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<li><a class="reference internal" href="#">Developer's tips for 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="#random-data-in-tests">Random data in tests</a></li>
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<div class="section" id="developer-s-tips-for-testing">
<span id="testing"></span><h1>Developer's tips for testing<a class="headerlink" href="#developer-s-tips-for-testing" title="Permalink to this headline">¶</a></h1>
<p>Matplotlib's testing infrastructure depends on <a class="reference external" href="http://doc.pytest.org/en/latest/">pytest</a>. The tests are in
<code class="file docutils literal notranslate"><span class="pre">lib/matplotlib/tests</span></code>, and customizations to the pytest testing
infrastructure are in <a class="reference internal" href="../api/testing_api.html#module-matplotlib.testing" title="matplotlib.testing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">matplotlib.testing</span></code></a>.</p>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">¶</a></h2>
<p>Install the latest version of Matplotlib as documented in
<a class="reference internal" href="contributing.html#installing-for-devs"><span class="std std-ref">Retrieving and installing the latest version of the code</span></a> In particular, follow the instructions to use a
local FreeType build.</p>
<p>The following software is required to run the tests:</p>
<ul class="simple">
<li><a class="reference external" href="http://doc.pytest.org/en/latest/">pytest</a> (>=3.6)</li>
<li><a class="reference external" href="https://www.ghostscript.com/">Ghostscript</a> (>= 9.0, to render PDF files)</li>
<li><a class="reference external" href="https://inkscape.org">Inkscape</a> (to render SVG files)</li>
</ul>
<p>Optionally you can install:</p>
<ul class="simple">
<li><a class="reference external" href="https://pytest-cov.readthedocs.io/en/latest/">pytest-cov</a> (>=2.3.1) to collect coverage information</li>
<li><a class="reference external" href="https://pypi.org/project/pytest-flake8/">pytest-flake8</a> to test coding standards using <a class="reference external" href="https://pypi.org/project/flake8/">flake8</a></li>
<li><a class="reference external" href="https://pypi.org/project/pytest-timeout/">pytest-timeout</a> to limit runtime in case of stuck tests</li>
<li><a class="reference external" href="https://pypi.org/project/pytest-xdist/">pytest-xdist</a> to run tests in parallel</li>
</ul>
</div>
<div class="section" id="running-the-tests">
<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 pytest installed and run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span>
</pre></div>
</div>
<p>in the root directory of the repository.</p>
<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pytest</span></code> can be configured via a lot of <a class="reference external" href="http://doc.pytest.org/en/latest/usage.html">commandline parameters</a>. Some
particularly useful ones are:</p>
<table border="1" class="docutils">
<colgroup>
<col width="43%" />
<col width="57%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><code class="docutils literal notranslate"><span class="pre">-v</span></code> or <code class="docutils literal notranslate"><span class="pre">--verbose</span></code></td>
<td>Be more verbose</td>
</tr>
<tr class="row-even"><td><code class="docutils literal notranslate"><span class="pre">-n</span> <span class="pre">NUM</span></code></td>
<td>Run tests in parallel over NUM
processes (requires <a class="reference external" href="https://pypi.org/project/pytest-xdist/">pytest-xdist</a>)</td>
</tr>
<tr class="row-odd"><td><code class="docutils literal notranslate"><span class="pre">--timeout=SECONDS</span></code></td>
<td>Set timeout for results from each test
process (requires <a class="reference external" href="https://pypi.org/project/pytest-timeout/">pytest-timeout</a>)</td>
</tr>
<tr class="row-even"><td><code class="docutils literal notranslate"><span class="pre">--capture=no</span></code> or <code class="docutils literal notranslate"><span class="pre">-s</span></code></td>
<td>Do not capture stdout</td>
</tr>
<tr class="row-odd"><td><code class="docutils literal notranslate"><span class="pre">--flake8</span></code></td>
<td>Check coding standards using <a class="reference external" href="https://pypi.org/project/flake8/">flake8</a>
(requires <a class="reference external" href="https://pypi.org/project/pytest-flake8/">pytest-flake8</a>)</td>
</tr>
</tbody>
</table>
<p>To run a single test from the command line, you can provide a file path,
optionally followed by the function separated by two colons, e.g., (tests do
not need to be installed, but Matplotlib should be):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="n">lib</span><span class="o">/</span><span class="n">matplotlib</span><span class="o">/</span><span class="n">tests</span><span class="o">/</span><span class="n">test_simplification</span><span class="o">.</span><span class="n">py</span><span class="p">::</span><span class="n">test_clipping</span>
</pre></div>
</div>
<p>or, if tests are installed, a dot-separated path to the module, optionally
