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Developer's tips for testing

Matplotlib's testing infrastructure depends on pytest. The tests are in lib/matplotlib/tests, and customizations to the pytest testing infrastructure are in matplotlib.testing.


Install the latest version of Matplotlib as documented in Retrieving and installing the latest version of the code In particular, follow the instructions to use a local FreeType build

The following software is required to run the tests:

Optionally you can install:

Running the tests

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



pytest .

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

--pep8 Perform pep8 checks (requires pytest-pep8)
-m "not network" Disable tests that require network access

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

--verbose Be more verbose
--n NUM Run tests in parallel over NUM processes (requires pytest-xdist)
--timeout=SECONDS Set timeout for results from each test process (requires pytest-timeout)
--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/

or, if tests are installed, a dot-separated path to the module, optionally followed by the function separated by two colons, such as:

pytest --pyargs 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:

pytest --verbose -n 5

Depending on your version of Python and pytest-xdist, you may need to set PYTHONHASHSEED to a fixed value when running in parallel:

PYTHONHASHSEED=0 pytest --verbose -n 5

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 matplotlib.test():

import matplotlib

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:

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 rc params). The pytest fixture mpl_test_settings() will automatically clean these up; there is no need to do anything further.

Random data in tests

Random data can 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:

import numpy as np

and Python's random number generator:

import random

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:

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.

Known failing tests

If you're writing a test, you may mark it as a known failing test with the pytest.mark.xfail() 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:

import pytest

def test_simple_fail():
    '''very simple example test that should fail'''
    assert 1 + 1 == 3

Note that the first argument to the xfail() 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 reason='message' keyword argument.

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 module are in

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 -- here's an example.

Using tox

Tox is a tool for running tests against multiple Python environments, including multiple versions of Python (e.g., 3.5, 3.6) and even different Python implementations altogether (e.g., CPython, PyPy, Jython, etc.)

Testing all versions of Python (3.5, 3.6, ...) 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 py36,py37

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 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.