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Matplotlib has a testing infrastructure based on nose, making it easy to write new tests. The tests are in matplotlib.tests, and customizations to the nose testing infrastructure are in matplotlib.testing. (There is other old testing cruft around, please ignore it while we consolidate our testing to these locations.)


The following software is required to run the tests:

Optionally you can install:

  • coverage to collect coverage information
  • pep8 to test coding standards

Running the tests

Running the tests is simple. Make sure you have nose installed and run:

python tests.py

in the root directory of the distribution. The script takes a set of commands, such as:

--pep8 pep8 checks
--no-pep8 Do not perform pep8 checks
--no-network Disable tests that require network access

Additional arguments are passed on to nosetests. See the nose documentation for supported arguments. Some of the more important ones are given here:

--verbose Be more verbose
--processes=NUM Run tests in parallel over NUM processes
--process-timeout=SECONDS Set timeout for results from test runner process
--nocapture Do not capture stdout

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):

python tests.py matplotlib.tests.test_simplification:test_clipping

If you want to run the full test suite, but want to save wall time try running the tests in parallel:

python tests.py --nocapture --nose-verbose --processes=5 --process-timeout=300

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 matplotlib.test():

import matplotlib


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

pip install nose
pip install mock

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:

from nose.tools import assert_equal

def test_simple():
    very simple example test

Nose determines which functions are tests by searching for functions beginning with “test” in their name.

If the test has side effects that need to be cleaned up, such as creating figures using the pyplot interface, use the @cleanup decorator:

from matplotlib.testing.decorators import cleanup

def test_create_figure():
    very simple example test that creates a figure using pyplot.
    fig = figure()

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:

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

def test_spines_axes_positions():
    # SF bug 2852168
    fig = plt.figure()
    x = np.linspace(0,2*np.pi,100)
    y = 2*np.sin(x)
    ax = fig.add_subplot(1,1,1)
    ax.set_title('centered spines')

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

The image_comparison() decorator defaults to generating png, pdf and svg output, but in interest of keeping the size of the library from ballooning we should only include the svg or pdf outputs if the test is explicitly exercising a feature dependent on that backend.

There are two optional keyword arguments to the image_comparison decorator:

  • extensions: If you only wish to test additional image formats (rather than just png), 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 png. For example only copy the files ending in pdf, not in _pdf.png.
  • tol: This is the image matching tolerance, the default 1e-3. If some variation is expected in the image between runs, this value may be adjusted.

Freetype version

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 freetype 2.5.3-21 on Fedora 21 and only the fonts that ship with matplotlib (regenerated in PR #4031 / commit 005cfde02751d274f2ab8016eddd61c3b3828446) and travis is using freetype 2.4.8 on ubuntu.

Known failing tests

If you’re writing a test, you may mark it as a known failing test with the knownfailureif() 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:

from nose.tools import assert_equal
from matplotlib.testing.decorators import knownfailureif

def test_simple_fail():
    '''very simple example test that should fail'''

Note that the first argument to the knownfailureif() decorator is a fail condition, which can be a value such as True, False, or ‘indeterminate’, or may be a dynamically evaluated expression.

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.

Let’s say you’ve added a new module named whizbang.py and you want to add tests for it in matplotlib.tests.test_whizbang. To add this module to the list of default tests, append its name to default_test_modules in lib/matplotlib/__init__.py.

Using Travis CI

Travis CI is a hosted CI system “in the cloud”.

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 .travis.yml in the root of the repository to see how to test the project.

Travis CI is already enabled for the main matplotlib GitHub repository – for example, see its Travis page.

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 the Travis CI Getting Started page. This generally isn’t necessary, since any pull request submitted against the main matplotlib repository will be tested.

Once this is configured, you can see the Travis CI results at http://travis-ci.org/your_GitHub_user_name/matplotlib – here’s an example.

Using tox

Tox 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.)

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.

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 py26,py27

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.