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:
Running the tests is simple. Make sure you have nose installed and run the script tests.py in the root directory of the distribution. The script can take any of the usual nosetest arguments, such as
|-d||detailed error messages|
|--with-coverage||enable collecting coverage information|
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, eg. (this is assuming the test is installed):
python tests.py matplotlib.tests.test_simplification:test_clipping
An alternative implementation that does not look at command line arguments works from within Python:
import matplotlib matplotlib.test()
Running tests by any means other than matplotlib.test() does not load the nose “knownfailureif” (Known failing tests) plugin, causing known-failing tests to fail for real.
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 """ assert_equal(1+1,2)
Nose determines which functions are tests by searching for functions beginning with “test” in their name.
If the test as 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 @cleanup def test_create_figure(): """ very simple example test that creates a figure using pyplot. """ fig = figure() ...
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 @image_comparison(baseline_images=['spines_axes_positions']) 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') ax.plot(x,y) ax.spines['right'].set_position(('axes',0.1)) ax.yaxis.set_ticks_position('right') ax.spines['top'].set_position(('axes',0.25)) ax.xaxis.set_ticks_position('top') ax.spines['left'].set_color('none') ax.spines['bottom'].set_color('none')
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.*) to the baseline_images tree in the source directory (in this case lib/matplotlib/tests/baseline_images/test_category) and put them under source code revision control (with git add). When rerunning the tests, they should now pass.
There are two optional keyword arguments to the image_comparison decorator:
- extensions: If you only wish to test some of the image formats (rather than the default png, svg and pdf formats), pass a list of the extensions to test.
- 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.
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 @knownfailureif(True) def test_simple_fail(): '''very simple example test that should fail''' assert_equal(1+1,3)
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.
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.
Tox is a tool for running tests against multiple Python environments, including multiple versions of Python (e.g.: 2.6, 2.7, 3.2, etc.) and even different Python implementations altogether (e.g.: CPython, PyPy, Jython, etc.)
Testing all 4 versions of Python (2.6, 2.7, 3.1, and 3.2) requires having four 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, or use pythonbrew.
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.
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.
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.