Concatenate two numpy arrays in the 4th dimension

Question

I have two numpy arrays with three dimensions (3 x 4 x 5) and I want to concatenate them so the result has four dimensions (3 x 4 x 5 x 2). In Matlab, this can be done with

Answer 1

Here you go:

import numpy as np
a = np.ones((3,4,5))
b = np.ones((3,4,5))
c = np.concatenate((a[...,np.newaxis],b[...,np.newaxis]),axis=3)

Answer 2

It's not necessarily the most elegant, but I've used variations of

c = rollaxis(array([a,b]), 0, 4)

in the past.

Answer 3

The accepted answer above is great. But I'll add the following because I'm a math dork and it's a nice use of the fact that a.shape is a.T.shape[::-1]...i.e. taking a transpose reverses the order of the indices of a numpy array. So if you have your building blocks in an array called blocks, then the solution above is:

new = np.concatenate([block[..., np.newaxis] for block in blocks],
                     axis=len(blocks[0].shape))

but you could also do

new2 = np.array([block.T for block in blocks]).T

which I think reads more cleanly. It's worth noting that the already-accepted answer runs more quickly:

%%timeit
new = np.concatenate([block[..., np.newaxis] for block in blocks],
                     axis=len(blocks[0].shape))
1000 loops, best of 3: 321 µs per loop

while

%%timeit
new2 = np.array([block.T for block in blocks]).T
1000 loops, best of 3: 407 µs per loop

Answer 4

What about

c = np.stack((a,b), axis=3)

Answer 5

How about the following:

c = concatenate((a[:,:,:,None],b[:,:,:,None]), axis=3)

This gives a (3 x 4 x 5 x 2) array, which I believe is laid out in the manner you require.

Here, None is synonymous to np.newaxis: Numpy: Should I use newaxis or None?

edit As suggested by @Joe Kington, the code could be cleaned up a little bit by using an ellipsis:

c = concatenate((a[...,None],b[...,None]), axis=3)

Answer 6

This works for me:

 c = numpy.array([a,b])

Though it would be nice if it worked your way, too.