pax_global_header00006660000000000000000000000064152654425730014527gustar00rootroot0000000000000052 comment=fc1b1299e7b5aab58368ce92620dc7eec2a50db5 unyt-3.2.0/000077500000000000000000000000001526544257300125305ustar00rootroot00000000000000unyt-3.2.0/.editorconfig000066400000000000000000000004441526544257300152070ustar00rootroot00000000000000# http://editorconfig.org root = true [*] indent_style = space indent_size = 4 trim_trailing_whitespace = true insert_final_newline = true charset = utf-8 end_of_line = lf [*.bat] indent_style = tab end_of_line = crlf [LICENSE] insert_final_newline = false [Makefile] indent_style = tab unyt-3.2.0/.git-blame-ignore-revs000066400000000000000000000003031526544257300166240ustar00rootroot00000000000000# automated linting (black + isort + blacken-docs) 982fc992fa6cb636d87ccc445e695cb5f964eb12 # flynt + pyupgrade 381650d795922aeba997cbe46685d3ca21ccba51 d707555f9386c5b7fe8f50b12d52257375f9840a unyt-3.2.0/.gitattributes000066400000000000000000000000361526544257300154220ustar00rootroot00000000000000unyt/_version.py export-subst unyt-3.2.0/.github/000077500000000000000000000000001526544257300140705ustar00rootroot00000000000000unyt-3.2.0/.github/ISSUE_TEMPLATE.md000066400000000000000000000004731526544257300166010ustar00rootroot00000000000000* unyt version: * Python version: * Operating System: ### Description Describe what you were trying to get done. Tell us what happened, what went wrong, and what you expected to happen. ### What I Did ``` Paste the command(s) you ran and the output. If there was a crash, please include the traceback here. ``` unyt-3.2.0/.github/dependabot.yml000066400000000000000000000006031526544257300167170ustar00rootroot00000000000000version: 2 updates: - package-ecosystem: github-actions directory: /.github/workflows schedule: interval: monthly cooldown: default-days: 7 groups: gha-patches: update-types: - patch - package-ecosystem: uv directory: / schedule: interval: monthly cooldown: default-days: 7 groups: uv.lock-patches: update-types: - patch unyt-3.2.0/.github/workflows/000077500000000000000000000000001526544257300161255ustar00rootroot00000000000000unyt-3.2.0/.github/workflows/bleeding-edge.yaml000066400000000000000000000025061526544257300214670ustar00rootroot00000000000000name: CI (bleeding edge) # this workflow is heavily inspired from pandas, see # https://github.com/pandas-dev/pandas/blob/master/.github/workflows/python-dev.yml # check stability against dev version of Python, numpy, and matplotlib # and sympy pre-releases (but avoid pre-releases of sympy's dependencies) on: push: branches: - master pull_request: paths: - .github/workflows/bleeding-edge.yaml # Run every Wednesday at 0:01 UTC schedule: - cron: 1 0 * * 3 workflow_dispatch: jobs: build: runs-on: ubuntu-latest name: CPython-dev timeout-minutes: 60 concurrency: group: ${{ github.ref }}-dev cancel-in-progress: true steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.15' enable-cache: false - name: Configure uv run: | echo "UV_PRERELEASE=allow" >> "$GITHUB_ENV" echo "UV_INDEX=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" >> "$GITHUB_ENV" echo "UV_INDEX_STRATEGY=unsafe-best-match" >> "$GITHUB_ENV" - run: uv sync --group test - name: Run tests run: uv run --no-sync pytest -vvv unyt unyt-3.2.0/.github/workflows/ci.yml000066400000000000000000000125551526544257300172530ustar00rootroot00000000000000name: CI on: push: branches: - master pull_request: workflow_dispatch: jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: fetch-depth: 0 persist-credentials: false - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.14' enable-cache: false - run: uv build unyt-module-test-function: runs-on: ubuntu-latest needs: [build] steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.14' cache-suffix: test-module - run: uv run --group test python -c "import unyt ; unyt.test()" test: runs-on: ${{ matrix.os }} needs: [build] strategy: fail-fast: false matrix: os: [ubuntu-latest] python-version: - '3.11' - '3.12' - '3.13' - '3.14' - '3.15' sync-args: [null] pytest-args: [null] label: [null] # Test all on ubuntu, test ends on macos and windows include: - os: macos-latest python-version: '3.11' sync-args: null pytest-args: null label: null - os: windows-latest python-version: '3.11' sync-args: null pytest-args: null label: null - os: macos-latest python-version: '3.15' sync-args: null pytest-args: null - os: windows-latest python-version: '3.15' sync-args: null pytest-args: null label: null # with integration deps - os: ubuntu-latest python-version: '3.14' sync-args: --group integration pytest-args: null label: integration # with lowest dependencies - os: ubuntu-22.04 python-version: '3.11' sync-args: --resolution=lowest --group integration pytest-args: null label: oldestdeps # with doctests - os: ubuntu-latest python-version: '3.11' sync-args: --group doc pytest-args: --doctest-modules --doctest-plus label: doctests steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: fetch-depth: 0 persist-credentials: false - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: ${{ matrix.python-version }} - name: Install test env run: uv sync --group covcheck ${{ matrix.sync-args }} - name: Run tests shell: bash # for windows compat run: | uv run --no-sync coverage run --parallel-mode -m \ pytest --color=yes ${{ matrix.pytest-args }} - name: Upload coverage data # only using reports from ubuntu because # combining reports from multiple platforms is tricky if: startsWith( matrix.os , 'ubuntu' ) uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: name: unyt_coverage_data-${{matrix.os}}-${{matrix.python-version}}-${{matrix.label}} path: .coverage.* if-no-files-found: ignore include-hidden-files: true coverage: name: Combine & check coverage. runs-on: ubuntu-latest needs: [test] steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: fetch-depth: 0 persist-credentials: false - uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.14' - uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8.0.1 with: pattern: unyt_coverage_data-* merge-multiple: true - name: Check coverage run: | uv tool install coverage coverage combine coverage html --skip-covered --skip-empty coverage report --skip-covered --format=markdown >> "$GITHUB_STEP_SUMMARY" coverage report --fail-under=99 - name: Upload HTML report uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: name: unyt_coverage_report path: htmlcov docs: runs-on: ubuntu-latest needs: [build] steps: - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 with: fetch-depth: 0 persist-credentials: false - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.11' - run: uv sync --group doc --group test - name: Build docs run: | uv run --no-sync --directory=docs \ python -m sphinx -M html "." "_build" -W - name: Upload build uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: name: unyt_docs path: docs/_build/html if-no-files-found: error unyt-3.2.0/.github/workflows/publish.yml000066400000000000000000000015451526544257300203230ustar00rootroot00000000000000name: Publish to PyPI on: push: tags: v* pull_request: paths: - .github/workflows/publish.yml jobs: pypi-publish: name: Upload release to PyPI runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/unyt permissions: id-token: write steps: - name: Checkout Source uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1 - name: Setup Python uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0 with: python-version: '3.14' enable-cache: false - name: Build distributions run: uv build - name: Publish package distributions to PyPI if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # v1.14.2 unyt-3.2.0/.gitignore000066400000000000000000000026631526544257300145270ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # setuptools_scm generated file unyt/_version.py # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # this can probably be removed at some point # https://github.com/pypa/pip/issues/6213 pip-wheel-metadata/ # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # dotenv .env # virtualenv .venv venv/ ENV/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # Autogenerated API Documentation docs/modules/ # benchmark outputs benchmarks/*.json benchmarks/*.png # Pycharm config .idea/ unyt-3.2.0/.mailmap000066400000000000000000000010301526544257300141430ustar00rootroot00000000000000Nathan Goldbaum Nathan Goldbaum Nathan Goldbaum Nathan Goldbaum Kacper Kowalik Kacper Kowalik (Xarthisius) Kyle Sunden ksunden Kyle Sunden Kyle Sunden John ZuHone jzuhone Ricarda Beckmann RicardaBeckmann unyt-3.2.0/.pre-commit-config.yaml000066400000000000000000000025701526544257300170150ustar00rootroot00000000000000minimum_pre_commit_version: 1.15.0 ci: autofix_prs: false autoupdate_schedule: monthly exclude: "^(\ paper/.*.py\ )" repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: no-commit-to-branch - id: check-shebang-scripts-are-executable - id: check-executables-have-shebangs - id: check-yaml - id: check-toml - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.12.23 hooks: - id: uv-lock # TODO: replace this with ruff when it supports embedded python blocks # see https://github.com/astral-sh/ruff/issues/8237 - repo: https://github.com/adamchainz/blacken-docs rev: 1.20.0 hooks: - id: blacken-docs additional_dependencies: [black==24.10.0] - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.16.10 hooks: - id: ruff-format - id: ruff-check args: [--fix, --show-fixes] - repo: https://github.com/pre-commit/pygrep-hooks rev: v1.10.0 hooks: - id: rst-backticks - repo: https://github.com/codespell-project/codespell rev: v2.4.3 hooks: - id: codespell args: [--skip="*.pdf", -L, 'smoot,copin,celcius,ue,bu,dne,ond'] unyt-3.2.0/.readthedocs.yaml000066400000000000000000000012401526544257300157540ustar00rootroot00000000000000# .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-22.04 tools: python: "3.12" commands: - asdf plugin add uv - asdf install uv latest - asdf global uv latest - uv sync --frozen --group doc - uv run --no-sync python -m sphinx -M html "." "_build" -W # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf unyt-3.2.0/AUTHORS.rst000066400000000000000000000024571526544257300144170ustar00rootroot00000000000000======= Credits ======= Contributors ------------ * Daniel Bates * Ricarda Beckmann * Andrei Berceanu * Josh Borrow * Yi-Hao Chen * Bili Dong * Sam Dotson * Bing Gao * Nathan Goldbaum * David Hannasch * Chris Havlin * Peter Hayman * Cameron Hummels * Thomas Hisch * Paul Ivanov * Suoqing Ji * Lee Johnston * Ben Kimock * Kacper Kowalik * Iason Krommydas * Nathan Musoke * Andrew Myers * Kyle Oman * Osnippet * Clément Robert * Will Roper * Simon Schopferer * Sam Skillman * Britton Smith * Josh Soref * Kyle Sunden * Ben Thompson * Matthew Turk * Miguel de Val-Borro * Jeroen Van Goey * Mike Zingale * John ZuHone This library was adapted from `dimensionful `_ which was written by Casey Stark . unyt-3.2.0/CONTRIBUTING.rst000066400000000000000000000162261526544257300152000ustar00rootroot00000000000000.. highlight:: shell ============ Contributing ============ Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. Code of Conduct --------------- The development of ``unyt`` happens in the context of the `yt community code of conduct `_. If for any reason you feel that the code of conduct has been violated in the context of ``unyt`` development, please send an e-mail to confidential@yt-project.org with details describing the incident. All emails sent to this address will be treated with the strictest confidence by an individual who does not normally participate in yt development. Types of Contributions ---------------------- You can contribute in many ways: Report Bugs ~~~~~~~~~~~ Report bugs at https://github.com/yt-project/unyt/issues. If you are reporting a bug, please include: * Your operating system name and version. * Any details about your local setup that might be helpful in troubleshooting. This includes things like Python version and versions of any libraries being used, including unyt. * If possible, detailed steps to reproduce the bug. Fix Bugs ~~~~~~~~ Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it. Implement Features ~~~~~~~~~~~~~~~~~~ Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it. Write Documentation ~~~~~~~~~~~~~~~~~~~ unyt could always use more documentation, whether as part of the official unyt docs, in docstrings, or even on the web in blog posts, articles, and such. Submit Feedback ~~~~~~~~~~~~~~~ The best way to send feedback is to file an issue at https://github.com/yt-project/unyt/issues. If you are proposing a feature: * Explain in detail how it would work. * Keep the scope as narrow as possible, to make it easier to implement. * Remember that this is a volunteer-driven project, and that contributions are welcome :) Get Started! ------------ Ready to contribute? Here's how to set up ``unyt`` for local development. The ``unyt`` test suite makes use of ``uv``, which makes it easy to run tests on multiple python versions. This guide uses ``uv`` extensively, but note that you do not have to use ``uv``-managed Python interpreters, see uv's documentation https://docs.astral.sh/uv/concepts/python-versions/#requiring-or-disabling-managed-python-versions 1. Fork the ``unyt`` repo on GitHub. 2. Clone your fork locally:: $ git clone git@github.com:your_name_here/unyt.git 4. Setup a development environment $ cd unyt/ $ uv sync 5. Create a branch for local development:: $ git switch -c name-of-your-bugfix-or-feature 6. Edit files in the ``unyt`` repository, using your local python installation to test your edits. 7. When you're done making changes, check that your changes pass linting, and run the tests $ pre-commit run --all-files $ uv run --group test pytest unyt --doctest-modules --doctest-rst --doctest-plus 8. Commit your changes and push your branch to GitHub:: $ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature 9. Submit a pull request through the GitHub website. Testing unyt ------------ We use the ``pytest`` test runner as well as ``uv`` to manage running tests on various versions of python. To run the tests on your copy of the ``unyt`` repository using your current python environment, run ``pytest`` in the root of the repository using the following arguments:: $ cd unyt/ $ uv run --group test pytest unyt --doctest-modules --doctest-rst --doctest-plus These enable testing the docstrings and doctest examples scattered throughout the unyt and its documentation. Integration tests require additional dependencies, which you can get by passing the following additional argument to ``uv run``: ``--group integration`` If you would like to run the tests on different Python versions, add ``--python=``, where ```` can be a version number or a path. Pull Request Guidelines ----------------------- Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests for functionality that is not already tested. We strive for 100% test coverage and pull requests should not add any new untested code. You can generate coverage reports locally by first generating coverage statistics with ``pytest`` through ``coverage`` as ``uv run --group covcheck coverage run -m pytest``. To generate reports from the coverage statistics, you can use ``uv run coverage report`` to print to screen or ``uv run coverage html`` to generate an html that you can open in a browser. 2. If the pull request adds functionality the docs should be updated. If your new functionality adds new functions or classes to the public API, please add docstrings. If you modified an existing function or class in the public API, please update the existing docstrings. If you modify private implementation details, please use your judgment on documenting it with comments or docstrings. 3. The pull request should work for Python 3.11, 3.12, 3.13, 3.14 and 3.15 Check in the GitHub interface for your pull request and make sure that the tests pass for all supported Python versions. Deploying --------- A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst and adding any new contributors to AUTHORS.rst). The version number must also be updated, preferably in its own PR. The preferred way to do it is through ``uv version --bump ``. If doing a bugfix release, you may need to create a - or checkout an existing - backport branch named ``vX.Y.x`` where ``X`` and ``Y`` represent the relevant major and minor version numbers, and the lowercase ``x`` is literal. Otherwise you may just release from the development branch. Once you are ready, create a tag: $ git tag vX.Y.Z # where X, Y and Z should be meaningful major, minor and micro version numbers In any case, take care that the version number for the tag matches what was chosen for the version number. If the tests pass you can then subsequently manually do a test publication:: $ uv build # builds a source distribution and a wheel under dist/ $ uvx twine check dist/* $ uv publish --publish-url https://test.pypi.org/legacy/ Then, using a fresh environment here, and from outside the repository, test the result:: $ uv sync --only-group test $ uv pip install unyt --reinstall --only-binary --index https://test.pypi.org/simple/ --index-strategy=unsafe-best-match $ uv run --no-sync python -c "import unyt; unyt.test()" $ uv pip install unyt --reinstall --no-binary --index https://test.pypi.org/simple/ --index-strategy=unsafe-best-match $ uv run --no-sync python -c "import unyt; unyt.test()" Finally, if everything works well, push the tag to the upstream repository:: $ git push upstream --tag # assuming the mother repo yt-project/unyt is set as a remote under the name "upstream" unyt-3.2.0/HISTORY.rst000066400000000000000000001327231526544257300144330ustar00rootroot00000000000000======= History ======= 3.2.0 (2026-10-16) ------------------ This new release of ``unyt`` fixes bugs discovered since the v3.1.0 release, as well as including new features. * The instructions for how to test release artifacts before publication have been amended (`PR #623 `_). Thanks to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Make ``np.squeeze`` return the correct type for the shape of the result (`PR #628 `_). Thanks to Kyle Oman (@kyleaoman on GitHub) for the contribution. * The computation of the MD5 digest of ``UnitRegistry`` no longer raises a ``ValueError`` on FIPS-enabled systems (such as certain HPC systems, `PR #643 `_). Thank you to Bing Gao (@binggao1230 on GitHub) for the contribution. * It is now possible to develop other classes that can handle multiplication and division operations with the ``Unit`` class (`PR #640 `_). Thanks to Kyle Oman (@kyleaoman on GitHub) for the contribution. * ``np.digitize`` is now unit-aware (`PR #644 `_). Thank you to Bing Gao (@binggao1230 on GitHub) for the contribution. * The documented ``dtype`` argument in ``Unit.get_conversion_factor`` is now honored (`PR #661 `_). Thank you to @youdie006 on GitHub for the contribution. * Python 3.15 is now supported (`PR #671 `_). Support for Python 3.10 has been dropped (`PR #673 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contributions. * Binary ``ufunc`` operations between ``unyt_array`` objects and other array-like objects are now properly handled (`PR #674 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Conversions between temperature interval units and temperature units are no longer allowed, as they have no physical meaning (`PR #676 `_). Thank you to @shaneraphel on GitHub for the contribution. * ``unyt_array`` objects now properly return ``NotImplemented`` in array interoperability methods (`PR #677 `_). Thank you to Iason Krommydas (@ikrommyd on GitHub) for the contribution. * If a ``unyt_array`` is created from another ``unyt_array`` and a new ``dtype`` is specified, the new requested ``dtype`` is now used (`PR #679 `_). Thanks to Will Roper (@WillJRoper on GitHub) for the contribution. * The conversion factors for the nautical mile and knot units were slightly inaccurate and have been fixed (`PR #680 `_). Thank you to Shubham Padkonde (@Shubham-Padkonde on GitHub) for the contribution. * Multiplying a ``float32`` array by the ``Msun`` unit no longer promotes the array to ``float64`` (`PR #681 `_). Thank you to Will Roper (@WillJRoper on GitHub) for the contribution. 3.1.0 (2026-01-12) ------------------ This new release of ``unyt`` fixes bugs discovered since the v3.0.4 release, as well as including new features. * Fix the implementation of ``np.block`` so that multiplication is not used to attach units to the result (`PR #571 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Increase the ability for subclasses of ``unyt_array`` to manipulate function calls, in particular so that the result of a binary operation of an ``unyt_array`` with a subclass will return the subclass (`PR #572 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Add the angular units "spat", "turn", "pla", "gradian", and "gon" (`PR #573 `_). Thank you to John ZuHone (@jzuhone on GitHub) for the contribution. * Use ``uv`` to run the test suite instead of ``tox`` (`PR #574 `_ and `PR #594 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contributions. * Start testing against Python 3.14 in CI (`PR #581 `_ and `PR #594 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contributions. * Fix incompatibilities and deprecations with NumPy 2.4 (`PR #595 `_, `PR #597 `_, `PR #607 `_, `PR #614 `_). * Switch build-backend from ``setuptools`` to ``flit-core`` (`PR #601 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix histograms with mismatched units of input and bins (`PR #610 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix a bug where ``np.average`` would strip units on sum of weights when input and weights shapes differ (`PR #611 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. 3.0.4 (2025-03-13) ------------------ This new bugfix release of ``unyt`` fixes bugs discovered since the v3.0.3 release. It also adds support for foe and bethe units: * Add foe and bethe units (`PR #521 `_). Thank you to Michael Zingale (@zingale on GitHub) for the contribution. Following is a list of all bug fixes and documentation fixes included in the release. * Fix incompatibilities with numpy 2.1 (`PR #512 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix stephan-boltzmann constant accuracy and add radiation constant (`PR #520 `_). Thank you to Mike Zingale (@zingale on GitHub) for the contribution. * Fix raising a unyt array to an array power in sensible cases (`PR #524 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Typo fixes and documentation fix (`PR #529 `_). Thank you to Jeroen Van Goey (@BioGeek on GitHub) for the contribution. * Fix return units from numpy.ftt functions (`PR #531 `_). Thank you to Josh Borrow (@JBorrow on GitHub) for the contribution. * Fix incorrect output unit for ``np.prod`` with an axis argument (`PR #537 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * ``np.histogram*`` functions give correct units when weights and/or density are set (`PR #539 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix an issue where ``np.histogramdd`` could create infinite recursion on some inputs (`PR #541 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * ``linspace`` and ``logspace`` give incorrect results or crash with some inputs (`PR #544 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix typo in array function implementations (ftt -> fft) (`PR #547 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Apply_over_axes no longer assumes user-supplied function preserves units (`PR #548 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Allow subclassing in ``unyt_array.__array_func__`` (`PR #550 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix unit handling for ``np.take`` and ``unyt_array.take`` (`PR #551 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix a regression where ``np.linspace`` 's num argument would be ignored for ``unyt_array`` instances (`PR #553 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Handle ``np.vecdot`` as a ufunc rather than an arrayfunc (`PR #557 `_). Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. * Fix an issue where hdf5 io wouldn't roundtrip properly for a ``unyt_quantity`` object (`PR #560 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Make ``test_unique_values`` order-agnostic and fix testing against numpy 2.3 dev (`PR #565 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. 3.0.3 (2024-07-02) ------------------ This new bugfix release of ``unyt`` fixes bugs discovered since the v3.0.2 release. * Fix defects when running test suite in isolation from the project's pytest configuration (`PR #495 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Drop test case for unpickling old pickle files as too sensitive to upstream changes (`PR #498 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix signature incompatibilities in nep 18 wrapped functions (`PR #500 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an incompatibility with sympy 1.13.0rc1 (`PR #504 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Adjust doctests to changes in array repr from numpy 2.0 (`PR #506 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * TST: declare np.unstack as subclass-safe (fix incompatibility with Numpy 2.1) (`PR #509 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. 3.0.2 (2024-03-13) ------------------ This new bugfix release of ``unyt`` fixes bugs discovered since the v3.0.1 release, and is intended to be compatible with the upcoming NumPy 2.0 release. * Fix minimal requirement on setuptools_scm (`PR #471 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Explicitly forbid destructive edits to the default unit registry (`PR #475 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an issue where array functions would raise ``UnitInconsistencyError`` when operands' units differ by some dimensionless factor (`PR #478 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Implement and test array functions new in numpy 2.0 (`PR #483 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix compat with numpy dev for ``np.trapezoid`` (previously named np.trapz) (`PR #486 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Implement missing support for ``np.cbrt`` (`PR #491 `_). Thank you to @yuyttenhove for the contribution. * Fix compatibility with numpy 2.0 copy semantics (`PR #492 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. 3.0.1 (2023-11-02) ------------------ This new bugfix release of ``unyt`` fixes a few bugs since the v3.0.0 release. * Fix an issue where array functions would raise ``UnitConsistencyError`` on ``unyt_array`` objects using non-default unit registries (`PR #463 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an issue where array functions would crash (``AttributeError``) when passed non-``ndarray`` array-like objects (e.g. Python lists) (`PR #464 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix backward compatibility for calling ``numpy.histogram`` with implicit range units (`PR #466 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. 3.0.0 (2023-11-01) ------------------ This new major release of ``unyt`` fixes a number of issues and adds a number of new features. Major contributions include: * Support for Python 3.8 has been dropped. * Support for Python 3.12 has been added. * Support for NumPy <1.19.3 has been dropped. * Support for SymPy <1.7 has been dropped. * A new ``unyt_dask_array`` class, which implements a subclass of standard `dask arrays `_ with units attached, has been added (`PR #185 `_). See :ref:`dask` for more details. Thank you to Chris Havlin (@chrishavlin on Github) for the contribution. * A number of new metric and non-metric units have been added in `PR #441 `_. Thank you to John ZuHone (@jzuhone on GitHub) for the contribution. * A number of common values for the solar metallicity found in the literature have been added as new metallicity units (`PR #315 `_). See :ref:`metal_conversions` for more details. Thank you to John ZuHone (@jzuhone on GitHub) for the contribution. * The "liter" unit has been added (`PR #305 `_). Thank you to Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. * The following common systems engineering units for energy have been added: ``MMBTU``, ``therm``, ``quad``, and ``Wh`` (`PR #294 `_). Thank you to Sam Dotson (@samgdotson on GitHub) for the contribution. * The ``@returns`` decorator (documented in :ref:`checking_units`) now allows dimension-checking of multiple return values (`PR #435 `_). Thank you to Daniel Bates (@db434 on GitHub) for the contribution. * A number of PRs to support `NEP 18 `_, including the following (thank you to Clément Robert, @neutrinoceros on GitHub, and Nathan Goldbaum, @ngoldbaum on Github, for the contributions): - `PR #200 `_. - `PR #293 `_. - `PR #295 `_. - `PR #304 `_. - `PR #309 `_. - `PR #313 `_. - `PR #316 `_. - `PR #317 `_. - `PR #319 `_. - `PR #320 `_. - `PR #324 `_. - `PR #325 `_. - `PR #329 `_. - `PR #338 `_. - `PR #348 `_. - `PR #351 `_. - `PR #352 `_. - `PR #388 `_. - `PR #394 `_. - `PR #395 `_. - `PR #396 `_. - `PR #397 `_. - `PR #398 `_. * A fix for for the LaTeX representation of Planck units (`PR #379 `_). Thank you to Peter Hayman (@haymanpf on GitHub) for the contribution. * A fix for a bug that prevented the conversion of dimensionless arrays to their corresponding `AstroPy Quantities `_ (`PR #437 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * A fix for a bug in subtraction of temperature quantities that resulted in ``degC`` units being returned instead of ``delta_degC`` units (`PR #413 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fixes for issues with the comparison of temperature quantities (`PR #408 `_ and `PR #412 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Support for versions of NumPy < 1.19 has been dropped in this version (`PR #403 `_). Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * A number of PRs to support NumPy 2.0, thank you to Clément Robert (@neutrinoceros on GitHub) for the contributions: - `PR #434 `_. - `PR #442 `_. - `PR #443 `_. - `PR #445 `_. - `PR #448 `_. - `PR #455 `_. - `PR #456 `_. 2.9.5 (2023-02-22) ------------------ * Fix a regression where arrays elements with dtype ``'int8'`` would not compare to floats as intended. See `PR #371 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) and Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. * Raise an error in case an array element is assigned to a new value with incompatible units. See `PR #375 `_ and `PR #376 `_. Thank you to Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. 2.9.4 (2023-02-06) ------------------ * Make ``unyt_quantity.from_string`` parse ints. See `PR #278 `_. Thank you to Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. * TST: migrate from tox-pyenv to tox-gh-actions #344 See `PR #344 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Correctly test string comparison depending on numpy version #358 See `PR #358 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Multiple fixes for ``unyt_quantity.from_string`` - fix a bug where ``unyt_quantity.from_string`` would drop part of the unit expression - fix a bug where ``unyt_quantity.from_string`` would choke on unit expressions starting with ``'*'`` or ``'/'`` - fix a bug where ``unyt_quantity.from_string`` would choke on space-separated unit expressions - fix roundtrip for ``unyt_quantity.from_string`` and ``unyt_quantity.to_string`` methods - simplify unit regexp (``'**/2'`` isn't a valid exponent) - fix a bug where malformed string input would be incorrectly parsed by ``unyt_quantity.from_string`` See `PR #362 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution, and to Chris Byrohl (@cbyrohl on GitHub) for the report. 2.9.3 (2022-12-07) ------------------ * Fix a future incompatibility with numpy 1.25 (unreleased) where comparing ``unyt_array`` objects to non-numeric objects (e.g. strings) would cause a crash. See `PR #333 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) and Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. 2.9.2 (2022-07-20) ------------------ * Fix an issue where taking powers of units was backwards-incompatible with previous versions of ``unyt`` when the exponent is not zero. See `PR #249 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * The import time for ``unyt`` has been reduced by skipping version checking of other packages. See `PR #251 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. 2.9.0 (2022-07-14) ------------------ * Dropped support for Python 3.6 and 3.7. * Added support for Python 3.8, 3.9 and 3.10. * Fix an issue where SI prefixes of the ``degC`` units would give incorrect values in conversions. See `PR #176 `_. Thank you to Lee Johnston (@l-johnston on GitHub) for the contribution. * Fix an issue when using ``matplotlib_support``, plot an empty unyt array, would result in an error when changing units. See `PR #180 `_. Thank you to Josh Borrow (@JBorrow on GitHub) for the contribution. * Fix an issue where units would be printed twice in formatted strings with an ``unyt_array`` embedded. See `PR #188 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Add a method to parse a ``unyt_quantity`` from a string expression. See `PR #191 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an issue where a ``unyt_array`` with dtype int8 could not be converted to a different unit. See `PR #197 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * The import time for ``unyt`` has been reduced. See `PR #199 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an issue where taking an ``unyt_array`` or ``unyt_quantity`` to a zero power would retain the units of the original array or quantity instead of converting to a dimensionless array. See `PR #204 `_. Thank you to Josh Borrow (@JBorrow on GitHub) for the contribution. * Add support for coercing iterables of ``unyt_array`` objects with nonuniform dimensionally equivalent units to a single ``unyt_array``. See `PR #211 `_. Thank you to Nathan Goldbaum (@ngoldbaum on GitHub) for the contribution. * Add the civil engineering units ``pli``, ``plf``, ``psf``, ``kli``, ``klf``, and ``ksf``. See `PR #217 `_. Thank you to @osnippet on GitHub for the contribution. * Fix typos in constants and unit prefixes. See `PR #218 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Fix an issue where multiplying a 1-element ``unyt_array`` would return a ``unyt_quantity``. See `PR #225 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the contribution. * Add the Rydberg constant ``R_∞`` and unit ``Ry``, add the dimension ``angular_frequency`` and the unit ``rpm``, and increase the precision of Avogadro's number. See `PR #228 `_. * Fix an issue where ``np.divide.reduce`` would return incorrect units for ``unyt_array`` instances. See `PR #230 `_. Thank you to Kyle Oman (@kyleaoman on GitHub) for the contribution. 2.8.0 (2020-10-05) ------------------ * Dropped support for Python 3.5. * Add ``delta_degC`` and ``delta_degF`` units to support temperature difference arithmetic. See `PR #152 `_. Thank you to Lee Johnston (@l-johnston on GitHub) for the contribution. * Fix an issue where a subsequent load of the unit registry with units that are equal but not identical leads to a crash. See `PR #158 `_. Thank you to Matthew Turk (@matthewturk on GitHub) for the initial bug report and fix. * Add force unit ``kip`` and pressure unit ``psi``. Thank you to P. Talley (@otaithleigh on GitHub) for the contribution. See `PR #162 `_. * Fix an issue where arithmetic operations on units defined in different registries and having the conversion defined in one direction would lead to a crash. See `PR #164 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the initial bug report and fix. 2.7.2 (2020-06-29) ------------------ * The ``unyt.returns`` and ``unyt.accepts`` decorators now work correctly for functions that accept or return data with dimensionless units. See `PR #146 `_. Thank you to Simon Schopferer (@simfinite on GitHub) for the initial bug report and fix. * Data used in the tests are packaged with the source distribution and ``unyt.test()`` is now itself run as part of unyt's continuous integration tests. See `PR #149 `_ and `PR #150 `_. Thank you to Miguel de Val-Borro (@migueldvb on GitHub) for the initial bug report and fix. * The ``degC`` and ``degF`` units now render as ``°C`` and ``°F`` by default, ``°C`` and ``°F`` are now recognized as valid unit names as well. Thank you to Lee Johnston (@l-johnston on GitHub) for the contribution. * Use a more canonical representation of the micro symbol when printing units with the micro prefix, avoiding issues with displaying unit names in Matplotlib plot labels. See `PR #153 `_. Thank you to Matthew Turk (@matthewturk on GitHub) for the bug report and fix. * Add more alternative spellings for solar units. See `PR #155 `_. Thank you to Clément Robert (@neutrinoceros on GitHub) for the initial bug report. 2.7.1 (2020-02-17) ------------------ * Fix compatibility with ``unyt_array`` subclasses that do not have the new ``name`` argument in their initializer. See `PR #140 `_. * Fix an issue where custom units added to a unit registry were not restored correctly when reloading a unit registry from a JSON or pickle representation. See `PR #140 `_. 2.7.0 (2020-02-06) ------------------ * The ``unyt_array`` and ``unyt_quantity`` classes now have a new, optional ``name`` attribute. The primary purpose of this attribute is to enable automatic generation of matplotlib plot labels. The ``name`` attribute is propagated through unit conversions and copies but is not propagated through mathematical operations. See `PR #129 `_ and the documentation for details. * Add support for the ``Neper`` and ``Bel`` units with logarithmic dimensions. This includes support for the ``decibel`` unit. Note that logarithmic units can only be used with other logarithmic units and must be applied and stripped manually. See `PR #133 `_ and `PR #134 `_. * Add support for the SI unit of inductance, ``H``. See `PR #135 `_. * Fix formatting of error message produced when raising a quantity to a power with units. See `PR #131 `_. Thank you to Lee Johnston (@l-johnston on GitHub) for all of the above contributions. * Fix incorrect unit metadata when loading a pickled array saved by ``yt.units``. See `PR #137 `_. 2.6.0 (2020-01-22) ------------------ * Matplotlib support is no longer enabled by importing ``unyt``. Instead, it is now necessary to use the ``unyt.matplotlib_support`` context manager in code where you want unyt to automatically generate plot labels. Enabling Matplotlib support by default in the previous release caused crashes in previously working code for some users so we have decided to make the plotting support optional. See the documentation for more details. We are sorry for introducing a new feature that broke some user's code. See `PR #126 `_. Thank you to Lee Johnston (@l-johnston on GitHub) for the contribution. * Updated the contribution guide to include more details about setting up multiple Python versions for the ``tox`` tests. 2.5.0 (2020-01-20) ------------------ * Importing unyt now registers unyt with Matplotlib's interface for handling units. See the `Matplotlib `_ and `unyt `_ documentation for more details. See `PR #122 `_ and `PR #124 `_. Thank you to Lee Johnston (@l-johnston on GitHub) for the contribution. * Updated the LaTeX formatting of solar units so they do not get rendered italicized. See `PR #120 `_. Thank you to Josh Borrow (@JBorrow on GitHub) for the contribution. * Reduce floating point round-off error when data are converted from integer to float dtypes. See `PR #119 `_. 2.4.1 (2020-01-10) ------------------ * Add support for the latest releases of h5py, sympy, NumPy, and PyTest. See `PR #115 `_. * Fix the hash implementation so that identical units cannot have distinct hashes. See `PR #114 `_ and `PR #117 `_. Thank you to Ben Kimock (@saethlin on GitHub) for the contribution. 2.4.0 (2019-10-25) ------------------ * Improve performance for creating quantities or small arrays via multiplication with a unit object. Creating an array or quantity from data that does not have a numeric dtype will now raise ``UnitOperationError`` instead of ``UnitDtypeError``, which has been removed. See `PR #111 `_. * Comparing data with units that have different dimensions using the ``==`` and ``!=`` operators will no longer raise an error. Other comparison operators will continue to raise errors. See `PR #109 `_. * Fixed a corner case in the implementation of ``clip``. See `PR #108 `_. Thank you to Matthew Turk (@matthewturk on GitHub) for the contribution. * Added ``%`` as a valid dimensionless unit with a value of ``0.01``, also available under the name ``percent``. See `PR #106 `_. Thank you to Thomas Hisch for the contribution. * Added ``bar`` to the default unit lookup table. See `PR #103 `_. Thank you to Thomas Hisch (@thisch on GitHub) for the contribution. 2.3.1 (2019-08-21) ------------------ * Added support for the ``clip`` ufunc added in NumPy 1.17. See `PR #102 `_. 2.3.0 (2019-08-14) ------------------ * Added ``unyt.dimensions.accepts`` and ``unyt.dimensions.returns``, decorators that can be used to ensure that data passed into a decorated function has units that are dimensionally consistent with the function's expected inputs. See `PR #98 `_. Thank you to Andrei Berceanu (@berceanu on GitHub) for the contribution. * Added ``unyt.allclose_units`` and improved documentation for writing tests for code that uses ``unyt``. This is a wrapper for ``numpy.allclose`` that also checks the units of the input arrays. See `PR #94 `_. Thank you to Andrei Berceanu (@berceanu on GitHub) for the contribution. 2.2.2 (2019-07-03) ------------------ * Fix erroneous conversions of E&M units to their "native" unit system, for example, converting Gauss to CGS units would return Tesla and converting Tesla to MKS units would return Gauss. See `PR #96 `_. 2.2.1 (2019-07-02) ------------------ * Add support for loading JSON unit registries saved by ``yt.units``. See `PR #93 `_. * Correct the value of the ``light_year`` unit. See `PR #93 `_. * It is now possible to define a ``UnitSystem`` object with a quantity. See `PR #86 `_. * Incorrect units for Planck units have been fixed. See `PR #85 `_. Thank you to Nathan Musoke (@musoke on GitHub) for the contribution. * Updated value of Newton's constant to latest CODATA value. See `PR #84 `_. 2.2.0 (2019-04-03) ------------------ * Several performance optimizations. This includes a slight change to the behavior of MKS/CGS E&M unit conversions that makes the conversion rules slightly more relaxed. See `PR #82 `_. 2.1.1 (2019-03-27) ------------------ * Fixed an issue with restoring unit registries from JSON output. See `PR #81 `_. 2.1.0 (2019-03-26) ------------------ This release includes a few minor new features and bugfixes for the 2.0.0 release. * Added support for the matmul ``@`` operator. See `PR #80 `_. * Allow defining unit systems using ``Unit`` instances instead of string unit names. See `PR #71 `_. Thank you to Josh Borrow (@JBorrow on GitHub) for the contribution. * Fix incorrect behavior when ``uhstack`` is called with the ``axis`` argument. See `PR #73 `_. * Add ``"rsun"``, ``"lsun"``, and ``"au"`` as alternate spellings for the ``"Rsun"``, ``"Lsun"``, and ``"AU"`` units. See `PR #77 `_. * Improvements for working with code unit systems. See `PR #78 `_. * Reduce impact of floating point round-off noise on unit comparisons. See `PR #79 `_. 2.0.0 (2019-03-08) ------------------ ``unyt`` 2.0.0 includes a number of exciting new features as well as some bugfixes. There are some small backwards incompatible changes in this release related to automatic unit simplification and handling of dtypes. Please see the release notes below for more details. If you are upgrading from ``unyt 1.x`` we suggest testing to make sure these changes do not significantly impact you. If you run into issues please let us know by `opening an issue on GitHub `_. * Dropped support for Python 2.7 and Python 3.4. Added support for Python 3.7. * Added ``Unit.simplify()``, which cancels pairs of terms in a unit expression that have inverse dimensions and made it so the results of ``unyt_array`` multiplication and division will automatically simplify units. This means operations that combine distinct dimensionally equivalent units will cancel in many situations. For example .. code-block:: >>> from unyt import kg, g >>> print((12 * kg) / (4 * g)) 3000.0 dimensionless older versions of ``unyt`` would have returned ``4.0 kg/g``. See `PR #58 `_ for more details. This change may cause the units of operations to have different, equivalent simplified units than they did with older versions of ``unyt``. * Added the ability to resolve non-canonical unit names to the equivalent canonical unit names. This means it is now possible to refer to a unit name using an alternative non-canonical unit name when importing the unit from the ``unyt`` namespace as well as when a unit name is passed as a string to ``unyt``. For example: .. code-block:: >>> from unyt import meter, second >>> data = 1000.0 * meter / second >>> data.to("kilometer/second") unyt_quantity(1., 'km/s') >>> data.to("metre/s") unyt_quantity(1000., 'm/s') The documentation now has a table of units recognized by ``unyt`` along with known alternative spellings for each unit. * Added support for unicode unit names, including ``μm`` for micrometer and ``Ω`` for ohm. See `PR #59 `_. * Substantially improved support for data that does not have a ``float64`` dtype. Rather than coercing all data to ``float64`` ``unyt`` will now preserve the dtype of data. Data that is not already a numpy array will be coerced to a dtype by calling ``np.array`` internally. Converting integer data to a new unit will convert the data to floats, if this causes a loss of precision then a warning message will be printed. See `PR #55 `_ for details. This change may cause data to be loaded into ``unyt`` with a different dtype. On Windows the default integer dtype is ``int32``, so data may begin to be recognized as ``int32`` or converted to ``float32`` where before it was interpreted as ``float64`` by default. * Unit registries are now associated with a unit system. This means that it's possible to create a unit registry that is associated with a non-MKS unit system so that conversions to "base" units will end up in that non-MKS system. For example: .. code-block:: >>> from unyt import UnitRegistry, unyt_quantity >>> ureg = UnitRegistry(unit_system="cgs") >>> data = unyt_quantity(12, "N", registry=ureg) >>> data.in_base() unyt_quantity(1200000., 'dyn') See `PR #62 `_ for details. * Added two new utility functions, ``unyt.unit_systems.add_constants`` and ``unyt.unit_systems.add_symbols`` that can populate a namespace with a set of unit symbols in the same way that the top-level ``unyt`` namespace is populated. For example, the author of a library making use of ``unyt`` could create an object that users can use to access unit data like this: .. code-block:: >>> from unyt.unit_systems import add_symbols >>> from unyt.unit_registry import UnitRegistry >>> class UnitContainer: ... def __init__(self): ... add_symbols(vars(self), registry=UnitRegistry()) ... >>> units = UnitContainer() >>> units.kilometer km >>> units.microsecond μs See `PR #68 `_. * The ``unyt`` codebase is now automatically formatted by `black `_. See `PR #57 `_. * Add missing "microsecond" name from top-level ``unyt`` namespace. See `PR #48 `_. * Add support for ``numpy.argsort`` by defining ``unyt_array.argsort``. See `PR #52 `_. * Add Farad unit and fix issues with conversions between MKS and CGS electromagnetic units. See `PR #54 `_. * Fixed incorrect conversions between inverse velocities and ``statohm``. See `PR #61 `_. * Fixed issues with installing ``unyt`` from source with newer versions of ``pip``. See `PR #63 `_. * Fixed bug when using ``define_unit`` that caused crashes when using a custom unit registry. Thank you to Bili Dong (@qobilidob on GitHub) for the pull request. See `PR #64 `_. We would also like to thank Daniel Gomez (@dangom), Britton Smith (@brittonsmith), Lee Johnston (@l-johnston), Meagan Lang (@langmm), Eric Chen (@ericchen), Justin Gilmer (@justinGilmer), and Andy Perez (@sharkweek) for reporting issues. 1.0.7 (2018-08-13) ------------------ Trigger zenodo archiving. 1.0.6 (2018-08-13) ------------------ Minor paper updates to finalize JOSS submission. 1.0.5 (2018-08-03) ------------------ ``unyt`` 1.0.5 includes changes that reflect the peew review process for the JOSS method paper. The peer reviewers were Stuart Mumfork (`@cadair `_), Trevor Bekolay (`@tbekolay `_), and Yan Grange (`@ygrange `_). The editor was Kyle Niemeyer (`@kyleniemeyer `_). The ``unyt`` development team thank our reviewers and editor for their help getting the ``unyt`` paper out the door as well as for the numerous comments and suggestions that improved the paper and package as a whole. In addition we'd like to thank Mike Zingale, Meagan Lang, Maksin Ratkin, DougAJ4, Ma Jianjun, Paul Ivanov, and Stephan Hoyer for reporting issues. * Added docstrings for the custom exception classes defined by ``unyt``. See `PR #44 `_. * Added improved documentation to the contributor guide on how to run the tests and what the PR review guidelines are. See `PR #43 `_. * Updates to the text of the method paper in response to reviewer suggestions. See `PR #42 `_. * It is now possible to run the tests on an installed copy of ``unyt`` by executing ``unyt.test()``. See `PR #41 `_. * Minor edit to LICENSE file so GitHub recognizes it. See `PR #40 `_. Thank you to Kyle Sunden (`@ksunden `_) for the contribution. * Add spatial frequency as a dimension and added support in the ``spectral`` equivalence for the spatial frequency dimension. See `PR #38 `_ Thank you to Kyle Sunden (`@ksunden `_) for the contribution. * Add support for Python 3.7. See `PR #37 `_. * Importing ``unyt`` will now fail if ``numpy`` and ``sympy`` are not installed. See `PR #35 `_ * Testing whether a unit name is contained in a unit registry using the Python ``in`` keyword will now work correctly for all unit names. See `PR #31 `_. * The aliases for megagram in the top-level unyt namespace were incorrectly set to reference kilogram and now have the correct value. See `PR #29 `_. * Make it possible to take scalars to dimensionless array powers with a properly broadcasted result without raising an error about units. See `PR #23 `_. * Whether or not a unit is allowed to be SI-prefixable (for example, meter is SI-prefixable to form centimeter, kilometer, and many other units) is now stored as metadata in the unit registry rather than as global state inside ``unyt``. See `PR #21 `_. * Made adjustments to the rules for converting between CGS and MKS E&M units so that errors are only raised when going between unit systems and not merely when doing a complicated unit conversion involving E&M units. See `PR #20 `_. * ``round(q)`` where ``q`` is a ``unyt_quantity`` instance will no longer raise an error and will now return the nearest rounded float. See `PR #19 `_. * Fixed a typo in the readme. Thank you to Paul Ivanov (`@ivanov `_) for `the fix `_. * Added smoot as a unit. See `PR #14 `_. 1.0.4 (2018-06-08) ------------------ * Expand installation instructions * Mention paper and arxiv submission in the readme. 1.0.3 (2018-06-06) ------------------ * Fix readme rendering on pypi 1.0.2 (2018-06-06) ------------------ * Added a paper to be submitted to the Journal of Open Source Software. * Tweaks for the readme 1.0.1 (2018-05-24) ------------------ * Don't use setup_requires in setup.py 1.0.0 (2018-05-24) ------------------ * First release on PyPI. * unyt began life as a submodule of yt named yt.units. * It was separated from yt.units as its own package in 2018. unyt-3.2.0/LICENSE000066400000000000000000000027611526544257300135430ustar00rootroot00000000000000 BSD 3-Clause License Copyright (c) 2018-, yt Development Team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * 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. * 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. unyt-3.2.0/README.rst000066400000000000000000000072671526544257300142330ustar00rootroot00000000000000==== unyt ==== .. image:: https://img.shields.io/pypi/v/unyt.svg :target: https://pypi.python.org/pypi/unyt .. image:: https://img.shields.io/conda/vn/conda-forge/unyt.svg :target: https://anaconda.org/conda-forge/unyt :alt: conda-forge .. image:: https://github.com/yt-project/unyt/actions/workflows/ci.yml/badge.svg?branch=main :target: https://github.com/yt-project/unyt/actions/workflows/ci.yml .. image:: https://github.com/yt-project/unyt/actions/workflows/bleeding-edge.yaml/badge.svg?branch=main :target: https://github.com/yt-project/unyt/actions/workflows/bleeding-edge.yaml .. image:: https://readthedocs.org/projects/unyt/badge/?version=latest :target: https://unyt.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: http://joss.theoj.org/papers/dbc27acb614dd33eb02b029ef20e7fe7/status.svg :target: http://joss.theoj.org/papers/dbc27acb614dd33eb02b029ef20e7fe7 :alt: Code Paper | .. image:: https://raw.githubusercontent.com/yt-project/unyt/master/docs/_static/yt_logo_small.png :target: https://yt-project.org :alt: The yt Project A package for handling numpy arrays with units. Often writing code that deals with data that has units can be confusing. A function might return an array but at least with plain NumPy arrays, there is no way to easily tell what the units of the data are without somehow knowing *a priori*. The ``unyt`` package (pronounced like "unit") provides a subclass of NumPy's ``ndarray`` class that knows about units. For example, one could do: >>> import unyt as u >>> distance_traveled = [3.4, 5.8, 7.2] * u.mile >>> print(distance_traveled.to('km')) [ 5.4717696 9.3341952 11.5872768] km And a whole lot more! See `the documentation `_ for installation instructions, more examples, and full API reference. This package only depends on ``numpy`` and ``sympy``. Notably, it does *not* depend on ``yt`` and it is written in pure Python. Code of Conduct --------------- The ``unyt`` package is part of `The yt Project `_. Participating in ``unyt`` development therefore happens under the auspices of the `yt community code of conduct `_. If for any reason you feel that the code of conduct has been violated, please send an e-mail to confidential@yt-project.org with details describing the incident. All emails sent to this address will be treated with the strictest confidence by an individual who does not normally participate in yt development. License ------- The unyt package is licensed under the BSD 3-clause license. Citation -------- If you make use of unyt in work that leads to a publication we would appreciate a mention in the text of the paper or in the acknowledgements along with a citation to our `paper `_ in the Journal of Open Source Software. You can use the following BibTeX:: @article{Goldbaum2018, doi = {10.21105/joss.00809}, url = {https://doi.org/10.21105/joss.00809}, year = {2018}, month = {aug}, publisher = {The Open Journal}, volume = {3}, number = {28}, pages = {809}, author = {Nathan J. Goldbaum and John A. ZuHone and Matthew J. Turk and Kacper Kowalik and Anna L. Rosen}, title = {unyt: Handle, manipulate, and convert data with units in Python}, journal = {Journal of Open Source Software} } Or the following citation format: Goldbaum et al., (2018). unyt: Handle, manipulate, and convert data with units in Python . Journal of Open Source Software, 3(28), 809, https://doi.org/10.21105/joss.00809 unyt-3.2.0/benchmarks/000077500000000000000000000000001526544257300146455ustar00rootroot00000000000000unyt-3.2.0/benchmarks/README.txt000066400000000000000000000002331526544257300163410ustar00rootroot00000000000000The data for a benchmark performed with unyt 1.0.1, Pint 0.8.1 and astropy 3.0.1 are available on figshare: https://doi.org/10.6084/m9.figshare.6399875.v1 unyt-3.2.0/benchmarks/bench.py000066400000000000000000000167601526544257300163100ustar00rootroot00000000000000import matplotlib matplotlib.use("agg") from collections import OrderedDict import contextlib import io from matplotlib import pyplot as plt import numpy as np import os import pyperf import subprocess import sys @contextlib.contextmanager def stdoutIO(stdout=None): old = sys.stdout if stdout is None: stdout = io.StringIO() sys.stdout = stdout yield stdout sys.stdout = old def run_perf(args, json_name): if os.path.exists(json_name): return args = args + ["-o", json_name] print(args) p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = p.communicate() print(out.decode()) print(err.decode()) def make_plot(extension): ratios = OrderedDict() stddevs = OrderedDict() benchmarks = OrderedDict() np_bench = pyperf.Benchmark.load(open("{}_{}".format("numpy", extension))) np_mean = np_bench.mean() np_stddev = np_bench.stdev() for package in setup: if package == "numpy": continue benchmarks[package] = pyperf.Benchmark.load(open(f"{package}_{extension}")) mean = benchmarks[package].mean() stddev = benchmarks[package].stdev() ratios[package] = mean / np_mean stddevs[package] = ratios[package] * np.sqrt( (np_stddev / np_mean) ** 2 + (stddev / mean) ** 2 ) fig, ax = plt.subplots() packages = list(ratios.keys()) ax.bar(packages, ratios.values(), yerr=stddevs.values()) fig.suptitle(extension.replace(".json", "").replace("_", " ").title()) ax.set_ylabel("numpy overhead (x time for numpy); lower is better") plt.savefig(extension.replace(".json", ".png")) plt.close(fig) if ratios["unyt"] != min(ratios.values()): rvalues = list(ratios.values()) svalues = list(stddevs.values()) unyt_index = packages.index("unyt") min_index = rvalues.index(min(rvalues)) if ratios["unyt"] > 3 * svalues[min_index] + rvalues[min_index]: for package in ratios: script = get_script(benchmarks, package) with stdoutIO() as s: exec(script) res = s.getvalue().replace("\n", "") print( "{}: {} +- {} ({})".format( package, ratios[package], stddevs[package], res ) ) print(get_script(benchmarks, "unyt")) def get_script(benchmarks, package): meta = benchmarks[package].get_metadata() setup_s = meta["timeit_setup"][1:-1] bench_s = "print(" + meta["timeit_stmt"][1:-1] + ")" script = setup_s + "; " + bench_s script = script.replace("; ", "\n") return script setup = OrderedDict( [ ("numpy", "import numpy as np"), ("pint", "from pint import UnitRegistry; u = UnitRegistry()"), ("astropy", "import astropy.units as u"), ("unyt", "import unyt as u"), ("quantities", "import quantities as u"), ] ) base_args = ["python", "-m", "pyperf", "timeit"] shared_setup = "import numpy as np; import operator" base_setups = OrderedDict( [ ("small_list", "data = [1., 2., 3.]"), ("small_tuple", "data = (1., 2., 3.)"), ("small_array", "data = np.array([1., 2., 3.])"), ("big_list", "data = (np.arange(1e6)+1).tolist()"), ("big_array", "data = (np.arange(1e6)+1)"), ] ) op_ufuncs = OrderedDict( [ ("operator.add", "np.add"), ("operator.sub", "np.subtract"), ("operator.mul", "np.multiply"), ("operator.truediv", "np.true_divide"), ("operator.eq", "np.equal"), ] ) for bs in base_setups: for package in sorted(setup): print(package) setup_s = "; ".join([shared_setup, setup[package], base_setups[bs]]) args = base_args + ["-s", setup_s + " "] if package == "numpy": args.append("np.array(data)") else: args.append("data*u.g") json_name = f"{package}_{bs}_create.json" run_perf(args, json_name) if "list" in bs or "tuple" in bs: continue args = base_args + ["-s", setup_s + "; data=np.asarray(data); out=data.copy()"] if package == "numpy": args[-1] += "; " else: if package != "pint": args[-1] += "*u.g" args[-1] += "; data = data*u.g " args.append("data**2") json_name = f"{package}_{bs}_square.json" run_perf(args, json_name) args[-1] = "np.power(data, 2)" json_name = f"{package}_{bs}_npsquare.json" run_perf(args, json_name) args[-1] = "np.power(data, 2, out=out)" json_name = f"{package}_{bs}_npsquareout.json" run_perf(args, json_name) args[-1] = "data**0.5" json_name = f"{package}_{bs}_sqrt.json" run_perf(args, json_name) args[-1] = "np.sqrt(data)" json_name = f"{package}_{bs}_npsqrt.json" run_perf(args, json_name) args[-1] = "np.sqrt(data, out=out)" json_name = f"{package}_{bs}_npsqrtout.json" run_perf(args, json_name) make_plot(f"{bs}_create.json") if "list" not in bs and "tuple" not in bs: make_plot(f"{bs}_square.json") make_plot(f"{bs}_npsquare.json") make_plot(f"{bs}_npsquareout.json") make_plot(f"{bs}_sqrt.json") make_plot(f"{bs}_npsqrt.json") make_plot(f"{bs}_npsqrtout.json") for bs in base_setups: if "list" in bs or "tuple" in bs: continue for op, ufunc in op_ufuncs.items(): for bench, bench_name in [ (op + r"(data1, data2)", op + "12.json"), (op + r"(data2, data1)", op + "21.json"), (ufunc + r"(data1, data2)", ufunc + "12.json"), (ufunc + r"(data2, data1)", ufunc + "21.json"), (ufunc + r"(data1, data2, out=out)", ufunc + "12out.json"), (ufunc + r"(data2, data1, out=out)", ufunc + "21out.json"), ]: for unit_choice in [("g", "g"), ("kg", "g")]: for package in sorted(setup): print(package) setup_s = ( "; ".join([shared_setup, setup[package], base_setups[bs]]) + "; " ) if "out" in bench: if package not in ("pint", "numpy") and "equal" not in bench: setup_s += f"out=data*u.{unit_choice[0]}; " else: setup_s += "out=np.array(data); " if package == "numpy": setup_s += "; ".join( [r"data1 = np.array(data)", r"data2 = np.array(data)"] ) if unit_choice[0] != unit_choice[1]: _bench = bench.replace("data1", ".001*data1") else: setup_s += "; ".join( [ f"data1 = data*u.{unit_choice[0]}", f"data2 = data*u.{unit_choice[1]}", ] ) _bench = bench args = base_args + ["-s", setup_s + " "] json_name = "{}_{}_{}{}".format( package, bs, unit_choice[0], unit_choice[1] ) run_perf(args + [_bench], json_name + "_" + bench_name) make_plot(f"{bs}_{unit_choice[0]}{unit_choice[1]}_{bench_name}") unyt-3.2.0/codemeta.json000066400000000000000000000030321526544257300152020ustar00rootroot00000000000000{ "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld", "@type": "Code", "author": [ { "@id": "0000-0001-5557-267X", "@type": "Person", "email": "ngoldbau@illinois.edu", "name": "Nathan Goldbaum", "affiliation": "University of Illinois at Urbana Champaign" }, { "@id": "0000-0003-3175-2347", "@type": "Person", "email": "jzuhone@cfa.harvard.edu", "name": "John ZuHone", "affiliation": "Harvard-Smithsonian Center for Astrophysics" }, { "@id": "0000-0002-5294-0198", "@type": "Person", "email": "mjturk@illinois.edu", "name": "Matthew Turk", "affiliation": "University of Illinois at Urbana Champaign" }, { "@id": "0000-0003-1709-3744", "@type": "Person", "email": "kowalikk@illinois.edu", "name": "Kacper Kowalik", "affiliation": "University of Illinois at Urbana Champaign" }, { "@id": "0000-0003-4423-0660", "@type": "Person", "email": "anna.rosen@cfa.harvard.edu", "name": "Anna Rosen", "affiliation": "Harvard-Smithsonian Center for Astrophysics" } ], "identifier": "", "codeRepository": "https://github.com/yt-project/unyt", "datePublished": "2018-08-13", "dateModified": "2018-08-13", "dateCreated": "2018-08-13", "description": "Handle, manipulate, and convert data with units in Python", "keywords": "units, python, numpy, sympy, quantities", "license": "BSD", "title": "unyt", "version": "v1.0.7" } unyt-3.2.0/docs/000077500000000000000000000000001526544257300134605ustar00rootroot00000000000000unyt-3.2.0/docs/Makefile000066400000000000000000000012171526544257300151210ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. 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BR@f4H{vk4^R͞VEsc"]TLY6P,wξn Κt]'s*uD ZrqA F{MQ0@.ff :[p-4lG '<\]3y@dX(VɽT\ٹm'eںS{Z O-&e!`NgzlR^y%I pqGz-~!mZYo[w{=рr>K"FD܆"fS|iqSLMBo;#A"Q&E`R[4uzM]4Zq&;ېh'. rBm'RHK?bq1Lc>QfJ#NI7e}<Ѿ~wZI릮5ijK[k$o՚'HWLىXYmh-| K)j~PDUoj_u> ,biBTR~xNv"jZtaqѹ6d]Mn#7R :MhHdf. {TSѤ E̔Ȥ܅e[7jώVmo]5t_)CE%P'pbP}N1ΧWD7L$1t4h|2.q U8mԵA&Z@m?XB0(@\eBC#A[jt#NN@bq,㝟Ċ?Qxka".G1haRH* %pUR;xwp 7#JV))d2%Sm|8< Ƌ@<gG}h{'*mM@\5Ikɇ"Mb%cAe XAs>Cu $$z Qڦ?ڂ~;*6s ӧhrD/OSACOC,f0Dq! 0O J_;u:P :wXbVue4r/僱(;aㇽxa{DETEOqMIy4" _i/_>NÖD'"9~h9KF6A@\8_wahj6lBJQ&Y-fiE)]KP*8;R" FR}u61k5;&q <;RSKٗiW~=N)eSרN*eszdZ5^g?8;6vuZ]]wEi4N]nj. lću*c;h4JlufEi>F:7SgFjU%HT 2+'0k}Ens~5(ix}sO&T4.$=D;qz~zfWv Yd!.d ݱC| RSEz*D/%H_R | x/a8pr +t> p)t$\a 2?OjBLD8[?]s:bwq|EkZRC9&9c3z |koU  'Xƛ<|!| wJBN'ZJO(:qbhNotyV\ЈtԩfPԑ P̿y/ъ+t KIIU|fq"_?s'Vt-\E1̀ƐV]t&G Px͕1CNOZ` ^mFu;nxP\̵x{yf# 'rӻZVk!*:IWR@B !1?)]v(=q'ӆF˨Caf!\$|*uu*UT rjPS8.Du1Tbm!ʽӰLJ'CNPSSYcd y T,Me':Vm$sTXB bptN؝!tVqJ!Q~*aR+}eFj+Q 2ΦNDk{ אw/;;8+y5 B:f8: U|3z6đ`|f$ߊ5dL8mt$K9+|M#c-2'H\؂Ǯyc2?,Dz;!#;A:]${\ԂH-= (m8҂#jق;X"^arY!1ŷ,;'%0~Mj3KY 5>Di/oޤBW/pР&y$ 0 iV= ฝىOljBTJUAӨZD P6=sYj^DJvqvc@>KR SǪ?t>EĆ־ɹ`*CN?\6xNc#*MN +P tu2%w0'Xv%M]NB'uL"،ei!fLhh9pP]:Ф@mjij<.- VmShGr{$@461[#Yf^M ,y ̵K,eo@T'< : hHФ@5 !ph_pԼ\<O4aߣMNcf&5g_ 3MԛhFk>"S@#LABƙ<~K ~DbS<ФL0S"yR;ֹ !0|и@CfhRI(0pXn$ rMR/>C\U%`wm{pDs|(P.sO&VGR;E4 y;@r!5E "<p݃84A M$EI|ÖBXQZM U::zWr\NgśL+A`d )=~ُ@b7e$VagM 4)  8j=G3\R-/_: @,xpj@CAfP$S+̖T65)Ф@X ryͷs[P^M΢&՝y:,ytG5t>7M 4)Ф@M 4)Ф@M 4)Ф@M 4)Ф>]uKIENDB`unyt-3.2.0/docs/authors.rst000066400000000000000000000000341526544257300156740ustar00rootroot00000000000000.. include:: ../AUTHORS.rst unyt-3.2.0/docs/citation.rst000066400000000000000000000020421526544257300160220ustar00rootroot00000000000000================== Citing :mod:`unyt` ================== If you make use of unyt in work that leads to a publication we would appreciate a mention in the text of the paper or in the acknowledgements along with a citation to our `paper `_ in the Journal of Open Source Software. You can use the following BibTeX:: @article{Goldbaum2018, doi = {10.21105/joss.00809}, url = {https://doi.org/10.21105/joss.00809}, year = {2018}, month = {aug}, publisher = {The Open Journal}, volume = {3}, number = {28}, pages = {809}, author = {Nathan J. Goldbaum and John A. ZuHone and Matthew J. Turk and Kacper Kowalik and Anna L. Rosen}, title = {unyt: Handle, manipulate, and convert data with units in Python}, journal = {Journal of Open Source Software} } Or the following citation format: Goldbaum et al., (2018). unyt: Handle, manipulate, and convert data with units in Python . Journal of Open Source Software, 3(28), 809, https://doi.org/10.21105/joss.00809 unyt-3.2.0/docs/conf.py000077500000000000000000000131111526544257300147570ustar00rootroot00000000000000#!/usr/bin/env python # # unyt documentation build configuration file, created by # sphinx-quickstart on Fri Jun 9 13:47:02 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import os import sys import unyt # If extensions (or modules to document with autodoc) are in another # directory, add these directories to sys.path here. If the directory is # relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # sys.path.insert(0, os.curdir + os.sep + "extensions") # -- General configuration --------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.doctest", "sphinx.ext.napoleon", "sphinx.ext.mathjax", "show_all_units", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = "unyt" copyright = "2022, The yt Project" author = "The yt Project" # The version info for the project you're documenting, acts as replacement # for |version| and |release|, also used in various other places throughout # the built documents. # # The short X.Y version. version = unyt.__version__ # The full version, including alpha/beta/rc tags. release = unyt.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "modules/modules.rst"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "alabaster" # Theme options are theme-specific and customize the look and feel of a # theme further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # 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"] # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "_static/yt_icon.png" # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = "unytdoc" # -- Options for LaTeX output ------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass # [howto, manual, or own class]). latex_documents = [ (master_doc, "unyt.tex", "unyt Documentation", "The yt Project", "manual") ] # -- Options for manual page output ------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "unyt", "unyt Documentation", [author], 1)] # -- Options for Texinfo output ---------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "unyt", "unyt Documentation", author, "unyt", "One line description of project.", "Miscellaneous", ) ] autodoc_member_order = "bysource" def run_apidoc(_): try: from sphinx.ext.apidoc import main except ImportError: from sphinx.apidoc import main sys.path.append(os.path.join(os.path.dirname(__file__), "..")) cur_dir = os.path.abspath(os.path.dirname(__file__)) api_doc_dir = os.path.join(cur_dir, "modules") module = os.path.join(cur_dir, "..", "unyt") ignore = os.path.join(cur_dir, "..", "unyt", "tests") os.environ["SPHINX_APIDOC_OPTIONS"] = "members,undoc-members,show-inheritance" main(["-M", "-f", "-e", "-T", "-d 0", "-o", api_doc_dir, module, ignore]) def setup(app): app.connect("builder-inited", run_apidoc) unyt-3.2.0/docs/contributing.rst000066400000000000000000000000411526544257300167140ustar00rootroot00000000000000.. include:: ../CONTRIBUTING.rst unyt-3.2.0/docs/extensions/000077500000000000000000000000001526544257300156575ustar00rootroot00000000000000unyt-3.2.0/docs/extensions/show_all_units.py000066400000000000000000000145521526544257300212720ustar00rootroot00000000000000from docutils.parsers.rst import Directive try: maketrans = "".maketrans except AttributeError: # python2 fallback from string import maketrans import numpy as np from sympy.core.mul import Mul from sympy.core.symbol import Symbol import unyt import unyt.dimensions as dims from unyt import Unit from unyt._unit_lookup_table import name_alternatives, physical_constants from unyt.exceptions import UnitsNotReducible from unyt.unit_registry import default_unit_registry from unyt.unit_systems import _split_prefix all_dims = {} for d in dims.__dict__.keys(): if isinstance(getattr(dims, d), (Symbol, Mul)): if d == "dimensionless": continue all_dims[getattr(dims, d)] = d def setup(app): app.add_directive("show_all_units", ShowAllUnits) app.add_directive("show_all_constants", ShowAllConstants) setup.app = app setup.config = app.config setup.confdir = app.confdir retdict = {"version": "0.1"} return retdict class ShowAllUnits(Directive): required_arguments = 0 optional_arguments = 0 def run(self): lines = [] for name, alt_names in name_alternatives.items(): prefix, base = _split_prefix(name, default_unit_registry.lut) if prefix != "": continue lut_entry = default_unit_registry[name] u = Unit(name) try: dimensions = all_dims[u.dimensions] except KeyError: if u.is_dimensionless: dimensions = "dimensionless" else: dimensions = u.dimensions try: mks_value = (1 * u).in_mks() except UnitsNotReducible: mks_value = "N/A" try: cgs_value = (1 * u).in_cgs() except UnitsNotReducible: cgs_value = "N/A" def generate_table_value(value): if value == "N/A": return value approx_string = f"{value:.4e}" real_string = str(value) fv = value.value close_value = float(f"{fv:.4e}") if (close_value - fv) / fv < 1e-6 and len(str(fv)) > 8: return approx_string if fv < 1e-4 or fv > 1e4 or len(str(fv)) > 8: return approx_string return real_string latex_repr = "``" + u.latex_repr + "``" if latex_repr == "````": latex_repr = "" with np.printoptions(precision=4, suppress=False, floatmode="maxprec"): lines.append( ( name, str(dimensions), generate_table_value(mks_value), generate_table_value(cgs_value), latex_repr, str(lut_entry[4]), ", ".join([a for a in alt_names if a != name]), ) ) lines.insert( 0, [ "Unit Name", "Dimensions", "MKS value", "CGS Value", "LaTeX Representation", "SI Prefixable?", "Alternate Names", ], ) lines = as_rest_table(lines, full=False).split("\n") rst_file = self.state_machine.document.attributes["source"] self.state_machine.insert_input(lines, rst_file) return [] class ShowAllConstants(Directive): required_arguments = 0 optional_arguments = 0 def run(self): lines = [] for name, (_value, _unit, alternate_names) in physical_constants.items(): val = getattr(unyt.physical_constants, name) if val > 1e4 or val < 1e-4: default_value = f"{val:.4e}" else: default_value = str(val) lines.append((name, default_value, ", ".join(alternate_names))) lines.insert(0, ["Constant Name", "Value", "Alternate Names"]) lines = as_rest_table(lines, full=False).split("\n") rst_file = self.state_machine.document.attributes["source"] self.state_machine.insert_input(lines, rst_file) return [] def as_rest_table(data, full=False): """ Originally from ActiveState recipes, copy/pasted from GitHub where it is listed with an MIT license. https://github.com/ActiveState/code/tree/master/recipes/Python/579054_Generate_Sphinx_table """ data = data if data else [["No Data"]] table = [] # max size of each column sizes = list(map(max, zip(*[[len(str(elt)) for elt in member] for member in data]))) num_elts = len(sizes) if full: start_of_line = "| " vertical_separator = " | " end_of_line = " |" line_marker = "-" else: start_of_line = "" vertical_separator = " " end_of_line = "" line_marker = "=" meta_template = vertical_separator.join( [f"{{{{{i}:{{{i}}}}}}}" for i in range(num_elts)] ) template = f"{start_of_line}{meta_template.format(*sizes)}{end_of_line}" # determine top/bottom borders if full: to_separator = maketrans("| ", "+-") else: to_separator = maketrans("|", "+") start_of_line = start_of_line.translate(to_separator) vertical_separator = vertical_separator.translate(to_separator) end_of_line = end_of_line.translate(to_separator) separator = f"{start_of_line}{vertical_separator.join([x * line_marker for x in sizes])}{end_of_line}" # determine header separator th_separator_tr = maketrans("-", "=") start_of_line = start_of_line.translate(th_separator_tr) line_marker = line_marker.translate(th_separator_tr) vertical_separator = vertical_separator.translate(th_separator_tr) end_of_line = end_of_line.translate(th_separator_tr) th_separator = f"{start_of_line}{vertical_separator.join([x * line_marker for x in sizes])}{end_of_line}" # prepare result table.append(separator) # set table header titles = data[0] table.append(template.format(*titles)) table.append(th_separator) for d in data[1:-1]: table.append(template.format(*d)) if full: table.append(separator) table.append(template.format(*data[-1])) table.append(separator) return "\n".join(table) unyt-3.2.0/docs/history.rst000066400000000000000000000000341526544257300157100ustar00rootroot00000000000000.. include:: ../HISTORY.rst unyt-3.2.0/docs/index.rst000066400000000000000000000020761526544257300153260ustar00rootroot00000000000000unyt ==== This is the documentation for ``unyt`` (pronounced like "unit"). ``unyt`` is a Python library for working with data that has physical units. It was originally developed as part of `the yt Project `_ and was split out as an independent project so that other Python projects can easily make use of it. The unyt library defines the :class:`unyt.array.unyt_array` and :class:`unyt.array.unyt_quantity` classes for handling arrays and scalars with units, respectively. In addition, ``unyt`` provides a number of predefined units and physical constants that can be directly imported from from the ``unyt`` namespace: .. doctest:: >>> from unyt import G, Mearth, Rearth >>> v_esc = (2 * G * Mearth / Rearth) ** (1.0 / 2) >>> print(v_esc.to("km/s")) 11.254544657958482 km/s .. toctree:: :maxdepth: 3 :caption: Contents: installation usage unit_listing API Documentation contributing citation authors history Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` unyt-3.2.0/docs/installation.rst000066400000000000000000000043251526544257300167170ustar00rootroot00000000000000.. highlight:: shell ============ Installation ============ Stable release -------------- To install :mod:`unyt`, run this command in your terminal: .. code-block:: console $ python -m pip install unyt If you have a C compiler available, we also suggest installing `fastcache`_, which will improve the performance of `SymPy`_. If you don't have `pip`_ installed, this `Python installation guide`_ can guide you through the process. If you use `conda`_, :mod:`unyt` is available via `conda-forge`_: .. code-block:: console $ conda install -c conda-forge unyt It is not necessary to explicitly install ``fastcache`` if you use ``conda`` because it will be installed automatically as a dependency of ``SymPy``. .. _pip: https://pip.pypa.io .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/ .. _fastcache: https://github.com/pbrady/fastcache .. _SymPy: http://sympy.org/ .. _conda: https://conda.io/ .. _conda-forge: https://conda-forge.org/ From source ----------- The sources for :mod:`unyt` can be downloaded from the `Github repo`_. If you don't need an editable install, the following line is all you need to run .. code-block:: console $ python -m pip install git+https//github.com/yt-project/unyt.git If you would like to make an "editable" where you can directly edit the Python source files of the installed version of ``unyt``, then you can download the latest version from Github either with ``git`` .. code-block:: console $ git clone git://github.com/yt-project/unyt or download the `tarball`_: .. code-block:: console $ curl -OL https://github.com/yt-project/unyt/tarball/main Once you have a copy of the source, you can install it by navigating to the root of the installation and issuing the following command: .. code-block:: console $ python -m pip install -e . .. _Github repo: https://github.com/yt-project/unyt .. _tarball: https://github.com/yt-project/unyt/tarball/main Running the tests ----------------- You can check that :mod:`unyt` is working properly by running the unit tests on your installed copy: .. doctest:: >>> import unyt >>> unyt.test() # doctest: +SKIP Note that you'll need ``pytest`` installed for this function to run. unyt-3.2.0/docs/make.bat000066400000000000000000000013761526544257300150740ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=python -msphinx ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=unyt if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The Sphinx module was not found. Make sure you have Sphinx installed, echo.then set the SPHINXBUILD environment variable to point to the full echo.path of the 'sphinx-build' executable. Alternatively you may add the echo.Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd unyt-3.2.0/docs/unit_listing.rst000066400000000000000000000002671526544257300167270ustar00rootroot00000000000000.. _unit-listing: Listing of Units ================ .. show_all_units:: .. _constant-listing: Listing of Physical Constants ============================= .. show_all_constants:: unyt-3.2.0/docs/usage.rst000066400000000000000000001674311526544257300153320ustar00rootroot00000000000000======================== Working with :mod:`unyt` ======================== Basic Usage +++++++++++ To use unyt in a project:: >>> import unyt The top-level :mod:`unyt` namespace defines both a number of useful functions as well as a number of units and physical constants populated from the :mod:`unyt.unit_symbols` and :mod:`unyt.physical_constants` namespaces you can use to attach units to NumPy arrays and other common Python data container types like ``list`` and ``tuple``. For an exhaustive listing of units and physical constants defined in :mod:`unyt`, see :ref:`unit-listing`. .. warning:: Both unit symbols and physical constants are defined in the top-level :mod:`unyt` namespace. Some names occur as both unit symbols and physical constants, e.g. ``"me"``, ``"mp"``, ``"E_pl"``, etc. In such cases, the top-level namespace defaults to exporting the physical constant with the duplicate name. This means that there may be a (very rare) case where an :class:`Unit ` object that at one point can be imported from the top-level namespace may at a later point be only imported as an :class:`unyt_quantity ` object with the same name. As described below, there are other ways to import the unit symbols and/or physical constants besides the top-level namespace. An Example from High School Physics ----------------------------------- To see how you might use :mod:`unyt` to solve a problem where units might be a headache, let's estimate the orbital periods of Jupiter's Galilean moons, assuming they have circular orbits and their masses are negligible compared to Jupiter. Under these assumptions, the orbital period is .. math:: T = 2\pi\left( \frac{r^3}{GM}\right)^{1/2}. For this exercise let's calculate the orbital period in days. While it's possible to do this using plain old floating point numbers (you probably had to do something similar on a calculator in a high school physics class, looking up and plugging in conversion factors by hand), it's much easier to do this sort of thing symbolically and let :mod:`unyt` handle the unit conversions. To do this we'll need to know the mass of Jupiter (fortunately that is built into :mod:`unyt`) and the semi-major axis of the orbits of Jupiter's moons, which we can look up from `Wikipedia `_ and enter by hand:: >>> from unyt import Mjup, G, AU >>> from math import pi ... >>> moons = ['Io', 'Europa', 'Ganymede', 'Callisto'] >>> semimajor_axis = [.002819, .0044856, .00715526, .01258513]*AU ... >>> period = 2*pi*(semimajor_axis**3/(G*Mjup))**0.5 >>> period = period.to('d') ... >>> for moon, period in zip(moons, period): ... print('{}: {:04.2f}'.format(moon, period)) Io: 1.77 day Europa: 3.55 day Ganymede: 7.15 day Callisto: 16.69 day Let's break up this example into a few components so you can see what's going on. First, we import the unit symbols we need from the :mod:`unyt` namespace:: >>> from unyt import Mjup, G, AU The :mod:`unyt` namespace has a large number of units and physical constants you can import to apply units to data in your own code. You can see how that works in the example:: >>> semimajor_axis = [.002819, .0044856, .00715526, .01258513]*AU >>> semimajor_axis unyt_array([0.002819 , 0.0044856 , 0.00715526, 0.01258513], 'AU') By multiplying by ``AU``, we converted the Python list into a :class:`unyt.unyt_array ` instance. This is a class that's built into :mod:`unyt`, has units attached to it, and knows how to convert itself into different dimensionally equivalent units:: >>> semimajor_axis.value array([0.002819 , 0.0044856 , 0.00715526, 0.01258513]) >>> semimajor_axis.units AU >>> print(semimajor_axis.to('km')) [ 421716.39764641 671036.20903964 1070411.66066813 1882708.6511216 ] km Next, we calculated the orbital period by translating the orbital period formula to Python and then converting the answer to the units we want in the end, days:: >>> period = 2*pi*(semimajor_axis**3/(G*Mjup))**0.5 >>> period unyt_array([ 152864.59689789, 306828.08975058, 618162.17963649, 1441952.18891597], 's') >>> period.to('d') unyt_array([ 1.76926617, 3.55125104, 7.15465486, 16.68926145], 'day') Note that we haven't added any conversion factors between different units, that's all handled internally by :mod:`unyt`. Also note how the :meth:`unyt_array.to ` method was able to automatically handle the conversion from seconds to days and how the shorthand ``"d"`` was automatically interpreted as ``"day"``. Arithmetic and units -------------------- The real power of working with :mod:`unyt` is its ability to add, subtract, multiply, and divide quantities and arrays with units in mathematical formulas while automatically handling unit conversions and detecting when you have made a mistake in your units in a mathematical formula. To see what I mean by that, let's take a look at the following examples:: >>> from unyt import cm, m, ft, yard >>> print(3.*cm + 4.*m - 5.*ft + 6.*yard) 799.24 cm Despite the fact that the four unit symbols used in the above example correspond to four different units, :mod:`unyt` is able to automatically convert the value of all three units into a common unit and return the result in those units. Note that for expressions where the return units are ambiguous, :mod:`unyt` always returns data in the units of the leftmost object in an expression:: >>> print(4*m + 3*cm - 5*ft + 6*yard) # doctest: +FLOAT_CMP 7.9924 m One can also form more complex units out of atomic unit symbols. For example, here is how we'd create an array with units of meters per second and print out the values in the array in miles per hour:: >>> from unyt import m, s >>> velocities = [20., 22., 25.]*m/s >>> print(velocities.to('mile/hr')) [44.73872584 49.21259843 55.9234073 ] mile/hr Similarly one can multiply two units together to create new compound units:: >>> from unyt import N, m >>> energy = 3*N * 4*m >>> print(energy) 12 N*m >>> print(energy.to('erg')) 120000000.0 erg In general, one can multiply or divide by an arbitrary rational power of a unit symbol. Most commonly this shows up in mathematical formulas in terms of square roots. For example, let's calculate the gravitational free-fall time for a person to fall from the surface of the Earth through to a hole dug all the way to the center of the Earth. It turns out that this time `is given by `_: .. math:: t_{\rm ff} = \sqrt{\frac{3\pi}{32 G \rho}} where :math:`\rho` is the average density of the Earth. >>> from unyt import G, Mearth, Rearth >>> from math import pi >>> import numpy as np ... >>> rho = Mearth / (4./3 * pi* Rearth**3) >>> print(rho.to('g/cm**3')) 5.581225129861083 g/cm**3 >>> tff = np.sqrt(3*pi/(32*G*rho)) >>> print(tff.to('min')) 14.820022043294829 min If you make a mistake by adding two things that have different dimensions, :mod:`unyt` will raise an error to let you know that you have a bug in your code: >>> from unyt import kg, m >>> 3*kg + 5*m # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... unyt.exceptions.UnitOperationError: The operator for unyt_arrays with units "kg" (dimensions "(mass)") and "m" (dimensions "(length)") is not well defined. while this example is trivial when one writes more complicated formulae it can be easy to accidentally write expressions that are not dimensionally sound. Sometimes this can be annoying to deal with, particularly if one is mixing data that has units attached with data from some outside source with no units. To quickly patch over this lack of unit metadata (which could be applied by explicitly attaching units at I/O time), one can use the ``units`` attribute of the :class:`unyt.unyt_array ` class to quickly apply units to a scalar, list, or array: >>> from unyt import cm, s >>> velocities = [10, 20, 30] * cm/s >>> velocities + 12 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... unyt.exceptions.UnitOperationError: The operator for unyt_arrays with units "cm/s" (dimensions "(length)/(time)") and "dimensionless" (dimensions "1") is not well defined. >>> velocities + 12*velocities.units unyt_array([22, 32, 42], 'cm/s') Powers, Logarithms, Exponentials, and Trigonometric Functions ------------------------------------------------------------- The :mod:`unyt` library represents powers using standard Python syntax. This means you must use ``**`` and not ``^``, even when writing a unit as a string: >>> from unyt import kg, m >>> print((10.*kg/m**3).to('g/cm**3')) 0.01 g/cm**3 Formally it does not make sense to exponentiate, take the logarithm of, or apply a transcendental function to a quantity with units. However, the :mod:`unyt` library makes the practical affordance to allow this, simply ignoring the units present and returning a result without units. This makes it easy to work with data that has units both in linear space and in log space: >>> from unyt import g, cm >>> import numpy as np >>> print(np.log10(1e-23*g/cm**3)) -23.0 The one exception to this rule is for trigonometric functions applied to data with angular units: >>> from unyt import degree, radian >>> import numpy as np >>> np.sin(np.pi/4*radian) array(0.70710678) >>> np.sin(45.