Testing#

Note

This section is about general testing of napari. Other testing related information can be found in:

Plugin testing guidelines - information on testing plugins.
app-model testing- information about testing app-model aspects of napari.

Overview#

We use unit tests, integration tests, and functional tests to ensure that napari works as intended. We have:

Unit tests which test if individual modules or functions work correctly in isolation.
Integration tests which test if different modules or functions work properly when combined.
Functional tests which test if slices of napari functionality work as intended in the whole system.

Testing philosophy#

To get the most return on investment (ROI) from our coding, we strive to test as much as we can with unit tests, requiring fewer integration tests, and the least number of functional tests as depicted in the test pyramid below from softwaretestinghelp.com:

Unit tests are at the base of the pyramid because they are the easiest to write and the quickest to run. The time and effort to implement and maintain tests increases from unit tests to integration and functional tests.

Test organization#

All of napari tests are located in folders named _tests. We use the pytest library and test runner to execute our tests locally and in continuous integration, CI.

Integration and functional tests#

Our integration and functional tests are located in the napari/_tests folder at the top of the repository.

Unit tests#

To keep unit tests close to their related source code, these tests are located in module folders. For example, units tests for the Image layer are located in /napari/layers/image/_tests alongside the Image layer’s module code /napari/layers/image.

napari is made up of its core library code and its GUI code. As a general practice, we strive to unit test as much of our core library code, models and utils, independently of our GUI code.

Core library unit tests#

Core library tests are located in the following folders:

GUI unit tests#

Our GUI code have tests in the following folders:

These GUI tests are ignored when we run them in the subset of our continuous integration workflows. Workflows that run in a “headless” environment (without a Qt backend). Testing of core library, or “non-GUI” code, that requires a specific GUI backend are also found in these folders.

napari plugin tests#

The napari/plugins folder contains most tests related to plugins.

pytest fixtures#

Pytest fixtures are used to set up test state, such as setup and teardown, and provide frequently test data. These fixtures reduce repetitive code when writing and running tests. The fixtures can be found in:

napari/conftest.py - available globally to all of napari.
napari/utils/_testsupport.py - available globally to all of napari and to all tests in the same virtual environment that napari is in (as this testsupport.py file is exported).

`make_napari_viewer` fixture#

One often used fixture is make_napari_viewer. This fixture can take an argument show which is either True or False. In case your test depends on rendering of the viewer, it should be set to True. This is, for example, the case when testing a screenshot functionality. Otherwise, it is best to set the argument to False to prevent the viewer from fully rendering.

napari builtin plugin fixtures#

napari comes with a number of samples and examples builtin. There are also fixtures for testing these napari builtin plugins that provide contributions that come builtin with napari. These fixtures are found in napari_builtins/_tests/conftest.py and are available to tests stored in napari_builtins/_tests.

Running tests#

To run our test suite locally, run pytest on the command line. When running tests, use a napari development installation.

Run GUI tests locally#

Since napari can run as an interactive application, some tests require showing GUI elements (such as testing screenshots) and window focus (such as testing drag and drop behavior). By default, these tests are only run during continuous integration. If you’d like to enable GUI element tests to run locally, you can set the environment variables NAPARI_POPUP_TESTS=1, NAPARI_FOCUS_TESTS=1, or CI=1 before the pytest command:

CI=1 pytest

Note: setting CI=1 will also disable certain tests that take too long on CI.

Also, if running the GUI tests that use pyautogui on macOS, be sure to set the Terminal app Accessibility permissions in System Settings > Privacy & Security > Accessibility so pyautogui can control the mouse, keyboard, etc.

Use tox to run tests locally#

It is also possible to run tests locally using tox. We use tox to run test in CI. The main difference between running pytest locally or tox locally is that tox will create a virtual environment for each test environment, so it will take a bit more time. Though, tox will be more similar to the CI environment. To run test using tox using Python 3.10 and pyqt5 on Linux, enter:

tox -e py310-linux-pyqt5

To get list of all available environments that may be run:

tox list

Run tests without pop-up windows#

Some tests create visible napari viewers, which pop up on your monitor then quickly disappear. This can be annoying if you are trying to use your computer while the tests are running. You can avoid pop-up windows opening two different ways:

Use the QT_QPA_PLATFORM=offscreen environment variable with pytest or tox. This tells Qt to render windows “offscreen”, which is slower but will avoid the distracting pop-ups.
```
QT_QPA_PLATFORM=offscreen pytest napari
```
or
```
QT_QPA_PLATFORM=offscreen tox -e py310-linux-pyqt5
```
If you are using Linux or WSL (Windows Subsystem for Linux), you can use the xvfb-run command. This will run the tests in a virtual X server.
```
xvfb-run pytest napari
```
or
```
xvfb-run tox -e py310-linux-pyqt5
```

where the tox environment selector py310-linux-pyqt5 must match your OS and Python version.

