From 97910366927e216c494d833596ed03afcc0e622c Mon Sep 17 00:00:00 2001 From: Dave Bunten Date: Wed, 30 Oct 2024 14:36:56 -0600 Subject: [PATCH] Migrate `CytoDataFrame` from `coSMicQC` as standalone package (#4) * initial migration Co-Authored-By: Jenna Tomkinson <107513215+jenna-tomkinson@users.noreply.github.com> * linting and test shifting * updates for docs * updates and linting * reduce environment and other references * readd pypi stuff * add link to pypi badge * activate uncovered test * remove poe steps and dep --------- Co-authored-by: Jenna Tomkinson <107513215+jenna-tomkinson@users.noreply.github.com> --- .github/ISSUE_TEMPLATE/issue.yml | 49 + .github/PULL_REQUEST_TEMPLATE.md | 37 + .github/dependabot.yml | 16 + .github/release-drafter.yml | 21 + .github/workflows/draft-release.yml | 23 + .github/workflows/publish-docs.yml | 50 + .github/workflows/publish-pypi.yml | 34 + .github/workflows/run-tests.yml | 50 + .gitignore | 41 +- .pre-commit-config.yaml | 73 + CITATION.cff | 163 + CODE_OF_CONDUCT.md | 3 + CONTRIBUTING.md | 3 + LICENSE | 2 +- README.md | 39 +- .../cosmicqc-example-cytodataframe.png | Bin 0 -> 759619 bytes docs/src/_static/custom.css | 39 + docs/src/code_of_conduct.md | 132 + docs/src/conf.py | 100 + docs/src/contributing.md | 135 + docs/src/index.md | 19 + docs/src/presentations.md | 10 + docs/src/presentations/sbi2-2024.md | 7 + docs/src/python-api.md | 22 + media/coverage-badge.svg | 1 + poetry.lock | 2714 +++++++++++++++++ pyproject.toml | 130 + src/cytodataframe/__init__.py | 9 + src/cytodataframe/frame.py | 866 ++++++ src/cytodataframe/image.py | 66 + tests/__init__.py | 0 tests/conftest.py | 144 + .../show_report/cosmicqc_example_report.png | Bin 0 -> 127173 bytes .../CFRet_data/shrink_source_data.py | 24 + .../NF1_plate2_export_masks.cppipe | 227 ++ .../gather_and_create_data.py | 83 + .../NF1_plate2_export_masks.cppipe | 227 ++ .../create_image_data.py | 73 + .../create_mask_data.py | 51 + .../nuclear_speckles/shrink_source_data.py | 31 + tests/test_frame.py | 193 ++ tests/test_image.py | 53 + tests/utils.py | 77 + 43 files changed, 6007 insertions(+), 30 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 CITATION.cff create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 docs/src/_static/cosmicqc-example-cytodataframe.png create mode 100644 docs/src/_static/custom.css create mode 100644 docs/src/code_of_conduct.md create mode 100644 docs/src/conf.py create mode 100644 docs/src/contributing.md create mode 100644 docs/src/index.md create mode 100644 docs/src/presentations.md create mode 100644 docs/src/presentations/sbi2-2024.md create mode 100644 docs/src/python-api.md create mode 100644 media/coverage-badge.svg create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 src/cytodataframe/__init__.py create mode 100644 src/cytodataframe/frame.py create mode 100644 src/cytodataframe/image.py create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 100644 tests/data/coSMicQC/show_report/cosmicqc_example_report.png create mode 100644 tests/data/cytotable/CFRet_data/shrink_source_data.py create mode 100644 tests/data/cytotable/NF1_cellpainting_data/NF1_plate2_export_masks.cppipe create mode 100644 tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py create mode 100644 tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe create mode 100644 tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py create mode 100644 tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py create mode 100644 tests/data/cytotable/nuclear_speckles/shrink_source_data.py create mode 100644 tests/test_frame.py create mode 100644 tests/test_image.py create mode 100644 tests/utils.py diff --git a/.github/ISSUE_TEMPLATE/issue.yml b/.github/ISSUE_TEMPLATE/issue.yml new file mode 100644 index 0000000..eff3c20 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/issue.yml @@ -0,0 +1,49 @@ +# 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 in the + [issues](https://github.com/WayScience/CytoDataFrame/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 + [issues](https://github.com/WayScience/CytoDataFrame/issues) + 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..9f03218 --- /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..9c752b0 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,16 @@ +# 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" 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..133c00f --- /dev/null +++ b/.github/workflows/publish-docs.yml @@ -0,0 +1,50 @@ +--- +# 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: Setup for poetry + run: | + python -m pip install poetry poetry-dynamic-versioning + - name: poetry deps + run: poetry install + - name: Build documentation + run: | + mkdir pages + touch pages/.nojekyll + cd docs + poetry run 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: Add index redirector to latest docs + run: | + cp docs/redirector.html pages/redirector.html + - name: Add media folder to latest docs + run: | + cp -r docs/src/media pages/media + - 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..6dd028a --- /dev/null +++ b/.github/workflows/publish-pypi.yml @@ -0,0 +1,34 @@ +--- +# 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: Python setup + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Setup for poetry + run: | + python -m pip install poetry poetry-dynamic-versioning + - name: Install environment + run: poetry install --no-interaction --no-ansi + - name: poetry build distribution content + run: poetry 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..e03a3ce --- /dev/null +++ b/.github/workflows/run-tests.yml @@ -0,0 +1,50 @@ +--- +# used for running tests +name: tests + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + pre_commit_checks: + runs-on: ubuntu-22.04 + 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: Setup for poetry + run: | + python -m pip install poetry + - name: Install environment + run: poetry install --no-interaction --no-ansi + - uses: pre-commit/action@v3.0.1 + run_tests: + strategy: + matrix: + python_version: ["3.9", "3.10", "3.11", "3.12"] + os: [ubuntu-22.04, macos-13] + 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: Setup for poetry + run: | + python -m pip install poetry + - name: Install environment + run: poetry install --no-interaction --no-ansi + - name: Run pytest + # run all tests except those marked generate_report_image + run: poetry run pytest -m "not generate_report_image" diff --git a/.gitignore b/.gitignore index 82f9275..770b919 100644 --- a/.gitignore +++ b/.gitignore @@ -82,34 +82,13 @@ target/ profile_default/ ipython_config.py -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +# https://pdm.fming.dev/#use-with-ide .pdm.toml -.pdm-python -.pdm-build/ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ @@ -154,9 +133,15 @@ dmypy.json # Cython debug symbols cython_debug/ -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ +# test data ignores +*.tif +*.tiff +*.sqlite +*.parquet +*.zip +*.csv + +.DS_Store + +# jupyter notebook build files from myst-nb +docs/jupyter_execute diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..0131be9 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,73 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +default_language_version: + python: python3.11 +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + exclude: | + (?x)^( + .*\.svg + )$ + - id: check-yaml + - id: check-added-large-files + - id: detect-private-key + - repo: https://github.com/python-poetry/poetry + rev: "1.8.0" + hooks: + - id: poetry-check + - repo: https://github.com/tox-dev/pyproject-fmt + rev: "2.4.3" + hooks: + - id: pyproject-fmt + - repo: https://github.com/codespell-project/codespell + rev: v2.3.0 + hooks: + - id: codespell + exclude: | + (?x)^( + .*\.lock | + .*\.json | + .*\.ipynb | + .*\.cppipe + )$ + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.18 + hooks: + - id: mdformat + additional_dependencies: + - mdformat-gfm + - repo: https://github.com/citation-file-format/cffconvert + rev: b6045d78aac9e02b039703b030588d54d53262ac + hooks: + - id: validate-cff + - repo: https://github.com/adrienverge/yamllint + rev: v1.35.1 + hooks: + - id: yamllint + - repo: https://github.com/rhysd/actionlint + rev: v1.7.3 + hooks: + - id: actionlint + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.7.0" + hooks: + - id: ruff-format + - id: ruff + - repo: local + hooks: + - id: code-cov-gen + name: Generate code coverage + language: system + entry: poetry run coverage run -m pytest + pass_filenames: false + always_run: true + - repo: https://github.com/Weird-Sheep-Labs/coverage-pre-commit + rev: 0.1.1 + hooks: + - id: coverage-xml + - id: coverage-badge + args: ["-o", "media/coverage-badge.svg"] diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..2fb5a5c --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,163 @@ +# This CITATION.cff file was generated with cffinit. +# Visit https://bit.ly/cffinit to generate yours today! +--- +cff-version: 1.2.0 +title: CytoDataFrame +message: >- + If you use this software, please cite it using the + metadata from this file. +type: software +authors: + - given-names: David + family-names: Bunten + orcid: 'https://orcid.org/0000-0001-6041-3665' + - given-names: Jenna + family-names: Tomkinson + orcid: 'https://orcid.org/0000-0003-2676-5813' + - given-names: Gregory + family-names: Way + orcid: 'https://orcid.org/0000-0002-0503-9348' +repository-code: 'https://github.com/WayScience/CytoDataFrame' +abstract: >- + An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks. +keywords: + - python + - single-cell-analysis + - profiling + - dataframes + - data-analysis + - way-lab +license: BSD-3-Clause +references: + - authors: + - name: "Way Lab CFReT_data Team" + date-accessed: "2024-05-13" + title: Way Lab CFReT_data CytoTable Data + type: data + repository-code: "https://github.com/WayScience/CFReT_data" + url: "https://github.com/WayScience/CFReT_data/blob/main/3.process_cfret_features/data/converted_profiles/localhost231120090001_converted.parquet" + scope: "localhost231120090001_converted.parquet" + notes: >- + Data from CFReT_data project is used to help validate + expected results. Data is generated from CellProfiler + and CytoTable. + identifiers: + - description: "Github Link with Contributors" + type: url + value: "https://github.com/WayScience/CFReT_data/graphs/contributors" + - authors: + - name: "Way Lab NF1_cellpainting_data Team" + date-accessed: "2024-06-28" + title: Way Lab NF1_cellpainting_data CytoTable Data + type: data + repository-code: "https://github.com/WayScience/nf1_cellpainting_data" + notes: >- + Data from NF1_cellpainting_data project is used to help validate + expected results. Data is generated from CellProfiler + and CytoTable. We use the following files from