Skip to content

Latest commit

 

History

History
349 lines (274 loc) · 17 KB

CONTRIBUTING.md

File metadata and controls

349 lines (274 loc) · 17 KB
Raw

How to Contribute

Contributing code

Creating a development environment

It is recommended to use conda or mamba to create a development environment for movement. In the following we assume you have conda installed, but the same commands will also work with mamba/micromamba.

First, create and activate a conda environment with some prerequisites:

conda create -n movement-dev -c conda-forge python=3.10 pytables
conda activate movement-dev

The above method ensures that you will get packages that often can't be installed via pip, including hdf5.

To install movement for development, clone the GitHub repository, and then run from inside the repository:

pip install -e .[dev]  # works on most shells
pip install -e '.[dev]'  # works on zsh (the default shell on macOS)

This will install the package in editable mode, including all dependencies required for development.

Finally, initialise the pre-commit hooks:

pre-commit install

Pull requests

In all cases, please submit code to the main repository via a pull request (PR). We recommend, and adhere, to the following conventions:

  • Please submit draft PRs as early as possible to allow for discussion.
  • The PR title should be descriptive e.g. "Add new function to do X" or "Fix bug in Y".
  • The PR description should be used to provide context and motivation for the changes.
  • One approval of a PR (by a repo owner) is enough for it to be merged.
  • Unless someone approves the PR with optional comments, the PR is immediately merged by the approving reviewer.
  • Ask for a review from someone specific if you think they would be a particularly suited reviewer.
  • PRs are preferably merged via the "squash and merge" option, to keep a clean commit history on the main branch.

A typical PR workflow would be:

  • Create a new branch, make your changes, and stage them.
  • When you try to commit, the pre-commit hooks will be triggered.
  • Stage any changes made by the hooks, and commit.
  • You may also run the pre-commit hooks manually, at any time, with pre-commit run -a.
  • Make sure to write tests for any new features or bug fixes. See testing below.
  • Don't forget to update the documentation, if necessary. See contributing documentation below.
  • Push your changes to GitHub and open a draft pull request, with a meaningful title and a thorough description of the changes.
  • If all checks (e.g. linting, type checking, testing) run successfully, you may mark the pull request as ready for review.
  • Respond to review comments and implement any requested changes.
  • One of the maintainers will approve the PR and add it to the merge queue.
  • Success 🎉 !! Your PR will be (squash-)merged into the main branch.

Development guidelines

Formatting and pre-commit hooks

Running pre-commit install will set up pre-commit hooks to ensure a consistent formatting style. Currently, these include:

  • ruff does a number of jobs, including code linting and auto-formatting.
  • mypy as a static type checker.
  • check-manifest to ensure that the right files are included in the pip package.
  • codespell to check for common misspellings.

These will prevent code from being committed if any of these hooks fail. To run them individually (from the root of the repository), you can use:

ruff .
mypy -p movement
check-manifest
codespell

To run all the hooks before committing:

pre-commit run  # for staged files
pre-commit run -a  # for all files in the repository

Some problems will be automatically fixed by the hooks. In this case, you should stage the auto-fixed changes and run the hooks again:

git add .
pre-commit run

If a problem cannot be auto-fixed, the corresponding tool will provide information on what the issue is and how to fix it. For example, ruff might output something like:

movement/io/load_poses.py:551:80: E501 Line too long (90 > 79)

This pinpoints the problem to a single code line and a specific ruff rule violation. Sometimes you may have good reasons to ignore a particular rule for a specific line of code. You can do this by adding an inline comment, e.g. # noqa: E501. Replace E501 with the code of the rule you want to ignore.

For docstrings, we adhere to the numpydoc style. Make sure to provide docstrings for all public functions, classes, and methods. This is important as it allows for automatic generation of the API reference.

Testing

We use pytest for testing and aim for ~100% test coverage (as far as is reasonable). All new features should be tested. Write your test methods and classes in the tests folder.

For some tests, you will need to use real experimental data. Do not include these data in the repository, especially if they are large. We store several sample datasets in an external data repository. See sample data for more information.

Continuous integration

All pushes and pull requests will be built by GitHub actions. This will usually include linting, testing and deployment.

A GitHub actions workflow (.github/workflows/test_and_deploy.yml) has been set up to run (on each push/PR):

  • Linting checks (pre-commit).
  • Testing (only if linting checks pass)
  • Release to PyPI (only if a git tag is present and if tests pass).

