When I usually need to do something like that, I just construct a tuple of slice() objects. No need to use swapaxes(). Or am I missing something?
On Sat, Feb 1, 2025 at 10:24 AM Michael Mullen <mich...@adialante.com> wrote: > Hello, > > I am writing a class handling NumPy arrays, and basing it off some > computations I have done in C++ using the Eigen library. For tensors whose > length are only known at runtime, the Eigen chip method is useful for > slicing a tensor along a specific axis while keeping all other axes the > same. I have not found a similar method in NumPy, but a simple > implementation is > > def chip(A, axis, vals): > return np.swapaxes(np.swapaxes(A, axis, -1)[..., vals], -1, axis) > > Example usage would be (to slice the 3rd axis of a 4D array): > A = np.random.randn(10,11,12,13) > B = chip(A, axis=2, vals=np.arange(0,6)) > B.shape #(10, 11, 6, 13) > > Since this may be useful for others, despite its simplicity, I thought it > may be useful to have something similar in NumPy. > > Best, > Mike > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: ben.v.r...@gmail.com >
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