问题描述
此问题在这里解决了使用numpy生成高斯内核.但是，我不了解使用kernlen和nsig的输入是什么，以及它们与通常用于描述高斯分布的均值/标准差之间的关系.
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.
如何生成用mean = (8, 10)和sigma = 3描述的2d高斯核?理想的输出将是代表高斯分布的二维数组.
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.
推荐答案
您可以使用astropy，尤其是 Gaussian2D 模型，来自/modeling/#id2"rel =" nofollow noreferrer> astropy.modeling.models 模块:
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
这篇关于给定均值和标准差，生成高斯核的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！