Contributing to pygmtools
First, thank you for contributing to
How to contribute
The preferred workflow for contributing to
pygmtools is to fork the
main repository on
GitHub, clone, and develop on a branch. Steps:
Fork the project repository by clicking on the ‘Fork’ button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see this guide.
Clone your fork of the repo from your GitHub account to your local disk:
$ git clone email@example.com:YourUserName/pygmtools.git $ cd pygmtools
featurebranch to hold your development changes:
$ git checkout -b my-feature
Always use a
featurebranch. It is good practice to never work on the
Develop the feature on your feature branch. Add changed files using
git addand then
$ git add modified_files $ git commit
to record your changes in Git, then push the changes to your GitHub account with:
$ git push -u origin my-feature
Follow these instructions to create a pull request from your fork. This will email the committers and an automatic check will run.
(If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.)
Pull Request Checklist
We recommended that your contribution complies with the following rules before you submit a pull request:
Follow the PEP8 Guidelines.
If your pull request addresses an issue, please use the pull request title to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is created.
All public methods should have informative docstrings with sample usage presented as doctests when appropriate.
When adding additional functionality, provide at least one example script under the function’s API. Have a look at other functions’ examples for reference. You are also encouraged to add new examples to the
examples/folder to demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in
If you have modified any examples, please build the documentation before you commit, and please also commit any changes in
Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with non-regression tests. These tests verify the correct behavior of the fix or feature. In this manner, further modifications on the code base are granted to be consistent with the desired behavior. For the Bug-fixes case, at the time of the PR, these tests should fail for the code base in master and pass for the PR code.
At least one paragraph of narrative documentation with links to references in the literature and the example.
You can also check for common programming errors with the following tools:
No pyflakes warnings, check with:
$ pip install pyflakes $ pyflakes path/to/module.py
No PEP8 warnings, check with:
$ pip install pep8 $ pep8 path/to/module.py
AutoPEP8 can help you fix some of the easy redundant errors:
$ pip install autopep8 $ autopep8 path/to/pep8.py
We use Github issues to track all bugs and feature requests; feel free to open an issue if you have found a bug or wish to see a feature implemented.
It is recommended to check that your issue complies with the following rules before submitting:
Verify that your issue is not being currently addressed by other issues or pull requests.
Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks.
Please include your operating system type and version number, as well as your Python, pygmtools, numpy, and scipy versions. Please also provide the name of your running backend, and the GPU/CUDA versions if you are using GPU. This information can be found by running the following environment report (
$ python3 -c 'import pygmtools; pygmtools.env_report()'
If you are using GPU, make sure to install
pynvmlbefore running the above script:
pip install pynvml.
Please be specific about what estimators and/or functions are involved and the shape of the data, as appropriate; please include a reproducible code snippet or link to a gist. If an exception is raised, please provide the traceback.
We are glad to accept any sort of documentation: function docstrings,
reStructuredText documents, tutorials, examples, etc.
reStructuredText documents live in the source code repository under the
You can edit the documentation using any text editor and then generate
the HTML output by typing
make html from the
The resulting HTML files are in
docs/_build/ and are viewable in
any web browser. The example files in
examples/ are also built.
If you want to skip building the examples, please use the command
For building the documentation, you will need the packages listed in
docs/requirements.txt. Please use
python==3.8 to keep it consistent
with the read-the-doc builder online. If you have modified any examples,
please build the documentation before you commit, and please also commit
any changes in
When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data.
This Contribution guide is strongly inpired by the one of the scikit-learn team.