I think that my issue should be really simple, yet I can not find any help on the Internet whatsoever. I am very new to Python, so it is possible that I am missing somethin
sigma.resize() returns None because it operates in-place. np.resize(sigma, shape), on the other hand, returns the result but instead of padding with zeros, it pads with repeats of the array.
Also, the shape() function returns the shape of the input. If you just want to predefine a shape, just use a tuple.
import numpy as np
...
shape = (6, 6) #This will be some pre-determined size
sigma = np.diag(S) #diagonalise the matrix - this works
sigma.resize(shape) #Resize the matrix and fill with zeros
However, this will first flatten out your original array, and then reconstruct it into the given shape, destroying the original ordering. If you just want to "pad" with zeros, instead of using resize() you can just directly index into a generated zero-matrix.
# This assumes that you have a 2-dimensional array
zeros = np.zeros(shape, dtype=np.int32)
zeros[:sigma.shape[0], :sigma.shape[1]] = sigma