pax_global_header00006660000000000000000000000064146745356060014532gustar00rootroot0000000000000052 comment=c2b76d8c8d9c12c6f36196ead8b9e25aa5dc1e76 MyST-NB-1.1.2/000077500000000000000000000000001467453560600126645ustar00rootroot00000000000000MyST-NB-1.1.2/.github/000077500000000000000000000000001467453560600142245ustar00rootroot00000000000000MyST-NB-1.1.2/.github/dependabot.yml000066400000000000000000000011651467453560600170570ustar00rootroot00000000000000# To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for all configuration options: # https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: "pip" directory: "/" schedule: interval: "monthly" - package-ecosystem: "github-actions" directory: "/" schedule: interval: "monthly" groups: actions: patterns: - "*" labels: - "github_actions" MyST-NB-1.1.2/.github/workflows/000077500000000000000000000000001467453560600162615ustar00rootroot00000000000000MyST-NB-1.1.2/.github/workflows/tests.yml000066400000000000000000000062771467453560600201620ustar00rootroot00000000000000name: continuous-integration on: push: branches: [master] tags: - 'v*' pull_request: jobs: tests: strategy: fail-fast: false matrix: os: [ubuntu-latest] python-version: ["3.9", "3.10", "3.11", "3.12"] sphinx: [">=5,<9"] # Newest Sphinx (any) myst-parser: [">=1,<3"] # Newest MyST Parser (any) include: # Just check the other platforms once - os: windows-latest python-version: "3.12" sphinx: "~=8.0" myst-parser: "~=4.0" - os: macos-latest python-version: "3.12" sphinx: "~=8.0" myst-parser: "~=4.0" # Oldest known-compatible dependencies - os: ubuntu-latest python-version: "3.9" sphinx: "==5.0.0" myst-parser: "==1.0.0" # Mid-range dependencies - os: ubuntu-latest python-version: "3.11" sphinx: "==7.0.0" myst-parser: "==2.0.0" # Newest known-compatible dependencies - os: ubuntu-latest python-version: "3.12" sphinx: "==8.0.2" myst-parser: "==4.0.0" runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: pip - name: Install myst-nb with Sphinx ${{ matrix.sphinx }} run: | pip install --upgrade pip pip install --upgrade "Sphinx${{ matrix.sphinx }}" "myst-parser${{ matrix.myst-parser }}" -e .[testing] - name: Run pytest run: pytest --durations=10 coverage: needs: [tests] runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: "3.11" cache: pip - name: Install dependencies run: | pip install --upgrade pip pip install -e .[testing] - name: Run pytest run: pytest --durations=10 --cov=myst_nb --cov-report=xml --cov-report=term-missing - name: Create cov run: coverage xml # for some reason the tests/conftest.py::check_nbs fixture breaks pytest-cov's cov-report outputting # this is why we run `coverage xml` afterwards (required by codecov) # TEMPORARY FIX: Disable codecov until we can get it working again - name: Upload to Codecov uses: codecov/codecov-action@v4 if: false with: name: myst-nb-pytests flags: pytests files: ./coverage.xml publish: name: Publish to PyPi needs: [tests] if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags') runs-on: ubuntu-latest steps: - name: Checkout source uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.10" - name: install flit run: | pip install flit~=3.4 - name: Build and publish run: | flit publish env: FLIT_USERNAME: __token__ FLIT_PASSWORD: ${{ secrets.PYPI_KEY }} MyST-NB-1.1.2/.gitignore000066400000000000000000000035401467453560600146560ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ (PosixPath('/* # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # Jupyter Cache .jupyter_cache # OSX .DS_Store .vscode/ todos.md _archive/ MyST-NB-1.1.2/.pre-commit-config.yaml000066400000000000000000000023051467453560600171450ustar00rootroot00000000000000# Install pre-commit hooks via # pre-commit install exclude: > (?x)^( \.vscode/settings\.json| tests/commonmark/commonmark\.json| .*\.xml| tests/.*\.txt )$ ci: autoupdate_schedule: 'monthly' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 hooks: - id: check-json - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - id: check-added-large-files - id: check-case-conflict - id: check-merge-conflict - id: mixed-line-ending - id: trailing-whitespace - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.1.4 hooks: - id: ruff args: ["--fix", "--show-fixes"] - id: ruff-format - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.6.1 hooks: - id: mypy args: [--config-file=pyproject.toml] additional_dependencies: - importlib_metadata - myst-parser~=2.0.0 - "sphinx~=7.3.7" - nbclient - types-PyYAML files: > (?x)^( myst_nb/.+\.py| )$ - repo: https://github.com/codespell-project/codespell rev: v2.2.6 hooks: - id: codespell args: ["-S", "*.ipynb"] MyST-NB-1.1.2/.readthedocs.yml000066400000000000000000000004031467453560600157470ustar00rootroot00000000000000version: 2 build: os: ubuntu-22.04 tools: python: "3.11" python: install: - method: pip path: . extra_requirements: - rtd - requirements: docs/requirements.txt sphinx: builder: html fail_on_warning: true MyST-NB-1.1.2/CHANGELOG.md000066400000000000000000001126201467453560600144770ustar00rootroot00000000000000# Change Log ## v1.1.1 - 2024-06-27 ([full changelog](https://github.com/executablebooks/MyST-NB/compare/v1.1.0...6ce30cd41fa82543e0f315ac8bbee82669b0cc82)) ### Bugs fixed - FIX: output metadata overwrites image size for all following images [#609](https://github.com/executablebooks/MyST-NB/pull/609) ([@aeisenbarth](https://github.com/aeisenbarth)) - FIX: remove incorrect license classifier [#603](https://github.com/executablebooks/MyST-NB/pull/603) ([@agoose77](https://github.com/agoose77)) ### Maintenance and upkeep improvements - MAINT: bump version [#614](https://github.com/executablebooks/MyST-NB/pull/614) ([@agoose77](https://github.com/agoose77)) - MAINT: appease mypy [#612](https://github.com/executablebooks/MyST-NB/pull/612) ([@agoose77](https://github.com/agoose77)) - MAINT: fix specs for CI matrix [#611](https://github.com/executablebooks/MyST-NB/pull/611) ([@agoose77](https://github.com/agoose77)) - MAINT: bump version [#592](https://github.com/executablebooks/MyST-NB/pull/592) ([@agoose77](https://github.com/agoose77)) ### Documentation improvements - DOCS: set printoptions to disable modern scalar printing [#613](https://github.com/executablebooks/MyST-NB/pull/613) ([@agoose77](https://github.com/agoose77)) - DOCS: extra comma forgotten [#606](https://github.com/executablebooks/MyST-NB/pull/606) ([@jeertmans](https://github.com/jeertmans)) - DOCS: update shown code to match source [#598](https://github.com/executablebooks/MyST-NB/pull/598) ([@OriolAbril](https://github.com/OriolAbril)) ### Contributors to this release ([GitHub contributors page for this release](https://github.com/executablebooks/MyST-NB/graphs/contributors?from=2024-04-12&to=2024-06-27&type=c)) [@aeisenbarth](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aaeisenbarth+updated%3A2024-04-12..2024-06-27&type=Issues) | [@agoose77](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aagoose77+updated%3A2024-04-12..2024-06-27&type=Issues) | [@jeertmans](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ajeertmans+updated%3A2024-04-12..2024-06-27&type=Issues) | [@OriolAbril](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AOriolAbril+updated%3A2024-04-12..2024-06-27&type=Issues) | [@sstroemer](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Asstroemer+updated%3A2024-04-12..2024-06-27&type=Issues) | [@welcome](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Awelcome+updated%3A2024-04-12..2024-06-27&type=Issues) ## v1.1.0 - 2024-04-12 ([full changelog](https://github.com/executablebooks/MyST-NB/compare/v1.0.0...9943ec214c35844c4535d0184f7840574fc7ab03)) ### Enhancements made - ENH: pass-through image metadata [#588](https://github.com/executablebooks/MyST-NB/pull/588) ([@flying-sheep](https://github.com/flying-sheep)) ### Maintenance and upkeep improvements - MAINT: bump version [#592](https://github.com/executablebooks/MyST-NB/pull/592) ([@agoose77](https://github.com/agoose77)) - MAINT: use `findall` instead of `traverse` [#585](https://github.com/executablebooks/MyST-NB/pull/585) ([@agoose77](https://github.com/agoose77)) - MAINT: restore default line length [#577](https://github.com/executablebooks/MyST-NB/pull/577) ([@agoose77](https://github.com/agoose77)) ### Other merged PRs - build(deps): bump actions/setup-python from 4 to 5 [#576](https://github.com/executablebooks/MyST-NB/pull/576) ([@dependabot](https://github.com/dependabot)) ### Contributors to this release ([GitHub contributors page for this release](https://github.com/executablebooks/MyST-NB/graphs/contributors?from=2023-11-08&to=2024-04-12&type=c)) [@agoose77](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aagoose77+updated%3A2023-11-08..2024-04-12&type=Issues) | [@cisaacstern](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acisaacstern+updated%3A2023-11-08..2024-04-12&type=Issues) | [@dependabot](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Adependabot+updated%3A2023-11-08..2024-04-12&type=Issues) | [@flying-sheep](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aflying-sheep+updated%3A2023-11-08..2024-04-12&type=Issues) | [@ma-sadeghi](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ama-sadeghi+updated%3A2023-11-08..2024-04-12&type=Issues) | [@peytondmurray](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Apeytondmurray+updated%3A2023-11-08..2024-04-12&type=Issues) | [@PhilipVinc](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3APhilipVinc+updated%3A2023-11-08..2024-04-12&type=Issues) | [@sphuber](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Asphuber+updated%3A2023-11-08..2024-04-12&type=Issues) | [@welcome](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Awelcome+updated%3A2023-11-08..2024-04-12&type=Issues) ## v1.0.0 - 2023-11-08 ([full changelog](https://github.com/executablebooks/MyST-NB/compare/e8fd165...48edb5d852eb73b09eae962c7518045f836633d5)) ### New features added - FEAT: allow any value for expr [#429](https://github.com/executablebooks/MyST-NB/pull/429) ([@agoose77](https://github.com/agoose77)) ### Bugs fixed - FIX: update tests for newer matplotlib [#559](https://github.com/executablebooks/MyST-NB/pull/559) ([@agoose77](https://github.com/agoose77)) - FIX: remove warnings for 3.11+ [#547](https://github.com/executablebooks/MyST-NB/pull/547) ([@agoose77](https://github.com/agoose77)) - FIX: Show traceback before raising ExecutionError [#531](https://github.com/executablebooks/MyST-NB/pull/531) ([@paugier](https://github.com/paugier)) ### Maintenance and upkeep improvements - MAINT: uncap nbformat [#568](https://github.com/executablebooks/MyST-NB/pull/568) ([@agoose77](https://github.com/agoose77)) - MAINT: unpin jupyer-cache [#567](https://github.com/executablebooks/MyST-NB/pull/567) ([@agoose77](https://github.com/agoose77)) - MAINT: bump minimum Python version [#566](https://github.com/executablebooks/MyST-NB/pull/566) ([@agoose77](https://github.com/agoose77)) - MAINT: enable ruff formatter [#565](https://github.com/executablebooks/MyST-NB/pull/565) ([@agoose77](https://github.com/agoose77)) - MAINT: test wider matrix [#552](https://github.com/executablebooks/MyST-NB/pull/552) ([@agoose77](https://github.com/agoose77)) - MAINT: update linting [#551](https://github.com/executablebooks/MyST-NB/pull/551) ([@agoose77](https://github.com/agoose77)) - MAINT: Patch `file_regression` fixture for Sphinx backwards compatibility [#536](https://github.com/executablebooks/MyST-NB/pull/536) ([@je-cook](https://github.com/je-cook)) - MAINT: remove python=3.7 as EOL is June 2023 [#516](https://github.com/executablebooks/MyST-NB/pull/516) ([@mmcky](https://github.com/mmcky)) ### Documentation improvements - DOCS: Fix broken pytest fixture [#546](https://github.com/executablebooks/MyST-NB/pull/546) ([@peytondmurray](https://github.com/peytondmurray)) - ๐Ÿ“š DOCS: Fix typos and add codespell pre-commit hook [#475](https://github.com/executablebooks/MyST-NB/pull/475) ([@kianmeng](https://github.com/kianmeng)) ### API and Breaking Changes - UPGRADE: Support Sphinx 7 [#524](https://github.com/executablebooks/MyST-NB/pull/524) ([@LecrisUT](https://github.com/LecrisUT)) - UPGRADE: myst-parser 1.0 [#479](https://github.com/executablebooks/MyST-NB/pull/479) ([@aleivag](https://github.com/aleivag)) ### Other merged PRs - [pre-commit.ci] pre-commit autoupdate [#564](https://github.com/executablebooks/MyST-NB/pull/564) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - [pre-commit.ci] pre-commit autoupdate [#561](https://github.com/executablebooks/MyST-NB/pull/561) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - [pre-commit.ci] pre-commit autoupdate [#556](https://github.com/executablebooks/MyST-NB/pull/556) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - Update readthedocs Config File to fix Test Error [#554](https://github.com/executablebooks/MyST-NB/pull/554) ([@michaelweinold](https://github.com/michaelweinold)) - [pre-commit.ci] pre-commit autoupdate [#553](https://github.com/executablebooks/MyST-NB/pull/553) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - build(deps): update ipython requirement from <=8.16.0 to <=8.16.1 [#550](https://github.com/executablebooks/MyST-NB/pull/550) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#549](https://github.com/executablebooks/MyST-NB/pull/549) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - build(deps): update ipython requirement from !=8.1.0,<8.16 to !=8.1.0,<8.17 [#548](https://github.com/executablebooks/MyST-NB/pull/548) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#542](https://github.com/executablebooks/MyST-NB/pull/542) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - build(deps): bump actions/checkout from 3 to 4 [#539](https://github.com/executablebooks/MyST-NB/pull/539) ([@dependabot](https://github.com/dependabot)) - build(deps): update ipython requirement from !=8.1.0,<8.15 to !=8.1.0,<8.16 [#538](https://github.com/executablebooks/MyST-NB/pull/538) ([@dependabot](https://github.com/dependabot)) - Update copyright year to 2023 [#537](https://github.com/executablebooks/MyST-NB/pull/537) ([@GlobalMin](https://github.com/GlobalMin)) - build(deps-dev): update jupytext requirement from <1.15.0,>=1.11.2 to >=1.11.2,<1.16.0 [#534](https://github.com/executablebooks/MyST-NB/pull/534) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#529](https://github.com/executablebooks/MyST-NB/pull/529) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - build(deps-dev): update coconut requirement from <2.3.0,>=1.4.3 to >=1.4.3,<3.1.0 [#527](https://github.com/executablebooks/MyST-NB/pull/527) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#526](https://github.com/executablebooks/MyST-NB/pull/526) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - Update ipython requirement from !=8.1.0,<8.5 to !=8.1.0,<8.15 [#521](https://github.com/executablebooks/MyST-NB/pull/521) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#518](https://github.com/executablebooks/MyST-NB/pull/518) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - Update coconut requirement from <2.3.0,>=1.4.3 to >=1.4.3,<3.1.0 [#517](https://github.com/executablebooks/MyST-NB/pull/517) ([@dependabot](https://github.com/dependabot)) - [pre-commit.ci] pre-commit autoupdate [#515](https://github.com/executablebooks/MyST-NB/pull/515) ([@pre-commit-ci](https://github.com/pre-commit-ci)) - Update ipykernel requirement from ~=5.5 to >=5.5,<7.0 [#512](https://github.com/executablebooks/MyST-NB/pull/512) ([@dependabot](https://github.com/dependabot)) - Update sphinx-book-theme requirement from ~=0.3.0 to >=0.3,<1.1 [#510](https://github.com/executablebooks/MyST-NB/pull/510) ([@dependabot](https://github.com/dependabot)) - Update jupytext requirement from ~=1.11.2 to >=1.11.2,<1.15.0 [#509](https://github.com/executablebooks/MyST-NB/pull/509) ([@dependabot](https://github.com/dependabot)) ### Contributors to this release ([GitHub contributors page for this release](https://github.com/executablebooks/MyST-NB/graphs/contributors?from=2023-04-24&to=2023-11-29&type=c)) [@agoose77](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aagoose77+updated%3A2023-04-24..2023-11-29&type=Issues) | [@aleivag](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aaleivag+updated%3A2023-04-24..2023-11-29&type=Issues) | [@choldgraf](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acholdgraf+updated%3A2023-04-24..2023-11-29&type=Issues) | [@chrisjsewell](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Achrisjsewell+updated%3A2023-04-24..2023-11-29&type=Issues) | [@cisaacstern](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acisaacstern+updated%3A2023-04-24..2023-11-29&type=Issues) | [@codecov](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acodecov+updated%3A2023-04-24..2023-11-29&type=Issues) | [@dependabot](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Adependabot+updated%3A2023-04-24..2023-11-29&type=Issues) | [@GlobalMin](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AGlobalMin+updated%3A2023-04-24..2023-11-29&type=Issues) | [@je-cook](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aje-cook+updated%3A2023-04-24..2023-11-29&type=Issues) | [@joeldodson](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ajoeldodson+updated%3A2023-04-24..2023-11-29&type=Issues) | [@kianmeng](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Akianmeng+updated%3A2023-04-24..2023-11-29&type=Issues) | [@kloczek](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Akloczek+updated%3A2023-04-24..2023-11-29&type=Issues) | [@LecrisUT](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3ALecrisUT+updated%3A2023-04-24..2023-11-29&type=Issues) | [@michaelweinold](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Amichaelweinold+updated%3A2023-04-24..2023-11-29&type=Issues) | [@mmcky](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ammcky+updated%3A2023-04-24..2023-11-29&type=Issues) | [@paugier](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Apaugier+updated%3A2023-04-24..2023-11-29&type=Issues) | [@peytondmurray](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Apeytondmurray+updated%3A2023-04-24..2023-11-29&type=Issues) | [@PhilipVinc](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3APhilipVinc+updated%3A2023-04-24..2023-11-29&type=Issues) | [@pre-commit-ci](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Apre-commit-ci+updated%3A2023-04-24..2023-11-29&type=Issues) | [@rowanc1](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Arowanc1+updated%3A2023-04-24..2023-11-29&type=Issues) | [@sphuber](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Asphuber+updated%3A2023-04-24..2023-11-29&type=Issues) | [@tupui](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Atupui+updated%3A2023-04-24..2023-11-29&type=Issues) | [@WarrenWeckesser](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AWarrenWeckesser+updated%3A2023-04-24..2023-11-29&type=Issues) | [@welcome](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Awelcome+updated%3A2023-04-24..2023-11-29&type=Issues) | [@Yoshanuikabundi](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AYoshanuikabundi+updated%3A2023-04-24..2023-11-29&type=Issues) ## v0.17.2 - 2023-04-21 This is primarily a maintenance release to support newer versions of dependencies and fix a few bugs. ### Maintenance and upkeep improvements - MAINT: Create dependabot.yml [#499](https://github.com/executablebooks/MyST-NB/pull/499) ([@choldgraf](https://github.com/choldgraf)) - MAINT: Fix codecov jobs and update pre-commit [#460](https://github.com/executablebooks/MyST-NB/pull/460) ([@choldgraf](https://github.com/choldgraf)) - UPDATE: jupyter-cache v0.6.0 [#498](https://github.com/executablebooks/MyST-NB/pull/498) ([@choldgraf](https://github.com/choldgraf)) ### Documentation improvements - DOCS: Hint to avoid Extension error in sphinx-build [#494](https://github.com/executablebooks/MyST-NB/pull/494) ([@kolibril13](https://github.com/kolibril13), [@choldgraf](https://github.com/choldgraf)) - DOCS: fix link to gallery [#483](https://github.com/executablebooks/MyST-NB/pull/483) ([@michaelaye](https://github.com/michaelaye), [@choldgraf](https://github.com/choldgraf), [@agoose77](https://github.com/agoose77)) - Add note about how to use cell tags [#490](https://github.com/executablebooks/MyST-NB/pull/490) ([@kolibril13](https://github.com/kolibril13), [@choldgraf](https://github.com/choldgraf)) - Update quickstart.md to include docs folder [#489](https://github.com/executablebooks/MyST-NB/pull/489) ([@kolibril13](https://github.com/kolibril13), [@choldgraf](https://github.com/choldgraf)) - docs: update to latest `sphinx-design` [#486](https://github.com/executablebooks/MyST-NB/pull/486) ([@agoose77](https://github.com/agoose77), [@choldgraf](https://github.com/choldgraf)) ### Bug fixes - fix: use jsdelivr CDN for ipywidgets [#491](https://github.com/executablebooks/MyST-NB/pull/491) ([@agoose77](https://github.com/agoose77), [@choldgraf](https://github.com/choldgraf)) - Add ipywidgets javascript [#469](https://github.com/executablebooks/MyST-NB/pull/469) ([@OriolAbril](https://github.com/OriolAbril), [@agoose77](https://github.com/agoose77)) ### Contributors to this release The following people contributed discussions, new ideas, code and documentation contributions, and review. See [our definition of contributors](https://github-activity.readthedocs.io/en/latest/#how-does-this-tool-define-contributions-in-the-reports). ([GitHub contributors page for this release](https://github.com/executablebooks/MyST-NB/graphs/contributors?from=2022-09-30&to=2023-04-21&type=c)) @agoose77 ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aagoose77+updated%3A2022-09-30..2023-04-21&type=Issues)) | @choldgraf ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acholdgraf+updated%3A2022-09-30..2023-04-21&type=Issues)) | @chrisjsewell ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Achrisjsewell+updated%3A2022-09-30..2023-04-21&type=Issues)) | @dependabot ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Adependabot+updated%3A2022-09-30..2023-04-21&type=Issues)) | @kolibril13 ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Akolibril13+updated%3A2022-09-30..2023-04-21&type=Issues)) | @michaelaye ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Amichaelaye+updated%3A2022-09-30..2023-04-21&type=Issues)) | @OriolAbril ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AOriolAbril+updated%3A2022-09-30..2023-04-21&type=Issues)) | @pre-commit-ci ([activity](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Apre-commit-ci+updated%3A2022-09-30..2023-04-21&type=Issues)) ## v0.17.1 - 2022-30-09 [Full changelog](https://github.com/executablebooks/MyST-NB/compare/v0.17.0...v0.17.1) - ๐Ÿ‘Œ IMPROVE: `hide-output` button (#450) This now uses the same margin color as the cell source and, when the cell source is present, is "connected" to that, to form a single element. See [Hide cell contents](docs/render/hiding.md) for more information. ## v0.17.0 - 2022-29-09 [Full changelog](https://github.com/executablebooks/MyST-NB/compare/v0.16.0...v0.17.0) - ๐Ÿ‘Œ IMPROVE: Replace sphinx-togglebutton with built-in functionality (#446) This allows for tighter integration with myst-nb: - Nicer rendering of the hidden content buttons - Customisation of the hide/show prompts See [Hide cell contents](docs/render/hiding.md) for more information. - ๐Ÿ› FIX: Inline exec variables with multiple outputs (#440) Previously, it was assumed that a variable evaluation would only ever create 0 or 1 outputs. Multiple are now allowed. - ๐Ÿ‘Œ IMPROVE: cache bust changes to CSS (#447) - ๐Ÿ‘Œ IMPROVE: Move CSS colors to variables (#448) ## v0.16.0 - 2022-06-13 [Full changelog](https://github.com/executablebooks/MyST-NB/compare/v0.15.0...v0.16.0) - โฌ†๏ธ UPGRADE: Sphinx v5 and drop v3 (see [changelog](https://www.sphinx-doc.org/en/master/changes.html)), myst-parser v0.18 (see [changelog](https://myst-parser.readthedocs.io/en/latest/develop/_changelog.html)) - โฌ†๏ธ UPGRADE: Add Python 3.10 support ## v0.15.0 - 2022-05-05 [Full changelog](https://github.com/executablebooks/MyST-NB/compare/v0.14.0...v0.15.0) โœจ NEW: Add `inline` execution mode and `eval` role/directive, for inserting code variables directly into the text flow of your documentation! See [Inline variable evaluation](docs/render/inline.md) for more information. ## v0.14.0 - 2022-04-27 [Full changelog](https://github.com/executablebooks/MyST-NB/compare/v0.13.2...v0.14.0) This release encompasses a **major** rewrite of the entire library and its documentation, primarily in [#380](https://github.com/executablebooks/MyST-NB/pull/380) and [#405](https://github.com/executablebooks/MyST-NB/pull/405). ### Breaking Changes โ€ผ๏ธ #### Configuration A number of configuration option names have been changed, such that they now share the `nb_` prefix. Most of the deprecated names will be auto-converted at the start of the build, emitting a warning such as: ``` WARNING: 'jupyter_execute_notebooks' is deprecated for 'nb_execution_mode' [mystnb.config] ``` `nb_render_priority` has been removed and replaced by `nb_mime_priority_overrides`, which has a different format and is more flexible. See [Outputs MIME priority](docs/render/format_code_cells.md) for more information. As per the changes in [`myst_parser`](inv:myst#develop/_changelog), the `dollarmath` syntax extension is no longer included by default. To re-add this extension, ensure that it is specified in your `conf.py`: `myst_enable_extensions = ["dollarmath"]`. For cell-level configuration the top-level key `render` has now been deprecated for `mystnb`. For example, replace: ````markdown ```{code-cell} --- render: image: width: 200px --- ... ``` ```` with: ````markdown ```{code-cell} --- mystnb: image: width: 200px --- ... ``` ```` `render` will currently still be read, if present, and will issue a `[mystnb.cell_metadata_key]` warning. The `jupyter_sphinx_require_url` and `jupyter_sphinx_embed_url` configuration options are no longer used by this package, and are replaced by `nb_ipywidgets_js`. See the [configuration section](docs/configuration.md) for more details. #### Dependencies The [ipywidgets](https://ipywidgets.readthedocs.io) package has been removed from the requirements. If required, please install it specifically. #### AST structure and rendering plugins The structure of the docutils AST and nodes produced by MyST-NB has been fully changed, for compatibility with the new [docutils only functionality](docs/docutils.md). See [the API documentation](docs/reference/api.rst) for more details. The renderer plugin system (used by the `myst_nb.renderers` entry point) has also been completely rewritten, so any current existing renderers will no longer work. There is also now a new `myst_nb.mime_renderers` entry point, to allow for targeted rendering of specific code-cell output MIME types. See [Customise the render process](docs/render/format_code_cells.md) for more information. #### Glue functionality By default, `glue` roles and directives now only work for keys within the same document. To reference glued content in a different document, the `glue:any` directive allows for a `doc` option and `glue:any`/`glue:text` roles allow the (relative) doc path to be added, for example: ````markdown ```{glue:any} var_text :doc: other.ipynb ``` {glue:text}`other.ipynb::var_float:.2E` ```` This cross-document functionality is currently restricted to only `text/plain` and `text/html` output MIME types, not images. See [Embedding outputs as variables](docs/render/glue.md) for more details. ### Dependency changes โฌ†๏ธ - Removed: - `ipywidgets` - `jupyter_sphinx` - `nbconvert` - Updated: - `Python`: `3.6+ -> 3.7+` - `myst_parser`: [`0.15 -> 0.17`](inv:myst#develop/_changelog) - `jupyter-cache`: [`0.4 -> 0.5`](https://github.com/executablebooks/jupyter-cache/blob/master/CHANGELOG.md) - `sphinx-togglebutton`: [`0.1 -> 0.3`](https://sphinx-togglebutton.readthedocs.io/en/latest/changelog.html) ### New and improved โœจ The following is a non-exhaustive list of new features and improvements, see the rest of the documentation for all the changes. - Multi-level configuration (global (`conf.py`) < notebook level metadata < cell level metadata) - Plus new config options including: `nb_number_source_lines`, `nb_remove_code_source`, `nb_remove_code_outputs`, `nb_render_error_lexer`, `nb_execution_raise_on_error`, `nb_kernel_rgx_aliases` - See the [configuration section](docs/configuration.md) for more details. - Added `mystnb-quickstart` and `mystnb-to-jupyter` CLI commands. - MyST text-based notebooks can now be specified by just: ```yaml --- file_format: mystnb kernelspec: name: python3 --- ``` as opposed to the alternative jupytext top-matter. See [Text-based Notebooks](docs/authoring/text-notebooks.md) for more details. - docutils API/CLI with command line tools, e.g. `mystnb-docutils-html` - Includes `glue` roles and directives - See [single page builds](docs/docutils.md) for more details. - Parallel friendly (e.g. `sphinx-build -j 4` can execute four notebooks in parallel) - Page specific loading of ipywidgets JavaScript, i.e. only when ipywidgets are present in the notebook. - Added raw cell rendering, with the `raw-cell` directive. See [Raw cells authoring](docs/authoring/jupyter-notebooks.md) for more details. - Added MIME render plugins. See [Customise the render process](docs/render/format_code_cells.md) for more details. - Better log info/warnings, with `type.subtype` specifiers for warning suppression. See [Warning suppression](docs/configuration.md) for more details. - Reworked jupyter-cache integration to be easier to use (including parallel execution) - Added image options to `glue:figure` - New `glue:md` role/directive includes nested parsing of MyST Markdown. See [Embedding outputs as variables](docs/render/glue.md) for more details. - Improved `nb-exec-table` directive (includes links to documents, etc) ### Additional Pull Requests - ๐Ÿ‘Œ IMPROVE: Update ANSI CSS colors by @saulshanabrook in [#384](https://github.com/executablebooks/MyST-NB/pull/384) - โœจ NEW: Add `nb_execution_raise_on_error` config by @chrisjsewell in [#404](https://github.com/executablebooks/MyST-NB/pull/404) - ๐Ÿ‘Œ IMPROVE: Add image options to `glue:figure` by @chrisjsewell in [#403](https://github.com/executablebooks/MyST-NB/pull/403) ## v0.13.2 - 2022-02-10 This release improves for cell outputs and brings UI improvements for toggling cell inputs and outputs. It also includes several bugfixes. - Add CSS support for 8-bit ANSI colours [#379](https://github.com/executablebooks/MyST-NB/pull/379) ([@thiippal](https://github.com/thiippal)) - Use configured `nb_render_plugin` for glue nodes [#337](https://github.com/executablebooks/MyST-NB/pull/337) ([@bryanwweber](https://github.com/bryanwweber)) - UPGRADE: sphinx-togglebutton v0.3.0 [#390](https://github.com/executablebooks/MyST-NB/pull/390) ([@choldgraf](https://github.com/choldgraf)) ## 0.13.1 - 2021-10-04 โœจ NEW: `nb_merge_streams` configuration [[PR #364](https://github.com/executablebooks/MyST-NB/pull/364)] If `nb_merge_streams=True`, all stdout / stderr output streams are merged into single outputs. This ensures deterministic outputs. ## 0.13.0 - 2021-09-02 ### Upgraded to `sphinx` v4 โฌ†๏ธ The primary change in this release is to update the requirements of myst-nb from `sphinx>=2,<4` to `sphinx>=3,<5` to support `sphinx>=4` [[PR #356](https://github.com/executablebooks/MyST-NB/pull/356)]. - ๐Ÿ‘Œ IMPROVE: Allows more complex suffixes in notebooks [[PR #328](https://github.com/executablebooks/MyST-NB/pull/328)] - โฌ†๏ธ UPDATE: myst-parser to `0.15.2` [[PR #353](https://github.com/executablebooks/MyST-NB/pull/353)] - โฌ†๏ธ UPGRADE: nbconvert 6 support [[PR #326](https://github.com/executablebooks/MyST-NB/pull/326)] - โฌ†๏ธ UPGRADE: markdown-it-py v1.0 [[PR #320](https://github.com/executablebooks/MyST-NB/pull/320)] - ๐Ÿ”ง MAINT: Pin ipykernel to ~v5.5 [[PR #347](https://github.com/executablebooks/MyST-NB/pull/347)] - ๐Ÿ”ง MAINT: Make a more specific selector for no-border [[PR #344](https://github.com/executablebooks/MyST-NB/pull/344)] Many thanks to @akhmerov, @bollwyvl, @choldgraf, @chrisjsewell, @juhuebner, @mmcky ## 0.12.1 - 2021-04-25 - โฌ†๏ธ UPDATE: jupyter_sphinx to `0.3.2`: fixes `Notebook code has no file extension metadata` warning) - โฌ†๏ธ UPDATE: importlib_metadata to `3.6`: to use new entry point loading interface - Official support for Python 3.9 (`0.12.2` and `0.12.3` fix a regression, when working with the entry point loading interface) ## 0.12.0 - 2021-02-23 This release adds an experimental MyST-NB feature to enable loading of code from a file for `code-cell` directives using a `:load: ` option. Usage information is available in the [docs](https://myst-nb.readthedocs.io/en/latest/use/markdown.html#syntax-for-code-cells) ## 0.11.1 - 2021-01-20 Minor update to handle MyST-Parser `v0.13.3` and `v4.5` notebooks. ## 0.11.0 - 2021-01-12 This release updates MyST-Parser to `v0.13`, which is detailed in the [myst-parser changelog](https://myst-parser.readthedocs.io/en/latest/develop/_changelog.html). The primary change is to the extension system, with extensions now all loaded *via* `myst_enable_extensions = ["dollarmath", ...]`, and a number of extensions added or improved. ## 0.10.2 - 2021-01-12 Minor fixes: - ๐Ÿ› FIX: empty myst file read - ๐Ÿ› FIX: remove cell background-color CSS for cells - ๐Ÿ”ง MAINTAIN: Pin jupyter-sphinx version ## 0.10.1 - 2020-09-08 โฌ†๏ธ UPGRADE: myst-parser v0.12.9 : Minor bug fixes and enhancements / new features ## 0.10.0 - 2020-08-28 โฌ†๏ธ UPGRADE: jupyter-sphinx v0.3, jupyter-cache v0.4.1 and nbclient v0.5. : These upgrades allow for full Windows OS compatibility, and improve the stability of notebook execution on small machines. ๐Ÿ‘Œ IMPROVE: Formatting of stderr is now similar to stdout, but with a slight red background. ๐Ÿงช TESTS: Add Windows CI ## 0.9.2 - 2020-08-27 โฌ†๏ธ UPGRADE: myst-parser patch version : to ensure a few new features and bug fixes are incorporated (see its [CHANGELOG.md](https://github.com/executablebooks/MyST-Parser/blob/master/CHANGELOG.md)) ## 0.9.1 - 2020-08-24 More configuration! - โœจ NEW: Add stderr global configuration: `nb_output_stderr` (see [removing stderr](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#removing-stdout-and-stderr)) - โœจ NEW: Add `nb_render_key` configuration (see [formatting outputs](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#images)) - ๐Ÿ› FIX: `auto` execution not recognising (and skipping) notebooks with existing outputs ## 0.9.0 - 2020-08-24 This versions see's many great changes; utilising the โฌ†๏ธ upgrade to `myst-parser=v0.12` and accompanying โฌ†๏ธ upgrade to `sphinx=v3`, as well as major refactors to the execution ([#236](https://github.com/executablebooks/MyST-NB/commit/2bc0c11cedbad6206f70546819fad85d779ce449)) and code output rendering ([#243](https://github.com/executablebooks/MyST-NB/commit/04f3bbb928cf1794e140de6a919fb58578753300)). Plus much more configuration options, to allow for a more configurable workflow (the defaults work great as well!). Below is a summary of the changes, and you can also check out many examples in the documentation, , and the MyST-Parser Changelog for all the new Markdown parsing features available: . ### New โœจ - Custom notebook formats: Configuration and logic has been added for designating additional file types to be converted to Notebooks, which are then executed & parsed in the same manner as regular Notebooks. See [Custom Notebook Formats](https://myst-nb.readthedocs.io/en/latest/examples/custom-formats.html) for details. - Allow for configuration of render priority (per output format) with `nb_render_priority`. - The code cell output renderer class is now loaded from an entry-point, with a configurable name, meaning that anyone can provide their own renderer subclass. See [Customise the render process](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#customise-the-render-process) for details. - Assignment of metadata tags `remove-stdout` and `remove-stderr` for removal of the relevant outputs ([see here](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#removing-stdout-and-stderr)) - Render `text/markdown` MIME types with an integrated CommonMark parser ([see here](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#markdown)). - Add code output image formatting, *via* cell metadata, including size, captions and labelling ([see here](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#images)). - Notebook outputs ANSI lexer which is applied to stdout/stderr and text/plain outputs, and is configurable *via* `nb_render_text_lexer` ([see here](https://myst-nb.readthedocs.io/en/latest/use/formatting_outputs.html#ansi-outputs)). - Capture execution data in sphinx env, which can be output into the documentation, with the `nb-exec-table` directive. See [Execution statistics](https://myst-nb.readthedocs.io/en/latest/use/execute.html#execution-statistics) for details. ### Improved ๐Ÿ‘Œ - Standardise auto/cache execution Both now call the same underlying function (from `jupyter-cache`) and act the same. This improves `auto`, by making it output error reports and not raising an exception on an error. Additional config has also been added: `execution_allow_errors` and `execution_in_temp`. As for for `timeout`, `allow_errors` can also be set in the notebook `metadata.execution.allow_errors` This presents one breaking change, in that `cache` will now by default execute in a the local folder as the CWD (not a temporary one). ### Fixed ๐Ÿ› - Code cell source code is now assigned the correct lexer when using custom kernels ([39c1bb9](https://github.com/executablebooks/MyST-NB/commit/39c1bb99e73b35812474366f2f1760850fe40a57)) ### Documented ๐Ÿ“š - Add example of using kernels other than Python ([676eb2c](https://github.com/executablebooks/MyST-NB/commit/676eb2c46b1ca605980180479c845b43ec64c5fb)) ### Refactored โ™ป๏ธ - Add more signature typing and docstrings - Move config value validation to separate function - Rename functions in cache.py and improve their logical flow - Rename variable stored in sphinx environment, to share same suffix: - `path_to_cache` -> `nb_path_to_cache` - `allowed_nb_exec_suffixes` -> `nb_allowed_exec_suffixes` - `excluded_nb_exec_paths` -> `nb_excluded_exec_paths` - Initial Nb output rendering: - Ensure source (path, lineno) are correctly propagated to `CellOutputBundleNode` - Capture cell level metadata in `CellOutputBundleNode` - New `CellOutputRenderer` class to contain render methods - Simplify test code, using sphinx `get_doctree` and `get_and_resolve_doctree` methods ## 0.8.5 - 2020-08-11 ### Improved ๐Ÿ‘Œ - Add configuration for traceback in stderr (#218) ### Fixed ๐Ÿ› - MIME render priority lookup ### Upgrades โฌ†๏ธ - myst-parser -> 0.9 - jupyter-cache to v0.3.0 ### Documented ๐Ÿ“š - More explanation of myst notebooks (#213) - Update contributing guide ## Contributors for previously releases Thanks to all these contributors ๐Ÿ™: [@AakashGfude](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AAakashGfude+updated%3A2020-03-28..2020-08-11&type=Issues) | [@akhmerov](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aakhmerov+updated%3A2020-03-28..2020-08-11&type=Issues) | [@amueller](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aamueller+updated%3A2020-03-28..2020-08-11&type=Issues) | [@choldgraf](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acholdgraf+updated%3A2020-03-28..2020-08-11&type=Issues) | [@chrisjsewell](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Achrisjsewell+updated%3A2020-03-28..2020-08-11&type=Issues) | [@codecov](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Acodecov+updated%3A2020-03-28..2020-08-11&type=Issues) | [@consideRatio](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3AconsideRatio+updated%3A2020-03-28..2020-08-11&type=Issues) | [@jstac](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ajstac+updated%3A2020-03-28..2020-08-11&type=Issues) | [@matthew-brett](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Amatthew-brett+updated%3A2020-03-28..2020-08-11&type=Issues) | [@mmcky](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Ammcky+updated%3A2020-03-28..2020-08-11&type=Issues) | [@phaustin](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aphaustin+updated%3A2020-03-28..2020-08-11&type=Issues) | [@rossbar](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Arossbar+updated%3A2020-03-28..2020-08-11&type=Issues) | [@rowanc1](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Arowanc1+updated%3A2020-03-28..2020-08-11&type=Issues) | [@seanpue](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Aseanpue+updated%3A2020-03-28..2020-08-11&type=Issues) | [@stefanv](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Astefanv+updated%3A2020-03-28..2020-08-11&type=Issues) | [@TomDonoghue](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3ATomDonoghue+updated%3A2020-03-28..2020-08-11&type=Issues) | [@tonyfast](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Atonyfast+updated%3A2020-03-28..2020-08-11&type=Issues) | [@welcome](https://github.com/search?q=repo%3Aexecutablebooks%2FMyST-NB+involves%3Awelcome+updated%3A2020-03-28..2020-08-11&type=Issues) MyST-NB-1.1.2/LICENSE000066400000000000000000000027711467453560600137000ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2020, ExecutableBookProject All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. MyST-NB-1.1.2/README.md000066400000000000000000000027331467453560600141500ustar00rootroot00000000000000# MyST-NB [![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![PyPI][pypi-badge]][pypi-link] [![Conda Version][conda-badge]][conda-link] A collection of tools for working with Jupyter Notebooks in Sphinx. The primary tool this package provides is a Sphinx parser for `ipynb` files. This allows you to directly convert Jupyter Notebooks into Sphinx documents. It relies heavily on the [`MyST` parser](https://github.com/executablebooks/myst-parser). For more information, [see the MyST-NB documentation](https://myst-nb.readthedocs.io/en/latest/) ## Contributing We welcome all contributions! See the [Contributing Guide](https://myst-nb.readthedocs.io/en/latest/reference/contributing.html) for more details. [github-ci]: https://github.com/executablebooks/MyST-NB/workflows/continuous-integration/badge.svg?branch=master [github-link]: https://github.com/executablebooks/MyST-NB [rtd-badge]: https://readthedocs.org/projects/myst-nb/badge/?version=latest [rtd-link]: https://myst-nb.readthedocs.io/en/latest/?badge=latest [codecov-badge]: https://codecov.io/gh/executablebooks/MyST-NB/branch/master/graph/badge.svg [codecov-link]: https://codecov.io/gh/executablebooks/MyST-NB [pypi-badge]: https://img.shields.io/pypi/v/myst-nb.svg [pypi-link]: https://pypi.org/project/myst-nb [conda-badge]: https://img.shields.io/conda/vn/conda-forge/myst-nb.svg [conda-link]: https://anaconda.org/conda-forge/myst-nb MyST-NB-1.1.2/codecov.yml000066400000000000000000000002421467453560600150270ustar00rootroot00000000000000coverage: status: project: default: target: 80% threshold: 0.5% patch: default: target: 80% threshold: 0.5% MyST-NB-1.1.2/docs/000077500000000000000000000000001467453560600136145ustar00rootroot00000000000000MyST-NB-1.1.2/docs/_static/000077500000000000000000000000001467453560600152425ustar00rootroot00000000000000MyST-NB-1.1.2/docs/_static/custom.css000066400000000000000000000005101467453560600172620ustar00rootroot00000000000000/** Add a counter before subsections **/ h1 { counter-reset: subsection; } h2 { counter-reset: subsubsection; } h2::before { counter-increment: subsection; content: counter(subsection) ". "; } h3::before { counter-increment: subsubsection; content: counter(subsection) "." counter(subsubsection) ". "; } MyST-NB-1.1.2/docs/_static/logo-square.svg000066400000000000000000000021231467453560600202170ustar00rootroot00000000000000logo-square MyST-NB-1.1.2/docs/_static/logo-wide.svg000066400000000000000000000063611467453560600176570ustar00rootroot00000000000000 MyST-NB-1.1.2/docs/authoring/000077500000000000000000000000001467453560600156145ustar00rootroot00000000000000MyST-NB-1.1.2/docs/authoring/basics.md000066400000000000000000000065171467453560600174130ustar00rootroot00000000000000 (authoring/intro)= # The basics ## Default file formats As well as writing in the Sphinx default format, [RestructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) (`.rst`), loading `myst_nb` will also parse: - Markdown files (`.md`) - Jupyter notebooks (`.ipynb`) - MyST-NB text-based notebooks (`.md` + top-matter) ## Custom file extensions You can change which file extensions are parsed by MyST-NB using the [source_suffix](https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-source_suffix) option in your `conf.py`, e.g.: ```python extensions = ["myst_nb"] source_suffix = { '.rst': 'restructuredtext', '.ipynb': 'myst-nb', '.myst': 'myst-nb', } ``` ## Other notebook formats See the [](write/custom_formats) section, for how to integrate other Notebook formats into your build, and integration with [jupytext](https://github.com/mwouts/jupytext). ## MyST Markdown For all file formats, Markdown authoring is the backbone of MyST-NB. By default, the MyST flavour of Markdown is enabled, which extends [CommonMark](https://commonmark.org/) with RST inspired syntaxes to provide the additional functionality required for technical writing. In particular MyST adds targets, roles and directives syntax, allowing you to utilise all available Docutils/Sphinx features: ::::{grid} 2 :gutter: 1 :::{grid-item-card} RestructuredText ``` .. _target: Header ------ :role-name:`content` .. directive-name:: argument :parameter: value content ``` ::: :::{grid-item-card} MyST Markdown ```` (target)= # Header {role-name}`content` ```{directive-name} argument :parameter: value content ``` ```` ::: :::: :::{seealso} See the [](authoring/jupyter-notebooks) section, for more details on how to author Jupyter notebooks. ::: ## Text-based notebooks MyST-NB text-based notebooks are a special format for storing Jupyter notebooks in a text file. They map directly to a Notebook file, without directly storing the code execution outputs. To designate a Markdown file as a text-based notebook, add the following top-matter to the start of the file: ```yaml --- file_format: mystnb kernelspec: name: python3 --- ``` The `kernelspec.name` should relate to a [Jupyter kernel](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) installed in your environment. MyST-NB will also recognise [jupytext](https://jupytext.readthedocs.io) top-matter, such as: ```yaml --- kernelspec: name: python3 display_name: python3 jupytext: text_representation: extension: .md format_name: myst format_version: '0.13' jupytext_version: 1.13.8 --- ``` Code cells are then designated by the `code-cell` directive: ````markdown ```{code-cell} :tags: [my-tag] print("Hello world!") ``` ```` and Markdown can be split into cells by the `+++` break syntax: ```markdown Markdown cell 1 +++ {"tags": ["my-tag"]} Markdown cell 2, with metadata tags ``` :::{seealso} See the [](authoring/text-notebooks) section, for more details on text-based notebooks, and integration with [jupytext](https://jupytext.readthedocs.io). ::: ## Configuration MyST-NB parsing, execution and rendering can be configured at three levels of specificity; globally, per file, and per notebook cell, with the most specific configuration taking precedence. See the [](config/intro) section, for more details on how to configure MyST-NB. MyST-NB-1.1.2/docs/authoring/custom-formats.Rmd000066400000000000000000000054531467453560600212520ustar00rootroot00000000000000--- author: "Marc Wouts" date: "June 16, 2018" jupyter: kernelspec: display_name: Python language: python name: python3 --- (write/custom_formats)= # Custom Formats You can designate additional file types to be converted to Notebooks, and then executed / parsed in the same manner as regular Notebooks, by adding the following configuration to your `conf.py`: ```python nb_custom_formats = { ".mysuffix": "mylibrary.converter_function" } ``` - The string should be a Python function that will be loaded by `import mylibrary.converter_function` - The function should take a file's contents (as a `str`) and return an [nbformat.NotebookNode](inv:nbformat#api) If the function takes additional keyword arguments, then you can specify these as dictionary in a second argument. For example this is what the default conversion would look like: ```python nb_custom_formats = { '.ipynb': ['nbformat.reads', {'as_version': 4}], } ``` :::{important} By default, Markdown cells in the Notebook will be parsed using the same MyST parser configuration as for other Markdown files ([see available configuration options](config/intro)). But, if this is incompatible with your file format, then you can specify for the Markdown to be parsed as **strictly CommonMark**, using a third argument: ```python nb_custom_formats = { '.ipynb': ['nbformat.reads', {'as_version': 4}, True], } ``` ::: Finally, for text-based formats, MyST-NB also searches for an optional `source_map` key in the output Notebook's metadata. This key should be a list mapping each cell to the starting line number in the original source file, for example for a notebook with three cells: ```json { "metadata": { "source_map": [10, 21, 53] } } ``` This mapping allows for "true" error reporting, as described in [](myst/error-reporting). ## Using Jupytext A common conversion tool is [jupytext](https://github.com/mwouts/jupytext), which has been used to convert this very `.Rmd` file to a notebook! The configuration looks like: ```python nb_custom_formats = { ".Rmd": ["jupytext.reads", {"fmt": "Rmd"}] } ``` :::{important} For full compatibility with `myst-nb`, `jupytext>=1.11.2` should be used. ::: For example: ``` \```{python echo=TRUE} import pandas as pd series = pd.Series({'A':1, 'B':3, 'C':2}) pd.DataFrame({"Columne A": series}) \``` ``` ```{python echo=TRUE} import pandas as pd series = pd.Series({'A':1, 'B':3, 'C':2}) pd.DataFrame({"Columne A": series}) ``` ``` \```{python bar_plot, echo=FALSE, fig.height=5, fig.width=8} series.plot(kind='bar', title='Sample plot') \``` ``` ```{python bar_plot, echo=FALSE, fig.height=5, fig.width=8} series.plot(kind='bar', title='Sample plot') ``` ## Acknowledgements Thanks to [nbsphinx](https://nbsphinx.readthedocs.io), for providing the initial implementation which this was built on. MyST-NB-1.1.2/docs/authoring/index.md000066400000000000000000000002131467453560600172410ustar00rootroot00000000000000# Authoring Create content in one or more formats. ```{toctree} :maxdepth: 1 basics jupyter-notebooks text-notebooks custom-formats ``` MyST-NB-1.1.2/docs/authoring/jupyter-notebooks.md000066400000000000000000000111161467453560600216410ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- (authoring/jupyter-notebooks)= # Jupyter Notebooks This notebook is a demonstration of directly-parsing Jupyter Notebooks into Sphinx using the MyST parser.[^download] [^download]: This notebook can be downloaded as **{nb-download}`jupyter-notebooks.ipynb`** and {download}`jupyter-notebooks.md` ## Markdown :::{seealso} For more information about what you can write with MyST Markdown, see the [MyST Parser documentation](inv:myst#intro/get-started). ::: ### Configuration The MyST-NB parser derives from [the base MyST-Parser](inv:myst#intro/get-started), and so all the same configuration options are available. See the [MyST configuration options](inv:myst#sphinx/config-options) for the full set of options, and [MyST syntax guide](inv:myst#syntax/core) for all the syntax options. To build documentation from this notebook, the following options are set: ```python myst_enable_extensions = [ "amsmath", "colon_fence", "deflist", "dollarmath", "html_image", ] myst_url_schemes = ("http", "https", "mailto") ``` :::{note} Loading the `myst_nb` extension also activates the [`myst_parser`](inv:myst#index) extension, for enabling the MyST flavour of Markdown. It is not required to add this explicitly in the list of `extensions`. ::: ### Syntax As you can see, markdown is parsed as expected. Embedding images should work as expected. For example, here's the MyST-NB logo: ```md ![myst-nb logo](../_static/logo-wide.svg) ``` ![myst-nb logo](../_static/logo-wide.svg) By adding `"html_image"` to the `myst_enable_extensions` list in the sphinx configuration ([see here](inv:myst#syntax/images)), you can even add HTML `img` tags with attributes: ```html logo ``` logo Because MyST-NB is using the MyST-markdown parser, you can include rich markdown with Sphinx in your notebook. For example, here's a note admonition block: :::::{note} **Wow**, a note! It was generated with this code ([as explained here](inv:myst:std:label#syntax/admonitions)): ````md :::{note} **Wow**, a note! ::: ```` ::::: If you wish to use "bare" LaTeX equations, then you should add `"amsmath"` to the `myst_enable_extensions` list in the sphinx configuration. This is [explained here](inv:myst:std:label#syntax/amsmath), and works as such: ```latex \begin{equation} \frac {\partial u}{\partial x} + \frac{\partial v}{\partial y} = - \, \frac{\partial w}{\partial z} \end{equation} \begin{align*} 2x - 5y &= 8 \\ 3x + 9y &= -12 \end{align*} ``` \begin{equation} \frac {\partial u}{\partial x} + \frac{\partial v}{\partial y} = - \, \frac{\partial w}{\partial z} \end{equation} \begin{align*} 2x - 5y &= 8 \\ 3x + 9y &= -12 \end{align*} Also you can use features like **equation numbering** and referencing in the notebooks: ```md $$e^{i\pi} + 1 = 0$$ (euler) ``` $$e^{i\pi} + 1 = 0$$ (euler) Euler's identity, equation {math:numref}`euler`, was elected one of the most beautiful mathematical formulas. You can see the syntax used for this example [here in the MyST documentation](inv:myst:std:label#syntax/math). ## Code cells and outputs You can run cells, and the cell outputs will be captured and inserted into the resulting Sphinx site. ### `__repr__` and HTML outputs For example, here's some simple Python: ```{code-cell} ipython3 import matplotlib.pyplot as plt import numpy as np data = np.random.rand(3, 100) * 100 data[:, :10] ``` This will also work with HTML outputs ```{code-cell} ipython3 import pandas as pd df = pd.DataFrame(data.T, columns=['a', 'b', 'c']) df.head() ``` as well as math outputs ```{code-cell} ipython3 from IPython.display import Math Math(r"\sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6}") ``` This works for error messages as well: ```{code-cell} ipython3 :tags: [raises-exception] print("This will be properly printed...") print(thiswont) ``` ### Images Images that are generated from your code (e.g., with Matplotlib) will also be embedded. ```{code-cell} ipython3 fig, ax = plt.subplots() ax.scatter(*data, c=data[2]) ``` ## Raw cells The [raw cell type](https://nbformat.readthedocs.io/en/latest/format_description.html#raw-nbconvert-cells) can be used to specifically render the content as a specific [MIME media type](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types). ````markdown ```{raw-cell} :format: text/html

My cat is very grumpy.

``` ```` ```{raw-cell} :format: text/html

My cat is very grumpy.