*degree) array(0.70710678) Logarithmic Quantities and Units ******************************** The logarithmic quantities level-of-power and level-of-field and the units neper and bel are supported. In the next example, we represent the power measurements, ``p``, as a logarithmic quantity at reference level, ``p_ref``, in the units decibel. >>> import numpy as np >>> from unyt import dB, mW >>> dB.dimensions (logarithmic) >>> p = [1, 100]*mW >>> p_ref = 1*mW >>> level_of_power = 10*np.log10(p/p_ref)*dB >>> level_of_power unyt_array([ 0., 20.], 'dB') You can convert the logarithmic quantity back to physical units through exponentiation, just remember to remove the units using the :meth:`unyt_array.v ` property. >>> 10**(level_of_power.v/10)*p_ref unyt_array([ 1., 100.], 'mW') Printing Units -------------- The print formatting of :class:`unyt_array ` can be controlled identically to NumPy arrays, using ``numpy.setprintoptions``: >>> import numpy as np >>> import unyt as u ... >>> with np.printoptions(precision=4): ... print([1.123456789]*u.km) [1.1235] km Print a :math:`\rm{\LaTeX}` representation of a set of units using the :meth:`unyt.unit_object.Unit.latex_representation` function or :attr:`unyt.unit_object.Unit.latex_repr` attribute: >>> from unyt import g, cm >>> (g/cm**3).units.latex_representation() '\\frac{\\rm{g}}{\\rm{cm}^{3}}' >>> (g/cm**3).units.latex_repr '\\frac{\\rm{g}}{\\rm{cm}^{3}}' Simplifying Units ----------------- Unit expressions can often be simplified to cancel pairs of factors with compatible dimensions. For example, we can form a unit with dimensions of length by dividing a unit with dimensions of length squared by another unit with dimensions of length:: >>> from unyt import m, cm >>> m**2/cm m**2/cm The :class:`Unit ` class has a :meth:`simplify() ` method that we can call to create a new unit object to that includes the dimensionless ratio ``m/cm`` as a constant coefficient:: >>> (m**2/cm).simplify() 100*m This will also work for units that are the reciprocals of each other, for example: >>> from unyt import s, Hz >>> (s*Hz).simplify() (dimensionless) Products and quotients of unit objects will not be simplified unless ``simplify()`` is called explicitly. However, products and quotients of arrays and quantities will be simplified to make interactive work more intuitive:: >>> from unyt import erg, minute, hour >>> power = [20, 40, 80] * erg / minute >>> elapsed_time = 3*hour >>> print(power*elapsed_time) [ 3600. 7200. 14400.] erg .. _checking_units: Checking Units -------------- If you write a function that accepts data with units as an argument or returns data with units, you can ensure the dimensional correctness of the inputs or outputs using the :meth:`@accepts ` and :meth:`@returns ` decorators:: >>> from unyt.dimensions import length, time >>> from unyt import accepts, returns >>> import unyt as u >>> @returns(length) ... @accepts(a=time, v=length/time) ... def foo(a, v): ... return a * v ... >>> res = foo(a=2*u.s, v=3*u.m/u.s) >>> print(res) 6 m :meth:`@accepts ` can specify the dimensions of any subset of inputs and :meth:`@returns ` must always describe all outputs. >>> @returns(length, length/time**2) ... @accepts(v=length/time) ... def bar(a, v): ... return a * v, v / a ... >>> res = bar(a=2*u.s, v=3*u.m/u.s) >>> print(*res) 6 m 1.5 m/s**2 .. note:: Using these decorators may incur some performance overhead, especially for small arrays. Temperature Units ----------------- The temperature unit degree Celsius has the symbol ``°C``, but since the degree character is an invalid Python identifier, :mod:`unyt` uses the symbol ``degC``. Printing a degree Celsius quantity will show the correct symbol. >>> from unyt import degC >>> Ta = 23*degC >>> print(Ta) 23 °C The ``degC`` symbol has alternative names ``degree_Celsius``, ``Celsius`` and ``°C``. >>> from unyt import degree_Celsius, unyt_array >>> Ta = 23*degree_Celsius >>> print(Ta) 23 °C >>> Ta = unyt_array([-40, 23, 70], '°C') >>> print(Ta) [-40 23 70] °C These comments also apply to degree Fahrenheit. Performing arithmetic with temperature quantities can be ambiguous. To clarify intent, :mod:`unyt` has the convenience units ``delta_degC`` and ``delta_degF``. >>> from unyt import degC, delta_degC, V >>> t1 = 23*degC >>> t2 = 1*delta_degC >>> print(t1 + t2) 24.0 °C >>> print(t2 - t1) -22.0 °C >>> tempco = 10.0*V/delta_degC >>> print(tempco*2*delta_degC) 20.0 V Unit Conversions and Unit Systems +++++++++++++++++++++++++++++++++ Converting Data to Arbitrary Units ---------------------------------- If you have some data that you want to convert to a different set of units and you know which units you would like to convert it to, you can make use of the :meth:`unyt_array.to ` function: >>> from unyt import mile >>> (1.0*mile).to('ft') unyt_quantity(5280., 'ft') If you try to convert to a unit with different dimensions, :mod:`unyt` will raise an error: >>> from unyt import mile >>> (1.0*mile).to('lb') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... unyt.exceptions.UnitConversionError: Cannot convert between 'mile' (dim '(length)') and 'lb' (dim '(mass)'). While we recommend using :meth:`unyt_array.to ` in most cases to convert arrays or quantities to different units, if you would like to explicitly emphasize that this operation has to do with units, we also provide the more verbose name :meth:`unyt_array.in_units ` which behaves identically to :meth:`unyt_array.to `. Converting Units In-Place ------------------------- The :meth:`unyt_array.to ` method makes a copy of the array data. For most cases this is fine, but when dealing with big arrays, or when performance is a concern, it sometimes is preferable to convert the data in an array in-place, without copying the data to a new array. This can be accomplished with the :meth:`unyt_array.convert_to_units ` function: >>> from unyt import mile >>> data = [1., 2., 3.]*mile >>> data unyt_array([1., 2., 3.], 'mile') >>> data.convert_to_units('km') >>> data unyt_array([1.609344, 3.218688, 4.828032], 'km') Converting to MKS and CGS Base Units ------------------------------------ If you don't necessarily know the units you want to convert data to ahead of time, it's often convenient to specify a unit system to convert to. The :class:`unyt_array ` has built-in conversion methods for the two most popular unit systems, MKS (meter kilogram second) and CGS (centimeter gram second). For CGS these are :meth:`unyt_array.in_cgs ` and :meth:`unyt_array.convert_to_cgs `. These functions create a new copy of an array in CGS units and convert an array in-place to CGS respectively. For MKS, there are the :meth:`unyt_array.in_mks ` and :meth:`unyt_array.convert_to_mks ` methods, which play analogous roles. See below for details on CGS and MKS electromagnetic units. .. _metal_conversions: Metallicity Unit Conversions ---------------------------- In the astrophysical context, "metals" are all of the elements that have atomic numbers greater than 2, i.e. everything heavier than helium. The "solar metallicity" is the mass fraction of metals in the solar atmosphere, and is used in a variety of contexts. Often, the metallicity of other astrophysical objects is expressed in terms of the solar metallicity, given by the unit :math:`Z_\odot`. The default mass fraction corresponding to :math:`Z_\odot` in :mod:`unyt` is 0.01295, corresponding to the value used in the `Cloudy Code `_. Metal mass fractions (by definition dimensionless) can be converted to :math:`Z_\odot` (and vice versa): >>> from unyt import dimensionless >>> M_Z = 0.0259*dimensionless >>> M_Z unyt_quantity(0.0259, 'dimensionless') >>> M_Z.convert_to_units("Z_sun") >>> M_Z unyt_quantity(2., 'Zsun') However, the value of this mass fraction conversion must be measured, and various estimates of it disagree somewhat. Different sub-disciplines of astronomy often use different estimates in the literature. :mod:`unyt` provides other metallicity unit conversions to several typical values in use. The available units (and their mass fraction conversion factors) are: * ``"Zsun_angr"``: 0.01937, from `Anders E. & Grevesse N. (1989, Geochimica et Cosmochimica Acta 53, 197) `_ * ``"Zsun_aspl"``: 0.01337, from `Asplund M., Grevesse N., Sauval A.J. & Scott P. (2009, ARAA, 47, 481) `_ * ``"Zsun_feld"``: 0.01909, from `Feldman U. (1992, Physica Scripta, 46, 202) `_ * ``"Zsun_lodd"``: 0.01321, from `Lodders, K (2003, ApJ 591, 1220) `_ These can be used in the same way as above: >>> from unyt import dimensionless >>> M_Z = 0.0259*dimensionless >>> M_Z unyt_quantity(0.0259, 'dimensionless') >>> M_Z.convert_to_units("Zsun_angr") >>> M_Z unyt_quantity(1.33711926, 'Zsun_angr') Other Unit Systems ------------------ The :mod:`unyt` library currently has built-in support for a number of unit systems, as detailed in the table below. Note that all unit systems currently use "radian" as the base angle unit. If a unit system in the table below has "Other Units" specified, this is a mapping from dimension to a unit name. These units override the unit system's default unit for that dimension. If no unit is explicitly specified of a dimension then the base unit for that dimension is calculated at runtime by combining the base units for the unit system into the appropriate dimension. +--------------+--------------------+--------------------------+ | Unit system | Base Units | Other Units | +==============+====================+==========================+ | cgs | cm, g, s | * Energy: erg | | | | * Specific Energy: erg/g | | | | * Pressure: dyne/cm**2 | | | | * Force: dyne | | | | * Power: erg/s | | | | * Magnetic Field: G | | | | * Charge: esu | | | | * Current: statA | +--------------+--------------------+--------------------------+ | mks | m, kg, s | * Energy: J | | | | * Specific Energy: J/kg | | | | * Pressure: Pa | | | | * Force: N | | | | * Power: W | | | | * Magnetic Field: T | | | | * Charge: C | +--------------+--------------------+--------------------------+ | imperial | ft, lb, s | * Energy: ft*lbf | | | | * Temperature: R | | | | * Pressure: lbf/ft**2 | | | | * Force: lbf | | | | * Power: hp | +--------------+--------------------+--------------------------+ | galactic | kpc, Msun, kyr | * Energy: kev | | | | * Magnetic Field: uG | +--------------+--------------------+--------------------------+ | solar | AU, Mearth, yr | | +--------------+--------------------+--------------------------+ Note that in MKS units the current unit, Ampere, is a base unit in the unit system. In CGS units the electromagnetic units like Gauss and statA are decomposable in terms of the base mass, length, and time units in the unit system. For this reason quantities defined in E&M units in CGS units are not readily convertible to MKS units and vice versa since the units are not dimensionally equivalent. The :mod:`unyt` library does have limited support for converting electromagnetic units between MKS and CGS, however only simple conversions of data with a single specific unit are supported and no conversions are allowed for complex combinations of units. For example converting between Gauss and Tesla is supported: >>> from unyt import T >>> (1.0*T).to('G') unyt_quantity(10000., 'G') But converting a more complicated compound unit will raise an error: >>> from unyt import C, T, V >>> (1.0*C*T*V).in_cgs() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... unyt.exceptions.UnitsNotReducible: The unit "C*T*V" (dimensions "(length)**2*(mass)**2/((current_mks)*(time)**4)") cannot be reduced to an expression within the cgs system of units. If you need to work with complex expressions involving electromagnetic units, we suggest sticking to either CGS or SI units for the full calculation. There is no general way to convert an arbitrary quantity between CGS and SI units if the quantity involves electromagnetic units. Instead, it is necessary to do the conversion on the equations under consideration, and then recompute the necessary quantity in the transformed set of equations. This requires understanding the context for a calculation, which unfortunately is beyond the scope of a library like :mod:`unyt`. You can convert data to a unit system :mod:`unyt` knows about using the :meth:`unyt_array.in_base ` and :meth:`unyt_array.convert_to_base ` methods: >>> from unyt import g, cm, horsepower >>> (1e-9*g/cm**2).in_base('galactic') unyt_quantity(4.78843804, 'Msun/kpc**2') >>> data = [100., 500., 700.]*horsepower >>> data unyt_array([100., 500., 700.], 'hp') >>> data.convert_to_base('mks') >>> data unyt_array([ 74569.98715823, 372849.93579114, 521989.91010759], 'W') Defining and Using New Unit Systems *********************************** To define a new custom unit system, one need only create a new instance of the :class:`unyt.UnitSystem ` class. The class initializer accepts a set of base units to define the unit system. If you would like to additionally customize any derived units in the unit system, you can do this using item setting. As an example, let's define an atomic unit system based on typical scales for atoms and molecules: >>> from unyt import UnitSystem >>> atomic_unit_system = UnitSystem('atomic', 'nm', 'mp', 'fs', 'nK', 'rad') >>> atomic_unit_system['energy'] = 'eV' >>> atomic_unit_system atomic Unit System Base Units: length: nm mass: mp time: fs temperature: nK angle: rad current_mks: A luminous_intensity: cd logarithmic: Np Other Units: energy: eV >>> print(atomic_unit_system) atomic >>> atomic_unit_system['number_density'] nm**(-3) >>> atomic_unit_system['angular_momentum'] mp*nm**2/fs It is also legal to define a unit system using :class:`unyt.Unit ` instances: >>> from unyt.unit_symbols import Msun, second, megaparsec >>> UnitSystem('cosmological', megaparsec, Msun, second) cosmological Unit System Base Units: length: Mpc mass: Msun time: s temperature: K angle: rad current_mks: A luminous_intensity: cd logarithmic: Np Other Units: Or with a quantity: >>> UnitSystem('quasmological', 3*megaparsec, .8*Msun, 42*second) quasmological Unit System Base Units: length: 3*Mpc mass: 0.8*Msun time: 42*s temperature: K angle: rad current_mks: A luminous_intensity: cd logarithmic: Np Other Units: Once you have defined a new unit system that will register the new system with a global registry of unit systems known to the :mod:`unyt` library. That means you will immediately be able to use it just like the built-in unit systems: >>> from unyt import W >>> (1.0*W).in_base('atomic') unyt_quantity(0.59746607, 'mp*nm**2/fs**3') If you would like your unit system to include an MKS current unit (e.g. something that is directly convertible to the MKS Ampere unit), then specify a ``current_mks_unit`` in the :class:`UnitSystem ` initializer. Equivalencies +++++++++++++ An equivalency is a way to define a mapping to convert from one unit to another even if the two units are not dimensionally equivalent. This usually involves some sort of shorthand or heuristic understanding of the problem under consideration. Only use one of these equivalencies if it makes sense to use it for the problem you are working on. The :mod:`unyt` library implements the following equivalencies: * ``"thermal"``: conversions between temperature and energy (:math:`E = k_BT`) * ``"spectral"``: conversions between wavelength, spatial frequency, frequency, and energy for photons (:math:`E = h\nu = hc/\lambda`, :math:`c = \lambda\nu`) * ``"mass_energy"``: conversions between mass and energy (:math:`E = mc^2`) * ``"lorentz"``: conversions between velocity and Lorentz factor (:math:`\gamma = 1/\sqrt{1-(v/c)^2}`) * ``"schwarzschild"``: conversions between mass and Schwarzschild radius (:math:`R_S = 2GM/c^2`) * ``"compton"``: conversions between mass and Compton wavelength (:math:`\lambda = h/mc`) You can convert data to a specific set of units via an equivalency appropriate for the units of the data. To see the equivalencies that are available for an array, use the :meth:`unit_array.list_equivalencies ` method: >>> from unyt import gram, km >>> gram.list_equivalencies() mass_energy: mass <-> energy schwarzschild: mass <-> length compton: mass <-> length >>> km.list_equivalencies() spectral: length <-> spatial_frequency <-> frequency <-> energy schwarzschild: mass <-> length compton: mass <-> length All of the unit conversion methods described above have an ``equivalence`` keyword argument that allows one to optionally specify an equivalence to use for the unit conversion operation. For example, let's use the ``schwarzschild`` equivalence to calculate the mass of a black hole with a radius of one AU: >>> from unyt import AU >>> (1.0*AU).to('Msun', equivalence='schwarzschild') unyt_quantity(50656851.7815179, 'Msun') Both the methods that convert data in-place and the ones that return a copy support optionally specifying equivalence. In addition to the methods described above, :mod:`unyt` also supplies two more conversion methods that *require* an equivalence to be specified: :meth:`unyt_array.to_equivalent ` and :meth:`unyt_array.convert_to_equivalent `. These are identical to their counterparts described above, except that equivalence is a required argument to the function rather than an optional keyword argument. Use these functions when you want to emphasize that an equivalence is being used. If the equivalence has optional keyword arguments, these can be passed to the unit conversion function. For example, here's an example where we specify a custom mean molecular weight (``mu``) for the ``number_density`` equivalence: >>> from unyt import g, cm >>> rho = 1e-23 * g/cm**3 >>> rho.to('cm**-3', equivalence='number_density', mu=1.4) unyt_quantity(4.26761476, 'cm**(-3)') For full API documentation and an autogenerated listing of the built-in equivalencies in :mod:`unyt` as well as a short usage example for each, see the :mod:`unyt.equivalencies` API listing. Dealing with code that doesn't use :mod:`unyt` ++++++++++++++++++++++++++++++++++++++++++++++ Optimally, a function will work the same irrespective of whether the data passed in has units attached or not: >>> from unyt import cm >>> def square(x): ... return x**2 >>> print(square(3.)) 9.0 >>> print(square(3.*cm)) 9.0 cm**2 However in the real world that is not always the case. In this section we describe strategies for dealing with that situation. Stripping units off of data --------------------------- The :mod:`unyt` library provides a number of ways to convert :class:`unyt_quantity ` instances into floats and :class:`unyt_array ` instances into NumPy arrays. These methods either return a copy of the data as a NumPy array or return a view onto the underlying array data owned by a :class:`unyt_array ` instance. To obtain a new array containing a copy of the original data, use either the :meth:`unyt_array.to_value ` function or the :attr:`unyt_array.value ` or :attr:`unyt_array.v ` properties. All of these are equivalent to passing a :class:`unyt_array ` to the ``numpy.array()`` function: >>> from unyt import g >>> import numpy as np >>> data = [1., 2., 3.]*g >>> data unyt_array([1., 2., 3.], 'g') >>> np.array(data) array([1., 2., 3.]) >>> data.to_value('kg') array([0.001, 0.002, 0.003]) >>> data.value array([1., 2., 3.]) >>> data.v array([1., 2., 3.]) Similarly, to obtain a ndarray containing a view of the data in the original array, use either the :attr:`unyt_array.ndview ` property (or :attr:`unyt_array.d ` for shorts): >>> data.view(np.ndarray) array([1., 2., 3.]) >>> data.ndview array([1., 2., 3.]) >>> data.d array([1., 2., 3.]) Applying units to data ---------------------- .. note:: A NumPy array that shares memory with another NumPy array points to the array that owns the data with the ``base`` attribute. If ``arr1.base is arr2`` is ``True`` then ``arr1`` is a view onto ``arr2`` and ``arr2.base`` will be ``None``. When a :class:`unyt_array ` instance is created from a NumPy array and a :class:`Unit `, data from the NumPy array will be copied: >>> from unyt import g >>> data = np.random.random((100, 100)) >>> data_with_units = data*g >>> data_with_units.base is data False If you would like to create a view rather than a copy, you can apply units like this: >>> from unyt import unyt_array >>> data_with_units = unyt_array(data, g) >>> data_with_units.base is data True Any set of units can be used for either of these operations. For example, if you already have an existing array, you could do this to create a new array with the same units: >>> more_data = [4, 5, 6]*data_with_units.units >>> more_data unyt_array([4, 5, 6], 'g') Working with code that uses ``astropy.units`` --------------------------------------------- The :mod:`unyt` library can convert data contained inside of an Astropy ``Quantity`` instance. It can also produce a ``Quantity`` from an existing :class:`unyt_array ` instance. To convert data from ``astropy.units`` to :mod:`unyt` use the :func:`unyt_array.from_astropy ` function: >>> from astropy.units import km >>> from unyt import unyt_quantity >>> unyt_quantity.from_astropy(km) unyt_quantity(1., 'km') >>> a = [1, 2, 3]*km >>> a >>> unyt_array.from_astropy(a) unyt_array([1., 2., 3.], 'km') To convert data *to* ``astropy.units`` use the :meth:`unyt_array.to_astropy ` method: >>> from unyt import g, cm >>> data = [3, 4, 5]*g/cm**3 >>> data.to_astropy() >>> (4*cm).to_astropy() Working with code that uses ``Pint`` ------------------------------------ The :mod:`unyt` library can also convert data contained in ``Pint`` ``Quantity`` instances. To convert data from ``Pint`` to :mod:`unyt`, use the :func:`unyt_array.from_pint ` function: >>> from pint import UnitRegistry >>> import numpy as np >>> ureg = UnitRegistry() >>> a = np.arange(4) >>> b = ureg.Quantity(a, "erg/cm**3") >>> b >>> c = unyt_array.from_pint(b) >>> c unyt_array([0, 1, 2, 3], 'erg/cm**3') And to convert data contained in a :class:`unyt_array ` instance, use the :meth:`unyt_array.to_pint ` method: >>> from unyt import cm, s >>> a = 4*cm**2/s >>> print(a) 4 cm**2/s >>> a.to_pint() >>> b = [1, 2, 3]*cm >>> b.to_pint() Reading quantities from text ---------------------------- Quantities can also be parsed from strings with the :func:`unyt_quantity.from_string ` function: >>> from unyt import unyt_quantity >>> unyt_quantity.from_string("1 cm") unyt_quantity(1, 'cm') >>> unyt_quantity.from_string("1e3 Msun") unyt_quantity(1000., 'Msun') >>> unyt_quantity.from_string("1e-3 g/cm**3") unyt_quantity(0.001, 'g/cm**3') This method is helpful to read data from text files, for instance configuration files. It is intended to be as flexible as possible on the string format, though it requires that the numerical value and the unit name be separated with some kind of whitespace. User-Defined Units ++++++++++++++++++ Often it is convenient to define new custom units. This can happen when you need to make use of a unit that the :mod:`unyt` library does not have a definition for already. It can also happen when dealing with data that uses a custom unit system or when writing software that needs to deal with such data in a flexible way, particularly when the units might change from dataset to dataset. This comes up often when modeling a physical system since it is often convenient to rescale data from a physical unit system to an internal "code" unit system in which the values of the variables under consideration are close to unity. This approach can help minimize floating point round-off error but is often done for convenience or to non-dimensionalize the problem under consideration. The :mod:`unyt` library provides two approaches for dealing with this problem. For more toy one-off use-cases, we suggest using :func:`unyt.define_unit ` which allows defining a new unit name in the global, default unit system that :mod:`unyt` ships with by default. This function makes it possible to easily define a new unit that is unknown to the :mod:`unyt` library: >>> import unyt as u >>> ninety_pounds = 90.0*u.lb >>> one_pound = 1.0*u.lb >>> u.define_unit("firkin", ninety_pounds) >>> print((3*u.firkin)/one_pound) 270.0 dimensionless This is primarily useful for one-off definitions of units that the :mod:`unyt` library does not already have predefined. For more complex uses cases that need more flexibility, it is possible to use a custom unit system by ensuring that the data you are working with makes use of a :class:`UnitRegistry ` customized for your use case, as described below. Dealing with data types +++++++++++++++++++++++ The :mod:`unyt` library supports creating :class:`unyt.unyt_array ` and :class:`unyt.unyt_quantity ` instances with arbitrary integer or floating point data types: >>> import numpy as np >>> from unyt import km ... >>> int_data = [1, 2, 3]*km >>> int_data unyt_array([1, 2, 3], 'km') >>> float32_data = np.array([1, 2, 3], dtype='float32')*km >>> float32_data unyt_array([1., 2., 3.], dtype=float32, units='km') The ``dtype`` of a ``unyt_array`` instance created by multiplying an iterable by a unit will be the same as passing the iterable to ``np.array()``. You can also manually specify the ``dtype`` by calling ``np.array()`` yourself or by using the ``unyt_array`` initializer directly: >>> np.array([1, 2, 3], dtype='float64')*km unyt_array([1., 2., 3.], 'km') Operations that convert an integer array to a new unit will convert the array to the floating point type with an equivalent size. For example, Calling ``in_units`` on a 32 bit integer array with units of kilometers will return a 32 bit floating point array. >>> data = np.array([1, 2, 3], dtype='int32')*km >>> data.in_units('mile') unyt_array([0.6213712, 1.2427424, 1.8641136], dtype=float32, units='mile') In-place operations will also mutate the dtype from float to integer in these cases, again in a way that will preserve the byte size of the data. >>> data.convert_to_units('mile') >>> data unyt_array([0.6213712, 1.2427424, 1.8641136], dtype=float32, units='mile') It is possible that arrays containing large integers (16777217 for 32 bit and 9007199254740993 for 64 bit) will lose precision when converting data to a different unit. In these cases a warning message will be printed. Integrating :mod:`unyt` Into a Python Library +++++++++++++++++++++++++++++++++++++++++++++ The :mod:`unyt` library began life as the unit system for the ``yt`` data analysis and visualization package, in the form of ``yt.units``. In this role, :mod:`unyt` was deeply integrated into a larger Python library. Due to these origins, it is straightforward to build applications that ensure unit consistency by making use of :mod:`unyt`. Below we discuss a few topics that most often come up when integrating :mod:`unyt` into a new or existing Python library. Unit registries --------------- It is also possible to define a custom database of units completely independent of the global default unit database exposed by the :mod:`unyt` namespace or to create namespaces in your own package that expose listings of units. In these cases it becomes important to understand how ``unyt`` stores unit metadata in an internal database, how to add custom entries to the database, how to modify them, and how to persist custom units. In practice, the unit metadata for a unit object is contained in an instance of the :class:`UnitRegistry ` class. Every :class:`Unit ` instance contains a reference to a :class:`UnitRegistry ` instance: >>> from unyt import g >>> g.registry # doctest: +ELLIPSIS All the unit objects in the :mod:`unyt` namespace make use of the default unit registry, importable as :data:`unyt.unit_registry.default_unit_registry`. This registry object contains all of the real-world physical units that the :mod:`unyt` library ships with out of the box. The unit registry itself contains a look-up table that maps from unit names to the metadata necessary to construct a unit. Note that the unit registry only contains metadata for "base" units, and not, for example, SI-prefixed units like centimeter of kilogram, it will instead only contain entries for meter and gram. Sometimes it is convenient to create a unit registry containing new units that are not available in the default unit registry. A common example would be adding a ``code_length`` unit that corresponds to the scaling to from physical lengths to an internal unit system. In practice, this value is arbitrary, but will be fixed for a given problem. Let's create a unit registry and a custom ``"code_length"`` unit to it, and then create a ``"code_length"`` unit and a quantity with units of ``"code_length"``. For the sake of example, let's set the value of ``"code_length"`` equal to 10 meters. >>> from unyt import UnitRegistry, Unit >>> from unyt.dimensions import length >>> reg = UnitRegistry() >>> reg.add("code_length", base_value=10.0, dimensions=length, ... tex_repr=r"\rm{Code Length}") >>> 'code_length' in reg True >>> u = Unit('code_length', registry=reg) >>> data = 3*u >>> print(data) 3 code_length As you can see, you can test whether a unit name is in a registry using the Python ``in`` operator. In an application that depends on ``unyt``, it is often convenient to define methods or functions to automatically attach the correct unit registry to unit objects associated with an object. For example, consider a ``Simulation`` class. Let's give this class two methods named ``array`` and ``quantity`` to create new :mod:`unyt_array ` and :mod:`unyt_quantity ` instances, respectively: >>> class Simulation: ... def __init__(self, registry): ... self.registry = registry ... ... def array(self, value, units): ... return unyt_array(value, units, registry=self.registry) ... ... def quantity(self, value, units): ... return unyt_quantity(value, units, registry=self.registry) ... >>> registry = UnitRegistry() >>> registry.add("code_length", base_value=3.2, dimensions=length) >>> s = Simulation(registry) >>> s.array([1, 2, 3], 'code_length') unyt_array([1, 2, 3], 'code_length') We can create an array with ``"code_length"`` here because ``s.registry``, the ``UnitRegistry`` instance associated with our Simulation instance has a ``"code_length"`` unit defined. As for arrays with different units, for operations between arrays created with different unit registries, the result of the operation will use the same unit registry as the leftmost unit. This can sometimes lead to surprising behaviors where data will seem to "forget" about custom units. In this situation it is important to make sure ahead of time that all data are created with units using the same unit registry. If for some reason that is not possible (for example, when comparing data from two different simulations with different internal units), then care must be taken when working with custom units. To avoid these sorts of ambiguities it is best to do work in physical units as much as possible. When writing tests, it is convenient to use :mod:`unyt.testing`. In particular, :func:`assert_allclose_units ` can be used to check for floating-point equality. >>> from unyt import assert_allclose_units, m >>> import numpy as np >>> actual = [1e-5, 1e-3, 1e-1] * m >>> desired = actual.to("cm") >>> assert_allclose_units(actual, desired) Custom Unit Systems ------------------- By default :mod:`unyt` uses the SI MKS unit system. However, libraries can create a unit registry using another unit system to expose that unit system to their users by creating a unit registry with a custom unit system. For example, to make CGS units the default unit for all operations, one might use a CGS ``UnitRegistry`` to instancitate the ``Simulation`` class like so:: >>> class Simulation: ... def __init__(self, registry): ... self.registry = registry ... ... def array(self, value, units): ... return unyt_array(value, units, registry=self.registry) ... ... def quantity(self, value, units): ... return unyt_quantity(value, units, registry=self.registry) ... >>> registry = UnitRegistry(unit_system='cgs') >>> registry.add("code_length", base_value=3.2, dimensions=length) >>> s_cgs = Simulation(registry) >>> data = s_cgs.array([1, 2, 3], 'code_length') >>> data unyt_array([1, 2, 3], 'code_length') >>> data.in_base() unyt_array([320., 640., 960.], 'cm') Note that the ``base_value`` parameter of :meth:`UnitRegistry.add ` must be specified in MKS units. All unit data are stored internally in :mod:`unyt` in MKS units. You can also use two helper functions provided by :mod:`unyt`, :func:`unyt.unit_systems.add_constants` and :func:`unyt.unit_systems.add_symbols`, to populate a namespace with a set of predefined unit symbols or physical constants. This namespace could correspond to the names importable from a module or the names of attributes of an object, or any other generic dictionary. One example of doing this would be to make a ``UnitContainer`` class that contains units that are compatible with the ``Simulation`` instance we named ``s_cgs`` in the example above:: >>> from unyt.unit_systems import add_symbols >>> class UnitContainer: ... def __init__(self, registry): ... add_symbols(vars(self), registry) >>> units = UnitContainer(s_cgs.registry) >>> units.kilometer km >>> units.code_length code_length >>> (10.0 * units.kilometer).in_base() unyt_quantity(1000000., 'cm') >>> (10.0 * units.kilometer).in_units('code_length') unyt_quantity(3125., 'code_length') Note how the result of the call to ``in_base()`` comes out in centimeters because of the the CGS unit system used by the :class:`UnitRegistry ` instance associated with the ``Simulation``. Writing Data with Units to Disk ------------------------------- The :mod:`unyt` library has support for serializing data stored in a :class:`unyt.unyt_array ` instance to HDF5 files, text files, and via the Python pickle protocol. We give brief examples below, but first describe how to handle saving units manually as string metadata. Dealing with units as strings ***************************** If all you want to do is save data to disk in a physical unit or you are working in a physical unit system, then you only need to save the unit name as a string and treat the array data you are trying to save as a regular NumPy array, as in this example: >>> import numpy as np >>> import os >>> from unyt import cm ... >>> data = [1, 2, 3]*cm >>> np.save('my_data_cm.npy', data) >>> new_data = np.load('my_data_cm.npy') >>> new_data array([1, 2, 3]) >>> new_data_with_units = new_data * cm >>> os.remove('my_data_cm.npy') Of course in this example using ``numpy.save`` we need to hard-code the units because the ``.npy`` format doesn't have a way to store metadata along with the array data. We could have stored metadata in a sidecar file, but this is much more natural with ``hdf5`` via ``h5py``: >>> import h5py >>> import os >>> from unyt import cm, unyt_array ... >>> data = [1, 2, 3]*cm ... >>> with h5py.File('my_data.h5', 'a') as f: ... d = f.create_dataset('my_data', data=data) ... f['my_data'].attrs['units'] = str(data.units) ... >>> with h5py.File('my_data.h5', 'r') as f: ... new_data = f['my_data'][:] ... unit_str = f['my_data'].attrs['units'] ... >>> new_data = unyt_array(new_data, unit_str) >>> new_data unyt_array([1, 2, 3], 'cm') >>> os.remove('my_data.h5') HDF5 Files ********** The :mod:`unyt` library provides a hook for writing data both to a new HDF5 file and an existing file and then subsequently reading that data back in to restore the array. This works via the :meth:`unyt_array.write_hdf5 ` and :meth:`unyt_array.from_hdf5 ` methods. The simplest way to use these functions is to write data to a file that does not exist yet: >>> from unyt import cm, unyt_array >>> import os >>> data = [1, 2, 3]*cm >>> data.write_hdf5('my_data.h5') ... >>> unyt_array.from_hdf5('my_data.h5') unyt_array([1, 2, 3], 'cm') >>> os.remove('my_data.h5') By default the data will be written to the root group of the HDF5 file in a dataset named ``'array_data'``. You can also specify that you would like the data to be saved in a particular group or dataset in the file: >>> data.write_hdf5('my_data.h5', dataset_name='my_special_data', ... group_name='my_special_group') >>> unyt_array.from_hdf5('my_data.h5', dataset_name='my_special_data', ... group_name='my_special_group') unyt_array([1, 2, 3], 'cm') >>> os.remove('my_data.h5') You can even write to files and groups that already exist: >>> with h5py.File('my_data.h5', 'w') as f: ... g = f.create_group('my_custom_group') ... >>> data.write_hdf5('my_data.h5', group_name='my_custom_group') ... >>> with h5py.File('my_data.h5') as f: ... print(f['my_custom_group/array_data'][:]) [1 2 3] >>> os.remove('my_data.h5') If the dataset that you would like to write to already exists, :mod:`unyt` will clobber that dataset. Note that with this method of saving data to HDF5 files, the :class:`unyt.UnitRegistry ` instance associated with the units of the data will be saved in the HDF5 file. This means that if you create custom units and save a unit to disk, you will be able to convert data to those custom units even if you are dealing with those units later after restoring the data from disk. Here is a short example illustrating this: >>> import os >>> from unyt import UnitRegistry >>> reg = UnitRegistry() >>> reg.add("code_length", base_value=10.0, dimensions=length, ... tex_repr=r"\rm{Code Length}") >>> u = Unit('cm', registry=reg) >>> data = [1., 2., 3.]*u >>> data.write_hdf5('my_code_data.h5') >>> read_data = data.from_hdf5('my_code_data.h5') >>> read_data unyt_array([1., 2., 3.], 'cm') >>> read_data.to('code_length') unyt_array([0.001, 0.002, 0.003], 'code_length') >>> os.remove('my_code_data.h5') Text Files ********** The :mod:`unyt` library also has wrappers around ``numpy.savetxt`` and ``numpy.loadtxt`` for saving data as an ASCII table. For example: >>> import unyt as u >>> import os >>> data = [[1, 2, 3]*u.cm, [4, 5, 6]*u.kg] >>> u.savetxt('my_data.txt', data) >>> with open('my_data.txt') as f: ... print("".join(f.readlines())) # doctest: +NORMALIZE_WHITESPACE # Units # cm kg 1.000000000000000000e+00 4.000000000000000000e+00 2.000000000000000000e+00 5.000000000000000000e+00 3.000000000000000000e+00 6.000000000000000000e+00 >>> os.remove('my_data.txt') Pickles ******* .. note:: Pickle files are great for serializing data to disk or over a network for internal usage by a package. They are ill-suited for long-term data storage or for communicating data between different Python installations. If you want to use pickle files for data storage, consider using a format designed for long-term data storage, like HDF5. Both :class:`unyt.unyt_array ` and :class:`unyt.Unit ` instances can be saved using the pickle protocol: >>> from unyt import kg >>> import pickle >>> import numpy as np ... >>> assert kg == pickle.loads(pickle.dumps(kg)) >>> data = [1, 2, 3]*kg >>> reloaded_data = pickle.loads(pickle.dumps(data)) >>> assert np.array_equal(data.value, reloaded_data.value) >>> assert data.units == reloaded_data.units As for HDF5 data, the unit registry associated with the unit object is saved to the pickle. If you have custom units defined, the reloaded data will know about your custom unit and be able to convert data to and from the custom unit. Handling errors from :mod:`unyt` -------------------------------- :mod:`unyt` sometimes raises exceptions with unique exception types, e.g., to signal invalid operations, like summation of quantities with different dimensions. It is possible to catch any exceptions from unyt as >>> from unyt import cm, s >>> from unyt.exceptions import UnytError >>> a = 1 * cm >>> b = 1 / s >>> try: ... a + b ... except UnytError: ... pass However, it is in general advised to only catch specific exceptions types that are known-possible outcomes. All custom exceptions types live in the :mod:`unyt.exceptions` module and may be imported from there. Performance Considerations -------------------------- Tracking units in an application will inevitably add overhead. Judging where overhead is important or not depends on what real-world workflows look like. Ultimately, profiling code is the best way to find out whether handling units is a performance bottleneck. Optimally handling units will be amortized over the cost of an operation. While this is true for large arrays (bigger than about one million elements), this is *not* true for small arrays that contain only a few elements. In addition, it is sometimes easy to write code that needlessly checks unit consistency when we know ahead of time that data are already in the correct units. Often we can get away with only checking unit consistency once and then stripping units after that. A good rule of thumb is that units should be checked on input, stripped off of data during a calculation, and then re-applied when returning data from a function. In other words, apply or check units at interfaces, but during an internal calculation it is often worth stripping units, especially if the calculation involves many operations on arrays with only a few elements. :class:`unyt_array.name ` attribute +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ The unyt_array has a name attribute for use in structured-data applications or similar applications that require labeled data. For example, Numpy has record arrays and when constructed as shown below, it is possible to retain the units while taking advantage of the labeled record fields. >>> import numpy as np >>> from unyt import unyt_array >>> x = unyt_array([0, 1, 2], "s", name="time") >>> y = unyt_array([3, 4, 5], "m", name="distance") >>> data = (x, y) >>> dt = [(a.name, "O") for a in data] >>> data_points = np.array(list(zip(*data)), dtype=dt).view(np.recarray) >>> data_points[0].time unyt_quantity(0, 's') >>> data_points[0].distance unyt_quantity(3, 'm') .. note:: The name attribute does not propagate through mathematical operations. Other operations such as indexing, copying, and unit conversion, will preserve the name attribute where the semantic meaning of the quantity remains the same. Plotting with Matplotlib ++++++++++++++++++++++++ .. note:: - This is an experimental feature. Please report issues. - This feature works in Matplotlib versions 2.2.4 and above - Matplotlib is not a dependency of Unyt Matplotlib is Unyt aware. After enabling support in :mod:`unyt` using the :class:`unyt.matplotlib_support ` context manager, Matplotlib will label the x and y axes with the units. >>> import matplotlib.pyplot as plt >>> from unyt import matplotlib_support, s, K >>> x = [0.0, 60.0, 120.0]*s >>> y = [298.15, 308.15, 318.15]*K >>> with matplotlib_support: ... plt.plot(x, y) ... plt.show() [] .. image:: _static/mpl_fig1.png You can change the plotted units without affecting the original data. >>> with matplotlib_support: ... plt.plot(x, y, xunits="min", yunits=("J", "thermal")) ... plt.show() [] .. image:: _static/mpl_fig2.png It is also possible to set the label style; the choices ``"()"``, ``"[]"`` and ``"/"`` are supported. >>> matplotlib_support.label_style = "[]" >>> with matplotlib_support: ... plt.plot(x, y) ... plt.show() [] .. image:: _static/mpl_fig3.png The axis label will include the unyt_array.name attribute if set. >>> x.name = "Time" >>> y.name = "Temperature" >>> with matplotlib_support: ... plt.plot(x, y) ... plt.show() [] .. image:: _static/mpl_fig4.png With label_style set to "/", the axis label conforms to the SI standard where the axis label is a mathematical expression rather than a caption. In this case, set the unyt_array.name attribute to the latex expression for the physical quantity symbol. >>> x.name = "$t$" >>> y.name = "" >>> matplotlib_support.label_style = "/" >>> with matplotlib_support: ... plt.plot(x, y) ... plt.show() [] .. image:: _static/mpl_fig5.png There are three ways to use the context manager: 1. As a conventional context manager in a ``with`` statement as shown above 2. As a feature toggle in an interactive session: >>> import matplotlib.pyplot as plt >>> from unyt import s, K, matplotlib_support >>> matplotlib_support.enable() >>> plt.plot([0, 1, 2]*s, [3, 4, 5]*K) [] >>> plt.show() >>> matplotlib_support.disable() 3. As an enable for a complete session: >>> import unyt >>> unyt.matplotlib_support() >>> import matplotlib.pyplot as plt .. _dask: Working with Dask arrays ++++++++++++++++++++++++ :mod:`unyt` provides the ability to wrap dask arrays with :mod:`unyt` behavior. The main access point is the :mod:`unyt.dask_array.unyt_from_dask` function, which allows you to build a :mod:`unyt_dask_array` from a plain dask array analogous to the creation of a :mod:`unyt_array` from a plain :mod:`numpy.ndarray`: >>> from unyt import dask_array as uda >>> import dask.array as da >>> x = da.arange(10000, chunks=(1000,)) >>> x_da = uda.unyt_from_dask(x, 'm') Methods that hang off of a :mod:`unyt_dask_array` object and operations on :mod:`unyt_dask_array` objects will generally preserve units: >>> x_da.sum().compute() unyt_quantity(49995000, 'm') >>> (x_da[:5000] * x_da[5000:]).compute()[:5] unyt_array([ 0, 5001, 10004, 15009, 20016], 'm**2') One important caveat is that using Dask array functions may strip units: >>> da.sum(x_da).compute() np.int64(49995000) For simple reductions, you can use the :mod:`reduce_with_units` function: >>> result = uda.reduce_with_units(da.sum, x_da) >>> result.compute() unyt_quantity(49995000, 'm') But more complex operations may require more careful management of units. Note that :mod:`reduce_with_units` will accept any of the positional or keyword arguments for the array function: >>> import numpy as np >>> x = da.ones((10000, 3), chunks=(1000, 1000)) >>> x[:,0] = np.nan >>> x_da = uda.unyt_from_dask(x, 'm') >>> result = uda.reduce_with_units(da.nansum, x_da, axis=1) >>> result.compute()[:5] unyt_array([2., 2., 2., 2., 2.], 'm') As a final note: the initial Dask array provided to :mod:`dask_array.unyt_from_dask` can be constructed in any of the usual ways of constructing Dask arrays -- from :mod:`NumPy`-like array instantiation as in the above examples to reading from file or delayed operations. 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perf from matplotlib import pyplot as plt from matplotlib.patches import Patch ALPHA_MAP = {"small": 0.3333, "medium": 0.666, "big": 1.0} COLOR_MAP = {"unyt": "C0", "astropy": "C1", "pint": "C2"} SIZE_LABELS = {"small": "3", "medium": "$10^3$", "big": "$10^6$"} def make_plot(benchmark_name, benchmarks, fig_filename): plt.style.use("tableau-colorblind10") plt.rc("font", family="stixgeneral") plt.rc("mathtext", fontset="cm") fig, ax = plt.subplots() ratios = OrderedDict() stddevs = OrderedDict() width = 0.1 packages = ["pint", "astropy", "unyt"] package_offsets = [-width, 0, width] size_offsets = [-width * 3, 0, width * 3] static_offset = 0 sizes = ["big", "medium", "small"] yticks = [] all_yticks = [] for ind, benchmark in enumerate(benchmarks): if "create" not in benchmark: benchmark = "array_" + benchmark for size, size_offset in zip(sizes, size_offsets): np_fname = "../benchmarks/numpy_{}_{}.json".format(size, benchmark) with open(np_fname, "r") as f: np_bench = perf.Benchmark.load(f) np_mean = np_bench.mean() np_stddev = np_bench.stdev() for package, package_offset in zip(packages, package_offsets): fname = "../benchmarks/{}_{}_{}.json".format(package, size, benchmark) with open(fname, "r") as f: pbench = perf.Benchmark.load(f) mean = pbench.mean() stddev = pbench.stdev() ratios[package] = mean / np_mean stddevs[package] = ratios[package] * np.sqrt( (np_stddev / np_mean) ** 2 + (stddev / mean) ** 2 ) ytick = ind + 1 + size_offset + package_offset + static_offset ax.barh( ytick, ratios[package], width, xerr=stddevs[package], color=COLOR_MAP[package], label=" ".join([package, SIZE_LABELS[size]]), alpha=ALPHA_MAP[size], ) all_yticks.append(ytick) if package == "astropy" and size == "medium": yticks.append(ytick) static_offset += 0.01 legend_patches = [Patch(color=c) for c in COLOR_MAP.values()] leg = ax.legend(legend_patches, COLOR_MAP.keys(), loc=1) size_patches = [Patch(color="k", alpha=a) for a in ALPHA_MAP.values()] size_labels = [SIZE_LABELS[l] for l in ALPHA_MAP.keys()] ax.legend(size_patches, size_labels, loc=4, title="Number of elements") ax.add_artist(leg) ax.set_xlabel(r"$T_{\rm package} / T_{\rm numpy}$") ax.set_xscale("symlog", linthresh=1) ax.set_xlim(0, 1000) ax.set_yticks(yticks) ax.set_xticks( [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 2, 3, 4, 5, 6, 7, 8, 9, 20, 30, 40, 50, 60, 70, 80, 90, 200, 300, 400, 500, 600, 700, 800, 900, ], minor=True, ) ax.set_xticks([0, 1, 10, 100, 1000]) ax.set_xticklabels(["0", "1", "10", "100", "1000"]) ax.set_yticklabels(benchmarks.values()) ax.plot([1, 1], [0, len(benchmarks) + 1], "--", color="k", lw=0.75, alpha=0.5) spacing = len(benchmarks) // 2 + 1 ax.set_ylim(all_yticks[0] - 0.05 * spacing, all_yticks[-1] + 0.05 * spacing) fig.suptitle(benchmark_name) plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.savefig(fig_filename, dpi=300) plt.close(fig) make_plot( benchmark_name="Apply units to data", benchmarks={"list_create": "List", "array_create": "Array"}, fig_filename="apply.png", ) make_plot( benchmark_name="Unary operations", benchmarks={"sqrt": r"$\mathtt{data**0.5}$", "square": r"$\mathtt{data**2}$"}, fig_filename="unary.png", ) make_plot( benchmark_name="Binary operations, different units", benchmarks={ "kgg_operator.add12": r"$\mathtt{a + b}$", "kgg_operator.sub12": r"$\mathtt{a - b}$", "kgg_operator.mul12": r"$\mathtt{a * b}$", "kgg_operator.truediv12": r"$\mathtt{a / b}$", "kgg_operator.eq12": r"$\mathtt{a == b}$", }, fig_filename="binary_different_units.png", ) make_plot( benchmark_name="Binary operations, same units", benchmarks={ "gg_operator.add12": r"$\mathtt{a + b}$", "gg_operator.sub12": r"$\mathtt{a - b}$", "gg_operator.mul12": r"$\mathtt{a * b}$", "gg_operator.truediv12": r"$\mathtt{a / b}$", "gg_operator.eq12": r"$\mathtt{a == b}$", }, fig_filename="binary_same_units.png", ) make_plot( benchmark_name="NumPy ufunc", benchmarks={ "kgg_np.add12": r"$\mathtt{np.add(a, b)}$", "kgg_np.subtract12": r"$\mathtt{np.subtract(a, b)}$", "kgg_np.multiply12": r"$\mathtt{np.multiply(a, b)}$", "kgg_np.true_divide12": r"$\mathtt{np.divide(a, b)}$", "kgg_np.equal12": r"$\mathtt{np.equal(a, b)}$", "npsqrt": r"$\mathtt{np.sqrt(data)}$", "npsquare": r"$\mathtt{np.power(data, 2)}$", }, fig_filename="ufunc.png", ) make_plot( benchmark_name="In-place ufunc", benchmarks={ "kgg_np.add12out": r"$\mathtt{np.add(a, b, out=out)}$", "kgg_np.subtract12out": r"$\mathtt{np.subtract(a, b, out=out)}$", "kgg_np.multiply12out": r"$\mathtt{np.multiply(a, b, out=out)}$", "kgg_np.true_divide12out": r"$\mathtt{np.divide(a, b, out=out)}$", "kgg_np.equal12out": r"$\mathtt{np.equal(a, b, out=out)}$", "npsqrtout": r"$\mathtt{np.sqrt(data, out=out)}$", "npsquareout": r"$\mathtt{np.power(data, 2, out=out)}$", }, fig_filename="ufuncout.png", ) unyt-3.2.0/paper/binary_different_units.png000066400000000000000000002365401526544257300211130ustar00rootroot00000000000000PNG  IHDR^sBIT|d pHYs.#.#x?v9tEXtSoftwarematplotlib version 2.2.2, 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It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy. }, volume = 3, pages = {e103}, journal = {PeerJ Computer Science}, issn = {2376-5992}, url = {https://doi.org/10.7717/peerj-cs.103}, doi = {10.7717/peerj-cs.103} } @ARTICLE{vanderwalt2011, author={S. van der Walt and S. C. Colbert and G. Varoquaux}, journal={Computing in Science Engineering}, title={The NumPy Array: A Structure for Efficient Numerical Computation}, year={2011}, volume={13}, number={2}, pages={22-30}, keywords={data structures;high level languages;mathematics computing;numerical analysis;Python programming language;high level language;numerical computation;numerical data;numpy array;Arrays;Computational efficiency;Finite element methods;Numerical analysis;Performance evaluation;Resource management;Vector quantization;NumPy;Python;numerical computations;programming libraries;scientific programming}, doi={10.1109/MCSE.2011.37}, ISSN={1521-9615}, month={March}, } @book{NumPy, place={USA}, title={A guide to NumPy}, publisher={Trelgol Publishing}, author={Oliphant, Travis E}, year={2006} } @Book{h5py, keywords = {python, hdf5}, year = {2013}, publisher = {O'Reilly}, title = {Python and HDF5}, author = {Andrew Collette} } @unpublished{bekolay2013, title={A comprehensive look at representing physical quantities in Python}, author={Bekolay, Trevor}, year={2013}, note={SciPy Conference}, url={https://www.youtube.com/watch?v=N-edLdxiM40} } @misc{dimensionful, author = {Stark, Casey W.}, title = {dimensionful}, year = {2012}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/caseywstark/dimensionful}} } @ARTICLE{yt, author = {{Turk}, M.~J. and {Smith}, B.~D. and {Oishi}, J.~S. and {Skory}, S. and {Skillman}, S.~W. and {Abel}, T. and {Norman}, M.~L.}, title = "{yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data}", journal = {The Astrophysical Journal Supplement Series}, archivePrefix = "arXiv", eprint = {1011.3514}, primaryClass = "astro-ph.IM", keywords = {cosmology: theory, methods: data analysis, methods: numerical}, year = 2011, month = jan, volume = 192, eid = {9}, pages = {9}, doi = {10.1088/0067-0049/192/1/9}, adsurl = {http://adsabs.harvard.edu/abs/2011ApJS..192....9T}, adsnote = {Provided by the SAO/NASA Astrophysics 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(Or, Real-World Applications of the Hubble Constant Using Observed and Simulated Data)}", journal = {Publications of the Astronomical Society of Australia}, archivePrefix = "arXiv", eprint = {1308.4150}, keywords = {cosmology, galaxies, methods: observational}, year = 2013, month = oct, volume = 30, eid = {e052}, pages = {e052}, doi = {10.1017/pasa.2013.31}, adsurl = {http://adsabs.harvard.edu/abs/2013PASA...30...52C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @book{nasa1999, title={Mars Climate Orbiter Mishap Investigation Board: Phase I Report}, author={Mars Climate Orbiter Mishap Investigation Board}, url={https://books.google.com/books?id=4OMIHQAACAAJ}, year={1999}, publisher={Jet Propulsion Laboratory} } unyt-3.2.0/paper/paper.md000066400000000000000000000557341526544257300153060ustar00rootroot00000000000000--- title: 'unyt: Handle, manipulate, and convert data with units in Python' tags: - units - quantities - Python - NumPy - sympy authors: - name: Nathan J. Goldbaum orcid: 0000-0001-5557-267X affiliation: 1 - name: John A. ZuHone orcid: 0000-0003-3175-2347 affiliation: 2 - name: Matthew J. Turk orcid: 0000-0002-5294-0198 affiliation: 1 - name: Kacper Kowalik orcid: 0000-0003-1709-3744 affiliation: 1 - name: Anna L. Rosen orcid: 0000-0003-4423-0660 affiliation: 2 affiliations: - name: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign. 1205 W Clark St, Urbana, IL USA 61801 index: 1 - name: Harvard-Smithsonian Center for Astrophysics. 60 Garden St, Cambridge, MA USA 02138 index: 2 date: 24 May 2018 bibliography: paper.bib --- # Summary Software that processes real-world data or that models a physical system must have some way of managing units. This might be as simple as the convention that all floating point numbers are understood to be in the same physical unit system (for example, the SI MKS units system). While simple approaches like this do work in practice, they also are fraught with possible error, both by programmers modifying the code who unintentionally misinterpret the units, and by users of the software who must take care to supply data in the correct units or who need to infer the units of data returned by the software. Famously, NASA lost contact with the Mars Climate Orbiter spacecraft after it crash-landed on the surface of Mars due to the use of English Imperial units rather than metric units in the spacecraft control software [@nasa1999]. The `unyt` library is designed both to aid quick calculations at an interactive Python prompt and to be tightly integrated into a larger Python application or library. The top-level `unyt` namespace ships with a large number of predefined units and physical constants to aid setting up quick calculations without needing to look up unit data or the value of a physical constant. Using the `unyt` library as an interactive calculation aid only requires knowledge of basic Python syntax and awareness of a few of the methods of the `unyt_array` class — for example, the `unyt_array.to()` method to convert data to a different unit. As the complexity of the usage increases, `unyt` provides a number of optional features to aid these cases, including custom unit registries containing both predefined physical units as well as user-defined units, built-in output to disk via the pickle protocol and to HDF5 files using the `h5py` library [@h5py], and round-trip converters for unit objects defined by other popular Python unit libraries. Physical units in the `unyt` class are defined in terms of the dimensions of the unit, a string representation, and a floating point scaling to the MKS unit system. Rather than implementing algebra for unit expressions, we rely on the `SymPy` symbolic algebra library [@SymPy] to handle symbolic algebraic manipulation. The `unyt.Unit` object can represent arbitrary units formed out of base dimensions in the SI unit system: time, length, mass, temperature, luminance, and electric current. We currently treat units such as mol with the seventh SI base dimension, amount of substance, as dimensionless, although we are open to changing this based on feedback from users. The `unyt` library supports forming quantities defined in other unit systems — in particular CGS Gaussian units common in astrophysics as well as geometrized "natural" units common in relativistic calculations. In addition, `unyt` ships with a number of other useful predefined unit systems including imperial units; Planck units; a unit system for calculations in the solar system; and a "galactic" unit system based on the solar mass, kiloparsecs, and Myr, a convention common in galactic astronomy. In addition to the `unyt.Unit` class, `unyt` also provides a two subclasses of the NumPy [@NumPy] `ndarray` [@vanderwalt2011], `unyt.unyt_array` and `unyt.unyt_quantity` to represent arrays and scalars with units attached, respectively. The `unyt` library also provides a `unyt.UnitRegistry` class to allow custom systems of units, for example to track the internal unit system used in a simulation. These subclasses are tightly integrated with the NumPy ufunc system, which ensures that algebraic calculations that include data with units automatically check to make sure the units are consistent, and allow automatic converting of the final answer of a calculation into a convenient unit. We direct readers interested in usage examples and a guide for integrating `unyt` into an existing Python application or workflow to the unyt documentation hosted at http://unyt.readthedocs.io/en/latest/. # Comparison with `Pint` and ``astropy.units`` The scientific Python ecosystem has a long history of efforts to develop a library to handle unit conversions and enforce unit consistency. For a relatively recent review of these efforts, see [@bekolay2013]. While we won't exhaustively cover extant Python libraries for handling units in this paper, we will focus on `Pint` [@Pint] and `astropy.units` [@astropy], which both provide a robust implementation of an array container with units attached and are commonly used in research software projects. At time of writing a GitHub search for `import astropy.units` returns approximately 10,500 results and a search for `import pint` returns approximately 1,500 results. While `unyt` provides functionality that overlaps with `astropy.units` and `Pint`, there are important differences which we elaborate on below. In addition, it is worth noting that all three codebases had origins at roughly the same time period. `Pint` initially began development in 2012 according to the git repository logs. Likewise `astropy.units` began development in 2012 and was released as part of `astropy 0.2` in 2013, although the initial implementation was adapted from the `pynbody` library [@pynbody], which started its units implementation in 2010 according to the git repository logs. In the case of `unyt`, it originated via the `dimensionful` library [@dimensionful] in 2012. Later, `dimensionful` was elaborated on and improved to become `yt.units`, the unit system for the `yt` library [@yt] at a `yt` developer workshop in 2013 and was subsequently released as part of `yt 3.0` in 2014. One of the design goals for the `yt` unit system was the ability to dynamically define "code" units (e.g. units internal to data loaded by yt) as well as units that depend on details of the dataset — in particular cosmological comoving units and the "little $h$" factor, used to parameterize the Hubble constant in cosmology calculations [@croton2013]. For cosmology simulations in particular, comparing data with different unit systems can be tricky because one might want to use data from multiple outputs in a time series, with each output having a different mapping from internal units to physical units. This despite the fact that each output in the time series represents the same physical system and common workflows involve combining data from multiple outputs. This requirement to manage complex custom units and interoperate between custom unit systems drove the `yt` community to independently develop a custom unit system solution. We have decided to repackage and improve `yt.units` in the form of `unyt` to both make it easier to work on and improve the unit system and encourage use of the unit system for scientific Python users who do not want to install a heavy-weight dependency like `yt`. Below we present a table comparing `unyt` with `astropy.units` and `Pint`. Estimates for lines of code in the library were generated using the `cloc` tool [@cloc]; blank and comment lines are excluded from the estimate. Test coverage was estimated using the `coveralls` output for `Pint` and `astropy.units` and using the `codecov.io` output for `unyt`. | Library | `unyt` | `astropy.units` | `Pint` | |--------------------------------|----------------|-----------------|------------| | Lines of code | 5128 | 10163 | 8908 | | Lines of code excluding tests | 3195 | 5504 | 4499 | | Test Coverage | 99.91% | 93.63% | 77.44% | We offer lines of code as a very rough estimate for the "hackability" of the codebase. In general, smaller codebases with higher test coverage have fewer defects [@Lipow1982; @Koru2007; @Gopinath2014]. This comparison is somewhat unfair in favor of `unyt` in that `astropy.units` only depends on NumPy and `Pint` has no dependencies, while `unyt` depends on both `SymPy` and NumPy. Much of the reduction in the size of the `unyt` library can be attributed to offloading the handling of algebra to `SymPy` rather than needing to implement the algebra of unit symbols directly in `unyt`. For potential users who are wary of adding `SymPy` as a dependency, that might argue in favor of using `Pint` in favor of `unyt`. ## `astropy.units` The `astropy.units` subpackage provides a `PrefixUnit` class, a `Quantity` class that represents both scalar and array data with attached units, and a large number of predefined unit symbols. The preferred way to create `Quantity` instances is via multiplication with a `PrefixUnit` instance. Similar to `unyt`, the `Quantity` class is implemented via a subclass of the NumPy `ndarray` class. Indeed, in many ways the everyday usage patterns of `astropy.units` and `unyt` are similar, although `unyt` is not quite a drop-in replacement for `astropy.units` as there are some API differences. The main functional difference between `astropy.units` and `unyt` is that `astropy.units` is a subpackage of the larger `astropy` package. This means that depending on `astropy.units` requires installing a large collection of astronomically focused software included in the `astropy` package, including a substantial amount of compiled C code. This presents a barrier to usage for potential users of `astropy.units` who are not astronomers or do not need the observational astronomy capabilities provided by `astropy`. ## `Pint` The `Pint` package provides a different API for accessing units compared with `unyt` and `astropy.units`. Rather than making units immediately importable from the `Pint` namespace, `Pint` instead requires users to instantiate a `UnitRegistry` instance (unrelated to the `unyt.UnitRegistry` class), which in turn has `Unit` instances as attributes. Just like with `unyt` and `astropy.units`, creating a `Quantity` instance requires multiplying an array or scalar by a `Unit` instance. Exposing the `UnitRegistry` directly to all users like this does force users of the library to think about which system of units they are working with, which may be beneficial in some cases, however it also means that users have a bit of extra cognitive overhead they need to deal with every time they use `Pint`. ![A benchmark comparing the ratio of the time to apply units to lists and NumPy `ndarray` instances to the time to interpret the same list or `ndarray` to an `ndarray`. This ratio, $T_{\rm package}/T_{\rm numpy}$, corresponds to the overhead of converting data to work with one of the three packages. Values close to unity correspond to zero or negligible overhead, while values larger than unity correspond to measurable overhead. Optimally all values would be near unity. In practice, applying units to small arrays incurs substantial overhead. Each test is shown for three different sizes of input data, including inputs with size 3, 1,000, and 1,000,000. The black lines at the top of the bars indicate the sample standard deviation. The $T_{\rm numpy}$ time is calculated by benchmarking the time to perform `np.asarray(data)` where `data` is either a `list` or an `ndarray`.](apply.png) In addition, the `Quantity` class provided by `Pint` is not a subclass of NumPy's `ndarray`. Instead, it is a wrapper around an internal `ndarray` buffer. This simplifies the implementation of `Pint` by avoiding the somewhat arcane process for creating an `ndarray` subclass, although the `Pint` `Quantity` class must also be careful to emulate the full NumPy `ndarray` API so that it can be a drop-in replacement for `ndarray`. Finally, in comparing the output of our benchmarks of `Pint`, `astropy.units`, and `unyt`, we found that in-place operations making use of a NumPy `ufunc` will unexpectedly strip units in `Pint`. For example, if `a` and `b` are `Pint` `Quantity` instances, `np.add(a, b, out=out))` will operate on `a` and `b` as if neither have units attached. Interestingly, without the `out` keyword, `Pint` does get the correct answer, so it is possible that this is a bug in `Pint`, and we have reported it as such upstream (see https://github.com/hgrecco/pint/issues/644). ## Performance Comparison Checking units will always add some overhead over using hard-coded unit conversion factors. Thus a library that is entrusted with checking units in an application should incur the minimum possible overhead to avoid triggering performance regressions after integrating unit checking into an application. Optimally, a unit library will add zero overhead regardless of the size of the array. In practice that is not the case for any of the three libraries under consideration, and there is a minimum array size above which the overhead of doing a mathematical operation exceeds the overhead of checking units. It is thus worth benchmarking unit libraries in a fair manner, comparing with the same operation implemented using plain NumPy. ![A benchmark comparing the time to square an array and to take the square root of an array. See Figure 1 for a detailed explanation of the plot style.](unary.png) Here we present such a set of benchmarks. We made use of the `perf` [@perf] Python benchmarking tool, which not only provides facilities for establishing the statistical significance of a benchmark run, but also can tune a linux system to turn off operating system and hardware features like CPU throttling that might introduce variance in a benchmark. We made use of a Dell Latitude E7270 laptop equipped with an Intel i5-6300U CPU clocked at 2.4 GHz. The testing environment was based on `Python 3.6.3` and had `NumPy 1.14.2`, `sympy 1.1.1`, `fastcache 1.0.2`, `Astropy 3.0.1`, and `Pint 0.8.1` installed. `fastcache` [@fastcache] is an optional dependency of `SymPy` that provides an optimized LRU cache implemented in C that can substantially speed up `SymPy`. The system was instrumented using `perf system tune` to turn off CPU features that might interfere with stable benchmarks. We did not make any boot-time Linux kernel parameter changes. ![A benchmark comparing the time to perform various binary arithmetic operations on input operans that have different but dimensionallty compatible units. See Figure 1 for a detailed explanation of the plot style.](binary_different_units.png) For each of the benchmarks we show the ratio of the time to perform an operation with one of `unyt`, `Pint`, and `astopy.units`, $T_{\rm package}$, to the time it takes for NumPy to perform the equivalent operation, $T_{\rm numpy}$. For example, for the comparison of the performance of `np.add(a, b)` where `a` and `b` have different units with the same dimension, the corresponding benchmark to generate $T_{\rm numpy}$ would use the code `np.add(a, c*b)` where `a` and `b` would be `ndarray` instances and `c` would be the floating point conversion factor between the units of `a` and `b`. Much of the time in $T_{\rm package}$ relative to $T_{\rm numpy}$ is spent in the respective packages calculating the appropriate conversion factor `c`. Thus the comparisons below depict very directly the overhead for using a unit library over an equivalent operation that uses hard-coded unit-conversion factors. In some cases when the operands of an operation have different dimensionally compatible units, using a unit library will produce a result *faster* than a pure-numpy implementation. In cases where this happens, the resulting $T_{\rm package}/T_{\rm numpy}$ measurement will come out less than unity. As an example, consider the operation of one milligram multiplied by 3 kilograms. In this case one could write down the answer as e.g. `3000 mg`, `0.003 kg`, or `3 kg*g`. In the former two cases, one of the operands needs to be scaled by a floating point conversion factor to ensure that both operands have the same unit before the pure-numpy implementation can actually evaluate the result, since each array can only have one unit. In the latter case the conversion factor is implicitly handled by the unit metadata. Note that this effect will only happen if the operands of an operation have different units. If the units are the same there is no need to calculate a conversion factor to convert the operands to the same unit system, so the pure-numpy operation avoids the extra cost that a unit library avoids in all cases. ![A benchmark comparing the time to perform various binary arithmetic operations on input operands that have the same units. See Figure 1 for a detailed explanation of the plot style.](binary_same_units.png) ### Applying units to data In Figure 1 we plot the overhead for applying units to data, showing both Python lists and NumPy `ndarray` instances as the input to apply data to. Since all three libraries eventually convert input data to a NumPy `ndarray`, the comparison with array inputs more explicitly shows the overhead for *just* applying units to data. When applying units to a list, all three libraries as well as NumPy need to first copy the contents of the list into a NumPy array or a subclass of `ndarray`. This explains why the overhead is systematically lower when starting with a list. ![A benchmark comparing the overhead for computing various NumPy `ufunc` operations. The operands of all binary `ufuncs` have the same units. See Figure 1 for a detailed explanation of the plot style.](ufunc.png) In all cases, `unyt` either is fastest by a statistically significant margin, or ties with `astropy`. Even for large input arrays, `Pint` still has statistically significant overhead, while both `unyt` and `astropy.units` have negligible overhead once the input array size reaches $10^6$ elements. ### Unary arithmetic operations Expressions involving powers of data with units, including integer and fractional powers, are very common in the physical sciences. It is therefore very important for a library that handles units to be able to track this case in a performant way. In Figure 2 we present a benchmark comparing `Pint`, `unyt`, and `astropy.units` for the squaring and square root operation. In all cases, `unyt` has the lowest overhead, with `Pint` coming in second, and `astropy.units` trailing. Note that the x-axis is plotted on a log scale, so `astropy` is as much as 4 times slower than `unyt` for these operations. ### Binary arithmetic operations Binary operations form the core of arithmetic. It is vital for a library that handles unit manipulation to both transparently convert units when necessary and to ensure that expressions involving quantities with units are dimensionally consistent. In Figure 3 and 4 we present benchmarks for binary arithmetic expressions, both with input data that has the same units and with input data with different units but the same dimensions. In most cases, `unyt` has less overhead than both `astropy` and `Pint`, although there are a few anomalies that are worth explaining in more detail. For comparison operations, `Pint` exhibits a slowdown even on large input arrays. This is not present for other binary operations, so it is possible that this overhead might be eliminated with a code change in `Pint`. For multiplication on large arrays, all three libraries have measured overhead of $\sim 0.5$ than that of the "equivalent" Numpy operation. See the discussion in the "Performance Comparison" section above for why this happens, but briefly, in all three libraries there is no need to multiply the result of a multiplication operation by an additional constant to ensure the result has a well-defined unit, while a pure NumPy implementation would need to multiply the result by a constant to ensure both operands of the operation are in the same units. For division, both `Pint` and `astropy.units` exhibit the same behavior as for multiplication, and for similar reasons: the result of the division operation is output with units given by the ratio of the input units. On the other hand, `unyt` will automatically cancel the dimensionally compatible units in the ratio and return a result with dimensionless units. To make that concrete, in `astropy` and `Pint`, the result of `(4*g) / (2*kg)` is `2 g/kg` while `unyt` would report `.002`. ![The same as Figure 5, but with in-place `ufunc` operations. See Figure 1 for a detailed explanation of the plot style.](ufuncout.png) ### NumPy `ufunc` performance Lastly, in Figures 5 and 6, we present benchmarks of NumPy `ufunc` operations. A NumPy `ufunc` is a fast C implementation of a basic mathematical operation. This includes arithmetic operators as well as trigonometric and special functions. By using a `ufunc` directly, one bypasses the Python object protocol and short-circuits directly to the low-level NumPy math kernels. We show both directly using the NumPy `ufunc` operators (Figure 5) as well as using the same operators with a pre-allocated output array to benchmark in-place operations. As for the other benchmarks, `unyt` tends to have the lowest amount of overhead, although there are some significant exceptions. For `np.power`, `Pint` has the lowest overhead, except for very large input arrays, where the overhead for all three libraries is negligible. On the other hand, for `np.sqrt`, `np.equal`, `np.add`, and `np.subtract`, `Pint` still has statistically significant overhead for large input arrays. Finally, for the in-place `ufunc` comparison, `Pint` has the lowest overhead for all operations. However, as discussed above, this is because of a bug in `Pint` which causes the library to ignore units when calling a `ufunc` with the `out` keyword set. # Conclusions In this paper we present the `unyt` library, giving background on the reasons for its existence and some historical context for its origin. We also present a set of benchmarks for common arithmetic operations, comparing the performance of `unyt` with `Pint` and `astropy.units`. In general, we find that `unyt` either outperforms or matches the performance `astropy.units` and `Pint`, depending on the operation and size of the input data. We also demonstrate that the `unyt` library constitutes a smaller codebase with higher test coverage than both `Pint` and `astropy.units`. # Acknowledgements NJG would like to thank Brandon Carswell and Alex Farthing of the NCSA IT staff for providing a laptop with Linux installed for the performance benchmark. This work was supported by NSF grant OAC-1663914 (NJG, MJT), by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4561 (MJT) and by NASA through Einstein Postdoctoral Fellowship grant number PF7-180166 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060 (ALR). # References unyt-3.2.0/paper/ufunc.png000066400000000000000000003243561526544257300155020ustar00rootroot00000000000000PNG  IHDR^sBIT|d pHYs.#.#x?v9tEXtSoftwarematplotlib version 2.2.2, http://matplotlib.org/ IDATxy|M9HB$B!T5kV*EMUү׽_WUm5yrqWC`*HPC1DdџqNFIvyC#J`p9uIǏw:^?x掉 /={j֬۷bŊ9-[޽{;ݻW͚5slڻwf͚+W:k߾KYfйsg_^M9_)S'NԤIB@^#`W^U-tYcUl믿F={O]{ZJ{VJJ[Ըq.vޭzJTT)ݸqiLZZƍ+VI&ߥ`˗W~\T^W*UH|9>{6mC߭[ԡC[)}Jr9jjڴiЊ+<0mۦqGyDV#}u[o%k׮9la?$&&jӦMb1R2e\`3fs4}n=777uŊiFN:;w] `Juɓ]{tl!W"6mw%?ހj ~0Ӛ0aw]ƍٚbJ嗲e˺zj>WRݼyDQ bXӧOСCշo__nZZn*UoΝ;[nrz2ȑ#eaÆyyN[m]X|KJJ2,vBCCիW/yzz:o>ƪbŊj׮s+**Jwo;wtҪ]7nl_fR2P+I7nܨI&ڵ>CY, {g?~*Wg}V6mp'___=6l{= ܹs]ιtRթSG=^u/[oFuֺ|r~>9q5*U҆ 2}eUP9>lrJܹ霑#GfxNHH9&Mrӿ㑑~zZJ]tQJ4h EGGۏw[7nÇUre 6L֭[ժU+UPA=zЄ 4ydjٲ*T#FŠL/00PVO?$M2E5RΝ:<󌒓n:Y:g|٣[fǏkʕZr;rƍշotsx (QB?VJO[lI;wTw|Y=O?9ժUK˗WfCf҅ gϞ {jl j9TR?_~EK.Ujjjt]~~~_b ݺu+s:u *{?O{L2z?դ$-[L׿yf;INNol6>3ݼyS .Rׯ_א!CrJ/T׮]UlYl6m߾]]v͛7駟jɒ%ZrZj L`ҤIDov#sΥRSSUzu={֡{goGEEAtøQF^G:w׫J*:|9駟jĈ};v0hܽ{z)oV Z!!!8pϛh͟?_>>>tNҨQwߩyҥʗ/_UsULL[]vm̙3jӦ֮]k>_{S?j׮P4o;ߺuK]tQlll\H"z}^4nÆ Zn9jժ9۴i AܹsڼyƏD1112eZn-???>}Z/֨Q+h_Bye뫯r ;8j„ NgӒqJ+I=z9-[Tɒ%sYriر2d+㯽{YFaaa3f_?z!_|E׋QvtU^]֭sJR~qqq2'( S~|V}QHݻW]vu:QF9mݺpIau::t({‚XbZzY))5r]rfʕ+n֬Uj Vjڴ{HNN'|v)!!_|ym޼Y?<<< ul{-[L/_)g)gUW͛5k{νcccuycmۦիWKԩmfZӽ'x"s0 UhhVwM0A7o6)RC=sy̶X,N3f]uqEDDܹs_f=Z9rt颟Y͚5p7|>RRR4{L+9dU`9UVuv˱}Ů 4HfRǎMfyP50B 4|ˡ?--M}я?~ؠֽ+ysz^Nٳq-TmW_UӦM4oҥ?}W8qb+{߯3gi;m՗\m۶ۍ75F#F@(|K.$7[6#oZ5Էc~< :`)Sȑ#ڰaCC4x`-ZȠ >Wa\V%";wΡoP%P0xy{{+44T>>>N}]޽ۀSkVǎ\n͛혘*$UVM˗/wzmJJz衋/Trbرݻwzj-ZT}򐇎=jt `(`yM6ͩ?**JݺuSbbU!'ռys3go޼Y111zgU\.tޠJ` ?5jԿ~ZʀS^b.]$IZl$6;CCCu֭l* C 7o4iO8TtY5kִ5{lܹ۟sg+Dn;whΜ9ٞgРAZpan!`* _Xbn%͖\$Ţ1c8͛7O˖-۷գGkMsPj-[:M2Ek={Էo, A T$Io߾y*U5k8 LWGDDZUz!|jjjзo_1119r͡} ߹sGپν?Ӵ,[?+WO?ŋtR;~_P0 .HΟ?s5kL_~eνwp||6mڔ7nhƌN9Y*r:#M97oZu-UREy~{?Wpɓ~Y2ýcbccXa|Vsg3y׻ jٲe.m;V}ՓO>_A T8 鏐ɓ=_ĬhܸÇ?OOٓu~7v|||ݸqé/++:/]<vYGZiȐ!vׯ_\'thYsfo޼C>SNe3g8oܸ,՘UW rSߝ;w\}g/;GEEwW߾}5vX3F]tQŊ5m4*UJ~m@aB 4:SLL33c -f3;EFF^z=zfϞ'UVjذl6VXbŊ9/kњ3gSvMGt aWAPзy WiXeܹs~yyyi}N}tYfiڴiׯ*WoFK,Qz/_\ݻw?NݺuK׿,՘Ճ:]ԩS;8q"sQ]r劖,YiӦi ۷榥K*(((Z0r_C 111ڿ-[E9mzKZR r|ׯI& Ws7n:^͚5ڵkU|y;-U~iuA{̙HZ) u]+Wրh׮]:~.]2 O? cǎӧkZ~իWWΝW^yE~۷{zTjUUX1ϟ{Qխ[WԢE b^=zfΜ///-YD:u҄ ۏYV5ixG{ٳ uZ ~W˖-ռysUPA[v ʋϪ?e޽0WJF9]ѣzVkjҤIѩS'exT\9-^8_… m?y_;vֿ,s?ﲞҥKۦNjKNN.]6x`ێ;l6[rrrܹs6f8qbرzY:vܹlvx?,sssI͝;7_g}ޭV[nߓf/YV[vl!!!k׮97zlOaYMLLլYfX2B ofl׮]UV-{TmϞ=.#556g[ʕ]n2dҥKY@ `Ȇ;wƍҥK ݻuUvjݺӳWmۦ'|RnnnYɓUu{ nҎ;$usw!bŊ^dtQ]zUŋWժUժU+,Y Lo3fz0,X Iz  `޽[O=TpY,K ϰ`Z6Mcǎ$;#ӵo>+WNVZZ*///UTI{֎;ٳgk񒤿*^x0Zbzƍk߾}Z4/кzj˔)˗ PhuQ^^^.hΝz*C (Tm۶駟Vҥkʔ):~֭kt+ & `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0 `0F(nܸ]vrww7"@A .O=||| |9k.u2ڵk /]40[@r, vZ=#U.A m^xxãwf?`@V=#]AKBB|}}G}TV?E>       (jt"777 0 VUAAAFdLY IDATLLg ))IK,#777+vXm޼Ysj„ c~z=ͳZh"ϯa@~hݺ7nH>33Fnnn1֭ӝ;w,@1vX+WhҥǗ.]ҥKҀF׮]Uzu{?~6oެ^z݈аZ{СCھ}$iJJJbg<PO+VgΜ)kԨMU`8`إX~rwwȑ#7jΝڹs^y,aXSSSsF(E.Ashs $>}:qǎshl<+#C￯[nfgϞX{z뭷.Pd<uj߾}u̙3ZØcǎiԨQ*Yn߾-IrRΝ;:tM&IR۶m~~~~w=CժUիW x jٲ,Xj֭1b5kS*00>vܹZr>lKIIQtt}ƍt{YvLLL~Uwϒl={ծ]tm#x 4Hݺu/]Azgd-]T/yZdve[d]vܹsN״lO6nܘa`|/???uUTdI{K/fo۶Mj۶5ka0a h@Pbъ+4o<}:v,TݻkС*W$jժzԭ[75iDV l٢oqj֬M꣏>R5w\͜9ygϞU``fgZ{NrJuMY%Jh˖->|٣ŋQF5jy,4lJLLeX W|@Fڵ.UѢEa5o\%JPҥձcGڵKcǎ5<@" H+,,R`ɓ'ŋj׮n߾mt9Zh0)&&F6lP˖-. 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"///կ_x嗛-;.֭[Cꫯ'ڵkcΜ9q种={v :4fΜ}$I+N8!׿f{[hQ,Z(&O rL{qgĉ+n!v3gΌ+2v={; OlDDw /06l}z(>>f1pZIDAT4o]6R ⳟl!H K$"H&Oܤ8ӟR1FinݚID${~8㌤w~_5ӟt^hQҧOdСɦM]qID$SNovG>_n\$"3f~饗&C!:th|1nܸ;#mW_eeeq嗧ƍ^zi?b…ѫWnq}G?Ѹ[ƍ#HL:5MyyykjK>Gi>s m۶ַqeEΝ~??>綾^˗ǥ^~6UsSߚ ./">`3 }q߾}#"b͚5ND7yyyq7_ѣ}d;rߦMbǎQSS;wx{M7{쨮ݻG.] ;(++k<>яK/s̉;wFIIID)w}y֭[[$""N9ԩ~??jժ8skk}#"-$_M$vޝz}ΝGDȑ#a""bʕm۶(--=`3gΌ.}Қرc}("]>T o""LӦMsӞ|UW]uHOU]]N {{'|3:c#"*VXFJ[e˖կ~/be˖Xzu^4hPsZdI|UVŜ9s:f̘ذaCDA|$nݺsEiϞ=S/_<""__bƌ۷oGeeA 1{8c㤓N:~֧ pt~z?&Ms̉_|ߎ;v~ ؟[@>V˲`ٞ=?|}]P;wΝ;rL?k׮^Ν; ={̌32p̟??k׮_~>e]ٳgO>y3o޼#?ֹ4(\pAK/:&Izݕ򗿴ulٲ%rQ=\z뭌=copw$o߾$+|8< g{xjСCgܸq;wneܣݻw6nܘw}SSSKN.]2lذL2%ÇO>}?w>|x~2pbxό?OI3I2mڴ93C5nذ!ofFdڵGL[OM&In* 744d̘1 &9x$ ,… seeԨQό32w,Y$խgϞOGG{ɲe2xKwpX4k9>;6}׿8(޽{|y'$zk?I/վ|8-Z(=\d񩪪ʺu۷/{auYfK_RVX{3d˃ ʴiӲjժ[.ضm۲iӦOzJq7n\OgeO @Qf̘wygvڕ7~]]]>3oF^x$xܹs_z&OL:Ο?? ?}Ӗo9kkkzMĨjiiiI:6mڔ>/{466N-ܒomF 6?iV\}._W8̙33s 80'OΨQ9̙3'Jjkkӵk׌1"~{jjj$k֬ 7ܐM6Uܹs뗅 s׾YrޚwyYrqu:tho~ﱦ&?ه%˗/σ>ܹsN:\s5[`An̛7/Ǐ?؎e˖\tE^UUU۷oL[n%̛oYI/~17tSO|ׯ̙3/SNu]'K/ͺuңG##Fȑ#{رcs=fV^G>|xϟj~s|޽{SWWgy&s"PCV~:v2B ,Ȅ r}eر֭[);v͛jժ<ٲeKV^:ݮ?lذ\2[n,}Lkf9rd tcs{۶m˄ 2f̘z뭭u)ݻwO3x9묳O[~ϦA-#h8N444ri+hsqVXueܹinnN̝;7_}N> ^~W:p&N]v466fYxqvU9ܜիWo]wݕ_&MƍS[[n}3tTWWgԩ˳hѢvm'bTѓcf͚=:7oNSSS*}ٳ5b)˗/o~˩Nuuuٓ~K/ 7ܐ*'P{7 ,ȬYwޜy晙={SB(% !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( P0@!`B !B( PM3XȃIENDB`unyt-3.2.0/pyproject.toml000066400000000000000000000101561526544257300154470ustar00rootroot00000000000000[build-system] requires = ["flit_core>=3.11,<5"] build-backend = "flit_core.buildapi" [project] name = "unyt" version = "3.2.0" description = "A package for handling numpy arrays with units" authors = [ { name = "The yt project", email = "yt-dev@python.org" }, ] license = "BSD-3-Clause" license-files = ["LICENSE"] classifiers = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Natural Language :: English", "Programming Language :: Python :: 3", "Programming Language :: Python :: Implementation :: CPython", ] keywords = [ "unyt", ] requires-python = ">=3.11" dependencies = [ "numpy>=1.23.2, <3.0", "sympy>=1.9.0", "packaging>=22.0", ] [project.readme] file = "README.rst" content-type = "text/x-rst" [project.urls] Homepage = "https://github.com/yt-project/unyt" Documentation = "https://unyt.readthedocs.io/en/stable/index.html" Changelog = "https://unyt.readthedocs.io/en/stable/history.html" [dependency-groups] test = [ "pytest-doctestplus>=1.4.0", "pytest>=9.0.2", ] covcheck = [ {include-group = "test"}, "coverage[toml] ; python_version < '3.11'", "coverage>=7.10.0", ] doc = [ {include-group = "integration"}, "docutils>=0.21.2", "sphinx>=7.4.7", ] integration = [ "astropy>=5.0.5", "dask[array,diagnostics]>=2024.1.0", "h5py>=3.8.0", "matplotlib>=3.6.0", "pint>=0.20", ] [tool.uv] # never build the following packages from source. Prefer more recent versions # with binary distributions even with --resolution=lowest[-direct] no-build-package = [ "astropy", "bokeh", "contourpy", "coverage", "h5py", "locket", "numpy", "markupsafe", "matplotlib", "pandas", "toolz", "tornado", "pyyaml", "kiwisolver", "pillow", "pyerfa", ] constraint-dependencies = [ "mpmath>=1.2.1", # via sympy "toolz>=1.0.0", # via dask "pyparsing>=3.1.0", # via matplotlib ] # ensure that uv resolves envs that are compatible with macOS arm64, # even in drastic conditions like --resolution=lowest[-direct], # taking into account tool.uv.no-build-package required-environments = [ "sys_platform == 'darwin' and platform_machine == 'arm64'", ] [tool.flit.sdist] include = [ "unyt", "unyt.tests", "unyt.tests.data", "unyt._mpl_array_converter", ] [tool.black] exclude = ''' ( /( \.eggs | \.git | \.hg | \.mypy_cache | \.tox | \.venv | _build | buck-out | build | dist | _version.py ) ) ''' [tool.ruff] exclude = [ ".*/", "benchmarks/*.py", "paper/*.py", "*/_version.py", "*/__init__.py", ] [tool.ruff.lint] ignore = [ "E501", "B904", "B905", # zip-without-explicit-strict ] select = [ "E", "F", "W", "C4", # flake8-comprehensions "B", # flake8-bugbear "YTT", # flake8-2020 "I", # isort "UP", # pyupgrade "NPY", # numpy specific rules ] [tool.ruff.lint.isort] combine-as-imports = true [tool.pytest.ini_options] addopts = "-ra --ignore=benchmarks --ignore=paper --ignore=unyt/_mpl_array_converter --color=yes" filterwarnings = [ "error", "ignore:Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.:UserWarning", "ignore:FigureCanvasAgg is non-interactive, and thus cannot be shown:UserWarning", "ignore:distutils Version classes are deprecated. Use packaging.version instead.:DeprecationWarning", 'ignore:datetime\.datetime\.utcfromtimestamp\(\) is deprecated:DeprecationWarning', # https://github.com/dateutil/dateutil/pull/1285 'ignore:mpnumeric is deprecated:DeprecationWarning', # sympy 1.12 VS mpmath 1.4, solution: https://github.com/sympy/sympy/pull/25290 'ignore:`np.compat`, which was used during the Python 2 to 3 transition, is deprecated since 1.26.0, and will be removed:DeprecationWarning' # from dask 2021.x ] [tool.coverage.run] source = [ "unyt", ] omit = [ "docs/*", "unyt/_version.py", "unyt/_on_demand_imports.py", "unyt/tests/test_linters.py", ] [tool.coverage.report] show_missing = true skip_covered = false unyt-3.2.0/unyt/000077500000000000000000000000001526544257300135275ustar00rootroot00000000000000unyt-3.2.0/unyt/__init__.py000066400000000000000000000055561526544257300156530ustar00rootroot00000000000000""" The unyt package. Note that the symbols defined in :mod:`unyt.physical_constants` and :mod:`unyt.unit_symbols` are importable from this module. For example:: >>> from unyt import km, clight >>> print((km/clight).to('ns')) 3335.64095198152 ns In addition, the following functions and classes are importable from the top-level ``unyt`` namespace: * :func:`unyt.array.loadtxt` * :func:`unyt.array.savetxt` * :func:`unyt.test` * :func:`unyt.array.uconcatenate` * :func:`unyt.array.ucross` * :func:`unyt.array.udot` * :func:`unyt.array.uhstack` * :func:`unyt.array.uintersect1d` * :func:`unyt.array.unorm` * :func:`unyt.array.ustack` * :func:`unyt.array.uunion1d` * :func:`unyt.array.uvstack` * :class:`unyt.array.unyt_array` * :class:`unyt.array.unyt_quantity` * :func:`unyt.unit_object.define_unit` * :class:`unyt.unit_object.Unit` * :class:`unyt.unit_registry.UnitRegistry` * :class:`unyt.unit_systems.UnitSystem` * :func:`unyt.testing.assert_allclose_units` * :func:`unyt.array.allclose_units` * :func:`unyt.dimensions.accepts` * :func:`unyt.dimensions.returns` """ from unyt import physical_constants, unit_symbols from unyt.array import ( # NOQA: F401 allclose_units, loadtxt, savetxt, uconcatenate, ucross, udot, uhstack, uintersect1d, unorm, unyt_array, unyt_quantity, ustack, uunion1d, uvstack, ) from unyt.dimensions import accepts, returns # NOQA: F401 from unyt.testing import assert_allclose_units # NOQA: F401 from unyt.unit_object import Unit, define_unit # NOQA: F401 from unyt.unit_registry import UnitRegistry # NOQA: F401 from unyt.unit_systems import UnitSystem # NOQA: F401 # function to only import quantities into this namespace # we go through the trouble of doing this instead of "import *" # to avoid including extraneous variables (e.g. floating point # constants used to *construct* a physical constant) in this namespace def import_units(module, namespace): """Import Unit objects from a module into a namespace""" for key, value in module.