From 02ec1744a353488604bb48ab76a9130e4eb5278f Mon Sep 17 00:00:00 2001 From: d33bs Date: Mon, 3 Nov 2025 16:06:06 -0700 Subject: [PATCH 01/23] initialize work --- .github/ISSUE_TEMPLATE/issue.yml | 46 + .github/PULL_REQUEST_TEMPLATE.md | 37 + .github/dependabot.yml | 27 + .github/release-drafter.yml | 21 + .github/workflows/draft-release.yml | 23 + .github/workflows/publish-docs.yml | 41 + .github/workflows/publish-pypi.yml | 33 + .github/workflows/run-tests.yml | 46 + .gitignore | 6 +- .pre-commit-config.yaml | 58 + .python-version | 1 + CITATION.cff | 34 + CODE_OF_CONDUCT.md | 132 + CONTRIBUTING.md | 129 + README.md | 1 + docs/src/conf.py | 85 + docs/src/index.md | 16 + docs/src/python-api.md | 11 + pyproject.toml | 110 + src/ome_arrow/__init__.py | 12 + src/ome_arrow/convert.py | 211 ++ src/ome_arrow/meta.py | 96 + src/ome_arrow/utils.py | 25 + src/ome_arrow/view.py | 94 + tests/conftest.py | 69 + .../AS_09125_050116030001_D03f00d0.tif | Bin 0 -> 233336 bytes .../AS_09125_050116030001_D03f00d1.tif | Bin 0 -> 240924 bytes .../AS_09125_050116030001_D03f00d2.tif | Bin 0 -> 293652 bytes tests/test_meta.py | 17 + uv.lock | 3319 +++++++++++++++++ 30 files changed, 4698 insertions(+), 2 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/issue.yml create mode 100644 .github/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/dependabot.yml create mode 100644 .github/release-drafter.yml create mode 100644 .github/workflows/draft-release.yml create mode 100644 .github/workflows/publish-docs.yml create mode 100644 .github/workflows/publish-pypi.yml create mode 100644 .github/workflows/run-tests.yml create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version create mode 100644 CITATION.cff create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 docs/src/conf.py create mode 100644 docs/src/index.md create mode 100644 docs/src/python-api.md create mode 100644 pyproject.toml create mode 100644 src/ome_arrow/__init__.py create mode 100644 src/ome_arrow/convert.py create mode 100644 src/ome_arrow/meta.py create mode 100644 src/ome_arrow/utils.py create mode 100644 src/ome_arrow/view.py create mode 100644 tests/conftest.py create mode 100644 tests/data/examplehuman/AS_09125_050116030001_D03f00d0.tif create mode 100644 tests/data/examplehuman/AS_09125_050116030001_D03f00d1.tif create mode 100644 tests/data/examplehuman/AS_09125_050116030001_D03f00d2.tif create mode 100644 tests/test_meta.py create mode 100644 uv.lock diff --git a/.github/ISSUE_TEMPLATE/issue.yml b/.github/ISSUE_TEMPLATE/issue.yml new file mode 100644 index 0000000..4e449ed --- /dev/null +++ b/.github/ISSUE_TEMPLATE/issue.yml @@ -0,0 +1,46 @@ +# GitHub Issue template for bug reports +name: Open a GitHub issue +description: > + Please use this form to send along new ideas for content or + changes that might be helpful! + +body: + - type: checkboxes + attributes: + label: Is this a duplicate of an existing idea for this project? + description: > + Please make sure to search the existing issues first + to see whether the same issue was reported already. + If you find an existing issue, please don't hesitate to comment + on it or add a reaction to existing content! + options: + - label: > + I found no existing covering this topic. + required: true + + - type: textarea + id: description + attributes: + label: What is your idea? + description: > + Please provide a specific description of what you'd like to see + including the context and what the result might look like. + placeholder: > + For example: "When x happens I see y. + The following might be a good way to address this ..." + validations: + required: true + + - type: checkboxes + attributes: + label: Would you like to work on a solution for this? + description: > + This is a community-driven project and we + love new contributors (including through opening or adding to issues)! + This is an optional check to help us understand your interest to be + involved (especially if you already have a good understanding + of how to implement it). + We are happy to guide you in the contribution process and please + don't hesitate to reach out for help along the way. + options: + - label: Yes I am willing to submit a PR for this! diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..66a3c90 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,37 @@ + + +# Description + + + +## What kind of change(s) are included? + +- [ ] Documentation (changes docs or other related content) +- [ ] Bug fix (fixes an issue). +- [ ] Enhancement (adds functionality). +- [ ] Breaking change (these changes would cause existing functionality to not work as expected). + +# Checklist + +Please ensure that all boxes are checked before indicating that this pull request is ready for review. + +- [ ] I have read and followed the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines. +- [ ] I have searched for existing content to ensure this is not a duplicate. +- [ ] I have performed a self-review of these additions (including spelling, grammar, and related). +- [ ] These changes pass all pre-commit checks. +- [ ] I have added comments to my code to help provide understanding +- [ ] I have added a test which covers the code changes found within this PR +- [ ] I have deleted all non-relevant text in this pull request template. diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..b9c048a --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,27 @@ +# GitHub Dependabot configuration +# Note: there is no interaction between this +# configuration and dependabot security updates. +# See here for more information: +# https://docs.github.com/en/code-security/dependabot/dependabot-security-updates/about-dependabot-security-updates#about-dependabot-security-updates + +version: 2 +updates: + # GitHub Actions checks + # See here for more information: + # https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuring-dependabot-version-updates + - package-ecosystem: "github-actions" + directory: "/" + schedule: + # Check for updates to GitHub Actions every week + interval: "weekly" + + # Perform checks and updates for python uv environment. + # This depends on changes to dependabot via: + # https://github.com/dependabot/dependabot-core/pull/10040 + # See here for more information: + # https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#pip-and-pip-compile + - package-ecosystem: "pip" + directory: "/" + schedule: + # Check for updates to uv environment files every week + interval: "weekly" diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml new file mode 100644 index 0000000..3d8f4ed --- /dev/null +++ b/.github/release-drafter.yml @@ -0,0 +1,21 @@ +--- +# template configuration for release-drafter +# see: https://github.com/release-drafter/release-drafter +name-template: 'v$RESOLVED_VERSION' +tag-template: 'v$RESOLVED_VERSION' +version-resolver: + major: + labels: + - 'release-major' + minor: + labels: + - 'release-minor' + patch: + labels: + - 'release-patch' + default: patch +change-template: '- $TITLE (@$AUTHOR via #$NUMBER)' +template: | + ## Changes + + $CHANGES diff --git a/.github/workflows/draft-release.yml b/.github/workflows/draft-release.yml new file mode 100644 index 0000000..4847e3e --- /dev/null +++ b/.github/workflows/draft-release.yml @@ -0,0 +1,23 @@ +--- +# workflow for drafting releases on GitHub +# see: https://github.com/release-drafter/release-drafter +name: release drafter + +on: + push: + branches: + - main + +jobs: + draft_release: + permissions: + # write permission is required to create a github release + contents: write + # write permission is required for autolabeler + # otherwise, read permission is required at least + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: release-drafter/release-drafter@v6 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml new file mode 100644 index 0000000..e4c7fdf --- /dev/null +++ b/.github/workflows/publish-docs.yml @@ -0,0 +1,41 @@ +--- +# used for publishing documentation on push to main or published release +name: publish docs + +on: + push: + branches: + - main + release: + types: + - published + +jobs: + build: + # only build and deploy docs if the actor is not dependabot + if: ${{ github.actor != 'dependabot[bot]' }} + runs-on: ubuntu-22.04 + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 + - name: Build documentation + run: | + mkdir pages + touch pages/.nojekyll + cd docs + uv run --frozen sphinx-build src build + # remove any doctrees dirs which aren't needed for publishing + find ./build -type d -name '.doctrees' -exec rm -rf {} + + cp -r build/* ../pages/ + - name: Deploy documentation + uses: JamesIves/github-pages-deploy-action@v4 + with: + branch: pages + folder: pages diff --git a/.github/workflows/publish-pypi.yml b/.github/workflows/publish-pypi.yml new file mode 100644 index 0000000..71b18e5 --- /dev/null +++ b/.github/workflows/publish-pypi.yml @@ -0,0 +1,33 @@ +--- +# used for publishing packages to pypi on release +name: publish pypi release + +on: + release: + types: + - published + +jobs: + publish_pypi: + runs-on: ubuntu-latest + environment: release + permissions: + # IMPORTANT: this permission is mandatory for trusted publishing + id-token: write + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Fetch tags + run: git fetch --all --tags + - name: Python setup + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 + - name: uv build distribution content + run: uv build + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml new file mode 100644 index 0000000..f9a1d29 --- /dev/null +++ b/.github/workflows/run-tests.yml @@ -0,0 +1,46 @@ +--- +# used for running tests +name: tests + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + pre_commit_checks: + runs-on: ubuntu-24.04 + steps: + # checks out the repo + - uses: actions/checkout@v4 + # run pre-commit + - name: Python setup + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 + - uses: pre-commit/action@v3.0.1 + # run pre-commit ci lite for automated fixes + - uses: pre-commit-ci/lite-action@v1.1.0 + if: ${{ !cancelled() }} + run_tests: + strategy: + matrix: + python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + os: [ubuntu-24.04, macos-14] + runs-on: ${{ matrix.os }} + env: + OS: ${{ matrix.os }} + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Python setup + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python_version }} + - name: Install the latest version of uv + uses: astral-sh/setup-uv@v6 + - name: Run pytest + run: uv run --frozen pytest diff --git a/.gitignore b/.gitignore index b7faf40..870936d 100644 --- a/.gitignore +++ b/.gitignore @@ -182,9 +182,9 @@ cython_debug/ .abstra/ # Visual Studio Code -# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore # that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore -# and can be added to the global gitignore or merged into this file. However, if you prefer, +# and can be added to the global gitignore or merged into this file. However, if you prefer, # you could uncomment the following to ignore the entire vscode folder # .vscode/ @@ -205,3 +205,5 @@ cython_debug/ marimo/_static/ marimo/_lsp/ __marimo__/ + +_version.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..0b3cd97 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,58 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: detect-private-key +- repo: https://github.com/tox-dev/pyproject-fmt + rev: "v2.11.0" + hooks: + - id: pyproject-fmt +- repo: https://github.com/citation-file-format/cffconvert + rev: 5295f87c0e261da61a7b919fc754e3a77edd98a7 + hooks: + - id: validate-cff +- repo: https://github.com/codespell-project/codespell + rev: v2.4.1 + hooks: + - id: codespell + exclude: | + (?x)^( + .*\.lock | + .*\.csv | + .*\.cff | + .*\.ipynb + )$ +- repo: https://github.com/executablebooks/mdformat + rev: 0.7.21 + hooks: + - id: mdformat + additional_dependencies: + - mdformat-gfm +- repo: https://github.com/adrienverge/yamllint + rev: v1.37.1 + hooks: + - id: yamllint + exclude: pre-commit-config.yaml +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.14.3" + hooks: + - id: ruff-format + - id: ruff-check +- repo: https://github.com/rhysd/actionlint + rev: v1.7.8 + hooks: + - id: actionlint +- repo: https://gitlab.com/vojko.pribudic.foss/pre-commit-update + rev: v0.6.0 + hooks: + - id: pre-commit-update + args: ["--keep", "mdformat", "--keep", "pre-commit-update", "--keep", "cffconvert"] +- repo: https://github.com/jendrikseipp/vulture + rev: 'v2.14' + hooks: + - id: vulture diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..149191e --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,34 @@ +# This CITATION.cff file was generated with cffinit. +# Visit https://bit.ly/cffinit to generate yours today! +--- +cff-version: 1.2.0 +title: ome_arrow +message: >- + If you use this software, please cite it using the + metadata from the CITATION.cff file. +type: software +authors: + - given-names: Dave + family-names: Bunten + orcid: "https://orcid.org/0000-0001-6041-3665" +repository-code: "https://github.com/d33bs/ome-arrow" +abstract: >- + Using OME specifications with Apache Arrow for fast, queryable, and language agnostic bioimage data. +keywords: + - python +license: BSD-3-Clause +references: + - authors: + - name: "ExampleHuman CellProfiler Data Team" + date-accessed: "2022-09-01" + title: ExampleHuman CellProfiler Data + type: data + repository-code: "https://github.com/CellProfiler/examples/tree/master/ExampleHuman" + url: "https://cellprofiler.org/examples" + scope: "ExampleHuman" + notes: >- + ExampleHuman CellProfiler data is used to help validate expected results. + identifiers: + - description: "README.md with Citation Information" + type: url + value: "https://github.com/CellProfiler/examples/blob/master/ExampleHuman/README.md" \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..bde3cb7 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,132 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socioeconomic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +- Demonstrating empathy and kindness toward other people +- Being respectful of differing opinions, viewpoints, and experiences +- Giving and gracefully accepting constructive feedback +- Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +- Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +- The use of sexualized language or imagery, and sexual attention or advances of + any kind +- Trolling, insulting or derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or email address, + without their explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official email address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to community leaders responsible for enforcement. +Please reach out to the maintainers of this repository by using their GitHub profile email address contact information to privately notify us of any incidents of this nature. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][mozilla coc]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][faq]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[faq]: https://www.contributor-covenant.org/faq +[homepage]: https://www.contributor-covenant.org +[mozilla coc]: https://github.com/mozilla/diversity +[translations]: https://www.contributor-covenant.org/translations +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..aa0b66a --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,129 @@ +# Contributing + +First of all, thank you so much for contributing! 