Tips for speeding up local testing#

Very often when developing new code, you don’t need or want to run the entire test suite (which can take many minutes to finish). With pytest, it’s easy to run a subset of your tests:

# run tests in a specific subdirectory
pytest napari/components

# run tests in a specific file
pytest napari/components/_tests/test_add_layers.py

# run a specific test within a specific file
pytest napari/components/_tests/test_add_layers.py::test_add_layers_with_plugins

# select tests based on substring match of test name:
pytest napari/layers/ -k 'points and not bindings'

In general, it pays to learn a few of the tips and tricks of running pytest.

Testing coverage locally#

We aim for good test coverage, and we use codecov during continuous integration to make sure we maintain good coverage. If you’d like to test coverage locally as you develop new code, you can install pytest-cov and take advantage of a few handy commands:

# run the full test suite with coverage
pytest --cov=napari

# instead of coverage in the console, get a nice browser-based cov-report
pytest --cov=napari --cov-report=html
open htmlcov/index.html  # look at the report

# run a subset of tests with coverage
pytest --cov=napari.layers.shapes --cov-report=html napari/layers/shapes
open htmlcov/index.html  # look at the report

Writing tests#

Writing tests for new code is a critical part of keeping napari maintainable as it grows. Tests are written in files with names that begin with test_* and these test files are contained in one of the _tests directories.

Writing tests is a learned skill. If you are starting out writing tests, pytest’s documentation provides good examples. Reading existing tests is also helpful for understanding how tests are written. If you have questions, ask them in our chat.

Mocking: “Fake it till you make it”#

It can be confusing to write unit tests for individual functions, when the function being tested in turn depends on the output from some other function or method. This makes it tempting to write integration tests that “just test the whole thing together”. A useful tool in this case is the mock object library. “Mocking” lets you patch or replace parts of the code being tested with “fake” behavior or return values, so that you can test how a given function would perform if it were to receive some value from the upstream code. For a few examples of using mocks when testing napari, search the codebase for unittest.mock.

Property-based testing with Hypothesis#

Property-based tests allow you to test that “for any X, …” - with a much nicer developer experience than using truly random data. We use Hypothesis for unit or integration tests where there are simple properties like x == load(save(x)) or when Napari implements a function we can check against the equivalent in a trusted library for at least some inputs.

Writing tests of the GUI#

Fixtures are used when testing the GUI. Fixtures are helpful for setting state such as setup and teardown of the GUI. When using fixtures with mocks, the GUI behavior can be simulated and tested.

Testing with `Qt` and `napari.Viewer`#

There are a couple things to keep in mind when writing a test where a Qt event loop or a Viewer is required. The important thing is that any widgets you create during testing need to be cleaned up at the end of each test. We thus recommend that you use the following fixtures when needing a widget or Viewer in a test.

qapp and qtbot#

If you need to use any Qt related code in your test, you need to ensure that a QApplication is created. To to this we suggest you use the qapp fixture from pytest-qt, a napari testing dependency.

If you need to instantiate a Qt GUI object (e.g., a widget) for your test, we recommend that you use the qtbot fixture. qtbot, which itself depends on qapp , allows you to test user input (e.g., mouse clicks) by sending events to Qt objects.

Note

Fixtures in pytest can be a little mysterious, since it’s not always clear where they are coming from. The pytest-qt qapp and qtbot fixtures can be used in two ways; by adding them to the list of arguments of your test function:

def test_something(qtbot):
    ...

or by using pytest’s usefixtures, which avoids adding an unused argument to your test function:

@pytest.mark.usefixtures('qtbot')
def test_something():
    ...

qtbot also provides a convenient add_widget/addWidget method that will ensure that the widget gets closed and properly cleaned at the end of the test. This can prevents segfaults when running several tests. The wait_until/waitUntil method is also useful to wait for a desired condition. The example below adds a QtDims widget, plays the Dims and checks that the QtDim widget is playing before we make any assertions.

def test_something_else(qtbot):
    dims = Dims(ndim=3, ndisplay=2, range=((0, 10, 1), (0, 20, 1), (0, 30, 1)))
    view = QtDims(dims)
    qtbot.addWidget(view)
    # Loop to prevent finishing before the assertions in this test.
    view.play(loop_mode='loop')
    qtbot.waitUntil(lambda: view.is_playing)
    ...

`qt_viewer` and `viewer_model`#

Since napari==0.5.4 we have implemented the qt_viewer pytest fixture which can be used for tests that are only using the ViewerModel api or are only checking rendering of the viewer. For the current moment, it is only for internal use and is not exported to the global scope, as it is defined in conftest.py file.

The qt_viewer fixture returns the instance of the QtViewer class. This class does not provide the same api as the ViewerModel class, but has an associated ViewerModel instance, which can be accessed by the viewer attribute. Alternatively, you could use the viewer_model fixture, which returns this instance of ViewerModel class.

def test_something(qt_viewer):
    qt_viewer.viewer.add_image(np.random.random((10, 10)))
    assert len(viewer.layers) == 1
    assert viewer.layers[0].name == 'Image'

or

def test_something(qt_viewer, viewer_model):
    viewer_model.add_image(np.random.random((10, 10)))
    assert len(viewer.layers) == 1
    assert viewer.layers[0].name == 'Image'

The qt_viewer fixture takes care of proper teardown of all qt widgets related to the viewer, including hiding and clearing any references to viewer instances. If you need to adjust the QtViewer for a given test file you can use the qt_viewer_ fixture.

