.. _coding-guide:

************
Coding guide
************

Committing changes
==================

When committing changes to matplotlib, there are a few things to bear
in mind.

* if your changes are non-trivial, please make an entry in the
  :file:`CHANGELOG`

* if you change the API, please document it in :file:`doc/api/api_changes.rst`,
  and consider posting to `matplotlib-devel
  <http://lists.sourceforge.net/mailman/listinfo/matplotlib-devel>`_

* Are your changes python2.6 compatible?  We support python2.6 and later

* Can you pass :file:`examples/tests/backend_driver.py`?  This is our
  poor man's unit test.

* Can you add a test to :file:`lib/matplotlib/tests` to test your changes?

* If you have altered extension code, do you pass
  :file:`unit/memleak_hawaii3.py`?

* if you have added new files or directories, or reorganized existing
  ones, are the new files included in the match patterns in
  :file:`MANIFEST.in`.  This file determines what goes into the source
  distribution of the mpl build.

* Keep the maintenance branches and master in sync where it makes sense.

Style guide
===========

Importing and name spaces
-------------------------

For `numpy <http://www.numpy.org>`_, use::

  import numpy as np
  a = np.array([1,2,3])

For masked arrays, use::

  import numpy.ma as ma

For matplotlib main module, use::

  import matplotlib as mpl
  mpl.rcParams['xtick.major.pad'] = 6

For matplotlib modules (or any other modules), use::

  import matplotlib.cbook as cbook

  if cbook.iterable(z):
      pass

We prefer this over the equivalent ``from matplotlib import cbook``
because the latter is ambiguous as to whether ``cbook`` is a module or a
function.  The former makes it explicit that you
are importing a module or package.  There are some modules with names
that match commonly used local variable names, eg
:mod:`matplotlib.lines` or :mod:`matplotlib.colors`. To avoid the clash,
use the prefix 'm' with the ``import some.thing as
mthing`` syntax, eg::

    import matplotlib.lines as mlines
    import matplotlib.transforms as transforms   # OK
    import matplotlib.transforms as mtransforms  # OK, if you want to disambiguate
    import matplotlib.transforms as mtrans       # OK, if you want to abbreviate

Naming, spacing, and formatting conventions
-------------------------------------------

In general, we want to hew as closely as possible to the standard
coding guidelines for python written by Guido in `PEP 0008
<http://www.python.org/dev/peps/pep-0008>`_, though we do not do this
throughout.

* functions and class methods: ``lower`` or
  ``lower_underscore_separated``

* attributes and variables: ``lower`` or ``lowerUpper``

* classes: ``Upper`` or ``MixedCase``

Prefer the shortest names that are still readable.

Configure your editor to use spaces, not hard tabs. The standard
indentation unit is always four spaces;
if there is a file with
tabs or a different number of spaces it is a bug -- please fix it.
To detect and fix these and other whitespace errors (see below),
use `reindent.py
<http://svn.python.org/projects/doctools/trunk/utils/reindent.py>`_ as
a command-line script.  Unless you are sure your editor always
does the right thing, please use reindent.py before committing your
changes in git.

Keep docstrings_ uniformly indented as in the example below, with
nothing to the left of the triple quotes.  The
:func:`matplotlib.cbook.dedent` function is needed to remove excess
indentation only if something will be interpolated into the docstring,
again as in the example below.

Limit line length to 80 characters.  If a logical line needs to be
longer, use parentheses to break it; do not use an escaped newline.
It may be preferable to use a temporary variable to replace a single
long line with two shorter and more readable lines.

Please do not commit lines with trailing white space, as it causes
noise in git diffs.  Tell your editor to strip whitespace from line
ends when saving a file.  If you are an emacs user, the following in
your ``.emacs`` will cause emacs to strip trailing white space upon
saving for python, C and C++:

.. code-block:: cl

  ; and similarly for c++-mode-hook and c-mode-hook
  (add-hook 'python-mode-hook
            (lambda ()
            (add-hook 'write-file-functions 'delete-trailing-whitespace)))

for older versions of emacs (emacs<22) you need to do:

.. code-block:: cl

  (add-hook 'python-mode-hook
            (lambda ()
            (add-hook 'local-write-file-hooks 'delete-trailing-whitespace)))

Keyword argument processing
---------------------------

Matplotlib makes extensive use of ``**kwargs`` for pass-through
customizations from one function to another.  A typical example is in
:func:`matplotlib.pylab.text`.  The definition of the pylab text
function is a simple pass-through to
:meth:`matplotlib.axes.Axes.text`::