followed by the function separated by two colons, such as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="o">--</span><span class="n">pyargs</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">tests</span><span class="o">.</span><span class="n">test_simplification</span><span class="p">::</span><span class="n">test_clipping</span>
</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 notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="o">--</span><span class="n">verbose</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span>
</pre></div>
</div>
<p>Depending on your version of Python and pytest-xdist, you may need to set
<code class="docutils literal notranslate"><span class="pre">PYTHONHASHSEED</span></code> to a fixed value when running in parallel:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">PYTHONHASHSEED</span><span class="o">=</span><span class="mi">0</span> <span class="n">pytest</span> <span class="o">--</span><span class="n">verbose</span> <span class="o">-</span><span class="n">n</span> <span class="mi">5</span>
</pre></div>
</div>
<p>An alternative implementation that does not look at command line arguments
and works from within Python is to run the tests from the Matplotlib library
function <code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.test()</span></code>:</p>
<div class="highlight-default notranslate"><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>
</div>
<div class="section" id="writing-a-simple-test">
<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 notranslate"><span class="pre">matplotlib.tests.test_basic</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">test_simple</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> very simple example test</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="mi">2</span>
</pre></div>
</div>
<p>Pytest determines which functions are tests by searching for files whose names
begin with <code class="docutils literal notranslate"><span class="pre">"test_"</span></code> and then within those files for functions beginning with
<code class="docutils literal notranslate"><span class="pre">"test"</span></code> or classes beginning with <code class="docutils literal notranslate"><span class="pre">"Test"</span></code>.</p>
<p>Some tests have internal side effects that need to be cleaned up after their
execution (such as created figures or modified rc params). The pytest fixture
<code class="xref py py-func docutils literal notranslate"><span class="pre">mpl_test_settings()</span></code> will automatically clean
these up; there is no need to do anything further.</p>
</div>
<div class="section" id="random-data-in-tests">
<h2>Random data in tests<a class="headerlink" href="#random-data-in-tests" title="Permalink to this headline">¶</a></h2>
<p>Random data is a very convenient way to generate data for examples,
however the randomness is problematic for testing (as the tests
must be deterministic!). To work around this set the seed in each test.
For numpy use:</p>
<div class="highlight-default notranslate"><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="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">19680801</span><span class="p">)</span>
</pre></div>
</div>
<p>and Python's random number generator:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">19680801</span><span class="p">)</span>
</pre></div>
</div>
<p>The seed is John Hunter's birthday.</p>
</div>
<div class="section" id="writing-an-image-comparison-test">
<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 <a class="reference internal" href="../api/testing_api.html#matplotlib.testing.decorators.image_comparison" title="matplotlib.testing.decorators.image_comparison"><code class="xref py py-obj docutils literal notranslate"><span class="pre">image_comparison</span></code></a>
decorator. For example, this test generates a single image and automatically
tests it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.testing.decorators</span> <span class="kn">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">'line_dashes'</span><span class="p">],</span> <span class="n">remove_text</span><span class="o">=</span><span class="kc">True</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_line_dashes</span><span class="p">():</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span> <span class="n">lw</span><span class="o">=</span><span class="mi">5</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="file docutils literal notranslate"><span class="pre">result_images/test_lines/test_line_dashes.png</span></code>) to the correct
subdirectory of <code class="file docutils literal notranslate"><span class="pre">baseline_images</span></code> tree in the source directory (in this
case <code class="file docutils literal notranslate"><span class="pre">lib/matplotlib/tests/baseline_images/test_lines</span></code>). Put this new
file under source code revision control (with <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">add</span></code>). When rerunning
the tests, they should now pass.</p>
<p>Baseline images take a lot of space in the Matplotlib repository.
