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
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