Contributions are welcome, and they are greatly appreciated! Every little bit helps. You can contribute in many ways:
Types of Contributions¶
Usage in Publications¶
If you use Sympl to perform research, your publication is a valuable resource for others looking to learn the ways they can leverage Sympl’s capabilities. If you have used Sympl in a publication, please let us know so we can add it to the list.
Working on projects that use Sympl¶
Sympl is only as useful as the components it has available. You can make Sympl more useful for others by contributing to model projects which use Sympl, or by writing/wrapping model components and deploying them in your own Python packages.
Presenting Sympl to Others¶
Sympl is meant to be an accessible, community-driven tool. You can help the community of users grow and be more effective in many ways, such as:
- Running a workshop
- Offering to be a resource for others to ask questions
- Presenting research that uses Sympl
If you or someone you know is contributing to the Sympl community by presenting it or assisting others with the model, please let us know so we can add that person to the contributors list.
Report bugs at https://github.com/mcgibbon/sympl/issues.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.
Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.
Sympl could always use more documentation. You could:
- Clean up or add to the official Sympl docs and docstrings.
- Write useful and clear examples that are missing from the examples folder.
- Create a Jupyter notebook that uses Sympl and share it with others.
- Prepare reproducible model scripts to distribute with a paper using Sympl.
- Anything else that communicates useful information about Sympl.
The best way to send feedback is to file an issue at https://github.com/mcgibbon/sympl/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that contributions are welcome :)
Ready to contribute? Here’s how to set up sympl for local development.
Fork the sympl repo on GitHub.
Clone your fork locally:
$ git clone firstname.lastname@example.org:your_name_here/sympl.git
Install your local copy in development mode:
$ cd sympl/ $ python setup.py develop
Create a branch for local development:
$ git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
$ flake8 sympl tests $ python setup.py test or py.test $ tox
To get flake8 and tox, just pip install them.
Commit your changes and push your branch to GitHub:
$ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website.
Pull Request Guidelines¶
Before you submit a pull request, check that it meets these guidelines:
- The pull request should include tests.
- If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
- The pull request should work for Python 2.7, 3.4 and 3.5. Check https://travis-ci.org/mcgibbon/sympl/pull_requests and make sure that the tests pass for all supported Python versions.
In the Sympl code, we follow PEP 8 style guidelines (tested by flake8). You can test style by running “tox -e flake8” from the root directory of the repository. There are some exceptions to PEP 8:
- All lines should be shorter than 80 characters. However, lines longer than this are permissible if this increases readability (particularly for lines representing complicated equations).
- Space should be assigned around arithmetic operators in a way that maximizes readability. For some cases, this may mean not including whitespace around certain operations to make the separation of terms clearer, e.g. “Cp*T + g*z + Lv*q”.
- While state dictionary keys are full and verbose, within components they may be assigned to shorter names if it makes the code clearer.
- We can take advantage of known scientific abbreviations for quantities within components (e.g. “T” for “air_temperature”) even thought they do not follow pothole_case.
To run a subset of tests:
$ py.test tests.test_timestepping