``` MyST-NB-1.1.2/docs/authoring/text-notebooks.md000066400000000000000000000146611467453560600211330ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- (authoring/text-notebooks)= # Text-based Notebooks MyST Markdown notebooks allow you to write your Jupyter Notebook entirely in markdown, utilising the MyST Markdown notebook format. This allows you to store notebook metadata, Markdown, and cell inputs in a text-based format that is easy to read and use with text-based tools. MyST notebooks have a 1-to-1 mapping with Jupyter notebook, so can be [converted to `.ipynb` files](converting-ipynb) and [opened as notebooks in Jupyter interfaces](myst-nb/jupyter-interfaces) (with jupytext installed). When used with `myst_nb`, MyST notebooks are also integrated directly into the {ref}`Execution and Caching ` machinery.[^download] [^download]: This notebook can be downloaded as **{nb-download}`text-notebooks.ipynb`** and {download}`text-notebooks.md` ## The MyST notebook Structure MyST Markdown Notebooks (or MyST notebooks for short) have four main types of content: - **cell/notebook level metadata** that are written as YAML wrapped in `---` - **markdown cells** that can be written as CommonMark or MyST Markdown - **code cells** that are written with the MyST Markdown `code-cell` directive syntax - **raw cells** that are written with the MyST Markdown `raw-cell` directive syntax ### Notebook-level metadata Begin a MyST notebook file with YAML top-matter metadata, containing at least the `file_format: mystnb` signifier. This will be used as **notebook-level metadata** for the resulting Jupyter Notebook. This metadata takes the following form: ```yaml --- file_format: mystnb kernelspec: name: python3 otherkey1: val1 otherkey2: val2 --- # Notebook title ... ``` The kernel that your code cells use is determined by the `kernelspec.name` field, and should relate to a [Jupyter kernel](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) installed in your environment and registered with Jupyter. If no kernel is given, then the default kernel will be used. ### Syntax for Markdown Anything in-between code cells will be treated as Markdown. You can use any Markdown that is valid MyST. If you are using MyST notebooks with the `myst_nb` Sphinx extension, you can write Sphinx directives and roles. However, note that most Jupyter notebook environments may not be able to render MyST Markdown syntax. **To denote a break between two markdown cells**, use the following syntax: ``` Some markdown +++ {"optionalkey": "val"} More markdown ``` This will result in two markdown cells in the resulting notebook. The key:val pairs specified in the `{}` brackets will be cell-level metadata in the second markdown cell. ### Syntax for code cells When writing MyST notebooks, use the following syntax to define a code cell: ````md ```{code-cell} ipython3 a = "This is some" b = "Python code!" print(f"{a} {b}") ``` ```` The argument after `{code-cell}` (above, `ipython3`) is optional, and is used for readability purposes. The content inside `{code-cell}` makes up the content of the cell, and will be executed at build time. This will result in the following output after building your site: ```{code-cell} ipython3 a = "This is some" b = "Python code!" print(f"{a} {b}") ``` ### Cell-level metadata You can begin `code-cell` blocks with top-matter metadata. These will be used as **cell-level metadata** in the resulting notebook cell. The same metadata tags can be used as you would in a normal notebook, for example those discussed in {ref}`use/hiding/code`: ````md ```{code-cell} ipython3 --- tags: [hide-output] --- for i in range(20): print("Millhouse did not test cootie positive") ``` ```` Yields the following: ```{code-cell} ipython3 --- tags: [hide-output] --- for i in range(20): print("Millhouse did not test cootie positive") ``` There is also an **alternative short-hand syntax** for cell-level metadata. This takes the following form: ````md ```{code-cell} :key: val print("hi") ``` ```` For example, the following syntax adds a `raises-exception` tag to the cell, which means our code will execute without halting the kernel: ````md ```{code-cell} ipython3 :tags: [raises-exception] raise ValueError("oopsie!") ``` ```` ```{code-cell} ipython3 :tags: [raises-exception] raise ValueError("oopsie!") ``` (converting-ipynb)= ## Convert between MyST notebooks and `.ipynb` MyST notebooks can be converted to Jupyter notebooks using the `mystnb-to-jupyter` CLI command. ```console $ mystnb-to-jupyter path/to/text-notebook.md Wrote notebook to: path/to/text-notebook.ipynb ``` MyST notebooks can also be converted back-and-forth from `ipynb` files using [jupytext](https://jupytext.readthedocs.io), a Python library for two-way conversion of `ipynb` files with many text-based formats. To let jupytext know the format of the notebook, add the notebook top-matter similar to: ```yaml --- kernelspec: name: python3 display_name: python3 jupytext: text_representation: extension: .md format_name: myst format_version: '0.13' jupytext_version: 1.13.8 --- ``` Then you can run: - To convert `.ipynb` to a MyST notebook, run: `jupytext notebook.ipynb --to myst` - To convert a MyST notebook to `.ipynb`, run: `jupytext mystfile.md --to ipynb` :::{seealso} For more information, see the [Jupytext Documentation](https://jupytext.readthedocs.io), and specifically the [MyST Markdown format](https://jupytext.readthedocs.io/en/latest/formats.html#myst-markdown). ::: (myst-nb/jupyter-interfaces)= ## MyST notebooks in Jupyter interfaces You can use MyST notebooks in Jupyter interfaces by using Jupytext extensions. This allows you to open a MyST Markdown Notebook as a "regular" Jupyter Notebook in Jupyter Lab and the Classic Notebook interface. For more information, see [the Jupytext documentation](https://jupytext.readthedocs.io). (myst-nb/jupyter-book)= ## MyST notebooks in Jupyter Book In addition to using MyST notebooks with Sphinx, you may also use them with the Jupyter Book project. See {doc}`jb:file-types/myst-notebooks`. ## Code from Files ```{warning} This is an experimental feature that is **not** part of the core `MyST` markup specification, and may be removed in the future. Using `:load:` will also overwrite any code written into the directive. ``` `myst_nb` provides a convenience feature for importing executable code into a `{code-cell}` from a file. This can be useful when you want to share code between documents. To do this you specify a `load` metadata attribute such as: ````md ```{code-cell} ipython3 :load: ``` ```` MyST-NB-1.1.2/docs/computation/000077500000000000000000000000001467453560600161565ustar00rootroot00000000000000MyST-NB-1.1.2/docs/computation/coconut-lang.md000066400000000000000000000040671467453560600211000ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: coconut --- # Jupyter kernels A Jupyter Notebook can utilise any program kernel that implements the [Jupyter messaging protocol](http://jupyter-client.readthedocs.io/en/latest/messaging.html) for executing code. There are kernels available for [Python](http://ipython.org/notebook.html), [Julia](https://github.com/JuliaLang/IJulia.jl), [Ruby](https://github.com/minad/iruby), [Haskell](https://github.com/gibiansky/IHaskell) and [many other languages](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels). In this notebook we demonstrate executing code with the [Coconut Programming Language](http://coconut-lang.org), a variant of Python built for *simple, elegant, Pythonic functional programming*. In the first example we will define a recursive `factorial` function, a fundamentally functional approach that doesnโ€™t involve any state changes or loops: ```{code-cell} coconut def factorial(n): """Compute n! where n is an integer >= 0.""" case n: match 0: return 1 match x is int if x > 0: return x * factorial(x-1) else: raise TypeError("the argument to factorial must be an integer >= 0") 3 |> factorial |> print ``` Although this example is very basic, pattern-matching is both one of Coconutโ€™s most powerful and most complicated features. In the second example, we implement the quick sort algorithm. This quick_sort algorithm works using a bunch of new constructs: ```{code-cell} coconut def quick_sort(l): """Sort the input iterator using the quick sort algorithm.""" match [head] :: tail in l: tail = reiterable(tail) yield from quick_sort(left) :: [head] :: quick_sort(right) where: left = (x for x in tail if x < head) right = (x for x in tail if x >= head) # By yielding nothing if the match falls through, we implicitly return an empty iterator. [3,0,4,2,1] |> quick_sort |> list |> print ``` Finally, we see that exceptions are raised as one would expect: ```{code-cell} coconut :tags: [raises-exception] x ``` MyST-NB-1.1.2/docs/computation/execute.md000066400000000000000000000175111467453560600201470ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- (execute/intro)= # Execute and cache MyST-NB can automatically run and cache notebooks contained in your project using [jupyter-cache]. Notebooks can either be run each time the documentation is built, or cached locally so that re-runs occur only when code cells have changed. Execution and caching behaviour is controlled with configuration at a global or per-file level, as outlined in the [configuration section](config/intro). See the sections below for a description of each configuration option and their effect. (execute/modes)= ## Notebook execution modes To trigger the execution of notebook pages, use the global `nb_execution_mode` configuration key, or per-file `execution_mode` key: | Mode | Description | | -------- | -------------------------------------------------------------------- | | `off` | Do not execute the notebook | | `force` | Always execute the notebook (before parsing) | | `auto` | Execute notebooks with missing outputs (before parsing) | | `cache` | Execute notebook and store/retrieve outputs from a cache | | `inline` | Execute the notebook during parsing (allows for variable evaluation) | By default this is set to: ```python nb_execution_mode = "auto" ``` This will only execute notebooks that are missing at least one output. If a notebook has *all* of its outputs populated, then it will not be executed. To force the execution of all notebooks, regardless of their outputs, change the above configuration value to: ```python nb_execution_mode = "force" ``` To cache execution outputs with [jupyter-cache], change the above configuration value to: ```python nb_execution_mode = "cache" ``` See {ref}`execute/cache` for more information. To execute notebooks inline during parsing, change the above configuration value to: ```python nb_execution_mode = "inline" ``` This allows for the use of `eval` roles/directives to embed variables, evaluated from the execution kernel, inline of the Markdown. See {ref}`render/eval` for more information. To turn off notebook execution, change the above configuration value to: ```python nb_execution_mode = "off" ``` ## Exclude notebooks from execution To exclude certain file patterns from execution, use the following configuration: ```python nb_execution_excludepatterns = ['list', 'of', '*patterns'] ``` Any file that matches one of the items in `nb_execution_excludepatterns` will not be executed. (execute/cache)= ## Cache execution outputs As mentioned above, you can cache the results of executing a notebook page by setting: ```python nb_execution_mode = "cache" ``` In this case, when a page is executed, its outputs will be stored in a local database. This allows you to be sure that the outputs in your documentation are up-to-date, while saving time avoiding unnecessary re-execution. It also allows you to store your `.ipynb` files (or their `.md` equivalent) in your `git` repository *without their outputs*, but still leverage a cache to save time when building your site. :::{tip} You should only use this option when notebooks have deterministic execution outputs: - You use the same environment to run them (e.g. the same installed packages) - They run no non-deterministic code (e.g. random numbers) - They do not depend on external resources (e.g. files or network connections) that change over time ::: When you re-build your site, the following will happen: - Notebooks that have not seen changes to their **code cells** or **metadata** since the last build will not be re-executed. Instead, their outputs will be pulled from the cache and inserted into your site. - Notebooks that **have any change to their code cells** will be re-executed, and the cache will be updated with the new outputs. By default, the cache will be placed in the parent of your build folder. Generally, this is in `_build/.jupyter_cache`, and it will also be specified in the build log, e.g. ``` Using jupyter-cache at: ./docs/_build/.jupyter_cache ``` You may also specify a path to the location of a jupyter cache you'd like to use: ```python nb_execution_cache_path = "path/to/mycache" ``` The path should point to an **empty folder**, or a folder where a **jupyter cache already exists**. Once you have run the documentation build, you can inspect the contents of the cache with the following command: ```console $ jcache notebook -p docs/_build/.jupyter_cache list ``` See [jupyter-cache] for more information. [jupyter-cache]: https://github.com/executablebooks/jupyter-cache "the Jupyter Cache Project" ## Execute with a different kernel name If you require your notebooks to run with a different kernel, to those specified in the actual files, you can set global aliases with e.g. ```python nb_kernel_rgx_aliases = {"oth.*": "python3"} ``` The mapping keys are [regular expressions](https://www.regular-expressions.info/) so, for example `oth.*` will match any kernel name starting with `oth`. ## Executing in temporary folders By default, the command working directory (cwd) that a notebook runs in will be the directory it is located in. However, you can set `nb_execution_in_temp=True` in your `conf.py`, to change this behaviour such that, for each execution, a temporary directory will be created and used as the cwd. (execute/timeout)= ## Execution timeout The execution of notebooks is managed by {doc}`nbclient `. The `nb_execution_timeout` sphinx option defines the maximum time (in seconds) each notebook cell is allowed to run. If the execution takes longer an exception will be raised. The default is 30 s, so in cases of long-running cells you may want to specify an higher value. The timeout option can also be set to `None` or -1 to remove any restriction on execution time. This global value can also be overridden per notebook by adding this to you notebooks metadata: ```json { "metadata": { "execution": { "timeout": 30 } } ``` (execute/allow_errors)= ## Raise errors in code cells In some cases, you may want to intentionally show code that doesn't work (e.g., to show the error message). You can achieve this at "three levels": Globally, by setting `nb_execution_allow_errors=True` in your `conf.py`. Per notebook (overrides global), by adding this to you notebooks metadata: ```json { "metadata": { "execution": { "allow_errors": true } } ``` Per cell, by adding a `raises-exception` tag to your code cell. This can be done via a Jupyter interface, or via the `{code-cell}` directive like so: ````md ```{code-cell} :tags: [raises-exception] print(thisvariabledoesntexist) ``` ```` Which produces: ```{code-cell} --- tags: [raises-exception] --- print(thisvariabledoesntexist) ``` (execute/raise_on_error)= ## Error reporting: Warning vs. Failure When an error occurs in a context where `nb_execution_allow_errors=False`, the default behaviour is for this to be reported as a warning. This warning will simply be logged and not cause the build to fail unless `sphinx-build` is run with the [`-W` option](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#cmdoption-sphinx-build-W). If you would like unexpected execution errors to cause a build failure rather than a warning regardless of the `-W` option, you can achieve this by setting `nb_execution_raise_on_error=True` in your `conf.py`. (execute/statistics)= ## Execution statistics As notebooks are executed, certain statistics are stored in a dictionary, and saved on the [sphinx environment object](https://www.sphinx-doc.org/en/master/extdev/envapi.html#sphinx.environment.BuildEnvironment) in `env.metadata[docname]`. You can access this in a post-transform in your own sphinx extensions, or use the built-in `nb-exec-table` directive: ````md ```{nb-exec-table} ``` ```` which produces: ```{nb-exec-table} ``` MyST-NB-1.1.2/docs/computation/index.md000066400000000000000000000001321467453560600176030ustar00rootroot00000000000000# Computations Execute code and cache its output. ```{toctree} execute coconut-lang ``` MyST-NB-1.1.2/docs/conf.py000066400000000000000000000243651467453560600151250ustar00rootroot00000000000000# Configuration file for the Sphinx documentation builder. # https://www.sphinx-doc.org/en/master/usage/configuration.html import os # -- Project information ----------------------------------------------------- project = "MyST-NB" copyright = "2023, Executable Book Project" author = "Executable Book Project" master_doc = "index" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "myst_nb", "sphinx_copybutton", "sphinx_book_theme", "sphinx.ext.intersphinx", "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx_design", ] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] myst_enable_extensions = [ "amsmath", "colon_fence", "deflist", "dollarmath", "html_image", ] nb_custom_formats = {".Rmd": ["jupytext.reads", {"fmt": "Rmd"}]} nb_execution_mode = "cache" nb_execution_show_tb = "READTHEDOCS" in os.environ nb_execution_timeout = 60 # Note: 30 was timing out on RTD nb_ipywidgets_js = { "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js": { "integrity": "sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=", "crossorigin": "anonymous", }, "https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@*/dist/embed-amd.js": { "data-jupyter-widgets-cdn": "https://cdn.jsdelivr.net/npm/", "crossorigin": "anonymous", }, } # nb_render_image_options = {"width": "200px"} # application/vnd.plotly.v1+json and application/vnd.bokehjs_load.v0+json suppress_warnings = ["mystnb.unknown_mime_type"] intersphinx_mapping = { "python": ("https://docs.python.org/3.8", None), "jb": ("https://jupyterbook.org/", None), "myst": ("https://myst-parser.readthedocs.io/en/latest/", None), "markdown_it": ("https://markdown-it-py.readthedocs.io/en/latest", None), "nbclient": ("https://nbclient.readthedocs.io/en/latest", None), "nbformat": ("https://nbformat.readthedocs.io/en/latest", None), "sphinx": ("https://www.sphinx-doc.org/en/master", None), } intersphinx_cache_limit = 5 # ignore these type annotations nitpick_ignore = [ ("py:class", klass) for klass in [ "Path", "docutils.nodes.document", "docutils.nodes.Node", "docutils.nodes.Element", "nodes.Element", "docutils.nodes.container", "docutils.nodes.system_message", "DocutilsNbRenderer", "SphinxNbRenderer", "nbformat.notebooknode.NotebookNode", "nbf.NotebookNode", "NotebookNode", "pygments.lexer.RegexLexer", "LoggerType", "ExecutionResult", "MdParserConfig", "NbParserConfig", "NbReader", # Literal values are not supported "typing_extensions.Literal", "typing_extensions.Literal[show, remove, remove - warn, warn, error, severe]", "off", "force", "auto", "cache", "commonmark", "gfm", "myst", ] ] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_title = "" html_theme = "sphinx_book_theme" html_logo = "_static/logo-wide.svg" html_favicon = "_static/logo-square.svg" html_theme_options = { "github_url": "https://github.com/executablebooks/myst-nb", "repository_url": "https://github.com/executablebooks/myst-nb", "repository_branch": "master", "home_page_in_toc": True, "path_to_docs": "docs", "show_navbar_depth": 1, "use_edit_page_button": True, "use_repository_button": True, "use_download_button": True, "launch_buttons": { "binderhub_url": "https://mybinder.org", "notebook_interface": "classic", }, "navigation_with_keys": False, } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_css_files = ["custom.css"] copybutton_selector = "div:not(.output) > div.highlight pre" def setup(app): """Add functions to the Sphinx setup.""" import subprocess from typing import cast from docutils import nodes from docutils.parsers.rst import directives from myst_parser.config.main import MdParserConfig from sphinx.application import Sphinx from sphinx.util.docutils import SphinxDirective from myst_nb.core.config import NbParserConfig, Section app = cast(Sphinx, app) # this is required to register the coconut kernel with Jupyter, # to execute docs/examples/coconut-lang.md subprocess.check_call(["coconut", "--jupyter"]) class _ConfigBase(SphinxDirective): """Directive to automate printing of the configuration.""" @staticmethod def table_header(): return [ "```````{list-table}", ":header-rows: 1", "", "* - Name", " - Type", " - Default", " - Description", ] @staticmethod def field_default(value): default = " ".join(f"{value!r}".splitlines()) if len(default) > 20: default = default[:20] + "..." return default @staticmethod def field_type(field): ctype = " ".join(str(field.type).splitlines()) ctype = ctype.replace("typing.", "") ctype = ctype.replace("typing_extensions.", "") for tname in ("str", "int", "float", "bool"): ctype = ctype.replace(f"", tname) return ctype class MystNbConfigDirective(_ConfigBase): required_arguments = 1 option_spec = { "sphinx": directives.flag, "section": lambda x: directives.choice( x, ["config", "read", "execute", "render"] ), } def run(self): """Run the directive.""" level_name = directives.choice( self.arguments[0], ["global_lvl", "file_lvl", "cell_lvl"] ) level = Section[level_name] config = NbParserConfig() text = self.table_header() count = 0 for name, value, field in config.as_triple(): # filter by sphinx options if "sphinx" in self.options and field.metadata.get("sphinx_exclude"): continue # filter by level sections = field.metadata.get("sections") or [] if level not in sections: continue # filter by section if "section" in self.options: section = Section[self.options["section"]] if section not in sections: continue if level == Section.global_lvl: name = f"nb_{name}" elif level == Section.cell_lvl: name = field.metadata.get("cell_key", name) description = " ".join(field.metadata.get("help", "").splitlines()) default = self.field_default(value) ctype = self.field_type(field) text.extend( [ f"* - `{name}`", f" - `{ctype}`", f" - `{default}`", f" - {description}", ] ) count += 1 if not count: return [] text.append("```````") node = nodes.Element() self.state.nested_parse(text, 0, node) return node.children class MystConfigDirective(_ConfigBase): option_spec = { "sphinx": directives.flag, } def run(self): """Run the directive.""" config = MdParserConfig() text = self.table_header() count = 0 for name, value, field in config.as_triple(): # filter by sphinx options if "sphinx" in self.options and field.metadata.get("sphinx_exclude"): continue name = f"myst_{name}" description = " ".join(field.metadata.get("help", "").splitlines()) default = self.field_default(value) ctype = self.field_type(field) text.extend( [ f"* - `{name}`", f" - `{ctype}`", f" - `{default}`", f" - {description}", ] ) count += 1 if not count: return [] text.append("```````") node = nodes.Element() self.state.nested_parse(text, 0, node) return node.children class DocutilsCliHelpDirective(SphinxDirective): """Directive to print the docutils CLI help.""" has_content = False required_arguments = 0 optional_arguments = 0 final_argument_whitespace = False option_spec = {} def run(self): """Run the directive.""" import io from docutils import nodes from docutils.frontend import OptionParser from myst_nb.docutils_ import Parser as DocutilsParser stream = io.StringIO() OptionParser( components=(DocutilsParser,), usage="mystnb-docutils- [options] [ []]", ).print_help(stream) return [nodes.literal_block("", stream.getvalue())] app.add_directive("myst-config", MystConfigDirective) app.add_directive("mystnb-config", MystNbConfigDirective) app.add_directive("docutils-cli-help", DocutilsCliHelpDirective) MyST-NB-1.1.2/docs/configuration.md000066400000000000000000000115731467453560600170140ustar00rootroot00000000000000 (config/intro)= # Configuration MyST-NB can be configured at three levels of specificity; globally, per file, and per notebook cell, with the most specific configuration taking precedence. ## Global configuration Overriding the default configuration at the global level is achieved by specifying variables in the Sphinx `conf.py` file. All `myst_nb` configuration variables are prefixed with `nb_`, e.g. ```python nb_execution_timeout = 60 ``` ### Reading Control how files are read. ```{mystnb-config} global_lvl :sphinx: :section: read ``` ### Execution This configuration is used to control how Jupyter Notebooks are executed at build-time. ```{mystnb-config} global_lvl :sphinx: :section: execute ``` ### Rendering These configuration options affect the look and feel of notebook parsing and output rendering. ```{mystnb-config} global_lvl :sphinx: :section: render ``` ## File level configuration Overriding the default configuration at a per-file level is achieved by specifying variables in the files metadata, under the `mystnb` key. In Jupyter notebooks, this is added in the notebook-level metadata, e.g. ```json { "metadata": { "mystnb": { "execution_timeout": 60 } } } ``` In text-based notebooks, this is added in the YAML top-matter, e.g. ```yaml --- file_format: mystnb mystnb: execution_timeout: 60 --- ``` ### Execution This configuration is used to control how Jupyter Notebooks are executed at build-time. ```{mystnb-config} file_lvl :sphinx: :section: execute ``` ### Rendering These configuration options affect the look and feel of notebook parsing and output rendering. ```{mystnb-config} file_lvl :sphinx: :section: render ``` ## Cell level configuration Overriding the default configuration at a per-cell level is achieved by specifying variables in the cell metadata, under the `mystnb` key. In Jupyter notebooks, this is added in the cell-level metadata, e.g. ```json { "cell_type": "code", "source": ["print('hello world')"], "metadata": { "mystnb": { "number_source_lines": true } } } ``` In text-based notebooks, this is added in the code cell YAML, e.g. ````markdown ```{code-cell} ipython3 --- mystnb: number_source_lines: true --- print('hello world') ``` ```` ```{mystnb-config} cell_lvl :sphinx: ``` ### Cell tags As a convenience for users in Jupyter interfaces, some cell level configuration can be achieved by specifying tags in the cell metadata. Tags are a list of strings under the `tags` key in the cell metadata, e.g. ```json { "cell_type": "code", "source": ["print('hello world')"], "metadata": { "tags": ["my-tag1", "my-tag2"] } } ``` Tag | Description --------------- | --- `remove-cell` | Remove the cell from the rendered output. `remove-input` | Remove the code cell input/source from the rendered output. `remove-output` | Remove the code cell output from the rendered output. `remove-stderr` | Remove the code cell output stderr from the rendered output. Additionally, for code execution, these tags are provided (via `nbclient`): Tag | Description ------------------ | --- `skip-execution` | Skip this cell, when executing the notebook `raises-exception` | Expect the code cell to raise an Exception (and continue execution) ## Markdown parsing configuration The MyST-NB parser derives from {ref}`the base MyST-Parser `, and so all the same configuration options are available. As referenced in {ref}`MyST configuration options `, the full set of global options are: ```{myst-config} :sphinx: ``` (myst/error-reporting)= ## Warning suppression When Sphinx encounters and error or raises a warning, it will print the location and source file of the text that generated that error. This works slightly differently depending on whether you use markdown files or Jupyter Notebook files. For markdown (`.md`) files, Sphinx will correctly report the line number that the error or warning is associated with: ``` source/path:4: (WARNING/2) Unknown mime type: 'xyz' [mystnb.unknown_mime_type] ``` For Jupyter Notebook (`.ipynb`) files, these errors also correspond to a cell index. To allow for this, we use a special format of line number corresponding to: ` * 10000 + LINE_NUMBER`. For example, the following error corresponds to **Cell 1, line 4**: ``` source/path:10004: (WARNING/2) Unknown mime type: 'xyz' [mystnb.unknown_mime_type] ``` In general, if your build logs any warnings, you should either fix them or raise an Issue if you think the warning is erroneous. However, in some circumstances if you wish to suppress the warning you can use the Sphinx [`suppress_warnings`](https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-suppress_warnings) configuration option. All myst-nb warnings are prepended by their type, and can be suppressed by e.g. ```python suppress_warnings = ["mystnb.unknown_mime_type"] ``` MyST-NB-1.1.2/docs/docutils.md000066400000000000000000000053241467453560600157700ustar00rootroot00000000000000(render/single-page)= # Single page builds ```{versionadded} 0.14.0 ``` Sphinx, and thus MyST-NB, is built on top of the [Docutils](https://docutils.sourceforge.io/docs/) package. MyST-NB offers a renderer, parser and CLI-interface for working directly with Docutils, independent of Sphinx, as described below. :::{note} Since these tools are independent of Sphinx, this means they cannot parse any Sphinx or Sphinx extensions specific roles or directives. ::: On installing MyST-NB, the following CLI-commands are made available: - `mystnb-docutils-html`: converts notebooks to HTML - `mystnb-docutils-html5`: converts notebooks to HTML5 - `mystnb-docutils-latex`: converts notebooks to LaTeX - `mystnb-docutils-xml`: converts notebooks to docutils-native XML - `mystnb-docutils-pseudoxml`: converts notebooks to pseudo-XML (to visualise the AST structure) Each command can be piped stdin or take a file path as an argument: ```console $ mystnb-docutils-html --help $ mystnb-docutils-html --nb-execution-mode="off" hello-world.ipynb $ mystnb-docutils-html --nb-read-as-md="yes" hello-world.md ``` The commands are based on the [Docutils Front-End Tools](https://docutils.sourceforge.io/docs/user/tools.html), and so follow the same argument and options structure, included many of the MyST NB specific options detailed in the [](config/intro) section. :::{dropdown} Shared Docutils CLI Options ```{docutils-cli-help} ``` ::: The CLI commands can also utilise the [`docutils.conf` configuration file](https://docutils.sourceforge.io/docs/user/config.html) to configure the behaviour of the CLI commands. For example: ```ini # These entries affect all processing: [general] nb_execution_mode: off # These entries affect specific HTML output: [html writers] embed-stylesheet: no [html5 writer] stylesheet-dirs: path/to/html5_polyglot/ stylesheet-path: minimal.css, responsive.css ``` You can also use the {py:class}`myst_nb.docutils_.Parser` class programmatically with the [Docutils publisher API](https://docutils.sourceforge.io/docs/api/publisher.html): ```python from docutils.core import publish_string from nbformat import writes from nbformat.v4 import new_notebook from myst_nb.docutils_ import Parser source = writes(new_notebook()) output = publish_string( source=source, writer_name="html5", settings_overrides={ "nb_execution_mode": "off", "embed_stylesheet": False, }, parser=Parser(), ) ``` Finally, you can include MyST Markdown files within a RestructuredText file, using the [`include` directive](https://docutils.sourceforge.io/docs/ref/rst/directives.html#include): ```rst .. include:: include.ipynb :parser: myst_nb.docutils_ ``` ```{important} The `parser` option requires `docutils>=0.17` ``` MyST-NB-1.1.2/docs/index.md000066400000000000000000000050221467453560600152440ustar00rootroot00000000000000--- sd_hide_title: true --- # Overview ::::{grid} :reverse: :gutter: 3 4 4 4 :margin: 1 2 1 2 :::{grid-item} :columns: 12 4 4 4 ```{image} _static/logo-square.svg :width: 200px :class: sd-m-auto ``` ::: :::{grid-item} :columns: 12 8 8 8 :child-align: justify :class: sd-fs-5 ```{rubric} Jupyter Notebook Publishing ``` A Sphinx and Docutils extension for compiling Jupyter Notebooks into high quality documentation formats. ```{button-ref} quickstart :ref-type: doc :color: primary :class: sd-rounded-pill Get Started ``` ::: :::: ---------------- ::::{grid} 1 2 2 3 :gutter: 1 1 1 2 :::{grid-item-card} {material-regular}`edit_note;2em` Write :link: authoring/intro :link-type: ref Mix Jupyter notebooks with text-based notebooks, Markdown and RST documents.\ Use MyST Markdown syntax to support technical authoring features such as cross-referencing, figures, and admonitions. +++ [Learn more ยป](authoring/intro) ::: :::{grid-item-card} {material-regular}`published_with_changes;2em` Compute :link: execute/intro :link-type: ref Generate dynamic outputs using Jupyter kernels, with configurable execution handling.\ Cache execution outputs, for fast re-builds. +++ [Learn more ยป](execute/intro) ::: :::{grid-item-card} {material-regular}`preview;2em` Render :link: render/code-cells :link-type: ref Convert Jupyter execution outputs to rich embedded content.\ Insert computed variables within the document flow.\ Build single or collections of documents into multiple formats (HTML, PDF, ...). +++ [Learn more ยป](render/code-cells) ::: :::: ---------------- MyST-NB is a module within the [Executable Books Project](https://executablebooks.org), an international collaboration to build open source tools that facilitate publishing computational narratives using the Jupyter ecosystem. It is also a core component of [Jupyter Book](inv:jb#intro). Check out the [Gallery of Jupyter Books](https://executablebooks.org/en/latest/gallery), for inspiration from across the community. See also, the [MyST-Markdown VS Code extension](https://marketplace.visualstudio.com/items?itemName=ExecutableBookProject.myst-highlight) and [jupyterlab-myst](https://github.com/executablebooks/jupyterlab-myst), for editor tools to author your notebooks. ```{toctree} :hidden: :maxdepth: 2 quickstart ``` ```{toctree} :caption: Guides :hidden: :maxdepth: 2 authoring/index computation/index render/index configuration docutils ``` ```{toctree} :caption: Reference :hidden: :titlesonly: :maxdepth: 1 reference/api reference/changelog reference/contributing ``` MyST-NB-1.1.2/docs/quickstart.md000066400000000000000000000031371467453560600163340ustar00rootroot00000000000000# Get Started `myst-nb` is distributed as a Python package and requires no non-Python dependencies. Use pip to install `myst-nb`: ```bash pip install myst-nb ``` You can use the `mystnb-quickstart` CLI to quickly create an example Sphinx + MyST-NB project: ```bash mystnb-quickstart my_project/docs/ ``` or simply add `myst_nb` to your existing Sphinx configuration: ```python extensions = [ ..., "myst_nb" ] ``` By default, MyST-NB will now parse both markdown (`.md`) and notebooks (`.ipynb`). ```{button-ref} authoring/intro :ref-type: myst :color: primary Begin authoring your content {material-regular}`navigate_next;2em` ``` Once you have finished authoring your content, you can now use the [sphinx-build CLI](https://www.sphinx-doc.org/en/master/man/sphinx-build.html) to build your documentation, e.g. ```bash sphinx-build -nW --keep-going -b html docs/ docs/_build/html ``` :::{tip} MyST-NB is parallel-friendly, so you can also distribute the build (and execution of notebooks) over *N* processes with: `sphinx-build -j 4` ::: ```{admonition} The execution environment is the same as your Sphinx environment Your Sphinx build shares the same environment with the notebooks you execute during a build. Ensure that you call the correct `sphinx-build` command when building your documentation, or the environment needed to run the notebooks may not be correct. This often happens if you see an `Extension error` in the build log, or an error from `jupyter-cache`. ``` :::{seealso} Check out [Read the Docs](https://docs.readthedocs.io) for hosting and *continuous deployment* of documentation ::: MyST-NB-1.1.2/docs/reference/000077500000000000000000000000001467453560600155525ustar00rootroot00000000000000MyST-NB-1.1.2/docs/reference/api.rst000066400000000000000000000044521467453560600170620ustar00rootroot00000000000000.. _api/main: Python API ========== The parsing of a notebook consists of a number of stages, with each stage separated into a separate module: 1. The configuration is set (from a file or CLI) 2. The parser is called with an input string and source 3. The parser reads the input string to a notebook node 4. The notebook is converted to a Markdown-It tokens syntax tree 5. The notebook code outputs are potentially updated, via execution or from a cache 6. The syntax tree is transformed to a docutils document AST (calling the renderer plugin) 7. The docutils document is processed by docutils/sphinx, to create the desired output format(s) Configuration ------------- .. autoclass:: myst_nb.core.config.NbParserConfig :members: Parsers ------- .. autoclass:: myst_nb.docutils_.Parser :members: .. autoclass:: myst_nb.sphinx_.Parser :members: Read ---- .. autoclass:: myst_nb.core.read.NbReader :members: .. autofunction:: myst_nb.core.read.create_nb_reader .. autofunction:: myst_nb.core.read.is_myst_markdown_notebook .. autofunction:: myst_nb.core.read.read_myst_markdown_notebook Execute ------- .. autofunction:: myst_nb.core.execute.create_client .. autoclass:: myst_nb.core.execute.base.NotebookClientBase :members: .. autoclass:: myst_nb.core.execute.direct.NotebookClientDirect .. autoclass:: myst_nb.core.execute.cache.NotebookClientCache .. autoclass:: myst_nb.core.execute.inline.NotebookClientInline .. autoexception:: myst_nb.core.execute.base.ExecutionError .. autoexception:: myst_nb.core.execute.base.EvalNameError .. autoclass:: myst_nb.core.execute.base.ExecutionResult :members: Render plugin ------------- .. autoclass:: myst_nb.core.render.MimeData :members: .. autoclass:: myst_nb.core.render.NbElementRenderer :members: .. autoclass:: myst_nb.core.render.MimeRenderPlugin :members: :undoc-members: .. autoclass:: myst_nb.core.render.ExampleMimeRenderPlugin :members: :undoc-members: Lexers ------ .. autoclass:: myst_nb.core.lexers.AnsiColorLexer :members: :undoc-members: :show-inheritance: Loggers ------- .. autoclass:: myst_nb.core.loggers.DocutilsDocLogger :members: :undoc-members: :show-inheritance: .. autoclass:: myst_nb.core.loggers.SphinxDocLogger :members: :undoc-members: :show-inheritance: MyST-NB-1.1.2/docs/reference/changelog.md000066400000000000000000000000721467453560600200220ustar00rootroot00000000000000```{include} ../../CHANGELOG.md :relative-docs: docs/ ``` MyST-NB-1.1.2/docs/reference/contributing.md000066400000000000000000000123771467453560600206150ustar00rootroot00000000000000# Contribute to MyST-NB [![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![Code style: black][black-badge]][black-link] We welcome all contributions! See the [EBP Contributing Guide](https://executablebooks.org/en/latest/contributing.html) for general details, and below for guidance specific to MyST-NB. ## Installation To install `MyST-NB` for package development (you will need a recent version of pip (>=22.0)): ```bash git clone https://github.com/executablebooks/MyST-NB cd MyST-NB git checkout master pip install -e .[code_style,testing,rtd] ``` ## How the Jupyter Notebook parser works MyST-NB is built on top of the MyST markdown parser. This is a flavor of markdown designed to work with the Sphinx ecosystem. It is a combination of CommonMark markdown, with a few extra syntax pieces added for use in Sphinx (for example, roles and directives). MyST-NB will do the following: * Check for any pages in your documentation folder that end in `.ipynb`. For each one: * Loop through the notebook's cells, converting cell contents into the Sphinx AST. * If it finds executable code cells, include their outputs in-line with the code. * If it finds markdown cells, use the MyST parser to convert them into Sphinx. Eventually, it will also provide support for writing pure-markdown versions of notebooks that can be executed and read into Sphinx. ## Code Style Code style is tested using [ruff](https://docs.astral.sh/ruff), with the configuration set in `pyproject.toml`, and code formatted with [black](https://github.com/ambv/black). Installing with `myst-nb[code_style]` makes the [pre-commit](https://pre-commit.com/) package available, which will ensure this style is met before commits are submitted, by reformatting the code and testing for lint errors. It can be setup by: ```shell >> cd MyST-NB >> pre-commit install ``` Optionally you can run `black` and `ruff` separately: ```shell >> black . >> ruff . ``` Editors like VS Code also have automatic code reformat utilities, which can adhere to this standard. All functions and class methods should be annotated with types and include a docstring. The preferred docstring format is outlined in `MyST-NB/docstring.fmt.mustache` and can be used automatically with the [autodocstring](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring) VS Code extension. ## Testing For code tests, MyST-NB uses [pytest](https://docs.pytest.org)): ```shell >> cd MyST-NB >> pytest ``` You can also use [tox](https://tox.readthedocs.io), to run the tests in multiple isolated environments (see the `tox.ini` file for available test environments): ```shell >> cd MyST-NB >> tox ``` For documentation build tests: ```shell >> cd MyST-NB >> tox ``` or ```shell >> cd MyST-NB/docs >> make clean >> make html-strict ``` ## Unit Testing Testing is one of the most important aspects of your PR. You should write test cases and verify your implementation by following the testing guide above. If you modify code related to existing unit tests, you must run appropriate commands and confirm that the tests still pass. Note that we are using [pytest](https://docs.pytest.org/en/latest/) for testing, [pytest-regression](https://pytest-regressions.readthedocs.io/en/latest/) to self-generate/re-generate expected outcomes of test and [pytest-cov](https://pytest-cov.readthedocs.io/en/latest/) for checking coverage. To run tests along with coverage: ``` pytest -v --cov=myst_nb ``` To run tests along with generation of an html coverage report: ``` pytest -v --cov=myst_nb --cov-report=html ``` ### Test File and Directory Naming Conventions Tests are found in the [tests](https://github.com/executablebooks/MyST-NB/tree/master/tests) directory. In order for `pytest` to find the test scripts correctly, the name of each test script should start with `test_` prefix. ### How to Write Tests There are many examples of unit tests under the [tests](https://github.com/executablebooks/MyST-NB/tree/master/tests) directory, so reading some of them is a good and recommended way. Prefer using the `fixtures` and the classes defined in [conftest.py](https://github.com/executablebooks/MyST-NB/blob/master/tests/conftest.py) as much as possible. If using [pytest-regression](https://pytest-regressions.readthedocs.io/en/latest/), a new directory with `test_` prefix is expected to be created in the first test run. This will store your expected output against which subsequent test outputs will be compared. ### Code Coverage report [pytest-cov](https://pytest-cov.readthedocs.io/en/latest/) is used to generate code coverage report. Make sure that your test cases cover most of the code written by you. [github-ci]: https://github.com/executablebooks/MyST-NB/workflows/continuous-integration/badge.svg?branch=master [github-link]: https://github.com/executablebooks/MyST-NB [codecov-badge]: https://codecov.io/gh/executablebooks/MyST-NB/branch/master/graph/badge.svg [codecov-link]: https://codecov.io/gh/executablebooks/MyST-NB [rtd-badge]: https://readthedocs.org/projects/myst-nb/badge/?version=latest [rtd-link]: https://myst-nb.readthedocs.io/en/latest/?badge=latest [black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg [black-link]: https://github.com/ambv/black MyST-NB-1.1.2/docs/render/000077500000000000000000000000001467453560600150735ustar00rootroot00000000000000MyST-NB-1.1.2/docs/render/format_code_cells.md000066400000000000000000000275561467453560600211000ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- (render/code-cells)= # Format code cells Code cell rendering behaviour is controlled with configuration at a global, per-file, or per-cell level, as outlined in the [configuration section](config/intro). See the sections below for a description of these configuration option and their effect. (render/input/number)= ## Number source lines You can control whether the number of source lines is displayed for code cells, globally with `nb_number_source_lines = True`, per-file with `number_source_lines` in the notebook metadata, or per-cell with `number_source_lines` in the cell metadata. For example: ````markdown ```{code-cell} ipython3 --- mystnb: number_source_lines: true --- a = 1 b = 2 c = 1 ``` ```` ```{code-cell} ipython3 --- mystnb: number_source_lines: true --- a = 1 b = 2 c = 1 ``` (render/output/stdout-stderr)= ## stdout and stderr outputs (render/output/stderr)= ### Remove stdout or stderr In some cases you may not wish to display stdout/stderr outputs in your final documentation, for example, if they are only for debugging purposes. You can tell MyST-NB to remove these outputs, per cell, using the `remove-stdout` and `remove-stderr` [cell tags](https://jupyter-notebook.readthedocs.io/en/stable/changelog.html#cell-tags), like so: ````md ```{code-cell} ipython3 :tags: [remove-input,remove-stdout,remove-stderr] import pandas, sys print("this is some stdout") print("this is some stderr", file=sys.stderr) # but what I really want to show is: pandas.DataFrame({"column 1": [1, 2, 3]}) ``` ```` ```{code-cell} ipython3 :tags: [remove-input,remove-stdout,remove-stderr] import pandas, sys print("this is some stdout") print("this is some stderr", file=sys.stderr) # but what I really want to show is: pandas.DataFrame({"column 1": [1, 2, 3]}) ``` Alternatively, you can configure how stdout is dealt with at a global configuration level, using the `nb_output_stderr` configuration value. This can be set to: - `"show"` (default): show all stderr (unless a `remove-stderr` tag is present) - `"remove"`: remove all stderr - `"remove-warn"`: remove all stderr, but log a warning to sphinx if any found - `"warn"`, `"error"` or `"severe"`: log to sphinx, at a certain level, if any found. (render/output/group-stderr-stdout)= ### Group into single streams Cells may print multiple things to `stdout` and `stderr`. For example, if a cell prints status updates throughout its execution, each of these is often printed to `stdout`. By default, these outputs may be split across multiple items, and will be rendered as separate "chunks" in your built documentation. If you'd like each of the outputs in `stderr` and `stdout` to be merged into a single stream for each, use the following configuration: ```python nb_merge_streams = True ``` This will ensure that all `stderr` and `stdout` outputs are merged into a single group. This also makes cell outputs more deterministic. Normally, slight differences in timing may result in different orders of `stderr` and `stdout` in the cell output, while this setting will sort them properly. (render/output/priority)= ## Outputs MIME priority When Jupyter executes a code cell it can produce multiple outputs, and each of these outputs can contain multiple [MIME media types](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types), for use by different output formats (like HTML or LaTeX). MyST-NB stores a base priority dictionary for most of the common [Sphinx builder names](https://www.sphinx-doc.org/en/master/usage/builders/index.html), mapping MIME types to a priority number (lower is higher priority): ```{code-cell} ipython3 :tags: [hide-output] import yaml from myst_nb.core.render import base_render_priority print(yaml.dump(base_render_priority())) ``` Items in this dictionary can be overridden by the `nb_mime_priority_overrides` configuration option, in your `conf.py`. For example, the following configuration applies in order: - Sets `text/plain` as the highest priority for `html` output. - Disables `image/jpeg` for `latex` output - Adds a custom MIME type `customtype` for all builders (`*` applies to all builders) ```python nb_mime_priority_overrides = [ ('html', 'text/plain', 0), ('latex', 'image/jpeg', None), ('*', 'customtype', 20) ] ``` ```{versionchanged} 0.14.0 `nb_mime_priority_overrides` replaces `nb_render_priority` ``` :::{seealso} [](render/output/customise), for a more advanced means of customisation. ::: (render/output/images)= ## Images and Figures With the default renderer, for any image types output by the code, we can apply formatting *via* cell metadata. The top-level metadata key can be set using `nb_cell_metadata_key` in your `conf.py`, and is set to `mystnb` by default. Then for the image we can apply all the variables of the standard [image directive](https://docutils.sourceforge.io/docs/ref/rst/directives.html#image): - **width**: length or percentage (%) of the current line width - **height**: length - **scale**: integer percentage (the "%" symbol is optional) - **align**: "top", "middle", "bottom", "left", "center", or "right" - **classes**: space separated strings - **alt**: string Units of length are: 'em', 'ex', 'px', 'in', 'cm', 'mm', 'pt', 'pc' You can also wrap the output in a [`figure`](https://docutils.sourceforge.io/docs/ref/rst/directives.html#figure), that can include: - **align**: "left", "center", or "right" - **caption**: a string, which must contain a single paragraph and is rendered as MyST Markdown (subsequent paragraphs are added as a legend) - **caption_before**: a boolean, if true, the caption is rendered before the figure (default is false) - **name**: by which to reference the figure - **classes**: space separated strings ````md ```{code-cell} ipython3 --- mystnb: image: width: 300px alt: fun-fish classes: shadow bg-primary figure: caption: | Hey everyone its **party** time! (and I'm a legend) name: fun-fish-ref --- from IPython.display import Image Image("images/fun-fish.png") ``` ```` ```{code-cell} ipython3 --- mystnb: image: width: 300px alt: fun-fish classes: shadow bg-primary figure: caption: | Hey everyone its **party** time! (and I'm a legend) name: fun-fish-ref --- from IPython.display import Image Image("images/fun-fish.png") ``` Now we can link to the image from anywhere in our documentation: [swim to the fish](fun-fish-ref) You can create figures for any mime outputs, including tables: ````md ```{code-cell} ipython3 --- mystnb: figure: align: center caption_before: true caption: This is my table caption, above the table --- import pandas df = pandas.DataFrame({"column 1": [1, 2, 3]}) df = df.style.set_table_attributes('class="dataframe align-center"') df ``` ```` ```{code-cell} ipython3 --- mystnb: figure: align: center caption_before: true caption: This is my table caption, above the table --- import pandas df = pandas.DataFrame({"column 1": [1, 2, 3]}) df = df.style.set_table_attributes('class="dataframe align-center"') df ``` (render/output/markdown)= ## Markdown The format of output `text/markdown` can be specified by `render_markdown_format` configuration: - `commonmark` (default): Restricted to the [CommonMark specification](https://commonmark.org/). - `gfm`: Restricted to the [GitHub-flavored markdown](https://github.github.com/gfm/). - Note, this requires the installation of the [linkify-it-py package](https://pypi.org/project/linkify-it-py) - `myst`: Uses [the MyST parser](https://myst-parser.readthedocs.io/en/latest/) with the same configuration as the current document. CommonMark formatting will output basic Markdown syntax: ```{code-cell} ipython3 from IPython.display import display, Markdown display(Markdown('**_some_ markdown** and an [a reference](https://example.com)!')) ``` and even internal images can be rendered! ```{code-cell} ipython3 display(Markdown('![figure](../_static/logo-wide.svg)')) ``` But setting the Markdown format to `myst` will allow for more advanced formatting, such as including internal references, tables, and even other directives, either using: - `myst_render_markdown_format = "myst"` in the `conf.py` to set globally, or - `markdown_format` in the cell metadata to set per-cell. `````md ````{code-cell} ipython3 --- mystnb: markdown_format: myst --- display(Markdown('**_some_ markdown** and an [internal reference](render/output/markdown)!')) display(Markdown(""" | a | b | c | |---|---|---| | 1 | 2 | 3 | """)) display(Markdown(""" ```{note} A note admonition! ``` """)) ```` ````` The parsed Markdown is integrated into the wider documentation, and so it is possible, for example, to include internal references: ````{code-cell} ipython3 --- mystnb: markdown_format: myst --- display(Markdown('**_some_ markdown** and an [internal reference](render/output/markdown)!')) display(Markdown(""" | a | b | c | |---|---|---| | 1 | 2 | 3 | """)) display(Markdown(""" ```{note} A note admonition! ``` """)) ```` (render/output/ansi)= ## ANSI Outputs By default, the standard output/error streams and text/plain MIME outputs may contain ANSI escape sequences to change the text and background colors. ```{code-cell} ipython3 import sys print("BEWARE: \x1b[1;33;41mugly colors\x1b[m!", file=sys.stderr) print("AB\x1b[43mCD\x1b[35mEF\x1b[1mGH\x1b[4mIJ\x1b[7m" "KL\x1b[49mMN\x1b[39mOP\x1b[22mQR\x1b[24mST\x1b[27mUV") ``` This uses the built-in {py:class}`~myst_nb.core.lexers.AnsiColorLexer` [pygments lexer](https://pygments.org/). You can change the lexer used in the `conf.py`, for example to turn off lexing: ```python nb_render_text_lexer = "none" ``` The following code[^acknowledge] shows the 8 basic ANSI colors it is based on. Each of the 8 colors has an โ€œintenseโ€ variation, which is used for bold text. [^acknowledge]: Borrowed from [nbsphinx](https://nbsphinx.readthedocs.io/en/0.7.1/code-cells.html#ANSI-Colors)! ```{code-cell} ipython3 text = " XYZ " formatstring = "\x1b[{}m" + text + "\x1b[m" print( " " * 6 + " " * len(text) + "".join("{:^{}}".format(bg, len(text)) for bg in range(40, 48)) ) for fg in range(30, 38): for bold in False, True: fg_code = ("1;" if bold else "") + str(fg) print( " {:>4} ".format(fg_code) + formatstring.format(fg_code) + "".join( formatstring.format(fg_code + ";" + str(bg)) for bg in range(40, 48) ) ) ``` :::{note} ANSI also supports a set of 256 indexed colors. This is currently not supported, but we hope to introduce it at a later date (raise an issue on the repository if you require it!). ::: (render/output/customise)= ## Customise the render process The render process is governed by subclasses of {py:class}`~myst_nb.core.render.NbElementRenderer`, which dictate how to create the `docutils` AST nodes for elements of the notebook. Implementations are loaded *via* Python [entry points](https://packaging.python.org/guides/distributing-packages-using-setuptools/#entry-points), in the `myst_nb.renderers` group. So it is possible to inject your own subclass to fully override rendering. For example, the renderer loaded in this package is: ```toml [project.entry-points."myst_nb.renderers"] default = "myst_nb.core.render:NbElementRenderer" ``` You can then select the renderer plugin in your `conf.py`: ```python nb_render_plugin = "default" ``` Plugins can also override rendering of particular output MIME types, using the `myst_nb.mime_renderers` entry point group to supply functions with signature: {py:class}`~myst_nb.core.render.MimeRenderPlugin`. For example {py:class}`myst_nb.core.render.ExampleMimeRenderPlugin`, is loaded in this package: ```toml [project.entry-points."myst_nb.mime_renderers"] example = "myst_nb.core.render:ExampleMimeRenderPlugin" ``` Meaning we can now render `custommimetype` in all output formats: ```{code-cell} ipython3 from IPython.display import display display({"custommimetype": "Some text"}, raw=True) ``` MyST-NB-1.1.2/docs/render/glue.md000066400000000000000000000333031467453560600163530ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- (glue/main)= # Saving variables to embed (glue) The `glue` submodule allows you to store variables in the notebooks outputs, by keys, then reference those keys to embed the outputs inline of your text content.[^download] [^download]: This notebook can be downloaded as **{nb-download}`glue.ipynb`** and {download}`glue.md` :::{versionchanged} 0.14.0 The `glue` roles and directives now only identify keys in the same notebook, by default. To glue keys from other notebooks, see {ref}`glue/crossdoc`. ::: +++ (glue/gluing)= ## Save variables in code cells You can use `myst_nb.glue()` to assign the output of a variable to a key of your choice. `glue` will store all of the information that is normally used to display that variable (that is, whatever happens when you display the variable by putting it at the end of a cell). Choose a key that you will remember, as you will use it later. The following code glues a variable inside the notebook: ```{code-cell} ipython3 from myst_nb import glue a = "my variable!" glue("my_variable", a) ``` You can then insert it into your text like so: {glue}`my_variable`. That was accomplished with the following code: `` {glue}`my_variable` ``. ### Saving different variable types You can glue anything in your notebook and display it later with `{glue}`. Here we'll show how to glue and paste **numbers and images**. We'll simulate some data and run a simple bootstrap on it. We'll hide most of this process below, to focus on the glueing part. +++ ```{code-cell} ipython3 :tags: [hide-cell] # Simulate some data and bootstrap the mean of the data import numpy as np np.set_printoptions(legacy="1.25") import pandas as pd import matplotlib.pyplot as plt n_points = 10000 n_boots = 1000 mean, sd = (3, .2) data = sd*np.random.randn(n_points) + mean bootstrap_indices = np.random.randint(0, n_points, n_points*n_boots).reshape((n_boots, n_points)) ``` In the cell below, `data` contains our data, and `bootstrap_indices` is a collection of sample indices in each bootstrap. Below we'll calculate a few statistics of interest, and `glue()` them into the notebook. ```{code-cell} ipython3 # Calculate the mean of a bunch of random samples means = data[bootstrap_indices].mean(0) # Calculate the 95% confidence interval for the mean clo, chi = np.percentile(means, [2.5, 97.5]) # Store the values in our notebook glue("boot_mean", means.mean()) glue("boot_clo", clo) glue("boot_chi", chi) ``` By default, `glue` will display the value of the variable you are gluing. This is useful for sanity-checking its value at glue-time. If you'd like to **prevent display**, use the `display=False` option. Note that below, we also *overwrite* the value of `boot_chi` (but using the same value): ```{code-cell} ipython3 glue("boot_chi_notdisplayed", chi, display=False) ``` You can also glue visualizations, such as matplotlib figures (here we use `display=False` to ensure that the figure isn't plotted twice): ```{code-cell} ipython3 # Visualize the histogram with the intervals fig, ax = plt.subplots() ax.hist(means) for ln in [clo, chi]: ax.axvline(ln, ls='--', c='r') ax.set_title("Bootstrap distribution and 95% CI") # And a wider figure to show a timeseries fig2, ax = plt.subplots(figsize=(6, 2)) ax.plot(np.sort(means), lw=3, c='r') ax.set_axis_off() glue("boot_fig", fig, display=False) glue("sorted_means_fig", fig2, display=False) ``` The same can be done for DataFrames (or other table-like objects) as well. ```{code-cell} ipython3 bootstrap_subsets = data[bootstrap_indices][:3, :5].T df = pd.DataFrame(bootstrap_subsets, columns=["first", "second", "third"]) glue("df_tbl", df) ``` ```{tip} Since we are going to paste this figure into our document at a later point, you may wish to remove the output here, using the `remove-output` tag (see {ref}`use/removing`). ``` +++ (glue/pasting)= ## Embedding variables in the page Once you have glued variables into a notebook, you can then **paste** those variables into your text in your book anywhere you like (even on other pages). These variables can be pasted using one of the roles or directives in the `glue:` *family*. +++ ### The `glue` role/directive The simplest role and directive are `glue` (also known as `glue:any`), which paste the glued output inline or as a block respectively, with no additional formatting. Simply add: ````md ```{glue} your-key ``` ```` For example, we'll paste the plot we generated above with the following text: ````md ```{glue} boot_fig ``` ```` Here's how it looks: ```{glue} boot_fig ``` Or we can paste inline objects like so: ```md Inline text; {glue}`boot_mean`, and figure; {glue}`boot_fig`. ``` Inline text; {glue}`boot_mean`, and figure; {glue}`boot_fig`. ```{tip} We recommend using wider, shorter figures when plotting in-line, with a ratio around 6x2. For example, here's is an in-line figure of sorted means from our bootstrap: {glue}`sorted_means_fig`. It can be used to make a visual point that isn't too complex! For more ideas, check out [how sparklines are used](https://en.wikipedia.org/wiki/Sparkline). ``` Next we'll cover some more specific pasting functionality, which gives you more control over how the outputs look in your pages. +++ ## Controlling the output format You can control the pasted outputs by using a sub-command of `{glue}`. These are called like so: `` {glue:subcommand}`key` ``. These subcommands allow you to control more of the look, feel, and content of the pasted output. ```{tip} When you use `{glue}` you are actually using a short-hand for `{glue:any}`. This is a generic command that doesn't make many assumptions about what you are gluing. ``` +++ ### The `glue:text` role The `glue:text` role, is specific to `text/plain` outputs. For example, the following text: ```md The mean of the bootstrapped distribution was {glue:text}`boot_mean` (95% confidence interval {glue:text}`boot_clo`/{glue:text}`boot_chi`). ``` Is rendered as: The mean of the bootstrapped distribution was {glue:text}`boot_mean` (95% confidence interval {glue:text}`boot_clo`/{glue:text}`boot_chi`) ```{note} `glue:text` only works with glued variables that contain a `text/plain` output. ``` With `glue:text` we can add formatting to the output, by specifying a format spec string after a `:`: `` {glue:text}`mykey:` `` The `` should be a valid [Python format specifier](https://docs.python.org/3/library/string.html#format-specification-mini-language). This is particularly useful if you are displaying numbers and want to round the results. For example, the following: ``My rounded mean: {glue:text}`boot_mean:.2f` `` will be rendered like this: My rounded mean: {glue:text}`boot_mean:.2f` (95% CI: {glue:text}`boot_clo:.2f`/{glue:text}`boot_chi:.2f`). ````{warning} As of NumPy 2.0, the `text/plain` representation of [NumPy objects has changed](https://numpy.org/devdocs/release/2.0.0-notes.html#representation-of-numpy-scalars-changed). Using text formatting with NumPy>=2.0 will give warnings like: ``` sphinx.errors.SphinxWarning: :257:Failed to format text/plain data: could not convert string to float: 'np.float64(0.9643970836113095)' [mystnb.glue] ``` This can be resolved by either formatting the number before glueing or by setting NumPy to use legacy print options, as shown below. ```python var = np.float(1.0) # Format the variable before glueing glue("var_glue", f"{var:.2f}") # Or set NumPy legacy print options np.setprintoptions(legacy="1.25") glue("var_glue", var) ``` ```` +++ ### The `glue:figure` directive With `glue:figure` you can apply more formatting to figure like objects, such as giving them a caption and referenceable label: :::{table} `glue:figure` directive options | Option | Type | Description | | ------ | ---- | ----------- | | alt | text | Alternate text of an image | | height | length | The desired height of an image | | width | length or percentage | The width of an image | | scale | percentage | The uniform scaling factor of an image | | class | text | A space-separated list of class names for the image | | figwidth | length or percentage | The width of the figure | | figclass | text | A space-separated list of class names for the figure | | align | text | left, center, or right | | name | text | referenceable label for the figure | ::: ````md ```{glue:figure} boot_fig :alt: "Alternative title" :figwidth: 300px :name: "fig-boot" This is a **caption**, with an embedded `{glue:text}` element: {glue:text}`boot_mean:.2f`! ``` ```` ```{glue:figure} boot_fig :alt: "Alternative title" :figwidth: 300px :name: "fig-boot" This is a **caption**, with an embedded `{glue:text}` element: {glue:text}`boot_mean:.2f`! ``` ```md Here is a {ref}`reference to the figure ` ``` Here is a {ref}`reference to the figure ` +++ Here's a table: ````md ```{glue:figure} df_tbl :figwidth: 300px :name: "tbl:df" A caption for a pandas table. ``` ```` ```{glue:figure} df_tbl :figwidth: 300px :name: "tbl:df" A caption for a pandas table. ``` +++ ### The `glue:math` directive The `glue:math` directive, is specific to latex math outputs (glued variables that contain a `text/latex` mimetype), and works similarly to the [sphinx math directive](https://www.sphinx-doc.org/en/1.8/usage/restructuredtext/directives.html#math). :::{table} `glue:math` directive options | Option | Type | Description | | ------ | ---- | ----------- | | nowrap | flag | Prevent any wrapping of the given math in a math environment | | class | text | A space-separated list of class names | | label or name | text | referenceable label for the figure | ::: ```{code-cell} ipython3 import sympy as sym f = sym.Function('f') y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) glue("sym_eq", sym.rsolve(f,y(n),[1,4])) ``` ````md Insert the equation here: ```{glue:math} sym_eq :label: eq-sym ``` Which we reference as Equation {eq}`eq-sym` ```` Insert the equation here: ```{glue:math} sym_eq :label: eq-sym ``` Which we reference as Equation {eq}`eq-sym`. ```{note} `glue:math` only works with glued variables that contain a `text/latex` output. ``` ### The `glue:md` role/directive With `glue:md`, you can output `text/markdown`, that will be integrated into your page. ````{code-cell} ipython3 from IPython.display import Markdown glue("inline_md", Markdown( "inline **markdown** with a [link](glue/main), " "and a nested glue value: {glue}`boot_mean`" ), display=False) glue("block_md", Markdown(""" #### A heading Then some text, and anything nested. ```python print("Hello world!") ``` """ ), display=False) ```` The format of the markdown can be specified as: - `commonmark` (default): Restricted to the [CommonMark specification](https://commonmark.org/). - `gfm`: Restricted to the [GitHub-flavored markdown](https://github.github.com/gfm/). - Note, this requires the installation of the [linkify-it-py package](https://pypi.org/project/linkify-it-py) - `myst`: The MyST parser configuration for the the current document. For example, the following role/directive will glue inline/block MyST Markdown, as if it was part of the original document. ````md Here is some {glue:md}`inline_md:myst`! ```{glue:md} block_md :format: myst ``` ```` Here is some {glue:md}`inline_md:myst`! ```{glue:md} block_md :format: myst ``` +++ (glue/crossdoc)= ## Embedding outputs from other pages Certain `glue` roles and directives can be used to paste content from other notebooks, by specifying the (relative) path to them. :::{tip} Sometimes you'd like to use variables from notebooks that are not meant to be shown to users. In this case, you should bundle the notebook with the rest of your content pages, but include `orphan: true` in the metadata of the notebook. ::: For example, the following example pastes glue variables from {ref}`orphaned-nb`: ````markdown - A cross-pasted `any` role: {glue}`orphaned_nb.ipynb::var_text` - A cross-pasted `text` role: {glue:text}`orphaned_nb.ipynb::var_float:.2E` A cross-pasted `any` directive: ```{glue} var_text :doc: orphaned_nb.ipynb ``` ```` - A cross-pasted `any` role: {glue}`orphaned_nb.ipynb::var_text` - A cross-pasted `text` role: {glue:text}`orphaned_nb.ipynb::var_float:.2E` A cross-pasted `any` directive: ```{glue} var_text :doc: orphaned_nb.ipynb ``` +++ ## Advanced use-cases Here are a few more specific and advanced uses of the `glue` submodule. ### Embedding into tables In addition to pasting blocks of outputs, or in-line with text, you can also paste directly into tables. This allows you to compose complex collections of structured data using outputs that were generated in other notebooks. For example the following table: ````md | name | plot | mean | ci | |:-------------------------------:|:---------------------------:|---------------------------|----------------------------------------------------| | histogram and raw text | {glue}`boot_fig` | {glue}`boot_mean` | {glue}`boot_clo`-{glue}`boot_chi` | | sorted means and formatted text | {glue}`sorted_means_fig` | {glue:text}`boot_mean:.3f`| {glue:text}`boot_clo:.3f`-{glue:text}`boot_chi:.3f`| ```` Results in: | name | plot | mean | ci | |:-------------------------------:|:---------------------------:|---------------------------|---------------------------------------------------| | histogram and raw text | {glue}`boot_fig` | {glue}`boot_mean` | {glue}`boot_clo`-{glue}`boot_chi` | | sorted means and formatted text | {glue}`sorted_means_fig` | {glue:text}`boot_mean:.3f` | {glue:text}`boot_clo:.3f`-{glue:text}`boot_chi:.3f` | MyST-NB-1.1.2/docs/render/hiding.md000066400000000000000000000107021467453560600166570ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- # Hide cell contents You can use Jupyter Notebook **cell tags** to control some of the behavior of the rendered notebook.