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

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>Contributing — Matplotlib 2.0.2.post4416.dev0+g1df513f documentation</title>

type="text/css" />

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<h3>Navigation</h3>

<ul>

<a href="../genindex.html" title="General Index"

accesskey="I">index</a></li>

<a href="../py-modindex.html" title="Python Module Index"

>modules</a> |</li>

<a href="testing.html" title="Developer’s tips for testing"

accesskey="N">next</a> |</li>

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accesskey="P">previous</a> |</li>

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Travis-CI: <a href="https://travis-ci.org/matplotlib/matplotlib">

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<br/>

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

<ul>

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

<li><a class="reference internal" href="#submitting-a-bug-report">Submitting a bug report</a></li>

<li><a class="reference internal" href="#retrieving-and-installing-the-latest-version-of-the-code">Retrieving and installing the latest version of the code</a></li>

<li><a class="reference internal" href="#contributing-code">Contributing code</a><ul>

<li><a class="reference internal" href="#how-to-contribute">How to contribute</a></li>

<li><a class="reference internal" href="#contributing-pull-requests">Contributing pull requests</a></li>

<li><a class="reference internal" href="#issues-for-new-contributors">Issues for New Contributors</a></li>

</ul>

</li>

<li><a class="reference internal" href="#other-ways-to-contribute">Other ways to contribute</a></li>

<li><a class="reference internal" href="#coding-guidelines">Coding guidelines</a><ul>

<li><a class="reference internal" href="#new-modules-and-files-installation">New modules and files: installation</a></li>

<li><a class="reference internal" href="#c-c-extensions">C/C++ extensions</a></li>

<li><a class="reference internal" href="#keyword-argument-processing">Keyword argument processing</a></li>

<li><a class="reference internal" href="#developing-a-new-backend">Developing a new backend</a></li>

<li><a class="reference internal" href="#writing-examples">Writing examples</a></li>

</ul>

</li>

</ul>

</li>

</ul>

<h3>Related Topics</h3>

<ul>

<li><a href="../contents.html">Documentation overview</a><ul>

<li><a href="index.html">The Matplotlib Developers’ Guide</a><ul>

<li>Previous: <a href="index.html" title="previous chapter">The Matplotlib Developers’ Guide</a></li>

<li>Next: <a href="testing.html" title="next chapter">Developer’s tips for testing</a></li>

</ul></li>

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</div>

<li><a href="../_sources/devel/contributing.rst.txt"

rel="nofollow">Show Source</a></li>

</ul>

</div>

<h3>Quick search</h3>

</form>

</div>

</div>

<span id="id1"></span><h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline">¶</a></h1>

<p>This project is a community effort, and everyone is welcome to

contribute.</p>

<p>The project is hosted on <a class="reference external" href="https://github.com/matplotlib/matplotlib">https://github.com/matplotlib/matplotlib</a></p>

<h2>Submitting a bug report<a class="headerlink" href="#submitting-a-bug-report" title="Permalink to this headline">¶</a></h2>

<p>If you find a bug in the code or documentation, do not hesitate to submit a

ticket to the

<a class="reference external" href="https://github.com/matplotlib/matplotlib/issues">Bug Tracker</a>. You are also

welcome to post feature requests or pull requests.</p>

<p>If you are reporting a bug, please do your best to include the following:</p>

<li><p class="first">A short, top-level summary of the bug. In most cases, this should be 1-2

sentences.</p>

</li>

<li><p class="first">A short, self-contained code snippet to reproduce the bug, ideally allowing

a simple copy and paste to reproduce. Please do your best to reduce the code

snippet to the minimum required.</p>

</li>

<li><p class="first">The actual outcome of the code snippet</p>

</li>

<li><p class="first">The expected outcome of the code snippet</p>

</li>

<li><p class="first">The Matplotlib version, Python version and platform that you are using. You

can grab the version with the following commands:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib</span>

<span class="gp">>>> </span><span class="n">matplotlib</span><span class="o">.</span><span class="n">__version__</span>

<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">platform</span>

<span class="gp">>>> </span><span class="n">platform</span><span class="o">.</span><span class="n">python_version</span><span class="p">()</span>

</pre></div>

</div>

</li>

</ol>

</div></blockquote>

<p>We have preloaded the issue creation page with a Markdown template that you can

use to organize this information.</p>

<p>Thank you for your help in keeping bug reports complete, targeted and descriptive.</p>

