t0\t184940\tUnfamiliar\t15\tmeg/u143.bmp\n171.335\t0\t188468\tScrambled\t17\tmeg/s026.bmp\n174.525\t0\t191978\tScrambled\t18\tmeg/s026.bmp\n177.599\t0\t195359\tFamous\t5\tmeg/f036.bmp\n180.706\t0\t198777\tFamous\t6\tmeg/f036.bmp\n183.864\t0\t202250\tScrambled\t17\tmeg/s101.bmp\n186.987\t0\t205686\tUnfamiliar\t15\tmeg/u134.bmp\n190.111\t0\t209122\tUnfamiliar\t13\tmeg/u016.bmp\n193.235\t0\t212558\tUnfamiliar\t14\tmeg/u016.bmp\n196.409\t0\t216050\tScrambled\t19\tmeg/s150.bmp\n199.533\t0\t219486\tUnfamiliar\t13\tmeg/u049.bmp\n202.724\t0\t222996\tScrambled\t17\tmeg/s129.bmp\n205.931\t0\t226524\tScrambled\t18\tmeg/s129.bmp\n208.938\t0\t229832\tFamous\t5\tmeg/f038.bmp\n212.195\t0\t233415\tScrambled\t17\tmeg/s001.bmp\n215.369\t0\t236906\tScrambled\t19\tmeg/s101.bmp\n218.544\t0\t240398\tUnfamiliar\t13\tmeg/u084.bmp\n221.567\t0\t243724\tUnfamiliar\t14\tmeg/u084.bmp\n224.775\t0\t247252\tScrambled\t17\tmeg/s029.bmp\n227.932\t0\t250725\tScrambled\t18\tmeg/s029.bmp\n231.089\t0\t254198\tUnfamiliar\t15\tmeg/u049.bmp\n234.38\t0\t257818\tUnfamiliar\t13\tmeg/u145.bmp\n237.47\t0\t261217\tScrambled\t17\tmeg/s030.bmp\n240.594\t0\t264653\tFamous\t7\tmeg/f038.bmp\n243.735\t0\t268108\tScrambled\t17\tmeg/s060.bmp\n247.093\t0\t271802\tScrambled\t19\tmeg/s001.bmp\n250.233\t0\t275256\tUnfamiliar\t13\tmeg/u050.bmp\n253.34\t0\t278674\tUnfamiliar\t14\tmeg/u050.bmp\n256.581\t0\t282239\tScrambled\t17\tmeg/s076.bmp\n259.705\t0\t285675\tScrambled\t17\tmeg/s137.bmp\n262.828\t0\t289111\tUnfamiliar\t15\tmeg/u145.bmp\n265.969\t0\t292566\tUnfamiliar\t13\tmeg/u020.bmp\n269.277\t0\t296205\tUnfamiliar\t14\tmeg/u020.bmp\n272.384\t0\t299622\tScrambled\t19\tmeg/s030.bmp\n275.675\t0\t303242\tUnfamiliar\t13\tmeg/u056.bmp\n278.965\t0\t306862\tScrambled\t19\tmeg/s060.bmp\n282.174\t0\t310391\tFamous\t5\tmeg/f032.bmp\n285.23\t0\t313753\tScrambled\t17\tmeg/s077.bmp\n288.387\t0\t317226\tScrambled\t18\tmeg/s077.bmp\n291.495\t0\t320644\tScrambled\t19\tmeg/s076.bmp\n294.685\t0\t324154\tFamous\t5\tmeg/f133.bmp\n297.793\t0\t327572\tFamous\t6\tmeg/f133.bmp\n300.966\t0\t331063\tScrambled\t19\tmeg/s137.bmp\n304.04\t0\t334444\tUnfamiliar\t13\tmeg/u064.bmp\n307.398\t0\t338138\tUnfamiliar\t14\tmeg/u064.bmp\n310.405\t0\t341445\tScrambled\t17\tmeg/s113.bmp\n313.429\t0\t344772\tUnfamiliar\t15\tmeg/u056.bmp\n316.585\t0\t348244\tFamous\t5\tmeg/f015.bmp\n319.576\t0\t351534\tFamous\t6\tmeg/f015.bmp\n322.835\t0\t355118\tFamous\t7\tmeg/f032.bmp\n325.975\t0\t358572\tFamous\t5\tmeg/f005.bmp\n329.165\t0\t362081\tFamous\t6\tmeg/f005.bmp\n332.423\t0\t365665\tFamous\t5\tmeg/f115.bmp\n335.563\t0\t369119\tFamous\t5\tmeg/f040.bmp\n338.804\t0\t372684\tFamous\t6\tmeg/f040.bmp\n342.028\t0\t376231\tScrambled\t17\tmeg/s109.bmp\n345.319\t0\t379851\tScrambled\t19\tmeg/s113.bmp\n348.494\t0\t383343\tScrambled\t17\tmeg/s003.bmp\n351.7\t0\t386870\tScrambled\t18\tmeg/s003.bmp\n354.891\t0\t390380\tUnfamiliar\t13\tmeg/u141.bmp\n358.115\t0\t393926\tScrambled\t17\tmeg/s009.bmp\n361.189\t0\t397308\tScrambled\t18\tmeg/s009.bmp\n364.446\t0\t400891\tFamous\t7\tmeg/f115.bmp\n367.737\t0\t404511\tScrambled\t17\tmeg/s046.bmp\n370.945\t0\t408039\tScrambled\t18\tmeg/s046.bmp\n374.269\t0\t411696\tUnfamiliar\t13\tmeg/u138.bmp\n377.426\t0\t415169\tUnfamiliar\t14\tmeg/u138.bmp\n380.634\t0\t418697\tScrambled\t19\tmeg/s109.bmp\n383.925\t0\t422317\tScrambled\t17\tmeg/s094.bmp\n386.998\t0\t425698\tScrambled\t18\tmeg/s094.bmp\n390.223\t0\t429245\tFamous\t5\tmeg/f095.bmp\n393.279\t0\t432607\tFamous\t6\tmeg/f095.bmp\n396.537\t0\t436191\tUnfamiliar\t15\tmeg/u141.bmp\n399.761\t0\t439737\tScrambled\t17\tmeg/s132.bmp\n403.002\t0\t443302\tScrambled\t18\tmeg/s132.bmp\n406.025\t0\t446628\tScrambled\t17\tmeg/s055.bmp\n409.233\t0\t450156\tUnfamiliar\t13\tmeg/u005.bmp\n412.424\t0\t453666\tUnfamiliar\t13\tmeg/u054.bmp\n415.597\t0\t457157\tFamous\t5\tmeg/f022.bmp\n418.738\t0\t460612\tFamous\t6\tmeg/f022.bmp\n422.079\t0\t464287\tScrambled\t17\tmeg/s095.bmp\n425.253\t0\t467778\tScrambled\t17\tmeg/s024.bmp\n428.294\t0\t471123\tScrambled\t19\tmeg/s055.bmp\n431.501\t0\t474651\tFamous\t5\tmeg/f025.bmp\n434.541\t0\t477995\tFamous\t6\tmeg/f025.bmp\n437.615\t0\t481376\tUnfamiliar\t15\tmeg/u005.bmp\n440.672\t0\t484739\tFamous\t5\tmeg/f137.bmp\n443.946\t0\t488341\tUnfamiliar\t15\tmeg/u054.bmp\n447.271\t0\t491998\tUnfamiliar\t13\tmeg/u107.bmp\n450.445\t0\t495489\tUnfamiliar\t13\tmeg/u108.bmp\n453.618\t0\t498980\tScrambled\t19\tmeg/s095.bmp\n456.809\t0\t502490\tFamous\t5\tmeg/f093.bmp\n459.95\t0\t505945\tFamous\t6\tmeg/f093.bmp\n463.224\t0\t509546\tScrambled\t19\tmeg/s024.bmp\n466.247\t0\t512872\tUnfamiliar\t13\tmeg/u121.bmp\n469.538\t0\t516492\tFamous\t5\tmeg/f048.bmp\n472.73\t0\t520003\tFamous\t7\tmeg/f137.bmp\n475.886\t0\t523475\tFamous\t5\tmeg/f131.bmp\n479.044\t0\t526948\tUnfamiliar\t15\tmeg/u107.bmp\n482.235\t0\t530458\tFamous\t5\tmeg/f078.bmp\n485.425\t0\t533968\tFamous\t6\tmeg/f078.bmp\n488.716\t0\t537588\tUnfamiliar\t15\tmeg/u108.bmp\n" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-02_events.tsv", "content": 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"onset\tduration\tonset_sample\tstim_type\ttrigger\tstim_file\n23.9691\t0\t26366\tFamous\t5\tmeg/f140.bmp\n27.1773\t0\t29895\tFamous\t6\tmeg/f140.bmp\n30.3845\t0\t33423\tScrambled\t17\tmeg/s064.bmp\n33.5418\t0\t36896\tScrambled\t18\tmeg/s064.bmp\n36.7155\t0\t40387\tScrambled\t17\tmeg/s092.bmp\n39.8727\t0\t43860\tScrambled\t18\tmeg/s092.bmp\n43.03\t0\t47333\tUnfamiliar\t13\tmeg/u041.bmp\n46.1873\t0\t50806\tUnfamiliar\t14\tmeg/u041.bmp\n49.4282\t0\t54371\tUnfamiliar\t13\tmeg/u099.bmp\n52.7027\t0\t57973\tUnfamiliar\t13\tmeg/u031.bmp\n55.8764\t0\t61464\tFamous\t5\tmeg/f071.bmp\n58.9\t0\t64790\tFamous\t5\tmeg/f018.bmp\n62.1573\t0\t68373\tFamous\t6\tmeg/f018.bmp\n65.4655\t0\t72012\tUnfamiliar\t13\tmeg/u035.bmp\n68.7564\t0\t75632\tUnfamiliar\t14\tmeg/u035.bmp\n71.98\t0\t79178\tUnfamiliar\t13\tmeg/u039.bmp\n75.2709\t0\t82798\tUnfamiliar\t15\tmeg/u099.bmp\n78.5618\t0\t86418\tScrambled\t17\tmeg/s013.bmp\n81.7691\t0\t89946\tScrambled\t18\tmeg/s013.bmp\n85.06\t0\t93566\tUnfamiliar\t15\tmeg/u031.bmp\n88.1173\t0\t96929\tFamous\t5\tmeg/f079.bmp\n91.3082\t0\t100439\tFamous\t7\tmeg/f071.bmp\n94.5155\t0\t103967\tUnfamiliar\t13\tmeg/u045.bmp\n97.6391\t0\t107403\tScrambled\t17\tmeg/s112.bmp\n100.83\t0\t110913\tUnfamiliar\t13\tmeg/u087.bmp\n104.004\t0\t114404\tUnfamiliar\t15\tmeg/u039.bmp\n107.128\t0\t117841\tFamous\t5\tmeg/f008.bmp\n110.369\t0\t121406\tFamous\t6\tmeg/f008.bmp\n113.493\t0\t124842\tFamous\t5\tmeg/f129.bmp\n116.716\t0\t128388\tFamous\t6\tmeg/f129.bmp\n119.79\t0\t131769\tFamous\t7\tmeg/f079.bmp\n122.948\t0\t135243\tFamous\t5\tmeg/f088.bmp\n126.038\t0\t138642\tUnfamiliar\t15\tmeg/u045.bmp\n129.179\t0\t142097\tUnfamiliar\t13\tmeg/u085.bmp\n132.336\t0\t145570\tScrambled\t19\tmeg/s112.bmp\n135.51\t0\t149061\tUnfamiliar\t13\tmeg/u027.bmp\n138.734\t0\t152607\tUnfamiliar\t15\tmeg/u087.bmp\n141.992\t0\t156191\tFamous\t5\tmeg/f050.bmp\n145.115\t0\t159627\tFamous\t6\tmeg/f050.bmp\n148.239\t0\t163063\tUnfamiliar\t13\tmeg/u129.bmp\n151.514\t0\t166665\tScrambled\t17\tmeg/s117.bmp\n154.604\t0\t170064\tFamous\t7\tmeg/f088.bmp\n157.778\t0\t173556\tFamous\t5\tmeg/f021.bmp\n160.918\t0\t177010\tFamous\t6\tmeg/f021.bmp\n164.26\t0\t180686\tUnfamiliar\t15\tmeg/u085.bmp\n167.601\t0\t184361\tUnfamiliar\t13\tmeg/u088.bmp\n170.675\t0\t187742\tUnfamiliar\t14\tmeg/u088.bmp\n173.832\t0\t191215\tUnfamiliar\t15\tmeg/u027.bmp\n177.039\t0\t194743\tScrambled\t17\tmeg/s037.bmp\n180.28\t0\t198308\tFamous\t5\tmeg/f060.bmp\n183.37\t0\t201707\tUnfamiliar\t15\tmeg/u129.bmp\n186.645\t0\t205309\tFamous\t5\tmeg/f019.bmp\n189.768\t0\t208745\tFamous\t6\tmeg/f019.bmp\n192.993\t0\t212292\tScrambled\t19\tmeg/s117.bmp\n196.083\t0\t215691\tUnfamiliar\t13\tmeg/u060.bmp\n199.257\t0\t219183\tScrambled\t17\tmeg/s065.bmp\n202.447\t0\t222692\tScrambled\t18\tmeg/s065.bmp\n205.705\t0\t226276\tScrambled\t17\tmeg/s136.bmp\n208.946\t0\t229841\tScrambled\t19\tmeg/s037.bmp\n212.104\t0\t233314\tFamous\t5\tmeg/f094.bmp\n215.094\t0\t236603\tFamous\t6\tmeg/f094.bmp\n218.1\t0\t239910\tFamous\t7\tmeg/f060.bmp\n221.225\t0\t243347\tUnfamiliar\t13\tmeg/u018.bmp\n224.515\t0\t246967\tUnfamiliar\t14\tmeg/u018.bmp\n227.756\t0\t250532\tScrambled\t17\tmeg/s034.bmp\n230.78\t0\t253858\tUnfamiliar\t15\tmeg/u060.bmp\n233.87\t0\t257257\tScrambled\t17\tmeg/s110.bmp\n237.127\t0\t260840\tFamous\t5\tmeg/f122.bmp\n240.302\t0\t264332\tScrambled\t19\tmeg/s136.bmp\n243.292\t0\t267621\tFamous\t5\tmeg/f007.bmp\n246.633\t0\t271296\tFamous\t6\tmeg/f007.bmp\n249.824\t0\t274806\tScrambled\t17\tmeg/s107.bmp\n252.981\t0\t278279\tScrambled\t17\tmeg/s133.bmp\n256.021\t0\t281623\tScrambled\t18\tmeg/s133.bmp\n259.045\t0\t284949\tScrambled\t19\tmeg/s034.bmp\n262.035\t0\t288239\tFamous\t5\tmeg/f069.bmp\n265.159\t0\t291675\tScrambled\t19\tmeg/s110.bmp\n268.215\t0\t295037\tFamous\t5\tmeg/f142.bmp\n271.44\t0\t298584\tFamous\t7\tmeg/f122.bmp\n274.647\t0\t302112\tFamous\t5\tmeg/f058.bmp\n277.838\t0\t305622\tFamous\t6\tmeg/f058.bmp\n280.895\t0\t308985\tFamous\t5\tmeg/f144.bmp\n284.153\t0\t312568\tFamous\t6\tmeg/f144.bmp\n287.294\t0\t316023\tScrambled\t19\tmeg/s107.bmp\n290.384\t0\t319422\tFamous