[^download] If you are using cell tags for the first time, you can read more about them in this tutorial [^download]: This notebook can be downloaded as **{nb-download}`hiding.ipynb`** and {download}`hiding.md` (use/hiding/code)= ## Hide code cells You can use **cell tags** to control the content hidden with code cells at the cell level. Add the following tags to a cell's metadata to control what to hide in code cells: * **`hide-input`** tag to hide the cell inputs * **`hide-output`** to hide the cell outputs * **`hide-cell`** to hide the entire cell For example, we'll show cells with each below. ```{code-cell} ipython3 :tags: [remove-cell] import matplotlib.pyplot as plt import numpy as np data = np.random.rand(2, 100) * 100 ``` Here is a cell with a `hide-input` tag. ```{code-cell} ipython3 :tags: [hide-input] # This cell has a hide-input tag fig, ax = plt.subplots() points =ax.scatter(*data, c=data[0], s=data[0]) ``` Here's a cell with a `hide-output` tag: ```{code-cell} ipython3 :tags: [hide-output] # This cell has a hide-output tag fig, ax = plt.subplots() points =ax.scatter(*data, c=data[0], s=data[0]) ``` Here's a cell with both `hide-input` and `hide-output` tags: ```{code-cell} ipython3 :tags: [hide-input, hide-output] # This cell has a hide-output tag fig, ax = plt.subplots() points =ax.scatter(*data, c=data[0], s=data[0]) ``` Here's a cell with a `hide-cell` tag: ```{code-cell} ipython3 :tags: [hide-cell] # This cell has a hide-cell tag fig, ax = plt.subplots() points =ax.scatter(*data, c=data[0], s=data[0]) ``` Finally, a cell with both `remove-input` (see below) and `hide-output` tags: ```{code-cell} ipython3 :tags: [remove-input, hide-output] fig, ax = plt.subplots() points = ax.scatter(*data, c=data[0], s=data[0]) ``` You can control the hide/show prompts by using the `code_prompt_show` and `code_prompt_hide` configuration options. The optional `{type}` placeholder will be replaced with `content`, `source`, or `outputs`, depending on the hide tag. See the {ref}`config/intro` section for more details. ````markdown ```{code-cell} ipython3 :tags: [hide-cell] :mystnb: : code_prompt_show: "My show prompt for {type}" : code_prompt_hide: "My hide prompt for {type}" print("hallo world") ``` ```` ```{code-cell} ipython3 :tags: [hide-cell] :mystnb: : code_prompt_show: "My show prompt for {type}" : code_prompt_hide: "My hide prompt for {type}" print("hallo world") ``` (use/hiding/markdown)= ## Hide markdown cells You cannot hide an entire markdown cell, but you can hide sections of markdown **content** by using roles and directives. For information on how to hide / toggle markdown content in Sphinx, see either [the `sphinx-togglebutton` documentation](https://sphinx-togglebutton.readthedocs.io/en/latest/) or the [`sphinx-design` dropdowns documentation](https://sphinx-design.readthedocs.io/en/latest/dropdowns.html). (use/removing)= ## Remove parts of cells Sometimes, you want to entirely remove parts of a cell so that it doesn't make it into the output at all. To do this at the global level, use the `nb_remove_code_source` or `nb_remove_code_outputs` configuration options, or at a per-file level, e.g. ```yaml --- mystnb: remove_code_source: true remove_code_outputs: true --- ``` See the [configuration section](config/intro) for more details. At a per-cell level you can use the same tag pattern described above, but with the word `remove_` instead of `hide_`. Use the following tags: * **`remove-input`** tag to remove the cell inputs * **`remove-output`** to remove the cell outputs * **`remove-cell`** to remove the entire cell +++ Here is a cell with a `remove-input` tag. The inputs will not make it into the page at all. ```{code-cell} ipython3 :tags: [remove-input] fig, ax = plt.subplots() points =ax.scatter(*data, c=data[0], s=data[0]) ``` Here's a cell with a `remove-output` tag: ```{code-cell} ipython3 :tags: [remove-output] fig, ax = plt.subplots() points = ax.scatter(*data, c=data[0], s=data[0]) ``` And the following cell has a `remove-cell` tag (there should be nothing below, since the cell will be gone). ```{code-cell} ipython3 :tags: [remove-cell] fig, ax = plt.subplots() points = ax.scatter(*data, c=data[0], s=data[0]) ``` MyST-NB-1.1.2/docs/render/images/000077500000000000000000000000001467453560600163405ustar00rootroot00000000000000MyST-NB-1.1.2/docs/render/images/fun-fish.png000066400000000000000000002636661467453560600206100ustar00rootroot00000000000000‰PNG  IHDR๔^V” IDATxœ์wœีูวฟg๎ฝห.m้‚;ุมWลฒ ฦ$ฦฤ๕%Fปฐ`/1ฑฝฑkLิXˆ`AมJ+Hpูฅ์.ปlน{หฬy?ๆฮนe)ฒ ‹๎ฯr๏9ๅ)็yฮsฮœ9บะ9 ซ*ฒhคXห+$ๆPh|(_:ช>ฃฃ 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UะnLฃ!W:™v”RXณfoฯž=๋สห๑›๛๏—‘y๓Q2™RblŒEm{F_2™๏ ƒ'“ษิ !Dssำู7x#ตx๑8Ž#KJJ ฅ„ใ87zธ๘กš์zๆิื๏›xCๆœฑะัมไ™3H:อw๚ฅปSฉแจRJJ)iธœiŽ:™vZZZะฺฺzg$I์ฺตkLJษm†aฒฐ™ะอnโทJ&จฌ ญฉฏวถตk0Œk?‡ฏ๋@>tw,;0 ๏ฮ็ๅ๛O=๕ณƒ~๘Q†’ินuQ@'ไ:ฒm 6๐X,๖ป{๏อฟ๘ใnนุ+ ไล๓๛š|5p๔(งOซž๑๑า๗ถ๛อ7zj๚ ยqจr#!ท ่„\J)ิิิ๘โ๑wVฌภ ห–฿ญ‡‹ืuOLงO3?ฮR๑ธ๑ษ๘ธQS3๛`4MQ’! €Nศ5‰DBƒƒ=;ŸxB<‰Lmy\๑Ž…ษ$ะฮฌ๎nDGGต๗KJllIฯฝ[RJH)QRRrc~BศดAk 0‡ฉจภฎgŸECUีฅ ีxหรr9 ฏแห/Ujpะ4“™ุV_ใึM›6e_z้W๎พฎTQ2Eะ น Ÿ}๖๚puตุ๙sจ๒๛/ {eval}`v1` If we update the variable, we can see the change reflected in subsequent evaluation: ```{code-cell} ipython3 v1 = "My new variable" ``` `` {eval}`v1` `` -> {eval}`v1` :::{important} Variable names must match the regex `[a-zA-Z][a-zA-Z0-9_]*` ::: ## Inserting different output types Any variable type can be inserted into the text flow using the `eval` role, and the most suitable output type will be used, based on the output format (see {ref}`render/output/priority` for more information). For example: ```{code-cell} ipython3 import ipywidgets as widgets slider = widgets.IntSlider(value=5, min=0, max=10) ``` An inline slider (`` {eval}`slider` ``): {eval}`slider` You can also use the `eval` directive to insert variables as blocks: ```{code-cell} ipython3 import matplotlib.pyplot as plt myplot, ax = plt.subplots(figsize=(6, 2)) mean = 2.0 ax.plot([1,2,3]) ax.grid() plt.close() ``` using: ````markdown ```{eval} myplot ``` ```` gives: ```{eval} myplot ``` ## Embedding outputs in figures The `eval:figure` directive allows you to embed outputs in a figure, with an optional caption and other formatting options. For example, we can embed the output of the above plot in a figure: `````markdown ```{eval:figure} myplot :name: myplot My plot with a mean value of {eval}`mean`. ``` ````` which gives: ```{eval:figure} myplot :name: myplot My plot with a mean value of {eval}`mean`. ``` That can be referenced with `` {ref}`myplot` ``: {ref}`myplot` The following directive options are available: :::{table} `eval:figure` directive options | Option | Type | Description | | ------ | ---- | ----------- | | figwidth | length or percentage | The width of the figure | | figclass | text | A space-separated list of class names for the figure | | name | text | referenceable label for the figure | | alt | text | Alternate text of an image | | height | length | The desired height of an image | | width | length or percentage | The width of an image | | scale | percentage | The uniform scaling factor of an image | | class | text | A space-separated list of class names for the image | | align | text | left, center, or right | ::: MyST-NB-1.1.2/docs/render/interactive.md000066400000000000000000000113741467453560600177400ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 --- # Widgets and interactive outputs Jupyter Notebooks have support for many kinds of interactive outputs. These should all be supported in MyST-NB by passing the output HTML through automatically. This page has a few common examples.[^download] [^download]: This notebook can be downloaded as **{nb-download}`interactive.ipynb`** and {download}`interactive.md` First off, we'll download a little bit of data and show its structure: ```{code-cell} ipython3 import plotly.express as px data = px.data.iris() data.head() ``` ## Plotting libraries ### Altair Interactive outputs will work under the assumption that the outputs they produce have self-contained HTML that works without requiring any external dependencies to load. See the [`Altair` installation instructions](https://altair-viz.github.io/getting_started/installation.html#installation) to get set up with Altair. Below is some example output. ```{code-cell} ipython3 import altair as alt alt.Chart(data=data).mark_point().encode( x="sepal_width", y="sepal_length", color="species", size='sepal_length' ) ``` ### Plotly Plotly is another interactive plotting library that provides a high-level API for visualization. See the [Plotly JupyterLab documentation](https://plotly.com/python/getting-started/#jupyterlab-support-python-35) to get started with Plotly in the notebook. Below is some example output. ```{code-cell} ipython3 import plotly.io as pio import plotly.express as px import plotly.offline as py df = px.data.iris() fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size="sepal_length") fig ``` :::{important} You may need to supply the `require.js` for plotly to display; in your `conf.py`: ```python html_js_files = ["https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"] ``` ::: ### Bokeh Bokeh provides several options for interactive visualizations, and is part of the PyViz ecosystem. See [the Bokeh with Jupyter documentation](https://docs.bokeh.org/en/latest/docs/user_guide/jupyter.html#userguide-jupyter) to get started. Below is some example output. ```{code-cell} ipython3 from bokeh.plotting import figure, show, output_notebook output_notebook() ``` ```{code-cell} ipython3 from bokeh.plotting import figure, show, output_notebook output_notebook() p = figure() p.circle(data["sepal_width"], data["sepal_length"], fill_color=data["species"], size=data["sepal_length"]) show(p) ``` ## ipywidgets :::{note} IPyWidgets uses a special JS package `@jupyter-widgets/html-manager` for rendering Jupyter widgets outside notebooks. `myst-nb` loads a specific version of this package, which may be incompatible with your installation of IPyWidgets. If this is the case, you might need to specify the appropriate `nb_ipywidgets_js` config value, e.g. for `0.20.0` ```yaml sphinx: recursive_update: true config: nb_ipywidgets_js: # Load IPywidgets bundle for embedding. "https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@0.20.0/dist/embed-amd.js": "data-jupyter-widgets-cdn": "https://cdn.jsdelivr.net/npm/" "crossorigin": "anonymous" ``` To determine which version of `@jupyter-widgets/html-manager` is required, find the `html-manager` JS package in the [`ipywidgets` repo](https://github.com/jupyter-widgets/ipywidgets), and identify its version. ::: You may also run code for Jupyter Widgets in your document, and the interactive HTML outputs will embed themselves in your side. See [the ipywidgets documentation](https://ipywidgets.readthedocs.io/en/latest/user_install.html) for how to get set up in your own environment. ```{admonition} Widgets often need a kernel Note that `ipywidgets` tend to behave differently from other interactive viz libraries. They interact both with Javascript, and with Python. Some functionality in `ipywidgets` may not work in rendered pages (because no Python kernel is running). You may be able to get around this with tools for remote kernels, like [thebelab](https://thebelab.readthedocs.org). ``` Here are some simple widget elements rendered below. ```{code-cell} ipython3 import ipywidgets as widgets widgets.IntSlider( value=7, min=0, max=10, step=1, description='Test:', disabled=False, continuous_update=False, orientation='horizontal', readout=True, readout_format='d' ) ``` ```{code-cell} ipython3 tab_contents = ['P0', 'P1', 'P2', 'P3', 'P4'] children = [widgets.Text(description=name) for name in tab_contents] tab = widgets.Tab() tab.children = children for ii in range(len(children)): tab.set_title(ii, f"tab_{ii}") tab ``` You can find [a list of possible Jupyter Widgets](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html) in the jupyter-widgets documentation. MyST-NB-1.1.2/docs/render/orphaned_nb.ipynb000066400000000000000000000022201467453560600204110ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(orphaned-nb)=\n", "\n", "# An orphaned notebook\n", "\n", "This defines a variable that we'll re-use in another notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'My orphaned variable!'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "orphaned_var" } }, "output_type": "display_data" } ], "source": [ "from myst_nb import glue\n", "glue(\"var_text\", \"My orphaned variable!\")\n", "glue(\"var_float\", 1.0/3.0)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "orphan": true }, "nbformat": 4, "nbformat_minor": 4 } MyST-NB-1.1.2/docs/requirements.txt000066400000000000000000000000621467453560600170760ustar00rootroot00000000000000# this is only required by coconut kernel ipython MyST-NB-1.1.2/myst_nb/000077500000000000000000000000001467453560600143375ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/__init__.py000066400000000000000000000021471467453560600164540ustar00rootroot00000000000000"""A docutils/sphinx parser for Jupyter Notebooks.""" __version__ = "1.1.2" def setup(app): """Sphinx extension setup.""" # we import this locally, so sphinx is not automatically imported from .sphinx_ext import sphinx_setup return sphinx_setup(app) def glue(name: str, variable, display: bool = True) -> None: """Glue a variable into the notebook's cell metadata. Parameters ---------- name: string A unique name for the variable. You can use this name to refer to the variable later on. variable: Python object A variable in Python for which you'd like to store its display value. This is not quite the same as storing the object itself - the stored information is what is *displayed* when you print or show the object in a Jupyter Notebook. display: bool Display the object you are gluing. This is helpful in sanity-checking the state of the object at glue-time. """ # we import this locally, so IPython is not automatically imported from myst_nb.ext.glue import glue return glue(name, variable, display) MyST-NB-1.1.2/myst_nb/_compat.py000066400000000000000000000003351467453560600163340ustar00rootroot00000000000000from docutils.nodes import Element def findall(node: Element): # findall replaces traverse in docutils v0.18 # note a difference is that findall is an iterator return getattr(node, "findall", node.traverse) MyST-NB-1.1.2/myst_nb/cli.py000066400000000000000000000120441467453560600154610ustar00rootroot00000000000000"""A basic CLI for quickstart of a myst_nb project.""" from __future__ import annotations import argparse from pathlib import Path from textwrap import indent import nbformat from .core.config import NbParserConfig from .core.read import read_myst_markdown_notebook def quickstart(args: list[str] | None = None): """Quickstart myst_nb project.""" namespace = create_quickstart_cli().parse_args(args) path = Path(namespace.path).resolve() verbose: bool = namespace.verbose overwrite: bool = namespace.overwrite if path.exists(): if not overwrite: raise FileExistsError(f"{path} already exists.") if verbose: print(f"Overwriting existing files in: {path}") # create directory path.mkdir(parents=True, exist_ok=True) # write .gitignore (path / ".gitignore").write_text(".ipynb_checkpoints\n_build\n", encoding="utf-8") # write conf.py (path / "conf.py").write_text(generate_conf_py(), encoding="utf-8") # write index.md (path / "index.md").write_text( generate_index(["notebook1", "notebook2"]), encoding="utf-8" ) # write notebook1.ipynb (path / "notebook1.ipynb").write_text(generate_jupyter_notebook(), encoding="utf-8") # write notebook2.md (path / "notebook2.md").write_text(generate_text_notebook(), encoding="utf-8") print(f"Created myst_nb project at: {path}") def create_quickstart_cli(): cli = argparse.ArgumentParser(description="Create a basic myst_nb project.") cli.add_argument( "path", metavar="PATH", type=str, help="Directory to output the project." ) cli.add_argument( "-o", "--overwrite", action="store_true", help="Overwrite existing files." ) cli.add_argument("-v", "--verbose", action="store_true", help="Increase verbosity.") return cli def generate_conf_py() -> str: """Generate `conf.py` content.""" content = ( """\ # outline for a myst_nb project with sphinx # build with: sphinx-build -nW --keep-going -b html . ./_build/html # load extensions extensions = ["myst_nb"] # specify project details master_doc = "index" project = "MyST-NB Quickstart" # basic build settings exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] nitpicky = True ## myst_nb default settings """.rstrip() + "\n" ) settings = "" config = NbParserConfig() for name, value, field in config.as_triple(): if field.metadata.get("sphinx_exclude"): continue if field.metadata.get("help"): settings += f'{field.metadata.get("help")}\n' settings += f"nb_{name} = {value!r}\n\n" content += "\n" + indent(settings, "# ").rstrip() + "\n" return content def generate_index(children: list[str]) -> str: """Generate `index.md` content.""" children_str = "\n".join(children) content = ( f"""\ # MyST-NB Quickstart ```{{toctree}} {children_str} ``` """.rstrip() + "\n" ) return content def generate_jupyter_notebook() -> str: """Generate `notebook.ipynb` content.""" nb = nbformat.v4.new_notebook() nb["metadata"] = { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3", }, } nb["cells"] = [ nbformat.v4.new_markdown_cell("# Jupyter Notebook"), nbformat.v4.new_code_cell("print('Hello, World!')"), ] return nbformat.writes(nb) def generate_text_notebook() -> str: """Generate `notebook.md` content.""" content = ( """\ --- file_format: mystnb kernelspec: name: python3 --- # Text-based Notebook ```{code-cell} print("Hello, World!") ``` """.rstrip() + "\n" ) return content def md_to_nb(args: list[str] | None = None): namespace = create_md_to_nb_cli().parse_args(args) path = Path(namespace.inpath).resolve() if namespace.outpath: outpath = Path(namespace.outpath).resolve() else: outpath = path.with_suffix(".ipynb") verbose: bool = namespace.verbose overwrite: bool = namespace.overwrite if outpath.exists(): if not overwrite: raise FileExistsError(f"{outpath} already exists.") if verbose: print(f"Overwriting existing file: {outpath}") nb = read_myst_markdown_notebook(path.read_text("utf8"), path=path) with outpath.open("w", encoding="utf8") as handle: nbformat.write(nb, handle) print(f"Wrote notebook to: {outpath}") def create_md_to_nb_cli(): cli = argparse.ArgumentParser( description="Convert a text-based notebook to a Jupyter notebook." ) cli.add_argument( "inpath", metavar="PATH_IN", type=str, help="Path to Markdown file." ) cli.add_argument( "outpath", metavar="PATH_OUT", nargs="?", type=str, help="Path to output to.", ) cli.add_argument( "-o", "--overwrite", action="store_true", help="Overwrite existing files." ) cli.add_argument("-v", "--verbose", action="store_true", help="Increase verbosity.") return cli MyST-NB-1.1.2/myst_nb/core/000077500000000000000000000000001467453560600152675ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/core/__init__.py000066400000000000000000000012071467453560600174000ustar00rootroot00000000000000"""Modules for core functionality. The parsing of a notebook consists of a number of stages, with each stage separated into a separate module: 1. The configuration is set (from a file or CLI) 2. The parser is called with an input string and source 3. The parser reads the input string to a notebook node 4. The notebook is converted to a Markdown-It tokens syntax tree 5. The notebook code outputs are potentially updated, via execution or from a cache 6. The syntax tree is transformed to a docutils document AST (calling the renderer plugin) 7. The docutils document is processed by docutils/sphinx, to create the desired output format(s) """ MyST-NB-1.1.2/myst_nb/core/config.py000066400000000000000000000527651467453560600171250ustar00rootroot00000000000000"""Configuration for myst-nb.""" import dataclasses as dc from enum import Enum from typing import Any, Callable, Dict, Iterable, Literal, Optional, Sequence, Tuple from myst_parser.config.dc_validators import ( ValidatorType, deep_iterable, deep_mapping, in_, instance_of, optional, validate_fields, ) from myst_nb.warnings_ import MystNBWarnings def custom_formats_converter(value: dict) -> Dict[str, Tuple[str, dict, bool]]: """Convert the custom format dict.""" if not isinstance(value, dict): raise TypeError(f"`nb_custom_formats` must be a dict: {value}") output: Dict[str, Tuple[str, dict, bool]] = {} for suffix, reader in value.items(): if not isinstance(suffix, str): raise TypeError(f"`nb_custom_formats` keys must be a string: {suffix}") if isinstance(reader, str): output[suffix] = (reader, {}, False) elif not isinstance(reader, Sequence): raise TypeError( f"`nb_custom_formats` values must be a string or sequence: {reader}" ) elif len(reader) == 2: output[suffix] = (reader[0], reader[1], False) elif len(reader) == 3: output[suffix] = (reader[0], reader[1], reader[2]) else: raise TypeError( f"`nb_custom_formats` values must be a string, of sequence of length " f"2 or 3: {reader}" ) if not isinstance(output[suffix][0], str): raise TypeError( f"`nb_custom_formats` values[0] must be a string: {output[suffix][0]}" ) # TODO check can be loaded as a python object? if not isinstance(output[suffix][1], dict): raise TypeError( f"`nb_custom_formats` values[1] must be a dict: {output[suffix][1]}" ) if not isinstance(output[suffix][2], bool): raise TypeError( f"`nb_custom_formats` values[2] must be a bool: {output[suffix][2]}" ) return output def ipywidgets_js_factory() -> Dict[str, Dict[str, str]]: """Create a default ipywidgets js dict.""" # see: https://ipywidgets.readthedocs.io/en/7.6.5/embedding.html return { # Load RequireJS, used by the IPywidgets for dependency management "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js": { "integrity": "sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=", "crossorigin": "anonymous", }, # Load IPywidgets bundle for embedding. "https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@1.0.6/dist/embed-amd.js": { "data-jupyter-widgets-cdn": "https://cdn.jsdelivr.net/npm/", "crossorigin": "anonymous", }, } def has_items(*validators) -> ValidatorType: """ A validator that performs validation per item of a sequence. :param validators: Validator to apply per item """ def _validator(inst, field: dc.Field, value, suffix=""): if not isinstance(value, Sequence): raise TypeError(f"{suffix}{field.name} must be a sequence: {value}") if len(value) != len(validators): raise TypeError( f"{suffix}{field.name!r} must be a sequence of length " f"{len(validators)}: {value}" ) for idx, (validator, member) in enumerate(zip(validators, value)): validator(inst, field, member, suffix=f"{suffix}[{idx}]") return _validator class Section(Enum): """Config section tags.""" global_lvl = "global" """Global level configuration.""" file_lvl = "notebook" """File level configuration.""" cell_lvl = "cell" """Cell level configuration.""" config = "config" """Meta configuration.""" read = "read" """Configuration for reading files.""" execute = "execute" """Configuration for executing notebooks.""" render = "render" """Configuration for rendering notebook elements.""" @dc.dataclass() class NbParserConfig: """Global configuration options for the MyST-NB parser. Note: in the docutils/sphinx configuration, these option names are prepended with ``nb_`` """ def __post_init__(self): self.custom_formats = custom_formats_converter(self.custom_formats) validate_fields(self) # file read options custom_formats: Dict[str, Tuple[str, dict, bool]] = dc.field( default_factory=dict, metadata={ "help": "Custom formats for reading notebook; suffix -> reader", "omit": ["docutils"], "sections": (Section.global_lvl, Section.read), }, ) # docutils does not support the custom formats mechanism read_as_md: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Read as the MyST Markdown format", "sphinx_exclude": True, }, repr=False, ) # configuration override keys (applied after file read) # TODO previously we had `nb_render_key` (default: "render"), # for cell.metadata.render.image and cell.metadata.render.figure`, # and also `timeout`/`allow_errors` in notebook.metadata.execution # do we still support these or deprecate? # (plus also cell.metadata.tags: # nbclient: `skip-execution` and `raises-exception`, # myst_nb: `remove_cell`, `remove-cell`, `remove_input`, `remove-input`, # `remove_output`, `remove-output`, `remove-stderr` # ) # see also: # https://nbformat.readthedocs.io/en/latest/format_description.html#cell-metadata metadata_key: str = dc.field( default="mystnb", metadata={ "validator": instance_of(str), "help": "Notebook level metadata key for config overrides", "sections": (Section.global_lvl, Section.config), }, ) cell_metadata_key: str = dc.field( default="mystnb", metadata={ "validator": instance_of(str), "help": "Cell level metadata key for config overrides", "legacy_name": "nb_render_key", "sections": (Section.global_lvl, Section.file_lvl, Section.config), }, ) # notebook execution options kernel_rgx_aliases: Dict[str, str] = dc.field( default_factory=dict, metadata={ "validator": deep_mapping(instance_of(str), instance_of(str)), "help": "Mapping of kernel name regex to replacement kernel name" "(applied before execution)", "omit": ["docutils"], "sections": (Section.global_lvl, Section.execute), }, ) eval_name_regex: str = dc.field( default=r"^[a-zA-Z_][a-zA-Z0-9_]*$", metadata={ "validator": instance_of(str), "help": "Regex that matches permitted values of eval expressions", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_mode: Literal["off", "force", "auto", "cache", "inline"] = dc.field( default="auto", metadata={ "validator": in_( [ "off", "auto", "force", "cache", "inline", ] ), "help": "Execution mode for notebooks", "legacy_name": "jupyter_execute_notebooks", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_cache_path: str = dc.field( default="", # No default, so that sphinx can set it inside outdir, if empty metadata={ "validator": instance_of(str), "help": "Path to folder for caching notebooks (default: )", "legacy_name": "jupyter_cache", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_excludepatterns: Sequence[str] = dc.field( default=(), metadata={ "validator": deep_iterable(instance_of(str)), "help": "Exclude (POSIX) glob patterns for notebooks", "legacy_name": "execution_excludepatterns", "omit": ["docutils"], "sections": (Section.global_lvl, Section.execute), }, ) execution_timeout: int = dc.field( default=30, metadata={ "validator": instance_of(int), "help": "Execution timeout (seconds)", "legacy_name": "execution_timeout", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_in_temp: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Use temporary folder for the execution current working directory", "legacy_name": "execution_in_temp", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_allow_errors: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Allow errors during execution", "legacy_name": "execution_allow_errors", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_raise_on_error: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Raise an exception on failed execution, " "rather than emitting a warning", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) execution_show_tb: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Print traceback to stderr on execution error", "legacy_name": "execution_show_tb", "sections": (Section.global_lvl, Section.file_lvl, Section.execute), }, ) # pre-processing options merge_streams: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Merge stdout/stderr execution output streams", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) # render options render_plugin: str = dc.field( default="default", metadata={ "validator": instance_of(str), "help": "The entry point for the execution output render class " "(in group `myst_nb.output_renderer`)", "sections": (Section.global_lvl, Section.file_lvl, Section.render), }, ) remove_code_source: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Remove code cell source", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) remove_code_outputs: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Remove code cell outputs", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) code_prompt_show: str = dc.field( default="Show code cell {type}", metadata={ "validator": instance_of(str), "help": "Prompt to expand hidden code cell {content|source|outputs}", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) code_prompt_hide: str = dc.field( default="Hide code cell {type}", metadata={ "validator": instance_of(str), "help": "Prompt to collapse hidden code cell {content|source|outputs}", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) number_source_lines: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Number code cell source lines", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) # we cannot directly obtain a sphinx builder name from docutils, # so must set it manually builder_name: str = dc.field( default="html", metadata={ "validator": instance_of(str), "help": "Builder name, to select render priority for mime types", "sphinx_exclude": True, }, repr=False, ) mime_priority_overrides: Sequence[Tuple[str, str, Optional[int]]] = dc.field( default=(), metadata={ "validator": deep_iterable( has_items( instance_of(str), instance_of(str), optional(instance_of(int)) ), ), "help": "Overrides for the base render priority of mime types: " "list of (builder name, mime type, priority)", # TODO how to allow this in docutils? "omit": ["docutils"], "sections": (Section.global_lvl, Section.file_lvl, Section.render), }, repr=False, ) output_stderr: Literal[ "show", "remove", "remove-warn", "warn", "error", "severe" ] = dc.field( default="show", metadata={ "validator": in_( [ "show", "remove", "remove-warn", "warn", "error", "severe", ] ), "help": "Behaviour for stderr output", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) render_text_lexer: str = dc.field( default="myst-ansi", # TODO allow None -> "none"? # TODO check it can be loaded? metadata={ "validator": optional(instance_of(str)), "help": "Pygments lexer applied to stdout/stderr and text/plain outputs", "cell_key": "text_lexer", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) render_error_lexer: str = dc.field( default="ipythontb", # TODO allow None -> "none"? # TODO check it can be loaded? metadata={ "validator": optional(instance_of(str)), "help": "Pygments lexer applied to error/traceback outputs", "cell_key": "error_lexer", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) render_image_options: Dict[str, str] = dc.field( default_factory=dict, # see https://docutils.sourceforge.io/docs/ref/rst/directives.html#image metadata={ "validator": deep_mapping(instance_of(str), instance_of((str, int))), "help": "Options for image outputs (class|alt|height|width|scale|align)", "omit": ["docutils"], # TODO backward-compatible change to "image_options"? "cell_key": "image", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) render_figure_options: Dict[str, str] = dc.field( default_factory=dict, # see https://docutils.sourceforge.io/docs/ref/rst/directives.html#figure metadata={ "validator": deep_mapping(instance_of(str), instance_of((str, int))), "help": "Options for figure outputs (classes|name|caption|caption_before)", "omit": ["docutils"], "cell_key": "figure", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) render_markdown_format: Literal["commonmark", "gfm", "myst"] = dc.field( default="commonmark", metadata={ "validator": in_(["commonmark", "gfm", "myst"]), "help": "The format to use for text/markdown rendering", "cell_key": "markdown_format", "sections": ( Section.global_lvl, Section.file_lvl, Section.cell_lvl, Section.render, ), }, ) # TODO jupyter_sphinx_require_url and jupyter_sphinx_embed_url (undocumented), # are no longer used by this package, replaced by ipywidgets_js # do we add any deprecation warnings? ipywidgets_js: Dict[str, Dict[str, str]] = dc.field( default_factory=ipywidgets_js_factory, metadata={ "validator": deep_mapping( instance_of(str), deep_mapping(instance_of(str), instance_of(str)) ), "help": "Javascript to be loaded on pages containing ipywidgets", "omit": ["docutils"], "sections": (Section.global_lvl, Section.render), }, repr=False, ) # write options for docutils output_folder: str = dc.field( default="build", metadata={ "validator": instance_of(str), "help": "Folder for external outputs (like images), skipped if empty", "sphinx_exclude": True, # in sphinx we always output to the build folder }, ) append_css: bool = dc.field( default=True, metadata={ "validator": instance_of(bool), "help": "Add default MyST-NB CSS to HTML outputs", "sphinx_exclude": True, }, ) metadata_to_fm: bool = dc.field( default=False, metadata={ "validator": instance_of(bool), "help": "Convert unhandled metadata to frontmatter", "sphinx_exclude": True, }, ) @classmethod def get_fields(cls) -> Tuple[dc.Field, ...]: return dc.fields(cls) def as_dict(self, dict_factory=dict) -> dict: return dc.asdict(self, dict_factory=dict_factory) def as_triple(self) -> Iterable[Tuple[str, Any, dc.Field]]: """Yield triples of (name, value, field).""" fields = {f.name: f for f in dc.fields(self.__class__)} for name, value in dc.asdict(self).items(): yield name, value, fields[name] def copy(self, **changes) -> "NbParserConfig": """Return a copy of the configuration with optional changes applied.""" return dc.replace(self, **changes) def __getitem__(self, field: str) -> Any: """Get a field value by name.""" if field in ("get_fields", "as_dict", "as_triple", "copy"): raise KeyError(field) try: return getattr(self, field) except AttributeError: raise KeyError(field) def get_cell_level_config( self, field_name: str, cell_metadata: Dict[str, Any], warning_callback: Callable[[str, MystNBWarnings], Any], ) -> Any: """Get a configuration value at the cell level. Takes the highest priority configuration from: `cell > document > global > default` :param field: the field name to get the value for :param cell_metadata: the metadata for the cell :param warning_callback: a callback to use to warn about issues (msg, subtype) :raises KeyError: if the field is not found """ field: dc.Field = self.__dataclass_fields__[field_name] cell_key = field.metadata.get("cell_key", field.name) if ( self.cell_metadata_key not in cell_metadata and "render" in cell_metadata and isinstance(cell_metadata["render"], dict) and cell_key in cell_metadata["render"] ): warning_callback( f"Deprecated `cell_metadata_key` 'render' " f"found, replace with {self.cell_metadata_key!r}", MystNBWarnings.CELL_METADATA_KEY, ) cell_meta = cell_metadata["render"] else: cell_meta = cell_metadata.get(self.cell_metadata_key, None) if cell_meta: try: if cell_key in cell_meta: value = cell_meta[cell_key] if "validator" in field.metadata: if isinstance(field.metadata["validator"], list): for validator in field.metadata["validator"]: validator(self, field, value) else: field.metadata["validator"](self, field, value) return value except Exception as exc: warning_callback( f"Cell metadata invalid: {exc}", MystNBWarnings.CELL_CONFIG, ) # default/global/file level should have already been merged return getattr(self, field.name) MyST-NB-1.1.2/myst_nb/core/execute/000077500000000000000000000000001467453560600167315ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/core/execute/__init__.py000066400000000000000000000053401467453560600210440ustar00rootroot00000000000000from __future__ import annotations from pathlib import Path, PurePosixPath from typing import TYPE_CHECKING from .base import ExecutionError, ExecutionResult, NotebookClientBase # noqa: F401 from .cache import NotebookClientCache from .direct import NotebookClientDirect from .inline import NotebookClientInline if TYPE_CHECKING: from nbformat import NotebookNode from myst_nb.core.config import NbParserConfig from myst_nb.core.loggers import LoggerType def create_client( notebook: NotebookNode, source: str, nb_config: NbParserConfig, logger: LoggerType, read_fmt: None | dict = None, ) -> NotebookClientBase: """Create a notebook execution client, to update its outputs. This function may execute the notebook if necessary, to update its outputs, or populate from a cache. :param notebook: The notebook to update. :param source: Path to or description of the input source being processed. :param nb_config: The configuration for the notebook parser. :param logger: The logger to use. :param read_fmt: The format of the input source (to parse to jupyter cache) :returns: The updated notebook, and the (optional) execution metadata. """ # path should only be None when using docutils programmatically, # e.g. source="" try: path = Path(source) if Path(source).is_file() else None except OSError: path = None # occurs on Windows for `source=""` # check if the notebook is excluded from execution by pattern if path is not None and nb_config.execution_excludepatterns: posix_path = PurePosixPath(path.as_posix()) for pattern in nb_config.execution_excludepatterns: if posix_path.match(pattern): logger.info(f"Excluded from execution by pattern: {pattern!r}") return NotebookClientBase(notebook, path, nb_config, logger) # 'auto' mode only executes the notebook if it is missing at least one output missing_outputs = ( len(cell.outputs) == 0 for cell in notebook.cells if cell["cell_type"] == "code" ) if nb_config.execution_mode == "auto" and not any(missing_outputs): logger.info("Skipped execution in 'auto' mode (all outputs present)") return NotebookClientBase(notebook, path, nb_config, logger) if nb_config.execution_mode in ("auto", "force"): return NotebookClientDirect(notebook, path, nb_config, logger) if nb_config.execution_mode == "cache": return NotebookClientCache(notebook, path, nb_config, logger, read_fmt=read_fmt) if nb_config.execution_mode == "inline": return NotebookClientInline(notebook, path, nb_config, logger) return NotebookClientBase(notebook, path, nb_config, logger) MyST-NB-1.1.2/myst_nb/core/execute/base.py000066400000000000000000000127511467453560600202230ustar00rootroot00000000000000"""Module for executing notebooks.""" from __future__ import annotations from pathlib import Path from typing import Any from nbformat import NotebookNode from typing_extensions import TypedDict, final from myst_nb.core.config import NbParserConfig from myst_nb.core.loggers import LoggerType from myst_nb.core.nb_to_tokens import nb_node_to_dict from myst_nb.ext.glue import extract_glue_data class ExecutionResult(TypedDict): """Result of executing a notebook.""" mtime: float """POSIX timestamp of the execution time""" runtime: float | None """runtime in seconds""" method: str """method used to execute the notebook""" succeeded: bool """True if the notebook executed successfully""" error: str | None """error type if the notebook failed to execute""" traceback: str | None """traceback if the notebook failed""" class ExecutionError(Exception): """An exception for failed execution and `execution_raise_on_error` is true.""" class EvalNameError(Exception): """An exception for if an evaluation variable name is invalid.""" class NotebookClientBase: """A base client for interacting with Jupyter notebooks. This class is intended to be used as a context manager, and should only be entered once. Subclasses should override the `start_client` and `close_client` methods. """ def __init__( self, notebook: NotebookNode, path: Path | None, nb_config: NbParserConfig, logger: LoggerType, **kwargs: Any, ): """Initialize the client.""" self._notebook = notebook self._path = path self._nb_config = nb_config self._logger = logger self._kwargs = kwargs self._glue_data: dict[str, NotebookNode] = {} self._exec_metadata: ExecutionResult | None = None # get or create source map of cell to source line # use 1-based indexing rather than 0, or pseudo base of the cell index _source_map: list[int] = notebook.metadata.get("source_map", None) self._source_map = [ (_source_map[i] if _source_map else ((i + 1) * 10000)) + 1 for i, _ in enumerate(notebook.cells) ] @final def __enter__(self) -> NotebookClientBase: """Enter the context manager.""" self.start_client() # extract glue data from the notebook self._glue_data = extract_glue_data( self.notebook, self._source_map, self.logger ) return self @final def __exit__(self, exc_type, exc_val, exc_tb): """Exit the context manager.""" self.close_client(exc_type, exc_val, exc_tb) def start_client(self): """Start the client.""" def finalise_client(self): """Finalise the client. This is called before final rendering and should be used, for example, to finalise the widget state on the metadata. """ def close_client(self, exc_type, exc_val, exc_tb): """Close the client.""" @property def notebook(self) -> NotebookNode: """Get the notebook.""" return self._notebook @property def path(self) -> Path | None: """Get the notebook path.""" return self._path @property def nb_config(self) -> NbParserConfig: """Get the notebook configuration.""" return self._nb_config @property def logger(self) -> LoggerType: """Get the logger.""" return self._logger @property def glue_data(self) -> dict[str, NotebookNode]: """Get the glue data.""" return self._glue_data @property def exec_metadata(self) -> ExecutionResult | None: """Get the execution metadata.""" return self._exec_metadata @exec_metadata.setter def exec_metadata(self, value: ExecutionResult): """Set the execution metadata.""" self._exec_metadata = value def cell_line(self, cell_index: int) -> int: """Get the source line number of a cell.""" return self._source_map[cell_index] @property def nb_metadata(self) -> dict[str, Any]: """Get the notebook level metadata.""" return nb_node_to_dict(self.notebook.get("metadata", {})) def nb_source_code_lexer(self) -> str | None: """Get the lexer for the notebook source code.""" metadata = self.notebook.get("metadata", {}) langinfo = metadata.get("language_info") or {} lexer = langinfo.get("pygments_lexer") or langinfo.get("name", None) if lexer is None: lexer = (metadata.get("kernelspec") or {}).get("language", None) return lexer def code_cell_outputs( self, cell_index: int ) -> tuple[int | None, list[NotebookNode]]: """Get the outputs of a cell. :returns: a tuple of the execution_count and the outputs :raises IndexError: if the cell index is out of range """ cells = self.notebook.get("cells", []) cell = cells[cell_index] return cell.get("execution_count", None), cell.get("outputs", []) def eval_variable(self, name: str) -> list[NotebookNode]: """Retrieve the value of a variable from the kernel. :param name: the name of the variable, must match the regex `[a-zA-Z][a-zA-Z0-9_]*` :returns: code cell outputs :raises NotImplementedError: if the execution mode does not support this feature :raises EvalNameError: if the variable name is invalid """ raise NotImplementedError MyST-NB-1.1.2/myst_nb/core/execute/cache.py000066400000000000000000000106741467453560600203560ustar00rootroot00000000000000"""Execute a notebook from the cache.""" from __future__ import annotations from contextlib import nullcontext, suppress from datetime import datetime import os from tempfile import TemporaryDirectory from typing import ContextManager from jupyter_cache import get_cache from jupyter_cache.base import CacheBundleIn from jupyter_cache.cache.db import NbProjectRecord from jupyter_cache.executors.utils import single_nb_execution from .base import ExecutionError, NotebookClientBase class NotebookClientCache(NotebookClientBase): """A notebook client that retrieves notebook outputs from the cache, or executes the notebook and adds them to the cache, on entrance of the context. """ def start_client(self): # setup the cache cache = get_cache(self.nb_config.execution_cache_path or ".jupyter_cache") # TODO config on what notebook/cell metadata to hash/merge # attempt to match the notebook to one in the cache cache_record = None with suppress(KeyError): cache_record = cache.match_cache_notebook(self.notebook) # use the cached notebook if it exists if cache_record is not None: self.logger.info(f"Using cached notebook: ID={cache_record.pk}") _, self._notebook = cache.merge_match_into_notebook(self.notebook) self.exec_metadata = { "mtime": cache_record.created.timestamp(), "runtime": cache_record.data.get("execution_seconds", None), "method": self.nb_config.execution_mode, "succeeded": True, "error": None, "traceback": None, } return if self.path is None: raise ValueError( "Input source must exist as file, if execution_mode is 'cache'" ) # attempt to execute the notebook read_fmt = self._kwargs.get("read_fmt", None) if read_fmt is not None: stage_record = cache.add_nb_to_project(str(self.path), read_data=read_fmt) else: stage_record = cache.add_nb_to_project(str(self.path)) # TODO do in try/except, in case of db write errors NbProjectRecord.remove_tracebacks([stage_record.pk], cache.db) cwd_context: ContextManager[str] = ( TemporaryDirectory() # type: ignore if self.nb_config.execution_in_temp else nullcontext(str(self.path.parent)) ) with cwd_context as cwd: cwd = os.path.abspath(cwd) self.logger.info( "Executing notebook using " + ("temporary" if self.nb_config.execution_in_temp else "local") + " CWD" ) result = single_nb_execution( self.notebook, cwd=cwd, allow_errors=self.nb_config.execution_allow_errors, timeout=self.nb_config.execution_timeout, meta_override=True, # TODO still support this? ) # handle success / failure cases # TODO do in try/except to be careful (in case of database write errors? if result.err is not None: msg = f"Executing notebook failed: {result.err.__class__.__name__}" if self.nb_config.execution_show_tb: msg += f"\n{result.exc_string}" self.logger.warning(msg, subtype="exec") if self.nb_config.execution_raise_on_error: raise ExecutionError(str(self.path)) from result.err NbProjectRecord.set_traceback(stage_record.uri, result.exc_string, cache.db) else: self.logger.info(f"Executed notebook in {result.time:.2f} seconds") cache_record = cache.cache_notebook_bundle( CacheBundleIn( self.notebook, stage_record.uri, data={"execution_seconds": result.time}, ), check_validity=False, overwrite=True, ) self.logger.info(f"Cached executed notebook: ID={cache_record.pk}") self.exec_metadata = { "mtime": datetime.now().timestamp(), "runtime": result.time, "method": self.nb_config.execution_mode, "succeeded": False if result.err else True, "error": f"{result.err.__class__.__name__}" if result.err else None, "traceback": result.exc_string if result.err else None, } MyST-NB-1.1.2/myst_nb/core/execute/direct.py000066400000000000000000000046271467453560600205660ustar00rootroot00000000000000"""Execute a notebook directly.""" from __future__ import annotations from contextlib import nullcontext from datetime import datetime import os from tempfile import TemporaryDirectory from typing import ContextManager from jupyter_cache.executors.utils import single_nb_execution from .base import ExecutionError, NotebookClientBase class NotebookClientDirect(NotebookClientBase): """A notebook client that executes the notebook directly, on entrance of the context. """ def start_client(self): # setup the execution current working directory cwd_context: ContextManager[str] if self.nb_config.execution_in_temp: cwd_context = TemporaryDirectory() else: if self.path is None: raise ValueError( "Input source must exist as file, if execution_in_temp=False" ) cwd_context = nullcontext(str(self.path.parent)) # execute in the context of the current working directory with cwd_context as cwd: cwd = os.path.abspath(cwd) self.logger.info( "Executing notebook using " + ("temporary" if self.nb_config.execution_in_temp else "local") + " CWD" ) result = single_nb_execution( self.notebook, cwd=cwd, allow_errors=self.nb_config.execution_allow_errors, timeout=self.nb_config.execution_timeout, meta_override=True, # TODO still support this? ) if result.err is not None: if self.nb_config.execution_raise_on_error: raise ExecutionError(str(self.path)) from result.err msg = f"Executing notebook failed: {result.err.__class__.__name__}" if self.nb_config.execution_show_tb: msg += f"\n{result.exc_string}" self.logger.warning(msg, subtype="exec") else: self.logger.info(f"Executed notebook in {result.time:.2f} seconds") self.exec_metadata = { "mtime": datetime.now().timestamp(), "runtime": result.time, "method": self.nb_config.execution_mode, "succeeded": False if result.err else True, "error": f"{result.err.__class__.__name__}" if result.err else None, "traceback": result.exc_string if result.err else None, } MyST-NB-1.1.2/myst_nb/core/execute/inline.py000066400000000000000000000167221467453560600205710ustar00rootroot00000000000000"""Execute a notebook inline.""" from __future__ import annotations import asyncio from datetime import datetime import re import shutil from tempfile import mkdtemp import time import traceback from nbclient.client import ( CellControlSignal, CellExecutionError, CellTimeoutError, DeadKernelError, NotebookClient, ensure_async, run_sync, ) import nbformat from nbformat import NotebookNode from myst_nb.ext.glue import extract_glue_data_cell from .base import EvalNameError, ExecutionError, NotebookClientBase class NotebookClientInline(NotebookClientBase): """A notebook client that executes the notebook inline, i.e. during the render. This allows for the client to be called in-between code cell executions, in order to extract variable state. """ def start_client(self): self._tmp_path = None if self.nb_config.execution_in_temp: self._tmp_path = mkdtemp() resources = {"metadata": {"path": self._tmp_path}} else: if self.path is None: raise ValueError( "Input source must exist as file, if execution_in_temp=False" ) resources = {"metadata": {"path": str(self.path.parent)}} self.logger.info("Starting inline execution client") self._time_start = time.perf_counter() self._client = ModifiedNotebookClient( self.notebook, record_timing=False, resources=resources, allow_errors=self.nb_config.execution_allow_errors, timeout=self.nb_config.execution_timeout, ) self._client.reset_execution_trackers() if self._client.km is None: self._client.km = self._client.create_kernel_manager() if not self._client.km.has_kernel: self._client.start_new_kernel() self._client.start_new_kernel_client() # retrieve the the language_info from the kernel assert self._client.kc is not None msg_id = self._client.kc.kernel_info() info_msg = self._client.wait_for_reply(msg_id) if info_msg is not None and "language_info" in info_msg["content"]: self.notebook.metadata["language_info"] = info_msg["content"][ "language_info" ] else: self.logger.warning("Failed to retrieve language info from kernel") self._last_cell_executed: int = -1 self._cell_error: None | Exception = None self._exc_string: None | str = None def finalise_client(self): try: self._client.set_widgets_metadata() except Exception as exc: self.logger.warning(f"Failed to set widgets metadata: {exc}") def close_client(self, exc_type, exc_val, exc_tb): self.logger.info("Stopping inline execution client") if self._client.owns_km: self._client._cleanup_kernel() del self._client _exec_time = time.perf_counter() - self._time_start self.exec_metadata = { "mtime": datetime.now().timestamp(), "runtime": _exec_time, "method": self.nb_config.execution_mode, "succeeded": False if self._cell_error else True, "error": f"{self._cell_error.__class__.__name__}" if self._cell_error else None, "traceback": self._exc_string, } if not self._cell_error: self.logger.info(f"Executed notebook in {_exec_time:.2f} seconds") else: msg = f"Executing notebook failed: {self._cell_error.__class__.__name__}" if self.nb_config.execution_show_tb: msg += f"\n{self._exc_string}" self.logger.warning(msg, subtype="exec") if self._tmp_path: shutil.rmtree(self._tmp_path, ignore_errors=True) def code_cell_outputs( self, cell_index: int ) -> tuple[int | None, list[NotebookNode]]: cells = self.notebook.get("cells", []) # ensure all cells up to and including the requested cell have been executed while (not self._cell_error) and cell_index > self._last_cell_executed: self._last_cell_executed += 1 try: next_cell = cells[self._last_cell_executed] except IndexError: break try: self._client.execute_cell( next_cell, self._last_cell_executed, execution_count=self._client.code_cells_executed + 1, ) except (CellExecutionError, CellTimeoutError) as err: if self.nb_config.execution_raise_on_error: raise ExecutionError(str(self.path)) from err self._cell_error = err self._exc_string = "".join(traceback.format_exc()) for key, cell_data in extract_glue_data_cell(next_cell): if key in self._glue_data: self.logger.warning( f"glue key {key!r} duplicate", subtype="glue", line=self.cell_line(self._last_cell_executed), ) self._glue_data[key] = cell_data cell = cells[cell_index] return cell.get("execution_count", None), cell.get("outputs", []) def eval_variable(self, name: str) -> list[NotebookNode]: if not re.match(self.nb_config.eval_name_regex, name): raise EvalNameError(name) return self._client.eval_expression(name) class ModifiedNotebookClient(NotebookClient): async def async_eval_expression(self, name: str) -> list[NotebookNode]: """Evaluate an expression in the kernel. This is a modified version of `async_execute_cell`, which executed a single cell, with `name` as the source and returns the result. """ assert self.kc is not None self.log.debug(f"Evaluating expression: {name}") parent_msg_id = await ensure_async( self.kc.execute( str(name), store_history=False, stop_on_error=False, ) ) cell = nbformat.v4.new_code_cell(source=str(name)) exec_timeout = self._get_timeout(cell) cell_index = -1 self.clear_before_next_output = False task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive()) task_poll_output_msg = asyncio.ensure_future( self._async_poll_output_msg(parent_msg_id, cell, cell_index) ) self.task_poll_for_reply = asyncio.ensure_future( self._async_poll_for_reply( parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive, ) ) try: await self.task_poll_for_reply except asyncio.CancelledError: # can only be cancelled by task_poll_kernel_alive when the kernel is dead task_poll_output_msg.cancel() raise DeadKernelError("Kernel died") except Exception as e: # Best effort to cancel request if it hasn't been resolved try: # Check if the task_poll_output is doing the raising for us if not isinstance(e, CellControlSignal): task_poll_output_msg.cancel() finally: raise return cell.outputs eval_expression = run_sync(async_eval_expression) MyST-NB-1.1.2/myst_nb/core/lexers.py000066400000000000000000000125701467453560600171500ustar00rootroot00000000000000"""Pygments lexers""" from __future__ import annotations import re # this is not added as an entry point in ipython, so we add it in this package from IPython.lib.lexers import IPythonTracebackLexer # noqa: F401 import pygments.lexer import pygments.token _ansi_code_to_color = { 0: "Black", 1: "Red", 2: "Green", 3: "Yellow", 4: "Blue", 5: "Magenta", 6: "Cyan", 7: "White", } def _token_from_lexer_state( bold: bool, faint: bool, fg_color: str | None, bg_color: str | None ): """Construct a token given the current lexer state. We can only emit one token even though we have a multiple-tuple state. To do work around this, we construct tokens like "Bold.Red". """ components: tuple[str, ...] = () if bold: components += ("Bold",) if faint: components += ("Faint",) if fg_color: components += (fg_color,) if bg_color: components += ("BG" + bg_color,) if len(components) == 0: return pygments.token.Text else: token = pygments.token.Token.Color for component in components: token = getattr(token, component) return token class AnsiColorLexer(pygments.lexer.RegexLexer): """Pygments lexer for text containing ANSI color codes. Adapted from https://github.com/chriskuehl/pygments-ansi-color """ name = "ANSI Color" aliases = ("myst-ansi",) flags = re.DOTALL | re.MULTILINE def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.reset_state() def reset_state(self): self.bold = False self.faint = False self.fg_color = None self.bg_color = None @property def current_token(self): return _token_from_lexer_state( self.bold, self.faint, self.fg_color, self.bg_color, ) def process(self, match): """Produce the next token and bit of text. Interprets the ANSI code (which may be a color code or some other code), changing the lexer state and producing a new token. If it's not a color code, we just strip it out and move on. Some useful reference for ANSI codes: * http://ascii-table.com/ansi-escape-sequences.php """ # "after_escape" contains everything after the start of the escape # sequence, up to the next escape sequence. We still need to separate # the content from the end of the escape sequence. after_escape = match.group(1) # TODO: this doesn't handle the case where the values are non-numeric. # This is rare but can happen for keyboard remapping, e.g. # '\x1b[0;59;"A"p' parsed = re.match( r"([0-9;=]*?)?([a-zA-Z])(.