# numpy.take_along_axis

numpy.take_along_axis(arr, indices, axis)

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

  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.

  Parameters
    arr ndarray (Ni…, M, Nk…)

      Source array

    indices ndarray (Ni…, J, Nk…)

      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`.

  Returns
    out: ndarray (Ni…, J, Nk…)

      The indexed result.

  See also

  `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

  Notes

  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]

  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]])

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