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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 <michael@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-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: ben.v.root@gmail.com