Use np.ma.is_masked() instead of isinstance(MaskedArray) in tests #2477
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…st failures are incorrect
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thank you @djkirkham this is a better test pattern, I think, asking whether the data is masked, rather than checking if it's type is 'maskedarray' the only changes here are test results which were not masked, but were of masked array type, so this is a testing pattern change only |
bjlittle
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Apr 10, 2017
| @@ -421,7 +421,7 @@ def assertDataAlmostEqual(self, data, reference_filename, **kwargs): | |||
| stats = json.load(reference_file) | |||
| self.assertEqual(stats.get('shape', []), list(data.shape)) | |||
| self.assertEqual(stats.get('masked', False), | |||
| isinstance(data, ma.MaskedArray)) | |||
| np.ma.is_masked(data)) | |||
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@djkirkham We have a soft coding standard where we use ma instead of np.ma, see here.
If it's not too much trouble, could you adopt this please, thanks.
bjlittle
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Apr 10, 2017
| ('min', np.float_(data.min())), | ||
| ('max', np.float_(data.max())), | ||
| ('shape', data.shape), | ||
| ('masked', np.ma.is_masked(data)), |
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@djkirkham Here is well ... np.ma.is_masked(data) -> ma.is_masked(data)
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Due to some changes in Numpy 1.12, some aggregator functions in np.ma return a MaskedArray instance with no mask rather than an ndarray. This causes some of our tests to fail which do an instance check for a MaskedArray.
I've modified the checking code to use np.ma.is_masked(), but this causes other tests to fail where the array being checked has always been a MaskedArray instance with no mask (thus, the reference file claims it is masked when it isn't). Commit dfa75a3 demonstrates this - see the Travis test failures. Note that because some tests contain multiple checks, not all of the checks which would fail are listed in the output.