</div>

<h2>Retrieving and installing the latest version of the code<a class="headerlink" href="#retrieving-and-installing-the-latest-version-of-the-code" title="Permalink to this headline">¶</a></h2>

<p>When working on the Matplotlib source, setting up a <a class="reference external" href="http://docs.python-guide.org/en/latest/dev/virtualenvs/">virtual

environment</a> or a

<a class="reference external" href="http://conda.pydata.org/docs/using/envs.html">conda environment</a> is

recommended.</p>

<p class="first admonition-title">Warning</p>

<p class="last">If you already have a version of Matplotlib installed, use an

virtual environment or uninstall using the same method you used

to install it. Installing multiple versions of Matplotlib via different

methods into the same environment may not always work as expected.</p>

</div>

<p>We use <a class="reference external" href="https://git-scm.com/">Git</a> for version control and

<a class="reference external" href="https://github.com/">GitHub</a> for hosting our main repository.</p>

<p>You can check out the latest sources with the command (see

<a class="reference internal" href="gitwash/set_up_fork.html#set-up-fork"><span class="std std-ref">Set up your fork</span></a> for more details):</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="p">:</span><span class="n">matplotlib</span><span class="o">/</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">git</span>

</pre></div>

</div>

<p>and navigate to the <code class="file docutils literal"><span class="pre">matplotlib</span></code> directory. If you have the proper privileges,

you can use <code class="docutils literal"><span class="pre">git@</span></code> instead of <code class="docutils literal"><span class="pre">https://</span></code>, which works through the ssh protocol

and might be easier to use if you are using 2-factor authentication.</p>

<p>To make sure the tests run locally you must build against the correct version

of freetype. To configure the build system to fetch and build it either export

the env <code class="docutils literal"><span class="pre">MPLLOCALFREETYPE</span></code> as:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">MPLLOCALFREETYPE</span><span class="o">=</span><span class="mi">1</span>

</pre></div>

</div>

<p>or copy <code class="file docutils literal"><span class="pre">setup.cfg.template</span></code> to <code class="file docutils literal"><span class="pre">setup.cfg</span></code> and edit it to contain</p>

<span class="n">local_freetype</span> <span class="o">=</span> <span class="kc">True</span>

</pre></div>

</div>

<p>To install Matplotlib (and compile the c-extensions) run the following

command from the top-level directory</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">v</span> <span class="o">-</span><span class="n">e</span> <span class="o">./</span>

</pre></div>

</div>

<p>This installs Matplotlib in ‘editable/develop mode’, i.e., builds

everything and places the correct link entries in the install

directory so that python will be able to import Matplotlib from the

source directory. Thus, any changes to the <code class="docutils literal"><span class="pre">*.py</span></code> files will be

reflected the next time you import the library. If you change the

c-extension source (which might happen if you change branches) you

will need to run:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span> <span class="n">build</span>

</pre></div>

</div>

<p>or re-run <code class="docutils literal"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-v</span> <span class="pre">-e</span> <span class="pre">./</span></code>.</p>

<p>Alternatively, if you do</p>

</pre></div>

</div>

<p>all of the files will be copied to the installation directory however,

you will have to rerun this command every time the source is changed.

Additionally you will need to copy <code class="file docutils literal"><span class="pre">setup.cfg.template</span></code> to

<code class="file docutils literal"><span class="pre">setup.cfg</span></code> and edit it to contain</p>

<span class="n">local_freetype</span> <span class="o">=</span> <span class="kc">True</span>

<span class="n">tests</span> <span class="o">=</span> <span class="kc">True</span>

</pre></div>

</div>

<p>In either case you can then run the tests to check your work

environment is set up properly:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">tests</span><span class="o">.</span><span class="n">py</span>

</pre></div>

</div>

<p class="last"><strong>Additional dependencies for testing</strong>: <a class="reference external" href="http://doc.pytest.org/en/latest/">pytest</a> (version 3.0 or later),

<a class="reference external" href="https://docs.python.org/dev/library/unittest.mock.html">mock</a> (if python < 3.3), <a class="reference external" href="https://www.ghostscript.com/">Ghostscript</a>, <a class="reference external" href="https://inkscape.org>">Inkscape</a></p>

</div>

<li><a class="reference internal" href="testing.html#testing"><span class="std std-ref">Developer’s tips for testing</span></a></li>

