CONTRIBUTING.txt

.. _howto_contribute:

How to contribute to ``skimage``
================================

Developing Open Source is great fun!  Join us on the `scikit-image mailing
list <https://mail.python.org/mailman/listinfo/scikit-image>`_ 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, you can find a list of
  `requested features on GitHub <https://github.com/scikit-image/scikit-image/wiki/Requested-features>`__.
  In addition, you can browse the
  `open issues on GitHub <https://github.com/scikit-image/scikit-image/issues?state=open>`__.
* 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

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
     <https://help.github.com/en/articles/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/mailman/listinfo/scikit-image>`_ 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``
(`<https://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 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 master`` and your feature branch
--------------------------------------------------------------

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::

   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 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::

   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
-----------------------

Once you've cloned your fork of the scikit-image repository,
you should set up a Python development environment tailored for scikit-image.
You may choose the environment manager of your choice.
Here we provide instructions for two popular environment managers:
``venv`` (pip based) and ``conda`` (Anaconda or Miniconda).

venv
====
When using ``venv``, you may find the following bash commands useful::

  # Create a virtualenv named ``skimage-dev`` that lives in the directory of
  # the same name
  python -m venv skimage-dev
  # Activate it
  source skimage-dev/bin/activate
  # Install all development and runtime dependencies of scikit-image
  pip install -r <(cat requirements/*.txt)
  # Build and install scikit-image from source
  pip install -e .
  # Test your installation
  pytest skimage

conda
=====

When using conda, you may find the following bash commands useful::

  # Create a conda environment named ``skimage-dev``
  conda create --name skimage-dev
  # Activate it
  conda activate skimage-dev
  # Install major development and runtime dependencies of scikit-image
  # (the rest can be installed from conda-forge or pip, if needed)
  conda install `for i in requirements/{default,build}.txt; do echo -n " --file $i "; done`
  # Install minimal testing dependencies
  conda install pytest
  # Install scikit-image from source
  pip install -e . --no-deps
  # Test your installation
  pytest skimage

Guidelines
----------

* All code should have tests (see `test coverage`_ below for more details).
* All code should be documented, to the same
  `standard <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-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 <https://mail.python.org/mailman/listinfo/scikit-image>`_ 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
  <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

   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
  :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
-------
``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 <https://docs.pytest.org/en/latest/>`__
testing framework, with tests located in the various
``skimage/submodule/tests`` folders.

To use ``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

Use ``--doctest-modules`` to run doctests.
For example, run all tests and all doctests using::

    $ PYTHONPATH=. pytest --doctest-modules skimage

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 ``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%
  ...


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://about.travis-ci.org/docs/user/getting-started/>`__
(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/>`__ 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

**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/articles/creating-an-access-token-for-command-line-use/
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')
            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).

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 <http://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.

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 master
~~~~~~~~~~~~~~~~~~~~~~~~~~~

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 master branch of the
``scikit-image`` repository. The command ``asv continuous`` is of help here::

  asv continuous master -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 master branch.

The output may look something like::

  $ asv continuous master -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 master are not significant
enough for airspeed velocity to report.