🎉 💯 + +This document contains guidelines on how to most effectively contribute within this repository. + +If you are stuck, please feel free to ask any questions or ask for help. + +## Code of conduct + +This project is governed by our [code of conduct](code_of_conduct.md). By participating, you are expected to uphold this code. +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to community leaders responsible for enforcement. +Please open a [new security advisory notice](https://github.com/d33bs/ome-arrow/security/advisories/new) (using defaults or "n/a" where unable to fill in the form) to privately notify us of any incidents of this nature. + +## Development + +This project leverages development environments managed by [uv](https://docs.astral.sh/uv/). +We use [pytest](https://docs.pytest.org/) for testing and [GitHub actions](https://docs.github.com/en/actions) for automated tests. + +### Development setup + +Perform the following steps to setup a Python development environment. + +1. [Install Python](https://www.python.org/downloads/) (we recommend using [`pyenv`](https://github.com/pyenv/pyenv) or similar) +1. [Install uv](https://docs.astral.sh/uv/getting-started/installation/) + +### Linting + +Work added to this project is automatically checked using [pre-commit](https://pre-commit.com/) via [GitHub Actions](https://docs.github.com/en/actions). +Pre-commit can work alongside your local [git with git-hooks](https://pre-commit.com/index.html#3-install-the-git-hook-scripts) + +After [installing pre-commit](https://pre-commit.com/#installation) within your development environment, the following command also can perform the same checks within your local development environment: + +```sh +% pre-commit run --all-files +``` + +We use these same checks within our automated tests which are managed by [GitHub Actions workflows](https://docs.github.com/en/actions/using-workflows). +These automated tests generally must pass in order to merge work into this repository. + +### Testing + +Work added to this project is automatically tested using [pytest](https://docs.pytest.org/) via [GitHub Actions](https://docs.github.com/en/actions). +Pytest is installed through the uv environment for this project. +We recommend testing your work before opening pull requests with proposed changes. + +You can run pytest on your work using the following example: + +```sh +% uv run pytest +``` + +## Making changes to this repository + +We welcome anyone to use [GitHub issues](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues) (requires a GitHub login) or create [pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) (to directly make changes within this repository) to modify content found within this repository. + +Specifically, there are several ways to suggest or make changes to this repository: + +1. Open a GitHub issue: https://github.com/d33bs/ome-arrow/issues +1. Create a pull request from a forked branch of the repository + +### Creating a pull request + +### Pull requests + +After you’ve decided to contribute code and have written it up, please file a pull request. +We specifically follow a [forked pull request model](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork). +Please create a fork of this repository, clone the fork, and then create a new, feature-specific branch. +Once you make the necessary changes on this branch, you should file a pull request to incorporate your changes into this (fork upstream) repository. + +The content and description of your pull request are directly related to the speed at which we are able to review, approve, and merge your contribution. +To ensure an efficient review process please perform the following steps: + +1. Follow all instructions in the [pull request template](https://github.com/d33bs/ome-arrow/blob/main/.github/PULL_REQUEST_TEMPLATE.md) +1. Triple check that your pull request is adding _one_ specific feature or additional group of content. + Small, bite-sized pull requests move so much faster than large pull requests. +1. After submitting your pull request, ensure that your contribution passes all status checks (e.g. passes all tests) + +Pull request review and approval is required by at least one project maintainer to merge. +We will do our best to review the code addition in a timely fashion. +Ensuring that you follow all steps above will increase our speed and ability to review. +We will check for accuracy, style, code coverage, and scope. + +## Versioning + +We use [`setuptools-scm`](https://github.com/pypa/setuptools-scm) to help version this software through [`PEP 440`](https://peps.python.org/pep-0440/) standards and [semver.org](https://semver.org/) standards. +Configuration for versioning is found within the `pyproject.toml` file. +All builds for packages include dynamic version data to help label distinct versions of the software. +`setuptools-scm` uses `git` tags to help distinguish version data. +We also use the `_version.py` file as a place to persist the version data for occaissions where the `git` history is unavailable or unwanted (this file is only present in package builds). +Versioning for the project is intended to align with GitHub Releases which provide `git` tag capabilities. + +### Releases + +We publish source code by using [GitHub Releases](https://docs.github.com/en/repositories/releasing-projects-on-github/about-releases) available [here](https://github.com/d33bs/ome-arrow/releases). + +#### Release Publishing Process + +Several manual and automated steps are involved with publishing releases. +See below for an overview of how this works. + +Notes about [semantic version](https://en.wikipedia.org/wiki/Software_versioning#Semantic_versioning) (semver) specifications: version specifications are controlled through [`setuptools-scm`](https://github.com/pypa/setuptools-scm) to create version data based on [git tags](https://git-scm.com/book/en/v2/Git-Basics-Tagging) and commits. +Release git tags are automatically applied through [GitHub Releases](https://docs.github.com/en/repositories/releasing-projects-on-github/about-releases) and related inferred changes from [`release-drafter`](https://github.com/release-drafter/release-drafter). + +1. Open a pull request and use a repository label for `release-` to label the pull request for visibility with [`release-drafter`](https://github.com/release-drafter/release-drafter). +1. On merging the pull request for the release, a [GitHub Actions workflow](https://docs.github.com/en/actions/using-workflows) defined in `draft-release.yml` leveraging [`release-drafter`](https://github.com/release-drafter/release-drafter) will draft a release for maintainers. +1. The draft GitHub release will include a version tag based on the GitHub PR label applied and `release-drafter`. +1. Make modifications as necessary to the draft GitHub release, then publish the release (the draft release does not normally need additional modifications). +1. On publishing the release, another GitHub Actions workflow defined in `publish-pypi.yml` will run to build and deploy the Python package to PyPI (utilizing the earlier modified `pyproject.toml` semantic version reference for labeling the release). + +## Documentation + +Documentation for this project is published using [Sphinx](https://www.sphinx-doc.org) with markdown and Jupyter notebook file compatibility provided by [myst-parser](https://myst-parser.readthedocs.io/en/latest/) and [myst-nb](https://myst-nb.readthedocs.io/en/latest/) to create a "documentation website" (also known as "docsite"). +The docsite is hosted