</ul>

</div>

<h2>Contributing code<a class="headerlink" href="#contributing-code" title="Permalink to this headline">¶</a></h2>

<h3>How to contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this headline">¶</a></h3>

<p>The preferred way to contribute to Matplotlib is to fork the <a class="reference external" href="https://github.com/matplotlib/matplotlib/">main

repository</a> on GitHub,

then submit a “pull request” (PR):</p>

<li><p class="first"><a class="reference external" href="https://github.com/join">Create an account</a> on

GitHub if you do not already have one.</p>

</li>

<li><p class="first">Fork the <a class="reference external" href="https://github.com/matplotlib/matplotlib">project repository</a>: click on the ‘Fork’ button

near the top of the page. This creates a copy of the code under your

account on the GitHub server.</p>

</li>

<li><p class="first">Clone this copy to your local disk:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span>$ git clone https://github.com:YourLogin/matplotlib.git

</pre></div>

</div>

</li>

<li><p class="first">Create a branch to hold your changes:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span>$ git checkout -b my-feature origin/master

</pre></div>

</div>

<p>and start making changes. Never work in the <code class="docutils literal"><span class="pre">master</span></code> branch!</p>

</li>

<li><p class="first">Work on this copy, on your computer, using Git to do the version

control. When you’re done editing e.g., <code class="docutils literal"><span class="pre">lib/matplotlib/collections.py</span></code>,

do:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span>$ git add lib/matplotlib/collections.py

$ git commit

</pre></div>

</div>

<p>to record your changes in Git, then push them to GitHub with:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span>$ git push -u origin my-feature

</pre></div>

</div>

</li>

</ol>

</div></blockquote>

<p>Finally, go to the web page of your fork of the Matplotlib repo,

and click ‘Pull request’ to send your changes to the maintainers for review.

You may want to consider sending an email to the mailing list for more

visibility.</p>

<li><a class="reference external" href="https://git-scm.com/documentation">Git documentation</a></li>

<li><a class="reference internal" href="gitwash/development_workflow.html#development-workflow"><span class="std std-ref">Development workflow</span></a>.</li>

<li><a class="reference internal" href="gitwash/index.html#using-git"><span class="std std-ref">Working with Matplotlib source code</span></a></li>

</ul>

</div>

<h3>Contributing pull requests<a class="headerlink" href="#contributing-pull-requests" title="Permalink to this headline">¶</a></h3>

<p>It is recommended to check that your contribution complies with the following

rules before submitting a pull request:</p>

<li><p class="first">If your pull request addresses an issue, please use the title to describe

the issue and mention the issue number in the pull request description

to ensure a link is created to the original issue.</p>

</li>

<li><p class="first">All public methods should have informative docstrings with sample

usage when appropriate. Use the

<a class="reference external" href="https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt">numpy docstring standard</a></p>

</li>

<li><p class="first">Formatting should follow <a class="reference external" href="https://www.python.org/dev/peps/pep-0008/">PEP8 recommendation</a>. You should consider

installing/enabling automatic PEP8 checking in your editor. Part of the

test suite is checking PEP8 compliance, things go smoother if the code is

mostly PEP8 compliant to begin with.</p>

</li>

<li><p class="first">Each high-level plotting function should have a simple example in

the <code class="docutils literal"><span class="pre">Example</span></code> section of the docstring. This should be as simple as

possible to demonstrate the method. More complex examples should go

in the <code class="docutils literal"><span class="pre">examples</span></code> tree.</p>

</li>

<li><p class="first">Changes (both new features and bugfixes) should be tested. See

<a class="reference internal" href="testing.html#testing"><span class="std std-ref">Developer’s tips for testing</span></a> for more details.</p>

</li>

<li><p class="first">Import the following modules using the standard scipy conventions:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">import</span> <span class="nn">numpy.ma</span> <span class="k">as</span> <span class="nn">ma</span>

<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="k">as</span> <span class="nn">cbook</span>

<span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</span>

</pre></div>

</div>

</li>

<li><p class="first">If your change is a major new feature, add an entry to the <code class="docutils literal"><span class="pre">What's</span> <span class="pre">new</span></code>

section by adding a new file in <code class="docutils literal"><span class="pre">doc/users/whats_new</span></code> (see

<code class="file docutils literal"><span class="pre">doc/users/whats_new/README</span></code> for more information).</p>

