Introduce indexing for 1d arrays (using an nd approach)#1
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| idx = np.atleast_1d(idx) | ||
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| # broadcast scalar to full 1d | ||
| if issparse(x): |
stefanv
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| try: | ||
| ix = next(iter(idx), None) | ||
| for _ in range(max_ndim): | ||
| if isinstance(ix, bool): | ||
| # 1st element is boolean, check the rest | ||
| idx = np.asanyarray(idx) | ||
| if idx.dtype.kind == 'b': | ||
| return idx | ||
| return None | ||
| ix = next(iter(ix), None) |
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z = np.add([[False, True, False], [False, False, False]], False, casting='equiv')
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np.lib.stride_tricks.as_strided([0], strides=(0, 0), shape=(5000, 5000)) |
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With dtype: np.lib.stride_tricks.as_strided(np.array([1], dtype=float), strides=(0, 0), shape=(50000, 50000)) |
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Turn on indexing for 1d csr_arrays!
This version rewrites
_validate_indicesfrom scratch using an N-D approachadds 1-d sections to getitem and setitem dunders
rewrites _validate_indices with an N-d approach.
Reduce helper functions to one
_compatible_boolean_arraywhich in-lines its helper functions.shorter, more readable, cleaner
also computes the shape of the output of this indexing.
passes all tests
adds 1d test for indexing (and passes those also)
__getitem__and__setitem__are still in the 1d/2d paradigm. Changing those will require changing the methods_get_intXsliceand friends. Probably a rewrite of that is needed for N-D. When to do that?This builds on the
csr-1dbranch, but should work withdok-1dbranch after turning on tests for it.And it should work for the 2d-arrays and matrix classes directly off of main (though test_indexing1d.py won't pass obviously).