__dict__.items(): if key not in namespace and isinstance(value, (unyt_quantity, Unit)): namespace[key] = value import_units(physical_constants, globals()) import_units(unit_symbols, globals()) del import_units def test(): # pragma: no cover """Execute the unit tests on an installed copy of unyt. Note that this function requires pytest to run. If pytest is not installed this function will raise ImportError. """ import os import pytest pytest.main([os.path.dirname(os.path.abspath(__file__))]) # isort: off from unyt.mpl_interface import matplotlib_support matplotlib_support = matplotlib_support() def __getattr__(item: str): if item == "__version__": from importlib.metadata import version return version("unyt") raise AttributeError(f"Module 'unyt' has no attribute {item!r}.") unyt-3.2.0/unyt/_array_functions.py000066400000000000000000001200671526544257300174540ustar00rootroot00000000000000import warnings from importlib.metadata import version from numbers import Number import numpy as np from packaging.version import Version from unyt import delta_degC, dimensionless from unyt.array import NULL_UNIT, unyt_array, unyt_quantity from unyt.dimensions import temperature from unyt.exceptions import ( InvalidUnitOperation, UnitInconsistencyError, UnytError, ) NUMPY_VERSION = Version(version("numpy")) # Functions for which passing units doesn't make sense # bail out with NotImplemented (escalated to TypeError by numpy) _UNSUPPORTED_FUNCTIONS = { # Polynomials np.poly, np.polyadd, np.polyder, np.polydiv, np.polyfit, np.polyint, np.polymul, np.polysub, np.polyval, np.roots, np.vander, # datetime64 is not a sensible dtype for unyt_array np.datetime_as_string, np.busday_count, np.busday_offset, np.is_busday, # not clear how to approach np.piecewise, # astropy.units doesn't have a simple implementation either np.packbits, np.unpackbits, np.ix_, } _HANDLED_FUNCTIONS = {} def implements(numpy_function): """Register an __array_function__ implementation for unyt_array objects.""" # See NEP 18 https://numpy.org/neps/nep-0018-array-function-protocol.html def decorator(func): _HANDLED_FUNCTIONS[numpy_function] = func return func return decorator @implements(np.array2string) def array2string(a, *args, **kwargs): return ( np.array2string._implementation(a, *args, **kwargs) + f", units={str(a.units)!r}" ) def product_helper(a, b, out, func): prod_units = getattr(a, "units", NULL_UNIT) * getattr(b, "units", NULL_UNIT) if out is None: return func._implementation(np.asarray(a), np.asarray(b)) * prod_units res = func._implementation(np.asarray(a), np.asarray(b), out=np.asarray(out)) if getattr(out, "units", None) is not None: out.units = prod_units return unyt_array(res, prod_units, bypass_validation=True) @implements(np.dot) def dot(a, b, out=None): return product_helper(a, b, out, np.dot) @implements(np.vdot) def vdot(a, b, /): return np.vdot._implementation(np.asarray(a), np.asarray(b)) * ( getattr(a, "units", NULL_UNIT) * getattr(b, "units", NULL_UNIT) ) @implements(np.inner) def inner(a, b, /): return np.inner._implementation(np.asarray(a), np.asarray(b)) * ( getattr(a, "units", NULL_UNIT) * getattr(b, "units", NULL_UNIT) ) @implements(np.outer) def outer(a, b, out=None): return product_helper(a, b, out, np.outer) @implements(np.kron) def kron(a, b): return np.kron._implementation(np.asarray(a), np.asarray(b)) * ( getattr(a, "units", NULL_UNIT) * getattr(b, "units", NULL_UNIT) ) @implements(np.linalg.inv) def linalg_inv(a, *args, **kwargs): return np.linalg.inv._implementation(np.asarray(a), *args, **kwargs) / a.units @implements(np.linalg.tensorinv) def linalg_tensorinv(a, *args, **kwargs): return np.linalg.tensorinv._implementation(a, *args, **kwargs) / a.units @implements(np.linalg.pinv) def linalg_pinv(a, *args, **kwargs): return np.linalg.pinv._implementation(a, *args, **kwargs).view(np.ndarray) / a.units @implements(np.linalg.svd) def linalg_svd(a, full_matrices=True, compute_uv=True, *args, **kwargs): ret_units = a.units retv = np.linalg.svd._implementation( np.asarray(a), full_matrices, compute_uv, *args, **kwargs ) if compute_uv: u, s, vh = retv return (u, s * ret_units, vh) else: return retv * ret_units def _sanitize_range(_range, units): # helper function to histogram* functions ndim = len(units) if _range is None: return _range new_range = np.empty((ndim, 2)) for i in range(ndim): ilim = _range[2 * i : 2 * (i + 1)] imin, imax = ilim if not (hasattr(imin, "units") and hasattr(imax, "units")): if len(units) == 1: # allow range to be pure numerical scalars # for backward compatibility with unyt 2.9.5 # see https://github.com/yt-project/unyt/issues/465 imin *= units[0] imax *= units[0] else: raise TypeError( f"Elements of range must both have a 'units' attribute. Got {_range}" ) new_range[i] = imin.to_value(units[i]), imax.to_value(units[i]) return new_range.squeeze() def _sanitize_bins(arr, bins): if np.isscalar(bins): # integer giving number of bins if hasattr(bins, "units"): if bins.to_value(dimensionless).is_integer(): return int(bins.to_value(dimensionless)) else: raise ValueError("Bin count must be a dimensionless integer.") else: return bins if hasattr(bins, "units"): # if bins has units, must be convertible to arr's units (assumed dimensionless if # absent) return bins.to_value(getattr(arr, "units", dimensionless)) elif hasattr(arr, "units"): # bins doesn't have units but arr does, only ok if arr is dimensionless return bins / arr.units.get_conversion_factor(dimensionless)[0] # neither arr nor bins have units (but maybe weights or density do) return bins def _histogram(a, *, bins=10, range=None, density=None, weights=None, normed=None): range = _sanitize_range(range, units=[getattr(a, "units", None)]) sanitized_bins = _sanitize_bins(a, bins) if NUMPY_VERSION >= Version("1.24"): counts, sanitized_bins = np.histogram._implementation( np.asarray(a), bins=sanitized_bins, range=range, density=density, weights=np.asarray(weights) if weights is not None else None, ) else: counts, sanitized_bins = np.histogram._implementation( np.asarray(a), bins=sanitized_bins, range=range, normed=normed, weights=np.asarray(weights) if weights is not None else None, density=density, ) # a and/or weights could have units, only apply if present # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want # a unyt_array if weights are not a unyt_array and not density if density and hasattr(a, "units"): counts /= a.units if weights is not None and hasattr(weights, "units"): counts *= weights.units return counts, ( sanitized_bins * getattr(a, "units", 1) if np.isscalar(bins) else bins ) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogram) def histogram(a, bins=10, range=None, density=None, weights=None): return _histogram(a, bins=bins, range=range, density=density, weights=weights) else: @implements(np.histogram) def histogram(a, bins=10, range=None, normed=None, weights=None, density=None): return _histogram( a, bins=bins, range=range, normed=normed, weights=weights, density=density ) def _histogram2d(x, y, *, bins=10, range=None, density=None, weights=None, normed=None): range = _sanitize_range( range, units=[getattr(x, "units", None), getattr(y, "units", None)] ) if np.isscalar(bins): sanitized_xbins, sanitized_ybins = ( _sanitize_bins(x, bins), _sanitize_bins(y, bins), ) else: sanitized_xbins = _sanitize_bins(x, bins[0]) sanitized_ybins = _sanitize_bins(y, bins[1]) if NUMPY_VERSION >= Version("1.24"): counts, obtained_xbins, obtained_ybins = np.histogram2d._implementation( np.asarray(x), np.asarray(y), bins=(sanitized_xbins, sanitized_ybins), range=range, density=density, weights=np.asarray(weights) if weights is not None else None, ) else: counts, obtained_xbins, obtained_ybins = np.histogram2d._implementation( np.asarray(x), np.asarray(y), bins=(sanitized_xbins, sanitized_ybins), range=range, normed=normed, weights=np.asarray(weights) if weights is not None else None, density=density, ) # x, y and/or weights could have units, only apply if present # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want # a unyt_array if weights are not a unyt_array and not density if density: if hasattr(x, "units"): counts /= x.units if hasattr(y, "units"): counts /= y.units if weights is not None and hasattr(weights, "units"): counts *= weights.units return ( counts, ( obtained_xbins * getattr(x, "units", 1) if np.isscalar(sanitized_xbins) else bins[0] ), ( obtained_ybins * getattr(y, "units", 1) if np.isscalar(sanitized_ybins) else bins[1] ), ) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogram2d) def histogram2d(x, y, bins=10, range=None, density=None, weights=None): return _histogram2d( x, y, bins=bins, range=range, density=density, weights=weights ) else: @implements(np.histogram2d) def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None): return _histogram2d( x, y, bins=bins, range=range, normed=normed, weights=weights, density=density, ) def _histogramdd( sample, *, bins=10, range=None, density=None, weights=None, normed=None ): range = _sanitize_range(range, units=[getattr(_, "units", None) for _ in sample]) if np.isscalar(bins): sanitized_bins = [_sanitize_bins(a, bins) for a in sample] else: sanitized_bins = [ _sanitize_bins(a, b) for a, b in zip(sample, bins, strict=True) ] if NUMPY_VERSION >= Version("1.24"): counts, obtained_bins = np.histogramdd._implementation( [np.asarray(_) for _ in sample], bins=sanitized_bins, range=range, density=density, weights=np.asarray(weights) if weights is not None else None, ) else: counts, obtained_bins = np.histogramdd._implementation( [np.asarray(_) for _ in sample], bins=sanitized_bins, range=range, normed=normed, weights=np.asarray(weights) if weights is not None else None, density=density, ) # sample(s) and/or weights could have units, only apply if present # don't getattr(..., "units", NULL_UNIT) because e.g. we don't want # a unyt_array if weights are not a unyt_array and not density if density: divider_units = 1 * NULL_UNIT for s in sample: if not hasattr(s, "units"): continue divider_units *= s.units if divider_units != NULL_UNIT: counts /= divider_units if weights is not None and hasattr(weights, "units"): counts *= weights.units return counts, tuple( ob * getattr(s, "units", 1) if np.isscalar(sb) else bins[i] for i, (ob, sb, s) in enumerate( zip(obtained_bins, sanitized_bins, sample, strict=True) ) ) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogramdd) def histogramdd(sample, bins=10, range=None, density=None, weights=None): return _histogramdd( sample, bins=bins, range=range, density=density, weights=weights ) else: @implements(np.histogramdd) def histogramdd( sample, bins=10, range=None, normed=None, weights=None, density=None ): return _histogramdd( sample, bins=bins, range=range, normed=normed, weights=weights, density=density, ) @implements(np.histogram_bin_edges) def histogram_bin_edges(a, *args, **kwargs): return ( np.histogram_bin_edges._implementation(np.asarray(a), *args, **kwargs) * a.units ) @implements(np.digitize) def digitize(x, bins, right=False): # convert bins to x's units before binning, reusing the same helper as the # histogram functions. This both makes the comparison unit-aware (so e.g. # grams and kilograms give consistent indices) and raises on dimensionally # incompatible bins instead of silently comparing raw values (see #633). if not hasattr(bins, "units"): # let _sanitize_bins operate on an array (it divides bins by a # conversion factor, which a plain list does not support) bins = np.asarray(bins) sanitized_bins = _sanitize_bins(x, bins) return np.digitize._implementation( np.asarray(x), np.asarray(sanitized_bins), right=right ) def get_units(objs): units = [] for sub in objs: if isinstance(sub, np.ndarray): units.append(getattr(sub, "units", NULL_UNIT)) elif isinstance(sub, Number): units.append(NULL_UNIT) else: units.extend(get_units(sub)) return units def _validate_units_consistency(objs): """ Return unique units or raise UnitInconsistencyError if units are mixed. """ # NOTE: we cannot validate that all arrays are unyt_arrays # by using this as a guard clause in unyt_array.__array_function__ # because it's already a necessary condition for numpy to use our # custom implementations units = get_units(objs) # filtering unique units using equality comparison implicitly allows for units # which have a dimensionless**(n) component to be treated as one and the same unique_units = [units[0], *(u for u in units if u != units[0])] if len(unique_units) == 1: return units[0] else: raise UnitInconsistencyError(*units) def _validate_units_consistency_v2(ref_units, *args) -> None: """ raise UnitInconsistencyError if units are mixed if all args are pure numbers, they are treated as having ref_units, otherwise they are treated as dimensionless """ if all(isinstance(_, Number) for _ in args): return else: _validate_units_consistency((1 * ref_units, *args)) @implements(np.concatenate) def concatenate(arrays, /, axis=0, out=None, *args, **kwargs): ret_units = _validate_units_consistency(arrays) if out is not None: out_view = np.asarray(out) else: out_view = out res = np.concatenate._implementation( [np.asarray(_) for _ in arrays], axis, out_view, *args, **kwargs ) if getattr(out, "units", None) is not None: out.units = ret_units return unyt_array(res, ret_units, bypass_validation=True) @implements(np.cross) def cross(a, b, *args, **kwargs): prod_units = getattr(a, "units", NULL_UNIT) * getattr(b, "units", NULL_UNIT) return ( np.cross._implementation(np.asarray(a), np.asarray(b), *args, **kwargs) * prod_units ) @implements(np.intersect1d) def intersect1d(ar1, ar2, assume_unique=False, return_indices=False): _validate_units_consistency((ar1, ar2)) retv = np.intersect1d._implementation( np.asarray(ar1), np.asarray(ar2), assume_unique=assume_unique, return_indices=return_indices, ) if return_indices: return retv else: return retv * ar1.units @implements(np.union1d) def union1d(ar1, ar2): _validate_units_consistency((ar1, ar2)) return np.union1d._implementation(np.asarray(ar1), np.asarray(ar2)) * ar1.units @implements(np.linalg.norm) def norm(x, *args, **kwargs): return np.linalg.norm._implementation(np.asarray(x), *args, **kwargs) * x.units @implements(np.vstack) def vstack(tup, **kwargs): ret_units = _validate_units_consistency(tup) return np.vstack._implementation([np.asarray(_) for _ in tup], **kwargs) * ret_units @implements(np.hstack) def hstack(tup, **kwargs): ret_units = _validate_units_consistency(tup) return np.vstack._implementation([np.asarray(_) for _ in tup], **kwargs) * ret_units @implements(np.dstack) def dstack(tup): ret_units = _validate_units_consistency(tup) return np.dstack._implementation([np.asarray(_) for _ in tup]) * ret_units @implements(np.column_stack) def column_stack(tup): ret_units = _validate_units_consistency(tup) return np.column_stack._implementation([np.asarray(_) for _ in tup]) * ret_units @implements(np.stack) def stack(arrays, axis=0, out=None, **kwargs): ret_units = _validate_units_consistency(arrays) if out is None: return ( np.stack._implementation([np.asarray(_) for _ in arrays], axis=axis) * ret_units ) res = np.stack._implementation( [np.asarray(_) for _ in arrays], axis=axis, out=np.asarray(out), **kwargs ) if getattr(out, "units", None) is not None: out.units = ret_units return unyt_array(res, ret_units, bypass_validation=True) @implements(np.around) def around(a, decimals=0, out=None): ret_units = a.units if out is None: return np.around._implementation(np.asarray(a), decimals=decimals) * ret_units res = np.around._implementation( np.asarray(a), decimals=decimals, out=np.asarray(out) ) if getattr(out, "units", None) is not None: out.units = ret_units return unyt_array(res, ret_units, bypass_validation=True) @implements(np.block) def block(arrays): ret_units = _validate_units_consistency(arrays) res = np.block._implementation(arrays) return unyt_array(res, ret_units, bypass_validation=True) @implements(np.fft.fft) def fft_fft(a, *args, **kwargs): return np.fft.fft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.fft2) def fft_fft2(a, *args, **kwargs): return np.fft.fft2._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.fftn) def fft_fftn(a, *args, **kwargs): return np.fft.fftn._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.hfft) def fft_hfft(a, *args, **kwargs): return np.fft.hfft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.rfft) def fft_rfft(a, *args, **kwargs): return np.fft.rfft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.rfft2) def fft_rfft2(a, *args, **kwargs): return np.fft.rfft2._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.rfftn) def fft_rfftn(a, *args, **kwargs): return np.fft.rfftn._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.ifft) def fft_ifft(a, *args, **kwargs): return np.fft.ifft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.ifft2) def fft_ifft2(a, *args, **kwargs): return np.fft.ifft2._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.ifftn) def fft_ifftn(a, *args, **kwargs): return np.fft.ifftn._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.ihfft) def fft_ihfft(a, *args, **kwargs): return np.fft.ihfft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.irfft) def fft_irfft(a, *args, **kwargs): return np.fft.irfft._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.irfft2) def fft_irfft2(a, *args, **kwargs): return np.fft.irfft2._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.irfftn) def fft_irfftn(a, *args, **kwargs): return np.fft.irfftn._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.fft.fftshift) def fft_fftshift(x, *args, **kwargs): return np.fft.fftshift._implementation(np.asarray(x), *args, **kwargs) * x.units @implements(np.fft.ifftshift) def fft_ifftshift(x, *args, **kwargs): return np.fft.ifftshift._implementation(np.asarray(x), *args, **kwargs) * x.units @implements(np.sort_complex) def sort_complex(a): return np.sort_complex._implementation(np.asarray(a)) * a.units def _array_comp_helper(a, b): au = getattr(a, "units", NULL_UNIT) bu = getattr(b, "units", NULL_UNIT) if bu != au and au != NULL_UNIT and bu != NULL_UNIT: b = b.in_units(au) elif bu == NULL_UNIT: b = np.array(b) * au elif au == NULL_UNIT: a = np.array(a) * bu return a, b @implements(np.isclose) def isclose(a, b, *args, **kwargs): a, b = _array_comp_helper(a, b) return np.isclose._implementation(np.asarray(a), np.asarray(b), *args, **kwargs) @implements(np.allclose) def allclose(a, b, *args, **kwargs): a, b = _array_comp_helper(a, b) return np.allclose._implementation(np.asarray(a), np.asarray(b), *args, **kwargs) @implements(np.array_equal) def array_equal(a1, a2, *args, **kwargs) -> bool: u1 = getattr(a1, "units", NULL_UNIT) u2 = getattr(a2, "units", NULL_UNIT) if u2 != u1: return False return np.array_equal._implementation( np.asarray(a1), np.asarray(a2), *args, **kwargs ) @implements(np.array_equiv) def array_equiv(a1, a2, *args, **kwargs) -> bool: u1 = getattr(a1, "units", NULL_UNIT) u2 = getattr(a2, "units", NULL_UNIT) if u2 != u1: return False return np.array_equiv._implementation( np.asarray(a1), np.asarray(a2), *args, **kwargs ) def _linspace( start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, *, device=None, ): _validate_units_consistency((start, stop)) kwargs = { "num": num, "endpoint": endpoint, "retstep": retstep, "dtype": dtype, "axis": axis, } if NUMPY_VERSION >= Version("2.1.0.dev0"): kwargs["device"] = device result = np.linspace._implementation(np.asarray(start), np.asarray(stop), **kwargs) if retstep: return result[0] * start.units, result[1] * start.units else: return result * start.units if NUMPY_VERSION >= Version("2.0.0dev0"): @implements(np.linspace) def linspace( start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, *, device=None, ): return _linspace( start, stop, num=num, endpoint=endpoint, retstep=retstep, dtype=dtype, axis=axis, device=device, ) else: @implements(np.linspace) def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): return _linspace( start, stop, num=num, endpoint=endpoint, retstep=retstep, dtype=dtype, axis=axis, ) @implements(np.logspace) def logspace(start, stop, num=50, endpoint=True, base=10, dtype=None, axis=0): startu = getattr(start, "units", NULL_UNIT) stopu = getattr(stop, "units", NULL_UNIT) if startu != NULL_UNIT or stopu != NULL_UNIT: raise TypeError( "The first two arguments to numpy.logspace must be dimensionless, " f"got units={startu} (arg1) and units={stopu} (arg2). If output with" " units is desired, apply the units to the `base` kwarg." ) result = np.logspace._implementation( np.asarray(start), np.asarray(stop), num=num, endpoint=endpoint, base=np.asarray(base), dtype=dtype, axis=axis, ) return result * getattr(base, "units", NULL_UNIT) @implements(np.geomspace) def geomspace(start, stop, *args, **kwargs): _validate_units_consistency((start, stop)) return ( np.geomspace._implementation( np.asarray(start), np.asarray(stop), *args, **kwargs ) * start.units ) @implements(np.copyto) def copyto(dst, src, *args, **kwargs): # note that np.copyto is heavily used internally # in numpy, and it may be used with fundamental datatypes, # so we don't attempt to pass ndarray views to keep generality np.copyto._implementation(dst, src, *args, **kwargs) if getattr(dst, "units", None) is not None: dst.units = getattr(src, "units", dst.units) @implements(np.prod) def prod(a, *args, **kwargs): res = np.prod._implementation(np.asarray(a), *args, **kwargs) return res * a.units ** (a.size // res.size) @implements(np.var) def var(a, *args, **kwargs): return np.var._implementation(np.asarray(a), *args, **kwargs) * a.units**2 @implements(np.trace) def trace(a, *args, **kwargs): return np.trace._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.percentile) def percentile(a, *args, **kwargs): return np.percentile._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.quantile) def quantile(a, *args, **kwargs): return np.quantile._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.nanpercentile) def nanpercentile(a, *args, **kwargs): return np.nanpercentile._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.nanquantile) def nanquantile(a, *args, **kwargs): return np.nanquantile._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.linalg.det) def linalg_det(a, *args, **kwargs): return ( np.linalg.det._implementation(np.asarray(a), *args, **kwargs) * a.units ** (a.shape[0]) ) @implements(np.linalg.lstsq) def linalg_lstsq(a, b, *args, **kwargs): x, residuals, rank, s = np.linalg.lstsq._implementation( np.asarray(a), np.asarray(b), *args, **kwargs ) au = getattr(a, "units", NULL_UNIT) bu = getattr(b, "units", NULL_UNIT) return (x * bu / au, residuals * bu / au, rank, s * au) @implements(np.linalg.solve) def linalg_solve(a, b, *args, **kwargs): au = getattr(a, "units", NULL_UNIT) bu = getattr(b, "units", NULL_UNIT) return ( np.linalg.solve._implementation(np.asarray(a), np.asarray(b), *args, **kwargs) * bu / au ) @implements(np.linalg.tensorsolve) def linalg_tensorsolve(a, b, *args, **kwargs): au = getattr(a, "units", NULL_UNIT) bu = getattr(b, "units", NULL_UNIT) return ( np.linalg.tensorsolve._implementation( np.asarray(a), np.asarray(b), *args, **kwargs ) * bu / au ) @implements(np.linalg.eig) def linalg_eig(a, *args, **kwargs): ret_units = a.units w, v = np.linalg.eig._implementation(np.asarray(a), *args, **kwargs) return w * ret_units, v @implements(np.linalg.eigh) def linalg_eigh(a, *args, **kwargs): ret_units = a.units w, v = np.linalg.eigh._implementation(np.asarray(a), *args, **kwargs) return w * ret_units, v @implements(np.linalg.eigvals) def linalg_eigvals(a, *args, **kwargs): return np.linalg.eigvals._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.linalg.eigvalsh) def linalg_eigvalsh(a, *args, **kwargs): return np.linalg.eigvalsh._implementation(np.asarray(a), *args, **kwargs) * a.units @implements(np.savetxt) def savetxt(fname, X, *args, **kwargs): warnings.warn( "numpy.savetxt does not preserve units, " "and will only save the raw numerical data from the unyt_array object.\n" "If this is the intended behaviour, call `numpy.savetxt(file, arr.d)` " "to silence this warning.\n" "If you want to preserve units, use `unyt.savetxt` " "(and `unyt.loadtxt`) instead.", stacklevel=4, ) return np.savetxt._implementation(fname, np.asarray(X), *args, **kwargs) @implements(np.apply_over_axes) def apply_over_axes(func, a, axes): res = func(a, axes[0]) if len(axes) > 1: # this function is recursive by nature, # here we intentionally do not call the base _implementation return np.apply_over_axes(func, res, axes[1:]) else: return res def diff_helper(func, arr, *args, **kwargs): u = getattr(arr, "units", NULL_UNIT) if u.dimensions is temperature: if u.base_offset: raise InvalidUnitOperation( "Quantities with units of Fahrenheit or Celsius " "cannot be multiplied, divided, subtracted or added." ) ret_units = delta_degC else: ret_units = u return func._implementation(np.asarray(arr), *args, **kwargs) * ret_units @implements(np.diff) def diff(a, *args, **kwargs): return diff_helper(np.diff, a, *args, **kwargs) @implements(np.ediff1d) def ediff1d(ary, *args, **kwargs): return diff_helper(np.ediff1d, ary, *args, **kwargs) @implements(np.ptp) def ptp(a, *args, **kwargs): return diff_helper(np.ptp, a, *args, **kwargs) @implements(np.cumprod) def cumprod(a, *args, **kwargs): raise UnytError( "numpy.cumprod (and other cumulative product function) cannot be used " "with a unyt_array as all return elements should (but cannot) " "have different units." ) if NUMPY_VERSION >= Version("2.1.0.dev0"): @implements(np.cumulative_prod) def cumulative_prod(x, /, *args, **kwargs): return cumprod(x, *args, **kwargs) @implements(np.pad) def pad(array, *args, **kwargs): return np.pad._implementation(np.asarray(array), *args, **kwargs) * array.units @implements(np.choose) def choose(a, choices, out=None, *args, **kwargs): if (au := getattr(a, "units", NULL_UNIT)) != NULL_UNIT: raise TypeError( f"The first argument to numpy.choose must be dimensionless, got units={au}" ) retu = _validate_units_consistency(choices) if out is None: return ( np.choose._implementation( a, [np.asarray(c) for c in choices], *args, **kwargs ) * retu ) res = np.choose._implementation( a, [np.asarray(c) for c in choices], *args, out=np.asarray(out), **kwargs, ) if getattr(out, "units", None) is not None: out.units = retu return unyt_array(res, retu, bypass_validation=True) @implements(np.fill_diagonal) def fill_diagonal(a, val, *args, **kwargs) -> None: _validate_units_consistency_v2(a.units, val) np.fill_diagonal._implementation(np.asarray(a), val, *args, **kwargs) @implements(np.insert) def insert(arr, obj, values, *args, **kwargs): _validate_units_consistency_v2(arr.units, values) return ( np.insert._implementation( np.asarray(arr), obj, np.asarray(values), *args, **kwargs ) * arr.units ) @implements(np.isin) def isin(element, test_elements, *args, **kwargs): _validate_units_consistency((element, test_elements)) return np.isin._implementation( np.asarray(element), np.asarray(test_elements), *args, **kwargs ) @implements(np.place) def place(arr, mask, vals, *args, **kwargs) -> None: _validate_units_consistency_v2(arr.units, vals) np.place._implementation(np.asarray(arr), mask, np.asarray(vals), *args, **kwargs) @implements(np.put) def put(a, ind, v, *args, **kwargs) -> None: _validate_units_consistency_v2(a.units, v) np.put._implementation(np.asarray(a), ind, np.asarray(v)) @implements(np.put_along_axis) def put_along_axis(arr, indices, values, axis, *args, **kwargs) -> None: _validate_units_consistency_v2(arr.units, values) np.put_along_axis._implementation( np.asarray(arr), indices, np.asarray(values), axis, *args, **kwargs ) @implements(np.putmask) def putmask(a, /, mask, values, *args, **kwargs) -> None: _validate_units_consistency_v2(a.units, values) np.putmask._implementation(np.asarray(a), mask, np.asarray(values), *args, **kwargs) @implements(np.searchsorted) def searchsorted(a, v, *args, **kwargs): _validate_units_consistency_v2(a.units, v) return np.searchsorted._implementation( np.asarray(a), np.asarray(v), *args, **kwargs ) @implements(np.select) def select(condlist, choicelist, default=0, *args, **kwargs): ref_units = choicelist[0].units _validate_units_consistency_v2(ref_units, choicelist, default) return ( np.select._implementation( condlist, [np.asarray(c) for c in choicelist], default ) * ref_units ) @implements(np.setdiff1d) def setdiff1d(ar1, ar2, *args, **kwargs): retu = _validate_units_consistency((ar1, ar2)) return ( np.setdiff1d._implementation(np.asarray(ar1), np.asarray(ar2), *args, **kwargs) * retu ) @implements(np.sinc) def sinc(x, *args, **kwargs): # this implementation becomes necessary after implementing where # we *want* this one to ignore units return np.sinc._implementation(np.asarray(x), *args, **kwargs) def clip_impl(a, a_min, a_max, out=None, *args, **kwargs): _validate_units_consistency_v2(a.units, a_min, a_max) if out is None: return ( np.clip._implementation( np.asarray(a), np.asarray(a_min), np.asarray(a_max), *args, **kwargs ) * a.units ) res = ( np.clip._implementation( np.asarray(a), np.asarray(a_min), np.asarray(a_max), *args, out=np.asarray(out), **kwargs, ) * a.units ) if getattr(out, "units", None) is not None: out.units = a.units return unyt_array(res, a.units, bypass_validation=True) if NUMPY_VERSION >= Version("2.1.0.dev0"): from numpy._globals import _NoValue @implements(np.clip) def clip(a, a_min=_NoValue, a_max=_NoValue, out=None, *args, **kwargs): return clip_impl(a, a_min, a_max, out, *args, **kwargs) else: @implements(np.clip) def clip(a, a_min, a_max, out=None, *args, **kwargs): return clip_impl(a, a_min, a_max, out, *args, **kwargs) @implements(np.where) def where(condition, /, *args, **kwargs): if len(args) == 0: return np.where._implementation(np.asarray(condition), **kwargs) elif len(args) < 2: # error message borrowed from numpy 1.24.1 raise ValueError("either both or neither of x and y should be given") x, y, *args = args retu = _validate_units_consistency((x, y)) return ( np.where._implementation( condition, np.asarray(x), np.asarray(y), *args, **kwargs ) * retu ) @implements(np.triu) def triu(m, *args, **kwargs): return np.triu._implementation(np.asarray(m), *args, **kwargs) * m.units @implements(np.tril) def tril(m, *args, **kwargs): return np.tril._implementation(np.asarray(m), *args, **kwargs) * m.units @implements(np.einsum) def einsum(*operands, out=None, **kwargs): subscripts, *operands = operands ret_units = _validate_units_consistency(operands) if out is not None: out_view = np.asarray(out) else: out_view = out res = np.einsum._implementation(subscripts, *operands, out=out_view) if getattr(out, "units", None) is not None: out.units = ret_units if res.ndim == 0: cls = unyt_quantity else: cls = unyt_array return cls(res, ret_units, bypass_validation=True) @implements(np.convolve) def convolve(a, v, *args, **kwargs): ret_units = np.prod(get_units((a, v))) return ( np.convolve._implementation(np.asarray(a), np.asarray(v), *args, **kwargs) * ret_units ) @implements(np.correlate) def correlate(a, v, *args, **kwargs): ret_units = np.prod(get_units((a, v))) return ( np.correlate._implementation(np.asarray(a), np.asarray(v), *args, **kwargs) * ret_units ) @implements(np.tensordot) def tensordot(a, b, *args, **kwargs): ret_units = np.prod(get_units((a, b))) return ( np.tensordot._implementation(np.asarray(a), np.asarray(b), *args, **kwargs) * ret_units ) @implements(np.unwrap) def unwrap(p, *args, **kwargs): ret_units = p.units return np.unwrap._implementation(np.asarray(p), *args, **kwargs) * ret_units @implements(np.interp) def interp(x, xp, fp, *args, **kwargs): _validate_units_consistency((x, xp)) # return array type should match fp's # so, the fallback multiplier is 1 instead of NULL_UNITS # This avoid leaking a dimensionless unyt_array if reference data # is a pure np.ndarray ret_units = getattr(fp, "units", 1) return ( np.interp(np.asarray(x), np.asarray(xp), np.asarray(fp), *args, **kwargs) * ret_units ) @implements(np.array_repr) def array_repr(arr, *args, **kwargs): rep = np.array_repr._implementation(np.asarray(arr), *args, **kwargs) rep = rep.replace("array", arr.__class__.__name__) units_repr = arr.units.__repr__() if "=" in rep: return rep[:-1] + ", units='" + units_repr + "')" else: return rep[:-1] + ", '" + units_repr + "')" if NUMPY_VERSION < Version("2.0.0dev0"): # functions that are removed in numpy 2.0.0 @implements(np.asfarray) # noqa: NPY201 def asfarray(a, dtype=np.double): ret_units = a.units arr = np.asfarray._implementation(np.asarray(a), dtype=dtype) # noqa: NPY201 return arr * ret_units _trapezoid_func = np.trapz # noqa: NPY201 elif NUMPY_VERSION >= Version("2.0.0dev0"): # functions that were added in numpy 2.0.0 @implements(np.linalg.outer) def linalg_outer(x1, x2, /): return product_helper(x1, x2, out=None, func=np.linalg.outer) _trapezoid_func = np.trapezoid @implements(_trapezoid_func) def trapezoid(y, x=None, dx=1.0, *args, **kwargs): ret_units = y.units if x is None: ret_units = ret_units * getattr(dx, "units", NULL_UNIT) else: ret_units = ret_units * getattr(x, "units", NULL_UNIT) if isinstance(x, np.ndarray): x = np.asarray(x) if isinstance(dx, np.ndarray): dx = np.asarray(dx) return ( _trapezoid_func._implementation(np.asarray(y), x, dx, *args, **kwargs) * ret_units ) if NUMPY_VERSION < Version("2.4.0.dev0"): @implements(np.in1d) # noqa: NPY201 def in1d(ar1, ar2, *args, **kwargs): _validate_units_consistency((ar1, ar2)) return np.isin._implementation( np.asarray(ar1), np.asarray(ar2), *args, **kwargs ) @implements(np.take) def take(a, indices, axis=None, out=None, mode="raise"): ret_units = getattr(a, "units", NULL_UNIT) if out is not None: out_view = np.asarray(out) else: out_view = None res = np.take._implementation( np.asarray(a), indices, axis=axis, out=out_view, mode=mode ) if getattr(out, "units", None) is not None: out.units = ret_units ret_cls = unyt_quantity if res.ndim == 0 else unyt_array return ret_cls(res, ret_units, bypass_validation=True) if NUMPY_VERSION < Version("2.4.1"): def _average(a, axis=None, weights=None, returned=False, *, keepdims=np._NoValue): if NUMPY_VERSION < Version("1.23"): res = np.average._implementation( np.asarray(a), axis=axis, weights=weights, returned=returned, ) else: res = np.average._implementation( np.asarray(a), axis=axis, weights=weights, returned=returned, keepdims=keepdims, ) if returned: avg, wsum = res else: avg = res if returned and isinstance(weights, unyt_array): wsum_cls = unyt_quantity if wsum.ndim == 0 else unyt_array wsum_units = weights.units wsum = wsum_cls(wsum, wsum_units, bypass_validation=True) if isinstance(a, unyt_array): avg_cls = unyt_quantity if avg.ndim == 0 else unyt_array avg_units = a.units avg = avg_cls(avg, avg_units, bypass_validation=True) if returned: return avg, wsum return avg if NUMPY_VERSION < Version("1.23.0"): @implements(np.average) def average(a, axis=None, weights=None, returned=False): return _average(a, axis=axis, weights=weights, returned=returned) elif NUMPY_VERSION < Version("2.4.1"): # will not be needed once https://github.com/numpy/numpy/pull/30522 is merged, # probably in numpy 2.4.1, but this will still be needed until earlier versions # are no longer supported @implements(np.average) def average(a, axis=None, weights=None, returned=False, *, keepdims=np._NoValue): return _average( a, axis=axis, weights=weights, returned=returned, keepdims=keepdims ) unyt-3.2.0/unyt/_deprecation.py000066400000000000000000000006751526544257300165450ustar00rootroot00000000000000import warnings def warn_deprecated( name, /, *, stacklevel: int = 3, replacement: str | None = None, since_version: str, ) -> None: msg = ( f"{name} is deprecated and will be removed in a future version\n" f"Instead, {replacement}\n" f"(deprecated since unyt v{since_version})" ) warnings.warn( msg, category=DeprecationWarning, stacklevel=stacklevel, ) unyt-3.2.0/unyt/_mpl_array_converter/000077500000000000000000000000001526544257300177435ustar00rootroot00000000000000unyt-3.2.0/unyt/_mpl_array_converter/__init__.py000066400000000000000000000113111526544257300220510ustar00rootroot00000000000000from weakref import WeakKeyDictionary from matplotlib.units import AxisInfo, ConversionInterface from unyt.array import unyt_array, unyt_quantity from unyt.unit_object import Unit class unyt_arrayConverter(ConversionInterface): """Matplotlib interface for unyt_array""" _instance = None _labelstyle = "()" _axisnames = WeakKeyDictionary() # ensure that unyt_arrayConverter is a singleton def __new__(cls): if unyt_arrayConverter._instance is None: unyt_arrayConverter._instance = super().__new__(cls) return unyt_arrayConverter._instance # When matplotlib first encounters a type in its units.registry, it will # call default_units to obtain the units. Then it calls axisinfo to # customize the axis - in our case, just set the label. Then matplotlib calls # convert. @staticmethod def axisinfo(unit, axis): """Set the axis label based on unit Parameters ---------- unit : Unit object, string, or tuple This parameter comes from unyt_arrayConverter.default_units() or from user code such as Axes.plot(), Axis.set_units(), etc. In user code, it is possible to convert the plotted units by specifying the new unit as a string, such as "ms", or as a tuple, such as ("J", "thermal") following the call signature of unyt_array.convert_to_units(). axis : Axis object Returns ------- AxisInfo object with the label formatted as in-line math latex """ if isinstance(unit, tuple): unit = unit[0] unit_obj = unit if isinstance(unit, Unit) else Unit(unit) name = unyt_arrayConverter._axisnames.get(axis, "") if unit_obj.is_dimensionless: label = name else: name += " " unit_str = unit_obj.latex_representation() if unyt_arrayConverter._labelstyle == "[]": label = name + "$\\left[" + unit_str + "\\right]$" elif unyt_arrayConverter._labelstyle == "/": axsym = "$q_{\\rm" + axis.axis_name + "}$" name = axsym if name == " " else name if "/" in unit_str: label = name + "$\\;/\\;\\left(" + unit_str + "\\right)$" else: label = name + "$\\;/\\;" + unit_str + "$" else: label = name + "$\\left(" + unit_str + "\\right)$" return AxisInfo(label=label.strip()) @staticmethod def default_units(x, axis): """Return the Unit object of the unyt_array x Parameters ---------- x : unyt_array axis : Axis object Returns ------- Unit object """ # In the case where the first matplotlib command is setting limits, # x may be a tuple of length two (with the same units). if isinstance(x, tuple): name = getattr(x[0], "name", "") units = x[0].units else: name = getattr(x, "name", "") units = x.units # maintain a mapping between Axis and name since Axis does not point to # its underlying data and we want to propagate the name to the axis # label in the subsequent call to axisinfo unyt_arrayConverter._axisnames[axis] = name if name is not None else "" return units @staticmethod def convert(value, unit, axis): """Convert the units of value to unit Parameters ---------- value : unyt_array, unyt_quantity, or sequence there of unit : Unit, string or tuple This parameter comes from unyt_arrayConverter.default_units() or from user code such as Axes.plot(), Axis.set_units(), etc. In user code, it is possible to convert the plotted units by specifying the new unit as a string, such as "ms", or as a tuple, such as ("J", "thermal") following the call signature of unyt_array.convert_to_units(). axis : Axis object Returns ------- unyt_array Raises ------ UnitConversionError if unit does not have the same dimensions as value or if we don't know how to convert value. """ converted_value = value if isinstance(unit, str) or isinstance(unit, Unit): unit = (unit,) if isinstance(value, (unyt_array, unyt_quantity)): converted_value = value.to(*unit) else: value_type = type(value) converted_value = [] for obj in value: converted_value.append(obj.to(*unit)) converted_value = value_type(converted_value) return converted_value unyt-3.2.0/unyt/_on_demand_imports.py000066400000000000000000000073101526544257300177420ustar00rootroot00000000000000""" A set of convenient on-demand imports """ from functools import wraps from importlib.util import find_spec class NotAModule: """ A class to implement an informative error message that will be outputted if someone tries to use an on-demand import without having the requisite package installed. """ def __init__(self, pkg_name, exc=None): self.pkg_name = pkg_name self._original_exception = exc error_note = ( f"Something went wrong while trying to lazy-import {pkg_name}. " f"Please make sure that {pkg_name} is properly installed.\n" "If the problem persists, please file an issue at " "https://github.com/yt-project/unyt/issues/new" ) if exc is None: self.error = ImportError(error_note) else: exc.add_note(error_note) self.error = exc def __getattr__(self, item): raise self.error def __call__(self, *args, **kwargs): raise self.error def __repr__(self) -> str: if self._original_exception is None: return f"NotAModule({self.pkg_name!r})" else: return f"NotAModule({self.pkg_name!r}, {self._original_exception!r})" class OnDemand: _default_factory: type[NotAModule] = NotAModule def __init_subclass__(cls): if not cls.__name__.endswith("_imports"): raise TypeError(f"class {cls}'s name needs to be suffixed '_imports'") def __new__(cls): if cls is OnDemand: raise TypeError("The OnDemand base class cannot be instantiated.") else: return object.__new__(cls) @property def _name(self) -> str: _name, _, _suffix = self.__class__.__name__.rpartition("_") return _name @property def __is_available__(self) -> bool: return find_spec(self._name) is not None def safe_import(func): @property @wraps(func) def inner(self): try: return func(self) except ImportError as exc: return self._default_factory(self._name, exc) return inner class astropy_imports(OnDemand): @safe_import def log(self): from astropy import log if log.exception_logging_enabled(): log.disable_exception_logging() return log @safe_import def units(self): from astropy import units self.log # noqa: B018 return units @safe_import def __version__(self): from astropy import __version__ return __version__ _astropy = astropy_imports() class h5py_imports(OnDemand): @safe_import def File(self): from h5py import File return File @safe_import def __version__(self): from h5py import __version__ return __version__ _h5py = h5py_imports() class pint_imports(OnDemand): @safe_import def UnitRegistry(self): from pint import UnitRegistry return UnitRegistry _pint = pint_imports() class matplotlib_imports(OnDemand): @safe_import def __version__(self): from matplotlib import __version__ return __version__ @safe_import def pyplot(self): from matplotlib import pyplot return pyplot @safe_import def units(self): from matplotlib import units return units @safe_import def use(self): from matplotlib import use return use _matplotlib = matplotlib_imports() class dask_imports(OnDemand): @safe_import def array(self): from dask import array return array @safe_import def __version__(self): from dask import __version__ return __version__ _dask = dask_imports() unyt-3.2.0/unyt/_parsing.py000066400000000000000000000052641526544257300157120ustar00rootroot00000000000000""" parsing utilities """ import token from sympy import Basic, Float, Integer, Rational, Symbol, sqrt from sympy.parsing.sympy_parser import auto_number, parse_expr, rationalize from unyt._unit_lookup_table import inv_name_alternatives from unyt.exceptions import UnitParseError def _auto_positive_symbol(tokens, local_dict, global_dict): """ Inserts calls to ``Symbol`` for undefined variables. Passes in positive=True as a keyword argument. Adapted from sympy.sympy.parsing.sympy_parser.auto_symbol """ result = [] tokens.append((None, None)) # so zip traverses all tokens for tok, nextTok in zip(tokens, tokens[1:]): tokNum, tokVal = tok nextTokNum, nextTokVal = nextTok if tokNum == token.NAME: name = tokVal if name in global_dict: obj = global_dict[name] if isinstance(obj, (Basic, type)) or callable(obj): result.append((token.NAME, name)) continue # try to resolve known alternative unit name try: used_name = inv_name_alternatives[str(name)] except KeyError: # if we don't know this name it's a user-defined unit name # so we should create a new symbol for it used_name = str(name) result.extend( [ (token.NAME, "Symbol"), (token.OP, "("), (token.NAME, repr(used_name)), (token.OP, ","), (token.NAME, "positive"), (token.OP, "="), (token.NAME, "True"), (token.OP, ")"), ] ) else: result.append((tokNum, tokVal)) return result global_dict = { "Symbol": Symbol, "Integer": Integer, "Float": Float, "Rational": Rational, "sqrt": sqrt, } unit_text_transform = (_auto_positive_symbol, auto_number, rationalize) def parse_unyt_expr(unit_expr): if not unit_expr: # Bug catch... # if unit_expr is an empty string, parse_expr fails hard... unit_expr = "1" # Avoid a parse error if someone uses the percent unit and the # parser tries to interpret it as the modulo operator unit_expr = unit_expr.replace("%", "percent") unit_expr = unit_expr.replace("°", "deg") try: unit_expr = parse_expr( unit_expr, global_dict=global_dict, transformations=unit_text_transform ) except Exception as e: msg = f"Unit expression '{unit_expr}' raised an error during parsing:\n{e!r}" raise UnitParseError(msg) return unit_expr unyt-3.2.0/unyt/_physical_ratios.py000066400000000000000000000142101526544257300174330ustar00rootroot00000000000000import numpy as np # # Physical Constants and Units Conversion Factors # # Values for these constants, unless otherwise noted, are drawn from IAU, # IUPAC, NIST, and NASA data, whichever is newer. # http://maia.usno.navy.mil/NSFA/IAU2009_consts.html # http://goldbook.iupac.org/list_goldbook_phys_constants_defs.html # http://physics.nist.gov/cuu/Constants/index.html # http://nssdc.gsfc.nasa.gov/planetary/factsheet/jupiterfact.html # Elementary masses mass_electron_kg = 9.10938291e-31 amu_kg = 1.660538921e-27 amu_grams = amu_kg * 1.0e3 mass_hydrogen_kg = 1.007947 * amu_kg mass_proton_kg = 1.672623110e-27 # Solar values (see Mamajek 2012) # https://sites.google.com/site/mamajeksstarnotes/bc-scale mass_sun_kg = 1.98841586e30 temp_sun_kelvin = 5870.0 luminosity_sun_watts = 3.8270e26 # Solar abundances from various references metallicity_sun = 0.01295 # Cloudy 17.03 metallicity_sun_angr = 0.01937 # Anders & Grevesse (1989) metallicity_sun_aspl = 0.01337 # Asplund et al. (2009) metallicity_sun_feld = 0.01909 # Feldman (1992) metallicity_sun_lodd = 0.01321 # Lodders (2003) # Conversion Factors: X au * mpc_per_au = Y mpc # length mpc_per_mpc = 1e0 mpc_per_kpc = 1e-3 mpc_per_pc = 1e-6 mpc_per_au = 4.84813682e-12 mpc_per_rsun = 2.253962e-14 mpc_per_rearth = 2.06470307893e-16 mpc_per_rjup = 2.26566120943e-15 mpc_per_miles = 5.21552871e-20 mpc_per_km = 3.24077929e-20 mpc_per_m = 3.24077929e-23 kpc_per_m = mpc_per_m / mpc_per_kpc pc_per_m = mpc_per_m / mpc_per_pc km_per_pc = 3.08567758e13 cm_per_pc = 3.08567758e18 cm_per_mpc = 3.08567758e21 km_per_m = 1e-3 km_per_cm = 1e-5 m_per_cm = 1e-2 ly_per_m = 1.05702341e-16 rsun_per_m = 1.4378145e-9 rearth_per_m = 1.56961033e-7 # Mean (volumetric) radius rjup_per_m = 1.43039006737e-8 # Mean (volumetric) radius au_per_m = 6.68458712e-12 ang_per_m = 1.0e10 m_per_fpc = 0.0324077929 kpc_per_mpc = 1.0 / mpc_per_kpc pc_per_mpc = 1.0 / mpc_per_pc au_per_mpc = 1.0 / mpc_per_au rsun_per_mpc = 1.0 / mpc_per_rsun rearth_per_mpc = 1.0 / mpc_per_rearth rjup_per_mpc = 1.0 / mpc_per_rjup miles_per_mpc = 1.0 / mpc_per_miles km_per_mpc = 1.0 / mpc_per_km m_per_mpc = 1.0 / mpc_per_m m_per_kpc = 1.0 / kpc_per_m m_per_km = 1.0 / km_per_m cm_per_km = 1.0 / km_per_cm cm_per_m = 1.0 / m_per_cm pc_per_km = 1.0 / km_per_pc m_per_pc = 1.0 / pc_per_m m_per_ly = 1.0 / ly_per_m m_per_rsun = 1.0 / rsun_per_m m_per_rearth = 1.0 / rearth_per_m m_per_rjup = 1.0 / rjup_per_m m_per_au = 1.0 / au_per_m m_per_ang = 1.0 / ang_per_m # time # "IAU Style Manual" by G.A. Wilkins, Comm. 5, in IAU Transactions XXB (1989) sec_per_Gyr = 31.5576e15 sec_per_Myr = 31.5576e12 sec_per_kyr = 31.5576e9 sec_per_year = 31.5576e6 sec_per_day = 86400.0 sec_per_hr = 3600.0 sec_per_min = 60.0 day_per_year = 365.25 # velocities, accelerations speed_of_light_m_per_s = 2.99792458e8 speed_of_light_cm_per_s = speed_of_light_m_per_s * 100.0 standard_gravity_m_per_s2 = 9.80665 # some constants newton_mks = 6.67408e-11 planck_mks = 6.62606957e-34 elementary_charge_C = 1.6021766208e-19 # permeability of Free Space mu_0 = 4.0e-7 * np.pi # permittivity of Free Space eps_0 = 1.0 / (speed_of_light_m_per_s**2 * mu_0) avogadros_number = 6.022141410704091e23 rydberg_constant_mks = ( 0.125 * mass_electron_kg * elementary_charge_C**4 / (eps_0**2 * planck_mks**3 * speed_of_light_m_per_s) ) # temperature / energy boltzmann_constant_J_per_K = 1.3806488e-23 erg_per_eV = 1.602176562e-12 J_per_eV = erg_per_eV * 1.0e-7 erg_per_keV = erg_per_eV * 1.0e3 J_per_keV = J_per_eV * 1.0e3 K_per_keV = J_per_keV / boltzmann_constant_J_per_K keV_per_K = 1.0 / K_per_keV keV_per_erg = 1.0 / erg_per_keV eV_per_erg = 1.0 / erg_per_eV kelvin_per_rankine = 5.0 / 9.0 watt_per_horsepower = 745.69987158227022 erg_per_s_per_watt = 1e7 J_per_BTU = 1055.0559 J_per_watt_hour = 3600 btu_per_mmbtu = 1e6 * J_per_BTU btu_per_therm = 1e5 * J_per_BTU btu_per_quad = 1e15 * J_per_BTU rydberg_unit_mks = planck_mks * speed_of_light_m_per_s * rydberg_constant_mks stefan_boltzmann_W_per_sqm_per_K4 = ( 2.0 * np.pi**5 * boltzmann_constant_J_per_K**4 / (15.0 * speed_of_light_m_per_s**2 * planck_mks**3) ) radiation_constant_J_per_m3_per_K4 = ( 4.0 * stefan_boltzmann_W_per_sqm_per_K4 / speed_of_light_m_per_s ) J_per_foe = 1.0e44 # Solar System masses # Standish, E.M. (1995) "Report of the IAU WGAS Sub-Group on Numerical # Standards", in Highlights of Astronomy (I. Appenzeller, ed.), Table 1, # Kluwer Academic Publishers, Dordrecht. # REMARK: following masses include whole systems (planet + moons) mass_jupiter_kg = mass_sun_kg / 1047.3486 mass_mercury_kg = mass_sun_kg / 6023600.0 mass_venus_kg = mass_sun_kg / 408523.71 mass_earth_kg = mass_sun_kg / 328900.56 mass_mars_kg = mass_sun_kg / 3098708.0 mass_saturn_kg = mass_sun_kg / 3497.898 mass_uranus_kg = mass_sun_kg / 22902.98 mass_neptune_kg = mass_sun_kg / 19412.24 # flux jansky_mks = 1.0e-26 # Cosmological constants # Calculated with H = 100 km/s/Mpc, value given in units of h^2 g cm^-3 # Multiply by h^2 to get the critical density in units of g cm^-3 rho_crit_g_cm3_h2 = 1.8788e-29 primordial_H_mass_fraction = 0.76 # Misc. Approximations mass_mean_atomic_cosmology = 1.22 mass_mean_atomic_galactic = 2.3 # Miscellaneous HUGE = 1.0e90 TINY = 1.0e-40 # Planck units hbar_mks = 0.5 * planck_mks / np.pi planck_mass_kg = np.sqrt(hbar_mks * speed_of_light_m_per_s / newton_mks) planck_length_m = np.sqrt(hbar_mks * newton_mks / speed_of_light_m_per_s**3) planck_time_s = planck_length_m / speed_of_light_m_per_s planck_energy_J = planck_mass_kg * speed_of_light_m_per_s * speed_of_light_m_per_s planck_temperature_K = planck_energy_J / boltzmann_constant_J_per_K planck_charge_C = np.sqrt(4.0 * np.pi * eps_0 * hbar_mks * speed_of_light_m_per_s) # Imperial and other non-metric units kg_per_pound = 0.45359237 pascal_per_atm = 101325.0 m_per_inch = 0.0254 m_per_ft = 0.3048 m_per_mile = 1609.344 m_per_nautical_mile = 1852.0 # https://en.wikipedia.org/wiki/Comparison_of_the_imperial_and_US_customary_measurement_systems uk_fl_oz_per_L = 0.0284130625e-3 us_fl_oz_per_L = 0.0295735295625e-3 # logarithmic units # IEC 60027-3: https://webstore.iec.ch/publication/94 # NIST Special Publication 811: https://www.nist.gov/pml/special-publication-811 neper_per_bel = np.log(10) / 2 unyt-3.2.0/unyt/_pint_conversions.py000066400000000000000000000030201526544257300176350ustar00rootroot00000000000000""" Stuff for pint conversions """ pint_aliases = { "meter": "m", "second": "s", "gram": "g", "joule": "J", "franklin": "esu", "dyne": "dyn", "parsec": "pc", "mole": "mol", "rankine": "R", "watt": "W", "pascal": "Pa", "tesla": "T", "kelvin": "K", "year": "yr", "minute": "min", "hour": "hr", "volt": "V", "ampere": "A", "foot": "ft", "coulomb": "C", "newton": "N", "hertz": "Hz", "arcsecond": "arcsec", "arcminute": "arcmin", "speed_of_light": "c", "esu_per_second": "statA", "atomic_mass_unit": "amu", "astronomical_unit": "au", "light_year": "ly", "electron_mass": "me", "proton_mass": "mp", } pint_prefixes = { "yotta": "Y", "zetta": "Z", "exa": "E", "peta": "P", "tera": "T", "giga": "G", "mega": "M", "kilo": "k", "deci": "d", "centi": "c", "milli": "m", "micro": "u", "nano": "n", "pico": "p", "femto": "f", "atto": "a", "zepto": "z", "yocto": "y", } def convert_pint_units(unit_expr): uexpr = unit_expr pfx = "" for prefix in pint_prefixes: if unit_expr.startswith(prefix): pfx = pint_prefixes[prefix] uexpr = uexpr[len(prefix) :] break if uexpr in pint_aliases: uexpr = pint_aliases[uexpr] if pfx == "": return uexpr else: return pfx + uexpr # If we can't figure it out just pass it and see # what happens return unit_expr unyt-3.2.0/unyt/_unit_lookup_table.py000066400000000000000000000721621526544257300177670ustar00rootroot00000000000000""" The default unit symbol lookup table. """ from collections import OrderedDict, defaultdict import numpy as np from unyt import dimensions from unyt._physical_ratios import ( J_per_BTU, J_per_eV, J_per_foe, J_per_watt_hour, amu_grams, amu_kg, avogadros_number, boltzmann_constant_J_per_K, btu_per_mmbtu, btu_per_quad, btu_per_therm, elementary_charge_C, eps_0, jansky_mks, kelvin_per_rankine, kg_per_pound, luminosity_sun_watts, m_per_ang, m_per_au, m_per_ft, m_per_inch, m_per_ly, m_per_mile, m_per_nautical_mile, m_per_pc, m_per_rearth, m_per_rjup, m_per_rsun, mass_earth_kg, mass_electron_kg, mass_hydrogen_kg, mass_jupiter_kg, mass_mars_kg, mass_mercury_kg, mass_neptune_kg, mass_proton_kg, mass_saturn_kg, mass_sun_kg, mass_uranus_kg, mass_venus_kg, metallicity_sun, metallicity_sun_angr, metallicity_sun_aspl, metallicity_sun_feld, metallicity_sun_lodd, mu_0, neper_per_bel, newton_mks, pascal_per_atm, planck_charge_C, planck_energy_J, planck_length_m, planck_mass_kg, planck_mks, planck_temperature_K, planck_time_s, radiation_constant_J_per_m3_per_K4, rydberg_constant_mks, rydberg_unit_mks, sec_per_day, sec_per_hr, sec_per_min, sec_per_year, speed_of_light_m_per_s, standard_gravity_m_per_s2, stefan_boltzmann_W_per_sqm_per_K4, temp_sun_kelvin, uk_fl_oz_per_L, us_fl_oz_per_L, watt_per_horsepower, ) # Lookup a unit symbol with the symbol string, and provide a tuple with the # conversion factor to cgs and dimensionality. default_unit_symbol_lut = OrderedDict( [ # base ("m", (1.0, dimensions.length, 0.0, r"\rm{m}", True)), ("g", (1.0e-3, dimensions.mass, 0.0, r"\rm{g}", True)), ("s", (1.0, dimensions.time, 0.0, r"\rm{s}", True)), ("K", (1.0, dimensions.temperature, 0.0, r"\rm{K}", True)), ("rad", (1.0, dimensions.angle, 0.0, r"\rm{rad}", True)), ("A", (1.0, dimensions.current_mks, 0.0, r"\rm{A}", True)), ("cd", (1.0, dimensions.luminous_intensity, 0.0, r"\rm{cd}", True)), ("mol", (1.0 / amu_grams, dimensions.dimensionless, 0.0, r"\rm{mol}", True)), # some cgs ("dyn", (1.0e-5, dimensions.force, 0.0, r"\rm{dyn}", True)), ("erg", (1.0e-7, dimensions.energy, 0.0, r"\rm{erg}", True)), ("Ba", (0.1, dimensions.pressure, 0.0, r"\rm{Ba}", True)), ("G", (0.1**0.5, dimensions.magnetic_field_cgs, 0.0, r"\rm{G}", True)), ("statC", (1.0e-3**1.5, dimensions.charge_cgs, 0.0, r"\rm{statC}", True)), ("statA", (1.0e-3**1.5, dimensions.current_cgs, 0.0, r"\rm{statA}", True)), ( "statV", ( 0.1 * 1.0e-3**0.5, dimensions.electric_potential_cgs, 0.0, r"\rm{statV}", True, ), ), ("statohm", (100.0, dimensions.resistance_cgs, 0.0, r"\rm{statohm}", True)), ("Mx", (1.0e-3**1.5, dimensions.magnetic_flux_cgs, 0.0, r"\rm{Mx}", True)), # some SI ("J", (1.0, dimensions.energy, 0.0, r"\rm{J}", True)), ("W", (1.0, dimensions.power, 0.0, r"\rm{W}", True)), ("Hz", (1.0, dimensions.rate, 0.0, r"\rm{Hz}", True)), ("N", (1.0, dimensions.force, 0.0, r"\rm{N}", True)), ("C", (1.0, dimensions.charge_mks, 0.0, r"\rm{C}", True)), ("T", (1.0, dimensions.magnetic_field_mks, 0.0, r"\rm{T}", True)), ("Pa", (1.0, dimensions.pressure, 0.0, r"\rm{Pa}", True)), ("bar", (1.0e5, dimensions.pressure, 0.0, r"\rm{bar}", True)), ("V", (1.0, dimensions.electric_potential, 0.0, r"\rm{V}", True)), ("F", (1.0, dimensions.capacitance, 0.0, r"\rm{F}", True)), ("H", (1.0, dimensions.inductance, 0.0, r"\rm{H}", True)), ("Ω", (1.0, dimensions.resistance, 0.0, r"\Omega", True)), ("Wb", (1.0, dimensions.magnetic_flux, 0.0, r"\rm{Wb}", True)), ("lm", (1.0, dimensions.luminous_flux, 0.0, r"\rm{lm}", True)), ( "lx", (1.0, dimensions.luminous_flux / dimensions.area, 0.0, r"\rm{lx}", True), ), ("degC", (1.0, dimensions.temperature, -273.15, r"^\circ\rm{C}", True)), ("delta_degC", (1.0, dimensions.temperature, 0, r"\Delta^\circ\rm{C}", True)), ("L", (1e-3, dimensions.volume, 0, r"\rm{L}", True)), ("ha", (1.0e4, dimensions.area, 0.0, r"\rm{ha}", False)), ("t", (1.0e3, dimensions.mass, 0.0, r"\rm{t}", False)), # Imperial and other non-metric units ("mil", (1e-3 * m_per_inch, dimensions.length, 0.0, r"\rm{mil}", False)), ("inch", (m_per_inch, dimensions.length, 0.0, r"\rm{in}", False)), ("ft", (m_per_ft, dimensions.length, 0.0, r"\rm{ft}", False)), ("yd", (0.9144, dimensions.length, 0.0, r"\rm{yd}", False)), ("mile", (m_per_mile, dimensions.length, 0.0, r"\rm{mile}", False)), ("nmi", (m_per_nautical_mile, dimensions.length, 0.0, r"\rm{nmi}", False)), ( "mph", (m_per_mile / sec_per_hr, dimensions.velocity, 0.0, r"\rm{mph}", False), ), ( "kt", ( m_per_nautical_mile / sec_per_hr, dimensions.velocity, 0.0, r"\rm{kt}", False, ), ), ("acre", (4046.8564224, dimensions.area, 0.0, r"\rm{acre}", False)), ("furlong", (m_per_ft * 660.0, dimensions.length, 0.0, r"\rm{fur}", False)), ( "degF", ( kelvin_per_rankine, dimensions.temperature, -459.67, r"^\circ\rm{F}", False, ), ), ( "delta_degF", ( kelvin_per_rankine, dimensions.temperature, 0.0, r"\Delta^\circ\rm{F}", False, ), ), ( "R", (kelvin_per_rankine, dimensions.temperature, 0.0, r"^\circ\rm{R}", False), ), ( "lbf", ( kg_per_pound * standard_gravity_m_per_s2, dimensions.force, 0.0, r"\rm{lbf}", False, ), ), ( "kip", ( 1000 * kg_per_pound * standard_gravity_m_per_s2, dimensions.force, 0.0, r"\rm{kip}", False, ), ), ("lb", (kg_per_pound, dimensions.mass, 0.0, r"\rm{lb}", False)), ("atm", (pascal_per_atm, dimensions.pressure, 0.0, r"\rm{atm}", False)), ("hp", (watt_per_horsepower, dimensions.power, 0.0, r"\rm{hp}", False)), ("oz", (kg_per_pound / 16.0, dimensions.mass, 0.0, r"\rm{oz}", False)), ("ton", (kg_per_pound * 2000.0, dimensions.mass, 0.0, r"\rm{US ton}", False)), ( "ton_UK", (kg_per_pound * 2240.0, dimensions.mass, 0.0, r"\rm{UK ton}", False), ), ( "slug", ( kg_per_pound * standard_gravity_m_per_s2 / m_per_ft, dimensions.mass, 0.0, r"\rm{slug}", False, ), ), ("fl_oz_US", (us_fl_oz_per_L, dimensions.volume, 0.0, r"\rm{US fl oz}", False)), ("fl_oz_UK", (uk_fl_oz_per_L, dimensions.volume, 0.0, r"\rm{UK fl oz}", False)), ( "pt_US", (us_fl_oz_per_L * 16.0, dimensions.volume, 0.0, r"\rm{US pt}", False), ), ( "pt_UK", (uk_fl_oz_per_L * 20.0, dimensions.volume, 0.0, r"\rm{UK pt}", False), ), ( "qt_US", (us_fl_oz_per_L * 32.0, dimensions.volume, 0.0, r"\rm{US qt}", False), ), ( "qt_UK", (uk_fl_oz_per_L * 40.0, dimensions.volume, 0.0, r"\rm{UK qt}", False), ), ( "gal_US", (us_fl_oz_per_L * 128.0, dimensions.volume, 0.0, r"\rm{US gal}", False), ), ( "gal_UK", (uk_fl_oz_per_L * 160.0, dimensions.volume, 0.0, r"\rm{UK gal}", False), ), ("cal", (4.184, dimensions.energy, 0.0, r"\rm{cal}", True)), ("BTU", (J_per_BTU, dimensions.energy, 0.0, r"\rm{BTU}", False)), ("MMBTU", (btu_per_mmbtu, dimensions.energy, 0.0, r"\rm{MMBTU}", False)), ("therm", (btu_per_therm, dimensions.energy, 0.0, r"\rm{therm}", False)), ("quad", (btu_per_quad, dimensions.energy, 0.0, r"\rm{quad}", False)), ("Wh", (J_per_watt_hour, dimensions.energy, 0.0, r"\rm{Wh}", True)), ( "pli", ( kg_per_pound * standard_gravity_m_per_s2 / m_per_inch, dimensions.tension, 0.0, r"\rm{pli}", False, ), ), ( "plf", ( kg_per_pound * standard_gravity_m_per_s2 / m_per_ft, dimensions.tension, 0.0, r"\rm{plf}", False, ), ), ( "psi", ( kg_per_pound * standard_gravity_m_per_s2 / m_per_inch**2, dimensions.pressure, 0.0, r"\rm{psi}", False, ), ), ( "psf", ( kg_per_pound * standard_gravity_m_per_s2 / m_per_ft**2, dimensions.pressure, 0.0, r"\rm{psf}", False, ), ), ( "kli", ( 1000 * kg_per_pound * standard_gravity_m_per_s2 / m_per_inch, dimensions.tension, 0.0, r"\rm{kli}", False, ), ), ( "klf", ( 1000 * kg_per_pound * standard_gravity_m_per_s2 / m_per_ft, dimensions.tension, 0.0, r"\rm{klf}", False, ), ), ( "ksi", ( 1000 * kg_per_pound * standard_gravity_m_per_s2 / m_per_inch**2, dimensions.pressure, 0.0, r"\rm{ksi}", False, ), ), ( "ksf", ( 1000 * kg_per_pound * standard_gravity_m_per_s2 / m_per_ft**2, dimensions.pressure, 0.0, r"\rm{ksf}", False, ), ), ("smoot", (1.7018, dimensions.length, 0.0, r"\rm{smoot}", False)), # dimensionless stuff ("dimensionless", (1.0, dimensions.dimensionless, 0.0, r"", False)), ("%", (0.01, dimensions.dimensionless, 0.0, r"\%", False)), # times ("min", (sec_per_min, dimensions.time, 0.0, r"\rm{min}", False)), ("hr", (sec_per_hr, dimensions.time, 0.0, r"\rm{hr}", False)), ("day", (sec_per_day, dimensions.time, 0.0, r"\rm{d}", False)), ("week", (7.0 * sec_per_day, dimensions.time, 0.0, r"\rm{week}", False)), ( "fortnight", (14.0 * sec_per_day, dimensions.time, 0.0, r"\rm{fortnight}", False), ), ("yr", (sec_per_year, dimensions.time, 0.0, r"\rm{yr}", True)), # Velocities ("c", (speed_of_light_m_per_s, dimensions.velocity, 0.0, r"\rm{c}", False)), # Solar units ("Msun", (mass_sun_kg, dimensions.mass, 0.0, r"\rm{M}_\odot", False)), ("Rsun", (m_per_rsun, dimensions.length, 0.0, r"\rm{R}_\odot", False)), ("Lsun", (luminosity_sun_watts, dimensions.power, 0.0, r"\rm{L}_\odot", False)), ( "Tsun", (temp_sun_kelvin, dimensions.temperature, 0.0, r"\rm{T}_\odot", False), ), ( "Zsun", (metallicity_sun, dimensions.dimensionless, 0.0, r"\rm{Z}_\odot", False), ), ( "Zsun_angr", ( metallicity_sun_angr, dimensions.dimensionless, 0.0, r"\rm{Z}_\odot", False, ), ), ( "Zsun_aspl", ( metallicity_sun_aspl, dimensions.dimensionless, 0.0, r"\rm{Z}_\odot", False, ), ), ( "Zsun_feld", ( metallicity_sun_feld, dimensions.dimensionless, 0.0, r"\rm{Z}_\odot", False, ), ), ( "Zsun_lodd", ( metallicity_sun_lodd, dimensions.dimensionless, 0.0, r"\rm{Z}_\odot", False, ), ), ("Mjup", (mass_jupiter_kg, dimensions.mass, 0.0, r"\rm{M}_{\rm{Jup}}", False)), ("Mearth", (mass_earth_kg, dimensions.mass, 0.0, r"\rm{M}_\oplus", False)), ("Rjup", (m_per_rjup, dimensions.length, 0.0, r"\rm{R}_\mathrm{Jup}", False)), ("Rearth", (m_per_rearth, dimensions.length, 0.0, r"\rm{R}_\oplus", False)), # astro distances ("AU", (m_per_au, dimensions.length, 0.0, r"\rm{AU}", False)), ("ly", (m_per_ly, dimensions.length, 0.0, r"\rm{ly}", False)), ("pc", (m_per_pc, dimensions.length, 0.0, r"\rm{pc}", True)), # angles ("degree", (np.pi / 180.0, dimensions.angle, 0.0, r"^\circ", False)), ("arcmin", (np.pi / 10800.0, dimensions.angle, 0.0, r"\rm{arcmin}", False)), ("arcsec", (np.pi / 648000.0, dimensions.angle, 0.0, r"\rm{arcsec}", False)), ("mas", (np.pi / 648000000.0, dimensions.angle, 0.0, r"\rm{mas}", False)), ("hourangle", (np.pi / 12.0, dimensions.angle, 0.0, r"\rm{HA}", False)), ("sr", (1.0, dimensions.solid_angle, 0.0, r"\rm{sr}", False)), ("lat", (-np.pi / 180.0, dimensions.angle, 90.0, r"\rm{Latitude}", False)), ("lon", (np.pi / 180.0, dimensions.angle, -180.0, r"\rm{Longitude}", False)), ( "rpm", (2.0 * np.pi / 60.0, dimensions.angular_frequency, 0.0, r"\rm{RPM}", False), ), ( "rev", (2.0 * np.pi, dimensions.angle, 0.0, r"\rm{rev}", False), ), ("spat", (4.0 * np.pi, dimensions.solid_angle, 0.0, r"\rm{sp}", False)), ("gradian", (np.pi / 200.0, dimensions.angle, 0.0, r"^\rm{g}", False)), # misc ("eV", (J_per_eV, dimensions.energy, 0.0, r"\rm{eV}", True)), # note: 1 foe = 1 Bethe ("foe", (J_per_foe, dimensions.energy, 0.0, r"\rm{foe}", False)), ("bethe", (J_per_foe, dimensions.energy, 0.0, r"\rm{B}", False)), ("amu", (amu_kg, dimensions.mass, 0.0, r"\rm{amu}", False)), ("Å", (m_per_ang, dimensions.length, 0.0, r"\AA", False)), ("Jy", (jansky_mks, dimensions.specific_flux, 0.0, r"\rm{Jy}", True)), ("counts", (1.0, dimensions.dimensionless, 0.0, r"\rm{counts}", False)), ("photons", (1.0, dimensions.dimensionless, 0.0, r"\rm{photons}", False)), ("me", (mass_electron_kg, dimensions.mass, 0.0, r"m_e", False)), ("mp", (mass_hydrogen_kg, dimensions.mass, 0.0, r"m_p", False)), ("Sv", (1.0, dimensions.specific_energy, 0.0, r"\rm{Sv}", True)), ("Ry", (rydberg_unit_mks, dimensions.energy, 0.0, r"\rm{Ry}", False)), ( "rayleigh", (2.5e9 / np.pi, dimensions.count_intensity, 0.0, r"\rm{R}", False), ), ("lambert", (1.0e4 / np.pi, dimensions.luminance, 0.0, r"\rm{L}", False)), ("nt", (1.0, dimensions.luminance, 0.0, r"\rm{nt}", False)), # Planck units ("m_pl", (planck_mass_kg, dimensions.mass, 0.0, r"m_{\rm{P}}", False)), ("l_pl", (planck_length_m, dimensions.length, 0.0, r"\ell_{\rm{P}}", False)), ("t_pl", (planck_time_s, dimensions.time, 0.0, r"t_{\rm{P}}", False)), ( "T_pl", (planck_temperature_K, dimensions.temperature, 0.0, r"T_{\rm{P}}", False), ), ("q_pl", (planck_charge_C, dimensions.charge_mks, 0.0, r"q_{\rm{P}}", False)), ("E_pl", (planck_energy_J, dimensions.energy, 0.0, r"E_{\rm{P}}", False)), # Geometrized units ("m_geom", (mass_sun_kg, dimensions.mass, 0.0, r"\rm{M}_\odot", False)), ( "l_geom", ( newton_mks * mass_sun_kg / speed_of_light_m_per_s**2, dimensions.length, 0.0, r"\rm{M}_\odot", False, ), ), ( "t_geom", ( newton_mks * mass_sun_kg / speed_of_light_m_per_s**3, dimensions.time, 0.0, r"\rm{M}_\odot", False, ), ), # logarithmic units ("B", (neper_per_bel, dimensions.logarithmic, 0.0, r"\rm{B}", True)), ("Np", (1.0, dimensions.logarithmic, 0.0, r"\rm{Np}", True)), ] ) # This dictionary formatting from magnitude package, credit to Juan Reyero. unit_prefixes = OrderedDict( [ ("Y", (1e24, "yotta")), ("Z", (1e21, "zetta")), ("E", (1e18, "exa")), ("P", (1e15, "peta")), ("T", (1e12, "tera")), ("G", (1e9, "giga")), ("M", (1e6, "mega")), ("k", (1e3, "kilo")), ("h", (1e2, "hecto")), ("da", (1e1, "deca")), ("d", (1e-1, "deci")), ("c", (1e-2, "centi")), ("m", (1e-3, "mili")), # typo, kept for backward compatibility ("m", (1e-3, "milli")), ("µ", (1e-6, "micro")), # ('MICRO SIGN' U+00B5) ("u", (1e-6, "micro")), ("μ", (1e-6, "micro")), # ('GREEK SMALL LETTER MU' U+03BC) ("n", (1e-9, "nano")), ("p", (1e-12, "pico")), ("f", (1e-15, "femto")), ("a", (1e-18, "atto")), ("z", (1e-21, "zepto")), ("y", (1e-24, "yocto")), ] ) default_base_units = { dimensions.mass: "kg", dimensions.length: "m", dimensions.time: "s", dimensions.temperature: "K", dimensions.angle: "radian", dimensions.current_mks: "A", dimensions.luminous_intensity: "cd", } physical_constants = OrderedDict( [ ("me", (mass_electron_kg, "kg", ["mass_electron", "electron_mass"])), ("Na", (avogadros_number, "mol**-1", ["Avogadros_number", "avogadros_number"])), ("mp", (mass_proton_kg, "kg", ["proton_mass", "mass_proton"])), ("mh", (mass_hydrogen_kg, "kg", ["hydrogen_mass", "mass_hydrogen"])), ("c", (speed_of_light_m_per_s, "m/s", ["clight", "speed_of_light"])), ( "σ_T", ( 6.65245854533e-29, "m**2", [ "sigma_thompson", "thompson_cross_section", "cross_section_thompson", ] # typos, kept for backwards compatibility + ["sigma_thomson", "thomson_cross_section", "cross_section_thomson"], ), ), ( "qp", ( elementary_charge_C, "C", ["proton_charge", "elementary_charge", "charge_proton"], ), ), ("qe", (-elementary_charge_C, "C", ["electron_charge", "charge_electron"])), ("kb", (boltzmann_constant_J_per_K, "J/K", ["kboltz", "boltzmann_constant"])), ( "G", ( newton_mks, "m**3/kg/s**2", ["newtons_constant", "gravitational_constant"], ), ), ("h", (planck_mks, "J*s", ["planck_constant"])), ("hbar", (0.5 * planck_mks / np.pi, "J*s", ["reduced_planck_constant"])), ( "σ", ( stefan_boltzmann_W_per_sqm_per_K4, "W/m**2/K**4", ["stefan_boltzmann_constant"], ), ), ( "a", ( radiation_constant_J_per_m3_per_K4, "J/m**3/K**4", ["radiation_density_constant"], ), ), ("Tcmb", (2.726, "K", ["CMB_temperature"])), ( "Msun", ( mass_sun_kg, "kg", ["msun", "m_sun", "m_Sun", "M_sun", "M_Sun", "solar_mass", "mass_sun"], ), ), ("Mjup", (mass_jupiter_kg, "kg", ["mjup", "jupiter_mass", "mass_jupiter"])), ("mercury_mass", (mass_mercury_kg, "kg", ["mass_mercury"])), ("venus_mass", (mass_venus_kg, "kg", ["mass_venus"])), ("Mearth", (mass_earth_kg, "kg", ["mearth", "earth_mass", "mass_earth"])), ("mars_mass", (mass_mars_kg, "kg", ["mass_mars"])), ("saturn_mass", (mass_saturn_kg, "kg", ["mass_saturn"])), ("uranus_mass", (mass_uranus_kg, "kg", ["mass_uranus"])), ("neptune_mass", (mass_neptune_kg, "kg", ["mass_neptune"])), ("m_pl", (planck_mass_kg, "kg", ["planck_mass"])), ("l_pl", (planck_length_m, "m", ["planck_length"])), ("t_pl", (planck_time_s, "s", ["planck_time"])), ("E_pl", (planck_energy_J, "J", ["planck_energy"])), ("q_pl", (planck_charge_C, "C", ["planck_charge"])), ("T_pl", (planck_temperature_K, "K", ["planck_temperature"])), ("mu_0", (mu_0, "N/A**2", ["vacuum_permeability", "magnetic_constant", "μ_0"])), ( "eps_0", ( eps_0, "C**2/N/m**2", ["vacuum_permittivity", "electric_constant", "ε_0", "epsilon_0"], ), ), ("R_inf", (rydberg_constant_mks, "m**-1", ["rydberg_constant", "R_∞"])), ("standard_gravity", (standard_gravity_m_per_s2, "m/s**2", [])), ] ) default_unit_name_alternatives = OrderedDict( [ # base ("m", ("meter", "metre")), ("g", ("gram", "gramme")), ("s", ("second",)), ("K", ("degree_kelvin", "kelvin")), ("rad", ("radian",)), ("A", ("ampere", "amp", "Amp")), ("cd", ("candela",)), ("mol", ("mole",)), # some cgs ("dyn", ("dyne",)), ("erg", ("ergs",)), ("Ba", ("barye",)), ("G", ("gauss",)), ("statC", ("statcoulomb", "esu", "ESU", "electrostatic_unit")), ("statA", ("statampere",)), ("statV", ("statvolt",)), ("Mx", ("maxwell",)), # some SI ("J", ("joule",)), ("W", ("watt",)), ("Hz", ("hertz",)), ("N", ("newton",)), ("C", ("coulomb",)), ("T", ("tesla",)), ("Pa", ("pascal",)), ("V", ("volt",)), ("F", ("farad",)), ("H", ("henry",)), ("Ω", ("ohm", "Ohm")), ("Wb", ("weber",)), ("lm", ("lumen",)), ("lx", ("lux",)), ("degC", ("degree_celsius", "degree_Celsius", "celcius", "celsius", "°C")), ("L", ("liter", "litre", "l")), ("ha", ("hectare",)), ( "t", ( "tonne", "metric_ton", ), ), # Imperial and other non-metric units ("mil", ("thou", "thousandth")), ("inch", ("in",)), ("ft", ("foot",)), ("yd", ("yard",)), ("furlong", ("fur",)), ("degF", ("degree_fahrenheit", "degree_Fahrenheit", "fahrenheit", "°F")), ("R", ("degree_rankine", "rankine")), ("lbf", ("pound_force",)), ("kip", ("kilopound", "kipf")), ("lb", ("pound", "pound_mass", "lbm")), ("ton", ("ton_US", "ton_US_short", "short_ton")), ("ton_UK", ("ton_US_long", "long_ton")), ("atm", ("atmosphere",)), ("hp", ("horsepower",)), ("oz", ("ounce",)), ("nmi", ("nautical_mile",)), ("fl_oz_US", ("fluid_ounce_US",)), ("fl_oz_UK", ("fluid_ounce_UK",)), ("pt_US", ("pint_US",)), ("pt_UK", ("pint_UK",)), ("qt_US", ("quart_US",)), ("qt_UK", ("quart_UK",)), ("gal_US", ("gallon_US",)), ("gal_UK", ("gallon_UK",)), ("kt", ("knot",)), ("cal", ("calorie",)), ( "BTU", ( "british_thermal_unit", "btu", ), ), ("MMBTU", ("mmbtu",)), ("therm", ("therms",)), ("quad", ("quads",)), ( "Wh", ( "watt_hour", "watt_hours", ), ), ("pli", ("pounds_per_inch",)), ("plf", ("pounds_per_ft",)), ("psi", ("pounds_per_square_inch",)), ("psf", ("pounds_per_square_ft",)), ("kli", ("kips_per_inch",)), ("klf", ("kips_per_ft",)), ("ksi", ("kips_per_square_inch",)), ("ksf", ("kips_per_square_ft",)), # dimensionless stuff ("dimensionless", ("_", "")), ("B", ("bel",)), ("Np", ("neper",)), # times ("min", ("minute",)), ("hr", ("hour",)), ("day", ("d",)), ("yr", ("year",)), # Solar units ( "Msun", ("msun", "m_sun", "M_sun", "m_Sun", "solar_mass", "solMass", "mass_sun"), ), ("Rsun", ("rsun", "r_sun", "R_sun", "r_Sun", "solar_radius", "solRadius")), ( "Lsun", ("lsun", "l_sun", "L_sun", "l_Sun", "solar_luminosity", "solLuminosity"), ), ( "Tsun", ("t_sun", "tsun", "T_sun", "t_Sun", "solar_temperature", "solTemperature"), ), ( "Zsun", ("z_sun", "zsun", "Z_sun", "z_Sun", "solar_metallicity", "solMetallicity"), ), ("Mjup", ("m_jup", "jupiter_mass")), ("Mearth", ("m_earth", "earth_mass")), ("Rjup", ("r_jup", "jupiter_radius")), ("Rearth", ("r_earth", "earth_radius")), # astro distances ("AU", ("au", "astronomical_unit")), ("pc", ("parsec",)), ("ly", ("light_year",)), # angles ("degree", ("deg",)), ("arcmin", ("arcminute",)), ("arcsec", ("arcsecond",)), ("mas", ("milliarcsecond",)), ("hourangle", ("HA",)), ("sr", ("steradian",)), ("lat", ("latitude", "degree_latitude")), ("lon", ("longitude", "degree_longitude")), ("rev", ("revolution", "turn", "pla")), ("gradian", ("gon",)), # misc ("eV", ("electronvolt",)), ("amu", ("atomic_mass_unit",)), ("Å", ("angstrom",)), ("Jy", ("jansky",)), ("counts", ("count",)), ("photons", ("photon",)), ("me", ("electron_mass",)), ("mp", ("proton_mass",)), ("Sv", ("sievert",)), ("nt", ("nit",)), ("%", ("percent",)), # Planck units ("m_pl", ("planck_mass",)), ("l_pl", ("planck_length",)), ("t_pl", ("planck_time",)), ("T_pl", ("planck_temperature",)), ("q_pl", ("planck_charge",)), ("E_pl", ("planck_energy",)), ] ) def generate_name_alternatives(): names = defaultdict(list) inv_names = {} seen = set() def append_name(n, okey, key): if key not in seen: n.append(key) inv_names[key] = okey seen.add(key) else: if okey[0] not in ["u", "μ"]: raise RuntimeError( # pragma: no cover f"Duplicate unit name found: {key}, {okey}" ) for key, entry in default_unit_symbol_lut.items(): append_name(names[key], key, key) # Are we SI prefixable or not? if entry[4]: for prefix in unit_prefixes: # This is specifically to work around # https://github.com/yt-project/unyt/issues/145 if prefix in ["u", "μ", "µ"]: used_prefix = "μ" else: used_prefix = prefix append_name(names[prefix + key], used_prefix + key, prefix + key) elif len(key) > 3 and key.title() != key: if all(len(k) > 3 for k in key.split("_")): append_name(names[key], key, key.title()) if key in default_unit_name_alternatives: alternatives = default_unit_name_alternatives[key] # Are we SI prefixable or not? if entry[4]: for a in alternatives: for up, up_data in unit_prefixes.items(): if len(a) < 4: append_name(names[up + key], up + key, up + a) alt = up_data[1] + a if alt not in seen: append_name(names[up + key], up + key, alt) if alt.title() not in names[up + key]: append_name(names[up + key], up + key, alt.title()) for alt in alternatives: append_name(names[key], key, alt) if not alt.islower() or len(alt) < 4: continue if alt.title() not in names[key]: append_name(names[key], key, alt.title()) return names, inv_names name_alternatives, inv_name_alternatives = generate_name_alternatives() unyt-3.2.0/unyt/array.py000066400000000000000000002627751526544257300152420ustar00rootroot00000000000000""" unyt_array class. """ import copy import re import sys import warnings from functools import lru_cache from importlib.metadata import version from numbers import Number as numeric_type import numpy as np from numpy import ( absolute, add, arccos, arccosh, arcsin, arcsinh, arctan, arctan2, arctanh, bitwise_and, bitwise_or, bitwise_xor, cbrt, ceil, clip, conj, copysign, cos, cosh, deg2rad, divide, divmod as divmod_, equal, exp, exp2, expm1, fabs, floor, floor_divide, fmax, fmin, fmod, frexp, greater, greater_equal, heaviside, hypot, invert, iscomplex, isfinite, isinf, isnan, isnat, isreal, ldexp, left_shift, less, less_equal, log, log1p, log2, log10, logaddexp, logaddexp2, logical_and, logical_not, logical_or, logical_xor, matmul, maximum, minimum, mod, modf, multiply, negative, nextafter, not_equal, ones_like, positive, power, rad2deg, reciprocal, remainder, right_shift, rint, sign, signbit, sin, sinh, spacing, sqrt, square, subtract, tan, tanh, true_divide, trunc, ) from packaging.version import Version from sympy import Rational from unyt._on_demand_imports import _astropy, _dask, _pint from unyt._pint_conversions import convert_pint_units from unyt._unit_lookup_table import default_unit_symbol_lut from unyt.dimensions import angle, temperature from unyt.equivalencies import equivalence_registry from unyt.exceptions import ( InvalidUnitEquivalence, InvalidUnitOperation, IterableUnitCoercionError, MKSCGSConversionError, SymbolNotFoundError, UnitConversionError, UnitOperationError, UnitsNotReducible, ) from unyt.unit_object import Unit, _check_em_conversion, _em_conversion from unyt.unit_registry import ( UnitRegistry, _correct_old_unit_registry, _sanitize_unit_system, default_unit_registry, ) from unyt.unit_symbols import delta_degC, delta_degF from ._deprecation import warn_deprecated NUMPY_VERSION = Version(version("numpy")) if NUMPY_VERSION >= Version("2.0.0.dev0"): from numpy import vecdot NULL_UNIT = Unit() POWER_MAPPING = {multiply: lambda x: x, divide: lambda x: 2 - x} DISALLOWED_DTYPES = ( "S", # bytestring "a", # bytestring "U", # (unicode) bytes "O", # Python object "M", # datetime "m", # timedelta ) __doctest_requires__ = { ("unyt_array.from_pint", "unyt_array.to_pint"): ["pint"], ("unyt_array.from_astropy", "unyt_array.to_astropy"): ["astropy"], } _COPY_IF_NEEDED = None if Version(version("numpy")) >= Version("2.0.0dev0") else False # This is partially adapted from the following SO thread # https://stackoverflow.com/questions/41668588/regex-to-match-scientific-notation _NUMB_PATTERN = ( r"[+/-]?(?:((?:\d\.?\d*[Ee][+\-]?\d+)|(?:\d+\.\d*|\d*\.\d+))|\d+|inf\s|nan\s)" ) # *all* greek letters are considered valid unit string elements. # This may be an overshoot. We rely on unyt.Unit to do the actual validation _UNIT_PATTERN = r"((\s*[*/]\s*)?[α-ωΑ-Ωa-zA-Z]+(\*\*([+-]?\d+|\([+-]?\d+\)))?)+" _QUAN_PATTERN = rf"{_NUMB_PATTERN}\s*{_UNIT_PATTERN}" _NUMB_REGEXP = re.compile(_NUMB_PATTERN) _UNIT_REGEXP = re.compile(_UNIT_PATTERN) _QUAN_REGEXP = re.compile(_QUAN_PATTERN) def _iterable(obj): try: len(obj) except Exception: return False return True @lru_cache(maxsize=128, typed=False) def _sqrt_unit(unit): return 1, unit**0.5 @lru_cache(maxsize=128, typed=False) def _cbrt_unit(unit): return 1, unit ** (1.0 / 3.0) @lru_cache(maxsize=128, typed=False) def _multiply_units(unit1, unit2): try: ret = (unit1 * unit2).simplify() except SymbolNotFoundError: # Some operators are not natively commutative when operands are # defined within different unit registries, and conversion # is defined one way but not the other. ret = (unit2 * unit1).simplify() return ret.as_coeff_unit() @lru_cache(maxsize=128, typed=False) def _preserve_units(unit1, unit2=None): if unit2 is None or unit1.dimensions is not temperature: return 1, unit1 if unit1.base_offset == 0.0 and unit2.base_offset != 0.0: return 1, unit2 return 1, unit1 @lru_cache(maxsize=128, typed=False) def _difference_units(unit1, unit2=None): if unit1.dimensions is not temperature: return _preserve_units(unit1, unit2) s1 = repr(unit1) if unit2 is not None and unit2 != unit1: s2 = repr(unit2) if s1 in s2 and s2.startswith("delta_"): return 1, unit1 elif s2 in s1 and s1.startswith("delta_"): return 1, unit2 else: raise InvalidUnitOperation( "Quantities with units of Fahrenheit or Celsius " "cannot be multiplied, divided, subtracted or " "added with data that has different units." ) if unit1.base_offset == 0.0: return 1, unit1 if s1 == "degF": return 1, delta_degF elif s1 == "degC": return 1, delta_degC else: # This is supposed to be unreachable raise RuntimeError( "Could not determine difference temperature units " f"in operation ({unit1} - {unit2}).\n" "If you see this error please file an issue at " "https://github.com/yt-project/unyt/issues/new" ) @lru_cache(maxsize=128, typed=False) def _power_unit(unit, power): return 1, unit**power @lru_cache(maxsize=128, typed=False) def _square_unit(unit): return 1, unit * unit @lru_cache(maxsize=128, typed=False) def _divide_units(unit1, unit2): try: ret = (unit1 / unit2).simplify() except SymbolNotFoundError: ret = (1 / (unit2 / unit1).simplify()).units return ret.as_coeff_unit() @lru_cache(maxsize=128, typed=False) def _reciprocal_unit(unit): return 1, unit**-1 def _passthrough_unit(unit, unit2=None): return 1, unit def _return_without_unit(unit, unit2=None): return 1, None def _arctan2_unit(unit1, unit2): return 1, NULL_UNIT def _comparison_unit(unit1, unit2=None): return 1, None def _invert_units(unit): raise TypeError("Bit-twiddling operators are not defined for unyt_array instances") def _bitop_units(unit1, unit2): raise TypeError("Bit-twiddling operators are not defined for unyt_array instances") def _coerce_iterable_units(input_object, registry=None): if isinstance(input_object, np.ndarray): ret = input_object elif _iterable(input_object): if any(isinstance(o, unyt_array) for o in input_object): ff = getattr(input_object[0], "units", NULL_UNIT) if any(ff != getattr(_, "units", NULL_UNIT) for _ in input_object): ret = [] for datum in input_object: try: ret.append(datum.in_units(ff.units)) except UnitConversionError: raise IterableUnitCoercionError(str(input_object)) ret = unyt_array(np.array(ret), ff, registry=registry) # This will create a copy of the data in the iterable. else: ret = unyt_array(np.array(input_object), ff, registry=registry) else: ret = np.asarray(input_object) else: ret = np.asarray(input_object) if ret.dtype.char in DISALLOWED_DTYPES: raise IterableUnitCoercionError(str(input_object)) return ret def _sanitize_units_convert(possible_units, registry): if isinstance(possible_units, Unit): return possible_units # let Unit() try to parse this if it's not already a Unit unit = Unit(possible_units, registry=registry) return unit def _apply_power_mapping(ufunc, in_unit, in_size, in_shape, input_kwarg_dict): # a reduction of a multiply or divide corresponds to # a repeated product which we implement as an exponent mul = 1 power_map = POWER_MAPPING[ufunc] if input_kwarg_dict.get("axis", None) is not None: unit = in_unit ** (power_map(in_shape[input_kwarg_dict["axis"]])) else: unit = in_unit ** (power_map(in_size)) return mul, unit unary_operators = ( negative, absolute, rint, sign, conj, exp, exp2, log, log2, log10, expm1, log1p, sqrt, cbrt, square, reciprocal, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh, arccosh, arctanh, deg2rad, rad2deg, invert, logical_not, isreal, iscomplex, isfinite, isinf, isnan, signbit, floor, ceil, trunc, modf, frexp, fabs, spacing, positive, isnat, ones_like, ) binary_operators = ( add, subtract, multiply, divide, logaddexp, logaddexp2, true_divide, power, remainder, mod, arctan2, hypot, bitwise_and, bitwise_or, bitwise_xor, left_shift, right_shift, greater, greater_equal, less, less_equal, not_equal, equal, logical_and, logical_or, logical_xor, maximum, minimum, fmax, fmin, copysign, nextafter, ldexp, fmod, divmod_, heaviside, ) if NUMPY_VERSION >= Version("2.0.0.dev0"): binary_operators += (vecdot,) trigonometric_operators = (sin, cos, tan) multiple_output_operators = {modf: 2, frexp: 2, divmod_: 2} LARGE_INPUT = {4: 16777217, 8: 9007199254740993} def _update_array_dtype_inplace(array, *, dtype) -> None: # change the dtypes in-place, this does not change the # underlying memory buffer dt = np.dtype(dtype) assert dt.kind == "f" if NUMPY_VERSION >= Version("2.5.0dev0"): array._set_dtype(dt) else: array.dtype = dt def _update_array_values_inplace(array, *, dtype) -> None: new_values = array.astype(dtype) _update_array_dtype_inplace(array, dtype=dtype) # actually fill in the new float values now that our # dtype is correct np.copyto(array, new_values) class unyt_array(np.ndarray): """ An ndarray subclass that attaches a symbolic unit object to the array data. Parameters ---------- input_array : iterable A tuple, list, or array to attach units to units : String unit name, unit symbol object, or astropy unit The units of the array. Powers must be specified using python syntax (cm**3, not cm^3). registry : :class:`unyt.unit_registry.UnitRegistry` The registry to create units from. If units is already associated with a unit registry and this is specified, this will be used instead of the registry associated with the unit object. dtype : numpy dtype or dtype name The dtype of the array data. Defaults to the dtype of the input data, or, if none is found, uses np.float64 bypass_validation : boolean If True, all input validation is skipped. Using this option may produce corrupted, invalid units or array data, but can lead to significant speedups in the input validation logic adds significant overhead. If set, units *must* be a valid unit object. Defaults to False. name : string The name of the array. Defaults to None. This attribute does not propagate through mathematical operations, but is preserved under indexing and unit conversions. Examples -------- >>> from unyt import unyt_array >>> a = unyt_array([1, 2, 3], 'cm') >>> b = unyt_array([4, 5, 6], 'm') >>> a + b unyt_array([401., 502., 603.], 'cm') >>> b + a unyt_array([4.01, 5.02, 6.03], 'm') NumPy ufuncs will pass through units where appropriate. >>> from unyt import g, cm >>> import numpy as np >>> a = (np.arange(8) - 4)*g/cm**3 >>> np.abs(a) unyt_array([4, 3, 2, 1, 0, 1, 2, 3], 'g/cm**3') and strip them when it would be annoying to deal with them. >>> np.log10(np.arange(8)+1) array([0. , 0.30103 , 0.47712125, 0.60205999, 0.69897 , 0.77815125, 0.84509804, 0.90308999]) """ _ufunc_registry = { add: _preserve_units, subtract: _difference_units, multiply: _multiply_units, divide: _divide_units, logaddexp: _return_without_unit, logaddexp2: _return_without_unit, true_divide: _divide_units, floor_divide: _divide_units, negative: _passthrough_unit, power: _power_unit, remainder: _preserve_units, mod: _preserve_units, fmod: _preserve_units, absolute: _passthrough_unit, fabs: _passthrough_unit, rint: _return_without_unit, sign: _return_without_unit, conj: _passthrough_unit, exp: _return_without_unit, exp2: _return_without_unit, log: _return_without_unit, log2: _return_without_unit, log10: _return_without_unit, expm1: _return_without_unit, log1p: _return_without_unit, sqrt: _sqrt_unit, cbrt: _cbrt_unit, square: _square_unit, reciprocal: _reciprocal_unit, sin: _return_without_unit, cos: _return_without_unit, tan: _return_without_unit, sinh: _return_without_unit, cosh: _return_without_unit, tanh: _return_without_unit, arcsin: _return_without_unit, arccos: _return_without_unit, arctan: _return_without_unit, arctan2: _arctan2_unit, arcsinh: _return_without_unit, arccosh: _return_without_unit, arctanh: _return_without_unit, hypot: _preserve_units, deg2rad: _return_without_unit, rad2deg: _return_without_unit, bitwise_and: _bitop_units, bitwise_or: _bitop_units, bitwise_xor: _bitop_units, invert: _invert_units, left_shift: _bitop_units, right_shift: _bitop_units, greater: _comparison_unit, greater_equal: _comparison_unit, less: _comparison_unit, less_equal: _comparison_unit, not_equal: _comparison_unit, equal: _comparison_unit, logical_and: _comparison_unit, logical_or: _comparison_unit, logical_xor: _comparison_unit, logical_not: _return_without_unit, maximum: _preserve_units, minimum: _preserve_units, fmax: _preserve_units, fmin: _preserve_units, isreal: _return_without_unit, iscomplex: _return_without_unit, isfinite: _return_without_unit, isinf: _return_without_unit, isnan: _return_without_unit, signbit: _return_without_unit, copysign: _passthrough_unit, nextafter: _preserve_units, modf: _passthrough_unit, ldexp: _bitop_units, frexp: _return_without_unit, floor: _passthrough_unit, ceil: _passthrough_unit, trunc: _passthrough_unit, spacing: _passthrough_unit, positive: _passthrough_unit, divmod_: _passthrough_unit, isnat: _return_without_unit, heaviside: _preserve_units, matmul: _multiply_units, clip: _passthrough_unit, } if NUMPY_VERSION >= Version("2.0.0.dev0"): _ufunc_registry[vecdot] = _multiply_units __array_priority__ = 2.0 def __new__( cls, input_array, units=None, registry=None, dtype=None, *, bypass_validation=False, name=None, ): input_units = units if bypass_validation is True: if dtype is None: dtype = input_array.dtype obj = input_array.view(type=cls, dtype=dtype) obj.units = input_units if registry is not None: obj.units.registry = registry obj.name = name return obj if isinstance(input_array, unyt_array): # Unpack the input array and coerce to the correct type and dtype ret = np.asarray(input_array, dtype=dtype).view(cls) if input_units is None: if registry is None: ret.units = input_array.units else: units = Unit(str(input_array.units), registry=registry) ret.units = units elif isinstance(input_units, Unit): ret.units = input_units else: ret.units = Unit(input_units, registry=registry) ret.name = name return ret elif isinstance(input_array, np.ndarray): pass elif _iterable(input_array) and input_array: if isinstance(input_array[0], unyt_array): return _coerce_iterable_units(input_array, registry) # Input array is an already formed ndarray instance # We first cast to be our class type obj = np.asarray(input_array, dtype=dtype).view(cls) # Check units type if input_units is None: # Nothing provided. Make dimensionless... units = Unit() elif isinstance(input_units, Unit): if registry and registry is not input_units.registry: units = Unit(str(input_units), registry=registry) else: units = input_units else: # units kwarg set, but it's not a Unit object. # don't handle all the cases here, let the Unit class handle if # it's a str. units = Unit(input_units, registry=registry) # Attach the units and name obj.units = units obj.name = name return obj def __repr__(self): return np.array_repr(self) def __str__(self): return str(self.view(np.ndarray)) + " " + str(self.units) def __format__(self, format_spec): return f"{self.d.