\t5\tmeg/f056.bmp\n293.541\t0\t322895\tFamous\t6\tmeg/f056.bmp\n296.632\t0\t326295\tUnfamiliar\t13\tmeg/u123.bmp\n299.839\t0\t329823\tFamous\t7\tmeg/f069.bmp\n302.913\t0\t333204\tUnfamiliar\t13\tmeg/u081.bmp\n306.07\t0\t336677\tUnfamiliar\t14\tmeg/u081.bmp\n309.244\t0\t340168\tFamous\t7\tmeg/f142.bmp\n312.535\t0\t343788\tUnfamiliar\t13\tmeg/u063.bmp\n315.792\t0\t347371\tUnfamiliar\t14\tmeg/u063.bmp\n319.05\t0\t350955\tScrambled\t17\tmeg/s149.bmp\n322.09\t0\t354299\tScrambled\t18\tmeg/s149.bmp\n325.281\t0\t357809\tScrambled\t17\tmeg/s088.bmp\n328.405\t0\t361245\tScrambled\t17\tmeg/s084.bmp\n331.612\t0\t364773\tUnfamiliar\t15\tmeg/u123.bmp\n334.753\t0\t368228\tScrambled\t17\tmeg/s038.bmp\n337.944\t0\t371738\tScrambled\t18\tmeg/s038.bmp\n340.95\t0\t375045\tUnfamiliar\t13\tmeg/u112.bmp\n344.125\t0\t378537\tUnfamiliar\t14\tmeg/u112.bmp\n347.398\t0\t382138\tUnfamiliar\t13\tmeg/u021.bmp\n350.64\t0\t385704\tFamous\t5\tmeg/f059.bmp\n353.864\t0\t389250\tScrambled\t19\tmeg/s088.bmp\n357.021\t0\t392723\tScrambled\t17\tmeg/s081.bmp\n360.211\t0\t396232\tScrambled\t19\tmeg/s084.bmp\n363.368\t0\t399705\tFamous\t5\tmeg/f104.bmp\n366.543\t0\t403197\tFamous\t6\tmeg/f104.bmp\n369.716\t0\t406688\tUnfamiliar\t13\tmeg/u076.bmp\n372.724\t0\t409996\tUnfamiliar\t14\tmeg/u076.bmp\n375.815\t0\t413396\tUnfamiliar\t13\tmeg/u118.bmp\n379.105\t0\t417015\tUnfamiliar\t15\tmeg/u021.bmp\n382.178\t0\t420396\tUnfamiliar\t13\tmeg/u149.bmp\n385.319\t0\t423851\tFamous\t7\tmeg/f059.bmp\n388.595\t0\t427454\tScrambled\t17\tmeg/s031.bmp\n391.867\t0\t431054\tScrambled\t18\tmeg/s031.bmp\n394.858\t0\t434344\tScrambled\t19\tmeg/s081.bmp\n398.065\t0\t437872\tUnfamiliar\t13\tmeg/u096.bmp\n401.239\t0\t441363\tUnfamiliar\t14\tmeg/u096.bmp\n404.614\t0\t445075\tScrambled\t17\tmeg/s120.bmp\n407.755\t0\t448530\tScrambled\t17\tmeg/s047.bmp\n410.912\t0\t452003\tScrambled\t18\tmeg/s047.bmp\n413.935\t0\t455329\tUnfamiliar\t15\tmeg/u118.bmp\n417.243\t0\t458967\tScrambled\t17\tmeg/s102.bmp\n420.484\t0\t462532\tUnfamiliar\t15\tmeg/u149.bmp\n423.641\t0\t466005\tFamous\t5\tmeg/f077.bmp\n426.715\t0\t469386\tFamous\t6\tmeg/f077.bmp\n429.939\t0\t472933\tUnfamiliar\t13\tmeg/u057.bmp\n433.179\t0\t476497\tScrambled\t17\tmeg/s074.bmp\n436.32\t0\t479952\tScrambled\t18\tmeg/s074.bmp\n439.444\t0\t483388\tScrambled\t19\tmeg/s120.bmp\n442.685\t0\t486953\tFamous\t5\tmeg/f113.bmp\n445.842\t0\t490426\tScrambled\t17\tmeg/s104.bmp\n448.999\t0\t493899\tScrambled\t19\tmeg/s102.bmp\n452.04\t0\t497244\tUnfamiliar\t13\tmeg/u047.bmp\n455.23\t0\t500753\tUnfamiliar\t14\tmeg/u047.bmp\n458.321\t0\t504153\tFamous\t5\tmeg/f141.bmp\n461.578\t0\t507736\tUnfamiliar\t15\tmeg/u057.bmp\n464.735\t0\t511209\tFamous\t5\tmeg/f135.bmp\n467.876\t0\t514664\tFamous\t6\tmeg/f135.bmp\n471.117\t0\t518229\tUnfamiliar\t13\tmeg/u126.bmp\n474.307\t0\t521738\tUnfamiliar\t14\tmeg/u126.bmp\n477.465\t0\t525211\tFamous\t7\tmeg/f113.bmp\n480.488\t0\t528537\tScrambled\t17\tmeg/s070.bmp\n483.645\t0\t532010\tScrambled\t19\tmeg/s104.bmp\n486.87\t0\t535557\tScrambled\t17\tmeg/s130.bmp\n" }, { "path": "tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-06_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-02/ses-meg/sub-02_ses-meg_scans.tsv", "content": "filename\tacq_time\nmeg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif\t##-##-##T09:49:10\nmeg/sub-02_ses-meg_task-facerecognition_run-02_meg.fif\t##-##-##T10:01:34\nmeg/sub-02_ses-meg_task-facerecognition_run-03_meg.fif\t##-##-##T10:11:25\nmeg/sub-02_ses-meg_task-facerecognition_run-04_meg.fif\t##-##-##T10:20:51\nmeg/sub-02_ses-meg_task-facerecognition_run-05_meg.fif\t##-##-##T10:31:09\nmeg/sub-02_ses-meg_task-facerecognition_run-06_meg.fif\t##-##-##T10:41:15\n" }, { "path": "tests/data/ds000117/sub-02/ses-mri/anat/sub-02_ses-mri_acq-mprage_T1w.json", "content": "{\n \"AnatomicalMRICoordinateSystem\": \"NIfTI\",\n \"AnatomicalMRICoordinateUnits\": \"voxels\",\n \"AnatomicalLandmarkCoordinates\": {\n \"Nasion\": [46,112,99],\n \"LPA\": [133,71,22],\n \"RPA\": [141,72,173]\n }\n}" }, { "path": "tests/data/ds000117/sub-02/ses-mri/dwi/sub-02_ses-mri_dwi.bval", "content": "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 \n" }, { "path": "tests/data/ds000117/sub-02/ses-mri/dwi/sub-02_ses-mri_dwi.bvec", "content": "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 -0.96543651819229 0.43138152360916 -0.76459014415741 0.68220269680023 -0.73489046096801 0.71736860275268 -0.1915717869997 -0.76524639129638 -0.12156687676906 0.54024440050125 -0.14661329984664 -0.57721072435379 0.84557461738586 0.30962398648262 -0.12417509406805 -0.72918784618377 0.47633138298988 0.48192557692527 -0.12921124696731 0.42382287979126 0.34370183944702 -0.75468689203262 0.29488214850425 0.05937920883297 -0.00240350398235 0.60929822921752 -0.22387877106666 0.71009385585784 0.28569409251213 \n0 -0.02968616597354 0.99073135852813 0.54209250211715 -0.81056135892868 -0.63224965333938 -0.29120826721191 0.03026523999869 0.87829613685607 0.0756150111556 -0.82679963111877 -0.79487478733062 -0.55308210849762 -0.48777076601982 0.62854999303817 0.02722834423184 -0.5289801955223 0.00618236232549 -0.43813782930374 -0.92386263608932 0.4854601919651 0.79799860715866 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}, { "path": "tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-02_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-05_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-08_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-09_events.tsv", "content": 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}, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090409/meg/sub-emptyroom_ses-20090409_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090409/sub-emptyroom_ses-20090409_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090409_task-noise_meg.fif\t##-##-##T11:05:49\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090506/meg/sub-emptyroom_ses-20090506_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090506/sub-emptyroom_ses-20090506_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090506_task-noise_meg.fif\t##-##-##T14:51:49\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090511/meg/sub-emptyroom_ses-20090511_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090511/sub-emptyroom_ses-20090511_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090511_task-noise_meg.fif\t##-##-##T09:39:23\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090515/meg/sub-emptyroom_ses-20090515_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090515/sub-emptyroom_ses-20090515_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090515_task-noise_meg.fif\t##-##-##T15:52:30\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090518/meg/sub-emptyroom_ses-20090518_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090518/sub-emptyroom_ses-20090518_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090518_task-noise_meg.fif\t##-##-##T09:33:15\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090601/meg/sub-emptyroom_ses-20090601_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20090601/sub-emptyroom_ses-20090601_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20090601_task-noise_meg.fif\t##-##-##T10:05:18\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20091126/meg/sub-emptyroom_ses-20091126_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20091126/sub-emptyroom_ses-20091126_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20091126_task-noise_meg.fif\t##-##-##T15:39:18\n" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20091208/meg/sub-emptyroom_ses-20091208_task-noise_meg.fif", "content": "" }, { "path": "tests/data/ds000117/sub-emptyroom/ses-20091208/sub-emptyroom_ses-20091208_scans.tsv", "content": "filename\tacq_time\nmeg/sub-emptyroom_ses-20091208_task-noise_meg.fif\t##-##-##T09:54:18\n" }, { "path": "tests/data/ds000117/task-facerecognition_beh.json", "content": "{\n \"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.