Skip to content

Latest commit

 

History

History
88 lines (49 loc) · 4.34 KB

CONTRIBUTING.md

File metadata and controls

88 lines (49 loc) · 4.34 KB

Contributing

General guidelines

If you haven't contributed to open-source before, we recommend you read this excellent guide by GitHub on how to contribute to open source. The guide is long, so you can gloss over things you're familiar with.

If you're not already familiar with it, we follow the fork and pull model on GitHub. Also, check out this recommended git workflow.

As a rough guide for cooltools:

  • contributors should preferably work on their forks and submit pull requests to the main branch
  • core maintainers can work on feature branches in the main fork and then submit pull requests to the main branch
  • core maintainers can push directly to the main branch if it's urgently needed

Contributing Code

This project has a number of requirements for all code contributed.

  • We follow the PEP-8 style convention.
  • We use flake8 to automatically lint the code and maintain code style. You can use a code formatter like black or autopep8 to help keep the linter happy.
  • We use Numpy-style docstrings.
  • User-facing API changes or new features should have documentation added.

Ideally, provide full test coverage for new code submitted in PRs.

Setting up Your Development Environment

For setting up an isolated virtual environment for development, we recommend using conda. After forking and cloning the repository, install in "editable" (i.e. development) mode using the -e option:

$ git clone https://github.com/open2c/cooltools.git
$ cd cooltools
$ pip install -e .

Editable mode installs the package by creating a "link" to your working (repo) directory.

Unit Tests

It is best if all new functionality and/or bug fixes have unit tests added with each use-case.

We use pytest as our unit testing framework with the pytest-cov extension to check code coverage and pytest-flake8 to check code style. You don't need to configure these extensions yourself. This automatically checks code style and functionality, and prints code coverage, even though it doesn't fail on low coverage.

Once you've configured your environment, you can just cd to the root of your repository and run

$ pytest

Unit tests are automatically run on Travis CI for pull requests.

Coverage

The pytest script automatically reports coverage, both on the terminal for missing line numbers, and in annotated HTML form in htmlcov/index.html.

Documentation

If a feature is stable and relatively finalized, it is time to add it to the documentation. If you are adding any private/public functions, it is best to add docstrings, to aid in reviewing code and also for the API reference.

We use Numpy style docstrings and Sphinx to document this library. Sphinx, in turn, uses reStructuredText as its markup language for adding code.

We use the Sphinx Autosummary extension to generate API references. You may want to look at docs/api.rst to see how these files look and where to add new functions, classes or modules.

We also use the nbsphinx extension to render tutorial pages from Jupyter notebooks.

To build the documentation:

$ make docs

After this, you can find an HTML version of the documentation in docs/_build/html/index.html.

Documentation from master and tagged releases is automatically built and hosted thanks to readthedocs.

Acknowledgement

If you've contributed significantly and would like your authorship to be included in subsequent uploads to Zenodo, please make a separate PR to add your name and affiliation to the .zenodo.json file.


This document was modified from the guidelines from the sparse project.