How to contribute to skimage¶

Development process¶

Here’s the long and short of it:

1. 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:

     cd scikit-image
     

   • 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 scikit-image 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
     

   • 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 scikit-image (http://scikit-image.org/docs/dev/gitwash/index.html).

4. 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.
   • A pull request must be approved by two core team members before merging.

5. Document changes

   If your change introduces any API modifications, please update doc/source/api_changes.txt.

   If your change introduces a deprecation, add a reminder to TODO.txt for the team to remove the deprecated functionality in the future.

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.)

Guidelines¶

• 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 and approval by two core team members. 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 (np.uint8 instead of "uint8").

• 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 M and 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 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.:

  hough(canny(my_image))
  

• Use 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 with nogil:.

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 coverage.py (using easy_install coverage) 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%
...

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-image fork

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 Coveralls to inspect the current test overage.

Building docs¶

To build docs, run make from the doc directory. make help lists all targets.

pip install sphinx

sudo apt-get install -qq texlive texlive-latex-extra dvipng

sudo tlmgr install ucs dvipng

Deprecation cycle¶

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

• a deprecation cycle is necessary for any breaking API change, meaning a

  change where the function, invoked with the same arguments, would return a different result after the change. This includes: * changing the order of arguments or keyword arguments, or * adding arguments or keyword arguments to a function, or * changing a function’s name or submodule, or * changing the default value of a function’s arguments.

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:

• In the signature, set default to None, and modify the docstring to specify that it’s True.
• In the function, _if_ rescale is set to None, set to True and warn that the default will change to False in version N+3.
• In 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).

Bugs¶

Please report bugs on GitHub.