  # in pylab.py
  def text(*args, **kwargs):
      ret =  gca().text(*args, **kwargs)
      draw_if_interactive()
      return ret

:meth:`~matplotlib.axes.Axes.text` in simplified form looks like this,
i.e., it just passes all ``args`` and ``kwargs`` on to
:meth:`matplotlib.text.Text.__init__`::

  # in axes.py
  def text(self, x, y, s, fontdict=None, withdash=False, **kwargs):
      t = Text(x=x, y=y, text=s, **kwargs)

and :meth:`~matplotlib.text.Text.__init__` (again with liberties for
illustration) just passes them on to the
:meth:`matplotlib.artist.Artist.update` method::

  # in text.py
  def __init__(self, x=0, y=0, text='', **kwargs):
      Artist.__init__(self)
      self.update(kwargs)

``update`` does the work looking for methods named like
``set_property`` if ``property`` is a keyword argument.  I.e., no one
looks at the keywords, they just get passed through the API to the
artist constructor which looks for suitably named methods and calls
them with the value.

As a general rule, the use of ``**kwargs`` should be reserved for
pass-through keyword arguments, as in the example above.  If all the
keyword args are to be used in the function, and not passed
on, use the key/value keyword args in the function definition rather
than the ``**kwargs`` idiom.

In some cases, you may want to consume some keys in the local
function, and let others pass through.  You can ``pop`` the ones to be
used locally and pass on the rest.  For example, in
:meth:`~matplotlib.axes.Axes.plot`, ``scalex`` and ``scaley`` are
local arguments and the rest are passed on as
:meth:`~matplotlib.lines.Line2D` keyword arguments::

  # in axes.py
  def plot(self, *args, **kwargs):
      scalex = kwargs.pop('scalex', True)
      scaley = kwargs.pop('scaley', True)
      if not self._hold: self.cla()
      lines = []
      for line in self._get_lines(*args, **kwargs):
          self.add_line(line)
          lines.append(line)

Note: there is a use case when ``kwargs`` are meant to be used locally
in the function (not passed on), but you still need the ``**kwargs``
idiom.  That is when you want to use ``*args`` to allow variable
numbers of non-keyword args.  In this case, python will not allow you
to use named keyword args after the ``*args`` usage, so you will be
forced to use ``**kwargs``.  An example is
:meth:`matplotlib.contour.ContourLabeler.clabel`::

  # in contour.py
  def clabel(self, *args, **kwargs):
      fontsize = kwargs.get('fontsize', None)
      inline = kwargs.get('inline', 1)
      self.fmt = kwargs.get('fmt', '%1.3f')
      colors = kwargs.get('colors', None)
      if len(args) == 0:
          levels = self.levels
          indices = range(len(self.levels))
      elif len(args) == 1:
         ...etc...

.. _docstrings:

Documentation and docstrings
============================

Matplotlib uses artist introspection of docstrings to support
properties.  All properties that you want to support through ``setp``
and ``getp`` should have a ``set_property`` and ``get_property``
method in the :class:`~matplotlib.artist.Artist` class.  Yes, this is
not ideal given python properties or enthought traits, but it is a
historical legacy for now.  The setter methods use the docstring with
the ACCEPTS token to indicate the type of argument the method accepts.
Eg. in :class:`matplotlib.lines.Line2D`::

  # in lines.py
  def set_linestyle(self, linestyle):
      """
      Set the linestyle of the line

      ACCEPTS: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' | ' ' | '' ]
      """

Since matplotlib uses a lot of pass-through ``kwargs``, eg. in every
function that creates a line (:func:`~matplotlib.pyplot.plot`,
:func:`~matplotlib.pyplot.semilogx`,
:func:`~matplotlib.pyplot.semilogy`, etc...), it can be difficult for
the new user to know which ``kwargs`` are supported.  Matplotlib uses
a docstring interpolation scheme to support documentation of every
function that takes a ``**kwargs``.  The requirements are:

1. single point of configuration so changes to the properties don't
   require multiple docstring edits.

2. as automated as possible so that as properties change, the docs
   are updated automagically.