the repository: + - "Plate_2_nf1_analysis.sqlite" + - "Plate_2.parquet" + identifiers: + - description: "Github Link with Contributors" + type: url + value: "https://github.com/WayScience/nf1_cellpainting_data/graphs/contributors" + - title: >- + Plate 2 (Cell Painting images from Plate 2 for NF1_cellpainting_data project) + type: data + url: https://figshare.com/articles/dataset/Plate_2/22233700 + notes: >- + Image data for related NF1_cellpainting_data parquet sqlite. + authors: + - family-names: Tomkinson + given-names: Jenna + - family-names: Mattson-Hoss + given-names: Michelle + - family-names: Sarnoff + given-names: Herb + - family-names: Way + given-names: Gregory + date-published: "2023-04-12" + identifiers: + - type: doi + value: 10.6084/m9.figshare.22233700.v4 + - authors: + - name: "Way Lab and Alexander Lab Nuclear Speckles Collaboration" + date-accessed: "2024-09-04" + title: Way Lab and Alexander Lab Nuclear Speckles Collaboration Data + type: data + repository-code: https://github.com/WayScience/nuclear_speckle_image_profiling + notes: >- + Data from a collaborative project focusing on nuclear speckles + with the Way Lab and Alexander Lab s used to help validate + expected results. Parquet data is generated from CellProfiler + and CytoTable. Images courtesy of Katherine Alexander + and the Alexander Lab. + identifiers: + - description: "Github Link with Contributors" + type: url + value: "https://github.com/WayScience/nuclear_speckle_image_profiling/graphs/contributors" + - authors: + - family-names: Chandrasekaran + given-names: Srinivas Niranj + - family-names: Cimini + given-names: Beth A. + - family-names: Goodale + given-names: Amy + - family-names: Miller + given-names: Lisa + - family-names: Kost-Alimova + given-names: Maria + - family-names: Jamali + given-names: Nasim + - family-names: Doench + given-names: John G. + - family-names: Fritchman + given-names: Briana + - family-names: Skepner + given-names: Adam + - family-names: Melanson + given-names: Michelle + - family-names: Kalinin + given-names: Alexandr A. + - family-names: Arevalo + given-names: John + - family-names: Haghighi + given-names: Marzieh + - family-names: Caicedo + given-names: Juan C. + - family-names: Kuhn + given-names: Daniel + - family-names: Hernandez + given-names: Desiree + - family-names: Berstler + given-names: James + - family-names: Shafqat-Abbasi + given-names: Hamdah + - family-names: Root + given-names: David E. + - family-names: Swalley + given-names: Susanne E. + - family-names: Garg + given-names: Sakshi + - family-names: Singh + given-names: Shantanu + - family-names: Carpenter + given-names: Anne E. + date-accessed: "2024-08-21" + title: >- + Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations + type: article + issn: 1548-7105 + issue: 6 + journal: Nature Methods + pages: 1114-1121 + volume: 21 + url: https://doi.org/10.1038/s41592-024-02241-6 + date-published: "2024-06-01" + identifiers: + - type: doi + value: 10.1038/s41592-024-02241-6 + notes: >- + JUMP (cpg0000-jump-pilot) was used to help demonstrate CytoDataFrame performance + with large data. See here for more information: + https://github.com/broadinstitute/cellpainting-gallery diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..6611dd1 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,3 @@ +# Contributor Covenant Code of Conduct + +Please see our full code of conduct at https://WayScience.github.io/CytoDataFrame/main/code_of_conduct diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..51e5e5e --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,3 @@ +# Contributing + +Please see our [contributing](https://WayScience.github.io/CytoDataFrame/main/contributing) documentation for more details on contributions, development, and testing. diff --git a/LICENSE b/LICENSE index 66cabec..92cf4cb 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright (c) 2024, The Way Lab +Copyright (c) 2024, Way Science Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: diff --git a/README.md b/README.md index 7a9e432..dc416d5 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,39 @@ # CytoDataFrame -An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks. + +[![PyPI - Version](https://img.shields.io/pypi/v/cytodataframe)](https://pypi.org/project/CytoDataFrame/) +[![Build Status](https://github.com/WayScience/CytoDataFrame/actions/workflows/run-tests.yml/badge.svg?branch=main)](https://github.com/WayScience/CytoDataFrame/actions/workflows/run-tests.yml?query=branch%3Amain) +![Coverage Status](https://raw.githubusercontent.com/WayScience/CytoDataFrame/main/media/coverage-badge.svg) +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) +[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/) + +![](https://raw.githubusercontent.com/WayScience/coSMicQC/refs/heads/main/docs/presentations/2024-09-18-SBI2-Conference/images/cosmicqc-example-cytodataframe.png) +_CytoDataFrame extends Pandas functionality to help display single-cell profile data alongside related images._ + +CytoDataFrame is an advanced in-memory data analysis format designed for single-cell profiling, integrating not only the data profiles but also their corresponding microscopy images and segmentation masks. +Traditional single-cell profiling often excludes the associated images from analysis, limiting the scope of research. +CytoDataFrame bridges this gap, offering a purpose-built solution for comprehensive analysis that incorporates both the data and images, empowering more detailed and visual insights in single-cell research. + +CytoDataFrame development began within [coSMicQC](https://github.com/WayScience/coSMicQC) - a single-cell profile quality control package. + +## Installation + +Install CytoDataFrame from source using the following: + +```shell +# install from pypi +pip install cytodataframe + +# or install directly from source +pip install git+https://github.com/WayScience/CytoDataFrame.git +``` + +## Contributing, Development, and Testing + +Please see our [contributing](https://WayScience.github.io/CytoDataFrame/main/contributing) documentation for more details on contributions, development, and testing. + +## References + +- [coSMicQC](https://github.com/WayScience/coSMicQC) +- [pycytominer](https://github.com/cytomining/pycytominer) +- [CellProfiler](https://github.com/CellProfiler/CellProfiler) +- [CytoTable](https://github.com/cytomining/CytoTable) diff --git a/docs/src/_static/cosmicqc-example-cytodataframe.png b/docs/src/_static/cosmicqc-example-cytodataframe.png new file mode 100644 index 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0000000..0d87ac3 --- /dev/null +++ b/docs/src/_static/custom.css @@ -0,0 +1,39 @@ +div.cell_output table { + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 1em; + table-layout: fixed; + background: white; + overflow: scroll; + max-width: 600px; +} + +.pt-5, .py-5 { + padding-top: 1rem !important; +} + +section { + margin-top: 0rem; + margin-bottom: 2rem; +} + +.cell_input div.highlight, .cell_output pre, .cell_input pre, .cell_output .output { + border: none; + box-shadow: none; + overflow: auto; +} + +#main img{ + max-width:600px; +} + +.page-toc { + font-size: .875rem; + padding-top: 0 !important; + color: #494949; + max-height: 100vh; + overflow: hidden auto; + word-break: break-all; +} diff --git a/docs/src/code_of_conduct.md b/docs/src/code_of_conduct.md new file mode 100644 index 0000000..14f473b --- /dev/null +++ b/docs/src/code_of_conduct.md @@ -0,0 +1,132 @@ +# 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 open a [new security advisory notice](https://github.com/WayScience/CytoDataFrame/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. +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/docs/src/conf.py b/docs/src/conf.py new file mode 100644 index 0000000..6e50b48 --- /dev/null +++ b/docs/src/conf.py @@ -0,0 +1,100 @@ +# 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 = "CytoDataFrame" +# is used here due to sphinx decision-making: https://github.com/sphinx-doc/sphinx/issues/8132 +copyright = "2024, WayScience Community" # noqa: A001 +author = "WayScience 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", + "sphinx_multiversion", + "sphinx_wagtail_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 = { + "external_links": [ + { + "url": "https://github.com/WayScience/coSMicQC", + "name": "coSMicQC", + }, + ], + "header_links_before_dropdown": 5, + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/WayScience/CytoDataFrame", + "icon": "fa-brands fa-github", + }, + ], + "logo": {"text": "CytoDataFrame"}, + "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 theme options + +# Options for myst-nb +# turn off notebook execution for docs builds +# (we rely on the notebook already being processed +# prior to the publish to help navigate compute needs) +nb_execution_mode = "off" + +# set option to avoid rendering default variables +autodoc_preserve_defaults = True + +# enable anchor creation +myst_heading_anchors = 3 diff --git a/docs/src/contributing.md b/docs/src/contributing.md new file mode 100644 index 0000000..7277af9 --- /dev/null +++ b/docs/src/contributing.md @@ -0,0 +1,135 @@ +# 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/WayScience/CytoDataFrame/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 Python [Poetry](https://python-poetry.org/). +We leverage interactions with the Docker through Python to achieve reproducible results through containers. +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 Poetry](https://python-poetry.org/docs/#installation) +1. [Install Poetry Environment](https://python-poetry.org/docs/basic-usage/#installing-dependencies): `poetry install` + +### 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 Poetry 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 +% poetry 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/WayScience/CytoDataFrame/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/WayScience/CytoDataFrame/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 [`poetry-dynamic-versioning`](https://github.com/mtkennerly/poetry-dynamic-versioning) to help version this software through [`PEP 440`](https://peps.python.org/pep-0440/) 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. +`poetry-dynamic-versioning` uses `git` tags to help distinguish version data. +We also use the `__init__.py` file as a place to persist the version data for occaissions where the `git` history is unavailable or unwanted. + +The following command is used to add `poetry-dynamic-versioning` to Poetry for use with this project: `poetry self add "poetry-dynamic-versioning[plugin]"`. +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/wayscience/CytoDataFrame/releases). +We publish a related Python package through the [Python