An alternative approach for image comparison tests is to use the
<a class="reference internal" href="../api/testing_api.html#matplotlib.testing.decorators.check_figures_equal" title="matplotlib.testing.decorators.check_figures_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_figures_equal</span></code></a> decorator, which should be
used to decorate a function taking two <code class="xref py py-obj docutils literal notranslate"><span class="pre">Figure</span></code> parameters and draws the same
images on the figures using two different methods (the tested method and the
baseline method). The decorator will arrange for setting up the figures and
then collect the drawn results and compare them.</p>
<p>See the documentation of <a class="reference internal" href="../api/testing_api.html#matplotlib.testing.decorators.image_comparison" title="matplotlib.testing.decorators.image_comparison"><code class="xref py py-obj docutils literal notranslate"><span class="pre">image_comparison</span></code></a> and
<a class="reference internal" href="../api/testing_api.html#matplotlib.testing.decorators.check_figures_equal" title="matplotlib.testing.decorators.check_figures_equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_figures_equal</span></code></a> for additional information
about their use.</p>
</div>
<div class="section" id="known-failing-tests">
<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 notranslate"><span class="pre">pytest.mark.xfail()</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>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pytest</span>
<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">xfail</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>
<span class="k">assert</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="mi">3</span>
</pre></div>
</div>
<p>Note that the first argument to the <code class="xref py py-func docutils literal notranslate"><span class="pre">xfail()</span></code> decorator is a
fail condition, which can be a value such as True, False, or may be a
dynamically evaluated expression. If a condition is supplied, then a reason
must also be supplied with the <code class="docutils literal notranslate"><span class="pre">reason='message'</span></code> keyword argument.</p>
</div>
<div class="section" id="creating-a-new-module-in-matplotlib-tests">
<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 notranslate"><span class="pre">mathtext.py</span></code> module
are in <code class="docutils literal notranslate"><span class="pre">test_mathtext.py</span></code>.</p>
</div>
<div class="section" id="using-travis-ci">
<h2>Using Travis CI<a class="headerlink" href="#using-travis-ci" title="Permalink to this headline">¶</a></h2>
<p><a class="reference external" href="https://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 notranslate"><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="https://docs.travis-ci.com/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="https://travis-ci.org/your_GitHub_user_name/matplotlib">https://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>
<div class="section" id="using-tox">
<h2>Using tox<a class="headerlink" href="#using-tox" title="Permalink to this headline">¶</a></h2>
<p><a class="reference external" href="https://tox.readthedocs.io/en/latest/">Tox</a> is a tool for running tests
against multiple Python environments, including multiple versions of Python
(e.g., 3.6, 3.7) and even different Python implementations altogether
(e.g., CPython, PyPy, Jython, etc.), as long as all these versions are
available on your system's $PATH (consider using your system package manager,
e.g. apt-get, yum, or Homebrew, to install them).</p>
<p>tox makes it easy to determine if your working copy introduced any
regressions before submitting a pull request. Here's how to use it:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install tox
$ tox
</pre></div>
</div>
<p>You can also run tox on a subset of environments:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ tox -e py36,py37
</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 notranslate"><span class="pre">detox</span></code>. Give this a try:</p>
<div class="highlight-bash notranslate"><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 notranslate"><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 notranslate"><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 notranslate"><span class="pre">tox.ini</span></code> file, see the <a class="reference external" href="https://tox.readthedocs.io/en/latest/config.html">Tox
Configuration Specification</a>.</p>
</div>
<div class="section" id="building-old-versions-of-matplotlib">
<h2>Building old versions of Matplotlib<a class="headerlink" href="#building-old-versions-of-matplotlib" title="Permalink to this headline">¶</a></h2>
<p>When running a <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">bisect</span></code> to see which commit introduced a certain bug,
you may (rarely) need to build very old versions of Matplotlib. The following
constraints need to be taken into account:</p>
<ul class="simple">
<li>Matplotlib 1.3 (or earlier) requires numpy 1.8 (or earlier).</li>
</ul>
</div>
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