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take_along_axis(arr, indices, axis)

This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to look up values in the latter. These slices can be different lengths.

Functions returning an index along an axis, like argsort and argpartition , produce suitable indices for this function.

versionadded

Notes

This is equivalent to (but faster than) the following use of ndindex and :None:None:`s_`, which sets each of ii and kk to a tuple of indices:

Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
J = indices.shape[axis]  # Need not equal M
out = np.empty(Ni + (J,) + Nk)

for ii in ndindex(Ni):
    for kk in ndindex(Nk):
        a_1d       = a      [ii + s_[:,] + kk]
        indices_1d = indices[ii + s_[:,] + kk]
        out_1d     = out    [ii + s_[:,] + kk]
        for j in range(J):
            out_1d[j] = a_1d[indices_1d[j]]

Equivalently, eliminating the inner loop, the last two lines would be:

out_1d[:] = a_1d[indices_1d]

Parameters

arr : ndarray (Ni..., M, Nk...): Source array
indices : ndarray (Ni..., J, Nk...): Indices to take along each 1d slice of :None:None:`arr`. This must match the dimension of arr, but dimensions Ni and Nj only need to broadcast against :None:None:`arr`.
axis : int: The axis to take 1d slices along. If axis is None, the input array is treated as if it had first been flattened to 1d, for consistency with sort and argsort .

Returns

out: ndarray (Ni..., J, Nk...): The indexed result.

Take values from the input array by matching 1d index and data slices.

Examples

For this sample array

>>> a = np.array([[10, 30, 20], [60, 40, 50]])

We can sort either by using sort directly, or argsort and this function

>>> np.sort(a, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])

>>> ai = np.argsort(a, axis=1); ai
array([[0, 2, 1],
       [1, 2, 0]])

>>> np.take_along_axis(a, ai, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])

The same works for max and min, if you expand the dimensions:

>>> np.expand_dims(np.max(a, axis=1), axis=1)
array([[30],
       [60]])

>>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
... ai
array([[1],
       [0]])

>>> np.take_along_axis(a, ai, axis=1)
array([[30],
       [60]])

If we want to get the max and min at the same time, we can stack the indices first

>>> ai_min = np.expand_dims(np.argmin(a, axis=1), axis=1)
... ai_max = np.expand_dims(np.argmax(a, axis=1), axis=1)
... ai = np.concatenate([ai_min, ai_max], axis=1)
... ai
array([[0, 1],
       [1, 0]])

>>> np.take_along_axis(a, ai, axis=1)
array([[10, 30],
       [40, 60]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.take dask.array.routines.choose numpy.argmax numpy.argpartition numpy.put_along_axis numpy.argmin numpy.choose numpy.argsort

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