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Contributing code

How to contribute

The preferred way to contribute to scikit-video is to fork the [main repository](http://github.com/scikit-video/scikit-video) on GitHub:

  1. Fork the [project repository](http://github.com/scikit-video/scikit-video): click on the 'Fork' button near the top of the page. This creates a copy of the code under your account on the GitHub server.

  2. Clone this copy to your local disk:

    $ git clone git@github.com:YourLogin/scikit-video.git $ cd scikit-video

  3. Create a branch to hold your changes:

    $ git checkout -b my-feature

    and start making changes. Never work in the master branch!

  4. Work on this copy on your computer using Git to do the version control. When you're done editing, do:

    $ git add modified_files $ git commit

    to record your changes in Git, then push them to GitHub with:

    $ git push -u origin my-feature

Finally, go to the web page of your fork of the scikit-video repo, and click 'Pull request' to send your changes to the maintainer for review. This will send an email to the committer.

(If any of the above seems like magic to you, then look up the [Git documentation](http://git-scm.com/documentation) on the web.)

It is recommended to check that your contribution complies with the following rules before submitting a pull request:

  • All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

  • All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the toplevel source folder):

    $ make

  • When adding additional functionality, provide at least one example script in the examples/ folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in scikit-video.
  • At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.

You can also check for common programming errors with the following tools:

  • Code with good unittest coverage (at least 80%), check with:

    $ pip install nose coverage $ nosetests --with-coverage path/to/tests_for_package

  • 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

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the mailing list or on the GitHub issue).

Documentation

We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.

You can edit the documentation using any text editor and then generate the HTML output by typing make html from the doc/ directory. Alternatively, make can be used to quickly generate the documentation without the example gallery. The resulting HTML files will be placed in _build/html/ and are viewable in a web browser. See the README file in the doc/ directory for more information.

For building the documentation, you will need [sphinx](http://sphinx.pocoo.org/), [matplotlib](http://matplotlib.sourceforge.net/), and [pillow](http://pillow.readthedocs.org/en/latest/).

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 and a figure (coming from an example) illustrating it.