The functions :attr:`matplotlib.artist.kwdocd` and
:func:`matplotlib.artist.kwdoc` to facilitate this.  They combine
python string interpolation in the docstring with the matplotlib
artist introspection facility that underlies ``setp`` and ``getp``.
The ``kwdocd`` is a single dictionary that maps class name to a
docstring of ``kwargs``.  Here is an example from
:mod:`matplotlib.lines`::

  # in lines.py
  artist.kwdocd['Line2D'] = artist.kwdoc(Line2D)

Then in any function accepting :class:`~matplotlib.lines.Line2D`
pass-through ``kwargs``, eg. :meth:`matplotlib.axes.Axes.plot`::

  # in axes.py
  def plot(self, *args, **kwargs):
      """
      Some stuff omitted

      The kwargs are Line2D properties:
      %(Line2D)s

      kwargs scalex and scaley, if defined, are passed on
      to autoscale_view to determine whether the x and y axes are
      autoscaled; default True.  See Axes.autoscale_view for more
      information
      """
      pass
  plot.__doc__ = cbook.dedent(plot.__doc__) % artist.kwdocd

Note there is a problem for :class:`~matplotlib.artist.Artist`
``__init__`` methods, eg. :meth:`matplotlib.patches.Patch.__init__`,
which supports ``Patch`` ``kwargs``, since the artist inspector cannot
work until the class is fully defined and we can't modify the
``Patch.__init__.__doc__`` docstring outside the class definition.
There are some some manual hacks in this case, violating the
"single entry point" requirement above -- see the
``artist.kwdocd['Patch']`` setting in :mod:`matplotlib.patches`.

.. _custom_backend:

Developing a new backend
========================

If you are working on a custom backend, the *backend* setting in
:file:`matplotlibrc` (:ref:`customizing-matplotlib`) supports an
external backend via the ``module`` directive.  if
:file:`my_backend.py` is a matplotlib backend in your
:envvar:`PYTHONPATH`, you can set use it on one of several ways

* in matplotlibrc::

    backend : module://my_backend

* with the use directive is your script::

    import matplotlib
    matplotlib.use('module://my_backend')

* from the command shell with the -d flag::

    > python simple_plot.py -d module://my_backend



.. _sample-data:

Writing examples
================

We have hundreds of examples in subdirectories of
:file:`matplotlib/examples`, and these are automatically generated
when the website is built to show up both in the `examples
<http://matplotlib.org/examples/index.html>`_ and `gallery
<http://matplotlib.org/gallery.html>`_ sections of the website.  Many
people find these examples from the website, and do not have ready
access to the file:`examples` directory in which they reside.  Thus
any example data that is required for the example should be added to
the `sample_data <https://github.com/matplotlib/sample_data>`_ git
repository.  Then in your example code you can load it into a file
handle with::

    import matplotlib.cbook as cbook
    fh = cbook.get_sample_data('mydata.dat')

The file will be fetched from the git repo using urllib and updated
when the revision number changes.


If you prefer just to get the full path to the file instead of a file
object::

    import matplotlib.cbook as cbook
    datafile = cbook.get_sample_data('mydata.dat', asfileobj=False)
    print 'datafile', datafile


Writing a new pyplot function
=============================
A large portion of the pyplot interface is automatically generated by the
`boilerplate.py` script (in the root of the source tree).  To add or remove
a plotting method from pyplot, edit the appropriate list in `boilerplate.py`
and then run the script which will update the content in
`lib/matplotlib/pyplot.py`. Both the changes in `boilerplate.py` and
`lib/matplotlib/pyplot.py` should be checked into the repository.

Testing
=======

Matplotlib has a testing infrastructure based on nose_, making it easy
to write new tests. The tests are in :mod:`matplotlib.tests`, and
customizations to the nose testing infrastructure are in
:mod:`matplotlib.testing`. (There is other old testing cruft around,
please ignore it while we consolidate our testing to these locations.)

.. _nose: http://somethingaboutorange.com/mrl/projects/nose/

Requirements
------------

The following software is required to run the tests:

  - nose_, version 1.0 or later

  - `Ghostscript <http://pages.cs.wisc.edu/~ghost/>`_ (to render PDF
    files)

  - `Inkscape <http://inkscape.org>`_ (to render SVG files)

Running the tests
-----------------

Running the tests is simple. Make sure you have nose installed and run
the script :file:`tests.py` in the root directory of the distribution.
The script can take any of the usual `nosetest arguments`_, such as

===================  ===========
``-v``               increase verbosity
``-d``               detailed error messages
``--with-coverage``  enable collecting coverage information
===================  ===========

To run a single test from the command line, you can provide a
dot-separated path to the module followed by the function separated by
a colon, eg.  (this is assuming the test is installed)::

  python tests.py matplotlib.tests.test_simplification:test_clipping

An alternative implementation that does not look at command line
arguments works from within Python::

  import matplotlib
  matplotlib.test()


.. _`nosetest arguments`: http://somethingaboutorange.com/mrl/projects/nose/1.0.0/usage.html


Running tests by any means other than `matplotlib.test()`
does not load the nose "knownfailureif" (Known failing tests) plugin,
causing known-failing tests to fail for real.