*)$", after_escape, re.DOTALL | re.MULTILINE, ) if parsed is None: # This shouldn't ever happen if we're given valid text + ANSI, but # people can provide us with utter junk, and we should tolerate it. text = after_escape else: value, code, text = parsed.groups() if code == "m": # "m" is "Set Graphics Mode" # Special case \x1b[m is a reset code if value == "": self.reset_state() else: try: values = [int(v) for v in value.split(";")] except ValueError: # Shouldn't ever happen, but could with invalid ANSI. values = [] while len(values) > 0: value = values.pop(0) fg_color = _ansi_code_to_color.get(value - 30) bg_color = _ansi_code_to_color.get(value - 40) if fg_color: self.fg_color = fg_color elif bg_color: self.bg_color = bg_color elif value == 1: self.bold = True elif value == 2: self.faint = True elif value == 22: self.bold = False self.faint = False elif value == 39: self.fg_color = None elif value == 49: self.bg_color = None elif value == 0: self.reset_state() elif value in (38, 48): try: five = values.pop(0) color = values.pop(0) except IndexError: continue else: if five != 5: continue if not 0 <= color <= 255: continue if value == 38: self.fg_color = f"C{color}" else: self.bg_color = f"C{color}" yield match.start(), self.current_token, text tokens = { "root": [(r"\x1b\[([^\x1b]*)", process), (r"[^\x1b]+", pygments.token.Text)], } MyST-NB-1.1.2/myst_nb/core/loggers.py000066400000000000000000000122041467453560600173020ustar00rootroot00000000000000"""This module provides equivalent loggers for both docutils and sphinx. These loggers act like standard Python logging.Logger objects, but route messages via the docutils/sphinx reporting systems. They are initialised with a docutils document, in order to provide the source location of the log message, and can also both handle ``line`` and ``subtype`` keyword arguments: ``logger.warning("message", line=1, subtype="foo")`` """ import logging from typing import Union from docutils import nodes DEFAULT_LOG_TYPE = "mystnb" class SphinxDocLogger(logging.LoggerAdapter): """Wraps a Sphinx logger, which routes messages to the docutils document reporter. The document path and message type are automatically included in the message, and ``line`` is allowed as a keyword argument, as well as the standard sphinx logger keywords: ``subtype``, ``color``, ``once``, ``nonl``. As per the sphinx logger, warnings are suppressed, if their ``type.subtype`` are included in the ``suppress_warnings`` configuration. These are also appended to the end of messages. """ def __init__(self, document: nodes.document, type_name: str = DEFAULT_LOG_TYPE): from sphinx.util import logging as sphinx_logging docname = document.settings.env.docname self.logger = sphinx_logging.getLogger(f"{type_name}-{docname}") # default extras to parse to sphinx logger # location can be: docname, (docname, lineno), or a node self.extra = {"docname": docname, "type": type_name} def process(self, msg, kwargs): self.extra: dict kwargs["extra"] = self.extra if "type" in kwargs: # override type self.extra["type"] = kwargs.pop("type") subtype = ("." + kwargs["subtype"]) if "subtype" in kwargs else "" if kwargs.get("line", None) is not None: # add line to location # note this will be overridden by the location keyword self.extra["location"] = (self.extra["docname"], kwargs.pop("line")) else: self.extra["location"] = self.extra["docname"] if "parent" in kwargs: # TODO ideally here we would append a system_message to this node, # then it could replace myst_parser.SphinxRenderer.create_warning self.extra["parent"] = kwargs.pop("parent") return f"{msg} [{self.extra['type']}{subtype}]", kwargs class DocutilsDocLogger(logging.LoggerAdapter): """A logger which routes messages to the docutils document reporter. The document path and message type are automatically included in the message, and ``line`` is allowed as a keyword argument. The standard sphinx logger keywords are allowed but ignored: ``subtype``, ``color``, ``once``, ``nonl``. ``type.subtype`` are also appended to the end of messages. """ KEYWORDS = [ "type", "subtype", "location", "nonl", "color", "once", "line", "parent", ] def __init__(self, document: nodes.document, type_name: str = DEFAULT_LOG_TYPE): self.logger: logging.Logger = logging.getLogger( f"{type_name}-{document.source}" ) # docutils handles the level of output logging self.logger.setLevel(logging.DEBUG) if not self.logger.handlers: self.logger.addHandler(DocutilsLogHandler(document)) # default extras to parse to sphinx logger # location can be: docname, (docname, lineno), or a node self.extra = {"type": type_name, "line": None, "parent": None} def process(self, msg, kwargs): kwargs["extra"] = self.extra subtype = ("." + kwargs["subtype"]) if "subtype" in kwargs else "" for keyword in self.KEYWORDS: if keyword in kwargs: kwargs["extra"][keyword] = kwargs.pop(keyword) etype = "" if not self.extra else self.extra.get("type", "") return f"{msg} [{etype}{subtype}]", kwargs class DocutilsLogHandler(logging.Handler): """Handle logging via a docutils reporter.""" def __init__(self, document: nodes.document) -> None: """Initialize a new handler.""" super().__init__() self._document = document reporter = self._document.reporter self._name_to_level = { "DEBUG": reporter.DEBUG_LEVEL, "INFO": reporter.INFO_LEVEL, "WARN": reporter.WARNING_LEVEL, "WARNING": reporter.WARNING_LEVEL, "ERROR": reporter.ERROR_LEVEL, "CRITICAL": reporter.SEVERE_LEVEL, "FATAL": reporter.SEVERE_LEVEL, } def emit(self, record: logging.LogRecord) -> None: """Handle a log record.""" levelname = record.levelname.upper() level = self._name_to_level.get(levelname, self._document.reporter.DEBUG_LEVEL) node = self._document.reporter.system_message( level, record.msg, **({"line": record.line} if record.line is not None else {}), # type: ignore ) if record.parent is not None: # type: ignore record.parent.append(node) # type: ignore LoggerType = Union[DocutilsDocLogger, SphinxDocLogger] MyST-NB-1.1.2/myst_nb/core/nb_to_tokens.py000066400000000000000000000126361467453560600203350ustar00rootroot00000000000000"""Module for parsing notebooks to Markdown-it tokens.""" from __future__ import annotations from typing import Any from markdown_it.main import MarkdownIt from markdown_it.rules_core import StateCore from markdown_it.token import Token from nbformat import NotebookNode from myst_nb.core.loggers import LoggerType def nb_node_to_dict(node: NotebookNode) -> dict[str, Any]: """Recursively convert a notebook node to a dict.""" return _nb_node_to_dict(node) def _nb_node_to_dict(item: Any) -> Any: """Recursively convert any notebook nodes to dict.""" if isinstance(item, NotebookNode): return {k: _nb_node_to_dict(v) for k, v in item.items()} return item def notebook_to_tokens( notebook: NotebookNode, mdit_parser: MarkdownIt, mdit_env: dict[str, Any], logger: LoggerType, ) -> list[Token]: # disable front-matter, since this is taken from the notebook mdit_parser.disable("front_matter", ignoreInvalid=True) # this stores global state, such as reference definitions # Parse block tokens only first, leaving inline parsing to a second phase # (required to collect all reference definitions, before assessing references). block_tokens = [Token("nb_initialise", "", 0, map=[0, 0])] for cell_index, nb_cell in enumerate(notebook.cells): # skip empty cells if len(nb_cell["source"].strip()) == 0: continue # skip cells tagged for removal tags = nb_cell.metadata.get("tags", []) if ("remove_cell" in tags) or ("remove-cell" in tags): continue # generate tokens tokens: list[Token] if nb_cell["cell_type"] == "markdown": # https://nbformat.readthedocs.io/en/5.1.3/format_description.html#markdown-cells # TODO if cell has tag output-caption, then use as caption for next/preceding cell? tokens = [ Token( "nb_cell_markdown_open", "", 1, hidden=True, meta={ "index": cell_index, "metadata": nb_node_to_dict(nb_cell["metadata"]), }, map=[0, len(nb_cell["source"].splitlines()) - 1], ), ] with mdit_parser.reset_rules(): # enable only rules up to block rules = mdit_parser.core.ruler.get_active_rules() mdit_parser.core.ruler.enableOnly(rules[: rules.index("inline")]) tokens.extend(mdit_parser.parse(nb_cell["source"], mdit_env)) tokens.append( Token( "nb_cell_markdown_close", "", -1, hidden=True, ), ) elif nb_cell["cell_type"] == "raw": # https://nbformat.readthedocs.io/en/5.1.3/format_description.html#raw-nbconvert-cells tokens = [ Token( "nb_cell_raw", "code", 0, content=nb_cell["source"], meta={ "index": cell_index, "metadata": nb_node_to_dict(nb_cell["metadata"]), }, map=[0, 0], ) ] elif nb_cell["cell_type"] == "code": # https://nbformat.readthedocs.io/en/5.1.3/format_description.html#code-cells # we don't copy the outputs here, since this would # greatly increase the memory consumption, # instead they will referenced by the cell index tokens = [ Token( "nb_cell_code", "code", 0, content=nb_cell["source"], meta={ "index": cell_index, "metadata": nb_node_to_dict(nb_cell["metadata"]), }, map=[0, 0], ) ] else: pass # TODO create warning # update token's source lines, using either a source_map (index -> line), # set when converting to a notebook, or a pseudo base of the cell index smap = notebook.metadata.get("source_map", None) start_line = smap[cell_index] if smap else (cell_index + 1) * 10000 start_line += 1 # use base 1 rather than 0 for token in tokens: if token.map: token.map = [start_line + token.map[0], start_line + token.map[1]] # also update the source lines for duplicate references for dup_ref in mdit_env.get("duplicate_refs", []): if "fixed" not in dup_ref: dup_ref["map"] = [ start_line + dup_ref["map"][0], start_line + dup_ref["map"][1], ] dup_ref["fixed"] = True # add tokens to list block_tokens.extend(tokens) block_tokens.append(Token("nb_finalise", "", 0, map=[0, 0])) # Now all definitions have been gathered, run the inline parsing phase state = StateCore("", mdit_parser, mdit_env, block_tokens) with mdit_parser.reset_rules(): rules = mdit_parser.core.ruler.get_active_rules() mdit_parser.core.ruler.enableOnly(rules[rules.index("inline") :]) mdit_parser.core.process(state) return state.tokens MyST-NB-1.1.2/myst_nb/core/read.py000066400000000000000000000341541467453560600165630ustar00rootroot00000000000000"""Module for reading notebook formats from a string input.""" from __future__ import annotations import dataclasses as dc from functools import partial import json from pathlib import Path from typing import Callable, Iterator from docutils.parsers.rst import Directive from markdown_it.renderer import RendererHTML from myst_parser.config.main import MdParserConfig from myst_parser.parsers.mdit import create_md_parser import nbformat as nbf import yaml from myst_nb.core.config import NbParserConfig from myst_nb.core.loggers import DocutilsDocLogger, SphinxDocLogger NOTEBOOK_VERSION = 4 """The notebook version that readers should return.""" @dc.dataclass() class NbReader: """A data class for reading a notebook format.""" read: Callable[[str], nbf.NotebookNode] """The function to read a notebook from a (utf8) string.""" md_config: MdParserConfig """The configuration for parsing markdown cells.""" read_fmt: dict | None = dc.field(default=None) """The type of the reader, if known.""" def standard_nb_read(text: str) -> nbf.NotebookNode: """Read a standard .ipynb notebook from a string.""" return nbf.reads(text, as_version=NOTEBOOK_VERSION) def create_nb_reader( path: str, md_config: MdParserConfig, nb_config: NbParserConfig, content: None | str | Iterator[str], ) -> NbReader | None: """Create a notebook reader, given a string, source path and configuration. Note, we do not directly parse to a notebook, since jupyter-cache functionality requires the reader. :param path: Path to the input source being processed. :param nb_config: The configuration for parsing Notebooks. :param md_config: The default configuration for parsing Markown. :param content: The input string (optionally used to check for text-based notebooks) :returns: the notebook reader, and the (potentially modified) MdParserConfig, or None if the input cannot be read as a notebook. """ # the import is here so this module can be loaded without sphinx from sphinx.util import import_object # get all possible readers readers = nb_config.custom_formats.copy() # add the default reader readers.setdefault(".ipynb", (standard_nb_read, {}, False)) # type: ignore # we check suffixes ordered by longest first, to ensure we get the "closest" match iterator = sorted(readers.items(), key=lambda x: len(x[0]), reverse=True) for suffix, (reader, reader_kwargs, commonmark_only) in iterator: if Path(path).suffix == suffix: if isinstance(reader, str): # attempt to load the reader as an object path reader = import_object(reader) if commonmark_only: # Markdown cells should be read as Markdown only md_config = dc.replace(md_config, commonmark_only=True) return NbReader(partial(reader, **(reader_kwargs or {})), md_config) # type: ignore # a Markdown file is a special case, since we only treat it as a notebook, # if it starts with certain "top-matter" if content is not None and is_myst_markdown_notebook(content): return NbReader( partial( read_myst_markdown_notebook, config=md_config, add_source_map=True, path=path, ), md_config, {"type": "plugin", "name": "myst_nb_md"}, ) # if we get here, we did not find a reader return None def is_myst_markdown_notebook(text: str | Iterator[str]) -> bool: """Check if the input is a MyST Markdown notebook. This is identified by the presence of a top-matter section, containing either:: --- file_format: mystnb --- or:: --- jupytext: text_representation: format_name: myst --- :param text: The input text. :returns: True if the input is a markdown notebook. """ if isinstance(text, str): if not text.startswith("---"): # skip creating the line list in memory return False text = (line for line in text.splitlines()) try: if not next(text).startswith("---"): return False except StopIteration: return False top_matter = [] for line in text: if line.startswith("---") or line.startswith("..."): break top_matter.append(line.rstrip() + "\n") try: metadata = yaml.safe_load("".join(top_matter)) assert isinstance(metadata, dict) except Exception: return False if "file_format" in metadata and metadata["file_format"] == "mystnb": return True if ( metadata.get("jupytext", {}) .get("text_representation", {}) .get("format_name", None) != "myst" ): return False return True # TODO move this to reader, since not strictly part of function objective # or just allow nbformat/nbclient to handle the failure # if "name" not in metadata.get("kernelspec", {}): # raise IOError( # "A myst notebook text-representation requires " "kernelspec/name metadata" # ) # if "display_name" not in metadata.get("kernelspec", {}): # raise IOError( # "A myst notebook text-representation requires " # "kernelspec/display_name metadata" # ) def myst_nb_reader_plugin(uri: str) -> nbf.NotebookNode: """Read a myst notebook from a string. Used as plugin for jupyter-cache. """ return read_myst_markdown_notebook( Path(uri).read_text("utf8"), add_source_map=True, path=uri ) def read_myst_markdown_notebook( text, config: MdParserConfig | None = None, code_directive="{code-cell}", raw_directive="{raw-cell}", add_source_map=False, path: str | Path | None = None, ) -> nbf.NotebookNode: """Convert text written in the myst format to a notebook. :param text: the file text :param code_directive: the name of the directive to search for containing code cells :param raw_directive: the name of the directive to search for containing raw cells :param add_source_map: add a `source_map` key to the notebook metadata, which is a list of the starting source line number for each cell. :param path: path to notebook (required for :load:) :raises MystMetadataParsingError if the metadata block is not valid JSON/YAML NOTE: we assume here that all of these directives are at the top-level, i.e. not nested in other directives. """ config = config or MdParserConfig() # parse markdown file up to the block level (i.e. don't worry about inline text) inline_config = dc.replace( config, disable_syntax=(list(config.disable_syntax) + ["inline"]) ) parser = create_md_parser(inline_config, RendererHTML) tokens = parser.parse(text + "\n") lines = text.splitlines() md_start_line = 0 # get the document metadata metadata_nb = {} if tokens[0].type == "front_matter": metadata = tokens.pop(0) md_start_line = metadata.map[1] if metadata.map else 0 try: metadata_nb = yaml.safe_load(metadata.content) except (yaml.parser.ParserError, yaml.scanner.ScannerError) as error: raise MystMetadataParsingError(f"Notebook metadata: {error}") # add missing display name to the metadata, as required by the nbformat schema: # https://github.com/jupyter/nbformat/blob/f712d60f13c5b168313222cbf4bee7face98a081/nbformat/v4/nbformat.v4.5.schema.json#L16 if ( "kernelspec" in metadata_nb and "name" in metadata_nb["kernelspec"] and "display_name" not in metadata_nb["kernelspec"] ): metadata_nb["kernelspec"]["display_name"] = metadata_nb["kernelspec"]["name"] # create an empty notebook nbf_version = nbf.v4 kwargs = {"metadata": nbf.from_dict(metadata_nb)} notebook = nbf_version.new_notebook(**kwargs) source_map = [] # this is a list of the starting line number for each cell def _flush_markdown(start_line, token, md_metadata): """When we find a cell we check if there is preceding text.o""" endline = token.map[0] if token else len(lines) md_source = _strip_blank_lines("\n".join(lines[start_line:endline])) meta = nbf.from_dict(md_metadata) if md_source: source_map.append(start_line) notebook.cells.append( nbf_version.new_markdown_cell(source=md_source, metadata=meta) ) # iterate through the tokens to identify notebook cells nesting_level = 0 md_metadata: dict = {} for token in tokens: nesting_level += token.nesting if nesting_level != 0: # we ignore fenced block that are nested, e.g. as part of lists, etc continue token_map = token.map or [0, 0] if token.type == "fence" and token.info.startswith(code_directive): _flush_markdown(md_start_line, token, md_metadata) options, body_lines = _read_fenced_cell(token, len(notebook.cells), "Code") # Parse :load: or load: tags and populate body with contents of file if "load" in options: body_lines = _load_code_from_file( path, options["load"], token, body_lines ) meta = nbf.from_dict(options) source_map.append(token_map[0] + 1) notebook.cells.append( nbf_version.new_code_cell(source="\n".join(body_lines), metadata=meta) ) md_metadata = {} md_start_line = token_map[1] elif token.type == "fence" and token.info.startswith(raw_directive): _flush_markdown(md_start_line, token, md_metadata) options, body_lines = _read_fenced_cell(token, len(notebook.cells), "Raw") meta = nbf.from_dict(options) source_map.append(token_map[0] + 1) notebook.cells.append( nbf_version.new_raw_cell(source="\n".join(body_lines), metadata=meta) ) md_metadata = {} md_start_line = token_map[1] elif token.type == "myst_block_break": _flush_markdown(md_start_line, token, md_metadata) md_metadata = _read_cell_metadata(token, len(notebook.cells)) md_start_line = token_map[1] _flush_markdown(md_start_line, None, md_metadata) if add_source_map: notebook.metadata["source_map"] = source_map return notebook class MystMetadataParsingError(Exception): """Error when parsing metadata from myst formatted text""" class _LoadFileParsingError(Exception): """Error when parsing files for code-blocks/code-cells""" def _strip_blank_lines(text): text = text.rstrip() while text and text.startswith("\n"): text = text[1:] return text class _MockDirective: option_spec = {"options": True} required_arguments = 0 optional_arguments = 1 has_content = True def _read_fenced_cell(token, cell_index, cell_type): from myst_parser.parsers.directives import parse_directive_text result = parse_directive_text( directive_class=_MockDirective, first_line="", content=token.content, validate_options=False, ) if result.warnings: raise MystMetadataParsingError( "{} cell {} at line {} could not be read: {}".format( cell_type, cell_index, token.map[0] + 1, result.warnings[0] ) ) return result.options, result.body def _read_cell_metadata(token, cell_index): metadata = {} if token.content: try: metadata = json.loads(token.content.strip()) except Exception as err: raise MystMetadataParsingError( "Markdown cell {} at line {} could not be read: {}".format( cell_index, token.map[0] + 1, err ) ) if not isinstance(metadata, dict): raise MystMetadataParsingError( "Markdown cell {} at line {} is not a dict".format( cell_index, token.map[0] + 1 ) ) return metadata def _load_code_from_file( nb_path: None | str | Path, file_name: str, token, body_lines: list[str] ): """load source code from a file.""" if nb_path is None: raise _LoadFileParsingError("path to notebook not supplied for :load:") file_path = Path(nb_path).parent.joinpath(file_name).resolve() if len(body_lines): pass # TODO this would make the reader dependent on sphinx # line = token.map[0] if token.map else 0 # msg = ( # f"{nb_path}:{line} content of code-cell is being overwritten by " # f":load: {file_name}" # ) # LOGGER.warning(msg) try: body_lines = file_path.read_text().split("\n") except Exception: raise _LoadFileParsingError(f"Can't read file from :load: {file_path}") return body_lines class UnexpectedCellDirective(Directive): """The `{code-cell}`` and ``{raw-cell}`` directives, are special cases, which are picked up by the MyST Markdown reader to convert them into notebooks. If any are left in the parsed Markdown, it probably means that they were nested inside another directive, which is not allowed. Therefore, we log a warning if it is triggered, and discard it. """ optional_arguments = 1 final_argument_whitespace = True has_content = True def run(self): """Run the directive.""" message = ( "Found an unexpected `code-cell` or `raw-cell` directive. " "Either this file was not converted to a notebook, " "because Jupytext header content was missing, " "or the `code-cell` was not converted, because it is nested. " "See https://myst-nb.readthedocs.io/en/latest/use/markdown.html " "for more information." ) document = self.state.document if hasattr(document.settings, "env"): logger = SphinxDocLogger(document) else: logger = DocutilsDocLogger(document) # type: ignore logger.warning(message, line=self.lineno, subtype="nbcell") return [] MyST-NB-1.1.2/myst_nb/core/render.py000066400000000000000000001347171467453560600171350ustar00rootroot00000000000000"""Module for rendering notebook components to docutils nodes. Note, this module purposely does not import any Sphinx modules at the top-level, in order for docutils-only use. """ from __future__ import annotations from binascii import a2b_base64 from contextlib import contextmanager import dataclasses as dc from functools import lru_cache import hashlib import json from mimetypes import guess_extension import os from pathlib import Path import re from typing import TYPE_CHECKING, Any, ClassVar, Iterator, Sequence, Union from docutils import nodes from docutils.parsers.rst import directives as options_spec from importlib_metadata import entry_points from myst_parser.config.main import MdParserConfig from myst_parser.mdit_to_docutils.base import token_line from myst_parser.parsers.mdit import create_md_parser from nbformat import NotebookNode from typing_extensions import Protocol from myst_nb.core.config import NbParserConfig from myst_nb.core.execute import NotebookClientBase from myst_nb.core.loggers import LoggerType # DEFAULT_LOG_TYPE, from myst_nb.core.utils import coalesce_streams from myst_nb.warnings_ import MystNBWarnings, create_warning if TYPE_CHECKING: from markdown_it.tree import SyntaxTreeNode from myst_nb.docutils_ import DocutilsNbRenderer, DocutilsRenderer from myst_nb.sphinx_ import SphinxNbRenderer, SphinxRenderer SelfType = Union["MditRenderMixin", DocutilsRenderer, SphinxRenderer] WIDGET_STATE_MIMETYPE = "application/vnd.jupyter.widget-state+json" WIDGET_VIEW_MIMETYPE = "application/vnd.jupyter.widget-view+json" RENDER_ENTRY_GROUP = "myst_nb.renderers" MIME_RENDER_ENTRY_GROUP = "myst_nb.mime_renderers" _ANSI_RE = re.compile("\x1b\\[(.*?)([@-~])") _QUOTED_RE = re.compile(r"^([\"']).*\1$") class MditRenderMixin: """Mixin for rendering markdown-it tokens to docutils nodes. This has shared methods for both the `DocutilsRenderer` and `SphinxRenderer` """ # required by mypy md_options: dict[str, Any] document: nodes.document render_children: Any add_line_and_source_path: Any add_line_and_source_path_r: Any current_node: Any current_node_context: Any create_highlighted_code_block: Any @property def nb_config(self: SelfType) -> NbParserConfig: """Get the notebook element renderer.""" return self.md_options["nb_config"] @property def nb_client(self: SelfType) -> NotebookClientBase: """Get the notebook element renderer.""" return self.md_options["nb_client"] @property def nb_renderer(self: SelfType) -> NbElementRenderer: """Get the notebook element renderer.""" return self.document["nb_renderer"] def get_cell_level_config( self: SelfType, field: str, cell_metadata: dict[str, Any], line: int | None = None, ) -> Any: """Get a configuration value at the cell level. Takes the highest priority configuration from: `cell > document > global > default` :param field: the field name from ``NbParserConfig`` to get the value for :param cell_metadata: the metadata for the cell """ def _callback(msg: str, subtype: MystNBWarnings): create_warning(self.document, msg, line=line, subtype=subtype) return self.nb_config.get_cell_level_config(field, cell_metadata, _callback) def render_nb_initialise(self, token: SyntaxTreeNode) -> None: """Run rendering at the start of the notebook.""" def render_nb_finalise(self, token: SyntaxTreeNode) -> None: """Run rendering at the end of the notebook.""" self.nb_client.finalise_client() self.nb_renderer.render_nb_finalise(self.nb_client.nb_metadata) def render_nb_cell_markdown(self: SelfType, token: SyntaxTreeNode) -> None: """Render a notebook markdown cell.""" # TODO this is currently just a "pass-through", but we could utilise the metadata # it would be nice to "wrap" this in a container that included the metadata, # but unfortunately this would break the heading structure of docutils/sphinx. # perhaps we add an "invisible" (non-rendered) marker node to the document tree, self.render_children(token) def render_nb_cell_raw(self: SelfType, token: SyntaxTreeNode) -> None: """Render a notebook raw cell.""" line = token_line(token, 0) _nodes = self.nb_renderer.render_raw_cell( token.content, token.meta["metadata"], token.meta["index"], line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) def render_nb_cell_code(self: SelfType, token: SyntaxTreeNode) -> None: """Render a notebook code cell.""" cell_index = token.meta["index"] cell_line = token_line(token, 0) or None tags = token.meta["metadata"].get("tags", []) exec_count, outputs = self._get_nb_code_cell_outputs(token) classes = ["cell"] for tag in tags: classes.append(f"tag_{tag.replace(' ', '_')}") # TODO do we need this -/_ duplication of tag names, or can we deprecate one? hide_cell = "hide-cell" in tags remove_input = ( self.get_cell_level_config( "remove_code_source", token.meta["metadata"], line=cell_line ) or ("remove_input" in tags) or ("remove-input" in tags) ) hide_input = "hide-input" in tags remove_output = ( self.get_cell_level_config( "remove_code_outputs", token.meta["metadata"], line=cell_line ) or ("remove_output" in tags) or ("remove-output" in tags) ) hide_output = "hide-output" in tags # if we are remove both the input and output, we can skip the cell if remove_input and remove_output: return hide_mode = None if hide_cell: hide_mode = "all" elif hide_input and hide_output: hide_mode = "input+output" elif hide_input: hide_mode = "input" elif hide_output: hide_mode = "output" # create a container for all the input/output cell_container = nodes.container( nb_element="cell_code", cell_index=cell_index, # TODO some way to use this to allow repr of count in outputs like HTML? exec_count=exec_count, cell_metadata=token.meta["metadata"], classes=classes, ) if hide_mode: cell_container["hide_mode"] = hide_mode code_prompt_show = self.get_cell_level_config( "code_prompt_show", token.meta["metadata"], line=cell_line ) code_prompt_hide = self.get_cell_level_config( "code_prompt_hide", token.meta["metadata"], line=cell_line ) cell_container["prompt_show"] = code_prompt_show cell_container["prompt_hide"] = code_prompt_hide self.add_line_and_source_path(cell_container, token) with self.current_node_context(cell_container, append=True): # render the code source code if not remove_input: cell_input = nodes.container( nb_element="cell_code_source", classes=["cell_input"] ) self.add_line_and_source_path(cell_input, token) with self.current_node_context(cell_input, append=True): self._render_nb_cell_code_source(token) # render the execution output, if any if outputs and (not remove_output): cell_output = nodes.container( nb_element="cell_code_output", classes=["cell_output"] ) self.add_line_and_source_path(cell_output, token) with self.current_node_context(cell_output, append=True): self._render_nb_cell_code_outputs(token, outputs) def _get_nb_source_code_lexer( self: SelfType, cell_index: int, warn_missing: bool = True, line: int | None = None, ) -> str | None: """Get the lexer name for code cell source.""" lexer = self.nb_client.nb_source_code_lexer() if lexer is None and warn_missing: # TODO this will create a warning for every cell, but perhaps # it should only be a single warning for the notebook (as previously) # TODO allow user to set default lexer? create_warning( self.document, f"No source code lexer found for notebook cell {cell_index + 1}", # wtype=DEFAULT_LOG_TYPE, subtype=MystNBWarnings.LEXER, line=line, append_to=self.current_node, ) return lexer def _render_nb_cell_code_source(self: SelfType, token: SyntaxTreeNode) -> None: """Render a notebook code cell's source.""" cell_index = token.meta["index"] line = token_line(token, 0) or None node = self.create_highlighted_code_block( token.content, self._get_nb_source_code_lexer(cell_index, line=line), number_lines=self.get_cell_level_config( "number_source_lines", token.meta["metadata"], line=line, ), source=self.document["source"], line=token_line(token), ) self.add_line_and_source_path(node, token) self.current_node.append(node) def _get_nb_code_cell_outputs( self, token: SyntaxTreeNode ) -> tuple[int | None, list[NotebookNode]]: """Get the outputs for a code cell and its execution count.""" cell_index = token.meta["index"] line = token_line(token, 0) or None exec_count, outputs = self.nb_client.code_cell_outputs(cell_index) if self.get_cell_level_config("merge_streams", token.meta["metadata"], line): # TODO should this be saved on the output notebook outputs = coalesce_streams(outputs) return exec_count, outputs def _render_nb_cell_code_outputs( self, token: SyntaxTreeNode, outputs: list[NotebookNode] ) -> None: """Render a notebook code cell's outputs.""" # for display_data/execute_result this is different in the # docutils/sphinx implementation, # since sphinx delays MIME type selection until a post-transform # (when the output format is known) # TODO how to output MyST Markdown? # currently text/markdown is set to be rendered as CommonMark only, # with headings disallowed, # to avoid "side effects" if the mime is discarded but contained # targets, etc, and because we can't parse headings within containers. # perhaps we could have a config option to allow this? # - for non-commonmark, the text/markdown would always be considered # the top priority, and all other mime types would be ignored. # - for headings, we would also need to parsing the markdown # at the "top-level", i.e. not nested in container(s) raise NotImplementedError @dc.dataclass() class MimeData: """Mime data from an execution output (display_data / execute_result) e.g. notebook.cells[0].outputs[0].data['text/plain'] = "Hello, world!" see: https://nbformat.readthedocs.io/en/5.1.3/format_description.html#display-data """ mime_type: str """Mime type key of the output.data""" content: str | bytes """Data value of the output.data""" cell_metadata: dict[str, Any] = dc.field(default_factory=dict) """Cell level metadata of the output""" output_metadata: dict[str, Any] = dc.field(default_factory=dict) """Output level metadata of the output""" cell_index: int | None = None """Index of the cell in the notebook""" output_index: int | None = None """Index of the output in the cell""" line: int | None = None """Source line of the cell""" @property def string(self) -> str: """Get the content as a string.""" try: return self.content.decode("utf-8") # type: ignore except AttributeError: return self.content # type: ignore class NbElementRenderer: """A class for rendering notebook elements.""" def __init__( self, renderer: DocutilsNbRenderer | SphinxNbRenderer, logger: LoggerType ) -> None: """Initialize the renderer. :params output_folder: the folder path for external outputs (like images) """ self._renderer = renderer self._logger = logger @property def renderer(self) -> DocutilsNbRenderer | SphinxNbRenderer: """The renderer this output renderer is associated with.""" return self._renderer @property def config(self) -> NbParserConfig: """The notebook parser config""" return self._renderer.nb_config @property def logger(self) -> LoggerType: """The logger for this renderer. In extension to a standard logger, this logger also for `line` and `subtype` kwargs to the `log` methods. """ # TODO the only problem with logging here, is that we cannot generate # nodes.system_message to append to the document. return self._logger @property def source(self): """The source of the notebook.""" return self.renderer.document["source"] def write_file( self, path: list[str], content: bytes, overwrite=False, exists_ok=False ) -> str: """Write a file to the external output folder. :param path: the path to write the file to, relative to the output folder :param content: the content to write to the file :param overwrite: whether to overwrite an existing file :param exists_ok: whether to ignore an existing file if overwrite is False :returns: URI to use for referencing the file """ output_folder = self.config.output_folder filepath = Path(output_folder).joinpath(*path) if not output_folder: pass # do not output anything if output_folder is not set (docutils only) elif filepath.exists(): if overwrite: filepath.write_bytes(content) elif not exists_ok: # TODO raise or just report? raise FileExistsError(f"File already exists: {filepath}") else: filepath.parent.mkdir(parents=True, exist_ok=True) filepath.write_bytes(content) if self.renderer.sphinx_env: # sphinx expects paths in POSIX format, relative to the documents path, # or relative to the source folder if prepended with '/' filepath = filepath.resolve() if os.name == "nt": # Can't get relative path between drives on Windows return filepath.as_posix() # Path().relative_to() doesn't work when not a direct subpath return "/" + os.path.relpath(filepath, self.renderer.sphinx_env.app.srcdir) else: return str(filepath) def add_js_file(self, key: str, uri: str | None, kwargs: dict[str, str]) -> None: """Register a JavaScript file to include in the HTML output of this document. :param key: the key to use for referencing the file :param uri: the URI to the file, or None and supply the file contents in kwargs['body'] """ if "nb_js_files" not in self.renderer.document: self.renderer.document["nb_js_files"] = {} # TODO handle duplicate keys (whether to override/ignore) self.renderer.document["nb_js_files"][key] = (uri, kwargs) def render_nb_finalise(self, metadata: dict) -> None: """Finalise the render of the notebook metadata.""" # add ipywidgets state JavaScript, # The JSON inside the script tag is identified and parsed by: # https://github.com/jupyter-widgets/ipywidgets/blob/32f59acbc63c3ff0acf6afa86399cb563d3a9a86/packages/html-manager/src/libembed.ts#L36 # see also: https://ipywidgets.readthedocs.io/en/7.6.5/embedding.html ipywidgets = metadata.get("widgets", None) ipywidgets_mime = (ipywidgets or {}).get(WIDGET_STATE_MIMETYPE, {}) if ipywidgets_mime.get("state", None): self.add_js_file( "ipywidgets_state", None, { "type": "application/vnd.jupyter.widget-state+json", "body": sanitize_script_content(json.dumps(ipywidgets_mime)), }, ) for i, (path, kwargs) in enumerate(self.config.ipywidgets_js.items()): self.add_js_file(f"ipywidgets_{i}", path, kwargs) def render_raw_cell( self, content: str, metadata: dict, cell_index: int, source_line: int ) -> list[nodes.Element]: """Render a raw cell. https://nbformat.readthedocs.io/en/5.1.3/format_description.html#raw-nbconvert-cells :param content: the raw cell content :param metadata: the cell level metadata :param cell_index: the index of the cell :param source_line: the line number of the cell in the source document """ mime_type = metadata.get("format") if not mime_type: # skip without warning, since e.g. jupytext saves raw cells with no format return [] return self.render_mime_type( MimeData( mime_type, content, metadata, cell_index=cell_index, line=source_line ) ) def render_stdout( self, output: NotebookNode, cell_metadata: dict[str, Any], cell_index: int, source_line: int, ) -> list[nodes.Element]: """Render a notebook stdout output. https://nbformat.readthedocs.io/en/5.1.3/format_description.html#stream-output :param output: the output node :param metadata: the cell level metadata :param cell_index: the index of the cell containing the output :param source_line: the line number of the cell in the source document """ if "remove-stdout" in cell_metadata.get("tags", []): return [] lexer = self.renderer.get_cell_level_config( "render_text_lexer", cell_metadata, line=source_line ) node = self.renderer.create_highlighted_code_block( output["text"], lexer, source=self.source, line=source_line ) node["classes"] += ["output", "stream"] return [node] def render_stderr( self, output: NotebookNode, cell_metadata: dict[str, Any], cell_index: int, source_line: int, ) -> list[nodes.Element]: """Render a notebook stderr output. https://nbformat.readthedocs.io/en/5.1.3/format_description.html#stream-output :param output: the output node :param metadata: the cell level metadata :param cell_index: the index of the cell containing the output :param source_line: the line number of the cell in the source document """ if "remove-stderr" in cell_metadata.get("tags", []): return [] output_stderr = self.renderer.get_cell_level_config( "output_stderr", cell_metadata, line=source_line ) msg = f"stderr was found in the cell outputs of cell {cell_index + 1}" outputs = [] if output_stderr == "remove": return [] elif output_stderr == "remove-warn": self.logger.warning(msg, subtype="stderr", line=source_line) return [] elif output_stderr == "warn": self.logger.warning(msg, subtype="stderr", line=source_line) elif output_stderr == "error": self.logger.error(msg, subtype="stderr", line=source_line) elif output_stderr == "severe": self.logger.critical(msg, subtype="stderr", line=source_line) lexer = self.renderer.get_cell_level_config( "render_text_lexer", cell_metadata, line=source_line ) node = self.renderer.create_highlighted_code_block( output["text"], lexer, source=self.source, line=source_line ) node["classes"] += ["output", "stderr"] outputs.append(node) return outputs def render_error( self, output: NotebookNode, cell_metadata: dict[str, Any], cell_index: int, source_line: int, ) -> list[nodes.Element]: """Render a notebook error output. https://nbformat.readthedocs.io/en/5.1.3/format_description.html#error :param output: the output node :param metadata: the cell level metadata :param cell_index: the index of the cell containing the output :param source_line: the line number of the cell in the source document """ traceback = strip_ansi("\n".join(output["traceback"])) lexer = self.renderer.get_cell_level_config( "render_error_lexer", cell_metadata, line=source_line ) node = self.renderer.create_highlighted_code_block( traceback, lexer, source=self.source, line=source_line ) node["classes"] += ["output", "traceback"] return [node] def render_mime_type(self, data: MimeData) -> list[nodes.Element]: """Render a notebook mime output, as a block level element.""" # try plugin renderers for renderer in load_mime_renders(): nodes = renderer.handle_mime(self, data, False) if nodes is not None: return nodes # try default renderers if data.mime_type == "text/plain": return self.render_text_plain(data) if data.mime_type in { "image/png", "image/jpeg", "application/pdf", "image/svg+xml", "image/gif", }: return self.render_image(data) if data.mime_type == "text/html": return self.render_text_html(data) if data.mime_type == "text/latex": return self.render_text_latex(data) if data.mime_type == "application/javascript": return self.render_javascript(data) if data.mime_type == WIDGET_VIEW_MIMETYPE: return self.render_widget_view(data) if data.mime_type == "text/markdown": return self.render_markdown(data) return self.render_unhandled(data) def render_unhandled(self, data: MimeData) -> list[nodes.Element]: """Render a notebook output of unknown mime type.""" self.logger.warning( f"skipping unknown output mime type: {data.mime_type}", subtype="unknown_mime_type", line=data.line, ) return [] def render_markdown(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/markdown mime data output.""" fmt = self.renderer.get_cell_level_config( "render_markdown_format", data.cell_metadata, line=data.line ) return self._render_markdown_base(data, fmt=fmt, inline=False) def render_text_plain(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/plain mime data output.""" lexer = self.renderer.get_cell_level_config( "render_text_lexer", data.cell_metadata, line=data.line ) node = self.renderer.create_highlighted_code_block( data.string, lexer, source=self.source, line=data.line ) node["classes"] += ["output", "text_plain"] return [node] def render_text_html(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/html mime data output.""" return [ nodes.raw(text=data.string, format="html", classes=["output", "text_html"]) ] def render_text_latex(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/latex mime data output.""" # TODO should we always assume this is math? return [ nodes.math_block( text=strip_latex_delimiters(data.string), nowrap=False, number=None, classes=["output", "text_latex"], ) ] def render_image(self, data: MimeData) -> list[nodes.Element]: """Render a notebook image mime data output.""" # Adapted from: # https://github.com/jupyter/nbconvert/blob/45df4b6089b3bbab4b9c504f9e6a892f5b8692e3/nbconvert/preprocessors/extractoutput.py#L43 # ensure that the data is a bytestring if data.mime_type in { "image/png", "image/jpeg", "image/gif", "application/pdf", }: # data is b64-encoded as text data_bytes = a2b_base64(data.content) elif isinstance(data.content, str): # ensure correct line separator data_bytes = os.linesep.join(data.content.splitlines()).encode("utf-8") # create filename extension = ( guess_extension(data.mime_type) or "." + data.mime_type.rsplit("/")[-1] ) # latex does not recognize the '.jpe' extension extension = ".jpeg" if extension == ".jpe" else extension # ensure de-duplication of outputs by using hash as filename # TODO note this is a change to the current implementation, # which names by {notbook_name}-{cell_index}-{output-index}.{extension} data_hash = hashlib.sha256(data_bytes).hexdigest() filename = f"{data_hash}{extension}" # TODO should we be trying to clear old files? uri = self.write_file([filename], data_bytes, overwrite=False, exists_ok=True) image_node = nodes.image(uri=uri) # apply attributes to the image node # TODO backwards-compatible re-naming to image_options? image_options = self.renderer.get_cell_level_config( "render_image_options", data.cell_metadata, line=data.line ).copy() # Overwrite with metadata stored in output image_options.update( { key: str(value) for key, value in data.output_metadata.get(data.mime_type, {}).items() if key in {"width", "height"} } ) for key, spec in [ ("classes", options_spec.class_option), ("alt", options_spec.unchanged), ("height", options_spec.length_or_unitless), ("width", options_spec.length_or_percentage_or_unitless), ("scale", options_spec.percentage), ("align", lambda a: options_spec.choice(a, ("left", "center", "right"))), ]: if key not in image_options: continue try: image_node[key] = spec(image_options[key]) except Exception as exc: msg = f"Invalid image option ({key!r}; {image_options[key]!r}): {exc}" self.logger.warning(msg, subtype="image", line=data.line) return [image_node] def render_javascript(self, data: MimeData) -> list[nodes.Element]: """Render a notebook application/javascript mime data output.""" content = sanitize_script_content(data.string) mime_type = "application/javascript" return [ nodes.raw( text=f'', format="html", ) ] def render_widget_view(self, data: MimeData) -> list[nodes.Element]: """Render a notebook application/vnd.jupyter.widget-view+json mime output.""" # TODO note ipywidgets present? content = sanitize_script_content(json.dumps(data.string)) return [ nodes.raw( text=f'', format="html", ) ] def render_mime_type_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook mime output, as an inline level element.""" # try plugin renderers for renderer in load_mime_renders(): nodes = renderer.handle_mime(self, data, True) if nodes is not None: return nodes # try built-in renderers if data.mime_type == "text/plain": return self.render_text_plain_inline(data) if data.mime_type in { "image/png", "image/jpeg", "application/pdf", "image/svg+xml", "image/gif", }: return self.render_image_inline(data) if data.mime_type == "text/html": return self.render_text_html_inline(data) if data.mime_type == "text/latex": return self.render_text_latex_inline(data) if data.mime_type == "application/javascript": return self.render_javascript_inline(data) if data.mime_type == WIDGET_VIEW_MIMETYPE: return self.render_widget_view_inline(data) if data.mime_type == "text/markdown": return self.render_markdown_inline(data) return self.render_unhandled_inline(data) def render_unhandled_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook output of unknown mime type.""" self.logger.warning( f"skipping unknown output mime type: {data.mime_type}", subtype="unknown_mime_type", line=data.line, ) return [] def render_markdown_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/markdown mime data output.""" fmt = self.renderer.get_cell_level_config( "render_markdown_format", data.cell_metadata, line=data.line ) return self._render_markdown_base(data, fmt=fmt, inline=True) def render_text_plain_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/plain mime data output.""" content = data.string if data.output_metadata.get("strip_text_quotes", False) and _QUOTED_RE.match( content ): content = content[1:-1] node = nodes.inline(data.string, content, classes=["output", "text_plain"]) return [node] def render_text_html_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/html mime data output.""" return self.render_text_html(data) def render_text_latex_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook text/latex mime data output.""" # TODO should we always assume this is math? return [ nodes.math( text=strip_latex_delimiters(data.string), nowrap=False, number=None, classes=["output", "text_latex"], ) ] def render_image_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook image mime data output.""" return self.render_image(data) def render_javascript_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook application/javascript mime data output.""" return self.render_javascript(data) def render_widget_view_inline(self, data: MimeData) -> list[nodes.Element]: """Render a notebook application/vnd.jupyter.widget-view+json mime output.""" return self.render_widget_view(data) def _render_markdown_base( self, data: MimeData, *, fmt: str, inline: bool ) -> list[nodes.Element]: """Base render for a notebook markdown mime output (block or inline).""" pseudo_element = nodes.Element() # element to hold the parsed markdown current_parser = self.renderer.md current_md_config = self.renderer.md_config try: # potentially replace the parser temporarily if fmt == "myst": # use the current configuration to render the markdown pass elif fmt == "commonmark": # use an isolated, CommonMark only, parser self.renderer.md_config = MdParserConfig(commonmark_only=True) self.renderer.md = create_md_parser( self.renderer.md_config, self.renderer.__class__ ) elif fmt == "gfm": # use an isolated, GitHub Flavoured Markdown only, parser self.renderer.md_config = MdParserConfig(gfm_only=True) self.renderer.md = create_md_parser( self.renderer.md_config, self.renderer.__class__ ) else: self.logger.warning( f"skipping unknown markdown format: {fmt}", subtype="unknown_markdown_format", line=data.line, ) return [] with self.renderer.current_node_context(pseudo_element): self.renderer.nested_render_text( data.string, data.line or 0, inline=inline, ) finally: # restore the parser self.renderer.md = current_parser self.renderer.md_config = current_md_config return pseudo_element.children class EntryPointError(Exception): """Exception raised when an entry point cannot be loaded.""" @lru_cache(maxsize=10) def load_renderer(name: str) -> type[NbElementRenderer]: """Load a renderer, given a name within the ``RENDER_ENTRY_GROUP`` entry point group """ all_eps = entry_points() if hasattr(all_eps, "select"): # importlib_metadata >= 3.6 or importlib.metadata in python >=3.10 eps = all_eps.select(group=RENDER_ENTRY_GROUP, name=name) found = name in eps.names else: eps = {ep.name: ep for ep in all_eps.get(RENDER_ENTRY_GROUP, [])} # type: ignore found = name in eps if found: klass = eps[name].load() if not issubclass(klass, NbElementRenderer): raise EntryPointError( f"Entry Point for {RENDER_ENTRY_GROUP}:{name} " f"is not a subclass of `NbElementRenderer`: {klass}" ) return klass raise EntryPointError(f"No Entry Point found for {RENDER_ENTRY_GROUP}:{name}") class MimeRenderPlugin(Protocol): """Protocol for a mime renderer plugin.""" mime_priority_overrides: ClassVar[Sequence[tuple[str, str, int | None]]] = () """A list of (builder name, mime type, priority).""" @staticmethod def handle_mime( renderer: NbElementRenderer, data: MimeData, inline: bool ) -> None | list[nodes.Element]: """A function that renders a mime type to docutils nodes, or returns None to reject.""" class ExampleMimeRenderPlugin(MimeRenderPlugin): """Example mime renderer for `custommimetype`.""" mime_priority_overrides = [("*", "custommimetype", 1)] @staticmethod def handle_mime( renderer: NbElementRenderer, data: MimeData, inline: int ) -> None | list[nodes.Element]: if not inline and data.mime_type == "custommimetype": return [ nodes.paragraph( text=f"This is a custom mime type, with content: {data.content!r}", classes=["output", "text_plain"], ) ] return None @lru_cache def load_mime_renders() -> list[MimeRenderPlugin]: all_eps = entry_points() if hasattr(all_eps, "select"): # importlib_metadata >= 3.6 or importlib.metadata in python >=3.10 return [ep.load() for ep in all_eps.select(group=MIME_RENDER_ENTRY_GROUP)] return [ep.load() for ep in all_eps.get(MIME_RENDER_ENTRY_GROUP, [])] # type: ignore def strip_ansi(text: str) -> str: """Strip ANSI escape sequences from a string""" return _ANSI_RE.sub("", text) def sanitize_script_content(content: str) -> str: """Sanitize the content of a ``", r"<\/script>") def strip_latex_delimiters(source): r"""Remove LaTeX math delimiters that would be rendered by the math block. These are: ``\(โ€ฆ\)``, ``\[โ€ฆ\]``, ``$โ€ฆ$``, and ``$$โ€ฆ$$``. This is necessary because sphinx does not have a dedicated role for generic LaTeX, while Jupyter only defines generic LaTeX output, see https://github.com/jupyter/jupyter-sphinx/issues/90 for discussion. """ source = source.strip() delimiter_pairs = (pair.split() for pair in r"\( \),\[ \],$$ $$,$ $".split(",")) for start, end in delimiter_pairs: if source.startswith(start) and source.endswith(end): return source[len(start) : -len(end)] return source @contextmanager def create_figure_context( self: DocutilsNbRenderer | SphinxNbRenderer, figure_options: dict[str, Any] | None, line: int, ) -> Iterator: """Create a context manager, which optionally wraps new nodes in a figure node. A caption may also be added before or after the nodes. """ if not isinstance(figure_options, dict): yield return # note: most of this is copied directly from sphinx.Figure # create figure node figure_node = nodes.figure() figure_node.line = line figure_node.source = self.document["source"] # add attributes to figure node if figure_options.get("classes"): # TODO change to class? figure_node["classes"] += str(figure_options["classes"]).split() if figure_options.get("align") in ("center", "left", "right"): figure_node["align"] = figure_options["align"] # add target name if figure_options.get("name"): name = nodes.fully_normalize_name(str(figure_options.get("name"))) figure_node["names"].append(name) self.document.note_explicit_target(figure_node, figure_node) # create caption node caption = None if figure_options.get("caption", ""): node = nodes.Element() # anonymous container for parsing with self.current_node_context(node): self.nested_render_text(str(figure_options["caption"]), line) first_node = node.children[0] legend_nodes = node.children[1:] if isinstance(first_node, nodes.paragraph): caption = nodes.caption(first_node.rawsource, "", *first_node.children) caption.source = self.document["source"] caption.line = line elif not (isinstance(first_node, nodes.comment) and len(first_node) == 0): create_warning( self.document, "Figure caption must be a paragraph or empty comment.", line=line, # wtype=DEFAULT_LOG_TYPE, subtype=MystNBWarnings.FIG_CAPTION, ) self.current_node.append(figure_node) old_current_node = self.current_node self.current_node = figure_node if caption and figure_options.get("caption_before", False): figure_node.append(caption) if legend_nodes: figure_node += nodes.legend("", *legend_nodes) yield if caption and not figure_options.get("caption_before", False): figure_node.append(caption) if legend_nodes: figure_node += nodes.legend("", *legend_nodes) self.current_node = old_current_node def base_render_priority() -> dict[str, dict[str, int | None]]: """Create a base render priority dict: name -> mime type -> priority (ascending).""" # See formats at https://www.sphinx-doc.org/en/master/usage/builders/index.html # generated with: # [(b.name, b.format, b.supported_image_types) for b in app.registry.builders.values()] return { "epub": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "html": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "dirhtml": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "singlehtml": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "applehelp": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/png": 40, "image/gif": 50, "image/jpeg": 60, "image/tiff": 70, "image/jp2": 80, "image/svg+xml": 90, "text/markdown": 100, "text/latex": 110, "text/plain": 120, }, "devhelp": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/png": 40, "image/gif": 50, "image/jpeg": 60, "text/markdown": 70, "text/latex": 80, "text/plain": 90, }, "htmlhelp": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/png": 40, "image/gif": 50, "image/jpeg": 60, "text/markdown": 70, "text/latex": 80, "text/plain": 90, }, "json": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "pickle": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "qthelp": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, # deprecated RTD builders # https://github.com/readthedocs/readthedocs-sphinx-ext/blob/master/readthedocs_ext/readthedocs.py "readthedocs": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "readthedocsdirhtml": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "readthedocssinglehtml": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "readthedocssinglehtmllocalmedia": { "application/vnd.jupyter.widget-view+json": 10, "application/javascript": 20, "text/html": 30, "image/svg+xml": 40, "image/png": 50, "image/gif": 60, "image/jpeg": 70, "text/markdown": 80, "text/latex": 90, "text/plain": 100, }, "changes": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "dummy": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "gettext": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "latex": { "application/pdf": 10, "image/png": 20, "image/jpeg": 30, "text/latex": 40, "text/markdown": 50, "text/plain": 60, }, "linkcheck": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "man": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "texinfo": { "image/png": 10, "image/jpeg": 20, "image/gif": 30, "text/latex": 40, "text/markdown": 50, "text/plain": 60, }, "text": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "xml": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, "pseudoxml": {"text/latex": 10, "text/markdown": 20, "text/plain": 30}, } def get_mime_priority( builder: str, overrides: Sequence[tuple[str, str, int | None]] ) -> list[str]: """Return the priority list for the builder. Takes the base priority list, overrides from the config, then sorts by priority in ascending order. """ base = base_render_priority().get(builder, {}) overrides = list(overrides) for plugin in load_mime_renders(): overrides = list(getattr(plugin, "mime_priority_overrides", [])) + overrides for override in overrides: if override[0] == "*" or override[0] == builder: base[override[1]] = override[2] sort = sorted( ((k, p) for k, p in base.items() if p is not None), key=lambda x: x[1] ) return [k for k, _ in sort] MyST-NB-1.1.2/myst_nb/core/utils.py000066400000000000000000000040761467453560600170100ustar00rootroot00000000000000"""Shared utilities.""" from __future__ import annotations import re from nbformat import NotebookNode _RGX_CARRIAGERETURN = re.compile(r".*\r(?