给定均值和标准差，生成高斯核

Question

This question here addresses how to generate a Gaussian kernel using numpy. However I do not understand what the inputs used kernlen and nsig are and how they relate to the mean/standard deviation usually used to describe a Gaussian distribtion.

How would I generate a 2d Gaussian kernel described by, say mean = (8, 10) and sigma = 3 ? The ideal output would be a 2-dimensional array representing the Gaussian distribution.

Answer 1

You could use astropy , especially the Gaussian2D model from the astropy.modeling.models module:

from astropy.modeling.models import Gaussian2D

g2d = Gaussian2D(x_mean=8, y_mean=10, x_stddev=3, y_stddev=3)  # specify properties

g2d(*np.mgrid[0:100, 0:100])  # specify the grid for the array