How to contribute to skfuzzy

Developing Open Source is great fun! Join us on the scikit-fuzzy mailing list and tell us which challenges you’d like to solve.

  • Guidance is available for those new to scientific programming in Python.
  • If you’re looking for something to implement, you can browse the open issues on GitHub or suggest a new, useful feature.
  • The technical detail of the development process is summed up below. Refer to the gitwash for a step-by-step tutorial.

Development process

Here’s the long and short of it:

  1. If you are a first-time contributor:

    • Go to and click the “fork” button to create your own copy of the project.

    • Clone the project to your local computer:

      git clone
    • Add the upstream repository:

      git remote add upstream
    • Now, you have remote repositories named:

      • upstream, which refers to the scikit-fuzzy repository
      • origin, which refers to your personal fork
  2. Develop your contribution:

    • Pull the latest changes from upstream:

      git checkout master
      git pull upstream master
    • Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as ‘transform-speedups’:

      git checkout -b transform-speedups
    • Commit locally as you progress (git add and git commit)

  3. To submit your contribution:

    • Push your changes back to your fork on GitHub:

      git push origin transform-speedups
    • Go to GitHub. The new branch will show up with a green Pull Request button - click it.

    • If you want, post on the mailing list to explain your changes or to ask for review.

For a more detailed discussion, read these detailed documents on how to use Git with scikit-fuzzy (../gitwash/index.html).

  1. Review process:

    • Reviewers (the other developers and interested community members) will write inline and/or general comments on your Pull Request (PR) to help you improve its implementation, documentation and style. Every single developer working on the project has their code reviewed, and we’ve come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don’t let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you’re donating!).
    • To update your pull request, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the pull request will update automatically.
    • Travis-CI, a continuous integration service, is triggered after each Pull Request update to build the code, run unit tests, measure code coverage and check coding style (PEP8) of your branch. The Travis tests must pass before your PR can be merged. If Travis fails, you can find out why by clicking on the “failed” icon (red cross) and inspecting the build and test log.
  2. Document changes

    Before merging your commits, you must add a description of your changes to the release notes of the upcoming version in doc/release/release_dev.txt.


To reviewers: if it is not obvious, add a short explanation of what a branch did to the merge message and, if closing a bug, also add “Closes #123” where 123 is the issue number.

Divergence between upstream master and your feature branch

Do not ever merge the main branch into yours. If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, rebase onto master:

git checkout master
git pull upstream master
git checkout transform-speedups
git rebase master

If any conflicts occur, fix the according files and continue:

git add conflict-file1 conflict-file2
git rebase --continue

However, you should only rebase your own branches and must generally not rebase any branch which you collaborate on with someone else.

Finally, you must push your rebased branch:

git push --force origin transform-speedups

(If you are curious, here’s a further discussion on the dangers of rebasing. Also see this LWN article.)


  • All code should have tests (see test coverage below for more details).
  • All code should be documented, to the same standard as NumPy and SciPy.
  • For new functionality, always add an example to the gallery.
  • No changes are ever committed without review. Ask on the mailing list if you get no response to your pull request. Never merge your own pull request.
  • Examples in the gallery should have a maximum figure width of 8 inches.

Stylistic Guidelines

  • Set up your editor to remove trailing whitespace. Follow PEP08. Check code with pyflakes / flake8.

  • Use numpy data types instead of strings, e.g., np.uint8 instead of "uint8".

  • Use the following import conventions:

    import numpy as np
    import matplotlib.pyplot as plt
    cimport numpy as cnp # in Cython code
  • When documenting array parameters, use image : (M, N) ndarray and then refer to M and N in the docstring, if necessary.

  • Functions should support all input image dtypes. Use utility functions such as img_as_float to help convert to an appropriate type. The output format can be whatever is most efficient. This allows us to string together several functions into a pipeline, e.g.:

  • Use Py_ssize_t as data type for all indexing, shape and size variables in C/C++ and Cython code.

Test coverage

Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%.

To measure the test coverage, install (using easy_install coverage) and then run:

$ make coverage

This will print a report with one line for each file in skfuzzy, detailing the test coverage:

Name                                        Stmts   Miss  Cover   Missing
skfuzzy.cluster                                 2      0   100%
skfuzzy.defuzzify                               2      0   100%
skfuzzy.filters                                 2      0   100%

Activate Travis-CI for your fork (optional)

Travis-CI checks all unittests in the project to prevent breakage.

Before sending a pull request, you may want to check that Travis-CI successfully passes all tests. To do so,

  • Go to Travis-CI and follow the Sign In link at the top
  • Go to your profile page and switch on your scikit-fuzzy fork

It corresponds to steps one and two in Travis-CI documentation (Step three is already done in scikit-fuzzy).

Thus, as soon as you push your code to your fork, it will trigger Travis-CI, and you will receive an email notification when the process is done.

Every time Travis is triggered, it also calls on Coveralls to inspect the current test overage.