\"\n}\n" }, { "path": "tests/data/ds000117/task-facerecognition_bold.json", "content": "{\n \"RepetitionTime\": 2.0,\n \"TaskName\": \"facerecognition\",\n \"EchoTime\": 0.030,\n \"FlipAngle\": 78,\n \"PulseSequenceType\": \"EPI\",\n \"Manufacturer\": \"Siemens\",\n \"ManufacturersModelName\": \"TIM TRIO\",\n \"MagneticFieldStrength\": 3\n}\n" }, { "path": "tests/data/ds000117/task-facerecognition_channels.tsv", "content": "name\ttype\tunits\tdescription\tsampling_frequency\thigh_cutoff\tlow_cutoff\tnotch\tsoftware_filters\tstatus\nMEG0113\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0112\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0111\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0122\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0123\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0121\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0132\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0133\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0131\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0143\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0142\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0141\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0213\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0212\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0211\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0222\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0223\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0221\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0232\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0233\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0231\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0243\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0242\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0241\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0313\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0312\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0311\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0322\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0323\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0321\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0333\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0332\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0331\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0343\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0342\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0341\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0413\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0412\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0411\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0422\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0423\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0421\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0432\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0433\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0431\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0443\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0442\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0441\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0513\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0512\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0511\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0523\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0522\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0521\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0532\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0533\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0531\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0542\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0543\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0541\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0613\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0612\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0611\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0622\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0623\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0621\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0633\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0632\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0631\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0642\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0643\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0641\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0713\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0712\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0711\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0723\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0722\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0721\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0733\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0732\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0731\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0743\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0742\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0741\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0813\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0812\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0811\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0822\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0823\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0821\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0913\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0912\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0911\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0923\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0922\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0921\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0932\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0933\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0931\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0942\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0943\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG0941\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1013\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1012\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1011\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1023\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1022\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1021\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1032\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1033\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1031\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1043\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1042\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1041\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1112\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1113\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1111\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1123\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1122\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1121\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1133\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1132\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1131\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1142\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1143\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1141\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1213\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1212\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1211\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1223\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1222\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1221\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1232\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1233\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1231\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1243\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1242\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1241\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1312\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1313\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1311\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1323\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1322\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1321\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1333\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1332\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1331\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1342\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1343\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1341\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1412\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1413\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1411\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1423\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1422\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1421\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1433\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1432\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1431\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1442\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1443\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1441\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1512\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1513\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1511\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1522\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1523\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