.. _howto_contribute:
How to contribute to scikit-image
=================================
Developing Open Source is great fun! Join us on the `scikit-image mailing
list <https://mail.python.org/mailman3/lists/scikit-image.python.org/>`_ and tell us
which of the following challenges you'd like to solve.
* Mentoring is available for those new to scientific programming in Python.
* If you're looking for something to implement or to fix, you can browse the
`open issues on GitHub <https://github.com/scikit-image/scikit-image/issues?q=is%3Aopen>`__.
* The technical detail of the `development process`_ is summed up below.
Refer to the :doc:`gitwash <gitwash/index>` for a step-by-step tutorial.
.. contents::
:local:
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
<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
.. note::
Although our code is hosted on `github
<https://github.com/scikit-image/>`_, our dataset is stored on `gitlab
<https://gitlab.com/scikit-image/data>`_ and fetched with `pooch
<https://github.com/fatiando/pooch>`_. New data must be submitted on
gitlab. Once merged, the data registry ``skimage/data/_registry.py``
in the main codebase on github must be updated.
2. Develop your contribution:
* Pull the latest changes from upstream::
git checkout main
git pull upstream main
* 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
<https://help.github.com/en/github/authenticating-to-github/connecting-to-github-with-ssh>`_).
* 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
<https://mail.python.org/mailman3/lists/scikit-image.python.org/>`_ to explain your changes or
to ask for review.
For a more detailed discussion, read these :doc:`detailed documents
<gitwash/index>` on how to use Git with ``scikit-image`` (:ref:`using-git`).
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 a 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 the 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 <https://travis-ci.org/>`__, 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.
.. 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.
Divergence between ``upstream main`` and your feature branch
------------------------------------------------------------
If GitHub indicates that the branch of your Pull Request can no longer
be merged automatically, merge the main branch into yours::
git fetch upstream main
git merge upstream/main
If any conflicts occur, they need to be fixed before continuing. See
which files are in conflict using::
git status
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 main branch
>>>>>>> main
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::
git commit
.. note::
Advanced Git users are encouraged to `rebase instead of merge
<https://scikit-image.org/docs/dev/gitwash/development_workflow.html#rebasing-on-trunk>`__,
but we squash and merge most PRs either way.
Build environment setup
-----------------------
Please refer to :ref:`installing-scikit-image` for development installation
instructions.
Guidelines
----------
* All code should have tests (see `test coverage`_ below for more details).
* All code should be documented, to the same
`standard <https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard>`_ as NumPy and SciPy.
* For new functionality, always add an example to the gallery (see
:ref:`Sphinx-Gallery<sphinx_gallery>` below for more details).
* No changes are ever committed without review and approval by two core
team members. Ask on the
`mailing list <https://mail.python.org/mailman3/lists/scikit-image.python.org/>`_ if
you get no response to your pull request.
**Never merge your own pull request.**
Stylistic Guidelines
--------------------
* Set up your editor to remove trailing whitespace. Follow `PEP08
<https://www.python.org/dev/peps/pep-0008/>`__. 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
# only in Cython code
cimport numpy as cnp
cnp.import_array()
* 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
:ref:`Coordinate conventions <numpy-images-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:``.
Testing
-------
See the testing section of the Installation guide.
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
`pytest-cov <https://pytest-cov.readthedocs.io/en/latest/>`__
(using ``pip 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%
...
Activate Travis-CI for your fork (optional)
-------------------------------------------
Travis-CI checks all unit tests 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 <https://travis-ci.org/>`__ and follow the Sign In link at
the top
* Go to your `profile page <https://travis-ci.org/profile>`__ and switch on
your scikit-image fork
It corresponds to steps one and two in
`Travis-CI documentation <https://docs.travis-ci.com/user/tutorial/#to-get-started-with-travis-ci-using-github>`__
(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
<https://codecov.io>`_ to inspect the current test overage.
Building docs
-------------
To build docs, run ``make`` from the ``doc`` directory. ``make help`` lists
all targets. For example, to build the HTML documentation, you can run:
.. code:: sh
make html
Then, all the HTML files will be generated in ``scikit-image/doc/build/html/``.
To rebuild a full clean documentation, run:
.. code:: sh
make clean
make html
Requirements
~~~~~~~~~~~~
`Sphinx <http://www.sphinx-doc.org/en/stable/>`_,
`Sphinx-Gallery <https://sphinx-gallery.github.io>`_,
and LaTeX are needed to build the documentation.