Versioning and releases

We use semantic versioning, which includes MAJOR.MINOR.PATCH version numbers:

  • PATCH = small bugfix
  • MINOR = new feature
  • MAJOR = breaking change

We use setuptools_scm to automatically version movement. It has been pre-configured in the pyproject.toml file. setuptools_scm will automatically infer the version using git. To manually set a new semantic version, create a tag and make sure the tag is pushed to GitHub. Make sure you commit any changes you wish to be included in this version. E.g. to bump the version to 1.0.0:

git add .
git commit -m "Add new changes"
git tag -a v1.0.0 -m "Bump to version 1.0.0"
git push --follow-tags

Alternatively, you can also use the GitHub web interface to create a new release and tag.

The addition of a GitHub tag triggers the package's deployment to PyPI. The version number is automatically determined from the latest tag on the main branch.

Contributing documentation

The documentation is hosted via GitHub pages at movement.neuroinformatics.dev. Its source files are located in the docs folder of this repository. They are written in either reStructuredText or markdown. The index.md file corresponds to the homepage of the documentation website. Other .rst or .md files are linked to the homepage via the toctree directive.

We use Sphinx and the PyData Sphinx Theme to build the source files into HTML output. This is handled by a GitHub actions workflow (.github/workflows/docs_build_and_deploy.yml). The build job is triggered on each PR, ensuring that the documentation build is not broken by new changes. The deployment job is only triggered whenever a tag is pushed to the main branch, ensuring that the documentation is published in sync with each PyPI release.

Editing the documentation

To edit the documentation, first clone the repository, and install movement in a development environment.

Now create a new branch, edit the documentation source files (.md or .rst in the docs folder), and commit your changes. Submit your documentation changes via a pull request, following the same guidelines as for code changes. Make sure that the header levels in your .md or .rst files are incremented consistently (H1 > H2 > H3, etc.) without skipping any levels.

Adding new pages

If you create a new documentation source file (e.g. my_new_file.md or my_new_file.rst), you will need to add it to the toctree directive in index.md for it to be included in the documentation website:

:maxdepth: 2
:hidden:

existing_file
my_new_file

Adding external links

If you are adding references to an external link (e.g. https://github.com/neuroinformatics-unit/movement/issues/1) in a .md file, you will need to check if a matching URL scheme (e.g. https://github.com/neuroinformatics-unit/movement/) is defined in myst_url_schemes in docs/source/conf.py. If it is, the following [](scheme:loc) syntax will be converted to the full URL during the build process:

[link text](movement-github:issues/1)

If it is not yet defined and you have multiple external links pointing to the same base URL, you will need to add the URL scheme to myst_url_schemes in docs/source/conf.py.

Updating the API reference

If your PR introduces new public-facing functions, classes, or methods, make sure to add them to the docs/source/api_index.rst page, so that they are included in the API reference, e.g.:

My new module
--------------
.. currentmodule:: movement.new_module
.. autosummary::
    :toctree: api

    new_function
    NewClass

For this to work, your functions/classes/methods will need to have docstrings that follow the numpydoc style.

Updating the examples

We use sphinx-gallery to create the examples. To add new examples, you will need to create a new .py file in examples/. The file should be structured as specified in the relevant sphinx-gallery documentation.

We are using sphinx-gallery's integration with binder to provide interactive versions of the examples. If your examples rely on packages that are not among movement's dependencies, you will need to add them to the docs/source/environment.yml file. That file is used by binder to create the conda environment in which the examples are run. See the relevant section of the binder documentation.

Building the documentation locally

We recommend that you build and view the documentation website locally, before you push it. To do so, first install the requirements for building the documentation:

pip install -r docs/requirements.txt

Then, from the root of the repository, run:

sphinx-build docs/source docs/build

You can view the local build by opening docs/build/index.html in a browser. To refresh the documentation, after making changes, remove the docs/build folder and re-run the above command:

rm -rf docs/build && sphinx-build docs/source docs/build

To check that external links are correctly resolved, run:

sphinx-build docs/source docs/build -b linkcheck

If the linkcheck step incorrectly marks links with valid anchors as broken, you can skip checking the anchors in specific links by adding the URLs to linkcheck_anchors_ignore_for_url in docs/source/conf.py, e.g.:

# The linkcheck builder will skip verifying that anchors exist when checking
# these URLs
linkcheck_anchors_ignore_for_url = [
    "https://gin.g-node.org/G-Node/Info/wiki/",
    "https://neuroinformatics.zulipchat.com/",
]

Sample data

We maintain some sample datasets to be used for testing, examples and tutorials on an external data repository. Our hosting platform of choice is called GIN and is maintained by the German Neuroinformatics Node. GIN has a GitHub-like interface and git-like CLI functionalities.

