Sourceskimage¶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 git@github.com:your-username/scikit-image.git
Add the upstream repository:
git remote add upstream git@github.com:scikit-image/scikit-image.git
Now, you have remote repositories named:
upstream, which refers to the scikit-image repositoryorigin, which refers to your personal forkDevelop 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)
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-image
(http://scikit-image.org/docs/dev/gitwash/index.html).
Review process:
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.
Note
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.
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.)
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:.
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%
...
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 Coveralls to inspect the current test overage.
To build docs, run make from the docs directory. make help lists
all targets.
Sphinx (>= 1.3) and Latex is needed to build doc.
Sphinx:
pip install sphinx
Latex Ubuntu:
sudo apt-get install -qq texlive texlive-latex-extra dvipng
Latex Mac:
Install the full MacTex installation or install the smaller BasicTex and add ucs and dvipng packages:
sudo tlmgr install ucs dvipng
This set of instructions was used to create scikit-image/tools/deploy-docs.sh
public_repo and
user:email onlygem install travis. On OSX,
you can get gem via brew install ruby.travis encrypt GH_TOKEN=<token> from inside a scikit-image repo.travis.yml.https://help.github.com/articles/creating-an-access-token-for-command-line-use/ http://docs.travis-ci.com/user/encryption-keys/
Please report bugs on GitHub.