Developing Open Source is great fun! Join us on the scikit-fuzzy mailing list and tell us which challenges you’d like to solve.
Here’s the long and short of it:
If you are a first-time contributor:
Go to https://github.com/scikit-fuzzy/scikit-fuzzy and click the “fork” button to create your own copy of the project.
Clone the project to your local computer:
git clone email@example.com:your-username/scikit-fuzzy.git
Add the upstream repository:
git remote add upstream firstname.lastname@example.org:scikit-fuzzy/scikit-fuzzy.git
Now, you have remote repositories named:
upstream, which refers to the
origin, which refers to your personal fork
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
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.
Before merging your commits, you must add a description of your changes
to the release notes of the upcoming version in
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.
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
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
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
N in the docstring, if necessary.
Functions should support all input image dtypes. Use utility functions such
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.:
Py_ssize_t as data type for all indexing, shape and size variables
in C/C++ and Cython code.
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
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% ...
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,
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.