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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<h3><a href="../contents.html">Table Of Contents</a></h3>

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<li><a class="reference internal" href="#">Coding guide</a><ul>

<li><a class="reference internal" href="#pull-request-checklist">Pull request checklist</a><ul>

<li><a class="reference internal" href="#branch-selection">Branch selection</a></li>

<li><a class="reference internal" href="#style">Style</a></li>

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<li><a class="reference internal" href="#testing">Testing</a></li>

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<h1>Coding guide<a class="headerlink" href="#coding-guide" title="Permalink to this headline">¶</a></h1>

<h2>Pull request checklist<a class="headerlink" href="#pull-request-checklist" title="Permalink to this headline">¶</a></h2>

This checklist should be consulted when creating pull requests to make

sure they are complete before merging. These are not intended to be

rigidly followed—it’s just an attempt to list in one place all of

the items that are necessary for a good pull request. Of course, some

items will not always apply.

<h3>Branch selection<a class="headerlink" href="#branch-selection" title="Permalink to this headline">¶</a></h3>

<li>In general, simple bugfixes that are unlikely to introduce new bugs

of their own should be merged onto the maintenance branch. New

features, or anything that changes the API, should be made against

master. The rules are fuzzy here – when in doubt, try to get some

consensus.<ul>

<li>Once changes are merged into the maintenance branch, they should

be merged into master.</li>

</ul>

</li>

</ul>

</div>

<h3>Style<a class="headerlink" href="#style" title="Permalink to this headline">¶</a></h3>

<ul>

<li>Formatting should follow <a class="reference external" href="http://www.python.org/dev/peps/pep-0008/">PEP8</a>. Exceptions to these

rules are acceptable if it makes the code objectively more readable.

<li>You may want to consider installing automatic PEP8 checking in

your editor.</li>

</ul>

</li>

<li>No tabs (only spaces). No trailing whitespace.

<li>Configuring your editor to remove these things upon saving will

save a lot of trouble.</li>

</ul>

</li>

<li>Import the following modules using the standard scipy conventions:

<div class="highlight-python"><div class="highlight"><pre>import numpy as np

import numpy.ma as ma

import matplotlib as mpl

from matplotlib import pyplot as plt

import matplotlib.cbook as cbook

import matplotlib.collections as mcol

import matplotlib.patches as mpatches

</pre></div>

</div>

</li>

<li>See below for additional points about

<a class="reference internal" href="#keyword-argument-processing">Keyword argument processing</a>, if code in your pull request

does that.

</li>

<li>Adding a new pyplot function involves generating code. See

<a class="reference internal" href="#new-pyplot-function">Writing a new pyplot function</a> for more information.

</li>

</ul>

</div>

<h3>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h3>

<li>Every new feature should be documented. If it’s a new module, don’t

forget to add it to the API docs.</li>

<li>Docstrings should be in <a class="reference external" href="https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt">numpydoc format</a>.

Don’t be thrown off by the fact that many of the existing docstrings

are not in that format. We are working to standardize on

<tt class="xref py py-obj docutils literal">numpydoc</tt>.</li>

<li>Each high-level plotting function should have a simple example in

the <tt class="xref py py-obj docutils literal">Example</tt> section. This should be as simple as possible to

demonstrate the method. More complex examples should go in the

<tt class="xref py py-obj docutils literal">examples</tt> tree.</li>

<li>Build the docs and make sure all formatting warnings are addressed.</li>

<li>See <a class="reference internal" href="documenting_mpl.html#documenting-matplotlib">Documenting matplotlib</a> for our documentation style guide.</li>

<li>If your changes are non-trivial, please make an entry in the

<tt class="file docutils literal">CHANGELOG</tt>.</li>

<li>If your change is a major new feature, add an entry to

<tt class="file docutils literal">doc/users/whats_new.rst</tt>.</li>

<li>If you change the API in a backward-incompatible way, please

document it in <tt class="file docutils literal">doc/api/api_changes.rst</tt>.</li>

</ul>

</div>

<h3>Testing<a class="headerlink" href="#testing" title="Permalink to this headline">¶</a></h3>

Using the test framework is discussed in detail in the section

<a class="reference internal" href="testing.html#testing">Testing</a>.