Writing a simple test
---------------------

Many elements of Matplotlib can be tested using standard tests. For
example, here is a test from :mod:`matplotlib.tests.test_basic`::

  from nose.tools import assert_equal

  def test_simple():
      '''very simple example test'''
      assert_equal(1+1,2)

Nose determines which functions are tests by searching for functions
beginning with "test" in their name.

Writing an image comparison test
--------------------------------

Writing an image based test is only slightly more difficult than a
simple test. The main consideration is that you must specify the
"baseline", or expected, images in the
:func:`~matplotlib.testing.decorators.image_comparison` decorator. For
example, this test generates a single image and automatically tests
it::

  import numpy as np
  import matplotlib
  from matplotlib.testing.decorators import image_comparison
  import matplotlib.pyplot as plt

  @image_comparison(baseline_images=['spines_axes_positions'])
  def test_spines_axes_positions():
      # SF bug 2852168
      fig = plt.figure()
      x = np.linspace(0,2*np.pi,100)
      y = 2*np.sin(x)
      ax = fig.add_subplot(1,1,1)
      ax.set_title('centered spines')
      ax.plot(x,y)
      ax.spines['right'].set_position(('axes',0.1))
      ax.yaxis.set_ticks_position('right')
      ax.spines['top'].set_position(('axes',0.25))
      ax.xaxis.set_ticks_position('top')
      ax.spines['left'].set_color('none')
      ax.spines['bottom'].set_color('none')

The first time this test is run, there will be no baseline image to
compare against, so the test will fail.  Copy the output images (in
this case `result_images/test_category/spines_axes_positions.*`) to
the `baseline_images` tree in the source directory (in this case
`lib/matplotlib/tests/baseline_images/test_category`) and put them
under source code revision control (with `git add`).  When rerunning
the tests, they should now pass.

There are two optional keyword arguments to the `image_comparison`
decorator:

   - `extensions`: If you only wish to test some of the image formats
     (rather than the default `png`, `svg` and `pdf` formats), pass a
     list of the extensions to test.

   - `tol`: This is the image matching tolerance, the default `1e-3`.
     If some variation is expected in the image between runs, this
     value may be adjusted.

Known failing tests
-------------------

If you're writing a test, you may mark it as a known failing test with
the :func:`~matplotlib.testing.decorators.knownfailureif`
decorator. This allows the test to be added to the test suite and run
on the buildbots without causing undue alarm. For example, although
the following test will fail, it is an expected failure::

  from nose.tools import assert_equal
  from matplotlib.testing.decorators import knownfailureif

  @knownfailureif(True)
  def test_simple_fail():
      '''very simple example test that should fail'''
      assert_equal(1+1,3)

Note that the first argument to the
:func:`~matplotlib.testing.decorators.knownfailureif` decorator is a
fail condition, which can be a value such as True, False, or
'indeterminate', or may be a dynamically evaluated expression.

Creating a new module in matplotlib.tests
-----------------------------------------

Let's say you've added a new module named
``matplotlib.tests.test_whizbang_features``.  To add this module to
the list of default tests, append its name to ``default_test_modules``
in :file:`lib/matplotlib/__init__.py`.

Using tox
---------

`Tox <http://tox.testrun.org/>`_ is a tool for running tests against multiple
Python environments, including multiple versions of Python (e.g.: 2.6, 2.7,
3.2, etc.) and even different Python implementations altogether (e.g.: CPython,
PyPy, Jython, etc.)

Testing all 4 versions of Python (2.6, 2.7, 3.1, and 3.2) requires having four
versions of Python installed on your system and on the PATH. Depending on your
operating system, you may want to use your package manager (such as apt-get,
yum or MacPorts) to do this, or use `pythonbrew
<https://github.com/utahta/pythonbrew>`_.

tox makes it easy to determine if your working copy introduced any regressions
before submitting a pull request. Here's how to use it:

.. code-block:: bash

    $ pip install tox
    $ tox

You can also run tox on a subset of environments:

.. code-block:: bash

    $ tox -e py26,py27

Tox processes everything serially so it can take a long time to test several
environments. To speed it up, you might try using a new, parallelized version
of tox called ``detox``. Give this a try:

.. code-block:: bash

    $ pip install -U -i http://pypi.testrun.org detox
    $ detox

Tox is configured using a file called ``tox.ini``. You may need to edit this
file if you want to add new environments to test (e.g.: ``py33``) or if you
want to tweak the dependencies or the way the tests are run. For more info on
the ``tox.ini`` file, see the `Tox Configuration Specification
<http://tox.testrun.org/latest/config.html>`_.