1521\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1533\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1532\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1531\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1543\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1542\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1541\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1613\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1612\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1611\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1622\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1623\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1621\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1632\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1633\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1631\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1643\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1642\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1641\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1713\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1712\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1711\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1722\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1723\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1721\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1732\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1733\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1731\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1743\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1742\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1741\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1813\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1812\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1811\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1822\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1823\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1821\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1832\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1833\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1831\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1843\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1842\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1841\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1912\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1913\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1911\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1923\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1922\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1921\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1932\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1933\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1931\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1943\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1942\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG1941\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2013\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2012\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2011\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2023\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2022\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2021\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2032\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2033\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2031\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2042\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2043\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2041\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2113\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2112\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2111\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2122\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2123\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2121\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2133\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2132\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2131\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2143\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2142\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2141\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2212\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2213\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2211\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2223\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2222\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2221\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2233\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2232\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2231\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2242\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2243\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2241\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2312\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2313\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2311\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2323\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2322\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2321\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2332\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2333\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2331\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2343\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2342\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2341\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2412\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2413\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2411\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2423\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2422\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2421\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2433\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2432\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2431\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2442\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2443\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2441\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2512\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2513\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2511\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2522\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2523\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2521\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2533\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2532\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2531\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2543\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2542\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2541\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2612\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2613\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2611\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2623\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2622\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2621\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2633\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2632\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2631\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2642\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2643\tMEGGRAD\tT/m\tplanar_gradiometer\t1100\t350\t0\tn/a\tn/a\tgood\nMEG2641\tMEGMAG\tT\tmagnetometer\t1100\t350\t0\tn/a\tn/a\tgood\nEEG001\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG002\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG003\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG004\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG005\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG006\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG007\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG008\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG009\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG010\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG011\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG012\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG013\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG014\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG015\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG016\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG017\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG018\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG019\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG020\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG021\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG022\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG023\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG024\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG025\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG026\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG027\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG028\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG029\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG030\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG031\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG032\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG033\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG034\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG035\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG036\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG037\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG038\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG039\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG040\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG041\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG042\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG043\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG044\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG045\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG046\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG047\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG048\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG049\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG050\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG051\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG052\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG053\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG054\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG055\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG056\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG057\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG058\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG059\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG060\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG061\tHEOG\tV\thorizontaleog\t1100\t350\t0\tn/a\tn/a\tgood\nEEG062\tVEOG\tV\tverticaleog\t1100\t350\t0\tn/a\tn/a\tgood\nEEG063\tECG\tV\tcardiac\t1100\t350\t0\tn/a\tn/a\tgood\nEEG064\tMISC\tn/a\tunused\t1100\t350\t0\tn/a\tn/a\tgood\nEEG065\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG066\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG067\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG068\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG069\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG070\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG071\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG072\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG073\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nEEG074\tEEG\tV\teegchannel\t1100\t350\t0\tn/a\tn/a\tgood\nSTI101\tTRIG\tn/a\tdigitaltrigger\t1100\tInf\t0\tn/a\tn/a\tgood\nSTI201\tTRIG\tn/a\tothertrigger\t1100\tInf\t0\tn/a\tn/a\tgood\nSTI301\tTRIG\tn/a\tothertrigger\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC201\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC202\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC203\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC204\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC205\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC206\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC301\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC302\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC303\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC304\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC305\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nMISC306\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI001\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI002\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI003\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI004\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI005\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI006\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI007\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI008\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\nCHPI009\tMISC\tn/a\tn/a\t1100\tInf\t0\tn/a\tn/a\tgood\n" }, { "path": "tests/data/ds000117/task-facerecognition_meg.json", "content": "{\n \"TaskName\": \"facerecognition\",\n \"SamplingFrequency\": 1100,\n \"Manufacturer\": \"Elekta\",\n \"ManufacturersModelName\": \"Neuromag Vectorview 306\",\n \"MEGChannelCount\": 306,\n \"MEGREFChannelCount\": 0,\n \"EEGChannelCount\": 70,\n \"EOGChannelCount\": 2,\n \"ECGChannelCount\": 1,\n \"EMGChannelCount\": 0,\n \"MiscChannelCount\": 12,\n \"TriggerChannelCount\": 3,\n \"PowerLineFrequency\": 50,\n \"HeadCoilFrequency\": [293,307,314,321,328],\n \"EEGPlacementScheme\": \"extended 10-10% system\",\n \"EEGReference\": \"nose\",\n \"DewarPosition\": \"upright\",\n \"RecordingType\": \"continuous\",\n \"EpochLength\": \"Inf\",\n \"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.\",\n \"ContinuousHeadLocalization\": true,\n \"SubjectArtefactDescription\": \"None\",\n \"DigitizedLandmarks\": true,\n \"DigitizedHeadPoints\": true,\n \"SoftwareFilters\": {\n \"SpatialCompensation\": {\n \"None\": []\n }\n },\n \"InstitutionName\": \"MRC Cognition & Brain Sciences Unit\",\n \"InstitutionAddress\": \"15 Chaucer Road, Cambridge, UK\",\n \"AssociatedEmptyRoom\": \"sub-emptyroom/ses-20090409/meg/sub-emptyroom_ses-20090409_task-noise_meg.fif.gz\"\n}" }, { "path": "tests/data/ds005/CHANGES", "content": "" }, { "path": "tests/data/ds005/README", "content": "" }, { "path": "tests/data/ds005/dataset_description.json", "content": "{\n \"BIDSVersion\": \"1.0.0rc2\",\n \"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.\",\n \"Name\": \"Mixed-gambles task\",\n \"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\"\n}" }, { "path": "tests/data/ds005/derivatives/events/dataset_description.json", "content": "{\n \"GeneratedBy\": [{\n \"Name\": \"events\"\n }],\n \"BIDSVersion\": \"1.7.1\",\n \"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.\",\n \"Name\": \"Mixed-gambles task\",\n \"ReferencesAndLinks\": \"Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. 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gain\tn/a\t0.3835\tn/a\t40\t17\t11.42\t2\t1\t1.252\n472.0\t3\tparametric gain\tn/a\t-0.1165\tn/a\t20\t17\t-8.58\t4\t0\t1.3559999999999999\n476.0\t3\tparametric gain\tn/a\t-0.0165\tn/a\t24\t19\t-7.94\t4\t0\t0.991\n" }, { "path": "tests/data/ds005/task-mixedgamblestask_bold.json", "content": "{\n \"RepetitionTime\": 2.0,\n \"TaskName\": \"mixed-gambles task\",\n \"SliceTiming\": [0.0, 0.0571, 0.1143, 0.1714, 0.2286, 0.2857],\n \"NullTestMetadata\": null\n}\n" }, { "path": "tests/data/ds005_conflict/dataset_description.json", "content": "{\n \"BIDSVersion\": \"1.0.0rc2\",\n \"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.\",\n \"Name\": \"Mixed-gambles task\",\n \"ReferencesAndLinks\": \"Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. 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gain\tn/a\t0.3806\tn/a\t40\t16\t24.16\t1\t1\t0.777\n426.0\t3\tparametric gain\tn/a\t0.1306\tn/a\t30\t17\t13.17\t2\t1\t1.239\n432.0\t3\tparametric gain\tn/a\t-0.0194\tn/a\t24\t5\t19.05\t1\t1\t1.073\n436.0\t3\tparametric gain\tn/a\t-0.2194\tn/a\t16\t7\t9.07\t2\t1\t1.063\n440.0\t3\tparametric gain\tn/a\t-0.0694\tn/a\t22\t18\t4.18\t2\t1\t1.5919999999999999\n444.0\t3\tparametric gain\tn/a\t0.0306\tn/a\t26\t19\t7.19\t2\t1\t1.128\n448.0\t3\tparametric gain\tn/a\t-0.0194\tn/a\t24\t20\t4.2\t2\t1\t1.258\n452.0\t3\tparametric gain\tn/a\t0.2306\tn/a\t34\t6\t28.06\t1\t1\t1.794\n460.0\t3\tparametric gain\tn/a\t-0.0194\tn/a\t24\t16\t8.16\t2\t1\t0.914\n468.0\t3\tparametric gain\tn/a\t-0.0694\tn/a\t22\t6\t16.06\t1\t1\t1.849\n474.0\t3\tparametric gain\tn/a\t-0.3194\tn/a\t12\t7\t5.07\t2\t1\t1.122\n" }, { "path": "tests/data/ds005_conflict/sub-01/func/sub-01_task-mixedgamblestask_run-03_events.tsv", "content": "onset\tduration\ttrial_type\tdistance from indifference\tparametric gain\tparametric loss\tgain\tloss\tPTval\trespnum\trespcat\tRT\n0.0\t3\tparametric gain\tn/a\t0.0835\tn/a\t28\t17\t11.17\t2\t1\t1.203\n4.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t7\t7.07\t2\t1\t1.123\n8.0\t3\tparametric gain\tn/a\t0.0835\tn/a\t28\t18\t10.18\t2\t1\t0.9690000000000001\n14.0\t3\tparametric gain\tn/a\t-0.2165\tn/a\t16\t11\t5.11\t2\t1\t1.102\n18.0\t3\tparametric gain\tn/a\t0.3835\tn/a\t40\t20\t20.2\t1\t1\t0.9690000000000001\n22.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t16\t2.16\t2\t1\t1.261\n28.