__format__(format_spec)} {self.units}" # # Start unit conversion methods # def convert_to_units(self, units, equivalence=None, **kwargs): """ Convert the array to the given units in-place. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. Parameters ---------- units : Unit object or string The units you want to convert to. equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this object, try the ``list_equivalencies`` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import cm, km >>> length = [3000, 2000, 1000]*cm >>> length.convert_to_units('m') >>> print(length) [30. 20. 10.] m """ units = _sanitize_units_convert(units, self.units.registry) if equivalence is None: conv_data = _check_em_conversion( self.units, units, registry=self.units.registry ) if any(conv_data): new_units, (conv_factor, offset) = _em_conversion( self.units, conv_data, units ) else: new_units = units (conv_factor, offset) = self.units.get_conversion_factor( new_units, self.dtype ) if offset and ( str(self.units.expr).startswith("delta_") or str(new_units.expr).startswith("delta_") ): raise InvalidUnitOperation( "Interval temperature units cannot be converted to or " "from offset temperature units without a reference point." ) self.units = new_units values = self.d # if our dtype is an integer do the following somewhat awkward # dance to change the dtype in-place. We can't use astype # directly because that will create a copy and not update self if self.dtype.kind in ("u", "i"): # create a copy of the original data in floating point # form, it's possible this may lose precision for very # large integers dsize = values.dtype.itemsize if dsize == 1: raise ValueError( "Can't convert memory buffer in place. " f"Input dtype ({self.dtype}) has a smaller itemsize than the " "smallest floating point representation possible." ) large = LARGE_INPUT.get(dsize, 0) if large and np.any(np.abs(values) > large): warnings.warn( f"Overflow encountered while converting to units '{new_units}'", RuntimeWarning, stacklevel=2, ) _update_array_values_inplace(values, dtype=f"f{dsize}") _update_array_dtype_inplace(self, dtype=values.dtype) values *= conv_factor if offset: np.subtract(values, offset, values) else: self.convert_to_equivalent(units, equivalence, **kwargs) def convert_to_base(self, unit_system=None, equivalence=None, **kwargs): """ Convert the array in-place to the equivalent base units in the specified unit system. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. Parameters ---------- unit_system : string, optional The unit system to be used in the conversion. If not specified, the configured base units are used (defaults to MKS). equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this object, try the ``list_equivalencies`` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import erg, s >>> E = 2.5*erg/s >>> E.convert_to_base("mks") >>> E unyt_quantity(2.5e-07, 'W') """ self.convert_to_units( self.units.get_base_equivalent(unit_system), equivalence=equivalence, **kwargs, ) def convert_to_cgs(self, equivalence=None, **kwargs): """ Convert the array and in-place to the equivalent cgs units. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. Parameters ---------- equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this object, try the ``list_equivalencies`` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import Newton >>> data = [1., 2., 3.]*Newton >>> data.convert_to_cgs() >>> data unyt_array([100000., 200000., 300000.], 'dyn') """ self.convert_to_units( self.units.get_cgs_equivalent(), equivalence=equivalence, **kwargs ) def convert_to_mks(self, equivalence=None, **kwargs): """ Convert the array and units to the equivalent mks units. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. Parameters ---------- equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this object, try the ``list_equivalencies`` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import dyne, erg >>> data = [1., 2., 3.]*erg >>> data unyt_array([1., 2., 3.], 'erg') >>> data.convert_to_mks() >>> data unyt_array([1.e-07, 2.e-07, 3.e-07], 'J') """ self.convert_to_units(self.units.get_mks_equivalent(), equivalence, **kwargs) def in_units(self, units, equivalence=None, **kwargs): """ Creates a copy of this array with the data converted to the supplied units, and returns it. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. Parameters ---------- units : Unit object or string The units you want to get a new quantity in. equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this object, try the ``list_equivalencies`` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import c, gram >>> m = 10*gram >>> E = m*c**2 >>> print(E.in_units('erg')) 8.987551787368176e+21 erg >>> print(E.in_units('J')) 898755178736817.6 J """ units = _sanitize_units_convert(units, self.units.registry) if equivalence is None: conv_data = _check_em_conversion( self.units, units, registry=self.units.registry ) if any(conv_data): new_units, (conversion_factor, offset) = _em_conversion( self.units, conv_data, units ) offset = 0 else: new_units = units (conversion_factor, offset) = self.units.get_conversion_factor( new_units, self.dtype ) if offset and ( str(self.units.expr).startswith("delta_") or str(new_units.expr).startswith("delta_") ): raise InvalidUnitOperation( "Interval temperature units cannot be converted to or " "from offset temperature units without a reference point." ) dsize = max(2, self.dtype.itemsize) if self.dtype.kind in ("u", "i"): large = LARGE_INPUT.get(dsize, 0) if large and np.any(np.abs(self.d) > large): warnings.warn( f"Overflow encountered while converting to units '{new_units}'", RuntimeWarning, stacklevel=2, ) new_dtypekind = "c" if self.dtype.kind == "c" else "f" new_dtype = np.dtype(new_dtypekind + str(dsize)) ret = np.asarray(self.ndview * conversion_factor, dtype=new_dtype) if offset: np.subtract(ret, offset, ret) try: new_array = type(self)( ret, new_units, bypass_validation=True, name=self.name ) except TypeError: # subclasses might not take name as a kwarg new_array = type(self)(ret, new_units, bypass_validation=True) return new_array else: return self.to_equivalent(units, equivalence, **kwargs) def to(self, units, equivalence=None, **kwargs): """ Creates a copy of this array with the data converted to the supplied units, and returns it. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. .. note:: All additional keyword arguments are passed to the equivalency, which should be used if that particular equivalency requires them. Parameters ---------- units : Unit object or string The units you want to get a new quantity in. equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the :meth:`list_equivalencies` method. Default: None kwargs: optional Any additional keyword arguments are supplied to the equivalence Raises ------ If the provided unit does not have the same dimensions as the array this will raise a UnitConversionError Examples -------- >>> from unyt import c, gram >>> m = 10*gram >>> E = m*c**2 >>> print(E.to('erg')) 8.987551787368176e+21 erg >>> print(E.to('J')) 898755178736817.6 J """ return self.in_units(units, equivalence=equivalence, **kwargs) def to_value(self, units=None, equivalence=None, **kwargs): """ Creates a copy of this array with the data in the supplied units, and returns it without units. Output is therefore a bare NumPy array. Optionally, an equivalence can be specified to convert to an equivalent quantity which is not in the same dimensions. .. note:: All additional keyword arguments are passed to the equivalency, which should be used if that particular equivalency requires them. Parameters ---------- units : Unit object or string, optional The units you want to get the bare quantity in. If not specified, the value will be returned in the current units. equivalence : string, optional The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the :meth:`list_equivalencies` method. Default: None Examples -------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> print(a.to_value('cm')) [300000. 400000. 500000.] """ if units is None: v = self.value else: v = self.in_units(units, equivalence=equivalence, **kwargs).value if isinstance(self, unyt_quantity): return float(v) else: return v def in_base(self, unit_system=None): """ Creates a copy of this array with the data in the specified unit system, and returns it in that system's base units. Parameters ---------- unit_system : string, optional The unit system to be used in the conversion. If not specified, the configured default base units of are used (defaults to MKS). Examples -------- >>> from unyt import erg, s >>> E = 2.5*erg/s >>> print(E.in_base("mks")) 2.5e-07 W """ us = _sanitize_unit_system(unit_system, self) try: conv_data = _check_em_conversion( self.units, unit_system=us, registry=self.units.registry ) except MKSCGSConversionError: raise UnitsNotReducible(self.units, us) if any(conv_data): um = us.units_map u = self.units if u.dimensions in um and u.expr == um[self.units.dimensions]: return self.copy() to_units, (conv, offset) = _em_conversion(u, conv_data, unit_system=us) else: to_units = self.units.get_base_equivalent(unit_system) conv, offset = self.units.get_conversion_factor(to_units, self.dtype) ret = self.v * conv if offset: ret = ret - offset return type(self)(ret, to_units) def in_cgs(self): """ Creates a copy of this array with the data in the equivalent cgs units, and returns it. Returns ------- unyt_array object with data in this array converted to cgs units. Example ------- >>> from unyt import Newton, km >>> print((10*Newton/km).in_cgs()) 10.0 g/s**2 """ return self.in_base("cgs") def in_mks(self): """ Creates a copy of this array with the data in the equivalent mks units, and returns it. Returns ------- unyt_array object with data in this array converted to mks units. Example ------- >>> from unyt import mile >>> print((1.*mile).in_mks()) 1609.344 m """ return self.in_base("mks") def convert_to_equivalent(self, unit, equivalence, **kwargs): """ Convert the array in-place to the specified units, assuming the given equivalency. The dimensions of the specified units and the dimensions of the original array need not match so long as there is an appropriate conversion in the specified equivalency. Parameters ---------- unit : string The unit that you wish to convert to. equivalence : string The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the :meth:`list_equivalencies` method. Examples -------- >>> from unyt import K >>> a = [10, 20, 30]*(1e7*K) >>> a.convert_to_equivalent("keV", "thermal") >>> a unyt_array([ 8.6173324, 17.2346648, 25.8519972], 'keV') """ conv_unit = Unit(unit, registry=self.units.registry) if self.units.same_dimensions_as(conv_unit): self.convert_to_units(conv_unit) return this_equiv = equivalence_registry[equivalence](in_place=True) if self.has_equivalent(equivalence): this_equiv.convert(self, conv_unit.dimensions, **kwargs) self.convert_to_units(conv_unit) # set name to None since the semantic meaning has changed self.name = None else: raise InvalidUnitEquivalence(equivalence, self.units, conv_unit) def to_equivalent(self, unit, equivalence, **kwargs): """ Return a copy of the unyt_array in the units specified units, assuming the given equivalency. The dimensions of the specified units and the dimensions of the original array need not match so long as there is an appropriate conversion in the specified equivalency. Parameters ---------- unit : string The unit that you wish to convert to. equivalence : string The equivalence you wish to use. To see which equivalencies are supported for this unitful quantity, try the :meth:`list_equivalencies` method. Examples -------- >>> from unyt import K >>> a = 1.0e7*K >>> print(a.to_equivalent("keV", "thermal")) 0.8617332401096504 keV """ conv_unit = Unit(unit, registry=self.units.registry) if self.units.same_dimensions_as(conv_unit): return self.in_units(conv_unit) this_equiv = equivalence_registry[equivalence]() if self.has_equivalent(equivalence): new_arr = this_equiv.convert(self, conv_unit.dimensions, **kwargs) return new_arr.in_units(conv_unit) else: raise InvalidUnitEquivalence(equivalence, self.units, unit) def list_equivalencies(self): """ Lists the possible equivalencies associated with this unyt_array or unyt_quantity. Example ------- >>> from unyt import km >>> (1.0*km).list_equivalencies() spectral: length <-> spatial_frequency <-> frequency <-> energy schwarzschild: mass <-> length compton: mass <-> length """ self.units.list_equivalencies() def has_equivalent(self, equivalence): """ Check to see if this unyt_array or unyt_quantity has an equivalent unit in *equiv*. Example ------- >>> from unyt import km, keV >>> (1.0*km).has_equivalent('spectral') True >>> print((1*km).to_equivalent('MHz', equivalence='spectral')) 0.299792458 MHz >>> print((1*keV).to_equivalent('angstrom', equivalence='spectral')) 12.39841931521966 Å """ return self.units.has_equivalent(equivalence) def ndarray_view(self): """ Returns a view into the array as a numpy array Returns ------- View of this array's data. Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> a.ndarray_view() array([3, 4, 5]) This function returns a view that shares the same underlying memory as the original array. >>> b = a.ndarray_view() >>> b.base is a.base True >>> b[2] = 4 >>> b array([3, 4, 4]) >>> a unyt_array([3, 4, 4], 'km') """ return self.view(np.ndarray) def to_ndarray(self): """ Creates a copy of this array with the unit information stripped Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> b = a.to_ndarray() >>> b array([3, 4, 5]) The returned array will contain a copy of the data contained in the original array. >>> a.base is not b.base True """ return np.array(self) def argsort(self, axis=-1, kind="quicksort", order=None): """ Returns the indices that would sort the array. See the documentation of ndarray.argsort for details about the keyword arguments. Example ------- >>> from unyt import km >>> data = [3, 8, 7]*km >>> print(np.argsort(data)) [0 2 1] km >>> print(data.argsort()) [0 2 1] """ return self.view(np.ndarray).argsort(axis, kind, order) @classmethod def from_astropy(cls, arr, unit_registry=None): """ Convert an AstroPy "Quantity" to a unyt_array or unyt_quantity. Parameters ---------- arr : AstroPy Quantity The Quantity to convert from. unit_registry : unyt.UnitRegistry, optional A unyt unit registry to use in the conversion. If one is not supplied, the default one will be used. Example ------- >>> from astropy.units import km >>> unyt_quantity.from_astropy(km) unyt_quantity(1., 'km') >>> a = [1, 2, 3]*km >>> a >>> unyt_array.from_astropy(a) unyt_array([1., 2., 3.], 'km') """ # Converting from AstroPy Quantity try: u = arr.unit _arr = arr except AttributeError: u = arr _arr = 1.0 * u ap_units = [] for base, exponent in zip(u.bases, u.powers): unit_str = base.to_string() # we have to do this because AstroPy is silly and defines # hour as "h" if unit_str == "h": unit_str = "hr" ap_units.append(f"{unit_str}**({Rational(exponent)})") ap_units = "*".join(ap_units) if isinstance(_arr.value, np.ndarray) and _arr.shape != (): return unyt_array(_arr.value, ap_units, registry=unit_registry) else: return unyt_quantity(_arr.value, ap_units, registry=unit_registry) def to_astropy(self, **kwargs): """ Creates a new AstroPy quantity with the same unit information. Example ------- >>> from unyt import g, cm >>> data = [3, 4, 5]*g/cm**3 >>> data.to_astropy() """ if self.units.is_dimensionless: s_units = "" else: s_units = str(self.units) return self.value * _astropy.units.Unit(s_units, **kwargs) @classmethod def from_pint(cls, arr, unit_registry=None): """ Convert a Pint "Quantity" to a unyt_array or unyt_quantity. Parameters ---------- arr : Pint Quantity The Quantity to convert from. unit_registry : unyt.UnitRegistry, optional A unyt unit registry to use in the conversion. If one is not supplied, the default one will be used. Examples -------- >>> from pint import UnitRegistry >>> import numpy as np >>> ureg = UnitRegistry() >>> a = np.arange(4) >>> b = ureg.Quantity(a, "erg/cm**3") >>> b >>> c = unyt_array.from_pint(b) >>> c unyt_array([0, 1, 2, 3], 'erg/cm**3') """ p_units = [] for base, exponent in arr._units.items(): bs = convert_pint_units(base) p_units.append(f"{bs}**({Rational(exponent)})") p_units = "*".join(p_units) if isinstance(arr.magnitude, np.ndarray): return unyt_array(arr.magnitude, p_units, registry=unit_registry) else: return unyt_quantity(arr.magnitude, p_units, registry=unit_registry) def to_pint(self, unit_registry=None): """ Convert a unyt_array or unyt_quantity to a Pint Quantity. Parameters ---------- arr : unyt_array or unyt_quantity The unitful quantity to convert from. unit_registry : Pint UnitRegistry, optional The Pint UnitRegistry to use in the conversion. If one is not supplied, the default one will be used. NOTE: This is not the same as a unyt.UnitRegistry object. Examples -------- >>> from unyt import cm, s >>> a = 4*cm**2/s >>> print(a) 4 cm**2/s >>> a.to_pint() """ if unit_registry is None: unit_registry = _pint.UnitRegistry() powers_dict = self.units.expr.as_powers_dict() units = [] for unit, pow in powers_dict.items(): # we have to do this because Pint doesn't recognize # "yr" as "year" if str(unit).endswith("yr") and len(str(unit)) in [2, 3]: unit = str(unit).replace("yr", "year") units.append(f"{unit}**({Rational(pow)})") units = "*".join(units) return unit_registry.Quantity(self.value, units) @staticmethod def from_string(s, unit_registry=None): """ Parse a string to a unyt_quantity object. Parameters ---------- s: str A string representing a single quantity. unit_registry: unyt.UnitRegistry, optional A unyt unit registry to use in the conversion. If one is not supplied, the default one will be used. Examples -------- >>> from unyt import unyt_quantity >>> unyt_quantity.from_string("1cm") unyt_quantity(1, 'cm') >>> unyt_quantity.from_string("+1e3 Mearth") unyt_quantity(1000., 'Mearth') >>> unyt_quantity.from_string("-10. kg") unyt_quantity(-10., 'kg') >>> unyt_quantity.from_string(".66\tum") unyt_quantity(0.66, 'μm') >>> unyt_quantity.from_string("42") unyt_quantity(42, '(dimensionless)') >>> unyt_quantity.from_string("1.0 g/cm**3") unyt_quantity(1., 'g/cm**3') """ v = s.strip() if re.fullmatch(_NUMB_REGEXP, v): num = re.match(_NUMB_REGEXP, v).group() unit = Unit() elif re.fullmatch(_UNIT_REGEXP, v): num = 1 unit = Unit(re.match(_UNIT_REGEXP, v).group()) elif not re.fullmatch(_QUAN_REGEXP, v): raise ValueError(f"Received invalid quantity expression '{s}'.") else: res = re.search(_NUMB_REGEXP, v) num = res.group() res = re.search(_UNIT_REGEXP, v[res.span()[1] :]) unit = res.group().strip() if unit.startswith(("/", "*")): unit = f"1{unit}" try: num = int(num) except ValueError: num = float(num) return num * Unit(unit, registry=unit_registry) def to_string(self): # this is implemented purely for symmetry's sake return str(self) # # End unit conversion methods # def write_hdf5(self, filename, dataset_name=None, info=None, group_name=None): r"""Writes a unyt_array to hdf5 file. Parameters ---------- filename: string The filename to create and write a dataset to dataset_name: string The name of the dataset to create in the file. info: dictionary A dictionary of supplementary info to write to append as attributes to the dataset. group_name: string An optional group to write the arrays to. If not specified, the arrays are datasets at the top level by default. Examples -------- >>> from unyt import cm >>> a = [1,2,3]*cm >>> myinfo = {'field':'dinosaurs', 'type':'field_data'} >>> a.write_hdf5('test_array_data.h5', dataset_name='dinosaurs', ... info=myinfo) # doctest: +SKIP """ import pickle from unyt._on_demand_imports import _h5py as h5py if info is None: info = {} info["units"] = str(self.units) lut = {} for k, v in self.units.registry.lut.items(): if k not in default_unit_registry.lut: lut[k] = v info["unit_registry"] = np.void(pickle.dumps(lut)) if dataset_name is None: dataset_name = "array_data" f = h5py.File(filename, "a") if group_name is not None: if group_name in f: g = f[group_name] else: g = f.create_group(group_name) else: g = f if dataset_name in g.keys(): d = g[dataset_name] # Overwrite without deleting if we can get away with it. if d.shape == self.shape and d.dtype == self.dtype: d[...] = self for k in d.attrs.keys(): del d.attrs[k] else: del f[dataset_name] d = g.create_dataset(dataset_name, data=self) else: d = g.create_dataset(dataset_name, data=self) for k, v in info.items(): d.attrs[k] = v f.close() @classmethod def from_hdf5(cls, filename, dataset_name=None, group_name=None): r"""Attempts read in and convert a dataset in an hdf5 file into a unyt_array. Parameters ---------- filename: string The filename to of the hdf5 file. dataset_name: string The name of the dataset to read from. If the dataset has a units attribute, attempt to infer units as well. group_name: string An optional group to read the arrays from. If not specified, the arrays are datasets at the top level by default. """ import pickle from unyt._on_demand_imports import _h5py as h5py if dataset_name is None: dataset_name = "array_data" f = h5py.File(filename, "r") if group_name is not None: g = f[group_name] else: g = f dataset = g[dataset_name] data = dataset[...] units = dataset.attrs.get("units", "") unit_lut = default_unit_symbol_lut.copy() unit_lut_load = pickle.loads(dataset.attrs["unit_registry"].tobytes()) unit_lut.update(unit_lut_load) f.close() registry = UnitRegistry(lut=unit_lut, add_default_symbols=False) return cls(data, units, registry=registry) # # Start convenience methods # @property def value(self): """ Creates a copy of this array with the unit information stripped Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> b = a.value >>> b array([3, 4, 5]) The returned array will contain a copy of the data contained in the original array. >>> a.base is not b.base True """ return np.array(self) @property def v(self): """ Creates a copy of this array with the unit information stripped Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> b = a.v >>> b array([3, 4, 5]) The returned array will contain a copy of the data contained in the original array. >>> a.base is not b.base True """ return np.array(self) @property def ndview(self): """ Returns a view into the array as a numpy array Returns ------- View of this array's data. Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> a.ndview array([3, 4, 5]) This function returns a view that shares the same underlying memory as the original array. >>> b = a.ndview >>> b.base is a.base True >>> b[2] = 4 >>> b array([3, 4, 4]) >>> a unyt_array([3, 4, 4], 'km') """ return self.view(np.ndarray) @property def d(self): """ Returns a view into the array as a numpy array Returns ------- View of this array's data. Example ------- >>> from unyt import km >>> a = [3, 4, 5]*km >>> a unyt_array([3, 4, 5], 'km') >>> a.d array([3, 4, 5]) This function returns a view that shares the same underlying memory as the original array. >>> b = a.d >>> b.base is a.base True >>> b[2] = 4 >>> b array([3, 4, 4]) >>> a unyt_array([3, 4, 4], 'km') """ return self.view(np.ndarray) @property def unit_quantity(self): """ Return a quantity with a value of 1 and the same units as this array Example ------- >>> from unyt import km >>> a = [4, 5, 6]*km >>> a.unit_quantity unyt_quantity(1, 'km') >>> print(a + 7*a.unit_quantity) [11 12 13] km """ return unyt_quantity(1, self.units) @property def uq(self): """ Return a quantity with a value of 1 and the same units as this array Example ------- >>> from unyt import km >>> a = [4, 5, 6]*km >>> a.uq unyt_quantity(1, 'km') >>> print(a + 7*a.uq) [11 12 13] km """ return unyt_quantity(1, self.units) @property def unit_array(self): """ Return an array filled with ones with the same units as this array Example ------- >>> from unyt import km >>> a = [4, 5, 6]*km >>> a.unit_array unyt_array([1, 1, 1], 'km') >>> print(a + 7*a.unit_array) [11 12 13] km """ return np.ones_like(self) @property def ua(self): """ Return an array filled with ones with the same units as this array Example ------- >>> from unyt import km >>> a = [4, 5, 6]*km >>> a.unit_array unyt_array([1, 1, 1], 'km') >>> print(a + 7*a.unit_array) [11 12 13] km """ return np.ones_like(self) def __getitem__(self, item): ret = super().__getitem__(item) if getattr(ret, "shape", None) == (): ret = unyt_quantity(ret, bypass_validation=True, name=self.name) ret.units = self.units return ret def __setitem__(self, item, value): if hasattr(value, "units"): if value.units != self.units and value.units != NULL_UNIT: value = value.to(self.units) super().__setitem__(item, value) def __pow__(self, p, mod=None, /): """ Power function """ # see https://github.com/yt-project/unyt/issues/203 if np.isscalar(p) and p == 0.0: ret = self.unit_array ret.units = Unit("dimensionless") return ret else: return super().__pow__(p, mod) def __eq__(self, other): try: return super().__eq__(other) except (IterableUnitCoercionError, UnitOperationError, TypeError): return np.zeros(self.shape, dtype="bool") def __ne__(self, other): try: return super().__ne__(other) except (IterableUnitCoercionError, UnitOperationError, TypeError): return np.ones(self.shape, dtype="bool") # # Start operation methods # def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): if type(self) in (unyt_array, unyt_quantity): for x in inputs: if isinstance(x, unyt_array) and type(x) not in ( unyt_array, unyt_quantity, ): # This object is a unyt array/quantity (not a subclass) but one of the # inputs is a subclass. We return NotImplemented to give subclasses # a chance to define the result. return NotImplemented if ufunc not in self._ufunc_registry: return NotImplemented func = getattr(ufunc, method) if "out" not in kwargs: if ufunc in multiple_output_operators: out = (None,) * multiple_output_operators[ufunc] out_func = out else: out = None out_func = None else: # we need to get both the actual "out" object and a view onto it # in case we need to do in-place operations out = kwargs.pop("out") if ufunc in multiple_output_operators: out_func = [] for arr in out: out_func.append(arr.view(np.ndarray)) out_func = tuple(out_func) else: out = out[0] if out.dtype.kind in ("u", "i"): _update_array_values_inplace(out, dtype=f"f{out.dtype.itemsize}") out_func = out.view(np.ndarray) if len(inputs) == 1: # Unary ufuncs inp = inputs[0] u = getattr(inp, "units", None) if u.dimensions is angle and ufunc in trigonometric_operators: # ensure np.sin(90*degrees) works as expected inp = inp.in_units("radian").v # evaluate the ufunc out_arr = func(np.asarray(inp), out=out_func, **kwargs) if ufunc in (multiply, divide) and method == "reduce": mul, unit = _apply_power_mapping(ufunc, u, inp.size, inp.shape, kwargs) else: # get unit of result mul, unit = self._ufunc_registry[ufunc](u) # use type(self) here so we can support user-defined # subclasses of unyt_array ret_class = type(self) elif len(inputs) == 2: # binary ufuncs i0 = inputs[0] i1 = inputs[1] if "dask" in sys.modules and isinstance(i1, _dask.array.core.Array): # need to short circuit all this to handle binary operations # like unyt_quantity(2,'m') / unyt_dask_array_instance # only need to check the second argument as if the first arg # is a unyt_dask_array, it won't end up here. return i1.__array_ufunc__(ufunc, method, *inputs, **kwargs) # coerce inputs to be ndarrays if they aren't already try: inp0 = _coerce_iterable_units(i0) inp1 = _coerce_iterable_units(i1) except IterableUnitCoercionError: return NotImplemented u0 = getattr(i0, "units", None) or getattr(inp0, "units", None) u1 = getattr(i1, "units", None) or getattr(inp1, "units", None) ret_class = _get_binary_op_return_class(type(i0), type(i1)) if ret_class is NotImplemented: return NotImplemented if u0 is None: u0 = Unit(registry=getattr(u1, "registry", None)) if u1 is None and ufunc is not power: u1 = Unit(registry=getattr(u0, "registry", None)) elif ufunc is power: u1 = inp1 if inp0.shape == () or inp1.shape == (): if isinstance(u1, unyt_array) and not u1.units.is_dimensionless: raise UnitOperationError(ufunc, u0, u1.units) if u1.shape == (): u1 = float(u1) else: u1 = 1.0 elif inp0.shape == inp1.shape: if isinstance(u1, unyt_array) and not u1.units.is_dimensionless: raise UnitOperationError(ufunc, u0, getattr(u1, "units", None)) if ( (isinstance(u0, Unit) and not u0.is_dimensionless) or isinstance(u0, unyt_array) and not u0.units.is_dimensionless ): # u0 has units if np.ptp(u1) != 0: raise UnitOperationError( ufunc, u0, getattr(u1, "units", None) ) first_element_slice = (0,) * u1.ndim u1 = float(u1[first_element_slice]) else: raise UnitOperationError(ufunc, u0, u1) unit_operator = self._ufunc_registry[ufunc] if ( unit_operator is _preserve_units and u0.dimensions is temperature and u1 is not None and u1.base_offset != 0.0 and u0.base_offset == 0.0 and str(u0.expr) in ["K", "R"] ): raise UnitOperationError(ufunc, u0, u1) if unit_operator in ( _preserve_units, _comparison_unit, _arctan2_unit, _difference_units, ): # check "is" equality first for speed if u0 is not u1 and u0 != u1: # we allow adding, multiplying, comparisons with # zero-filled arrays, lists, etc or scalar zero. We # do not allow zero-filled unyt_array instances for # performance reasons. If we did allow it, every # binary operation would need to scan over all the # elements of both arrays to check for arrays filled # with zeros if not isinstance(i0, unyt_array) or not isinstance(i1, unyt_array): any_nonzero = [np.count_nonzero(i0), np.count_nonzero(i1)] if any_nonzero[0] == 0: u0 = u1 elif any_nonzero[1] == 0: u1 = u0 if not u0.same_dimensions_as(u1): if unit_operator is _comparison_unit: # we allow comparisons between data with # units and dimensionless data if u0.is_dimensionless: u0 = u1 elif u1.is_dimensionless: u1 = u0 else: # comparison with different units, so need to check if # this is == and != which we allow and handle in a # special way using an early return from __array_ufunc__ if ufunc in (equal, not_equal): if ufunc is equal: func = np.zeros_like else: func = np.ones_like ret = func(np.asarray(inp1), dtype=bool) if out is not None: out[:] = ret[:] if isinstance(out, unyt_array): out.units = Unit( "", registry=self.units.registry ) if ret.shape == (): ret = bool(ret) return ret else: raise UnitOperationError(ufunc, u0, u1) else: raise UnitOperationError(ufunc, u0, u1) conv, offset = u1.get_conversion_factor(u0, inp1.dtype) new_dtype = np.dtype("f" + str(inp1.dtype.itemsize)) conv = new_dtype.type(conv) if ( offset is not None and u1.base_offset != 0.0 and not repr(u0).startswith("delta_") ): raise InvalidUnitOperation( "Quantities with units of Fahrenheit or Celsius " "cannot be multiplied, divided, subtracted or " "added with data that has different units." ) inp1 = np.asarray(inp1, dtype=new_dtype) * conv # get the unit of the result mul, unit = unit_operator(u0, u1) # A scalar quantity acts as a unit scale factor here, so it should # not change the precision chosen by the non-quantity operand. if unit_operator is _multiply_units: if ( isinstance(i0, unyt_quantity) and not isinstance(i1, unyt_quantity) and inp1.dtype.kind in ("f", "c") ): inp0 = inp0.astype(inp1.dtype) elif ( isinstance(i1, unyt_quantity) and not isinstance(i0, unyt_quantity) and inp0.dtype.kind in ("f", "c") ): inp1 = inp1.astype(inp0.dtype) # actually evaluate the ufunc out_arr = func( inp0.view(np.ndarray), inp1.view(np.ndarray), out=out_func, **kwargs ) if unit_operator in (_multiply_units, _divide_units): if unit.is_dimensionless and unit.base_value != 1.0: if not u0.is_dimensionless: if u0.dimensions == u1.dimensions: out_arr = np.multiply( out_arr.view(np.ndarray), unit.base_value, out=out_func ) unit = Unit(registry=unit.registry) if ( u0.base_offset and u0.dimensions is temperature or u1.base_offset and u1.dimensions is temperature ): raise InvalidUnitOperation( "Quantities with units of Fahrenheit or Celsius " "cannot be multiplied, divided, subtracted or added." ) else: if ufunc is clip: inp = [] for i in inputs: if isinstance(i, unyt_array): inp.append(i.to(inputs[0].units).view(np.ndarray)) else: inp.append(i) if out is not None: _out = out.view(np.ndarray) else: _out = None out_arr = ufunc(*inp, out=_out) unit = inputs[0].units ret_class = type(inputs[0]) mul = 1 else: raise RuntimeError( f"Support for the {ufunc} ufunc with {len(inputs)} " "inputs has not been added to unyt_array." ) if unit is None: out_arr = np.array(out_arr, copy=_COPY_IF_NEEDED) elif ufunc in (modf, divmod_): out_arr = tuple(ret_class(o, unit) for o in out_arr) elif out_arr.shape == (): out_arr = unyt_quantity(np.asarray(out_arr), unit) elif out_arr.size == 1: out_arr = unyt_array(np.asarray(out_arr), unit) else: if issubclass(ret_class, unyt_quantity): # This happens if you do ndarray * unyt_quantity. # Explicitly casting to unyt_array avoids creating a # unyt_quantity with size > 1 out_arr = unyt_array(out_arr, unit) else: out_arr = ret_class(out_arr, unit, bypass_validation=True) if out is not None: if ( unit is not None and hasattr(out, "units") and (mul, unit) == out.units.as_coeff_unit() ): return out if mul != 1: multiply(out, mul, out=out) if np.shares_memory(out_arr, out): mul = 1 if isinstance(out, unyt_array): try: out.units = out_arr.units except AttributeError: # out_arr is an ndarray out.units = Unit("", registry=self.units.registry) elif isinstance(out, tuple): for o, oa in zip(out, out_arr): if o is None: continue o.units = oa.units if mul == 1: return out_arr return mul * out_arr def __array_function__(self, func, types, args, kwargs): # Follow NEP 18 guidelines # https://numpy.org/neps/nep-0018-array-function-protocol.html from unyt._array_functions import _HANDLED_FUNCTIONS, _UNSUPPORTED_FUNCTIONS if func in _UNSUPPORTED_FUNCTIONS: # following NEP 18, return NotImplemented as a sentinel value # which will lead to raising a TypeError, while # leaving other arguments a chance to take the lead return NotImplemented if func not in _HANDLED_FUNCTIONS: # default to numpy's private implementation return func._implementation(*args, **kwargs) # Note: this allows subclasses that don't override # __array_function__ to handle unyt_array objects if not all(issubclass(t, unyt_array) or t is np.ndarray for t in types): return NotImplemented return _HANDLED_FUNCTIONS[func](*args, **kwargs) def copy(self, order="C"): """ Return a copy of the array. Parameters ---------- order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the copy. 'C' means C-order, 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. (Note that this function and :func:`numpy.copy` are very similar, but have different default values for their order= arguments.) See also -------- numpy.copy numpy.copyto Examples -------- >>> from unyt import km >>> x = [[1,2,3],[4,5,6]] * km >>> y = x.copy() >>> x.fill(0) >>> print(x) [[0 0 0] [0 0 0]] km >>> print(y) [[1 2 3] [4 5 6]] km """ name = getattr(self, "name", None) try: return type(self)(np.copy(np.asarray(self)), self.units, name=name) except TypeError: # subclasses might not take name as a kwarg return type(self)(np.copy(np.asarray(self)), self.units) def __array_finalize__(self, obj): self.units = getattr(obj, "units", NULL_UNIT) self.name = getattr(obj, "name", None) def __pos__(self): """Posify the data.""" # this needs to be defined for all numpy versions, see # numpy issue #9081 return type(self)(super().__pos__(), self.units) def dot(self, b, out=None): """dot product of two arrays. Refer to `numpy.dot` for full documentation. See Also -------- numpy.dot : equivalent function Examples -------- >>> from unyt import km, s >>> a = np.eye(2)*km >>> b = (np.ones((2, 2)) * 2)*s >>> print(a.dot(b)) [[2. 2.] [2. 2.]] km*s This array method can be conveniently chained: >>> print(a.dot(b).dot(b)) [[8. 8.] [8. 8.]] km*s**2 """ res_units = self.units * getattr(b, "units", NULL_UNIT) ret = self.view(np.ndarray).dot(np.asarray(b), out=out) * res_units if out is not None: out.units = res_units return ret def take(self, indices, axis=None, out=None, mode="raise"): """method Return an array formed from the elements of `a` at the given indices. Refer to :func:`numpy.take` for full documentation. See also -------- numpy.take : equivalent function """ from ._array_functions import take return take(self, indices, axis=axis, out=out, mode=mode) def __reduce__(self): """Pickle reduction method See the documentation for the standard library pickle module: http://docs.python.org/2/library/pickle.html Unit metadata is encoded in the zeroth element of third element of the returned tuple, itself a tuple used to restore the state of the ndarray. This is always defined for numpy arrays. """ np_ret = super().__reduce__() obj_state = np_ret[2] unit_state = (((str(self.units), self.units.registry.lut),) + obj_state[:],) new_ret = np_ret[:2] + unit_state + np_ret[3:] return new_ret def __setstate__(self, state): """Pickle setstate method This is called inside pickle.read() and restores the unit data from the metadata extracted in __reduce__ and then serialized by pickle. """ super().__setstate__(state[1:]) unit, lut = state[0] lut = _correct_old_unit_registry(lut) registry = UnitRegistry(lut=lut, add_default_symbols=False) self.units = Unit(unit, registry=registry) def __deepcopy__(self, memodict=None): """copy.deepcopy implementation This is necessary for stdlib deepcopy of arrays and quantities. """ ret = super().__deepcopy__(memodict) try: return type(self)( ret, copy.deepcopy(self.units), name=self.name, bypass_validation=True ) except TypeError: # subclasses might not take name as a kwarg return type(self)(ret, copy.deepcopy(self.units), bypass_validation=True) def squeeze(self, axis=None): """method Remove axes of length one from `a`. Refer to :func:`numpy.squeeze` for full documentation. See also -------- numpy.squeeze : equivalent function """ ret = super().squeeze(axis=axis) if ret.ndim == 0: return ret.view(type=unyt_quantity) else: return ret class unyt_quantity(unyt_array): """ A scalar associated with a unit. Parameters ---------- input_scalar : an integer or floating point scalar The scalar to attach units to units : String unit specification, unit symbol object, or astropy units The units of the quantity. Powers must be specified using python syntax (cm**3, not cm^3). registry : A UnitRegistry object The registry to create units from. If units is already associated with a unit registry and this is specified, this will be used instead of the registry associated with the unit object. dtype : data-type The dtype of the array data. bypass_validation : boolean If True, all input validation is skipped. Using this option may produce corrupted, invalid units or array data, but can lead to significant speedups in the input validation logic adds significant overhead. If set, units *must* be a valid unit object. Defaults to False. name : string The name of the scalar. Defaults to None. This attribute does not propagate through mathematical operations, but is preserved under indexing and unit conversions. Examples -------- >>> a = unyt_quantity(3., 'cm') >>> b = unyt_quantity(2., 'm') >>> print(a + b) 203.0 cm >>> print(b + a) 2.03 m NumPy ufuncs will pass through units where appropriate. >>> import numpy as np >>> from unyt import g, cm >>> a = 12*g/cm**3 >>> print(np.abs(a)) 12 g/cm**3 and strip them when it would be annoying to deal with them. >>> print(np.log10(a)) 1.0791812460476249 """ def __new__( cls, input_scalar, units=None, registry=None, dtype=None, *, bypass_validation=False, name=None, ): input_units = units if not ( bypass_validation or isinstance(input_scalar, (numeric_type, np.number, np.ndarray)) ): raise RuntimeError("unyt_quantity values must be numeric") if input_units is None: units = getattr(input_scalar, "units", None) else: units = input_units ret = unyt_array.__new__( cls, np.asarray(input_scalar), units, registry, dtype=dtype, bypass_validation=bypass_validation, name=name, ) if ret.size > 1: raise RuntimeError("unyt_quantity instances must be scalars") return ret def __round__(self): return type(self)(round(float(self)), self.units) def reshape(self, *shape, order="C"): # this is necessary to support some numpy operations # natively, like numpy.meshgrid, which internally performs # reshaping, e.g., arr.reshape(1, -1), which doesn't affect the size, # but does change the object's internal representation to a >0D array # see https://github.com/yt-project/unyt/issues/224 if len(shape) == 1: shape = shape[0] if shape == () or shape is None: return super().reshape(shape, order=order) else: return unyt_array(self).reshape(shape, order=order) def _validate_numpy_wrapper_units(v, arrs): if not any(isinstance(a, unyt_array) for a in arrs): return v if not all(isinstance(a, unyt_array) for a in arrs): raise RuntimeError("Not all of your arrays are unyt_arrays.") a1 = arrs[0] if not all(a.units == a1.units for a in arrs[1:]): raise RuntimeError("Your arrays must have identical units.") v.units = a1.units return v def uconcatenate(arrs, axis=0): """Concatenate a sequence of arrays. This wrapper around numpy.concatenate preserves units. All input arrays must have the same units. See the documentation of numpy.concatenate for full details. Examples -------- >>> from unyt import cm >>> A = [1, 2, 3]*cm >>> B = [2, 3, 4]*cm >>> uconcatenate((A, B)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... unyt_array([1, 2, 3, 2, 3, 4], 'cm') """ warn_deprecated( "unyt.uconcatenate", replacement="use numpy.concatenate", since_version="3.0" ) v = np.concatenate._implementation(arrs, axis=axis) v = _validate_numpy_wrapper_units(v, arrs) return v def ucross(arr1, arr2, registry=None, axisa=-1, axisb=-1, axisc=-1, axis=None): """Applies the cross product to two YT arrays. This wrapper around numpy.cross preserves units. See the documentation of numpy.cross for full details. """ warn_deprecated( "unyt.ucross", replacement=( "use numpy.cross (note that the *registry* argument will not be ported)" ), since_version="3.0", ) v = np.cross._implementation( arr1, arr2, axisa=axisa, axisb=axisb, axisc=axisc, axis=axis ) units = arr1.units * arr2.units arr = unyt_array(v, units, registry=registry) return arr def uintersect1d(arr1, arr2, assume_unique=False): """Find the sorted unique elements of the two input arrays. A wrapper around numpy.intersect1d that preserves units. All input arrays must have the same units. See the documentation of numpy.intersect1d for full details. Examples -------- >>> from unyt import cm >>> A = [1, 2, 3]*cm >>> B = [2, 3, 4]*cm >>> uintersect1d(A, B) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... unyt_array([2, 3], 'cm') """ warn_deprecated( "unyt.uintersect1d", replacement="use numpy.intersect1d", since_version="3.0" ) return np.intersect1d(arr1, arr2, assume_unique=assume_unique) def uunion1d(arr1, arr2): """Find the union of two arrays. A wrapper around numpy.intersect1d that preserves units. All input arrays must have the same units. See the documentation of numpy.intersect1d for full details. Examples -------- >>> from unyt import cm >>> A = [1, 2, 3]*cm >>> B = [2, 3, 4]*cm >>> uunion1d(A, B) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... unyt_array([1, 2, 3, 4], 'cm') """ warn_deprecated( "unyt.uunion1d", replacement="use numpy.union1d", since_version="3.0" ) return np.union1d(arr1, arr2) def unorm(data, ord=None, axis=None, keepdims=False): """Matrix or vector norm that preserves units This is a wrapper around np.linalg.norm that preserves units. See the documentation for that function for descriptions of the keyword arguments. Examples -------- >>> from unyt import km >>> data = [1, 2, 3]*km >>> print(unorm(data)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... 3.7416573867739413 km """ warn_deprecated( "unyt.unorm", replacement="use numpy.linalg.norm", since_version="3.0" ) norm = np.linalg.norm._implementation(data, ord=ord, axis=axis, keepdims=keepdims) if norm.shape == (): return unyt_quantity(norm, data.units) return unyt_array(norm, data.units) def udot(op1, op2): """Matrix or vector dot product that preserves units This is a wrapper around np.dot that preserves units. Examples -------- >>> from unyt import km, s >>> a = np.eye(2)*km >>> b = (np.ones((2, 2)) * 2)*s >>> print(udot(a, b)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... [[2. 2.] [2. 2.]] km*s """ warn_deprecated("unyt.udot", replacement="use numpy.dot", since_version="3.0") dot = np.dot._implementation(op1.d, op2.d) units = op1.units * op2.units if dot.shape == (): return unyt_quantity(dot, units) return unyt_array(dot, units) def uvstack(arrs): """Stack arrays in sequence vertically (row wise) while preserving units This is a wrapper around np.vstack that preserves units. Examples -------- >>> from unyt import km >>> a = [1, 2, 3]*km >>> b = [2, 3, 4]*km >>> print(uvstack([a, b])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... [[1 2 3] [2 3 4]] km """ warn_deprecated("unyt.uvstack", replacement="use numpy.vstack", since_version="3.0") v = np.vstack._implementation(arrs) v = _validate_numpy_wrapper_units(v, arrs) return v def uhstack(arrs): """Stack arrays in sequence horizontally while preserving units This is a wrapper around np.hstack that preserves units. Examples -------- >>> from unyt import km >>> a = [1, 2, 3]*km >>> b = [2, 3, 4]*km >>> print(uhstack([a, b])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... [1 2 3 2 3 4] km >>> a = [[1],[2],[3]]*km >>> b = [[2],[3],[4]]*km >>> print(uhstack([a, b])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... [[1 2] [2 3] [3 4]] km """ warn_deprecated("unyt.uhstack", replacement="use numpy.hstack", since_version="3.0") v = np.hstack._implementation(arrs) v = _validate_numpy_wrapper_units(v, arrs) return v def ustack(arrs, axis=0): """Join a sequence of arrays along a new axis while preserving units The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if ``axis=0`` it will be the first dimension and if ``axis=-1`` it will be the last dimension. This is a wrapper around np.stack that preserves units. See the documentation for np.stack for full details. Examples -------- >>> from unyt import km >>> a = [1, 2, 3]*km >>> b = [2, 3, 4]*km >>> print(ustack([a, b])) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): DeprecationWarning: ... [[1 2 3] [2 3 4]] km """ warn_deprecated("unyt.ustack", replacement="use numpy.stack", since_version="3.0") v = np.stack._implementation(arrs, axis=axis) v = _validate_numpy_wrapper_units(v, arrs) return v def _get_binary_op_return_class(cls1, cls2): if cls1 is cls2: return cls1 if cls1 in (Unit, np.ndarray, np.matrix, np.ma.masked_array) or issubclass( cls1, (numeric_type, np.number, list, tuple) ): return cls2 if cls2 in (Unit, np.ndarray, np.matrix, np.ma.masked_array) or issubclass( cls2, (numeric_type, np.number, list, tuple) ): return cls1 if issubclass(cls1, unyt_quantity): return cls2 if issubclass(cls2, unyt_quantity): return cls1 if issubclass(cls1, cls2): return cls1 if issubclass(cls2, cls1): return cls2 else: return NotImplemented def loadtxt(fname, dtype="float", delimiter="\t", usecols=None, comments="#"): r""" Load unyt_arrays with unit information from a text file. Each row in the text file must have the same number of values. Parameters ---------- fname : str Filename to read. dtype : data-type, optional Data-type of the resulting array; default: float. delimiter : str, optional The string used to separate values. By default, this is any whitespace. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. comments : str, optional The character used to indicate the start of a comment; default: '#'. Examples -------- >>> temp, velx = loadtxt( ... "sphere.dat", usecols=(1,2), delimiter="\t") # doctest: +SKIP """ f = open(fname) next_one = False units = [] num_cols = -1 for line in f.readlines(): words = line.strip().split() if len(words) == 0: continue if line[0] == comments: if next_one: units = words[1:] if len(words) == 2 and words[1] == "Units": next_one = True else: # Here we catch the first line of numbers col_words = line.strip().split(delimiter) for word in col_words: # test that word can be converted to a number complex(word) num_cols = len(col_words) break f.close() if len(units) != num_cols: units = ["dimensionless"] * num_cols arrays = np.loadtxt( fname, dtype=dtype, comments=comments, delimiter=delimiter, converters=None, unpack=True, usecols=usecols, ndmin=0, ) if len(arrays.shape) < 2: arrays = [arrays] if usecols is not None: units = [units[col] for col in usecols] ret = tuple(unyt_array(arr, unit) for arr, unit in zip(arrays, units)) if len(ret) == 1: return ret[0] return ret def savetxt( fname, arrays, fmt="%.18e", delimiter="\t", header="", footer="", comments="#" ): r""" Write unyt_arrays with unit information to a text file. Parameters ---------- fname : str The file to write the unyt_arrays to. arrays : list of unyt_arrays or single unyt_array The array(s) to write to the file. fmt : str or sequence of strs, optional A single format (%10.5f), or a sequence of formats. delimiter : str, optional String or character separating columns. header : str, optional String that will be written at the beginning of the file, before the unit header. footer : str, optional String that will be written at the end of the file. comments : str, optional String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# ', as expected by e.g. ``unyt.loadtxt``. Examples -------- >>> import unyt as u >>> a = [1, 2, 3]*u.cm >>> b = [8, 10, 12]*u.cm/u.s >>> c = [2, 85, 9]*u.g >>> savetxt("sphere.dat", [a,b,c], header='My sphere stuff', ... delimiter="\t") # doctest: +SKIP """ if not isinstance(arrays, list): arrays = [arrays] units = [] for array in arrays: if hasattr(array, "units"): units.append(str(array.units)) else: units.append("dimensionless") if header != "" and not header.endswith("\n"): header += "\n" header += " Units\n " + "\t".join(units) np.savetxt( fname, np.transpose(arrays), header=header, fmt=fmt, delimiter=delimiter, footer=footer, newline="\n", comments=comments, ) def allclose_units(actual, desired, rtol=1e-7, atol=0, **kwargs): """Returns False if two objects are not equal up to desired tolerance This is a wrapper for :func:`numpy.allclose` that also verifies unit consistency Parameters ---------- actual : array-like Array obtained (possibly with attached units) desired : array-like Array to compare with (possibly with attached units) rtol : float, optional Relative tolerance, defaults to 1e-7 atol : float or quantity, optional Absolute tolerance. If units are attached, they must be consistent with the units of ``actual`` and ``desired``. If no units are attached, assumes the same units as ``desired``. Defaults to zero. Raises ------ RuntimeError If units of ``rtol`` are not dimensionless See Also -------- :func:`unyt.testing.assert_allclose_units` Notes ----- Also accepts additional keyword arguments accepted by :func:`numpy.allclose`, see the documentation of that function for details. Examples -------- >>> import unyt as u >>> actual = [1e-5, 1e-3, 1e-1]*u.m >>> desired = actual.to("cm") >>> allclose_units(actual, desired) True """ # Create a copy to ensure this function does not alter input arrays act = unyt_array(actual) des = unyt_array(desired) try: des = des.in_units(act.units) except (UnitOperationError, UnitConversionError): return False rt = unyt_array(rtol) if not rt.units.is_dimensionless: raise RuntimeError(f"Units of rtol ({rt.units}) are not dimensionless") if not isinstance(atol, unyt_array): at = unyt_quantity(atol, des.units) else: at = atol try: at = at.in_units(act.units) except (UnitOperationError, UnitConversionError): return False # units have been validated, so we strip units before calling numpy # to avoid spurious errors act = act.value des = des.value rt = rt.value at = at.value return np.allclose(act, des, rt, at, **kwargs) unyt-3.2.0/unyt/dask_array.py000066400000000000000000000537051526544257300162330ustar00rootroot00000000000000""" a dask array class (unyt_dask_array) and helper functions for unyt. """ import sys from functools import wraps import numpy as np import unyt.array as ua if "pytest" in sys.modules: # should only happen if pytest is installed *and* already imported, # so we can skip collecting doctests from this module when dask isn't installed # while avoiding making pytest itself a hard dependency to this module. # This check is constructed to work with direct invocation (pytest unyt) # as well as through python -m pytest import pytest pytest.importorskip("dask") del pytest from dask.array.core import Array as DaskArray, finalize as dask_finalize # noqa: E402 # the following attributes hang off of dask.array.core.Array and do not modify units _use_unary_decorator = { "min", "max", "sum", "mean", "std", "cumsum", "squeeze", "rechunk", "clip", "view", "swapaxes", "round", "copy", "repeat", "astype", "reshape", "topk", } def _unary_dask_decorator(dask_func, current_unyt_dask): """ a decorator for the simpler dask functions that can just copy over the current unit info after applying the dask function. this includes functions that return single values (e.g., min()) or functions that change the array in ways that do not affect units like reshaping or rounding. Parameters ---------- dask_func: func handle the dask function handle to call current_unyt_dask: unyt_dask_array the current instance of a unyt_dask_array Returns a new unyt_dask_array instance with appropriate units """ @wraps(dask_func) def wrapper(*args, **kwargs): da = dask_func(*args, **kwargs) # will return standard dask array return _create_with_quantity(da, current_unyt_dask._unyt_array) return wrapper # the following list are the unyt_quantity/array attributes that will also # be attributes of the unyt_dask_array. These methods are wrapped with a unit # conversion decorator. _unyt_funcs_to_track = {"to", "in_units", "in_cgs", "in_base", "in_mks"} # helper sanitizing functions for handling ufunc, array operations def _extract_unyt(obj): # returns the hidden _unyt_quantity if it exists in obj, otherwise return obj if _is_iterable(obj): return [_extract_unyt(ob) for ob in obj] return obj._unyt_array if isinstance(obj, unyt_dask_array) else obj def _extract_dask(obj): # returns a plain dask array if the obj is a unyt_dask_array, otherwise return obj if _is_iterable(obj): return [_extract_dask(ob) for ob in obj] return obj.to_dask() if isinstance(obj, unyt_dask_array) else obj def _extract_unyt_val(obj): # returns the value of a unyt_quantity if obj is a unyt_quantity if _is_iterable(obj): return [_extract_unyt_val(ob) for ob in obj] return obj.to_value() if isinstance(obj, ua.unyt_quantity) else obj def _check_unyt_inputs(ui_0, ui_1): # returns True if the args need to be converted to the same unit, based on # if the units have the same dimensions but different units. if hasattr(ui_0, "units") and hasattr(ui_1, "units"): un_0, un_1 = ui_0.units, ui_1.units return un_0 != un_1 and un_0.dimensions == un_1.dimensions return False def _is_iterable(obj): return type(obj) is tuple or type(obj) is list # noqa E721 def _sanitize_unit_args(*input): # returns sanitized inputs and unyt_inputs for calling the ufunc unyt_inputs = _extract_unyt(input) if len(unyt_inputs) > 1: # note 1: even though we rely on the ufunc applied to the unyt quantities # to get the final units of our unyt_dask_array after an operation, # we need to first ensure that if our arguments have the same dimensions # they are in the same units. This happens internally for unyt_quantities # but we also need to apply those internal unit conversions to our # dask_unyt_array objects, so we do those checks manually here. # note 2: we do NOT check for validity of the operation here. The subsequent # call to the ufunc with the unyt_inputs will enforce the unyt rules # (e.g., addition must have same dimensions). ui_0, ui_1 = unyt_inputs[0], unyt_inputs[1] if _check_unyt_inputs(ui_0, ui_1): # convert to the unit with the larger base input = list(input) if ui_0.units.base_value < ui_1.units.base_value: input[0] = input[0].to(ui_1.units) else: input[1] = input[1].to(ui_0.units) unyt_inputs = _extract_unyt(input) return input, unyt_inputs def _prep_ufunc(ufunc, *input, extract_dask=False, **kwargs): # this function: # (1) sanitizes inputs for calls to __array_func__, __array__ and _elementwise # (2) applies the function to the hidden unyt quantities # (3) (optional) makes inputs extra clean: converts unyt_dask_array args to # plain dask array objects # apply the operation to the hidden unyt_quantities input, unyt_inputs = _sanitize_unit_args(*input) unyt_result = ufunc(*unyt_inputs, **kwargs) if extract_dask: input = _extract_dask(input) input = _extract_unyt_val(input) return input, unyt_result def _post_ufunc(dask_superfunk, unyt_result): # a decorator to attach hidden unyt quantity to result of a ufunc, array or # elemwise calculation def wrapper(*args, **kwargs): dask_result = dask_superfunk(*args, **kwargs) if hasattr(unyt_result, "units"): return _create_with_quantity(dask_result, unyt_result) return dask_result return wrapper def _special_dec(the_func): # decorator for special operations like __mul__ , __truediv__, which will # bypass __getattribute__ def wrapper(self, *args, **kwargs): funcname = the_func.__name__ ufunc = getattr(ua.unyt_quantity, funcname) ufunc_args = (self,) + args args, unyt_result = _prep_ufunc(ufunc, *ufunc_args, extract_dask=True, **kwargs) # remove the first arg, cause we need to supply self as first arg _ = args.pop(0) daskresult = the_func(self, *args, **kwargs) if hasattr(unyt_result, "units"): return _create_with_quantity(daskresult, unyt_result) return daskresult # __lt__, __le__, etc. will hit this return wrapper class unyt_dask_array(DaskArray): """ a dask.array.core.Array subclass that tracks units. This class is only recommended for advanced usage, most cases should use the unyt_from_dask helper function to instantiate a new unyt_dask_array. Parameters ---------- The following parameters are those for a standard dask.array.core.Array: dask : dict Task dependency graph name : string Name of array in dask shape : tuple of ints Shape of the entire array chunks: iterable of tuples block sizes along each dimension dtype : str or dtype Typecode or data-type for the new Dask Array meta : empty ndarray empty ndarray created with same NumPy backend, ndim and dtype as the Dask Array being created (overrides dtype) The following keyword-only parameters are the same as for a standard unyt.unyt_array (check there for definitions): units registry bypass_validation unyt_name """ def __new__( cls, dask_graph, name, chunks, dtype=None, meta=None, shape=None, *, units=None, registry=None, bypass_validation=False, unyt_name=None, ): # get the base dask array obj = super().__new__( cls, dask_graph, name, chunks, dtype, meta, shape, ) # attach our unyt sidecar quantity dtype = obj.dtype obj._unyt_array = ua.unyt_array( [1.0], units, registry, dtype, bypass_validation=bypass_validation, name=unyt_name, ) obj.units = obj._unyt_array.units obj.unyt_name = obj._unyt_array.name # set the unit conversion attributes so they are discoverable. no name # conflicts for now, but this could be an issue if _unyt_funcs_to_track # is expanded. for attr in _unyt_funcs_to_track: setattr(obj, attr, getattr(obj._unyt_array, attr)) return obj def _elemwise(self, ufunc, *args, **kwargs): args, unyt_result = _prep_ufunc(ufunc, *args, **kwargs) return _post_ufunc(super()._elemwise, unyt_result)(ufunc, *args, **kwargs) def __array_ufunc__(self, numpy_ufunc, method, *inputs, **kwargs): if numpy_ufunc not in ua.unyt_array._ufunc_registry: return NotImplemented inputs, unyt_result = _prep_ufunc( numpy_ufunc, *inputs, extract_dask=True, **kwargs ) wrapped_func = _post_ufunc(super().__array_ufunc__, unyt_result) return wrapped_func(numpy_ufunc, method, *inputs, **kwargs) def __array_function__(self, func, types, args, kwargs): from unyt._array_functions import _UNSUPPORTED_FUNCTIONS if func in _UNSUPPORTED_FUNCTIONS: return NotImplemented args, unyt_result = _prep_ufunc(func, *args, extract_dask=True, **kwargs) types = [type(i) for i in args] wrapped_func = _post_ufunc(super().__array_function__, unyt_result) return wrapped_func(func, types, args, kwargs) def __getitem__(self, index): return _unary_dask_decorator(super().__getitem__, self)(index) def __setitem__(self, key, value): # requires dask >= 2021.4.1 super().__setitem__(key, value) def __repr__(self): disp_str = super().__repr__().replace("dask.array", "unyt_dask_array") units_str = f", units={self.units}>" return disp_str.replace(">", units_str) def _repr_html_(self): # controls jupyter notebook display of an array. called from _repr_html_ base_table = super()._repr_html_() table = base_table.split("\n") new_table = [] for row in table: if "" in row: u = self.units newrow = f" Units {u} {u} " new_table.append(newrow) new_table.append(row) return "\n".join(new_table) def to_dask(self): """ convert to a plain dask array Returns ------- dask.array object Examples -------- >>> from unyt import dask_array >>> import dask.array as da >>> x = da.random.random((10000, 10000), chunks=(1000, 1000)) >>> x_da = dask_array.unyt_from_dask(x, 'm') >>> x_da.to_dask() ... # doctest: +NORMALIZE_WHITESPACE dask.array """ (_, args) = super().__reduce__() return DaskArray(*args) def __reduce__(self): (_, args) = super().__reduce__() unyt_state = { "_unyt_array": self._unyt_array, "units": self.units, "unyt_name": self.unyt_name, } # reminder: the 3rd object returned by a __reduce__ tuple is the object # state. when there is no __setstate__, it must be a dict and it will be # added to the __dict__ attribute. return unyt_dask_array, args, unyt_state def __getattribute__(self, name): if name in _unyt_funcs_to_track: return self._track_conversion(name) result = super().__getattribute__(name) if name in _use_unary_decorator: return _unary_dask_decorator(result, self) return result def _track_conversion(self, unyt_func_name): """ a decorator to use with unyt functions that convert units Parameters ---------- unyt_func_name: str the name of the function to call from the sidecar _unyt_quantity. Must be an attribute of unyt.unyt_quantity. current_unyt_dask: unyt_dask_array the current instance of a unyt_dask_array Returns a new unyt_dask_array instance with appropriate units """ def wrapper(*args, **kwargs): # current value of sidecar quantity init_val = self._unyt_array.value # get the unyt function handle and call it the_func = getattr(self._unyt_array, unyt_func_name) new_unyt_quantity = the_func(*args, **kwargs) # calculate the conversion factor and pull out the new name and units # might be able to use _get_conversion_factor here too... factor = new_unyt_quantity.value / init_val # apply the factor, return new new_unyt_quantity[:] = 1 # reset sidecar value new_obj = _create_with_quantity(self * factor, new_unyt_quantity) return new_obj # functools wrap fails here because unyt_func_name is a string, copy manually: wrapper.__doc__ = getattr(self._unyt_array, unyt_func_name).__doc__ return wrapper def __dask_postcompute__(self): # a dask hook to catch after .compute(), see # https://docs.dask.org/en/latest/custom-collections.html#example-dask-collection return _finalize_unyt, ((self.units,)) def _set_unit_state(self, units, new_unyt_array, unyt_name): # sets just the unit state of the object self.units = units self._unyt_array = new_unyt_array self.unyt_name = unyt_name # These methods bypass __getattribute__ and numpy hooks, so they are defined # explicitly here (but are handled generically by the _special_dec decorator). @_special_dec def __abs__(self): return super().__abs__() @_special_dec def __pow__(self, other): return super().__pow__(other) @_special_dec def __mul__(self, other): return super().__mul__(other) @_special_dec def __rmul__(self, other): return super().__rmul__(other) @_special_dec def __truediv__(self, other): return super().__truediv__(other) @_special_dec def __rtruediv__(self, other): return super().__rtruediv__(other) @_special_dec def __add__(self, other): return super().__add__(other) @_special_dec def __sub__(self, other): return super().__sub__(other) @_special_dec def __lt__(self, other): return super().__lt__(other) @_special_dec def __le__(self, other): return super().__le__(other) @_special_dec def __gt__(self, other): return super().__gt__(other) @_special_dec def __ge__(self, other): return super().__ge__(other) @_special_dec def __eq__(self, other): return super().__eq__(other) @_special_dec def __ne__(self, other): return super().__ne__(other) def prod(self, *args, **kwargs): _, unit = ua._apply_power_mapping( np.multiply, self._unyt_array.units, self.size, self.shape, kwargs ) dask_result = super().prod(*args, **kwargs) return _create_with_quantity(dask_result, ua.unyt_array([1.0], unit)) def _finalize_unyt(results, unit_name): """ the function to call from the __dask_postcompute__ hook. Parameters ---------- results : the dask results object unit_name : the units of the result Returns ------- unyt_array or unyt_quantity """ # here, we first call the standard finalize function for a dask array # and then return a standard unyt_array from the now in-memory result if # the result is an array, otherwise return a unyt_quantity. result = dask_finalize(results) if type(result) is np.ndarray: return ua.unyt_array(result, unit_name) else: return ua.unyt_quantity(result, unit_name) def _create_with_quantity(dask_array, new_unyt_array): """ this function instantiates a new unyt_dask_array instance and then sets the unit state, including units. Used to wrap dask operations Parameters ---------- dask_array : a standard dask array or a unyt_dask_array new_unyt_array : a standard unyt array remaining arguments get passed to unyt.unyt_array, check there for a description. """ if isinstance(dask_array, unyt_dask_array): dask_array = dask_array.to_dask() out = unyt_from_dask(dask_array) # attach the unyt_array units = new_unyt_array.units unyt_name = new_unyt_array.name out._set_unit_state(units, new_unyt_array, unyt_name) return out def unyt_from_dask( dask_array, units=None, *, registry=None, bypass_validation=False, unyt_name=None, ): """ creates a unyt_dask_array from a standard dask array. This function will create a new copy of the dask graph associated with an array. Parameters ---------- dask_array : a standard dask array units : String unit name, unit symbol object, or astropy unit The units of the array. Powers must be specified using python syntax (cm**3, not cm^3). registry : :class:`unyt.unit_registry.UnitRegistry` The registry to create units from. If units is already associated with a unit registry and this is specified, this will be used instead of the registry associated with the unit object. bypass_validation : boolean If True, all input validation is skipped. Using this option may produce corrupted, invalid units or array data, but can lead to significant speedups in the input validation logic adds significant overhead. If set, units *must* be a valid unit object. Defaults to False. unyt_name : string The name of the array. Defaults to None. This attribute does not propagate through mathematical operations, but is preserved under indexing and unit conversions. Notes ----- All of the above parameters with the exception of the dask_array are the same as for a standard unyt.unyt_array. Examples -------- >>> from unyt import dask_array, unyt_quantity >>> import dask.array as da >>> x = da.ones((1000, 1000), chunks=(100, 100)) >>> x_da = dask_array.unyt_from_dask(x, 'm') >>> type(x_da) >>> x_da.units m >>> x_da.mean().units m >>> x_da.mean().compute() unyt_quantity(1., 'm') >>> x_da.to('cm').mean().compute() unyt_quantity(100., 'cm') >>> (x_da.to('cm')/unyt_quantity(10, 's')).mean().compute() unyt_quantity(10., 'cm/s') """ # reduce the dask array to pull out the arguments required for instantiating # a new dask.array.core.Array object and then initialize our new unyt_dask # array (cls, args) = dask_array.__reduce__() da = unyt_dask_array( *args, units=units, registry=registry, bypass_validation=bypass_validation, unyt_name=unyt_name, ) return da # note: the unyt_dask_array class has no way of catching daskified reductions. # operations like dask.array.min() get routed through dask.array.reductions.min() # and will return plain arrays or float/int values. # # When these operations exist as attributes, they can be called and will return # unyt objects. i.e., : # # import dask; import unyt # x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), unyt.m) # dask.array.min(x_da).compute() # returns a plain float # x_da.min().compute() # returns a unyt quantity # # but when the functions do not exist as attributes, like dask.array.nanmin(), # it is difficult to handle without manually wrapping all of those reductions # functions and exposing them here. The following function, reduce_with_units, # is a compromise: it is a simple helper function for calling # a general dask.array.reductions method on a unyt_dask array to correctly # handle units. # # check https://github.com/yt-project/unyt/issues/269 for further discussion _nan_ops = {"nansum", "nanmean", "nanmedian", "nanstd", "nanmax", "nanmin", "nancumsum"} _passthrough_reductions = {"diagonal", "median"} _allowed_funcs = _nan_ops.union(_passthrough_reductions, _use_unary_decorator) def reduce_with_units(dask_func, unyt_dask_in, *args, **kwargs): """ Call a dask.array.reduction function and preserve units. Parameters ---------- dask_func : function a function handle from dask.array.reduction (e.g., dask.array.min, dask.array.var, dask.array.nanstd) to call. unyt_dask_in : unyt_dask_array the unyt dask array, first argument to dask_func args: any arguments to the dask_func kwargs: any keyword arguments to the dask_func Returns ------- unyt_dask_array: the result of dask_func with units preserved Examples -------- >>> import dask.array >>> from unyt.dask_array import unyt_from_dask, reduce_with_units >>> a = dask.array.ones((10000,), chunks=(100,)) >>> a = unyt_from_dask(a, 'm') >>> b = reduce_with_units(dask.array.median, a, axis=0) >>> b.compute() unyt_quantity(1., 'm') """ if dask_func.__name__ in _allowed_funcs: # e.g., min, max, nanstd. functions that return the same units can # be called directly as the dask function will treat unyt_dask_in as # a standard dask array and then we copy over the initial units. return _unary_dask_decorator(dask_func, unyt_dask_in)( unyt_dask_in, *args, **kwargs ) else: # the operation may change the units npfunc = getattr(np, dask_func.__name__, None) if npfunc: newargs, unyt_result = _prep_ufunc( npfunc, unyt_dask_in, *args, extract_dask=True, **kwargs ) return _post_ufunc(dask_func, unyt_result)(*newargs) else: raise ValueError("could not deduce np equivalent of dask reduction") unyt-3.2.0/unyt/dimensions.py000066400000000000000000000244231526544257300162560ustar00rootroot00000000000000""" Dimensions of physical quantities """ from functools import wraps from itertools import chain from sympy import Rational, Symbol, sympify from unyt._deprecation import warn_deprecated #: mass mass = Symbol("(mass)", positive=True) #: length length = Symbol("(length)", positive=True) #: time time = Symbol("(time)", positive=True) #: temperature temperature = Symbol("(temperature)", positive=True) #: angle angle = Symbol("(angle)", positive=True) #: current_mks current_mks = Symbol("(current_mks)", positive=True) #: luminous_intensity luminous_intensity = Symbol("(luminous_intensity)", positive=True) #: dimensionless dimensionless = sympify(1) #: logarithmic logarithmic = Symbol("(logarithmic)", positive=True) #: A list of all of the base dimensions base_dimensions = [ mass, length, time, temperature, angle, current_mks, dimensionless, luminous_intensity, logarithmic, ] # # Derived dimensions # # rate rate = 1 / time # frequency (alias for rate) frequency = rate # angular frequency angular_frequency = angle * rate # spatial frequency spatial_frequency = 1 / length #: solid_angle solid_angle = angle * angle #: velocity velocity = length / time #: acceleration acceleration = length / time**2 #: jerk jerk = length / time**3 #: snap snap = length / time**4 #: crackle crackle = length / time**5 #: pop pop = length / time**6 #: area area = length * length #: volume volume = area * length #: momentum momentum = mass * velocity #: force force = mass * acceleration #: surface tension tension = force / length #: pressure pressure = force / area #: energy energy = force * length #: power power = energy / time #: flux flux = power / area #: specific_flux specific_flux = flux / rate #: number_density number_density = 1 / (length * length * length) #: density density = mass * number_density #: angular_momentum angular_momentum = mass * length * velocity #: specific_angular_momentum specific_angular_momentum = angular_momentum / mass #: specific_energy specific_energy = energy / mass #: count_flux count_flux = 1 / (area * time) #: count_intensity count_intensity = count_flux / solid_angle #: luminous_flux luminous_flux = luminous_intensity * solid_angle #: luminance luminance = luminous_intensity / area # Gaussian electromagnetic units #: charge_cgs charge_cgs = (energy * length) ** Rational(1, 2) # proper 1/2 power #: current_cgs current_cgs = charge_cgs / time #: electric_field_cgs electric_field_cgs = charge_cgs / length**2 #: magnetic_field_cgs magnetic_field_cgs = electric_field_cgs #: electric_potential_cgs electric_potential_cgs = energy / charge_cgs #: resistance_cgs resistance_cgs = electric_potential_cgs / current_cgs #: magnetic_flux_cgs magnetic_flux_cgs = magnetic_field_cgs * area # SI electromagnetic units #: charge charge = charge_mks = current_mks * time #: electric_field electric_field = electric_field_mks = force / charge_mks #: magnetic_field magnetic_field = magnetic_field_mks = electric_field_mks / velocity #: electric_potential electric_potential = electric_potential_mks = energy / charge_mks #: resistance resistance = resistance_mks = electric_potential_mks / current_mks #: capacitance capacitance = capacitance_mks = charge / electric_potential #: magnetic_flux magnetic_flux = magnetic_flux_mks = magnetic_field_mks * area #: inductance inductance = inductance_mks = magnetic_flux_mks / current_mks #: a list containing all derived_dimensions derived_dimensions = [ rate, velocity, acceleration, jerk, snap, crackle, pop, momentum, force, energy, power, charge_cgs, electric_field_cgs, magnetic_field_cgs, solid_angle, flux, specific_flux, volume, luminous_flux, area, current_cgs, charge_mks, electric_field_mks, magnetic_field_mks, electric_potential_cgs, electric_potential_mks, resistance_cgs, resistance_mks, magnetic_flux_mks, magnetic_flux_cgs, luminance, spatial_frequency, angular_frequency, ] #: a list containing all dimensions dimensions = base_dimensions + derived_dimensions #: a dict containing a bidirectional mapping from #: mks dimension to cgs dimension em_dimensions = { magnetic_field_mks: magnetic_field_cgs, magnetic_flux_mks: magnetic_flux_cgs, charge_mks: charge_cgs, current_mks: current_cgs, electric_potential_mks: electric_potential_cgs, resistance_mks: resistance_cgs, } for k, v in list(em_dimensions.items()): em_dimensions[v] = k def accepts(**arg_units): """Decorator for checking units of function arguments. Parameters ---------- arg_units: dict Mapping of function arguments to dimensions, of the form 'arg1'=dimension1 etc where ``'arg1'`` etc are the function arguments and ``dimension1`` etc are SI base units (or combination of units), eg. length/time. Notes ----- Keyword args are not dimensionally check, being directly passed to the decorated function. Function arguments that don't have attached units can be skipped can bypass dimensionality checking by not being passed to the decorator. See ``baz`` in the examples, where ``a`` has no units. Examples -------- >>> import unyt as u >>> from unyt.dimensions import length, time >>> @accepts(a=time, v=length/time) ... def foo(a, v): ... return a * v ... >>> res = foo(a= 2 * u.s, v = 3 * u.m/u.s) >>> print(res) 6 m >>> @accepts(a=length, v=length/time) ... def bar(a, v): ... return a * v ... >>> bar(a= 2 * u.s, v = 3 * u.m/u.s) Traceback (most recent call last): ... TypeError: arg 'a=2 s' does not match (length) >>> @accepts(v=length/time) ... def baz(a, v): ... return a * v ... >>> res = baz(a= 2, v = 3 * u.m/u.s) >>> print(res) 6 m/s """ def check_accepts(f): """Decorates original function. Parameters ---------- f : function Function being decorated. Returns ------- new_f: function Decorated function. """ names_of_args = f.__code__.co_varnames @wraps(f) def new_f(*args, **kwargs): """The new function being returned from the decorator. Check units of `args` and `kwargs`, then run original function. Raises ------ TypeError If the units do not match. """ for arg_name, arg_value in chain(zip(names_of_args, args), kwargs.items()): if arg_name in arg_units: # function argument needs to be checked dimension = arg_units[arg_name] if not _has_dimensions(arg_value, dimension): raise TypeError( f"arg '{arg_name}={arg_value}' does not match {dimension}" ) return f(*args, **kwargs) return new_f return check_accepts def returns(*r_units, r_unit=None): """Decorator for checking function return units. Parameters ---------- *r_units: :py:class:`sympy.core.symbol.Symbol` SI base unit (or combination of units), eg. length/time of the value(s) returned by the original function r_unit: :py:class:`sympy.core.symbol.Symbol` Deprecated version of `r_units` which supports only one named return value. Examples -------- >>> import unyt as u >>> from unyt.dimensions import length, time >>> @returns(length) ... def f(a, v): ... return a * v ... >>> res = f(a= 2 * u.s, v = 3 * u.m/u.s) >>> print(res) 6 m >>> @returns(length/time) ... def f(a, v): ... return a * v ... >>> f(a= 2 * u.s, v = 3 * u.m/u.s) Traceback (most recent call last): ... TypeError: result '6 m' does not match (length)/(time) >>> @returns(length, length/time**2) ... def f(a, v): ... return a * v, v / a ... >>> res = f(a= 2 * u.s, v = 3 * u.m/u.s) >>> print(*res) 6 m 1.5 m/s**2 """ # Convert deprecated arguments into current ones where possible. if r_unit is not None: if len(r_units) > 0: raise ValueError( "Cannot specify `r_unit` and other return values simultaneously" ) else: warn_deprecated( "@unyt.returns(r_unit=...)", replacement="use @unyt.returns(...)", since_version="3.0", ) r_units = (r_unit,) def check_returns(f): """Decorates original function. Parameters ---------- f : function Function being decorated. Returns ------- new_f: function Decorated function. """ @wraps(f) def new_f(*args, **kwargs): """The decorated function, which checks the return units. Raises ------ TypeError If the units do not match. """ results = f(*args, **kwargs) # Make results a tuple so we can treat single and multiple return values the # same way. if isinstance(results, tuple): result_tuple = results else: result_tuple = (results,) for result, dimension in zip(result_tuple, r_units): if not _has_dimensions(result, dimension): raise TypeError(f"result '{result}' does not match {dimension}") return results return new_f return check_returns def _has_dimensions(quant, dim): """Checks the argument has the right dimensionality. Parameters ---------- quant : :py:class:`unyt.array.unyt_quantity` Quantity whose dimensionality we want to check. dim : :py:class:`sympy.core.symbol.Symbol` SI base unit (or combination of units), eg. length/time Returns ------- bool True if check successful. Examples -------- >>> import unyt as u >>> from unyt.dimensions import length, time >>> _has_dimensions(3 * u.m/u.s, length/time) True >>> _has_dimensions(3, length) False """ try: arg_dim = quant.units.dimensions except AttributeError: arg_dim = dimensionless return arg_dim == dim unyt-3.2.0/unyt/equivalencies.py000066400000000000000000000355301526544257300167440ustar00rootroot00000000000000""" Equivalencies between different kinds of units """ from collections import OrderedDict import numpy as np from unyt.dimensions import ( density, dimensionless, energy, flux, length, mass, number_density, rate, spatial_frequency, temperature, velocity, ) from unyt.exceptions import InvalidUnitEquivalence equivalence_registry = OrderedDict() class _RegisteredEquivalence(type): def __init__(cls, name, b, d): type.__init__(cls, name, b, d) if hasattr(cls, "type_name"): equivalence_registry[cls.type_name] = cls class Equivalence(metaclass=_RegisteredEquivalence): def __init__(self, in_place=False): self.in_place = in_place def convert(self, x, new_dims, **kwargs): if x.units.dimensions in self._dims and new_dims in self._dims: return self._convert(x, new_dims, **kwargs) else: raise InvalidUnitEquivalence(self, x.units, new_dims) def _get_out(self, x): if self.in_place: return x return None class NumberDensityEquivalence(Equivalence): """Equivalence between mass and number density, given a mean molecular weight. Given a number density :math:`n`, the mass density :math:`\\rho` is: .. math:: \\rho = \\mu m_{\\rm H} n And similarly .. math:: n = \\rho (\\mu m_{\\rm H})^{-1} Parameters ---------- mu : float The mean molecular weight. Defaults to 0.6 which is valid for fully ionized gas with primordial composition. Example ------- >>> print(NumberDensityEquivalence()) number density: density <-> number density >>> from unyt import Msun, pc >>> rho = 3*Msun/pc**3 >>> rho.to_equivalent('cm**-3', 'number_density', mu=1.4) unyt_quantity(86.64869896, 'cm**(-3)') """ type_name = "number_density" _dims = (density, number_density) def _convert(self, x, new_dims, mu=0.6): from unyt import physical_constants as pc if new_dims == number_density: return np.true_divide(x, mu * pc.mh, out=self._get_out(x)) elif new_dims == density: return np.multiply(x, mu * pc.mh, out=self._get_out(x)) def __str__(self): return "number density: density <-> number density" class ThermalEquivalence(Equivalence): """Equivalence between temperature and energy via the Boltzmann constant Given a temperature :math:`T` in an absolute scale (e.g. Kelvin or Rankine), the equivalent thermal energy :math:`E` for that temperature is given by: .. math:: E = k_B T And .. math:: T = E/k_B Where :math:`k_B` is Boltzmann's constant. Example ------- >>> print(ThermalEquivalence()) thermal: temperature <-> energy >>> from unyt import Kelvin >>> temp = 1e6*Kelvin >>> temp.to_equivalent('keV', 'thermal') unyt_quantity(0.08617332, 'keV') """ type_name = "thermal" _dims = (temperature, energy) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == energy: return np.multiply(x, pc.kboltz, out=self._get_out(x)) elif new_dims == temperature: return np.true_divide(x, pc.kboltz, out=self._get_out(x)) def __str__(self): return "thermal: temperature <-> energy" class MassEnergyEquivalence(Equivalence): """Equivalence between mass and energy in special relativity Given a body with mass :math:`m`, the self-energy :math:`E` of that mass is given by .. math:: E = m c^2 where :math:`c` is the speed of light. Example ------- >>> print(MassEnergyEquivalence()) mass_energy: mass <-> energy >>> from unyt import g >>> (3.5*g).to_equivalent('J', 'mass_energy') unyt_quantity(3.14564313e+14, 'J') """ type_name = "mass_energy" _dims = (mass, energy) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == energy: return np.multiply(x, pc.clight * pc.clight, out=self._get_out(x)) elif new_dims == mass: return np.true_divide(x, pc.clight * pc.clight, out=self._get_out(x)) def __str__(self): return "mass_energy: mass <-> energy" class SpectralEquivalence(Equivalence): """Equivalence between wavelength, frequency, and energy of a photon. Given a photon with wavelength :math:`\\lambda`, spatial frequency :math:`\\bar\\nu`, frequency :math:`\\nu` and Energy :math:`E`, these quantities are related by the following forumlae: .. math:: E = h \\nu = h c / \\lambda = h c \\bar\\nu where :math:`h` is Planck's constant and :math:`c` is the speed of light. Example ------ >>> print(SpectralEquivalence()) spectral: length <-> spatial_frequency <-> frequency <-> energy >>> from unyt import angstrom, km >>> (3*angstrom).to_equivalent('keV', 'spectral') unyt_quantity(4.13280644, 'keV') >>> (1*km).to_equivalent('MHz', 'spectral') unyt_quantity(0.29979246, 'MHz') """ type_name = "spectral" _dims = (length, rate, energy, spatial_frequency) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == energy: if x.units.dimensions == length: return np.true_divide(pc.clight * pc.h_mks, x, out=self._get_out(x)) elif x.units.dimensions == rate: return np.multiply(x, pc.h_mks, out=self._get_out(x)) elif x.units.dimensions == spatial_frequency: return np.multiply(x, pc.h_mks * pc.clight, out=self._get_out(x)) elif new_dims == length: if x.units.dimensions == rate: return np.true_divide(pc.clight, x, out=self._get_out(x)) elif x.units.dimensions == energy: return np.true_divide(pc.h_mks * pc.clight, x, out=self._get_out(x)) elif x.units.dimensions == spatial_frequency: return np.true_divide(1, x, out=self._get_out(x)) elif new_dims == rate: if x.units.dimensions == length: return np.true_divide(pc.clight, x, out=self._get_out(x)) elif x.units.dimensions == energy: return np.true_divide(x, pc.h_mks, out=self._get_out(x)) elif x.units.dimensions == spatial_frequency: return np.multiply(x, pc.clight, out=self._get_out(x)) elif new_dims == spatial_frequency: if x.units.dimensions == length: return np.true_divide(1, x, out=self._get_out(x)) elif x.units.dimensions == energy: return np.true_divide(x, pc.clight * pc.h_mks, out=self._get_out(x)) elif x.units.dimensions == rate: return np.true_divide(x, pc.clight, out=self._get_out(x)) def __str__(self): return "spectral: length <-> spatial_frequency <-> frequency " + "<-> energy" class SoundSpeedEquivalence(Equivalence): """Equivalence between the sound speed, temperature, and thermal energy of an ideal gas For an ideal gas with sound speed :math:`c_s`, temperature :math:`T`, and thermal energy :math:`E`, the following equalities will hold: .. math:: c_s = \\sqrt{\\frac{\\gamma k_B T}{\\mu m_{\\rm H}}} and .. math:: E = c_s^2 \\mu m_{\\rm H} / \\gamma = k_B T where :math:`k_B` is Boltzmann's constant, :math:`\\mu` is the mean molecular weight of the gas, and :math:`\\gamma` is the ratio of specific heats. Parameters ---------- gamma : float The ratio of specific heats. Defaults to 5/3, which is correct for monatomic species. mu : float The mean molecular weight. Defaults to 0.6, which is valid for fully ionized gas with primordial composition. Example ------- >>> print(SoundSpeedEquivalence()) sound_speed (ideal gas): velocity <-> temperature <-> energy >>> from unyt import Kelvin, km, s >>> hot = 1e6*Kelvin >>> hot.to_equivalent('km/s', 'sound_speed') unyt_quantity(151.37249927, 'km/s') >>> hot.to_equivalent('keV', 'sound_speed') unyt_quantity(0.08617332, 'keV') >>> cs = 100*km/s >>> cs.to_equivalent('K', 'sound_speed') unyt_quantity(436421.39881617, 'K') >>> cs.to_equivalent('keV', 'sound_speed') unyt_quantity(0.03760788, 'keV') """ type_name = "sound_speed" _dims = (velocity, temperature, energy) def _convert(self, x, new_dims, mu=0.6, gamma=5.0 / 3.0): from unyt import physical_constants as pc if new_dims == velocity: if x.units.dimensions == temperature: v2 = np.multiply( pc.kboltz * gamma / (mu * pc.mh), x, out=self._get_out(x) ) elif x.units.dimensions == energy: v2 = np.multiply(gamma / (mu * pc.mh), x, out=self._get_out(x)) return np.sqrt(v2, out=self._get_out(x)) elif new_dims == temperature: if x.units.dimensions == velocity: v2 = np.multiply(x, x, out=self._get_out(x)) kT = np.multiply(v2, mu * pc.mh / gamma, out=self._get_out(x)) return np.true_divide(kT, pc.kboltz, out=self._get_out(x)) else: return np.true_divide(x, pc.kboltz, out=self._get_out(x)) else: if x.units.dimensions == velocity: v2 = np.multiply(x, x, out=self._get_out(x)) return np.multiply(mu * pc.mh / gamma, v2, out=self._get_out(x)) else: return np.multiply(x, pc.kboltz, out=self._get_out(x)) def __str__(self): return "sound_speed (ideal gas): velocity <-> temperature <-> energy" class LorentzEquivalence(Equivalence): """Equivalence between velocity and the Lorentz gamma factor. For a body with velocity :math:`v`, the Lorentz gamma factor, :math:`\\gamma` is .. math:: \\gamma = \\frac{1}{\\sqrt{1 - v^2/c^2}} and similarly .. math:: v = \\frac{c}{\\sqrt{1 - \\gamma^2}} where :math:`c` is the speed of light. Example ------- >>> print(LorentzEquivalence()) lorentz: velocity <-> dimensionless >>> from unyt import c, dimensionless >>> v = 0.99*c >>> print(v.to_equivalent('', 'lorentz')) 7.088812050083393 dimensionless >>> fast = 99.9*dimensionless >>> fast.to_equivalent('c', 'lorentz') unyt_quantity(0.9999499, 'c') >>> fast.to_equivalent('km/s', 'lorentz') unyt_quantity(299777.43797656, 'km/s') """ type_name = "lorentz" _dims = (dimensionless, velocity) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == dimensionless: beta = np.true_divide(x, pc.clight, out=self._get_out(x)) beta2 = np.multiply(beta, beta, out=self._get_out(x)) inv_gamma_2 = np.subtract(1, beta2, out=self._get_out(x)) inv_gamma = np.sqrt(inv_gamma_2, out=self._get_out(x)) gamma = np.true_divide(1.0, inv_gamma, out=self._get_out(x)) return gamma elif new_dims == velocity: gamma2 = np.multiply(x, x, out=self._get_out(x)) inv_gamma_2 = np.true_divide(1, gamma2, out=self._get_out(x)) beta2 = np.subtract(1, inv_gamma_2, out=self._get_out(x)) beta = np.sqrt(beta2, out=self._get_out(x)) return np.multiply(pc.clight, beta, out=self._get_out(x)) def __str__(self): return "lorentz: velocity <-> dimensionless" class SchwarzschildEquivalence(Equivalence): """Equivalence between the mass and radius of a Schwarzschild black hole A Schwarzschild black hole of mass :math:`M` has radius :math:`R` .. math:: R = \\frac{2 G M}{c^2} and similarly .. math:: M = \\frac{R c^2}{2 G} where :math:`G` is Newton's gravitational constant and :math:`c` is the speed of light. Example ------- >>> print(SchwarzschildEquivalence()) schwarzschild: mass <-> length >>> from unyt import Msun, AU >>> (10*Msun).to_equivalent('km', 'schwarzschild') unyt_quantity(29.53161626, 'km') >>> (1*AU).to_equivalent('Msun', 'schwarzschild') unyt_quantity(50656851.7815179, 'Msun') """ type_name = "schwarzschild" _dims = (mass, length) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == length: return np.multiply( 2.0 * pc.G / (pc.clight * pc.clight), x, out=self._get_out(x) ) elif new_dims == mass: return np.multiply( 0.5 * pc.clight * pc.clight / pc.G, x, out=self._get_out(x) ) def __str__(self): return "schwarzschild: mass <-> length" class ComptonEquivalence(Equivalence): """Equivalence between the Compton wavelength of a particle and its mass. .. math:: \\lambda_c = h/mc Example ------- >>> print(ComptonEquivalence()) compton: mass <-> length >>> from unyt import me, fm >>> me.to_equivalent('angstrom', 'compton') unyt_quantity(0.0242631, 'Å') >>> (10*fm).to_equivalent('me', 'compton') unyt_quantity(242.63102371, 'me') """ type_name = "compton" _dims = (mass, length) def _convert(self, x, new_dims): from unyt import physical_constants as pc return np.true_divide(pc.h_mks / pc.clight, x, out=self._get_out(x)) def __str__(self): return "compton: mass <-> length" class EffectiveTemperatureEquivalence(Equivalence): """Equivalence between the emitted flux across all wavelengths and temperature of a blackbody For a blackbody emitter with Temperature :math:`T` emitting radiation with a flux :math:`F`, the following equality holds: .. math:: F = \\sigma T^4 where :math:`\\sigma` is the Stefan-Boltzmann constant. Example ------- >>> print(EffectiveTemperatureEquivalence()) effective_temperature: flux <-> temperature >>> from unyt import K, W, m >>> (5000.