How to "scale" a numpy array?

scipy.misc.imresize can scale images. It can be used to scale numpy arrays, too:

#!/usr/bin/env python

import numpy as np
import scipy.misc

def scale_array(x, new_size):
    min_el = np.min(x)
    max_el = np.max(x)
    y = scipy.misc.imresize(x, new_size, mode='L', interp='nearest')
    y = y / 255 * (max_el - min_el) + min_el
    return y

x = np.array([[1, 1],
              [0, 1]])
n = 2
new_size = n * np.array(x.shape)
y = scale_array(x, new_size)
print(y)

You could use repeat:

In [6]: a.repeat(2,axis=0).repeat(2,axis=1)
Out[6]: 
array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [0, 0, 1, 1],
       [0, 0, 1, 1]])

I am not sure if there's a neat way to combine the two operations into one.

You should use the Kronecker product, numpy.kron:

Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first

import numpy as np
a = np.array([[1, 1],
              [0, 1]])
n = 2
np.kron(a, np.ones((n,n)))

which gives what you want:

array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [0, 0, 1, 1],
       [0, 0, 1, 1]])