=[^\n])") _RGX_BACKSPACE = re.compile(r"[^\n]\b") def coalesce_streams(outputs: list[NotebookNode]) -> list[NotebookNode]: """Merge all stream outputs with shared names into single streams. This ensure deterministic outputs. Adapted from: https://github.com/computationalmodelling/nbval/blob/master/nbval/plugin.py. """ if not outputs: return [] new_outputs = [] streams: dict[str, NotebookNode] = {} for output in outputs: if output["output_type"] == "stream": if output["name"] in streams: out = output["text"].strip() if out: streams[output["name"]]["text"] += f"{out}\n" else: output["text"] = output["text"].strip() + "\n" new_outputs.append(output) streams[output["name"]] = output else: new_outputs.append(output) # process \r and \b characters for output in streams.values(): old = output["text"] while len(output["text"]) < len(old): old = output["text"] # Cancel out anything-but-newline followed by backspace output["text"] = _RGX_BACKSPACE.sub("", output["text"]) # Replace all carriage returns not followed by newline output["text"] = _RGX_CARRIAGERETURN.sub("", output["text"]) # We also want to ensure stdout and stderr are always in the same consecutive order, # because they are asynchronous, so order isn't guaranteed. for i, output in enumerate(new_outputs): if output["output_type"] == "stream" and output["name"] == "stderr": if ( len(new_outputs) >= i + 2 and new_outputs[i + 1]["output_type"] == "stream" and new_outputs[i + 1]["name"] == "stdout" ): stdout = new_outputs.pop(i + 1) new_outputs.insert(i, stdout) return new_outputs MyST-NB-1.1.2/myst_nb/core/variables.py000066400000000000000000000145121467453560600176140ustar00rootroot00000000000000"""Utilities for rendering code output variables.""" from __future__ import annotations from ast import literal_eval import dataclasses as dc from typing import Any from docutils import nodes from myst_nb._compat import findall from myst_nb.core.loggers import DocutilsDocLogger, SphinxDocLogger from myst_nb.core.render import MimeData, NbElementRenderer, get_mime_priority def is_sphinx(document: nodes.document) -> bool: """Return True if we are in sphinx, otherwise docutils.""" return hasattr(document.settings, "env") def create_warning( message: str, document: nodes.document, line: int, subtype: str ) -> nodes.system_message: """Create a warning.""" logger: DocutilsDocLogger | SphinxDocLogger if is_sphinx(document): logger = SphinxDocLogger(document) else: logger = DocutilsDocLogger(document) logger.warning(message, line=line, subtype=subtype) return nodes.system_message( message, type="WARNING", level=2, line=line, source=document["source"], ) def set_source_info(node: nodes.Node, source: str, line: int) -> None: """Set the source info for a node and its descendants.""" for _node in findall(node)(include_self=True): _node.source = source _node.line = line class RetrievalError(Exception): """An error occurred while retrieving the variable output.""" @dc.dataclass() class VariableOutput: """A class to store display_data/execute_result output for a variable.""" data: dict[str, Any] """the mime-bundle: mime-type -> data""" metadata: dict[str, Any] nb_renderer: NbElementRenderer vtype: str """The variable type (for use in warnings).""" index: int = 0 """The index of the output.""" def render_variable_outputs( outputs: list[VariableOutput], document: nodes.document, line: int, source: str, *, inline: bool = False, render: dict[str, Any] | None = None, ) -> list[nodes.Node]: """Given output data for a variable, return the docutils/sphinx nodes relevant to this data. :param outputs: The output data. :param document: The current docutils document. :param line: The current source line number of the directive or role. :param source: The current source path or description. :param inline: Whether to render the output as inline (or block). :param render: Cell-level render metadata. :returns: the docutils/sphinx nodes """ _nodes = [] for output in outputs: _nodes.extend( _render_variable_output( output, document, line, source, inline=inline, render=render, ) ) return _nodes def _render_variable_output( output: VariableOutput, document: nodes.document, line: int, source: str, *, inline: bool = False, render: dict[str, Any] | None = None, ) -> list[nodes.Node]: cell_metadata = {} if render: cell_metadata[output.nb_renderer.config.cell_metadata_key] = render if is_sphinx(document): _nodes = _render_output_sphinx( output, cell_metadata, source, line, inline, ) else: _nodes = _render_output_docutils( output, cell_metadata, document, line, inline, ) # TODO rendering should perhaps return if it succeeded explicitly, # and whether system_messages or not (required for roles) return _nodes def _render_output_docutils( output: VariableOutput, cell_metadata: dict[str, Any], document: nodes.document, line: int, inline=False, ) -> list[nodes.Node]: """Render the output in docutils (select mime priority directly).""" mime_priority = get_mime_priority( output.nb_renderer.config.builder_name, output.nb_renderer.config.mime_priority_overrides, ) try: mime_type = next(x for x in mime_priority if x in output.data) except StopIteration: if output.data: return [ create_warning( "No output mime type found from render_priority " f"(output<{output.index}>)", document, line, output.vtype, ) ] return [] else: mime_data = MimeData( mime_type, output.data[mime_type], cell_metadata=cell_metadata, output_metadata=output.metadata, line=line, ) if inline: return output.nb_renderer.render_mime_type_inline(mime_data) return output.nb_renderer.render_mime_type(mime_data) def _render_output_sphinx( output: VariableOutput, cell_metadata: dict[str, Any], source: str, line: int, inline=False, ) -> list[nodes.Node]: """Render the output in sphinx (defer mime priority selection).""" mime_bundle = nodes.container(nb_element="mime_bundle") set_source_info(mime_bundle, source, line) for mime_type, content in output.data.items(): mime_container = nodes.container(mime_type=mime_type) set_source_info(mime_container, source, line) mime_data = MimeData( mime_type, content, cell_metadata=cell_metadata, output_metadata=output.metadata, line=line, ) if inline: _nodes = output.nb_renderer.render_mime_type_inline(mime_data) else: _nodes = output.nb_renderer.render_mime_type(mime_data) if _nodes: mime_container.extend(_nodes) mime_bundle.append(mime_container) return [mime_bundle] def format_plain_text(text: str, fmt_spec: str) -> str: """Format plain text for display in a docutils node.""" # literal eval, to remove surrounding quotes try: value = literal_eval(text) except (SyntaxError, ValueError): value = text if fmt_spec == "": return str(value) type_char = fmt_spec[-1] if type_char == "s": value = str(value) elif type_char in ("b", "c", "d", "o", "x", "X"): value = int(value) elif type_char in ("e", "E", "f", "F", "g", "G", "n", "%"): value = float(value) return format(value, fmt_spec) MyST-NB-1.1.2/myst_nb/docutils_.py000066400000000000000000000364241467453560600167070ustar00rootroot00000000000000"""The docutils parser implementation for myst-nb.""" from __future__ import annotations from dataclasses import dataclass, field from functools import lru_cache, partial from importlib import resources as import_resources import os from typing import Any from docutils import nodes from docutils.core import default_description, publish_cmdline from docutils.parsers.rst.directives import _directives from docutils.parsers.rst.roles import _roles from markdown_it.token import Token from markdown_it.tree import SyntaxTreeNode from myst_parser.config.main import MdParserConfig, merge_file_level from myst_parser.mdit_to_docutils.base import DocutilsRenderer, token_line from myst_parser.parsers.docutils_ import Parser as MystParser from myst_parser.parsers.docutils_ import create_myst_config, create_myst_settings_spec from myst_parser.parsers.mdit import create_md_parser import nbformat from nbformat import NotebookNode from pygments.formatters import get_formatter_by_name from myst_nb import static from myst_nb.core.config import NbParserConfig from myst_nb.core.execute import create_client from myst_nb.core.loggers import DocutilsDocLogger # DEFAULT_LOG_TYPE, from myst_nb.core.nb_to_tokens import nb_node_to_dict, notebook_to_tokens from myst_nb.core.read import ( NbReader, UnexpectedCellDirective, read_myst_markdown_notebook, standard_nb_read, ) from myst_nb.core.render import ( MditRenderMixin, MimeData, NbElementRenderer, create_figure_context, get_mime_priority, load_renderer, ) from myst_nb.ext.eval import load_eval_docutils from myst_nb.ext.glue import load_glue_docutils from myst_nb.warnings_ import MystNBWarnings, create_warning DOCUTILS_EXCLUDED_ARGS = list( {f.name for f in NbParserConfig.get_fields() if f.metadata.get("docutils_exclude")} ) @dataclass class DocutilsApp: roles: dict[str, Any] = field(default_factory=dict) directives: dict[str, Any] = field(default_factory=dict) @lru_cache(maxsize=1) def get_nb_roles_directives() -> DocutilsApp: app = DocutilsApp() app.directives["code-cell"] = UnexpectedCellDirective app.directives["raw-cell"] = UnexpectedCellDirective load_eval_docutils(app) load_glue_docutils(app) return app class Parser(MystParser): """Docutils parser for Jupyter Notebooks, containing MyST Markdown.""" supported: tuple[str, ...] = ("mystnb", "ipynb") """Aliases this parser supports.""" settings_spec = ( "MyST-NB options", None, create_myst_settings_spec(NbParserConfig, "nb_"), *MystParser.settings_spec, ) """Runtime settings specification.""" config_section = "myst-nb parser" def parse(self, inputstring: str, document: nodes.document) -> None: # register/unregister special directives and roles app = get_nb_roles_directives() for name, directive in app.directives.items(): _directives[name] = directive for name, role in app.roles.items(): _roles[name] = role try: return self._parse(inputstring, document) finally: for name in app.directives: _directives.pop(name, None) for name in app.roles: _roles.pop(name, None) def _parse(self, inputstring: str, document: nodes.document) -> None: """Parse source text. :param inputstring: The source string to parse :param document: The root docutils node to add AST elements to """ document_source = document["source"] # get a logger for this document logger = DocutilsDocLogger(document) # get markdown parsing configuration try: md_config = create_myst_config(document.settings) except (TypeError, ValueError) as error: logger.error(f"myst configuration invalid: {error.args[0]}") md_config = MdParserConfig() # get notebook rendering configuration try: nb_config = create_myst_config(document.settings, NbParserConfig, "nb_") except (TypeError, ValueError) as error: logger.error(f"myst-nb configuration invalid: {error.args[0]}") nb_config = NbParserConfig() # convert inputstring to notebook # note docutils does not support the full custom format mechanism if nb_config.read_as_md: nb_reader = NbReader( partial( read_myst_markdown_notebook, config=md_config, add_source_map=True, ), md_config, {"type": "plugin", "name": "myst_nb_md"}, ) else: nb_reader = NbReader(standard_nb_read, md_config) notebook = nb_reader.read(inputstring) # update the global markdown config with the file-level config warning = lambda wtype, msg: create_warning( # noqa: E731 document, msg, line=1, append_to=document, subtype=wtype ) nb_reader.md_config = merge_file_level( nb_reader.md_config, notebook.metadata, warning ) # Update mystnb configuration with notebook level metadata if nb_config.metadata_key in notebook.metadata: overrides = nb_node_to_dict(notebook.metadata[nb_config.metadata_key]) try: nb_config = nb_config.copy(**overrides) except Exception as exc: logger.warning( f"Failed to update configuration with notebook metadata: {exc}", subtype="config", ) else: logger.debug( "Updated configuration with notebook metadata", subtype="config" ) # Setup the markdown parser mdit_parser = create_md_parser(nb_reader.md_config, DocutilsNbRenderer) mdit_parser.options["document"] = document mdit_parser.options["nb_config"] = nb_config mdit_renderer: DocutilsNbRenderer = mdit_parser.renderer # type: ignore mdit_env: dict[str, Any] = {} # load notebook element renderer class from entry-point name # this is separate from DocutilsNbRenderer, so that users can override it renderer_name = nb_config.render_plugin nb_renderer: NbElementRenderer = load_renderer(renderer_name)( mdit_renderer, logger ) # we temporarily store nb_renderer on the document, # so that roles/directives can access it document.attributes["nb_renderer"] = nb_renderer # we currently do this early, so that the nb_renderer has access to things mdit_renderer.setup_render(mdit_parser.options, mdit_env) # type: ignore # parse notebook structure to markdown-it tokens # note, this does not assume that the notebook has been executed yet mdit_tokens = notebook_to_tokens(notebook, mdit_parser, mdit_env, logger) # open the notebook execution client, # this may execute the notebook immediately or during the page render with create_client(notebook, document_source, nb_config, logger) as nb_client: mdit_parser.options["nb_client"] = nb_client # convert to docutils AST, which is added to the document mdit_renderer.render(mdit_tokens, mdit_parser.options, mdit_env) # save final execution data if nb_client.exec_metadata: document["nb_exec_data"] = nb_client.exec_metadata if nb_config.output_folder: # write final (updated) notebook to output folder (utf8 is standard encoding) content = nbformat.writes(notebook).encode("utf-8") nb_renderer.write_file(["processed.ipynb"], content, overwrite=True) # if we are using an HTML writer, dynamically add the CSS to the output if nb_config.append_css and hasattr(document.settings, "stylesheet"): css_paths = [] css_paths.append( nb_renderer.write_file( ["mystnb.css"], (import_resources.files(static) / "mystnb.css").read_bytes(), overwrite=True, ) ) fmt = get_formatter_by_name("html", style="default") css_paths.append( nb_renderer.write_file( ["pygments.css"], fmt.get_style_defs(".code").encode("utf-8"), overwrite=True, ) ) css_paths = [os.path.abspath(path) for path in css_paths] # stylesheet and stylesheet_path are mutually exclusive if document.settings.stylesheet_path: document.settings.stylesheet_path.extend(css_paths) if document.settings.stylesheet: document.settings.stylesheet.extend(css_paths) # TODO also handle JavaScript # remove temporary state document.attributes.pop("nb_renderer") class DocutilsNbRenderer(DocutilsRenderer, MditRenderMixin): """A docutils-only renderer for Jupyter Notebooks.""" def render_nb_initialise(self, token: SyntaxTreeNode) -> None: metadata = self.nb_client.nb_metadata special_keys = ["kernelspec", "language_info", "source_map"] for key in special_keys: # save these special keys on the document, rather than as docinfo if key in metadata: self.document[f"nb_{key}"] = metadata.get(key) if self.nb_config.metadata_to_fm: # forward the remaining metadata to the front_matter renderer special_keys.append("widgets") top_matter = {k: v for k, v in metadata.items() if k not in special_keys} self.render_front_matter( Token( # type: ignore "front_matter", "", 0, map=[0, 0], content=top_matter, # type: ignore[arg-type] ), ) def _render_nb_cell_code_outputs( self, token: SyntaxTreeNode, outputs: list[NotebookNode] ) -> None: """Render a notebook code cell's outputs.""" cell_index = token.meta["index"] metadata = token.meta["metadata"] line = token_line(token) # render the outputs mime_priority = get_mime_priority( self.nb_config.builder_name, self.nb_config.mime_priority_overrides ) for output_index, output in enumerate(outputs): if output.output_type == "stream": if output.name == "stdout": _nodes = self.nb_renderer.render_stdout( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) elif output.name == "stderr": _nodes = self.nb_renderer.render_stderr( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) else: pass # TODO warning elif output.output_type == "error": _nodes = self.nb_renderer.render_error( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) elif output.output_type in ("display_data", "execute_result"): # Note, this is different to the sphinx implementation, # here we directly select a single output, based on the mime_priority, # as opposed to output all mime types, and select in a post-transform # (the mime_priority must then be set for the output format) try: mime_type = next(x for x in mime_priority if x in output["data"]) except StopIteration: if output["data"]: create_warning( self.document, "No output mime type found from render_priority " f"(cell<{cell_index}>.output<{output_index}>", line=line, append_to=self.current_node, # wtype=DEFAULT_LOG_TYPE, subtype=MystNBWarnings.MIME_TYPE, ) else: figure_options = ( self.get_cell_level_config( "render_figure_options", metadata, line=line ) or None ) with create_figure_context(self, figure_options, line): _nodes = self.nb_renderer.render_mime_type( MimeData( mime_type, output["data"][mime_type], cell_metadata=metadata, output_metadata=output.get("metadata", {}), cell_index=cell_index, output_index=output_index, line=line, ), ) self.current_node.extend(_nodes) self.add_line_and_source_path_r(_nodes, token) else: create_warning( self.document, f"Unsupported output type: {output.output_type}", line=line, append_to=self.current_node, # wtype=DEFAULT_LOG_TYPE, subtype=MystNBWarnings.OUTPUT_TYPE, ) def _run_cli( writer_name: str, builder_name: str, writer_description: str, argv: list[str] | None ): """Run the command line interface for a particular writer.""" publish_cmdline( parser=Parser(), writer_name=writer_name, description=( f"Generates {writer_description} from standalone MyST Notebook sources.\n" f"{default_description}\n" "External outputs are written to `--nb-output-folder`.\n" ), # to see notebook execution info by default settings_overrides={"report_level": 1, "nb_builder_name": builder_name}, argv=argv, ) def cli_html(argv: list[str] | None = None) -> None: """Cmdline entrypoint for converting MyST to HTML.""" _run_cli("html", "html", "(X)HTML documents", argv) def cli_html5(argv: list[str] | None = None): """Cmdline entrypoint for converting MyST to HTML5.""" _run_cli("html5", "html", "HTML5 documents", argv) def cli_latex(argv: list[str] | None = None): """Cmdline entrypoint for converting MyST to LaTeX.""" _run_cli("latex", "latex", "LaTeX documents", argv) def cli_xml(argv: list[str] | None = None): """Cmdline entrypoint for converting MyST to XML.""" _run_cli("xml", "xml", "Docutils-native XML", argv) def cli_pseudoxml(argv: list[str] | None = None): """Cmdline entrypoint for converting MyST to pseudo-XML.""" _run_cli("pseudoxml", "html", "pseudo-XML", argv) MyST-NB-1.1.2/myst_nb/ext/000077500000000000000000000000001467453560600151375ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/ext/__init__.py000066400000000000000000000000001467453560600172360ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/ext/download.py000066400000000000000000000020271467453560600173210ustar00rootroot00000000000000import os from pathlib import Path from docutils import nodes from sphinx.addnodes import download_reference from sphinx.util.docutils import ReferenceRole from myst_nb.sphinx_ import SphinxEnvType class NbDownloadRole(ReferenceRole): """Role to download an executed notebook.""" def run(self): """Run the role.""" # get a path relative to the current document self.env: SphinxEnvType path = Path(self.env.mystnb_config.output_folder).joinpath( *(self.env.docname.split("/")[:-1] + self.target.split("/")) ) reftarget = ( path.as_posix() if os.name == "nt" else ("/" + os.path.relpath(path, self.env.app.srcdir)) ) node = download_reference(self.rawtext, reftarget=reftarget) self.set_source_info(node) title = self.title if self.has_explicit_title else self.target node += nodes.literal( self.rawtext, title, classes=["xref", "download", "myst-nb"] ) return [node], [] MyST-NB-1.1.2/myst_nb/ext/eval/000077500000000000000000000000001467453560600160665ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/ext/eval/__init__.py000066400000000000000000000175561467453560600202150ustar00rootroot00000000000000"""Roles/directives for evaluating variables in the notebook.""" from __future__ import annotations from functools import partial from typing import TYPE_CHECKING, Any from docutils import nodes from docutils.parsers.rst import directives as spec from myst_nb.core.execute.base import EvalNameError from myst_nb.core.render import NbElementRenderer from myst_nb.core.variables import ( RetrievalError, VariableOutput, create_warning, render_variable_outputs, ) from myst_nb.ext.utils import DirectiveBase, RoleBase try: from sphinx.domains import Domain except ImportError: # for docutils only use Domain = object # type: ignore if TYPE_CHECKING: from sphinx.application import Sphinx from myst_nb.docutils_ import DocutilsApp eval_warning = partial(create_warning, subtype="eval") def retrieve_eval_data(document: nodes.document, key: str) -> list[VariableOutput]: """Retrieve the glue data from a specific document.""" if "nb_renderer" not in document: raise RetrievalError("This document does not have a running kernel") element: NbElementRenderer = document["nb_renderer"] # evaluate the variable try: outputs = element.renderer.nb_client.eval_variable(key) except NotImplementedError: raise RetrievalError("This document does not have a running kernel") except EvalNameError: raise RetrievalError( f"The expression {key!r} is not valid according to the configured pattern" ) except Exception as exc: raise RetrievalError(f"variable evaluation error: {exc}") if not outputs: raise RetrievalError(f"expression {key!r} does not return any outputs") # the returned outputs could be one of the following: # https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs for output in outputs: if output.get("output_type") == "error": msg = f"{output.get('ename', '')}: {output.get('evalue', '')}" raise RetrievalError(msg) return [ VariableOutput( data=output.get("data", {}), metadata=output.get("metadata", {}), nb_renderer=element, vtype="eval", index=i, ) for i, output in enumerate(outputs) ] class EvalRoleAny(RoleBase): """A role for evaluating value outputs from the kernel, using render priority to decide the output mime type. """ def run(self) -> tuple[list[nodes.Node], list[nodes.system_message]]: try: data = retrieve_eval_data(self.document, self.text) except RetrievalError as exc: return [], [eval_warning(str(exc), self.document, self.line)] # for text/plain, we want to strip quotes from strings for output in data: output.metadata["strip_text_quotes"] = True _nodes = render_variable_outputs( data, self.document, self.line, self.source, inline=True, ) return _nodes, [] class EvalDirectiveAny(DirectiveBase): """A directive for evaluating value outputs from the kernel, using render priority to decide the output mime type. """ required_arguments = 1 # the key final_argument_whitespace = False has_content = False def run(self) -> list[nodes.Node]: """Run the directive.""" try: data = retrieve_eval_data(self.document, self.arguments[0]) except RetrievalError as exc: return [eval_warning(str(exc), self.document, self.line)] return render_variable_outputs( data, self.document, self.line, self.source, inline=False, ) class EvalFigureDirective(DirectiveBase): """A directive for pasting code outputs from notebooks, wrapped in a figure. Mirrors: https://github.com/docutils-mirror/docutils/blob/9649abee47b4ce4db51be1d90fcb1fb500fa78b3/docutils/parsers/rst/directives/images.py#95 """ required_arguments = 1 # the key final_argument_whitespace = False has_content = True def align(argument): return spec.choice(argument, ("left", "center", "right")) def figwidth_value(argument): return spec.length_or_percentage_or_unitless(argument, "px") option_spec = { # note we don't add converters for image options, # since this is handled in `NbElementRenderer.render_image` "alt": spec.unchanged, "height": spec.unchanged, "width": spec.unchanged, "scale": spec.unchanged, "class": spec.unchanged, "figwidth": figwidth_value, "figclass": spec.class_option, "align": align, "name": spec.unchanged, } def run(self): try: data = retrieve_eval_data(self.document, self.arguments[0]) except RetrievalError as exc: return [eval_warning(str(exc), self.document, self.line)] render: dict[str, Any] = {} for key in ("alt", "height", "width", "scale", "class"): if key in self.options: render.setdefault("image", {})[ key.replace("classes", "class") ] = self.options[key] mime_nodes = render_variable_outputs( data, self.document, self.line, self.source, render=render ) # note: most of this is copied directly from sphinx.Figure # create figure node figure_node = nodes.figure("", *mime_nodes) self.set_source_info(figure_node) # add attributes figwidth = self.options.pop("figwidth", None) figclasses = self.options.pop("figclass", None) align = self.options.pop("align", None) if figwidth is not None: figure_node["width"] = figwidth if figclasses: figure_node["classes"] += figclasses if align: figure_node["align"] = align # add target self.add_name(figure_node) # create the caption and legend if self.content: node = nodes.Element() # anonymous container for parsing self.state.nested_parse(self.content, self.content_offset, node) first_node = node[0] if isinstance(first_node, nodes.paragraph): caption = nodes.caption(first_node.rawsource, "", *first_node.children) caption.source = first_node.source caption.line = first_node.line figure_node += caption elif not (isinstance(first_node, nodes.comment) and len(first_node) == 0): error = eval_warning( "Figure caption must be a paragraph or empty comment.", self.document, self.lineno, ) return [figure_node, error] if len(node) > 1: figure_node += nodes.legend("", *node[1:]) return [figure_node] class NbEvalDomain(Domain): """A sphinx domain for defining eval roles and directives. Note, the only reason we use this, is that sphinx will not allow for `:` in a directive/role name, if it is part of a domain. """ name = "eval" label = "NotebookEval" # data version, bump this when the format of self.data changes data_version = 1 directives = {"figure": EvalFigureDirective} roles: dict = {} def merge_domaindata(self, *args, **kwargs): pass def resolve_any_xref(self, *args, **kwargs): return [] def load_eval_sphinx(app: Sphinx) -> None: """Load the eval domain.""" app.add_role("eval", EvalRoleAny(), override=True) app.add_directive("eval", EvalDirectiveAny, override=True) app.add_domain(NbEvalDomain) def load_eval_docutils(app: DocutilsApp) -> None: app.roles["eval"] = EvalRoleAny() app.directives["eval"] = EvalDirectiveAny app.directives["eval:figure"] = EvalFigureDirective MyST-NB-1.1.2/myst_nb/ext/execution_tables.py000066400000000000000000000124231467453560600210500ustar00rootroot00000000000000"""Sphinx elements to create tables of statistics on executed notebooks. The `nb-exec-table` directive adds a placeholder node to the document, which is then replaced by a table of statistics in a post-transformation (once all the documents have been executed and these statistics are available). """ from __future__ import annotations from datetime import datetime import posixpath from typing import Any, Callable, DefaultDict from docutils import nodes from sphinx.addnodes import pending_xref from sphinx.application import Sphinx from sphinx.transforms.post_transforms import SphinxPostTransform from sphinx.util import logging from sphinx.util.docutils import SphinxDirective from myst_nb._compat import findall from myst_nb.sphinx_ import NbMetadataCollector, SphinxEnvType SPHINX_LOGGER = logging.getLogger(__name__) METADATA_KEY = "has_exec_table" def setup_exec_table_extension(app: Sphinx) -> None: """Add the Sphinx extension to the Sphinx application.""" app.add_node(ExecutionStatsNode) app.add_directive("nb-exec-table", ExecutionStatsTable) app.connect("env-updated", update_exec_tables) app.add_post_transform(ExecutionStatsPostTransform) class ExecutionStatsNode(nodes.General, nodes.Element): """A placeholder node, for adding a notebook execution statistics table.""" class ExecutionStatsTable(SphinxDirective): """Add a notebook execution statistics table.""" has_content = True final_argument_whitespace = True def run(self): """Add a placeholder node to the document, and mark it as having a table.""" self.env: SphinxEnvType NbMetadataCollector.set_doc_data(self.env, self.env.docname, METADATA_KEY, True) return [ExecutionStatsNode()] def update_exec_tables(app: Sphinx, env: SphinxEnvType): """If a document has been re-executed, return all documents containing tables. These documents will be updated with the new statistics. """ if not NbMetadataCollector.new_exec_data(env): return None to_update = [ docname for docname, data in NbMetadataCollector.get_doc_data(env).items() if data.get(METADATA_KEY) ] if to_update: SPHINX_LOGGER.info( f"Updating {len(to_update)} file(s) with execution table [mystnb]" ) return to_update class ExecutionStatsPostTransform(SphinxPostTransform): """Replace the placeholder node with the final table nodes.""" default_priority = 8 # before ReferencesResolver (10) and MystReferenceResolver(9) def run(self, **kwargs) -> None: """Replace the placeholder node with the final table nodes.""" self.env: SphinxEnvType for node in findall(self.document)(ExecutionStatsNode): node.replace_self( make_stat_table( self.env.docname, NbMetadataCollector.get_doc_data(self.env) ) ) _key2header: dict[str, str] = { "mtime": "Modified", "method": "Method", "runtime": "Run Time (s)", "succeeded": "Status", } _key2transform: dict[str, Callable[[Any], str]] = { "mtime": lambda x: datetime.fromtimestamp(x).strftime("%Y-%m-%d %H:%M") if x else "", "method": str, "runtime": lambda x: "-" if x is None else str(round(x, 2)), "succeeded": lambda x: "โœ…" if x is True else "โŒ", } def make_stat_table( parent_docname: str, metadata: DefaultDict[str, dict] ) -> nodes.table: """Create a table of statistics on executed notebooks.""" # top-level element table = nodes.table() table["classes"] += ["colwidths-auto"] # self.set_source_info(table) # column settings element ncols = len(_key2header) + 1 tgroup = nodes.tgroup(cols=ncols) table += tgroup colwidths = [round(100 / ncols, 2)] * ncols for colwidth in colwidths: colspec = nodes.colspec(colwidth=colwidth) tgroup += colspec # header thead = nodes.thead() tgroup += thead row = nodes.row() thead += row for name in ["Document"] + list(_key2header.values()): row.append(nodes.entry("", nodes.paragraph(text=name))) # body tbody = nodes.tbody() tgroup += tbody for docname in sorted(metadata): data = metadata[docname].get("exec_data") if not data: continue row = nodes.row() tbody += row # document name doclink = pending_xref( refdoc=parent_docname, reftarget=posixpath.relpath(docname, posixpath.dirname(parent_docname)), reftype="doc", refdomain="std", refexplicit=True, refwarn=True, classes=["xref", "doc"], ) doclink += nodes.inline(text=docname) paragraph = nodes.paragraph() paragraph += doclink row.append(nodes.entry("", paragraph)) # other rows for name in _key2header.keys(): paragraph = nodes.paragraph() if name == "succeeded" and data[name] is False: paragraph += nodes.abbreviation( text=_key2transform[name](data[name]), explanation=(data["error"] or ""), ) else: paragraph += nodes.Text(_key2transform[name](data[name])) row.append(nodes.entry("", paragraph)) return table MyST-NB-1.1.2/myst_nb/ext/glue/000077500000000000000000000000001467453560600160735ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/ext/glue/__init__.py000066400000000000000000000100771467453560600202110ustar00rootroot00000000000000"""Functionality for storing special data in notebook code cells, which can then be inserted into the document body. """ from __future__ import annotations from typing import TYPE_CHECKING, Any import IPython from IPython.display import display as ipy_display from nbformat import NotebookNode from myst_nb.core.loggers import LoggerType if TYPE_CHECKING: from sphinx.application import Sphinx from myst_nb.docutils_ import DocutilsApp GLUE_PREFIX = "application/papermill.record/" def load_glue_sphinx(app: Sphinx) -> None: """Load the eval domain.""" from .directives import PasteAnyDirective from .domain import NbGlueDomain from .roles import PasteRoleAny app.add_directive("glue", PasteAnyDirective, override=True) app.add_role("glue", PasteRoleAny(), override=True) app.add_domain(NbGlueDomain) def load_glue_docutils(app: DocutilsApp) -> None: from .directives import ( PasteAnyDirective, PasteFigureDirective, PasteMarkdownDirective, PasteMathDirective, ) from .roles import PasteMarkdownRole, PasteRoleAny, PasteTextRole for name, role in [ ("glue", PasteRoleAny()), ("glue:", PasteRoleAny()), ("glue:any", PasteRoleAny()), ("glue:text", PasteTextRole()), ("glue:md", PasteMarkdownRole()), ]: app.roles[name] = role for name, directive in [ ("glue", PasteAnyDirective), ("glue:", PasteAnyDirective), ("glue:any", PasteAnyDirective), ("glue:figure", PasteFigureDirective), ("glue:math", PasteMathDirective), ("glue:md", PasteMarkdownDirective), ]: app.directives[name] = directive def glue(name: str, variable: Any, display: bool = True) -> None: """Glue a variable into the notebook's cell metadata. Parameters ---------- name: string A unique name for the variable. You can use this name to refer to the variable later on. variable: Python object A variable in Python for which you'd like to store its display value. This is not quite the same as storing the object itself - the stored information is what is *displayed* when you print or show the object in a Jupyter Notebook. display: bool Display the object you are gluing. This is helpful in sanity-checking the state of the object at glue-time. """ mimebundle, metadata = IPython.core.formatters.format_display_data(variable) mime_prefix = "" if display else GLUE_PREFIX metadata["scrapbook"] = dict(name=name, mime_prefix=mime_prefix) ipy_display( {mime_prefix + k: v for k, v in mimebundle.items()}, raw=True, metadata=metadata ) def extract_glue_data( notebook: NotebookNode, source_map: list[int], logger: LoggerType, ) -> dict[str, NotebookNode]: """Extract all the glue data from the notebook.""" # note this assumes v4 notebook format data: dict[str, NotebookNode] = {} for index, cell in enumerate(notebook.cells): if cell.cell_type != "code": continue for key, cell_data in extract_glue_data_cell(cell): if key in data: logger.warning( f"glue key {key!r} duplicate", subtype="glue", line=source_map[index], ) data[key] = cell_data return data def extract_glue_data_cell(cell: NotebookNode) -> list[tuple[str, NotebookNode]]: """Extract glue data from a single cell.""" outputs = [] data = [] for output in cell.get("outputs", []): meta = output.get("metadata", {}) if "scrapbook" not in meta: outputs.append(output) continue key = meta["scrapbook"]["name"] mime_prefix = len(meta["scrapbook"].get("mime_prefix", "")) output["data"] = {k[mime_prefix:]: v for k, v in output["data"].items()} data.append((key, output)) if not mime_prefix: # assume that the output is a displayable object outputs.append(output) cell.outputs = outputs return data MyST-NB-1.1.2/myst_nb/ext/glue/crossref.py000066400000000000000000000104531467453560600202760ustar00rootroot00000000000000"""Sphinx only cross-document gluing. Note, we restrict this to a only a subset of mime-types and data -> nodes transforms, since adding these nodes in a post-transform will not apply any transforms to them. """ from __future__ import annotations from functools import lru_cache import json from pathlib import Path from typing import Any, Sequence from docutils import nodes from sphinx.transforms.post_transforms import SphinxPostTransform from sphinx.util import logging as sphinx_logging from myst_nb._compat import findall from myst_nb.core.loggers import DEFAULT_LOG_TYPE from myst_nb.core.render import get_mime_priority from myst_nb.core.variables import format_plain_text from .utils import PendingGlueReference SPHINX_LOGGER = sphinx_logging.getLogger(__name__) @lru_cache(maxsize=3) def read_glue_cache(folder: str, docname: str) -> dict[str, Any]: """Read a glue cache from the build folder, for a particular document.""" docpath = docname.split("/") path = Path(folder).joinpath(*docpath[:-1]).joinpath(f"{docpath[-1]}.glue.json") if not path.exists(): return {} with path.open("r") as f: return json.load(f) class ReplacePendingGlueReferences(SphinxPostTransform): """Sphinx only cross-document gluing. Note, we restrict this to a only a subset of mime-types and data -> nodes transforms, since adding these nodes in a post-transform will not apply any transforms to them. """ default_priority = 5 def apply(self, **kwargs): """Apply the transform.""" cache_folder = self.env.mystnb_config.output_folder # type: ignore bname = self.app.builder.name priority_list = get_mime_priority( bname, self.config["nb_mime_priority_overrides"] ) node: PendingGlueReference for node in list(findall(self.document)(PendingGlueReference)): data = read_glue_cache(cache_folder, node.refdoc) if node.key not in data: SPHINX_LOGGER.warning( f"Glue reference {node.key!r} not found in doc {node.refdoc!r} " f"[{DEFAULT_LOG_TYPE}.glue_ref]", type=DEFAULT_LOG_TYPE, subtype="glue_ref", location=node, ) node.parent.remove(node) continue output = data[node.key] if node.gtype == "text": _nodes = generate_text_nodes(node, output) else: _nodes = generate_any_nodes(node, output, priority_list) if _nodes: node.replace_self(_nodes) else: node.parent.remove(node) def ref_warning(msg: str, node) -> None: """Log a warning for a reference.""" SPHINX_LOGGER.warning( f"{msg} [{DEFAULT_LOG_TYPE}.glue_ref]", type=DEFAULT_LOG_TYPE, subtype="glue_ref", location=node, ) def generate_any_nodes( node: PendingGlueReference, output: dict[str, Any], priority_list: Sequence[str] ) -> list[nodes.Element]: """Generate nodes for a cell, according to the highest priority mime type.""" data = output["data"] for mime_type in priority_list: if mime_type not in data: continue if mime_type == "text/plain": if node.inline: return [nodes.literal(data[mime_type], data[mime_type])] else: return [nodes.literal_block(data[mime_type], data[mime_type])] if mime_type == "text/html": return [ nodes.raw( text=data[mime_type], format="html", classes=["output", "text_html"] ) ] ref_warning( f"No allowed mime type found in {node.key!r}: {list(output['data'])}", node ) return [] def generate_text_nodes(node: PendingGlueReference, output: dict[str, Any]): """Generate nodes for a cell, for formatted text/plain.""" data = output["data"] if "text/plain" not in data: ref_warning(f"No text/plain found in {node.key!r}", node) return [] try: text = format_plain_text(data["text/plain"], node["fmt_spec"]) except Exception as exc: ref_warning(f"Failed to format text/plain: {exc}", node) return [] return [nodes.inline(text, text, classes=["pasted-text"])] MyST-NB-1.1.2/myst_nb/ext/glue/directives.py000066400000000000000000000223101467453560600206040ustar00rootroot00000000000000"""Directives which can be used by both docutils and sphinx. We intentionally do no import sphinx in this module, in order to allow docutils-only use without sphinx installed. """ from typing import TYPE_CHECKING, Any, Dict, List from docutils import nodes from docutils.parsers.rst import directives as spec from myst_nb.core.render import MimeData, strip_latex_delimiters from myst_nb.core.variables import RetrievalError, is_sphinx, render_variable_outputs from myst_nb.ext.utils import DirectiveBase from .utils import ( PendingGlueReferenceError, create_pending_glue_ref, glue_warning, retrieve_glue_data, ) if TYPE_CHECKING: from sphinx.domains.math import MathDomain from sphinx.environment import BuildEnvironment class PasteAnyDirective(DirectiveBase): """A directive for pasting code outputs from notebooks, using render priority to decide the output mime type. """ required_arguments = 1 # the key final_argument_whitespace = True has_content = False option_spec = {"doc": spec.unchanged} def run(self) -> List[nodes.Node]: """Run the directive.""" key = self.arguments[0] if "doc" in self.options: try: ref = create_pending_glue_ref( self.document, self.source, self.line, self.options["doc"], key ) except PendingGlueReferenceError as exc: return [ glue_warning( str(exc), self.document, self.line, ) ] return [ref] try: data = retrieve_glue_data(self.document, key) except RetrievalError as exc: return [ glue_warning( f"{exc} (use 'doc' option, to glue from another document)", self.document, self.line, ) ] return render_variable_outputs([data], self.document, self.line, self.source) def md_fmt(argument): return spec.choice(argument, ("commonmark", "gfm", "myst")) class PasteMarkdownDirective(DirectiveBase): """A directive for pasting markdown outputs from notebooks as MyST Markdown.""" required_arguments = 1 # the key final_argument_whitespace = True has_content = False option_spec = { "format": md_fmt, } def run(self) -> List[nodes.Node]: """Run the directive.""" key = self.arguments[0] try: result = retrieve_glue_data(self.document, key) except RetrievalError as exc: return [glue_warning(str(exc), self.document, self.line)] if "text/markdown" not in result.data: return [ glue_warning( f"No text/markdown found in {key!r} data", self.document, self.line, ) ] # TODO this "override" feels a bit hacky cell_key = result.nb_renderer.config.cell_metadata_key mime = MimeData( "text/markdown", result.data["text/markdown"], cell_metadata={ cell_key: {"markdown_format": self.options.get("format", "commonmark")}, }, output_metadata=result.metadata, line=self.line, ) _nodes = result.nb_renderer.render_markdown(mime) self.set_source_info(_nodes) return _nodes class PasteFigureDirective(DirectiveBase): """A directive for pasting code outputs from notebooks, wrapped in a figure. Mirrors: https://github.com/docutils-mirror/docutils/blob/9649abee47b4ce4db51be1d90fcb1fb500fa78b3/docutils/parsers/rst/directives/images.py#95 """ required_arguments = 1 # the key final_argument_whitespace = True has_content = True def align(argument): return spec.choice(argument, ("left", "center", "right")) def figwidth_value(argument): return spec.length_or_percentage_or_unitless(argument, "px") option_spec = { # note we don't add converters for image options, # since this is handled in `NbElementRenderer.render_image` "alt": spec.unchanged, "height": spec.unchanged, "width": spec.unchanged, "scale": spec.unchanged, "class": spec.unchanged, "figwidth": figwidth_value, "figclass": spec.class_option, "align": align, "name": spec.unchanged, } def run(self): try: data = retrieve_glue_data(self.document, self.arguments[0]) except RetrievalError as exc: return [glue_warning(str(exc), self.document, self.line)] render: Dict[str, Any] = {} for key in ("alt", "height", "width", "scale", "class"): if key in self.options: render.setdefault("image", {})[ key.replace("classes", "class") ] = self.options[key] paste_nodes = render_variable_outputs( [data], self.document, self.line, self.source, render=render ) # note: most of this is copied directly from sphinx.Figure # create figure node figure_node = nodes.figure("", *paste_nodes) self.set_source_info(figure_node) # add attributes figwidth = self.options.pop("figwidth", None) figclasses = self.options.pop("figclass", None) align = self.options.pop("align", None) if figwidth is not None: figure_node["width"] = figwidth if figclasses: figure_node["classes"] += figclasses if align: figure_node["align"] = align # add target self.add_name(figure_node) # create the caption and legend if self.content: node = nodes.Element() # anonymous container for parsing self.state.nested_parse(self.content, self.content_offset, node) first_node = node[0] if isinstance(first_node, nodes.paragraph): caption = nodes.caption(first_node.rawsource, "", *first_node.children) caption.source = first_node.source caption.line = first_node.line figure_node += caption elif not (isinstance(first_node, nodes.comment) and len(first_node) == 0): error = glue_warning( "Figure caption must be a paragraph or empty comment.", self.document, self.lineno, ) return [figure_node, error] if len(node) > 1: figure_node += nodes.legend("", *node[1:]) return [figure_node] class PasteMathDirective(DirectiveBase): """A directive for pasting latex outputs from notebooks as math.""" required_arguments = 1 # the key final_argument_whitespace = True has_content = False option_spec = { "class": spec.class_option, "nowrap": spec.flag, # these are equivalent "label": spec.unchanged, "name": spec.unchanged, } def run(self) -> List[nodes.Node]: """Run the directive.""" key = self.arguments[0] try: result = retrieve_glue_data(self.document, key) except RetrievalError as exc: return [glue_warning(str(exc), self.document, self.line)] if "text/latex" not in result.data: return [ glue_warning( f"No text/latex found in {key!r} data", self.document, self.lineno, ) ] latex = strip_latex_delimiters(str(result.data["text/latex"])) label = self.options.get("label", self.options.get("name")) node = nodes.math_block( latex, latex, nowrap="nowrap" in self.options, label=label, number=None, classes=["pasted-math"] + (self.options.get("class") or []), ) self.add_name(node) self.set_source_info(node) if is_sphinx(self.document): return self.add_target(node) return [node] def add_target(self, node: nodes.math_block) -> List[nodes.Node]: """Add target to the node.""" # adapted from sphinx.directives.patches.MathDirective env: "BuildEnvironment" = self.document.settings.env node["docname"] = env.docname # assign label automatically if math_number_all enabled if node["label"] == "" or (env.config.math_number_all and not node["label"]): seq = env.new_serialno("sphinx.ext.math#equations") node["label"] = "%s:%d" % (env.docname, seq) # no targets and numbers are needed if not node["label"]: return [node] # register label to domain domain: "MathDomain" = env.get_domain("math") # type: ignore domain.note_equation(env.docname, node["label"], location=node) node["number"] = domain.get_equation_number_for(node["label"]) # add target node node_id = nodes.make_id("equation-%s" % node["label"]) target = nodes.target("", "", ids=[node_id]) self.document.note_explicit_target(target) return [target, node] MyST-NB-1.1.2/myst_nb/ext/glue/domain.py000066400000000000000000000022411467453560600177130ustar00rootroot00000000000000"""A domain to register in sphinx. This is required for any directive/role names using `:`. """ from sphinx.domains import Domain from .directives import ( PasteAnyDirective, PasteFigureDirective, PasteMarkdownDirective, PasteMathDirective, ) from .roles import PasteMarkdownRole, PasteRoleAny, PasteTextRole class NbGlueDomain(Domain): """A sphinx domain for defining glue roles and directives. Note, the only reason we use this, is that sphinx will not allow for `:` in a directive/role name, if it is part of a domain. """ name = "glue" label = "NotebookGlue" # data version, bump this when the format of self.data changes data_version = 1 roles = { "": PasteRoleAny(), "any": PasteRoleAny(), "text": PasteTextRole(), "md": PasteMarkdownRole(), } directives = { "": PasteAnyDirective, "any": PasteAnyDirective, "figure": PasteFigureDirective, "math": PasteMathDirective, "md": PasteMarkdownDirective, } def merge_domaindata(self, *args, **kwargs): pass def resolve_any_xref(self, *args, **kwargs): return [] MyST-NB-1.1.2/myst_nb/ext/glue/roles.py000066400000000000000000000134161467453560600175760ustar00rootroot00000000000000"""Roles which can be used by both docutils and sphinx. We intentionally do no import sphinx in this module, in order to allow docutils-only use without sphinx installed. """ from __future__ import annotations from docutils import nodes from myst_nb.core.render import MimeData from myst_nb.core.variables import ( RetrievalError, format_plain_text, render_variable_outputs, ) from myst_nb.ext.utils import RoleBase from .utils import ( PendingGlueReferenceError, create_pending_glue_ref, glue_warning, retrieve_glue_data, ) class PasteRoleAny(RoleBase): """A role for pasting inline code outputs from notebooks, using render priority to decide the output mime type. """ def run(self) -> tuple[list[nodes.Node], list[nodes.system_message]]: # check if this is a pending reference doc_key = self.text.split("::", 1) if len(doc_key) == 2: doc, key = doc_key try: ref = create_pending_glue_ref( self.document, self.source, self.line, doc, key, inline=True ) except PendingGlueReferenceError as exc: return [], [ glue_warning( str(exc), self.document, self.line, ) ] return [ref], [] try: data = retrieve_glue_data(self.document, self.text) except RetrievalError as exc: return [], [glue_warning(str(exc), self.document, self.line)] paste_nodes = render_variable_outputs( [data], self.document, self.line, self.source, inline=True, ) return paste_nodes, [] class PasteTextRole(RoleBase): """A role for pasting text/plain outputs from notebooks. The role content should follow the format: ``:::``, where: - ```` (optional) is the relative path to another notebook, defaults to local. - ```` is the key - ```` (optional) is a format specifier, defined in: https://docs.python.org/3/library/string.html#format-specification-mini-language, it must end in the type character. """ def run(self) -> tuple[list[nodes.Node], list[nodes.system_message]]: # check if we have both key:format in the key key_format = self.text.rsplit(":", 1) if len(key_format) == 2: key, fmt_spec = key_format else: key = key_format[0] fmt_spec = "s" # check if this is a pending reference doc_key = key.split("::", 1) if len(doc_key) == 2: doc, key = doc_key try: ref = create_pending_glue_ref( self.document, self.source, self.line, doc, key, inline=True, gtype="text", fmt_spec=fmt_spec, ) except PendingGlueReferenceError as exc: return [], [ glue_warning( str(exc), self.document, self.line, ) ] return [ref], [] # now retrieve the data try: result = retrieve_glue_data(self.document, key) except RetrievalError as exc: return [], [ glue_warning( f"{exc} (use `path::key`, to glue from another document)", self.document, self.line, ) ] if "text/plain" not in result.data: return [], [ glue_warning( f"No text/plain found in {key!r} data", self.document, self.line ) ] try: text = format_plain_text(result.data["text/plain"], fmt_spec) except Exception as exc: return [], [ glue_warning( f"Failed to format text/plain data: {exc}", self.document, self.line ) ] node = nodes.inline(text, text, classes=["pasted-text"]) self.set_source_info(node) return [node], [] class PasteMarkdownRole(RoleBase): """A role for pasting markdown outputs from notebooks as inline MyST Markdown.""" def run(self) -> tuple[list[nodes.Node], list[nodes.system_message]]: # check if we have both key:format in the key parts = self.text.rsplit(":", 1) if len(parts) == 2: key, fmt = parts else: key = parts[0] fmt = "commonmark" # TODO - check fmt is valid # retrieve the data try: result = retrieve_glue_data(self.document, key) except RetrievalError as exc: return [], [glue_warning(str(exc), self.document, self.line)] if "text/markdown" not in result.data: return [], [ glue_warning( f"No text/markdown found in {key!r} data", self.document, self.line, ) ] # TODO this feels a bit hacky cell_key = result.nb_renderer.renderer.nb_config.cell_metadata_key mime = MimeData( "text/markdown", result.data["text/markdown"], cell_metadata={ cell_key: {"markdown_format": fmt}, }, output_metadata=result.metadata, line=self.line, ) _nodes = result.nb_renderer.render_markdown_inline(mime) for node in _nodes: self.set_source_info(node) return _nodes, [] MyST-NB-1.1.2/myst_nb/ext/glue/utils.py000066400000000000000000000051511467453560600176070ustar00rootroot00000000000000"""Utilities for working with docutils and sphinx. We intentionally do no import sphinx in this module, in order to allow docutils-only use without sphinx installed. """ from __future__ import annotations from functools import partial from typing import TYPE_CHECKING, Any from docutils import nodes from myst_nb.core.render import NbElementRenderer from myst_nb.core.variables import ( RetrievalError, VariableOutput, create_warning, is_sphinx, ) if TYPE_CHECKING: from sphinx.environment import BuildEnvironment glue_warning = partial(create_warning, subtype="glue") class PendingGlueReference(nodes.Element): """A glue reference to another document.""" @property def refdoc(self) -> str: return self.attributes["refdoc"] @property def key(self) -> str: return self.attributes["key"] @property def inline(self) -> bool: return self.attributes.get("inline", False) @property def gtype(self) -> str | None: return self.attributes.get("gtype", None) class PendingGlueReferenceError(Exception): """An error occurred while resolving a pending glue reference.""" def create_pending_glue_ref( document: nodes.document, source: str, line: int, rel_doc: str, key: str, inline: bool = False, gtype: str | None = None, **kwargs: Any, ) -> PendingGlueReference: """Create a pending glue reference.""" if not is_sphinx(document): raise PendingGlueReferenceError( "Pending glue references are only supported in sphinx." ) env: BuildEnvironment = document.settings.env _, filepath = env.relfn2path(rel_doc, env.docname) refdoc = env.path2doc(filepath) if refdoc is None: raise PendingGlueReferenceError( f"Pending glue reference document not found: {filepath!r}." ) ref = PendingGlueReference( refdoc=refdoc, key=key, inline=inline, gtype=gtype, **kwargs ) ref.source = source ref.line = line return ref def retrieve_glue_data(document: nodes.document, key: str) -> VariableOutput: """Retrieve the glue data from a specific document.""" msg = f"No key {key!r} found in glue data for this document." if "nb_renderer" not in document: raise RetrievalError(msg) element: NbElementRenderer = document["nb_renderer"] glue_data = element.renderer.nb_client.glue_data if key not in glue_data: raise RetrievalError(msg) return VariableOutput( data=glue_data[key]["data"], metadata=glue_data[key].get("metadata", {}), nb_renderer=element, vtype="glue", ) MyST-NB-1.1.2/myst_nb/ext/utils.py000066400000000000000000000046211467453560600166540ustar00rootroot00000000000000"""Extension utilities for extensions. We intentionally do no import sphinx in this module, in order to allow docutils-only use without sphinx installed. """ from __future__ import annotations from typing import Any from docutils import nodes from docutils.parsers.rst import Directive from docutils.parsers.rst.states import Inliner from docutils.utils import unescape from myst_nb._compat import findall def set_source_info(node: nodes.Node, source: str, line: int) -> None: """Set the source info for a node and its descendants.""" for _node in findall(node)(include_self=True): _node.source = source _node.line = line class RoleBase: """A base class for creating a role.""" @property def document(self) -> nodes.document: """Get the document.""" return self.inliner.document def set_source_info(self, node: nodes.Node) -> None: """Set the source info for a node and its descendants.""" set_source_info(node, self.source, self.line) def __call__( self, name: str, rawtext: str, text: str, lineno: int, inliner: Inliner, options=None, content=(), ) -> tuple[list[nodes.Node], list[nodes.system_message]]: self.text: str = unescape(text) self.inliner = inliner self.rawtext = rawtext source, line = inliner.reporter.get_source_and_line(lineno) self.source: str = source self.line: int = line return self.run() def run(self) -> tuple[list[nodes.Node], list[nodes.system_message]]: """Run the role.""" raise NotImplementedError class DirectiveBase(Directive): """A base class for creating a directive.""" @property def document(self) -> nodes.document: return self.state.document def __init__(self, *args, **kwargs) -> None: self.arguments: list[str] self.options: dict[str, Any] self.content: str super().__init__(*args, **kwargs) source, line = self.state_machine.get_source_and_line(self.lineno) self.source: str = source self.line: int = line def set_source_info(self, node: nodes.Node) -> None: """Set source and line number to the node and its descendants.""" nodes = node if isinstance(node, (list, tuple)) else [node] for _node in nodes: set_source_info(_node, self.source, self.line) MyST-NB-1.1.2/myst_nb/sphinx_.py000066400000000000000000000562541467453560600163750ustar00rootroot00000000000000"""The sphinx parser implementation for myst-nb.""" from __future__ import annotations from collections import defaultdict from html import escape import json from pathlib import Path import re from typing import Any, DefaultDict, cast from docutils import nodes from markdown_it.token import Token from markdown_it.tree import SyntaxTreeNode from myst_parser.config.main import MdParserConfig, merge_file_level from myst_parser.mdit_to_docutils.base import token_line from myst_parser.mdit_to_docutils.sphinx_ import SphinxRenderer from myst_parser.parsers.mdit import create_md_parser from myst_parser.parsers.sphinx_ import MystParser import nbformat from sphinx.application import Sphinx from sphinx.environment import BuildEnvironment from sphinx.environment.collectors import EnvironmentCollector from sphinx.transforms.post_transforms import SphinxPostTransform from sphinx.util import logging as sphinx_logging from sphinx.util.docutils import SphinxTranslator from myst_nb._compat import findall from myst_nb.core.config import NbParserConfig from myst_nb.core.execute import ExecutionResult, create_client from myst_nb.core.loggers import DEFAULT_LOG_TYPE, SphinxDocLogger from myst_nb.core.nb_to_tokens import nb_node_to_dict, notebook_to_tokens from myst_nb.core.read import create_nb_reader from myst_nb.core.render import ( MditRenderMixin, MimeData, NbElementRenderer, create_figure_context, get_mime_priority, load_renderer, ) from myst_nb.warnings_ import MystNBWarnings, create_warning SPHINX_LOGGER = sphinx_logging.getLogger(__name__) class SphinxEnvType(BuildEnvironment): """Sphinx build environment, including attributes set by myst_nb.""" myst_config: MdParserConfig mystnb_config: NbParserConfig nb_metadata: DefaultDict[str, dict] nb_new_exec_data: bool class Parser(MystParser): """Sphinx parser for Jupyter Notebook formats, containing MyST Markdown.""" supported = ("myst-nb",) translate_section_name = None config_section = "myst-nb parser" config_section_dependencies = ("parsers",) env: SphinxEnvType def parse(self, inputstring: str, document: nodes.document) -> None: """Parse source text. :param inputstring: The source string to parse :param document: The root docutils node to add AST elements to """ assert self.env is not None, "env not set" document_path = self.env.doc2path(self.env.docname) # get a logger for this document logger = SphinxDocLogger(document) # get markdown parsing configuration md_config: MdParserConfig = self.env.myst_config # get notebook rendering configuration nb_config: NbParserConfig = self.env.mystnb_config # create a reader for the notebook nb_reader = create_nb_reader(document_path, md_config, nb_config, inputstring) # If the nb_reader is None, then we default to a standard Markdown parser if nb_reader is None: return super().parse(inputstring, document) notebook = nb_reader.read(inputstring) # update the global markdown config with the file-level config warning = lambda wtype, msg: create_warning( # noqa: E731 document, msg, line=1, append_to=document, subtype=wtype ) nb_reader.md_config = merge_file_level( nb_reader.md_config, notebook.metadata, warning ) # potentially replace kernel name with alias kernel_name = notebook.metadata.get("kernelspec", {}).get("name", None) if kernel_name is not None and nb_config.kernel_rgx_aliases: for rgx, alias in nb_config.kernel_rgx_aliases.items(): if re.fullmatch(rgx, kernel_name): logger.debug( f"Replaced kernel name: {kernel_name!r} -> {alias!r}", subtype="kernel", ) notebook.metadata["kernelspec"]["name"] = alias break # Update mystnb configuration with notebook level metadata if nb_config.metadata_key in notebook.metadata: overrides = nb_node_to_dict(notebook.metadata[nb_config.metadata_key]) overrides.pop("output_folder", None) # this should not be overridden try: nb_config = nb_config.copy(**overrides) except Exception as exc: logger.warning( f"Failed to update configuration with notebook metadata: {exc}", subtype="config", ) else: logger.debug( "Updated configuration with notebook metadata", subtype="config" ) # Setup the parser mdit_parser = create_md_parser(nb_reader.md_config, SphinxNbRenderer) mdit_parser.options["document"] = document mdit_parser.options["nb_config"] = nb_config mdit_renderer: SphinxNbRenderer = mdit_parser.renderer # type: ignore mdit_env: dict[str, Any] = {} # load notebook element renderer class from entry-point name # this is separate from SphinxNbRenderer, so that users can override it renderer_name = nb_config.render_plugin nb_renderer: NbElementRenderer = load_renderer(renderer_name)( mdit_renderer, logger ) # we temporarily store nb_renderer on the document, # so that roles/directives can access it document.attributes["nb_renderer"] = nb_renderer # we currently do this early, so that the nb_renderer has access to things mdit_renderer.setup_render(mdit_parser.options, mdit_env) # type: ignore # parse notebook structure to markdown-it tokens # note, this does not assume that the notebook has been executed yet mdit_tokens = notebook_to_tokens(notebook, mdit_parser, mdit_env, logger) # open the notebook execution client, # this may execute the notebook immediately or during the page render with create_client( notebook, document_path, nb_config, logger, nb_reader.read_fmt ) as nb_client: mdit_parser.options["nb_client"] = nb_client # convert to docutils AST, which is added to the document mdit_renderer.render(mdit_tokens, mdit_parser.options, mdit_env) # save final execution data if nb_client.exec_metadata: NbMetadataCollector.set_exec_data( self.env, self.env.docname, nb_client.exec_metadata ) if nb_client.exec_metadata["traceback"]: # store error traceback in outdir and log its path reports_file = Path(self.env.app.outdir).joinpath( "reports", *(self.env.docname + ".err.log").split("/") ) reports_file.parent.mkdir(parents=True, exist_ok=True) reports_file.write_text( nb_client.exec_metadata["traceback"], encoding="utf8" ) logger.warning( f"Notebook exception traceback saved in: {reports_file}", subtype="exec", ) # write final (updated) notebook to output folder (utf8 is standard encoding) path = self.env.docname.split("/") ipynb_path = path[:-1] + [path[-1] + ".ipynb"] content = nbformat.writes(notebook).encode("utf-8") nb_renderer.write_file(ipynb_path, content, overwrite=True) # write glue data to the output folder, # and store the keys to environment doc metadata, # so that they may be used in any post-transform steps if nb_client.glue_data: glue_path = path[:-1] + [path[-1] + ".glue.json"] nb_renderer.write_file( glue_path, json.dumps(nb_client.glue_data, cls=BytesEncoder).encode("utf8"), overwrite=True, ) NbMetadataCollector.set_doc_data( self.env, self.env.docname, "glue", list(nb_client.glue_data.keys()) ) # move some document metadata to environment metadata, # so that we can later read it from the environment, # rather than having to load the whole doctree for key, (uri, kwargs) in document.attributes.pop("nb_js_files", {}).items(): NbMetadataCollector.add_js_file( self.env, self.env.docname, key, uri, kwargs ) # remove temporary state document.attributes.pop("nb_renderer") class SphinxNbRenderer(SphinxRenderer, MditRenderMixin): """A sphinx renderer for Jupyter Notebooks.""" def render_nb_initialise(self, token: SyntaxTreeNode) -> None: env = cast(BuildEnvironment, self.sphinx_env) metadata = self.nb_client.nb_metadata special_keys = ["kernelspec", "language_info", "source_map"] for key in special_keys: if key in metadata: # save these special keys on the metadata, rather than as docinfo # note, sphinx_book_theme checks kernelspec is in the metadata env.metadata[env.docname][key] = metadata.get(key) # forward the remaining metadata to the front_matter renderer special_keys.append("widgets") top_matter = {k: v for k, v in metadata.items() if k not in special_keys} self.render_front_matter( Token( # type: ignore "front_matter", "", 0, map=[0, 0], content=top_matter, # type: ignore[arg-type] ), ) def _render_nb_cell_code_outputs( self, token: SyntaxTreeNode, outputs: list[nbformat.NotebookNode] ) -> None: """Render a notebook code cell's outputs.""" line = token_line(token, 0) cell_index = token.meta["index"] metadata = token.meta["metadata"] # render the outputs for output_index, output in enumerate(outputs): if output.output_type == "stream": if output.name == "stdout": _nodes = self.nb_renderer.render_stdout( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) elif output.name == "stderr": _nodes = self.nb_renderer.render_stderr( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) else: pass # TODO warning elif output.output_type == "error": _nodes = self.nb_renderer.render_error( output, metadata, cell_index, line ) self.add_line_and_source_path_r(_nodes, token) self.current_node.extend(_nodes) elif output.output_type in ("display_data", "execute_result"): # Note, this is different to the docutils implementation, # where we directly select a single output, based on the mime_priority. # Here, we do not know the mime priority until we know the output format # so we output all the outputs during this parsing phase # (this is what sphinx caches as "output format agnostic" AST), # and replace the mime_bundle with the format specific output # in a post-transform (run per output format on the cached AST) figure_options = ( self.get_cell_level_config( "render_figure_options", metadata, line=line ) or None ) with create_figure_context(self, figure_options, line): mime_bundle = nodes.container(nb_element="mime_bundle") with self.current_node_context(mime_bundle): for mime_type, data in output["data"].items(): mime_container = nodes.container(mime_type=mime_type) with self.current_node_context(mime_container): _nodes = self.nb_renderer.render_mime_type( MimeData( mime_type, data, cell_metadata=metadata, output_metadata=output.get("metadata", {}), cell_index=cell_index, output_index=output_index, line=line, ) ) self.current_node.extend(_nodes) if mime_container.children: self.current_node.append(mime_container) if mime_bundle.children: self.add_line_and_source_path_r([mime_bundle], token) self.current_node.append(mime_bundle) else: create_warning( self.document, f"Unsupported output type: {output.output_type}", line=line, append_to=self.current_node, # wtype=DEFAULT_LOG_TYPE, subtype=MystNBWarnings.OUTPUT_TYPE, ) class SelectMimeType(SphinxPostTransform): """Select the mime type to render from mime bundles, based on the builder and its associated priority list. """ default_priority = 4 # TODO set correct priority def run(self, **kwargs: Any) -> None: """Run the transform.""" # get priority list for this builder # TODO allow for per-notebook/cell priority dicts? bname = self.app.builder.name priority_list = get_mime_priority( bname, self.config["nb_mime_priority_overrides"] ) def condition(node): return ( isinstance(node, nodes.container) and node.attributes.get("nb_element", "") == "mime_bundle" ) # remove/replace_self will not work with an iterator for node in list(findall(self.document)(condition)): # get available mime types mime_types = [node["mime_type"] for node in node.children] if not mime_types: node.parent.remove(node) continue # select top priority index = None for mime_type in priority_list: try: index = mime_types.index(mime_type) except ValueError: continue else: break if index is None: mime_string = ",".join(repr(m) for m in mime_types) SPHINX_LOGGER.warning( f"No mime type available in priority list for builder {bname!r} " f"({mime_string}) [{DEFAULT_LOG_TYPE}.mime_priority]", type=DEFAULT_LOG_TYPE, subtype="mime_priority", location=node, ) node.parent.remove(node) elif not node.children[index].children: node.parent.remove(node) else: node.replace_self(node.children[index].children) class NbMetadataCollector(EnvironmentCollector): """Collect myst-nb specific metadata, and handle merging of parallel builds.""" @staticmethod def set_doc_data(env: SphinxEnvType, docname: str, key: str, value: Any) -> None: """Add nb metadata for a docname to the environment.""" if not hasattr(env, "nb_metadata"): env.nb_metadata = defaultdict(dict) env.nb_metadata.setdefault(docname, {})[key] = value @staticmethod def get_doc_data(env: SphinxEnvType) -> DefaultDict[str, dict]: """Get myst-nb docname -> metadata dict.""" if not hasattr(env, "nb_metadata"): env.nb_metadata = defaultdict(dict) return env.nb_metadata @classmethod def set_exec_data( cls, env: SphinxEnvType, docname: str, value: ExecutionResult ) -> None: """Add nb metadata for a docname to the environment.""" cls.set_doc_data(env, docname, "exec_data", value) # TODO this does not take account of cache data cls.note_exec_update(env) @classmethod def get_exec_data(cls, env: SphinxEnvType, docname: str) -> ExecutionResult | None: """Get myst-nb docname -> execution data.""" return cls.get_doc_data(env)[docname].get("exec_data") def get_outdated_docs( # type: ignore[override] self, app: Sphinx, env: SphinxEnvType, added: set[str], changed: set[str], removed: set[str], ) -> list[str]: # called before any docs are read env.nb_new_exec_data = False return [] @staticmethod def note_exec_update(env: SphinxEnvType) -> None: """Note that a notebook has been executed.""" env.nb_new_exec_data = True @staticmethod def new_exec_data(env: SphinxEnvType) -> bool: """Return whether any notebooks have updated execution data.""" return getattr(env, "nb_new_exec_data", False) @classmethod def add_js_file( cls, env: SphinxEnvType, docname: str, key: str, uri: str | None, kwargs: dict[str, str], ): """Register a JavaScript file to include in the HTML output.""" if not hasattr(env, "nb_metadata"): env.nb_metadata = defaultdict(dict) js_files = env.nb_metadata.setdefault(docname, {}).setdefault("js_files", {}) # TODO handle whether overrides are allowed js_files[key] = (uri, kwargs) @classmethod def get_js_files( cls, env: SphinxEnvType, docname: str ) -> dict[str, tuple[str | None, dict[str, str]]]: """Get myst-nb docname -> execution data.""" return cls.get_doc_data(env)[docname].get("js_files", {}) def clear_doc( # type: ignore[override] self, app: Sphinx, env: SphinxEnvType, docname: str, ) -> None: if not hasattr(env, "nb_metadata"): env.nb_metadata = defaultdict(dict) env.nb_metadata.pop(docname, None) def process_doc(self, app: Sphinx, doctree: nodes.document) -> None: pass def merge_other( # type: ignore[override] self, app: Sphinx, env: SphinxEnvType, docnames: set[str], other: SphinxEnvType, ) -> None: if not hasattr(env, "nb_metadata"): env.nb_metadata = defaultdict(dict) other_metadata = getattr(other, "nb_metadata", defaultdict(dict)) for docname in docnames: env.nb_metadata[docname] = other_metadata[docname] if other.nb_new_exec_data: env.nb_new_exec_data = True class BytesEncoder(json.JSONEncoder): """A JSON encoder that accepts b64 (and other *ascii*) bytestrings.""" def default(self, obj): if isinstance(obj, bytes): return obj.decode("ascii") return json.JSONEncoder.default(self, obj) class HideCodeCellNode(nodes.Element): """Node for hiding cell input.""" @classmethod def add_to_app(cls, app: Sphinx): app.add_node(cls, html=(visit_HideCellInput, depart_HideCellInput)) def visit_HideCellInput(self: SphinxTranslator, node: HideCodeCellNode): classes = " ".join(node["classes"]) self.body.append(f'