Packaging Index (PyPI)](https://pypi.org/) available [here](https://pypi.org/project/CytoDataFrame/). + +#### Release Publishing Process + +Several manual and automated steps are involved with publishing CytoDataFrame 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: +CytoDataFrame version specifications are controlled through [`poetry-dynamic-versioning`](https://github.com/mtkennerly/poetry-dynamic-versioning) which leverages [`dunamai`](https://github.com/mtkennerly/dunamai) to create version data based on [git tags](https://git-scm.com/book/en/v2/Git-Basics-Tagging) and commits. +CytoDataFrame 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) (for example, see [CytoDataFrame#24](https://github.com/wayscience/CytoDataFrame/pull/24) as a reference of a semver patch update). +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) +poetry run sphinx-build docs/src docs/build + +# build multi-version sphinx documentation +# (used in production) +poetry run sphinx-multiversion docs/src docs/build +``` diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..18c343a --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,19 @@ + + +```{include} ../../README.md +--- +relative-docs: docs/src/ +relative-images: +--- +``` + +```{toctree} +--- +caption: 'Contents:' +maxdepth: 3 +--- +python-api +presentations +contributing +code_of_conduct +``` diff --git a/docs/src/presentations.md b/docs/src/presentations.md new file mode 100644 index 0000000..651e42e --- /dev/null +++ b/docs/src/presentations.md @@ -0,0 +1,10 @@ +# Presentations + +```{toctree} +--- +caption: "List of presentations" +maxdepth: 2 +glob: +--- +presentations/* +``` diff --git a/docs/src/presentations/sbi2-2024.md b/docs/src/presentations/sbi2-2024.md new file mode 100644 index 0000000..f58ade3 --- /dev/null +++ b/docs/src/presentations/sbi2-2024.md @@ -0,0 +1,7 @@ +# SBI2 Annual Confrerence 2024 Poster + +We presented `CytoDataFrame` within the context of `coSMicQC` during the [SBI2 annual conference (2024)](https://sbi2.org/conference/) poster sessions. + +See below for the [poster](https://wayscience.github.io/coSMicQC/media/sbi2-2024-cosmicqc-poster.pdf) or reference our Zenodo record here: [https://zenodo.org/records/13829960](https://zenodo.org/records/13829960) + + diff --git a/docs/src/python-api.md b/docs/src/python-api.md new file mode 100644 index 0000000..5616923 --- /dev/null +++ b/docs/src/python-api.md @@ -0,0 +1,22 @@ +# Python API + +```{eval-rst} + +cytodataframe.frame 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from = "src" } ] + +[tool.poetry.dependencies] +python = ">=3.9,<3.13" +# used for data management +pandas = [ + { version = "<2.2.2", python = "<3.9" }, + { version = "^2.2.2", python = ">=3.9" }, +] +# used for data ingest and export +pyarrow = "^16.0.0" +# used for report generation capabilities +plotly = "^5.22.0" +# used for plotly dependencies +kaleido = "0.2.1" +# used for environment detection +ipython = "^8.12.3" +# used for image processing +scikit-image = [ + { version = "^0.19.3", python = "<3.9" }, + { version = ">0.19.3", python = ">=3.9" }, +] +# dependency of scikit-image +pywavelets = [ + { version = "^1.4.1", python = "<3.9" }, + { version = ">1.4.1", python = ">=3.9" }, +] +# used for image modifications in cytodataframe +opencv-python = "^4.10.0.84" +# used for report visualizations from within cytodataframe +cosmicqc = "^0.0.11" + +[tool.poetry.group.dev.dependencies] +# provides testing capabilities for project +pytest = "^8.2.0" +# used for report html export to image for tests +html2image = "^2.0.4.3" +# used for test data generation +sqlalchemy = ">=1.3.6,<2" +# added to generate test coverage reports +coverage = "^7.6.0" + +[tool.poetry.group.docs.dependencies] +# used for rendering docs into docsite +sphinx = "^7.0.0" +# used for rendering markdown through sphinx +myst-parser = "^3.0.0" +# used for rendering notebooks through myst parser +myst-nb = "^1.1.1" +# used for extension sphinx-multiversion and to fix git-based challenges with default branch handling +sphinx-multiversion = { git = "https://github.com/J-RN/sphinx-multiversion", rev = "a77f0c862dace3a62c18fc866da60ef7dde3873d" } +# used for gathering version data for docsite +dunamai = "^1.22.0" +# used for theming the docsite +pydata-sphinx-theme = "^0.16.0" + +[tool.poetry-dynamic-versioning] +enable = true +style = "pep440" +vcs = "git" + +# specify where version replacement is performed +[tool.poetry-dynamic-versioning.substitution] +files = [ "src/CytoDataFrame/__init__.py" ] + +# defines various development tasks + +[tool.setuptools_scm] +root = "." + +[tool.ruff] +target-version = "py38" +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 `E402` and `F401` (unused imports) in all `__init__.py` files +lint.per-file-ignores."__init__.py" = [ "E402", "F401" ] +# ignore typing rules for tests +lint.per-file-ignores."tests/*" = [ "ANN201", "PLR0913" ] + +[tool.pytest.ini_options] +markers = [ + "generate_report_image: tests which involve the creation of report images.", +] + +[tool.coverage.run] +# settings to avoid errors with cv2 and coverage +# see here for more: https://github.com/nedbat/coveragepy/issues/1653 +omit = [ + "config.py", + "config-3.py", +] + +[tool.bandit] +exclude_dirs = [ "tests" ] + diff --git a/src/cytodataframe/__init__.py b/src/cytodataframe/__init__.py new file mode 100644 index 0000000..81154a8 --- /dev/null +++ b/src/cytodataframe/__init__.py @@ -0,0 +1,9 @@ +""" +Initialization for cytodataframe package +""" + +from .frame import CytoDataFrame + +# note: version placeholder is updated during build +# by poetry-dynamic-versioning. +__version__ = "0.0.0" diff --git a/src/cytodataframe/frame.py b/src/cytodataframe/frame.py new file mode 100644 index 0000000..5ffe79b --- /dev/null +++ b/src/cytodataframe/frame.py @@ -0,0 +1,866 @@ +""" +Defines a CytoDataFrame class. +""" + +import base64 +import pathlib +import random +import re +import webbrowser +from io import BytesIO, StringIO +from typing import ( + Any, + Callable, + ClassVar, + Dict, + List, + Optional, + Tuple, + TypeVar, + Union, +) + +import numpy as np +import pandas as pd +import plotly +import plotly.colors as pc +import plotly.express as px +import plotly.graph_objects as go +import skimage +import skimage.io +import skimage.measure +from IPython import get_ipython +from jinja2 import Environment, FileSystemLoader +from pandas._config import ( + get_option, +) +from pandas.io.formats import ( + format as fmt, +) +from PIL import Image, ImageDraw + +from .image import adjust_image_brightness, is_image_too_dark + +# provide backwards compatibility for Self type in earlier Python versions. +# see: https://peps.python.org/pep-0484/#annotating-instance-and-class-methods +CytoDataFrame_type = TypeVar("CytoDataFrame_type", bound="CytoDataFrame") + + +class CytoDataFrame(pd.DataFrame): + """ + A class designed to enhance single-cell data handling by wrapping + pandas DataFrame capabilities, providing advanced methods for quality control, + comprehensive analysis, and image-based data processing. + + This class can initialize with either a pandas DataFrame or a file path (CSV, TSV, + TXT, or Parquet). When initialized with a file path, it reads the data into a + pandas DataFrame. It also includes capabilities to export data. + + Attributes: + _metadata (ClassVar[list[str]]): + A class-level attribute that includes custom attributes. + _custom_attrs (dict): + A dictionary to store custom attributes, such as data source, + context directory, and bounding box information. + """ + + _metadata: ClassVar = ["_custom_attrs"] + + def __init__( # noqa: PLR0912 + self: CytoDataFrame_type, + data: Union[CytoDataFrame_type, pd.DataFrame, str, pathlib.Path], + data_context_dir: Optional[str] = None, + data_bounding_box: Optional[pd.DataFrame] = None, + data_mask_context_dir: Optional[str] = None, + **kwargs: Dict[str, Any], + ) -> None: + """ + Initializes the CytoDataFrame with either a DataFrame or a file path. + + Args: + data (Union[CytoDataFrame_type, pd.DataFrame, str, pathlib.Path]): + The data source, either a pandas DataFrame or a file path. + data_context_dir (Optional[str]): + Directory context for the image data within the DataFrame. + data_bounding_box (Optional[pd.DataFrame]): + Bounding box data for the DataFrame images. + data_mask_context_dir: Optional[str]: + Directory context for the mask data for images. + **kwargs: + Additional keyword arguments to pass to the pandas read functions. + """ + + self._custom_attrs = { + "data_source": None, + "data_context_dir": None, + "data_bounding_box": None, + "data_mask_context_dir": None, + } + + if data_context_dir is not None: + self._custom_attrs["data_context_dir"] = data_context_dir + + if data_mask_context_dir is not None: + self._custom_attrs["data_mask_context_dir"] = data_mask_context_dir + + if isinstance(data, CytoDataFrame): + self._custom_attrs["data_source"] = data._custom_attrs["data_source"] + self._custom_attrs["data_context_dir"] = data._custom_attrs[ + "data_context_dir" + ] + self._custom_attrs["data_mask_context_dir"] = data._custom_attrs[ + "data_mask_context_dir" + ] + super().__init__(data) + elif isinstance(data, (pd.DataFrame, pd.Series)): + self._custom_attrs["data_source"] = ( + "pandas.DataFrame" + if isinstance(data, pd.DataFrame) + else "pandas.Series" + ) + super().__init__(data) + elif isinstance(data, (str, pathlib.Path)): + data_path = pathlib.Path(data) + self._custom_attrs["data_source"] = str(data_path) + + if data_context_dir is None: + self._custom_attrs["data_context_dir"] = str(data_path.parent) + else: + self._custom_attrs["data_context_dir"] = data_context_dir + + if data_path.suffix in {".csv", ".tsv", ".txt"} or data_path.suffixes == [ + ".csv", + ".gz", + ]: + data = pd.read_csv(data_path, **kwargs) + elif data_path.suffix == ".parquet": + data = pd.read_parquet(data_path, **kwargs) + else: + raise ValueError("Unsupported file format for CytoDataFrame.") + + super().__init__(data) + + else: + super().__init__(data) + + if data_bounding_box is None: + self._custom_attrs["data_bounding_box"] = self.get_bounding_box_from_data() + + else: + self._custom_attrs["data_bounding_box"] = data_bounding_box + + def __getitem__(self: CytoDataFrame_type, key: Union[int, str]) -> Any: # noqa: ANN401 + """ + Returns an element or a slice of the underlying pandas DataFrame. + + Args: + key: + The key or slice to access the data. + + Returns: + pd.DataFrame or any: + The selected element or slice of data. + """ + + result = super().__getitem__(key) + + if isinstance(result, pd.Series): + return result + + elif isinstance(result, pd.DataFrame): + return CytoDataFrame( + super().