.. _coding-guide:

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

.. _version-control:

Version control
===============

svn checkouts
-------------

Checking out everything in the trunk (matplotlib and toolkits)::

   svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk \
   matplotlib --username=youruser --password=yourpass

Checking out the main source::

   svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/\
   matplotlib mpl --username=youruser --password=yourpass

Branch checkouts, eg the maintenance branch::

   svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/branches/\
   v0_91_maint mpl91 --username=youruser --password=yourpass

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:`API_CHANGES`,
  and consider posting to `matplotlib-devel
  <http://lists.sourceforge.net/mailman/listinfo/matplotlib-devel>`_

* Are your changes python2.4 compatible?  We still support 2.4, so
  avoid features new to 2.5

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

* If you have altered extension code, do you pass
  :file:`unit/memleak_hawaii.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 branch and trunk in sync where it makes sense.
  If there is a bug on both that needs fixing, use `svnmerge.py
  <http://www.orcaware.com/svn/wiki/Svnmerge.py>`_ to keep them in
  sync.  The basic procedure is:

  * install ``svnmerge.py`` in your PATH::

      > wget http://svn.collab.net/repos/svn/trunk/contrib/client-side/\
        svnmerge/svnmerge.py

  * get a svn copy of the maintenance branch and the trunk (see above)

  * Michael advises making the change on the branch and committing
    it.  Make sure you svn upped on the trunk and have no local
    modifications, and then from the svn trunk do::

        > svnmerge.py merge

    If you wish to merge only specific revisions (in an unusual
    situation), do::

        > svnmerge.py merge -rNNN1-NNN2

    where the ``NNN`` are the revision numbers.  Ranges are also
    acceptable.

    The merge may have found some conflicts (code that must be
    manually resolved).  Correct those conflicts, build matplotlib and
    test your choices.  If you have resolved any conflicts, you can
    let svn clean up the conflict files for you::

        > svn -R resolved .

    ``svnmerge.py`` automatically creates a file containing the commit
    messages, so you are ready to make the commit::

       > svn commit -F svnmerge-commit-message.txt


.. _style-guide:

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 checking changes into
svn.

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 svn 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`.


.. _licenses:

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 different 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 sagemath, linux, gcc and emacs.

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 two primary reasons why early matplotlib developers selected
a BSD compatible license. 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 in general, who use matplotlib often make significant
contributions, since they have the resources to get a job done, even a
boring one, if they need it in their code. Two of the matplotlib
backends (FLTK and WX) were contributed by private companies.

The other reason is licensing 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.