\n') self.body.append('\n') self.body.append(f'\n') self.body.append(f'{escape(node["prompt_hide"])}\n') self.body.append("\n") def depart_HideCellInput(self: SphinxTranslator, node: HideCodeCellNode): self.body.append("
\n") class HideInputCells(SphinxPostTransform): """Hide input cells in the HTML output.""" default_priority = 199 formats = ("html",) def run(self, **kwargs): for node in findall(self.document)(nodes.container): if ( node.get("nb_element") == "cell_code" and node.get("hide_mode") and node.children ): hide_mode = node.get("hide_mode") has_input = node.children[0].get("nb_element") == "cell_code_source" has_output = node.children[-1].get("nb_element") == "cell_code_output" if has_input and hide_mode == "all": # wrap everything and place a summary above the input wrap_node = HideCodeCellNode( prompt_show=node["prompt_show"].replace("{type}", "content"), prompt_hide=node["prompt_hide"].replace("{type}", "content"), ) wrap_node["classes"].append("above-input") wrap_node.extend(node.children) node.children = [wrap_node] if has_input and has_output and hide_mode in ("output", "input+output"): node.children[0]["classes"].append("above-output-prompt") if has_input and hide_mode in ("input", "input+output"): # wrap just the input and place a summary above the input wrap_node = HideCodeCellNode( prompt_show=node["prompt_show"].replace("{type}", "source"), prompt_hide=node["prompt_hide"].replace("{type}", "source"), ) wrap_node["classes"].append("above-input") code = node.children[0] wrap_node.append(code) node.replace(code, wrap_node) if has_input and has_output and hide_mode in ("output", "input+output"): # wrap just the output and place a summary below the input wrap_node = HideCodeCellNode( prompt_show=node["prompt_show"].replace("{type}", "output"), prompt_hide=node["prompt_hide"].replace("{type}", "output"), ) wrap_node["classes"].append("below-input") output = node.children[-1] wrap_node.append(output) node.replace(output, wrap_node) if ( (not has_input) and has_output and hide_mode in ("all", "input+output", "output") ): # wrap just the output and place a summary above the output wrap_node = HideCodeCellNode( prompt_show=node["prompt_show"].replace("{type}", "outputs"), prompt_hide=node["prompt_hide"].replace("{type}", "outputs"), ) wrap_node["classes"].append("above-output") output = node.children[-1] wrap_node.append(output) node.replace(output, wrap_node) MyST-NB-1.1.2/myst_nb/sphinx_ext.py000066400000000000000000000213371467453560600171100ustar00rootroot00000000000000"""Setup for the myst-nb sphinx extension.""" from __future__ import annotations import contextlib import hashlib from importlib import resources as import_resources import os from pathlib import Path import sys from types import ModuleType from typing import Any, Iterator, cast from myst_parser.sphinx_ext.main import setup_sphinx as setup_myst_parser from sphinx.application import Sphinx from sphinx.util import logging as sphinx_logging from sphinx.util.fileutil import copy_asset_file from myst_nb import __version__, static from myst_nb.core.config import NbParserConfig from myst_nb.core.loggers import DEFAULT_LOG_TYPE from myst_nb.core.read import UnexpectedCellDirective from myst_nb.ext.download import NbDownloadRole from myst_nb.ext.eval import load_eval_sphinx from myst_nb.ext.glue import load_glue_sphinx from myst_nb.ext.glue.crossref import ReplacePendingGlueReferences from myst_nb.sphinx_ import ( HideCodeCellNode, HideInputCells, NbMetadataCollector, Parser, SelectMimeType, SphinxEnvType, ) SPHINX_LOGGER = sphinx_logging.getLogger(__name__) OUTPUT_FOLDER = "jupyter_execute" # used for deprecated config values, # so we can tell if they have been set by a user, and warn them _UNSET = "--unset--" def sphinx_setup(app: Sphinx): """Initialize Sphinx extension.""" # note, for core events overview, see: # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events # Add myst-parser configuration and transforms (but does not add the parser) setup_myst_parser(app) # add myst-nb configuration variables for name, default, field in NbParserConfig().as_triple(): if not field.metadata.get("sphinx_exclude"): # TODO add types? app.add_config_value(f"nb_{name}", default, "env", Any) # type: ignore[arg-type] if "legacy_name" in field.metadata: app.add_config_value( f"{field.metadata['legacy_name']}", _UNSET, "env", Any, # type: ignore[arg-type] ) # Handle non-standard deprecation app.add_config_value("nb_render_priority", _UNSET, "env", Any) # type: ignore[arg-type] # generate notebook configuration from Sphinx configuration # this also validates the configuration values app.connect("builder-inited", create_mystnb_config) # add parser and default associated file suffixes app.add_source_parser(Parser) app.add_source_suffix(".md", "myst-nb", override=True) app.add_source_suffix(".ipynb", "myst-nb") # add additional file suffixes for parsing app.connect("config-inited", add_nb_custom_formats) # ensure notebook checkpoints are excluded from parsing app.connect("config-inited", add_exclude_patterns) # add collector for myst nb specific data app.add_env_collector(NbMetadataCollector) # TODO add an event which, if any files have been removed, # all jupyter-cache stage records with a non-existent path are removed # (just to keep it "tidy", but won't affect run) # add directive to ensure all notebook cells are converted app.add_directive("code-cell", UnexpectedCellDirective, override=True) app.add_directive("raw-cell", UnexpectedCellDirective, override=True) # add directive for downloading an executed notebook app.add_role("nb-download", NbDownloadRole()) # add directive for evaluating glue and kernel variables load_eval_sphinx(app) load_glue_sphinx(app) # add post-transform for selecting mime type from a bundle app.add_post_transform(SelectMimeType) app.add_post_transform(ReplacePendingGlueReferences) # setup collapsible content app.add_post_transform(HideInputCells) HideCodeCellNode.add_to_app(app) # add HTML resources add_css(app) app.connect("build-finished", add_global_html_resources) # note, this event is only available in Sphinx >= 3.5 app.connect("html-page-context", add_per_page_html_resources) # Note lexers are registered as `pygments.lexers` entry-points # and so do not need to be added here. # setup extension for execution statistics tables # import here, to avoid circular import from myst_nb.ext.execution_tables import setup_exec_table_extension setup_exec_table_extension(app) return { "version": __version__, "parallel_read_safe": True, "parallel_write_safe": True, } def add_nb_custom_formats(app: Sphinx, config): """Add custom conversion formats.""" for suffix in config.nb_custom_formats: app.add_source_suffix(suffix, "myst-nb", override=True) def create_mystnb_config(app): """Generate notebook configuration from Sphinx configuration""" # Ignore type checkers because the attribute is dynamically assigned from sphinx.util.console import bold values = {} for name, _, field in NbParserConfig().as_triple(): if not field.metadata.get("sphinx_exclude"): values[name] = app.config[f"nb_{name}"] if "legacy_name" in field.metadata: legacy_value = app.config[field.metadata["legacy_name"]] if legacy_value != _UNSET: legacy_name = field.metadata["legacy_name"] SPHINX_LOGGER.warning( f"{legacy_name!r} is deprecated for 'nb_{name}' " f"[{DEFAULT_LOG_TYPE}.config]", type=DEFAULT_LOG_TYPE, subtype="config", ) values[name] = legacy_value if app.config["nb_render_priority"] != _UNSET: SPHINX_LOGGER.warning( "'nb_render_priority' is deprecated for 'nb_mime_priority_overrides'" f"{DEFAULT_LOG_TYPE}.config", type=DEFAULT_LOG_TYPE, subtype="config", ) try: app.env.mystnb_config = NbParserConfig(**values) SPHINX_LOGGER.info( bold("myst-nb v%s:") + " %s", __version__, app.env.mystnb_config ) except (TypeError, ValueError) as error: SPHINX_LOGGER.critical("myst-nb configuration invalid: %s", error.args[0]) raise # update the output_folder (for writing external files like images), # and the execution_cache_path (for caching notebook outputs) # to a set path within the sphinx build folder output_folder = Path(app.outdir).parent.joinpath(OUTPUT_FOLDER).resolve() exec_cache_path: None | str | Path = app.env.mystnb_config.execution_cache_path if not exec_cache_path: exec_cache_path = Path(app.outdir).parent.joinpath(".jupyter_cache").resolve() app.env.mystnb_config = app.env.mystnb_config.copy( output_folder=str(output_folder), execution_cache_path=str(exec_cache_path) ) SPHINX_LOGGER.info(f"Using jupyter-cache at: {exec_cache_path}") def add_exclude_patterns(app: Sphinx, config): """Add default exclude patterns (if not already present).""" if "**.ipynb_checkpoints" not in config.exclude_patterns: config.exclude_patterns.append("**.ipynb_checkpoints") def _get_file_hash(path: Path): """Get the hash of a file.""" return hashlib.sha256(path.read_bytes()).hexdigest() @contextlib.contextmanager def _import_resources_path(package: ModuleType, resource: str) -> Iterator[Path]: if sys.version_info < (3, 9): with import_resources.path(package, resource) as path: yield path else: with import_resources.as_file( import_resources.files(package).joinpath(resource) ) as path: yield path def add_css(app: Sphinx): """Add CSS for myst-nb.""" with _import_resources_path(static, "mystnb.css") as source_path: hash = _get_file_hash(source_path) app.add_css_file(f"mystnb.{hash}.css") def add_global_html_resources(app: Sphinx, exception): """Add HTML resources that apply to all pages.""" # see https://github.com/sphinx-doc/sphinx/issues/1379 if app.builder is not None and app.builder.format == "html" and not exception: with _import_resources_path(static, "mystnb.css") as source_path: hash = _get_file_hash(source_path) destination = os.path.join( app.builder.outdir, "_static", f"mystnb.{hash}.css" ) copy_asset_file(str(source_path), destination) def add_per_page_html_resources( app: Sphinx, pagename: str, *args: Any, **kwargs: Any ) -> None: """Add JS files for this page, identified from the parsing of the notebook.""" if app.env is None or app.builder is None or app.builder.format != "html": return js_files = NbMetadataCollector.get_js_files(cast(SphinxEnvType, app.env), pagename) for path, kwargs in js_files.values(): app.add_js_file(path, **kwargs) # type: ignore[arg-type] MyST-NB-1.1.2/myst_nb/static/000077500000000000000000000000001467453560600156265ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/static/__init__.py000066400000000000000000000000001467453560600177250ustar00rootroot00000000000000MyST-NB-1.1.2/myst_nb/static/mystnb.css000066400000000000000000001147041467453560600176630ustar00rootroot00000000000000/* Variables */ :root { --mystnb-source-bg-color: #f7f7f7; --mystnb-stdout-bg-color: #fcfcfc; --mystnb-stderr-bg-color: #fdd; --mystnb-traceback-bg-color: #fcfcfc; --mystnb-source-border-color: #ccc; --mystnb-source-margin-color: green; --mystnb-stdout-border-color: #f7f7f7; --mystnb-stderr-border-color: #f7f7f7; --mystnb-traceback-border-color: #ffd6d6; --mystnb-hide-prompt-opacity: 70%; --mystnb-source-border-radius: .4em; --mystnb-source-border-width: 1px; } /* Whole cell */ div.container.cell { padding-left: 0; margin-bottom: 1em; } /* Removing all background formatting so we can control at the div level */ .cell_input div.highlight, .cell_output pre, .cell_input pre, .cell_output .output { border: none; box-shadow: none; } .cell_output .output pre, .cell_input pre { margin: 0px; } /* Input cells */ div.cell div.cell_input, div.cell details.above-input>summary { padding-left: 0em; padding-right: 0em; border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; background-color: var(--mystnb-source-bg-color); border-left-color: var(--mystnb-source-margin-color); border-left-width: medium; border-radius: var(--mystnb-source-border-radius); } div.cell_input>div, div.cell_output div.output>div.highlight { margin: 0em !important; border: none !important; } /* All cell outputs */ .cell_output { padding-left: 1em; padding-right: 0em; margin-top: 1em; } /* Text outputs from cells */ .cell_output .output.text_plain, .cell_output .output.traceback, .cell_output .output.stream, .cell_output .output.stderr { margin-top: 1em; margin-bottom: 0em; box-shadow: none; } .cell_output .output.text_plain, .cell_output .output.stream { background: var(--mystnb-stdout-bg-color); border: 1px solid var(--mystnb-stdout-border-color); } .cell_output .output.stderr { background: var(--mystnb-stderr-bg-color); border: 1px solid var(--mystnb-stderr-border-color); } .cell_output .output.traceback { background: var(--mystnb-traceback-bg-color); border: 1px solid var(--mystnb-traceback-border-color); } /* Collapsible cell content */ div.cell details.above-input div.cell_input { border-top-left-radius: 0; border-top-right-radius: 0; border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed; } div.cell div.cell_input.above-output-prompt { border-bottom-left-radius: 0; border-bottom-right-radius: 0; } div.cell details.above-input>summary { border-bottom-left-radius: 0; border-bottom-right-radius: 0; border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed; padding-left: 1em; margin-bottom: 0; } div.cell details.above-output>summary { background-color: var(--mystnb-source-bg-color); padding-left: 1em; padding-right: 0em; border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; border-radius: var(--mystnb-source-border-radius); border-left-color: var(--mystnb-source-margin-color); border-left-width: medium; } div.cell details.below-input>summary { background-color: var(--mystnb-source-bg-color); padding-left: 1em; padding-right: 0em; border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; border-top: none; border-bottom-left-radius: var(--mystnb-source-border-radius); border-bottom-right-radius: var(--mystnb-source-border-radius); border-left-color: var(--mystnb-source-margin-color); border-left-width: medium; } div.cell details.hide>summary>span { opacity: var(--mystnb-hide-prompt-opacity); } div.cell details.hide[open]>summary>span.collapsed { display: none; } div.cell details.hide:not([open])>summary>span.expanded { display: none; } @keyframes collapsed-fade-in { 0% { opacity: 0; } 100% { opacity: 1; } } div.cell details.hide[open]>summary~* { -moz-animation: collapsed-fade-in 0.3s ease-in-out; -webkit-animation: collapsed-fade-in 0.3s ease-in-out; animation: collapsed-fade-in 0.3s ease-in-out; } /* Math align to the left */ .cell_output .MathJax_Display { text-align: left !important; } /* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */ div.cell_output table { border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 1em; table-layout: fixed; } div.cell_output thead { border-bottom: 1px solid black; vertical-align: bottom; } div.cell_output tr, div.cell_output th, div.cell_output td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } div.cell_output th { font-weight: bold; } div.cell_output tbody tr:nth-child(odd) { background: #f5f5f5; } div.cell_output tbody tr:hover { background: rgba(66, 165, 245, 0.2); } /** source code line numbers **/ span.linenos { opacity: 0.5; } /* Inline text from `paste` operation */ span.pasted-text { font-weight: bold; } span.pasted-inline img { max-height: 2em; } tbody span.pasted-inline img { max-height: none; } /* Font colors for translated ANSI escape sequences Color values are copied from Jupyter Notebook 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MyST-NB-1.1.2/myst_nb/warnings_.py000066400000000000000000000060101467453560600166750ustar00rootroot00000000000000"""Central handling of warnings for the myst-nb extension.""" from __future__ import annotations from enum import Enum from typing import Sequence from docutils import nodes from myst_parser.warnings_ import MystWarnings from myst_parser.warnings_ import create_warning as myst_parser_create_warnings __all__ = [ "MystWarnings", "MystNBWarnings", "create_warning", ] class MystNBWarnings(Enum): """MySTNB warning types.""" LEXER = "lexer" """Issue resolving lexer""" FIG_CAPTION = "fig_caption" """Issue resoliving figure caption""" MIME_TYPE = "mime_type" """Issue resolving MIME type""" OUTPUT_TYPE = "output_type" """Issue resolving Output type""" CELL_METADATA_KEY = "cell_metadata_key" """Issue with a key in a cell's `metadata` dictionary.""" CELL_CONFIG = "cell_config" """Issue with a cell's configuration or metadata.""" def _is_suppressed_warning( type: str, subtype: str, suppress_warnings: Sequence[str] ) -> bool: """Check whether the warning is suppressed or not. Mirrors: https://github.com/sphinx-doc/sphinx/blob/47d9035bca9e83d6db30a0726a02dc9265bd66b1/sphinx/util/logging.py """ if type is None: return False subtarget: str | None for warning_type in suppress_warnings: if "." in warning_type: target, subtarget = warning_type.split(".", 1) else: target, subtarget = warning_type, None if target == type and subtarget in (None, subtype, "*"): return True return False def create_warning( document: nodes.document, message: str, subtype: MystNBWarnings | MystWarnings, *, line: int | None = None, append_to: nodes.Element | None = None, ) -> nodes.system_message | None: """Generate a warning, logging if it is necessary. If the warning type is listed in the ``suppress_warnings`` configuration, then ``None`` will be returned and no warning logged. """ # Pass off Myst Parser warnings to that package if isinstance(subtype, MystWarnings): myst_parser_create_warnings( document=document, message=message, subtype=subtype, line=line, append_to=append_to, ) wtype = "myst-nb" # figure out whether to suppress the warning, if sphinx is available, # it will have been set up by the Sphinx environment, # otherwise we will use the configuration set by docutils suppress_warnings: Sequence[str] = [] try: suppress_warnings = document.settings.env.app.config.suppress_warnings except AttributeError: suppress_warnings = document.settings.myst_suppress_warnings or [] if _is_suppressed_warning(wtype, subtype.value, suppress_warnings): return None kwargs = {"line": line} if line is not None else {} message = f"{message} [{wtype}.{subtype.value}]" msg_node = document.reporter.warning(message, **kwargs) if append_to is not None: append_to.append(msg_node) return msg_node MyST-NB-1.1.2/pyproject.toml000066400000000000000000000076671467453560600156200ustar00rootroot00000000000000 [build-system] requires = ["flit_core >=3.4,<4"] build-backend = "flit_core.buildapi" [project] name = "myst-nb" dynamic = ["version"] description = "A Jupyter Notebook Sphinx reader built on top of the MyST markdown parser." authors = [{name = "ExecutableBookProject", email = "chrisj_sewell@hotmail.com"}] maintainers = [{name = "Angus Hollands", email = "goosey15@gmail.com"}] readme = "README.md" license = {file = "LICENSE"} classifiers = [ "Development Status :: 5 - Production/Stable", "Framework :: Sphinx :: Extension", "Intended Audience :: Developers", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Text Processing :: Markup", ] keywords = [ "markdown", "lexer", "parser", "jupyter", "docutils", "sphinx", ] requires-python = ">=3.9" dependencies = [ "importlib_metadata", "ipython", "jupyter-cache>=0.5", "nbclient", "myst-parser>=1.0.0", "nbformat>=5.0", "pyyaml", "sphinx>=5", "typing-extensions", # ipykernel is not a requirement of the library, # but is a common requirement for users (registers the python3 kernel) "ipykernel", ] [project.urls] Homepage = "https://github.com/executablebooks/myst-nb" Documentation = "https://myst-nb.readthedocs.io" [project.entry-points."myst_nb.renderers"] default = "myst_nb.core.render:NbElementRenderer" [project.entry-points."myst_nb.mime_renderers"] example = "myst_nb.core.render:ExampleMimeRenderPlugin" [project.entry-points."pygments.lexers"] myst-ansi = "myst_nb.core.lexers:AnsiColorLexer" ipythontb = "myst_nb.core.lexers:IPythonTracebackLexer" [project.entry-points."jcache.readers"] myst_nb_md = "myst_nb.core.read:myst_nb_reader_plugin" [project.optional-dependencies] code_style = ["pre-commit"] rtd = [ "alabaster", "altair", "bokeh", "coconut>=1.4.3", "ipykernel>=5.5", "ipywidgets", "jupytext>=1.11.2", "matplotlib", "numpy", "pandas", "plotly", "sphinx-book-theme>=0.3", "sphinx-copybutton", "sphinx-design", "sphinxcontrib-bibtex", "sympy", ] testing = [ "coverage>=6.4", "beautifulsoup4", "ipykernel>=5.5", # for issue with 8.1.0 see https://github.com/ipython/ipython/issues/13554 "ipython!=8.1.0", "ipywidgets>=8", "jupytext>=1.11.2", # Matplotlib outputs are sensitive to the matplotlib version "matplotlib==3.7.*", "nbdime", "numpy", "pandas", "pyarrow", "pytest", "pytest-cov>=3", "pytest-regressions", "pytest-param-files", "sympy>=1.10.1", ] [project.scripts] mystnb-quickstart = "myst_nb.cli:quickstart" mystnb-to-jupyter = "myst_nb.cli:md_to_nb" mystnb-docutils-html = "myst_nb.docutils_:cli_html" mystnb-docutils-html5 = "myst_nb.docutils_:cli_html5" mystnb-docutils-latex = "myst_nb.docutils_:cli_latex" mystnb-docutils-xml = "myst_nb.docutils_:cli_xml" mystnb-docutils-pseudoxml = "myst_nb.docutils_:cli_pseudoxml" [tool.flit.module] name = "myst_nb" [tool.flit.sdist] exclude = [ "docs/", "tests/", ] [tool.mypy] show_error_codes = true check_untyped_defs = true strict_equality = true no_implicit_optional = true warn_unused_ignores = true [[tool.mypy.overrides]] module = ["myst_nb.*"] # can only follow these imports when more of the code is typed follow_imports = "skip" [[tool.mypy.overrides]] module = ["docutils.*", "nbformat.*", "jupyter_cache.*", "IPython.*", "pygments.*"] ignore_missing_imports = true [tool.ruff.lint] ignore = [ "E203", # Whitespace before punctuation ] [tool.ruff.lint.isort] force-sort-within-sections = true MyST-NB-1.1.2/tests/000077500000000000000000000000001467453560600140265ustar00rootroot00000000000000MyST-NB-1.1.2/tests/conftest.py000066400000000000000000000224271467453560600162340ustar00rootroot00000000000000import json import os from pathlib import Path import re import uuid import bs4 from docutils.nodes import image as image_node from nbconvert.filters import strip_ansi from nbdime.diffing.notebooks import ( diff_notebooks, set_notebook_diff_ignores, set_notebook_diff_targets, ) from nbdime.prettyprint import pretty_print_diff import nbformat as nbf import pytest import sphinx from sphinx import version_info as sphinx_version_info from sphinx.util.console import nocolor pytest_plugins = "sphinx.testing.fixtures" # -Diff Configuration-# NB_VERSION = 4 set_notebook_diff_ignores({"/nbformat_minor": True}) set_notebook_diff_targets(metadata=False) TEST_FILE_DIR = Path(__file__).parent.joinpath("notebooks") @pytest.fixture(autouse=True, scope="session") def build_matplotlib_font_cache(): """This is to mitigate errors on CI VMs, where you can get the message: "Matplotlib is building the font cache" in output notebooks """ from matplotlib.font_manager import FontManager FontManager() @pytest.fixture() def get_test_path(): def _get_test_path(name): return TEST_FILE_DIR.joinpath(name) return _get_test_path class SphinxFixture: """A class returned by the ``sphinx_run`` fixture, to run sphinx, and retrieve aspects of the build. """ def __init__(self, app, filenames): self.app = app self.env = app.env self.files = [os.path.splitext(ff) for ff in filenames] self.software_versions = ( f".sphinx{sphinx.version_info[0]}" # software version tracking for fixtures ) # self.nb_file = nb_file # self.nb_name = os.path.splitext(nb_file)[0] def build(self): """Run the sphinx build.""" # TODO reset streams before each build, # but this was wiping the warnings of a build self.app.build() def status(self): """Return the stdout stream of the sphinx build.""" return self.app._status.getvalue().strip() def warnings(self): """Return the stderr stream of the sphinx build.""" return self.app._warning.getvalue().strip() def invalidate_files(self): """Invalidate the files, such that it will be flagged for a re-read.""" for name, _ in self.files: self.env.all_docs.pop(name) def get_resolved_doctree(self, docname=None): """Load and return the built docutils.document, after post-transforms.""" docname = docname or self.files[0][0] doctree = self.env.get_and_resolve_doctree(docname, self.app.builder) doctree["source"] = docname return doctree def get_doctree(self, docname=None): """Load and return the built docutils.document.""" docname = docname or self.files[0][0] doctree = self.env.get_doctree(docname) doctree["source"] = docname return doctree def get_html(self, index=0): """Return the built HTML file.""" name = self.files[index][0] _path = self.app.outdir / (name + ".html") if not _path.exists(): pytest.fail("html not output") return bs4.BeautifulSoup(_path.read_text(), "html.parser") def get_nb(self, index=0): """Return the output notebook (after any execution).""" name = self.files[index][0] _path = self.app.srcdir / "_build" / "jupyter_execute" / (name + ".ipynb") if not _path.exists(): pytest.fail("notebook not output") return _path.read_text(encoding="utf-8") def get_report_file(self, index=0): """Return the report file for a failed execution.""" name = self.files[index][0] _path = self.app.outdir / "reports" / (name + ".err.log") if not _path.exists(): pytest.fail("report log not output") return _path.read_text() @pytest.fixture() def sphinx_params(request): """Parameters that are specified by 'pytest.mark.sphinx_params' are passed to the ``sphinx_run`` fixture:: @pytest.mark.sphinx_params("name.ipynb", conf={"option": "value"}) def test_something(sphinx_run): ... The first file specified here will be set as the master_doc """ markers = request.node.iter_markers("sphinx_params") kwargs = {} if markers is not None: for info in reversed(list(markers)): kwargs.update(info.kwargs) kwargs["files"] = info.args return kwargs @pytest.fixture() def sphinx_run(sphinx_params, make_app, tmp_path): """A fixture to setup and run a sphinx build, in a sandboxed folder. The `myst_nb` extension is added by default, and the first file will be set as the masterdoc """ assert len(sphinx_params["files"]) > 0, sphinx_params["files"] conf = sphinx_params.get("conf", {}) buildername = sphinx_params.get("buildername", "html") confoverrides = { "extensions": ["myst_nb"], "master_doc": os.path.splitext(sphinx_params["files"][0])[0], "exclude_patterns": ["_build"], "nb_execution_show_tb": True, } confoverrides.update(conf) current_dir = os.getcwd() if "working_dir" in sphinx_params: base_dir = Path(sphinx_params["working_dir"]) / str(uuid.uuid4()) else: base_dir = tmp_path srcdir = base_dir / "source" srcdir.mkdir(exist_ok=True) os.chdir(base_dir) (srcdir / "conf.py").write_text( "# conf overrides (passed directly to sphinx):\n" + "\n".join( ["# " + ll for ll in json.dumps(confoverrides, indent=2).splitlines()] ) + "\n" ) for nb_file in sphinx_params["files"]: nb_path = TEST_FILE_DIR.joinpath(nb_file) assert nb_path.exists(), nb_path (srcdir / nb_file).parent.mkdir(exist_ok=True) (srcdir / nb_file).write_text( nb_path.read_text(encoding="utf-8"), encoding="utf-8" ) nocolor() # For compatibility with multiple versions of sphinx, convert pathlib.Path to # sphinx.testing.path.path here. if sphinx_version_info >= (7, 2): app_srcdir = srcdir else: from sphinx.testing.path import path app_srcdir = path(os.fspath(srcdir)) app = make_app( buildername=buildername, srcdir=app_srcdir, confoverrides=confoverrides ) yield SphinxFixture(app, sphinx_params["files"]) # reset working directory os.chdir(current_dir) def empty_non_deterministic_outputs(cell): if "outputs" in cell and len(cell.outputs): for item in cell.outputs: if "data" in item and "image/png" in item.data: item.data["image/png"] = "" if "filenames" in item.get("metadata", {}): item["metadata"]["filenames"] = { k: os.path.basename(v) for k, v in item["metadata"]["filenames"].items() } if "traceback" in item: item["traceback"] = [strip_ansi(line) for line in item["traceback"]] @pytest.fixture() def check_nbs(): def _check_nbs(obtained_filename, expected_filename): obtained_nb = nbf.read(str(obtained_filename), nbf.NO_CONVERT) expect_nb = nbf.read(str(expected_filename), nbf.NO_CONVERT) obtained_nb.nbformat_minor = 5 expect_nb.nbformat_minor = 5 for cell in expect_nb.cells: empty_non_deterministic_outputs(cell) cell.id = "none" for cell in obtained_nb.cells: empty_non_deterministic_outputs(cell) cell.id = "none" diff = diff_notebooks(obtained_nb, expect_nb) filename_without_path = str(expected_filename)[ str(expected_filename).rfind("/") + 1 : ] if diff: raise AssertionError( pretty_print_diff(obtained_nb, diff, str(filename_without_path)) ) return _check_nbs @pytest.fixture() def clean_doctree(): def _func(doctree): if os.name == "nt": # on Windows file paths are absolute findall = getattr(doctree, "findall", doctree.traverse) for node in findall(image_node): # type: image_node if "candidates" in node: node["candidates"]["*"] = ( "_build/jupyter_execute/" + os.path.basename(node["candidates"]["*"]) ) if "uri" in node: node["uri"] = "_build/jupyter_execute/" + os.path.basename( node["uri"] ) return doctree return _func # comparison files will need updating # alternatively the resolution of https://github.com/ESSS/pytest-regressions/issues/32 @pytest.fixture() def file_regression(file_regression): return FileRegression(file_regression) class FileRegression: ignores = ( # TODO: Remove when support for Sphinx<=6 is dropped, re.escape(" translation_progress=\"{'total': 0, 'translated': 0}\""), # TODO: Remove when support for Sphinx<7.2 is dropped, r"original_uri=\"[^\"]*\"\s", ) def __init__(self, file_regression): self.file_regression = file_regression def check(self, data, **kwargs): return self.file_regression.check(self._strip_ignores(data), **kwargs) def _strip_ignores(self, data): for ig in self.ignores: data = re.sub(ig, "", data) return data MyST-NB-1.1.2/tests/nb_fixtures/000077500000000000000000000000001467453560600163565ustar00rootroot00000000000000MyST-NB-1.1.2/tests/nb_fixtures/basic.txt000066400000000000000000000117141467453560600202040ustar00rootroot00000000000000Markdown Cell: . cells: - cell_type: markdown metadata: {} source: | # A Title . A Title . Code Cell (no output): . cells: - cell_type: code metadata: {} execution_count: null source: | a=1 print(a) outputs: [] . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block classes="code" xml:space="preserve"> a=1 print(a) . Code Cell (with lexer): . metadata: language_info: name: python pygments_lexer: ipython3 cells: - cell_type: code metadata: {} execution_count: null source: a=1 outputs: [] . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" nb_language_info="{'name': 'python', 'pygments_lexer': 'ipython3'}" source="<string>"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block classes="code ipython3" xml:space="preserve"> <inline classes="n"> a <inline classes="o"> = <inline classes="mi"> 1 . Code Cell (simple output): . cells: - cell_type: code metadata: {} execution_count: 1 source: | a=1 print(a) outputs: - name: stdout output_type: stream text: "1" . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block classes="code" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="code myst-ansi output stream" xml:space="preserve"> 1 . Raw Cell . cells: - cell_type: raw metadata: {"format": "text/html"} source: | <div> <h1>A Title</h1> </div> . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <h1>A Title</h1> </div> . Mixed Cells: . cells: - cell_type: markdown metadata: {} source: | # A Title - cell_type: code metadata: {} execution_count: null source: | a=1 print(a) outputs: [] - cell_type: markdown metadata: {} source: | b . <document ids="a-title" names="a\ title" nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>" title="A Title"> <title> A Title <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block classes="code" xml:space="preserve"> a=1 print(a) <paragraph> b . Reference definitions defined in different cells: . cells: - cell_type: markdown metadata: {} source: | [a]: before - cell_type: markdown metadata: {} source: | [a] [b] - cell_type: markdown metadata: {} source: | [b]: after . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>"> <paragraph> <reference refuri="before"> a <reference refuri="after"> b . Footnote definitions defined in different cells: . cells: - cell_type: markdown metadata: {} source: | [^a]: before - cell_type: markdown metadata: {} source: | [^a] [^b] - cell_type: markdown metadata: {} source: | [^b]: after . <document nb_kernelspec="{'name': 'python3', 'display_name': 'Python 3', 'language': ''}" source="<string>"> <paragraph> <footnote_reference auto="1" ids="footnote-reference-1" refid="a"> 1 <footnote_reference auto="1" ids="footnote-reference-2" refid="b"> 2 <transition classes="footnotes"> <footnote auto="1" backrefs="footnote-reference-1" ids="a" names="a"> <label> 1 <paragraph> before <footnote auto="1" backrefs="footnote-reference-2" ids="b" names="b"> <label> 2 <paragraph> after . ����������������������������������������������������MyST-NB-1.1.2/tests/nb_fixtures/reporter_warnings.txt�����������������������������������������������0000664�0000000�0000000�00000002255�14674535606�0022675�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������Unknown Role: . cells: - cell_type: markdown metadata: {} source: | a - cell_type: markdown metadata: {} source: | {unknown}`a` . <string>:20002: (WARNING/2) Unknown interpreted text role "unknown". [myst.role_unknown] . Unknown directive: . cells: - cell_type: markdown metadata: {} source: | a ```{xyz} ``` . <string>:10003: (WARNING/2) Unknown directive type: 'xyz' [myst.directive_unknown] . Directive parsing error: . cells: - cell_type: markdown metadata: {} source: | ```{class} ``` . <string>:10002: (ERROR/3) Directive 'class': 1 argument(s) required, 0 supplied . Directive run error: . cells: - cell_type: markdown metadata: {} source: | ```{date} x ``` . <string>:10002: (ERROR/3) Invalid context: the "date" directive can only be used within a substitution definition. . Duplicate reference definition: . cells: - cell_type: markdown metadata: {} source: | [a]: b - cell_type: markdown metadata: {} source: | d [a]: c . <string>:20004: (WARNING/2) Duplicate reference definition: A [myst.duplicate_def] . ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/����������������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0016031�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/basic_failing.ipynb���������������������������������������������������0000664�0000000�0000000�00000001526�14674535606�0021652�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "source": [ "raise Exception('oopsie!')" ], "outputs": [] } ], "metadata": { "test_name": "notebook1", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/basic_nometadata.md���������������������������������������������������0000664�0000000�0000000�00000000253�14674535606�0021631�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������# a title this was created using `jupytext --to myst tests/notebooks/basic_unrun.ipynb` but with the jupytext metadata removed. ```{code-cell} ipython3 a=1 print(a) ``` �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/basic_relative.ipynb��������������������������������������������������0000664�0000000�0000000�00000001757�14674535606�0022062�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Test reading a relative file to make sure relative paths work\n", "from PIL import Image\n", "\n", "image = Image.open(\"./example.jpg\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "test_name": "notebook1", "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } �����������������MyST-NB-1.1.2/tests/notebooks/basic_run.ipynb�������������������������������������������������������0000664�0000000�0000000�00000001736�14674535606�0021050�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "source": [ "a=1\n", "print(a)" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ] } ], "metadata": { "test_name": "notebook1", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������MyST-NB-1.1.2/tests/notebooks/basic_stderr.ipynb����������������������������������������������������0000664�0000000�0000000�00000002277�14674535606�0021550�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "source": [ "import sys\n", "print('hallo', file=sys.stderr)" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "hallo\n" ] } ] }, { "cell_type": "code", "execution_count": 1, "metadata": {"tags": ["remove-stderr"]}, "source": [ "import sys\n", "print('hallo', file=sys.stderr)" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "hallo\n" ] } ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/basic_unrun.ipynb�����������������������������������������������������0000664�0000000�0000000�00000001525�14674535606�0021407�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "source": [ "a=1\n", "print(a)" ], "outputs": [] } ], "metadata": { "test_name": "notebook1", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/basic_unrun.md��������������������������������������������������������0000664�0000000�0000000�00000000366�14674535606�0020670�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- file_format: mystnb kernelspec: display_name: Python 3 language: python name: python3 author: Chris --- # a title this was created using `jupytext --to myst tests/notebooks/basic_unrun.ipynb` ```{code-cell} ipython3 a=1 print(a) ``` ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/complex_outputs.ipynb�������������������������������������������������0000664�0000000�0000000�00000321050�14674535606�0022347�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sympy as sym\n", "sym.init_printing(use_latex=True)\n", "import numpy as np\n", "from IPython.display import Image, Latex" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "# Markdown" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## General" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "Some markdown text.\n", "\n", "A list:\n", "\n", "- something\n", "- something else\n", "\n", "A numbered list\n", "\n", "1. something\n", "2. something else\n", "\n", "non-ascii characters TODO" ] }, { "cell_type": "markdown", "metadata": { "ipub": {} }, "source": [ "This is a long section of text, which we only want in a document (not a presentation)\n", "some text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true, "slideonly": true } }, "source": [ "This is an abbreviated section of the document text, which we only want in a presentation\n", "\n", "- summary of document text" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## References and Citations" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "References to \\cref{fig:example}, \\cref{tbl:example}, =@eqn:example_sympy and \\cref{code:example_mpl}.\n", "\n", "A latex citation.\\cite{zelenyak_molecular_2016}\n", "\n", "A html citation.<cite data-cite=\"kirkeminde_thermodynamic_2012\">(Kirkeminde, 2012)</cite> " ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## Todo notes" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "\\todo[inline]{an inline todo}\n", "\n", "Some text.\\todo{a todo in the margins}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text Output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ipub": { "text": { "format": { "backgroundcolor": "\\color{blue!10}" } } } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\n" ] } ], "source": [ "print(\"\"\"\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Images and Figures" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ipub": { "figure": { "caption": "A nice picture.", "label": "fig:example", "placement": "!bh" } } }, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": { "image/jpeg": { "height": 400 } }, "output_type": "execute_result" } ], "source": [ "Image('example.jpg',height=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying a plot with its code" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "fig:example_mpl" } }, "source": [ "A matplotlib figure, with the caption set in the markdowncell above the figure." ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_mpl" } }, "source": [ "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "a", "label": "code:example_mpl", "widefigure": false }, "figure": { "caption": "", "label": "fig:example_mpl", "widefigure": false } } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "width": 432, "height": 288 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(np.random.rand(10), np.random.rand(10), \n", " label='data label')\n", "plt.ylabel(r'a y label with latex $\\alpha$')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tables (with pandas)" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_pd" } }, "source": [ "The plotting code for a pandas Dataframe table (\\cref{tbl:example})." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_pd", "placement": "H", "widefigure": false }, "table": { "alternate": "gray!20", "caption": "An example of a table created with pandas dataframe.", "label": "tbl:example", "placement": "H" } } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b</th>\n", " <th>c</th>\n", " <th>d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>$\\delta$</td>\n", " <td>l</td>\n", " <td>0.391</td>\n", " <td>0.607</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>x</td>\n", " <td>m</td>\n", " <td>0.132</td>\n", " <td>0.205</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>y</td>\n", " <td>n</td>\n", " <td>0.969</td>\n", " <td>0.726</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/latex": [ "\\begin{tabular}{lllrr}\n", "\\toprule\n", "{} & a & b & c & d \\\\\n", "\\midrule\n", "0 & \\$\\textbackslash delta\\$ & l & 0.391 & 0.607 \\\\\n", "1 & x & m & 0.132 & 0.205 \\\\\n", "2 & y & n & 0.969 & 0.726 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n" ], "text/plain": [ " a b c d\n", "0 $\\delta$ l 0.391 0.607\n", "1 x m 0.132 0.205\n", "2 y n 0.969 0.726" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d'])\n", "df.a = [r'$\\delta$','x','y']\n", "df.b = ['l','m','n']\n", "df.set_index(['a','b'])\n", "df.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Equations (with ipython or sympy)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ipub": { "equation": { "label": "eqn:example_ipy" } } }, "outputs": [ { "data": { "text/latex": [ "$$ a = b+c $$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Latex('$$ a = b+c $$')" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_sym" } }, "source": [ "The plotting code for a sympy equation (=@eqn:example_sympy)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_sym", "placement": "H", "widefigure": false }, "equation": { "environment": "equation", "label": "eqn:example_sympy" } } }, "outputs": [ { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle \\left(\\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} - \\frac{2 \\sqrt{5} i}{5}\\right) + \\left(- \\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} + \\frac{2 \\sqrt{5} i}{5}\\right)$" ], "text/plain": [ " \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž\n", "(โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ\n", " โŽ2 5 โŽ  โŽ2 5 โŽ " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "sym.rsolve(f,y(n),[1,4])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**_some_ markdown**" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Markdown\n", "display(Markdown('**_some_ markdown**'))" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "ipub": { "bibliography": "example.bib", "biboptions": [ "super", "sort" ], "bibstyle": "unsrtnat", "language": "portuges", "listcode": true, "listfigures": true, "listtables": true, "pandoc": { "at_notation": true, "use_numref": true }, "sphinx": { "bib_title": "My Bibliography" }, "titlepage": { "author": "Authors Name", "email": "authors@email.com", "institution": [ "Institution1", "Institution2" ], "logo": "logo_example.png", "subtitle": "Sub-Title", "supervisors": [ "First Supervisor", "Second Supervisor" ], "tagline": "A tagline for the report.", "title": "Main-Title" }, "toc": { "depth": 2 } }, "jupytext": { "metadata_filter": { "notebook": "ipub" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "example.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": true }, "nav_menu": {}, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "161px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/complex_outputs_unrun.ipynb�������������������������������������������0000664�0000000�0000000�00000022225�14674535606�0023600�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "init_cell": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sympy as sym\n", "sym.init_printing(use_latex=True)\n", "import numpy as np\n", "from IPython.display import Image, Latex" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "# Markdown" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## General" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "Some markdown text.\n", "\n", "A list:\n", "\n", "- something\n", "- something else\n", "\n", "A numbered list\n", "\n", "1. something\n", "2. something else\n", "\n", "non-ascii characters TODO" ] }, { "cell_type": "markdown", "metadata": { "ipub": {} }, "source": [ "This is a long section of text, which we only want in a document (not a presentation)\n", "some text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true, "slideonly": true } }, "source": [ "This is an abbreviated section of the document text, which we only want in a presentation\n", "\n", "- summary of document text" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## References and Citations" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "References to \\cref{fig:example}, \\cref{tbl:example}, =@eqn:example_sympy and \\cref{code:example_mpl}.\n", "\n", "A latex citation.\\cite{zelenyak_molecular_2016}\n", "\n", "A html citation.<cite data-cite=\"kirkeminde_thermodynamic_2012\">(Kirkeminde, 2012)</cite> " ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## Todo notes" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "\\todo[inline]{an inline todo}\n", "\n", "Some text.\\todo{a todo in the margins}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text Output" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ipub": { "text": { "format": { "backgroundcolor": "\\color{blue!10}" } } } }, "outputs": [], "source": [ "print(\"\"\"\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Images and Figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying a plot with its code" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "fig:example_mpl" } }, "source": [ "A matplotlib figure, with the caption set in the markdowncell above the figure." ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_mpl" } }, "source": [ "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tables (with pandas)" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_pd" } }, "source": [ "The plotting code for a pandas Dataframe table (\\cref{tbl:example})." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_pd", "placement": "H", "widefigure": false }, "table": { "alternate": "gray!20", "caption": "An example of a table created with pandas dataframe.", "label": "tbl:example", "placement": "H" } } }, "outputs": [], "source": [ "np.random.seed(0) \n", "df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d'])\n", "df.a = [r'$\\delta$','x','y']\n", "df.b = ['l','m','n']\n", "df.set_index(['a','b'])\n", "df.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Equations (with ipython or sympy)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ipub": { "equation": { "label": "eqn:example_ipy" } } }, "outputs": [], "source": [ "Latex('$$ a = b+c $$')" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_sym" } }, "source": [ "The plotting code for a sympy equation (=@eqn:example_sympy)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_sym", "placement": "H", "widefigure": false }, "equation": { "environment": "equation", "label": "eqn:example_sympy" } } }, "outputs": [], "source": [ "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "sym.rsolve(f,y(n),[1,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive outputs\n", "\n", "## ipywidgets" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import ipywidgets as widgets\n", "widgets.Layout(model_id=\"1337h4x0R\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import display, Markdown\n", "display(Markdown('**_some_ markdown**'))" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "ipub": { "bibliography": "example.bib", "biboptions": [ "super", "sort" ], "bibstyle": "unsrtnat", "language": "portuges", "listcode": true, "listfigures": true, "listtables": true, "pandoc": { "at_notation": true, "use_numref": true }, "sphinx": { "bib_title": "My Bibliography" }, "titlepage": { "author": "Authors Name", "email": "authors@email.com", "institution": [ "Institution1", "Institution2" ], "logo": "logo_example.png", "subtitle": "Sub-Title", "supervisors": [ "First Supervisor", "Second Supervisor" ], "tagline": "A tagline for the report.", "title": "Main-Title" }, "toc": { "depth": 2 } }, "jupytext": { "notebook_metadata_filter": "ipub" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "example.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": true }, "nav_menu": {}, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "161px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/custom-formats.Rmd����������������������������������������������������0000664�0000000�0000000�00000000617�14674535606�0021464�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- title: "Test chunk options in Rmd/Jupyter conversion" author: "Marc Wouts" date: "June 16, 2018" jupyter: kernelspec: display_name: Python language: python name: python3 --- # Custom Formats ```{python echo=TRUE} import pandas as pd x = pd.Series({'A':1, 'B':3, 'C':2}) ``` ```{python bar_plot, echo=FALSE, fig.height=5, fig.width=8} x.plot(kind='bar', title='Sample plot') ``` �����������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/example.jpg�����������������������������������������������������������0000664�0000000�0000000�00000160761�14674535606�0020201�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������ุแzExif��MM�*������������������b�������j(�������1����$���r2�������–‡i�������ฌ���ุ� €��'� €��'Adobe Photoshop CC 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ปู���������������MyST-NB-1.1.2/tests/notebooks/file_level_config.ipynb�����������������������������������������������0000664�0000000�0000000�00000000670�14674535606�0022532�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "id": "be9f2c4c", "metadata": {}, "source": [ "# Title\n", "\n", "name\n", ": definition" ] } ], "metadata": { "file_format": "mystnb", "kernelspec": { "display_name": "python3", "name": "python3" }, "myst": { "enable_extensions": [ "deflist" ] }, "mystnb": { "execution_mode": "off" } }, "nbformat": 4, "nbformat_minor": 5 } ������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/file_level_config.md��������������������������������������������������0000664�0000000�0000000�00000000243�14674535606�0022005�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- file_format: mystnb kernelspec: name: python3 myst: enable_extensions: - "deflist" mystnb: execution_mode: "off" --- # Title name : definition �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/hide_cell_content.ipynb�����������������������������������������������0000664�0000000�0000000�00000003756�14674535606�0022551�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hide Code Cell Content" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hide-input\n" ] } ], "source": [ "print(\"hide-input\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hide-output\n" ] } ], "source": [ "print(\"hide-output\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hide-cell\n" ] } ], "source": [ "print(\"hide-cell\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "hide-cell" ], "mystnb": { "code_prompt_show": "My show message", "code_prompt_hide": "My hide message" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hide-cell custom message\n" ] } ], "source": [ "print(\"hide-cell custom message\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "321f99720af1749431335326d75386e6232ab33d0a78426e9f427a66c2c329a4" } } }, "nbformat": 4, "nbformat_minor": 2 } ������������������MyST-NB-1.1.2/tests/notebooks/ipywidgets.ipynb������������������������������������������������������0000664�0000000�0000000�00000052612�14674535606�0021272�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sympy as sym\n", "sym.init_printing(use_latex=True)\n", "import numpy as np\n", "from IPython.display import Image, Latex" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "# Markdown" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## General" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "Some markdown text.\n", "\n", "A list:\n", "\n", "- something\n", "- something else\n", "\n", "A numbered list\n", "\n", "1. something\n", "2. something else\n", "\n", "non-ascii characters TODO" ] }, { "cell_type": "markdown", "metadata": { "ipub": {} }, "source": [ "This is a long section of text, which we only want in a document (not a presentation)\n", "some text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true, "slideonly": true } }, "source": [ "This is an abbreviated section of the document text, which we only want in a presentation\n", "\n", "- summary of document text" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## References and Citations" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "References to \\cref{fig:example}, \\cref{tbl:example}, =@eqn:example_sympy and \\cref{code:example_mpl}.\n", "\n", "A latex citation.\\cite{zelenyak_molecular_2016}\n", "\n", "A html citation.<cite data-cite=\"kirkeminde_thermodynamic_2012\">(Kirkeminde, 2012)</cite> " ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## Todo notes" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "\\todo[inline]{an inline todo}\n", "\n", "Some text.\\todo{a todo in the margins}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text Output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ipub": { "text": { "format": { "backgroundcolor": "\\color{blue!10}" } } } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\n" ] } ], "source": [ "print(\"\"\"\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Images and Figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying a plot with its code" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "fig:example_mpl" } }, "source": [ "A matplotlib figure, with the caption set in the markdowncell above the figure." ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_mpl" } }, "source": [ "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tables (with pandas)" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_pd" } }, "source": [ "The plotting code for a pandas Dataframe table (\\cref{tbl:example})." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_pd", "placement": "H", "widefigure": false }, "table": { "alternate": "gray!20", "caption": "An example of a table created with pandas dataframe.", "label": "tbl:example", "placement": "H" } } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b</th>\n", " <th>c</th>\n", " <th>d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>$\\delta$</td>\n", " <td>l</td>\n", " <td>0.603</td>\n", " <td>0.545</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>x</td>\n", " <td>m</td>\n", " <td>0.438</td>\n", " <td>0.892</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>y</td>\n", " <td>n</td>\n", " <td>0.792</td>\n", " <td>0.529</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/latex": [ "\\begin{tabular}{lllrr}\n", "\\toprule\n", "{} & a & b & c & d \\\\\n", "\\midrule\n", "0 & \\$\\textbackslash delta\\$ & l & 0.603 & 0.545 \\\\\n", "1 & x & m & 0.438 & 0.892 \\\\\n", "2 & y & n & 0.792 & 0.529 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n" ], "text/plain": [ " a b c d\n", "0 $\\delta$ l 0.603 0.545\n", "1 x m 0.438 0.892\n", "2 y n 0.792 0.529" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(0) \n", "df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d'])\n", "df.a = [r'$\\delta$','x','y']\n", "df.b = ['l','m','n']\n", "df.set_index(['a','b'])\n", "df.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Equations (with ipython or sympy)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ipub": { "equation": { "label": "eqn:example_ipy" } } }, "outputs": [ { "data": { "text/latex": [ "$$ a = b+c $$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Latex('$$ a = b+c $$')" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_sym" } }, "source": [ "The plotting code for a sympy equation (=@eqn:example_sympy)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_sym", "placement": "H", "widefigure": false }, "equation": { "environment": "equation", "label": "eqn:example_sympy" } } }, "outputs": [ { "data": { "image/png": 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", "text/latex": [ "$\\displaystyle \\left(\\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} - \\frac{2 \\sqrt{5} i}{5}\\right) + \\left(- \\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} + \\frac{2 \\sqrt{5} i}{5}\\right)$" ], "text/plain": [ " \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž\n", "(โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ\n", " โŽ2 5 โŽ  โŽ2 5 โŽ " ] }, "execution_count": 5, "metadata": { "filenames": { "image/png": "/private/var/folders/_w/bsp9j6414gs4gdlnhhcnqm9c0000gn/T/pytest-of-matthewmckay/pytest-37/test_complex_outputs_unrun_cac0/source/_build/jupyter_execute/complex_outputs_unrun_22_0.png" } }, "output_type": "execute_result" } ], "source": [ "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "sym.rsolve(f,y(n),[1,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive outputs\n", "\n", "## ipywidgets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1337h4x0R", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Layout()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "widgets.Layout(model_id=\"1337h4x0R\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**_some_ markdown**" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Markdown\n", "display(Markdown('**_some_ markdown**'))" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "ipub": { "bibliography": "example.bib", "biboptions": [ "super", "sort" ], "bibstyle": "unsrtnat", "language": "portuges", "listcode": true, "listfigures": true, "listtables": true, "pandoc": { "at_notation": true, "use_numref": true }, "sphinx": { "bib_title": "My Bibliography" }, "titlepage": { "author": "Authors Name", "email": "authors@email.com", "institution": [ "Institution1", "Institution2" ], "logo": "logo_example.png", "subtitle": "Sub-Title", "supervisors": [ "First Supervisor", "Second Supervisor" ], "tagline": "A tagline for the report.", "title": "Main-Title" }, "toc": { "depth": 2 } }, "jupytext": { "notebook_metadata_filter": "ipub" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "example.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": true }, "nav_menu": {}, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "161px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "1337h4x0R": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ����������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/kernel_alias.md�������������������������������������������������������0000664�0000000�0000000�00000000152�14674535606�0021002�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- file_format: mystnb kernelspec: name: other --- # a title ```{code-cell} ipython3 print("hi") ``` ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/latex_build/����������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0020325�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/latex_build/index.ipynb�����������������������������������������������0000664�0000000�0000000�00000001142�14674535606�0022475�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MyST: An example project\n", "\n", "```{toctree}\n", "other\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 } ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/latex_build/other.ipynb�����������������������������������������������0000664�0000000�0000000�00000001656�14674535606�0022521�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(title_ref)=\n", "# Title\n", "\n", "```{contents}\n", "---\n", "depth: 2\n", "---\n", "```\n", "\n", "Content\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 4 } ����������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/merge_streams.ipynb���������������������������������������������������0000664�0000000�0000000�00000003365�14674535606�0021740�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "source": [ "import sys\n", "print('stdout1', file=sys.stdout)\n", "print('stdout2', file=sys.stdout)\n", "print('stderr1', file=sys.stderr)\n", "print('stderr2', file=sys.stderr)\n", "print('stdout3', file=sys.stdout)\n", "print('stderr3', file=sys.stderr)\n", "1" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "stdout1\n", "stdout2\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "stderr1\n", "stderr2\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "stdout3\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "stderr3\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "1" ] }, "metadata": {}, "execution_count": 1 } ], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/merge_streams_parallel.ipynb������������������������������������������0000664�0000000�0000000�00000006136�14674535606�0023613�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-09-19T21:44:29.809012Z", "iopub.status.busy": "2024-09-19T21:44:29.808809Z", "iopub.status.idle": "2024-09-19T21:44:29.978481Z", "shell.execute_reply": "2024-09-19T21:44:29.977891Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from concurrent.futures import ProcessPoolExecutor\n", "\n", "with ProcessPoolExecutor() as executor:\n", " for i in executor.map(print, [0] * 10):\n", " pass" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/metadata_figure.ipynb�������������������������������������������������0000664�0000000�0000000�00000362170�14674535606�0022226�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formatting code outputs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "myst": { "figure": { "caption": "Hey everyone its **party** time!