__getitem__(key), + data_context_dir=self._custom_attrs["data_context_dir"], + data_bounding_box=self._custom_attrs["data_bounding_box"], + data_mask_context_dir=self._custom_attrs["data_mask_context_dir"], + ) + + def _wrap_method( + self: CytoDataFrame_type, + method: Callable, + *args: List[Any], + **kwargs: Dict[str, Any], + ) -> Any: # noqa: ANN401 + """ + Wraps a given method to ensure that the returned result + is an CytoDataFrame if applicable. + + Args: + method (Callable): + The method to be called and wrapped. + *args (List[Any]): + Positional arguments to be passed to the method. + **kwargs (Dict[str, Any]): + Keyword arguments to be passed to the method. + + Returns: + Any: + The result of the method call. If the result is a pandas DataFrame, + it is wrapped in an CytoDataFrame instance with additional context + information (data context directory and data bounding box). + + """ + result = method(*args, **kwargs) + if isinstance(result, pd.DataFrame): + result = CytoDataFrame( + result, + data_context_dir=self._custom_attrs["data_context_dir"], + data_bounding_box=self._custom_attrs["data_bounding_box"], + data_mask_context_dir=self._custom_attrs["data_mask_context_dir"], + ) + return result + + def sort_values( + self: CytoDataFrame_type, *args: List[Any], **kwargs: Dict[str, Any] + ) -> CytoDataFrame_type: + """ + Sorts the DataFrame by the specified column(s) and returns a + new CytoDataFrame instance. + + Note: we wrap this method within CytoDataFrame to help ensure the consistent + return of CytoDataFrames in the context of pd.Series (which are + treated separately but have specialized processing within the + context of sort_values). + + Args: + *args (List[Any]): + Positional arguments to be passed to the pandas + DataFrame's `sort_values` method. + **kwargs (Dict[str, Any]): + Keyword arguments to be passed to the pandas + DataFrame's `sort_values` method. + + Returns: + CytoDataFrame_type: + A new instance of CytoDataFrame sorted by the specified column(s). + + """ + + return self._wrap_method(super().sort_values, *args, **kwargs) + + def get_bounding_box_from_data( + self: CytoDataFrame_type, + ) -> Optional[CytoDataFrame_type]: + """ + Retrieves bounding box data from the DataFrame based + on predefined column groups. + + This method identifies specific groups of columns representing bounding box + coordinates for different cellular components (cytoplasm, nuclei, cells) and + checks for their presence in the DataFrame. If all required columns are present, + it filters and returns a new CytoDataFrame instance containing only these + columns. + + Returns: + Optional[CytoDataFrame_type]: + A new instance of CytoDataFrame containing the bounding box columns if + they exist in the DataFrame. Returns None if the required columns + are not found. + + """ + # Define column groups and their corresponding conditions + column_groups = { + "cyto": [ + "Cytoplasm_AreaShape_BoundingBoxMaximum_X", + "Cytoplasm_AreaShape_BoundingBoxMaximum_Y", + "Cytoplasm_AreaShape_BoundingBoxMinimum_X", + "Cytoplasm_AreaShape_BoundingBoxMinimum_Y", + ], + "nuclei": [ + "Nuclei_AreaShape_BoundingBoxMaximum_X", + "Nuclei_AreaShape_BoundingBoxMaximum_Y", + "Nuclei_AreaShape_BoundingBoxMinimum_X", + "Nuclei_AreaShape_BoundingBoxMinimum_Y", + ], + "cells": [ + "Cells_AreaShape_BoundingBoxMaximum_X", + "Cells_AreaShape_BoundingBoxMaximum_Y", + "Cells_AreaShape_BoundingBoxMinimum_X", + "Cells_AreaShape_BoundingBoxMinimum_Y", + ], + } + + # Determine which group of columns to select based on availability in self.data + selected_group = None + for group, cols in column_groups.items(): + if all(col in self.columns.tolist() for col in cols): + selected_group = group + break + + # Assign the selected columns to self.bounding_box_df + if selected_group: + return self.filter(items=column_groups[selected_group]) + + return None + + def export( + self: CytoDataFrame_type, file_path: str, **kwargs: Dict[str, Any] + ) -> None: + """ + Exports the underlying pandas DataFrame to a file. + + Args: + file_path (str): + The path where the DataFrame should be saved. + **kwargs: + Additional keyword arguments to pass to the pandas to_* methods. + """ + + data_path = pathlib.Path(file_path) + + # export to csv + if ".csv" in data_path.suffixes: + self.to_csv(file_path, **kwargs) + # export to tsv + elif any(elem in data_path.suffixes for elem in (".tsv", ".txt")): + self.to_csv(file_path, sep="\t", **kwargs) + # export to parquet + elif data_path.suffix == ".parquet": + self.to_parquet(file_path, **kwargs) + else: + raise ValueError("Unsupported file format for export.") + + @staticmethod + def is_notebook_or_lab() -> bool: + """ + Determines if the code is being executed in a Jupyter notebook (.ipynb) + returning false if it is not. + + This method attempts to detect the interactive shell environment + using IPython's `get_ipython` function. It checks the class name of the current + IPython shell to distinguish between different execution environments. + + Returns: + bool: + - `True` + if the code is being executed in a Jupyter notebook (.ipynb). + - `False` + otherwise (e.g., standard Python shell, terminal IPython shell, + or scripts). + """ + try: + # check for type of session via ipython + shell = get_ipython().__class__.__name__ + if "ZMQInteractiveShell" in shell: + return True + elif "TerminalInteractiveShell" in shell: + return False + else: + return False + except NameError: + return False + + def show_report( + self: CytoDataFrame_type, + report_path: Optional[str] = None, + auto_open: bool = True, + color_palette: Optional[List[str]] = None, + ) -> None: + """ + Generates and displays a report based on the current DataFrame's data + quality control (DQC) columns. + + This method organizes the DQC columns from the DataFrame, creates + visualizations for each threshold set, and then either displays the + visualizations inline (if running in a Jupyter notebook or lab) or + opens an HTML report in the default web browser. + + Args: + report_path (Optional[str]): + The file path where the HTML report should be saved and displayed. + If `None`, the report will be displayed inline if in a notebook + or lab environment. + auto_open: bool: + Whether to automatically open the report. + color_palette Optional(List[str]): + Optional list for color palette to use. + + Raises: + ValueError: If the DataFrame does not contain any DQC columns. + """ + + # find all cosmicqc columns in the data using the prefix `cqc.` + cqc_cols = [col for col in self.columns.tolist() if "cqc." in col] + # organize column data into the threshold set name, threshold is_outlier col, + # and the threshold score columns as list + organized_columns = [ + [ + # name of the threshold set + threshold_set, + # column which includes boolean is_outlier data for threshold set + next( + ( + col + for col in cqc_cols + if f"cqc.{threshold_set}.is_outlier" in col + ), + None, + ), + # columns which show the data associated with thresholds + [col for col in cqc_cols if f"cqc.{threshold_set}.Z_Score." in col], + ] + for threshold_set in sorted({col.split(".")[1] for col in cqc_cols}) + ] + + # create figures for visualization based on the name, outlier status, + # and threshold scores + figures = [ + self.create_threshold_set_outlier_visualization( + df=self, + threshold_set_name=set_name, + col_outlier=col_outlier, + cols_threshold_scores=cols_threshold_scores, + color_palette=color_palette, + ) + for set_name, col_outlier, cols_threshold_scores in organized_columns + ] + + # if we're running in a notebook or jupyter lab, show the figures as-is + if self.is_notebook_or_lab() or report_path is None: + # if we should automatically open, show the figures + if auto_open: + for figure in figures: + figure.show() + + return figures + + # otherwise, create an html file with figures and open it with default browser + else: + html_path = self.create_figure_group_html( + figures=figures, report_path=report_path + ) + + # if we should auto open, show the html file in default web browser + if auto_open: + webbrowser.open(f"file://{pathlib.Path(html_path).resolve()}") + + print(f"Opened default web browser for report {html_path}") + + return html_path + + @staticmethod + def create_figure_group_html( + figures: List[plotly.graph_objs._figure.Figure], + report_path: Optional[str] = None, + ) -> str: + """ + Generates an HTML file containing multiple Plotly figures. + + This method takes a list of Plotly figure objects, converts them to HTML, + and embeds them into a template HTML file. The resulting HTML file is then + saved to the specified path. + + Args: + figures (List[plotly.graph_objs._figure.Figure]): + A list of Plotly figure objects to be included in the HTML report. + report_path (str): + The file path where the HTML report will be saved. + Defaults to "cosmicqc_outlier_report.html" when None. + + Returns: + str: The path to the saved HTML report. + """ + + # if we have none for the report path, use a default name. + if report_path is None: + report_path = "cosmicqc_outlier_report.html" + + # create wrapped html for figures + figure_html = "".join( + [ + f"

{fig.to_html(full_html=False)}
" + for fig in figures + ] + ) + + # configure jinja environment + env = Environment( + loader=FileSystemLoader(f"{pathlib.Path(__file__).parent!s}/data") + ) + # load a jinja template + template = env.get_template("report_template.html") + + # Render the template with Plotly figure HTML + rendered_html = template.render(figure_html=figure_html) + + # write the html to file + with open(report_path, "w") as f: + f.write(rendered_html) + + # return the path of the file + return report_path + + def create_threshold_set_outlier_visualization( + self: CytoDataFrame_type, + df: pd.DataFrame, + threshold_set_name: str, + col_outlier: str, + cols_threshold_scores: List[str], + color_palette: Optional[List[str]] = None, + ) -> plotly.graph_objs._figure.Figure: + """ + Creates a Plotly figure visualizing the Z-score distributions and outliers + for a given threshold set. + + This method generates histograms for each Z-score column in the given DataFrame, + colors them based on outlier status, and overlays them into a single figure. + + Args: + df (pd.DataFrame): + The DataFrame containing the data to be visualized. + threshold_set_name (str): + The name of the threshold set being visualized. + col_outlier (str): + The column name indicating outlier status. + cols_threshold_scores (List[str]): + A list of column names representing the Z-scores to be visualized. + color_palette Optional(List[str]): + Optional list for color palette to use. + Defaults to use Dark24 color palette from Plotly. + + Returns: + plotly.graph_objs._figure.Figure: + A Plotly figure object containing the visualization. + """ + + # Create histograms using plotly.express with pattern_shape and random color + figures = [ + px.histogram( + df, + x=col, + color=col_outlier, + nbins=50, + pattern_shape=col_outlier, + opacity=0.7, + ) + for col in cols_threshold_scores + ] + + # Create a combined figure + fig = go.Figure() + + # check that we have enough colors for figures if provided + if color_palette is not None and len(color_palette) < len(figures): + raise ReferenceError( + f"Color palette length must match figure length of {len(figures)}." + ) + + # Add traces from each histogram and modify colors, names, and pattern shapes + for idx, fig_hist in enumerate(figures): + if color_palette is None: + # Create a default list of colors from a Plotly color palette + fig_color = random.choice(pc.qualitative.Dark24) + else: + # otherwise, use static color palette based on the number of figures + fig_color = color_palette[idx] + + for trace in fig_hist.data: + trace.marker.color = fig_color + trace.marker.pattern.shape = ( + "x" if trace.name == "True" else "" + ) # Use pattern shapes + renamed_col = cols_threshold_scores[idx].replace( + f"cqc.{threshold_set_name}.Z_Score.", "" + ) + trace.name = ( + f"{renamed_col} ({'outlier' if trace.name == 'True' else 'inlier'})" + ) + # Update hovertemplate to match the name in the key + trace.hovertemplate = ( + f"{renamed_col}
