[FIXED] Use .take() to index multidimensional array

Issue

I have a multidimensional array of shape (n,x,y). For this example can use this array

A = array([[[ 0,  1,  2],
            [ 3,  4,  5],
            [ 6,  7,  8],
            [ 9, 10, 11]],

           [[12, 13, 14],
            [15, 16, 17],
            [18, 19, 20],
            [21, 22, 23]],

           [[24, 25, 26],
            [27, 28, 29],
            [30, 31, 32],
            [33, 34, 35]]])

I then have another multidimensional array that has index values that I want to use on the original array, A. This has shape (z,2) and the values represent row values index’s

Row_values = array([[0,1],
                    [0,2],
                    [1,2],
                    [1,3]])

So I want to use all the index values in row_values to apply to each of the three arrays in A so I end up with a final array of shape (12,2,3)

Result = ([[[0,1,2],
            [3,4,5]],
           [[0,1,2],
            [6,7,8]],
           [[3,4,5],
            [6,7,8]]
           [[3,4,5],
            [9,10,11],
           [[12,13,14],
            [15,16,17]],
           [[12,13,14],
            [18,19,20]],
           [[15,16,17],
            [18,19,20]],
           [[15,16,17],
            [21,22,23]],
           [[24,25,26],
            [27,28,29]],
           [[24,25,26],
            [30,31,32]],
           [[27,28,29],
            [30,31,32]],
           [[27,28,29],
            [33,34,35]]]

I have tried using np.take() but haven’t been able to make it work. Not sure if there’s another numpy function that is easier to use

Solution

We can advantage of NumPy's advanced indexing and using np.repeat and np.tile along with it.

cidx = np.tile(Row_values, (A.shape[0], 1))
ridx = np.repeat(np.arange(A.shape[0]), Row_values.shape[0])

out = A[ridx[:, None], cidx]
# out.shape -> (12, 2, 3)

---

Using np.take

np.take(A, Row_values, axis=1).reshape((-1, 2, 3))
# Or
A[:, Row_values].reshape((-1, 2, 3))

Output:

array([[[ 0,  1,  2],
        [ 3,  4,  5]],

       [[ 0,  1,  2],
        [ 6,  7,  8]],

       [[ 3,  4,  5],
        [ 6,  7,  8]],

       [[ 3,  4,  5],
        [ 9, 10, 11]],

       [[12, 13, 14],
        [15, 16, 17]],

       [[12, 13, 14],
        [18, 19, 20]],

       [[15, 16, 17],
        [18, 19, 20]],

       [[15, 16, 17],
        [21, 22, 23]],

       [[24, 25, 26],
        [27, 28, 29]],

       [[24, 25, 26],
        [30, 31, 32]],

       [[27, 28, 29],
        [30, 31, 32]],

       [[27, 28, 29],
        [33, 34, 35]]])

Answered By - Ch3steR