# jax.numpy.take_along_axisÂ¶

jax.numpy.take_along_axis(arr, indices, axis)[source]Â¶

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

LAX-backend implementation of take_along_axis(). Original docstring below.

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.

New in version 1.15.0.

arr: ndarray (Niâ€¦, M, Nkâ€¦)

Source array

indices: ndarray (Niâ€¦, J, Nkâ€¦)

Indices to take along each 1d slice of arr. This must match the dimension of arr, but dimensions Ni and Nj only need to broadcast against 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.

out: ndarray (Niâ€¦, J, Nkâ€¦)

The indexed result.

This is equivalent to (but faster than) the following use of ndindex and 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]


take : Take along an axis, using the same indices for every 1d slice put_along_axis :

Put values into the destination array by matching 1d index and data slices

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]])