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t11\t27.11\t2\t1\t1.35\n32.0\t3\tparametric gain\tn/a\t0.2835\tn/a\t36\t10\t26.1\t1\t1\t2.157\n40.0\t3\tparametric gain\tn/a\t0.3835\tn/a\t40\t18\t22.18\t1\t1\t1.6780000000000002\n44.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t10\t0.1\t2\t1\t1.581\n50.0\t3\tparametric gain\tn/a\t-0.0165\tn/a\t24\t6\t18.06\t1\t1\t1.133\n56.0\t3\tparametric gain\tn/a\t-0.1165\tn/a\t20\t8\t12.08\t2\t1\t2.2230000000000003\n62.0\t3\tparametric gain\tn/a\t0.3835\tn/a\t40\t12\t28.12\t1\t1\t1.046\n68.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t10\t12.1\t2\t1\t1.5519999999999998\n76.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t6\t12.06\t2\t1\t1.2790000000000001\n80.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t16\t22.16\t2\t1\t2.4530000000000003\n90.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t20\t10.2\t2\t1\t1.328\n98.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t14\t8.14\t2\t1\t1.008\n102.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t18\t0.18\t2\t1\t2.222\n106.0\t3\tparametric gain\tn/a\t-0.2165\tn/a\t16\t19\t-2.81\t4\t0\t1.767\n110.0\t3\tparametric gain\tn/a\t-0.0165\tn/a\t24\t10\t14.1\t2\t1\t1.024\n116.0\t3\tparametric gain\tn/a\t-0.3165\tn/a\t12\t19\t-6.81\t4\t0\t1.176\n120.0\t3\tparametric gain\tn/a\t0.0835\tn/a\t28\t9\t19.09\t1\t1\t2.383\n126.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t10\t20.1\t1\t1\t1.936\n130.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t15\t3.15\t2\t1\t1.551\n142.0\t3\tparametric gain\tn/a\t0.1835\tn/a\t32\t14\t18.14\t2\t1\t1.4169999999999998\n146.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t11\t23.11\t1\t1\t1.52\n152.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t19\t19.19\t1\t1\t2.488\n156.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t10\t4.1\t2\t1\t1.202\n160.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t8\t26.08\t1\t1\t0.888\n164.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t11\t19.11\t1\t1\t1.546\n168.0\t3\tparametric gain\tn/a\t0.1835\tn/a\t32\t13\t19.13\t2\t1\t2.403\n174.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t11\t7.11\t2\t1\t1.176\n178.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t7\t23.07\t1\t1\t1.3519999999999999\n186.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t13\t5.13\t2\t1\t0.8340000000000001\n190.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t19\t-4.81\t4\t0\t0.9690000000000001\n198.0\t3\tparametric gain\tn/a\t-0.3165\tn/a\t12\t11\t1.11\t2\t1\t1.858\n202.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t13\t-2.87\t4\t0\t1.403\n206.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t16\t6.16\t2\t1\t1.136\n212.0\t3\tparametric gain\tn/a\t-0.3165\tn/a\t12\t16\t-3.84\t4\t0\t0.971\n218.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t17\t21.17\t2\t1\t1.811\n226.0\t3\tparametric gain\tn/a\t0.3835\tn/a\t40\t14\t26.14\t1\t1\t1.651\n230.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t5\t17.05\t1\t1\t0.914\n234.0\t3\tparametric gain\tn/a\t-0.2165\tn/a\t16\t16\t0.16\t2\t1\t2.441\n240.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t5\t29.05\t1\t1\t0.866\n250.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t19\t-8.81\t4\t0\t0.836\n254.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t16\t-1.84\t4\t0\t1.9069999999999998\n258.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t8\t22.08\t1\t1\t0.9690000000000001\n262.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t14\t20.14\t2\t1\t2.204\n266.0\t3\tparametric gain\tn/a\t0.1335\tn/a\t30\t14\t16.14\t2\t1\t1.97\n274.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t15\t11.15\t2\t1\t1.068\n282.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t20\t-9.8\t4\t0\t1.077\n286.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t7\t27.07\t1\t1\t1.051\n292.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t5\t5.05\t2\t1\t1.445\n296.0\t3\tparametric gain\tn/a\t-0.1165\tn/a\t20\t20\t0.2\t2\t1\t1.67\n300.0\t3\tparametric gain\tn/a\t0.0835\tn/a\t28\t20\t8.2\t2\t1\t0.956\n304.0\t3\tparametric gain\tn/a\t0.2835\tn/a\t36\t5\t31.05\t1\t1\t1.11\n308.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t7\t19.07\t1\t1\t2.516\n316.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t17\t9.17\t2\t1\t1.254\n326.0\t3\tparametric gain\tn/a\t-0.2165\tn/a\t16\t15\t1.15\t2\t1\t1.51\n330.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t10\t16.1\t2\t1\t0.757\n338.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t15\t23.15\t1\t1\t1.963\n346.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t11\t15.11\t2\t1\t0.9179999999999999\n356.0\t3\tparametric gain\tn/a\t0.0835\tn/a\t28\t5\t23.05\t1\t1\t0.855\n362.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t18\t-3.82\t4\t0\t1.391\n368.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t12\t10.12\t2\t1\t0.966\n372.0\t3\tparametric gain\tn/a\t0.2335\tn/a\t34\t15\t19.15\t1\t1\t2.406\n376.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t13\t25.13\t1\t1\t0.87\n380.0\t3\tparametric gain\tn/a\t-0.3665\tn/a\t10\t8\t2.08\t2\t1\t1.271\n394.0\t3\tparametric gain\tn/a\t-0.3165\tn/a\t12\t9\t3.09\t2\t1\t1.15\n398.0\t3\tparametric gain\tn/a\t0.2835\tn/a\t36\t11\t25.11\t1\t1\t1.564\n404.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t5\t9.05\t2\t1\t1.695\n408.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t11\t11.11\t2\t1\t1.11\n412.0\t3\tparametric gain\tn/a\t-0.1665\tn/a\t18\t19\t-0.81\t4\t0\t1.286\n418.0\t3\tparametric gain\tn/a\t-0.0665\tn/a\t22\t8\t14.08\t2\t1\t1.7369999999999999\n428.0\t3\tparametric gain\tn/a\t0.3335\tn/a\t38\t6\t32.06\t1\t1\t1.063\n434.0\t3\tparametric gain\tn/a\t-0.3165\tn/a\t12\t8\t4.08\t2\t1\t0.887\n438.0\t3\tparametric gain\tn/a\t-0.2665\tn/a\t14\t11\t3.11\t2\t1\t1.247\n442.0\t3\tparametric gain\tn/a\t0.1835\tn/a\t32\t12\t20.12\t2\t1\t1.545\n446.0\t3\tparametric gain\tn/a\t-0.1165\tn/a\t20\t11\t9.11\t2\t1\t0.8490000000000001\n450.0\t3\tparametric gain\tn/a\t0.0335\tn/a\t26\t14\t12.14\t2\t1\t0.9440000000000001\n454.0\t3\tparametric gain\tn/a\t0.1835\tn/a\t32\t8\t24.08\t1\t1\t0.825\n466.0\t3\tparametric gain\tn/a\t0.3835\tn/a\t40\t17\t23.17\t1\t1\t1.963\n472.0\t3\tparametric gain\tn/a\t-0.1165\tn/a\t20\t17\t3.17\t2\t1\t1.161\n476.0\t3\tparametric gain\tn/a\t-0.0165\tn/a\t24\t19\t5.19\t2\t1\t0.888\n" }, { "path": "tests/data/ds005_derivs/dummy/dataset_description.json", "content": "{\n \"GeneratedBy\": [{\n \"Name\": \"dummy\"\n }],\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\",\n \"DatasetType\": \"derivative\"\n}\n" }, { "path": "tests/data/ds005_derivs/dummy-vx.x.x/dataset_description.json", "content": "{\n \"GeneratedBy\": [{\n \"Name\": \"dummy\"\n }],\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\",\n \"DatasetType\": \"derivative\"\n}\n" }, { "path": "tests/data/ds005_derivs/format_errs/no_dataset_type/dataset_description.json", "content": "{\n \"PipelineDescription\": {\n \"Name\": \"dummy\"\n },\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\"\n}\n" }, { "path": "tests/data/ds005_derivs/format_errs/no_pipeline_description/dataset_description.json", "content": "{\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\",\n \"DatasetType\": \"derivative\"\n}\n" }, { "path": "tests/data/ds005_derivs/format_errs/no_type_or_description/dataset_description.json", "content": "{\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\"\n}\n" }, { "path": "tests/data/ds005_derivs/other/dummy/dataset_description.json", "content": "{\n \"GeneratedBy\": [{\n \"Name\": \"dummy\"\n }],\n \"BIDSVersion\": \"1.0.0rc2\",\n \"Name\": \"Mixed-gambles task -- dummy derivative\",\n \"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\",\n \"DatasetType\": \"derivative\"\n}\n" }, { "path": "tests/data/synthetic/T1w.json", "content": "{\n \"RepetitionTime\": 2.5,\n \"EchoTime\": 0.0029,\n \"FlipAngle\": 8\n}\n" }, { "path": "tests/data/synthetic/code/create_synthetic_ds.sh", "content": "#!