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Development process
:doc:`Read this overview <gitwash/index>` of how to use Git with
``skimage``. Here's the long and short of it:
* Go to `
<>`_ and follow the
instructions on making your own fork.
* Create a new 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 'your_name-transform-speedups'.
* Commit locally as you progress.
* Push your changes back to github and create a pull request by
clicking "request pull" in GitHub.
* Optionally, mail the mailing list, explaining your changes.
You may also read this summary by Fernando Perez of the IPython
project on how they manage to keep review overhead to a minimum:
.. note::
Do *not* merge the main branch into yours. You may rebase,
as long as you are `aware of its dangers <>`_
(also see `LWN article <>`_).
* To reviewers: add a short explanation of what a branch did to the merge
message or, if closing a bug, add "Closes gh-XXXX".
* 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. If possible, also add a section to the user guide.
* Follow the `Python PEPs <>`_
where possible.
* No major changes should be committed without review. Ask on the
`mailing list <>`_ if
you get no response to your pull request.
* Examples in the gallery should have a maximum figure width of 8 inches.
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
` <>`__
(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%
Please `report bugs on Github <>`_.
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