*K).to_equivalent('W/m**2', 'effective_temperature') unyt_quantity(35439828.89119583, 'W/m**2') >>> (100.*W/m**2).to_equivalent('K', 'effective_temperature') unyt_quantity(204.92601755, 'K') """ type_name = "effective_temperature" _dims = (flux, temperature) def _convert(self, x, new_dims): from unyt import physical_constants as pc if new_dims == flux: x4 = np.power(x, 4, out=self._get_out(x)) return np.multiply( pc.stefan_boltzmann_constant_mks, x4, out=self._get_out(x) ) elif new_dims == temperature: T4 = np.true_divide( x, pc.stefan_boltzmann_constant_mks, out=self._get_out(x) ) ret = np.power(T4, 0.25, out=self._get_out(x)) return ret def __str__(self): return "effective_temperature: flux <-> temperature" unyt-3.2.0/unyt/exceptions.py000066400000000000000000000231361526544257300162670ustar00rootroot00000000000000""" Exception classes defined by unyt """ class UnytError(Exception): """ A generic exception type that all unyt exceptions are derived from. It may be raised directly in rare situations that do not fit any existing exceptions and don't justify the creation of a new one. Since it serves as a base class for all unyt exceptions, it may be used as a catch-all exception type in dependent code. For instance >>> from unyt import cm, s >>> from unyt.exceptions import UnytError >>> a = 1 * cm >>> b = 1 / s >>> try: ... a + b ... except UnytError: ... pass However, it is generally recommended to only capture exceptions as specific as possible. """ pass class UnitOperationError(UnytError, ValueError): """An exception that is raised when unit operations are not allowed Example ------- >>> import unyt as u >>> 3*u.g + 4*u.m\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.UnitOperationError: The operator for unyt_arrays with units "g" (dimensions "(mass)") and "m" (dimensions "(length)") is not well defined. """ def __init__(self, operation, unit1, unit2=None): self.operation = operation self.unit1 = unit1 self.unit2 = unit2 super().__init__() def __str__(self): err = ( f"The {self.operation} operator for unyt_arrays with units " f"'{self.unit1}' (dimensions '{self.unit1.dimensions}') " ) if self.unit2 is not None: err += f"and '{self.unit2}' (dimensions '{self.unit2.dimensions}') " err += "is not well defined." return err class UnitInconsistencyError(UnitOperationError): """An error raised on forbidden numpy API expecting arrays with identical units. Example ------- >>> import unyt as u >>> import numpy as np >>> np.concatenate([[1, 2, 3] * u.cm, [4, 5, 6] * u.s])\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.UnitInconsistencyError: Expected all unyt_array arguments to have identical units. Received mixed units (cm, s) """ def __init__(self, *units): self.units = units def __str__(self): return ( "Expected all unyt_array arguments to have identical units. " f"Received mixed units ({', '.join(str(u) for u in self.units)})" ) class UnitConversionError(UnytError): """An error raised when converting to a unit with different dimensions. Example ------- >>> import unyt as u >>> data = 3*u.g >>> data.to('m') # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.UnitConversionError: Cannot convert between 'g' (dim '(mass)') and 'm' (dim '(length)'). """ def __init__(self, unit1, dimension1, unit2, dimension2): self.unit1 = unit1 self.unit2 = unit2 self.dimension1 = dimension1 self.dimension2 = dimension2 super().__init__() def __str__(self): err = ( f"Cannot convert between '{self.unit1}' (dim '{self.dimension1}') " f"and '{self.unit2}' (dim '{self.dimension2}')." ) return err class MissingMKSCurrent(UnytError): """Raised when querying a unit system for MKS current dimensions Since current is a base dimension for SI or SI-like unit systems but not in CGS or CGS-like unit systems, dimensions that include the MKS current dimension (the dimension of ampere) are not representable in CGS-like unit systems. When a CGS-like unit system is queried for such a dimension, this error is raised. Example ------- >>> from unyt.unit_systems import cgs_unit_system as us >>> from unyt import ampere >>> us[ampere.dimensions]\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.MissingMKSCurrent: The cgs unit system does not have a MKS current base unit """ def __init__(self, unit_system_name): self.unit_system_name = unit_system_name super().__init__() def __str__(self): return ( f"The {self.unit_system_name} unit system does not have " "a MKS current base unit" ) class MKSCGSConversionError(UnytError): """Raised when conversion between MKS and CGS units cannot be performed This error is raised and caught internally and will expose itself to the user as part of a chained exception leading to a UnitConversionError. """ pass class UnitsNotReducible(UnytError): """Raised when a unit cannot be safely represented in a unit system Example ------- >>> from unyt import A, cm >>> data = 12*A/cm >>> data.in_cgs()\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.UnitsNotReducible: The unit "A/cm" (dimensions "(current_mks)/(length)") cannot be reduced to an expression within the cgs system of units. """ def __init__(self, unit, units_base): self.unit = unit self.units_base = units_base super().__init__() def __str__(self): return ( f"The unit '{self.unit}' (dimensions '{self.unit.dimensions}') cannot be " f"reduced to an expression within the {self.units_base} system of units." ) class IterableUnitCoercionError(UnytError): """Raised when an iterable cannot be converted to a unyt_array Example ------- >>> from unyt import g, cm, unyt_array >>> data = [2*cm, 3*g] >>> unyt_array(data)\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.IterableUnitCoercionError: Received an input or operand that cannot be converted to a unyt_array with uniform units: [unyt_quantity(2., 'cm'), unyt_quantity(3., 'g')] """ def __init__(self, op): self.op = op super().__init__() def __str__(self): err = ( "Received an input or operand that cannot be converted " f"to a unyt_array with uniform units: {self.op}" ) return err class InvalidUnitEquivalence(UnytError): """Raised an equivalence does not apply to a unit conversion Example ------- >>> import unyt as u >>> data = 12*u.g >>> data.to('erg', equivalence='thermal')\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.InvalidUnitEquivalence: The unit equivalence 'thermal' does not exist for the units 'g' and 'erg'. """ def __init__(self, equiv, unit1, unit2): self.equiv = equiv self.unit1 = unit1 self.unit2 = unit2 super().__init__() def __str__(self): from unyt.unit_object import Unit if isinstance(self.unit2, Unit): msg = ( "The unit equivalence '%s' does not exist for the units '%s' and '%s'." ) else: msg = ( "The unit equivalence '%s' does not exist for units '%s' " "to convert to a new unit with dimensions '%s'." ) return msg % (self.equiv, self.unit1, self.unit2) class InvalidUnitOperation(UnytError): """Raised when an operation on a unit object is not allowed Example ------- >>> from unyt import cm, g >>> cm + g # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.InvalidUnitOperation: addition with unit objects is not allowed """ pass class SymbolNotFoundError(UnytError): """Raised when a unit name is not available in a unit registry Example ------- >>> from unyt.unit_registry import default_unit_registry >>> default_unit_registry['made_up_unit']\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.SymbolNotFoundError: The symbol 'made_up_unit' does not exist in this registry. """ pass class UnitParseError(UnytError): """Raised when a string unit name is not parseable as a valid unit Example ------- >>> from unyt import Unit >>> Unit('hello')\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.UnitParseError: Could not find unit symbol 'hello' in the provided symbols. """ pass class IllDefinedUnitSystem(UnytError): """Raised when the dimensions of the base units of a unit system are inconsistent. Example ------- >>> from unyt.unit_systems import UnitSystem >>> UnitSystem('atomic', 'nm', 'fs', 'nK', 'rad')\ # doctest: +IGNORE_EXCEPTION_DETAIL +NORMALIZE_WHITESPACE Traceback (most recent call last): ... unyt.exceptions.IllDefinedUnitSystem: Cannot create unit system with inconsistent mapping from dimensions to units. Received: OrderedDict([((length), nm), ((mass), fs), ((time), nK), ((temperature), rad), ((angle), rad), ((current_mks), A), ((luminous_intensity), cd)]) """ def __init__(self, units_map): self.units_map = units_map super().__init__() def __str__(self): return ( "Cannot create unit system with inconsistent mapping from " f"dimensions to units. Received:\n{self.units_map}" ) unyt-3.2.0/unyt/mpl_interface.py000066400000000000000000000045321526544257300167150ustar00rootroot00000000000000""" Matplotlib offers support for custom classes, such as unyt_array, allowing customization of axis information and unit conversion. In the case of unyt, the axis label is set based on the unyt_array.name and unyt_array.units attributes. It is also possible to convert the plotted units. This feature is optional and has to be enabled using the matplotlib_support context manager. """ from unyt.array import unyt_array, unyt_quantity from ._on_demand_imports import _matplotlib __all__ = ["matplotlib_support"] class matplotlib_support: """Context manager for enabling the feature When used in a with statement, the feature is enabled during the context and then disabled after it exits. Parameters ---------- label_style : str One of the following set, ``{'()', '[]', '/'}``. These choices correspond to the following unit labels: * ``'()'`` -> ``'(unit)'`` * ``'[]'`` -> ``'[unit]'`` * ``'/'`` -> ``'q_x / unit'`` """ @property def array_converter(self): from ._mpl_array_converter import unyt_arrayConverter unyt_arrayConverter._labelstyle = self.label_style return unyt_arrayConverter def __init__(self, label_style="()"): self._labelstyle = label_style self._enabled = False def __call__(self): self.__enter__() @property def label_style(self): """str: One of the following set, ``{'()', '[]', '/'}``. These choices correspond to the following unit labels: * ``'()'`` -> ``'(unit)'`` * ``'[]'`` -> ``'[unit]'`` * ``'/'`` -> ``'q_x / unit'`` """ return self._labelstyle @label_style.setter def label_style(self, label_style="()"): self._labelstyle = label_style self.array_converter._labelstyle = label_style def __enter__(self): _matplotlib.units.registry[unyt_array] = self.array_converter() _matplotlib.units.registry[unyt_quantity] = self.array_converter() self._enabled = True def __exit__(self, exc_type, exc_val, exc_tb): _matplotlib.units.registry.pop(unyt_array) _matplotlib.units.registry.pop(unyt_quantity) self._enabled = False def enable(self): self.__enter__() def disable(self): if self._enabled: self.__exit__(None, None, None) unyt-3.2.0/unyt/physical_constants.py000066400000000000000000000012331526544257300200100ustar00rootroot00000000000000""" Predefined useful physical constants Note that all of these names can be imported from the top-level unyt namespace. For example:: >>> from unyt.physical_constants import gravitational_constant, solar_mass >>> from unyt import AU >>> from math import pi >>> >>> period = 2 * pi * ((1 * AU)**3 / (gravitational_constant * solar_mass))**0.5 >>> period.in_units('day') unyt_quantity(365.26236846, 'day') .. show_all_constants:: """ from unyt.unit_registry import default_unit_registry as _default_unit_registry from unyt.unit_systems import add_constants as _add_constants _add_constants(globals(), registry=_default_unit_registry) unyt-3.2.0/unyt/testing.py000066400000000000000000000052421526544257300155610ustar00rootroot00000000000000""" Utilities for writing tests """ import warnings from unyt.array import NULL_UNIT, allclose_units def assert_allclose_units(actual, desired, rtol=1e-7, atol=0, **kwargs): """Raise an error if two objects are not equal up to desired tolerance This is a wrapper for :func:`numpy.testing.assert_allclose` that also verifies unit consistency Parameters ---------- actual : array-like Array obtained (possibly with attached units) desired : array-like Array to compare with (possibly with attached units) rtol : float, optional Relative tolerance, defaults to 1e-7 atol : float or quantity, optional Absolute tolerance. If units are attached, they must be consistent with the units of ``actual`` and ``desired``. If no units are attached, assumes the same units as ``desired``. Defaults to zero. See Also -------- :func:`unyt.array.allclose_units` Notes ----- Also accepts additional keyword arguments accepted by :func:`numpy.testing.assert_allclose`, see the documentation of that function for details. Examples -------- >>> import unyt as u >>> actual = [1e-5, 1e-3, 1e-1]*u.m >>> desired = actual.to("cm") >>> assert_allclose_units(actual, desired) """ if not allclose_units(actual, desired, rtol, atol, **kwargs): raise AssertionError def assert_array_equal_units(x, y, **kwargs): """A thin wrapper around :func:`numpy.testing.assert_array_equal` that also verifies unit consistency Arrays without units are considered dimensionless. Parameters ---------- x : array_like The actual object to check. y : array_like The desired, expected object. See Also -------- :func:`numpy.testing.assert_array_equal` Notes ----- Also accepts additional keyword arguments accepted by :func:`numpy.testing.assert_array_equel`, see the documentation of that function for details. """ # see https://github.com/yt-project/unyt/issues/281 from numpy.testing import assert_array_equal assert_array_equal(x, y, **kwargs) if not (xu := getattr(x, "units", NULL_UNIT)) == ( yu := getattr(y, "units", NULL_UNIT) ): raise AssertionError(f"Arguments' units do not match (got {xu} and {yu})") def _process_warning(op, message, warning_class, args=(), kwargs=None): if kwargs is None: kwargs = {} with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") op(*args, **kwargs) assert len(w) == 1 assert issubclass(w[0].category, warning_class) assert str(w[0].message) == message unyt-3.2.0/unyt/tests/000077500000000000000000000000001526544257300146715ustar00rootroot00000000000000unyt-3.2.0/unyt/tests/__init__.py000066400000000000000000000000001526544257300167700ustar00rootroot00000000000000unyt-3.2.0/unyt/tests/data/000077500000000000000000000000001526544257300156025ustar00rootroot00000000000000unyt-3.2.0/unyt/tests/data/__init__.py000066400000000000000000000000001526544257300177010ustar00rootroot00000000000000unyt-3.2.0/unyt/tests/data/old_json_registry.txt000066400000000000000000000312301526544257300221010ustar00rootroot00000000000000{"g": [1.0, "Symbol('(mass)', positive=True)", 0.0, "\\rm{g}"], "s": [1.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{s}"], "K": [1.0, "Symbol('(temperature)', positive=True)", 0.0, "\\rm{K}"], "radian": [1.0, "Symbol('(angle)', positive=True)", 0.0, "\\rm{radian}"], "dyne": [1.0, "Mul(Symbol('(length)', positive=True), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{dyn}"], "erg": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{erg}"], "esu": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(3, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{esu}"], "gauss": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(-1, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{G}"], "degC": [1.0, "Symbol('(temperature)', positive=True)", -273.15, "^\\circ\\rm{C}"], "statA": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(3, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{statA}"], "statV": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(1, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{statV}"], "statohm": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(-1)), Symbol('(time)', positive=True))", 0.0, "\\rm{statohm}"], "m": [100.0, "Symbol('(length)', positive=True)", 0.0, "\\rm{m}"], "J": [10000000.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{J}"], "W": [10000000.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-3)))", 0.0, "\\rm{W}"], "Hz": [1.0, "Pow(Symbol('(time)', positive=True), Integer(-1))", 0.0, "\\rm{Hz}"], "N": [100000.0, "Mul(Symbol('(length)', positive=True), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{N}"], "C": [1.0, "Mul(Symbol('(current_mks)', positive=True), Symbol('(time)', positive=True))", 0.0, "\\rm{C}"], "A": [1.0, "Symbol('(current_mks)', positive=True)", 0.0, "\\rm{A}"], "T": [1000.0, "Mul(Pow(Symbol('(current_mks)', positive=True), Integer(-1)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{T}"], "Pa": [10.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(-1)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{Pa}"], "V": [10000000.0, "Mul(Pow(Symbol('(current_mks)', positive=True), Integer(-1)), Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-3)))", 0.0, "\\rm{V}"], "ohm": [10000000.0, "Mul(Pow(Symbol('(current_mks)', positive=True), Integer(-2)), Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-3)))", 0.0, "\\Omega"], "ft": [30.48, "Symbol('(length)', positive=True)", 0.0, "\\rm{ft}"], "mile": [160934.4, "Symbol('(length)', positive=True)", 0.0, "\\rm{mile}"], "degF": [0.5555555555555556, "Symbol('(temperature)', positive=True)", -459.67, "^\\circ\\rm{F}"], "R": [0.5555555555555556, "Symbol('(temperature)', positive=True)", 0.0, "^\\circ\\rm{R}"], "lbf": [444822.16152605, "Mul(Symbol('(length)', positive=True), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{lbf}"], "lbm": [453.59237, "Symbol('(mass)', positive=True)", 0.0, "\\rm{lbm}"], "atm": [1013250.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(-1)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{atm}"], "h": [0.702, "Integer(1)", 0.0, "h"], "dimensionless": [1.0, "Integer(1)", 0.0, ""], "min": [60.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{min}"], "hr": [3600.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{hr}"], "day": [86400.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{d}"], "d": [86400.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{d}"], "yr": [31557600.0, "Symbol('(time)', positive=True)", 0.0, "\\rm{yr}"], "c": [29979245800.0, "Mul(Symbol('(length)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{c}"], "Msun": [1.98841586e+33, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_\\odot"], "msun": [1.98841586e+33, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_\\odot"], "Rsun": [69550001060.63751, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\odot"], "rsun": [69550001060.63751, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\odot"], "R_sun": [69550001060.63751, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\odot"], "r_sun": [69550001060.63751, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\odot"], "Lsun": [3.827e+33, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-3)))", 0.0, "\\rm{L}_\\odot"], "Tsun": [5870.0, "Symbol('(temperature)', positive=True)", 0.0, "\\rm{T}_\\odot"], "Zsun": [0.01295, "Integer(1)", 0.0, "\\rm{Z}_\\odot"], "Mjup": [1.8985234333630655e+30, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_{\\rm{Jup}}"], "Mearth": [6.045644495102106e+27, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_\\oplus"], "AU": [14959787075076.674, "Symbol('(length)', positive=True)", 0.0, "\\rm{AU}"], "au": [14959787075076.674, "Symbol('(length)', positive=True)", 0.0, "\\rm{AU}"], "ly": [9.460528409678268e+17, "Symbol('(length)', positive=True)", 0.0, "\\rm{ly}"], "pc": [3.0856775809623245e+18, "Symbol('(length)', positive=True)", 0.0, "\\rm{pc}"], "degree": [0.017453292519943295, "Symbol('(angle)', positive=True)", 0.0, "^\\circ"], "arcmin": [0.0002908882086657216, "Symbol('(angle)', positive=True)", 0.0, "\\rm{arcmin}"], "arcsec": [4.84813681109536e-06, "Symbol('(angle)', positive=True)", 0.0, "\\rm{arcsec}"], "mas": [4.8481368110953594e-09, "Symbol('(angle)', positive=True)", 0.0, "\\rm{mas}"], "hourangle": [0.2617993877991494, "Symbol('(angle)', positive=True)", 0.0, "\\rm{HA}"], "steradian": [1.0, "Pow(Symbol('(angle)', positive=True), Integer(2))", 0.0, "\\rm{sr}"], "lat": [-0.017453292519943295, "Symbol('(angle)', positive=True)", 90.0, "\\rm{Latitude}"], "lon": [0.017453292519943295, "Symbol('(angle)', positive=True)", -180.0, "\\rm{Longitude}"], "eV": [1.602176562e-12, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{eV}"], "amu": [1.660538921e-24, "Symbol('(mass)', positive=True)", 0.0, "\\rm{amu}"], "angstrom": [1e-08, "Symbol('(length)', positive=True)", 0.0, "\\AA"], "Jy": [1e-23, "Mul(Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{Jy}"], "counts": [1.0, "Integer(1)", 0.0, "\\rm{counts}"], "photons": [1.0, "Integer(1)", 0.0, "\\rm{photons}"], "me": [9.10938291e-28, "Symbol('(mass)', positive=True)", 0.0, "m_e"], "mp": [1.6737352238051868e-24, "Symbol('(mass)', positive=True)", 0.0, "m_p"], "mol": [6.02214129011674e+23, "Integer(1)", 0.0, "\\rm{mol}"], "Sv": [10000.0, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{Sv}"], "rayleigh": [79577.47154594767, "Mul(Pow(Symbol('(angle)', positive=True), Integer(-2)), Pow(Symbol('(length)', positive=True), Integer(-2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{R}"], "solMass": [1.98841586e+33, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_\\odot"], "solRad": [69550001060.63751, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\odot"], "solLum": [3.827e+33, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-3)))", 0.0, "\\rm{L}_\\odot"], "dyn": [1.0, "Mul(Symbol('(length)', positive=True), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{dyn}"], "sr": [1.0, "Pow(Symbol('(angle)', positive=True), Integer(2))", 0.0, "\\rm{sr}"], "rad": [1.0, "Symbol('(angle)', positive=True)", 0.0, "\\rm{rad}"], "deg": [0.017453292519943295, "Symbol('(angle)', positive=True)", 0.0, "^\\circ"], "Fr": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(3, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{Fr}"], "G": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(-1, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{G}"], "Angstrom": [1e-08, "Symbol('(length)', positive=True)", 0.0, "\\AA"], "statC": [1.0, "Mul(Pow(Symbol('(length)', positive=True), Rational(3, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{statC}"], "m_pl": [2.1764701184435688e-05, "Symbol('(mass)', positive=True)", 0.0, "m_{\\rm{P}}"], "l_pl": [1.6162283157597786e-33, "Symbol('(length)', positive=True)", 0.0, "\\ell_{\\rm{P}}"], "t_pl": [5.391157357800438e-44, "Symbol('(time)', positive=True)", 0.0, "t_{\\rm{P}}"], "T_pl": [1.4168076561665009e+32, "Symbol('(temperature)', positive=True)", 0.0, "T_{\\rm{P}}"], "q_pl": [1.8755459562040954e-18, "Mul(Symbol('(current_mks)', positive=True), Symbol('(time)', positive=True))", 0.0, "q_{\\rm{P}}"], "E_pl": [1.9561137903170922e+16, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "E_{\\rm{P}}"], "m_geom": [1.98841586e+33, "Symbol('(mass)', positive=True)", 0.0, "\\rm{M}_\\odot"], "l_geom": [147658.08127593446, "Symbol('(length)', positive=True)", 0.0, "\\rm{M}_\\odot"], "t_geom": [4.925343427950227e-06, "Symbol('(time)', positive=True)", 0.0, "\\rm{M}_\\odot"], "R_earth": [637100801.9550942, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\oplus"], "r_earth": [637100801.9550942, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\oplus"], "R_jup": [6991100000.006706, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\mathrm{Jup}"], "r_jup": [6991100000.006706, "Symbol('(length)', positive=True)", 0.0, "\\rm{R}_\\mathrm{Jup}"], "code_length": [1.4065766789943524e+26, "Symbol('(length)', positive=True)", 0.0, "\\rm{code\\ length}"], "code_mass": [7.032854288130045e+48, "Symbol('(mass)', positive=True)", 0.0, "\\rm{code\\ mass}"], "code_density": [2.527210293640965e-30, "Mul(Pow(Symbol('(length)', positive=True), Integer(-3)), Symbol('(mass)', positive=True))", 0.0, "\\rm{code\\ density}"], "code_specific_energy": [2.1385728000000008e+18, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{code\\ specific\\ energy}"], "code_time": [1885954521538155.5, "Symbol('(time)', positive=True)", 0.0, "\\rm{code\\ time}"], "code_magnetic": [8.24114664867782e-06, "Mul(Pow(Symbol('(length)', positive=True), Rational(-1, 2)), Pow(Symbol('(mass)', positive=True), Rational(1, 2)), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{code\\ magnetic}"], "code_temperature": [1.0, "Symbol('(temperature)', positive=True)", 0.0, "\\rm{code\\ temperature}"], "code_pressure": [1.4057425005421192e-08, "Mul(Pow(Symbol('(length)', positive=True), Integer(-1)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{code\\ pressure}"], "code_velocity": [1462385995.556577, "Mul(Symbol('(length)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-1)))", 0.0, "\\rm{code\\ velocity}"], "code_metallicity": [1.0, "Integer(1)", 0.0, "\\rm{code\\ metallicity}"], "a": [1.0000000027810865, "Integer(1)", 0.0, "\\rm{a}"], "mcm": [100.00000027810864, "Symbol('(length)', positive=True)", 0.0, "\\rm{m}/(1+z)"], "pccm": [3.08567758954386e+18, "Symbol('(length)', positive=True)", 0.0, "\\rm{pc}/(1+z)"], "AUcm": [14959787116681.133, "Symbol('(length)', positive=True)", 0.0, "\\rm{AU}/(1+z)"], "aucm": [14959787116681.133, "Symbol('(length)', positive=True)", 0.0, "\\rm{au}/(1+z)"], "Mpccm": [3.08567758954386e+24, "Symbol('(length)', positive=True)", 0.0, "\\rm{Mpc}/(1+z)"], "cm": [1.0, "Symbol('(length)', positive=True)", 0.0, "\\rm{cm}"], "unitary": [1.4065766789943524e+26, "Symbol('(length)', positive=True)", 0.0, "\\rm{unitary}"], "keV": [1.602176562e-09, "Mul(Pow(Symbol('(length)', positive=True), Integer(2)), Symbol('(mass)', positive=True), Pow(Symbol('(time)', positive=True), Integer(-2)))", 0.0, "\\rm{keV}"]} unyt-3.2.0/unyt/tests/sample_subclasses.py000066400000000000000000001304121526544257300207540ustar00rootroot00000000000000""" Tests for support of subclasses of unyt_array and unyt_quantity. """ import warnings from collections.abc import Callable, Collection, Iterable from importlib.metadata import version from numbers import Number as numeric_type import numpy as np from packaging.version import Version import unyt from unyt import unyt_array, unyt_quantity from unyt._array_functions import ( _trapezoid_func, allclose as unyt_allclose, around as unyt_around, array2string as unyt_array2string, array_equal as unyt_array_equal, array_equiv as unyt_array_equiv, array_repr as unyt_array_repr, block as unyt_block, choose as unyt_choose, clip as unyt_clip, column_stack as unyt_column_stack, concatenate as unyt_concatenate, convolve as unyt_convolve, copyto as unyt_copyto, correlate as unyt_correlate, cross as unyt_cross, diff as unyt_diff, dot as unyt_dot, dstack as unyt_dstack, ediff1d as unyt_ediff1d, einsum as unyt_einsum, fft_fft as unyt_fft_fft, fft_fft2 as unyt_fft_fft2, fft_fftn as unyt_fft_fftn, fft_fftshift as unyt_fft_fftshift, fft_hfft as unyt_fft_hfft, fft_ifft as unyt_fft_ifft, fft_ifft2 as unyt_fft_ifft2, fft_ifftn as unyt_fft_ifftn, fft_ifftshift as unyt_fft_ifftshift, fft_ihfft as unyt_fft_ihfft, fft_irfft as unyt_fft_irfft, fft_irfft2 as unyt_fft_irfft2, fft_irfftn as unyt_fft_irfftn, fft_rfft as unyt_fft_rfft, fft_rfft2 as unyt_fft_rfft2, fft_rfftn as unyt_fft_rfftn, fill_diagonal as unyt_fill_diagonal, geomspace as unyt_geomspace, histogram as unyt_histogram, histogram2d as unyt_histogram2d, histogram_bin_edges as unyt_histogram_bin_edges, histogramdd as unyt_histogramdd, hstack as unyt_hstack, inner as unyt_inner, insert as unyt_insert, interp as unyt_interp, intersect1d as unyt_intersect1d, isclose as unyt_isclose, isin as unyt_isin, kron as unyt_kron, linalg_det as unyt_linalg_det, linalg_eig as unyt_linalg_eig, linalg_eigh as unyt_linalg_eigh, linalg_eigvals as unyt_linalg_eigvals, linalg_eigvalsh as unyt_linalg_eigvalsh, linalg_inv as unyt_linalg_inv, linalg_lstsq as unyt_linalg_lstsq, linalg_pinv as unyt_linalg_pinv, linalg_solve as unyt_linalg_solve, linalg_svd as unyt_linalg_svd, linalg_tensorinv as unyt_linalg_tensorinv, linalg_tensorsolve as unyt_linalg_tensorsolve, linspace as unyt_linspace, logspace as unyt_logspace, nanpercentile as unyt_nanpercentile, nanquantile as unyt_nanquantile, norm as unyt_linalg_norm, # not linalg_norm, doesn't follow usual pattern outer as unyt_outer, pad as unyt_pad, percentile as unyt_percentile, place as unyt_place, prod as unyt_prod, ptp as unyt_ptp, put as unyt_put, put_along_axis as unyt_put_along_axis, putmask as unyt_putmask, quantile as unyt_quantile, savetxt as unyt_savetxt, searchsorted as unyt_searchsorted, select as unyt_select, setdiff1d as unyt_setdiff1d, sinc as unyt_sinc, sort_complex as unyt_sort_complex, stack as unyt_stack, take as unyt_take, tensordot as unyt_tensordot, trace as unyt_trace, trapezoid as unyt_trapezoid, tril as unyt_tril, triu as unyt_triu, union1d as unyt_union1d, unwrap as unyt_unwrap, var as unyt_var, vdot as unyt_vdot, vstack as unyt_vstack, where as unyt_where, ) from unyt.array import _iterable, multiple_output_operators NUMPY_VERSION = Version(version("numpy")) if NUMPY_VERSION >= Version("2.0.0dev0"): from unyt._array_functions import linalg_outer as unyt_linalg_outer if NUMPY_VERSION < Version("2.0.0dev0"): from unyt._array_functions import asfarray as unyt_asfarray _HANDLED_FUNCTIONS = {} class ExtraAttributeError(Exception): """ Raised when function arguments have incompatible ``extra_attr``. """ def __init__(self, message: str = "Tried to mix different extra_attr!") -> None: self.message = message # first we define helper functions to handle repetitive operations in wrapping unyt & # numpy functions (we will actually wrap the functions below): def _copy_extra_attr_if_present( from_obj: object, to_obj: object, copy_units=False ) -> object: """ Copy extra_attr across two objects. """ if not ( isinstance(to_obj, subclass_uarray) and isinstance(from_obj, subclass_uarray) ): return to_obj if copy_units: to_obj.units = from_obj.units if hasattr(from_obj, "extra_attr"): to_obj.extra_attr = from_obj.extra_attr return to_obj def _propagate_extra_attr_to_result(func: Callable) -> Callable: """ Wrapper that copies the extra attribute from first input argument to first output. Many functions take one input (or have a first input that has a close correspondence to the output) and one output. This helper copies the ``extra_attr`` attribute from the first input argument to the output. Can be used as a decorator on functions (the first argument is then the first argument of the function) or methods (the first argument is then ``self``). If the output is not a ``subclass_uarray`` it is not promoted (and then no attributes are copied). """ def wrapped(obj, *args, **kwargs): return _copy_extra_attr_if_present(obj, func(obj, *args, **kwargs)) return wrapped def _prepare_array_func_args(*args, _default_cm: bool = True, **kwargs) -> dict: """ For this minimalist class we check that the extra_attr matches and raise if not. """ args_extra_attrs = [ (hasattr(arg, "extra_attr"), getattr(arg, "extra_attr", None)) for arg in args ] kwarg_extra_attrs = { k: (hasattr(kwarg, "extra_attr"), getattr(kwarg, "extra_attr", None)) for k, kwarg in kwargs.items() } extra_attr_values_where_present = [ea[1] for ea in args_extra_attrs if ea[0]] + [ ea[1] for ea in kwarg_extra_attrs.values() if ea[0] ] # here we check that all of the extra_attr match (could be True, False or None): if not len(set(extra_attr_values_where_present)) <= 1: raise ExtraAttributeError # we could modify the args and kwargs before returning them here to "prepare" them return { "args": args, "kwargs": kwargs, "extra_attr": ( extra_attr_values_where_present[0] if len(extra_attr_values_where_present) > 0 else None ), } def _return_helper( res: np.ndarray, helper_result: dict, out: np.ndarray | None = None, ) -> "subclass_uarray": """ Helper function to attach our extra_attr to return values of wrapped functions. """ res = _promote_unyt_to_subclass(res) if isinstance(res, subclass_uarray): # also recognizes subclass_uquantity res.extra_attr = helper_result["extra_attr"] if isinstance(out, subclass_uarray): # also recognizes subclass_uquantity out.extra_attr = helper_result["extra_attr"] return res def _ensure_result_is_subclass_uarray_or_uquantity(func: Callable) -> Callable: """ Wrapper that converts any ``unyt_array`` or ``unyt_quantity`` instances in function output to subclass equivalents. If the wrapped function returns a ``tuple`` (as many numpy functions do) it is iterated over (but not recursively) and each element with a unyt class type is upgraded to its subclass equivalent. If anything but a ``tuple`` is returned, that object is promoted to the subclass equivalent if it is of a unyt class type. """ def wrapped(*args, **kwargs) -> object: # omit docstring so that sphinx picks up docstring of wrapped function result = func(*args, **kwargs) if isinstance(result, tuple): return tuple( _ensure_array_or_quantity_matches_shape(_promote_unyt_to_subclass(item)) for item in result ) else: return _ensure_array_or_quantity_matches_shape( _promote_unyt_to_subclass(result) ) return wrapped def _promote_unyt_to_subclass(input_object: object) -> object: """ Upgrades the input unyt instance to its subclass equivalent. """ if isinstance(input_object, unyt_quantity) and not isinstance( input_object, subclass_uquantity ): return input_object.view(subclass_uquantity) elif isinstance(input_object, unyt_array) and not isinstance( input_object, subclass_uarray ): return input_object.view(subclass_uarray) else: return input_object def _ensure_array_or_quantity_matches_shape(input_object: object) -> object: """ Convert scalars to ``subclass_uquantity`` and arrays to ``subclass_uarray``. """ if ( isinstance(input_object, subclass_uarray) and not isinstance(input_object, subclass_uquantity) and input_object.shape == () ): return input_object.view(subclass_uquantity) elif isinstance(input_object, subclass_uquantity) and input_object.shape != (): return input_object.view(subclass_uarray) else: return input_object def _default_unary_wrapper(unyt_func: Callable) -> Callable: """ Wrapper helper for unary functions with typical behaviour. Can be used as a decorator. """ def wrapper(*args, **kwargs): helper_result = _prepare_array_func_args(*args, **kwargs) res = unyt_func(*helper_result["args"], **helper_result["kwargs"]) if "out" in kwargs: return _return_helper(res, helper_result, out=kwargs["out"]) else: return _return_helper(res, helper_result) return wrapper def _default_binary_wrapper(unyt_func: Callable) -> Callable: """ Wrapper helper for binary functions with typical behaviour. Can be used as a decorator. """ def wrapper(*args, **kwargs): helper_result = _prepare_array_func_args(*args, **kwargs) res = unyt_func(*helper_result["args"], **helper_result["kwargs"]) if "out" in kwargs: return _return_helper(res, helper_result, out=kwargs["out"]) else: return _return_helper(res, helper_result) return wrapper # here an alias, but often comparisons have their own particular default logic: _default_comparison_wrapper = _default_binary_wrapper def _default_oplist_wrapper(unyt_func: Callable) -> Callable: """ Wrapper helper for functions accepting a list of operands with typical behaviour. Can be used as a decorator. """ def wrapper(*args, **kwargs): helper_result = _prepare_array_func_args(*args, **kwargs) helper_result_oplist = _prepare_array_func_args(*args[0]) res = unyt_func( helper_result_oplist["args"], *helper_result["args"][1:], **helper_result["kwargs"], ) return _return_helper(res, helper_result_oplist) return wrapper def implements(numpy_function: Callable) -> Callable: """ Register an __array_function__ implementation for subclass_uarray objects. Intended for use as a decorator. """ # See NEP 18 https://numpy.org/neps/nep-0018-array-function-protocol.html def decorator(func: Callable) -> Callable: """ Actually register the specified function. """ _HANDLED_FUNCTIONS[numpy_function] = func return func return decorator # Next we wrap functions from unyt and numpy. There's not much point in writing docstrings # or type hints for all of these. # Now we wrap functions that unyt handles explicitly (below that will be those not handled # explicitly): @implements(np.array2string) def array2string(a, *args, **kwargs): res = unyt_array2string(a, *args, **kwargs) return res + f" extra_attr={a.extra_attr}" implements(np.dot)(_default_binary_wrapper(unyt_dot)) implements(np.vdot)(_default_binary_wrapper(unyt_vdot)) implements(np.inner)(_default_binary_wrapper(unyt_inner)) implements(np.outer)(_default_binary_wrapper(unyt_outer)) implements(np.kron)(_default_binary_wrapper(unyt_kron)) implements(np.histogram_bin_edges)(_default_unary_wrapper(unyt_histogram_bin_edges)) implements(np.linalg.inv)(_default_unary_wrapper(unyt_linalg_inv)) implements(np.linalg.tensorinv)(_default_unary_wrapper(unyt_linalg_tensorinv)) implements(np.linalg.pinv)(_default_unary_wrapper(unyt_linalg_pinv)) implements(np.linalg.svd)(_default_unary_wrapper(unyt_linalg_svd)) def _histogram(a, bins=10, range=None, density=None, weights=None, normed=None): helper_result = _prepare_array_func_args( a, bins=bins, range=range, density=density, weights=weights, normed=normed ) kwargs = { "bins": helper_result["kwargs"]["bins"], "range": helper_result["kwargs"]["range"], "density": helper_result["kwargs"]["density"], "weights": helper_result["kwargs"]["weights"], } if NUMPY_VERSION < Version("1.24"): kwargs["normed"] = helper_result["kwargs"]["normed"] counts, bins = unyt_histogram(*helper_result["args"], **kwargs) counts = _promote_unyt_to_subclass(counts) if isinstance(counts, subclass_uarray): # also recognizes subclass_uquantity counts.extra_attr = helper_result["extra_attr"] return counts, _return_helper(bins, helper_result) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogram) def histogram(a, bins=10, range=None, density=None, weights=None): return _histogram(a, bins=bins, range=range, density=density, weights=weights) else: @implements(np.histogram) def histogram(a, bins=10, range=None, normed=None, density=None, weights=None): return _histogram(a, bins=bins, range=range, density=density, weights=weights) def _histogram2d(x, y, *, bins=10, range=None, density=None, weights=None, normed=None): if range is not None: xrange, yrange = range else: xrange, yrange = None, None try: N = len(bins) except TypeError: N = 1 if N != 2: xbins = ybins = bins elif N == 2: xbins, ybins = bins helper_result_x = _prepare_array_func_args(x, bins=xbins, range=xrange) helper_result_y = _prepare_array_func_args(y, bins=ybins, range=yrange) if not density: helper_result_w = _prepare_array_func_args(weights=weights, normed=normed) if (helper_result_x["kwargs"]["range"] is None) and ( helper_result_y["kwargs"]["range"] is None ): safe_range = None else: safe_range = ( helper_result_x["kwargs"]["range"], helper_result_y["kwargs"]["range"], ) kwargs = { "bins": ( helper_result_x["kwargs"]["bins"], helper_result_y["kwargs"]["bins"], ), "range": safe_range, "density": density, "weights": helper_result_w["kwargs"]["weights"], } if NUMPY_VERSION < Version("1.24"): kwargs["normed"] = helper_result_w["kwargs"]["normed"] counts, xbins, ybins = unyt_histogram2d( helper_result_x["args"][0], helper_result_y["args"][0], **kwargs, ) if weights is not None: counts = _promote_unyt_to_subclass(counts) if isinstance( counts, subclass_uarray ): # also recognizes subclass_uquantity counts.extra_attr = helper_result_w["extra_attr"] else: # density=True # now x, y and weights must be compatible because they will combine # we unpack input to the helper to get everything checked for compatibility helper_result = _prepare_array_func_args( x, y, xbins=xbins, ybins=ybins, xrange=xrange, yrange=yrange, weights=weights, normed=normed, ) if (helper_result["kwargs"]["xrange"] is None) and ( helper_result["kwargs"]["yrange"] is None ): safe_range = None else: safe_range = ( helper_result["kwargs"]["xrange"], helper_result["kwargs"]["yrange"], ) kwargs = { "bins": ( helper_result["kwargs"]["xbins"], helper_result["kwargs"]["ybins"], ), "range": safe_range, "density": density, "weights": helper_result["kwargs"]["weights"], } if NUMPY_VERSION < Version("1.24"): kwargs["normed"] = helper_result["kwargs"]["normed"] counts, xbins, ybins = unyt_histogram2d( helper_result["args"][0], helper_result["args"][1], **kwargs ) counts = _promote_unyt_to_subclass(counts) if isinstance(counts, subclass_uarray): # also recognizes subclass_uquantity counts.extra_attr = helper_result["extra_attr"] return ( counts, _return_helper(xbins, helper_result_x), _return_helper(ybins, helper_result_y), ) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogram2d) def histogram2d(x, y, bins=10, range=None, density=None, weights=None): return _histogram2d( x, y, bins=bins, range=range, density=density, weights=weights ) else: @implements(np.histogram2d) def histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None): return _histogram2d( x, y, bins=bins, range=range, normed=normed, weights=weights, density=density, ) def _histogramdd(sample, bins=10, range=None, density=None, weights=None, normed=None): D = len(sample) if range is not None: ranges = range else: ranges = D * [None] if np.isscalar(bins): bins = D * [bins] helper_results = [ _prepare_array_func_args(s, bins=b, range=r) for s, b, r in zip(sample, bins, ranges) ] if not density: helper_result_w = _prepare_array_func_args(weights=weights) all_ranges = [ helper_result["kwargs"]["range"] for helper_result in helper_results ] if set(all_ranges) == {None}: safe_range = None else: safe_range = [ helper_result["kwargs"]["range"] for helper_result in helper_results ] kwargs = { "bins": [ helper_result["kwargs"]["bins"] for helper_result in helper_results ], "range": safe_range, "density": density, "weights": helper_result_w["kwargs"]["weights"], } if NUMPY_VERSION < Version("1.24"): kwargs["normed"] = normed counts, bins = unyt_histogramdd( [helper_result["args"][0] for helper_result in helper_results], **kwargs, ) if weights is not None: counts = _promote_unyt_to_subclass(counts) if isinstance(counts, subclass_uarray): counts.extra_attr = helper_result_w["extra_attr"] else: # density=True # now sample and weights must be compatible because they will combine # we unpack input to the helper to get everything checked for compatibility helper_result = _prepare_array_func_args( *sample, bins=bins, range=range, weights=weights ) kwargs = { "bins": helper_result["kwargs"]["bins"], "range": helper_result["kwargs"]["range"], "density": density, "weights": helper_result["kwargs"]["weights"], } if NUMPY_VERSION < Version("1.24"): kwargs["normed"] = normed counts, bins = unyt_histogramdd( helper_result["args"], **kwargs, ) counts = _promote_unyt_to_subclass(counts) if isinstance(counts, subclass_uarray): # also recognizes subclass_uquantity counts.extra_attr = helper_result["extra_attr"] return ( counts, tuple( _return_helper(b, helper_result) for b, helper_result in zip(bins, helper_results) ), ) if NUMPY_VERSION >= Version("1.24"): @implements(np.histogramdd) def histogramdd(sample, bins=10, range=None, density=None, weights=None): return _histogramdd( sample, bins=bins, range=range, density=density, weights=weights ) else: @implements(np.histogramdd) def histogramdd( sample, bins=10, range=None, normed=None, weights=None, density=None ): return _histogramdd( sample, bins=bins, range=range, normed=normed, weights=weights, density=density, ) implements(np.concatenate)(_default_oplist_wrapper(unyt_concatenate)) implements(np.cross)(_default_binary_wrapper(unyt_cross)) implements(np.intersect1d)(_default_binary_wrapper(unyt_intersect1d)) implements(np.union1d)(_default_binary_wrapper(unyt_union1d)) implements(np.linalg.norm)(_default_unary_wrapper(unyt_linalg_norm)) implements(np.vstack)(_default_oplist_wrapper(unyt_vstack)) implements(np.hstack)(_default_oplist_wrapper(unyt_hstack)) implements(np.dstack)(_default_oplist_wrapper(unyt_dstack)) implements(np.column_stack)(_default_oplist_wrapper(unyt_column_stack)) implements(np.stack)(_default_oplist_wrapper(unyt_stack)) implements(np.around)(_default_unary_wrapper(unyt_around)) def _prepare_array_block_args(lst, recursing=False): """ Block accepts only a nested list of array "blocks". We need to recurse on this. """ helper_results = [] if isinstance(lst, list): for item in lst: if isinstance(item, list): helper_results += _prepare_array_block_args(item, recursing=True) else: helper_results.append(_prepare_array_func_args(item)) if recursing: return helper_results eas = [hr["extra_attr"] for hr in helper_results] if set(eas) == {True}: ret_ea = True elif set(eas) == {None}: ret_ea = None elif set(eas) == {False}: ret_ea = False else: # mixed values raise ExtraAttributeError ret_lst = lst return { "args": ret_lst, "kwargs": {}, "extra_attr": ret_ea, } @implements(np.block) def block(arrays): # block is a special case since we need to recurse more than one level # down the list of arrays. helper_result_block = _prepare_array_block_args(arrays) res = unyt_block(helper_result_block["args"]) return _return_helper(res, helper_result_block) implements(np.fft.fft)(_default_unary_wrapper(unyt_fft_fft)) implements(np.fft.fft2)(_default_unary_wrapper(unyt_fft_fft2)) implements(np.fft.fftn)(_default_unary_wrapper(unyt_fft_fftn)) implements(np.fft.hfft)(_default_unary_wrapper(unyt_fft_hfft)) implements(np.fft.rfft)(_default_unary_wrapper(unyt_fft_rfft)) implements(np.fft.rfft2)(_default_unary_wrapper(unyt_fft_rfft2)) implements(np.fft.rfftn)(_default_unary_wrapper(unyt_fft_rfftn)) implements(np.fft.ifft)(_default_unary_wrapper(unyt_fft_ifft)) implements(np.fft.ifft2)(_default_unary_wrapper(unyt_fft_ifft2)) implements(np.fft.ifftn)(_default_unary_wrapper(unyt_fft_ifftn)) implements(np.fft.ihfft)(_default_unary_wrapper(unyt_fft_ihfft)) implements(np.fft.irfft)(_default_unary_wrapper(unyt_fft_irfft)) implements(np.fft.irfft2)(_default_unary_wrapper(unyt_fft_irfft2)) implements(np.fft.irfftn)(_default_unary_wrapper(unyt_fft_irfftn)) implements(np.fft.fftshift)(_default_unary_wrapper(unyt_fft_fftshift)) implements(np.fft.ifftshift)(_default_unary_wrapper(unyt_fft_ifftshift)) implements(np.sort_complex)(_default_unary_wrapper(unyt_sort_complex)) implements(np.isclose)(_default_comparison_wrapper(unyt_isclose)) implements(np.allclose)(_default_comparison_wrapper(unyt_allclose)) implements(np.array_equal)(_default_comparison_wrapper(unyt_array_equal)) implements(np.array_equiv)(_default_comparison_wrapper(unyt_array_equiv)) implements(np.linspace)(_default_binary_wrapper(unyt_linspace)) implements(np.logspace)(_default_binary_wrapper(unyt_logspace)) implements(np.geomspace)(_default_binary_wrapper(unyt_geomspace)) implements(np.copyto)(_default_binary_wrapper(unyt_copyto)) implements(np.prod)(_default_unary_wrapper(unyt_prod)) implements(np.var)(_default_unary_wrapper(unyt_var)) implements(np.trace)(_default_unary_wrapper(unyt_trace)) implements(np.percentile)(_default_unary_wrapper(unyt_percentile)) implements(np.quantile)(_default_unary_wrapper(unyt_quantile)) implements(np.nanpercentile)(_default_unary_wrapper(unyt_nanpercentile)) implements(np.nanquantile)(_default_unary_wrapper(unyt_nanquantile)) implements(np.linalg.det)(_default_unary_wrapper(unyt_linalg_det)) implements(np.diff)(_default_unary_wrapper(unyt_diff)) implements(np.ediff1d)(_default_unary_wrapper(unyt_ediff1d)) implements(np.ptp)(_default_unary_wrapper(unyt_ptp)) # implements(np.cumprod)(...) Omitted because unyt just raises if called. # implements(np.cumulative_prod)(...) Omitted because unyt just raises if called. implements(np.pad)(_default_unary_wrapper(unyt_pad)) implements(np.choose)(_default_binary_wrapper(unyt_choose)) implements(np.insert)(_default_unary_wrapper(unyt_insert)) @implements(np.linalg.lstsq) def linalg_lstsq(a, b, rcond=None): helper_result = _prepare_array_func_args(a, b, rcond=rcond) ress = unyt_linalg_lstsq(*helper_result["args"], **helper_result["kwargs"]) return ( _return_helper(ress[0], helper_result), _return_helper(ress[1], helper_result), ress[2], _return_helper(ress[3], helper_result), ) implements(np.linalg.solve)(_default_binary_wrapper(unyt_linalg_solve)) implements(np.linalg.tensorsolve)(_default_binary_wrapper(unyt_linalg_tensorsolve)) @implements(np.linalg.eig) def linalg_eig(a): helper_result = _prepare_array_func_args(a) ress = unyt_linalg_eig(*helper_result["args"], **helper_result["kwargs"]) return (_return_helper(ress[0], helper_result), ress[1]) @implements(np.linalg.eigh) def linalg_eigh(a, UPLO="L"): helper_result = _prepare_array_func_args(a, UPLO=UPLO) ress = unyt_linalg_eigh(*helper_result["args"], **helper_result["kwargs"]) return (_return_helper(ress[0], helper_result), ress[1]) implements(np.linalg.eigvals)(_default_unary_wrapper(unyt_linalg_eigvals)) implements(np.linalg.eigvalsh)(_default_unary_wrapper(unyt_linalg_eigvalsh)) @implements(np.savetxt) def savetxt( fname, X, fmt="%.18e", delimiter=" ", newline="\n", header="", footer="", comments="# ", encoding=None, ): warnings.warn( "numpy.savetxt does not preserve units or extra_attr information, " "and will only save the raw numerical data from the subclass_uarray object.\n" "If this is the intended behaviour, call `numpy.savetxt(file, arr.d)` " "to silence this warning.\n", stacklevel=4, ) helper_result = _prepare_array_func_args( fname, X, fmt=fmt, delimiter=delimiter, newline=newline, header=header, footer=footer, comments=comments, encoding=encoding, ) with warnings.catch_warnings(): warnings.filterwarnings( action="ignore", category=UserWarning, message="numpy.savetxt does not preserve units", ) unyt_savetxt(*helper_result["args"], **helper_result["kwargs"]) return @implements(np.apply_over_axes) def apply_over_axes(func, a, axes): res = func(a, axes[0]) if len(axes) > 1: # this function is recursive by nature, # here we intentionally do not call the base _implementation return np.apply_over_axes(func, res, axes[1:]) else: return res implements(np.fill_diagonal)(_default_binary_wrapper(unyt_fill_diagonal)) implements(np.isin)(_default_comparison_wrapper(unyt_isin)) implements(np.place)(_default_unary_wrapper(unyt_place)) implements(np.put)(_default_unary_wrapper(unyt_put)) implements(np.put_along_axis)(_default_unary_wrapper(unyt_put_along_axis)) implements(np.putmask)(_default_unary_wrapper(unyt_putmask)) implements(np.searchsorted)(_default_binary_wrapper(unyt_searchsorted)) @implements(np.select) def select(condlist, choicelist, default=0): helper_result = _prepare_array_func_args(condlist, choicelist, default=default) helper_result_choicelist = _prepare_array_func_args(*choicelist) res = unyt_select( helper_result["args"][0], helper_result_choicelist["args"], **helper_result["kwargs"], ) return _return_helper(res, helper_result) implements(np.setdiff1d)(_default_binary_wrapper(unyt_setdiff1d)) implements(np.sinc)(_default_unary_wrapper(unyt_sinc)) implements(np.clip)(_default_unary_wrapper(unyt_clip)) @implements(np.where) def where(condition, *args): helper_result = _prepare_array_func_args(condition, *args) if len(args) == 0: # just condition res = unyt_where(*helper_result["args"], **helper_result["kwargs"]) elif len(args) < 2: # pragma: no cover # error message borrowed from numpy 1.24.1 raise ValueError("either both or neither of x and y should be given") res = unyt_where(*helper_result["args"], **helper_result["kwargs"]) return _return_helper(res, helper_result) implements(np.triu)(_default_unary_wrapper(unyt_triu)) implements(np.tril)(_default_unary_wrapper(unyt_tril)) @implements(np.einsum) def einsum( subscripts, *operands, out=None, dtype=None, order="K", casting="safe", optimize=False, ): helper_result = _prepare_array_func_args( subscripts, operands, out=out, dtype=dtype, order=order, casting=casting, optimize=optimize, ) helper_result_operands = _prepare_array_func_args(*operands) res = unyt_einsum( helper_result["args"][0], *helper_result_operands["args"], **helper_result["kwargs"], ) return _return_helper(res, helper_result_operands, out=out) implements(np.convolve)(_default_binary_wrapper(unyt_convolve)) implements(np.correlate)(_default_binary_wrapper(unyt_correlate)) implements(np.tensordot)(_default_binary_wrapper(unyt_tensordot)) implements(np.unwrap)(_default_unary_wrapper(unyt_unwrap)) implements(np.interp)(_default_unary_wrapper(unyt_interp)) @implements(np.array_repr) def array_repr(arr, max_line_width=None, precision=None, suppress_small=None): helper_result = _prepare_array_func_args( arr, max_line_width=max_line_width, precision=precision, suppress_small=suppress_small, ) rep = unyt_array_repr(*helper_result["args"], **helper_result["kwargs"])[:-1] if hasattr(arr, "extra_attr"): rep += f", extra_attr='{arr.extra_attr}'" rep += ")" return rep if NUMPY_VERSION < Version("2.0.0dev0"): implements(np.asfarray)(_default_unary_wrapper(unyt_asfarray)) # NOQA: NPY201 if NUMPY_VERSION >= Version("2.0.0dev0"): implements(np.linalg.outer)(_default_binary_wrapper(unyt_linalg_outer)) implements(_trapezoid_func)(_default_binary_wrapper(unyt_trapezoid)) implements(np.isin)(_default_comparison_wrapper(unyt_isin)) implements(np.take)(_default_unary_wrapper(unyt_take)) # Now we wrap functions that unyt does not handle explicitly: implements(np.average)(_propagate_extra_attr_to_result(np.average._implementation)) implements(np.max)(_propagate_extra_attr_to_result(np.max._implementation)) implements(np.min)(_propagate_extra_attr_to_result(np.min._implementation)) implements(np.mean)(_propagate_extra_attr_to_result(np.mean._implementation)) implements(np.median)(_propagate_extra_attr_to_result(np.median._implementation)) implements(np.sort)(_propagate_extra_attr_to_result(np.sort._implementation)) implements(np.sum)(_propagate_extra_attr_to_result(np.sum._implementation)) implements(np.partition)(_propagate_extra_attr_to_result(np.partition._implementation)) if NUMPY_VERSION >= Version("2.0.0dev0"): implements(np.linalg.cross)( _default_binary_wrapper(np.linalg.cross._implementation) ) @implements(np.meshgrid) def meshgrid(*xi, **kwargs): # meshgrid is a unique case: arguments never interact with each other, so we don't # want to use our _prepare_array_func_args helper (that will try to enforce # consistency). # However we can't just use _propagate_extra_attr_to_result because we # need to iterate over arguments. res = np.meshgrid._implementation(*xi, **kwargs) ret_type = tuple if NUMPY_VERSION >= Version("2.0.0dev0") else list return ret_type(_copy_extra_attr_if_present(x, r) for (x, r) in zip(xi, res)) class subclass_uarray(unyt_array): """ Minimalist subclass of unyt_array. """ def __new__( cls, input_array: Iterable, units: str | unyt.unit_object.Unit = None, *, registry: unyt.unit_registry.UnitRegistry = None, dtype: np.dtype | str = None, bypass_validation: bool = False, name: str = None, extra_attr: bool = None, ) -> "subclass_uarray": """ Closely inspired by the :meth:`unyt.array.unyt_array.__new__` constructor. """ if bypass_validation is True: obj = super().__new__( cls, input_array, units=units, registry=registry, dtype=dtype, bypass_validation=bypass_validation, name=name, ) # dtype, units, registry & name are handled by unyt obj.extra_attr = extra_attr return obj if isinstance(input_array, subclass_uarray): if input_array.extra_attr != extra_attr: raise ValueError obj = input_array.view(cls) return obj elif isinstance(input_array, np.ndarray) and input_array.dtype != object: # Guard np.ndarray so it doesn't get caught by _iterable in next # case. ndarray with object dtype goes to next case to properly # handle e.g. ndarrays containing subclass_uquantity's pass elif _iterable(input_array) and input_array: # check compatibility: helper_result = _prepare_array_func_args(*input_array) input_array = helper_result["args"] extra_attr = ( helper_result["extra_attr"] if extra_attr is None else extra_attr ) obj = super().__new__( cls, input_array, units=units, registry=registry, dtype=dtype, bypass_validation=bypass_validation, name=name, ) if isinstance(obj, unyt_array) and not isinstance(obj, cls): obj = obj.view(cls) # attach our attribute: obj.extra_attr = extra_attr return obj def __array_finalize__(self, obj: "subclass_uarray") -> None: super().__array_finalize__(obj) if obj is None: return self.extra_attr = getattr(obj, "extra_attr", None) def __str__(self) -> str: # pragma: no cover return super().__str__() + f" {self.extra_attr}" def __repr__(self) -> str: # pragma: no cover return super().__repr__() + f" {self.extra_attr}" # Wrap functions that return copies of subclass_uarrays so that our # attribute gets passed through: astype = _propagate_extra_attr_to_result(unyt_array.astype) in_units = _propagate_extra_attr_to_result(unyt_array.in_units) byteswap = _propagate_extra_attr_to_result(unyt_array.byteswap) compress = _propagate_extra_attr_to_result(unyt_array.compress) diagonal = _propagate_extra_attr_to_result(unyt_array.diagonal) flatten = _propagate_extra_attr_to_result(unyt_array.flatten) ravel = _propagate_extra_attr_to_result(unyt_array.ravel) repeat = _propagate_extra_attr_to_result(unyt_array.repeat) swapaxes = _propagate_extra_attr_to_result(unyt_array.swapaxes) transpose = _propagate_extra_attr_to_result(unyt_array.transpose) view = _propagate_extra_attr_to_result(unyt_array.view) __copy__ = _propagate_extra_attr_to_result(unyt_array.__copy__) __deepcopy__ = _propagate_extra_attr_to_result(unyt_array.__deepcopy__) in_cgs = _propagate_extra_attr_to_result(unyt_array.in_cgs) take = _propagate_extra_attr_to_result( _ensure_result_is_subclass_uarray_or_uquantity(unyt_array.take) ) reshape = _propagate_extra_attr_to_result( _ensure_result_is_subclass_uarray_or_uquantity(unyt_array.reshape) ) __getitem__ = _propagate_extra_attr_to_result( _ensure_result_is_subclass_uarray_or_uquantity(unyt_array.__getitem__) ) dot = _default_binary_wrapper(unyt_array.dot) # Also wrap some array "properties": T = property(_propagate_extra_attr_to_result(unyt_array.transpose)) ua = property(_propagate_extra_attr_to_result(np.ones_like)) unit_array = property(_propagate_extra_attr_to_result(np.ones_like)) def __array_ufunc__( self, ufunc: np.ufunc, method: str, *inputs, **kwargs ) -> object: """ Handles :mod:`numpy` ufunc calls on ``subclass_uarray`` input. ``numpy`` facilitates wrapping array classes by handing off to this function when a function of :class:`numpy.ufunc` type is called with arguments from an inheriting array class. Since we inherit from ``unyt_array`, we let it handle what to do with the units and take care of processing the extra_attr via our helper functions. """ helper_result = _prepare_array_func_args(*inputs, **kwargs) result = _ensure_result_is_subclass_uarray_or_uquantity( super().__array_ufunc__ )(ufunc, method, *inputs, **kwargs) # if we get a tuple we have multiple return values to deal with if isinstance(result, tuple): for r in result: if isinstance(r, subclass_uarray): # also recognizes subclass_uquantity r.extra_attr = helper_result["extra_attr"] elif isinstance(result, subclass_uarray): # also recognizes subclass_uquantity result.extra_attr = helper_result["extra_attr"] if "out" in kwargs: out = kwargs.pop("out") assert ufunc not in multiple_output_operators out = out[0] if isinstance(out, subclass_uarray): # also recognizes subclass_uquantity out.extra_attr = helper_result["extra_attr"] return result def __array_function__( self, func: Callable, types: Collection, args: tuple, kwargs: dict ): """ Handles ``numpy`` function calls on ``subclass_uarray`` input. ``numpy`` facilitates wrapping array classes by handing off to this function when a numpy-defined function is called with arguments from an inheriting array class. Since we inherit from ``unyt_array``, we let it handle what to do with the units and take care of processing the extra_attr via our helper functions. """ # Follow NEP 18 guidelines # https://numpy.org/neps/nep-0018-array-function-protocol.html from unyt._array_functions import ( _HANDLED_FUNCTIONS as _UNYT_HANDLED_FUNCTIONS, _UNSUPPORTED_FUNCTIONS as _UNYT_UNSUPPORTED_FUNCTIONS, ) # Let's claim to support everything supported by unyt. # If we can't do this in future, follow their pattern of # defining out own _UNSUPPORTED_FUNCTIONS in a _array_functions.py file _UNSUPPORTED_FUNCTIONS = _UNYT_UNSUPPORTED_FUNCTIONS if func in _UNSUPPORTED_FUNCTIONS: # pragma: no cover # following NEP 18, return NotImplemented as a sentinel value # which will lead to raising a TypeError, while # leaving other arguments a chance to take the lead return NotImplemented if not all(issubclass(t, subclass_uarray) or t is np.ndarray for t in types): # Note: this allows subclasses that don't override # __array_function__ to handle subclass_uarray objects return NotImplemented if func in _HANDLED_FUNCTIONS: function_to_invoke = _HANDLED_FUNCTIONS[func] elif func in _UNYT_HANDLED_FUNCTIONS: function_to_invoke = _UNYT_HANDLED_FUNCTIONS[func] else: # default to numpy's private implementation function_to_invoke = func._implementation return function_to_invoke(*args, **kwargs) def __mul__( self, b: int | float | np.ndarray | unyt.unit_object.Unit ) -> "subclass_uarray": """ Multiply this ``subclass_uarray``. We delegate most cases to ``unyt_array``, but we need to handle the case where the second argument is a ``Unit``. """ if getattr(b, "is_Unit", False): return _copy_extra_attr_if_present( self, _ensure_result_is_subclass_uarray_or_uquantity(b.__mul__)( self.view(unyt_quantity) if self.shape == () else self.view(unyt_array) ), ) else: return super().__mul__(b) def __rmul__( self, b: int | float | np.ndarray | unyt.unit_object.Unit ) -> "subclass_uarray": """ Multiply this ``subclass_uarray`` (as the right argument). We delegate most cases to ``unyt_array``, but we need to handle the case where the second argument is a ``Unit``. """ if getattr(b, "is_Unit", False): return self.__mul__(b) else: return super().__rmul__(b) def __truediv__( self, b: int | float | np.ndarray | unyt.unit_object.Unit ) -> "subclass_uarray": """ Divide this ``subclass_uarray``. We delegate most cases to ``unyt_array``, but we need to handle the case where the second argument is a ``Unit``. """ if getattr(b, "is_Unit", False): return _copy_extra_attr_if_present( self, _ensure_result_is_subclass_uarray_or_uquantity((1 / b).__mul__)( self.view(unyt_quantity) if self.shape == () else self.view(unyt_array) ), ) else: return super().__truediv__(b) class subclass_uquantity(subclass_uarray, unyt_quantity): """ Minimalist subclass of unyt_quantity. """ def __new__( cls, input_scalar: numeric_type, units: str | unyt.unit_object.Unit | None = None, *, registry: unyt.unit_registry.UnitRegistry | None = None, dtype: np.dtype | str | None = None, bypass_validation: bool = False, name: str | None = None, extra_attr: bool | None = None, ) -> "subclass_uquantity": """ Closely inspired by the ``unyt_quantity.__new__`` constructor. """ if bypass_validation is True: result = super().__new__( cls, np.asarray(input_scalar), units=units, registry=registry, dtype=dtype, bypass_validation=bypass_validation, name=name, extra_attr=extra_attr, ) if not isinstance( input_scalar, (numeric_type, np.number, np.ndarray) ): # pragma: no cover raise RuntimeError("subclass_uquantity values must be numeric") # Use values from kwargs, if None use values from input_scalar units = getattr(input_scalar, "units", None) if units is None else units name = getattr(input_scalar, "name", None) if name is None else name if isinstance(input_scalar, subclass_uquantity): if input_scalar.extra_attr != extra_attr: raise ValueError extra_attr = ( getattr(input_scalar, "extra_attr", None) if extra_attr is None else extra_attr ) result = super().__new__( cls, np.asarray(input_scalar), units=units, registry=registry, dtype=dtype, bypass_validation=bypass_validation, name=name, extra_attr=extra_attr, ) if result.size > 1: # pragma: no cover raise RuntimeError("subclass_uquantity instances must be scalars") return result __round__ = _propagate_extra_attr_to_result( _ensure_result_is_subclass_uarray_or_uquantity(unyt_quantity.__round__) ) unyt-3.2.0/unyt/tests/test_array_functions.py000066400000000000000000002174131526544257300215200ustar00rootroot00000000000000# tests for NumPy __array_function__ support import inspect import re import warnings from importlib.metadata import version import numpy as np import pytest from numpy.testing import assert_allclose from packaging.version import Version from unyt import A, K, Msun, cm, degC, delta_degC, dimensionless, g, km, rad, s from unyt._array_functions import ( _HANDLED_FUNCTIONS as HANDLED_FUNCTIONS, _UNSUPPORTED_FUNCTIONS as UNSUPPORTED_FUNCTIONS, _sanitize_bins, _sanitize_range, ) from unyt.array import unyt_array, unyt_quantity from unyt.exceptions import ( UnitConversionError, UnitInconsistencyError, UnytError, ) from unyt.testing import assert_array_equal_units from unyt.unit_object import Unit from unyt.unit_registry import UnitRegistry NUMPY_VERSION = Version(version("numpy")) VAR_POSITIONAL = inspect.Parameter.VAR_POSITIONAL VAR_KEYWORD = inspect.Parameter.VAR_KEYWORD POSITIONAL_ONLY = inspect.Parameter.POSITIONAL_ONLY KEYWORD_ONLY = inspect.Parameter.KEYWORD_ONLY POSITIONAL_OR_KEYWORD = inspect.Parameter.POSITIONAL_OR_KEYWORD # this is a subset of NOT_HANDLED_FUNCTIONS for which there's nothing to do # because they don't apply to (real) numeric types # or they work as expected out of the box # This is not necessarily complete ! NOOP_FUNCTIONS = { np.all, # expects booleans np.amax, # works out of the box (tested) np.amin, # works out of the box (tested) np.angle, # expects complex numbers np.any, # works out of the box (tested) np.append, # we get it for free with np.concatenate (tested) np.apply_along_axis, # works out of the box (tested) np.argmax, # returns pure numbers np.argmin, # returns pure numbers np.argpartition, # returns pure numbers np.argsort, # returns pure numbers np.argwhere, # returns pure numbers np.array_str, # hooks into __str__ np.atleast_1d, # works out of the box (tested) np.atleast_2d, # works out of the box (tested) np.atleast_3d, # works out of the box (tested) np.can_cast, # works out of the box (tested) np.common_type, # works out of the box (tested) np.result_type, # works out of the box (tested) np.iscomplex, # works out of the box (tested) np.iscomplexobj, # works out of the box (tested) np.isreal, # works out of the box (tested) np.isrealobj, # works out of the box (tested) np.nan_to_num, # works out of the box (tested) np.nanargmax, # return pure numbers np.nanargmin, # return pure numbers np.nanmax, # works out of the box (tested) np.nanmean, # works out of the box (tested) np.nanmedian, # works out of the box (tested) np.nanmin, # works out of the box (tested) np.trim_zeros, # works out of the box (tested) np.max, # works out of the box (tested) np.mean, # works out of the box (tested) np.median, # works out of the box (tested) np.min, # works out of the box (tested) np.ndim, # return pure numbers np.shape, # returns pure numbers np.size, # returns pure numbers np.sort, # works out of the box (tested) np.sum, # works out of the box (tested) np.repeat, # works out of the box (tested) np.tile, # works out of the box (tested) np.shares_memory, # works out of the box (tested) np.nonzero, # works out of the box (tested) np.count_nonzero, # returns pure numbers np.flatnonzero, # works out of the box (tested) np.isneginf, # works out of the box (tested) np.isposinf, # works out of the box (tested) np.empty_like, # works out of the box (tested) np.full_like, # works out of the box (tested) np.ones_like, # works out of the box (tested) np.zeros_like, # works out of the box (tested) np.copy, # works out of the box (tested) np.meshgrid, # works out of the box (tested) np.transpose, # works out of the box (tested) np.reshape, # works out of the box (tested) np.resize, # works out of the box (tested) np.roll, # works out of the box (tested) np.rollaxis, # works out of the box (tested) np.rot90, # works out of the box (tested) np.expand_dims, # works out of the box (tested) np.squeeze, # works because ndarray.squeeze is handled np.flip, # works out of the box (tested) np.fliplr, # works out of the box (tested) np.flipud, # works out of the box (tested) np.delete, # works out of the box (tested) np.partition, # works out of the box (tested) np.broadcast_to, # works out of the box (tested) np.broadcast_arrays, # works out of the box (tested) np.split, # works out of the box (tested) np.array_split, # works out of the box (tested) np.dsplit, # works out of the box (tested) np.hsplit, # works out of the box (tested) np.vsplit, # works out of the box (tested) np.swapaxes, # works out of the box (tested) np.moveaxis, # works out of the box (tested) np.nansum, # works out of the box (tested) np.std, # works out of the box (tested) np.nanstd, # works out of the box (tested) np.nanvar, # works out of the box (tested) np.nanprod, # works out of the box (tested) np.diag, # works out of the box (tested) np.diag_indices_from, # returns pure numbers np.diagflat, # works out of the box (tested) np.diagonal, # works out of the box (tested) np.ravel, # returns pure numbers np.ravel_multi_index, # returns pure numbers np.unravel_index, # returns pure numbers np.fix, # works out of the box (tested) np.round, # is implemented via np.around np.may_share_memory, # returns pure numbers (booleans) np.linalg.matrix_power, # works out of the box (tested) np.linalg.cholesky, # works out of the box (tested) np.linalg.multi_dot, # works out of the box (tested) np.linalg.matrix_rank, # returns pure numbers np.linalg.qr, # works out of the box (tested) np.linalg.slogdet, # undefined units np.linalg.cond, # works out of the box (tested) np.gradient, # works out of the box (tested) np.cumsum, # works out of the box (tested) np.nancumsum, # works out of the box (tested) np.nancumprod, # we get it for free with np.cumprod (tested) np.bincount, # works out of the box (tested) np.unique, # works out of the box (tested) np.min_scalar_type, # returns dtypes np.extract, # works out of the box (tested) np.setxor1d, # we get it for free with previously implemented functions (tested) np.lexsort, # returns pure numbers np.tril_indices_from, # returns pure numbers np.triu_indices_from, # returns pure numbers np.imag, # works out of the box (tested) np.real, # works out of the box (tested) np.real_if_close, # works out of the box (tested) np.einsum_path, # returns pure numbers np.cov, # returns pure numbers np.corrcoef, # returns pure numbers np.compress, # works out of the box (tested) np.take_along_axis, # works out of the box (tested) } if NUMPY_VERSION >= Version("2.0.0dev0"): # the following all work out of the box (tested) NOOP_FUNCTIONS |= { np.linalg.cross, np.linalg.diagonal, np.linalg.matmul, np.linalg.matrix_norm, np.linalg.matrix_transpose, np.linalg.svdvals, np.linalg.tensordot, np.linalg.trace, np.linalg.vecdot, np.linalg.vector_norm, np.astype, np.matrix_transpose, np.unique_all, np.unique_counts, np.unique_inverse, np.unique_values, } if NUMPY_VERSION >= Version("2.1.0dev0"): NOOP_FUNCTIONS |= { np.unstack, np.cumulative_sum, } if NUMPY_VERSION >= Version("2.4.1"): NOOP_FUNCTIONS |= {np.average} # Functions for which behaviour is intentionally left to default IGNORED_FUNCTIONS = { np.i0, # IO functions (no way to add units) np.save, np.savez, np.savez_compressed, } # map subsets of deprecated functions to the version they were removed # so that we can drop the ones that are not present in any version we support DEPRECATED_FUNCTIONS = { Version("1.20"): { "fv", "ipmt", "irr", "mirr", "nper", "npv", "pmt", "ppmt", "pv", "rate", }, Version("1.22"): { "alen", "asscalar", }, Version("2.0.0b1"): { "msort", "product", "cumproduct", "round_", "sometrue", "alltrue", }, # functions that are deprecated but not yet removed in any known version # should be added here Version("999.999.999"): set(), } NOT_HANDLED_FUNCTIONS = NOOP_FUNCTIONS | UNSUPPORTED_FUNCTIONS | IGNORED_FUNCTIONS with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) for removal_version, functions in DEPRECATED_FUNCTIONS.items(): if NUMPY_VERSION >= removal_version: continue for func in functions: NOT_HANDLED_FUNCTIONS.add(getattr(np, func)) def get_decorators(func): # adapted from # https://stackoverflow.com/questions/3232024/introspection-to-get-decorator-names-on-a-method import ast import inspect target = func decorators = {} def visit_FunctionDef(node): decorators[node.name] = [] for n in node.decorator_list: name = "" if isinstance(n, ast.Call): name = n.func.attr if isinstance(n.func, ast.Attribute) else n.func.id else: name = n.attr if isinstance(n, ast.Attribute) else n.id decorators[node.name].append(name) node_iter = ast.NodeVisitor() node_iter.visit_FunctionDef = visit_FunctionDef try: node_iter.visit(ast.parse(inspect.getsource(target))) return decorators[func.__name__] except TypeError: # may be raised if inspecting a C compiled function # in which case, we return with empty hands return [] def get_wrapped_functions(*modules): """get functions that support __array_function__ in modules This was adapted from astropy's tests """ wrapped_functions = {} for mod in modules: for name, f in mod.__dict__.items(): if callable(f) and hasattr(f, "__wrapped__"): if ( f is np.printoptions or f.__name__.startswith("_") or "deprecate" in get_decorators(f) ): continue wrapped_functions[mod.__name__ + "." + name] = f return dict(sorted(wrapped_functions.items())) def test_wrapping_completeness(): """Ensure we wrap all numpy functions that support __array_function__""" handled_numpy_functions = set(HANDLED_FUNCTIONS.keys()) # ensure no functions appear in both NOT_HANDLED_FUNCTIONS and HANDLED_FUNCTIONS assert NOT_HANDLED_FUNCTIONS.isdisjoint(handled_numpy_functions), ( NOT_HANDLED_FUNCTIONS.intersection(handled_numpy_functions) ) # get list of functions that support wrapping by introspection on numpy module wrappable_functions = get_wrapped_functions(np, np.fft, np.linalg) for function in HANDLED_FUNCTIONS: # ensure we only have wrappers for functions that support wrapping assert function in wrappable_functions.values() all_funcs = NOT_HANDLED_FUNCTIONS.union(handled_numpy_functions) # ensure all functions in numpy that support wrapping either have wrappers # or are explicitly whitelisted for function in wrappable_functions.values(): assert function in all_funcs def test_unsupported_array_function_returns_notimplemented(): x = unyt_array([1, 2, 3], "cm") for func in UNSUPPORTED_FUNCTIONS: assert x.