Simulate numpy vectorized function on a meshgrid

Question

This is the example given on how to use numpy.meshgrid

x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)

What if I have a meshgrid like xx,yy above, but my function is a regular function that isn't vectorized, f(x,y), for example, the regular math.sin function ?

I know I can loop through the list of lists of xx,yy, but I want to try to simulate vectorized code.

Answer 1

If you don't care the speed, you can use numpy.vectorize():

import numpy as np
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)

import math
def f(x, y):
    return math.sin(x**2 + y**2) / (x**2 + y**2)

np.allclose(np.vectorize(f)(xx, yy), z)