/bin/bash\n\n# small bash script to create a synthetic BIDS data set\n\n# defines where the BIDS data set will be created\nstart_dir=$(pwd)\nraw_dir=${start_dir}/../\n\nsubject_list='01 02 03 04 05'\nsession_list='01'\n\ncreate_raw_dwi() {\n\n\ttarget_dir=$1\n\tsubject=$2\n\tses=$3\n\n\tthis_dir=${target_dir}/sub-${subject}/ses-${ses}/dwi\n\n\tmkdir -p \"${this_dir}\"\n\n\tsuffix='_dwi'\n\tsource_image=${target_dir}/../images/4d.nii.gz\n\tfilename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.nii.gz\n\tcp \"${source_image}\" \"${filename}\"\n\n\tsource_bval=${start_dir}/dwi.bval\n\tfilename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.bval\n\tcp \"${source_bval}\" \"${filename}\"\n\n\tsource_bvec=${start_dir}/dwi.bvec\n\tfilename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.bvec\n\tcp \"${source_bvec}\" \"${filename}\"\n\n}\n\ncreate_raw_fmap() {\n\n\ttarget_dir=$1\n\tsubject=$2\n\tses=$3\n\n\tthis_dir=${target_dir}/sub-${subject}/ses-${ses}/fmap\n\n\tmkdir -p ${this_dir}\n\n\tsource_image=${target_dir}/../images/3d.nii.gz\n\n\tfmap_suffix_list='_phasediff _magnitude1 _magnitude2'\n\n\tfor suffix in ${fmap_suffix_list}; do\n\t\tcp \"${source_image}\" \"${this_dir}/sub-${subject}_ses-${ses}${suffix}.nii.gz\"\n\tdone\n\n\tRepetitionTime=0.4\n\tEchoTime1=00519\n\tEchoTime2=0.00765\n\tFlipAngle=60\n\ttemplate='{\"FlipAngle\":%f, \"RepetitionTime\":%f, \"EchoTime1\":%f, \"EchoTime2\":%f, \"IntendedFor\": [\"%s\", \"%s\"], \"PhaseEncodingDirection\": \"j-\"}'\n\n\tsuffix='_bold'\n\n\ttask_name='nback'\n\tIntendedFor1=\"$(echo ses-${ses}/func/sub-${subject}_ses-${ses}_task-${task_name}_run-01${suffix}.nii.gz)\"\n\tIntendedFor2=\"$(echo ses-${ses}/func/sub-${subject}_ses-${ses}_task-${task_name}_run-02${suffix}.nii.gz)\"\n\tjson_string=$(printf \"$template\" \"$FlipAngle\" \"$RepetitionTime\" \"$EchoTime1\" \"$EchoTime2\" \"$IntendedFor1\" \"$IntendedFor2\")\n\techo \"$json_string\" >${this_dir}/sub-${subject}_ses-${ses}_phasediff.json\n\techo \"$json_string\" >${this_dir}/sub-${subject}_ses-${ses}_phasediff.json\n\n}\n\n# RAW DATASET\nfor subject in ${subject_list}; do\n\tfor ses in ${session_list}; do\n\t\tcreate_raw_dwi ${raw_dir} ${subject} ${ses}\n\t\tcreate_raw_fmap ${raw_dir} ${subject} ${ses}\n\tdone\n\ndone\n" }, { "path": "tests/data/synthetic/code/dwi.bval", "content": "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\n" }, { "path": "tests/data/synthetic/code/dwi.bvec", "content": "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 \n0 -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 \n0 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 \n" }, { "path": "tests/data/synthetic/dataset_description.json", "content": "{\n\t\"Name\": \"Synthetic dataset for inclusion in BIDS-examples\",\n\t\"BIDSVersion\": \"1.0.2\",\n\t\"License\": \"PD\",\n\t\"Authors\": [\"Markiewicz, C. J.\"]\n}\n" }, { "path": "tests/data/synthetic/derivatives/fmriprep/dataset_description.json", "content": "{\n\t\"GeneratedBy\": [{\n\t\t\"Name\": \"fmriprep\"\n\t}],\n\t\"Name\": \"synthetic\",\n\t\"BIDSVersion\": \"1.7.1\",\n\t\"License\": \"PD\",\n\t\"Authors\": [\"Markiewicz, C. J.\"],\n\t\"DatasetType\": \"derivative\"\n}\n" }, { "path": "tests/data/synthetic/derivatives/fmriprep/sub-01/ses-01/func/sub-01_ses-01_task-nback_run-01_desc-confounds_regressors.tsv", "content": 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-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 \n0 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 \n" }, { "path": "tests/data/synthetic/sub-04/ses-01/fmap/sub-04_ses-01_phasediff.json", "content": "{\n \"FlipAngle\": 60.000000,\n \"RepetitionTime\": 0.400000,\n \"EchoTime1\": 519.000000,\n \"EchoTime2\": 0.007650,\n \"IntendedFor\": [\n \"ses-01/func/sub-04_ses-01_task-nback_run-01_bold.nii.gz\",\n \"ses-01/func/sub-04_ses-01_task-nback_run-02_bold.nii.gz\"\n ],\n \"PhaseEncodingDirection\": \"j-\"\n}" }, { "path": "tests/data/synthetic/sub-04/ses-01/sub-04_ses-01_scans.tsv", "content": "filename\tacq_time\nanat/sub-04_ses-01_T1w.nii.gz\t1800-05-21T11:18:59\nfunc/sub-04_ses-01_task-nback_run-01_bold.nii.gz\t1800-05-21T11:23:59\nfunc/sub-04_ses-01_task-nback_run-02_bold.nii.gz\t1800-05-21T11:38:59\nfunc/sub-04_ses-01_task-rest_bold.nii.gz\t1800-05-21T11:53:59\n" }, { "path": "tests/data/synthetic/sub-04/ses-02/sub-04_ses-02_scans.tsv", "content": "filename\tacq_time\nanat/sub-04_ses-02_T1w.nii.gz\t1847-10-17T10:15:19\nfunc/sub-04_ses-02_task-nback_run-01_bold.nii.gz\t1847-10-17T10:20:19\nfunc/sub-04_ses-02_task-nback_run-02_bold.nii.gz\t1847-10-17T10:35:19\nfunc/sub-04_ses-02_task-rest_bold.nii.gz\t1847-10-17T10:50:19\n" }, { "path": "tests/data/synthetic/sub-04/sub-04_sessions.tsv", "content": "session_id\tsystolic_blood_pressure\nses-01\t111\nses-02\t115\n" }, { "path": "tests/data/synthetic/sub-05/ses-01/dwi/sub-05_ses-01_dwi.bval", "content": "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\n" }, { "path": "tests/data/synthetic/sub-05/ses-01/dwi/sub-05_ses-01_dwi.bvec", "content": "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 \n0 -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 \n0 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 \n" }, { "path": "tests/data/synthetic/sub-05/ses-01/fmap/sub-05_ses-01_phasediff.json", "content": "{\n \"FlipAngle\": 60.000000,\n \"RepetitionTime\": 0.400000,\n \"EchoTime1\": 519.000000,\n \"EchoTime2\": 0.007650,\n \"IntendedFor\": [\n \"ses-01/func/sub-05_ses-01_task-nback_run-01_bold.nii.gz\",\n \"ses-01/func/sub-05_ses-01_task-nback_run-02_bold.nii.gz\"\n ],\n \"PhaseEncodingDirection\": \"j-\"\n}" }, { "path": "tests/data/synthetic/sub-05/ses-01/sub-05_ses-01_scans.tsv", "content": "filename\tacq_time\nanat/sub-05_ses-01_T1w.nii.gz\t1899-07-07T16:04:21\nfunc/sub-05_ses-01_task-nback_run-01_bold.nii.gz\t1899-07-07T16:09:21\nfunc/sub-05_ses-01_task-nback_run-02_bold.nii.gz\t1899-07-07T16:24:21\nfunc/sub-05_ses-01_task-rest_bold.nii.gz\t1899-07-07T16:39:21\n" }, { "path": "tests/data/synthetic/sub-05/ses-02/sub-05_ses-02_scans.tsv", "content": "filename\tacq_time\nanat/sub-05_ses-02_T1w.nii.gz\t1868-01-31T20:57:43\nfunc/sub-05_ses-02_task-nback_run-01_bold.nii.gz\t1868-01-31T21:02:43\nfunc/sub-05_ses-02_task-nback_run-02_bold.nii.gz\t1868-01-31T21:17:43\nfunc/sub-05_ses-02_task-rest_bold.nii.gz\t1868-01-31T21:32:43\n" }, { "path": "tests/data/synthetic/sub-05/sub-05_sessions.tsv", "content": "session_id\tsystolic_blood_pressure\nses-01\t114\nses-02\t110\n" }, { "path": "tests/data/synthetic/task-nback_bold.json", "content": "{\n \"TaskName\": \"N-Back\",\n \"RepetitionTime\": 2.5,\n \"EchoTime\": 0.03\n}\n" }, { "path": "tests/data/synthetic/task-nback_events.tsv", "content": "onset\tduration\ttrial_type\tweight\n2.016\t1\tscene_nontarget_nbackparam\t-0.5\n4.0169999999999995\t1\tscene_target_nbackparam\t-0.5\n6.018\t1\tscene_target_nbackparam\t-0.5\n8.019\t1\tscene_nontarget_nbackparam\t-0.5\n10.02\t1\tscene_dist_rtparam\t0.348\n12.02\t1\tscene_nontarget_nbackparam\t-0.5\n14.005\t1\tscene_nontarget_nbackparam\t-0.5\n16.005\t1\tscene_dist_rtparam\t-0.122\n18.006\t1\tscene_nontarget_nbackparam\t-0.5\n20.007\t1\tscene_dist_rtparam\t0.10400000000000001\n24.025\t1\tfaces_nontarget_rtparam\t0.026000000000000002\n26.01\t1\tfaces_nontarget_rtparam\t-0.09699999999999999\n28.01\t1\tfaces_nontarget_rtparam\t-0.024\n30.011\t1\tfaces_nontarget_rtparam\t0.049\n32.012\t1\tfaces_nontarget_rtparam\t0.019\n34.013000000000005\t1\tfaces_dist_nbackparam\t-0.455\n36.014\t1\tfaces_target_rtparam\t-0.07400000000000001\n38.014\t1\tfaces_nontarget_rtparam\t-0.062\n40.015\t1\tfaces_nontarget_rtparam\t0.01\n42.016000000000005\t1\tfaces_dist_nbackparam\t-0.455\n68.01\t1\tscene_nontarget_nbackparam\t0.5\n70.01100000000001\t1\tscene_nontarget_nbackparam\t0.5\n72.012\t1\tscene_nontarget_nbackparam\t0.5\n74.013\t1\tscene_nontarget_nbackparam\t0.5\n76.013\t1\tscene_dist_rtparam\t0.136\n78.014\t1\tscene_target_nbackparam\t0.5\n80.015\t1\tscene_nontarget_nbackparam\t0.5\n82.016\t1\tscene_nontarget_nbackparam\t0.5\n84.01700000000001\t1\tscene_nontarget_nbackparam\t0.5\n86.01799999999999\t1\tscene_target_nbackparam\t0.5\n90.01899999999999\t1\tfaces_nontarget_rtparam\t-0.175\n92.02\t1\tfaces_nontarget_rtparam\t-0.195\n94.021\t1\tfaces_dist_nbackparam\t0.545\n96.022\t1\tfaces_dist_nbackparam\t0.545\n98.022\t1\tfaces_target_rtparam\t-0.068\n100.023\t1\tfaces_nontarget_rtparam\t0.018000000000000002\n102.024\t1\tfaces_nontarget_rtparam\t-0.019\n104.025\t1\tfaces_target_rtparam\t0.156\n106.009\t1\tfaces_nontarget_rtparam\t-0.035\n108.01\t1\tfaces_target_rtparam\t-0.057\n154.012\t1\tscene_nontarget_nbackparam\t-0.5\n156.013\t1\tscene_nontarget_nbackparam\t-0.5\n" }, { "path": "tests/data/synthetic/task-nback_physio.json", "content": "{\n \"SamplingFrequency\": 10.0,\n \"StartTime\": 22.8,\n \"Columns\": [\"respiratory\", \"cardiac\"]\n}\n" }, { "path": "tests/data/synthetic/task-nback_stim.json", "content": "{\n \"SamplingFrequency\": 2.0,\n \"StartTime\": 0.0,\n \"Columns\": [\"stimA\", \"stimB\"]\n}\n" }, { "path": "tests/data/synthetic/task-rest_bold.json", "content": "{\n \"TaskName\": \"Rest\",\n \"RepetitionTime\": 2.5,\n \"EchoTime\": 0.03\n}\n" }, { "path": "tests/data/synthetic/task-rest_physio.json", "content": "{\n \"SamplingFrequency\": 10.0,\n \"StartTime\": 0.0,\n \"Columns\": [\"respiratory\", \"cardiac\"]\n}\n" }, { "path": "tools/check_noqa_budget.py", "content": "#!/usr/bin/env python3\n\"\"\"Fail if '# noqa' directives increase compared to a git ref.\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport re\nimport subprocess\nimport sys\nfrom pathlib import Path\n\n\nNOQA_RE = re.compile(r\"#\\s*(?:ruff:\\s*)?noqa\\b\", re.IGNORECASE)\n\n\ndef git(*args: str) -> str:\n return subprocess.check_output([\"git\", *args], text=True).strip()\n\n\ndef tracked_python_files(ref: str) -> list[str]:\n output = git(\"ls-tree\", \"-r\", \"--name-only\", ref)\n return [line for line in output.splitlines() if line.endswith(\".py\")]\n\n\ndef file_content_at_ref(ref: str, path: str) -> str:\n return git(\"show\", f\"{ref}:{path}\")\n\n\ndef count_noqa_in_text(text: str) -> int:\n return sum(1 for line in text.splitlines() if NOQA_RE.search(line))\n\n\ndef count_noqa_at_ref(ref: str) -> int:\n total = 0\n for path in tracked_python_files(ref):\n try:\n text = file_content_at_ref(ref, path)\n except subprocess.CalledProcessError:\n # File may not exist in one side of comparison.