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Status: To Do
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Resolution: Unresolved
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Fix Version/s: None
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Component/s: utils
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Description
lsst.utils.assertFloatsAlmostEqual cannot handle lists of floats. Thus it cannot handle values returned from C++ as std::vector. Similarly for tuples of floats, lists of lists of floats, etc., though those may be rarer.
The usual workaround is to use self.assertTrue(numpy.allclose(arr1, arr2)) but it would be nice not to have to do that.
Also please consider adding a equal_nan flag like numpy.allclose. In both cases it is worth considering using numpy allclose as the code that performs the test.
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Disappointing to hear that numpy.testing uses number of decimals instead of relative/absolute tolerance. Otherwise I'd have been on board with migrating in that direction.
An ignoreNaNs option was added on DM-29370; I'm not sure if that's exactly what was requested here, but I imagine it's close enough. Sounds like there's at least a small amount of work to do here to support non-array sequence arguments, and that's still desired.
Maybe I am misunderstanding, but numpy.testing.assert_allclose offers absolute and relative tolerance parameters, as does numpy.allclose (though I default values are different). I don't even see an option for specifying the number of decimal points in numpy.testing.assert_allclose. Here is the documentation I found: https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_allclose.html#numpy-testing-assert-allclose
I have been burned by assert_allclose's default value of atol=0. This means that arrays that should be nearly zero can fail validation for perfectly reasonable values. But of course you have to know what a reasonable variation is in order to set a good non-zero value of atol. I always use assert_allclose if I can, but I also try to remember to set atol.
I very specifically referred to "numpy.testing.assert_array_almost_equal": https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_array_almost_equal.html
numpy's allclose defaults to rtol=1e-7, which is not very tight.
John Parejko I was answering Jim Bosch statement "numpy.testing uses number of decimals instead of relative/absolute tolerance". I think that statement is too broad.
In any case the docs you point to explicitly say that assert_arrays_almost_equal should be avoided in favor of assert_allclose or two other options I have never heard of before.
Note that numpy.allclose and numpy.testing.assert_allclose are different (though very similar) functions, with different default tolerance values. Perhaps it is best to always specify the tolerances explicitly.
As Jim says above, I don't think we want to advocate using the numpy.allclose or numpy.testing.assert_array_almost_equal, because they both have a much less stringent default tolerance, and the numpy.testing modules defines things completely differently (number of decimals, instead of relative/absolute tolerance).