through [GitHub Pages](https://pages.github.com/) and deployed through automated [GitHub Actions](https://docs.github.com/en/actions) jobs which trigger on pushes to the main branch or the publishing of a new release on GitHub. +Documentation is versioned as outlined earlier sections covering versioning details to help ensure users are able to understand each release independently of one another. + +It can sometimes be useful to test documentation builds locally before proposing changes within a pull request. +See below for some examples of how to build documentation locally. + +```shell +# build single-version sphinx documentation +# (useful for troubleshooting potential issues) +uv run sphinx-build docs/src docs/build + +# build multi-version sphinx documentation +# (used in production) +uv run sphinx-multiversion docs/src docs/build +``` diff --git a/README.md b/README.md index 0bf406a..abc669a 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,3 @@ # ome-arrow + Using OME specifications with Apache Arrow for fast, queryable, and language agnostic bioimage data. diff --git a/docs/src/conf.py b/docs/src/conf.py new file mode 100644 index 0000000..513f6f6 --- /dev/null +++ b/docs/src/conf.py @@ -0,0 +1,85 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import pathlib +import sys + +basedir = str(pathlib.Path(__file__).parent.parent.parent.resolve()) + +sys.path.insert(0, basedir) + +# -- Project information ----------------------------------------------------- + +project = "ome_arrow" +# is used here due to sphinx decision-making: https://github.com/sphinx-doc/sphinx/issues/8132 +copyright = "2024, DBMI Community" # noqa: A001 +author = "DBMI Community" + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "myst_nb", + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "pydata_sphinx_theme", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] # type: ignore + +# -- 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 = "pydata_sphinx_theme" + +html_theme_options = { + "header_links_before_dropdown": 5, + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/d33bs/ome-arrow", + "icon": "fa-brands fa-github", + }, + ], + "logo": {"text": "ome_arrow"}, + "use_edit_page_button": False, + "show_toc_level": 1, + "navbar_align": "left", + "navbar_center": ["navbar-nav"], + "footer_start": ["copyright"], + "footer_center": ["sphinx-version"], + "secondary_sidebar_items": { + "**/*": ["page-toc", "edit-this-page", "sourcelink"], + }, +} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] +html_css_files = ["custom.css"] + +# set option to avoid rendering default variables +autodoc_preserve_defaults = True + +# enable anchor creation +myst_heading_anchors = 3 diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..e811a07 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,16 @@ + + +```{include} ../../README.md +--- +relative-docs: docs/src/ +relative-images: +--- +``` + +```{toctree} +--- +caption: 'Contents:' +maxdepth: 3 +--- +python-api +``` diff --git a/docs/src/python-api.md b/docs/src/python-api.md new file mode 100644 index 0000000..a3b985b --- /dev/null +++ b/docs/src/python-api.md @@ -0,0 +1,11 @@ +# Python API + +```{eval-rst} +ome_arrow.meta +------------------- +.. automodule:: src.ome_arrow.meta + :members: + :private-members: + :undoc-members: + :show-inheritance: +``` diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..e046844 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,110 @@ +[build-system] +build-backend = "setuptools.build_meta" +requires = [ "setuptools>=64", "setuptools-scm>=8", "uv-dynamic-versioning"] + +[project] +name = "ome_arrow" +dynamic = ["version"] +description = "Using OME specifications with Apache Arrow for fast, queryable, and language agnostic bioimage data." +readme = "README.md" +authors = [ + { name = "Dave Bunten" }, +] +requires-python = ">=3.11" +classifiers = [ + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "pandas>=2.2.3", + "fire>=0.7.0", + "pyarrow>=22.0.0", + "matplotlib>=3.10.7", + "bioio>=3.0.0", + "bioio-tifffile>=1.3.0", +] + +[dependency-groups] +dev = [ + "poethepoet>=0.34", + "pytest>=8.3.5", +] +docs = [ + "myst-nb>=1.2", + "pydata-sphinx-theme>=0.16.1", + "sphinx>=8.2.3", +] +notebooks = [ + "black>=25.1", + "isort>=6.0.1", + "jupyterlab>=4.4.2", + "jupyterlab-code-formatter>=3.0.2", + "jupytext>=1.17.1", +] + +[tool.setuptools_scm] +root = "." +version_scheme = "no-guess-dev" +local_scheme = "no-local-version" +version_file = "src/ome_arrow/_version.py" + +[tool.setuptools.package-dir] +"" = "src" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.ruff] +target-version = "py311" +line-length = 88 +fix = true +lint.select = [ + # flake8-builtins + "A", + # flake8-annotations + "ANN", + # flake8-comprehensions + "C4", + # mccabe + "C90", + # pycodestyle + "E", + # pyflakes + "F", + # isort + "I", + # pylint + "PL", + # ruff + "RUF", + # flake8-simplify + "SIM", + "W", +] +# Ignore `F401` (unused imports) for `__init__.py` file +lint.per-file-ignores."__init__.py" = [ "F401" ] + +[tool.pytest.ini_options] +pythonpath = [ "." ] + +[tool.poe.tasks] +# tasks for development environments +test.shell = """ +pytest +""" +jupyter.shell = """ +jupyter lab +""" + +[tool.jupytext] +formats = "ipynb,py:light" + +[tool.vulture] +min_confidence = 90 +paths = [ "src/ome_arrow", "tests" ] + +[tool.uv-dynamic-versioning] +vcs = "git" +style = "semver" diff --git a/src/ome_arrow/__init__.py b/src/ome_arrow/__init__.py new file mode 100644 index 0000000..7477996 --- /dev/null +++ b/src/ome_arrow/__init__.py @@ -0,0 +1,12 @@ +""" +Init file for example package. +""" + +from ome_arrow.meta import * +from ome_arrow.convert import * +from ome_arrow.view import * +from ome_arrow.utils import * + +from ._version import version as ome_arrow_version + +__version__ = ome_arrow_version diff --git a/src/ome_arrow/convert.py b/src/ome_arrow/convert.py new file mode 100644 index 0000000..2e1582f --- /dev/null +++ b/src/ome_arrow/convert.py @@ -0,0 +1,211 @@ +""" +Converting to and from OME-Arrow formats. +""" + +from datetime import datetime +from typing import Optional, Sequence, List, Dict, Any + +import pyarrow as pa + +from datetime import datetime +from pathlib import Path +from typing import Optional, Sequence + +import numpy as np +import pyarrow as pa +from bioio import BioImage + +from ome_arrow.meta import OME_ARROW_STRUCT + + +def to_ome_arrow( + type_: str = "ome.arrow", + version: str = "1.0.0", + image_id: str = "unnamed", + name: str = "unknown", + acquisition_datetime: Optional[datetime] = None, + dimension_order: str = "XYZCT", + dtype: str = "uint16", + size_x: int = 1, + size_y: int = 1, + size_z: int = 1, + size_c: int = 1, + size_t: int = 1, + physical_size_x: float = 1.0, + physical_size_y: float = 1.0, + physical_size_z: float = 1.0, + physical_size_unit: str = "µm", + channels: Optional[List[Dict[str, Any]]] = None, + planes: Optional[List[Dict[str, Any]]] = None, + masks: Any = None, +) -> pa.StructScalar: + """Create a typed OME-Arrow StructScalar with sensible defaults. + + This builds and validates a nested dict that conforms to the given + StructType (e.g., OME_ARROW_STRUCT). You can override any field + explicitly; others use safe defaults. + + Args: + type_: Top-level type string ("ome.arrow" by default). + version: Specification version string. + image_id: Unique image identifier. + name: Human-friendly name. + acquisition_datetime: Datetime of acquisition (defaults to now). + dimension_order: Dimension order ("XYZCT" or "XYCT"). + dtype: Pixel data type string (e.g., "uint16"). + size_x, size_y, size_z, size_c, size_t: Axis sizes. + physical_size_x/y/z: Physical scaling