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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="#">Testing</a><ul>

<li><a class="reference internal" href="#requirements">Requirements</a></li>

<li><a class="reference internal" href="#running-the-tests">Running the tests</a></li>

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

<li><a class="reference internal" href="#writing-an-image-comparison-test">Writing an image comparison test</a></li>

<li><a class="reference internal" href="#freetype-version">Freetype version</a></li>

<li><a class="reference internal" href="#known-failing-tests">Known failing tests</a></li>

<li><a class="reference internal" href="#creating-a-new-module-in-matplotlib-tests">Creating a new module in matplotlib.tests</a></li>

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

<p>Matplotlib has a testing infrastructure based on <a class="reference external" href="http://nose.readthedocs.org/en/latest/">nose</a>, making it easy

to write new tests. The tests are in <code class="xref py py-mod docutils literal"><span class="pre">matplotlib.tests</span></code>, and

customizations to the nose testing infrastructure are in

<code class="xref py py-mod docutils literal"><span class="pre">matplotlib.testing</span></code>. (There is other old testing cruft around,

please ignore it while we consolidate our testing to these locations.)</p>

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

<p>The following software is required to run the tests:</p>

<li><a class="reference external" href="http://nose.readthedocs.org/en/latest/">nose</a>, version 1.0 or later</li>

<li><a class="reference external" href="http://www.voidspace.org.uk/python/mock/">mock</a>, when running python

versions < 3.3</li>

<li><a class="reference external" href="http://pages.cs.wisc.edu/~ghost/">Ghostscript</a> (to render PDF

files)</li>

<li><a class="reference external" href="http://inkscape.org">Inkscape</a> (to render SVG files)</li>

</ul>

</div></blockquote>

<p>Optionally you can install:</p>

<li><a class="reference external" href="http://nedbatchelder.com/code/coverage/">coverage</a> to collect coverage

information</li>

<li><a class="reference external" href="http://pep8.readthedocs.org/en/latest">pep8</a> to test coding standards</li>

</ul>

</div></blockquote>

</div>

<h2>Running the tests<a class="headerlink" href="#running-the-tests" title="Permalink to this headline">¶</a></h2>

<p>Running the tests is simple. Make sure you have nose installed and run:</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>in the root directory of the distribution. The script takes a set of

commands, such as:</p>

</colgroup>

<td>pep8 checks</td>

</tr>

<td>Do not perform pep8 checks</td>

</tr>

<tr class="row-odd"><td><code class="docutils literal"><span class="pre">--no-network</span></code></td>

<td>Disable tests that require network access</td>

</tr>

</tbody>

</table>

<p>Additional arguments are passed on to nosetests. See the nose

documentation for supported arguments. Some of the more important ones are given

here:</p>

</colgroup>

<tr class="row-odd"><td><code class="docutils literal"><span class="pre">--verbose</span></code></td>

<td>Be more verbose</td>

</tr>

<tr class="row-even"><td><code class="docutils literal"><span class="pre">--processes=NUM</span></code></td>

<td>Run tests in parallel over NUM processes</td>

</tr>

<tr class="row-odd"><td><code class="docutils literal"><span class="pre">--process-timeout=SECONDS</span></code></td>

<td>Set timeout for results from test runner process</td>

</tr>

<tr class="row-even"><td><code class="docutils literal"><span class="pre">--nocapture</span></code></td>

<td>Do not capture stdout</td>

</tr>

</tbody>

</table>

<p>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, e.g., (this is assuming the test is installed):</p>

</pre></div>

</div>

<p>If you want to run the full test suite, but want to save wall time try

running the tests in parallel:</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> <span class="o">--</span><span class="n">nocapture</span> <span class="o">--</span><span class="n">nose</span><span class="o">-</span><span class="n">verbose</span> <span class="o">--</span><span class="n">processes</span><span class="o">=</span><span class="mi">5</span> <span class="o">--</span><span class="n">process</span><span class="o">-</span><span class="n">timeout</span><span class="o">=</span><span class="mi">300</span>

</pre></div>

</div>

<p>An alternative implementation that does not look at command line

arguments works from within Python is to run the tests from the

matplotlib library function <code class="xref py py-func docutils literal"><span class="pre">matplotlib.test()</span></code>:</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">test</span><span class="p">()</span>

</pre></div>

</div>

<p>To run the tests you need to install nose and mock if using python 2.7:</p>

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

<span class="n">pip</span> <span class="n">install</span> <span class="n">mock</span>

</pre></div>

</div>

<h2>Writing a simple test<a class="headerlink" href="#writing-a-simple-test" title="Permalink to this headline">¶</a></h2>

<p>Many elements of Matplotlib can be tested using standard tests. For

example, here is a test from <code class="xref py py-mod docutils literal"><span class="pre">matplotlib.tests.test_basic</span></code>:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nose.tools</span> <span class="k">import</span> <span class="n">assert_equal</span>

