.. _testing: Testing ======= Matplotlib has a testing infrastructure based on nose_, making it easy to write new tests. The tests are in :mod:`matplotlib.tests`, and customizations to the nose testing infrastructure are in :mod:`matplotlib.testing`. (There is other old testing cruft around, please ignore it while we consolidate our testing to these locations.) .. _nose: http://nose.readthedocs.org/en/latest/ Requirements ------------ 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: - `coverage `_ to collect coverage information - `pep8 `_ to test coding standards Building matplotlib for image comparison tests ---------------------------------------------- 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 ----------------- 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 :func:`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 .. _`nosetest arguments`: http://nose.readthedocs.org/en/latest/usage.html Writing a simple test --------------------- Many elements of Matplotlib can be tested using standard tests. For example, here is a test from :mod:`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 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 :func:`~matplotlib.testing.decorators.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 :func:`~matplotlib.testing.decorators.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 :func:`~matplotlib.testing.decorators.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 :func:`~matplotlib.testing.decorators.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 :file:`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: .. code-block:: bash $ pip install tox $ tox You can also run tox on a subset of environments: .. code-block:: bash $ 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: .. code-block:: bash $ 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 `_.