" + + "Z-Score: %{x}
" + + "Single-cell Count (log): %{y}
" + + "" + ) + fig.add_trace(trace) + + # Update layout + fig.update_layout( + title=f"{threshold_set_name.replace('_', ' ').title()} Z-Score Outliers", + xaxis_title="Z-Score", + yaxis_title="Single-cell Count (log)", + yaxis_type="log", + # ensures that histograms are overlapping + barmode="overlay", + legend_title_text="Measurement Type and QC Status", + legend={ + "orientation": "v", + "yanchor": "top", + "y": 0.95, + "xanchor": "left", + "x": 1.02, + }, + ) + + return fig + + def find_image_columns(self: CytoDataFrame_type) -> bool: + pattern = r".*\.(tif|tiff)$" + return [ + column + for column in self.columns + if self[column] + .apply( + lambda value: isinstance(value, str) + and re.match(pattern, value, flags=re.IGNORECASE) + ) + .any() + ] + + @staticmethod + def draw_outline_on_image(actual_image_path: str, mask_image_path: str) -> Image: + """ + Draws outlines on a TIFF image based on a mask image and returns + the combined result. + + This method takes the path to a TIFF image and a mask image, creates + an outline from the mask, and overlays it on the TIFF image. The resulting + image, which combines the TIFF image with the mask outline, is returned. + + Args: + actual_image_path (str): Path to the TIFF image file. + mask_image_path (str): Path to the mask image file. + + Returns: + PIL.Image.Image: A PIL Image object that is the result of + combining the TIFF image with the mask outline. + + Raises: + FileNotFoundError: If the specified image or mask file does not exist. + ValueError: If the images are not in compatible formats or sizes. + """ + # Load the TIFF image + tiff_image_array = skimage.io.imread(actual_image_path) + + # Check if the image is 16-bit and grayscale + if tiff_image_array.dtype == np.uint16: + # Normalize the image to 8-bit for display purposes + tiff_image_array = (tiff_image_array / 256).astype(np.uint8) + + # Convert to PIL Image and then to 'RGBA' + tiff_image = Image.fromarray(tiff_image_array).convert("RGBA") + + # Check if the image is too dark and adjust brightness if needed + if is_image_too_dark(tiff_image): + tiff_image = adjust_image_brightness(tiff_image) + + # Load the mask image and convert it to grayscale + mask_image = Image.open(mask_image_path).convert("L") + mask_array = np.array(mask_image) + mask_array[mask_array > 0] = 255 # Ensure non-zero values are 255 (white) + + # Find contours of the mask + contours = skimage.measure.find_contours(mask_array, level=128) + + # Create an outline image with transparent background + outline_image = Image.new("RGBA", tiff_image.size, (0, 0, 0, 0)) + draw = ImageDraw.Draw(outline_image) + + for contour in contours: + # Swap x and y to match image coordinates + draw.line( + [(x, y) for y, x in np.round(contour).astype(int)], + fill=(0, 255, 0, 200), + width=2, + ) + + # Combine the TIFF image with the outline image + return Image.alpha_composite(tiff_image, outline_image) + + def process_image_data_as_html_display( + self: CytoDataFrame_type, + data_value: Any, # noqa: ANN401 + bounding_box: Tuple[int, int, int, int], + ) -> str: + if not pathlib.Path(data_value).is_file(): + # Use rglob to recursively search for a matching file + if candidate_paths := list( + pathlib.Path(self._custom_attrs["data_context_dir"]).rglob(data_value) + ): + # if we find a candidate, return the first one + candidate_path = candidate_paths[0] + else: + # we don't have any candidate paths so return the unmodified value + return data_value + + try: + if self._custom_attrs["data_mask_context_dir"] is not None and ( + matching_mask_file := list( + pathlib.Path(self._custom_attrs["data_mask_context_dir"]).rglob( + f"{pathlib.Path(candidate_path).stem}*" + ) + ) + ): + pil_image = self.draw_outline_on_image( + actual_image_path=candidate_path, + mask_image_path=matching_mask_file[0], + ) + + else: + # Read the TIFF image from the byte array + tiff_image = skimage.io.imread(candidate_path) + + # Convert the image array to a PIL Image + pil_image = Image.fromarray(tiff_image) + + cropped_img = pil_image.crop(bounding_box) + + # Save the PIL Image as PNG to a BytesIO object + png_bytes_io = BytesIO() + cropped_img.save(png_bytes_io, format="PNG") + + # Get the PNG byte data + png_bytes = png_bytes_io.getvalue() + + except (FileNotFoundError, ValueError): + # return the raw data value if we run into an exception of some kind + print("Unable to process image from {candidate_path}") + return data_value + + return ( + '' + ) + + def get_displayed_rows(self: CytoDataFrame_type) -> List[int]: + # Get the current display settings + max_rows = pd.get_option("display.max_rows") + min_rows = pd.get_option("display.min_rows") + + if len(self) <= max_rows: + # If the DataFrame has fewer rows than max_rows, all rows will be displayed + return self.index.tolist() + else: + # Calculate how many rows will be displayed at the beginning and end + half_min_rows = min_rows // 2 + start_display = self.index[:half_min_rows].tolist() + end_display = self.index[-half_min_rows:].tolist() + return start_display + end_display + + def _repr_html_( + self: CytoDataFrame_type, key: Optional[Union[int, str]] = None + ) -> str: + """ + Returns HTML representation of the underlying pandas DataFrame + for use within Juypyter notebook environments and similar. + + Referenced with modifications from: + https://github.com/pandas-dev/pandas/blob/v2.2.2/pandas/core/frame.py#L1216 + + Modifications added to help achieve image-based output for single-cell data + within the context of CytoDataFrame and coSMicQC. + + Mainly for Jupyter notebooks. + + Returns: + str: The data in a pandas DataFrame. + """ + + if self._info_repr(): + buf = StringIO() + self.info(buf=buf) + # need to escape the , should be the first line. + val = buf.getvalue().replace("<", r"<", 1) + val = val.replace(">", r">", 1) + return f"
{val}
" + + if get_option("display.notebook_repr_html"): + max_rows = get_option("display.max_rows") + min_rows = get_option("display.min_rows") + max_cols = get_option("display.max_columns") + show_dimensions = get_option("display.show_dimensions") + + # determine if we have image_cols to display + if image_cols := self.find_image_columns(): + # re-add bounding box cols if they are no longer available as in cases + # of masking or accessing various pandas attr's + bounding_box_externally_joined = False + + if self._custom_attrs["data_bounding_box"] is not None and not all( + col in self.columns.tolist() + for col in self._custom_attrs["data_bounding_box"].columns.tolist() + ): + data = self.join(other=self._custom_attrs["data_bounding_box"]) + bounding_box_externally_joined = True + else: + data = self.copy() + + # gather indices which will be displayed based on pandas configuration + display_indices = self.get_displayed_rows() + + # gather bounding box columns for use below + bounding_box_cols = self._custom_attrs["data_bounding_box"].columns.tolist() + + for image_col in image_cols: + data.loc[display_indices, image_col] = data.loc[display_indices].apply( + lambda row: self.process_image_data_as_html_display( + data_value=row[image_col], + bounding_box=( + # rows below are specified using the column name to + # determine which part of the bounding box the columns + # relate to (the list of column names could be in + # various order). + row[ + next( + col + for col in bounding_box_cols + if "Minimum_X" in col + ) + ], + row[ + next( + col + for col in bounding_box_cols + if "Minimum_Y" in col + ) + ], + row[ + next( + col + for col in bounding_box_cols + if "Maximum_X" in col + ) + ], + row[ + next( + col + for col in bounding_box_cols + if "Maximum_Y" in col + ) + ], + ), + ), + axis=1, + ) + + if bounding_box_externally_joined: + data = data.drop( + self._custom_attrs["data_bounding_box"].columns.tolist(), axis=1 + ) + + formatter = fmt.DataFrameFormatter( + data, + columns=None, + col_space=None, + na_rep="NaN", + formatters=None, + float_format=None, + sparsify=None, + justify=None, + index_names=True, + header=True, + index=True, + bold_rows=True, + # note: we avoid escapes to allow HTML rendering for images + escape=False, + max_rows=max_rows, + min_rows=min_rows, + max_cols=max_cols, + show_dimensions=show_dimensions, + decimal=".", + ) + + return fmt.DataFrameRenderer(formatter).to_html() + + else: + return None diff --git a/src/cytodataframe/image.py b/src/cytodataframe/image.py new file mode 100644 index 0000000..799b995 --- /dev/null +++ b/src/cytodataframe/image.py @@ -0,0 +1,66 @@ +""" +Helper functions for working with images in the context of CytoDataFrames. +""" + +import cv2 +import numpy as np +from PIL import Image, ImageEnhance + + +def is_image_too_dark(image: Image, pixel_brightness_threshold: float = 10.0) -> bool: + """ + Check if the image is too dark based on the mean brightness. + By "too dark" we mean not as visible to the human eye. + + Args: + image (Image): + The input PIL Image. + threshold (float): + The brightness threshold below which the image is considered too dark. + + Returns: + bool: + True if the image is too dark, False otherwise. + """ + # Convert the image to a numpy array and then to grayscale + img_array = np.array(image) + gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGBA2GRAY) + + # Calculate the mean brightness + mean_brightness = np.mean(gray_image) + + return mean_brightness < pixel_brightness_threshold + + +def adjust_image_brightness(image: Image) -> Image: + """ + Adjust the brightness of an image using histogram equalization. + + Args: + image (Image): + The