DM-11620: Numerical problem with Gaussian test in meas_base with pytest #94
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timj
reviewed
Aug 28, 2017
tests/test_PsfFlux.py
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| @@ -126,20 +129,21 @@ def testMonteCarlo(self): | |||
| fluxSigmas = [] | |||
| nSamples = 1000 | |||
| for repeat in range(nSamples): | |||
| exposure, catalog = self.dataset.realize(noise*flux, schema) | |||
| exposure, catalog = self.dataset.realize(noise*flux, schema, repeat) | |||
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maybe this should have the same comment that is found in the similar code in test_GaussianFlux.py? Also say randomSeed=repeat to make it obvious.
timj
reviewed
Aug 28, 2017
tests/test_SdssCentroid.py
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| @@ -96,7 +95,7 @@ def testMonteCarlo(self): | |||
| ySigmaList = [] | |||
| nSamples = 1000 | |||
| for repeat in range(nSamples): | |||
| exposure, catalog = self.dataset.realize(noise*flux, schema) | |||
| exposure, catalog = self.dataset.realize(noise*flux, schema, randomSeed=repeat) | |||
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Add a comment as for the other two times?
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timj
approved these changes
Aug 29, 2017
Rather than relying on the state of the RNG, we always use a local random state and seed it explicitly. This should make us robust against test failures due to "polluted" randomness.
pytest looks for classes starting with "Test" and will complain about being unable to find any tests. Renaming the class on import avoids this problem.
Some tests here depend on the noise realization in the test data or from the numpy random number generator, so we seed all dataset realizations explicitly. For the current test data and seed value of the random number seed (chosen solely based on the "test passes" criterion), they pass. This may no longer be true if the test data is regenerated or the seed value is changed.
Random seeding is now done explicitly in the unittests, so this is no longer used.
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