</li>

<li><p class="first">If you change the API in a backward-incompatible way, please

document it in <code class="xref py py-obj docutils literal"><span class="pre">doc/api/api_changes</span></code>, by adding a new file describing your

changes (see <code class="file docutils literal"><span class="pre">doc/api/api_changes/README</span></code> for more information)</p>

</li>

<li><p class="first">See below for additional points about

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

does that.</p>

</li>

</ul>

</div></blockquote>

<p>In addition, you can check for common programming errors with the following

tools:</p>

<li><p class="first">Code with a good unittest coverage (at least 70%, better 100%), check

with:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">coverage</span>

<span class="n">python</span> <span class="n">tests</span><span class="o">.</span><span class="n">py</span> <span class="o">--</span><span class="k">with</span><span class="o">-</span><span class="n">coverage</span>

</pre></div>

</div>

</li>

<li><p class="first">No pyflakes warnings, check with:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pyflakes</span>

<span class="n">pyflakes</span> <span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">module</span><span class="o">.</span><span class="n">py</span>

</pre></div>

</div>

</li>

</ul>

</div></blockquote>

<p class="last">The current state of the Matplotlib code base is not compliant with all

of those guidelines, but we expect that enforcing those constraints on all

new contributions will move the overall code base quality in the right

direction.</p>

</div>

<li><a class="reference internal" href="#coding-guidelines"><span class="std std-ref">Coding guidelines</span></a></li>

<li><a class="reference internal" href="testing.html#testing"><span class="std std-ref">Developer’s tips for testing</span></a></li>

<li><a class="reference internal" href="documenting_mpl.html#documenting-matplotlib"><span class="std std-ref">Developer’s tips for documenting matplotlib</span></a></li>

</ul>

</div>

<span id="new-contributors"></span><h3>Issues for New Contributors<a class="headerlink" href="#issues-for-new-contributors" title="Permalink to this headline">¶</a></h3>

<p>New contributors should look for the following tags when looking for issues.

We strongly recommend that new contributors tackle

<a class="reference external" href="https://github.com/matplotlib/matplotlib/labels/new-contributor-friendly">new-contributor-friendly</a>

issues (easy, well documented issues, that do not require an understanding of

the different submodules of Matplotlib) and

issues. This helps the contributor become familiar with the contribution

workflow, and for the core devs to become acquainted with the contributor;

besides which, we frequently underestimate how easy an issue is to solve!</p>

</div>

<span id="id2"></span><h2>Other ways to contribute<a class="headerlink" href="#other-ways-to-contribute" title="Permalink to this headline">¶</a></h2>

<p>Code is not the only way to contribute to Matplotlib. For instance,

documentation is also a very important part of the project and often doesn’t

get as much attention as it deserves. If you find a typo in the documentation,

or have made improvements, do not hesitate to send an email to the mailing

list or submit a GitHub pull request. Full documentation can be found under

the doc/ directory.</p>

<p>It also helps us if you spread the word: reference the project from your blog

and articles or link to it from your website!</p>

</div>

<span id="id3"></span><h2>Coding guidelines<a class="headerlink" href="#coding-guidelines" title="Permalink to this headline">¶</a></h2>

<h3>New modules and files: installation<a class="headerlink" href="#new-modules-and-files-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 are included in the match patterns in

<code class="file docutils literal"><span class="pre">MANIFEST.in</span></code>, and/or in <code class="xref py py-obj docutils literal"><span class="pre">package_data</span></code> in <code class="xref py py-obj docutils literal"><span class="pre">setup.py</span></code>.</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>Python/C interface code should be kept separate from the core C/C++

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

<code class="xref py py-obj docutils literal"><span class="pre">FOO_wrapper.cpp</span></code>.</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>

<span id="id4"></span><h3>Keyword argument processing<a class="headerlink" href="#keyword-argument-processing" title="Permalink to this headline">¶</a></h3>

<p>Matplotlib makes extensive use of <code class="docutils literal"><span class="pre">**kwargs</span></code> for pass-through

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

<a class="reference internal" href="../api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><code class="xref py py-func docutils literal"><span class="pre">matplotlib.pyplot.text()</span></code></a>. The definition of the pylab text

function is a simple pass-through to

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

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># in pylab.py</span>

<span class="k">def</span> <span class="nf">text</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">ret</span> <span class="o">=</span> <span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<span class="n">draw_if_interactive</span><span class="p">()</span>