\n", "name": "fun-fish" } } }, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": "<IPython.core.display.Image object>" }, "metadata": {}, "execution_count": 1 } ], "source": [ "from IPython.display import Image\n", "Image(\"fun-fish.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Link: [swim to the fish](fun-fish)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 } ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/metadata_image.ipynb��������������������������������������������������0000664�0000000�0000000�00000362010�14674535606�0022020�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formatting code outputs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "myst": { "image": { "alt": "fun-fish", "classes": "shadow bg-primary", "width": "300px" } } }, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": "<IPython.core.display.Image object>" }, "metadata": {}, "execution_count": 1 } ], "source": [ "from IPython.display import Image\n", "Image(\"fun-fish.png\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 } ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/metadata_image_output.ipynb�������������������������������������������0000664�0000000�0000000�00000231601�14674535606�0023441�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Output metadata" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": ["skip-execution"] }, "outputs": [ { "output_type": "execute_result", "data": { "image/jpeg": 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", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 100, "width": 500 } }, "execution_count": 1 } ], "source": [ "# Outputs included with width/height in output metadata,\n", "# cell is not executed\n", "from IPython.display import Image\n", "Image(filename=\"./example.jpg\", width=500, height=100)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# No outputs, cell is executed, image should have original size (370, 254)\n", "from IPython.display import Image\n", "Image(filename=\"./example.jpg\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "mystnb": { "execution_mode": "force" }, "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 } �������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/mystnb_codecell_file.md�����������������������������������������������0000664�0000000�0000000�00000000356�14674535606�0022524�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- jupytext: text_representation: extension: .md format_name: myst kernelspec: display_name: Python 3 language: python name: python3 author: Matt --- # a title ```{code-cell} ipython3 :load: mystnb_codecell_file.py ``` ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/mystnb_codecell_file.py�����������������������������������������������0000664�0000000�0000000�00000000043�14674535606�0022545�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������# flake8: noqa import numpy as np ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/mystnb_codecell_file_warnings.md��������������������������������������0000664�0000000�0000000�00000000400�14674535606�0024422�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- jupytext: text_representation: extension: .md format_name: myst kernelspec: display_name: Python 3 language: python name: python3 author: Aakash --- # a title ```{code-cell} ipython3 :load: mystnb_codecell_file.py i = 10 print(i) ``` ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/nb_exec_table.md������������������������������������������������������0000664�0000000�0000000�00000000622�14674535606�0021125�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- jupytext: text_representation: extension: .md format_name: myst format_version: '0.8' jupytext_version: 1.4.1+dev kernelspec: display_name: Python 3 language: python name: python3 author: Chris --- # Test the `nb-exec-table` directive ```{code-cell} ipython3 print("hi") ``` This directive should generate a table of executed notebook statistics. ```{nb-exec-table} ``` ��������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/sleep_10.ipynb��������������������������������������������������������0000664�0000000�0000000�00000001322�14674535606�0020502�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "init_cell": true }, "outputs": [], "source": [ "import time\n", "time.sleep(10)" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "execution": {}, "jupytext": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 } ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/sleep_10_metadata_timeout.ipynb���������������������������������������0000664�0000000�0000000�00000001350�14674535606�0024111�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "init_cell": true }, "outputs": [], "source": [ "import time\n", "time.sleep(10)" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "execution": { "timeout": 1 }, "jupytext": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/unknown_mimetype.ipynb������������������������������������������������0000664�0000000�0000000�00000001523�14674535606�0022505�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "source": [ "a=1\n", "print(a)" ], "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "unknown": "" } } ] } ], "metadata": { "test_name": "notebook1", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 } �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/with_eval.md����������������������������������������������������������0000664�0000000�0000000�00000003657�14674535606�0020350�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������--- file_format: mystnb kernelspec: name: python3 mystnb: execution_mode: 'inline' --- # Inline evaluation ```{code-cell} ipython3 a = 1 ``` Evaluated inline variable: {eval}`a` ```{eval} a ``` ```{code-cell} ipython3 import base64 from IPython.display import Image string = "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" img = Image(base64.b64decode(string)) ``` ```{eval:figure} img A caption ``` ���������������������������������������������������������������������������������MyST-NB-1.1.2/tests/notebooks/with_glue.ipynb�������������������������������������������������������0000664�0000000�0000000�00000100523�14674535606�0021064�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Glue Tests\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from myst_nb import glue" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'text1'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "key_text1" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "3.14159" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "key_float" } }, "output_type": "display_data" } ], "source": [ "glue(\"key_text1\", \"text1\")\n", "glue(\"key_float\", 3.14159)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/papermill.record/text/plain": "'undisplayed'" }, "metadata": { "scrapbook": { "mime_prefix": "application/papermill.record/", "name": "key_undisplayed" } }, "output_type": "display_data" } ], "source": [ "glue(\"key_undisplayed\", \"undisplayed\", display=False)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>header</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " header\n", "0 1\n", "1 2\n", "2 3" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "key_df" } }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "df = pd.DataFrame({\"header\": [1, 2, 3]})\n", "glue(\"key_df\", df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/papermill.record/image/png": 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\n", "application/papermill.record/text/plain": "<Figure size 432x288 with 1 Axes>" }, "metadata": { "scrapbook": { "mime_prefix": "application/papermill.record/", "name": "key_plt" } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot([1, 2, 3])\n", "glue(\"key_plt\", plt.gcf(), display=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Referencing the figs\n", "\n", "{glue:any}`key_text1`, {glue:}`key_plt`\n", "\n", "```{glue:any} key_df\n", "```\n", "\n", "and {glue:text}`key_text1` inline...\n", "\n", "and formatted {glue:text}`key_float:.2f`\n", "\n", "```{glue:} key_plt\n", "```\n", "\n", "and {glue:text}`key_undisplayed` inline...\n", "\n", "\n", "```{glue:figure} key_plt\n", ":name: abc\n", "\n", "A caption....\n", "```", "## A test title {glue:any}`key_text1`\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Math" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\left(\\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} - \\frac{2 \\sqrt{5} i}{5}\\right) + \\left(- \\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} + \\frac{2 \\sqrt{5} i}{5}\\right)$" ], "text/plain": [ "(sqrt(5)*I)**\\alpha*(1/2 - 2*sqrt(5)*I/5) + (-sqrt(5)*I)**\\alpha*(1/2 + 2*sqrt(5)*I/5)" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "sym_eq" } }, "output_type": "display_data" } ], "source": [ "import sympy as sym\n", "f = sym.Function('f')\n", "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "glue(\"sym_eq\", sym.rsolve(f,y(n),[1,4]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{glue:math} sym_eq\n", ":label: eq-sym\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_ansi_lexer.py��������������������������������������������������������������0000664�0000000�0000000�00000006150�14674535606�0017572�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������from pygments.token import Text, Token import pytest from myst_nb.core import lexers @pytest.mark.parametrize( ("bold", "faint", "fg_color", "bg_color", "expected"), ( (False, False, False, False, Text), (True, False, False, False, Token.Color.Bold), (True, False, "Red", False, Token.Color.Bold.Red), (True, False, "Red", "Blue", Token.Color.Bold.Red.BGBlue), (True, True, "Red", "Blue", Token.Color.Bold.Faint.Red.BGBlue), ), ) def test_token_from_lexer_state(bold, faint, fg_color, bg_color, expected): ret = lexers._token_from_lexer_state(bold, faint, fg_color, bg_color) assert ret == expected def _highlight(text): return tuple(lexers.AnsiColorLexer().get_tokens(text)) def test_plain_text(): assert _highlight("hello world\n") == ((Text, "hello world\n"),) def test_simple_colors(): assert _highlight( "plain text\n" "\x1b[31mred text\n" "\x1b[1;32mbold green text\n" "\x1b[39mfg color turned off\n" "\x1b[0mplain text after reset\n" "\x1b[1mbold text\n" "\x1b[43mbold from previous line with yellow bg\n" "\x1b[49mbg color turned off\n" "\x1b[2mfaint turned on\n" "\x1b[22mbold turned off\n" ) == ( (Text, "plain text\n"), (Token.Color.Red, "red text\n"), (Token.Color.Bold.Green, "bold green text\n"), (Token.Color.Bold, "fg color turned off\n"), (Text, "plain text after reset\n"), (Token.Color.Bold, "bold text\n"), (Token.Color.Bold.BGYellow, "bold from previous line with yellow bg\n"), (Token.Color.Bold, "bg color turned off\n"), (Token.Color.Bold.Faint, "faint turned on\n"), (Text, "bold turned off\n"), ) def test_highlight_empty_end_specifier(): ret = _highlight("plain\x1b[31mred\x1b[mplain\n") assert ret == ((Text, "plain"), (Token.Color.Red, "red"), (Text, "plain\n")) def test_ignores_unrecognized_ansi_color_codes(): """It should just strip and ignore any unrecognized color ANSI codes.""" assert _highlight( # unknown int code "\x1b[99m" "plain text\n" # invalid non-int code "\x1b[=m" "plain text\n" ) == ( (Text, "plain text\n"), (Text, "plain text\n"), ) def test_ignores_valid_ansi_non_color_codes(): """It should just strip and ignore any non-color ANSI codes. These include things like moving the cursor position, erasing lines, etc. """ assert _highlight( # restore cursor position "\x1b[u" "plain " # move cursor backwards 55 steps "\x1b[55C" "text\n" ) == ( # Ideally these would be just one token, but our regex isn't smart # enough yet. (Text, "plain "), (Text, "text\n"), ) def test_ignores_completely_invalid_escapes(): """It should strip and ignore invalid escapes. This shouldn't happen in valid ANSI text, but we could have an escape followed by garbage. """ assert _highlight("plain \x1b[%text\n") == ( (Text, "plain "), (Text, "%text\n"), ) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_cli.py���������������������������������������������������������������������0000664�0000000�0000000�00000003027�14674535606�0016210�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test the quickstart CLI""" import os from pathlib import Path import nbformat from sphinx import version_info as sphinx_version_info from myst_nb.cli import md_to_nb, quickstart def test_quickstart(tmp_path: Path, make_app): """Test the quickstart CLI builds a valid sphinx project.""" project_path = tmp_path / "project" quickstart([str(project_path)]) assert {p.name for p in project_path.iterdir()} == { ".gitignore", "conf.py", "index.md", "notebook1.ipynb", "notebook2.md", } # For compatibility with multiple versions of sphinx, convert pathlib.Path to # sphinx.testing.path.path here. if sphinx_version_info >= (7, 2): app_srcdir = project_path else: from sphinx.testing.path import path app_srcdir = path(os.fspath(project_path)) app = make_app(srcdir=app_srcdir, buildername="html") app.build() assert app._warning.getvalue().strip() == "" assert (project_path / "_build/html/index.html").exists() def test_md_to_nb(tmp_path: Path): """Test the md_to_nb CLI.""" path = tmp_path / "notebook.md" outpath = path.with_suffix(".ipynb") path.write_text( """\ --- kernelspec: name: python3 --- # Title +++ next cell """, "utf-8", ) md_to_nb([str(path)]) assert path.exists() with outpath.open("r") as handle: nb = nbformat.read(handle, as_version=4) assert nb.metadata == {"kernelspec": {"display_name": "python3", "name": "python3"}} assert len(nb.cells) == 2 ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_codecell_file.py�����������������������������������������������������������0000664�0000000�0000000�00000005574�14674535606�0020223�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test notebooks containing code cells with the `load` option.""" import pytest from sphinx.util.fileutil import copy_asset_file @pytest.mark.sphinx_params( "mystnb_codecell_file.md", conf={"nb_execution_mode": "cache", "source_suffix": {".md": "myst-nb"}}, ) def test_codecell_file(sphinx_run, file_regression, check_nbs, get_test_path): asset_path = get_test_path("mystnb_codecell_file.py") copy_asset_file(str(asset_path), str(sphinx_run.app.srcdir)) sphinx_run.build() assert sphinx_run.warnings() == "" assert set(sphinx_run.env.metadata["mystnb_codecell_file"].keys()) == { "jupytext", "author", "source_map", "wordcount", "kernelspec", "language_info", } assert set(sphinx_run.env.nb_metadata["mystnb_codecell_file"].keys()) == { "exec_data", } assert sphinx_run.env.metadata["mystnb_codecell_file"]["author"] == "Matt" assert sphinx_run.env.metadata["mystnb_codecell_file"]["kernelspec"] == { "display_name": "Python 3", "language": "python", "name": "python3", } try: file_regression.check( sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb", encoding="utf-8", ) finally: file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "mystnb_codecell_file_warnings.md", conf={"nb_execution_mode": "force", "source_suffix": {".md": "myst-nb"}}, ) def test_codecell_file_warnings(sphinx_run, file_regression, check_nbs, get_test_path): asset_path = get_test_path("mystnb_codecell_file.py") copy_asset_file(str(asset_path), str(sphinx_run.app.srcdir)) sphinx_run.build() # assert ( # "mystnb_codecell_file_warnings.md:14 content of code-cell " # "is being overwritten by :load: mystnb_codecell_file.py" # in sphinx_run.warnings() # ) assert set(sphinx_run.env.metadata["mystnb_codecell_file_warnings"].keys()) == { "jupytext", "author", "source_map", "wordcount", "kernelspec", "language_info", } assert set(sphinx_run.env.nb_metadata["mystnb_codecell_file_warnings"].keys()) == { "exec_data", } assert ( sphinx_run.env.metadata["mystnb_codecell_file_warnings"]["author"] == "Aakash" ) assert sphinx_run.env.metadata["mystnb_codecell_file_warnings"]["kernelspec"] == { "display_name": "Python 3", "language": "python", "name": "python3", } try: file_regression.check( sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb", encoding="utf-8", ) finally: file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) ������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_codecell_file/�������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0017636�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_codecell_file/test_codecell_file.ipynb�������������������������������������0000664�0000000�0000000�00000001672�14674535606�0024517�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "id": "77fd61dd", "metadata": {}, "source": [ "# a title" ] }, { "cell_type": "code", "execution_count": 1, "id": "2e32aa7d", "metadata": { "load": "mystnb_codecell_file.py" }, "outputs": [], "source": [ "# flake8: noqa\n", "\n", "import numpy as np\n" ] } ], "metadata": { "author": "Matt", "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "source_map": [ 11, 15 ] }, "nbformat": 4, "nbformat_minor": 5 } ����������������������������������������������������������������������MyST-NB-1.1.2/tests/test_codecell_file/test_codecell_file.xml���������������������������������������0000664�0000000�0000000�00000000771�14674535606�0024175�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="mystnb_codecell_file"> <section ids="a-title" names="a\ title"> <title> a title <container cell_index="1" cell_metadata="{'load': 'mystnb_codecell_file.py'}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> # flake8: noqa import numpy as np �������MyST-NB-1.1.2/tests/test_codecell_file/test_codecell_file_warnings.ipynb����������������������������0000664�0000000�0000000�00000001674�14674535606�0026431�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "id": "daa53dc3", "metadata": {}, "source": [ "# a title" ] }, { "cell_type": "code", "execution_count": 1, "id": "d218bd29", "metadata": { "load": "mystnb_codecell_file.py" }, "outputs": [], "source": [ "# flake8: noqa\n", "\n", "import numpy as np\n" ] } ], "metadata": { "author": "Aakash", "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "source_map": [ 11, 15 ] }, "nbformat": 4, "nbformat_minor": 5 } ��������������������������������������������������������������������MyST-NB-1.1.2/tests/test_codecell_file/test_codecell_file_warnings.xml������������������������������0000664�0000000�0000000�00000001002�14674535606�0026071�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="mystnb_codecell_file_warnings"> <section ids="a-title" names="a\ title"> <title> a title <container cell_index="1" cell_metadata="{'load': 'mystnb_codecell_file.py'}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> # flake8: noqa import numpy as np ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_docutils.py����������������������������������������������������������������0000664�0000000�0000000�00000005226�14674535606�0017272�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Run parsing tests against the docutils parser.""" from io import StringIO import json from pathlib import Path from docutils.core import publish_doctree, publish_string import pytest import sphinx import yaml from myst_nb.docutils_ import Parser FIXTURE_PATH = Path(__file__).parent.joinpath("nb_fixtures") @pytest.mark.param_file(FIXTURE_PATH / "basic.txt") def test_basic(file_params): """Test basic parsing.""" if ( "Footnote definitions defined in different cells" in file_params.title and sphinx.version_info[0] < 5 ): pytest.skip("footnote definition ids changes") dct = yaml.safe_load(file_params.content) dct.update({"nbformat": 4, "nbformat_minor": 4}) dct.setdefault("metadata", {}) dct["metadata"].setdefault( "kernelspec", {"name": "python3", "display_name": "Python 3", "language": ""} ) report_stream = StringIO() doctree = publish_doctree( json.dumps(dct), parser=Parser(), settings_overrides={ "nb_execution_mode": "off", "nb_output_folder": "", "myst_all_links_external": True, "warning_stream": report_stream, }, ) assert report_stream.getvalue().rstrip() == "" file_params.assert_expected(doctree.pformat(), rstrip=True) @pytest.mark.param_file(FIXTURE_PATH / "reporter_warnings.txt") def test_reporting(file_params): """Test that warnings and errors are reported as expected.""" dct = yaml.safe_load(file_params.content) dct.update({"metadata": {}, "nbformat": 4, "nbformat_minor": 4}) report_stream = StringIO() publish_doctree( json.dumps(dct), parser=Parser(), settings_overrides={ "nb_execution_mode": "off", "nb_output_folder": "", "warning_stream": report_stream, }, ) file_params.assert_expected(report_stream.getvalue(), rstrip=True) def test_html_resources(tmp_path): """Test HTML resources are correctly output.""" report_stream = StringIO() result = publish_string( json.dumps({"cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 4}), parser=Parser(), writer_name="html", settings_overrides={ "nb_execution_mode": "off", "nb_output_folder": str(tmp_path), "warning_stream": report_stream, "output_encoding": "unicode", "embed_stylesheet": False, }, ) assert report_stream.getvalue().rstrip() == "" assert "mystnb.css" in result assert "pygments.css" in result assert tmp_path.joinpath("mystnb.css").is_file() assert tmp_path.joinpath("pygments.css").is_file() ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_eval.py��������������������������������������������������������������������0000664�0000000�0000000�00000001013�14674535606�0016361�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test the `eval` directives and roles.""" import pytest @pytest.mark.sphinx_params("with_eval.md", conf={"nb_execution_mode": "inline"}) def test_sphinx(sphinx_run, clean_doctree, file_regression): """Test a sphinx build.""" sphinx_run.build() # print(sphinx_run.status()) # print(sphinx_run.warnings()) assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("with_eval")) file_regression.check( doctree.pformat(), encoding="utf-8", ) ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_eval/����������������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0016014�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_eval/test_sphinx.txt�������������������������������������������������������0000664�0000000�0000000�00000005745�14674535606�0021140�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="with_eval"> <section ids="inline-evaluation" names="inline\ evaluation"> <title> Inline evaluation <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> a = 1 <paragraph> Evaluated inline variable: <inline classes="output text_plain"> 1 <literal_block classes="output text_plain" language="myst-ansi" linenos="False" xml:space="preserve"> 1 <container cell_index="3" cell_metadata="{}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import base64 from IPython.display import Image string = "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" img = Image(base64.b64decode(string)) <figure ids="id1"> <image candidates="{'*': '_build/jupyter_execute/20d976ca74932f09b2e705bb1f2c57e10e6117b273b301c48a4fbfd32dc1a69b.png'}" uri="_build/jupyter_execute/20d976ca74932f09b2e705bb1f2c57e10e6117b273b301c48a4fbfd32dc1a69b.png"> <caption> A caption ���������������������������MyST-NB-1.1.2/tests/test_execute.py�����������������������������������������������������������������0000664�0000000�0000000�00000032030�14674535606�0017077�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test sphinx builds which execute notebooks.""" import os from pathlib import Path from IPython import version_info as ipy_version import pytest from myst_nb.core.execute import ExecutionError from myst_nb.sphinx_ import NbMetadataCollector def regress_nb_doc(file_regression, sphinx_run, check_nbs): try: file_regression.check( sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb", encoding="utf-8", ) finally: doctree_string = sphinx_run.get_doctree().pformat() # TODO this is a difference in the hashing on the CI, # with complex_outputs_unrun.ipynb equation PNG, after execution # sympy doctree_string = doctree_string.replace( "91b3db0f47514d451e7c0f5a501d31c4e101b7112050634ad0788405f417782a", "e2dfbe330154316cfb6f3186e8f57fc4df8aee03b0303ed1345fc22cd51f66de", ) doctree_string = doctree_string.replace( "438c56ea3dcf99d86cd64df1b23e2b436afb25846434efb1cfec7b660ef01127", "e2dfbe330154316cfb6f3186e8f57fc4df8aee03b0303ed1345fc22cd51f66de", ) if os.name == "nt": # on Windows image file paths are absolute doctree_string = doctree_string.replace( Path(sphinx_run.app.srcdir).as_posix() + "/", "" ) file_regression.check(doctree_string, extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "auto"}) def test_basic_unrun_auto(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] regress_nb_doc(file_regression, sphinx_run, check_nbs) assert NbMetadataCollector.new_exec_data(sphinx_run.env) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_unrun") assert data assert data["method"] == "auto" assert data["succeeded"] is True @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "cache"}) def test_basic_unrun_cache(sphinx_run, file_regression, check_nbs): """The outputs should be populated.""" sphinx_run.build() assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] regress_nb_doc(file_regression, sphinx_run, check_nbs) assert NbMetadataCollector.new_exec_data(sphinx_run.env) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_unrun") assert data assert data["method"] == "cache" assert data["succeeded"] is True @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "inline"}) def test_basic_unrun_inline(sphinx_run, file_regression, check_nbs): """The outputs should be populated.""" sphinx_run.build() assert sphinx_run.warnings() == "" assert "test_name" in sphinx_run.app.env.metadata["basic_unrun"] regress_nb_doc(file_regression, sphinx_run, check_nbs) assert NbMetadataCollector.new_exec_data(sphinx_run.env) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_unrun") assert data assert data["method"] == "inline" assert data["succeeded"] is True @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "cache"}) def test_rebuild_cache(sphinx_run): """The notebook should only be executed once.""" sphinx_run.build() assert NbMetadataCollector.new_exec_data(sphinx_run.env) sphinx_run.invalidate_files() sphinx_run.build() assert "Using cached" in sphinx_run.status() @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "force"}) def test_rebuild_force(sphinx_run): """The notebook should be executed twice.""" sphinx_run.build() assert NbMetadataCollector.new_exec_data(sphinx_run.env) sphinx_run.invalidate_files() sphinx_run.build() assert NbMetadataCollector.new_exec_data(sphinx_run.env) @pytest.mark.sphinx_params( "basic_unrun.ipynb", conf={ "nb_execution_mode": "cache", "nb_execution_excludepatterns": ["basic_*"], }, ) def test_exclude_path(sphinx_run, file_regression): """The notebook should not be executed.""" sphinx_run.build() assert not NbMetadataCollector.new_exec_data(sphinx_run.env) assert "Executing" not in sphinx_run.status(), sphinx_run.status() file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) @pytest.mark.skipif(ipy_version[0] < 8, reason="Error message changes for ipython v8") @pytest.mark.sphinx_params("basic_failing.ipynb", conf={"nb_execution_mode": "cache"}) def test_basic_failing_cache(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.warnings()) assert "Executing notebook failed" in sphinx_run.warnings() regress_nb_doc(file_regression, sphinx_run, check_nbs) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_failing") assert data assert data["method"] == "cache" assert data["succeeded"] is False sphinx_run.get_report_file() @pytest.mark.skipif(ipy_version[0] < 8, reason="Error message changes for ipython v8") @pytest.mark.sphinx_params("basic_failing.ipynb", conf={"nb_execution_mode": "auto"}) def test_basic_failing_auto(sphinx_run, file_regression, check_nbs): sphinx_run.build() assert "Executing notebook failed" in sphinx_run.warnings() regress_nb_doc(file_regression, sphinx_run, check_nbs) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_failing") assert data assert data["method"] == "auto" assert data["succeeded"] is False assert data["traceback"] sphinx_run.get_report_file() @pytest.mark.skipif(ipy_version[0] < 8, reason="Error message changes for ipython v8") @pytest.mark.sphinx_params("basic_failing.ipynb", conf={"nb_execution_mode": "inline"}) def test_basic_failing_inline(sphinx_run, file_regression, check_nbs): sphinx_run.build() assert "Executing notebook failed" in sphinx_run.warnings() regress_nb_doc(file_regression, sphinx_run, check_nbs) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "basic_failing") assert data assert data["method"] == "inline" assert data["succeeded"] is False assert data["traceback"] sphinx_run.get_report_file() @pytest.mark.skipif(ipy_version[0] < 8, reason="Error message changes for ipython v8") @pytest.mark.sphinx_params( "basic_failing.ipynb", conf={"nb_execution_mode": "cache", "nb_execution_allow_errors": True}, ) def test_allow_errors_cache(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert not sphinx_run.warnings() regress_nb_doc(file_regression, sphinx_run, check_nbs) @pytest.mark.skipif(ipy_version[0] < 8, reason="Error message changes for ipython v8") @pytest.mark.sphinx_params( "basic_failing.ipynb", conf={"nb_execution_mode": "auto", "nb_execution_allow_errors": True}, ) def test_allow_errors_auto(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert not sphinx_run.warnings() regress_nb_doc(file_regression, sphinx_run, check_nbs) @pytest.mark.sphinx_params( "basic_failing.ipynb", conf={"nb_execution_raise_on_error": True, "nb_execution_mode": "force"}, ) def test_raise_on_error_force(sphinx_run): with pytest.raises(ExecutionError, match="basic_failing.ipynb"): sphinx_run.build() @pytest.mark.sphinx_params( "basic_failing.ipynb", conf={"nb_execution_raise_on_error": True, "nb_execution_mode": "cache"}, ) def test_raise_on_error_cache(sphinx_run): with pytest.raises(ExecutionError, match="basic_failing.ipynb"): sphinx_run.build() @pytest.mark.sphinx_params( "complex_outputs_unrun.ipynb", conf={"nb_execution_mode": "cache"} ) def test_complex_outputs_unrun_cache(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) # Widget view and widget state should make it into the HTML scripts = sphinx_run.get_html().select("script") assert any( "application/vnd.jupyter.widget-view+json" in script.get("type", "") for script in scripts ) assert any( "application/vnd.jupyter.widget-state+json" in script.get("type", "") for script in scripts ) @pytest.mark.sphinx_params( "complex_outputs_unrun.ipynb", conf={"nb_execution_mode": "auto"} ) def test_complex_outputs_unrun_auto(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) # Widget view and widget state should make it into the HTML scripts = sphinx_run.get_html().select("script") assert any( "application/vnd.jupyter.widget-view+json" in script.get("type", "") for script in scripts ) assert any( "application/vnd.jupyter.widget-state+json" in script.get("type", "") for script in scripts ) @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "off"}) def test_no_execute(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) @pytest.mark.sphinx_params("basic_unrun.ipynb", conf={"nb_execution_mode": "cache"}) def test_jupyter_cache_path(sphinx_run, file_regression, check_nbs): sphinx_run.build() assert "Cached executed notebook" in sphinx_run.status() assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) # Testing relative paths within the notebook @pytest.mark.sphinx_params("basic_relative.ipynb", conf={"nb_execution_mode": "cache"}) def test_relative_path_cache(sphinx_run): sphinx_run.build() assert "Execution Failed" not in sphinx_run.status(), sphinx_run.status() @pytest.mark.sphinx_params("basic_relative.ipynb", conf={"nb_execution_mode": "force"}) def test_relative_path_force(sphinx_run): sphinx_run.build() assert "Execution Failed" not in sphinx_run.status(), sphinx_run.status() @pytest.mark.sphinx_params( "kernel_alias.md", conf={"nb_execution_mode": "force", "nb_kernel_rgx_aliases": {"oth.+": "python3"}}, ) def test_kernel_rgx_aliases(sphinx_run): sphinx_run.build() assert sphinx_run.warnings() == "" @pytest.mark.sphinx_params( "sleep_10.ipynb", conf={"nb_execution_mode": "cache", "nb_execution_timeout": 1}, ) def test_execution_timeout(sphinx_run): """execution should fail given the low timeout value""" sphinx_run.build() # print(sphinx_run.warnings()) assert "Executing notebook failed" in sphinx_run.warnings() @pytest.mark.sphinx_params( "sleep_10_metadata_timeout.ipynb", conf={"nb_execution_mode": "cache", "nb_execution_timeout": 60}, ) def test_execution_metadata_timeout(sphinx_run): """notebook timeout metadata has higher preference then execution_timeout config""" sphinx_run.build() # print(sphinx_run.warnings()) assert "Executing notebook failed" in sphinx_run.warnings() @pytest.mark.sphinx_params( "nb_exec_table.md", conf={"nb_execution_mode": "auto"}, ) def test_nb_exec_table(sphinx_run, file_regression): """Test that the table gets output into the HTML, including a row for the executed notebook. """ sphinx_run.build() # print(sphinx_run.status()) assert not sphinx_run.warnings() file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) # print(sphinx_run.get_html()) rows = sphinx_run.get_html().select("table.docutils tr") assert any("nb_exec_table" in row.text for row in rows) @pytest.mark.sphinx_params( "custom-formats.Rmd", conf={ "nb_execution_mode": "auto", "nb_custom_formats": {".Rmd": ["jupytext.reads", {"fmt": "Rmd"}]}, }, ) def test_custom_convert_auto(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) assert NbMetadataCollector.new_exec_data(sphinx_run.env) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "custom-formats") assert data assert data["method"] == "auto" assert data["succeeded"] is True @pytest.mark.sphinx_params( "custom-formats.Rmd", conf={ "nb_execution_mode": "cache", "nb_custom_formats": {".Rmd": ["jupytext.reads", {"fmt": "Rmd"}]}, }, ) def test_custom_convert_cache(sphinx_run, file_regression, check_nbs): """The outputs should be populated.""" sphinx_run.build() assert sphinx_run.warnings() == "" regress_nb_doc(file_regression, sphinx_run, check_nbs) assert NbMetadataCollector.new_exec_data(sphinx_run.env) data = NbMetadataCollector.get_exec_data(sphinx_run.env, "custom-formats") assert data assert data["method"] == "cache" assert data["succeeded"] is True ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/�������������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0016527�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_allow_errors_auto.ipynb���������������������������������������0000664�0000000�0000000�00000002540�14674535606�0024374�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "Exception", "evalue": "oopsie!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moopsie!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mException\u001b[0m: oopsie!" ] } ], "source": [ "raise Exception('oopsie!')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_allow_errors_auto.xml�����������������������������������������0000664�0000000�0000000�00000001726�14674535606�0024060�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_failing"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> raise Exception('oopsie!') <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output traceback" language="ipythontb" xml:space="preserve"> --------------------------------------------------------------------------- Exception Traceback (most recent call last) Cell In[1], line 1 ----> 1 raise Exception('oopsie!') Exception: oopsie! ������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_allow_errors_cache.ipynb��������������������������������������0000664�0000000�0000000�00000002540�14674535606�0024467�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "Exception", "evalue": "oopsie!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moopsie!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mException\u001b[0m: oopsie!" ] } ], "source": [ "raise Exception('oopsie!')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_allow_errors_cache.xml����������������������������������������0000664�0000000�0000000�00000001726�14674535606�0024153�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_failing"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> raise Exception('oopsie!') <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output traceback" language="ipythontb" xml:space="preserve"> --------------------------------------------------------------------------- Exception Traceback (most recent call last) Cell In[1], line 1 ----> 1 raise Exception('oopsie!') Exception: oopsie! ������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_auto.ipynb��������������������������������������0000664�0000000�0000000�00000002540�14674535606�0024434�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "Exception", "evalue": "oopsie!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moopsie!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mException\u001b[0m: oopsie!" ] } ], "source": [ "raise Exception('oopsie!')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_auto.xml����������������������������������������0000664�0000000�0000000�00000001726�14674535606�0024120�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_failing"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> raise Exception('oopsie!') <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output traceback" language="ipythontb" xml:space="preserve"> --------------------------------------------------------------------------- Exception Traceback (most recent call last) Cell In[1], line 1 ----> 1 raise Exception('oopsie!') Exception: oopsie! ������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_cache.ipynb�������������������������������������0000664�0000000�0000000�00000002540�14674535606�0024527�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "Exception", "evalue": "oopsie!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moopsie!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mException\u001b[0m: oopsie!" ] } ], "source": [ "raise Exception('oopsie!')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_cache.xml���������������������������������������0000664�0000000�0000000�00000001726�14674535606�0024213�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_failing"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> raise Exception('oopsie!') <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output traceback" language="ipythontb" xml:space="preserve"> --------------------------------------------------------------------------- Exception Traceback (most recent call last) Cell In[1], line 1 ----> 1 raise Exception('oopsie!') Exception: oopsie! ������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_inline.ipynb������������������������������������0000664�0000000�0000000�00000002540�14674535606�0024742�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "Exception", "evalue": "oopsie!", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moopsie!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mException\u001b[0m: oopsie!" ] } ], "source": [ "raise Exception('oopsie!')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_failing_inline.xml��������������������������������������0000664�0000000�0000000�00000001726�14674535606�0024426�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_failing"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> raise Exception('oopsie!') <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output traceback" language="ipythontb" xml:space="preserve"> --------------------------------------------------------------------------- Exception Traceback (most recent call last) Cell In[1], line 1 ----> 1 raise Exception('oopsie!') Exception: oopsie! ������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_auto.ipynb����������������������������������������0000664�0000000�0000000�00000001510�14674535606�0024166�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_auto.xml������������������������������������������0000664�0000000�0000000�00000001223�14674535606�0023646�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_cache.ipynb���������������������������������������0000664�0000000�0000000�00000001510�14674535606�0024261�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_cache.xml�����������������������������������������0000664�0000000�0000000�00000001223�14674535606�0023741�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_inline.ipynb��������������������������������������0000664�0000000�0000000�00000001510�14674535606�0024474�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_basic_unrun_inline.xml����������������������������������������0000664�0000000�0000000�00000001223�14674535606�0024154�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_complex_outputs_unrun_auto.ipynb������������������������������0000664�0000000�0000000�00000044023�14674535606�0026365�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sympy as sym\n", "sym.init_printing(use_latex=True)\n", "import numpy as np\n", "from IPython.display import Image, Latex" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "# Markdown" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## General" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "Some markdown text.\n", "\n", "A list:\n", "\n", "- something\n", "- something else\n", "\n", "A numbered list\n", "\n", "1. something\n", "2. something else\n", "\n", "non-ascii characters TODO" ] }, { "cell_type": "markdown", "metadata": { "ipub": {} }, "source": [ "This is a long section of text, which we only want in a document (not a presentation)\n", "some text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true, "slideonly": true } }, "source": [ "This is an abbreviated section of the document text, which we only want in a presentation\n", "\n", "- summary of document text" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## References and Citations" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "References to \\cref{fig:example}, \\cref{tbl:example}, =@eqn:example_sympy and \\cref{code:example_mpl}.\n", "\n", "A latex citation.\\cite{zelenyak_molecular_2016}\n", "\n", "A html citation.<cite data-cite=\"kirkeminde_thermodynamic_2012\">(Kirkeminde, 2012)</cite> " ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## Todo notes" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "\\todo[inline]{an inline todo}\n", "\n", "Some text.\\todo{a todo in the margins}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text Output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ipub": { "text": { "format": { "backgroundcolor": "\\color{blue!10}" } } } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\n" ] } ], "source": [ "print(\"\"\"\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Images and Figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying a plot with its code" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "fig:example_mpl" } }, "source": [ "A matplotlib figure, with the caption set in the markdowncell above the figure." ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_mpl" } }, "source": [ "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tables (with pandas)" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_pd" } }, "source": [ "The plotting code for a pandas Dataframe table (\\cref{tbl:example})." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_pd", "placement": "H", "widefigure": false }, "table": { "alternate": "gray!20", "caption": "An example of a table created with pandas dataframe.", "label": "tbl:example", "placement": "H" } } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b</th>\n", " <th>c</th>\n", " <th>d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>$\\delta$</td>\n", " <td>l</td>\n", " <td>0.603</td>\n", " <td>0.545</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>x</td>\n", " <td>m</td>\n", " <td>0.438</td>\n", " <td>0.892</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>y</td>\n", " <td>n</td>\n", " <td>0.792</td>\n", " <td>0.529</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a b c d\n", "0 $\\delta$ l 0.603 0.545\n", "1 x m 0.438 0.892\n", "2 y n 0.792 0.529" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(0) \n", "df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d'])\n", "df.a = [r'$\\delta$','x','y']\n", "df.b = ['l','m','n']\n", "df.set_index(['a','b'])\n", "df.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Equations (with ipython or sympy)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ipub": { "equation": { "label": "eqn:example_ipy" } } }, "outputs": [ { "data": { "text/latex": [ "$$ a = b+c $$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Latex('$$ a = b+c $$')" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_sym" } }, "source": [ "The plotting code for a sympy equation (=@eqn:example_sympy)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_sym", "placement": "H", "widefigure": false }, "equation": { "environment": "equation", "label": "eqn:example_sympy" } } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle \\left(\\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} - \\frac{2 \\sqrt{5} i}{5}\\right) + \\left(- \\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} + \\frac{2 \\sqrt{5} i}{5}\\right)$" ], "text/plain": [ " \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž\n", "(โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ\n", " โŽ2 5 โŽ  โŽ2 5 โŽ " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "sym.rsolve(f,y(n),[1,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive outputs\n", "\n", "## ipywidgets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1337h4x0R", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Layout()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipywidgets as widgets\n", "widgets.Layout(model_id=\"1337h4x0R\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**_some_ markdown**" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Markdown\n", "display(Markdown('**_some_ markdown**'))" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "ipub": { "bibliography": "example.bib", "biboptions": [ "super", "sort" ], "bibstyle": "unsrtnat", "language": "portuges", "listcode": true, "listfigures": true, "listtables": true, "pandoc": { "at_notation": true, "use_numref": true }, "sphinx": { "bib_title": "My Bibliography" }, "titlepage": { "author": "Authors Name", "email": "authors@email.com", "institution": [ "Institution1", "Institution2" ], "logo": "logo_example.png", "subtitle": "Sub-Title", "supervisors": [ "First Supervisor", "Second Supervisor" ], "tagline": "A tagline for the report.", "title": "Main-Title" }, "toc": { "depth": 2 } }, "jupytext": { "notebook_metadata_filter": "ipub" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "example.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": true }, "nav_menu": {}, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "161px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "1337h4x0R": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_complex_outputs_unrun_auto.xml��������������������������������0000664�0000000�0000000�00000033224�14674535606�0026045�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="complex_outputs_unrun"> <container cell_index="0" cell_metadata="{'init_cell': True, 'slideshow': {'slide_type': 'skip'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import matplotlib.pyplot as plt import pandas as pd import sympy as sym sym.init_printing(use_latex=True) import numpy as np from IPython.display import Image, Latex <section ids="markdown" names="markdown"> <title> Markdown <section ids="general" names="general"> <title> General <paragraph> Some markdown text. <paragraph> A list: <bullet_list bullet="-"> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> A numbered list <enumerated_list enumtype="arabic" prefix="" suffix="."> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> non-ascii characters TODO <paragraph> This is a long section of text, which we only want in a document (not a presentation) some text some more text some more text some more text some more text some more text some more text some more text some more text <paragraph> This is an abbreviated section of the document text, which we only want in a presentation <bullet_list bullet="-"> <list_item> <paragraph> summary of document text <section ids="references-and-citations" names="references\ and\ citations"> <title> References and Citations <paragraph> References to \cref{fig:example}, \cref{tbl:example}, =@eqn:example_sympy and \cref{code:example_mpl}. <paragraph> A latex citation.\cite{zelenyak_molecular_2016} <paragraph> A html citation. <raw format="html" xml:space="preserve"> <cite data-cite="kirkeminde_thermodynamic_2012"> (Kirkeminde, 2012) <raw format="html" xml:space="preserve"> </cite> <section ids="todo-notes" names="todo\ notes"> <title> Todo notes <paragraph> \todo[inline]{an inline todo} <paragraph> Some text.\todo{a todo in the margins} <section ids="text-output" names="text\ output"> <title> Text Output <container cell_index="11" cell_metadata="{'ipub': {'text': {'format': {'backgroundcolor': '\\color{blue!10}'}}}}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> print(""" This is some printed text, with a nicely formatted output. """) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> This is some printed text, with a nicely formatted output. <section ids="images-and-figures" names="images\ and\ figures"> <title> Images and Figures <section ids="displaying-a-plot-with-its-code" names="displaying\ a\ plot\ with\ its\ code"> <title> Displaying a plot with its code <paragraph> A matplotlib figure, with the caption set in the markdowncell above the figure. <paragraph> The plotting code for a matplotlib figure (\cref{fig:example_mpl}). <section ids="tables-with-pandas" names="tables\ (with\ pandas)"> <title> Tables (with pandas) <paragraph> The plotting code for a pandas Dataframe table (\cref{tbl:example}). <container cell_index="18" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_pd', 'placement': 'H', 'widefigure': False}, 'table': {'alternate': 'gray!20', 'caption': 'An example of a table created with pandas dataframe.', 'label': 'tbl:example', 'placement': 'H'}}}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> np.random.seed(0) df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d']) df.a = [r'$\delta$','x','y'] df.b = ['l','m','n'] df.set_index(['a','b']) df.round(3) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> a b c d 0 $\delta$ l 0.603 0.545 1 x m 0.438 0.892 2 y n 0.792 0.529 <container mime_type="text/html"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>a</th> <th>b</th> <th>c</th> <th>d</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>$\delta$</td> <td>l</td> <td>0.603</td> <td>0.545</td> </tr> <tr> <th>1</th> <td>x</td> <td>m</td> <td>0.438</td> <td>0.892</td> </tr> <tr> <th>2</th> <td>y</td> <td>n</td> <td>0.792</td> <td>0.529</td> </tr> </tbody> </table> </div> <section ids="equations-with-ipython-or-sympy" names="equations\ (with\ ipython\ or\ sympy)"> <title> Equations (with ipython or sympy) <container cell_index="20" cell_metadata="{'ipub': {'equation': {'label': 'eqn:example_ipy'}}}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> Latex('$$ a = b+c $$') <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Latex object> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> a = b+c <paragraph> The plotting code for a sympy equation (=@eqn:example_sympy). <container cell_index="22" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_sym', 'placement': 'H', 'widefigure': False}, 'equation': {'environment': 'equation', 'label': 'eqn:example_sympy'}}}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) sym.rsolve(f,y(n),[1,4]) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž (โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ โŽ2 5 โŽ  โŽ2 5 โŽ  <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/9bc81205a14646a235d284d1b68223d17f30f7f1d3d8ed3e52cf47830b02e3bb.png'}" uri="_build/jupyter_execute/9bc81205a14646a235d284d1b68223d17f30f7f1d3d8ed3e52cf47830b02e3bb.png"> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right) <section ids="interactive-outputs" names="interactive\ outputs"> <title> Interactive outputs <section ids="ipywidgets" names="ipywidgets"> <title> ipywidgets <container cell_index="24" cell_metadata="{}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import ipywidgets as widgets widgets.Layout(model_id="1337h4x0R") <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> Layout() <container mime_type="application/vnd.jupyter.widget-view+json"> <raw format="html" xml:space="preserve"> <script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "1337h4x0R"}</script> <container cell_index="25" cell_metadata="{}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> from IPython.display import display, Markdown display(Markdown('**_some_ markdown**')) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Markdown object> <container mime_type="text/markdown"> <paragraph> <strong> <emphasis> some markdown ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_complex_outputs_unrun_cache.ipynb�����������������������������0000664�0000000�0000000�00000044023�14674535606�0026460�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sympy as sym\n", "sym.init_printing(use_latex=True)\n", "import numpy as np\n", "from IPython.display import Image, Latex" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "# Markdown" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## General" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "Some markdown text.\n", "\n", "A list:\n", "\n", "- something\n", "- something else\n", "\n", "A numbered list\n", "\n", "1. something\n", "2. something else\n", "\n", "non-ascii characters TODO" ] }, { "cell_type": "markdown", "metadata": { "ipub": {} }, "source": [ "This is a long section of text, which we only want in a document (not a presentation)\n", "some text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n", "some more text\n" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true, "slideonly": true } }, "source": [ "This is an abbreviated section of the document text, which we only want in a presentation\n", "\n", "- summary of document text" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## References and Citations" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "References to \\cref{fig:example}, \\cref{tbl:example}, =@eqn:example_sympy and \\cref{code:example_mpl}.\n", "\n", "A latex citation.\\cite{zelenyak_molecular_2016}\n", "\n", "A html citation.<cite data-cite=\"kirkeminde_thermodynamic_2012\">(Kirkeminde, 2012)</cite> " ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "## Todo notes" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "slide": true } }, "source": [ "\\todo[inline]{an inline todo}\n", "\n", "Some text.\\todo{a todo in the margins}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text Output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ipub": { "text": { "format": { "backgroundcolor": "\\color{blue!10}" } } } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\n" ] } ], "source": [ "print(\"\"\"\n", "This is some printed text,\n", "with a nicely formatted output.\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Images and Figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying a plot with its code" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "fig:example_mpl" } }, "source": [ "A matplotlib figure, with the caption set in the markdowncell above the figure." ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_mpl" } }, "source": [ "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tables (with pandas)" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_pd" } }, "source": [ "The plotting code for a pandas Dataframe table (\\cref{tbl:example})." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_pd", "placement": "H", "widefigure": false }, "table": { "alternate": "gray!20", "caption": "An example of a table created with pandas dataframe.", "label": "tbl:example", "placement": "H" } } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b</th>\n", " <th>c</th>\n", " <th>d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>$\\delta$</td>\n", " <td>l</td>\n", " <td>0.603</td>\n", " <td>0.545</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>x</td>\n", " <td>m</td>\n", " <td>0.438</td>\n", " <td>0.892</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>y</td>\n", " <td>n</td>\n", " <td>0.792</td>\n", " <td>0.529</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a b c d\n", "0 $\\delta$ l 0.603 0.545\n", "1 x m 0.438 0.892\n", "2 y n 0.792 0.529" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(0) \n", "df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d'])\n", "df.a = [r'$\\delta$','x','y']\n", "df.b = ['l','m','n']\n", "df.set_index(['a','b'])\n", "df.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Equations (with ipython or sympy)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ipub": { "equation": { "label": "eqn:example_ipy" } } }, "outputs": [ { "data": { "text/latex": [ "$$ a = b+c $$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Latex('$$ a = b+c $$')" ] }, { "cell_type": "markdown", "metadata": { "ipub": { "caption": "code:example_sym" } }, "source": [ "The plotting code for a sympy equation (=@eqn:example_sympy)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ipub": { "code": { "asfloat": true, "caption": "", "label": "code:example_sym", "placement": "H", "widefigure": false }, "equation": { "environment": "equation", "label": "eqn:example_sympy" } } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle \\left(\\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} - \\frac{2 \\sqrt{5} i}{5}\\right) + \\left(- \\sqrt{5} i\\right)^{\\alpha} \\left(\\frac{1}{2} + \\frac{2 \\sqrt{5} i}{5}\\right)$" ], "text/plain": [ " \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \\alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž\n", "(โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ\n", " โŽ2 5 โŽ  โŽ2 5 โŽ " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = sym.Function('y')\n", "n = sym.symbols(r'\\alpha')\n", "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n", "sym.rsolve(f,y(n),[1,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive outputs\n", "\n", "## ipywidgets" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1337h4x0R", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Layout()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipywidgets as widgets\n", "widgets.Layout(model_id=\"1337h4x0R\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**_some_ markdown**" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Markdown\n", "display(Markdown('**_some_ markdown**'))" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "hide_input": false, "ipub": { "bibliography": "example.bib", "biboptions": [ "super", "sort" ], "bibstyle": "unsrtnat", "language": "portuges", "listcode": true, "listfigures": true, "listtables": true, "pandoc": { "at_notation": true, "use_numref": true }, "sphinx": { "bib_title": "My Bibliography" }, "titlepage": { "author": "Authors Name", "email": "authors@email.com", "institution": [ "Institution1", "Institution2" ], "logo": "logo_example.png", "subtitle": "Sub-Title", "supervisors": [ "First Supervisor", "Second Supervisor" ], "tagline": "A tagline for the report.", "title": "Main-Title" }, "toc": { "depth": 2 } }, "jupytext": { "notebook_metadata_filter": "ipub" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "example.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": true }, "nav_menu": {}, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "161px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "1337h4x0R": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_complex_outputs_unrun_cache.xml�������������������������������0000664�0000000�0000000�00000033224�14674535606�0026140�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="complex_outputs_unrun"> <container cell_index="0" cell_metadata="{'init_cell': True, 'slideshow': {'slide_type': 'skip'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import matplotlib.pyplot as plt import pandas as pd import sympy as sym sym.init_printing(use_latex=True) import numpy as np from IPython.display import Image, Latex <section ids="markdown" names="markdown"> <title> Markdown <section ids="general" names="general"> <title> General <paragraph> Some markdown text. <paragraph> A list: <bullet_list bullet="-"> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> A numbered list <enumerated_list enumtype="arabic" prefix="" suffix="."> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> non-ascii characters TODO <paragraph> This is a long section of text, which we only want in a document (not a presentation) some text some more text some more text some more text some more text some more text some more text some more text some more text <paragraph> This is an abbreviated section of the document text, which we only want in a presentation <bullet_list bullet="-"> <list_item> <paragraph> summary of document text <section ids="references-and-citations" names="references\ and\ citations"> <title> References and Citations <paragraph> References to \cref{fig:example}, \cref{tbl:example}, =@eqn:example_sympy and \cref{code:example_mpl}. <paragraph> A latex citation.\cite{zelenyak_molecular_2016} <paragraph> A html citation. <raw format="html" xml:space="preserve"> <cite data-cite="kirkeminde_thermodynamic_2012"> (Kirkeminde, 2012) <raw format="html" xml:space="preserve"> </cite> <section ids="todo-notes" names="todo\ notes"> <title> Todo notes <paragraph> \todo[inline]{an inline todo} <paragraph> Some text.