Matplotlib uses the pytest framework.
The tests are in
lib/matplotlib/tests, and customizations to the pytest
testing infrastructure are in
To run the tests you will need to set up Matplotlib for development. Note in particular the additional dependencies for testing.
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:
Be more verbose
Run tests in parallel over NUM processes (requires pytest-xdist)
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):
Writing a simple test#
Many elements of Matplotlib can be tested using standard tests. For
example, here is a test from
def test_simple(): """ very simple example test """ assert 1 + 1 == 2
Pytest determines which functions are tests by searching for files whose names
"test_" and then within those files for functions beginning with
"test" or classes beginning with
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
decorator. For example, this test generates a single image and automatically
from matplotlib.testing.decorators import image_comparison import matplotlib.pyplot as plt @image_comparison(baseline_images=['line_dashes'], remove_text=True, extensions=['png'], style='mpl20') 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
baseline_images tree in the source directory (in this
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.
It is preferred that new tests use
style='mpl20' as this leads to smaller
figures and reflects the newer look of default Matplotlib plots. Also, if the
texts (labels, tick labels, etc) are not really part of what is tested, use
remove_text=True as this will lead to smaller figures and reduce possible
issues with font mismatch on different platforms.
See the documentation of
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
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. The Tests workflow is skipped in forked repositories but you can trigger a run manually from the GitHub web interface.
You can see the GitHub Actions results at your_GitHub_user_name/matplotlib -- here's an example.
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
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.
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
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))"
An analogous copying of
is necessary for testing
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