\n continue\n total += count_noqa_in_text(text)\n return total\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--base-ref\",\n required=True,\n help=\"Git ref to compare against (e.g., origin/main).\",\n )\n args = parser.parse_args()\n\n try:\n base_count = count_noqa_at_ref(args.base_ref)\n head_count = count_noqa_at_ref(\"HEAD\")\n except subprocess.CalledProcessError as exc:\n print(f\"Failed to run git command: {exc}\", file=sys.stderr)\n return 2\n\n print(f\"Base ({args.base_ref}) # noqa count: {base_count}\")\n print(f\"Head (HEAD) # noqa count: {head_count}\")\n\n if head_count > base_count:\n print(\n f\"Error: '# noqa' count increased by {head_count - base_count}. \"\n \"Please remove added suppressions or justify and adjust policy.\",\n file=sys.stderr,\n )\n return 1\n\n print(\"OK: '# noqa' count did not increase.\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n" }, { "path": "tools/prep_zenodo.py", "content": "#!/usr/bin/env python3\n# /// script\n# requires-python = \">=3.9\"\n# dependencies = [\n# \"gitpython\",\n# ]\n# ///\nimport git\nimport json\nfrom subprocess import run, PIPE, CalledProcessError\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\n\n\ndef decommify(name):\n return ' '.join(name.split(', ')[::-1])\n\n\n# Users who have asked not to be cited at this time\n# XXX We should add a shortlog since the most recent tag and explicitly note\n# that a blacklisted user has contributed again recently, and verify they still\n# do not want to be cited.\nblacklist = {\n 'Cecile Madjar',\n 'Matthew Wardrop',\n 'github-actions[bot]',\n 'dependabot[bot]',\n}\n\n# List of repositories whose commits should be counted as contributions\ncodependents = [('https://github.com/grabbles/grabbit.git', '0.2.6')]\n\n# Last shablona commit\norigin_commit = 'd72caaf5933907ed699d57faddaec7bfc836ce6f'\n\ngit_root = Path(git.Repo('.', search_parent_directories=True).working_dir)\nzenodo_file = git_root / '.zenodo.json'\n\nzenodo = json.loads(zenodo_file.read_text()) if zenodo_file.exists() else {}\n\norig_creators = zenodo.get('creators', [])\ncreator_map = {\n decommify(creator['name']): creator for creator in orig_creators\n}\n\nshortlog = run(['git', 'shortlog', '-ns', f'{origin_commit}..'], stdout=PIPE)\ncounts = [\n line.split('\\t', 1)[::-1]\n for line in shortlog.stdout.decode().split('\\n')\n if line\n]\n\n# Get additional commit counts from dependencies\nwith TemporaryDirectory() as tmpdir:\n tmppath = Path(tmpdir)\n for repo in codependents:\n try:\n repo, ref = repo\n except (TypeError, ValueError):\n ref = None\n repo_dir = str(tmppath / repo.rsplit('/', 1)[1].split('.', 1)[0])\n try:\n clone = run(['git', 'clone', '-q', repo, repo_dir], check=True)\n except CalledProcessError as err:\n raise RuntimeError(f'Could not clone {repo}') from err\n\n if ref is None:\n tag = run(['git', '-C', repo_dir, 'tag'], stdout=PIPE)\n # latest tag\n ref = tag.stdout.decode().strip().rsplit('\\n', 1)[1]\n\n dep_shortlog = run(\n ['git', '-C', repo_dir, 'shortlog', '-ns', ref], stdout=PIPE\n )\n counts.extend(\n line.split('\\t', 1)[::-1]\n for line in dep_shortlog.stdout.decode().split('\\n')\n if line\n )\n\ncommit_counts = {}\nfor committer, commits in counts:\n commit_counts[committer] = commit_counts.get(committer, 0) + int(commits)\n\n# Stable sort:\n# Number of commits in reverse order\n# Ties broken by alphabetical order of first name\ncommitters = [\n committer\n for committer, _ in sorted(\n commit_counts.items(), key=lambda x: (-x[1], x[0])\n )\n]\n\n# Tal to the top\nfirst_author = 'Tal Yarkoni'\nif committers[0] != first_author:\n committers.remove(first_author)\n committers.insert(0, first_author)\n\ncreators = [\n creator_map.get(committer, {'name': committer})\n for committer in committers\n if committer not in blacklist\n]\n\nzenodo['creators'] = creators\nzenodo_file.write_text(json.dumps(zenodo, indent=2, ensure_ascii=False) + '\\n')\n" }, { "path": "tools/update_changes.sh", "content": "#!/bin/bash\n#\n# Collects the pull-requests since the latest release and\n# arranges them in the CHANGELOG.rst file.\n#\n# This is a script to be run before releasing a new version.\n#\n# Usage /bin/bash update_changes.sh 1.0.1\n#\n# Adapted from https://github.com/nipy/nipype/blob/98beb0a/tools/update_changes.sh\n\n# Setting # $ help set\nset -u # Treat unset variables as an error when substituting.\nset -x # Print command traces before executing command.\n\nROOT=$( git rev-parse --show-toplevel )\nCHANGES=$ROOT/CHANGELOG.rst\n\n# Elaborate today's release header\nHEADER=\"Version $1 ($(date '+%B %d, %Y'))\"\nUNDERLINE=$( printf \"%${#HEADER}s\" | tr \" \" \"-\" )\ncat < newchanges\nChangelog\n=========\n\n$HEADER\n$UNDERLINE\n\nEND\n\n# Search for PRs since previous release\nMERGE_COMMITS=$( git log --grep=\"Merge pull request\\|(#.*)$\" `git describe --tags --abbrev=0`..HEAD --pretty='format:%h' )\nfor COMMIT in ${MERGE_COMMITS//\\n}; do\n SUB=$( git log -n 1 --pretty=\"format:%s\" $COMMIT )\n if ( echo $SUB | grep \"^Merge pull request\" ); then\n # Merge commit\n PR=$( echo $SUB | sed -e \"s/Merge pull request \\#\\([0-9]*\\).*/\\1/\" )\n TITLE=$( git log -n 1 --pretty=\"format:%b\" $COMMIT )\n else\n # Squashed merge\n PR=$( echo $SUB | sed -e \"s/.*(\\#\\([0-9]*\\))$/\\1/\" )\n TITLE=$( echo $SUB | sed -e \"s/\\(.*\\)(\\#[0-9]*)$/\\1/\" )\n fi\n echo \"* $TITLE (#$PR)\" >> newchanges\ndone\n\n# Append old changes\ntail -n+3 $CHANGES >> newchanges\n\n# Replace old CHANGES with new file\nmv newchanges $CHANGES\n" }, { "path": "tools/update_requirements.py", "content": "#!/usr/bin/env python3\nimport sys\nfrom pathlib import Path\n\ntry:\n import tomllib\nexcept ImportError:\n import tomli as tomllib\n\nif sys.version_info < (3, 6):\n print('This script requires Python 3.6 to work correctly')\n sys.exit(1)\n\nrepo_root = Path(__file__).parent.parent\npyproject_toml = repo_root / 'pyproject.toml'\nreqs = repo_root / 'requirements.txt'\nmin_reqs = repo_root / 'min-requirements.txt'\ndoc_reqs = repo_root / 'doc-requirements.txt'\n\nwith open(pyproject_toml, 'rb') as fobj:\n config = tomllib.load(fobj)\nrequirements = config['project']['dependencies']\n# doc_requirements = config['project']['optional-dependencies']['doc']\n\nscript_name = Path(__file__).relative_to(repo_root)\n\nlines = [f'# Auto-generated by {script_name}', '']\n\n# Write requirements\n# lines[1:-1] = requirements\n# reqs.write_text('\\n'.join(lines))\n\n# Write minimum requirements\nlines[1:-1] = [req.replace('>=', '==').replace('~=', '==') for req in requirements]\nmin_reqs.write_text('\\n'.join(lines))\n\n# Write documentation requirements\n# lines[1:-1] = ['-r requirements.txt'] + doc_requirements\n# doc_reqs.write_text('\\n'.join(lines))\n" }, { "path": "tox.ini", "content": "[tox]\nrequires =\n tox>=4\n tox-uv\nenvlist =\n py31{0,1,2,3,4}-{full,pre}\n py310-min\nskip_missing_interpreters = true\n\n# Configuration that allows us to split tests across GitHub runners effectively\n[gh-actions]\npython =\n 3.10: py310\n 3.11: py311\n 3.12: py312\n 3.13: py313\n 3.14: py314\n\n[gh-actions:env]\nDEPENDS =\n min: min\n full: full\n pre: pre\n\n[testenv]\ndescription = Pytest with coverage\nlabels = test\npip_pre =\n pre: true\npass_env =\n # getpass.getuser() sources for Windows:\n LOGNAME\n USER\n LNAME\n USERNAME\n # Pass user color preferences through\n PY_COLORS\n FORCE_COLOR\n NO_COLOR\n CLICOLOR\n CLICOLOR_FORCE\nsetenv =\n pre: PIP_EXTRA_INDEX_URL=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple\n pre: UV_INDEX=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple\n pre: UV_INDEX_STRATEGY=unsafe-best-match\nextras = test\nrunner =\n full: uv-venv-lock-runner\n !full: uv-venv-runner\nuv_resolution =\n min: lowest-direct\n\ncommands =\n pytest --doctest-modules -v \\\n --cov src -cov tests --cov-report xml --cov-report term \\\n src/ tests/ {posargs:-n auto}\n\n[testenv:docs]\ndescription = Build documentation site\nlabels = docs\nallowlist_externals = make\nextras = doc\ncommands =\n make -C doc html\n\n[testenv:spellcheck]\ndescription = Check spelling\nlabels = check\ndeps =\n codespell[toml]\nskip_install = true\ncommands =\n codespell . {posargs}\n\n[testenv:build{,-strict}]\nlabels =\n check\n pre-release\ndeps =\n build\n twine\nskip_install = true\nset_env =\n build-strict: PYTHONWARNINGS=error\ncommands =\n python -m build\n python -m twine check dist/*\n\n[testenv:publish]\ndepends = build\nlabels = release\ndeps =\n twine\nskip_install = true\ncommands =\n python -m twine upload dist/*\n\n[testenv:noqa-budget]\ndescription = Fail if # noqa count increases vs base branch\nlabels = check\nskip_install = true\nallowlist_externals = git\npass_env =\n GITHUB_BASE_REF\n CI\ncommands =\n python tools/check_noqa_budget.py --base-ref {env:NOQA_BASE_REF:origin/{env:GITHUB_BASE_REF:main}}\n" } ]