ENH: allow numpy integer scalars to index fileslice #903
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This is done by allow 0-dimensional numpy integers to pass. Tests are added to show identical behaviour as normal integers for canonical_slicers and fileslice (the two places where `is_fancy` is used).
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Coverage 91.70% 91.70%
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Files 96 96
Lines 12311 12311
Branches 2173 2173
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Hits 11290 11290
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effigies
approved these changes
Apr 10, 2020
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This seems sensible to me. I think the comment can be improved.
I would ask @matthew-brett to weigh in, though. I'm not sure I follow the original concerns enough to be 100% sure that this isn't going to cause a subtle issue.
Refactoring of the `ndim` check and updating the comment Co-Authored-By: Chris Markiewicz <[email protected]>
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In the absence of objection, I'll plan to merge this tomorrow for inclusion in nibabel 3.1.0. |
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This is done by allowing 0-dimensional numpy integer arrays to return true in the
is_fancyfunction.Tests are added to show identical behaviour as normal integers for canonical_slicers and fileslice (the two places where
is_fancyis used).The reason for this is that I often find myself trying to index like
dataobj[..., idx], whereidxis an numpy integer scalar, and getting afancy indexing is not allowederror. This can be easily fixed by casting the numpy integer to a python one likedataobj[..., int(idx)]. However, as the indexing rules are identical for scalar numpy integers and python built-in integers (except that the former returns a copy, while the latter returns a view), I feel this casting should not be necessary.This is distinct from #531 as this focusses just on 0-dimensional scalars and still disallows indexing with 1- or higher dimensional arrays.