__array_function__(func, (unyt_array,), (x,), {}) is NotImplemented with pytest.raises(TypeError, match="no implementation found"): np.packbits(x) @pytest.mark.parametrize( "arrays", [ [np.array([1]), [2] * Unit()], [np.array([1]), [2] * Unit(registry=UnitRegistry())], [[1], [2] * Unit()], ], ) def test_unit_validation(arrays): # see https://github.com/yt-project/unyt/issues/462 # numpy.concatenate isn't essential to this test # what we're really testing is the unit consistency validation # underneath, but we do so using public API res = np.concatenate(arrays) assert res.units.is_dimensionless def test_unit_validation_dimensionless_factors(): # see https://github.com/yt-project/unyt/issues/477 # numpy.concatenate isn't essential to this test # what we're really testing is the unit consistency validation # underneath, but we do so using public API res = np.concatenate([[1] * cm, unyt_array([1], "cm*dimensionless")]) assert res.units is cm def test_array_repr(): arr = [1, 2, 3] * cm assert re.fullmatch(r"unyt_array\(\[1, 2, 3\], (units=)?'cm'\)", np.array_repr(arr)) def test_dot_vectors(): a = [1, 2, 3] * cm b = [1, 2, 3] * s res = np.dot(a, b) assert res.units == cm * s assert res.d == 14 # NOTE: explicitly setting the dtype of out arrays as `dtype=np.int_` is the # cross-platform way to guarantee that their dtype matches that of np.arange(x) # (on POSIX system it's int64, while on windows it's int32) @pytest.mark.parametrize( "out", [ None, np.empty((3, 3), dtype=np.int_), np.empty((3, 3), dtype=np.int_, order="C") * cm * s, np.empty((3, 3), dtype=np.int_, order="C") * km * s, ], ids=[ "None", "pure ndarray", "same units", "convertible units", ], ) def test_dot_matrices(out): a = np.reshape(np.arange(9), (3, 3)) * cm b = np.reshape(np.arange(9), (3, 3)) * s res = np.dot(a, b, out=out) if out is not None: np.testing.assert_array_equal(res, out) assert np.shares_memory(res, out) assert isinstance(res, unyt_array) assert isinstance(out, np.ndarray) if isinstance(out, unyt_array): # check that the result can be converted to predictable units res.in_units("cm * s") assert out.units == res.units def test_dot_mixed_ndarray_unyt_array(): a = np.ones((3, 3)) b = np.ones((3, 3)) * cm res = np.dot(a, b) assert isinstance(res, unyt_array) assert res.units == cm out = np.zeros((3, 3)) res = np.dot(a, b, out=out) assert isinstance(res, unyt_array) assert type(out) is np.ndarray assert np.shares_memory(out, res) np.testing.assert_array_equal(out, res) out = np.zeros((3, 3)) * km res = np.dot(a, b, out=out) assert isinstance(res, unyt_array) assert isinstance(out, unyt_array) assert res.units == out.units == cm assert np.shares_memory(res, out) # check this works with an ndarray as the first operand out = np.zeros((3, 3)) * km res = np.dot(b, a, out=out) assert isinstance(res, unyt_array) assert isinstance(out, unyt_array) assert res.units == out.units == cm assert np.shares_memory(res, out) def test_invalid_dot_matrices(): a = np.reshape(np.arange(9), (3, 3)) * cm b = np.reshape(np.arange(9), (3, 3)) * s out = np.empty((3, 3), dtype=np.int_, order="C") * s**2 res = np.dot(a, b, out=out) np.testing.assert_array_equal(res, out) assert out.units == res.units == cm * s def test_vdot(): a = np.arange(9) * cm b = np.arange(9) * s res = np.vdot(a, b) assert res.units == cm * s def test_inner(): a = np.array([1, 2, 3]) * cm b = np.array([0, 1, 0]) * s res = np.inner(a, b) assert res.d == 2 assert res.units == cm * s def test_outer(): a = np.array([1, 2, 3]) * cm b = np.array([0, 1, 0]) * s res = np.outer(a, b) expected = np.array([[0, 1, 0], [0, 2, 0], [0, 3, 0]]) np.testing.assert_array_equal(res.ndview, expected) assert res.units == cm * s def test_kron(): a = np.eye(2) * cm b = np.ones((2, 2)) * s res = np.kron(a, b) assert res.units == cm * s def test_linalg_inv(): rng = np.random.default_rng(0) arr = rng.random((3, 3)) * cm iarr = np.linalg.inv(arr) assert 1 * iarr.units == 1 / cm def test_linalg_tensorinv(): a = np.reshape(np.eye(4 * 6), (4, 6, 8, 3)) * cm ia = np.linalg.tensorinv(a) assert 1 * ia.units == 1 / cm def test_linalg_pinv(): rng = np.random.default_rng(0) a = rng.standard_normal(size=(9, 6)) * cm B = np.linalg.pinv(a) assert 1 * B.units == 1 / cm np.testing.assert_allclose(a, np.dot(a, np.dot(B, a))) np.testing.assert_allclose(B, np.dot(B, np.dot(a, B))) # see https://github.com/numpy/numpy/issues/22444 @pytest.mark.xfail( reason=( "as of numpy 1.21.2, the __array_function__ protocol doesn't let " "us overload np.linalg.pinv for stacks of matrices" ) ) def test_matrix_stack_linalg_pinv(): stack = [np.eye(4) * g for _ in range(3)] B = np.linalg.pinv(stack) assert 1 * B.units == 1 / g # see https://github.com/numpy/numpy/issues/22444 @pytest.mark.xfail( reason=( "as of numpy 1.21.2, the __array_function__ protocol doesn't let " "us overload np.linalg.pinv for stacks of matrices" ) ) def test_invalid_matrix_stack_linalg_pinv(): stack = [np.eye(4) * g, np.eye(4) * s] with pytest.raises( TypeError, match=re.escape( "numpy.linalg.pinv cannot operate on a stack " "of matrices with different units." ), ): np.linalg.pinv(stack) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.diagonal is new in numpy 2.0" ) def test_linalg_diagonal(): a = np.eye(3) * cm b = np.linalg.diagonal(a) assert type(b) is unyt_array assert b.units == a.units @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.trace is new in numpy 2.0" ) def test_linalg_trace(): a = np.eye(3) * cm b = np.linalg.trace(a) assert type(b) is unyt_quantity assert b.units == a.units @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.outer is new in numpy 2.0" ) def test_linalg_outer(): a = np.arange(10) * cm assert_array_equal_units(np.linalg.outer(a, a), np.outer(a, a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.cross is new in numpy 2.0" ) def test_linalg_cross(): a = np.arange(3) * cm assert_array_equal_units(np.linalg.cross(a, a), np.cross(a, a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.matmul is new in numpy 2.0" ) def test_linalg_matmul(): a = np.eye(3) * cm assert_array_equal_units(np.linalg.matmul(a, a), np.matmul(a, a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.matrix_norm is new in numpy 2.0", ) def test_linalg_matrix_norm(): a = np.eye(3) * cm assert_array_equal_units(np.linalg.matrix_norm(a), np.linalg.norm(a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="matrix_transpose is new in numpy 2.0" ) @pytest.mark.parametrize("namespace", [None, "linalg"]) def test_matrix_transpose(namespace): if namespace is None: func = np.matrix_transpose else: func = getattr(np, namespace).matrix_transpose a = np.arange(0, 9).reshape(3, 3) assert_array_equal_units(func(a), np.transpose(a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.vecdot is new in numpy 2.0" ) def test_linalg_vecdot(): a = np.arange(0, 9) assert_array_equal_units(np.linalg.vecdot(a, a), np.vdot(a, a)) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.vector_norm is new in numpy 2.0", ) def test_linalg_vector_norm(): a = np.arange(0, 9) assert_array_equal_units(np.linalg.vector_norm(a), np.linalg.norm(a)) def test_linalg_svd(): rng = np.random.default_rng(0) a = (rng.standard_normal(size=(9, 6)) + 1j * rng.standard_normal(size=(9, 6))) * cm u, s, vh = np.linalg.svd(a) assert type(u) is np.ndarray assert type(vh) is np.ndarray assert type(s) is unyt_array assert s.units == cm s = np.linalg.svd(a, compute_uv=False) assert type(s) is unyt_array assert s.units == cm @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.svdvals is new in numpy 2.0" ) def test_linalg_svdvals(): q = np.arange(9).reshape(3, 3) * cm _, ref, _ = np.linalg.svd(q) res = np.linalg.svdvals(q) assert type(res) is unyt_array assert_allclose(res, ref, rtol=5e-16) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="linalg.tensordot is new in numpy 2.0" ) def test_linalg_tensordot(): q = np.arange(9).reshape(3, 3) * cm ref = np.tensordot(q, q) res = np.linalg.tensordot(q, q) assert_array_equal_units(res, ref) class TestHistograms: def test_sanitize_range_on_None(self): assert _sanitize_range(None, [cm]) is None def test_sanitize_range_units_match(self): input_range = (1 * cm, 2 * cm) expected_range = (1 * cm, 2 * cm) assert_array_equal_units(_sanitize_range(input_range, [cm]), expected_range) def test_sanitize_range_units_mismatch(self): input_range = (1 * cm, 2) with pytest.raises( TypeError, match="Elements of range must both have a 'units' attribute." ): _sanitize_range(input_range, [cm, g]) def test_sanitize_range_pure_scalars(self): input_range = (1, 2) expected_range = (1 * cm, 2 * cm) assert_array_equal_units(_sanitize_range(input_range, [cm]), expected_range) def test_sanitize_bins_integer(self): a = np.arange(10) * cm bins = 10 assert _sanitize_bins(a, bins) == bins def test_sanitize_bins_convertible(self): a = np.arange(10) * cm bins = np.array([0, 1, 2]) * km np.testing.assert_array_equal(_sanitize_bins(a, bins), bins.to_value(a.units)) def test_sanitize_bins_missing_units_but_dimensionless(self): a = np.arange(10) * dimensionless bins = np.array([0, 1, 2]) np.testing.assert_array_equal(_sanitize_bins(a, bins), bins) def test_sanitize_bins_without_units(self): a = np.arange(10) bins = np.array([0, 1, 2]) np.testing.assert_array_equal(_sanitize_bins(a, bins), bins) def test_histogram(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm counts, bins = np.histogram(arr, bins=10, range=(arr.min(), arr.max())) assert type(counts) is np.ndarray assert bins.units == arr.units def test_histogram_implicit_units(self): # see https://github.com/yt-project/unyt/issues/465 rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm counts, bins = np.histogram( arr, bins=10, range=(arr.min().value, arr.max().value) ) assert type(counts) is np.ndarray assert bins.units == arr.units def test_histogram_with_density(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm density, bins = np.histogram( arr, bins=10, range=(arr.min(), arr.max()), density=True ) assert type(density) is unyt_array assert density.units == arr.units**-1 assert bins.units == arr.units def test_histogram_with_weights(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm w = rng.uniform(size=1000) * g wcounts, wbins = np.histogram( arr, bins=10, range=(arr.min(), arr.max()), weights=w ) assert type(wcounts) is unyt_array assert wcounts.units == w.units assert wbins.units == arr.units def test_histogram_with_weights_and_density(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm w = rng.uniform(size=1000) * g wdensity, wdbins = np.histogram( arr, bins=10, range=(arr.min(), arr.max()), density=True, weights=w ) assert type(wdensity) is unyt_array assert wdensity.units == w.units / arr.units assert wdbins.units == arr.units def test_histogram_with_weights_and_dimless_arr(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm w = rng.uniform(size=1000) * g wcounts2, wbins2 = np.histogram(arr.to_value(arr.units), weights=w) assert type(wcounts2) is unyt_array assert wcounts2.units == w.units assert not hasattr(wbins2, "units") def test_histogram2d(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s counts, xbins, ybins = np.histogram2d(x, y) assert counts.ndim == 2 assert xbins.units == x.units assert ybins.units == y.units def test_histogram2d_with_density(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s density, xbins, ybins = np.histogram2d(x, y, density=True) assert density.ndim == 2 assert type(density) is unyt_array assert density.units == (x.units * y.units) ** -1 assert xbins.units == x.units assert ybins.units == y.units def test_histogram2d_with_weights(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s w = rng.uniform(size=100) * g wcounts, xwbins, ywbins = np.histogram2d(x, y, weights=w) assert wcounts.ndim == 2 assert type(wcounts) is unyt_array assert wcounts.units == w.units assert xwbins.units == x.units assert ywbins.units == y.units def test_histogram2d_with_weights_and_density(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s w = rng.uniform(size=100) * g wdensity, xwdbins, ywdbins = np.histogram2d(x, y, weights=w, density=True) assert wdensity.ndim == 2 assert type(wdensity) is unyt_array assert wdensity.units == w.units / (x.units * y.units) assert xwdbins.units == x.units assert ywdbins.units == y.units def test_histogram2d_with_weights_and_dimless_arr(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s w = rng.uniform(size=100) * g wcounts2, xwbins2, ywbins2 = np.histogram2d( x.to_value(x.units), y.to_value(y.units), weights=w ) assert type(wcounts2) is unyt_array assert wcounts2.units == w.units assert not hasattr(xwbins2, "units") assert not hasattr(ywbins2, "units") def test_histogramdd(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(size=100) * s z = rng.normal(size=100) * g counts, (xbins, ybins, zbins) = np.histogramdd((x, y, z)) assert counts.ndim == 3 assert xbins.units == x.units assert ybins.units == y.units assert zbins.units == z.units def test_histogramdd_with_density(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s z = rng.normal(size=100) * g density, (xbins, ybins, zbins) = np.histogramdd((x, y, z), density=True) assert density.ndim == 3 assert type(density) is unyt_array assert density.units == (x.units * y.units * z.units) ** -1 assert xbins.units == x.units assert ybins.units == y.units assert zbins.units == z.units def test_histogramdd_with_weights(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s z = rng.normal(size=100) * g w = rng.uniform(size=100) * K wcounts, (xwbins, ywbins, zwbins) = np.histogramdd((x, y, z), weights=w) assert wcounts.ndim == 3 assert type(wcounts) is unyt_array assert wcounts.units == w.units assert xwbins.units == x.units assert ywbins.units == y.units assert zwbins.units == z.units def test_histogramdd_with_weights_and_density(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s z = rng.normal(size=100) * g w = rng.uniform(size=100) * K wdensity, (xwdbins, ywdbins, zwdbins) = np.histogramdd( (x, y, z), weights=w, density=True ) assert wdensity.ndim == 3 assert type(wdensity) is unyt_array assert wdensity.units == w.units / (x.units * y.units * z.units) assert xwdbins.units == x.units assert ywdbins.units == y.units assert zwdbins.units == z.units def test_histogramdd_with_weights_and_dimless_arr(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) * s z = rng.normal(size=100) * g w = rng.uniform(size=100) * K wcounts2, (xwbins2, ywbins2, zwbins2) = np.histogramdd( (x.to_value(x.units), y.to_value(y.units), z.to_value(z.units)), weights=w ) assert type(wcounts2) is unyt_array assert wcounts2.units == w.units assert not hasattr(xwbins2, "units") assert not hasattr(ywbins2, "units") assert not hasattr(zwbins2, "units") @pytest.mark.parametrize("weights", [None, [0, 1, 2], [0, 1, 2] * cm]) def test_histogramdd_recursion(self, weights): # regression test for https://github.com/yt-project/unyt/issues/540 sample = [unyt_array(np.arange(3), Msun)] np.histogramdd(sample, density=True, weights=weights) def test_histogramdd_with_density_and_non_unyt_samples(self): rng = np.random.default_rng(0) x = rng.normal(size=100) * cm y = rng.normal(loc=10, size=100) z = rng.normal(size=100) * g counts, (xbins, ybins, zbins) = np.histogramdd((x, y, z), density=True) assert counts.units == (x.units * z.units) ** -1 def test_histogram_bin_edges(self): rng = np.random.default_rng(0) arr = rng.normal(size=1000) * cm bins = np.histogram_bin_edges(arr) assert type(bins) is unyt_array assert bins.units == arr.units def test_histogram_input_and_bin_units_mismatch(self): # regression test for https://github.com/yt-project/unyt/issues/609 rng = np.random.default_rng(0) arr = rng.uniform(size=10) * cm bins = np.linspace(0, 1, 10) * km hist, resulting_bins = np.histogram(arr, bins=bins) assert np.all(bins == resulting_bins) def test_histogram_input_and_bin_dimensions_mismatch(self): # regression test for https://github.com/yt-project/unyt/issues/609 rng = np.random.default_rng(0) arr = rng.uniform(size=10) * cm bins = np.linspace(0, 1, 10) * s with pytest.raises(UnitConversionError): np.histogram(arr, bins=bins) def test_concatenate(): rng = np.random.default_rng(0) x1 = rng.normal(size=100) * cm x2 = rng.normal(size=100) * cm res = np.concatenate((x1, x2)) assert res.units == cm assert res.shape == (200,) def test_concatenate_different_units(): rng = np.random.default_rng(0) x1 = rng.normal(size=100) * cm x2 = rng.normal(size=100) * s with pytest.raises( UnitInconsistencyError, match=( r"Expected all unyt_array arguments to have identical units\. " r"Received mixed units \(cm, s\)" ), ): np.concatenate((x1, x2)) def test_cross(): x1 = [1, 2, 3] * cm x2 = [4, 5, 6] * s res = np.cross(x1, x2) assert res.units == cm * s def test_intersect1d(): x1 = [1, 2, 3, 4, 5, 6, 7, 8] * cm x2 = [0, 2, 4, 6, 8] * cm res = np.intersect1d(x1, x2) assert res.units == cm np.testing.assert_array_equal(res, [2, 4, 6, 8]) def test_intersect1d_return_indices(): x1 = [1, 2, 3, 4, 5, 6, 7, 8] * cm x2 = [0, 2, 4, 6, 8] * cm ures = np.intersect1d(x1, x2, return_indices=True) rres = np.intersect1d(x1.d, x2.d, return_indices=True) np.testing.assert_array_equal(ures, rres) def test_union1d(): x1 = [-1, 0, 1] * cm x2 = [-2, -1, -3] * cm res = np.union1d(x1, x2) assert res.units == cm np.testing.assert_array_equal(res, [-3, -2, -1, 0, 1]) def test_linalg_norm(): x = [1, 1, 1] * s res = np.linalg.norm(x) assert res.units == s assert res == pytest.approx(np.sqrt(3)) @pytest.mark.parametrize("func", [np.vstack, np.hstack, np.dstack, np.column_stack]) def test_xstack(func): x1 = [0, 1, 2] * cm x2 = [3, 4, 5] * cm res = func((x1, x2)) assert type(res) is unyt_array assert res.units == cm @pytest.mark.parametrize( "axis, expected", [(0, [[0, 1, 2], [3, 4, 5]]), (1, [[0, 3], [1, 4], [2, 5]])] ) def test_stack(axis, expected): x1 = [0, 1, 2] * cm x2 = [3, 4, 5] * cm res = np.stack((x1, x2), axis=axis) assert res.units == cm np.testing.assert_array_equal(res, expected) def test_amax(): x1 = [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]] * cm res = np.amax(x1) assert type(res) is unyt_quantity res = np.amax(x1, axis=1) assert type(res) is unyt_array def test_amin(): x1 = [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]] * cm res = np.amin(x1) assert type(res) is unyt_quantity res = np.amin(x1, axis=1) assert type(res) is unyt_array def test_around(): x1 = [[1, 2, 3], [1, 2, 3], [1, 2, 3.0]] * g res = np.around(x1, 2) assert type(res) is unyt_array assert res.units == g def test_atleast_nd(): x0 = 1.0 * cm x1 = np.atleast_1d(x0) assert type(x1) is unyt_array assert x1.ndim == 1 assert x1.units == cm x2 = np.atleast_2d(x0) assert type(x2) is unyt_array assert x2.ndim == 2 assert x2.units == cm x3 = np.atleast_3d(x0) assert type(x3) is unyt_array assert x3.ndim == 3 assert x3.units == cm def test_average(): x1 = [0.0, 1.0, 2.0] * cm res = np.average(x1) assert type(res) is unyt_quantity assert res == 1 * cm def test_average_with_returned(): # regression test for https://github.com/yt-project/unyt/issues/608 x = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]] * cm w = [1.0, 2.0, 3.0] * g avg, wsum = np.average(x, weights=w, axis=0, returned=True) assert type(wsum) is unyt_array assert wsum.units == g def test_trim_zeros(): x1 = [0, 1, 2, 3, 0] * cm res = np.trim_zeros(x1) assert type(res) is unyt_array def test_any(): assert not np.any([0, 0, 0] * cm) assert np.any([1, 0, 0] * cm) x = [1, 2, 3] * cm assert np.any(x >= 3) assert np.any(x >= 3 * cm) assert not np.any(x >= 3 * km) def test_append(): a = [0, 1, 2, 3] * cm b = np.append(a, [4, 5, 6] * cm) assert type(b) is unyt_array assert b.units == cm def test_append_inconsistent_units(): a = [0, 1, 2, 3] * cm with pytest.raises( UnitInconsistencyError, match=re.escape( r"Expected all unyt_array arguments to have identical units. " r"Received mixed units (cm, dimensionless)" ), ): np.append(a, [4, 5, 6]) @pytest.mark.skipif( NUMPY_VERSION >= Version("2.0.0dev0"), reason="np.asfarray is removed in numpy 2.0" ) def test_asfarray(): x1 = np.eye(3, dtype="int64") * cm x2 = np.asfarray(x1) # noqa: NPY201 assert type(x2) is unyt_array assert x2.units == cm assert x2.dtype == "float64" def test_block(): x1 = 1 * np.ones((3, 3)) * cm x2 = 2 * np.ones((3, 1)) * cm res = np.block([[x1, x2]]) assert type(res) is unyt_array assert res.units == cm def test_block_units_inconsistency(): # check that unit inconsistency is correctly detected # for nested lists x1 = 1 * np.ones((3, 3)) * cm x2 = [3 * cm, 3 * cm, 3 * km] with pytest.raises(UnitInconsistencyError): np.block([[x1, x2]]) def test_can_cast(): a = [0, 1, 2] * cm assert np.can_cast(a, "float64") assert np.can_cast(a, "int64") assert not np.can_cast(a, "float16") def test_isreal_like(): a = [1, 2, 3] * cm assert np.all(np.isreal(a)) assert np.isrealobj(a) assert not np.any(np.iscomplex(a)) assert not np.iscomplexobj(a) b = [1j, 2j, 3j] * cm assert not np.any(np.isreal(b)) assert not np.isrealobj(b) assert np.all(np.iscomplex(b)) assert np.iscomplexobj(b) @pytest.mark.parametrize( "func", [ np.fft.fft, np.fft.hfft, np.fft.rfft, np.fft.ifft, np.fft.ihfft, np.fft.irfft, ], ) def test_fft_1D(func): x1 = [0, 1, 2] * cm res = func(x1) assert type(res) is unyt_array assert res.units == (1 * cm).units @pytest.mark.parametrize( "func", [ np.fft.fft2, np.fft.fftn, np.fft.rfft2, np.fft.rfftn, np.fft.ifft2, np.fft.ifftn, np.fft.irfft2, np.fft.irfftn, ], ) def test_fft_ND(func): x1 = [[0, 1, 2], [0, 1, 2], [0, 1, 2]] * cm res = func(x1) assert type(res) is unyt_array assert res.units == (1 * cm).units @pytest.mark.parametrize("func", [np.fft.fftshift, np.fft.ifftshift]) def test_fft_shift(func): x1 = [[0, 1, 2], [0, 1, 2], [0, 1, 2]] * cm res = func(x1) assert type(res) is unyt_array assert res.units == cm if NUMPY_VERSION >= Version("2.0.0dev0"): _trapezoid_func = np.trapezoid else: _trapezoid_func = np.trapz # noqa: NPY201 def test_trapezoid_no_x(): y = [0, 1, 2, 3] * cm res = _trapezoid_func(y) assert type(res) is unyt_quantity assert res.units == cm def test_trapezoid_with_raw_x(): y = [0, 1, 2, 3] * cm x = [0, 1, 2, 3] res = _trapezoid_func(y, x) assert type(res) is unyt_quantity assert res.units == cm def test_trapezoid_with_unit_x(): y = [0, 1, 2, 3] * cm x = [0, 1, 2, 3] * s res = _trapezoid_func(y, x) assert type(res) is unyt_quantity assert res.units == cm * s def test_trapezoid_with_raw_dx(): y = [0, 1, 2, 3] * cm dx = 2.0 res = _trapezoid_func(y, dx=dx) assert type(res) is unyt_quantity assert res.units == cm def test_trapezoid_with_unit_dx(): y = [0, 1, 2, 3] * cm dx = 2.0 * s res = _trapezoid_func(y, dx=dx) assert type(res) is unyt_quantity assert res.units == cm * s @pytest.mark.parametrize( "op", ["min", "max", "mean", "median", "sum", "nanmin", "nanmax", "nanmean", "nanmedian"], ) def test_scalar_reduction(op): x = [0, 1, 2] * cm res = getattr(np, op)(x) assert type(res) is unyt_quantity assert res.units == cm @pytest.mark.parametrize("op", ["sort", "sort_complex"]) def test_sort(op): x = [2, 0, 1] * cm res = getattr(np, op)(x) assert type(res) is unyt_array assert res.units == cm def test_repeat(): x = [2, 0, 1] * cm res = np.repeat(x, 2) assert type(res) is unyt_array assert res.units == cm def test_tile(): x = [2, 0, 1] * cm res = np.tile(x, (2, 3)) assert type(res) is unyt_array assert res.units == cm def test_shares_memory(): x = [1, 2, 3] * cm assert np.shares_memory(x, x.view(np.ndarray)) def test_nonzero(): x = [1, 2, 0] * cm res = np.nonzero(x) assert len(res) == 1 np.testing.assert_array_equal(res[0], [0, 1]) res2 = np.flatnonzero(x) np.testing.assert_array_equal(res[0], res2) def test_isinf(): x = [1, float("inf"), float("-inf")] * cm res = np.isneginf(x) np.testing.assert_array_equal(res, [False, False, True]) res = np.isposinf(x) np.testing.assert_array_equal(res, [False, True, False]) def test_allclose(): x = [1, 2, 3] * cm y = [1, 2, 3] * km assert not np.allclose(x, y) @pytest.mark.parametrize( "a, b, expected", [ ([1, 2, 3] * cm, [1, 2, 3] * km, [False] * 3), ([1, 2, 3] * cm, [1, 2, 3], [True] * 3), ([1, 2, 3] * K, [-272.15, -271.15, -270.15] * degC, [True] * 3), ], ) def test_isclose(a, b, expected): res = np.isclose(a, b) np.testing.assert_array_equal(res, expected) def test_isclose_error(): x = [1, 2, 3] * cm y = [1, 2, 3] * g with pytest.raises(UnitConversionError): np.isclose(x, y) def test_linspace(): res = np.linspace(1 * cm, 11 * cm, 10) assert type(res) is unyt_array assert res.units == cm def test_linspace_with_retstep(): res, step = np.linspace(1 * cm, 11 * cm, 10, retstep=True) assert type(res) is unyt_array assert res.units == cm assert type(step) is unyt_quantity assert step.units == cm def test_logspace_with_units_raises(): with pytest.raises( TypeError, match="The first two arguments to numpy.logspace must be dimensionless", ): np.logspace(1 * cm, 2 * cm) def test_logspace_with_base(): res = np.logspace(1, 2, base=10 * cm) assert type(res) is unyt_array assert res.units == cm def test_logspace_with_dimless_input(): res = np.logspace(1 * dimensionless, 2 * dimensionless) assert type(res) is unyt_array assert res.units == dimensionless def test_geomspace(): res = np.geomspace(1 * cm, 11 * cm, 10) assert type(res) is unyt_array assert res.units == cm def test_full_like(): x = [1, 2, 3] * cm res = np.full_like(x, 6 * cm) assert type(res) is unyt_array assert res.units == cm @pytest.mark.parametrize( "func", [ np.empty_like, np.zeros_like, np.ones_like, ], ) def test_x_like(func): x = unyt_array([1, 2, 3], cm, dtype="float32") res = func(x) assert type(res) is unyt_array assert res.units == x.units assert res.shape == x.shape assert res.dtype == x.dtype def test_copy(): x = [1, 2, 3] * cm y = np.copy(x) # by default, subok=False, so we shouldn't # expect a unyt_array without switching this arg assert type(y) is np.ndarray def test_copy_subok(): x = [1, 2, 3] * cm y = np.copy(x, subok=True) assert type(y) is unyt_array assert y.units == cm def test_copyto(): x = [1, 2, 3] * cm y = np.empty_like(x) np.copyto(y, x) assert type(y) is unyt_array assert y.units == cm np.testing.assert_array_equal(x, y) def test_copyto_edge_cases(): x = [1, 2, 3] * cm y = [1, 2, 3] * g # copying to an array with a different unit is supported # to be in line with how we treat the 'out' param in most # numpy operations np.copyto(y, x) assert type(y) is unyt_array assert y.units == cm y = np.empty_like(x.view(np.ndarray)) np.copyto(y, x) assert type(y) is np.ndarray @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="astype is new in numpy 2.0" ) def test_astype(): x = np.array([1, 2, 3], dtype="int64") * cm res = np.astype(x, "int32") assert type(res) is unyt_array assert res.units == cm def test_meshgrid(): x = [1, 2, 3] * cm y = [1, 2, 3] * s x2d, y2d = np.meshgrid(x, y) assert type(x2d) is unyt_array assert type(y2d) is unyt_array assert x2d.units == cm assert y2d.units == s @pytest.mark.parametrize( "func, args, kwargs", [ (np.transpose, (), {}), (np.reshape, ((9, 2),), {}), (np.resize, ((3, 6),), {}), (np.expand_dims, (0,), {}), (np.squeeze, (), {}), (np.swapaxes, (0, 1), {}), (np.moveaxis, (0, 2), {}), (np.rot90, (), {}), (np.roll, (3,), {}), (np.rollaxis, (2,), {}), (np.flip, (), {}), (np.fliplr, (), {}), (np.flipud, (), {}), (np.broadcast_to, ((1, 1, 2, 3, 3),), {"subok": True}), (np.delete, (0, 1), {}), (np.partition, (2,), {}), ], ) def test_reshaper(func, args, kwargs): x = [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ], [ [10, 11, 12], [13, 14, 15], [16, 17, 18], ], ] ] * cm y = func(x, *args, **kwargs) assert type(y) is unyt_array assert y.units == cm def test_broadcast_arrays(): x = [1, 2, 3] * cm y = [ 4, ] * g res = np.broadcast_arrays(x, y, subok=True) assert all(type(_) is unyt_array for _ in res) @pytest.mark.parametrize( "func_name, args", [ ("split", (3, 2)), ("dsplit", (3,)), ("hsplit", (2,)), ("vsplit", (1,)), ("array_split", (3,)), pytest.param( "unstack", (), marks=pytest.mark.skipif( NUMPY_VERSION < Version("2.1.0dev0"), reason="np.unstack is new in NumPy 2.1", ), ), ], ) def test_xsplit(func_name, args): func = getattr(np, func_name) x = [ [ [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ], [ [10, 11, 12], [13, 14, 15], [16, 17, 18], ], ] ] * cm y = func(x, *args) assert all(type(_) is unyt_array for _ in y) assert all(_.units == cm for _ in y) @pytest.mark.parametrize( "func, expected_units", [ (np.prod, cm**9), (np.var, cm**2), (np.std, cm), (np.nanprod, cm**9), (np.nansum, cm), (np.nanvar, cm**2), (np.nanstd, cm), (np.trace, cm), ], ) def test_scalar_reducer(func, expected_units): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = func(x) assert type(y) is unyt_quantity assert y.units == expected_units @pytest.mark.parametrize("func", [np.prod, np.nanprod]) def test_prod_with_axis(func): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = func(x, axis=0) assert type(y) is unyt_array assert y.units == cm**3 @pytest.mark.parametrize( "func", [ np.percentile, np.quantile, np.nanpercentile, np.nanquantile, ], ) def test_percentile(func): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = func(x, 1) assert type(y) is unyt_quantity assert y.units == cm @pytest.mark.parametrize( "func", [ np.diag, np.diagflat, np.diagonal, ], ) def test_diagx(func): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = func(x) assert type(y) is unyt_array assert y.units == cm @pytest.mark.filterwarnings("ignore:numpy.fix is deprecated:DeprecationWarning") def test_fix(): y = np.fix(1.2 * cm) assert y == 1.0 * cm def test_linalg_matrix_power(): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = np.linalg.matrix_power(x, 2) assert type(y) is unyt_array assert y.units == cm**2 def test_linalg_det(): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm y = np.linalg.det(x) assert type(y) is unyt_quantity assert y.units == cm ** (len(x)) def test_linalg_cholesky(): x = np.eye(3) * cm y = np.linalg.cholesky(x) assert type(y) is unyt_array assert y.units == cm def test_linalg_lstsq(): a = np.eye(3) * cm b = np.ones(3).T * g # setting rcond explicitly to avoid a FutureWarning # see https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html x, residuals, rank, s = np.linalg.lstsq(a, b, rcond=-1) assert type(x) is unyt_array assert x.units == g / cm assert type(residuals) is unyt_array assert residuals.units == g / cm assert type(s) is unyt_array assert s.units == cm def test_linalg_multi_dot(): a = np.eye(3) * cm b = np.eye(3) * g c = np.eye(3) * s res = np.linalg.multi_dot([a, b, c]) assert type(res) is unyt_array assert res.units == cm * g * s def test_linalg_qr(): x = [ [1, 2, 3], [4, 5, 6], [7, 8, 9], ] * cm q, r = np.linalg.qr(x) assert type(q) is unyt_array assert q.units == cm assert type(r) is unyt_array assert r.units == cm @pytest.mark.parametrize("func", [np.linalg.solve, np.linalg.tensorsolve]) def test_linalg_solve(func): a = np.eye(3) * cm b = np.ones(3).T * g x = func(a, b) assert type(x) is unyt_array assert x.units == g / cm def is_any_dimless(x) -> bool: return (not hasattr(x, "units")) or x.units.is_dimensionles def test_linalg_cond(): a = np.eye(3) * cm res = np.linalg.cond(a) assert is_any_dimless(res) @pytest.mark.parametrize("func", [np.linalg.eig, np.linalg.eigh]) def test_eig(func): a = np.eye(3) * cm w, v = func(a) assert type(w) is unyt_array assert w.units == cm assert is_any_dimless(v) @pytest.mark.parametrize("func", [np.linalg.eigvals, np.linalg.eigvalsh]) def test_eigvals(func): a = np.eye(3) * cm w = func(a) assert type(w) is unyt_array assert w.units == cm def test_savetxt(tmp_path): a = [1, 2, 3] * cm with pytest.warns( UserWarning, match=re.escape( "numpy.savetxt does not preserve units, " "and will only save the raw numerical data from the unyt_array object.\n" "If this is the intended behaviour, call `numpy.savetxt(file, arr.d)` " "to silence this warning.\n" "If you want to preserve units, use `unyt.savetxt` " "(and `unyt.loadtxt`) instead." ), ): np.savetxt(tmp_path / "savefile.npy", a) # check that doing what the warning says doesn't trigger any other warning np.savetxt(tmp_path / "savefile.npy", a.d) def test_apply_along_axis(): a = np.eye(3) * cm ret = np.apply_along_axis(lambda x: x * cm, 0, a) assert type(ret) is unyt_array assert ret.units == cm**2 @pytest.mark.parametrize("axes, expected_units", [((0, 1), cm**4), ((0,), cm**2)]) def test_apply_over_axes(axes, expected_units): # the user-supplied function must be trusted to treat units # sensibly (mainly that it doesn't give a mix of units across # the resulting array), but we can check that units are # propagated correctly for well-behaved functions. a = np.eye(3) * cm ret = np.apply_over_axes(lambda x, axis: x[axis] ** 2, a, axes) assert isinstance(ret, unyt_array) # could be unyt_quantity assert ret.units == expected_units def test_array_equal(): a = [1, 2, 3] * cm b = [1, 2, 3] * cm c = [1, 2, 3] * km assert np.array_equal(a, b) assert not np.array_equal(a, c) def test_array_equiv(): a = [1, 2, 3] * cm b = [1, 2, 3] * cm c = [1, 2, 3] * km d = [[1, 2, 3]] * cm assert np.array_equiv(a, b) assert np.array_equiv(a, d) assert not np.array_equiv(a, c) def test_common_type(): a = np.array([1, 2, 3], dtype="float32") * cm b = np.array([1, 2, 3], dtype="float64") * cm dtype = np.common_type(a, b) assert dtype == np.dtype("float64") def test_result_type(): scalar = 3 * cm array = np.arange(7, dtype="i1") if NUMPY_VERSION >= Version("2.0.0dev0"): # promotion rules vary under NEP 50. The default behaviour is different # in numpy 2.0 VS numpy 1.x # see https://github.com/numpy/numpy/pull/23912 # see https://numpy.org/neps/nep-0050-scalar-promotion.html expected_dtype = scalar.dtype else: expected_dtype = array.dtype assert np.result_type(scalar, array) == expected_dtype @pytest.mark.parametrize( "func", [ np.diff, np.ediff1d, np.gradient, np.ptp, ], ) @pytest.mark.parametrize("input_units, output_units", [(cm, cm), (K, delta_degC)]) def test_deltas(func, input_units, output_units): x = np.arange(0, 4) * input_units res = func(x) assert isinstance(res, unyt_array) assert res.units == output_units @pytest.mark.parametrize( "func_name", [ "cumsum", "nancumsum", pytest.param( "cumulative_sum", marks=pytest.mark.skipif( NUMPY_VERSION < Version("2.1.0dev0"), reason="np.cumulative_sum is new in NumPy 2.1", ), ), ], ) def test_cumsum(func_name): a = [1, 2, 3] * cm func = getattr(np, func_name) res = func(a) assert type(res) is unyt_array assert res.units == cm @pytest.mark.parametrize( "func_name", [ "cumprod", "nancumprod", pytest.param( "cumulative_prod", marks=pytest.mark.skipif( NUMPY_VERSION < Version("2.1.0dev0"), reason="np.cumulative_prod is new in NumPy 2.1", ), ), ], ) def test_cumprod(func_name): a = [1, 2, 3] * cm func = getattr(np, func_name) with pytest.raises( UnytError, match=re.escape( r"numpy.cumprod (and other cumulative product function) cannot be used " r"with a unyt_array as all return elements should (but cannot) " r"have different units." ), ): func(a) def test_bincount(): a = [1, 2, 3] * cm res = np.bincount(a) assert type(res) is np.ndarray def test_unique(): a = [1, 2, 3] * cm res = np.unique(a) assert type(res) is unyt_array assert res.units == cm @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="unique_all is new in numpy 2.0" ) def test_unique_all(): q = np.arange(9).reshape(3, 3) * cm values, indices, inverse_indices, counts = np.unique( q, return_index=True, return_inverse=True, return_counts=True, equal_nan=False, ) res = np.unique_all(q) assert len(res) == 4 assert_array_equal_units(res.values, values) assert_array_equal_units(res.indices, indices) assert_array_equal_units(res.inverse_indices, inverse_indices) assert_array_equal_units(res.counts, counts) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="unique_counts is new in numpy 2.0" ) def test_unique_counts(): q = np.arange(9).reshape(3, 3) * cm values, counts = np.unique( q, return_counts=True, equal_nan=False, ) res = np.unique_counts(q) assert len(res) == 2 assert_array_equal_units(res.values, values) assert_array_equal_units(res.counts, counts) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="unique_inverse is new in numpy 2.0" ) def test_unique_inverse(): q = np.arange(9).reshape(3, 3) * cm values, inverse_indices = np.unique( q, return_inverse=True, equal_nan=False, ) res = np.unique_inverse(q) assert len(res) == 2 assert_array_equal_units(res.values, values) assert_array_equal_units(res.inverse_indices, inverse_indices) @pytest.mark.skipif( NUMPY_VERSION < Version("2.0.0dev0"), reason="unique_values is new in numpy 2.0" ) def test_unique_values(): q = np.arange(9).reshape(3, 3) * cm values = np.unique(q, equal_nan=False) res = np.unique_values(q) # np.unique_values' output is not guaranteed to be sorted, # so exact results may differ. # see https://github.com/numpy/numpy/issues/28493#issuecomment-2721303048 res.sort() assert_array_equal_units(res, values) @pytest.mark.parametrize("indices", [[0, 1], 0]) def test_take(indices): a = [1, 2, 3] * cm res = np.take(a, indices) if res.ndim == 0: assert type(res) is unyt_quantity else: assert type(res) is unyt_array assert res.units == cm @pytest.mark.parametrize("indices", [[0, 1], 0]) def test_ndarray_take(indices): a = [1, 2, 3] * cm res = a.take(indices) if res.ndim == 0: assert type(res) is unyt_quantity else: assert type(res) is unyt_array assert res.units == cm def test_pad(): a = [1, 2, 3] * cm res = np.pad(a, [0, 1]) assert type(res) is unyt_array assert res.units == cm def test_sinc(): a = [1, 2, 3] * cm res = np.sinc(a) # we *want* this one to ignore units assert type(res) is np.ndarray def test_choose_mixed_units(): choices = [[1, 2, 3] * cm, [4, 5, 6] * km] with pytest.raises(UnitInconsistencyError): np.choose([1, 0, 1], choices=choices) def test_choose(): choices = [[1, 2, 3] * cm, [4, 5, 6] * cm] res = np.choose([1, 0, 1], choices=choices) assert type(res) is unyt_array assert res.units == cm def test_extract(): a = [1, 2, 3] * cm res = np.extract(a > 1 * cm, a) assert type(res) is unyt_array assert res.units == cm def test_fill_diagonal_mixed_units(): a = np.zeros(9).reshape((3, 3)) * cm with pytest.raises(UnitInconsistencyError): np.fill_diagonal(a, 1 * km) @pytest.mark.parametrize("val", [1 * cm, 1]) def test_fill_diagonal(val): a = np.zeros(9).reshape((3, 3)) * cm np.fill_diagonal(a, val) assert type(a) is unyt_array assert a.units == cm def test_insert_mixed_units(): a = [1, 2, 3] * cm with pytest.raises(UnitInconsistencyError): np.insert(a, 1, 42 * km) @pytest.mark.parametrize("val", [42, 42 * cm]) def test_insert(val): a = [1, 2, 3] * cm res = np.insert(a, 1, val) assert type(res) is unyt_array assert res.units == cm def test_isin_mixed_units(): a = [1, 2, 3] * cm with pytest.raises(UnitInconsistencyError): np.isin(1, a) def test_isin(): a = [1, 2, 3] * cm assert np.isin(1 * cm, a) @pytest.mark.skipif( NUMPY_VERSION >= Version("2.4.0.dev0"), reason="in1d is removed in numpy 2.4" ) @pytest.mark.filterwarnings("ignore:`in1d` is deprecated. Use `np.isin` instead.") def test_in1d_mixed_units(): a = [1, 2, 3] * cm with pytest.raises(UnitInconsistencyError): np.in1d([1, 2], a) # noqa: NPY201 @pytest.mark.skipif( NUMPY_VERSION >= Version("2.4.0.dev0"), reason="in1d is removed in numpy 2.4" ) @pytest.mark.filterwarnings("ignore:`in1d` is deprecated. Use `np.isin` instead.") def test_in1d(): a = [1, 2, 3] * cm b = [1, 2] * cm assert np.all(np.in1d(b, a)) # noqa: NPY201 def test_place_mixed_units(): arr = np.arange(6).reshape(2, 3) * cm with pytest.raises(UnitInconsistencyError): np.place(arr, arr > 2, [44, 55]) def test_place(): arr = np.arange(6).reshape(2, 3) * cm np.place(arr, arr > 2, [44, 55] * cm) assert type(arr) is unyt_array assert arr.units == cm def test_put_mixed_units(): arr = np.arange(6).reshape(2, 3) * cm with pytest.raises(UnitInconsistencyError): np.put(arr, [1, 2], [44, 55]) def test_put(): arr = np.arange(6).reshape(2, 3) * cm np.put(arr, [1, 2], [44, 55] * cm) assert type(arr) is unyt_array assert arr.units == cm def test_put_along_axis_mixed_units(): arr = np.arange(6).reshape(2, 3) * cm with pytest.raises(UnitInconsistencyError): np.put_along_axis(arr, np.array([[1, 2], [0, 1]]), [44, 55], 1) def test_put_along_axis(): arr = np.arange(6).reshape(2, 3) * cm np.put_along_axis(arr, np.array([[1, 2], [0, 1]]), [44, 55] * cm, 1) assert type(arr) is unyt_array assert arr.units == cm def test_putmask_mixed_units(): arr = np.arange(6, dtype=np.int_).reshape(2, 3) * cm with pytest.raises(UnitInconsistencyError): np.putmask(arr, arr > 2 * cm, np.zeros_like(arr.d)) def test_putmask(): arr = np.arange(6, dtype=np.int_).reshape(2, 3) * cm np.putmask(arr, arr > 2 * cm, np.zeros_like(arr)) assert type(arr) is unyt_array assert arr.units == cm def test_searchsorted_mixed_units(): with pytest.raises(UnitInconsistencyError): np.searchsorted([1, 2, 3, 4, 5] * cm, 3 * km) @pytest.mark.parametrize("val", [3 * cm, 3]) def test_searchsorted(val): res = np.searchsorted([1, 2, 3, 4, 5] * cm, val) assert res == 2 def test_digitize_converts_bins(): # bins in different (but compatible) units must be converted to the data's # units before binning: 1, 2, 3 cm are all far below 1 km, so they all land # in the first bin (see #633) res = np.digitize([1, 2, 3] * cm, [0, 1, 2, 3] * km) assert_array_equal_units(res, np.array([1, 1, 1])) def test_digitize_right(): res = np.digitize([1, 2, 3] * cm, [0, 1, 2, 3] * km, right=True) assert_array_equal_units(res, np.array([1, 1, 1])) def test_digitize_dimensionless_plain_bins(): # a dimensionless array with plain (unit-less) bins must still work res = np.digitize([1, 2, 3] * dimensionless, [0, 1, 2, 3]) assert_array_equal_units(res, np.array([2, 3, 4])) def test_digitize_mixed_units(): with pytest.raises(UnitConversionError): np.digitize([1, 2, 3] * g, [0, 1, 2, 3] * s) @pytest.mark.parametrize( "choicelist_gen, default", [ ([lambda x: x**3, lambda x: x**2], 34 * cm), # invalid choicelist ([lambda x: x, lambda x: x + 3 * x.units], 34), # invalid default ], ) def test_select_mixed_units(choicelist_gen, default): a = [1, 2, 3, 4, 5, 6] * cm with pytest.raises(UnitInconsistencyError): np.select( [a > 3 * cm, a < 3 * cm], [f(a) for f in choicelist_gen], default=default ) def test_select(): a = [1, 2, 3, 4, 5, 6] * cm res = np.select([a > 3 * cm, a < 3 * cm], [a, a + 3 * cm], default=34 * cm) assert_array_equal_units(res, [4, 5, 34, 4, 5, 6] * cm) def test_setdiff1d_mixed_units(): a = [1, 2, 3] * cm b = [0, 1, 2] with pytest.raises(UnitInconsistencyError): np.setdiff1d(a, b) def test_setdiff1d(): a = [1, 2, 3] * cm b = [0, 1, 2] * cm res = np.setdiff1d(a, b) assert_array_equal_units(res, [3] * cm) def test_setxor1d_mixed_units(): a = [1, 2, 3] * cm b = [0, 1, 2] with pytest.raises(UnitInconsistencyError): np.setxor1d(a, b) def test_setxor1d(): a = [1, 2, 3] * cm b = [0, 1, 2] * cm res = np.setxor1d(a, b) assert_array_equal_units(res, [0, 3] * cm) def test_clip_mixed_units(): a = [1, 2, 3, 4, 5, 6] * cm with pytest.raises(UnitInconsistencyError): np.clip(a, 3 * cm, 4) @pytest.mark.parametrize("vmin,vmax", [(3 * cm, 4 * cm), (3, 4)]) def test_clip(vmin, vmax): a = [1, 2, 3, 4, 5, 6] * cm res = np.clip(a, vmin, vmax) assert_array_equal_units(res, [3, 3, 3, 4, 4, 4] * cm) def test_where_mixed_units(): x = [-1, 2, -3] * cm y = [0, 0, 0] with pytest.raises(UnitInconsistencyError): np.where(x > y, x, y) def test_where_single_arg(): x = [0, 2, -1, 0, 1] * cm res = np.where(x) assert isinstance(res, tuple) assert len(res) == 1 assert type(res[0]) is np.ndarray np.testing.assert_array_equal(res[0], [1, 2, 4]) def test_where_xy(): x = [-1, 2, -3] * cm y = [0, 0, 0] * cm res = np.where(x > y, x, y) assert type(res) is unyt_array assert res.units == cm @pytest.mark.parametrize( "func", [ np.imag, np.real, np.real_if_close, ], ) def test_complex_reductions(func): a = [1 + 1j for _ in range(3)] * A res = func(a) assert type(res) is unyt_array assert res.units == A @pytest.mark.parametrize( "func", [np.tril, np.triu], ) def test_triangles(func): a = np.eye(4) * cm res = func(a) assert type(res) is unyt_array assert res.units == cm def test_einsum(): a = np.eye(4) * cm # extract diagonal res = np.einsum("ii->i", a) assert type(res) is unyt_array assert res.units == cm # sum diagonal elements, the result should be a scalar res = np.einsum("ii", a) assert type(res) is unyt_quantity assert res.units == cm def test_convolve(): a = [1, 2, 3] * cm v = [4, 5, 6] * s res = np.convolve(a, v) assert type(res) is unyt_array assert res.units == cm * s def test_correlate(): a = [1, 2, 3] * cm v = [4, 5, 6] * s res = np.correlate(a, v) assert type(res) is unyt_array assert res.units == cm * s def test_tensordot(): a = np.arange(60.0).reshape(3, 4, 5) * cm b = np.arange(24.0).reshape(4, 3, 2) * s res = np.tensordot(a, b, axes=([1, 0], [0, 1])) assert type(res) is unyt_array assert res.units == cm * s def test_compress(): a = [1, 2, 3] * cm res = np.compress(a > 1, a) assert type(res) is unyt_array assert res.units == cm np.compress(a > 1, a, out=res) assert type(res) is unyt_array assert res.units == cm np.compress(a > 1, a, out=res.view(np.ndarray)) assert type(res) is unyt_array assert res.units == cm def test_take_along_axis(): a = np.array([[10, 30, 20], [60, 40, 50]]) * cm ai = np.argsort(a, axis=1) res = np.take_along_axis(a, ai, axis=1) assert type(res) is unyt_array assert res.units == cm def test_unwrap(): phase = np.linspace(0, np.pi, num=5) * rad phase[3:] += np.pi * rad res = np.unwrap(phase) assert type(res) is unyt_array assert res.units == rad def test_interp(): _x = np.array([1.1, 2.2, 3.3]) _xp = np.array([1, 2, 3]) _fp = np.array([4, 8, 12]) # any of the three input array-like might be unitful # let's test all relevant combinations # return type should match fp's with pytest.raises(UnitInconsistencyError): np.interp(_x * cm, _xp, _fp) with pytest.raises(UnitInconsistencyError): res = np.interp(_x, _xp * cm, _fp) res = np.interp(_x * cm, _xp * cm, _fp) assert type(res) is np.ndarray res = np.interp(_x, _xp, _fp * K) assert type(res) is unyt_array assert res.units == K res = np.interp(_x * cm, _xp * cm, _fp * K) assert type(res) is unyt_array assert res.units == K @pytest.mark.parametrize( "target, helper", sorted( HANDLED_FUNCTIONS.items(), key=lambda items: items[0].__name__, ), ids=lambda func: func.__name__, ) class TestFunctionHelpersSignatureCompatibility: """ Check that a helper function's signature is *at least* as flexible as the helped (target) function's. E.g., any argument that is allowed positionally, or as keyword, by the target must be re-exposed *somehow* by the helper. We explicitly allow helper's signature to be *more* flexible than the target signature by allowing *args and **kwargs catch-all arguments, which we use to limit code duplication, and also help with forward and backward compatibility. See https://github.com/astropy/astropy/issues/15703 """ # this test class is adapted from astropy.units @staticmethod def have_catchall_argument(parameters, kind) -> bool: return any(p.kind is kind for p in parameters.values()) @staticmethod def get_param_group(parameters, kinds: list) -> list[str]: return [name for name, p in parameters.items() if p.kind in kinds] def test_all_arguments_reexposed(self, target, helper): try: sig_target = inspect.signature(target) except ValueError: pytest.skip("Non Python function cannot be inspected at runtime") params_target = sig_target.parameters sig_helper = inspect.signature(helper) params_helper = sig_helper.parameters have_args_helper = self.have_catchall_argument(params_helper, VAR_POSITIONAL) have_kwargs_helper = self.have_catchall_argument(params_helper, VAR_KEYWORD) args_helper = list(params_helper.items()) pos_helper = 0 for nt, pt in params_target.items(): kt = pt.kind if kt in (POSITIONAL_ONLY, POSITIONAL_OR_KEYWORD): assert pos_helper < len(args_helper), ( "helper's signature is too short; " "some arguments are not properly re-exposed" ) nh, ph = args_helper[pos_helper] if (kh := ph.kind) is not VAR_POSITIONAL: assert nh == nt, f"argument {nt!r} isn't re-exposed as positional" assert kh is kt, ( f"helper is not re-exposing argument {nt!r} properly: " f"expected {kt}, got {kh}" ) pos_helper += 1 continue if kt in (KEYWORD_ONLY, POSITIONAL_OR_KEYWORD): if nt in params_helper: kh = params_helper[nt].kind assert kh is kt, ( f"helper is not re-exposing argument {nt!r} properly: " f"expected {kt}, got {kh}" ) elif kt is KEYWORD_ONLY: assert have_kwargs_helper, ( f"argument {nt!r} is not re-exposed as keyword" ) elif kt is POSITIONAL_OR_KEYWORD: assert have_args_helper and have_kwargs_helper, ( f"argument {nt!r} is not re-exposed as positional-or-keyword" ) elif kt is VAR_POSITIONAL: assert have_args_helper, "helper is missing a catch-all *args argument" elif kt is VAR_KEYWORD: assert have_kwargs_helper, ( "helper is missing a catch-all **kwargs argument" ) def test_known_arguments(self, target, helper): # validate that all exposed arguments map to something in the target try: sig_target = inspect.signature(target) except ValueError: pytest.skip("Non Python function cannot be inspected at runtime") params_target = sig_target.parameters sig_helper = inspect.signature(helper) params_helper = sig_helper.parameters for kind in (POSITIONAL_ONLY, POSITIONAL_OR_KEYWORD): args_target = self.get_param_group(params_helper, [kind]) args_helper = self.get_param_group(params_helper, [kind]) if (nhelper := len(args_helper)) > ( ntarget := len(args_target) ): # pragma: no cover unknown: list[str] = args_helper[ntarget:] raise AssertionError( f"Found unknown {kind} parameter(s) " "in helper's signature: " f"{unknown}, at position(s) {list(range(ntarget, nhelper))}" ) # keyword-allowed keyword_allowed_target = set( self.get_param_group(params_target, [KEYWORD_ONLY, POSITIONAL_OR_KEYWORD]) ) keyword_allowed_helper = set( self.get_param_group(params_helper, [KEYWORD_ONLY, POSITIONAL_OR_KEYWORD]) ) # additional private keyword-only argument are allowed because # they are only intended for testing purposes. # For instance, quantile has such a parameter '_q_unit' keyword_allowed_helper = { name for name in keyword_allowed_helper if not name.startswith("_") } diff = keyword_allowed_helper - keyword_allowed_target assert not diff, ( "Found some keyword-allowed parameters in helper " f"that are unknown to target: {diff}" ) # finally, check that default values are correctly replicated for name, ph in params_helper.items(): if name not in params_target: # In a few cases, the helper defines names that are not in # the target (e.g., a private name like _q_unit in quantile, # or a *args, **kwargs that captures further arguments # that do not matter. We let such cases slip by. continue pt = params_target[name] assert ph.default == pt.default, ( f"Default value mismatch for argument {name!r}. " f"Helper has {ph.default!r}, target has {pt.default!r}" ) def test_squeeze_array(): """Check that squeeze returns the correct type for the shape of the result.""" arr = np.ones((3, 1)) * cm arr_squeezed = np.squeeze(arr) assert arr_squeezed.ndim > 0 assert type(arr_squeezed) is unyt_array def test_squeeze_scalar(): """Check that squeeze returns the correct type for the shape of the result.""" arr = np.ones(1) * cm arr_squeezed = np.squeeze(arr) assert arr_squeezed.ndim == 0 assert type(arr_squeezed) is unyt_quantity def test_squeeze_with_axis(): """Check that squeeze obeys axis kwarg.""" arr = np.ones((1, 1)) * cm squeeze_axis = (1,) arr_squeezed = np.squeeze(arr, axis=1) assert arr_squeezed.ndim == arr.ndim - len(squeeze_axis) unyt-3.2.0/unyt/tests/test_dask_arrays.py000066400000000000000000000310041526544257300206030ustar00rootroot00000000000000import pickle from collections import defaultdict import numpy as np import pytest from numpy.testing import assert_array_equal from unyt import unyt_array, unyt_quantity from unyt._array_functions import _UNSUPPORTED_FUNCTIONS from unyt._on_demand_imports import _dask as dask if not dask.__is_available__: pytest.skip("dask isn't installed", allow_module_level=True) from unyt.dask_array import ( _create_with_quantity, _use_unary_decorator, reduce_with_units, unyt_dask_array, unyt_from_dask, ) from unyt.exceptions import UnitOperationError from unyt.unit_symbols import cm, g, m def test_unyt_dask_creation(): x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) assert type(x_da) is unyt_dask_array assert x_da.units == m assert type(x_da.compute()) is unyt_array x_da = _create_with_quantity(x, unyt_quantity(1, m)) assert type(x_da) is unyt_dask_array assert x_da.units == m assert type(x_da.compute()) is unyt_array x_dask = x_da.to_dask() assert_array_equal(x.compute(), x_dask.compute()) def test_unyt_dask_slice(): # tests __getitem__ x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) slc = x_da[:, 0] assert slc.units == m assert slc.compute().units == m assert type(slc.compute()) is unyt_array def test_unyt_set(): # tests __setitem__ x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_da[:, 0] = 3.0 assert (x_da[:, 0].compute() == 3.0).sum() == 10 def test_unit_conversions(): x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_da = x_da.to(cm) assert type(x_da) is unyt_dask_array assert x_da.units == cm assert x_da.compute().units == cm assert x_da[0, 0].compute().value == 100 x_da_2 = unyt_from_dask(x, m) result = x_da + x_da_2 assert type(result) is unyt_dask_array assert result.units == m assert result.compute().units == m x_da_2 = unyt_from_dask(x, "g") with pytest.raises(UnitOperationError): x_da + x_da_2 def test_conversion_to_dask(): x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_again = x_da.to_dask() assert_array_equal(x.compute(), x_again.compute()) assert type(x_again) is type(x) result = np.isfinite(x_da) # should return plain dask array assert type(result) is dask.array.core.Array def unary_test(the_func, unyt_dask_obj, unyt_array_in, *args, **kwargs): # the_func can be a numpy ufunc or dask.array numpy ufunc implementation result_delay = the_func(unyt_dask_obj, *args, **kwargs) correct_unyt = the_func(unyt_array_in, *args, **kwargs) assert result_delay.units == correct_unyt.units # units should already match unary_result_test(result_delay, correct_unyt) def unary_result_test(dask_unyt_delayed, correct_unyt): # computes a delayed dask_unyt_array and compares resulting units and values result = dask_unyt_delayed.compute() assert result.units == correct_unyt.units assert type(result) is type(correct_unyt) # value comparison: if type(result) is unyt_array: assert_array_equal(result, correct_unyt) else: assert result == correct_unyt def test_unary(): x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_unyt = unyt_array(np.ones((10, 10)), m) for unary_op in [np.sqrt, dask.array.sqrt]: unary_test(unary_op, x_da, x_unyt) # some of the daskified ufunc attributes need to be called directly unary_result_test(x_da.sum(), x_unyt.sum()) unary_result_test(x_da.min(), x_unyt.min()) unary_result_test(x_da.max(), x_unyt.max()) unary_result_test(x_da.mean(), x_unyt.mean()) unary_result_test(x_da.std(), x_unyt.std()) unary_result_test(x_da.cumsum(0), x_unyt.cumsum(0)) unary_result_test(abs(x_da), abs(x_unyt)) # __abs__ @pytest.mark.parametrize( "logical_op", ["__lt__", "__le__", "__gt__", "__ge__", "__eq__", "__ne__"] ) @pytest.mark.parametrize("other", [None, unyt_quantity(2, m)]) def test_logical(logical_op, other): def check_operator(arg1, arg2): # comparisons should return plain dask arrays func = getattr(arg1, logical_op) result = func(arg2) assert type(result) is dask.array.Array x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) if other is None: other = 2 * x_da # test another unyt-dask array check_operator(x_da, other) check_operator(other, x_da) def test_binary(): x = dask.array.ones((10, 10), chunks=(2, 2)) x2 = dask.array.full((10, 10), 2, chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_da_2 = unyt_from_dask(x2, g) # multiplications result = x_da * x_da_2 assert result.units == m * g result = x_da_2 * x_da assert result.units == m * g result = x_da_2 * 2 assert result.units == g result = 2 * x_da_2 assert result.units == g result = x_da_2 * unyt_quantity(2, "m") assert result.units == m * g result = unyt_quantity(2, "m") * x_da_2 assert result.units == m * g # divisions result = x_da_2 / x_da assert result.units == g / m result = x_da / x_da_2 assert result.units == m / g result = x_da_2 / 2 assert result.units == g result = 2 / x_da_2 # __rtruediv__ assert result.units == g**-1 result = x_da_2 / unyt_quantity(2, "m") assert result.units == g / m result = unyt_quantity(2, "m") / x_da_2 assert result.units == m / g result = x_da**2 assert result.units == m * m def test_addition(): x = dask.array.ones((10, 10), chunks=(2, 2)) x2 = dask.array.full((10, 10), 2, chunks=(2, 2)) x_da = unyt_from_dask(x, m) # two unyt_dask_array objects, any order, any units with same dimension x_da_2 = unyt_from_dask(x2, m) result = x_da + x_da_2 assert result.units == m result = x_da_2 + x_da assert result.units == m x_da_3 = unyt_from_dask(x2, "cm") result = x_da + x_da_3 assert result.units == m result = x_da_3 + x_da assert result.units == m # one unyt_dask_array, one unyt_quantity, any order, any units with same dim result = x_da + unyt_quantity(1, "m") assert result.units == m result = unyt_quantity(1, "m") + x_da assert result.units == m result = unyt_quantity(100, "cm") + x_da # test same dimensions assert result.units == m assert result.max().compute() == unyt_quantity(2, "m") result = x_da + unyt_quantity(100, "cm") # test same dimensions assert result.units == m assert result.max().compute() == unyt_quantity(200, "cm") def test_subtraction(): x = dask.array.ones((10, 10), chunks=(2, 2)) x2 = dask.array.full((10, 10), 2, chunks=(2, 2)) x_da = unyt_from_dask(x, m) # two unyt_dask_array objects, any order, any units with same dimension x_da_2 = unyt_from_dask(x2, m) result = x_da - x_da_2 assert result.units == m result = x_da_2 - x_da assert result.units == m x_da_3 = unyt_from_dask(x2, "cm") result = x_da - x_da_3 assert result.units == m result = x_da_3 - x_da assert result.units == m # one unyt_dask_array, one unyt_quantity, any order, any units with same dim result = x_da - unyt_quantity(1, "m") assert result.units == m result = unyt_quantity(1, "m") - x_da assert result.units == m result = unyt_quantity(100, "cm") - x_da # test same dimensions assert result.units == m assert result.max().compute() == unyt_quantity(0, "m") result = x_da - unyt_quantity(100, "cm") # test same dimensions assert result.units == m assert result.max().compute() == unyt_quantity(0, "cm") def test_unyt_type_result(): # test that the return type of a compute is unyt_array or unyt_quantity when # appropriate x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) x_unyt = unyt_array(np.ones((10, 10)), m) result = x_da.compute() assert type(result) is unyt_array assert_array_equal(result, x_unyt) result = x_da.min().compute() assert type(result) is unyt_quantity assert result == unyt_quantity(1, m) _func_args = defaultdict(lambda: ()) _func_args["reshape"] = ((100, 1),) _func_args["rechunk"] = ((5, 5),) _func_args["cumsum"] = (0,) _func_args["clip"] = (0, 2) _func_args["swapaxes"] = (0, 1) _func_args["repeat"] = (1, 0) _func_args["astype"] = (int,) _func_args["topk"] = (1,) @pytest.mark.parametrize( ("da_func", "args"), [(f, _func_args[f]) for f in _use_unary_decorator] ) def test_dask_passthroughs(da_func, args): # tests the array class functions that do not modify units x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) assert getattr(x_da, da_func)(*args).units == m def test_repr(): x = dask.array.ones((10, 10), chunks=(2, 2)) x_da = unyt_from_dask(x, m) assert "unyt_dask_array" in x_da.__repr__() table_str = x_da._repr_html_() assert "Units" in table_str @pytest.mark.parametrize( ("dask_func_str", "actual", "axis", "check_nan"), [ ("mean", unyt_quantity(1.0, "m"), None, True), ("min", unyt_quantity(1.0, "m"), None, True), ("max", unyt_quantity(1.0, "m"), None, True), ("std", unyt_quantity(0.0, "m"), None, True), ( "median", unyt_quantity(1.0, "m"), 1, True, ), # median requires an axis for dask ("var", unyt_quantity(0.0, "m**2"), None, True), ("sum", unyt_quantity(100.0, "m"), None, True), ( "cumsum", unyt_array(np.ones((10, 10)), "m").cumsum(axis=1), 1, True, ), ("average", unyt_quantity(1.0, "m"), None, False), ("diagonal", unyt_array(np.ones((10,)), "m"), None, False), ("unique", unyt_array([1.0], "m"), None, False), ], ) def test_dask_array_reductions(dask_func_str, actual, axis, check_nan): extra_kwargs = {} if axis is not None: extra_kwargs["axis"] = axis func_strs = [ dask_func_str, ] if check_nan: func_strs.append("nan" + dask_func_str) for func_str in func_strs: dask_func = getattr(dask.array, func_str) x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) result = reduce_with_units(dask_func, x_da, **extra_kwargs).compute() assert_array_equal(result, actual) def test_bad_dask_array_reductions(): x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) with pytest.raises( ValueError, match="could not deduce np equivalent of dask reduction" ): reduce_with_units(lambda: None, x_da).compute() def test_prod(): x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) assert x_da.prod().units == m**100 def test_pickle(): x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) x_da2 = pickle.loads(pickle.dumps(x_da)) assert_array_equal(x_da, x_da2) assert x_da.units == x_da2.units def test_np_array_funcs(): # test some numpy array functions to catch __array_function__ x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) actual = np.sum(x_da).compute() assert actual == unyt_quantity(100, m) plain_dask = dask.array.arange(100, chunks=(20,)) x_da = unyt_from_dask(plain_dask, m) actual = np.mean(x_da).compute() expected = unyt_quantity(np.mean(plain_dask).compute(), m) assert actual == expected actual = np.std(x_da[x_da > 10.0]).compute() expected = unyt_quantity(np.std(plain_dask[plain_dask > 10]).compute(), m) assert actual == expected actual = np.max(np.power(x_da, 2)).compute() expected = unyt_quantity(np.max(np.power(plain_dask, 2)).compute(), m * m) assert actual == expected def test_unsupported_ufunc_returns_notimplemented(): # a user-defined ufunc is guaranteed not to be in unyt_array._ufunc_registry ufunc = np.frompyfunc(lambda x: x, 1, 1) x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) assert x_da.__array_ufunc__(ufunc, "__call__", x_da) is NotImplemented with pytest.raises(TypeError, match="all returned NotImplemented"): ufunc(x_da) def test_unsupported_array_function_returns_notimplemented(): x_da = unyt_from_dask(dask.array.ones((10, 10), chunks=(2, 2)), m) types = (unyt_dask_array,) for func in _UNSUPPORTED_FUNCTIONS: assert x_da.__array_function__(func, types, (x_da,), {}) is NotImplemented with pytest.raises(TypeError, match="no implementation found"): np.packbits(x_da) unyt-3.2.0/unyt/tests/test_define_unit.py000066400000000000000000000023331526544257300205740ustar00rootroot00000000000000import pytest from unyt.array import unyt_quantity from unyt.unit_object import define_unit from unyt.unit_registry import UnitRegistry def test_define_unit(): define_unit("kph", (1.0, "km/hr")) a = unyt_quantity(2.0, "kph") b = unyt_quantity(1.0, "km") c = unyt_quantity(1.0, "hr") assert a == 2.0 * b / c d = unyt_quantity(1000.0, "cm**3") define_unit("Baz", d, prefixable=True) e = unyt_quantity(1.0, "mBaz") f = unyt_quantity(1.0, "cm**3") assert e == f define_unit("Foo", (1.0, "V/sqrt(s)")) g = unyt_quantity(1.0, "Foo") volt = unyt_quantity(1.0, "V") second = unyt_quantity(1.0, "s") assert g == volt / second ** (0.5) # Test custom registry reg = UnitRegistry() define_unit("Foo", (1, "m"), registry=reg) define_unit("Baz", (1, "Foo**2"), registry=reg) h = unyt_quantity(1, "Baz", registry=reg) i = unyt_quantity(1, "m**2", registry=reg) assert h == i def test_define_unit_error(): from unyt import define_unit with pytest.raises(RuntimeError): define_unit("foobar", "baz") with pytest.raises(RuntimeError): define_unit("foobar", 12) with pytest.raises(RuntimeError): define_unit("C", (1.0, "A*s")) unyt-3.2.0/unyt/tests/test_mpl_interface.py000066400000000000000000000162361526544257300211220ustar00rootroot00000000000000"""Test Matplotlib ConversionInterface""" import numpy as np import pytest from unyt import K, m, matplotlib_support, s, unyt_array, unyt_quantity from unyt._on_demand_imports import NotAModule, _matplotlib try: from unyt._mpl_array_converter import unyt_arrayConverter except ImportError: pass check_matplotlib = pytest.mark.skipif( isinstance(_matplotlib.pyplot, NotAModule), reason="matplotlib not installed" ) @pytest.fixture def ax(): _matplotlib.use("agg") matplotlib_support.enable() matplotlib_support.label_style = "()" fig, ax = _matplotlib.pyplot.subplots() yield ax _matplotlib.pyplot.close() matplotlib_support.disable() @check_matplotlib def test_label(ax): x = [0, 1, 2] * s y = [3, 4, 5] * K matplotlib_support.label_style = "()" ax.plot(x, y) expected_xlabel = "$\\left(\\rm{s}\\right)$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "$\\left(\\rm{K}\\right)$" assert ax.yaxis.get_label().get_text() == expected_ylabel _matplotlib.pyplot.close() @check_matplotlib def test_convert_unit(ax): x = [0, 1, 2] * s y = [1000, 2000, 3000] * K ax.plot(x, y, yunits="Celsius") expected = y.to("Celsius") line = ax.lines[0] original_y_array = line.get_data()[1] converted_y_array = line.convert_yunits(original_y_array) results = converted_y_array == expected assert results.all() @check_matplotlib def test_convert_equivalency(ax): x = [0, 1, 2] * s y = [1000, 2000, 3000] * K ax.clear() ax.plot(x, y, yunits=("J", "thermal")) expected = y.to("J", "thermal") line = ax.lines[0] original_y_array = line.get_data()[1] converted_y_array = line.convert_yunits(original_y_array) results = converted_y_array == expected assert results.all() @check_matplotlib def test_dimensionless(ax): x = [0, 1, 2] * s y = [3, 4, 5] * K / K ax.plot(x, y) expected_ylabel = "" assert ax.yaxis.get_label().get_text() == expected_ylabel @check_matplotlib def test_conversionerror(ax): x = [0, 1, 2] * s y = [3, 4, 5] * K ax.plot(x, y) ax.xaxis.callbacks.exception_handler = None with pytest.raises(_matplotlib.units.ConversionError): ax.xaxis.set_units("V") @check_matplotlib def test_ndarray_label(ax): x = [0, 1, 2] * s y = np.arange(3, 6) matplotlib_support.label_style = "()" ax.plot(x, y) expected_xlabel = "$\\left(\\rm{s}\\right)$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "" assert ax.yaxis.get_label().get_text() == expected_ylabel @check_matplotlib def test_list_label(ax): x = [0, 1, 2] * s y = [3, 4, 5] matplotlib_support.label_style = "()" ax.plot(x, y) expected_xlabel = "$\\left(\\rm{s}\\right)$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "" assert ax.yaxis.get_label().get_text() == expected_ylabel @check_matplotlib def test_errorbar(ax): x = unyt_array([8, 9, 10], "cm") y = unyt_array([8, 9, 10], "kg") y_scatter = [unyt_array([0.1, 0.2, 0.3], "kg"), unyt_array([0.1, 0.2, 0.3], "kg")] x_lims = (unyt_quantity(5, "cm"), unyt_quantity(12, "cm")) y_lims = (unyt_quantity(5, "kg"), unyt_quantity(12, "kg")) ax.errorbar(x, y, yerr=y_scatter) x_lims = (unyt_quantity(5, "cm"), unyt_quantity(12, "cm")) y_lims = (unyt_quantity(5, "kg"), unyt_quantity(12, "kg")) ax.set_xlim(*x_lims) ax.set_ylim(*y_lims) @check_matplotlib def test_hist2d(ax): rng = np.random.default_rng() x = rng.normal(size=50000) * s y = 3 * x + rng.normal(size=50000) * s ax.hist2d(x, y, bins=(50, 50)) @check_matplotlib def test_imshow(ax): rng = np.random.default_rng() data = np.reshape(rng.normal(size=10000), (100, 100)) ax.imshow(data, vmin=data.min(), vmax=data.max()) @check_matplotlib def test_hist(ax): rng = np.random.default_rng() data = rng.normal(size=10000) * s bin_edges = np.linspace(data.min(), data.max(), 50) ax.hist(data, bins=bin_edges) @check_matplotlib def test_matplotlib_support(): with pytest.raises(KeyError): _matplotlib.units.registry[unyt_array] matplotlib_support.enable() assert isinstance(_matplotlib.units.registry[unyt_array], unyt_arrayConverter) matplotlib_support.disable() assert unyt_array not in _matplotlib.units.registry.keys() assert unyt_quantity not in _matplotlib.units.registry.keys() # test as a callable matplotlib_support() assert isinstance(_matplotlib.units.registry[unyt_array], unyt_arrayConverter) @check_matplotlib def test_labelstyle(): x = [0, 1, 2] * s y = [3, 4, 5] * K matplotlib_support.label_style = "[]" assert matplotlib_support.label_style == "[]" matplotlib_support.enable() assert unyt_arrayConverter._labelstyle == "[]" fig, ax = _matplotlib.pyplot.subplots() ax.plot(x, y) expected_xlabel = "$\\left[\\rm{s}\\right]$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "$\\left[\\rm{K}\\right]$" assert ax.yaxis.get_label().get_text() == expected_ylabel matplotlib_support.label_style = "/" ax.clear() x.name = "$t$" ax.plot(x, y) expected_xlabel = "$t$ $\\;/\\;\\rm{s}$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "$q_{\\rmy}$$\\;/\\;\\rm{K}$" assert ax.yaxis.get_label().get_text() == expected_ylabel x = [0, 1, 2] * m / s ax.clear() ax.plot(x, y) expected_xlabel = "$q_{\\rmx}$$\\;/\\;\\left(\\rm{m} / \\rm{s}\\right)$" assert ax.xaxis.get_label().get_text() == expected_xlabel _matplotlib.pyplot.close() matplotlib_support.disable() @check_matplotlib def test_name(ax): x = unyt_array([0, 1, 2], "s", name="time") assert x.name == "time" y = unyt_array([3, 4, 5], "m", name="distance") ax.plot(x, y) expected_xlabel = "time $\\left(\\rm{s}\\right)$" assert ax.xaxis.get_label().get_text() == expected_xlabel expected_ylabel = "distance $\\left(\\rm{m}\\right)$" assert ax.yaxis.get_label().get_text() == expected_ylabel ax.clear() ax.plot(x, y, xunits="ms") expected_xlabel = "time $\\left(\\rm{ms}\\right)$" @check_matplotlib def test_multiple_subplots(): x1 = unyt_array([0, 1, 2], "s", name="time") y1 = unyt_array([6, 7, 8], "m", name="distance") x2 = unyt_array([3, 4, 5], "V", name="voltage") y2 = unyt_array([9, 10, 11], "A", name="current") matplotlib_support.enable() fig, ax = _matplotlib.pyplot.subplots(nrows=1, ncols=2) ax[0].plot(x1, y1) ax[1].plot(x2, y2) expected_labels = [ "time $\\left(\\rm{s}\\right)$", "distance $\\left(\\rm{m}\\right)$", "voltage $\\left(\\rm{V}\\right)$", "current $\\left(\\rm{A}\\right)$", ] generated_labels = [] for subplot in ax: xlabel = subplot.xaxis.get_label().get_text() ylabel = subplot.yaxis.get_label().get_text() generated_labels.extend((xlabel, ylabel)) assert generated_labels == expected_labels _matplotlib.pyplot.close() matplotlib_support.disable() @check_matplotlib def test_empty_plot(ax): ax.set_xlim(*unyt_array([0, 1], "s")) _matplotlib.pyplot.close() unyt-3.2.0/unyt/tests/test_no_duplicates.py000066400000000000000000000007221526544257300211340ustar00rootroot00000000000000def test_no_duplicates(): import unyt from unyt import physical_constants, unit_symbols from unyt.array import unyt_quantity symbol_names = {key for key in unit_symbols.__dict__ if not key.startswith("_")} constant_names = { key for key in physical_constants.__dict__ if not key.startswith("_") } dups = symbol_names.intersection(constant_names) for dup in dups: assert isinstance(getattr(unyt, dup), unyt_quantity) unyt-3.2.0/unyt/tests/test_subclass.py000066400000000000000000001167511526544257300201340ustar00rootroot00000000000000import os import warnings from copy import copy, deepcopy from importlib.metadata import version import numpy as np import pytest from packaging.version import Version import unyt as u from .sample_subclasses import ExtraAttributeError, subclass_uarray, subclass_uquantity NUMPY_VERSION = Version(version("numpy")) trapezoid_fname = "trapz" if NUMPY_VERSION < Version("2.0.0dev0") else "trapezoid" savetxt_file = "saved_array.txt" def getfunc(fname): """ Helper for our tests: get the function handle from a name (possibly with attribute access). """ func = np for attr in fname.split("."): func = getattr(func, attr) return func def sub_arr(x, unit=u.Mpc): """ Helper for our tests: turn an array into a subclass_uarray. """ return subclass_uarray(x, unit, extra_attr=True) def sub_q(x, unit=u.Mpc): """ Helper for our tests: turn a scalar into a subclass_uquantity. """ return subclass_uquantity(x, unit, extra_attr=True) def arg_to_ua(arg): """ Helper for our tests: recursively convert subclass_* in an argument (possibly an iterable) to their unyt_* equivalents. """ if type(arg) in (list, tuple): return type(arg)([arg_to_ua(a) for a in arg]) else: return to_ua(arg) def to_ua(x): """ Helper for our tests: turn a subclass_* object into its unyt_* equivalent. """ return u.unyt_array(x) if hasattr(x, "extra_attr") else x def check_result(x_s, x_u, ignore_values=False): """ Helper for our tests: check that a result with subclass input matches what we expected based on the result with unyt input. We check: - that the type of the result makes sense, recursing if needed. - that the value of the result matches (unless ignore_values=False). - that the units match. """ if x_u is None: assert x_s is None return if isinstance(x_u, str): assert isinstance(x_s, str) return if isinstance(x_u, type) or isinstance(x_u, np.dtype): assert x_u == x_s return if type(x_u) in (list, tuple): assert type(x_u) is type(x_s) assert len(x_u) == len(x_s) for x_c_i, x_u_i in zip(x_s, x_u): check_result(x_c_i, x_u_i) return # careful, unyt_quantity is a subclass of unyt_array: if isinstance(x_u, u.unyt_quantity): assert isinstance(x_s, subclass_uquantity) elif isinstance(x_u, u.unyt_array): assert isinstance(x_s, subclass_uarray) and not isinstance( x_s, subclass_uquantity ) else: assert not isinstance(x_s, subclass_uarray) if not ignore_values: assert np.allclose(x_s, x_u) return assert x_s.units == x_u.units if not ignore_values: assert np.allclose(x_s.to_value(x_s.units), x_u.to_value(x_u.units)) if isinstance(x_s, subclass_uarray): # includes subclass_uquantity assert x_s.extra_attr is True return class TestSubclassArrayInit: """ Test different ways of initializing a subclass_uarray. """ def test_init_from_ndarray(self): """ Check initializing from a bare numpy array. """ arr = subclass_uarray(np.ones(5), units=u.Mpc, extra_attr=True) assert hasattr(arr, "extra_attr") assert isinstance(arr, subclass_uarray) def test_init_from_list(self): """ Check initializing from a list of values. """ arr = subclass_uarray( [1, 1, 1, 1, 1], units=u.Mpc, extra_attr=True, ) assert hasattr(arr, "extra_attr") assert isinstance(arr, subclass_uarray) def test_init_from_unyt_array(self): """ Check initializing from a unyt_array. """ arr = subclass_uarray( u.unyt_array(np.ones(5), units=u.Mpc), extra_attr=False, ) assert hasattr(arr, "extra_attr") assert isinstance(arr, subclass_uarray) def test_init_from_list_of_unyt_arrays(self): """ Check initializing from a list of unyt_array's. Note that unyt won't recurse deeper than one level on inputs, so we don't test deeper than one level of lists. """ arr = subclass_uarray( [u.unyt_array(1, units=u.Mpc) for _ in range(5)], extra_attr=True, ) assert hasattr(arr, "extra_attr") assert isinstance(arr, subclass_uarray) def test_init_from_list_of_subclass_arrays(self): """ Check initializing from a list of subclass_uarray's. Note that unyt won't recurse deeper than one level on inputs, so we don't test deeper than one level of lists. """ arr = subclass_uarray( [ subclass_uarray( [1], units=u.Mpc, extra_attr=True, ) for _ in range(5) ] ) assert isinstance(arr, subclass_uarray) assert hasattr(arr, "extra_attr") and arr.extra_attr is True def test_expected_init_failures(self): # we refuse mixed extra_attr: with pytest.raises(ExtraAttributeError): subclass_uarray( [ subclass_uquantity(1, u.Mpc, extra_attr=True), subclass_uquantity(1, u.Mpc, extra_attr=False), ] ) for cls, inp in ((subclass_uarray, [1]), (subclass_uquantity, 1)): # we refuse overriding an input subclass_uarray's information: with pytest.raises(ValueError): cls( cls(inp, units=u.Mpc, extra_attr=False), units=u.Mpc, extra_attr=True, ) # unless it matches, that's fine: cls( cls( inp, units=u.Mpc, extra_attr=True, ), units=u.Mpc, extra_attr=True, ) class TestNumpyFunctions: """ Check that numpy functions recognize our subclasses as input and handle them correctly. """ def test_explicitly_handled_funcs(self): """ Make sure we at least handle everything that unyt does, and anything that 'just worked' for unyt but that we need to handle by hand. We don't try to be exhaustive here, but at give some basic input to every function that we expect to be able to take subclass input. We then use our helpers defined above to convert the inputs to unyt equivalents and call the numpy function on both subclass and unyt input. Then we use our helpers to check the results for consistency. For instance, if with unyt input we got back a unyt_array, we should expect a subclass_uarray. We are not currently explicitly testing that the results of any specific function are numerically what we expected them to be (seems like overkill), nor that the extra_attr's make sense given the input. The latter would be a useful addition, but I can't think of a sensible way to implement this besides writing in the expectation for every output value of every function by hand. As long as no functions outright crash, the test will report the list of functions that we should have covered that we didn't cover in tests, and/or the list of functions whose output values were not what we expected based on running them with unyt input. Otherwise we just get a stack trace of the first function that crashed. """ from unyt._array_functions import _HANDLED_FUNCTIONS from unyt.tests.test_array_functions import NOOP_FUNCTIONS functions_to_check = { # FUNCTIONS UNYT HANDLES EXPLICITLY: "array2string": (sub_arr(np.arange(3)),), "dot": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "vdot": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "inner": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "outer": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "kron": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "histogram_bin_edges": (sub_arr(np.arange(3)),), "linalg.inv": (sub_arr(np.eye(3)),), "linalg.tensorinv": (sub_arr(np.eye(9).reshape((3, 3, 3, 3))),), "linalg.pinv": (sub_arr(np.eye(3)),), "linalg.svd": (sub_arr(np.eye(3)),), "histogram": (sub_arr(np.arange(3)),), "histogram2d": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "histogramdd": (sub_arr(np.arange(3)).reshape((1, 3)),), "concatenate": (sub_arr(np.eye(3)),), "cross": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "intersect1d": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "union1d": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "linalg.norm": (sub_arr(np.arange(3)),), "vstack": (sub_arr(np.arange(3)),), "hstack": (sub_arr(np.arange(3)),), "dstack": (sub_arr(np.arange(3)),), "column_stack": (sub_arr(np.arange(3)),), "stack": (sub_arr(np.arange(3)),), "around": (sub_arr(np.arange(3)),), "block": ([[sub_arr(np.arange(3))], [sub_arr(np.arange(3))]],), "fft.fft": (sub_arr(np.arange(3)),), "fft.fft2": (sub_arr(np.eye(3)),), "fft.fftn": (sub_arr(np.arange(3)),), "fft.hfft": (sub_arr(np.arange(3)),), "fft.rfft": (sub_arr(np.arange(3)),), "fft.rfft2": (sub_arr(np.eye(3)),), "fft.rfftn": (sub_arr(np.arange(3)),), "fft.ifft": (sub_arr(np.arange(3)),), "fft.ifft2": (sub_arr(np.eye(3)),), "fft.ifftn": (sub_arr(np.arange(3)),), "fft.ihfft": (sub_arr(np.arange(3)),), "fft.irfft": (sub_arr(np.arange(3)),), "fft.irfft2": (sub_arr(np.eye(3)),), "fft.irfftn": (sub_arr(np.arange(3)),), "fft.fftshift": (sub_arr(np.arange(3)),), "fft.ifftshift": (sub_arr(np.arange(3)),), "sort_complex": (sub_arr(np.arange(3)),), "isclose": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "allclose": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "array_equal": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "array_equiv": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "linspace": (sub_q(1), sub_q(2)), "logspace": ( sub_q(1, unit=u.dimensionless), sub_q(2, unit=u.dimensionless), ), "geomspace": (sub_q(1), sub_q(1)), "copyto": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "prod": (sub_arr(np.arange(3)),), "var": (sub_arr(np.arange(3)),), "trace": (sub_arr(np.eye(3)),), "percentile": (sub_arr(np.arange(3)), 30), "quantile": (sub_arr(np.arange(3)), 0.3), "nanpercentile": (sub_arr(np.arange(3)), 30), "nanquantile": (sub_arr(np.arange(3)), 0.3), "linalg.det": (sub_arr(np.eye(3)),), "diff": (sub_arr(np.arange(3)),), "ediff1d": (sub_arr(np.arange(3)),), "ptp": (sub_arr(np.arange(3)),), "cumprod": (sub_arr(np.arange(3)),), "pad": (sub_arr(np.arange(3)), 3), "choose": (np.arange(3), sub_arr(np.eye(3))), "insert": (sub_arr(np.arange(3)), 1, sub_q(1)), "linalg.lstsq": (sub_arr(np.eye(3)), sub_arr(np.eye(3))), "linalg.solve": (sub_arr(np.eye(3)), sub_arr(np.eye(3))), "linalg.tensorsolve": ( sub_arr(np.eye(24).reshape((6, 4, 2, 3, 4))), sub_arr(np.ones((6, 4))), ), "linalg.eig": (sub_arr(np.eye(3)),), "linalg.eigh": (sub_arr(np.eye(3)),), "linalg.eigvals": (sub_arr(np.eye(3)),), "linalg.eigvalsh": (sub_arr(np.eye(3)),), "savetxt": (savetxt_file, sub_arr(np.arange(3))), "fill_diagonal": ( sub_arr(np.eye(3)), sub_arr(np.arange(3)), ), "apply_over_axes": (lambda x, axis: x, sub_arr(np.eye(3)), (0, 1)), "isin": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "place": ( sub_arr(np.arange(3)), np.arange(3) > 0, sub_arr(np.arange(3)), ), "put": ( sub_arr(np.arange(3)), np.arange(3), sub_arr(np.arange(3)), ), "put_along_axis": ( sub_arr(np.arange(3)), np.arange(3), sub_arr(np.arange(3)), 0, ), "putmask": ( sub_arr(np.arange(3)), np.arange(3), sub_arr(np.arange(3)), ), "searchsorted": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "select": ( [np.arange(3) < 1, np.arange(3) > 1], [sub_arr(np.arange(3)), sub_arr(np.arange(3))], sub_q(1), ), "setdiff1d": ( sub_arr(np.arange(3)), sub_arr(np.arange(3, 6)), ), "sinc": (sub_arr(np.arange(3)),), "clip": (sub_arr(np.arange(3)), sub_q(1), sub_q(2)), "where": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "triu": (sub_arr(np.ones((3, 3))),), "tril": (sub_arr(np.ones((3, 3))),), "einsum": ("ii->i", sub_arr(np.eye(3))), "convolve": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "correlate": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "unwrap": (sub_arr(np.arange(3)),), "interp": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "array_repr": (sub_arr(np.arange(3)),), trapezoid_fname: (sub_arr(np.arange(3)),), "take": (sub_arr(np.arange(3)), np.arange(3)), # FUNCTIONS THAT UNYT DOESN'T HANDLE EXPLICITLY (THEY "JUST WORK"): "all": (sub_arr(np.arange(3)),), "amax": (sub_arr(np.arange(3)),), # implemented via max "amin": (sub_arr(np.arange(3)),), # implemented via min "angle": (sub_q(complex(1, 1)),), "any": (sub_arr(np.arange(3)),), "append": (sub_arr(np.arange(3)), sub_q(1)), "apply_along_axis": (lambda x: x, 0, sub_arr(np.eye(3))), "argmax": (sub_arr(np.arange(3)),), # implemented via max "argmin": (sub_arr(np.arange(3)),), # implemented via min "argpartition": ( sub_arr(np.arange(3)), 1, ), # implemented via partition "argsort": (sub_arr(np.arange(3)),), # implemented via sort "argwhere": (sub_arr(np.arange(3)),), "array_str": (sub_arr(np.arange(3)),), "atleast_1d": (sub_arr(np.arange(3)),), "atleast_2d": (sub_arr(np.arange(3)),), "atleast_3d": (sub_arr(np.arange(3)),), "average": (sub_arr(np.arange(3)),), "can_cast": (sub_arr(np.arange(3)), np.float64), "common_type": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "result_type": (sub_arr(np.ones(3)), sub_arr(np.ones(3))), "iscomplex": (sub_arr(np.arange(3)),), "iscomplexobj": (sub_arr(np.arange(3)),), "isreal": (sub_arr(np.arange(3)),), "isrealobj": (sub_arr(np.arange(3)),), "nan_to_num": (sub_arr(np.arange(3)),), "nanargmax": (sub_arr(np.arange(3)),), # implemented via max "nanargmin": (sub_arr(np.arange(3)),), # implemented via min "nanmax": (sub_arr(np.arange(3)),), # implemented via max "nanmean": (sub_arr(np.arange(3)),), # implemented via mean "nanmedian": (sub_arr(np.arange(3)),), # implemented via median "nanmin": (sub_arr(np.arange(3)),), # implemented via min "trim_zeros": (sub_arr(np.arange(3)),), "max": (sub_arr(np.arange(3)),), "mean": (sub_arr(np.arange(3)),), "median": (sub_arr(np.arange(3)),), "min": (sub_arr(np.arange(3)),), "ndim": (sub_arr(np.arange(3)),), "shape": (sub_arr(np.arange(3)),), "size": (sub_arr(np.arange(3)),), "sort": (sub_arr(np.arange(3)),), "sum": (sub_arr(np.arange(3)),), "repeat": (sub_arr(np.arange(3)), 2), "tile": (sub_arr(np.arange(3)), 2), "shares_memory": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "nonzero": (sub_arr(np.arange(3)),), "count_nonzero": (sub_arr(np.arange(3)),), "flatnonzero": (sub_arr(np.arange(3)),), "isneginf": (sub_arr(np.arange(3)),), "isposinf": (sub_arr(np.arange(3)),), "empty_like": (sub_arr(np.arange(3)),), "full_like": (sub_arr(np.arange(3)), sub_q(1)), "ones_like": (sub_arr(np.arange(3)),), "zeros_like": (sub_arr(np.arange(3)),), "copy": (sub_arr(np.arange(3)),), "meshgrid": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "transpose": (sub_arr(np.eye(3)),), "reshape": (sub_arr(np.arange(3)), (3,)), "resize": (sub_arr(np.arange(3)), 6), "roll": (sub_arr(np.arange(3)), 1), "rollaxis": (sub_arr(np.arange(3)), 0), "rot90": (sub_arr(np.eye(3)),), "expand_dims": (sub_arr(np.arange(3)), 0), "squeeze": (sub_arr(np.arange(3)),), "flip": (sub_arr(np.eye(3)),), "fliplr": (sub_arr(np.eye(3)),), "flipud": (sub_arr(np.eye(3)),), "delete": (sub_arr(np.arange(3)), 0), "partition": (sub_arr(np.arange(3)), 1), "broadcast_to": (sub_arr(np.arange(3)), 3), "broadcast_arrays": (sub_arr(np.arange(3)),), "split": (sub_arr(np.arange(3)), 1), "array_split": (sub_arr(np.arange(3)), 1), "dsplit": (sub_arr(np.arange(27)).reshape(3, 3, 3), 1), "hsplit": (sub_arr(np.arange(3)), 1), "vsplit": (sub_arr(np.eye(3)), 1), "swapaxes": (sub_arr(np.eye(3)), 0, 1), "moveaxis": (sub_arr(np.eye(3)), 0, 1), "nansum": (sub_arr(np.arange(3)),), # implemented via sum "std": (sub_arr(np.arange(3)),), "nanstd": (sub_arr(np.arange(3)),), "nanvar": (sub_arr(np.arange(3)),), "nanprod": (sub_arr(np.arange(3)),), "diag": (sub_arr(np.eye(3)),), "diag_indices_from": (sub_arr(np.eye(3)),), "diagflat": (sub_arr(np.eye(3)),), "diagonal": (sub_arr(np.eye(3)),), "ravel": (sub_arr(np.arange(3)),), "ravel_multi_index": (np.eye(2, dtype=int), (2, 2)), "unravel_index": (np.arange(3), (3,)), "fix": (sub_arr(np.arange(3)),), "round": (sub_arr(np.arange(3)),), # implemented via around "may_share_memory": ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ), "linalg.matrix_power": (sub_arr(np.eye(3)), 2), "linalg.cholesky": (sub_arr(np.eye(3)),), "linalg.multi_dot": ((sub_arr(np.eye(3)), sub_arr(np.eye(3))),), "linalg.matrix_rank": (sub_arr(np.eye(3)),), "linalg.qr": (sub_arr(np.eye(3)),), "linalg.slogdet": (sub_arr(np.eye(3)),), "linalg.cond": (sub_arr(np.eye(3)),), "tensordot": (sub_arr(np.eye(3)), sub_arr(np.eye(3))), "gradient": (sub_arr(np.arange(3)),), "cumsum": (sub_arr(np.arange(3)),), "nancumsum": (sub_arr(np.arange(3)),), "nancumprod": (sub_arr(np.arange(3)),), "bincount": (sub_arr(np.arange(3)),), "unique": (sub_arr(np.arange(3)),), "min_scalar_type": (sub_arr(np.arange(3)),), "extract": (0, sub_arr(np.arange(3))), "setxor1d": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "lexsort": (sub_arr(np.arange(3)),), "digitize": (sub_arr(np.arange(3)), sub_arr(np.arange(3))), "tril_indices_from": (sub_arr(np.eye(3)),), "triu_indices_from": (sub_arr(np.eye(3)),), "imag": (sub_arr(np.arange(3)),), "real": (sub_arr(np.arange(3)),), "real_if_close": (sub_arr(np.arange(3)),), "einsum_path": ( "ij,jk->ik", sub_arr(np.eye(3)), sub_arr(np.eye(3)), ), "cov": (sub_arr(np.arange(3)),), "corrcoef": (sub_arr(np.arange(3)),), "compress": (np.zeros(3), sub_arr(np.arange(3))), "take_along_axis": ( sub_arr(np.arange(3)), np.ones(3, dtype=int), 0, ), } if NUMPY_VERSION >= Version("2.0.0dev0"): functions_to_check["linalg.cross"] = ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ) functions_to_check["linalg.diagonal"] = (sub_arr(np.eye(3)),) functions_to_check["linalg.matmul"] = ( sub_arr(np.eye(3)), sub_arr(np.eye(3)), ) functions_to_check["linalg.matrix_norm"] = (sub_arr(np.eye(3)),) functions_to_check["linalg.matrix_transpose"] = (sub_arr(np.eye(3)),) functions_to_check["linalg.tensordot"] = ( sub_arr(np.eye(3)), sub_arr(np.eye(3)), ) functions_to_check["linalg.outer"] = ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ) functions_to_check["linalg.svdvals"] = (sub_arr(np.eye(3)),) functions_to_check["linalg.trace"] = (sub_arr(np.eye(3)),) functions_to_check["linalg.vecdot"] = ( sub_arr(np.arange(3)), sub_arr(np.arange(3)), ) functions_to_check["linalg.vector_norm"] = (sub_arr(np.arange(3)),) functions_to_check["astype"] = (sub_arr(np.arange(3)), float) functions_to_check["matrix_transpose"] = (sub_arr(np.eye(3)),) functions_to_check["unique_all"] = (sub_arr(np.arange(3)),) functions_to_check["unique_counts"] = (sub_arr(np.arange(3)),) functions_to_check["unique_inverse"] = (sub_arr(np.arange(3)),) functions_to_check["unique_values"] = (sub_arr(np.arange(3)),) functions_to_check["cumulative_sum"] = (sub_arr(np.arange(3)),) functions_to_check["cumulative_prod"] = (sub_arr(np.arange(3)),) functions_to_check["unstack"] = (sub_arr(np.arange(3)),) else: functions_to_check["asfarray"] = (sub_arr(np.arange(3)),) if NUMPY_VERSION < Version("2.4.0.dev0"): functions_to_check["in1d"] = (sub_arr(np.arange(3)), sub_arr(np.arange(3))) functions_checked = [] bad_funcs = {} for fname, args in functions_to_check.items(): ua_args = [] for arg in args: ua_args.append(arg_to_ua(arg)) func = getfunc(fname) try: with warnings.catch_warnings(): warnings.filterwarnings( action="ignore", category=FutureWarning, ) if "savetxt" in fname: warnings.filterwarnings( action="ignore", category=UserWarning, message="numpy.savetxt does not preserve units", ) if fname == "fix": warnings.filterwarnings( action="ignore", category=DeprecationWarning, message="numpy.fix is deprecated", ) try: ua_result = func(*ua_args) except: print(f"Crashed in {fname} with unyt input.") raise except u.exceptions.UnytError: raises_unyt_error = True else: raises_unyt_error = False if "savetxt" in fname and os.path.isfile(savetxt_file): os.remove(savetxt_file) functions_checked.append(func) if raises_unyt_error: with pytest.raises(u.exceptions.UnytError): result = func(*args) continue with warnings.catch_warnings(): warnings.filterwarnings( action="ignore", category=FutureWarning, ) if "savetxt" in fname: warnings.filterwarnings( action="ignore", category=UserWarning, message="numpy.savetxt does not preserve units or extra_attr", ) if fname == "fix": warnings.filterwarnings( action="ignore", category=DeprecationWarning, message="numpy.fix is deprecated", ) try: result = func(*args) except: # pragma: no cover print(f"Crashed in {fname} with subclass input.") raise if fname.split(".")[-1] in ( "fill_diagonal", "copyto", "place", "put", "put_along_axis", "putmask", ): # treat inplace modified values for relevant functions as result result = args[0] ua_result = ua_args[0] if "savetxt" in fname and os.path.isfile(savetxt_file): os.remove(savetxt_file) ignore_values = fname in {"empty_like"} # empty_like has arbitrary data try: check_result(result, ua_result, ignore_values=ignore_values) except AssertionError: # pragma: no cover bad_funcs["np." + fname] = result, ua_result if len(bad_funcs) > 0: # pragma: no cover raise AssertionError( "Some functions did not return expected types " "(obtained, obtained with unyt input): " + str(bad_funcs) ) unchecked_functions = [ f for f in set(_HANDLED_FUNCTIONS) | NOOP_FUNCTIONS if f not in functions_checked ] try: assert len(unchecked_functions) == 0 except AssertionError: # pragma: no cover raise AssertionError( "Did not check functions", [ (".".join((f.__module__, f.