numpy: ndarray.choose to Increase Array Size?

Here is how you can do that for that example:

import numpy as np

input_ = np.array([[0, 1],
                   [2, 3]])
subs = np.array([[[ 0,  1],
                  [ 2,  3]],
                 [[ 4,  5],
                  [ 6,  7]],
                 [[ 8,  9],
                  [10, 11]],
                 [[12, 13],
                  [14, 15]]])
res = subs[input_].transpose((0, 2, 1, 3)).reshape((4, 4))
print(res)
# [[ 0  1  4  5]
#  [ 2  3  6  7]
#  [ 8  9 12 13]
#  [10 11 14 15]]

EDIT:

A more general solution supporting more dimensions and input and substitutions with different number of dimensions:

import numpy as np

def expand_from(input_, subs):
    input_= np.asarray(input_)
    subs = np.asarray(subs)
    # Take from subs according to input
    res = subs[input_]
    # Input dimensions
    in_dims = input_.ndim
    # One dimension of subs is for indexing
    s_dims = subs.ndim - 1
    # Dimensions that correspond to each other on output
    num_matched = min(in_dims, s_dims)
    matched_dims = [(i, in_dims + i) for i in range(num_matched)]
    # Additional dimensions if there are any
    if in_dims > s_dims:
        extra_dims = list(range(num_matched, in_dims))
    else:
        extra_dims = list(range(2 * num_matched, in_dims + s_dims))
    # Dimensions order permutation
    dims_reorder = [d for m in matched_dims for d in m] + extra_dims
    # Output final shape
    res_shape = ([res.shape[d1] * res.shape[d2] for d1, d2 in matched_dims] +
                 [res.shape[d] for d in extra_dims])
    return res.transpose(dims_reorder).reshape(res_shape)

input_ = np.array([[0, 1],
                   [2, 3]])
subs = np.array([[[ 0,  1],
                  [ 2,  3]],
                 [[ 4,  5],
                  [ 6,  7]],
                 [[ 8,  9],
                  [10, 11]],
                 [[12, 13],
                  [14, 15]]])
output = expand_from(input_, subs)
print(output)
# [[ 0  1  4  5]
#  [ 2  3  6  7]
#  [ 8  9 12 13]
#  [10 11 14 15]]