Here’s the extension to handle generic ndarrays –
def indices_merged_arr_generic(arr, arr_pos="last"):
n = arr.ndim
grid = np.ogrid[tuple(map(slice, arr.shape))]
out = np.empty(arr.shape + (n+1,), dtype=np.result_type(arr.dtype, int))
if arr_pos=="first":
offset = 1
elif arr_pos=="last":
offset = 0
else:
raise Exception("Invalid arr_pos")
for i in range(n):
out[...,i+offset] = grid[i]
out[...,-1+offset] = arr
out.shape = (-1,n+1)
return out
Sample runs
2D case :
In [252]: arr
Out[252]:
array([[37, 32, 73],
[95, 80, 97]])
In [253]: indices_merged_arr_generic(arr)
Out[253]:
array([[ 0, 0, 37],
[ 0, 1, 32],
[ 0, 2, 73],
[ 1, 0, 95],
[ 1, 1, 80],
[ 1, 2, 97]])
In [254]: indices_merged_arr_generic(arr, arr_pos="first")
Out[254]:
array([[37, 0, 0],
[32, 0, 1],
[73, 0, 2],
[95, 1, 0],
[80, 1, 1],
[97, 1, 2]])
3D case :
In [226]: arr
Out[226]:
array([[[35, 45, 33],
[48, 38, 20],
[69, 31, 90]],
[[73, 65, 73],
[27, 51, 45],
[89, 50, 74]]])
In [227]: indices_merged_arr_generic(arr)
Out[227]:
array([[ 0, 0, 0, 35],
[ 0, 0, 1, 45],
[ 0, 0, 2, 33],
[ 0, 1, 0, 48],
[ 0, 1, 1, 38],
[ 0, 1, 2, 20],
[ 0, 2, 0, 69],
[ 0, 2, 1, 31],
[ 0, 2, 2, 90],
[ 1, 0, 0, 73],
[ 1, 0, 1, 65],
[ 1, 0, 2, 73],
[ 1, 1, 0, 27],
[ 1, 1, 1, 51],
[ 1, 1, 2, 45],
[ 1, 2, 0, 89],
[ 1, 2, 1, 50],
[ 1, 2, 2, 74]])