# 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.

## Requirements¶

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 repository.

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) --timeout=SECONDS Set timeout for results from each test process (requires pytest-timeout) --capture=no or -s Do not capture stdout --flake8 Check coding standards using flake8 (requires pytest-flake8)

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


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


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
matplotlib.test()


## 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 rcParams). 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 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
np.random.seed(19680801)


and Python's random number generator:

import random
random.seed(19680801)


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.

## 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

@pytest.mark.xfail
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 mathtext.py module are in test_mathtext.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 https://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., 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). 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:

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