Question

Possible Duplicate:
how to repeat along two axis

Let’s suppose we have the following matrix/image:

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

What I’d like to get is a 9×9 matrix that is a 3x magnified version of the above, having 3×3 ones in the top left corner, 3×3 0s in the middle top, etc.

The things I’ve already tried are:

scipy.ndimage.interpolation.zoom(x, 3, order=(anything)), for example order=0 returns this:

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

scipy.misc.imresize(x, (9,9), interp=”nearest”) (effectively from PIL), that comes up with a different creative (but wrong) solution.

Meanwhile, the MATLAB imresize solves the problem perfectly…

Any ideas? (note: all of these solutions should work, so before submitting, try it out :))

Answer 1

Kronecker product:

numpy.kron(x,numpy.ones((3,3)))

the result:

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