<span class="k">def</span> <span class="nf">test_simple</span><span class="p">():</span>

<span class="sd"> very simple example test</span>

<span class="n">assert_equal</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>

</pre></div>

</div>

<p>Nose determines which functions are tests by searching for functions

beginning with “test” in their name.</p>

<p>If the test has side effects that need to be cleaned up, such as

creating figures using the pyplot interface, use the <code class="docutils literal"><span class="pre">@cleanup</span></code>

decorator:</p>

<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.testing.decorators</span> <span class="k">import</span> <span class="n">cleanup</span>

<span class="nd">@cleanup</span>

<span class="k">def</span> <span class="nf">test_create_figure</span><span class="p">():</span>

<span class="sd"> very simple example test that creates a figure using pyplot.</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">()</span>

</pre></div>

</div>

<h2>Writing an image comparison test<a class="headerlink" href="#writing-an-image-comparison-test" title="Permalink to this headline">¶</a></h2>

<p>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

<code class="xref py py-func docutils literal"><span class="pre">image_comparison()</span></code> decorator. For

example, this test generates a single image and automatically tests

it:</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">matplotlib</span>

<span class="kn">from</span> <span class="nn">matplotlib.testing.decorators</span> <span class="k">import</span> <span class="n">image_comparison</span>

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

<span class="nd">@image_comparison</span><span class="p">(</span><span class="n">baseline_images</span><span class="o">=</span><span class="p">[</span><span class="s1">'spines_axes_positions'</span><span class="p">],</span>

<span class="n">extensions</span><span class="o">=</span><span class="p">[</span><span class="s1">'png'</span><span class="p">])</span>

<span class="k">def</span> <span class="nf">test_spines_axes_positions</span><span class="p">():</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>

<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'centered spines'</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s1">'axes'</span><span class="p">,</span><span class="mf">0.1</span><span class="p">))</span>

<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">'right'</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="s1">'axes'</span><span class="p">,</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">'top'</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'none'</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'none'</span><span class="p">)</span>

</pre></div>

</div>

<p>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 <code class="xref py py-obj docutils literal"><span class="pre">result_images/test_category/spines_axes_positions.png</span></code>) to

the correct subdirectory of <code class="xref py py-obj docutils literal"><span class="pre">baseline_images</span></code> tree in the source

directory (in this case

<code class="xref py py-obj docutils literal"><span class="pre">lib/matplotlib/tests/baseline_images/test_category</span></code>). Put this new

file under source code revision control (with <code class="xref py py-obj docutils literal"><span class="pre">git</span> <span class="pre">add</span></code>). When

rerunning the tests, they should now pass.</p>

<p>The <code class="xref py py-func docutils literal"><span class="pre">image_comparison()</span></code> decorator

defaults to generating <code class="docutils literal"><span class="pre">png</span></code>, <code class="docutils literal"><span class="pre">pdf</span></code> and <code class="docutils literal"><span class="pre">svg</span></code> output, but in

interest of keeping the size of the library from ballooning we should only

include the <code class="docutils literal"><span class="pre">svg</span></code> or <code class="docutils literal"><span class="pre">pdf</span></code> outputs if the test is explicitly exercising

a feature dependent on that backend.</p>

<p>There are two optional keyword arguments to the <code class="xref py py-obj docutils literal"><span class="pre">image_comparison</span></code>

decorator:</p>

<li><code class="xref py py-obj docutils literal"><span class="pre">extensions</span></code>: If you only wish to test additional image formats

(rather than just <code class="xref py py-obj docutils literal"><span class="pre">png</span></code>), pass any additional file types in the

list of the extensions to test. When copying the new

baseline files be sure to only copy the output files, not their

conversions to <code class="docutils literal"><span class="pre">png</span></code>. For example only copy the files

ending in <code class="docutils literal"><span class="pre">pdf</span></code>, not in <code class="docutils literal"><span class="pre">_pdf.png</span></code>.</li>

<li><code class="xref py py-obj docutils literal"><span class="pre">tol</span></code>: This is the image matching tolerance, the default <code class="xref py py-obj docutils literal"><span class="pre">1e-3</span></code>.