__name__)).replace("numpy", "np")) for f in unchecked_functions ], ) @pytest.mark.parametrize( "func_args", ( ( np.histogram, ( subclass_uarray( [1, 2, 3], u.m, extra_attr=True, ), ), ), ( np.histogram2d, ( subclass_uarray( [1, 2, 3], u.m, extra_attr=True, ), subclass_uarray( [1, 2, 3], u.K, extra_attr=True, ), ), ), ( np.histogramdd, ( [ subclass_uarray( [1, 2, 3], u.m, extra_attr=True, ), subclass_uarray( [1, 2, 3], u.K, extra_attr=True, ), subclass_uarray( [1, 2, 3], u.kg, extra_attr=True, ), ], ), ), ), ) @pytest.mark.parametrize( "weights", ( None, subclass_uarray([1, 2, 3], u.s, extra_attr=True), np.array([1, 2, 3]), ), ) @pytest.mark.parametrize("bins_type", ("int", "sub_arr")) @pytest.mark.parametrize("density", (None, True)) def test_histograms(self, func_args, weights, bins_type, density): """ Test that histograms give sensible output. Histograms are tricky with possible density and weights arguments, and the way that attributes need validation and propagation between the bins and values. They are also commonly used. They therefore need a bespoke test. """ func, args = func_args bins = { "int": 10, "sub_arr": [ subclass_uarray( np.linspace(0, 5, 11), u.kpc, extra_attr=True, ), subclass_uarray( np.linspace(0, 5, 11), u.K, extra_attr=True, ), subclass_uarray( np.linspace(0, 5, 11), u.Msun, extra_attr=True, ), ], }[bins_type] bins = ( bins[ { np.histogram: np.s_[0], np.histogram2d: np.s_[:2], np.histogramdd: np.s_[:], }[func] ] if bins_type == "sub_arr" else bins ) result = func(*args, bins=bins, density=density, weights=weights) ua_args = tuple( ( to_ua(arg) if not isinstance(arg, tuple) else tuple(to_ua(item) for item in arg) ) for arg in args ) ua_bins = ( to_ua(bins) if not isinstance(bins, tuple) else tuple(to_ua(item) for item in bins) ) ua_result = func( *ua_args, bins=ua_bins, density=density, weights=to_ua(weights) ) assert isinstance(ua_result, tuple) assert isinstance(result, tuple) assert len(result) == len(ua_result) for r, ua_r in zip(result, ua_result): check_result(r, ua_r) if not density and not isinstance(weights, subclass_uarray): assert not isinstance(result[0], subclass_uarray) else: assert result[0].extra_attr is True if density and not isinstance(weights, subclass_uarray): pass elif density and isinstance(weights, subclass_uarray): assert result[0].extra_attr is True elif not density and isinstance(weights, subclass_uarray): assert result[0].extra_attr is True ret_bins = { np.histogram: [result[1]], np.histogram2d: result[1:], np.histogramdd: result[1], }[func] for b in ret_bins: assert b.extra_attr is True def test_getitem(self): """ Make sure that we don't degrade to an ndarray on slicing. """ assert isinstance(sub_arr(np.arange(3))[0], subclass_uquantity) def test_reshape_to_scalar(self): """ Make sure that we convert to a subclass_uquantity when we reshape to a scalar. """ assert isinstance(sub_arr(np.ones(1)).reshape(()), subclass_uquantity) def test_iter(self): """ Make sure that we get subclass_uquantity's when iterating over a subclass_uarray. """ for sub_q in sub_arr(np.arange(3)): assert isinstance(sub_q, subclass_uquantity) def test_dot(self): """ Make sure that we get a subclass_uarray when we use array attribute dot. """ res = sub_arr(np.arange(3)).dot(sub_arr(np.arange(3))) assert isinstance(res, subclass_uquantity) assert res.extra_attr is True class TestSubclassQuantity: """ Test that the subclass_uquantity class works as desired, mostly around issues converting back and forth with subclass_uarray. """ @pytest.mark.parametrize( "func, args", [ ("astype", (float,)), ("in_units", (u.m,)), ("byteswap", ()), ("compress", ([True],)), ("flatten", ()), ("ravel", ()), ("repeat", (1,)), ("reshape", (1,)), ("take", ([0],)), ("transpose", ()), ("view", ()), ], ) def test_propagation_func(self, func, args): """ Test that functions that are supposed to propagate our attribute do so. """ sub_q = subclass_uquantity( 1, u.m, extra_attr=True, ) res = getattr(sub_q, func)(*args) assert res.extra_attr is True def test_round(self): """ Test that attributes propagate through the round builtin. """ sub_q = subclass_uquantity( 1.03, u.m, extra_attr=True, ) res = round(sub_q) assert res.value == 1.0 assert res.extra_attr is True def test_scalar_return_func(self): """ Make sure that default-wrapped functions that take a subclass_uarray and return a scalar convert to a subclass_uquantity. """ sub_arr = subclass_uarray( np.arange(3), u.m, extra_attr=True, ) res = np.min(sub_arr) assert isinstance(res, subclass_uquantity) @pytest.mark.parametrize("prop", ["T", "ua", "unit_array"]) def test_propagation_props(self, prop): """ Test that properties propagate our attributes as intended. """ sub_q = subclass_uquantity( 1, u.m, extra_attr=True, ) res = getattr(sub_q, prop) assert res.extra_attr is True def test_multiply_quantities(self): """ Test multiplying two quantities. """ sub_q = subclass_uquantity( 2, u.m, extra_attr=True, ) multiplied = sub_q * sub_q assert type(multiplied) is subclass_uquantity assert multiplied.extra_attr is True assert multiplied.to_value(u.m**2) == 4 class TestCosmoArrayCopy: """ Tests of explicit (deep)copying of subclass_uarray. """ def test_copy(self): """ Check that when we copy a subclass_uarray it preserves its values and attributes. """ units = u.Mpc arr = subclass_uarray( u.unyt_array(np.ones(5), units=units), extra_attr=True, ) copy_arr = copy(arr) assert np.allclose(arr.to_value(units), copy_arr.to_value(units)) assert arr.units == copy_arr.units assert arr.extra_attr == copy_arr.extra_attr def test_deepcopy(self): """ Check that when we deepcopy a subclass_uarray it preserves its values and attribute """ units = u.Mpc arr = subclass_uarray( u.unyt_array(np.ones(5), units=units), extra_attr=True, ) copy_arr = deepcopy(arr) assert np.allclose(arr.to_value(units), copy_arr.to_value(units)) assert arr.units == copy_arr.units assert arr.extra_attr == copy_arr.extra_attr def test_to_cgs(self): """ Check that using to_cgs properly preserves attributes. """ units = u.Mpc arr = subclass_uarray( u.unyt_array(np.ones(5), units=units), extra_attr=True, ) cgs_arr = arr.in_cgs() assert np.allclose(arr.to_value(u.cm), cgs_arr.to_value(u.cm)) assert cgs_arr.units == u.cm assert cgs_arr.extra_attr == arr.extra_attr class TestMultiplicationByUnyt: @pytest.mark.parametrize( "sub_arr", [ subclass_uarray( np.ones(3), u.Mpc, extra_attr=True, ), subclass_uquantity( np.ones(1), u.Mpc, extra_attr=True, ), ], ) def test_multiplication_by_unyt(self, sub_arr): """ We desire consistent behaviour for example for `subclass_uarray(...) * (1 * u.Mpc)` as for `subclass_uarray(...) * u.Mpc`. """ lmultiplied_by_quantity = sub_arr * ( 1 * u.Mpc ) # parentheses very important here lmultiplied_by_unyt = sub_arr * u.Mpc assert isinstance(lmultiplied_by_quantity, subclass_uarray) assert isinstance(lmultiplied_by_unyt, subclass_uarray) assert lmultiplied_by_unyt.extra_attr == lmultiplied_by_quantity.extra_attr assert np.allclose( lmultiplied_by_unyt.to_value(lmultiplied_by_quantity.units), lmultiplied_by_quantity.to_value(lmultiplied_by_quantity.units), ) ldivided_by_quantity = sub_arr / (1 * u.Mpc) # parentheses very important here ldivided_by_unyt = sub_arr / u.Mpc assert isinstance(ldivided_by_quantity, subclass_uarray) assert isinstance(ldivided_by_unyt, subclass_uarray) assert ldivided_by_unyt.extra_attr == ldivided_by_quantity.extra_attr assert np.allclose( ldivided_by_unyt.to_value(ldivided_by_quantity.units), ldivided_by_quantity.to_value(ldivided_by_quantity.units), ) rmultiplied_by_quantity = ( 1 * u.Mpc ) * sub_arr # parentheses very important here assert rmultiplied_by_quantity.extra_attr rmultiplied_by_unyt = u.Mpc * sub_arr assert isinstance(rmultiplied_by_quantity, subclass_uarray) assert isinstance(rmultiplied_by_unyt, subclass_uarray) assert rmultiplied_by_unyt.extra_attr == rmultiplied_by_quantity.extra_attr assert np.allclose( rmultiplied_by_unyt.to_value(rmultiplied_by_quantity.units), rmultiplied_by_quantity.to_value(rmultiplied_by_quantity.units), ) rdivided_by_quantity = (1 * u.Mpc) / sub_arr # parentheses very important here rdivided_by_unyt = u.Mpc / sub_arr assert isinstance(rdivided_by_quantity, subclass_uarray) assert isinstance(rdivided_by_unyt, subclass_uarray) assert rdivided_by_unyt.extra_attr == rdivided_by_quantity.extra_attr assert np.allclose( rdivided_by_unyt.to_value(rdivided_by_quantity.units), rdivided_by_quantity.to_value(rdivided_by_quantity.units), ) unyt-3.2.0/unyt/tests/test_temperature_interval_offset.py000066400000000000000000000012621526544257300241120ustar00rootroot00000000000000import pytest from unyt import K, degC, degF, delta_degC, delta_degF from unyt.exceptions import InvalidUnitOperation def test_interval_to_offset_raises(): with pytest.raises(InvalidUnitOperation): (3 * delta_degC).to(degC) with pytest.raises(InvalidUnitOperation): (3 * degC).to(delta_degC) with pytest.raises(InvalidUnitOperation): (3 * delta_degF).to(degF) with pytest.raises(InvalidUnitOperation): (3 * degF).to(delta_degF) def test_kelvin_celsius_affine_stays(): assert (0 * degC).to(K) == 273.15 * K assert (273.15 * K).to(degC) == 0 * degC def test_interval_to_kelvin_stays(): assert (3 * delta_degC).to(K) == 3 * K unyt-3.2.0/unyt/tests/test_unit_registry.py000066400000000000000000000132601526544257300212130ustar00rootroot00000000000000""" Test unit lookup tables and registry """ import os import pytest from numpy.testing import assert_allclose from unyt.dimensions import energy, length, mass, temperature, time from unyt.exceptions import SymbolNotFoundError, UnitParseError from unyt.unit_object import Unit from unyt.unit_registry import UnitRegistry from unyt.unit_systems import UnitSystem def test_add_modify_error(): from unyt import m ureg = UnitRegistry() with pytest.raises(UnitParseError): ureg.add("tayne", 1, length) with pytest.raises(UnitParseError): ureg.add("tayne", "blah", length) with pytest.raises(UnitParseError): ureg.add("tayne", 1.0, length, offset=1) with pytest.raises(UnitParseError): ureg.add("tayne", 1.0, length, offset="blah") ureg.add("tayne", 1.1, length) with pytest.raises(SymbolNotFoundError): ureg.remove("tayn") with pytest.raises(SymbolNotFoundError): ureg.modify("tayn", 1.2) ureg.modify("tayne", 1.0 * m) assert ureg["tayne"][:3] == ureg["m"][:3] def test_modify_symbol_from_default_unit_registry(): # see https://github.com/yt-project/unyt/issues/473 from unyt import km from unyt.unit_object import default_unit_registry with pytest.raises(TypeError): default_unit_registry.modify("cm", 10 * km) with pytest.raises(TypeError): default_unit_registry.remove("cm") def test_modify_cache_clear(): # see https://github.com/yt-project/unyt/issues/473 ureg = UnitRegistry() ureg.add("celery", 1.0, length) u0 = Unit("m", registry=ureg) u1 = Unit("celery", registry=ureg) assert u1 == u0 ureg.modify("celery", 0.5) # check that this equality still holds post registry modification assert u1 == u0 u2 = Unit("celery", registry=ureg) assert u2 != u1 assert 1.0 * u2 == 0.5 * u0 def test_remove_unit(): ureg = UnitRegistry() ureg.add("celery", 1.0, length) ureg.remove("celery") with pytest.raises(UnitParseError): Unit("celery", registry=ureg) def test_keys(): ureg = UnitRegistry() assert sorted(ureg.keys()) == sorted(ureg.lut.keys()) def test_prefixable_units(): ureg = UnitRegistry() pu = ureg.prefixable_units assert "m" in pu assert "pc" in pu assert "mol" in pu ureg.add("foobar", 1.0, length, prefixable=True) assert "foobar" in ureg.prefixable_units mfoobar = Unit("mfoobar", registry=ureg) foobar = Unit("foobar", registry=ureg) assert (1 * foobar) / (1 * mfoobar) == 1000 def test_registry_contains(): ureg = UnitRegistry() assert "m" in ureg assert "cm" in ureg assert "erg" in ureg assert "Merg" in ureg assert "foobar" not in ureg assert Unit("m", registry=ureg) in ureg def test_registry_json(): reg = UnitRegistry() reg.add("tayne", 1.0, length) json_reg = reg.to_json() unserialized_reg = UnitRegistry.from_json(json_reg) assert reg.lut == unserialized_reg.lut assert reg.lut["m"][1] is length assert reg.lut["erg"][1] is energy assert reg.lut["tayne"][1] is length OLD_JSON_PATH = os.sep.join([os.path.dirname(__file__), "data/old_json_registry.txt"]) def test_old_registry_multiple_load(): # See Issue #157 for details reg1 = UnitRegistry() reg1.add("code_length", 1.0, length) reg1.add("code_mass", 1.0, mass) reg1.add("code_time", 1.0, time) reg1.add("code_temperature", 1.0, temperature) UnitSystem( reg1.unit_system_id, "code_length", "code_mass", "code_time", "code_temperature", registry=reg1, ) cm = Unit("code_mass", registry=reg1) cl = Unit("code_length", registry=reg1) (cm / cl).latex_representation() with open(OLD_JSON_PATH) as f: json_data = f.read() reg2 = UnitRegistry.from_json(json_data) UnitSystem( reg2.unit_system_id, "code_length", "code_mass", "code_time", "code_temperature", registry=reg2, ) def test_old_registry_json(): with open(OLD_JSON_PATH) as f: json_text = f.read() reg = UnitRegistry.from_json(json_text) default_reg = UnitRegistry() loaded_keys = reg.keys() for k in default_reg.keys(): assert k in loaded_keys loaded_val = reg[k] val = default_reg[k] assert_allclose(loaded_val[0], val[0]) assert loaded_val[1:] == val[1:] def test_unit_system_id_under_fips(monkeypatch): # On FIPS-enabled systems, hashlib.md5() raises unless it is told the # digest is not used for security. unit_system_id only uses MD5 as a # fingerprint, so it must pass usedforsecurity=False; otherwise simply # importing unyt crashes on such systems (#580). import hashlib import unyt.unit_registry as unit_registry real_md5 = hashlib.md5 def fips_md5(*args, **kwargs): # Mimic a FIPS build: refuse to construct an MD5 hasher that is # flagged as being used for security. if kwargs.get("usedforsecurity", True): raise ValueError("[digital envelope routines] unsupported") return real_md5(*args, **kwargs) monkeypatch.setattr(unit_registry, "md5", fips_md5) reg = UnitRegistry() reg._unit_system_id = None # force recomputation through the patched md5 unit_system_id = reg.unit_system_id assert isinstance(unit_system_id, str) # The fingerprint must match the value computed with a regular md5. assert unit_system_id == real_md5(_hash_data_for(reg)).hexdigest() def _hash_data_for(reg): hash_data = bytearray() for k, v in sorted(reg.lut.items()): hash_data.extend(k.encode("utf8")) hash_data.extend(repr(v).encode("utf8")) return bytes(hash_data) unyt-3.2.0/unyt/tests/test_unit_systems.py000066400000000000000000000076131526544257300210570ustar00rootroot00000000000000""" Test unit systems. """ import pytest import unyt.unit_symbols as us from unyt import dimensions from unyt.exceptions import IllDefinedUnitSystem, MissingMKSCurrent from unyt.unit_object import Unit from unyt.unit_registry import UnitRegistry from unyt.unit_systems import ( UnitSystem, cgs_unit_system, mks_unit_system, unit_system_registry, ) def test_unit_systems(): goofy_unit_system = UnitSystem( "goofy", "ly", "lbm", "hr", temperature_unit="R", angle_unit="arcsec", current_mks_unit="mA", luminous_intensity_unit="cd", ) assert goofy_unit_system["temperature"] == Unit("R") assert goofy_unit_system[dimensions.solid_angle] == Unit("arcsec**2") assert goofy_unit_system["energy"] == Unit("lbm*ly**2/hr**2") goofy_unit_system["energy"] = "eV" assert goofy_unit_system["energy"] == Unit("eV") assert goofy_unit_system["magnetic_field_mks"] == Unit("lbm/(hr**2*mA)") assert "goofy" in unit_system_registry def test_unit_system_id(): reg1 = UnitRegistry() reg2 = UnitRegistry() assert reg1.unit_system_id == reg2.unit_system_id reg1.modify("g", 2.0) assert reg1.unit_system_id != reg2.unit_system_id reg1 = UnitRegistry() reg1.add("dinosaurs", 12.0, dimensions.length) assert reg1.unit_system_id != reg2.unit_system_id reg1 = UnitRegistry() reg1.remove("g") assert reg1.unit_system_id != reg2.unit_system_id reg1.add("g", 1.0e-3, dimensions.mass, prefixable=True) assert reg1.unit_system_id == reg2.unit_system_id def test_bad_unit_system(): with pytest.raises(IllDefinedUnitSystem): UnitSystem("atomic", "nm", "fs", "nK", "rad") with pytest.raises(IllDefinedUnitSystem): UnitSystem("atomic", "nm", "fs", "nK", "rad", registry=UnitRegistry()) with pytest.raises(IllDefinedUnitSystem): UnitSystem("atomic", us.nm, us.fs, us.nK, us.rad) with pytest.raises(IllDefinedUnitSystem): UnitSystem("atomic", us.nm, us.fs, us.nK, us.rad, registry=UnitRegistry()) def test_code_unit_system(): ureg = UnitRegistry() ureg.add("code_length", 2.0, dimensions.length) ureg.add("code_mass", 3.0, dimensions.mass) ureg.add("code_time", 4.0, dimensions.time) ureg.add("code_temperature", 5.0, dimensions.temperature) code_unit_system = UnitSystem( "my_unit_system", "code_length", "code_mass", "code_time", "code_temperature", registry=ureg, ) assert code_unit_system["length"] == Unit("code_length", registry=ureg) assert code_unit_system["length"].base_value == 2 assert code_unit_system["mass"] == Unit("code_mass", registry=ureg) assert code_unit_system["mass"].base_value == 3 assert code_unit_system["time"] == Unit("code_time", registry=ureg) assert code_unit_system["time"].base_value == 4 assert code_unit_system["temperature"] == Unit("code_temperature", registry=ureg) assert code_unit_system["temperature"].base_value == 5 def test_mks_current(): with pytest.raises(MissingMKSCurrent): cgs_unit_system[dimensions.current_mks] with pytest.raises(MissingMKSCurrent): cgs_unit_system[dimensions.magnetic_field] with pytest.raises(MissingMKSCurrent): cgs_unit_system[dimensions.current_mks] = "foo" with pytest.raises(MissingMKSCurrent): cgs_unit_system[dimensions.magnetic_field] = "bar" assert cgs_unit_system.has_current_mks is False assert mks_unit_system.has_current_mks is True def test_create_unit_system_from_unit_objects(): s = UnitSystem("test_units", us.Mpc, us.Msun, us.s) assert s["length"] == us.Mpc assert s["mass"] == us.Msun assert s["time"] == us.s def test_create_unit_system_from_quantity(): s = UnitSystem("test_units", us.Mpc, 3 * us.Msun, us.s) assert s["length"] == us.Mpc assert s["mass"] == Unit("3*Msun") assert s["time"] == us.s unyt-3.2.0/unyt/tests/test_units.py000066400000000000000000000672651526544257300174640ustar00rootroot00000000000000""" Test symbolic unit handling. """ import operator import pickle import numpy as np import pytest from numpy.testing import ( assert_allclose, assert_almost_equal, assert_array_almost_equal_nulp, assert_equal, ) from sympy import Symbol import unyt.unit_symbols as unit_symbols from unyt._physical_ratios import ( m_per_km, m_per_mpc, m_per_pc, mass_sun_kg, sec_per_year, ) from unyt._unit_lookup_table import ( default_unit_symbol_lut, name_alternatives, unit_prefixes, ) from unyt.array import unyt_quantity from unyt.dimensions import ( energy, length, magnetic_field_cgs, magnetic_field_mks, mass, power, rate, temperature, time, ) from unyt.exceptions import InvalidUnitOperation, UnitConversionError, UnitsNotReducible from unyt.testing import assert_allclose_units from unyt.unit_object import Unit, UnitParseError, default_unit_registry from unyt.unit_registry import UnitRegistry from unyt.unit_systems import UnitSystem, cgs_unit_system @pytest.mark.parametrize("symbol", ["nmi", "nautical_mile"]) def test_nautical_mile_conversion(symbol): assert_equal(unyt_quantity(1, symbol).to_value("m"), 1852.0) @pytest.mark.parametrize("symbol", ["kt", "knot"]) def test_knot_conversion(symbol): assert_allclose( unyt_quantity(1, symbol).to_value("m/s"), 1852.0 / 3600.0, rtol=1e-14, ) assert_allclose(unyt_quantity(1, symbol).to_value("nmi/hr"), 1.0) def test_no_conflicting_symbols(): """ Check unit symbol definitions for conflicts. """ full_set = set(default_unit_symbol_lut.keys()) # go through all possible prefix combos for symbol in default_unit_symbol_lut.keys(): if default_unit_symbol_lut[symbol][4]: keys = unit_prefixes.keys() else: keys = [symbol] for prefix in keys: new_symbol = f"{prefix}{symbol}" # test if we have seen this symbol assert new_symbol not in full_set, f"Duplicate symbol: {new_symbol}" full_set.add(new_symbol) def test_dimensionless(): """ Create dimensionless unit and check attributes. """ u1 = Unit() assert u1.is_dimensionless assert u1.expr == 1 assert u1.base_value == 1 assert u1.dimensions == 1 assert u1 != "hello!" assert (u1 == "hello") is False u2 = Unit("") assert u2.is_dimensionless assert u2.expr == 1 assert u2.base_value == 1 assert u2.dimensions == 1 assert_equal(u1.latex_repr, "") assert_equal(u2.latex_repr, "") def test_create_from_string(): """ Create units with strings and check attributes. """ u1 = Unit("kg * m**2 * s**-2") assert u1.dimensions == energy assert u1.base_value == 1.0 # make sure order doesn't matter u2 = Unit("m**2 * s**-2 * kg") assert u2.dimensions == energy assert u2.base_value == 1.0 # Test rationals u3 = Unit("kg**0.5 * m**-0.5 * s**-1") assert u3.dimensions == magnetic_field_cgs assert u3.base_value == 1.0 # sqrt functions u4 = Unit("sqrt(kg)/sqrt(m)/s") assert u4.dimensions == magnetic_field_cgs assert u4.base_value == 1.0 # commutative sqrt function u5 = Unit("sqrt(kg/m)/s") assert u5.dimensions == magnetic_field_cgs assert u5.base_value == 1.0 # nonzero CGS conversion factor u6 = Unit("Msun/pc**3") assert u6.dimensions == mass / length**3 assert_array_almost_equal_nulp( np.array([u6.base_value]), np.array([mass_sun_kg / m_per_pc**3]) ) with pytest.raises(UnitParseError): Unit("m**m") with pytest.raises(UnitParseError): Unit("m**g") with pytest.raises(UnitParseError): Unit("m+g") with pytest.raises(UnitParseError): Unit("m-g") with pytest.raises(UnitParseError): Unit("hello!") with pytest.raises(UnitParseError): Unit("True") with pytest.raises(UnitParseError): Unit("else") with pytest.raises(UnitParseError): Unit("hello(37)") with pytest.raises(UnitParseError): Unit("hello(foo=37)") cm = Unit("cm") data = 1 * cm assert Unit(data) == cm assert Unit(b"cm") == cm def test_create_from_expr(): """ Create units from sympy Exprs and check attributes. """ pc_mks = m_per_pc yr_mks = sec_per_year # Symbol expr s1 = Symbol("pc", positive=True) s2 = Symbol("yr", positive=True) # Mul expr s3 = s1 * s2 # Pow expr s4 = s1**2 * s2 ** (-1) u1 = Unit(s1) u2 = Unit(s2) u3 = Unit(s3) u4 = Unit(s4) assert u1.expr == s1 assert u2.expr == s2 assert u3.expr == s3 assert u4.expr == s4 assert_allclose_units(u1.base_value, pc_mks, 1e-12) assert_allclose_units(u2.base_value, yr_mks, 1e-12) assert_allclose_units(u3.base_value, pc_mks * yr_mks, 1e-12) assert_allclose_units(u4.base_value, pc_mks**2 / yr_mks, 1e-12) assert u1.dimensions == length assert u2.dimensions == time assert u3.dimensions == length * time assert u4.dimensions == length**2 / time def test_create_with_duplicate_dimensions(): """ Create units with overlapping dimensions. Ex: km/Mpc. """ u1 = Unit("J * s**-1") u2 = Unit("km/s/Mpc") km_mks = m_per_km Mpc_mks = m_per_mpc assert u1.base_value == 1 assert u1.dimensions == power assert_allclose_units(u2.base_value, km_mks / Mpc_mks, 1e-12) assert u2.dimensions == rate def test_create_new_symbol(): """ Create unit with unknown symbol. """ u1 = Unit("abc", base_value=42, dimensions=(mass / time)) assert u1.expr == Symbol("abc", positive=True) assert u1.base_value == 42 assert u1.dimensions == mass / time u1 = Unit("abc", base_value=42, dimensions=length**3) assert u1.expr == Symbol("abc", positive=True) assert u1.base_value == 42 assert u1.dimensions == length**3 u1 = Unit("abc", base_value=42, dimensions=length * (mass * length)) assert u1.expr == Symbol("abc", positive=True) assert u1.base_value == 42 assert u1.dimensions == length**2 * mass with pytest.raises(UnitParseError): Unit("abc", base_value=42, dimensions=length**length) with pytest.raises(UnitParseError): Unit("abc", base_value=42, dimensions=length ** (length * length)) with pytest.raises(UnitParseError): Unit("abc", base_value=42, dimensions=length - mass) with pytest.raises(UnitParseError): Unit("abc", base_value=42, dimensions=length + mass) def test_create_fail_on_unknown_symbol(): """ Fail to create unit with unknown symbol, without base_value and dimensions. """ with pytest.raises(UnitParseError): Unit(Symbol("jigawatts")) def test_create_fail_on_bad_symbol_type(): """ Fail to create unit with bad symbol type. """ with pytest.raises(UnitParseError): Unit([1]) # something other than Expr and str def test_create_fail_on_bad_dimensions_type(): """ Fail to create unit with bad dimensions type. """ with pytest.raises(UnitParseError): Unit("a", base_value=1, dimensions="(mass)") def test_create_fail_on_dimensions_content(): """ Fail to create unit with bad dimensions expr. """ a = Symbol("a") with pytest.raises(UnitParseError): Unit("a", base_value=1, dimensions=a) def test_create_fail_on_base_value_type(): """ Fail to create unit with bad base_value type. """ with pytest.raises(UnitParseError): Unit("a", base_value="a", dimensions=(mass / time)) def test_string_representation(): """ Check unit string representation. """ pc = Unit("pc") Myr = Unit("Myr") speed = pc / Myr dimensionless = Unit() assert str(pc) == "pc" assert str(Myr) == "Myr" assert str(speed) == "pc/Myr" assert repr(speed) == "pc/Myr" assert str(dimensionless) == "dimensionless" assert repr(dimensionless) == "(dimensionless)" def test_multiplication(): """ Multiply two units. """ msun_mks = mass_sun_kg pc_mks = m_per_pc # Create symbols msun_sym = Symbol("Msun", positive=True) pc_sym = Symbol("pc", positive=True) s_sym = Symbol("s", positive=True) # Create units u1 = Unit("Msun") u2 = Unit("pc") # Mul operation u3 = u1 * u2 assert u3.expr == msun_sym * pc_sym assert_allclose_units(u3.base_value, msun_mks * pc_mks, 1e-12) assert u3.dimensions == mass * length # Pow and Mul operations u4 = Unit("pc**2") u5 = Unit("Msun * s") u6 = u4 * u5 assert u6.expr == pc_sym**2 * msun_sym * s_sym assert_allclose_units(u6.base_value, pc_mks**2 * msun_mks, 1e-12) assert u6.dimensions == length**2 * mass * time def test_division(): """ Divide two units. """ pc_mks = m_per_pc km_mks = m_per_km # Create symbols pc_sym = Symbol("pc", positive=True) km_sym = Symbol("km", positive=True) s_sym = Symbol("s", positive=True) # Create units u1 = Unit("pc") u2 = Unit("km * s") u3 = u1 / u2 assert u3.expr == pc_sym / (km_sym * s_sym) assert_allclose_units(u3.base_value, pc_mks / km_mks, 1e-12) assert u3.dimensions == 1 / time def test_power(): """ Take units to some power. """ from sympy import nsimplify from unyt import dimensionless pc_mks = m_per_pc mK_mks = 1e-3 u1_dims = mass * length**2 * time**-3 * temperature**4 u1 = Unit("kg * pc**2 * s**-3 * mK**4") u2 = u1**2 assert u2.dimensions == u1_dims**2 assert_allclose_units(u2.base_value, (pc_mks**2 * mK_mks**4) ** 2, 1e-12) u3 = u1 ** (-1.0 / 3) assert u3.dimensions == nsimplify(u1_dims ** (-1.0 / 3)) assert_allclose_units(u3.base_value, (pc_mks**2 * mK_mks**4) ** (-1.0 / 3), 1e-12) assert u1**0.0 == dimensionless def test_equality(): """ Check unit equality with different symbols, but same dimensions and base_value. """ u1 = Unit("km * s**-1") u2 = Unit("m * ms**-1") assert u1 == u2 assert u1.copy() == u2 def test_invalid_operations(): u1 = Unit("cm") u2 = Unit("m") with pytest.raises(InvalidUnitOperation): u1 + u2 with pytest.raises(InvalidUnitOperation): u1 += u2 with pytest.raises(InvalidUnitOperation): 1 + u1 with pytest.raises(InvalidUnitOperation): u1 + 1 with pytest.raises(InvalidUnitOperation): u1 - u2 with pytest.raises(InvalidUnitOperation): u1 -= u2 with pytest.raises(InvalidUnitOperation): 1 - u1 with pytest.raises(InvalidUnitOperation): u1 - 1 with pytest.raises(InvalidUnitOperation): u1 *= u2 with pytest.raises(TypeError): u1 * "hello!" with pytest.raises(InvalidUnitOperation): u1 /= u2 with pytest.raises(TypeError): u1 / "hello!" with pytest.raises(InvalidUnitOperation): Unit("B") * Unit("V") with pytest.raises(InvalidUnitOperation): Unit("V") * Unit("B") with pytest.raises(InvalidUnitOperation): Unit("V") / Unit("Np") with pytest.raises(InvalidUnitOperation): Unit("dB") / Unit("dB") with pytest.raises(InvalidUnitOperation): Unit("B") ** 2 def test_base_equivalent(): """ Check base equivalent of a unit. """ Msun_mks = mass_sun_kg Mpc_mks = m_per_mpc u1 = Unit("Msun * Mpc**-3") u2 = Unit("kg * m**-3") u3 = u1.get_base_equivalent() assert u2.expr == u3.expr assert u2 == u3 assert_allclose_units(u1.base_value, Msun_mks / Mpc_mks**3, 1e-12) assert u2.base_value == 1 assert u3.base_value == 1 mass_density = mass / length**3 assert u1.dimensions == mass_density assert u2.dimensions == mass_density assert u3.dimensions == mass_density assert_allclose_units(u1.get_conversion_factor(u3)[0], Msun_mks / Mpc_mks**3, 1e-12) with pytest.raises(UnitConversionError): u1.get_conversion_factor(Unit("m")) with pytest.raises(UnitConversionError): u1.get_conversion_factor(Unit("degF")) reg = UnitRegistry(unit_system=cgs_unit_system) u = Unit("kg", registry=reg) assert u.get_base_equivalent() == Unit("g") u = Unit("kg") assert u.get_base_equivalent() == Unit("kg") u = Unit("A") assert u.get_base_equivalent(unit_system="mks") == Unit("A") @pytest.mark.parametrize( ("u1", "u2"), [ (Unit("degC"), Unit("degF")), (Unit("degC"), Unit("K")), (Unit("degF"), Unit("K")), ], ) def test_temperature_offsets(u1, u2): with pytest.raises(InvalidUnitOperation): operator.mul(u1, u2) with pytest.raises(InvalidUnitOperation): operator.truediv(u1, u2) assert u1 != u2 assert u2 != u1 assert not u1 == u2 assert not u2 == u1 def test_latex_repr(): registry = UnitRegistry() # create a fake comoving unit registry.add( "pccm", registry.lut["pc"][0] / (1 + 2), length, "\\rm{pc}/(1+z)", prefixable=True, ) test_unit = Unit("Mpccm", registry=registry) assert_almost_equal(test_unit.base_value, m_per_mpc / 3) assert_equal(test_unit.latex_repr, r"\rm{Mpc}/(1+z)") test_unit = Unit("cm**-3", base_value=1.0, registry=registry) assert_equal(test_unit.latex_repr, "\\frac{1}{\\rm{cm}^{3}}") test_unit = Unit("m_geom/l_geom**3") assert_equal(test_unit.latex_repr, "\\frac{1}{\\rm{M}_\\odot^{2}}") test_unit = Unit("1e9*cm") assert_equal(test_unit.latex_repr, "1.0 \\times 10^{9}\\ \\rm{cm}") test_unit = Unit("1.0*cm") assert_equal(test_unit.latex_repr, "\\rm{cm}") def test_latitude_longitude(): lat = unit_symbols.lat lon = unit_symbols.lon deg = unit_symbols.deg assert_equal(lat.units.base_offset, 90.0) assert_equal((deg * 90.0).in_units("lat").value, 0.0) assert_equal((deg * 180).in_units("lat").value, -90.0) assert_equal((lat * 0.0).in_units("deg"), deg * 90.0) assert_equal((lat * -90).in_units("deg"), deg * 180) assert_equal(lon.units.base_offset, -180.0) assert_equal((deg * 0.0).in_units("lon").value, -180.0) assert_equal((deg * 90.0).in_units("lon").value, -90.0) assert_equal((deg * 180).in_units("lon").value, 0.0) assert_equal((deg * 360).in_units("lon").value, 180.0) assert_equal((lon * -180.0).in_units("deg"), deg * 0.0) assert_equal((lon * -90.0).in_units("deg"), deg * 90.0) assert_equal((lon * 0.0).in_units("deg"), deg * 180.0) assert_equal((lon * 180.0).in_units("deg"), deg * 360) def test_creation_from_unyt_array(): from unyt import electrostatic_unit, elementary_charge_cgs u1 = Unit(electrostatic_unit) assert_equal(str(u1), "statC") assert_equal(u1, Unit("esu")) assert_equal(u1, electrostatic_unit.units) u2 = Unit(elementary_charge_cgs) assert_equal(str(u2), "4.80320467299766e-10*statC") assert_equal(u2, Unit("4.80320467299766e-10*statC")) assert_equal(u1, elementary_charge_cgs.units) assert_allclose((u1 / u2).base_value, electrostatic_unit / elementary_charge_cgs) with pytest.raises(UnitParseError): Unit([1, 2, 3] * elementary_charge_cgs) def test_list_same_dimensions(): from unyt import m reg = default_unit_registry for equiv in reg.list_same_dimensions(m): assert Unit(equiv).dimensions is length def test_decagram(): dag = Unit("dag") g = Unit("g") assert dag.get_conversion_factor(g) == (10.0, None) def test_conversion_factor_dtype(): # the documented dtype argument should control the precision of the # returned conversion factor (and offset), see GH issue #579 km = Unit("km") m = Unit("m") factor, offset = km.get_conversion_factor(m, dtype=np.dtype("float32")) assert isinstance(factor, np.float32) assert factor == np.float32(1000.0) assert offset is None # offset case: a conversion with a non-zero offset should return both the # factor and the offset with the requested precision degC = Unit("degC") degF = Unit("degF") factor, offset = degC.get_conversion_factor(degF, dtype=np.float32) assert isinstance(factor, np.float32) assert isinstance(offset, np.float32) # a narrower request is widened to a minimum of 2 bytes, mirroring the # behaviour used internally in unyt.array factor, _ = km.get_conversion_factor(m, dtype=np.int8) assert isinstance(factor, np.float16) # a complex request uses the width of its real part; taking the whole item # size would name a float128 that does not exist on every platform factor, _ = km.get_conversion_factor(m, dtype=np.complex128) assert isinstance(factor, np.float64) factor, _ = km.get_conversion_factor(m, dtype=np.complex64) assert isinstance(factor, np.float32) # regression: without a dtype the result is unchanged (plain Python floats) factor, offset = km.get_conversion_factor(m) assert type(factor) is float assert offset is None factor, offset = degC.get_conversion_factor(degF) assert type(factor) is float assert type(offset) is float def test_pickle(): cm = Unit("cm") assert cm == pickle.loads(pickle.dumps(cm)) def test_preserve_offset(): from unyt import degF, dimensionless new_unit = degF * dimensionless assert new_unit is not degF assert new_unit == degF assert new_unit.base_offset == degF.base_offset new_unit = degF / dimensionless assert new_unit is not degF assert new_unit == degF assert new_unit.base_offset == degF.base_offset with pytest.raises(InvalidUnitOperation): dimensionless / degF def test_code_unit(): from unyt import UnitRegistry ureg = UnitRegistry() ureg.add("code_length", 10.0, length) ureg.add("code_magnetic_field", 2.0, magnetic_field_mks) u = Unit("code_length", registry=ureg) assert u.is_code_unit is True assert u.get_base_equivalent() == Unit("m") u = Unit("cm") assert u.is_code_unit is False u = Unit("code_magnetic_field", registry=ureg) assert u.get_base_equivalent("mks") == Unit("T") with pytest.raises(UnitsNotReducible): assert u.get_base_equivalent("cgs") # see issue #60 u = Unit("s/m") assert u.get_mks_equivalent() == Unit("s/m") assert u.get_mks_equivalent() != Unit("ohm") assert u.get_cgs_equivalent() == Unit("s/cm") u = Unit("kC") assert u.get_cgs_equivalent() == Unit("kesu") assert u.get_cgs_equivalent().get_mks_equivalent() == u UnitSystem(ureg.unit_system_id, "code_length", "kg", "s", registry=ureg) u = Unit("cm", registry=ureg) ue = u.get_base_equivalent("code") assert str(ue) == "code_length" assert ue.base_value == 10 assert ue.dimensions is length class FakeDataset: unit_registry = ureg ds = FakeDataset() UnitSystem(ds, "code_length", "kg", "s", registry=ureg) u = Unit("cm", registry=ureg) ue = u.get_base_equivalent(ds) assert str(ue) == "code_length" assert ue.base_value == 10 assert ue.dimensions is length with pytest.raises(UnitParseError): Unit("code_length") def test_bad_equivalence(): from unyt import cm with pytest.raises(KeyError): cm.has_equivalent("dne") def test_em_unit_base_equivalent(): from unyt import A, cm with pytest.raises(UnitsNotReducible): (A / cm).get_base_equivalent("cgs") def test_symbol_lut_length(): for v in default_unit_symbol_lut.values(): assert len(v) == 5 def test_simplify(): import unyt as u answers = { u.Hz * u.s: "dimensionless", u.kg / u.g: "1000", u.Hz * u.s * u.km: "km", u.kHz * u.s: "1000", u.kHz * u.s * u.km: "1000*km", u.kHz * u.s**2: "1000*s", u.kHz * u.s**2 * u.km: "1000*km*s", u.Hz**-1 * u.s: "s/Hz", u.Hz**-1 * u.s * u.km: "km*s/Hz", u.Hz**1.5 * u.s**1.7: "sqrt(Hz)*s**(7/10)", u.Hz**1.5 * u.s**1.7 * u.km: "sqrt(Hz)*km*s**(7/10)", u.m**2 / u.cm**2: "10000", } for unit, answer in answers.items(): assert str(unit.simplify()) == answer def test_micro_prefix(): import unyt as u # both versions of unicode mu work correctly assert u.um == u.µm assert u.um == u.μm # parsing both versions works as well assert u.ug == u.Unit("µg") assert u.ug == u.Unit("μg") def test_name_alternatives(): import unyt from unyt._unit_lookup_table import ( default_unit_name_alternatives, inv_name_alternatives, name_alternatives, ) # concatenated list of all alternative unit names allowed_names = sum(name_alternatives.values(), []) # ensure the values are all tuples and not e.g. strings for val in default_unit_name_alternatives.values(): assert isinstance(val, tuple) # all names are unique assert len(set(allowed_names)) == len(allowed_names) # each allowed name has a key in the inverse dict assert len(inv_name_alternatives.keys()) == len(allowed_names) assert set(inv_name_alternatives.keys()) == set(allowed_names) for name in allowed_names: assert hasattr(unyt, name) assert hasattr(unyt.unit_symbols, name) def test_solar_unit_name_alternatives(): import unyt from unyt import Unit # check that m_sun, m_Sun, M_sun, M_Sun, msun, and Msun all work for lower_name_prefix in "mrltz": base_name = lower_name_prefix + "sun" for name_prefix in [lower_name_prefix, lower_name_prefix.upper()]: alternative_names = [name_prefix + suf for suf in ["sun", "_sun", "_Sun"]] for name in alternative_names: assert Unit(name) == Unit(base_name) assert hasattr(unyt, name) # only solar mass units are in physical constants if lower_name_prefix == "m": assert hasattr(unyt.physical_constants, name) def test_attosecond(): from unyt import Unit, attosecond, second assert Unit("as") == attosecond assert str(Unit("as")) == "as" assert Unit("as/s") == attosecond / second def test_micro(): from unyt import Unit assert str(Unit("um")) == "μm" assert str(Unit("us")) == "μs" def test_show_all_units_doc_table_ops(): for name in set(name_alternatives.keys()): u = Unit(name) (1 * u).in_mks() try: (1 * u).in_cgs() except UnitsNotReducible: pass def test_hPa_mbar(): assert Unit("hPa").dimensions == Unit("bar").dimensions assert (5 * Unit("hPa") == 5 * Unit("mbar")).all() assert (5 * Unit("hPa") != 1 * Unit("bar")).all() def test_percent(): a = 300 * Unit("percent") b = 3.0 * Unit("dimensionless") c = 300.0 * Unit("%") d = 300.0 * Unit("V*%/V") assert a == b assert str(a) == "300 %" assert repr(a) == "unyt_quantity(300, '%')" assert a == c assert c == d def test_equal_has_same_hash(): a = Unit("m") b = Unit("m") c = Unit("m*s/s") assert a == b assert b == c assert hash(a) == hash(b) assert hash(b) == hash(c) def test_bel_neper(): assert Unit("B").dimensions == Unit("Np").dimensions a = 1 * Unit("B") / (np.log(10) / 2) assert_allclose_units(a.to("Np"), 1 * Unit("Np")) a = 2 * Unit("B") b = 20 * Unit("decibel") assert (a == b).all() c = 2 * Unit("Np") d = 20 * Unit("decineper") assert (c == d).all() assert Unit("dB") ** 1 == Unit("dB") def test_henry(): assert (Unit("H") / Unit("Ω")).dimensions == time def test_degC(): assert Unit("degree_celsius") == Unit("degC") assert Unit("degree_Celsius") == Unit("degC") assert Unit("Celsius") == Unit("degC") assert Unit("°C") == Unit("degC") a = 1 * Unit("degC") assert str(a) == "1 °C" def test_degC_with_SIprefixes(): assert_allclose_units(1 * Unit("mdegC"), 0.001 * Unit("degC")) assert_allclose_units(1 * Unit("degC"), 1000 * Unit("mdegC")) assert_allclose_units(73 * Unit("degF"), 22777.779 * Unit("mdegC")) assert_allclose_units(22777.779 * Unit("mdegC"), 73 * Unit("degF")) assert_allclose_units(22777.779 * Unit("mdegC"), 532.67 * Unit("R")) assert_allclose_units(1 * Unit("mK"), -273149.0 * Unit("mdegC")) assert_allclose_units(1 * Unit("mdegC"), 273151.0 * Unit("mK")) def test_delta_degC(): a = 1 * Unit("delta_degC") assert str(a) == "1 Δ°C" def test_degF(): assert Unit("degree_fahrenheit") == Unit("degF") assert Unit("degree_Fahrenheit") == Unit("degF") assert Unit("Fahrenheit") == Unit("degF") assert Unit("°F") == Unit("degF") a = 1 * Unit("degF") assert str(a) == "1 °F" def test_delta_degF(): a = 1 * Unit("delta_degF") assert str(a) == "1 Δ°F" def test_mixed_registry_operations(): reg = UnitRegistry(unit_system="cgs") reg.add("fake_length", 0.001, length) a = unyt_quantity(1, units="fake_length", registry=reg) b = unyt_quantity(1, "cm") assert_almost_equal(a + b, b + a) assert_almost_equal(a - b, -(b - a)) assert_almost_equal(a * b, b * a) assert_almost_equal(b / a, b / a.in_units("km")) assert_almost_equal(a / b, a / b.in_units("km")) def test_other_argument_can_handle_multiplication_and_division(): class MyClass: def __init__(self, x): self.x = x def __mul__(self, other): return self.x * other def __rmul__(self, other): return self.__mul__(other) def __truediv__(self, other): return self.x / other def __rtruediv__(self, other): return other / self.x u = Unit("m") x = 5 mc = MyClass(x=x) assert_almost_equal(x * u, mc * u) # handled by MyClass.__mul__ assert_almost_equal(x * u, u * mc) # handled by MyClass.__mul__ assert_almost_equal(x / u, mc / u) # handled by MyClass.__truediv__ assert_almost_equal(u / x, u / mc) # handled by MyClass.__rtruediv__ def test_other_argument_typeerror_on_multiplication_or_division(): class MyClass: def __init__(self): return def __mul__(self, other): return other * None # always raises TypeError def __rtruediv__(self, other): return other / None # always raises TypeError u = Unit("m") mc = MyClass() mul_msg = r"unsupported operand type\(s\) for \*" div_msg = r"unsupported operand type\(s\) for \/" # only test for case when MyClass is right argument, when left it's its own problem with pytest.raises(TypeError, match=mul_msg): u * mc with pytest.raises(TypeError, match=div_msg): u / mc def test_other_argument_cannot_handle_multiplication_and_division(): class MyClass: def __init__(self, x): self.x = x u = Unit("m") x = 5 mc = MyClass(x=x) mul_msg = r"unsupported operand type\(s\) for \*" div_msg = r"unsupported operand type\(s\) for \/" with pytest.raises(TypeError, match=mul_msg): mc * u with pytest.raises(TypeError, match=mul_msg): u * mc with pytest.raises(TypeError, match=mul_msg): mc / u # handled as multiplication by inverse so expect mul_msg with pytest.raises(TypeError, match=div_msg): u / mc def test_other_argument_explicitly_cannot_handle_multiplication_and_division(): class MyClass: def __init__(self, x): self.x = x def __mul__(self, x): return NotImplemented def __rtruediv__(self, x): return NotImplemented u = Unit("m") x = 5 mc = MyClass(x=x) mul_msg = r"unsupported operand type\(s\) for \*" div_msg = r"unsupported operand type\(s\) for \/" with pytest.raises(TypeError, match=mul_msg): mc * u with pytest.raises(TypeError, match=mul_msg): u * mc with pytest.raises(TypeError, match=mul_msg): mc / u # handled as multiplication by inverse so expect mul_msg with pytest.raises(TypeError, match=div_msg): u / mc unyt-3.2.0/unyt/tests/test_unyt_array.py000066400000000000000000003061701526544257300205060ustar00rootroot00000000000000""" Test ndarray subclass that handles symbolic units. """ import copy import itertools import math import operator import os import pickle import re import shutil import tempfile from importlib.metadata import version import numpy as np import pytest from numpy import array from numpy.testing import ( assert_almost_equal, assert_array_almost_equal, assert_array_equal, assert_equal, ) from packaging.version import Version from unyt import K, Msun, R, Unit, degC, degF, delta_degC, delta_degF, dimensions from unyt._on_demand_imports import _astropy, _h5py, _pint from unyt._physical_ratios import metallicity_sun, speed_of_light_cm_per_s from unyt.array import ( binary_operators, loadtxt, savetxt, uconcatenate, ucross, udot, uintersect1d, unary_operators, unorm, unyt_array, unyt_quantity, ustack, uunion1d, uvstack, ) from unyt.exceptions import ( InvalidUnitEquivalence, InvalidUnitOperation, IterableUnitCoercionError, UnitConversionError, UnitOperationError, UnitParseError, UnitsNotReducible, ) from unyt.testing import ( _process_warning, assert_allclose_units, assert_array_equal_units, ) from unyt.unit_registry import UnitRegistry from unyt.unit_symbols import cm, degree, g, m NUMPY_VERSION = Version(version("numpy")) def operate_and_compare(a, b, op, answer): # Test generator for unyt_arrays tests assert_array_almost_equal(op(a, b), answer) def assert_isinstance(a, type): assert isinstance(a, type) def test_addition(): """ Test addition of two unyt_arrays """ # Same units a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "cm") a3 = [4 * cm, 5 * cm, 6 * cm] answer = unyt_array([5, 7, 9], "cm") operate_and_compare(a1, a2, operator.add, answer) operate_and_compare(a2, a1, operator.add, answer) operate_and_compare(a1, a3, operator.add, answer) operate_and_compare(a3, a1, operator.add, answer) operate_and_compare(a2, a1, np.add, answer) operate_and_compare(a1, a2, np.add, answer) operate_and_compare(a1, a3, np.add, answer) operate_and_compare(a3, a1, np.add, answer) # different units a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "m") a3 = [4 * m, 5 * m, 6 * m] answer1 = unyt_array([401, 502, 603], "cm") answer2 = unyt_array([4.01, 5.02, 6.03], "m") operate_and_compare(a1, a2, operator.add, answer1) operate_and_compare(a2, a1, operator.add, answer2) operate_and_compare(a1, a3, operator.add, answer1) operate_and_compare(a3, a1, operator.add, answer2) operate_and_compare(a1, a2, np.add, answer1) operate_and_compare(a2, a1, np.add, answer2) operate_and_compare(a1, a3, np.add, answer1) operate_and_compare(a3, a1, np.add, answer2) # Test dimensionless quantities a1 = unyt_array([1, 2, 3]) a2 = array([4, 5, 6]) a3 = [4, 5, 6] answer = unyt_array([5, 7, 9]) operate_and_compare(a1, a2, operator.add, answer) operate_and_compare(a2, a1, operator.add, answer) operate_and_compare(a1, a3, operator.add, answer) operate_and_compare(a3, a1, operator.add, answer) operate_and_compare(a1, a2, np.add, answer) operate_and_compare(a2, a1, np.add, answer) operate_and_compare(a1, a3, np.add, answer) operate_and_compare(a3, a1, np.add, answer) # Catch the different dimensions error a1 = unyt_array([1, 2, 3], "m") a2 = unyt_array([4, 5, 6], "kg") a3 = [7, 8, 9] a4 = unyt_array([10, 11, 12], "") with pytest.raises(UnitOperationError): operator.add(a1, a2) with pytest.raises(UnitOperationError): operator.iadd(a1, a2) with pytest.raises(UnitOperationError): operator.add(a1, a3) with pytest.raises(UnitOperationError): operator.iadd(a1, a3) with pytest.raises(UnitOperationError): operator.add(a3, a1) with pytest.raises(UnitOperationError): operator.iadd(a3, a1) with pytest.raises(UnitOperationError): operator.add(a1, a4) with pytest.raises(UnitOperationError): operator.iadd(a1, a4) with pytest.raises(UnitOperationError): operator.add(a4, a1) with pytest.raises(UnitOperationError): operator.iadd(a4, a1) # adding with zero is allowed irrespective of the units zeros = np.zeros(3) zeros_yta_dimless = unyt_array(zeros, "dimensionless") zeros_yta_length = unyt_array(zeros, "m") zeros_yta_mass = unyt_array(zeros, "kg") operands = [0, zeros, zeros_yta_length] for op in [operator.add, np.add]: for operand in operands: operate_and_compare(a1, operand, op, a1) operate_and_compare(operand, a1, op, a1) operate_and_compare(4 * m, operand, op, 4 * m) operate_and_compare(operand, 4 * m, op, 4 * m) operands = [ unyt_quantity(0), unyt_quantity(0, "kg"), zeros_yta_dimless, zeros_yta_mass, ] for op in [operator.add, np.add]: for operand in operands: with pytest.raises(UnitOperationError): operate_and_compare(a1, operand, op, a1) with pytest.raises(UnitOperationError): operate_and_compare(operand, a1, op, a1) with pytest.raises(UnitOperationError): operate_and_compare(4 * m, operand, op, 4 * m) with pytest.raises(UnitOperationError): operate_and_compare(operand, 4 * m, op, 4 * m) def test_subtraction(): """ Test subtraction of two unyt_arrays """ # Same units a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "cm") a3 = [4 * cm, 5 * cm, 6 * cm] answer1 = unyt_array([-3, -3, -3], "cm") answer2 = unyt_array([3, 3, 3], "cm") operate_and_compare(a1, a2, operator.sub, answer1) operate_and_compare(a2, a1, operator.sub, answer2) operate_and_compare(a1, a3, operator.sub, answer1) operate_and_compare(a3, a1, operator.sub, answer2) operate_and_compare(a1, a2, np.subtract, answer1) operate_and_compare(a2, a1, np.subtract, answer2) operate_and_compare(a1, a3, np.subtract, answer1) operate_and_compare(a3, a1, np.subtract, answer2) # different units a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "m") a3 = [4 * m, 5 * m, 6 * m] answer1 = unyt_array([-399, -498, -597], "cm") answer2 = unyt_array([3.99, 4.98, 5.97], "m") answer3 = unyt_array([399, 498, 597], "cm") operate_and_compare(a1, a2, operator.sub, answer1) operate_and_compare(a2, a1, operator.sub, answer2) operate_and_compare(a1, a3, operator.sub, answer1) operate_and_compare(a3, a1, operator.sub, answer3) operate_and_compare(a1, a2, np.subtract, answer1) operate_and_compare(a2, a1, np.subtract, answer2) operate_and_compare(a1, a3, np.subtract, answer1) operate_and_compare(a3, a1, np.subtract, answer3) # Test dimensionless quantities a1 = unyt_array([1, 2, 3]) a2 = array([4, 5, 6]) a3 = [4, 5, 6] answer1 = unyt_array([-3, -3, -3]) answer2 = unyt_array([3, 3, 3]) operate_and_compare(a1, a2, operator.sub, answer1) operate_and_compare(a2, a1, operator.sub, answer2) operate_and_compare(a1, a3, operator.sub, answer1) operate_and_compare(a3, a1, operator.sub, answer2) operate_and_compare(a1, a2, np.subtract, answer1) operate_and_compare(a2, a1, np.subtract, answer2) operate_and_compare(a1, a3, np.subtract, answer1) operate_and_compare(a3, a1, np.subtract, answer2) # Catch the different dimensions error a1 = unyt_array([1, 2, 3], "m") a2 = unyt_array([4, 5, 6], "kg") a3 = [7, 8, 9] a4 = unyt_array([10, 11, 12], "") with pytest.raises(UnitOperationError): operator.sub(a1, a2) with pytest.raises(UnitOperationError): operator.isub(a1, a2) with pytest.raises(UnitOperationError): operator.sub(a1, a3) with pytest.raises(UnitOperationError): operator.isub(a1, a3) with pytest.raises(UnitOperationError): operator.sub(a3, a1) with pytest.raises(UnitOperationError): operator.isub(a3, a1) with pytest.raises(UnitOperationError): operator.sub(a1, a4) with pytest.raises(UnitOperationError): operator.isub(a1, a4) with pytest.raises(UnitOperationError): operator.sub(a4, a1) with pytest.raises(UnitOperationError): operator.isub(a4, a1) # subtracting with zero is allowed irrespective of the units zeros = np.zeros(3) zeros_yta_dimless = unyt_array(zeros, "dimensionless") zeros_yta_length = unyt_array(zeros, "m") zeros_yta_mass = unyt_array(zeros, "kg") operands = [0, zeros, zeros_yta_length] for op in [operator.sub, np.subtract]: for operand in operands: operate_and_compare(a1, operand, op, a1) operate_and_compare(operand, a1, op, -a1) operate_and_compare(4 * m, operand, op, 4 * m) operate_and_compare(operand, 4 * m, op, -4 * m) operands = [ unyt_quantity(0), unyt_quantity(0, "kg"), zeros_yta_dimless, zeros_yta_mass, ] for op in [operator.sub, np.subtract]: for operand in operands: with pytest.raises(UnitOperationError): operate_and_compare(a1, operand, op, a1) with pytest.raises(UnitOperationError): operate_and_compare(operand, a1, op, -a1) with pytest.raises(UnitOperationError): operate_and_compare(4 * m, operand, op, 4 * m) with pytest.raises(UnitOperationError): operate_and_compare(operand, 4 * m, op, -4 * m) def test_multiplication(): """ Test multiplication of two unyt_arrays """ # Same units a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "cm") a3 = [4 * cm, 5 * cm, 6 * cm] answer = unyt_array([4, 10, 18], "cm**2") operate_and_compare(a1, a2, operator.mul, answer) operate_and_compare(a2, a1, operator.mul, answer) operate_and_compare(a1, a3, operator.mul, answer) operate_and_compare(a3, a1, operator.mul, answer) operate_and_compare(a1, a2, np.multiply, answer) operate_and_compare(a2, a1, np.multiply, answer) operate_and_compare(a1, a3, np.multiply, answer) operate_and_compare(a3, a1, np.multiply, answer) # different units, same dimension a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "m") a3 = [4 * m, 5 * m, 6 * m] answer1 = unyt_array([400, 1000, 1800], "cm**2") answer2 = unyt_array([0.04, 0.10, 0.18], "m**2") answer3 = unyt_array([4, 10, 18], "cm*m") operate_and_compare(a1, a2, operator.mul, answer1) operate_and_compare(a2, a1, operator.mul, answer2) operate_and_compare(a1, a3, operator.mul, answer1) operate_and_compare(a3, a1, operator.mul, answer2) operate_and_compare(a1, a2, np.multiply, answer3) operate_and_compare(a2, a1, np.multiply, answer3) operate_and_compare(a1, a3, np.multiply, answer3) operate_and_compare(a3, a1, np.multiply, answer3) # different dimensions a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([4, 5, 6], "g") a3 = [4 * g, 5 * g, 6 * g] answer = unyt_array([4, 10, 18], "cm*g") operate_and_compare(a1, a2, operator.mul, answer) operate_and_compare(a2, a1, operator.mul, answer) operate_and_compare(a1, a3, operator.mul, answer) operate_and_compare(a3, a1, operator.mul, answer) operate_and_compare(a1, a2, np.multiply, answer) operate_and_compare(a2, a1, np.multiply, answer) operate_and_compare(a1, a3, np.multiply, answer) operate_and_compare(a3, a1, np.multiply, answer) # One dimensionless, one unitful a1 = unyt_array([1, 2, 3], "cm") a2 = array([4, 5, 6]) a3 = [4, 5, 6] answer = unyt_array([4, 10, 18], "cm") operate_and_compare(a1, a2, operator.mul, answer) operate_and_compare(a2, a1, operator.mul, answer) operate_and_compare(a1, a3, operator.mul, answer) operate_and_compare(a3, a1, operator.mul, answer) operate_and_compare(a1, a2, np.multiply, answer) operate_and_compare(a2, a1, np.multiply, answer) operate_and_compare(a1, a3, np.multiply, answer) operate_and_compare(a3, a1, np.multiply, answer) # Both dimensionless quantities a1 = unyt_array([1, 2, 3]) a2 = array([4, 5, 6]) a3 = [4, 5, 6] answer = unyt_array([4, 10, 18]) operate_and_compare(a1, a2, operator.mul, answer) operate_and_compare(a2, a1, operator.mul, answer) operate_and_compare(a1, a3, operator.mul, answer) operate_and_compare(a3, a1, operator.mul, answer) operate_and_compare(a1, a2, np.multiply, answer) operate_and_compare(a2, a1, np.multiply, answer) operate_and_compare(a1, a3, np.multiply, answer) operate_and_compare(a3, a1, np.multiply, answer) # With np.multiply.reduce a = unyt_array([1.0, 2.0, 3.0], "cm") answer = unyt_quantity(6.0, "cm**3") assert_equal(np.multiply.reduce(a), answer) a = unyt_array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "cm") answer = unyt_array([6.0, 120.0], "cm**3") assert_equal(np.multiply.reduce(a, axis=1), answer) def test_multiplication_by_msun_preserves_precision(): values_to_test = ( np.float32(1), np.array([1, 2], dtype=np.float32), unyt_array([1, 2], "m", dtype=np.float32), ) value = unyt_quantity(np.float32(1), "m") for mass in (Msun, Msun.in_base("galactic")): for values in values_to_test: assert (values * mass).dtype == values.dtype assert (mass * values).dtype == values.dtype left = value * mass right = mass * value assert_equal(left, right) # With two quantities, neither operand is unit metadata, so normal NumPy # promotion applies. assert left.dtype == right.dtype == np.result_type(value.dtype, mass.dtype) def test_division(): """ Test division of two unyt_arrays """ # Same units a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([4.0, 5.0, 6.0], "cm") a3 = [4 * cm, 5 * cm, 6 * cm] answer1 = unyt_array([0.25, 0.4, 0.5]) answer2 = unyt_array([4, 2.5, 2]) op = operator.truediv operate_and_compare(a1, a2, op, answer1) operate_and_compare(a2, a1, op, answer2) operate_and_compare(a1, a3, op, answer1) operate_and_compare(a3, a1, op, answer2) operate_and_compare(a1, a2, np.divide, answer1) operate_and_compare(a2, a1, np.divide, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) # different units, same dimension a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([4.0, 5.0, 6.0], "m") a3 = [4 * m, 5 * m, 6 * m] answer1 = unyt_array([0.0025, 0.004, 0.005]) answer2 = unyt_array([400, 250, 200]) operate_and_compare(a1, a2, op, answer1) operate_and_compare(a2, a1, op, answer2) operate_and_compare(a1, a3, op, answer1) operate_and_compare(a3, a1, op, answer2) operate_and_compare(a1, a2, np.divide, answer1) operate_and_compare(a2, a1, np.divide, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) # different dimensions a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([4.0, 5.0, 6.0], "g") a3 = [4 * g, 5 * g, 6 * g] answer1 = unyt_array([0.25, 0.4, 0.5], "cm/g") answer2 = unyt_array([4, 2.5, 2], "g/cm") operate_and_compare(a1, a2, op, answer1) operate_and_compare(a2, a1, op, answer2) operate_and_compare(a1, a3, op, answer1) operate_and_compare(a3, a1, op, answer2) operate_and_compare(a1, a2, np.divide, answer1) operate_and_compare(a2, a1, np.divide, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) # One dimensionless, one unitful a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = array([4.0, 5.0, 6.0]) a3 = [4, 5, 6] answer1 = unyt_array([0.25, 0.4, 0.5], "cm") answer2 = unyt_array([4, 2.5, 2], "1/cm") operate_and_compare(a1, a2, op, answer1) operate_and_compare(a2, a1, op, answer2) operate_and_compare(a1, a3, op, answer1) operate_and_compare(a3, a1, op, answer2) operate_and_compare(a1, a2, np.divide, answer1) operate_and_compare(a2, a1, np.divide, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) # Both dimensionless quantities a1 = unyt_array([1.0, 2.0, 3.0]) a2 = array([4.0, 5.0, 6.0]) a3 = [4, 5, 6] answer1 = unyt_array([0.25, 0.4, 0.5]) answer2 = unyt_array([4, 2.5, 2]) operate_and_compare(a1, a2, op, answer1) operate_and_compare(a2, a1, op, answer2) operate_and_compare(a1, a3, op, answer1) operate_and_compare(a3, a1, op, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) operate_and_compare(a1, a3, np.divide, answer1) operate_and_compare(a3, a1, np.divide, answer2) # With np.multiply.reduce a = unyt_array([3.0, 2.0, 1.0], "cm") answer = unyt_quantity(1.5, "cm**-1") assert_equal(np.divide.reduce(a), answer) a = unyt_array([[3.0, 2.0, 1.0], [6.0, 5.0, 4.0]], "cm") answer = unyt_array([1.5, 0.3], "cm**-1") assert_equal(np.divide.reduce(a, axis=1), answer) def test_power(): """ Test power operator ensure units are correct. """ from unyt import cm cm_arr = np.array([1.0, 1.0]) * cm cm_quant = 1.0 * cm assert_equal((1 * cm) ** 3, unyt_quantity(1, "cm**3")) assert_equal(np.power((1 * cm), 3), unyt_quantity(1, "cm**3")) assert_equal((1 * cm) ** unyt_quantity(3), unyt_quantity(1, "cm**3")) with pytest.raises(UnitOperationError): np.power((1 * cm), unyt_quantity(3, "g")) with pytest.raises(InvalidUnitOperation): np.power(cm, cm) assert_equal(cm_arr**3, unyt_array([1, 1], "cm**3")) assert_equal(np.power(cm_arr, 3), unyt_array([1, 1], "cm**3")) assert_equal(cm_arr ** unyt_quantity(3), unyt_array([1, 1], "cm**3")) with pytest.raises(UnitOperationError): np.power(cm_arr, unyt_quantity(3, "g")) try: np.power(cm_arr, unyt_quantity(3, "g")) except UnitOperationError as err: assert isinstance(err.unit1, Unit) assert isinstance(err.unit2, Unit) # when the power is 0.0 numpy short-circuits via ones_like so we # need to test the special handling for that case assert_array_equal_units(cm_quant**0, unyt_quantity(1.0, "dimensionless")) assert_array_equal_units(cm_arr**0, unyt_quantity(1.0, "dimensionless")) @pytest.mark.parametrize( "p", [ pytest.param(unyt_array(0), id="scalar power 0"), pytest.param(unyt_array(1), id="scalar power 1"), pytest.param(unyt_array([0, 0, 0, 0]), id="uniform power 0"), pytest.param(unyt_array([1, 1, 1, 1]), id="uniform power 1"), pytest.param(unyt_array([0, 1, 2, 3]), id="non-uniform power"), ], ) def test_dimensionless_array_power(p): # see https://github.com/yt-project/unyt/issues/522 a = unyt_array([1, 2, 3, 4]) expected = a.value**p.value assert_array_equal(expected, (a**p).value) @pytest.mark.parametrize( "a, p", [ pytest.param( unyt_array([0, 1, 2, 3], "cm"), unyt_array([0, 1, 2, 3]), id="non-uniform power with arr non dimensionless", ), pytest.param( unyt_array([0, 1, 2, 3]), unyt_array([0, 0, 0, 0], "cm"), id="non dimensionless power", ), ], ) def test_invalid_array_power(a, p): # see https://github.com/yt-project/unyt/issues/522 with pytest.raises(UnitOperationError): a**p def test_comparisons(): """ Test numpy ufunc comparison operators for unit consistency. """ from unyt.array import unyt_array a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([2, 1, 3], "cm") a3 = unyt_array([0.02, 0.01, 0.03], "m") a4 = unyt_array([1, 2, 3], "g") dimless = np.array([2, 1, 3]) ops = (np.less, np.less_equal, np.greater, np.greater_equal, np.equal, np.not_equal) answers = ( [True, False, False], [True, False, True], [False, True, False], [False, True, True], [False, False, True], [True, True, False], ) for op, answer in zip(ops, answers): operate_and_compare(a1, a2, op, answer) for op, answer in zip(ops, answers): operate_and_compare(a1, dimless, op, answer) for op, answer in zip(ops, answers): operate_and_compare(a1, a3, op, answer) for op, answer in zip(ops, answers): operate_and_compare(a1, a3.in_units("cm"), op, answer) # Check that comparisons with dimensionless quantities work in both # directions. operate_and_compare(a3, dimless, np.less, [True, True, True]) operate_and_compare(dimless, a3, np.less, [False, False, False]) assert_equal(a1 < 2, [True, False, False]) assert_equal(a1 < 2, np.less(a1, 2)) assert_equal(2 < a1, [False, False, True]) assert_equal(2 < a1, np.less(2, a1)) # Check that comparisons with arrays that have different units with # different dimensions work properly operate_and_compare(a1, a4, np.equal, [False, False, False]) operate_and_compare(a1, a4, np.not_equal, [True, True, True]) # check that comparing quantities returns bools and not 0-D arrays el1, el4 = a1[0], a4[0] assert (el1 == el4) is False assert (el1 != el4) is True # comparisons that aren't == and != with pytest.raises(UnitOperationError): np.greater(a1, a4) with pytest.raises(UnitOperationError): a1 > a4 # noqa: B015 with pytest.raises(UnitOperationError): np.greater(el1, el4) with pytest.raises(UnitOperationError): el1 > el4 # noqa: B015 def test_unit_conversions(): """ Test operations that convert to different units or cast to ndarray """ from unyt.array import unyt_quantity from unyt.unit_object import Unit km = unyt_quantity(1.0, "km", dtype="float64") km_in_cm = km.in_units("cm") cm_unit = Unit("cm") kpc_unit = Unit("kpc") assert_equal(km_in_cm, km) assert_equal(km_in_cm.in_cgs(), 1e5) assert_equal(km_in_cm.in_mks(), 1e3) assert_equal(km_in_cm.units, cm_unit) km_view = km.ndarray_view() km.convert_to_units("cm") assert km_view.base is km.base assert_equal(km, unyt_quantity(1, "km")) assert_equal(km.in_cgs(), 1e5) assert_equal(km.in_mks(), 1e3) assert_equal(km.units, cm_unit) km.convert_to_units("kpc") assert km_view.base is km.base assert_array_almost_equal(km, unyt_quantity(1, "km")) assert_array_almost_equal(km.in_cgs(), unyt_quantity(1e5, "cm")) assert_array_almost_equal(km.in_mks(), unyt_quantity(1e3, "m")) assert_equal(km.units, kpc_unit) assert_isinstance(km.to_ndarray(), np.ndarray) assert_isinstance(km.ndarray_view(), np.ndarray) dyne = unyt_quantity(1.0, "dyne") assert_equal(dyne.in_cgs(), dyne) assert_equal(dyne.in_cgs(), 1.0) assert_equal(dyne.in_mks(), dyne) assert_equal(dyne.in_mks(), 1e-5) assert_equal(str(dyne.in_mks().units), "N") assert_equal(str(dyne.in_cgs().units), "dyn") em3 = unyt_quantity(1.0, "erg/m**3") assert_equal(em3.in_cgs(), em3) assert_equal(em3.in_cgs(), 1e-6) assert_equal(em3.in_mks(), em3) assert_equal(em3.in_mks(), 1e-7) assert_equal(str(em3.in_mks().units), "Pa") assert_equal(str(em3.in_cgs().units), "dyn/cm**2") em3_converted = unyt_quantity(1545436840.386756, "Msun/(Myr**2*kpc)") assert_equal(em3.in_base(unit_system="galactic"), em3) assert_array_almost_equal(em3.in_base(unit_system="galactic"), em3_converted) assert_equal(str(em3.in_base(unit_system="galactic").units), "Msun/(Myr**2*kpc)") dimless = unyt_quantity(1.0, "") assert_equal(dimless.in_cgs(), dimless) assert_equal(dimless.in_cgs(), 1.0) assert_equal(dimless.in_mks(), dimless) assert_equal(dimless.in_mks(), 1.0) assert_equal(str(dimless.in_cgs().units), "dimensionless") kg = unyt_quantity(1.0, "kg") assert kg.to(g).v == 1000 assert kg.in_units(g).v == 1000 kg.convert_to_units(g) assert kg.v == 1000 ten_grams = 10 * g assert kg.to(ten_grams).v == 100 assert kg.in_units(ten_grams).v == 100 kg.convert_to_units(ten_grams) assert kg.v == 100 with pytest.raises(UnitParseError): kg.to([1, 2] * g) with pytest.raises(UnitParseError): kg.in_units([1, 2] * g) with pytest.raises(UnitParseError): kg.convert_to_units([1, 2] * g) def test_temperature_conversions(): """ Test conversions between various supported temperatue scales. Also ensure we only allow compound units with temperature scales that have a proper zero point. """ from unyt.unit_object import InvalidUnitOperation km = unyt_quantity(1, "km", dtype="float64") balmy = unyt_quantity(300, "K", dtype="float64") balmy_F = unyt_quantity(80.33, "degF") balmy_C = unyt_quantity(26.85, "degC") balmy_R = unyt_quantity(540, "R") assert_array_almost_equal(balmy.in_units("degF").d, balmy_F.d) assert balmy.in_units("degF").units, balmy_F.units assert_array_almost_equal(balmy.in_units("degC").d, balmy_C.d) assert balmy.in_units("degC").units, balmy_C.units assert_array_almost_equal(balmy.in_units("R").d, balmy_R.d) assert balmy.in_units("R").units == balmy_R.units balmy_view = balmy.ndarray_view() balmy.convert_to_units("degF") assert balmy_view.base is balmy.base assert_array_almost_equal(np.array(balmy), np.array(balmy_F)) balmy.convert_to_units("degC") assert balmy_view.base is balmy.base assert_array_almost_equal(np.array(balmy), np.array(balmy_C)) balmy.convert_to_units("R") assert balmy_view.base is balmy.base assert_array_almost_equal(np.array(balmy), np.array(balmy_R)) balmy.convert_to_units("degF") assert balmy_view.base is balmy.base assert_array_almost_equal(np.array(balmy), np.array(balmy_F)) with pytest.raises(InvalidUnitOperation): np.multiply(balmy, km) with pytest.raises(InvalidUnitOperation): np.multiply(balmy, balmy) with pytest.raises(InvalidUnitOperation): np.multiply(balmy_F, balmy_F) with pytest.raises(InvalidUnitOperation): np.multiply(balmy_F, balmy_C) with pytest.raises(InvalidUnitOperation): np.divide(balmy, balmy) with pytest.raises(InvalidUnitOperation): np.divide(balmy_F, balmy_F) with pytest.raises(InvalidUnitOperation): np.divide(balmy_F, balmy_C) with pytest.raises(InvalidUnitOperation): balmy * km with pytest.raises(InvalidUnitOperation): balmy * balmy with pytest.raises(InvalidUnitOperation): balmy_F * balmy_F with pytest.raises(InvalidUnitOperation): balmy_F * balmy_C with pytest.raises(InvalidUnitOperation): 2 * balmy_F with pytest.raises(InvalidUnitOperation): balmy / balmy with pytest.raises(InvalidUnitOperation): balmy_F / balmy_F with pytest.raises(InvalidUnitOperation): balmy_F / balmy_C assert np.add(balmy_F, balmy_F) == unyt_quantity(80.33 * 2, "degF") with pytest.raises(InvalidUnitOperation): np.add(balmy_F, balmy_C) with pytest.raises(InvalidUnitOperation): balmy_F + balmy_C assert_equal(np.subtract(balmy_C, balmy_C), unyt_quantity(0, "degC")) with pytest.raises(InvalidUnitOperation): np.subtract(balmy_F, balmy_C) with pytest.raises(InvalidUnitOperation): balmy_F - balmy_C # Does CGS conversion from F to K work? assert_array_almost_equal(balmy.in_cgs(), unyt_quantity(300, "K")) def test_unyt_array_unyt_quantity_ops(): """ Test operations that combine unyt_array and unyt_quantity """ a = unyt_array(range(10, 1), "cm") b = unyt_quantity(5, "g") assert_isinstance(a * b, unyt_array) assert_isinstance(b * a, unyt_array) assert_isinstance(a / b, unyt_array) assert_isinstance(b / a, unyt_array) assert_isinstance(a * a, unyt_array) assert_isinstance(a / a, unyt_array) assert_isinstance(b * b, unyt_quantity) assert_isinstance(b / b, unyt_quantity) def test_selecting(): """ Test slicing of two unyt_arrays """ a = unyt_array(range(10), "cm") a_slice = a[:3] a_fancy_index = a[[1, 1, 3, 5]] a_array_fancy_index = a[array([[1, 1], [3, 5]])] a_boolean_index = a[a > 5] a_selection = a[0] assert_array_equal(a_slice, unyt_array([0, 1, 2], "cm")) assert_equal(a_slice.units, a.units) assert_array_equal(a_fancy_index, unyt_array([1, 1, 3, 5], "cm")) assert_equal(a_fancy_index.units, a.units) assert_array_equal(a_array_fancy_index, unyt_array([[1, 1], [3, 5]], "cm")) assert_equal(a_array_fancy_index.units, a.units) assert_array_equal(a_boolean_index, unyt_array([6, 7, 8, 9], "cm")) assert_equal(a_boolean_index.units, a.units) assert_isinstance(a_selection, unyt_quantity) assert_equal(a_selection.units, a.units) # .base points to the original array for a numpy view. If it is not a # view, .base is None. assert a_slice.base is a def test_iteration(): """ Test that iterating over a unyt_array returns a sequence of unyt_quantity instances """ a = np.arange(3) b = unyt_array(np.arange(3), "cm") for ia, ib in zip(a, b): assert_equal(ia, ib.value) assert_equal(ib.units, b.units) def test_unyt_array_pickle(): test_data = [unyt_quantity(12.0, "cm"), unyt_array([1, 2, 3], "km")] for data in test_data: tempf = tempfile.NamedTemporaryFile(delete=False) pickle.dump(data, tempf) tempf.close() with open(tempf.name, "rb") as fname: loaded_data = pickle.load(fname) os.unlink(tempf.name) assert_array_equal(data, loaded_data) assert_equal(data.units, loaded_data.units) assert_array_equal(array(data.in_cgs()), array(loaded_data.in_cgs())) assert_equal(float(data.units.base_value), float(loaded_data.units.base_value)) SYMPY_VERSION = Version(version("sympy")) def test_copy(): quan = unyt_quantity(1, "g") arr = unyt_array([1, 2, 3], "cm") assert_equal(copy.copy(quan), quan) assert_array_equal(copy.copy(arr), arr) assert_equal(copy.deepcopy(quan), quan) assert_array_equal(copy.deepcopy(arr), arr) memo = {} assert_equal(copy.deepcopy(quan, memo), quan) assert_array_equal(copy.deepcopy(arr), arr) assert_equal(quan.copy(), quan) assert_array_equal(arr.copy(), arr) assert_equal(np.copy(quan), quan) assert_array_equal(np.copy(arr), arr) # needed so the tests function on older numpy versions that have # different sets of ufuncs def yield_np_ufuncs(ufunc_list): for u in ufunc_list: ufunc = getattr(np, u, None) if ufunc is not None: yield ufunc def unary_ufunc_comparison(ufunc, a): out = a.copy() a_array = a.to_ndarray() if ufunc in (np.isreal, np.iscomplex): # According to the numpy docs, these two explicitly do not do # in-place copies. ret = ufunc(a) assert not hasattr(ret, "units") assert_array_equal(ret, ufunc(a)) elif ufunc in yield_np_ufuncs( [ "exp", "exp2", "log", "log2", "log10", "expm1", "log1p", "sin", "cos", "tan", "arcsin", "arccos", "arctan", "sinh", "cosh", "tanh", "arccosh", "arcsinh", "arctanh", "deg2rad", "rad2deg", "isfinite", "isinf", "isnan", "signbit", "sign", "rint", "logical_not", ] ): # These operations should return identical results compared to numpy. with np.errstate(invalid="ignore"): ret = ufunc(a, out=out) assert_array_equal(ret, out) assert_array_equal(ret, ufunc(a_array)) # In-place copies do not drop units. assert hasattr(out, "units") assert not hasattr(ret, "units") elif ufunc in yield_np_ufuncs( [ "absolute", "fabs", "conjugate", "floor", "ceil", "trunc", "negative", "spacing", "positive", ] ): ret = ufunc(a, out=out) assert_array_equal(ret, out) assert_array_equal(ret.to_ndarray(), ufunc(a_array)) assert ret.units == out.units elif ufunc in yield_np_ufuncs( ["ones_like", "square", "sqrt", "cbrt", "reciprocal"] ): if ufunc is np.ones_like: ret = ufunc(a) else: with np.errstate(invalid="ignore"): ret = ufunc(a, out=out) assert_array_equal(ret, out) with np.errstate(invalid="ignore"): assert_array_equal(ret.to_ndarray(), ufunc(a_array)) if ufunc is np.square: assert out.units == a.units**2 assert ret.units == a.units**2 elif ufunc is np.sqrt: assert out.units == a.units**0.5 assert ret.units == a.units**0.5 elif ufunc is np.cbrt: assert out.units == a.units ** (1.0 / 3.0) assert ret.units == a.units ** (1.0 / 3.0) elif ufunc is np.reciprocal: assert out.units == a.units**-1 assert ret.units == a.units**-1 elif ufunc is np.modf: ret1, ret2 = ufunc(a) npret1, npret2 = ufunc(a_array) assert_array_equal(ret1.to_ndarray(), npret1) assert_array_equal(ret2.to_ndarray(), npret2) elif ufunc is np.frexp: ret1, ret2 = ufunc(a) npret1, npret2 = ufunc(a_array) assert_array_equal(ret1, npret1) assert_array_equal(ret2, npret2) elif ufunc is np.invert: with pytest.raises(TypeError): ufunc(a.astype("int64")) elif hasattr(np, "isnat") and ufunc is np.isnat: # numpy 1.13 raises ValueError, numpy 1.14 and newer raise TypeError with pytest.raises((TypeError, ValueError)): ufunc(a) # no untested ufuncs assert ufunc in yield_np_ufuncs( [ "isreal", "iscomplex", "exp", "exp2", "log", "log2", "log10", "expm1", "log1p", "sin", "cos", "tan", "arcsin", "arccos", "arctan", "sinh", "cosh", "tanh", "arccosh", "arcsinh", "arctanh", "deg2rad", "rad2deg", "isfinite", "isinf", "isnan", "signbit", "sign", "rint", "logical_not", "absolute", "fabs", "conjugate", "floor", "ceil", "trunc", "negative", "spacing", "positive", "ones_like", "square", "sqrt", "cbrt", "reciprocal", "invert", "isnat", "modf", "frexp", ] ) def binary_ufunc_comparison(ufunc, a, b): if ufunc in [np.divmod]: out = (b.copy(), b.copy()) else: out = b.copy() if ufunc in yield_np_ufuncs( [ "add", "subtract", "remainder", "fmod", "mod", "arctan2", "hypot", "greater", "greater_equal", "less", "less_equal", "logical_and", "logical_or", "logical_xor", "maximum", "minimum", "fmax", "fmin", "nextafter", "heaviside", ] ): if a.units != b.units and a.units.dimensions != b.units.dimensions: with pytest.raises(UnitOperationError): ufunc(a, b) return if ufunc in yield_np_ufuncs( [ "bitwise_and", "bitwise_or", "bitwise_xor", "left_shift", "right_shift", "ldexp", ] ): with pytest.raises(TypeError): ufunc(a, b) return ret = ufunc(a, b, out=out) ret = ufunc(a, b) if ufunc is np.multiply: assert ret.units == (a.units * b.units).simplify().as_coeff_unit()[1] elif ufunc in (np.divide, np.true_divide, np.arctan2): assert ret.units.dimensions == (a.units / b.units).dimensions elif ufunc in ( np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal, np.equal, np.logical_and, np.logical_or, np.logical_xor, ): assert not isinstance(ret, unyt_array) and isinstance(ret, np.ndarray) if isinstance(ret, tuple): assert isinstance(out, tuple) assert len(out) == len(ret) for o, r in zip(out, ret): assert_array_equal(r, o) else: assert_array_equal(ret, out) if ufunc in (np.divide, np.true_divide, np.arctan2) and ( a.units.dimensions == b.units.dimensions ): assert_array_almost_equal( np.array(ret), ufunc(np.array(a.in_cgs()), np.array(b.in_cgs())) ) def test_ufuncs(): for ufunc in unary_operators: unary_ufunc_comparison( ufunc, unyt_array([0.3, 0.4, 0.5], "cm", dtype="float64") ) unary_ufunc_comparison(ufunc, unyt_array([12, 23, 47], "g", dtype="float64")) unary_ufunc_comparison( ufunc, unyt_array([2, 4, -6], "erg/m**3", dtype="float64") ) for ufunc in binary_operators: # arr**arr is undefined for arrays with units because # each element of the result would have different units. if ufunc is np.power: a = unyt_array([0.3, 0.4, 0.5], "cm") b = unyt_array([0.1, 0.2, 0.3], "dimensionless") c = np.array(b) d = unyt_array([1.0, 2.0, 3.0], "g") with pytest.raises(UnitOperationError): ufunc(a, b) with pytest.raises(UnitOperationError): ufunc(a, c) with pytest.raises(UnitOperationError): ufunc(a, d) binary_ufunc_comparison(ufunc, np.array(2.0), b) continue a = unyt_array([0.3, 0.4, 0.5], "cm") b = unyt_array([0.1, 0.2, 0.3], "cm") c = unyt_array([0.1, 0.2, 0.3], "m") d = unyt_array([0.1, 0.2, 0.3], "g") e = unyt_array([0.1, 0.2, 0.3], "erg/m**3") for pair in itertools.product([a, b, c, d, e], repeat=2): binary_ufunc_comparison(ufunc, pair[0], pair[1]) def test_dot_matmul(): arr = unyt_array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "cm") ev_result = arr.dot(unyt_array([1.0, 2.0, 3.0], "kg")) matmul_result = arr @ unyt_array([1.0, 2.0, 3.0], "kg") res = unyt_array([14.0, 32.0], "cm*kg") assert_equal(ev_result, res) assert_equal(ev_result.units, res.units) assert_isinstance(ev_result, unyt_array) assert_equal(matmul_result, res) assert_equal(matmul_result.units, res.units) assert_isinstance(matmul_result, unyt_array) ev_result = arr.dot(np.array([1.0, 2.0, 3.0])) matmul_result = arr @ np.array([1.0, 2.0, 3.0]) res = unyt_array([14.0, 32.0], "cm") assert_equal(ev_result, res) assert_equal(ev_result.units, res.units) assert_isinstance(ev_result, unyt_array) assert_equal(matmul_result, res) assert_equal(matmul_result.units, res.units) assert_isinstance(matmul_result, unyt_array) ev_result = arr.dot(arr.T) matmul_result = arr @ arr.T res = unyt_array([[14.0, 32.0], [32.0, 77.0]], "cm**2") assert_equal(ev_result, res) assert_equal(ev_result.units, res.units) assert_isinstance(ev_result, unyt_array) assert_equal(matmul_result, res) assert_equal(matmul_result.units, res.units) assert_isinstance(matmul_result, unyt_array) ev_result = arr.v.dot(arr.T) matmul_result = arr.v @ arr.T res = unyt_array([[14.0, 32.0], [32.0, 77.0]], "cm") assert_equal(ev_result, res) assert_equal(ev_result.units, res.units) assert_isinstance(ev_result, unyt_array) assert_equal(matmul_result, res) assert_equal(matmul_result.units, res.units) assert_isinstance(matmul_result, unyt_array) ev_result = arr.dot(arr.T.v) matmul_result = arr @ arr.T.v res = unyt_array([[14.0, 32.0], [32.0, 77.0]], "cm") assert_equal(ev_result, res) assert_equal(ev_result.units, res.units) assert_isinstance(ev_result, unyt_array) assert_equal(matmul_result, res) assert_equal(matmul_result.units, res.units) assert_isinstance(matmul_result, unyt_array) arr = unyt_array([[1.0, 2.0], [3.0, 4.0]], "kg") arr.dot(arr.T, out=arr) res = unyt_array([[5.0, 11.0], [11.0, 25.0]], "kg**2") assert_equal(arr, res) assert_equal(arr.units, res.units) assert_isinstance(arr, unyt_array) qv = unyt_array([1, 2, 3], "cm").dot(unyt_array([1, 2, 3], "cm")) mv = unyt_array([1, 2, 3], "cm") @ unyt_array([1, 2, 3], "cm") qa = unyt_quantity(14, "cm**2") assert qv == qa assert qv.units == qa.units assert_isinstance(qv, unyt_quantity) assert mv == qa assert mv.units == qa.units assert_isinstance(mv, unyt_quantity) qv = unyt_array([1, 2, 3], "cm").dot(np.array([1, 2, 3])) mv = unyt_array([1, 2, 3], "cm") @ np.array([1, 2, 3]) qa = unyt_quantity(14, "cm") assert qv == qa assert qv.units == qa.units assert_isinstance(qv, unyt_quantity) assert mv == qa assert mv.units == qa.units assert_isinstance(mv, unyt_quantity) def test_reductions(): arr = unyt_array([[1, 2, 3], [4, 5, 6]], "cm") answers = { "prod": ( unyt_quantity(720, "cm**6"), unyt_array([4, 10, 18], "cm**2"), unyt_array([6, 120], "cm**3"), ), "sum": ( unyt_quantity(21, "cm"), unyt_array([5.0, 7.0, 9.0], "cm"), unyt_array([6, 15], "cm"), ), "mean": ( unyt_quantity(3.5, "cm"), unyt_array([2.5, 3.5, 4.5], "cm"), unyt_array([2, 5], "cm"), ), "std": ( unyt_quantity(1.707825127659933, "cm"), unyt_array([1.5, 1.5, 1.5], "cm"), unyt_array([0.81649658, 0.81649658], "cm"), ), } for op, (result1, result2, result3) in answers.items(): ev_result = getattr(arr, op)() assert_almost_equal(ev_result, result1) assert_equal(ev_result.units, result1.units) assert_isinstance(ev_result, unyt_quantity) for axis, result in [(0, result2), (1, result3), (-1, result3)]: ev_result = getattr(arr, op)(axis=axis) assert_almost_equal(ev_result, result) assert_equal(ev_result.units, result.units) assert_isinstance(ev_result, unyt_array) def test_convenience(): for orig in [ [1.0, 2.0, 3.0], (1.0, 2.0, 3.0), np.array([1.0, 2.0, 3.0]), [[1.0], [2.0], [3.0]], np.array([[1.0], [2.0], [3.0]]), [[1.0, 2.0, 3.0]], np.array([[1.0, 2.0, 3.0]]), ]: arr = unyt_array(orig, "cm") arrou = unyt_array(orig, "1/cm") uoarr = unyt_array(1.0 / np.array(orig), "cm") assert_equal(arr.unit_quantity, unyt_quantity(1, "cm")) assert_equal(arr.uq, unyt_quantity(1, "cm")) assert_isinstance(arr.unit_quantity, unyt_quantity) assert_isinstance(arr.uq, unyt_quantity) assert_array_equal(arr.unit_array, unyt_array(np.ones_like(arr), "cm")) assert_array_equal(arr.ua, unyt_array(np.ones_like(arr), "cm")) assert_isinstance(arr.unit_array, unyt_array) assert_isinstance(arr.ua, unyt_array) for u in [arr.units, arr.unit_quantity, arr.unit_array, arr.uq, arr.ua]: assert_array_equal(u * orig, arr) assert_array_equal(orig * u, arr) assert_array_equal(orig / u, arrou) assert_array_equal(u / orig, uoarr) assert_array_equal(arr.ndview, arr.view(np.ndarray)) assert_array_equal(arr.d, arr.view(np.ndarray)) assert arr.ndview.base is arr.base assert arr.d.base is arr.base assert_array_equal(arr.value, np.array(arr)) assert_array_equal(arr.v, np.array(arr)) def test_registry_association(): reg = UnitRegistry() a = unyt_quantity(3, "cm", registry=reg) b = unyt_quantity(4, "m") c = unyt_quantity(6, "", registry=reg) d = 5 assert_equal(id(a.units.registry), id(reg)) def binary_op_registry_comparison(op): e = op(a, b) f = op(b, a) g = op(c, d) h = op(d, c) assert_equal(id(e.units.registry), id(reg)) assert_equal(id(f.units.registry), id(b.units.registry)) assert_equal(id(g.units.registry), id(h.units.registry)) assert_equal(id(g.units.registry), id(reg)) def unary_op_registry_comparison(op): c = op(a) d = op(b) assert_equal(id(c.units.registry), id(reg)) assert_equal(id(d.units.registry), id(b.units.registry)) binary_ops = [operator.add, operator.sub, operator.mul, operator.truediv] for op in binary_ops: binary_op_registry_comparison(op) for op in [operator.abs, operator.neg, operator.pos]: unary_op_registry_comparison(op) def test_to_value(): a = unyt_array([1.0, 2.0, 3.0], "kpc") assert_equal(a.to_value(), np.array([1.0, 2.0, 3.0])) assert_equal(a.to_value(), a.value) assert_equal(a.to_value("km"), a.in_units("km").value) b = unyt_quantity(5.5, "Msun") assert_equal(b.to_value(), 5.5) assert_equal(b.to_value("g"), b.in_units("g").value) def test_astropy(): pytest.importorskip("astropy") ap_arr = np.arange(10) * _astropy.units.km / _astropy.units.hr un_arr = unyt_array(np.arange(10), "km/hr") un_arr2 = unyt_array.from_astropy(ap_arr) ap_quan = 10.0 * _astropy.units.Msun**0.5 / (_astropy.units.kpc**3) un_quan = unyt_quantity(10.0, "sqrt(Msun)/kpc**3") un_quan2 = unyt_quantity.from_astropy(ap_quan) assert_array_equal(ap_arr, un_arr.to_astropy()) assert_array_equal(un_arr, unyt_array.from_astropy(ap_arr)) assert_array_equal(un_arr, un_arr2) assert_equal(ap_quan, un_quan.to_astropy()) assert_equal(un_quan, unyt_quantity.from_astropy(ap_quan)) assert_equal(un_quan, un_quan2) assert_array_equal(un_arr, unyt_array.from_astropy(un_arr.to_astropy())) assert_equal(un_quan, unyt_quantity.from_astropy(un_quan.to_astropy())) def test_astropy_dimensionless(): # see https://github.com/yt-project/unyt/issues/436 pytest.importorskip("astropy") arr = unyt_array([1, 2, 3], "") ap_arr = np.array([1, 2, 3]) * _astropy.units.Unit("") assert_array_equal(ap_arr, arr.to_astropy()) assert_array_equal(arr, unyt_array.from_astropy(ap_arr)) def test_pint(): pytest.importorskip("pint") def assert_pint_array_equal(arr1, arr2): assert_array_equal(arr1.magnitude, arr2.magnitude) assert str(arr1.units) == str(arr2.units) ureg = _pint.UnitRegistry() p_arr = np.arange(10) * ureg.km / ureg.year un_arr = unyt_array(np.arange(10), "km/yr") un_arr2 = unyt_array.from_pint(p_arr) p_quan = 10.0 * ureg.g**0.5 / (ureg.mm**3) un_quan = unyt_quantity(10.0, "sqrt(g)/mm**3") un_quan2 = unyt_quantity.from_pint(p_quan) assert_pint_array_equal(p_arr, un_arr.to_pint()) assert_array_equal(un_arr, unyt_array.from_pint(p_arr)) assert_array_equal(un_arr, un_arr2) assert_pint_array_equal(p_quan, un_quan.to_pint()) assert_equal(un_quan, unyt_quantity.from_pint(p_quan)) assert_equal(un_quan, un_quan2) assert_array_equal(un_arr, unyt_array.from_pint(un_arr.to_pint())) assert_equal(un_quan, unyt_quantity.from_pint(un_quan.to_pint())) def test_subclass(): class unyt_a_subclass(unyt_array): def __new__( cls, input_array, units=None, registry=None, bypass_validation=None ): return super().