Currently, the data repository contains sample pose estimation data files stored in the poses folder. For some of these files, we also host the associated video file (in the videos folder) and/or a single video frame (in the frames) folder. These can be used to develop and test visualisations, e.g. overlaying pose data on video frames. The metadata.yaml file holds metadata for each sample dataset, including information on data provenance as well as the mapping between pose data files and related video/frame files.

Fetching data

To fetch the data from GIN, we use the pooch Python package, which can download data from pre-specified URLs and store them locally for all subsequent uses. It also provides some nice utilities, like verification of sha256 hashes and decompression of archives.

The relevant functionality is implemented in the movement.sample_data.py module. The most important parts of this module are:

  1. The SAMPLE_DATA download manager object.
  2. The list_datasets() function, which returns a list of the available pose datasets (file names of the pose data files).
  3. The fetch_dataset_paths() function, which returns a dictionary containing local paths to the files associated with a particular sample dataset: poses, frame, video. If the relevant files are not already cached locally, they will be downloaded.
  4. The fetch_dataset() function, which downloads the files associated with a given sample dataset (same as fetch_dataset_paths()) and additionally loads the pose data into movement, returning an xarray.Dataset object. The local paths to the associated video and frame files are stored as dataset attributes, with names video_path and frame_path, respectively.

By default, the downloaded files are stored in the ~/.movement/data folder. This can be changed by setting the DATA_DIR variable in the movement.sample_data.py module.

Adding new data

Only core movement developers may add new files to the external data repository. To add a new file, you will need to:

  1. Create a GIN account
  2. Ask to be added as a collaborator on the movement data repository (if not already)
  3. Download the GIN CLI and set it up with your GIN credentials, by running gin login in a terminal.
  4. Clone the movement data repository to your local machine, by running gin get neuroinformatics/movement-test-data in a terminal.
  5. Add your new files to the poses, videos, and/or frames folders as appropriate. Follow the existing file naming conventions as closely as possible.
  6. Determine the sha256 checksum hash of each new file by running sha256sum <filename> in a terminal. For convenience, we've included a get_sha256_hashes.py script in the movement data repository. If you run this from the root of the data repository, within a Python environment with movement installed, it will calculate the sha256 hashes for all files in the poses, videos, and frames folders and write them to files named poses_hashes.txt, videos_hashes.txt, and frames_hashes.txt, respectively.
  7. Add metadata for your new files to metadata.yaml, including their sha256 hashes you've calculated. See the example entry below for guidance.
  8. Commit a specific file with gin commit -m <message> <filename>, or gin commit -m <message> . to commit all changes.
  9. Upload the committed changes to the GIN repository by running gin upload. Latest changes to the repository can be pulled via gin download. gin sync will synchronise the latest changes bidirectionally.

metadata.yaml example entry

"SLEAP_three-mice_Aeon_proofread.analysis.h5":
  sha256sum: "82ebd281c406a61536092863bc51d1a5c7c10316275119f7daf01c1ff33eac2a"
  source_software: "SLEAP"
  fps: 50
  species: "mouse"
  number_of_individuals: 3
  shared_by:
    name: "Chang Huan Lo"
    affiliation: "Sainsbury Wellcome Centre, UCL"
  frame:
    file_name: "three-mice_Aeon_frame-5sec.png"
    sha256sum: "889e1bbee6cb23eb6d52820748123579acbd0b2a7265cf72a903dabb7fcc3d1a"
  video:
    file_name: "three-mice_Aeon_video.avi"
    sha256sum: "bc7406442c90467f11a982fd6efd85258ec5ec7748228b245caf0358934f0e7d"
  note: "All labels were proofread (user-defined) and can be considered ground truth. It was exported from the .slp file with the same prefix."