numpy 3d to 2d transformation based on 2d mask array

Question

If I have an ndarray like this:

>>> a = np.arange(27).reshape(3,3,3)
>>> a
array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]         


        

Answer 1

Here is some magic numpy indexing that will do what you want, but unfortunately it's pretty unreadable.

def apply_mask(a, indices, axis):
    magic_index = [np.arange(i) for i in indices.shape]
    magic_index = np.ix_(*magic_index)
    magic_index = magic_index[:axis] + (indices,) + magic_index[axis:]
    return a[magic_index]

or equally unreadable:

def apply_mask(a, indices, axis):
    magic_index = np.ogrid[tuple(slice(i) for i in indices.shape)]
    magic_index.insert(axis, indices)
    return a[magic_index]

Answer 2

I use index_at() to create the full index:

import numpy as np

def index_at(idx, shape, axis=-1):
    if axis<0:
        axis += len(shape)
    shape = shape[:axis] + shape[axis+1:]
    index = list(np.ix_(*[np.arange(n) for n in shape]))
    index.insert(axis, idx)
    return tuple(index)

a = np.random.randint(0, 10, (3, 4, 5))

axis = 1
idx = np.argmax(a, axis=axis)
print a[index_at(idx, a.shape, axis=axis)]
print np.max(a, axis=axis)