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Contributing

Welcome to dcqc contributor's guide.

This document focuses on getting any potential contributor familiarized with the development processes, but other kinds of contributions are also appreciated.

If you are new to using git or have never collaborated in a project previously, please have a look at contribution-guide.org. Other resources are also listed in the excellent guide created by FreeCodeCamp 1.

Please notice, all users and contributors are expected to be open, considerate, reasonable, and respectful. When in doubt, Python Software Foundation's Code of Conduct is a good reference in terms of behavior guidelines.

Issue Reports

If you experience bugs or general issues with dcqc, please have a look on the issue tracker. If you don't see anything useful there, please feel free to fire an issue report.

:::{tip} Please don't forget to include the closed issues in your search. Sometimes a solution was already reported, and the problem is considered solved. :::

New issue reports should include information about your programming environment (e.g., operating system, Python version) and steps to reproduce the problem. Please try also to simplify the reproduction steps to a very minimal example that still illustrates the problem you are facing. By removing other factors, you help us to identify the root cause of the issue.

Documentation Improvements

You can help improve dcqc docs by making them more readable and coherent, or by adding missing information and correcting mistakes.

dcqc documentation uses Sphinx as its main documentation compiler. This means that the docs are kept in the same repository as the project code, and that any documentation update is done in the same way was a code contribution. The documentation is written using CommonMark with MyST extensions.

:::{tip} Please notice that the GitHub web interface provides a quick way of propose changes in dcqc's files. While this mechanism can be tricky for normal code contributions, it works perfectly fine for contributing to the docs, and can be quite handy.

If you are interested in trying this method out, please navigate to the docs folder in the source repository, find which file you would like to propose changes and click in the little pencil icon at the top, to open GitHub's code editor. Once you finish editing the file, please write a message in the form at the bottom of the page describing which changes have you made and what are the motivations behind them and submit your proposal. :::

When working on documentation changes in your local machine, you can compile them using tox :

tox -e docs

and use Python's built-in web server for a preview in your web browser (http://localhost:8000):

python3 -m http.server --directory 'docs/_build/html'

Code Contributions

Submit an issue

Before you work on any non-trivial code contribution it's best to first create a report in the issue tracker to start a discussion on the subject. This often provides additional considerations and avoids unnecessary work.

Clone the repository

  1. Create an user account on GitHub if you do not already have one.

  2. Fork the project repository: click on the Fork button near the top of the page. This creates a copy of the code under your account on GitHub.

  3. Clone this copy to your local disk:

    git clone git@github.com:Sage-Bionetworks-Workflows/py-dcqc.git
cd dcqc

  4. You should run:

    pipenv install --dev

    to create an isolated virtual environment containing package dependencies, including those needed for development (e.g. testing, documentation).

  5. Install pre-commit hooks:

    pipenv run pre-commit install

    dcqc comes with a lot of hooks configured to automatically help the developer to check the code being written.

Implement your changes

  1. Create a branch to hold your changes:

    git checkout -b my-feature

    and start making changes. Never work on the main branch!

  2. Start your work on this branch. Don't forget to add docstrings to new functions, modules and classes, especially if they are part of public APIs.

  3. Add yourself to the list of contributors in AUTHORS.md.

  4. When you’re done editing, do:

    git add <MODIFIED FILES>
git commit

    to record your changes in git.

    Please make sure to see the validation messages from pre-commit and fix any eventual issues. This should automatically use flake8/black to check/fix the code style in a way that is compatible with the project.

    :::{important} Don't forget to add unit tests and documentation in case your contribution adds an additional feature and is not just a bugfix.

    Moreover, writing a descriptive commit message is highly recommended. In case of doubt, you can check the commit history with:

    git log --graph --decorate --pretty=oneline --abbrev-commit --all

    to look for recurring communication patterns. :::

  5. Please check that your changes don't break any unit tests with:

    tox

    You can also use tox to run several other pre-configured tasks in the repository. Try tox -av to see a list of the available checks.

Submit your contribution

  1. If everything works fine, push your local branch to the remote server with:

    git push -u origin my-feature

  2. Go to the web page of your fork and click "Create pull request" to send your changes for review.

    Find more detailed information in creating a PR. You might also want to open the PR as a draft first and mark it as ready for review after the feedbacks from the continuous integration (CI) system or any required fixes.

Contributing Internal Tests

In py-dcqc, any test where the primary business logic is executed within the package itself is considered internal. One example is the Md5ChecksumTest.

When contributing an internal test be sure to do the following:

  1. Follow the steps above to set up py-dcqc and create your contribution.

  2. Include a class docstring that describes the purpose of the test.

  3. Include the following class attributes:

    • tier: A TestTier enum describing the complexity of the test contributed. Valid tier values include:
      • FILE_INTEGRITY
      • INTERNAL_CONFORMANCE
      • EXTERNAL_CONFORMANCE
      • SUBJECTIVE_CONFORMANCE
    • target: The target class that the test will be applied to. This value will be SingleTarget for individual files and PairedTarget for paired files.

