Unit test marking #104
Unit test marking #104
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Using markers to identify slow tests and precision sensitive tests.
Small CovarianceIO refactor.
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| return s | ||
| sacc_clone.save_fits(output_file_nm, overwrite=overwrite) |
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Hmm, not sure if not returning could break the integration with TXPipe. If you think it's better not returning it, I'd check what TXPipe is doing, otherwise I'd just add a TODO or sth like that.
In my opinion, I think it's convenient to get it returned there.
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Ah excellent call. I meant to be more careful about changing existing functionality. I will check and make sure nothing is breaking other tools.
| from tjpcov.covariance_builder import CovarianceBuilder | ||
| from scipy.linalg import block_diag | ||
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| INPUT_YML = "./tests/data/conf_covariance_builder_minimal.yaml" | ||
| OUTDIR = "tests/tmp/" | ||
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| def test_no_config_throws(): | ||
| mock_builder = MagicMock(CovarianceBuilder) |
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What magic is MagicMock doing?
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I use unittest.mock here to test the logic in the base class without needing to instantiate any concrete implementations. This is preferable because then we aren't implicitly testing something else.
MagicMock is a regular Mock that also allows you to mock dunder methods (e.g. __new__). In this case the added mocking allows us to directly call the __init__ function on the base class.
Adding markers to the pytest test configuration and marking tests that are precision sensitive or slow.
This is an incremental step to improve unit test speeds by separating unit tests (which users can run all of the time) from integration tests which can take very long. Ideally we can refactor the tests to have integration tests run nightly on github actions.
Also marking tests that depend on numerical precision as precision sensitive. By identifying these now, we can be sure the code isn't broken if scipy or numpy decides to make small modifications to their numerical precision.