@pytest.fixture
def qt_viewer(qt_viewer_):
    # in this file we need to have added data and 3d view for all tests in file
    qt_viewer_.viewer.add_image(np.random.random((5, 10, 10)))
    qt_viewer_.viewer.dims.ndisplay = 3
    return qt_viewer_

or

@pytest.fixture
def qt_viewer(qt_viewer_):
    # Make bigger viewer for all tests in file
    qt_viewer_.setGeometry(0, 0, 1000, 1000)
    return qt_viewer_

`make_napari_viewer`#

For more complex test cases where we need to fully test application behaviour (for example, using the viewer.window API) we can use make_napari_viewer pytest fixture. However, the creating and teardown of the whole viewer is more fragile and slower than using just the qt_viewer fixture. This fixture is available globally and to all tests in the same environment that napari is in (see Test organization for details). Thus, there is no need to import it, you simply include make_napari_viewer as a test function parameter, as shown in the Examples section below:

napari.utils._testsupport.make_napari_viewer()[source]#

A pytest fixture function that creates a napari viewer for use in testing.

This fixture will take care of creating a viewer and cleaning up at the end of the test. When using this function, it is not necessary to use a qtbot fixture, nor should you do any additional cleanup (such as using qtbot.addWidget or calling viewer.close()) at the end of the test. Duplicate cleanup may cause an error.

To use this fixture as a function in your tests:

def test_something_with_a_viewer(make_napari_viewer):
# make_napari_viewer takes any keyword arguments that napari.Viewer() takes viewer = make_napari_viewer()

It accepts all the same arguments as napari.Viewer, notably show which should be set to True for tests that require the Viewer to be visible (e.g., tests that check aspects of the Qt window or layer rendering). It also accepts the following test-related parameters:

ViewerClassType[napari.Viewer], optional: Override the viewer class being used. By default, will use napari.viewer.Viewer
strict_qtbool or str, optional: If True, a check will be performed after test cleanup to make sure that no top level widgets were created and not cleaned up during the test. If the string “raise” is provided, an AssertionError will be raised. Otherwise a warning is emitted. By default, this is False unless the test is being performed within the napari package. This can be made globally true by setting the ‘NAPARI_STRICT_QT’ environment variable.
block_plugin_discoverybool, optional: Block discovery of non-builtin plugins. Note: plugins can still be manually registered by using the ‘napari_plugin_manager’ fixture and the napari_plugin_manager.register() method. By default, True.

Examples

>>> def test_adding_shapes(make_napari_viewer):
...     viewer = make_napari_viewer()
...     viewer.add_shapes()
...     assert len(viewer.layers) == 1

>>> def test_something_with_plugins(make_napari_viewer):
...     viewer = make_napari_viewer(block_plugin_discovery=False)

>>> def test_something_with_strict_qt_tests(make_napari_viewer):
...     viewer = make_napari_viewer(strict_qt=True)

Testing `QWidget` visibility#

When checking that QWidget visibility is updated correctly, you may need to use qtbot.waitUntil or qtbot.waitExposed (see Writing tests of the GUI for details on qtbot). This is because visibility can take some time to change.

For example, the following code can be used to check that a widget correctly appears after it is created.

from qtpy.QtWidgets import QWidget


def test_widget_hidden(make_napari_viewer, qtbot):
    """Check widget visibility correctly updated after hide."""
    # create viewer and make it visible
    viewer = make_napari_viewer(show=True)
    viewer.window.add_dock_widget(QWidget(viewer), name='test')
    widget = viewer.window._dock_widgets['test']
    # wait until the `widget` appears
    qtbot.waitUntil(widget.isVisible)
    assert widget.isVisible()

Note that we need to make the viewer visible when creating it as we are checking visibility. Additionally, you can set the timeout for qtbot.waitUntil (default is 5 seconds).

Another function that may be useful for testing QWidget visibility is QWidget.isVisibleTo, which tells you if a widget is visible relative to an ancestor.

Skipping tests with GUI elements or need window focus#

When you want to mark a test that should be skipped during a test run, pytest has built-in decorators that can be added before the test. For example, @pytest.mark.skip or @pyteset.mark.skipif can decorate a test that you want to skip:

@pytest.mark.skip(reason="test is causing intermittent failures")
def test_hello_world_exists():
    # test source code

You can also use custom napari decorators to skip tests that use popups or need window focus. These decorators are defined in napari/_tests/utils. Tests that require showing GUI elements should be marked with skip_local_popups. If a test requires window focus, it should be marked with skip_local_focus. To use these custom skip decorators, import the decorator and apply it to a test:

from napari._tests.utils import skip_local_popups

@skip_local_popups
def test_popup_window_after_error():
    # test source code

This is so they can be excluded and run only during continuous integration (see Running tests for details).

Known testing issues#

There are several known issues with displaying GUI tests on windows in CI, and so certain tests have been disabled from windows in CI, see #1377 for more discussion.