input PIL Image. + + Returns: + Image: + The brightness-adjusted PIL Image. + """ + # Convert the image to numpy array and then to grayscale + img_array = np.array(image) + gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGBA2GRAY) + + # Apply histogram equalization to improve the contrast + equalized_image = cv2.equalizeHist(gray_image) + + # Convert back to RGBA + img_array[:, :, 0] = equalized_image # Update only the R channel + img_array[:, :, 1] = equalized_image # Update only the G channel + img_array[:, :, 2] = equalized_image # Update only the B channel + + # Convert back to PIL Image + enhanced_image = Image.fromarray(img_array) + + # Slightly reduce the brightness + enhancer = ImageEnhance.Brightness(enhanced_image) + reduced_brightness_image = enhancer.enhance(0.7) + + return reduced_brightness_image diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..a312b25 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,144 @@ +""" +Fixtures for testing via pytest. +See here for more information: +https://docs.pytest.org/en/7.1.x/explanation/fixtures.html +""" + +import pathlib + +import numpy as np +import pandas as pd +import pytest +import skimage +from PIL import Image + + +@pytest.fixture(name="cytotable_CFReT_data_df") +def fixture_cytotable_CFReT_df(): + """ + Return df to test CytoTable CFReT_data + """ + return pd.read_parquet( + "tests/data/cytotable/CFRet_data/test_localhost231120090001_converted.parquet" + ) + + +@pytest.fixture(name="cytotable_NF1_data_parquet_shrunken") +def fixture_cytotable_NF1_data_parquet_shrunken(): + """ + Return df to test CytoTable NF1 data through shrunken parquet file + """ + return ( + "tests/data/cytotable/NF1_cellpainting_data_shrunken/" + "Plate_2_with_image_data_shrunken.parquet" + ) + + +@pytest.fixture(name="cytotable_nuclear_speckles_data_parquet") +def fixture_cytotable_nuclear_speckle_data_parquet(): + """ + Return df to test CytoTable nuclear speckles data through shrunken parquet file + """ + return "tests/data/cytotable/nuclear_speckles/test_slide1_converted.parquet" + + +@pytest.fixture(name="basic_outlier_dataframe") +def fixture_basic_outlier_dataframe(): + """ + Creates basic example data for use in tests + """ + return pd.DataFrame({"example_feature": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}) + + +@pytest.fixture(name="basic_outlier_csv") +def fixture_basic_outlier_csv( + tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame +): + """ + Creates basic example data csv for use in tests + """ + + basic_outlier_dataframe.to_csv( + csv_path := tmp_path / "basic_example.csv", index=False + ) + + return csv_path + + +@pytest.fixture(name="basic_outlier_csv_gz") +def fixture_basic_outlier_csv_gz( + tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame +): + """ + Creates basic example data csv for use in tests + """ + + basic_outlier_dataframe.to_csv( + csv_gz_path := tmp_path / "example.csv.gz", index=False, compression="gzip" + ) + + return csv_gz_path + + +@pytest.fixture(name="basic_outlier_tsv") +def fixture_basic_outlier_tsv( + tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame +): + """ + Creates basic example data tsv for use in tests + """ + + basic_outlier_dataframe.to_csv( + tsv_path := tmp_path / "example.tsv", sep="\t", index=False + ) + + return tsv_path + + +@pytest.fixture(name="basic_outlier_parquet") +def fixture_basic_outlier_parquet( + tmp_path: pathlib.Path, basic_outlier_dataframe: pd.DataFrame +): + """ + Creates basic example data parquet for use in tests + """ + + basic_outlier_dataframe.to_parquet( + parquet_path := tmp_path / "example.parquet", index=False + ) + + return parquet_path + + +@pytest.fixture +def fixture_dark_image(): + # Create a dark image (50x50 pixels, almost black) + dark_img_array = np.zeros((50, 50, 3), dtype=np.uint8) + return Image.fromarray(dark_img_array) + + +@pytest.fixture +def fixture_mid_brightness_image(): + # Create an image with medium brightness (50x50 pixels, mid gray) + mid_brightness_img_array = np.full((50, 50, 3), 128, dtype=np.uint8) + return Image.fromarray(mid_brightness_img_array) + + +@pytest.fixture +def fixture_bright_image(): + # 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z&->x|kE*Jw<@8#Ez~-r@rY3{l?T(%v`+=Out~uoA*@Xq*71t2(y+$KL9x%X_6jfCL zeraF3@FIcFY-wq^(Ku=}BIorMv5^fBCzd|4%-zOUMg z4s-JgT)uSZT5^u;m`h*}V^$SAEe}95%+k^cCuX9G&n^?4QL)!u`oWxo9tB8fz*0K4 zR*^EYMLQFoWl={NdEur8+)?rP*GhZ5zai~7@yzxvr?a-25+HhK(+?n2R8(hzQz0RE zwc5d8n=c~P{h5Qj`$EQ=6_}xgZNTN-fi0jDceN_OrwA6m#$yds_9M*a6T0{0#~cqD zOJnsDLO9zN)@{3*8FJyVXLt++5ycCDaiV&N#XnPkcvoV#OYX+^q)Pjn=<{Gd(>*Z- zkrCip)SOtsO!!|pZl*84SWwwBXIWjp^`>ydMC2LKPrqVAa-uUzAiU8AU+*|RBSxV( z1$[A-Z]{1}[0-9]{1,2})_01_(?P[1-3]{1})_(?P[1-4]{1})_(?PDAPI|GFP|RFP) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name:None + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:Folder name + Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) + Regular expression to extract from folder name:Corrected_(?PPlate_[0-9]{1}) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name:None + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Assign files to their respective channel (only 3):', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:3 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does contain "DAPI") + Name to assign these images:DAPI + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "GFP") + Name to assign these images:GFP + Name to assign these objects:Nucleus + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "RFP") + Name to assign these images:RFP + Name to assign these objects:Cytoplasm + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['These are the current best parameters to segment nuclei from the DAPI channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAPI + Name the primary objects to be identified:Nuclei + Typical diameter of objects, in pixel units (Min,Max):30,90 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Minimum Cross-Entropy + +IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['These are the current best parameters to segment whole cells using the RFP (actin) channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nuclei + Name the objects to be identified:Cells + Select the method to identify the secondary objects:Propagation + Select the input image:RFP + Number of pixels by which to expand the primary objects:10 + Regularization factor:0.05 + Discard secondary objects touching the border of the image?:Yes + Discard the associated primary objects?:No + Name the new primary objects:Nuclei + Fill holes in identified objects?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['This module creates a third object from the first two where the nuclei is subtracted from the cells to create cytoplasm']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the larger identified objects:Cells + Select the smaller identified objects:Nuclei + Name the tertiary objects to be identified:Cytoplasm + Shrink smaller object prior to subtraction?:Yes + +ConvertObjectsToImage:[module_num:8|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nuclei + Name the output image:MaskNuclei + Select the color format:Binary (black & white) + Select the colormap:Default + +ConvertObjectsToImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Cells + Name the output image:MaskCells + Select the color format:Binary (black & white) + Select the colormap:Default + +ConvertObjectsToImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Cytoplasm + Name the output image:MaskCytoplasm + Select the color format:Binary (black & white) + Select the colormap:Default + +SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskNuclei + Select method for constructing file names:From image filename + Select image name for file prefix:DAPI + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskNuclei + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskCells + Select method for constructing file names:From image filename + Select image name for file prefix:RFP + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskCells + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes + +SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskCytoplasm + Select method for constructing file names:From image filename + Select image name for file prefix:RFP + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskCytoplasm + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes diff --git a/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py b/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py new file mode 100644 index 0000000..12d46eb --- /dev/null +++ b/tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py @@ -0,0 +1,83 @@ +""" +Used for downloading and preparing data for use +with coSMicQC tests. + +This file may be processed using the following command from the root +of the project repository: +`poetry run python tests/data/cytotable/NF1_cellpainting_data/gather_and_create_data.py` +""" + +import pathlib +import subprocess +import zipfile + +import pandas as pd + +# Define the paths +test_data_path = "tests/data/cytotable/NF1_cellpainting_data/" +sqlite_url = "https://github.com/WayScience/nf1_cellpainting_data/raw/main/2.cellprofiler_analysis/analysis_output/Plate_2/Plate_2_nf1_analysis.sqlite" +sqlite_file_path = test_data_path + "Plate_2_nf1_analysis.sqlite" +parquet_url = "https://github.com/WayScience/nf1_cellpainting_data/raw/main/3.processing_features/data/converted_data/Plate_2.parquet" +parquet_file_path = test_data_path + "Plate_2.parquet" +image_zip_url = "https://figshare.com/ndownloader/articles/22233700/versions/4" +image_zip_file_path = test_data_path + "Plate_2_images.zip" +image_extract_dir = test_data_path + "Plate_2_images" +joined_data_path = test_data_path + "Plate_2_with_image_data.parquet" + +# for url and filepath, download the url as the filepath on local system +for url, file_path in zip( + [sqlite_url, parquet_url, image_zip_url], + [sqlite_file_path, parquet_file_path, image_zip_file_path], +): + if not pathlib.Path(file_path).is_file(): + print(f"Downloading {file_path}...") + subprocess.run(["wget", "-O", file_path, url], check=True) + print(f"Downloaded {file_path}") + +# Check if the zip file exists +if not pathlib.Path(image_zip_file_path).is_file(): + print(f"{image_zip_file_path} does not exist.") +else: + # Create the extraction directory if it doesn't exist + if not pathlib.Path(image_extract_dir).is_dir(): + pathlib.Path(image_extract_dir).mkdir(parents=True) + + # Extract the zip file + with zipfile.ZipFile(image_zip_file_path, "r") as zip_ref: + zip_ref.extractall(image_extract_dir) + + print(f"Extracted {image_zip_file_path} to {image_extract_dir}") + +# form parquet table which includes image paths +df_cytotable = pd.read_parquet(parquet_file_path) + +# read