\todo{a todo in the margins} <section ids="text-output" names="text\ output"> <title> Text Output <container cell_index="11" cell_metadata="{'ipub': {'text': {'format': {'backgroundcolor': '\\color{blue!10}'}}}}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> print(""" This is some printed text, with a nicely formatted output. """) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> This is some printed text, with a nicely formatted output. <section ids="images-and-figures" names="images\ and\ figures"> <title> Images and Figures <section ids="displaying-a-plot-with-its-code" names="displaying\ a\ plot\ with\ its\ code"> <title> Displaying a plot with its code <paragraph> A matplotlib figure, with the caption set in the markdowncell above the figure. <paragraph> The plotting code for a matplotlib figure (\cref{fig:example_mpl}). <section ids="tables-with-pandas" names="tables\ (with\ pandas)"> <title> Tables (with pandas) <paragraph> The plotting code for a pandas Dataframe table (\cref{tbl:example}). <container cell_index="18" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_pd', 'placement': 'H', 'widefigure': False}, 'table': {'alternate': 'gray!20', 'caption': 'An example of a table created with pandas dataframe.', 'label': 'tbl:example', 'placement': 'H'}}}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> np.random.seed(0) df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d']) df.a = [r'$\delta$','x','y'] df.b = ['l','m','n'] df.set_index(['a','b']) df.round(3) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> a b c d 0 $\delta$ l 0.603 0.545 1 x m 0.438 0.892 2 y n 0.792 0.529 <container mime_type="text/html"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>a</th> <th>b</th> <th>c</th> <th>d</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>$\delta$</td> <td>l</td> <td>0.603</td> <td>0.545</td> </tr> <tr> <th>1</th> <td>x</td> <td>m</td> <td>0.438</td> <td>0.892</td> </tr> <tr> <th>2</th> <td>y</td> <td>n</td> <td>0.792</td> <td>0.529</td> </tr> </tbody> </table> </div> <section ids="equations-with-ipython-or-sympy" names="equations\ (with\ ipython\ or\ sympy)"> <title> Equations (with ipython or sympy) <container cell_index="20" cell_metadata="{'ipub': {'equation': {'label': 'eqn:example_ipy'}}}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> Latex('$$ a = b+c $$') <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Latex object> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> a = b+c <paragraph> The plotting code for a sympy equation (=@eqn:example_sympy). <container cell_index="22" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_sym', 'placement': 'H', 'widefigure': False}, 'equation': {'environment': 'equation', 'label': 'eqn:example_sympy'}}}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) sym.rsolve(f,y(n),[1,4]) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž (โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ โŽ2 5 โŽ  โŽ2 5 โŽ  <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/9bc81205a14646a235d284d1b68223d17f30f7f1d3d8ed3e52cf47830b02e3bb.png'}" uri="_build/jupyter_execute/9bc81205a14646a235d284d1b68223d17f30f7f1d3d8ed3e52cf47830b02e3bb.png"> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right) <section ids="interactive-outputs" names="interactive\ outputs"> <title> Interactive outputs <section ids="ipywidgets" names="ipywidgets"> <title> ipywidgets <container cell_index="24" cell_metadata="{}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import ipywidgets as widgets widgets.Layout(model_id="1337h4x0R") <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> Layout() <container mime_type="application/vnd.jupyter.widget-view+json"> <raw format="html" xml:space="preserve"> <script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "1337h4x0R"}</script> <container cell_index="25" cell_metadata="{}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> from IPython.display import display, Markdown display(Markdown('**_some_ markdown**')) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Markdown object> <container mime_type="text/markdown"> <paragraph> <strong> <emphasis> some markdown ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_custom_convert_auto.ipynb�������������������������������������0000664�0000000�0000000�00000033143�14674535606�0024737�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "raw", "id": "28b0321f", "metadata": {}, "source": [ "---\n", "title: \"Test chunk options in Rmd/Jupyter conversion\"\n", "author: \"Marc Wouts\"\n", "date: \"June 16, 2018\"\n", "---" ] }, { "cell_type": "markdown", "id": "58a3e81a", "metadata": {}, "source": [ "# Custom Formats" ] }, { "cell_type": "code", "execution_count": 1, "id": "be1f1414", "metadata": { "echo": true }, "outputs": [], "source": [ "import pandas as pd\n", "x = pd.Series({'A':1, 'B':3, 'C':2})" ] }, { "cell_type": "code", "execution_count": 2, "id": "64b71941", "metadata": { "fig.height": 5, "fig.width": 8, "name": "bar_plot", "tags": [ "remove_input" ] }, "outputs": [ { "data": { "text/plain": [ "<Axes: title={'center': 'Sample plot'}>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x.plot(kind='bar', title='Sample plot')" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".Rmd", "format_name": "rmarkdown" } }, "kernelspec": { "display_name": "Python", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 } �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_custom_convert_auto.xml���������������������������������������0000664�0000000�0000000�00000003152�14674535606�0024413�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="custom-formats"> <section ids="custom-formats" names="custom\ formats"> <title> Custom Formats <container cell_index="2" cell_metadata="{'echo': True}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import pandas as pd x = pd.Series({'A':1, 'B':3, 'C':2}) <container cell_index="3" cell_metadata="{'name': 'bar_plot', 'tags': ['remove_input'], 'fig.height': 5, 'fig.width': 8}" classes="cell tag_remove_input" exec_count="2" nb_element="cell_code"> <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <Axes: title={'center': 'Sample plot'}> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <Figure size 640x480 with 1 Axes> <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/a2e637020dfe58f670ba2c942d7a55e49ba48bed09312569ee15a84f5ac680cb.png'}" uri="_build/jupyter_execute/a2e637020dfe58f670ba2c942d7a55e49ba48bed09312569ee15a84f5ac680cb.png"> ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_custom_convert_cache.ipynb������������������������������������0000664�0000000�0000000�00000033143�14674535606�0025032�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "raw", "id": "4a49bac4", "metadata": {}, "source": [ "---\n", "title: \"Test chunk options in Rmd/Jupyter conversion\"\n", "author: \"Marc Wouts\"\n", "date: \"June 16, 2018\"\n", "---" ] }, { "cell_type": "markdown", "id": "df9ab2eb", "metadata": {}, "source": [ "# Custom Formats" ] }, { "cell_type": "code", "execution_count": 1, "id": "65052f1a", "metadata": { "echo": true }, "outputs": [], "source": [ "import pandas as pd\n", "x = pd.Series({'A':1, 'B':3, 'C':2})" ] }, { "cell_type": "code", "execution_count": 2, "id": "d7e1dd06", "metadata": { "fig.height": 5, "fig.width": 8, "name": "bar_plot", "tags": [ "remove_input" ] }, "outputs": [ { "data": { "text/plain": [ "<Axes: title={'center': 'Sample plot'}>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x.plot(kind='bar', title='Sample plot')" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".Rmd", "format_name": "rmarkdown" } }, "kernelspec": { "display_name": "Python", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 } �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_custom_convert_cache.xml��������������������������������������0000664�0000000�0000000�00000003152�14674535606�0024506�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="custom-formats"> <section ids="custom-formats" names="custom\ formats"> <title> Custom Formats <container cell_index="2" cell_metadata="{'echo': True}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import pandas as pd x = pd.Series({'A':1, 'B':3, 'C':2}) <container cell_index="3" cell_metadata="{'name': 'bar_plot', 'tags': ['remove_input'], 'fig.height': 5, 'fig.width': 8}" classes="cell tag_remove_input" exec_count="2" nb_element="cell_code"> <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <Axes: title={'center': 'Sample plot'}> <container nb_element="mime_bundle"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <Figure size 640x480 with 1 Axes> <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/a2e637020dfe58f670ba2c942d7a55e49ba48bed09312569ee15a84f5ac680cb.png'}" uri="_build/jupyter_execute/a2e637020dfe58f670ba2c942d7a55e49ba48bed09312569ee15a84f5ac680cb.png"> ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_exclude_path.xml����������������������������������������������0000664�0000000�0000000�00000000722�14674535606�0022756�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) ����������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_jupyter_cache_path.ipynb��������������������������������������0000664�0000000�0000000�00000001510�14674535606�0024467�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_jupyter_cache_path.xml����������������������������������������0000664�0000000�0000000�00000001223�14674535606�0024147�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_nb_exec_table.xml���������������������������������������������0000664�0000000�0000000�00000001535�14674535606�0023066�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="nb_exec_table"> <section ids="test-the-nb-exec-table-directive" names="test\ the\ nb-exec-table\ directive"> <title> Test the <literal> nb-exec-table directive <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> print("hi") <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> hi <paragraph> This directive should generate a table of executed notebook statistics. <ExecutionStatsNode> �������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_no_execute.ipynb����������������������������������������������0000664�0000000�0000000�00000001342�14674535606�0022767�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# a title\n", "\n", "some text\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "test_name": "notebook1" }, "nbformat": 4, "nbformat_minor": 2 } ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_execute/test_no_execute.xml������������������������������������������������0000664�0000000�0000000�00000000722�14674535606�0022447�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) ����������������������������������������������MyST-NB-1.1.2/tests/test_glue.py��������������������������������������������������������������������0000664�0000000�0000000�00000006202�14674535606�0016373�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test the `glue` directives and roles.""" from IPython.core.displaypub import DisplayPublisher from IPython.core.interactiveshell import InteractiveShell import nbformat import pytest from myst_nb.ext.glue import extract_glue_data, glue class MockDisplayPublisher(DisplayPublisher): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.publish_calls = [] def publish(self, data, **kwargs): kwargs["data"] = data self.publish_calls.append(kwargs) @pytest.fixture() def mock_ipython(): """A mock IPython shell for testing notebook cell executions.""" shell = InteractiveShell.instance() # type: InteractiveShell shell.display_pub = MockDisplayPublisher() yield shell.display_pub InteractiveShell.clear_instance() def test_glue_func_text(mock_ipython): glue("a", "b") assert mock_ipython.publish_calls == [ { "metadata": {"scrapbook": {"name": "a", "mime_prefix": ""}}, "data": {"text/plain": "'b'"}, } ] def test_glue_func_obj(mock_ipython): class Obj: def __repr__(self): return "repr" def _repr_html_(self): return "<p>repr</p>" glue("a", Obj()) assert mock_ipython.publish_calls == [ { "metadata": {"scrapbook": {"name": "a", "mime_prefix": ""}}, "data": {"text/html": "<p>repr</p>", "text/plain": "repr"}, } ] def test_glue_func_obj_no_display(mock_ipython): class Obj: def __repr__(self): return "repr" def _repr_html_(self): return "<p>repr</p>" glue("a", Obj(), display=False) assert mock_ipython.publish_calls == [ { "metadata": { "scrapbook": { "name": "a", "mime_prefix": "application/papermill.record/", } }, "data": { "application/papermill.record/text/html": "<p>repr</p>", "application/papermill.record/text/plain": "repr", }, } ] def test_extract_glue_data(get_test_path): path = get_test_path("with_glue.ipynb") with open(path) as handle: notebook = nbformat.read(handle, as_version=4) data = extract_glue_data(notebook, [], None) assert set(data) == { "key_text1", "key_float", "key_undisplayed", "key_df", "key_plt", "sym_eq", } @pytest.mark.sphinx_params("with_glue.ipynb", conf={"nb_execution_mode": "off"}) def test_parser(sphinx_run, clean_doctree, file_regression): """Test a sphinx build.""" # TODO test duplicate warning in docutils sphinx_run.build() # print(sphinx_run.status()) # print(sphinx_run.warnings()) assert sphinx_run.warnings() == "" assert sphinx_run.env.nb_metadata["with_glue"]["glue"] == [ "key_text1", "key_float", "key_undisplayed", "key_df", "key_plt", "sym_eq", ] doctree = clean_doctree(sphinx_run.get_resolved_doctree("with_glue")) file_regression.check( doctree.pformat(), encoding="utf-8", ) ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_glue/����������������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0016021�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_glue/test_parser.txt�������������������������������������������������������0000664�0000000�0000000�00000020500�14674535606�0021112�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="with_glue"> <section ids="glue-tests" names="glue\ tests"> <title> Glue Tests <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from myst_nb import glue <container cell_index="2" cell_metadata="{}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> glue("key_text1", "text1") glue("key_float", 3.14159) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output text_plain" language="myst-ansi" linenos="False" xml:space="preserve"> 'text1' <literal_block classes="output text_plain" language="myst-ansi" linenos="False" xml:space="preserve"> 3.14159 <container cell_index="3" cell_metadata="{}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> glue("key_undisplayed", "undisplayed", display=False) <container cell_index="4" cell_metadata="{'scrolled': True}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import pandas as pd df = pd.DataFrame({"header": [1, 2, 3]}) glue("key_df", df) <container classes="cell_output" nb_element="cell_code_output"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>header</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> <tr> <th>1</th> <td>2</td> </tr> <tr> <th>2</th> <td>3</td> </tr> </tbody> </table> </div> <container cell_index="5" cell_metadata="{}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import matplotlib.pyplot as plt plt.plot([1, 2, 3]) glue("key_plt", plt.gcf(), display=False) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png'}" uri="_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png"> <section ids="referencing-the-figs" names="referencing\ the\ figs"> <title> Referencing the figs <paragraph> <inline classes="output text_plain"> โ€˜text1โ€™ , <image candidates="{'*': '_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png'}" uri="_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>header</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1</td> </tr> <tr> <th>1</th> <td>2</td> </tr> <tr> <th>2</th> <td>3</td> </tr> </tbody> </table> </div> <paragraph> and <inline classes="pasted-text"> text1 inlineโ€ฆ <paragraph> and formatted <inline classes="pasted-text"> 3.14 <image candidates="{'*': '_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png'}" uri="_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png"> <paragraph> and <inline classes="pasted-text"> undisplayed inlineโ€ฆ <figure ids="abc" names="abc"> <image candidates="{'*': '_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png'}" uri="_build/jupyter_execute/8b394c6cdc09dc10c73e2d5f785aedc8eee615a4d219218f09d6732f7f8ef150.png"> <caption> A captionโ€ฆ. ```## A test title <inline classes="output text_plain"> โ€˜text1โ€™ <section ids="math" names="math"> <title> Math <container cell_index="8" cell_metadata="{}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sympy as sym f = sym.Function('f') y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) glue("sym_eq", sym.rsolve(f,y(n),[1,4])) <container classes="cell_output" nb_element="cell_code_output"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right) <target refid="equation-eq-sym"> <math_block classes="pasted-math" docname="with_glue" ids="equation-eq-sym" label="eq-sym" nowrap="False" number="1" xml:space="preserve"> \displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right) ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_parser.py������������������������������������������������������������������0000664�0000000�0000000�00000007763�14674535606�0016750�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Test parsing of already executed notebooks.""" import os from pathlib import Path import pytest @pytest.mark.sphinx_params("basic_run.ipynb", conf={"nb_execution_mode": "off"}) def test_basic_run(sphinx_run, file_regression): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert set(sphinx_run.env.metadata["basic_run"].keys()) == { "test_name", "wordcount", "kernelspec", "language_info", } assert set(sphinx_run.env.nb_metadata["basic_run"].keys()) == set() assert sphinx_run.env.metadata["basic_run"]["test_name"] == "notebook1" assert sphinx_run.env.metadata["basic_run"]["kernelspec"] == { "display_name": "Python 3", "language": "python", "name": "python3", } file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) filenames = { p.name for p in Path( os.fspath(sphinx_run.app.srcdir / "_build" / "jupyter_execute") ).iterdir() } assert filenames == {"basic_run.ipynb"} @pytest.mark.sphinx_params("complex_outputs.ipynb", conf={"nb_execution_mode": "off"}) def test_complex_outputs(sphinx_run, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" assert set(sphinx_run.env.metadata["complex_outputs"].keys()) == { "ipub", "hide_input", "nav_menu", "celltoolbar", "latex_envs", "jupytext", "toc", "varInspector", "wordcount", "kernelspec", "language_info", } assert set(sphinx_run.env.nb_metadata["complex_outputs"].keys()) == set() assert sphinx_run.env.metadata["complex_outputs"]["celltoolbar"] == "Edit Metadata" assert sphinx_run.env.metadata["complex_outputs"]["hide_input"] == "False" assert sphinx_run.env.metadata["complex_outputs"]["kernelspec"] == { "display_name": "Python 3", "language": "python", "name": "python3", } doctree_string = sphinx_run.get_doctree().pformat() if os.name == "nt": # on Windows image file paths are absolute doctree_string = doctree_string.replace( Path(sphinx_run.app.srcdir).as_posix() + "/", "" ) file_regression.check(doctree_string, extension=".xml", encoding="utf-8") filenames = { p.name.replace(".jpeg", ".jpg") for p in Path( os.fspath(sphinx_run.app.srcdir / "_build" / "jupyter_execute") ).iterdir() } # print(filenames) assert filenames == { "16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png", "a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg", "8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png", "complex_outputs.ipynb", } @pytest.mark.sphinx_params( "latex_build/index.ipynb", "latex_build/other.ipynb", conf={"nb_execution_mode": "off"}, buildername="latex", ) def test_toctree_in_ipynb(sphinx_run, file_regression): sphinx_run.build() print(sphinx_run.status()) print(sphinx_run.warnings()) file_regression.check( sphinx_run.get_doctree("latex_build/other").pformat(), extension=".xml" ) assert sphinx_run.warnings() == "" @pytest.mark.sphinx_params("ipywidgets.ipynb", conf={"nb_execution_mode": "off"}) def test_ipywidgets(sphinx_run): """Test that ipywidget state is extracted and JS is included in the HTML head.""" sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert "js_files" in sphinx_run.env.nb_metadata["ipywidgets"] assert set(sphinx_run.env.nb_metadata["ipywidgets"]["js_files"]) == { "ipywidgets_state", "ipywidgets_0", "ipywidgets_1", } head_scripts = sphinx_run.get_html().select("head > script") assert any("require.js" in script.get("src", "") for script in head_scripts) assert any("embed-amd.js" in script.get("src", "") for script in head_scripts) �������������MyST-NB-1.1.2/tests/test_parser/��������������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0016361�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_parser/test_basic_run.xml��������������������������������������������������0000664�0000000�0000000�00000001221�14674535606�0022103�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_run"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_parser/test_complex_outputs.xml��������������������������������������������0000664�0000000�0000000�00000036551�14674535606�0023426�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="complex_outputs"> <container cell_index="0" cell_metadata="{'init_cell': True, 'slideshow': {'slide_type': 'skip'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> import matplotlib.pyplot as plt import pandas as pd import sympy as sym sym.init_printing(use_latex=True) import numpy as np from IPython.display import Image, Latex <section ids="markdown" names="markdown"> <title> Markdown <section ids="general" names="general"> <title> General <paragraph> Some markdown text. <paragraph> A list: <bullet_list bullet="-"> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> A numbered list <enumerated_list enumtype="arabic" prefix="" suffix="."> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> non-ascii characters TODO <paragraph> This is a long section of text, which we only want in a document (not a presentation) some text some more text some more text some more text some more text some more text some more text some more text some more text <paragraph> This is an abbreviated section of the document text, which we only want in a presentation <bullet_list bullet="-"> <list_item> <paragraph> summary of document text <section ids="references-and-citations" names="references\ and\ citations"> <title> References and Citations <paragraph> References to \cref{fig:example}, \cref{tbl:example}, =@eqn:example_sympy and \cref{code:example_mpl}. <paragraph> A latex citation.\cite{zelenyak_molecular_2016} <paragraph> A html citation. <raw format="html" xml:space="preserve"> <cite data-cite="kirkeminde_thermodynamic_2012"> (Kirkeminde, 2012) <raw format="html" xml:space="preserve"> </cite> <section ids="todo-notes" names="todo\ notes"> <title> Todo notes <paragraph> \todo[inline]{an inline todo} <paragraph> Some text.\todo{a todo in the margins} <section ids="text-output" names="text\ output"> <title> Text Output <container cell_index="11" cell_metadata="{'ipub': {'text': {'format': {'backgroundcolor': '\\color{blue!10}'}}}}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> print(""" This is some printed text, with a nicely formatted output. """) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> This is some printed text, with a nicely formatted output. <section ids="images-and-figures" names="images\ and\ figures"> <title> Images and Figures <container cell_index="13" cell_metadata="{'ipub': {'figure': {'caption': 'A nice picture.', 'label': 'fig:example', 'placement': '!bh'}}}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> Image('example.jpg',height=400) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="image/jpeg"> <image candidates="{'*': '_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg'}" height="400" uri="_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Image object> <section ids="displaying-a-plot-with-its-code" names="displaying\ a\ plot\ with\ its\ code"> <title> Displaying a plot with its code <paragraph> A matplotlib figure, with the caption set in the markdowncell above the figure. <paragraph> The plotting code for a matplotlib figure (\cref{fig:example_mpl}). <container cell_index="17" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': 'a', 'label': 'code:example_mpl', 'widefigure': False}, 'figure': {'caption': '', 'label': 'fig:example_mpl', 'widefigure': False}}}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> plt.scatter(np.random.rand(10), np.random.rand(10), label='data label') plt.ylabel(r'a y label with latex $\alpha$') plt.legend(); <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png'}" height="288" uri="_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png" width="432"> <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <Figure size 432x288 with 1 Axes> <section ids="tables-with-pandas" names="tables\ (with\ pandas)"> <title> Tables (with pandas) <paragraph> The plotting code for a pandas Dataframe table (\cref{tbl:example}). <container cell_index="20" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_pd', 'placement': 'H', 'widefigure': False}, 'table': {'alternate': 'gray!20', 'caption': 'An example of a table created with pandas dataframe.', 'label': 'tbl:example', 'placement': 'H'}}}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d']) df.a = [r'$\delta$','x','y'] df.b = ['l','m','n'] df.set_index(['a','b']) df.round(3) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/html"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>a</th> <th>b</th> <th>c</th> <th>d</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>$\delta$</td> <td>l</td> <td>0.391</td> <td>0.607</td> </tr> <tr> <th>1</th> <td>x</td> <td>m</td> <td>0.132</td> <td>0.205</td> </tr> <tr> <th>2</th> <td>y</td> <td>n</td> <td>0.969</td> <td>0.726</td> </tr> </tbody> </table> </div> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \begin{tabular}{lllrr} \toprule {} & a & b & c & d \\ \midrule 0 & \$\textbackslash delta\$ & l & 0.391 & 0.607 \\ 1 & x & m & 0.132 & 0.205 \\ 2 & y & n & 0.969 & 0.726 \\ \bottomrule \end{tabular} <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> a b c d 0 $\delta$ l 0.391 0.607 1 x m 0.132 0.205 2 y n 0.969 0.726 <section ids="equations-with-ipython-or-sympy" names="equations\ (with\ ipython\ or\ sympy)"> <title> Equations (with ipython or sympy) <container cell_index="22" cell_metadata="{'ipub': {'equation': {'label': 'eqn:example_ipy'}}}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> Latex('$$ a = b+c $$') <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> a = b+c <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Latex object> <paragraph> The plotting code for a sympy equation (=@eqn:example_sympy). <container cell_index="24" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_sym', 'placement': 'H', 'widefigure': False}, 'equation': {'environment': 'equation', 'label': 'eqn:example_sympy'}}}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) sym.rsolve(f,y(n),[1,4]) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="image/png"> <image candidates="{'*': '_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png'}" uri="_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png"> <container mime_type="text/latex"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right) <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž \alpha โŽ›1 2โ‹…โˆš5โ‹…โ…ˆโŽž (โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ - โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ + (-โˆš5โ‹…โ…ˆ) โ‹…โŽœโ”€ + โ”€โ”€โ”€โ”€โ”€โ”€โŽŸ โŽ2 5 โŽ  โŽ2 5 โŽ  <container cell_index="25" cell_metadata="{}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> from IPython.display import display, Markdown display(Markdown('**_some_ markdown**')) <container classes="cell_output" nb_element="cell_code_output"> <container nb_element="mime_bundle"> <container mime_type="text/markdown"> <paragraph> <strong> <emphasis> some markdown <container mime_type="text/plain"> <literal_block classes="output text_plain" language="myst-ansi" xml:space="preserve"> <IPython.core.display.Markdown object> �������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_parser/test_toctree_in_ipynb.xml�������������������������������������������0000664�0000000�0000000�00000001733�14674535606�0023502�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="latex_build/other"> <target refid="title-ref"> <section ids="title title-ref" names="title title_ref"> <title refid="id1"> Title <topic classes="contents" ids="contents" names="contents"> <title> Contents <bullet_list> <list_item> <paragraph> <reference ids="id1" refid="title"> Title <paragraph> Content <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> print(1) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 �������������������������������������MyST-NB-1.1.2/tests/test_render_outputs.py����������������������������������������������������������0000664�0000000�0000000�00000016456�14674535606�0020535�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������"""Tests for rendering code cell outputs.""" import pytest from myst_nb.core.render import EntryPointError, load_renderer from sphinx.util.fileutil import copy_asset_file def test_load_renderer_not_found(): """Test that an error is raised when the renderer is not found.""" with pytest.raises(EntryPointError, match="No Entry Point found"): load_renderer("other") # TODO sometimes fails in full tests # def test_load_renderer_not_subclass(monkeypatch): # """Test that an error is raised when the renderer is not a subclass.""" # from importlib_metadata import EntryPoint # monkeypatch.setattr(EntryPoint, "load", lambda self: object) # with pytest.raises(EntryPointError, match="Entry Point .* not a subclass"): # load_renderer("default") @pytest.mark.sphinx_params("basic_run.ipynb", conf={"nb_execution_mode": "off"}) def test_basic_run(sphinx_run, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("basic_run") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("file_level_config.md") def test_file_level_config_md(sphinx_run, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("file_level_config") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("file_level_config.ipynb") def test_file_level_config_ipynb(sphinx_run, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("file_level_config") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("complex_outputs.ipynb", conf={"nb_execution_mode": "off"}) def test_complex_outputs(sphinx_run, clean_doctree, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("complex_outputs")) file_regression.check( doctree.pformat().replace(".jpeg", ".jpg"), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "complex_outputs.ipynb", conf={"nb_execution_mode": "off"}, buildername="latex", ) def test_complex_outputs_latex(sphinx_run, clean_doctree, file_regression): sphinx_run.build() assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("complex_outputs")) file_regression.check( doctree.pformat().replace(".jpeg", ".jpg"), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "basic_stderr.ipynb", conf={"nb_execution_mode": "off", "nb_output_stderr": "remove"}, ) def test_stderr_remove(sphinx_run, file_regression): """Test configuring all stderr outputs to be removed.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("basic_stderr") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("basic_stderr.ipynb", conf={"nb_execution_mode": "off"}) def test_stderr_tag(sphinx_run, file_regression): """Test configuring stderr outputs to be removed from a single cell, using `remove-stderr` in the `cell.metadata.tags`. """ sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("basic_stderr") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params( "merge_streams.ipynb", conf={"nb_execution_mode": "off", "nb_merge_streams": True}, ) def test_merge_streams(sphinx_run, file_regression): """Test configuring multiple concurrent stdout/stderr outputs to be merged.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("merge_streams") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params( "merge_streams_parallel.ipynb", conf={"nb_execution_mode": "off", "nb_merge_streams": True}, ) def test_merge_streams_parallel(sphinx_run, file_regression): """Test configuring multiple concurrent stdout/stderr outputs to be merged.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("merge_streams_parallel") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params( "metadata_image.ipynb", conf={"nb_execution_mode": "off", "nb_cell_metadata_key": "myst"}, ) def test_metadata_image(sphinx_run, clean_doctree, file_regression): """Test configuring image attributes to be rendered from cell metadata.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("metadata_image")) file_regression.check( doctree.pformat().replace(".jpeg", ".jpg"), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "metadata_image_output.ipynb", conf={"nb_execution_mode": "force"}, ) def test_metadata_image_output( sphinx_run, clean_doctree, file_regression, get_test_path ): """Test configuring image attributes to be rendered from cell metadata.""" asset_path = get_test_path("example.jpg") copy_asset_file(str(asset_path), str(sphinx_run.app.srcdir)) sphinx_run.build() assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("metadata_image_output")) file_regression.check( doctree.pformat().replace(".jpeg", ".jpg"), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "metadata_figure.ipynb", conf={"nb_execution_mode": "off", "nb_cell_metadata_key": "myst"}, ) def test_metadata_figure(sphinx_run, clean_doctree, file_regression): """Test configuring figure attributes to be rendered from cell metadata.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = clean_doctree(sphinx_run.get_resolved_doctree("metadata_figure")) doctree_string = doctree.pformat() # change, presumably with new docutils version doctree_string = doctree_string.replace( '<figure ids="fun-fish" names="fun-fish">', '<figure align="default" ids="fun-fish" names="fun-fish">', ) file_regression.check( doctree_string.replace(".jpeg", ".jpg"), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params("unknown_mimetype.ipynb", conf={"nb_execution_mode": "off"}) def test_unknown_mimetype(sphinx_run, file_regression): """Test that unknown mimetypes provide a warning.""" sphinx_run.build() warning = "skipping unknown output mime type: unknown [mystnb.unknown_mime_type]" assert warning in sphinx_run.warnings() doctree = sphinx_run.get_resolved_doctree("unknown_mimetype") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") @pytest.mark.sphinx_params("hide_cell_content.ipynb", conf={"nb_execution_mode": "off"}) def test_hide_cell_content(sphinx_run, file_regression): """Test that hiding cell contents produces the correct AST.""" sphinx_run.build() assert sphinx_run.warnings() == "" doctree = sphinx_run.get_resolved_doctree("hide_cell_content") file_regression.check(doctree.pformat(), extension=".xml", encoding="utf-8") ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0020147�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_basic_run.xml������������������������������������������0000664�0000000�0000000�00000001261�14674535606�0023675�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_run"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> some text <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> 1 �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_complex_outputs.xml������������������������������������0000664�0000000�0000000�00000027125�14674535606�0025211�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="complex_outputs"> <container cell_index="0" cell_metadata="{'init_cell': True, 'slideshow': {'slide_type': 'skip'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import matplotlib.pyplot as plt import pandas as pd import sympy as sym sym.init_printing(use_latex=True) import numpy as np from IPython.display import Image, Latex <section ids="markdown" names="markdown"> <title> Markdown <section ids="general" names="general"> <title> General <paragraph> Some markdown text. <paragraph> A list: <bullet_list bullet="-"> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> A numbered list <enumerated_list enumtype="arabic" prefix="" suffix="."> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> non-ascii characters TODO <paragraph> This is a long section of text, which we only want in a document (not a presentation) some text some more text some more text some more text some more text some more text some more text some more text some more text <paragraph> This is an abbreviated section of the document text, which we only want in a presentation <bullet_list bullet="-"> <list_item> <paragraph> summary of document text <section ids="references-and-citations" names="references\ and\ citations"> <title> References and Citations <paragraph> References to \cref{fig:example}, \cref{tbl:example}, =@eqn:example_sympy and \cref{code:example_mpl}. <paragraph> A latex citation.\cite{zelenyak_molecular_2016} <paragraph> A html citation. <raw format="html" xml:space="preserve"> <cite data-cite="kirkeminde_thermodynamic_2012"> (Kirkeminde, 2012) <raw format="html" xml:space="preserve"> </cite> <section ids="todo-notes" names="todo\ notes"> <title> Todo notes <paragraph> \todo[inline]{an inline todo} <paragraph> Some text.\todo{a todo in the margins} <section ids="text-output" names="text\ output"> <title> Text Output <container cell_index="11" cell_metadata="{'ipub': {'text': {'format': {'backgroundcolor': '\\color{blue!10}'}}}}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print(""" This is some printed text, with a nicely formatted output. """) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> This is some printed text, with a nicely formatted output. <section ids="images-and-figures" names="images\ and\ figures"> <title> Images and Figures <container cell_index="13" cell_metadata="{'ipub': {'figure': {'caption': 'A nice picture.', 'label': 'fig:example', 'placement': '!bh'}}}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> Image('example.jpg',height=400) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg'}" height="400" uri="_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg"> <section ids="displaying-a-plot-with-its-code" names="displaying\ a\ plot\ with\ its\ code"> <title> Displaying a plot with its code <paragraph> A matplotlib figure, with the caption set in the markdowncell above the figure. <paragraph> The plotting code for a matplotlib figure (\cref{fig:example_mpl}). <container cell_index="17" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': 'a', 'label': 'code:example_mpl', 'widefigure': False}, 'figure': {'caption': '', 'label': 'fig:example_mpl', 'widefigure': False}}}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> plt.scatter(np.random.rand(10), np.random.rand(10), label='data label') plt.ylabel(r'a y label with latex $\alpha$') plt.legend(); <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png'}" height="288" uri="_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png" width="432"> <section ids="tables-with-pandas" names="tables\ (with\ pandas)"> <title> Tables (with pandas) <paragraph> The plotting code for a pandas Dataframe table (\cref{tbl:example}). <container cell_index="20" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_pd', 'placement': 'H', 'widefigure': False}, 'table': {'alternate': 'gray!20', 'caption': 'An example of a table created with pandas dataframe.', 'label': 'tbl:example', 'placement': 'H'}}}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d']) df.a = [r'$\delta$','x','y'] df.b = ['l','m','n'] df.set_index(['a','b']) df.round(3) <container classes="cell_output" nb_element="cell_code_output"> <raw classes="output text_html" format="html" xml:space="preserve"> <div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>a</th> <th>b</th> <th>c</th> <th>d</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>$\delta$</td> <td>l</td> <td>0.391</td> <td>0.607</td> </tr> <tr> <th>1</th> <td>x</td> <td>m</td> <td>0.132</td> <td>0.205</td> </tr> <tr> <th>2</th> <td>y</td> <td>n</td> <td>0.969</td> <td>0.726</td> </tr> </tbody> </table> </div> <section ids="equations-with-ipython-or-sympy" names="equations\ (with\ ipython\ or\ sympy)"> <title> Equations (with ipython or sympy) <container cell_index="22" cell_metadata="{'ipub': {'equation': {'label': 'eqn:example_ipy'}}}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> Latex('$$ a = b+c $$') <container classes="cell_output" nb_element="cell_code_output"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> a = b+c <paragraph> The plotting code for a sympy equation (=@eqn:example_sympy). <container cell_index="24" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_sym', 'placement': 'H', 'widefigure': False}, 'equation': {'environment': 'equation', 'label': 'eqn:example_sympy'}}}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) sym.rsolve(f,y(n),[1,4]) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png'}" uri="_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png"> <container cell_index="25" cell_metadata="{}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from IPython.display import display, Markdown display(Markdown('**_some_ markdown**')) <container classes="cell_output" nb_element="cell_code_output"> <paragraph> <strong> <emphasis> some markdown �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_complex_outputs_latex.xml������������������������������0000664�0000000�0000000�00000024435�14674535606�0026407�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="complex_outputs"> <container cell_index="0" cell_metadata="{'init_cell': True, 'slideshow': {'slide_type': 'skip'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import matplotlib.pyplot as plt import pandas as pd import sympy as sym sym.init_printing(use_latex=True) import numpy as np from IPython.display import Image, Latex <section ids="markdown" names="markdown"> <title> Markdown <section ids="general" names="general"> <title> General <paragraph> Some markdown text. <paragraph> A list: <bullet_list bullet="-"> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> A numbered list <enumerated_list enumtype="arabic" prefix="" suffix="."> <list_item> <paragraph> something <list_item> <paragraph> something else <paragraph> non-ascii characters TODO <paragraph> This is a long section of text, which we only want in a document (not a presentation) some text some more text some more text some more text some more text some more text some more text some more text some more text <paragraph> This is an abbreviated section of the document text, which we only want in a presentation <bullet_list bullet="-"> <list_item> <paragraph> summary of document text <section ids="references-and-citations" names="references\ and\ citations"> <title> References and Citations <paragraph> References to \cref{fig:example}, \cref{tbl:example}, =@eqn:example_sympy and \cref{code:example_mpl}. <paragraph> A latex citation.\cite{zelenyak_molecular_2016} <paragraph> A html citation. <raw format="html" xml:space="preserve"> <cite data-cite="kirkeminde_thermodynamic_2012"> (Kirkeminde, 2012) <raw format="html" xml:space="preserve"> </cite> <section ids="todo-notes" names="todo\ notes"> <title> Todo notes <paragraph> \todo[inline]{an inline todo} <paragraph> Some text.\todo{a todo in the margins} <section ids="text-output" names="text\ output"> <title> Text Output <container cell_index="11" cell_metadata="{'ipub': {'text': {'format': {'backgroundcolor': '\\color{blue!10}'}}}}" classes="cell" exec_count="2" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print(""" This is some printed text, with a nicely formatted output. """) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> This is some printed text, with a nicely formatted output. <section ids="images-and-figures" names="images\ and\ figures"> <title> Images and Figures <container cell_index="13" cell_metadata="{'ipub': {'figure': {'caption': 'A nice picture.', 'label': 'fig:example', 'placement': '!bh'}}}" classes="cell" exec_count="3" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> Image('example.jpg',height=400) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg'}" height="400" uri="_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg"> <section ids="displaying-a-plot-with-its-code" names="displaying\ a\ plot\ with\ its\ code"> <title> Displaying a plot with its code <paragraph> A matplotlib figure, with the caption set in the markdowncell above the figure. <paragraph> The plotting code for a matplotlib figure (\cref{fig:example_mpl}). <container cell_index="17" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': 'a', 'label': 'code:example_mpl', 'widefigure': False}, 'figure': {'caption': '', 'label': 'fig:example_mpl', 'widefigure': False}}}" classes="cell" exec_count="4" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> plt.scatter(np.random.rand(10), np.random.rand(10), label='data label') plt.ylabel(r'a y label with latex $\alpha$') plt.legend(); <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png'}" height="288" uri="_build/jupyter_execute/16832f45917c1c9862c50f0948f64a498402d6ccde1f3a291da17f240797b160.png" width="432"> <section ids="tables-with-pandas" names="tables\ (with\ pandas)"> <title> Tables (with pandas) <paragraph> The plotting code for a pandas Dataframe table (\cref{tbl:example}). <container cell_index="20" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_pd', 'placement': 'H', 'widefigure': False}, 'table': {'alternate': 'gray!20', 'caption': 'An example of a table created with pandas dataframe.', 'label': 'tbl:example', 'placement': 'H'}}}" classes="cell" exec_count="5" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> df = pd.DataFrame(np.random.rand(3,4),columns=['a','b','c','d']) df.a = [r'$\delta$','x','y'] df.b = ['l','m','n'] df.set_index(['a','b']) df.round(3) <container classes="cell_output" nb_element="cell_code_output"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> \begin{tabular}{lllrr} \toprule {} & a & b & c & d \\ \midrule 0 & \$\textbackslash delta\$ & l & 0.391 & 0.607 \\ 1 & x & m & 0.132 & 0.205 \\ 2 & y & n & 0.969 & 0.726 \\ \bottomrule \end{tabular} <section ids="equations-with-ipython-or-sympy" names="equations\ (with\ ipython\ or\ sympy)"> <title> Equations (with ipython or sympy) <container cell_index="22" cell_metadata="{'ipub': {'equation': {'label': 'eqn:example_ipy'}}}" classes="cell" exec_count="6" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> Latex('$$ a = b+c $$') <container classes="cell_output" nb_element="cell_code_output"> <math_block classes="output text_latex" nowrap="False" number="True" xml:space="preserve"> a = b+c <paragraph> The plotting code for a sympy equation (=@eqn:example_sympy). <container cell_index="24" cell_metadata="{'ipub': {'code': {'asfloat': True, 'caption': '', 'label': 'code:example_sym', 'placement': 'H', 'widefigure': False}, 'equation': {'environment': 'equation', 'label': 'eqn:example_sympy'}}}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> y = sym.Function('y') n = sym.symbols(r'\alpha') f = y(n)-2*y(n-1/sym.pi)-5*y(n-2) sym.rsolve(f,y(n),[1,4]) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png'}" uri="_build/jupyter_execute/8c43e5c8cccf697754876b7fec1b0a9b731d7900bb585e775a5fa326b4de8c5a.png"> <container cell_index="25" cell_metadata="{}" classes="cell" exec_count="7" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from IPython.display import display, Markdown display(Markdown('**_some_ markdown**')) <container classes="cell_output" nb_element="cell_code_output"> <paragraph> <strong> <emphasis> some markdown �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_file_level_config_ipynb.xml����������������������������0000664�0000000�0000000�00000000523�14674535606�0026564�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="file_level_config"> <section ids="title" names="title"> <title> Title <definition_list classes="simple myst"> <definition_list_item> <term> name <definition> <paragraph> definition �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_file_level_config_md.xml�������������������������������0000664�0000000�0000000�00000000523�14674535606�0026043�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="file_level_config"> <section ids="title" names="title"> <title> Title <definition_list classes="simple myst"> <definition_list_item> <term> name <definition> <paragraph> definition �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_hide_cell_content.xml����������������������������������0000664�0000000�0000000�00000006635�14674535606�0025404�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="hide_cell_content"> <section ids="hide-code-cell-content" names="hide\ code\ cell\ content"> <title> Hide Code Cell Content <container cell_index="1" cell_metadata="{'tags': ['hide-input']}" classes="cell tag_hide-input" exec_count="1" hide_mode="input" nb_element="cell_code" prompt_hide="Hide code cell {type}" prompt_show="Show code cell {type}"> <HideCodeCellNode classes="above-input" prompt_hide="Hide code cell source" prompt_show="Show code cell source"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print("hide-input") <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> hide-input <container cell_index="2" cell_metadata="{'tags': ['hide-output']}" classes="cell tag_hide-output" exec_count="2" hide_mode="output" nb_element="cell_code" prompt_hide="Hide code cell {type}" prompt_show="Show code cell {type}"> <container classes="cell_input above-output-prompt" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print("hide-output") <HideCodeCellNode classes="below-input" prompt_hide="Hide code cell output" prompt_show="Show code cell output"> <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> hide-output <container cell_index="3" cell_metadata="{'tags': ['hide-cell']}" classes="cell tag_hide-cell" exec_count="4" hide_mode="all" nb_element="cell_code" prompt_hide="Hide code cell {type}" prompt_show="Show code cell {type}"> <HideCodeCellNode classes="above-input" prompt_hide="Hide code cell content" prompt_show="Show code cell content"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print("hide-cell") <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> hide-cell <container cell_index="4" cell_metadata="{'tags': ['hide-cell'], 'mystnb': {'code_prompt_show': 'My show message', 'code_prompt_hide': 'My hide message'}}" classes="cell tag_hide-cell" exec_count="5" hide_mode="all" nb_element="cell_code" prompt_hide="My hide message" prompt_show="My show message"> <HideCodeCellNode classes="above-input" prompt_hide="My hide message" prompt_show="My show message"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> print("hide-cell custom message") <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> hide-cell custom message ���������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_merge_streams.xml��������������������������������������0000664�0000000�0000000�00000002266�14674535606�0024573�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="merge_streams"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sys print('stdout1', file=sys.stdout) print('stdout2', file=sys.stdout) print('stderr1', file=sys.stderr) print('stderr2', file=sys.stderr) print('stdout3', file=sys.stdout) print('stderr3', file=sys.stderr) 1 <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> stdout1 stdout2 stdout3 <literal_block classes="output stderr" language="myst-ansi" linenos="False" xml:space="preserve"> stderr1 stderr2 stderr3 <literal_block classes="output text_plain" language="myst-ansi" linenos="False" xml:space="preserve"> 1 ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_merge_streams_parallel.xml�����������������������������0000664�0000000�0000000�00000002136�14674535606�0026443�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="merge_streams_parallel"> <container cell_index="0" cell_metadata="{'execution': {'iopub.execute_input': '2024-09-19T21:44:29.809012Z', 'iopub.status.busy': '2024-09-19T21:44:29.808809Z', 'iopub.status.idle': '2024-09-19T21:44:29.978481Z', 'shell.execute_reply': '2024-09-19T21:44:29.977891Z'}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from concurrent.futures import ProcessPoolExecutor with ProcessPoolExecutor() as executor: for i in executor.map(print, [0] * 10): pass <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" linenos="False" xml:space="preserve"> 0 0 0 0 0 0 0 0 0 0 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_metadata_figure.xml������������������������������������0000664�0000000�0000000�00000002521�14674535606�0025051�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="metadata_figure"> <section ids="formatting-code-outputs" names="formatting\ code\ outputs"> <title> Formatting code outputs <container cell_index="1" cell_metadata="{'myst': {'figure': {'caption': 'Hey everyone its **party** time!\n', 'name': 'fun-fish'}}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from IPython.display import Image Image("fun-fish.png") <container classes="cell_output" nb_element="cell_code_output"> <figure align="default" ids="fun-fish" names="fun-fish"> <image candidates="{'*': '_build/jupyter_execute/3eacaf6adad1a4305807616181bbee897bb29177e79e2092ddd0264b848ddb4e.png'}" uri="_build/jupyter_execute/3eacaf6adad1a4305807616181bbee897bb29177e79e2092ddd0264b848ddb4e.png"> <caption> Hey everyone its <strong> party time! <paragraph> Link: <reference internal="True" refid="fun-fish"> <inline classes="std std-ref"> swim to the fish �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_metadata_image.xml�������������������������������������0000664�0000000�0000000�00000001735�14674535606�0024660�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="metadata_image"> <section ids="formatting-code-outputs" names="formatting\ code\ outputs"> <title> Formatting code outputs <container cell_index="1" cell_metadata="{'myst': {'image': {'alt': 'fun-fish', 'classes': 'shadow bg-primary', 'width': '300px'}}}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> from IPython.display import Image Image("fun-fish.png") <container classes="cell_output" nb_element="cell_code_output"> <image alt="fun-fish" candidates="{'*': '_build/jupyter_execute/3eacaf6adad1a4305807616181bbee897bb29177e79e2092ddd0264b848ddb4e.png'}" classes="shadow bg-primary" uri="_build/jupyter_execute/3eacaf6adad1a4305807616181bbee897bb29177e79e2092ddd0264b848ddb4e.png" width="300px"> �����������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_metadata_image_output.xml������������������������������0000664�0000000�0000000�00000003451�14674535606�0026275�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="metadata_image_output"> <section ids="output-metadata" names="output\ metadata"> <title> Output metadata <container cell_index="1" cell_metadata="{'tags': ['skip-execution']}" classes="cell tag_skip-execution" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> # Outputs included with width/height in output metadata, # cell is not executed from IPython.display import Image Image(filename="./example.jpg", width=500, height=100) <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg'}" height="100" uri="_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg" width="500"> <container cell_index="2" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> # No outputs, cell is executed, image should have original size (370, 254) from IPython.display import Image Image(filename="./example.jpg") <container classes="cell_output" nb_element="cell_code_output"> <image candidates="{'*': '_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg'}" uri="_build/jupyter_execute/a4c9580c74dacf6f3316a3bd2e2a347933aa4463834dcf1bb8f20b4fcb476ae1.jpg"> �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_stderr_remove.xml��������������������������������������0000664�0000000�0000000�00000001570�14674535606�0024613�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_stderr"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sys print('hallo', file=sys.stderr) <container classes="cell_output" nb_element="cell_code_output"> <container cell_index="1" cell_metadata="{'tags': ['remove-stderr']}" classes="cell tag_remove-stderr" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sys print('hallo', file=sys.stderr) <container classes="cell_output" nb_element="cell_code_output"> ����������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_render_outputs/test_stderr_tag.xml�����������������������������������������0000664�0000000�0000000�00000001774�14674535606�0024077�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_stderr"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sys print('hallo', file=sys.stderr) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stderr" language="myst-ansi" linenos="False" xml:space="preserve"> hallo <container cell_index="1" cell_metadata="{'tags': ['remove-stderr']}" classes="cell tag_remove-stderr" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> import sys print('hallo', file=sys.stderr) <container classes="cell_output" nb_element="cell_code_output"> ����MyST-NB-1.1.2/tests/test_render_outputs/test_unknown_mimetype.xml�����������������������������������0000664�0000000�0000000�00000000635�14674535606�0025344�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="unknown_mimetype"> <container cell_index="0" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" linenos="False" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> ���������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based.py��������������������������������������������������������������0000664�0000000�0000000�00000004334�14674535606�0017565�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������import pytest @pytest.mark.sphinx_params( "basic_unrun.md", conf={"nb_execution_mode": "cache", "source_suffix": {".md": "myst-nb"}}, ) def test_basic_run(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert sphinx_run.warnings() == "" assert set(sphinx_run.env.metadata["basic_unrun"].keys()) == { "file_format", "author", "source_map", "wordcount", "kernelspec", "language_info", } assert set(sphinx_run.env.nb_metadata["basic_unrun"].keys()) == { "exec_data", } assert sphinx_run.env.metadata["basic_unrun"]["author"] == "Chris" assert sphinx_run.env.metadata["basic_unrun"]["kernelspec"] == { "display_name": "Python 3", "language": "python", "name": "python3", } file_regression.check( sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb", encoding="utf-8" ) file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "basic_unrun.md", conf={"nb_execution_mode": "off", "source_suffix": {".md": "myst-nb"}}, ) def test_basic_run_exec_off(sphinx_run, file_regression, check_nbs): sphinx_run.build() # print(sphinx_run.status()) assert set(sphinx_run.env.metadata["basic_unrun"].keys()) == { "file_format", "author", "source_map", "wordcount", "kernelspec", } assert set(sphinx_run.env.nb_metadata["basic_unrun"].keys()) == set() assert sphinx_run.env.metadata["basic_unrun"]["author"] == "Chris" file_regression.check( sphinx_run.get_nb(), check_fn=check_nbs, extension=".ipynb", encoding="utf-8" ) file_regression.check( sphinx_run.get_doctree().pformat(), extension=".xml", encoding="utf-8" ) @pytest.mark.sphinx_params( "basic_nometadata.md", conf={"nb_execution_mode": "off", "source_suffix": {".md": "myst-nb"}}, ) def test_basic_nometadata(sphinx_run): """A myst-markdown notebook with no jupytext metadata should raise a warning.""" sphinx_run.build() # print(sphinx_run.status()) assert "Found an unexpected `code-cell`" in sphinx_run.warnings() ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based/����������������������������������������������������������������0000775�0000000�0000000�00000000000�14674535606�0017207�5����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based/test_basic_run.ipynb��������������������������������������������0000664�0000000�0000000�00000002172�14674535606�0023260�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "id": "f0dc5a7d", "metadata": {}, "source": [ "# a title\n", "\n", "this was created using `jupytext --to myst tests/notebooks/basic_unrun.ipynb`" ] }, { "cell_type": "code", "execution_count": 1, "id": "49a06520", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "author": "Chris", "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": "0.8", "jupytext_version": "1.4.1+dev" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "source_map": [ 13, 19 ] }, "nbformat": 4, "nbformat_minor": 5 } ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based/test_basic_run.xml����������������������������������������������0000664�0000000�0000000�00000001374�14674535606�0022742�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> this was created using <literal> jupytext --to myst tests/notebooks/basic_unrun.ipynb <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="1" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="ipython3" xml:space="preserve"> a=1 print(a) <container classes="cell_output" nb_element="cell_code_output"> <literal_block classes="output stream" language="myst-ansi" xml:space="preserve"> 1 ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based/test_basic_run_exec_off.ipynb�����������������������������������0000664�0000000�0000000�00000001412�14674535606�0025112�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������{ "cells": [ { "cell_type": "markdown", "id": "f1b10cf9", "metadata": {}, "source": [ "# a title\n", "\n", "this was created using `jupytext --to myst tests/notebooks/basic_unrun.ipynb`" ] }, { "cell_type": "code", "execution_count": null, "id": "9daa25c2", "metadata": {}, "outputs": [], "source": [ "a=1\n", "print(a)" ] } ], "metadata": { "author": "Chris", "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": "0.8", "jupytext_version": "1.4.1+dev" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "source_map": [ 13, 19 ] }, "nbformat": 4, "nbformat_minor": 5 } ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tests/test_text_based/test_basic_run_exec_off.xml�������������������������������������0000664�0000000�0000000�00000001071�14674535606�0024572�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������<document source="basic_unrun"> <section ids="a-title" names="a\ title"> <title> a title <paragraph> this was created using <literal> jupytext --to myst tests/notebooks/basic_unrun.ipynb <container cell_index="1" cell_metadata="{}" classes="cell" exec_count="True" nb_element="cell_code"> <container classes="cell_input" nb_element="cell_code_source"> <literal_block language="python" xml:space="preserve"> a=1 print(a) �����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������MyST-NB-1.1.2/tox.ini�������������������������������������������������������������������������������0000664�0000000�0000000�00000002333�14674535606�0014200�0����������������������������������������������������������������������������������������������������ustar�00root����������������������������root����������������������������0000000�0000000������������������������������������������������������������������������������������������������������������������������������������������������������������������������# To use tox, see https://tox.readthedocs.io # Simply pip or conda install tox # If you use conda, you may also want to install tox-conda # then run `tox` or `tox -- {pytest args}` # To run in parallel using `tox -p` (this does not appear to work for this repo) # To rebuild the tox environment, for example when dependencies change, use # `tox -r` # Note: if the following error is encountered: `ImportError while loading conftest` # then then deleting compiled files has been found to fix it: `find . -name \*.pyc -delete` [tox] envlist = py311-sphinx7 [testenv] usedevelop = true [testenv:py{39,310,311,312}-sphinx{5,6,7,8}] extras = testing deps = sphinx5: sphinx>=5,<6 sphinx6: sphinx>=6,<7 sphinx7: sphinx>=7,<8 sphinx8: sphinx>=8,<9 commands = pytest {posargs} [testenv:docs-{update,clean}] extras = rtd deps = ipython setenv = BUILDER = {env:BUILDER:html} whitelist_externals = echo rm commands = clean: rm -rf docs/_build sphinx-build {posargs} -nW --keep-going -b {env:BUILDER} docs/ docs/_build/{env:BUILDER} commands_post = echo "open file://{toxinidir}/docs/_build/{env:BUILDER}/index.html" [pytest] markers = sphinx_params: Specify parameters to pass to the sphinx_run fixture �������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������