**Sphinx:**
Sphinx and other python packages needed to build the documentation
can be installed using: ``scikit-image/requirements/docs.txt`` file.
.. code:: sh
pip install -r requirements/docs.txt
.. _sphinx_gallery:
**Sphinx-Gallery:**
The above install command includes the installation of
`Sphinx-Gallery <https://sphinx-gallery.github.io>`_, which we use to create
the :ref:`examples_gallery`.
Refer to the Sphinx-Gallery documentation for complete instructions on syntax and usage.
If you are contributing an example to the gallery or editing an existing one,
build the docs (see above) and open a web browser to check how your edits
render at ``scikit-image/doc/build/html/auto_examples/``: navigate to the file
you have added or changed.
When adding an example, visit also
``scikit-image/doc/build/html/auto_examples/index.html`` to check how the new
thumbnail renders on the gallery's homepage. To change the thumbnail image,
please refer to `this section
<https://sphinx-gallery.github.io/stable/configuration.html#choosing-thumbnail>`_
of the Sphinx-Gallery docs.
Note that gallery examples should have a maximum figure width of 8 inches.
**LaTeX Ubuntu:**
.. code:: sh
sudo apt-get install -qq texlive texlive-latex-extra dvipng
**LaTeX Mac:**
Install the full `MacTex <https://www.tug.org/mactex/>`__ installation or
install the smaller
`BasicTex <https://www.tug.org/mactex/morepackages.html>`__ and add *ucs*
and *dvipng* packages:
.. code:: sh
sudo tlmgr install ucs dvipng
Fixing Warnings
~~~~~~~~~~~~~~~
- "citation not found: R###" There is probably an underscore after a
reference in the first line of a docstring (e.g. [1]\_). Use this
method to find the source file: $ cd doc/build; grep -rin R####
- "Duplicate citation R###, other instance in..."" There is probably a
[2] without a [1] in one of the docstrings
- Make sure to use pre-sphinxification paths to images (not the
\_images directory)
Auto-generating dev docs
~~~~~~~~~~~~~~~~~~~~~~~~
This set of instructions was used to create
scikit-image/tools/deploy-docs.sh
- Go to Github account settings -> personal access tokens
- Create a new token with access rights ``public_repo`` and
``user:email only``
- Install the travis command line tool: ``gem install travis``. On OSX,
you can get gem via ``brew install ruby``.
- Take then token generated by Github and run
``travis encrypt GH_TOKEN=<token>`` from inside a scikit-image repo
- Paste the output into the secure: field of ``.travis.yml``.
- The decrypted GH\_TOKEN env var will be available for travis scripts
https://help.github.com/en/github/authenticating-to-github/creating-a-personal-access-token-for-the-command-line
https://docs.travis-ci.com/user/encryption-keys/
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 behavior
- 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
.. code-block:: python
def a_function(image, rescale=True):
out = do_something(image, rescale=rescale)
return out
that has to be changed to
.. code-block:: python
def a_function(image, rescale=None):
if rescale is None:
warn('The default value of rescale will change '
'to `False` in version N+3.', stacklevel=2)
rescale = True
out = do_something(image, rescale=rescale)
return out
and in version N+3
.. code-block:: python
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 ``doc/release/release_dev.rst``, under deprecations, add "In
`a_function`, the `rescale` argument will default to `False` in 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).
Scikit-image uses warnings to highlight changes in its API so that users may
update their code accordingly. The ``stacklevel`` argument sets the location in
the callstack where the warnings will point. In most cases, it is appropriate
to set the ``stacklevel`` to ``2``. When warnings originate from helper
routines internal to the scikit-image library, it is may be more appropriate to
set the ``stacklevel`` to ``3``. For more information, see the documentation of
the `warn <https://docs.python.org/3/library/warnings.html#warnings.warn>`__
function in the Python standard library.
To test if your warning is being emitted correctly, try calling the function
from an IPython console. It should point you to the console input itself
instead of being emitted by the files in the scikit-image library.
* **Good**: ``ipython:1: UserWarning: ...``
* **Bad**: ``scikit-image/skimage/measure/_structural_similarity.py:155: UserWarning:``
Bugs
----
Please `report bugs on GitHub <https://github.com/scikit-image/scikit-image/issues>`_.
Benchmarks
----------
While not mandatory for most pull requests, we ask that performance related
PRs include a benchmark in order to clearly depict the use-case that is being
optimized for. A historical view of our snapshots can be found on
at the following `website <https://pandas.pydata.org/speed/scikit-image/>`_.
In this section we will review how to setup the benchmarks,
and three commands ``asv dev``, ``asv run`` and ``asv continuous``.
