Developing Open Source is great fun! Join us on the scikit-image mailing list and tell us which of the following 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-image/scikit-image and click the “fork” button to create your own copy of the project.
Clone the project to your local computer:
git clone https://github.com/your-username/scikit-image.git
Change the directory:
Add the upstream repository:
git remote add upstream https://github.com/scikit-image/scikit-image.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
Enter your GitHub username and password (repeat contributors or advanced users can remove this step by connecting to GitHub with SSH. See detailed instructions below if desired).
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
If your change introduces any API modifications, please update
If your change introduces a deprecation, add a reminder to
for the team to remove the deprecated functionality in the future.
To reviewers: if it is not obvious from the PR description, 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.
If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, merge the master branch into yours:
git fetch upstream master git merge upstream/master
If any conflicts occur, they need to be fixed before continuing. See which files are in conflict using:
Which displays a message like:
Unmerged paths: (use "git add <file>..." to mark resolution) both modified: file_with_conflict.txt
Inside the conflicted file, you’ll find sections like these:
<<<<<<< HEAD The way the text looks in your branch ======= The way the text looks in the master branch >>>>>>> master
Choose one version of the text that should be kept, and delete the rest:
The way the text looks in your branch
Now, add the fixed file:
git add file_with_conflict.txt
Once you’ve fixed all merge conflicts, do:
Advanced Git users are encouraged to rebase instead of merge, but we squash and merge most PRs either way.
Set up your editor to remove trailing whitespace. Follow PEP08. Check code with pyflakes / flake8.
Use numpy data types instead of strings (
np.uint8 instead of
Use the following import conventions:
import numpy as np import matplotlib.pyplot as plt from scipy import ndimage as ndi 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.
Refer to array dimensions as (plane), row, column, not as x, y, z. See Coordinate conventions in the user guide for more information.
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.
Use relative module imports, i.e.
from .._shared import xyz rather than
from skimage._shared import xyz.
Wrap Cython code in a pure Python function, which defines the API. This improves compatibility with code introspection tools, which are often not aware of Cython code.
For Cython functions, release the GIL whenever possible, using
scikit-image has an extensive test suite that ensures correct
execution on your system. The test suite has to pass before a pull
request can be merged, and tests should be added to cover any
modifications to the code base.
We make use of the pytest
testing framework, with tests located in the various
pytest, ensure that Cython extensions are built and that
the library is installed in development mode:
$ pip install -e .
Now, run all tests using:
$ PYTHONPATH=. pytest skimage
Or the tests for a specific submodule:
$ PYTHONPATH=. pytest skimage/morphology
Or tests from a specific file:
$ PYTHONPATH=. pytest skimage/morphology/tests/test_grey.py
Or a single test within that file:
$ PYTHONPATH=. pytest skimage/morphology/tests/test_grey.py::test_3d_fallback_black_tophat
--doctest-modules to run doctests.
For example, run all tests and all doctests using:
$ PYTHONPATH=. pytest --doctest-modules skimage
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 pytest-cov) and then run:
$ make coverage
This will print a report with one line for each file in skimage, detailing the test coverage:
Name Stmts Exec Cover Missing ------------------------------------------------------------------------------ skimage/color/colorconv 77 77 100% skimage/filter/__init__ 1 1 100% ...
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-image).
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 Codecov to inspect the current test overage.
To build docs, run
make from the
make help lists
Sphinx and LaTeX are needed to build the documentation.
pip install sphinx
sudo apt-get install -qq texlive texlive-latex-extra dvipng
sudo tlmgr install ucs dvipng
This set of instructions was used to create scikit-image/tools/deploy-docs.sh
gem install travis. On OSX, you can get gem via
brew install ruby.
travis encrypt GH_TOKEN=<token>from inside a scikit-image repo
If the behavior of the library has to be changed, a deprecation cycle must be followed to warn users.
a deprecation cycle is not necessary when:
- adding a new function, or
- adding a new keyword argument to the end of a function signature, or
- fixing what was buggy behaviour
change where the function, invoked with the same arguments, would return a different result after the change. This includes:
Usually, our policy is to put in place a deprecation cycle over two releases.
For the sake of illustration, we consider the modification of a default value in a function signature. In version N (therefore, next release will be N+1), we have
def a_function(image, rescale=True): out = do_something(image, rescale=rescale) return out
that has to be changed to
def a_function(image, rescale=None): if rescale is None: warn('The default value of rescale will change to `False` in version N+3') rescale = True out = do_something(image, rescale=rescale) return out
and in version N+3
def a_function(image, rescale=False): out = do_something(image, rescale=rescale) return out
Here is the process for a 2-release deprecation cycle:
doc/release/release_dev.rst, under deprecations, add “In a_function, the rescale argument will default to False in N+3.”
TODO.txt, create an item in the section related to version N+3 and write “change rescale default to False in a_function”.
Note that the 2-release deprecation cycle is not a strict rule and in some cases, the developers can agree on a different procedure upon justification (like when we can’t detect the change, or it involves moving or deleting an entire function for example).