<li>If the PR is a bugfix, add a test that fails prior to the change and

passes with the change. Include any relevant issue numbers in the

docstring of the test.</li>

<li>If this is a new feature, add a test that exercises as much of the

new feature as possible. (The <tt class="xref py py-obj docutils literal">--with-coverage</tt> option may be

useful here).</li>

<li>Make sure the Travis tests are passing before merging.<ul>

<li>The Travis tests automatically test on all of the Python versions

matplotlib supports whenever a pull request is created or updated.

The <tt class="xref py py-obj docutils literal">tox</tt> support in matplotlib may be useful for testing locally.</li>

</ul>

</li>

</ul>

</div>

<h3>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h3>

<li>If you have added new files or directories, or reorganized existing

ones, make sure the new files included in the match patterns in

<tt class="file docutils literal">MANIFEST.in</tt>, and/or in <tt class="xref py py-obj docutils literal">package_data</tt> in <tt class="xref py py-obj docutils literal">setup.py</tt>.</li>

</ul>

</div>

<h3>C/C++ extensions<a class="headerlink" href="#c-c-extensions" title="Permalink to this headline">¶</a></h3>

<li>Extensions may be written in C or C++.</li>

<li>Code style should conform to PEP7 (understanding that PEP7 doesn’t

address C++, but most of its admonitions still apply).</li>

<li>Interfacing with Python may be done either with the raw Python/C API

or Cython. Use of PyCXX is discouraged for new code.</li>

<li>Python/C interface code should be kept separate from the core C/C++

code. The interface code should be named <tt class="xref py py-obj docutils literal">FOO_wrap.cpp</tt>.</li>

<li>Header file documentation (aka docstrings) should be in Numpydoc

format. We don’t plan on using automated tools for these

docstrings, and the Numpydoc format is well understood in the

scientific Python community.</li>

</ul>

</div>

<h2>Style guide<a class="headerlink" href="#style-guide" title="Permalink to this headline">¶</a></h2>

<h3>Keyword argument processing<a class="headerlink" href="#keyword-argument-processing" title="Permalink to this headline">¶</a></h3>

Matplotlib makes extensive use of <tt class="docutils literal">**kwargs</tt> for pass-through

customizations from one function to another. A typical example is in

<tt class="xref py py-func docutils literal">matplotlib.pylab.text()</tt>. The definition of the pylab text

function is a simple pass-through to

<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text"><tt class="xref py py-meth docutils literal">matplotlib.axes.Axes.text()</tt></a>:

<div class="highlight-python"><div class="highlight"><pre># in pylab.py

def text(*args, **kwargs):

ret = gca().text(*args, **kwargs)

draw_if_interactive()

return ret

</pre></div>

</div>

<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text"><tt class="xref py py-meth docutils literal">text()</tt></a> in simplified form looks like this,

i.e., it just passes all <tt class="docutils literal">args</tt> and <tt class="docutils literal">kwargs</tt> on to

<tt class="xref py py-meth docutils literal">matplotlib.text.Text.__init__()</tt>:

<div class="highlight-python"><div class="highlight"><pre># in axes.py

def text(self, x, y, s, fontdict=None, withdash=False, **kwargs):

t = Text(x=x, y=y, text=s, **kwargs)

</pre></div>

</div>

and <tt class="xref py py-meth docutils literal">__init__()</tt> (again with liberties for

illustration) just passes them on to the

<a class="reference internal" href="../api/artist_api.html#matplotlib.artist.Artist.update" title="matplotlib.artist.Artist.update"><tt class="xref py py-meth docutils literal">matplotlib.artist.Artist.update()</tt></a> method:

<div class="highlight-python"><div class="highlight"><pre># in text.py

def __init__(self, x=0, y=0, text='', **kwargs):

Artist.__init__(self)

self.update(kwargs)

</pre></div>

</div>

<tt class="docutils literal">update</tt> does the work looking for methods named like

<tt class="docutils literal">set_property</tt> if <tt class="docutils literal">property</tt> 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 <tt class="docutils literal">**kwargs</tt> 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 <tt class="docutils literal">**kwargs</tt> idiom.

