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Coding guide

Committing changes

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

Style guide

Importing and name spaces

For numpy, 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, 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 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++:

; 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:

(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...

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`.

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

Writing examples

We have hundreds of examples in subdirectories of file:matplotlib/examples in the trunk, and these are automatically generated when the website it built to show up both in the examples and gallery 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 provided through the sample_data svn directory, which can then be accessed using :func:`matplotlib.cbook.get_sample_data`. First get a copy of the repository and svn add your data:

svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/sample_data
cp ~/path/to/mydata.dat sample_data/
cd sample_data
svn add mydata.dat
svn commit -m 'added my data'

and 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 svn repo using urllib and updated when the revision number changes.

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

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

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.)

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()

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.png'])
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')
    fig.savefig('spines_axes_positions.png')

The mechanism for comparing images is extremely simple -- it compares an image saved in the current directory with one from the Matplotlib sample_data repository. The correspondence is done by matching filenames, so ensure that:

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`.

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 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, 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. 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.