image data from the CellProfiler SQlite database +df_image = pd.read_sql( + sql="SELECT * FROM per_image;", con=f"sqlite:///{sqlite_file_path}" +) + +# merge the image data from the CellProfiler SQLite database +df_full = pd.merge( + left=df_cytotable, + right=df_image, + how="left", + left_on="Metadata_ImageNumber", + right_on="ImageNumber", +) + + +# modify the filename to match what was gathered from figshare +def modify_filename(file_path: str): + return file_path.replace("_illumcorrect.tiff", ".tif") + + +# apply the filename rename to several columns +df_full["Image_URL_DAPI"] = df_full["Image_URL_DAPI"].apply(modify_filename) +df_full["Image_URL_GFP"] = df_full["Image_URL_GFP"].apply(modify_filename) +df_full["Image_URL_RFP"] = df_full["Image_URL_RFP"].apply(modify_filename) +df_full["Image_FileName_DAPI"] = df_full["Image_FileName_DAPI"].apply(modify_filename) +df_full["Image_FileName_GFP"] = df_full["Image_FileName_GFP"].apply(modify_filename) +df_full["Image_FileName_RFP"] = df_full["Image_FileName_RFP"].apply(modify_filename) + +# export the result to parquet file +df_full.to_parquet(joined_data_path) diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe b/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe new file mode 100644 index 0000000..b9c4567 --- /dev/null +++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/NF1_plate2_export_masks.cppipe @@ -0,0 +1,227 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:424 +GitHash: +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is left blank as we are giving the path to the corrected images in the CLI']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Extract metadata from file names and folder names using regular expressions.', '', 'The only metadata that will be outputed in the SQLite DB file are:', '', 'Plate', 'Well', 'Site', '', 'The rest of the information is useful to make sure that the expression is working, but can be removed/not necessary.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{"Channel": "integer", "FileLocation": "text", "Frame": "text", "Plate": "text", "Series": "text", "Site": "integer", "Stain": "float", "Well": "text"} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:(?P[A-Z]{1}[0-9]{1,2})_01_(?P[1-3]{1})_(?P[1-4]{1})_(?PDAPI|GFP|RFP) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name:None + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:Folder name + Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) + Regular expression to extract from folder name:Corrected_(?PPlate_[0-9]{1}) + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name:None + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Assign files to their respective channel (only 3):', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:3 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does contain "DAPI") + Name to assign these images:DAPI + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "GFP") + Name to assign these images:GFP + Name to assign these objects:Nucleus + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "RFP") + Name to assign these images:RFP + Name to assign these objects:Cytoplasm + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['These are the current best parameters to segment nuclei from the DAPI channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAPI + Name the primary objects to be identified:Nuclei + Typical diameter of objects, in pixel units (Min,Max):30,90 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Minimum Cross-Entropy + +IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['These are the current best parameters to segment whole cells using the RFP (actin) channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nuclei + Name the objects to be identified:Cells + Select the method to identify the secondary objects:Propagation + Select the input image:RFP + Number of pixels by which to expand the primary objects:10 + Regularization factor:0.05 + Discard secondary objects touching the border of the image?:Yes + Discard the associated primary objects?:No + Name the new primary objects:Nuclei + Fill holes in identified objects?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:50 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + Thresholding method:Otsu + +IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['This module creates a third object from the first two where the nuclei is subtracted from the cells to create cytoplasm']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the larger identified objects:Cells + Select the smaller identified objects:Nuclei + Name the tertiary objects to be identified:Cytoplasm + Shrink smaller object prior to subtraction?:Yes + +ConvertObjectsToImage:[module_num:8|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Nuclei + Name the output image:MaskNuclei + Select the color format:Binary (black & white) + Select the colormap:Default + +ConvertObjectsToImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Cells + Name the output image:MaskCells + Select the color format:Binary (black & white) + Select the colormap:Default + +ConvertObjectsToImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:1|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input objects:Cytoplasm + Name the output image:MaskCytoplasm + Select the color format:Binary (black & white) + Select the colormap:Default + +SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskNuclei + Select method for constructing file names:From image filename + Select image name for file prefix:DAPI + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskNuclei + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskCells + Select method for constructing file names:From image filename + Select image name for file prefix:RFP + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskCells + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes + +SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:MaskCytoplasm + Select method for constructing file names:From image filename + Select image name for file prefix:RFP + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:_MaskCytoplasm + Saved file format:tiff + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:No + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + Save with lossless compression?:Yes diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py new file mode 100644 index 0000000..a63eec1 --- /dev/null +++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_image_data.py @@ -0,0 +1,73 @@ +""" +Creates and shrunken dataset for testing puproses +based on coSMicQC/tests/data/cytotable/NF1_cellpainting_data (Plate 2) + +This file may be processed using the following command from the root +of the project repository: +`poetry run python tests/data/cytotable/NF1_cellpainting_data_shrunken/create.py` +""" + +import pathlib +import shutil + +import pandas as pd + +source_data_path = "tests/data/cytotable/NF1_cellpainting_data/" +target_data_path = "tests/data/cytotable/NF1_cellpainting_data_shrunken/" +source_image_data_path = source_data_path + "Plate_2_images" +source_parquet_path = source_data_path + "Plate_2_with_image_data.parquet" +target_image_data_path = target_data_path + "Plate_2_images" +target_parquet_path = target_data_path + "Plate_2_with_image_data_shrunken.parquet" + +# create target image dir +pathlib.Path(target_image_data_path).mkdir(exist_ok=True) + +# read source data +source_df = pd.read_parquet(source_parquet_path) + +# get a sample of 5 from the source data +sampled_df = source_df.sample(n=5) + +# send the sampled df to parquet file +sampled_df.to_parquet(target_parquet_path) + + +def check_and_copy_file(filename: str): + """ + Checks for files in target dir and copies them if they don't already exist + """ + source_path = f"{source_image_data_path}/{filename}" + target_path = f"{target_image_data_path}/{filename}" + + # Check if the file already exists in the target directory + if not pathlib.Path(target_path).is_file(): + # Copy the file if it doesn't exist + shutil.copy(source_path, target_path) + return f"Copied {filename} to {target_image_data_path}" + else: + return f"{filename} already exists in {target_image_data_path}" + + +# apply the file copy and collect status information +sampled_df["Image_FileName_DAPI_status"] = sampled_df["Image_FileName_DAPI"].apply( + check_and_copy_file +) +sampled_df["Image_FileName_GFP_status"] = sampled_df["Image_FileName_GFP"].apply( + check_and_copy_file +) +sampled_df["Image_FileName_RFP_status"] = sampled_df["Image_FileName_RFP"].apply( + check_and_copy_file +) + +sampled_df + +# show the results using a mask on the dataframe for status +print( + sampled_df[ + [ + "Image_FileName_DAPI_status", + "Image_FileName_GFP_status", + "Image_FileName_RFP_status", + ] + ] +) diff --git a/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py new file mode 100644 index 0000000..d360508 --- /dev/null +++ b/tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py @@ -0,0 +1,51 @@ +""" +Creates image masks for images in +coSMicQC/tests/data/cytotable/NF1_cellpainting_data (Plate 2) + +Note: expects Docker to be installed as a CLI on the system. + +This file may be processed using the following command from the root +of the project repository: +`poetry run python \ +tests/data/cytotable/NF1_cellpainting_data_shrunken/create_mask_data.py` +""" + +import os +import pathlib +import subprocess + +# create a dir for segmentation masks +pathlib.Path("tests/data/cytotable/NF1_cellpainting_data_shrunken/Plate_2_masks").mkdir( + exist_ok=True +) + +# define docker command for CellProfiler use with provided pipeline file +command = [ + "docker", + "run", + "--platform", + "linux/amd64", + "--rm", + "-v", + f"{os.getcwd()}/tests/data/cytotable/NF1_cellpainting_data_shrunken:/app", + "cellprofiler/cellprofiler:4.2.4", + "cellprofiler", + "-c", + "-r", + "-p", + "/app/NF1_plate2_export_masks.cppipe", + "-o", + "/app/Plate_2_masks", + "-i", + "/app/Plate_2_images", +] + +# Run the command and show output +try: + result = subprocess.run(command, check=True, text=True, capture_output=True) + print("Command Output:\n", result.stdout) + print("Command Error:\n", result.stderr) +except subprocess.CalledProcessError as e: + print("Error:", e) + print("Command Output:\n", e.stdout) + print("Command Error:\n", e.stderr) diff --git a/tests/data/cytotable/nuclear_speckles/shrink_source_data.py b/tests/data/cytotable/nuclear_speckles/shrink_source_data.py new file mode 100644 index 0000000..210843f --- /dev/null +++ b/tests/data/cytotable/nuclear_speckles/shrink_source_data.py @@ -0,0 +1,31 @@ +""" +Module to shrink source data for testing. + +Original source of data (processing): +https://github.com/WayScience/nuclear_speckle_image_profiling +""" + +import os + +import