Writing unit tests

Satpy tests are written using the third-party pytest package.

Fixtures

The usage of Pytest fixtures is encouraged for code re-usability.

As the builtin fixtures (and those defined in conftest.py file) are injected by Pytest without them being imported explicitly, their usage can be very confusing for new developers. To lessen the confusion, it is encouraged to add a note at the top of the test modules listing all the automatically injected external fixtures that are used in the module:

# NOTE:
# The following fixtures are not defined in this file, but are used and injected by Pytest:
# - tmp_path
# - fixture_defined_in_conftest.py

Avoiding Warnings

Satpy tries to avoid all warnings being emitted during tests. A warning (ex. UserWarning) being emitted during tests suggests there is a new or upcoming change that will affect Satpy or its users. At the time of writing Satpy does not fail when warnings are encountered but this may change in the future.

Warnings encountered during testing should be handled in one of a couple different ways.

Fix the underlying issue. For example, if a dependency is changing behavior then update Satpy’s usage to not produce the warning.
Catch the specific warning as part of the test. For example, if a test is expecting to produce the warning should be making sure that it is, do:
```
with pytest.warns(UserWarning, match="the warning message"):
    # code being tested
```

Ignore the error at the test level:

@pytest.mark.filterwarnings("ignore:the warning message:UserWarning")
def test_something():
    # test code

Ignore the warning globally. This is typically reserved for dependency changes that are expected to be removed in a future version. These are configured in the pyproject.toml in the root of the repository in the tool.pytest.ini_options section. See existing warning filters there for examples.

Other warnings tips

Create semi-realistic test data and avoid da.zeros or da.ones when creating test data. A simple option is to use arange:
```
test_data = da.arange(100 * 200).reshape((100, 200)).rechunk(50)
```

If using pytest’s parametrize functionality and only some of the parameters should produce a warning, use contextlib.nullcontext:

exp_warning = pytest.warns(...) if condition else contextlib.nullcontext()
with exp_warning:
    # code being tested

Lastly, note that all Numpy “invalid value” warnings are ignored globally in the pyproject.toml file. So if your test involves NaNs in the data and you are expecting to see them or expecting to catch them then you may have to customize the warning filters with pytest’s filterwarnings. See Warnings and Errors for more details on what is recommended for users.