Using Travis CI
---------------

`Travis CI <http://travis-ci.org/>`_ is a hosted CI system "in the cloud".

Travis is configured to receive notifications of new commits to GitHub repos
(via GitHub "service hooks") and to run builds or tests when it sees these new
commits. It looks for a YAML file called ``.travis.yml`` in the root of the
repository to see how to test the project.

Travis CI is already enabled for the `main matplotlib GitHub repository
<https://github.com/matplotlib/matplotlib/>`_ -- for example, see `its Travis
page <http://travis-ci.org/#!/matplotlib/matplotlib>`_.

If you want to enable Travis CI for your personal matplotlib GitHub repo,
simply enable the repo to use Travis CI in either the Travis CI UI or the
GitHub UI (Admin | Service Hooks). For details, see `the Travis CI Getting
Started page <http://about.travis-ci.org/docs/user/getting-started/>`_.

Once this is configured, you can see the Travis CI results at
http://travis-ci.org/#!/your_GitHub_user_name/matplotlib -- here's `an example
<http://travis-ci.org/#!/msabramo/matplotlib>`_.

.. _license-discussion:

Licenses
========

Matplotlib only uses BSD compatible code.  If you bring in code from
another project make sure it has a PSF, BSD, MIT or compatible license
(see the Open Source Initiative `licenses page
<http://www.opensource.org/licenses>`_ for details on individual
licenses).  If it doesn't, you may consider contacting the author and
asking them to relicense it.  GPL and LGPL code are not acceptable in
the main code base, though we are considering an alternative way of
distributing L/GPL code through an separate channel, possibly a
toolkit.  If you include code, make sure you include a copy of that
code's license in the license directory if the code's license requires
you to distribute the license with it.  Non-BSD compatible licenses
are acceptable in matplotlib toolkits (eg basemap), but make sure you
clearly state the licenses you are using.

Why BSD compatible?
-------------------

The two dominant license variants in the wild are GPL-style and
BSD-style. There are countless other licenses that place specific
restrictions on code reuse, but there is an important difference to be
considered in the GPL and BSD variants.  The best known and perhaps
most widely used license is the GPL, which in addition to granting you
full rights to the source code including redistribution, carries with
it an extra obligation. If you use GPL code in your own code, or link
with it, your product must be released under a GPL compatible
license. I.e., you are required to give the source code to other
people and give them the right to redistribute it as well. Many of the
most famous and widely used open source projects are released under
the GPL, including linux, gcc, emacs and sage.

The second major class are the BSD-style licenses (which includes MIT
and the python PSF license). These basically allow you to do whatever
you want with the code: ignore it, include it in your own open source
project, include it in your proprietary product, sell it,
whatever. python itself is released under a BSD compatible license, in
the sense that, quoting from the PSF license page::

    There is no GPL-like "copyleft" restriction. Distributing
    binary-only versions of Python, modified or not, is allowed. There
    is no requirement to release any of your source code. You can also
    write extension modules for Python and provide them only in binary
    form.

Famous projects released under a BSD-style license in the permissive
sense of the last paragraph are the BSD operating system, python and
TeX.

There are several reasons why early matplotlib developers selected a
BSD compatible license. matplotlib is a python extension, and we
choose a license that was based on the python license (BSD
compatible).  Also, we wanted to attract as many users and developers
as possible, and many software companies will not use GPL code in
software they plan to distribute, even those that are highly committed
to open source development, such as `enthought
<http://enthought.com>`_, out of legitimate concern that use of the
GPL will "infect" their code base by its viral nature. In effect, they
want to retain the right to release some proprietary code. Companies
and institutions who use matplotlib often make significant
contributions, because they have the resources to get a job done, even
a boring one. Two of the matplotlib backends (FLTK and WX) were
contributed by private companies.  The final reason behind the
licensing choice is compatibility with the other python extensions for
scientific computing: ipython, numpy, scipy, the enthought tool suite
and python itself are all distributed under BSD compatible licenses.
