[Numpy-discussion] Numpy Advanced Indexing Question
Stéfan van der Walt
Thu Jul 17 04:00:05 CDT 2008
2008/7/17 Robert Kern <firstname.lastname@example.org>:
> So the way fancy indexing interacts with slices is a bit tricky, and
> this is why we couldn't use the nicer syntax of cube[:,:,idx_k]. All
> axes with fancy indices are collected together. Their index arrays are
> broadcasted and iterated over. *For each iterate*, all of the slices
> are collected, and those sliced axes are *added* to the output array.
> If you had used fancy indexing on all of the axes, then the iterate
> would be a scalar value pulled from the original array. If you mix
> fancy indexing and slices, the iterate is the *array* formed by the
> remaining slices.
> So if idx_k is shaped (ni,nj,3), for example, cube[:,:,idx_k] will
> have the shape (ni,nj,ni,nj,3). So
> Is that clear, or am I obfuscating the subject more?
Crystal, thank you for taking the time to explain! This is such
valuable information; we should consider adding a section to
numpy.doc.indexing or wherever is more appropriate.
>> For the constant slice case, I guess idx_k also have been (1, 1, 7)?
>> The construction of the cube could probably be done using only
>> cube.flat = np.arange(nk)
> If the RHS cannot be
> broadcasted to the right shape (in this case because it is not the
> same length as the final axis of the LHS), an error is raised. I find
> the reuse of the broadcasting concept to be more memorable, and robust
> over the (mostly) ad hoc use of plain repetition with .flat.
I've become used to exploiting the repeating property of .flat, and
forgot its dangers. Thanks for the reminder!
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