Prerequisites
~~~~~~~~~~~~~
Begin by installing `airspeed velocity <https://asv.readthedocs.io/en/stable/>`_
in your development environment. Prior to installation, be sure to activate your
development environment, then if using ``venv`` you may install the requirement with::
source skimage-dev/bin/activate
pip install asv
If you are using conda, then the command::
conda activate skimage-dev
conda install asv
is more appropriate. Once installed, it is useful to run the command::
asv machine
To let airspeed velocity know more information about your machine.
Writing a benchmark
~~~~~~~~~~~~~~~~~~~
To write benchmark, add a file in the ``benchmarks`` directory which contains a
a class with one ``setup`` method and at least one method prefixed with ``time_``.
The ``time_`` method should only contain code you wish to benchmark.
Therefore it is useful to move everything that prepares the benchmark scenario
into the ``setup`` method. This function is called before calling a ``time_``
method and its execution time is not factored into the benchmarks.
Take for example the ``TransformSuite`` benchmark:
.. code-block:: python
import numpy as np
from skimage import transform
class TransformSuite:
"""Benchmark for transform routines in scikit-image."""
def setup(self):
self.image = np.zeros((2000, 2000))
idx = np.arange(500, 1500)
self.image[idx[::-1], idx] = 255
self.image[idx, idx] = 255
def time_hough_line(self):
result1, result2, result3 = transform.hough_line(self.image)
Here, the creation of the image is completed in the ``setup`` method, and not
included in the reported time of the benchmark.
It is also possible to benchmark features such as peak memory usage. To learn
more about the features of `asv`, please refer to the official
`airpseed velocity documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.
Also, the benchmark files need to be importable when benchmarking old versions
of scikit-image. So if anything from scikit-image is imported at the top level,
it should be done as:
.. code-block:: python
try:
from skimage import metrics
except ImportError:
pass
The benchmarks themselves don't need any guarding against missing features,
only the top-level imports.
To allow tests of newer functions to be marked as "n/a" (not available)
rather than "failed" for older versions, the setup method itself can raise a
NotImplemented error. See the following example for the registration module:
.. code-block:: python
try:
from skimage import registration
except ImportError:
raise NotImplementedError("registration module not available")
Testing the benchmarks locally
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Prior to running the true benchmark, it is often worthwhile to test that the
code is free of typos. To do so, you may use the command::
asv dev -b TransformSuite
Where the ``TransformSuite`` above will be run once in your current environment
to test that everything is in order.
Running your benchmark
~~~~~~~~~~~~~~~~~~~~~~
The command above is fast, but doesn't test the performance of the code
adequately. To do that you may want to run the benchmark in your current
environment to see the performance of your change as you are developing new
features. The command ``asv run -E existing`` will specify that you wish to run
the benchmark in your existing environment. This will save a significant amount
of time since building scikit-image can be a time consuming task::
asv run -E existing -b TransformSuite
Comparing results to main
~~~~~~~~~~~~~~~~~~~~~~~~~
Often, the goal of a PR is to compare the results of the modifications in terms
speed to a snapshot of the code that is in the main branch of the
``scikit-image`` repository. The command ``asv continuous`` is of help here::
asv continuous main -b TransformSuite
This call will build out the environments specified in the ``asv.conf.json``
file and compare the performance of the benchmark between your current commit
and the code in the main branch.
The output may look something like::
$ asv continuous main -b TransformSuite
· Creating environments
· Discovering benchmarks
·· Uninstalling from conda-py3.7-cython-numpy1.15-scipy
·· Installing 544c0fe3 <benchmark_docs> into conda-py3.7-cython-numpy1.15-scipy.
· Running 4 total benchmarks (2 commits * 2 environments * 1 benchmarks)
[ 0.00%] · For scikit-image commit 37c764cb <benchmark_docs~1> (round 1/2):
[...]
[100.00%] ··· ...ansform.TransformSuite.time_hough_line 33.2±2ms
BENCHMARKS NOT SIGNIFICANTLY CHANGED.
In this case, the differences between HEAD and main are not significant
enough for airspeed velocity to report.
It is also possible to get a comparison of results for two specific revisions
for which benchmark results have previously been run via the `asv compare`
command::
asv compare v0.14.5 v0.17.2
Finally, one can also run ASV benchmarks only for a specific commit hash or
release tag by appending ``^!`` to the commit or tag name. For example to run
the skimage.filter module benchmarks on release v0.17.2:
asv run -b Filter v0.17.2^!
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