How to generate 2D gaussian with Python?

Question

I can generate Gaussian data with random.gauss(mu, sigma) function, but how can I generate 2D gaussian? Is there any function like that?

Answer 1

I'd like to add an approximation using exponential functions. This directly generates a 2d matrix which contains a movable, symmetric 2d gaussian.

I should note that I found this code on the scipy mailing list archives and modified it a little.

import numpy as np

def makeGaussian(size, fwhm = 3, center=None):
    """ Make a square gaussian kernel.

    size is the length of a side of the square
    fwhm is full-width-half-maximum, which
    can be thought of as an effective radius.
    """

    x = np.arange(0, size, 1, float)
    y = x[:,np.newaxis]

    if center is None:
        x0 = y0 = size // 2
    else:
        x0 = center[0]
        y0 = center[1]

    return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)

For reference and enhancements, it is hosted as a gist here. Pull requests welcome!