  4. Implement the major logic of the test in the compute_status method. This should include a condition for returning a status of TestStatus.PASS when the test conditions are met and TestStatus.FAIL when they are not.

    • For failing cases be sure to include a line setting the class' status_reason to a helpful string that will tell users why the test failed before returning the status.

Contributing External Tests

In py-dcqc, any test where the primary business logic is executed outside of this package itself is considered to be external. One example is the LibTiffInfoTest. For these tests, py-dcqc is responsible for packaging up a Nextflow process which is then executed in an nf-dcqc workflow run. Such tests are not possible to run in py-dcqc alone at this time. This makes contributing, testing, debugging, and using external tests a little more complicated that internal tests such as the Md5ChecksumTest which has all of its logic built into this package.

When contributing an internal test be sure to do the following:

  1. Follow the steps above to set up py-dcqc and create your contribution.

  2. Include a class docstring that describes the purpose of the test.

  3. Include the following class attributes:

    • tier: A TestTier enum describing the complexity of the test contributed. Valid tier values include:
      • FILE_INTEGRITY
      • INTERNAL_CONFORMANCE
      • EXTERNAL_CONFORMANCE
      • SUBJECTIVE_CONFORMANCE
    • pass_code: The exit code that will be returned by the command indicating a passed test.
    • fail_code: The exit code that will be returned by the command indicating a failed test.
    • failure_reason_location: The file (either "std_out" or "std_err") that will contain the reason for a failed test.
    • target: The target class that the test will be applied to. This value will be SingleTarget for individual files and PairedTarget for paired files.

  4. If possible, contribute an external test that returns different codes when it fails and when it errors out. Currently, a limitation of DCQC is that several external tests return the same exit_code when they fail and encounter an error. This will be addressed in future work that will add finer grained result interpretation.

Testing Your Changes

  1. Follow the instructions in the README.md file in the nf-dcqc respository to set up the workflow on your local machine.

    • Run git checkout dev to switch to the developer branch

  2. Build your local version of py-dcqc with your new changes with:

    src/docker/build.sh

    NOTE: This step assumes that you have docker installed and that it is running, and that you have pipx installed.

  3. Follow nf-dcqc instructions to create a nextflow run command that tests your contribution.

    • You should include at least two files in your nf-dcqc input file (example), one that you expect to pass your contributed test, and one that you expect to fail.
    • Include the local profile so that the workflow leverages your locally built py-orca container

    Example command (executed from within your local nf-dcqc repo clone):

    nextflow run main.nf -profile local,docker --input path/to/your/input.csv -- outdir output --required_tests <YOUR_TEST_NAME>

  4. Examine the final output.csv and suites.json files exported by the Nextflow workflow, if your contributed test bahaved as expected, you're done! If not, debug and make changes to your contribution and re-run the workflow.

Troubleshooting

The following tips can be used when facing problems to build or test the package:

  1. Make sure to fetch all the tags from the upstream repository. The command git describe --abbrev=0 --tags should return the version you are expecting. If you are trying to run CI scripts in a fork repository, make sure to push all the tags. You can also try to remove all the egg files or the complete egg folder, i.e., .eggs, as well as the *.egg-info folders in the src folder or potentially in the root of your project.

  2. Sometimes tox misses out when new dependencies are added, especially to setup.cfg and docs/requirements.txt. If you find any problems with missing dependencies when running a command with tox, try to recreate the tox environment using the -r flag. For example, instead of:

    tox -e docs

    Try running:

    tox -r -e docs

  3. Make sure to have a reliable tox installation that uses the correct Python version (e.g., 3.7+). When in doubt you can run:

    tox --version
# OR
which tox

    If you have trouble and are seeing weird errors upon running tox, you can also try to create a dedicated virtual environment with a tox binary freshly installed. For example:

    virtualenv .venv
source .venv/bin/activate
.venv/bin/pip install tox
.venv/bin/tox -e all

  4. Pytest can drop you in an interactive session in the case an error occurs. In order to do that you need to pass a --pdb option (for example by running tox -- -k <NAME OF THE FALLING TEST> --pdb). You can also setup breakpoints manually instead of using the --pdb option.

Maintainer tasks

Releases

If you are part of the group of maintainers and have correct user permissions on PyPI, the following steps can be used to release a new version for dcqc:

  1. Make sure all unit tests are successful.
  2. Tag the current commit on the main branch with a release tag, e.g., v1.2.3.
  3. Push the new tag to the upstream repository, e.g., git push upstream v1.2.3
  4. Clean up the dist and build folders with tox -e clean (or rm -rf dist build) to avoid confusion with old builds and Sphinx docs.
  5. Run tox -e build and check that the files in dist have the correct version (no .dirty or git hash) according to the git tag. Also check the sizes of the distributions, if they are too big (e.g., > 500KB), unwanted clutter may have been accidentally included.
  6. Run tox -e publish -- --repository pypi and check that everything was uploaded to PyPI correctly.

Footnotes

  1. Even though, these resources focus on open source projects and communities, the general ideas behind collaborating with other developers to collectively create software are general and can be applied to all sorts of environments, including private companies and proprietary code bases. ↩