In some cases, you may want to consume some keys in the local

function, and let others pass through. You can <tt class="docutils literal">pop</tt> the ones to be

used locally and pass on the rest. For example, in

<a class="reference internal" href="../api/axes_api.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot"><tt class="xref py py-meth docutils literal">plot()</tt></a>, <tt class="docutils literal">scalex</tt> and <tt class="docutils literal">scaley</tt> are

local arguments and the rest are passed on as

<a class="reference internal" href="../api/artist_api.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><tt class="xref py py-meth docutils literal">Line2D()</tt></a> keyword arguments:

<div class="highlight-python"><div class="highlight"><pre># 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)

</pre></div>

</div>

Note: there is a use case when <tt class="docutils literal">kwargs</tt> are meant to be used locally

in the function (not passed on), but you still need the <tt class="docutils literal">**kwargs</tt>

idiom. That is when you want to use <tt class="docutils literal">*args</tt> to allow variable

numbers of non-keyword args. In this case, python will not allow you

to use named keyword args after the <tt class="docutils literal">*args</tt> usage, so you will be

forced to use <tt class="docutils literal">**kwargs</tt>. An example is

<tt class="xref py py-meth docutils literal">matplotlib.contour.ContourLabeler.clabel()</tt>:

<div class="highlight-python"><div class="highlight"><pre># 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...

</pre></div>

</div>

<h2>Hints<a class="headerlink" href="#hints" title="Permalink to this headline">¶</a></h2>

This section describes how to add certain kinds of new features to

matplotlib.

<h3>Developing a new backend<a class="headerlink" href="#developing-a-new-backend" title="Permalink to this headline">¶</a></h3>

If you are working on a custom backend, the backend setting in

<tt class="file docutils literal">matplotlibrc</tt> (<a class="reference internal" href="../users/customizing.html#customizing-matplotlib">Customizing matplotlib</a>) supports an

external backend via the <tt class="docutils literal">module</tt> directive. if

<tt class="file docutils literal">my_backend.py</tt> is a matplotlib backend in your

<a class="reference internal" href="../faq/environment_variables_faq.html#envvar-PYTHONPATH"><tt class="xref std std-envvar docutils literal">PYTHONPATH</tt></a>, you can set use it on one of several ways

<ul>

<li>in matplotlibrc:

<div class="highlight-python"><pre>backend : module://my_backend</pre>

</div>

</li>

<li>with the use directive is your script:

<div class="highlight-python"><div class="highlight"><pre>import matplotlib

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

</pre></div>

</div>

</li>

<li>from the command shell with the -d flag:

<div class="highlight-python"><pre>> python simple_plot.py -d module://my_backend</pre>

</div>

</li>

</ul>

</div>

<h3>Writing examples<a class="headerlink" href="#writing-examples" title="Permalink to this headline">¶</a></h3>

We have hundreds of examples in subdirectories of

<tt class="file docutils literal">matplotlib/examples</tt>, and these are automatically generated

when the website is built to show up both in the <a class="reference external" href="http://matplotlib.org/examples/index.html">examples</a> and <a class="reference external" href="http://matplotlib.org/gallery.html">gallery</a> sections of the website.

Any sample data that the example uses should be kept small and

distributed with matplotlib in the

<tt class="xref py py-obj docutils literal">lib/matplotlib/mpl-data/sample_data/</tt> directory. Then in your

example code you can load it into a file handle with:

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.cbook as cbook

fh = cbook.get_sample_data('mydata.dat')

</pre></div>

</div>

<h3>Writing a new pyplot function<a class="headerlink" href="#writing-a-new-pyplot-function" title="Permalink to this headline">¶</a></h3>

A large portion of the pyplot interface is automatically generated by the

<tt class="xref py py-obj docutils literal">boilerplate.py</tt> script (in the root of the source tree). To add or remove

a plotting method from pyplot, edit the appropriate list in <tt class="xref py py-obj docutils literal">boilerplate.py</tt>

and then run the script which will update the content in

<tt class="xref py py-obj docutils literal">lib/matplotlib/pyplot.py</tt>. Both the changes in <tt class="xref py py-obj docutils literal">boilerplate.py</tt> and

<tt class="xref py py-obj docutils literal">lib/matplotlib/pyplot.py</tt> should be checked into the repository.

</div>

</div>

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