in µm. + physical_size_unit: Unit string, default "µm". + channels: List of channel dicts. Autogenerates one if None. + planes: List of plane dicts. Empty if None. + masks: Optional placeholder for future annotations. + + Returns: + pa.StructScalar: A validated StructScalar for the schema. + + Example: + >>> s = to_struct_scalar(OME_ARROW_STRUCT, image_id="img001") + >>> s.type == OME_ARROW_STRUCT + True + """ + # Sensible defaults for channels and planes + if channels is None: + channels = [ + { + "id": "ch-0", + "name": "default", + "emission_um": 0.0, + "excitation_um": 0.0, + "illumination": "Unknown", + "color_rgba": 0xFFFFFFFF, + } + ] + if planes is None: + planes = [ + {"z": 0, "t": 0, "c": 0, "pixels": [0] * (size_x * size_y)} + ] + + record = { + "type": type_, + "version": version, + "id": image_id, + "name": name, + "acquisition_datetime": acquisition_datetime or datetime.utcnow(), + "pixels_meta": { + "dimension_order": dimension_order, + "type": dtype, + "size_x": size_x, + "size_y": size_y, + "size_z": size_z, + "size_c": size_c, + "size_t": size_t, + "physical_size_x": physical_size_x, + "physical_size_y": physical_size_y, + "physical_size_z": physical_size_z, + "physical_size_x_unit": physical_size_unit, + "physical_size_y_unit": physical_size_unit, + "physical_size_z_unit": physical_size_unit, + "channels": channels, + }, + "planes": planes, + "masks": masks, + } + + # Validate against struct (raises if mismatched) + return pa.scalar(record, type=OME_ARROW_STRUCT) + + +def tiff_to_ome_arrow( + tiff_path: str | Path, + image_id: Optional[str] = None, + name: Optional[str] = None, + channel_names: Optional[Sequence[str]] = None, + acquisition_datetime: Optional[datetime] = None, + clamp_to_uint16: bool = True, +) -> pa.StructScalar: + """Read a TIFF via bioio and return a typed OME-Arrow StructScalar. + + Uses bioio to read TCZYX (or XY) data, flattens each YX plane, and + delegates struct creation to `to_struct_scalar`. + + Args: + tiff_path: Path to a TIFF readable by bioio. + image_id: Optional stable image identifier (defaults to stem). + name: Optional human label (defaults to file name). + channel_names: Optional channel names; defaults to C0..C{n-1}. + acquisition_datetime: Optional acquisition time (UTC now if None). + clamp_to_uint16: If True, clamp/cast planes to uint16. + + Returns: + pa.StructScalar validated against `struct`. + """ + p = Path(tiff_path) + img = BioImage(str(p)) + + # BioIO data is TCZYX (broadcasting missing dims to length 1). + arr = np.asarray(img.data) # shape: (T, C, Z, Y, X) + dims = img.dims + size_t = int(dims.T or 1) + size_c = int(dims.C or 1) + size_z = int(dims.Z or 1) + size_y = int(dims.Y or arr.shape[-2]) + size_x = int(dims.X or arr.shape[-1]) + if size_x <= 0 or size_y <= 0: + raise ValueError("Image must have positive Y and X dims.") + + # Physical pixel sizes (µm); default to 1.0 if unavailable. + pps = getattr(img, "physical_pixel_sizes", None) + try: + psize_x = float(getattr(pps, "X", None) or 1.0) + psize_y = float(getattr(pps, "Y", None) or 1.0) + psize_z = float(getattr(pps, "Z", None) or 1.0) + except Exception: + psize_x = psize_y = psize_z = 1.0 + + # Channels block. + if not channel_names or len(channel_names) != size_c: + channel_names = [f"C{i}" for i in range(size_c)] + channels = [{ + "id": f"ch-{i}", "name": channel_names[i], + "emission_um": 0.0, "excitation_um": 0.0, + "illumination": "Unknown", "color_rgba": 0xFFFFFFFF + } for i in range(size_c)] + + # Planes: flattened YX per (t, c, z). + planes: List[Dict[str, Any]] = [] + for t in range(size_t): + for c in range(size_c): + for z in range(size_z): + plane = arr[t, c, z] # (Y, X) + if clamp_to_uint16 and plane.dtype != np.uint16: + plane = np.clip(plane, 0, 65535).astype(np.uint16) + planes.append({ + "z": z, "t": t, "c": c, + "pixels": plane.ravel().tolist() + }) + + # Dimension order: no Z implies XYCT. + dim_order = "XYCT" if size_z == 1 else "XYZCT" + + # Delegate final struct creation + type validation. + return to_ome_arrow( + type_="ome.arrow", + version="1.0.0", + image_id=image_id or p.stem, + name=name or p.name, + acquisition_datetime=acquisition_datetime or datetime.utcnow(), + dimension_order=dim_order, + dtype="uint16", + size_x=size_x, + size_y=size_y, + size_z=size_z, + size_c=size_c, + size_t=size_t, + physical_size_x=psize_x, + physical_size_y=psize_y, + physical_size_z=psize_z, + physical_size_unit="µm", + channels=channels, + planes=planes, + masks=None, + ) \ No newline at end of file diff --git a/src/ome_arrow/meta.py b/src/ome_arrow/meta.py new file mode 100644 index 0000000..c33ffff --- /dev/null +++ b/src/ome_arrow/meta.py @@ -0,0 +1,96 @@ +import numpy as np +import pyarrow as pa +from ._version import version as ome_arrow_version + +OME_ARROW_TAG_TYPE = "ome.arrow" +OME_ARROW_TAG_VERSION = ome_arrow_version + +# OME_ARROW_STRUCT: ome-arrow record (describes one image/value). +# - type/version: quick identity & evolution. +# - id/name/acquisition_datetime: identity & provenance. +# - pixels_meta: pixels struct (sizes, units, channels). +# - planes: list of planes struct entries, one per (t,c,z). +# - masks: reserved for future labels/ROIs (placeholder). +OME_ARROW_STRUCT: pa.StructType = pa.struct( + [ + pa.field("type", pa.string()), # must be "ome.arrow" + pa.field("version", pa.string()), # e.g., "1.0.0" + pa.field("id", pa.string()), # stable image identifier + pa.field("name", pa.string()), # human label + pa.field("acquisition_datetime", pa.timestamp("us")), + # PIXELS: OME-like "Pixels" header summarizing shape & scale. + # - dimension_order: hint like "XYZCT" (or "XYCT" when Z==1). + # - type: numeric storage type (e.g., "uint16"). + # - size_*: axis lengths. + # - physical_size_* (+ *_unit): microscope scale in micrometers. + # - channels: list of channel struct entries (one per channel). + pa.field( + "pixels_meta", + pa.struct( + [ + pa.field("dimension_order", pa.string()), # "XYZCT" / "XYCT" + pa.field("type", pa.string()), # "uint8","uint16","float",... + pa.field("size_x", pa.int32()), # width (pixels) + pa.field("size_y", pa.int32()), # height (pixels) + pa.field("size_z", pa.int32()), # z-slices (1 for 2D) + pa.field("size_c", pa.int16()), # channels + pa.field("size_t", pa.int32()), # time points + pa.field("physical_size_x", pa.float32()), # µm per pixel (X) + pa.field("physical_size_y", pa.float32()), # µm per pixel (Y) + pa.field("physical_size_z", pa.float32()), # µm per z-step + pa.field("physical_size_x_unit", pa.string()), # usually "µm" + pa.field("physical_size_y_unit", pa.string()), + pa.field("physical_size_z_unit", pa.string()), + pa.field( + "channels", + pa.list_( + # CHANNELS: one entry per channel (e.g., DNA, Mito, ER). + # - emission_um / excitation_um: wavelengths (micrometers). + # - illumination: modality (e.g., "Epifluorescence"). + # - color_rgba: preferred display color + # (packed 0xRRGGBBAA). + pa.struct( + [ + pa.field("id", pa.string()), + pa.field("name", pa.string()), + pa.field("emission_um", pa.float32()), + pa.field("excitation_um", pa.float32()), + pa.field("illumination", pa.string()), + pa.field("color_rgba", pa.uint32()), + ] + ) + ), + ), + ] + ), + ), + # PLANES: one 2D image plane for a specific (t, c, z). + # - pixels: flattened numeric list (Y*X) for analysis-ready computation. + pa.field( + "planes", + pa.list_( + pa.struct( + [ + pa.field("z", pa.int32()), + pa.field("t", pa.int32()), + pa.field("c", pa.int16()), + pa.field( + "pixels", pa.list_(pa.uint16()) + ), # keep numeric (not PNG/JPEG) + ] + ) + ), + ), + pa.field("masks", pa.null()), # reserved for future annotations + ] +) + +# map numpy dtypes to ome-arrow types +DTYPE_MAP_NUMPY_TO_ARROW = { + np.dtype("uint8"): "uint8", + np.dtype("uint16"): "uint16", + np.dtype("uint32"): "uint32", + np.dtype("int16"): "int16", + np.dtype("float32"): "float", + np.dtype("float64"): "double", +} diff --git a/src/ome_arrow/utils.py b/src/ome_arrow/utils.py new file mode 100644 index 0000000..1821618 --- /dev/null +++ b/src/ome_arrow/utils.py @@ -0,0 +1,25 @@ +""" +Utility functions for ome-arrow. +""" + +from typing import Any +import pyarrow as pa + +def verify_ome_arrow(data: Any, struct: pa.StructType) -> bool: + """Return True if `data` conforms to the given Arrow StructType. + + This tries to convert `data` into a pyarrow scalar using `struct` + as the declared type. If conversion fails, the data does not match. + + Args: + data: A nested Python dict/list structure to test. + struct: The expected pyarrow.StructType schema. + + Returns: + bool: True if conversion succeeds, False otherwise. + """ + try: + pa.scalar(data, type=struct) + return True + except (TypeError, pa.ArrowInvalid, pa.ArrowTypeError): + return False \ No newline at end of file diff --git a/src/ome_arrow/view.py b/src/ome_arrow/view.py new file mode 100644 index 0000000..799a62a --- /dev/null +++ b/src/ome_arrow/view.py @@ -0,0 +1,94 @@ +""" +Viewing utilities for OME-Arrow data. +""" + +from typing import Any, Dict, Tuple + +import numpy as np +import pyarrow as pa +import matplotlib.pyplot as plt + + +def view_matplotlib( + row: Dict[str, Any] | pa.StructScalar, + ztc: Tuple[int, int, int] = (0, 0, 0), + autoscale: bool = True, + vmin: int | None = None, + vmax: int | None = None, + cmap: str = "gray", + show: bool = True, +) -> None: + """Display a single (z,t,c) plane from an OME-Arrow record. + + Minimal deps: pyarrow (if scalar), numpy, matplotlib. + + Args: + row: + OME-Arrow row as a Python dict (recommended) or StructScalar. + ztc: + (z, t, c) indices of the plane to display. Defaults to (0,0,0). + autoscale: + If True and vmin/vmax not passed, set vmin/vmax from data range. + vmin: + Optional lower limit for display scaling. + vmax: + Optional upper limit for display scaling. + cmap: + Matplotlib colormap for single-channel display. + show: + If True (default), calls plt.show() to display the image. + + Raises: + ValueError: If the requested plane is not found or shapes mismatch. + + Notes: + * Expects row["pixels_meta"] with size_x, size_y. + * Expects row["planes"] list of {z,t,c,pixels}, where pixels is a + flat uint16 sequence of length size_x * size_y. + """ + # Unwrap Arrow scalar to plain Python dict if needed. + if isinstance(row, pa.StructScalar): + row = row.as_py() + + pm = row["pixels_meta"] + sx, sy = int(pm["size_x"]), int(pm["size_y"]) + target = tuple(int(x) for x in ztc) + + plane = None + for p in row["planes"]: + if (int(p["z"]), int(p["t"]), int(p["c"])) == target: + plane = p + break + if plane is None: + raise ValueError(f"plane {target} not found") + + pix = plane["pixels"] + if len(pix) != sx * sy: + raise ValueError( + f"pixels len {len(pix)} != size_x*size_y ({sx*sy})" + ) + + # Make 2D array (row-major). Cast to uint16; copy avoids view traps. + img = np.asarray(pix, dtype=np.uint16).reshape(sy, sx).copy() + + # Decide intensity scaling. + if vmin is None or vmax is None: + if autoscale: + lo = int(img.min()) + hi = int(img.max()) + # Avoid degenerate scale when flat image. + if hi == lo: + hi = lo + 1 + vmin = lo if vmin is None else vmin + vmax = hi if vmax is None else vmax + + plt.figure() + plt.imshow(img, cmap=cmap, vmin=vmin, vmax=vmax) + z, t, c = target + plt.title(f"OME-Arrow plane z={z} t={t} c={c} ({sx}×{sy})") + plt.axis("off") + + if show: + plt.show() + + return plt \ No newline at end of file diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..9241a5c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,69 @@ +""" +conftest.py for pytest configuration. +""" + +import pytest +from datetime import datetime + +@pytest.fixture +def example_correct_data() -> dict: + """ + Example of correct ome-arrow data for testing. + """ + return { + "type": "ome.arrow", + "version": "1.0.0", + "id": "img-0001", + "name": "Example image", + "acquisition_datetime": datetime(2025, 1, 1, 12, 0, 0), + "pixels_meta": { + "dimension_order": "XYCT", # Z==1, so XYCT is fine + "type": "uint16", + "size_x": 4, # width + "size_y": 3, # height + "size_z": 1, # one z-slice + "size_c": 2, # two channels + "size_t": 1, # one timepoint + "physical_size_x": 0.65, + "physical_size_y": 0.65, + "physical_size_z": 1.00, + "physical_size_x_unit": "µm", + "physical_size_y_unit": "µm", + "physical_size_z_unit": "µm", + "channels": [ + { + "id": "C0", + "name": "DNA", + "emission_um": 0.46, + "excitation_um": 0.40, + "illumination": "Epifluorescence", + "color_rgba": 0x0000FFFF, # blue-ish + }, + { + "id": "C1", + "name": "Mito", + "emission_um": 0.59, + "excitation_um": 0.54, + "illumination": "Epifluorescence", + "color_rgba": 0xFF0000FF, # red-ish + }, + ], + }, + "planes": [ + { + "z": 0, + "t": 0, + "c": 0, + # 4*3 == 12 pixels (uint16 domain values) + 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "OMEArrow(\n", + " data=\"../../../tests/data/ome-artificial-5d-datasets/z-series.ome.tiff\"\n", + ").view(how=\"matplotlib\", cmap=\"binary\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff076e6e-d3a2-43bd-90a4-6701d6e00815", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/examples/ome-arrow_quiver.py b/docs/src/examples/ome-arrow_quiver.py new file mode 100644 index 0000000..056270c --- /dev/null +++ b/docs/src/examples/ome-arrow_quiver.py @@ -0,0 +1,35 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.17.1 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# # OME-Arrow quiver +# + +from ome_arrow import OMEArrow + +oa_image = OMEArrow( + data="../../../tests/data/examplehuman/AS_09125_050116030001_D03f00d0.tif" +) +oa_image + +oa_image.view(how="matplotlib") + +OMEArrow(data=oa_image.data) + +oa_image.export(how="numpy") + +OMEArrow( + data="../../../tests/data/ome-artificial-5d-datasets/z-series.ome.tiff" +).view(how="matplotlib", cmap="binary") + + diff --git a/pyproject.toml b/pyproject.toml index e046844..27a8871 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,8 @@ dependencies = [ "matplotlib>=3.10.7", "bioio>=3.0.0", "bioio-tifffile>=1.3.0", + "numpy>=2.2.6", + "bioio-ome-tiff>=1.4.0", ] [dependency-groups] diff --git a/src/ome_arrow/__init__.py b/src/ome_arrow/__init__.py index 7477996..705d7fe 100644 --- a/src/ome_arrow/__init__.py +++ b/src/ome_arrow/__init__.py @@ -1,12 +1,13 @@ """ -Init file for example package. +Init file for ome_arrow package. """ from ome_arrow.meta import * from ome_arrow.convert import * from ome_arrow.view import * from ome_arrow.utils import * +from ome_arrow.core import * -from ._version import version as ome_arrow_version +from ome_arrow._version import version as ome_arrow_version __version__ = ome_arrow_version diff --git a/src/ome_arrow/convert.py b/src/ome_arrow/convert.py index 2e1582f..e822b02 100644 --- a/src/ome_arrow/convert.py +++ b/src/ome_arrow/convert.py @@ -2,9 +2,11 @@ Converting to and from OME-Arrow formats. """ -from datetime import datetime +from datetime import datetime, timezone from typing import Optional, Sequence, List, Dict, Any +import pyarrow as pa +import numpy as np import pyarrow as pa from datetime import datetime @@ -14,13 +16,14 @@ import numpy as np import pyarrow as pa from bioio import BioImage +import bioio_tifffile +import bioio_ome_tiff -from ome_arrow.meta import OME_ARROW_STRUCT - +from ome_arrow.meta import OME_ARROW_STRUCT, OME_ARROW_TAG_TYPE, OME_ARROW_TAG_VERSION def to_ome_arrow( - type_: str = "ome.arrow", - version: str = "1.0.0", + type_: str = OME_ARROW_TAG_TYPE, + version: str = OME_ARROW_TAG_VERSION, image_id: str = "unnamed", name: str = "unknown", acquisition_datetime: Optional[datetime] = None, @@ -39,7 +42,8 @@ def to_ome_arrow( planes: Optional[List[Dict[str, Any]]] = None, masks: Any = None, ) -> pa.StructScalar: - """Create a typed OME-Arrow StructScalar with sensible defaults. + """ + Create a typed OME-Arrow StructScalar with sensible defaults. This builds and validates a nested dict that conforms to the given StructType (e.g., OME_ARROW_STRUCT). You can override any field @@ -68,6 +72,15 @@ def to_ome_arrow( >>> s.type == OME_ARROW_STRUCT True """ + + type_ = str(type_) + version = str(version) + image_id = str(image_id) + name = str(name) + dimension_order = str(dimension_order) + dtype = str(dtype) + physical_size_unit = str(physical_size_unit) + # Sensible defaults for channels and planes if channels is None: channels = [ @@ -80,17 +93,25 @@ def to_ome_arrow( "color_rgba": 0xFFFFFFFF, } ] + else: + # --- NEW: coerce channel text fields to str ------------------ + for ch in channels: + if "id" in ch: + ch["id"] = str(ch["id"]) + if "name" in ch: + ch["name"] = str(ch["name"]) + if "illumination" in ch: + ch["illumination"] = str(ch["illumination"]) + if planes is None: - planes = [ - {"z": 0, "t": 0, "c": 0, "pixels": [0] * (size_x * size_y)} - ] + planes = [{"z": 0, "t": 0, "c": 0, "pixels": [0] * (size_x * size_y)}] record = { "type": type_, "version": version, "id": image_id, "name": name, - "acquisition_datetime": acquisition_datetime or datetime.utcnow(), + "acquisition_datetime": acquisition_datetime or datetime.now(timezone.utc), "pixels_meta": { "dimension_order": dimension_order, "type": dtype, @@ -111,7 +132,6 @@ def to_ome_arrow( "masks": masks, } - # Validate against struct (raises if mismatched) return pa.scalar(record, type=OME_ARROW_STRUCT) @@ -123,7 +143,8 @@ def tiff_to_ome_arrow( acquisition_datetime: Optional[datetime] = None, clamp_to_uint16: bool = True, ) -> pa.StructScalar: - """Read a TIFF via bioio and return a typed OME-Arrow StructScalar. + """ + Read a TIFF and return a typed OME-Arrow StructScalar. Uses bioio to read TCZYX (or XY) data, flattens each YX plane, and delegates struct creation to `to_struct_scalar`. @@ -139,11 +160,19 @@ def tiff_to_ome_arrow( Returns: pa.StructScalar validated against `struct`. """ + p = Path(tiff_path) - img = BioImage(str(p)) - # BioIO data is TCZYX (broadcasting missing dims to length 1). - arr = np.asarray(img.data) # shape: (T, C, Z, Y, X) + img = BioImage( + image=str(p), + reader=( + bioio_ome_tiff.Reader + if str(p).lower().endswith(("ome.tif", "ome.tiff")) + else bioio_tifffile.Reader + ), + ) + + arr = np.asarray(img.data) # (T, C, Z, Y, X) dims = img.dims size_t = int(dims.T or 1) size_c = int(dims.C or 1) @@ -153,7 +182,6 @@ def tiff_to_ome_arrow( if size_x <= 0 or size_y <= 0: raise ValueError("Image must have positive Y and X dims.") - # Physical pixel sizes (µm); default to 1.0 if unavailable. pps = getattr(img, "physical_pixel_sizes", None) try: psize_x = float(getattr(pps, "X", None) or 1.0) @@ -162,38 +190,44 @@ def tiff_to_ome_arrow( except Exception: psize_x = psize_y = psize_z = 1.0 - # Channels block. + # --- NEW: coerce top-level strings -------------------------------- + img_id = str(image_id or p.stem) + display_name = str(name or p.name) + + # --- NEW: ensure channel_names is list[str] ------------------------ if not channel_names or len(channel_names) != size_c: channel_names = [f"C{i}" for i in range(size_c)] - channels = [{ - "id": f"ch-{i}", "name": channel_names[i], - "emission_um": 0.0, "excitation_um": 0.0, - "illumination": "Unknown", "color_rgba": 0xFFFFFFFF - } for i in range(size_c)] + channel_names = [str(x) for x in channel_names] + + channels = [ + { + "id": f"ch-{i}", + "name": channel_names[i], + "emission_um": 0.0, + "excitation_um": 0.0, + "illumination": "Unknown", + "color_rgba": 0xFFFFFFFF, + } + for i in range(size_c) + ] - # Planes: flattened YX per (t, c, z). planes: List[Dict[str, Any]] = [] for t in range(size_t): for c in range(size_c): for z in range(size_z): - plane = arr[t, c, z] # (Y, X) + plane = arr[t, c, z] if clamp_to_uint16 and plane.dtype != np.uint16: plane = np.clip(plane, 0, 65535).astype(np.uint16) - planes.append({ - "z": z, "t": t, "c": c, - "pixels": plane.ravel().tolist() - }) + planes.append( + {"z": z, "t": t, "c": c, "pixels": plane.ravel().tolist()} + ) - # Dimension order: no Z implies XYCT. dim_order = "XYCT" if size_z == 1 else "XYZCT" - # Delegate final struct creation + type validation. return to_ome_arrow( - type_="ome.arrow", - version="1.0.0", - image_id=image_id or p.stem, - name=name or p.name, - acquisition_datetime=acquisition_datetime or datetime.utcnow(), + image_id=img_id, + name=display_name, + acquisition_datetime=acquisition_datetime or datetime.now(timezone.utc), dimension_order=dim_order, dtype="uint16", size_x=size_x, @@ -208,4 +242,106 @@ def tiff_to_ome_arrow( channels=channels, planes=planes, masks=None, - ) \ No newline at end of file + ) + + +def to_numpy( + data: Dict[str, Any] | pa.StructScalar, + dtype: np.dtype = np.uint16, + strict: bool = True, + clamp: bool = False, +) -> np.ndarray: + """ + Convert an OME-Arrow record into a NumPy array shaped (T,C,Z,Y,X). + + The OME-Arrow "planes" are flattened YX slices indexed by (z, t, c). + This function reconstitutes them into a dense TCZYX ndarray. + + Args: + data: + OME-Arrow data as a Python dict or a `pa.StructScalar`. + dtype: + Output dtype (default: np.uint16). If different from plane + values, a cast (and optional clamp) is applied. + strict: + When True, raise if a plane has wrong pixel length. When + False, truncate/pad that plane to the expected length. + clamp: + If True, clamp values to the valid range of the target + dtype before casting. + + Returns: + np.ndarray: Dense array with shape (T, C, Z, Y, X). + + Raises: + KeyError: If required OME-Arrow fields are missing. + ValueError: If dimensions are invalid or planes are malformed. + + Examples: + >>> arr = ome_arrow_to_tczyx(my_row) # (T, C, Z, Y, X) + >>> arr.shape + (1, 2, 1, 512, 512) + """ + # Unwrap Arrow scalar to plain Python dict if needed. + if isinstance(data, pa.StructScalar): + data = data.as_py() + + pm = data["pixels_meta"] + sx, sy = int(pm["size_x"]), int(pm["size_y"]) + sz, sc, st = int(pm["size_z"]), int(pm["size_c"]), int(pm["size_t"]) + if sx <= 0 or sy <= 0 or sz <= 0 or sc <= 0 or st <= 0: + raise ValueError("All size_* fields must be positive integers.") + + expected_len = sx * sy + + # Prepare target array (T,C,Z,Y,X), zero-filled by default. + out = np.zeros((st, sc, sz, sy, sx), dtype=dtype) + + # Helper: cast (with optional clamp) to the output dtype. + if np.issubdtype(dtype, np.integer): + info = np.iinfo(dtype) + lo, hi = info.min, info.max + elif np.issubdtype(dtype, np.floating): + lo, hi = -np.inf, np.inf + else: + # Rare dtypes: no clamping logic; rely on astype. + lo, hi = -np.inf, np.inf + + def _cast_plane(a: np.ndarray) -> np.ndarray: + if clamp: + a = np.clip(a, lo, hi) + return a.astype(dtype, copy=False) + + # Fill planes. + for i, p in enumerate(data.get("planes", [])): + z = int(p["z"]) + t = int(p["t"]) + c = int(p["c"]) + + if not (0 <= z < sz and 0 <= t < st and 0 <= c < sc): + raise ValueError(f"planes[{i}] index out of range: (z,t,c)=({z},{t},{c})") + + pix = p["pixels"] + # Ensure sequence-like and correct length. + try: + n = len(pix) + except Exception as e: + raise ValueError(f"planes[{i}].pixels is not a sequence") from e + + if n != expected_len: + if strict: + raise ValueError( + f"planes[{i}].pixels length {n} != size_x*size_y {expected_len}" + ) + # Lenient