Testing#

Matplotlib uses the pytest framework.

The tests are in lib/matplotlib/tests, and customizations to the pytest testing infrastructure are in matplotlib.testing.

Requirements#

To run the tests you will need to set up Matplotlib for development. Note in particular the additional dependencies for testing.

Note

We will assume that you want to run the tests in a development setup.

While you can run the tests against a regular installed version of Matplotlib, this is a far less common use case. You still need the additional dependencies for testing. You have to additionally get the reference images from the repository, because they are not distributed with pre-built Matplotlib packages.

Running the tests#

In the root directory of your development repository run:

python -m pytest

pytest can be configured via a lot of command-line parameters. Some particularly useful ones are:

-v or --verbose

Be more verbose

-n NUM

Run tests in parallel over NUM processes (requires pytest-xdist)

--capture=no or -s

Do not capture stdout

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):

pytest lib/matplotlib/tests/test_simplification.py::test_clipping

Writing a simple test#

Many elements of Matplotlib can be tested using standard tests. For example, here is a test from matplotlib/tests/test_basic.py:

def test_simple():
    """
    very simple example test
    """
    assert 1 + 1 == 2

Pytest determines which functions are tests by searching for files whose names begin with "test_" and then within those files for functions beginning with "test" or classes beginning with "Test".

Some tests have internal side effects that need to be cleaned up after their execution (such as created figures or modified rcParams). The pytest fixture matplotlib.testing.conftest.mpl_test_settings will automatically clean these up; there is no need to do anything further.

Random data in tests#

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's default random number generator use:

import numpy as np
rng = np.random.default_rng(19680801)

and then use rng when generating the random numbers.

The seed is John Hunter's birthday.

Writing an image comparison test#

Writing an image-based test is only slightly more difficult than a simple test. The main consideration is that you must specify the "baseline", or expected, images in the image_comparison decorator. For example, this test generates a single image and automatically tests it:

from matplotlib.testing.decorators import image_comparison
import matplotlib.pyplot as plt

@image_comparison(baseline_images=['line_dashes'], remove_text=True,
                  extensions=['png'])
def test_line_dashes():
    fig, ax = plt.subplots()
    ax.plot(range(10), linestyle=(0, (3, 3)), lw=5)

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 result_images/test_lines/test_line_dashes.png) to the correct subdirectory of baseline_images tree in the source directory (in this case lib/matplotlib/tests/baseline_images/test_lines). Put this new file under source code revision control (with git add). When rerunning the tests, they should now pass.

Baseline images take a lot of space in the Matplotlib repository. An alternative approach for image comparison tests is to use the check_figures_equal decorator, which should be used to decorate a function taking two Figure 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.

See the documentation of image_comparison and check_figures_equal for additional information about their use.

Creating a new module in matplotlib.tests#

We try to keep the tests categorized by the primary module they are testing. For example, the tests related to the mathtext.py module are in test_mathtext.py.

Using GitHub Actions for CI#

GitHub Actions is a hosted CI system "in the cloud".

GitHub Actions is configured to receive notifications of new commits to GitHub repos and to run builds or tests when it sees these new commits. It looks for a YAML files in .github/workflows to see how to test the project.

GitHub Actions is already enabled for the main Matplotlib GitHub repository -- for example, see the Tests workflows.

GitHub Actions should be automatically enabled for your personal Matplotlib fork once the YAML workflow files are in it. It generally isn't necessary to look at these workflows, since any pull request submitted against the main Matplotlib repository will be tested.

You can see the GitHub Actions results at https://github.com/your_GitHub_user_name/matplotlib/actions -- here's an example.

Using tox#

Tox is a tool for running tests against multiple Python environments, including multiple versions of Python (e.g., 3.7, 3.8) 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).

tox makes it easy to determine if your working copy introduced any regressions before submitting a pull request. Here's how to use it:

$ pip install tox
$ tox

You can also run tox on a subset of environments:

$ tox -e py38,py39

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 detox. Give this a try:

$ pip install -U -i http://pypi.testrun.org detox
$ detox

Tox is configured using a file called tox.ini. You may need to edit this file if you want to add new environments to test (e.g., py33) or if you want to tweak the dependencies or the way the tests are run. For more info on the tox.ini file, see the Tox Configuration Specification.

Building old versions of Matplotlib#

When running a git bisect 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:

  • Matplotlib 1.3 (or earlier) requires numpy 1.8 (or earlier).

Testing released versions of Matplotlib#

Running the tests on an installation of a released version (e.g. PyPI package or conda package) also requires additional setup.

Note

For an end-user, there is usually no need to run the tests on released versions of Matplotlib. Official releases are tested before publishing.

Install additional dependencies#

Install the additional dependencies for testing.

Obtain the reference images#

Many tests compare the plot result against reference images. The reference images are not part of the regular packaged versions (pip wheels or conda packages). If you want to run tests with reference images, you need to obtain the reference images matching the version of Matplotlib you want to test.

To do so, either download the matching source distribution matplotlib-X.Y.Z.tar.gz from PyPI or alternatively, clone the git repository and git checkout vX.Y.Z. Copy the folder lib/matplotlib/tests/baseline_images to the folder matplotlib/tests of your the matplotlib installation to test. The correct target folder can be found using:

python -c "import matplotlib.tests; print(matplotlib.tests.__file__.rsplit('/', 1)[0])"

An analogous copying of lib/mpl_toolkits/tests/baseline_images is necessary for testing mpl_toolkits.

Run the tests#

To run the all the tests on your installed version of Matplotlib:

python -m pytest --pyargs matplotlib.tests

The test discovery scope can be narrowed to single test modules or even single functions:

python -m pytest --pyargs matplotlib.tests.test_simplification.py::test_clipping