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:
- nose, version 1.0 or later
- mock, when running python versions < 3.3
- Ghostscript (to render PDF files)
- Inkscape (to render SVG files)
Optionally you can install:
matplotlib’s test suite makes heavy use of image comparison tests, meaning the result of a plot is compared against a known good result. Unfortunately, different versions of FreeType produce differently formed characters, causing these image comparisons to fail. To make them reproducible, matplotlib can be built with a special local copy of FreeType. This is recommended for all matplotlib developers.
Add the following content to a setup.cfg
file at the root of the
matplotlib source directory:
[test]
local_freetype = True
or by setting the MPLLOCALFREETYPE
environmental variable to any true
value.
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
matplotlib.test()
Hint
To run the tests you need to install nose and mock if using python 2.7:
pip install nose
pip install mock
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 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
@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'],
extensions=['png'])
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.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 justpng
), 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 topng
. For example only copy the files ending in_pdf.png
.tol
: This is the image matching tolerance, the default1e-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
.
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.
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.