mode: fix length by truncation or zero-pad. + if n > expected_len: + pix = pix[:expected_len] + else: + pix = list(pix) + [0] * (expected_len - n) + + # Reshape to (Y,X) and cast. + arr2d = np.asarray(pix).reshape(sy, sx) + arr2d = _cast_plane(arr2d) + out[t, c, z] = arr2d + + return out diff --git a/src/ome_arrow/core.py b/src/ome_arrow/core.py new file mode 100644 index 0000000..7543b0d --- /dev/null +++ b/src/ome_arrow/core.py @@ -0,0 +1,148 @@ +""" +Core of the ome_arrow package, used for classes and such. +""" +from __future__ import annotations + +import pathlib +from typing import Any, Optional, Tuple + +import pyarrow as pa +import numpy as np +from ome_arrow.meta import OME_ARROW_STRUCT +from ome_arrow.view import view_matplotlib +from ome_arrow.convert import tiff_to_ome_arrow, to_numpy + + +class OMEArrow: + """ + Small convenience toolkit for working with ome-arrow data. + + If `input` is a TIFF path, this loads it via `tiff_to_ome_arrow`. + If `input` is a dict, it will be converted using `to_struct_scalar`. + If `input` is already a `pa.StructScalar`, it is used as-is. + + In Jupyter, evaluating the instance will render the first plane using + matplotlib (via `_repr_html_`). Call `view_matplotlib()` to select a + specific (z, t, c) plane. + + Args: + input: TIFF path, nested dict, or `pa.StructScalar`. + struct: Expected Arrow StructType (e.g., OME_ARROW_STRUCT). + """ + + def __init__( + self, + data: str | dict | pa.StructScalar, + ): + + if isinstance(data, str): + path = pathlib.Path(data) + if path.suffix.lower() in {".tif", ".tiff"}: + self.data = tiff_to_ome_arrow(path, OME_ARROW_STRUCT) + else: + raise ValueError( + "String input data must be a .tif/.tiff path." + ) + elif isinstance(data, pa.StructScalar): + self.data = data + elif isinstance(data, dict): + # Assumes dict matches the schema (or raises). + self.data = pa.scalar(data, type=OME_ARROW_STRUCT) + else: + raise TypeError("input data must be str, dict, or pa.StructScalar") + + def export( + self, + how: str = "numpy", + dtype: np.dtype = np.uint16, + strict: bool = True, + clamp: bool = False, + ) -> Any: + """ + Export the OME-Arrow content in a chosen representation. + + Args: + how: + "numpy" → TCZYX np.ndarray + "dict" → plain Python dict + "scalar" → pa.StructScalar (as-is) + dtype: + Target dtype for "numpy" export (default: np.uint16). + strict: + For "numpy": raise if a plane has wrong pixel length. + clamp: + For "numpy": clamp values into dtype range before cast. + + Returns: + The exported object per the `how` argument. + + Raises: + ValueError: If `how` is unknown. + """ + if how == "numpy": + return to_numpy( + self.data, dtype=dtype, strict=strict, clamp=clamp + ) + if how == "dict": + return self.data.as_py() + if how == "scalar": + return self.data + raise ValueError(f"Unknown export method: {how}") + + def view( + self, + how: str = "matplotlib", + ztc: Tuple[int, int, int] = (0, 0, 0), + autoscale: bool = True, + vmin: Optional[int] = None, + vmax: Optional[int] = None, + cmap: str = "gray", + show: bool = True, + ) -> Any: + """ + Render a (z, t, c) plane with various tools. + + Args: + ztc: Tuple of (z, t, c). Defaults to (0, 0, 0). + autoscale: When True and vmin/vmax unset, use data min/max. + vmin: Optional lower display bound. + vmax: Optional upper display bound. + cmap: Matplotlib colormap (single-channel). + + Returns: + A visualization of the object. + """ + + if how == "matplotlib": + view_matplotlib( + self.data, + ztc=ztc, + autoscale=autoscale, + vmin=vmin, + vmax=vmax, + cmap=cmap, + show=show + ) + else: + raise ValueError(f"Unknown view method: {how}") + + def _repr_html_(self) -> str: + """ + Auto-render first plane (0,0,0) as inline PNG in Jupyter. + """ + try: + view_matplotlib( + data=self.data, + ztc=(0, 0, 0), + autoscale=True, + vmin=None, + vmax=None, + cmap="gray", + show=False + ) + # return blank string to avoid showing class representation below image + return "" + except Exception as e: + # Fallback to a tiny text status if rendering fails. + return f"
OMEArrowKit: render failed: {e}
" + \ No newline at end of file diff --git a/src/ome_arrow/view.py b/src/ome_arrow/view.py index 799a62a..4971d46 100644 --- a/src/ome_arrow/view.py +++ b/src/ome_arrow/view.py @@ -8,23 +8,25 @@ import pyarrow as pa import matplotlib.pyplot as plt +import warnings + def view_matplotlib( - row: Dict[str, Any] | pa.StructScalar, + data: Dict[str, Any] | pa.StructScalar, ztc: Tuple[int, int, int] = (0, 0, 0), autoscale: bool = True, vmin: int | None = None, vmax: int | None = None, cmap: str = "gray", show: bool = True, -) -> None: +) -> plt: """Display a single (z,t,c) plane from an OME-Arrow record. Minimal deps: pyarrow (if scalar), numpy, matplotlib. Args: - row: - OME-Arrow row as a Python dict (recommended) or StructScalar. + data: + OME-Arrow data as a Python dict or StructScalar. ztc: (z, t, c) indices of the plane to display. Defaults to (0,0,0). autoscale: @@ -42,20 +44,20 @@ def view_matplotlib( ValueError: If the requested plane is not found or shapes mismatch. Notes: - * Expects row["pixels_meta"] with size_x, size_y. - * Expects row["planes"] list of {z,t,c,pixels}, where pixels is a + * Expects data["pixels_meta"] with size_x, size_y. + * Expects data["planes"] list of {z,t,c,pixels}, where pixels is a flat uint16 sequence of length size_x * size_y. """ # Unwrap Arrow scalar to plain Python dict if needed. - if isinstance(row, pa.StructScalar): - row = row.as_py() + if isinstance(data, pa.StructScalar): + data = data.as_py() - pm = row["pixels_meta"] + pm = data["pixels_meta"] sx, sy = int(pm["size_x"]), int(pm["size_y"]) target = tuple(int(x) for x in ztc) plane = None - for p in row["planes"]: + for p in data["planes"]: if (int(p["z"]), int(p["t"]), int(p["c"])) == target: plane = p break @@ -68,7 +70,7 @@ def view_matplotlib( f"pixels len {len(pix)} != size_x*size_y ({sx*sy})" ) - # Make 2D array (row-major). Cast to uint16; copy avoids view traps. + # Make 2D array. 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Bin 0 -> 376529 bytes tests/test_convert.py | 3 + uv.lock | 47 ++++ 17 files changed, 613 insertions(+), 49 deletions(-) create mode 100644 docs/src/examples/ome-arrow_quiver.ipynb create mode 100644 docs/src/examples/ome-arrow_quiver.py create mode 100644 src/ome_arrow/core.py create mode 100644 tests/data/ome-artificial-5d-datasets/4D-series.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/multi-channel-4D-series.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/multi-channel-time-series.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/multi-channel-z-series.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/multi-channel.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/single-channel.ome.tiff create mode 100644 tests/data/ome-artificial-5d-datasets/time-series.ome.tif create mode 100644 tests/data/ome-artificial-5d-datasets/z-series.ome.tiff create mode 100644 tests/test_convert.py diff --git a/docs/src/examples/ome-arrow_quiver.ipynb b/docs/src/examples/ome-arrow_quiver.ipynb new file mode 100644 index 0000000..f3b12aa --- /dev/null +++ b/docs/src/examples/ome-arrow_quiver.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d778aece-5867-49b6-8890-f5a387f22c44", + "metadata": {}, + "source": [ + "# OME-Arrow quiver\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0bb6c8cd-97b9-4b85-a63b-5e8f8d73577d", + "metadata": {}, + "outputs": [], + "source": [ + "from ome_arrow import OMEArrow" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5f629d57-0d8e-4fba-a824-a6a5c7cce4ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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