pandas as pd + +# note: we assume the dataset has been manually added to the +# directory containing this module. +filename = f"{os.path.dirname(__file__)}/slide1_converted.parquet" + +# read the data from parquet, sample a fraction of the data +df = pd.read_parquet(filename) + +# filter to only those data which include slide1_A1_M10_CH0_Z09_illumcorrect +df = df[ + ( + df["Image_FileName_A647"].str.contains( + img_str := "slide1_A1_M10_CH0_Z09_illumcorrect" + ) + ) + | (df["Image_FileName_DAPI"].str.contains(img_str)) + | (df["Image_FileName_GOLD"].str.contains(img_str)) +] + +# export to a new file +df.to_parquet(f"{os.path.dirname(__file__)}/test_slide1_converted.parquet") diff --git a/tests/test_frame.py b/tests/test_frame.py new file mode 100644 index 0000000..332089c --- /dev/null +++ b/tests/test_frame.py @@ -0,0 +1,193 @@ +""" +Tests cosmicqc CytoDataFrame module +""" + +import pathlib + +import cosmicqc +import pandas as pd +import plotly +import plotly.colors as pc +import pytest +from pyarrow import parquet + +from cytodataframe.frame import CytoDataFrame +from tests.utils import cytodataframe_image_display_contains_green_pixels + + +def test_cytodataframe_input( + tmp_path: pathlib.Path, + basic_outlier_dataframe: pd.DataFrame, + basic_outlier_csv: str, + basic_outlier_csv_gz: str, + basic_outlier_tsv: str, + basic_outlier_parquet: str, +): + # Tests CytoDataFrame with pd.DataFrame input. + sc_df = CytoDataFrame(data=basic_outlier_dataframe) + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == "pandas.DataFrame" + assert sc_df.equals(basic_outlier_dataframe) + + # test export + basic_outlier_dataframe.to_parquet( + control_path := f"{tmp_path}/df_input_example.parquet" + ) + sc_df.export(test_path := f"{tmp_path}/df_input_example1.parquet") + + assert parquet.read_table(control_path).equals(parquet.read_table(test_path)) + + # Tests CytoDataFrame with pd.Series input. + sc_df = CytoDataFrame(data=basic_outlier_dataframe.loc[0]) + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == "pandas.Series" + assert sc_df.equals(pd.DataFrame(basic_outlier_dataframe.loc[0])) + + # Tests CytoDataFrame with CSV input. + sc_df = CytoDataFrame(data=basic_outlier_csv) + expected_df = pd.read_csv(basic_outlier_csv) + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == str(basic_outlier_csv) + assert sc_df.equals(expected_df) + + # test export + sc_df.export(test_path := f"{tmp_path}/df_input_example.csv", index=False) + + pd.testing.assert_frame_equal(expected_df, pd.read_csv(test_path)) + + # Tests CytoDataFrame with CSV input. + sc_df = CytoDataFrame(data=basic_outlier_csv_gz) + expected_df = pd.read_csv(basic_outlier_csv_gz) + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == str(basic_outlier_csv_gz) + assert sc_df.equals(expected_df) + + # test export + sc_df.export(test_path := f"{tmp_path}/df_input_example.csv.gz", index=False) + + pd.testing.assert_frame_equal( + expected_df, pd.read_csv(test_path, compression="gzip") + ) + + # Tests CytoDataFrame with TSV input. + sc_df = CytoDataFrame(data=basic_outlier_tsv) + expected_df = pd.read_csv(basic_outlier_tsv, delimiter="\t") + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == str(basic_outlier_tsv) + assert sc_df.equals(expected_df) + + # test export + sc_df.export(test_path := f"{tmp_path}/df_input_example.tsv", index=False) + + pd.testing.assert_frame_equal(expected_df, pd.read_csv(test_path, sep="\t")) + + # Tests CytoDataFrame with parquet input. + sc_df = CytoDataFrame(data=basic_outlier_parquet) + expected_df = pd.read_parquet(basic_outlier_parquet) + + # test that we ingested the data properly + assert sc_df._custom_attrs["data_source"] == str(basic_outlier_parquet) + assert sc_df.equals(expected_df) + + # test export + sc_df.export(test_path := f"{tmp_path}/df_input_example2.parquet") + + assert parquet.read_table(basic_outlier_parquet).equals( + parquet.read_table(test_path) + ) + + # test CytoDataFrame with CytoDataFrame input + copy_sc_df = CytoDataFrame(data=sc_df) + + pd.testing.assert_frame_equal(copy_sc_df, sc_df) + + +def test_show_report(cytotable_CFReT_data_df: pd.DataFrame): + """ + Used for testing show report capabilities + """ + + df = cosmicqc.analyze.label_outliers( + df=cytotable_CFReT_data_df, + include_threshold_scores=True, + ) + + figures = df.show_report(auto_open=False) + + expected_number_figures = 3 + assert len(figures) == expected_number_figures + assert ( + next(iter({type(figure) for figure in figures})) + == plotly.graph_objs._figure.Figure + ) + + df.show_report( + report_path=(report_path := pathlib.Path("cosmicqc_example_report.html")), + auto_open=False, + ) + + assert report_path.is_file() + + +def test_repr_html( + cytotable_NF1_data_parquet_shrunken: str, + cytotable_nuclear_speckles_data_parquet: str, +): + """ + Tests how images are rendered through customized repr_html in CytoDataFrame. + """ + + # Ensure there's at least one greenish pixel in the image + assert cytodataframe_image_display_contains_green_pixels( + frame=CytoDataFrame( + data=cytotable_NF1_data_parquet_shrunken, + data_context_dir=f"{pathlib.Path(cytotable_NF1_data_parquet_shrunken).parent}/Plate_2_images", + data_mask_context_dir=f"{pathlib.Path(cytotable_NF1_data_parquet_shrunken).parent}/Plate_2_masks", + ), + image_cols=["Image_FileName_DAPI", "Image_FileName_GFP", "Image_FileName_RFP"], + ), "The NF1 images do not contain green outlines." + assert cytodataframe_image_display_contains_green_pixels( + frame=CytoDataFrame( + data=cytotable_nuclear_speckles_data_parquet, + data_context_dir=f"{pathlib.Path(cytotable_nuclear_speckles_data_parquet).parent}/images", + data_mask_context_dir=f"{pathlib.Path(cytotable_nuclear_speckles_data_parquet).parent}/masks", + ), + image_cols=[ + "Image_FileName_A647", + "Image_FileName_DAPI", + "Image_FileName_GOLD", + ], + ), "The nuclear speckles images do not contain green outlines." + + +@pytest.mark.generate_report_image +def fixture_generate_show_report_html_output(cytotable_CFReT_data_df: pd.DataFrame): + """ + Used for generating report output for use with other tests. + """ + + # create outliers dataframe + df = cosmicqc.analyze.label_outliers( + df=cytotable_CFReT_data_df, + include_threshold_scores=True, + ) + + # show a report + df.show_report( + report_path=( + report_path := pathlib.Path(__file__).parent + / "data" + / "coSMicQC" + / "show_report" + / "cosmicqc_example_report.html" + ), + color_palette=pc.qualitative.Dark24[0:2], + auto_open=False, + ) + + return report_path diff --git a/tests/test_image.py b/tests/test_image.py new file mode 100644 index 0000000..349eae8 --- /dev/null +++ b/tests/test_image.py @@ -0,0 +1,53 @@ +""" +Tests cosmicqc image module +""" + +from PIL import Image + +from cytodataframe.image import adjust_image_brightness, is_image_too_dark + + +def test_is_image_too_dark_with_dark_image(fixture_dark_image: Image): + assert is_image_too_dark(fixture_dark_image, pixel_brightness_threshold=10.0) + + +def test_is_image_too_dark_with_bright_image(fixture_bright_image: Image): + assert not is_image_too_dark(fixture_bright_image, pixel_brightness_threshold=10.0) + + +def test_is_image_too_dark_with_mid_brightness_image( + fixture_mid_brightness_image: Image, +): + assert not is_image_too_dark( + fixture_mid_brightness_image, pixel_brightness_threshold=10.0 + ) + + +def test_adjust_image_brightness_with_dark_image(fixture_dark_image: Image): + adjusted_image = adjust_image_brightness(fixture_dark_image) + # we expect that image to be too dark (it's all dark, so there's no adjustments) + assert is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0) + + +def test_adjust_image_brightness_with_bright_image(fixture_bright_image: Image): + adjusted_image = adjust_image_brightness(fixture_bright_image) + # Since the image was already bright, it should remain bright + assert not is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0) + + +def test_adjust_image_brightness_with_mid_brightness_image( + fixture_mid_brightness_image: Image, +): + adjusted_image = adjust_image_brightness(fixture_mid_brightness_image) + # The image should still not be too dark after adjustment + assert not is_image_too_dark(adjusted_image, pixel_brightness_threshold=10.0) + + +def test_adjust_nuclear_speckle_image_brightness( + fixture_nuclear_speckle_example_image: Image, +): + assert is_image_too_dark(fixture_nuclear_speckle_example_image) + assert not is_image_too_dark( + adjust_image_brightness(fixture_nuclear_speckle_example_image), + pixel_brightness_threshold=3.0, + ) diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..a1f7887 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,77 @@ +""" +Utilities for running pytest tests in CytoDataFrame +""" + +import base64 +import re +from io import BytesIO +from typing import List + +import numpy as np +from PIL import Image + +from cytodataframe import CytoDataFrame + + +def cytodataframe_image_display_contains_green_pixels( + frame: CytoDataFrame, image_cols: List[str] +) -> bool: + """ + Determines if relevant image from the CytoDataFrame HTML + contains green pixels. + + Args: + frame (CytoDataFrame): + A custom `CytoDataFrame` object which includes image paths. + image_cols (List[str]): + A list of column names in the `CytoDataFrame` + that contain images paths. + + Returns: + bool: + True if any greenish pixels are found in relevant + image within the HTML, otherwise False. + + Raises: + ValueError: + If no base64-encoded image data is found in the + HTML representation of the given columns. + """ + + # gather HTML output from CytoDataFrame + html_output = frame[image_cols]._repr_html_() + + # Extract all base64 image data from the HTML + matches = re.findall(r'data:image/png;base64,([^"]+)', html_output) + + # check that we have matches + if not len(matches) > 0: + raise ValueError("No base64 image data found in HTML") + + # Select the third base64 image data (indexing starts from 0) + # (we expect the first ones to not contain outlines based on the + # html and example data) + base64_data = matches[2] + + # Decode the base64 image data + image_data = base64.b64decode(base64_data) + image = Image.open(BytesIO(image_data)).convert("RGB") + + # Check for the presence of green pixels in the image + image_array = np.array(image) + + # gather color channels from image + red_channel = image_array[:, :, 0] + green_channel = image_array[:, :, 1] + blue_channel = image_array[:, :, 2] + + # Define a threshold to identify greenish pixels + green_threshold = 50 + green_pixels = ( + (green_channel > green_threshold) + & (green_channel > red_channel) + & (green_channel > blue_channel) + ) + + # return true/false if there's at least one greenish pixel in the image + return np.any(green_pixels)

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