<span class="k">return</span> <span class="n">ret</span>

</pre></div>

</div>

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

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

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

<span class="k">def</span> <span class="nf">text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">fontdict</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">withdash</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">t</span> <span class="o">=</span> <span class="n">Text</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">text</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

</pre></div>

</div>

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

illustration) just passes them on to the

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

<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">text</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">Artist</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>

<span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>

</pre></div>

</div>

<p><code class="docutils literal"><span class="pre">update</span></code> does the work looking for methods named like

<code class="docutils literal"><span class="pre">set_property</span></code> if <code class="docutils literal"><span class="pre">property</span></code> 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.</p>

<p>As a general rule, the use of <code class="docutils literal"><span class="pre">**kwargs</span></code> 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 <code class="docutils literal"><span class="pre">**kwargs</span></code> idiom.</p>

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

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

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

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

local arguments and the rest are passed on as

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

<span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">scalex</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'scalex'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>

<span class="n">scaley</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'scaley'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>

<span class="n">lines</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lines</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">lines</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>

</pre></div>

</div>

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

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

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

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

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

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

<a class="reference internal" href="../api/contour_api.html#matplotlib.contour.ContourLabeler.clabel" title="matplotlib.contour.ContourLabeler.clabel"><code class="xref py py-meth docutils literal"><span class="pre">matplotlib.contour.ContourLabeler.clabel()</span></code></a>:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># in contour.py</span>

<span class="k">def</span> <span class="nf">clabel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>

<span class="n">fontsize</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fontsize'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>

<span class="n">inline</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'inline'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

<span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fmt'</span><span class="p">,</span> <span class="s1">'</span><span class="si">%1.3f</span><span class="s1">'</span><span class="p">)</span>

<span class="n">colors</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'colors'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>

<span class="n">levels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span>

<span class="n">indices</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">))</span>

</pre></div>

</div>

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

<p>If you are working on a custom backend, the <em>backend</em> setting in

<code class="file docutils literal"><span class="pre">matplotlibrc</span></code> (<a class="reference internal" href="../tutorials/01_introductory/customizing.html#sphx-glr-tutorials-01-introductory-customizing-py"><span class="std std-ref">Customizing matplotlib</span></a>) supports an

external backend via the <code class="docutils literal"><span class="pre">module</span></code> directive. If

<code class="file docutils literal"><span class="pre">my_backend.py</span></code> is a Matplotlib backend in your

<span class="target" id="index-0"></span><a class="reference internal" href="../faq/environment_variables_faq.html#envvar-PYTHONPATH"><code class="xref std std-envvar docutils literal"><span class="pre">PYTHONPATH</span></code></a>, you can set it on one of several ways</p>

<ul>

<li><p class="first">in <code class="file docutils literal"><span class="pre">matplotlibrc</span></code>:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">backend</span> <span class="p">:</span> <span class="n">module</span><span class="p">:</span><span class="o">//</span><span class="n">my_backend</span>

</pre></div>

</div>

</li>

<li><p class="first">with the <span class="target" id="index-1"></span><a class="reference internal" href="../faq/environment_variables_faq.html#envvar-MPLBACKEND"><code class="xref std std-envvar docutils literal"><span class="pre">MPLBACKEND</span></code></a> environment variable:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="o">></span> <span class="n">export</span> <span class="n">MPLBACKEND</span><span class="o">=</span><span class="s2">"module://my_backend"</span>

<span class="o">></span> <span class="n">python</span> <span class="n">simple_plot</span><span class="o">.</span><span class="n">py</span>

</pre></div>

</div>

</li>

<li><p class="first">with the use directive in your script:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>

<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'module://my_backend'</span><span class="p">)</span>

</pre></div>

</div>

</li>

</ul>

</div>

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

<p>We have hundreds of examples in subdirectories of

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

when the website is built to show up in the <code class="xref py py-obj docutils literal"><span class="pre">examples</span></code> section of the website.</p>

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

distributed with Matplotlib in the

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

example code you can load it into a file handle with:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="k">as</span> <span class="nn">cbook</span>

<span class="n">fh</span> <span class="o">=</span> <span class="n">cbook</span><span class="o">.</span><span class="n">get_sample_data</span><span class="p">(</span><span class="s1">'mydata.dat'</span><span class="p">)</span>

</pre></div>

</div>

</div>

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