__new__( cls, input_array, units, registry=registry, bypass_validation=bypass_validation, ) a = unyt_a_subclass([4, 5, 6], "g") b = unyt_a_subclass([7, 8, 9], "kg") nu = unyt_a_subclass([10, 11, 12], "") nda = np.array([3, 4, 5]) yta = unyt_array([6, 7, 8], "mg") loq = [unyt_quantity(6, "mg"), unyt_quantity(7, "mg"), unyt_quantity(8, "mg")] ytq = unyt_quantity(4, "cm") ndf = np.float64(3) def op_comparison(op, inst1, inst2, compare_class): assert_isinstance(op(inst1, inst2), compare_class) assert_isinstance(op(inst2, inst1), compare_class) ops = [operator.mul, operator.truediv] for op in ops: for inst in (b, ytq, ndf, yta, nda, loq): op_comparison(op, a, inst, unyt_a_subclass) op_comparison(op, ytq, nda, unyt_array) op_comparison(op, ytq, yta, unyt_array) for op in (operator.add, operator.sub): op_comparison(op, nu, nda, unyt_a_subclass) op_comparison(op, a, b, unyt_a_subclass) op_comparison(op, a, yta, unyt_a_subclass) op_comparison(op, a, loq, unyt_a_subclass) assert_isinstance(a[0], unyt_quantity) assert_isinstance(a[:], unyt_a_subclass) assert_isinstance(a[:2], unyt_a_subclass) assert_isinstance(unyt_a_subclass(yta), unyt_a_subclass) assert_isinstance(a.to("kg"), unyt_a_subclass) assert_isinstance(a.copy(), unyt_a_subclass) assert_isinstance(copy.deepcopy(a), unyt_a_subclass) def test_string_operations_raise_errors(): a = unyt_array([1, 2, 3], "g") with pytest.raises(TypeError): a + "hello" with pytest.raises(TypeError): a * "hello" with pytest.raises(TypeError): a ** "hello" def test_string_ne(): a = unyt_array([1, 2, 3], "g") if NUMPY_VERSION >= Version("1.25.0.dev0"): ctx = pytest.raises(ValueError) else: ctx = pytest.warns(FutureWarning) with ctx: assert a != "hello" def test_string_operations_raise_errors_quantity(): q = 2 * g with pytest.raises(TypeError): q + "hello" with pytest.raises(TypeError): q * "hello" with pytest.raises(TypeError): q ** "hello" assert q != "hello" def test_h5_io(): pytest.importorskip("h5py") tmpdir = tempfile.mkdtemp() curdir = os.getcwd() os.chdir(tmpdir) reg = UnitRegistry() reg.add("code_length", 10.0, dimensions.length) rng = np.random.default_rng() warr = unyt_array(rng.random((256, 256)), "code_length", registry=reg) warr.write_hdf5("test.h5") iarr = unyt_array.from_hdf5("test.h5") assert_equal(warr, iarr) assert_equal(warr.units.registry["code_length"], iarr.units.registry["code_length"]) # test code to overwrite existing dataset warr.write_hdf5("test.h5") giarr = unyt_array.from_hdf5("test.h5") assert_equal(warr, giarr) # test code to overwrite existing dataset with data that has a different # shape rng = np.random.default_rng() warr = unyt_array(rng.random((255, 255)), "code_length", registry=reg) warr.write_hdf5("test.h5") giarr = unyt_array.from_hdf5("test.h5") assert_equal(warr, giarr) os.remove("test.h5") # write to a group that doesn't exist warr.write_hdf5( "test.h5", dataset_name="test_dset", group_name="/arrays/test_group" ) giarr = unyt_array.from_hdf5( "test.h5", dataset_name="test_dset", group_name="/arrays/test_group" ) assert_equal(warr, giarr) os.remove("test.h5") # write to a group that does exist with _h5py.File("test.h5", "a") as f: f.create_group("/arrays/test_group") warr.write_hdf5( "test.h5", dataset_name="test_dset", group_name="/arrays/test_group" ) giarr = unyt_array.from_hdf5( "test.h5", dataset_name="test_dset", group_name="/arrays/test_group" ) assert_equal(warr, giarr) os.remove("test.h5") os.chdir(curdir) shutil.rmtree(tmpdir) def test_h5_quantity_roundtrip(tmp_path): # regression test for https://github.com/yt-project/unyt/issues/559 pytest.importorskip("h5py") q1 = 5 * cm savefile = tmp_path / "savefile.hdf5" q1.write_hdf5( savefile, dataset_name="test_ds", group_name="test_group", ) q2 = unyt_quantity.from_hdf5( savefile, dataset_name="test_ds", group_name="test_group", ) assert_array_equal_units(q2, q1) def test_equivalencies(): import unyt as u # equivalence is ignored if the conversion doesn't need one data = 12.0 * u.g data.convert_to_equivalent("kg", None) assert data.value == 0.012 assert data.units == u.kg data = 12.0 * u.g data = data.to_equivalent("kg", None) assert data.value == 0.012 assert data.units == u.kg # incorrect usage of an equivalence raises errors with pytest.raises(InvalidUnitEquivalence): data.convert_to_equivalent("erg", "thermal") with pytest.raises(InvalidUnitEquivalence) as excinfo: data.convert_to_equivalent("m", "mass_energy") assert ( str(excinfo.value) == "The unit equivalence 'mass_energy: mass <-> energy' does not " "exist for units 'kg' to convert to a new unit with dimensions " "'(length)'." ) with pytest.raises(InvalidUnitEquivalence): data.to_equivalent("erg", "thermal") with pytest.raises(InvalidUnitEquivalence): data.to_equivalent("m", "mass_energy") # Mass-energy mp = u.mp.copy() mp.convert_to_units("keV", "mass_energy") assert_allclose_units(u.mp.in_units("keV", "mass_energy"), mp) assert_allclose_units(mp, u.mp * u.clight * u.clight) assert_allclose_units(u.mp, mp.in_units("g", "mass_energy")) mp.convert_to_units("g", "mass_energy") assert_allclose_units(u.mp, mp) # Thermal T = 1e8 * u.K E = T.in_units("W*hr", "thermal") assert_allclose_units(E, (u.kboltz * T).in_units("W*hr")) assert_allclose_units(T, E.in_units("K", "thermal")) T.convert_to_units("W*hr", "thermal") assert_allclose_units(E, T) T.convert_to_units("K", "thermal") assert_allclose_units(T, 1e8 * u.K) # Spectral # wavelength to frequency lam = 4000 * u.angstrom nu = lam.in_units("Hz", "spectral") assert_allclose_units(nu, u.clight / lam) lam.convert_to_units("MHz", "spectral") assert_allclose_units(lam, nu) assert lam.units == u.MHz.units assert nu.units == u.Hz.units # wavelength to photon energy lam = 4000 * u.angstrom hnu = lam.in_units("erg", "spectral") assert_allclose_units(hnu, u.h_mks * u.clight / lam) lam.convert_to_units("eV", "spectral") assert_allclose_units(lam, hnu) assert lam.units == u.eV.units assert hnu.units == u.erg.units # wavelength to spatial frequency lam = 4000 * u.angstrom nubar = lam.in_units("1/angstrom", "spectral") assert_allclose_units(nubar, 1 / lam) lam.convert_to_units("1/cm", "spectral") assert_allclose_units(lam, nubar) assert lam.units == (1 / u.cm).units assert nubar.units == (1 / u.angstrom).units # frequency to wavelength nu = 1.0 * u.MHz lam = nu.to("km", "spectral") assert_allclose_units(lam, u.clight / nu) nu.convert_to_units("m", "spectral") assert_allclose_units(lam, nu) assert lam.units == u.km.units assert nu.units == u.m.units # frequency to spatial frequency nu = 1.0 * u.MHz nubar = nu.to("1/km", "spectral") assert_allclose_units(nubar, nu / u.clight) nu.convert_to_units("1/m", "spectral") assert_allclose_units(nubar, nu) assert nubar.units == (1 / u.km).units assert nu.units == (1 / u.m).units # frequency to photon energy nu = 1.0 * u.MHz E = nu.to("erg", "spectral") assert_allclose_units(E, u.h_mks * nu) nu.convert_to_units("J", "spectral") assert_allclose_units(nu, E) assert nu.units == u.J.units assert E.units == u.erg.units # photon energy to frequency E = 13.6 * u.eV nu = E.to("Hz", "spectral") assert_allclose_units(nu, E / u.h_mks) E.convert_to_units("MHz", "spectral") assert_allclose_units(nu, E) assert E.units == u.MHz.units assert nu.units == u.Hz.units # photon energy to wavelength E = 13.6 * u.eV lam = E.to("nm", "spectral") assert_allclose_units(lam, u.h_mks * u.clight / E) E.convert_to_units("angstrom", "spectral") assert_allclose_units(E, lam) assert E.units == u.angstrom.units assert lam.units == u.nm.units # photon energy to spatial frequency E = 13.6 * u.eV nubar = E.to("1/nm", "spectral") assert_allclose_units(nubar, E / (u.h_mks * u.clight)) E.convert_to_units("1/angstrom", "spectral") assert_allclose_units(E, nubar) assert E.units == (1 / u.angstrom).units assert nubar.units == (1 / u.nm).units # spatial frequency to frequency nubar = 1500.0 / u.cm nu = nubar.to("Hz", "spectral") assert_allclose_units(nu, nubar * u.clight) nubar.convert_to_units("MHz", "spectral") assert_allclose_units(nu, nubar) assert nubar.units == u.MHz.units assert nu.units == u.Hz.units # spatial frequency to wavelength nubar = 1500.0 / u.cm lam = nubar.to("nm", "spectral") assert_allclose_units(lam, 1 / nubar) nubar.convert_to_units("angstrom", "spectral") assert_allclose_units(nubar, lam) assert nubar.units == u.angstrom.units assert lam.units == u.nm.units # spatial frequency to photon energy nubar = 1500.0 / u.cm E = nubar.to("erg", "spectral") assert_allclose_units(E, u.h_mks * u.clight * nubar) nubar.convert_to_units("J", "spectral") assert_allclose_units(nubar, E) assert nubar.units == u.J.units assert E.units == u.erg.units # Sound-speed # tempearature <-> velocity mu = 0.6 gg = 5.0 / 3.0 T = 1e8 * u.K c_s = T.in_units("km/s", equivalence="sound_speed") assert_allclose_units(c_s, np.sqrt(gg * u.kboltz * T / (mu * u.mh))) assert_allclose_units(T, c_s.in_units("K", "sound_speed")) T.convert_to_units("m/s", "sound_speed") assert_allclose_units(c_s, T) assert T.units == u.m.units / u.s.units assert c_s.units == u.km.units / u.s.units mu = 0.5 gg = 4.0 / 3.0 T = 1e8 * u.K c_s = T.in_units("km/s", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(c_s, np.sqrt(gg * u.kboltz * T / (mu * u.mh))) assert_allclose_units(T, c_s.in_units("K", "sound_speed", mu=mu, gamma=gg)) T.convert_to_units("m/s", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(c_s, T) assert T.units == u.m.units / u.s.units assert c_s.units == u.km.units / u.s.units # temperature <-> energy mu = 0.5 gg = 4.0 / 3.0 T = 1e8 * u.K kT = T.in_units("eV", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(kT, u.kboltz * T) T.convert_to_units("erg", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(T, kT) assert T.units == u.erg.units assert kT.units == u.eV.units assert_allclose_units(T.in_units("K", "sound_speed", mu=mu, gamma=gg), 1e8 * u.K) kT.convert_to_units("K", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(kT, 1e8 * u.K) # velocity <-> energy c_s = 300 * u.m / u.s kT = c_s.in_units("erg", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(kT, c_s**2 * mu * u.mh / gg) c_s.convert_to_units("J", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(c_s, kT) assert c_s.units == u.J.units assert kT.units == u.erg.units assert_allclose_units( kT.in_units("m/s", "sound_speed", mu=mu, gamma=gg), 300 * u.m / u.s ) c_s.convert_to_units("m/s", "sound_speed", mu=mu, gamma=gg) assert_allclose_units(c_s, 300 * u.m / u.s) # Lorentz v = 0.8 * u.clight g = v.in_units("dimensionless", "lorentz") g2 = unyt_quantity(1.0 / np.sqrt(1.0 - 0.8 * 0.8), "dimensionless") assert_allclose_units(g, g2) v.convert_to_units("", "lorentz") assert_allclose_units(v, g2) v.convert_to_units("c", "lorentz") v2 = g2.in_units("mile/hr", "lorentz") assert_allclose_units(v2, v.in_units("mile/hr")) # Schwarzschild msun = 1.0 * u.unit_symbols.Msun msun.convert_to_equivalent("km", "schwarzschild") R = u.mass_sun_mks.in_units("kpc", "schwarzschild") assert_allclose_units(msun, R) assert_allclose_units(R.in_mks(), 2 * u.G * u.mass_sun_mks / (u.clight**2)) assert_allclose_units(u.mass_sun_mks, R.in_units("kg", "schwarzschild")) R.convert_to_units("Msun", "schwarzschild") assert_allclose_units(u.mass_sun_mks, R) assert R.units == u.unit_symbols.Msun.units assert msun.units == u.km.units # Compton me = 1.0 * u.me me.convert_to_units("nm", "compton") length = u.me.in_units("angstrom", "compton") assert_allclose_units(length, me) assert_allclose_units(length, u.h_mks / (u.me * u.clight)) assert_allclose_units(u.me, length.in_units("g", "compton")) assert me.units == u.nm.units assert length.units == u.angstrom.units me.convert_to_units("me", "compton") assert_almost_equal(me.value, 1.0) # Number density rho = u.mp / u.m**3 n = rho.in_units("m**-3", "number_density") assert_allclose_units(n, rho / (u.mh * 0.6)) assert_allclose_units(rho, n.in_units("kg/m**3", "number_density")) rho.convert_to_units("cm**-3", "number_density") assert rho.units == (1 / u.cm**3).units assert n.units == (1 / u.m**3).units assert_allclose_units(n, rho) rho.convert_to_units("kg/m**3", "number_density") assert_allclose_units(u.mp / u.m**3, rho) assert rho.units == (u.kg / u.m**3).units rho = u.mp / u.m**3 n = rho.in_units("m**-3", equivalence="number_density", mu=0.75) assert_allclose_units(n, rho / (u.mh * 0.75)) assert_allclose_units( rho, n.in_units("kg/m**3", equivalence="number_density", mu=0.75) ) rho.convert_to_units("cm**-3", "number_density", mu=0.75) assert rho.units == (1 / u.cm**3).units assert n.units == (1 / u.m**3).units assert_allclose_units(n, rho) rho.convert_to_units("kg/m**3", "number_density", mu=0.75) assert_allclose_units(u.mp / u.m**3, rho) assert rho.units == (u.kg / u.m**3).units # Effective temperature T = 1e4 * u.K F = T.in_units("W/m**2", equivalence="effective_temperature") assert_allclose_units(F, u.stefan_boltzmann_constant * T**4) assert_allclose_units(T, F.in_units("K", equivalence="effective_temperature")) T.convert_to_units("erg/s/cm**2", "effective_temperature") assert_allclose_units(T, F) assert T.units == u.Unit("erg/cm**2/s") assert F.units == u.W / u.m**2 assert_almost_equal(T.in_units("K", "effective_temperature").value, 1e4) T.convert_to_units("K", "effective_temperature") assert_almost_equal(T.value, 1e4) assert T.units == u.K # to_value test assert_allclose_units( F.value, T.to_value("W/m**2", equivalence="effective_temperature") ) assert_allclose_units( n.value, rho.to_value("m**-3", equivalence="number_density", mu=0.75) ) def test_electromagnetic(): import unyt as u # Various tests of SI and CGS electromagnetic units t = 1.0 * u.Tesla g = 1.0 * u.gauss assert t.to("gauss") == 1e4 * u.gauss assert g.to("T") == 1e-4 * u.Tesla assert t.in_mks() == t assert g.in_cgs() == g t.convert_to_mks() assert t == 1.0 * u.Tesla g.convert_to_cgs() assert g == 1.0 * u.gauss qp_mks = u.qp_cgs.in_units("C") assert_equal(qp_mks.units.dimensions, dimensions.charge_mks) assert_almost_equal(qp_mks.v, 10.0 * u.qp.v / speed_of_light_cm_per_s) qp = 1.0 * u.qp_cgs assert_equal(qp, u.qp_cgs.in_units("esu")) qp.convert_to_units("C") assert_equal(qp.units.dimensions, dimensions.charge_mks) assert_almost_equal(qp.v, 10 * u.qp.v / u.clight.v) qp_cgs = u.qp.in_units("esu") assert_array_almost_equal(qp_cgs, u.qp_cgs) assert_equal(qp_cgs.units.dimensions, u.qp_cgs.units.dimensions) qp = u.qp.copy() qp.convert_to_units("esu") assert_almost_equal(qp_cgs, qp_cgs) assert qp.units == u.esu.units qp.convert_to_units("C") assert_almost_equal(u.qp, qp) assert qp.units == u.C.units qp_mks_k = u.qp_cgs.in_units("kC") assert_array_almost_equal(qp_mks_k.v, 1.0e-2 * u.qp_cgs.v / speed_of_light_cm_per_s) qp = 1.0 * u.qp_cgs qp.convert_to_units("kC") assert_almost_equal(qp, qp_mks_k) B = 1.0 * u.T B_cgs = B.in_units("gauss") assert_equal(B.units.dimensions, dimensions.magnetic_field_mks) assert_equal(B_cgs.units.dimensions, dimensions.magnetic_field_cgs) assert_array_almost_equal(B_cgs, unyt_quantity(1.0e4, "gauss")) B_cgs = B.in_cgs() assert_equal(B.units.dimensions, dimensions.magnetic_field_mks) assert_equal(B_cgs.units.dimensions, dimensions.magnetic_field_cgs) assert_array_almost_equal(B_cgs, unyt_quantity(1.0e4, "gauss")) B_cgs = B.in_base("cgs") assert_equal(B.units.dimensions, dimensions.magnetic_field_mks) assert_equal(B_cgs.units.dimensions, dimensions.magnetic_field_cgs) assert_array_almost_equal(B_cgs, unyt_quantity(1.0e4, "gauss")) B.convert_to_cgs() assert_almost_equal(B, B_cgs) B.convert_to_mks() B_cgs2 = B.to("gauss") assert_almost_equal(B_cgs, B_cgs2) B_mks2 = B_cgs2.to("T") assert_almost_equal(B, B_mks2) B = 1.0 * u.T u_mks = B * B / (2 * u.mu_0) assert_equal(u_mks.units.dimensions, dimensions.pressure) u_cgs = B_cgs * B_cgs / (8 * np.pi) assert_equal(u_mks, u_cgs.to(u_mks.units)) assert_equal(u_mks.to(u_cgs.units), u_cgs) assert_equal(u_mks.in_cgs(), u_cgs) assert_equal(u_cgs.in_mks(), u_mks) current = 1.0 * u.A I_cgs = current.in_units("statA") assert_array_almost_equal( I_cgs, unyt_quantity(0.1 * speed_of_light_cm_per_s, "statA") ) assert_array_almost_equal(I_cgs.in_units("mA"), current.in_units("mA")) assert_equal(I_cgs.units.dimensions, dimensions.current_cgs) current.convert_to_units("statA") assert current.units == u.statA.units current.convert_to_units("A") assert current.units == u.A.units I_cgs2 = current.to("statA") assert I_cgs2.units == u.statA.units assert_array_almost_equal( I_cgs2, unyt_quantity(0.1 * speed_of_light_cm_per_s, "statA") ) current = 1.0 * u.A R = unyt_quantity(1.0, "ohm") R_cgs = R.in_units("statohm") P_mks = current * current * R P_cgs = I_cgs * I_cgs * R_cgs assert_equal(P_mks.units.dimensions, dimensions.power) assert_equal(P_cgs.units.dimensions, dimensions.power) assert_almost_equal(P_cgs.in_cgs(), P_cgs) assert_almost_equal(P_mks.in_cgs(), P_cgs) assert_almost_equal(P_cgs.in_mks(), P_mks) assert_almost_equal(P_mks.in_mks(), P_mks) V = unyt_quantity(1.0, "statV") V_mks = V.in_units("V") assert_array_almost_equal(V_mks.v, 1.0e8 * V.v / speed_of_light_cm_per_s) data = 1.0 * u.C * u.T * u.V with pytest.raises(UnitConversionError): data.to("statC*G*statV") with pytest.raises(UnitConversionError): data.convert_to_units("statC*G*statV") with pytest.raises(UnitsNotReducible): data.in_cgs() data = 1.0 * u.statC * u.G * u.statV with pytest.raises(UnitConversionError): data.to("C*T*V") with pytest.raises(UnitConversionError): data.convert_to_units("C*T*V") assert_almost_equal(data.in_mks(), 6.67408e-18 * u.m**5 / u.s**4) mu_0 = 4.0e-7 * math.pi * u.N / u.A**2 eps_0 = 8.85418781782e-12 * u.m**-3 / u.kg * u.s**4 * u.A**2 assert_almost_equal((1.0 / (u.clight**2 * mu_0)).in_units(eps_0.units), eps_0) def test_unyt_array_coercion(): a = unyt_array([1, 2, 3], "cm") q = unyt_quantity(3, "cm") na = np.array([1, 2, 3]) assert_isinstance(a * q, unyt_array) assert_isinstance(q * na, unyt_array) assert_isinstance(q * 3, unyt_quantity) assert_isinstance(q * np.float64(3), unyt_quantity) assert_isinstance(q * np.array(3), unyt_quantity) def test_unyt_array_dtype_coercion(): a = unyt_array([1, 2, 3], "cm", dtype=np.float32) b = unyt_array(a, dtype=np.float64) assert b.dtype == np.float64 assert_array_equal(b, a) assert b.units == a.units def test_numpy_wrappers(): a1 = unyt_array([1, 2, 3], "cm") a2 = unyt_array([2, 3, 4, 5, 6], "cm") a3 = unyt_array([[1, 2, 3], [4, 5, 6]], "cm") a4 = unyt_array([7, 8, 9, 10, 11], "cm") catenate_answer = [1, 2, 3, 2, 3, 4, 5, 6] intersect_answer = [2, 3] union_answer = [1, 2, 3, 4, 5, 6] vstack_answer = [[2, 3, 4, 5, 6], [7, 8, 9, 10, 11]] vstack_answer_last_axis = [[2, 7], [3, 8], [4, 9], [5, 10], [6, 11]] cross_answer = [-2, 4, -2] norm_answer = np.sqrt(1**2 + 2**2 + 3**2) arr_norm_answer = [norm_answer, np.sqrt(4**2 + 5**2 + 6**2)] dot_answer = 14 with pytest.warns(DeprecationWarning): assert_array_equal(unyt_array(catenate_answer, "cm"), uconcatenate((a1, a2))) assert_array_equal(catenate_answer, np.concatenate((a1, a2))) with pytest.warns(DeprecationWarning): assert_array_equal(unyt_array(intersect_answer, "cm"), uintersect1d(a1, a2)) assert_array_equal(intersect_answer, np.intersect1d(a1, a2)) with pytest.warns(DeprecationWarning): assert_array_equal(unyt_array(union_answer, "cm"), uunion1d(a1, a2)) assert_array_equal(union_answer, np.union1d(a1, a2)) with pytest.warns(DeprecationWarning): assert_array_equal( unyt_array(cross_answer, "cm**2"), ucross(a1, a1 + (2 * a1.units)) ) assert_array_equal(cross_answer, np.cross(a1.v, a1.v + 2)) with pytest.warns(DeprecationWarning): assert_array_equal(unorm(a1), unyt_quantity(norm_answer, "cm")) assert_array_equal(np.linalg.norm(a1), norm_answer) with pytest.warns(DeprecationWarning): assert_array_equal(unorm(a3, axis=1), unyt_array(arr_norm_answer, "cm")) assert_array_equal(np.linalg.norm(a3, axis=1), arr_norm_answer) with pytest.warns(DeprecationWarning): assert_array_equal(udot(a1, a1), unyt_quantity(dot_answer, "cm**2")) with pytest.warns(DeprecationWarning): assert_array_equal(np.array(catenate_answer), uconcatenate((a1.v, a2.v))) with pytest.raises(RuntimeError): with pytest.warns(DeprecationWarning): uconcatenate((a1, a2.v)) with pytest.raises(RuntimeError): with pytest.warns(DeprecationWarning): uconcatenate((a1.to("m"), a2)) with pytest.warns(DeprecationWarning): assert_array_equal(unyt_array(vstack_answer, "cm"), uvstack([a2, a4])) assert_array_equal(vstack_answer, np.vstack([a2, a4])) with pytest.warns(DeprecationWarning): assert_array_equal(unyt_array(vstack_answer, "cm"), ustack([a2, a4])) assert_array_equal(vstack_answer, np.stack([a2, a4])) with pytest.warns(DeprecationWarning): assert_array_equal( unyt_array(vstack_answer_last_axis, "cm"), ustack([a2, a4], axis=-1) ) assert_array_equal(vstack_answer_last_axis, np.stack([a2, a4], axis=-1)) def test_dimensionless_conversion(): a = unyt_quantity(1, "Zsun") b = a.in_units("Zsun") a.convert_to_units("Zsun") assert a.units.base_value == metallicity_sun assert b.units.base_value == metallicity_sun def test_modified_unit_division(): reg1 = UnitRegistry() reg2 = UnitRegistry() reg1.modify("g", 50) a = unyt_quantity(3, "g", registry=reg1) b = unyt_quantity(3, "g", registry=reg2) ret = a / b assert ret == 50000.0 assert ret.units.is_dimensionless assert ret.units.base_value == 1.0 def test_loadtxt_and_savetxt(): tmpdir = tempfile.mkdtemp() curdir = os.getcwd() os.chdir(tmpdir) rng = np.random.default_rng() a = unyt_array(rng.random(10), "kpc") b = unyt_array(rng.random(10), "Msun") c = unyt_array(rng.random(10), "km/s") savetxt("arrays.dat", [a, b, c], delimiter=",") d, e = loadtxt("arrays.dat", usecols=(1, 2), delimiter=",") assert_array_equal(b, d) assert_array_equal(c, e) # adding newlines to the file doesn't matter savetxt("arrays.dat", [a, b, c], delimiter=",") with open("arrays.dat", "r+") as f: content = f.read() f.seek(0, 0) f.write("\n" + content) d, e = loadtxt("arrays.dat", usecols=(1, 2), delimiter=",") assert_array_equal(b, d) assert_array_equal(c, e) # data saved by numpy savetxt are loaded without units np.savetxt("arrays.dat", np.squeeze(np.transpose([a.v, b.v, c.v])), delimiter=",") d, e = loadtxt("arrays.dat", usecols=(1, 2), delimiter=",") assert_array_equal(b.v, d) assert_array_equal(c.v, e) # save a single array savetxt("arrays.dat", a) d = loadtxt("arrays.dat") assert_array_equal(a, d) # save an array with no units and an array with units with a header savetxt("arrays.dat", [a.v, b], header="this is a header!") d, e = loadtxt("arrays.dat") assert_array_equal(a.v, d) assert_array_equal(b, e) os.chdir(curdir) shutil.rmtree(tmpdir) def test_trig_ufunc_degrees(): rng = np.random.default_rng() for ufunc in (np.sin, np.cos, np.tan): degree_values = rng.random(10) * degree radian_values = degree_values.in_units("radian") assert_array_equal(ufunc(degree_values), ufunc(radian_values)) def test_builtin_sum(): from unyt import km arr = [1, 2, 3] * km assert_equal(sum(arr), 6 * km) def test_initialization_different_registries(): reg1 = UnitRegistry() reg2 = UnitRegistry() reg1.add("code_length", 1.0, dimensions.length) reg2.add("code_length", 3.0, dimensions.length) l1 = unyt_quantity(1.0, "code_length", registry=reg1) l2 = unyt_quantity(1.0, "code_length", registry=reg2) assert_almost_equal(float(l1.in_mks()), 1.0) assert_almost_equal(float(l2.in_mks()), 3.0) def test_ones_and_zeros_like(): data = unyt_array([1, 2, 3], "cm") zd = np.zeros_like(data) od = np.ones_like(data) assert_equal(zd, unyt_array([0, 0, 0], "cm")) assert_equal(zd.units, data.units) assert_equal(od, unyt_array([1, 1, 1], "cm")) assert_equal(od.units, data.units) def test_coerce_iterable(): from unyt import cm, g, m a = unyt_array([1, 2, 3], "cm") b = [1 * cm, 2 * m, 3 * cm] c = [1 * g, 2 * m, 3 * cm] assert_equal(a + b, unyt_array([2, 202, 6], "cm")) assert_equal(b + a, unyt_array([2, 202, 6], "cm")) with pytest.raises(TypeError): a + c with pytest.raises(TypeError): c + a assert_equal(unyt_array(b), unyt_array([1, 200, 3], "cm")) with pytest.raises(IterableUnitCoercionError): unyt_array(c) def test_bypass_validation(): from unyt import UnitRegistry, cm, unyt_array obj = unyt_array(np.array([1.0, 2.0, 3.0]), cm, bypass_validation=True) assert obj.units is cm reg = UnitRegistry() obj = unyt_array( np.array([1.0, 2.0, 3.0]), cm, registry=reg, bypass_validation=True ) assert obj.units == cm assert obj.units.registry is reg def test_creation(): from unyt import UnitRegistry, cm data = [1, 2, 3] * cm new_data = unyt_array(data) assert new_data.units is cm assert_array_equal(new_data.v, np.array([1, 2, 3], dtype="float64")) reg = UnitRegistry() new_data = unyt_array(data, registry=reg) assert_array_equal(new_data.v, np.array([1, 2, 3], dtype="float64")) assert new_data.units is not cm assert new_data.units == cm assert new_data.units.registry is reg new_data = unyt_array([1, 2, 3], cm) assert_array_equal(new_data.v, np.array([1, 2, 3], dtype="float64")) assert new_data.units is cm new_data = unyt_array([1, 2, 3], cm, registry=reg) assert_array_equal(new_data.v, np.array([1, 2, 3], dtype="float64")) assert new_data.units is not cm assert new_data.units == cm assert new_data.units.registry is reg with pytest.raises(RuntimeError): unyt_quantity("hello", "cm") with pytest.raises(RuntimeError): unyt_quantity(np.array([1, 2, 3]), "cm") def test_round(): from unyt import km assert_equal(round(3.3 * km), 3.0) assert_equal(round(3.5 * km), 4.0) assert_equal(round(3 * km), 3) assert_equal(round(3.7 * km), 4) with pytest.raises(TypeError): round([1, 2, 3] * km) @pytest.mark.parametrize("itemsize", (8, 16, 32, 64)) def test_conversion_from_int_types(itemsize): a = unyt_array([1], "cm", dtype=f"int{itemsize}") # check copy conversion a.in_units("m") # check in place conversion if itemsize == 8: with pytest.raises( ValueError, match=re.escape( "Can't convert memory buffer in place. " "Input dtype (int8) has a smaller itemsize than the " "smallest floating point representation possible." ), ): a.convert_to_units("m") else: a.convert_to_units("m") assert a.dtype == f"float{itemsize}" def test_integer_arrays(): from unyt import km, m, mile, ms, s def integer_semantics(inp): arr = inp * km assert arr.dtype == np.int_ arr = np.array(inp, dtype="int32") * km assert arr.dtype.name == "int32" ret = arr.in_units("mile") assert arr.dtype.name == "int32" answer = (inp * km).astype("int32").to("mile") assert_array_equal(ret, answer) assert ret.dtype.name == "float32" ret = arr.in_units("m") assert arr.dtype != ret.dtype assert ret.dtype.name == "float32" arr.convert_to_units("m") assert arr.dtype.name == "float32" arr = inp * km arr.convert_to_units("mile") assert arr.dtype.name == "float" + str(np.int_().dtype.itemsize * 8) for foo in [[1, 2, 3], 12, -8, 0, [1, -2, 3]]: integer_semantics(foo) arr1 = [1, 2, 3] * km arr2 = [4, 5, 6] * mile assert (arr1 + arr2).dtype.name == "float64" assert (arr1 * arr2).dtype == np.int_ assert (arr1 / arr2).dtype.name == "float64" arr1 = [1, 2, 3] * km arr2 = [4, 5, 6] * m assert (arr1 + arr2).dtype.name == "float64" assert (arr1 * arr2).dtype == np.int_ assert (arr1 / arr2).dtype.name == "float64" arr1 = [1, 2, 3] * km arr2 = [4, 5, 6] * km assert (arr1 + arr2).dtype == np.int_ assert (arr1 * arr2).dtype == np.int_ assert (arr1 / arr2).dtype.name == "float64" # see issue #118 for details assert 1000 * ms == 1 * s assert 1 * s == 1000 * ms def test_overflow_warnings(): from unyt import km data = [2**53, 2**54] * km message = "Overflow encountered while converting to units 'mile'" _process_warning(data.to, message, RuntimeWarning, ("mile",)) _process_warning(data.in_units, message, RuntimeWarning, ("mile",)) _process_warning(data.convert_to_units, message, RuntimeWarning, ("mile",)) def test_clip(): from unyt import km data = [1, 2, 3, 4, 5, 6] * km answer = [2, 2, 3, 4, 4, 4] * km ret = np.clip(data, 2, 4) assert_array_equal(ret, answer) assert ret.units == answer.units np.clip(data, 2, 4, out=data) assert_array_equal(data, answer) assert data.units == answer.units left_edge = [0.0, 0.0, 0.0] * km right_edge = [1.0, 1.0, 1.0] * km positions = [[0.0, 0.0, 0.0], [1.0, 1.0, -0.1], [1.5, 1.0, 0.9]] * km np.clip(positions, left_edge, right_edge, positions) assert positions.units == left_edge.units assert positions.max() == 1.0 * km assert positions.min() == 0.0 * km def test_name_attribute(): a = unyt_array([0, 1, 2], "s") assert a.name is None a.name = "time" assert a.name == "time" assert a[0].name == "time" a.convert_to_units("ms") assert a.name == "time" b = unyt_quantity(1, "m", name="distance") assert b.name == "distance" c = b.copy() assert c.name == "distance" c_1 = copy.deepcopy(b) assert c_1.name == "distance" d = b.in_units("mm") assert d.name == "distance" e = b.to("mm") assert e.name == "distance" f = unyt_array([3, 4, 5], "K", name="temperature") g = f.in_units("J", equivalence="thermal") assert g.name is None g_1 = f.to_equivalent("J", equivalence="thermal") assert g_1.name is None f.convert_to_equivalent("J", equivalence="thermal") assert f.name is None h = f.to("J", equivalence="thermal") assert h.name is None def test_neper_bel(): assert 0 * Unit("dB") + 20 * Unit("dB") == unyt_quantity(20, "dB") with pytest.raises(InvalidUnitOperation): unyt_array([1, 10], "V") * Unit("dB") with pytest.raises(InvalidUnitOperation): Unit("Np") * unyt_array([1, 10], "s") with pytest.raises(InvalidUnitOperation): unyt_array([0, 20], "dB") ** 2 with pytest.raises(InvalidUnitOperation): np.power(unyt_array([0, 20], "dB"), -2) def test_delta_degC(): t1 = 10 * degC t2 = 1 * K assert t1 + t2 == 11 * degC with pytest.raises(UnitOperationError): t2 + t1 t3 = 1 * delta_degC assert t1 + t3 == 11 * degC assert t3 + t1 == 11 * degC assert 1 * delta_degC + 2 * delta_degC == 3 * delta_degC assert 2 * delta_degC == unyt_quantity(2, "delta_degC") def test_delta_degF(): t1 = 10 * degF t2 = 1 * R assert t1 + t2 == 11 * degF with pytest.raises(UnitOperationError): t2 + t1 t3 = 1 * delta_degF assert t1 + t3 == 11 * degF assert t3 + t1 == 11 * degF assert 1 * delta_degF + 2 * delta_degF == 3 * delta_degF assert 2 * delta_degF == unyt_quantity(2, "delta_degF") @pytest.mark.parametrize( ("u0", "u1", "uout"), [ (K, K, K), (R, R, R), (degC, degC, delta_degC), (degF, degF, delta_degF), (degC, delta_degC, degC), (delta_degC, degC, degC), (degF, delta_degF, degF), (delta_degF, degF, degF), ], ) def test_delta_temperature_diff(u0, u1, uout): # using repr comparison because # 1) we don't care that Unit instances might not be the same # 2) some temperature units will compare as equal even when they are not identical (e.g. K and delta_degC) assert repr((2 * u0 - 1 * u1).units) == repr(uout) def test_mil(): assert_allclose_units(unyt_quantity(1, "mil"), unyt_quantity(0.001, "inch")) def test_kip(): assert_allclose_units(unyt_quantity(1, "lbf"), unyt_quantity(0.001, "kip")) def test_ksi(): assert_allclose_units(unyt_quantity(1, "lbf/inch**2"), unyt_quantity(0.001, "ksi")) def test_masked_array(): data = unyt_array([1, 2, 3], "s") mask = [False, False, True] marr = np.ma.MaskedArray(data, mask) assert_array_equal(marr.data, data) assert all(marr.mask == mask) assert marr.sum() == unyt_quantity(3, "s") assert np.ma.notmasked_contiguous(marr) == [slice(0, 2, None)] assert marr.argmax() == 1 assert marr.max() == unyt_quantity(2, "s") data = unyt_array([1, 2, np.inf], "s") marr = np.ma.MaskedArray(data) marr_masked = np.ma.masked_invalid(marr) assert all(marr_masked.mask == [False, False, True]) marr_masked.set_fill_value(unyt_quantity(3, "s")) assert_array_equal(marr_masked.filled(), unyt_array([1, 2, 3], "s")) marr_fixed = np.ma.fix_invalid(marr) assert_array_equal(marr_fixed.data, unyt_array([1, 2, 1e20], "s")) assert_array_equal(np.ma.filled(marr, unyt_quantity(3, "s")), data) assert_array_equal(np.ma.compressed(marr_masked), unyt_array([1, 2], "s")) # executing the repr should not raise an exception marr.__repr__() def test_complexvalued(tmp_path): freq = unyt_array([1j, 1j * 10], "Hz") arr = 1 / (Unit("F") * Unit("Ω") * freq) arr = arr.to("dimensionless") assert arr.units.is_dimensionless assert np.all(arr.v == np.asarray([-1j, -1j * 0.1])) arr = unyt_array([1j, 1j * 10], "mJ") arr.convert_to_base() assert_allclose_units(arr, unyt_array([1j * 0.001, 1j * 0.01], "J")) arr.convert_to_units("mJ") assert_allclose_units(arr, unyt_array([1j, 1j * 10], "mJ")) arr.convert_to_mks() assert_allclose_units(arr, unyt_array([1j * 0.001, 1j * 0.01], "J")) arr.convert_to_cgs() assert_allclose_units(arr, unyt_array([1j * 10000, 1j * 100000], "erg")) arr.convert_to_equivalent("K", "thermal") assert_allclose_units( arr, unyt_array([1j * 7.24297157e19, 1j * 7.24297157e20], "K") ) arr = arr.to_equivalent("J", "thermal") assert_allclose_units(arr, unyt_array([1j * 0.001, 1j * 0.01], "J")) assert_allclose_units(arr.to_ndarray(), np.asarray([1j * 0.001, 1j * 0.01])) assert_allclose_units(arr.to_value(), np.asarray([1j * 0.001, 1j * 0.01])) assert arr.tolist() == [1j * 0.001, 1j * 0.01] assert_allclose_units(arr.in_units("mJ"), unyt_array([1j, 1j * 10], "mJ")) assert_allclose_units(arr.in_base(), unyt_array([1j * 0.001, 1j * 0.01], "J")) assert_allclose_units(arr.in_cgs(), unyt_array([1j * 10000, 1j * 100000], "erg")) assert_allclose_units(arr.in_mks(), unyt_array([1j * 0.001, 1j * 0.01], "J")) fname = tmp_path / "testcomplexvalued.txt" savetxt(fname, arr) farr = loadtxt(fname, dtype=np.complex128) assert_allclose_units(farr, unyt_array([1j * 0.001, 1j * 0.01], "J")) def test_string_formatting(): d = unyt_array((1, 2, 3), "Msun") expected = "[1 2 3] Msun" assert f"{d}" == expected assert f"{d}" == expected @pytest.mark.parametrize( "s, expected, normalized", [ ("+1cm", 1.0 * Unit("cm"), "1 cm"), ("1cm", 1.0 * Unit("cm"), "1 cm"), ("1.cm", 1.0 * Unit("cm"), "1.0 cm"), ("1.0 cm", 1.0 * Unit("cm"), "1.0 cm"), ("1.0\tcm", 1.0 * Unit("cm"), "1.0 cm"), ("1.0\t cm", 1.0 * Unit("cm"), "1.0 cm"), ("1.0 cm", 1.0 * Unit("cm"), "1.0 cm"), ("1.0\t\tcm", 1.0 * Unit("cm"), "1.0 cm"), ("10e-1cm", 1.0 * Unit("cm"), "1.0 cm"), ("10E-1cm", 1.0 * Unit("cm"), "1.0 cm"), ("+1cm", 1.0 * Unit("cm"), "1 cm"), ("1um", 1.0 * Unit("μm"), "1 μm"), ("1μm", 1.0 * Unit("μm"), "1 μm"), ("-5 Msun", -5.0 * Unit("Msun"), "-5 Msun"), ("1e3km", 1e3 * Unit("km"), "1000.0 km"), ("-1e3 km", -1e3 * Unit("km"), "-1000.0 km"), ("1.0 g/cm**3", 1.0 * Unit("g/cm**3"), "1.0 g/cm**3"), ("1 g*cm**-3", 1.0 * Unit("g/cm**3"), "1 g/cm**3"), ("1.0 g*cm", 1.0 * Unit("g*cm"), "1.0 cm*g"), ("nan g", float("nan") * Unit("g"), "nan g"), ("-nan g", float("nan") * Unit("g"), "nan g"), ("inf g", float("inf") * Unit("g"), "inf g"), ("+inf g", float("inf") * Unit("g"), "inf g"), ("-inf g", -float("inf") * Unit("g"), "-inf g"), ("1", 1.0 * Unit(), "1 dimensionless"), ("g", 1.0 * Unit("g"), "1 g"), # from https://github.com/yt-project/unyt/issues/361 ("1 g**2/cm**2", 1.0 * Unit("g") ** 2 / Unit("cm") ** 2, "1 g**2/cm**2"), ("g**2/cm**2", 1.0 * Unit("g") ** 2 / Unit("cm") ** 2, "1 g**2/cm**2"), ("1*cm**2", 1.0 * Unit("cm") ** 2, "1 cm**2"), ("1/cm**2", 1.0 / Unit("cm") ** 2, "1 cm**(-2)"), ("1 / cm**2", 1.0 / Unit("cm") ** 2, "1 cm**(-2)"), ( "1e-3 g**2 / cm**2", 1e-3 * Unit("g") ** 2 / Unit("cm") ** 2, "0.001 g**2/cm**2", ), ], ) def test_valid_quantity_from_string(s, expected, normalized): actual = unyt_quantity.from_string(s) assert actual.to_string() == normalized roundtrip = unyt_quantity.from_string(actual.to_string()) if "nan" not in s: assert actual == expected assert roundtrip == expected assert actual.to_string() == normalized assert roundtrip.to_string() == normalized @pytest.mark.parametrize( "s", [ "++1cm", "--1cm", "cm10", "cm 10.", ".cm", "1cm**(-1", "1cm**/2", "1cm**3 hello", ], ) def test_invalid_expression_quantity_from_string(s): with pytest.raises(ValueError, match=r"^(Received invalid quantity expression )"): unyt_quantity.from_string(s) @pytest.mark.parametrize( "s", [ "10 cmmmm", "50. Km", ".6 MSUN", "infcm", # space sep is required here ], ) def test_invalid_unit_quantity_from_string(s): # using a lazy solution here # this test would need to be refactored if we want to add other cases # without a space separator between number and unit. un_str = s.split()[-1] with pytest.raises( UnitParseError, match=f"Could not find unit symbol '{un_str}' in the provided symbols.", ): unyt_quantity.from_string(s) def test_constant_type(): # see https://github.com/yt-project/unyt/issues/224 a = [1] * cm assert type(a) is unyt_array b = 2 * a assert type(b) is unyt_array def test_composite_meshgrid(): # see https://github.com/yt-project/unyt/issues/224 a = np.array(1) # pure numpy call to illustrate that the problem # is only with units np.meshgrid(np.array([1, 2]), a) np.meshgrid(np.array([1, 2]), a * m) @pytest.mark.parametrize( "shape, expected_output_shape", [ (1, (1,)), ((1,), (1,)), ((1, 1), (1, 1)), ((1, -1), (1, 1)), ], ) def test_reshape_quantity_to_array(shape, expected_output_shape): a = unyt_quantity(1, "m") b = a.reshape(shape) assert b.shape == expected_output_shape assert type(b) is unyt_array @pytest.mark.parametrize("shape", ((), None)) def test_reshape_quantity_noop(shape): a = unyt_quantity(1, "m") b = a.reshape(shape) assert b.shape == a.shape == () assert type(b) is unyt_quantity def test_reshape_quantity_via_shape_tuple(): # this is necessary to support np.tile a = unyt_quantity(1, "m") b = a.reshape(-1, 1) assert b.shape == (1, 1) assert type(b) is unyt_array def test_string_comparison(): # exercise comparison between a unyt_quantity object and a string # see regression https://github.com/numpy/numpy/issues/22744 a = 1 * cm assert not (a == "hello") assert a != "hello" def test_int8_comparison(): # see regression https://github.com/yt-project/unyt/issues/369 a = unyt_array(np.zeros(5, dtype=np.int8)) assert all(e == 0 for e in a) def test_setitem(): # see https://github.com/yt-project/unyt/issues/373 a = [1, 2, 3] * cm a[1] = 2 * m assert a[1].value == 200 assert a[1].units == cm with pytest.raises(UnitConversionError): a[1] = 2 * g a[1] = 2 assert a[1].value == 2 assert a[1].units == cm a[1] = unyt_quantity(2) assert a[1].value == 2 assert a[1].units == cm def test_division_by_float_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 # this case previously caused infinite recursion: out_in_place = unyt_array([2, 4, 6], 10 * m) result_in_place = np.true_divide(out_in_place, 2.0, out=out_in_place) assert_array_equal_units( out_in_place, unyt_array([1, 2, 3], 10 * m), ) assert_array_equal_units( result_in_place, unyt_array([1, 2, 3], 10 * m), ) def test_division_by_ndarray_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([2, 4, 6], 10 * m) out_ndarray = np.zeros(3) result_ndarray = np.true_divide(a, 2.0, out=out_ndarray) assert_array_equal_units( out_ndarray, np.array([10, 20, 30]), ) assert_array_equal_units( result_ndarray, unyt_array([10, 20, 30], m), ) def test_division_with_unyt_array_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([2, 4, 6], 10 * m) out_unyt_array_m = unyt_array([0, 0, 0], m) result_unyt_array_m = np.true_divide(a, 2.0, out=out_unyt_array_m) assert_array_equal_units( out_unyt_array_m, unyt_array([10, 20, 30], m), ) assert_array_equal_units( result_unyt_array_m, unyt_array([10, 20, 30], m), ) def test_division_with_unyt_array_quantity_units_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([2, 4, 6], 10 * m) # this case previously caused infinite recursion: out_unyt_array_10m = unyt_array([0, 0, 0], 10 * m) result_unyt_array_10m = np.true_divide(a, 2.0, out=out_unyt_array_10m) assert_array_equal_units( out_unyt_array_10m, unyt_array([1, 2, 3], 10 * m), ) assert_array_equal_units( result_unyt_array_10m, unyt_array([1, 2, 3], 10 * m), # QUERY: should this be [10, 20, 30], m? ) def test_division_with_unyt_array_different_units_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([2, 4, 6], 10 * m) out_unyt_array_cm = unyt_array([0, 0, 0], cm) result_unyt_array_cm = np.true_divide(a, 2.0, out=out_unyt_array_cm) assert_array_equal_units( out_unyt_array_cm, unyt_array([10, 20, 30], m), ) assert_array_equal_units( result_unyt_array_cm, unyt_array([10, 20, 30], m), ) def test_division_by_ndarray_with_inplace_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 # this case previously caused infinite recursion: out_div_ndarray = unyt_array([2, 4, 6], 10 * m) result_div_ndarray = np.true_divide( out_div_ndarray, np.array([2, 2, 2]), out=out_div_ndarray ) assert_array_equal_units( out_div_ndarray, unyt_array([1, 2, 3], 10 * m), ) assert_array_equal_units( result_div_ndarray, unyt_array([1, 2, 3], 10 * m), # QUERY: should this be [10, 20, 30], m? ) def test_division_by_unyt_array_with_inplace_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 # this case previously caused infinite recursion: out_div_unyt_array = unyt_array([2, 4, 6], 10 * m) result_div_unyt_array = np.true_divide( out_div_unyt_array, unyt_array([2, 2, 2], 10 * m), out=out_div_unyt_array ) assert_array_equal_units( out_div_unyt_array, unyt_array([1, 2, 3], "dimensionless"), ) assert_array_equal_units( result_div_unyt_array, unyt_array([1, 2, 3], "dimensionless"), ) def test_division_by_unyt_array_unyt_quantity_units_with_inplace_out_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 # this case previously caused infinite recursion: out_div_unyt_quantity = unyt_array([2, 4, 6], 10 * m) result_div_unyt_quantity = np.true_divide( out_div_unyt_quantity, unyt_quantity(2, 10 * m), out=out_div_unyt_quantity ) assert_array_equal_units( out_div_unyt_quantity, unyt_array([1, 2, 3], "dimensionless"), ) assert_array_equal_units( result_div_unyt_quantity, unyt_array([1, 2, 3], "dimensionless"), ) def test_multiplication_by_one_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 out_mul_one = unyt_array([1, 2, 3], 10 * m) result_mul_one = np.multiply(out_mul_one, 1, out=out_mul_one) assert_array_equal_units( out_mul_one, unyt_array([1, 2, 3], 10 * m), ) assert_array_equal_units( result_mul_one, unyt_array([1, 2, 3], 10 * m), ) def test_mean_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([2, 4, 6], 10 * m) # previously caused infinite recursion: assert_array_equal_units( a.mean(), unyt_quantity(4.0, 10 * m), ) def test_std_recursion(): # regression test for https://github.com/yt-project/unyt/issues/588 # see also https://github.com/yt-project/unyt/issues/540 a = unyt_array([1, 1, 5, 5], 10 * m) if NUMPY_VERSION >= Version("2.4.0"): # https://github.com/yt-project/unyt/issues/603 # https://github.com/numpy/numpy/pull/29951 expected = unyt_quantity(2.0, 10 * m) else: expected = unyt_quantity(20.0, m) # previously caused infinite recursion: assert_array_equal_units(a.std(), expected) def test_custom_unit_multiplication_with_out_kwarg(): # regression test for https://github.com/yt-project/unyt/issues/599 reg = UnitRegistry() reg.add( "code_length", base_value=10.0, dimensions=dimensions.length, ) u = Unit("code_length", registry=reg) data = 0 * u # previously crashed with: # UnitParseError: Could not find unit symbol 'code_length' in the provided symbols. np.multiply(data, data, out=data) def test_squeeze_method_array(): """Check that squeeze returns the correct type for the shape of the result.""" arr = np.ones((3, 1)) * cm arr_squeezed = arr.squeeze() assert arr_squeezed.ndim > 0 assert type(arr_squeezed) is unyt_array def test_squeeze_method_scalar(): """Check that squeeze returns the correct type for the shape of the result.""" arr = np.ones(1) * cm arr_squeezed = arr.squeeze() assert arr_squeezed.ndim == 0 assert type(arr_squeezed) is unyt_quantity def test_squeeze_method_with_axis(): """Check that squeeze obeys axis kwarg.""" arr = np.ones((1, 1)) * cm squeeze_axis = (1,) arr_squeezed = arr.squeeze(axis=1) assert arr_squeezed.ndim == arr.ndim - len(squeeze_axis) def test_other_argument_can_handle_binary_ufunc(): class MyClass: __array_ufunc__ = None def __init__(self, x: int): self.x = x def __mul__(self, b): return self.x * b def __rmul__(self, b): return self.x * b def __truediv__(self, b): return self.x / b def __rtruediv__(self, b): return b / self.x x = 5 mc = MyClass(x=x) uq = unyt_quantity(2, "m") ua = unyt_array([2], "m") for u in ua, uq: assert_almost_equal(x * u, mc * u) assert_almost_equal(x * u, u * mc) assert_almost_equal(x / u, mc / u) assert_almost_equal(u / x, u / mc) def test_other_argument_cannot_handle_binary_ufunc(): class MyClass: def __init__(self, x: int): self.x = x x = 5 mc = MyClass(x=x) uq = unyt_quantity(2, "m") ua = unyt_array([2], "m") for u in ua, uq: with pytest.raises(TypeError): mc * u with pytest.raises(TypeError): u * mc with pytest.raises(TypeError): mc / u with pytest.raises(TypeError): u / mc @pytest.mark.parametrize("n_inputs", [1, 2]) def test_unsupported_ufunc_returns_notimplemented(n_inputs): # a user-defined ufunc is guaranteed not to be in unyt_array._ufunc_registry ufunc = np.frompyfunc(lambda *args: args[0], n_inputs, 1) ua = unyt_array([1, 2, 3], "cm") uq = unyt_quantity(1, "cm") for u in ua, uq: inputs = (u,) * n_inputs assert u.__array_ufunc__(ufunc, "__call__", *inputs) is NotImplemented with pytest.raises(TypeError, match="all returned NotImplemented"): ufunc(*inputs) unyt-3.2.0/unyt/tests/test_unyt_testing.py000066400000000000000000000060161526544257300210410ustar00rootroot00000000000000""" Test unyt.testing module that contains utilities for writing tests. """ import pytest from unyt import accepts, meter, returns, second from unyt.array import unyt_array, unyt_quantity from unyt.dimensions import length, time from unyt.testing import assert_allclose_units def test_equality(): a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([1.0, 2.0, 3.0], "cm") assert_allclose_units(a1, a2) def test_unequal_error(): a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([4.0, 5.0, 6.0], "cm") with pytest.raises(AssertionError): assert_allclose_units(a1, a2) def test_conversion_error(): a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([1.0, 2.0, 3.0], "kg") with pytest.raises(AssertionError): assert_allclose_units(a1, a2) def test_runtime_error(): a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([1.0, 2.0, 3.0], "cm") with pytest.raises(RuntimeError): assert_allclose_units(a1, a2, rtol=unyt_quantity(1e-7, "cm")) def test_atol_conversion_error(): a1 = unyt_array([1.0, 2.0, 3.0], "cm") a2 = unyt_array([1.0, 2.0, 3.0], "cm") with pytest.raises(AssertionError): assert_allclose_units(a1, a2, atol=unyt_quantity(0.0, "kg")) def test_accepts(): @accepts(a=time, v=length / time) def foo(a, v): return a * v foo(a=2 * second, v=3 * meter / second) with pytest.raises(TypeError): foo(a=2 * meter, v=3 * meter / second) with pytest.raises(TypeError): foo(a=2 * second, v=3 * meter) def test_accepts_partial(): @accepts(a=time) def bar(a, v): return a * v bar(a=2 * second, v=3 * meter / second) bar(a=2 * second, v=3 * meter) with pytest.raises(TypeError): bar(a=2 * meter, v=3 * meter / second) @accepts(v=length / time) def baz(a, v): return a * v baz(a=2 * second, v=3 * meter / second) baz(a=2 * meter, v=3 * meter / second) with pytest.raises(TypeError): baz(a=2 * second, v=3 * meter) def test_returns(): @returns(length) def foo(a, v): return a * v # This usage is deprecated, but we still want to support it for now. with pytest.deprecated_call(): @returns(r_unit=length) def bar(a, v): return a * v for func in [foo, bar]: func(a=2 * second, v=3 * meter / second) with pytest.raises(TypeError): func(a=2 * meter, v=3 * meter / second) with pytest.raises(TypeError): func(a=2 * second, v=3 * meter) # We don't support a mixture of the two usage styles. with pytest.raises(ValueError): @returns(length, r_unit=time) def _(a, v): # pragma: no cover return a, v def test_returns_multiple(): @returns(time, length / time) def baz(a, v): return a, v baz(a=2 * second, v=3 * meter / second) with pytest.raises(TypeError): baz(a=2 * meter, v=3 * meter / second) with pytest.raises(TypeError): baz(a=2 * second, v=3 * meter) unyt-3.2.0/unyt/tests/test_version.py000066400000000000000000000002211526544257300177620ustar00rootroot00000000000000from importlib.metadata import version import unyt def test_version(): expected = version("unyt") assert unyt.__version__ == expected unyt-3.2.0/unyt/unit_object.py000066400000000000000000001103721526544257300164120ustar00rootroot00000000000000""" A class that represents a unit symbol. """ import copy import itertools import math from functools import lru_cache from numbers import Number as numeric_type import numpy as np from sympy import ( Add, Basic, Expr, Float, Mod, Mul, Number, Pow, Rational, Symbol, floor, latex, sympify, ) from sympy.core.numbers import One import unyt.dimensions as dims from unyt._parsing import parse_unyt_expr from unyt._physical_ratios import speed_of_light_cm_per_s from unyt.dimensions import ( angle, base_dimensions, current_mks, dimensionless, logarithmic, temperature, ) from unyt.equivalencies import equivalence_registry from unyt.exceptions import ( InvalidUnitOperation, MissingMKSCurrent, MKSCGSConversionError, UnitConversionError, UnitParseError, UnitsNotReducible, ) from unyt.unit_registry import _lookup_unit_symbol, default_unit_registry from unyt.unit_systems import _split_prefix sympy_one = sympify(1) def _get_latex_representation(expr, registry): symbol_table = {} for ex in expr.free_symbols: try: symbol_table[ex] = registry.lut[str(ex)][3] except KeyError: symbol_table[ex] = r"\rm{" + str(ex).replace("_", r"\ ") + "}" # invert the symbol table dict to look for keys with identical values invert_symbols = {} for key, value in symbol_table.items(): if value not in invert_symbols: invert_symbols[value] = [key] else: invert_symbols[value].append(key) # if there are any units with identical latex representations, substitute # units to avoid uncanceled terms in the final latex expression. for val in invert_symbols: symbols = invert_symbols[val] for i in range(1, len(symbols)): expr = expr.subs(symbols[i], symbols[0]) prefix = None l_expr = expr if isinstance(expr, Mul): coeffs = expr.as_coeff_Mul() if coeffs[0] == 1 or not isinstance(coeffs[0], Number): l_expr = coeffs[1] else: l_expr = coeffs[1] prefix = Float(coeffs[0], 2) latex_repr = latex( l_expr, symbol_names=symbol_table, mul_symbol="dot", fold_frac_powers=True, fold_short_frac=True, ) if prefix is not None: latex_repr = latex(prefix, mul_symbol="times") + "\\ " + latex_repr if latex_repr == "1": return "" else: return latex_repr class _ImportCache: __slots__ = ["_ua", "_uq"] def __init__(self): self._ua = None self._uq = None @property def ua(self): if self._ua is None: from unyt.array import unyt_array self._ua = unyt_array return self._ua @property def uq(self): if self._uq is None: from unyt.array import unyt_quantity self._uq = unyt_quantity return self._uq _import_cache_singleton = _ImportCache() class Unit: """ A symbolic unit, using sympy functionality. We only add "dimensions" so that sympy understands relations between different units. """ __slots__ = [ "expr", "is_atomic", "base_value", "base_offset", "dimensions", "_latex_repr", "registry", "is_Unit", ] # Set some assumptions for sympy. is_positive = True # make sqrt(m**2) --> m is_commutative = True is_number = False __array_priority__ = 3.0 def __new__( cls, unit_expr=sympy_one, base_value=None, base_offset=0.0, dimensions=None, registry=None, latex_repr=None, ): """ Create a new unit. May be an atomic unit (like a gram) or combinations of atomic units (like g / cm**3). Parameters ---------- unit_expr : Unit object, sympy.core.expr.Expr object, or str The symbolic unit expression. base_value : float The unit's value in yt's base units. base_offset : float The offset necessary to normalize temperature units to a common zero point. dimensions : sympy.core.expr.Expr A sympy expression representing the dimensionality of this unit. It must contain only mass, length, time, temperature and angle symbols. registry : UnitRegistry object The unit registry we use to interpret unit symbols. latex_repr : string A string to render the unit as LaTeX """ unit_cache_key = None # Simplest case. If user passes a Unit object, just use the expr. if hasattr(unit_expr, "is_Unit"): # grab the unit object's sympy expression. unit_expr = unit_expr.expr elif hasattr(unit_expr, "units") and hasattr(unit_expr, "value"): # something that looks like a unyt_array, grab the unit and value if unit_expr.shape != (): raise UnitParseError( "Cannot create a unit from a non-scalar unyt_array, " f"received: {unit_expr}" ) value = unit_expr.value if value == 1: unit_expr = unit_expr.units.expr else: unit_expr = unit_expr.value * unit_expr.units.expr # Parse a text unit representation using sympy's parser elif isinstance(unit_expr, (str, bytes)): if isinstance(unit_expr, bytes): unit_expr = unit_expr.decode("utf-8") # this cache substantially speeds up unit conversions if registry and unit_expr in registry._unit_object_cache: return registry._unit_object_cache[unit_expr] unit_cache_key = unit_expr unit_expr = parse_unyt_expr(unit_expr) # Make sure we have an Expr at this point. if not isinstance(unit_expr, Expr): raise UnitParseError( "Unit representation must be a string or " f"sympy Expr. '{unit_expr}' has type '{type(unit_expr)}'." ) if dimensions is None and unit_expr is sympy_one: dimensions = dimensionless if registry is None: # Caller did not set the registry, so use the default. registry = default_unit_registry # done with argument checking... # see if the unit is atomic. is_atomic = False if isinstance(unit_expr, Symbol): is_atomic = True # # check base_value and dimensions # if base_value is not None: # check that base_value is a float or can be converted to one try: base_value = float(base_value) except ValueError: raise UnitParseError( "Could not use base_value as a float. " f"base_value is '{base_value}' (type {type(base_value)})." ) # check that dimensions is valid if dimensions is not None: _validate_dimensions(dimensions) else: # lookup the unit symbols unit_data = _get_unit_data_from_expr(unit_expr, registry.lut) base_value = unit_data[0] dimensions = unit_data[1] if len(unit_data) > 2: base_offset = unit_data[2] latex_repr = unit_data[3] else: base_offset = 0.0 # Create obj with superclass construct. obj = super().__new__(cls) # Attach attributes to obj. obj.expr = unit_expr obj.is_atomic = is_atomic obj.base_value = base_value obj.base_offset = base_offset obj.dimensions = dimensions obj._latex_repr = latex_repr obj.registry = registry # lets us avoid isinstance calls obj.is_Unit = True # if we parsed a string unit expression, cache the result # for faster lookup later if unit_cache_key is not None: registry._unit_object_cache[unit_cache_key] = obj # Return `obj` so __init__ can handle it. return obj @property def latex_repr(self): """A LaTeX representation for the unit Examples -------- >>> from unyt import g, cm >>> (g/cm**3).units.latex_repr '\\\\frac{\\\\rm{g}}{\\\\rm{cm}^{3}}' """ if self._latex_repr is not None: return self._latex_repr if self.expr.is_Atom: expr = self.expr else: expr = self.expr.copy() self._latex_repr = _get_latex_representation(expr, self.registry) return self._latex_repr @property def units(self): return self def __hash__(self): return int(self.registry.unit_system_id, 16) ^ hash(self.expr) # end sympy conventions def __repr__(self): if self.expr == sympy_one: return "(dimensionless)" # @todo: don't use dunder method? return self.expr.__repr__() def __str__(self): if self.expr == sympy_one: return "dimensionless" unit_str = self.expr.__str__() if unit_str == "degC": return "°C" if unit_str == "delta_degC": return "Δ°C" if unit_str == "degF": return "°F" if unit_str == "delta_degF": return "Δ°F" # @todo: don't use dunder method? return unit_str # # Start unit operations # def __add__(self, u): raise InvalidUnitOperation("addition with unit objects is not allowed") def __radd__(self, u): raise InvalidUnitOperation("addition with unit objects is not allowed") def __sub__(self, u): raise InvalidUnitOperation("subtraction with unit objects is not allowed") def __rsub__(self, u): raise InvalidUnitOperation("subtraction with unit objects is not allowed") def __iadd__(self, u): raise InvalidUnitOperation( "in-place operations with unit objects are not allowed" ) def __isub__(self, u): raise InvalidUnitOperation( "in-place operations with unit objects are not allowed" ) def __imul__(self, u): raise InvalidUnitOperation( "in-place operations with unit objects are not allowed" ) def __itruediv__(self, u): raise InvalidUnitOperation( "in-place operations with unit objects are not allowed" ) def __rmul__(self, u): if not getattr(u, "is_Unit", False): data = np.array(u, subok=True) data_type = type(data) if ( issubclass(data_type, _import_cache_singleton.ua) and data_type is not _import_cache_singleton.ua and data_type is not _import_cache_singleton.uq ): return data.__mul__(self) return self.__mul__(u) def __mul__(self, u): """Multiply Unit with u (Unit object).""" if not getattr(u, "is_Unit", False): data = np.array(u, subok=True) if ( issubclass(data.__class__, _import_cache_singleton.ua) and type(data) is not _import_cache_singleton.ua and type(data) is not _import_cache_singleton.uq ): return data.__rmul__(self) unit = getattr(u, "units", None) if unit is not None: if self.dimensions is logarithmic: raise InvalidUnitOperation( f"Tried to multiply '{self}' and '{unit}'." ) units = unit * self else: units = self if data.dtype.kind not in ("f", "u", "i", "c"): # we don't know how to handle this, but the other argument might return NotImplemented if data.shape == (): return _import_cache_singleton.uq(data, units, bypass_validation=True) return _import_cache_singleton.ua(data, units, bypass_validation=True) elif self.dimensions is logarithmic and not u.is_dimensionless: raise InvalidUnitOperation(f"Tried to multiply '{self}' and '{u}'.") elif u.dimensions is logarithmic and not self.is_dimensionless: raise InvalidUnitOperation(f"Tried to multiply '{self}' and '{u}'.") base_offset = 0.0 if self.base_offset or u.base_offset: if u.dimensions in (temperature, angle) and self.is_dimensionless: base_offset = u.base_offset elif self.dimensions in (temperature, angle) and u.is_dimensionless: base_offset = self.base_offset else: raise InvalidUnitOperation( "Quantities with dimensions of angle or units of " "Fahrenheit or Celsius cannot be multiplied." ) return Unit( self.expr * u.expr, base_value=(self.base_value * u.base_value), base_offset=base_offset, dimensions=(self.dimensions * u.dimensions), registry=self.registry, ) def __truediv__(self, u): """Divide Unit by u (Unit object).""" if not isinstance(u, Unit): if isinstance(u, (numeric_type, list, tuple, np.ndarray)): from unyt.array import unyt_quantity return unyt_quantity(1.0, self) / u else: # we don't know how to handle this, but the other argument might return NotImplemented elif self.dimensions is logarithmic and not u.is_dimensionless: raise InvalidUnitOperation(f"Tried to divide '{self}' and '{u}'.") elif u.dimensions is logarithmic and not self.is_dimensionless: raise InvalidUnitOperation(f"Tried to divide '{self}' and '{u}'.") base_offset = 0.0 if self.base_offset or u.base_offset: if self.dimensions in (temperature, angle) and u.is_dimensionless: base_offset = self.base_offset else: raise InvalidUnitOperation( "Quantities with units of Farhenheit and Celsius cannot be divided." ) return Unit( self.expr / u.expr, base_value=(self.base_value / u.base_value), base_offset=base_offset, dimensions=(self.dimensions / u.dimensions), registry=self.registry, ) def __rtruediv__(self, u): return u * self**-1 def __pow__(self, p): """Take Unit to power p (float).""" try: p = Rational(str(p)).limit_denominator() except (ValueError, TypeError): raise InvalidUnitOperation( f"Tried to take a Unit object to the power '{p}' (type {type(p)}). " "Failed to cast it to a float." ) if self.dimensions is logarithmic and p != 1: raise InvalidUnitOperation(f"Tried to raise '{self}' to power '{p}'") return Unit( self.expr**p, base_value=(self.base_value**p), dimensions=(self.dimensions**p), registry=self.registry, ) def __eq__(self, u): """Test unit equality.""" return ( isinstance(u, Unit) and math.isclose(self.base_value, u.base_value) and math.isclose(self.base_offset, u.base_offset) and ( # use 'is' comparison dimensions to avoid expensive sympy operation self.dimensions is u.dimensions # fall back to expensive sympy comparison or self.dimensions == u.dimensions ) ) def copy(self, *, deep=False): expr = str(self.expr) base_value = copy.deepcopy(self.base_value) base_offset = copy.deepcopy(self.base_offset) dimensions = copy.deepcopy(self.dimensions) if deep: registry = copy.deepcopy(self.registry) else: registry = copy.copy(self.registry) return Unit(expr, base_value, base_offset, dimensions, registry) def __deepcopy__(self, memodict=None): return self.copy(deep=True) # # End unit operations # def same_dimensions_as(self, other_unit): """Test if the dimensions of *other_unit* are the same as this unit Examples -------- >>> from unyt import Msun, kg, mile >>> Msun.units.same_dimensions_as(kg.units) True >>> Msun.units.same_dimensions_as(mile.units) False """ # test first for 'is' equality to avoid expensive sympy operation if self.dimensions is other_unit.dimensions: return True return (self.dimensions / other_unit.dimensions) == sympy_one @property def is_dimensionless(self): """Is this a dimensionless unit? Returns ------- True for a dimensionless unit, False otherwise Examples -------- >>> from unyt import count, kg >>> count.units.is_dimensionless True >>> kg.units.is_dimensionless False """ return self.dimensions is sympy_one @property def is_code_unit(self): """Is this a "code" unit? Returns ------- True if the unit consists of atom units that being with "code". False otherwise """ for atom in self.expr.atoms(): if not (str(atom).startswith("code") or atom.is_Number): return False return True def list_equivalencies(self): """Lists the possible equivalencies associated with this unit object Examples -------- >>> from unyt import km >>> km.units.list_equivalencies() spectral: length <-> spatial_frequency <-> frequency <-> energy schwarzschild: mass <-> length compton: mass <-> length """ from unyt.equivalencies import equivalence_registry for k, v in equivalence_registry.items(): if self.has_equivalent(k): print(v()) def has_equivalent(self, equiv): """ Check to see if this unit object as an equivalent unit in *equiv*. Example ------- >>> from unyt import km >>> km.has_equivalent('spectral') True >>> km.has_equivalent('mass_energy') False """ try: this_equiv = equivalence_registry[equiv]() except KeyError: raise KeyError(f'No such equivalence "{equiv}".') old_dims = self.dimensions return old_dims in this_equiv._dims def get_base_equivalent(self, unit_system=None): """Create and return dimensionally-equivalent units in a specified base. >>> from unyt import g, cm >>> (g/cm**3).get_base_equivalent('mks') kg/m**3 >>> (g/cm**3).get_base_equivalent('solar') Mearth/AU**3 """ from unyt.unit_registry import _sanitize_unit_system unit_system = _sanitize_unit_system(unit_system, self) try: conv_data = _check_em_conversion( self.units, registry=self.registry, unit_system=unit_system ) um = unit_system.units_map if self.dimensions in um and self.expr == um[self.dimensions]: return self.copy() except MKSCGSConversionError: raise UnitsNotReducible(self.units, unit_system) if any(conv_data): new_units, _ = _em_conversion(self, conv_data, unit_system=unit_system) else: try: new_units = unit_system[self.dimensions] except MissingMKSCurrent: raise UnitsNotReducible(self.units, unit_system) return Unit(new_units, registry=self.registry) def get_cgs_equivalent(self): """Create and return dimensionally-equivalent cgs units. Example ------- >>> from unyt import kg, m >>> (kg/m**3).get_cgs_equivalent() g/cm**3 """ return self.get_base_equivalent(unit_system="cgs") def get_mks_equivalent(self): """Create and return dimensionally-equivalent mks units. Example ------- >>> from unyt import g, cm >>> (g/cm**3).get_mks_equivalent() kg/m**3 """ return self.get_base_equivalent(unit_system="mks") def get_conversion_factor(self, other_units, dtype=None): """Get the conversion factor and offset (if any) from one unit to another Parameters ---------- other_units: unit object The units we want the conversion factor for dtype: numpy dtype The dtype to return the conversion factor as Returns ------- conversion_factor : float old_units / new_units offset : float or None Offset between this unit and the other unit. None if there is no offset. Examples -------- >>> from unyt import km, cm, degree_fahrenheit, degree_celsius >>> km.get_conversion_factor(cm) (100000.0, None) >>> degree_celsius.get_conversion_factor(degree_fahrenheit) (1.7999999999999998, -31.999999999999886) """ return _get_conversion_factor(self, other_units, dtype) def latex_representation(self): """A LaTeX representation for the unit Examples -------- >>> from unyt import g, cm >>> (g/cm**3).latex_representation() '\\\\frac{\\\\rm{g}}{\\\\rm{cm}^{3}}' """ return self.latex_repr def as_coeff_unit(self): """Factor the coefficient multiplying a unit For units that are multiplied by a constant dimensionless coefficient, returns a tuple containing the coefficient and a new unit object for the unmultiplied unit. Example ------- >>> import unyt as u >>> unit = (u.m**2/u.cm).simplify() >>> unit 100*m >>> unit.as_coeff_unit() (100.0, m) """ coeff, mul = self.expr.as_coeff_Mul() coeff = float(coeff) ret = Unit( mul, self.base_value / coeff, self.base_offset, self.dimensions, self.registry, ) return coeff, ret def simplify(self): """Return a new equivalent unit object with a simplified unit expression >>> import unyt as u >>> unit = (u.m**2/u.cm).simplify() >>> unit 100*m """ expr = self.expr self.expr = _cancel_mul(expr, self.registry) return self def _factor_pairs(expr): factors = expr.as_ordered_factors() expanded_factors = [] for f in factors: if f.is_Number: continue base, exp = f.as_base_exp() if exp.q != 1: expanded_factors.append(base ** Mod(exp, 1)) exp = floor(exp) if exp >= 0: f = (base,) * exp else: f = (1 / base,) * abs(exp) expanded_factors.extend(f) return list(itertools.combinations(expanded_factors, 2)) def _create_unit_from_factor(factor, registry): base, exp = factor.as_base_exp() f = registry[str(base)] return Unit(base, f[0], f[2], f[1], registry, f[3]) ** exp def _cancel_mul(expr, registry): pairs_to_consider = _factor_pairs(expr) uncancelable_pairs = set() while len(pairs_to_consider): pair = pairs_to_consider.pop() if pair in uncancelable_pairs: continue u1 = _create_unit_from_factor(pair[0], registry) u2 = _create_unit_from_factor(pair[1], registry) prod = u1 * u2 if prod.dimensions == 1: expr = expr / pair[0] expr = expr / pair[1] value = prod.base_value if value != 1: if value.is_integer(): value = int(value) expr *= value else: uncancelable_pairs.add(pair) pairs_to_consider = _factor_pairs(expr) return expr # # Unit manipulation functions # # map from dimensions in one unit system to dimensions in other system, # canonical unit to convert to in that system, and floating point # conversion factor em_conversions = { ("C", dims.charge_mks): (dims.charge_cgs, "statC", 0.1 * speed_of_light_cm_per_s), ("statC", dims.charge_cgs): (dims.charge_mks, "C", 10.0 / speed_of_light_cm_per_s), ("T", dims.magnetic_field_mks): (dims.magnetic_field_cgs, "G", 1.0e4), ("G", dims.magnetic_field_cgs): (dims.magnetic_field_mks, "T", 1.0e-4), ("A", dims.current_mks): (dims.current_cgs, "statA", 0.1 * speed_of_light_cm_per_s), ("statA", dims.current_cgs): ( dims.current_mks, "A", 10.0 / speed_of_light_cm_per_s, ), ("V", dims.electric_potential_mks): ( dims.electric_potential_cgs, "statV", 1.0e-8 * speed_of_light_cm_per_s, ), ("statV", dims.electric_potential_cgs): ( dims.electric_potential_mks, "V", 1.0e8 / speed_of_light_cm_per_s, ), ("Ω", dims.resistance_mks): ( dims.resistance_cgs, "statohm", 1.0e9 / (speed_of_light_cm_per_s**2), ), ("statohm", dims.resistance_cgs): ( dims.resistance_mks, "Ω", 1.0e-9 * speed_of_light_cm_per_s**2, ), } em_conversion_dims = [k[1] for k in em_conversions.keys()] def _em_conversion(orig_units, conv_data, to_units=None, unit_system=None): """Convert between E&M & MKS base units. If orig_units is a CGS (or MKS) E&M unit, conv_data contains the corresponding MKS (or CGS) unit and scale factor converting between them. This must be done by replacing the expression of the original unit with the new one in the unit expression and multiplying by the scale factor. """ conv_unit, canonical_unit, scale = conv_data if conv_unit is None: conv_unit = canonical_unit new_expr = scale * canonical_unit.expr if unit_system is not None: # we don't know the to_units, so we get it directly from the # conv_data to_units = Unit(conv_unit.expr, registry=orig_units.registry) new_units = Unit(new_expr, registry=orig_units.registry) conv = new_units.get_conversion_factor(to_units) return to_units, conv @lru_cache(maxsize=128, typed=False) def _check_em_conversion(unit, to_unit=None, unit_system=None, registry=None): """Check to see if the units contain E&M units This function supports unyt's ability to convert data to and from E&M electromagnetic units. However, this support is limited and only very simple unit expressions can be readily converted. This function tries to see if the unit is an atomic base unit that is present in the em_conversions dict. If it does not contain E&M units, the function returns an empty tuple. If it does contain an atomic E&M unit in the em_conversions dict, it returns a tuple containing the unit to convert to and scale factor. If it contains a more complicated E&M unit and we are trying to convert between CGS & MKS E&M units, it raises an error. """ em_map = () if unit == to_unit or unit.dimensions not in em_conversion_dims: return em_map if unit.is_atomic: prefix, unit_wo_prefix = _split_prefix(str(unit), unit.registry.lut) else: prefix, unit_wo_prefix = "", str(unit) if (unit_wo_prefix, unit.dimensions) in em_conversions: em_info = em_conversions[unit_wo_prefix, unit.dimensions] em_unit = Unit(prefix + em_info[1], registry=registry) if to_unit is None: cmks_in_unit = current_mks in unit.dimensions.atoms() cmks_in_unit_system = unit_system.units_map[current_mks] cmks_in_unit_system = cmks_in_unit_system is not None if cmks_in_unit and cmks_in_unit_system: em_map = (unit_system[unit.dimensions], unit, 1.0) else: em_map = (None, em_unit, em_info[2]) elif to_unit.dimensions == em_unit.dimensions: em_map = (to_unit, em_unit, em_info[2]) if em_map: return em_map if unit_system is None: from unyt.unit_systems import unit_system_registry unit_system = unit_system_registry["mks"] for unit_atom in unit.expr.atoms(): if unit_atom.is_Number: continue bu = str(unit_atom) budims = Unit(bu, registry=registry).dimensions try: if str(unit_system[budims]) == bu: continue except MissingMKSCurrent: raise MKSCGSConversionError(unit) return em_map def _get_conversion_factor(old_units, new_units, dtype): """ Get the conversion factor between two units of equivalent dimensions. This is the number you multiply data by to convert from values in `old_units` to values in `new_units`. Parameters ---------- old_units: str or Unit object The current units. new_units : str or Unit object The units we want. dtype: NumPy dtype The dtype of the conversion factor Returns ------- conversion_factor : float `old_units / new_units` offset : float or None Offset between the old unit and new unit. """ if old_units.dimensions != new_units.dimensions: raise UnitConversionError( old_units, old_units.dimensions, new_units, new_units.dimensions ) old_basevalue = old_units.base_value old_baseoffset = old_units.base_offset new_basevalue = new_units.base_value new_baseoffset = new_units.base_offset ratio = old_basevalue / new_basevalue if dtype is not None: # Return the conversion factor (and offset, below) with the requested # precision, as documented. Internal call sites in array.py used to # re-cast the result manually because this argument was ignored; the # cast belongs here. A minimum width of 2 bytes mirrors the pattern # used in unyt/array.py, since 1-byte types have no float equivalent. requested = np.dtype(dtype) # A complex type stores two reals, so its matching real width is half # the item size; asking for f16 on a 16-byte complex would name a # float128 that does not exist on Windows or macOS. width = ( requested.itemsize // 2 if requested.kind == "c" else max(2, requested.itemsize) ) float_dtype = np.dtype(f"f{width}") ratio = float_dtype.type(ratio) if old_baseoffset == 0 and new_baseoffset == 0: return (ratio, None) else: # the dimensions are either temperature or angle (lat, lon) if old_units.dimensions == temperature: # for degree Celsius, back out the SI prefix scaling from # offset scaling for degree Fahrenheit old_prefix, _ = _split_prefix(str(old_units), old_units.registry.lut) if old_prefix != "": old_baseoffset /= old_basevalue new_prefix, _ = _split_prefix(str(new_units), new_units.registry.lut) if new_prefix != "": new_baseoffset /= new_basevalue offset = ratio * old_baseoffset - new_baseoffset if dtype is not None: offset = float_dtype.type(offset) return ratio, offset # # Helper functions # def _get_unit_data_from_expr(unit_expr, unit_symbol_lut): """ Grabs the total base_value and dimensions from a valid unit expression. Parameters ---------- unit_expr: Unit object, or sympy Expr object The expression containing unit symbols. unit_symbol_lut: dict Provides the unit data for each valid unit symbol. """ # Now for the sympy possibilities if isinstance(unit_expr, Number): if unit_expr is sympy_one: return (1.0, sympy_one) return (float(unit_expr), sympy_one) if isinstance(unit_expr, Symbol): return _lookup_unit_symbol(unit_expr.name, unit_symbol_lut) if isinstance(unit_expr, Pow): unit_data = _get_unit_data_from_expr(unit_expr.args[0], unit_symbol_lut) power = unit_expr.args[1] if isinstance(power, Symbol): raise UnitParseError(f"Invalid unit expression '{unit_expr}'.") conv = float(unit_data[0] ** power) unit = unit_data[1] ** power return (conv, unit) if isinstance(unit_expr, Mul): base_value = 1.0 dimensions = 1 for expr in unit_expr.args: unit_data = _get_unit_data_from_expr(expr, unit_symbol_lut) base_value *= unit_data[0] dimensions *= unit_data[1] return (float(base_value), dimensions) raise UnitParseError( f"Cannot parse for unit data from {str(unit_expr)!r}. Please supply " "an expression of only Unit, Symbol, Pow, and Mul objects." ) def _validate_dimensions(dimensions): if isinstance(dimensions, Mul): for dim in dimensions.args: _validate_dimensions(dim) elif isinstance(dimensions, Symbol): if dimensions not in base_dimensions: raise UnitParseError( f"Dimensionality expression contains an unknown symbol '{dimensions}'." ) elif isinstance(dimensions, Pow): if not isinstance(dimensions.args[1], Number): raise UnitParseError( f"Dimensionality expression '{dimensions}' contains a " "unit symbol as a power." ) elif isinstance(dimensions, (Add, Number)): if not isinstance(dimensions, One): raise UnitParseError( "Only dimensions that are instances of Pow, " "Mul, or symbols in the base dimensions are " f"allowed. Got dimensions '{dimensions}'" ) elif not isinstance(dimensions, Basic): raise UnitParseError(f"Bad dimensionality expression '{dimensions}'.") def define_unit( symbol, value, tex_repr=None, offset=None, prefixable=False, registry=None ): """ Define a new unit and add it to the specified unit registry. Parameters ---------- symbol : string The symbol for the new unit. value : tuple or :class:`unyt.array.unyt_quantity` The definition of the new unit in terms of some other units. For example, one would define a new "mph" unit with ``(1.0, "mile/hr")`` or with ``1.0*unyt.mile/unyt.hr`` tex_repr : string, optional The LaTeX representation of the new unit. If one is not supplied, it will be generated automatically based on the symbol string. offset : float, optional The default offset for the unit. If not set, an offset of 0 is assumed. prefixable : boolean, optional Whether or not the new unit can use SI prefixes. Default: False registry : :class:`unyt.unit_registry.UnitRegistry` or None The unit registry to add the unit to. If None, then defaults to the global default unit registry. If registry is set to None then the unit object will be added as an attribute to the top-level :mod:`unyt` namespace to ease working with the newly defined unit. See the example below. Examples -------- >>> from unyt import day >>> two_weeks = 14.0*day >>> one_day = 1.0*day >>> define_unit("two_weeks", two_weeks) >>> from unyt import two_weeks >>> print((3*two_weeks)/one_day) 42.0 dimensionless """ import unyt from unyt.array import _iterable, unyt_quantity if registry is None: registry = default_unit_registry if symbol in registry: raise RuntimeError( f"Unit symbol '{symbol}' already exists in the provided registry" ) if not isinstance(value, unyt_quantity): if _iterable(value) and len(value) == 2: value = unyt_quantity(value[0], value[1], registry=registry) else: raise RuntimeError('"value" needs to be a quantity or (value, unit) tuple!') base_value = float(value.in_base(unit_system="mks")) dimensions = value.units.dimensions registry.add( symbol, base_value, dimensions, prefixable=prefixable, tex_repr=tex_repr, offset=offset, ) if registry is default_unit_registry: u = Unit(symbol, registry=registry) setattr(unyt, symbol, u) NULL_UNIT = Unit() unyt-3.2.0/unyt/unit_registry.py000066400000000000000000000305551526544257300170200ustar00rootroot00000000000000""" A registry for units that can be added to and modified. """ import copy import json from functools import lru_cache from hashlib import md5 from sympy import sympify from unyt import dimensions as unyt_dims from unyt._unit_lookup_table import default_unit_symbol_lut, unit_prefixes from unyt.exceptions import SymbolNotFoundError, UnitParseError from unyt.unit_systems import _split_prefix, mks_unit_system, unit_system_registry def _sanitize_unit_system(unit_system, obj): if unit_system is None: try: unit_system = obj.units.registry.unit_system except AttributeError: unit_system = mks_unit_system if hasattr(unit_system, "name"): return unit_system_registry[unit_system.name] elif hasattr(unit_system, "unit_registry"): unit_system = unit_system.unit_registry.unit_system_id elif unit_system == "code": unit_system = obj.units.registry.unit_system_id return unit_system_registry[str(unit_system)] @lru_cache(maxsize=128, typed=False) def cached_sympify(u): """ Successive loads of unit systems produce the same calls to sympify in UnitRegistry.from_json. Even within a single load, this is a net improvement because there will often be a few cache hits """ return sympify(u, locals=vars(unyt_dims)) class UnitRegistry: """A registry for unit symbols""" _unit_system_id = None def __init__(self, add_default_symbols=True, lut=None, unit_system=None): self._unit_object_cache = {} if lut: self.lut = lut else: self.lut = {} self.unit_system = _sanitize_unit_system(unit_system, None) if add_default_symbols: self.lut.update(default_unit_symbol_lut) def __getitem__(self, key): try: ret = self.lut[str(key)] except KeyError: try: _lookup_unit_symbol(str(key), self.lut) ret = self.lut[str(key)] except UnitParseError: raise SymbolNotFoundError( f"The symbol '{key}' does not exist in this registry." ) return ret def __contains__(self, item): if str(item) in self.lut: return True try: _lookup_unit_symbol(str(item), self.lut) return True except UnitParseError: return False @property def unit_system_id(self): """ This is a unique identifier for the unit registry created from a FNV hash. It is needed to register a dataset's code unit system in the unit system registry. """ if self._unit_system_id is None: hash_data = bytearray() for k, v in sorted(self.lut.items()): hash_data.extend(k.encode("utf8")) hash_data.extend(repr(v).encode("utf8")) # This hash is only a fingerprint used to identify a unit # registry, never for security, so flag it accordingly. Without # usedforsecurity=False, importing unyt crashes on FIPS-enabled # systems where MD5 is disabled for security purposes (#580). m = md5(usedforsecurity=False) m.update(hash_data) self._unit_system_id = str(m.hexdigest()) return self._unit_system_id @property def prefixable_units(self): return [u for u in self.lut if self.lut[u][4]] def add( self, symbol, base_value, dimensions, tex_repr=None, offset=None, prefixable=False, ): """ Add a symbol to this registry. Parameters ---------- symbol : str The name of the unit base_value : float The scaling from the units value to the equivalent SI unit with the same dimensions dimensions : expr The dimensions of the unit tex_repr : str, optional The LaTeX representation of the unit. If not provided a LaTeX representation is automatically generated from the name of the unit. offset : float, optional If set, the zero-point offset to apply to the unit to convert to SI. This is mostly used for units like Farhenheit and Celsius that are not defined on an absolute scale. prefixable : bool If True, then SI-prefix versions of the unit will be created along with the unit itself. """ from unyt.unit_object import _validate_dimensions self._unit_system_id = None # Validate if not isinstance(base_value, float): raise UnitParseError( f"base_value ({base_value}) must be a float, got a {type(base_value)}." ) if offset is not None: if not isinstance(offset, float): raise UnitParseError( f"offset value ({offset}) must be a float, got a {type(offset)}." ) else: offset = 0.0 _validate_dimensions(dimensions) if tex_repr is None: # make educated guess that will look nice in most cases tex_repr = r"\rm{" + symbol.replace("_", r"\ ") + "}" # Add to lut self.lut[symbol] = (base_value, dimensions, offset, tex_repr, prefixable) def remove(self, symbol): """ Remove the entry for the unit matching `symbol`. Parameters ---------- symbol : str The name of the unit symbol to remove from the registry. """ self._unit_system_id = None if symbol not in self.lut: raise SymbolNotFoundError( r"Tried to remove the symbol {symbol!r}, but it does not exist " "in this registry." ) del self.lut[symbol] if symbol in self._unit_object_cache: del self._unit_object_cache[symbol] def modify(self, symbol, base_value): """ Change the base value of a unit symbol. Useful for adjusting code units after parsing parameters. Parameters ---------- symbol : str The name of the symbol to modify base_value : float The new base_value for the symbol. """ self._unit_system_id = None if symbol not in self.lut: raise SymbolNotFoundError( f"Tried to modify the symbol {symbol!r}, but it does not exist " "in this registry." ) if hasattr(base_value, "in_base"): new_dimensions = base_value.units.dimensions base_value = base_value.in_base("mks") base_value = base_value.value else: new_dimensions = self.lut[symbol][1] self.lut[symbol] = (float(base_value), new_dimensions) + self.lut[symbol][2:] if symbol in self._unit_object_cache: del self._unit_object_cache[symbol] def keys(self): """ Print out the units contained in the lookup table. """ return self.lut.keys() def to_json(self): """ Returns a json-serialized version of the unit registry """ sanitized_lut = {} for k, v in self.lut.items(): san_v = list(v) repr_dims = str(v[1]) san_v[1] = repr_dims sanitized_lut[k] = tuple(san_v) return json.dumps(sanitized_lut) @classmethod def from_json(cls, json_text): """ Returns a UnitRegistry object from a json-serialized unit registry Parameters ---------- json_text : str A string containing a json representation of a UnitRegistry """ data = json.loads(json_text) lut = _correct_old_unit_registry(data, sympify=True) return cls(lut=lut, add_default_symbols=False) def list_same_dimensions(self, unit_object): """ Return a list of base unit names that this registry knows about that are of equivalent dimensions to *unit_object*. """ equiv = [k for k, v in self.lut.items() if v[1] is unit_object.dimensions] equiv = sorted(set(equiv)) return equiv def __deepcopy__(self, memodict=None): lut = copy.deepcopy(self.lut) return type(self)(lut=lut) class _NonModifiableUnitRegistry(UnitRegistry): """The class of the default unit registry""" def modify(self, symbol, base_value): raise TypeError("Units from unyt's default registry cannot be modified.") def remove(self, symbol): raise TypeError("Units from unyt's default registry cannot be removed.") #: The default unit registry default_unit_registry = _NonModifiableUnitRegistry() def _lookup_unit_symbol(symbol_str, unit_symbol_lut): """ Searches for the unit data tuple corresponding to the given symbol. Parameters ---------- symbol_str : str The unit symbol to look up. unit_symbol_lut : dict Dictionary with symbols as keys and unit data tuples as values. """ if symbol_str in unit_symbol_lut: # lookup successful, return the tuple directly return unit_symbol_lut[symbol_str] # could still be a known symbol with a prefix prefix, symbol_wo_prefix = _split_prefix(symbol_str, unit_symbol_lut) if prefix: # lookup successful, it's a symbol with a prefix unit_data = unit_symbol_lut[symbol_wo_prefix] prefix_value = unit_prefixes[prefix][0] # Need to add some special handling for comoving units # this is fine for now, but it wouldn't work for a general # unit that has an arbitrary LaTeX representation if symbol_wo_prefix != "cm" and symbol_wo_prefix.endswith("cm"): sub_symbol_wo_prefix = symbol_wo_prefix[:-2] sub_symbol_str = symbol_str[:-2] else: sub_symbol_wo_prefix = symbol_wo_prefix sub_symbol_str = symbol_str latex_repr = unit_data[3].replace( "{" + sub_symbol_wo_prefix + "}", "{" + sub_symbol_str + "}" ) # Leave offset and dimensions the same, but adjust scale factor and # LaTeX representation ret = ( unit_data[0] * prefix_value, unit_data[1], unit_data[2], latex_repr, False, ) unit_symbol_lut[symbol_str] = ret return ret # no dice raise UnitParseError( f"Could not find unit symbol '{symbol_str}' in the provided symbols." ) def _correct_old_unit_registry(data, sympify=False): lut = {} for k, v in data.items(): unsan_v = list(v) if sympify: unsan_v[1] = cached_sympify(v[1]) if len(unsan_v) == 4: # old unit registry so we need to add SI-prefixability to the registry # entry, correct the base_value to be in MKS units, and swap dimensions to # use unyt's dimension singletons # add SI-prefixability to LUT entry if k in default_unit_symbol_lut: unsan_v.append(default_unit_symbol_lut[k][4]) else: unsan_v.append(False) dims = unsan_v[1] for dim_factor in dims.as_ordered_factors(): dim, power = dim_factor.as_base_exp() # Swap dimensions in the LUT entry to use unyt's dimension singletons for base_dim in unyt_dims.base_dimensions: # If they're *equal* but not *identical*, swap them if base_dim == dim and base_dim is not dim: if power != 1: unsan_v[1] /= dim**power unsan_v[1] *= base_dim**power else: # need a special case for power == 1 because id(symbol ** 1) # is not necessarily the same as id(symbol) unsan_v[1] /= dim unsan_v[1] *= base_dim break # correct base value to be in MKS units if dim == unyt_dims.mass: unsan_v[0] /= 1000 ** float(power) if dim == unyt_dims.length: unsan_v[0] /= 100 ** float(power) lut[k] = tuple(unsan_v) for k in default_unit_symbol_lut: if k not in lut: lut[k] = default_unit_symbol_lut[k] return lut unyt-3.2.0/unyt/unit_symbols.py000066400000000000000000000012211526544257300166240ustar00rootroot00000000000000""" Predefined useful aliases to physical units Note that all of these names can be imported from the top-level unyt namespace. For example:: >>> from unyt import cm, g, s >>> data = [3, 4, 5]*g*cm/s >>> print(data) [3 4 5] cm*g/s .. show_all_units:: """ from unyt._unit_lookup_table import name_alternatives as _name_alternatives from unyt.unit_object import Unit as _Unit from unyt.unit_registry import default_unit_registry as _registry _namespace = globals() for _canonical_name, _alt_names in _name_alternatives.items(): for _alt_name in _alt_names: _namespace[_alt_name] = _Unit(_canonical_name, registry=_registry) unyt-3.2.0/unyt/unit_systems.py000066400000000000000000000276351526544257300166640ustar00rootroot00000000000000""" Unit system class. """ from collections import OrderedDict from unyt import dimensions from unyt._parsing import parse_unyt_expr from unyt._unit_lookup_table import ( default_unit_symbol_lut as default_lut, inv_name_alternatives, physical_constants, unit_prefixes, ) from unyt.exceptions import IllDefinedUnitSystem, MissingMKSCurrent, UnitsNotReducible def add_symbols(namespace, registry): """Adds the unit symbols from :mod:`unyt.unit_symbols` to a namespace Parameters ---------- namespace : dict The dict to insert unit symbols into. The keys will be string unit names and values will be the corresponding unit objects. registry : :class:`unyt.unit_registry.UnitRegistry` The registry to create units from. Note that if you would like to use a custom unit system, ensure your registry was created using that unit system. Example ------- >>> from unyt.unit_registry import UnitRegistry >>> class MyClass(): ... def __init__(self): ... self.reg = UnitRegistry() ... add_symbols(vars(self), self.reg) >>> foo = MyClass() >>> foo.kilometer km >>> foo.joule J """ import unyt.unit_symbols as us from unyt.unit_object import Unit for name, unit in vars(us).items(): if name.startswith("_"): continue namespace[name] = Unit(unit.expr, registry=registry) for name in [k for k in registry.keys() if k not in namespace]: namespace[name] = Unit(name, registry=registry) def add_constants(namespace, registry): """Adds the quantities from :mod:`unyt.physical_constants` to a namespace Parameters ---------- namespace : dict The dict to insert quantities into. The keys will be string names and values will be the corresponding quantities. registry : :class:`unyt.unit_registry.UnitRegistry` The registry to create units from. Note that if you would like to use a custom unit system, ensure your registry was created using that unit system. Example ------- >>> from unyt.unit_registry import UnitRegistry >>> class MyClass(): ... def __init__(self): ... self.reg = UnitRegistry(unit_system='cgs') ... add_constants(vars(self), self.reg) >>> foo = MyClass() >>> foo.gravitational_constant unyt_quantity(6.67408e-08, 'cm**3/(g*s**2)') >>> foo.speed_of_light unyt_quantity(2.99792458e+10, 'cm/s') """ from unyt.array import unyt_quantity for constant_name in physical_constants: value, unit_name, alternate_names = physical_constants[constant_name] for name in alternate_names + [constant_name]: quan = unyt_quantity(value, unit_name, registry=registry) try: namespace[name] = quan.in_base(unit_system=registry.unit_system) except UnitsNotReducible: namespace[name] = quan namespace[name + "_mks"] = unyt_quantity( value, unit_name, registry=registry ) try: namespace[name + "_cgs"] = quan.in_cgs() except UnitsNotReducible: pass if name == "h": # backward compatibility for unyt 1.0, which defined hmks namespace["hmks"] = namespace["h_mks"].copy() namespace["hcgs"] = namespace["h_cgs"].copy() def _split_prefix(symbol_str, unit_symbol_lut): possible_prefix = symbol_str[0] if symbol_str[:2] == "da": possible_prefix = "da" if possible_prefix in unit_prefixes: # the first character could be a prefix, check the rest of the symbol symbol_wo_pref = symbol_str[1:] # deca is the only prefix with length 2 if symbol_str[:2] == "da": symbol_wo_pref = symbol_str[2:] possible_prefix = "da" entry = unit_symbol_lut.get(symbol_wo_pref, None) if entry and entry[4]: return possible_prefix, symbol_wo_pref return "", symbol_str def _get_system_unit_string(dims, base_units): # The dimensions of a unit object is the product of the base dimensions. # Use sympy to factor the dimensions into base CGS unit symbols. units = [] my_dims = dims.expand() if my_dims is dimensions.dimensionless: return "" for factor in my_dims.as_ordered_factors(): dim = list(factor.free_symbols)[0] unit_string = str(base_units[dim]) if factor.is_Pow: power_string = f"**({factor.as_base_exp()[1]})" else: power_string = "" units.append(f"({unit_string}){power_string}") return " * ".join(units) unit_system_registry = {} cmks = dimensions.current_mks class UnitSystem: """ Create a UnitSystem for facilitating conversions to a default set of units. Parameters ---------- name : string The name of the unit system. Will be used as the key in the *unit_system_registry* dict to reference the unit system by. length_unit : string or :class:`unyt.unit_object.Unit` The base length unit of this unit system. mass_unit : string or :class:`unyt.unit_object.Unit` The base mass unit of this unit system. time_unit : string or :class:`unyt.unit_object.Unit` The base time unit of this unit system. temperature_unit : string or :class:`unyt.unit_object.Unit`, optional The base temperature unit of this unit system. Defaults to "K". angle_unit : string or :class:`unyt.unit_object.Unit`, optional The base angle unit of this unit system. Defaults to "rad". mks_system: boolean, optional Whether or not this unit system has SI-specific units. Default: False current_mks_unit : string or :class:`unyt.unit_object.Unit`, optional The base current unit of this unit system. Defaults to "A". luminous_intensity_unit : string or :class:`unyt.unit_object.Unit`, optional The base luminous intensity unit of this unit system. Defaults to "cd". registry : :class:`unyt.unit_registry.UnitRegistry` object The unit registry associated with this unit system. Only useful for defining unit systems based on code units. """ def __init__( self, name, length_unit, mass_unit, time_unit, temperature_unit="K", angle_unit="rad", current_mks_unit="A", luminous_intensity_unit="cd", logarithmic_unit="Np", registry=None, ): self.registry = registry self.units_map = OrderedDict( [ (dimensions.length, length_unit), (dimensions.mass, mass_unit), (dimensions.time, time_unit), (dimensions.temperature, temperature_unit), (dimensions.angle, angle_unit), (dimensions.current_mks, current_mks_unit), (dimensions.luminous_intensity, luminous_intensity_unit), (dimensions.logarithmic, logarithmic_unit), ] ) for k, v in self.units_map.items(): if v is not None: if hasattr(v, "value") and hasattr(v, "units"): self.units_map[k] = v.value * v.units.expr else: self.units_map[k] = parse_unyt_expr(str(v)) for dimension, unit in self.units_map.items(): # CGS sets the current_mks unit to none, so catch it here if unit is None and dimension is dimensions.current_mks: continue if unit.is_Mul: unit = unit.as_coeff_Mul()[1] if ( self.registry is not None and self.registry[str(unit)][1] is not dimension ): raise IllDefinedUnitSystem(self.units_map) elif self.registry is None: bu = _split_prefix(str(unit), default_lut)[1] inferred_dimension = default_lut[inv_name_alternatives[bu]][1] if inferred_dimension is not dimension: raise IllDefinedUnitSystem(self.units_map) self._dims = [ "length", "mass", "time", "temperature", "angle", "current_mks", "luminous_intensity", "logarithmic", ] self.registry = registry self.base_units = self.units_map.copy() unit_system_registry[name] = self self.name = name def __getitem__(self, key): from unyt.unit_object import Unit if isinstance(key, str): key = getattr(dimensions, key) um = self.units_map if key not in um or um[key] is None: if cmks in key.free_symbols and self.units_map[cmks] is None: raise MissingMKSCurrent(self.name) units = _get_system_unit_string(key, self.units_map) self.units_map[key] = parse_unyt_expr(units) return Unit(units, registry=self.registry) return Unit(self.units_map[key], registry=self.registry) def __setitem__(self, key, value): if isinstance(key, str): if key not in self._dims: self._dims.append(key) key = getattr(dimensions, key) if self.units_map[cmks] is None and cmks in key.free_symbols: raise MissingMKSCurrent(self.name) self.units_map[key] = parse_unyt_expr(str(value)) def __str__(self): return self.name def __repr__(self): repr = f"{self.name} Unit System\n" repr += " Base Units:\n" for dim in self.base_units: if self.base_units[dim] is not None: repr += f" {str(dim).strip('()')}: {self.base_units[dim]}\n" repr += " Other Units:\n" for key in self._dims: dim = getattr(dimensions, key) if dim not in self.base_units: repr += f" {key}: {self.units_map[dim]}\n" return repr[:-1] @property def has_current_mks(self): """Does this unit system have an MKS current dimension?""" return self.units_map[cmks] is not None #: The CGS unit system cgs_unit_system = UnitSystem("cgs", "cm", "g", "s", current_mks_unit=None) cgs_unit_system["energy"] = "erg" cgs_unit_system["specific_energy"] = "erg/g" cgs_unit_system["pressure"] = "dyne/cm**2" cgs_unit_system["force"] = "dyne" cgs_unit_system["magnetic_field_cgs"] = "gauss" cgs_unit_system["charge_cgs"] = "esu" cgs_unit_system["current_cgs"] = "statA" cgs_unit_system["power"] = "erg/s" #: The MKS unit system mks_unit_system = UnitSystem("mks", "m", "kg", "s") mks_unit_system["energy"] = "J" mks_unit_system["specific_energy"] = "J/kg" mks_unit_system["pressure"] = "Pa" mks_unit_system["force"] = "N" mks_unit_system["magnetic_field"] = "T" mks_unit_system["charge"] = "C" mks_unit_system["frequency"] = "Hz" mks_unit_system["power"] = "W" mks_unit_system["electric_potential"] = "V" mks_unit_system["capacitance"] = "F" mks_unit_system["inductance"] = "H" mks_unit_system["resistance"] = "ohm" mks_unit_system["magnetic_flux"] = "Wb" mks_unit_system["luminous_flux"] = "lm" #: The imperial unit system imperial_unit_system = UnitSystem("imperial", "ft", "lb", "s", 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