simple numpy based 2d gaussian function

gistfile1.py

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)

Does this formula reflect the 2D gaussian here

How can this code be modified to make the gaussian asymmetric?

TE AMO

this is not a standard gaussian function as you do not take into account when off diagonal elms are non zero

I think that the return must be:

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

without the part: "4*np.log(2)"

According the equation. See: https://en.wikipedia.org/wiki/Gaussian_function

Does this formula reflect the 2D gaussian here

How can this code be modified to make the gaussian asymmetric?

Try this gist