听起来你要找的是一个Multivariate Normal Distribution。这在scipy中实现为scipy.stats.multivariate_normal。重要的是要记住，你要传递一个协方差矩阵给函数。所以为了简单起见，将非对角元素保留为零：[X variance , 0 ]

[ 0 ,Y Variance]

下面是一个使用此函数并生成结果分布的三维绘图的示例。我添加了colormap以使查看曲线更容易，但可以随意删除它。import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import multivariate_normal

from mpl_toolkits.mplot3d import Axes3D

#Parameters to set

mu_x = 0

variance_x = 3

mu_y = 0

variance_y = 15

#Create grid and multivariate normal

x = np.linspace(-10,10,500)

y = np.linspace(-10,10,500)

X, Y = np.meshgrid(x,y)

pos = np.empty(X.shape + (2,))

pos[:, :, 0] = X; pos[:, :, 1] = Y

rv = multivariate_normal([mu_x, mu_y], [[variance_x, 0], [0, variance_y]])

#Make a 3D plot

fig = plt.figure()

ax = fig.gca(projection='3d')

ax.plot_surface(X, Y, rv.pdf(pos),cmap='viridis',linewidth=0)

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

plt.show()

给你这个情节：

Matplotlib v2.2不赞成使用下面的编辑，并将在v3.1中删除

通过matplotlib.mlab.bivariate_normal提供更简单的版本

它接受以下参数，因此不必担心矩阵

matplotlib.mlab.bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0, mux=0.0, muy=0.0, sigmaxy=0.0)

这里X和Y再次是网格网格的结果，因此使用它重新创建上面的绘图：import numpy as np

import matplotlib.pyplot as plt

from matplotlib.mlab import bivariate_normal

from mpl_toolkits.mplot3d import Axes3D

#Parameters to set

mu_x = 0

sigma_x = np.sqrt(3)

mu_y = 0

sigma_y = np.sqrt(15)

#Create grid and multivariate normal

x = np.linspace(-10,10,500)

y = np.linspace(-10,10,500)

X, Y = np.meshgrid(x,y)

Z = bivariate_normal(X,Y,sigma_x,sigma_y,mu_x,mu_y)

#Make a 3D plot

fig = plt.figure()

ax = fig.gca(projection='3d')

ax.plot_surface(X, Y, Z,cmap='viridis',linewidth=0)

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

plt.show()

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