Functions

def gaussian_kernel(X, Y, sigma=1.0)

Get gaussian kernal for every point in array X to every point in array Y. If X/Y are the same, then this will just smooth array X. If X/Y are different, can be used to smooth one onto the other.

Parameters

X : numpy.ndarray

First array to compute gaussian kernel for

Y : numpy.ndarray

Second array to compute gaussian kernel for

sigma : float, optional

Standard deviation (sigma) for gaussian kernel, by default 1.

normalize: bool, optional

Whether or not to normalize the scalar values. Normalizing will ensure

that each point in x is a weighted sum of all points in Y with those weightings

totalling 1.0. Therefore,

Returns

numpy.ndarray

Array that can be multiple by scalar values to smooth them. Smoothing can be done inherently, or from one surface onto another.

Notes

https://math.stackexchange.com/questions/434629/3-d-generalization-of-the-gaussian-point-spread-function