How to calculate a Gaussian kernel matrix efficiently in numpy?

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I myself used the accepted answer for my image processing, but I find it (and the other answers) too dependent on other modules. Therefore, here is my compact solution:

import numpy as np
   
def gkern(l=5, sig=1.):
    """\
    creates gaussian kernel with side length `l` and a sigma of `sig`
    """
    ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)
    gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))
    kernel = np.outer(gauss, gauss)
    return kernel / np.sum(kernel)

Edit: Changed arange to linspace to handle even side lengths

Edit: Use separability for faster computation, thank you Yves Daoust.

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