If some variation is expected in the image between runs, this

value may be adjusted.</li>

</ul>

</div></blockquote>

</div>

<h2>Freetype version<a class="headerlink" href="#freetype-version" title="Permalink to this headline">¶</a></h2>

<p>Due to subtle differences in the font rendering under different

version of freetype some care must be taken when generating the

baseline images. Currently (early 2015), almost all of the images

were generated using <code class="docutils literal"><span class="pre">freetype</span> <span class="pre">2.5.3-21</span></code> on Fedora 21 and only the

fonts that ship with <code class="docutils literal"><span class="pre">matplotlib</span></code> (regenerated in PR #4031 / commit

005cfde02751d274f2ab8016eddd61c3b3828446) and travis is using

<code class="docutils literal"><span class="pre">freetype</span> <span class="pre">2.4.8</span></code> on ubuntu.</p>

</div>

<h2>Known failing tests<a class="headerlink" href="#known-failing-tests" title="Permalink to this headline">¶</a></h2>

<p>If you’re writing a test, you may mark it as a known failing test with

the <code class="xref py py-func docutils literal"><span class="pre">knownfailureif()</span></code>

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:</p>

<span class="kn">from</span> <span class="nn">matplotlib.testing.decorators</span> <span class="k">import</span> <span class="n">knownfailureif</span>

<span class="nd">@knownfailureif</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">test_simple_fail</span><span class="p">():</span>

<span class="sd">'''very simple example test that should fail'''</span>

</pre></div>

</div>

<p>Note that the first argument to the

<code class="xref py py-func docutils literal"><span class="pre">knownfailureif()</span></code> decorator is a

fail condition, which can be a value such as True, False, or

‘indeterminate’, or may be a dynamically evaluated expression.</p>

</div>

<h2>Creating a new module in matplotlib.tests<a class="headerlink" href="#creating-a-new-module-in-matplotlib-tests" title="Permalink to this headline">¶</a></h2>

<p>We try to keep the tests categorized by the primary module they are

testing. For example, the tests related to the <code class="docutils literal"><span class="pre">mathtext.py</span></code> module

are in <code class="docutils literal"><span class="pre">test_mathtext.py</span></code>.</p>

<p>Let’s say you’ve added a new module named <code class="docutils literal"><span class="pre">whizbang.py</span></code> and you want

to add tests for it in <code class="docutils literal"><span class="pre">matplotlib.tests.test_whizbang</span></code>. To add

this module to the list of default tests, append its name to

<code class="docutils literal"><span class="pre">default_test_modules</span></code> in <code class="file docutils literal"><span class="pre">lib/matplotlib/__init__.py</span></code>.</p>

</div>

<h2>Using Travis CI<a class="headerlink" href="#using-travis-ci" title="Permalink to this headline">¶</a></h2>

<p><a class="reference external" href="http://travis-ci.org/">Travis CI</a> is a hosted CI system “in the

cloud”.</p>

<p>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

<code class="docutils literal"><span class="pre">.travis.yml</span></code> in the root of the repository to see how to test the

project.</p>

<p>Travis CI is already enabled for the <a class="reference external" href="https://github.com/matplotlib/matplotlib/">main matplotlib GitHub

repository</a> – for

example, see <a class="reference external" href="https://travis-ci.org/matplotlib/matplotlib">its Travis page</a>.</p>

<p>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 <a class="reference external" href="http://about.travis-ci.org/docs/user/getting-started/">the

Travis CI Getting Started page</a>. This

generally isn’t necessary, since any pull request submitted against

the main matplotlib repository will be tested.</p>

<p>Once this is configured, you can see the Travis CI results at

<a class="reference external" href="http://travis-ci.org/your_GitHub_user_name/matplotlib">http://travis-ci.org/your_GitHub_user_name/matplotlib</a> – here’s <a class="reference external" href="https://travis-ci.org/msabramo/matplotlib">an

example</a>.</p>

</div>

<h2>Using tox<a class="headerlink" href="#using-tox" title="Permalink to this headline">¶</a></h2>

<p><a class="reference external" href="http://tox.testrun.org/">Tox</a> is a tool for running tests against

multiple Python environments, including multiple versions of Python

(e.g., 2.7, 3.4, 3.5) and even different Python implementations

altogether (e.g., CPython, PyPy, Jython, etc.)</p>

<p>Testing all versions of Python (2.6, 2.7, 3.*) requires

having multiple 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.</p>

<p>tox makes it easy to determine if your working copy introduced any

regressions before submitting a pull request. Here’s how to use it:</p>

<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install tox

$ tox

</pre></div>

</div>

<p>You can also run tox on a subset of environments:</p>

<div class="highlight-bash"><div class="highlight"><pre><span></span>$ tox -e py26,py27

</pre></div>

</div>

<p>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 <code class="docutils literal"><span class="pre">detox</span></code>. Give this a try:</p>

<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install -U -i http://pypi.testrun.org detox

$ detox

</pre></div>

</div>

<p>Tox is configured using a file called <code class="docutils literal"><span class="pre">tox.ini</span></code>. You may need to

edit this file if you want to add new environments to test (e.g.,

<code class="docutils literal"><span class="pre">py33</span></code>) or if you want to tweak the dependencies or the way the

tests are run. For more info on the <code class="docutils literal"><span class="pre">tox.ini</span></code> file, see the <a class="reference external" href="http://tox.testrun.org/latest/config.html">Tox

Configuration Specification</a>.</p>

</div>

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