在 3D plot python 中绘制平面上的 1D 高斯分布

Question

我有以下代码和它生成的图。我的目标是在第二个图（右）上绘制红色平面上的一维高斯分布。这样做的目的是表明重叠（表示条件）是高斯分布。我对正确的分布的确切方差不感兴趣，而只是直观地显示这一点。在python中有没有直接的方法来做到这一点？谢谢，Pimport numpy as npimport matplotlib.pyplot as pltfrom matplotlib.mlab import bivariate_normalfrom mpl_toolkits.mplot3d import Axes3D#Make a 3D plotfig = plt.figure(figsize=plt.figaspect(0.5))################ First Plot ###############Parameters to setmu_x = 0sigma_x = np.sqrt(5)mu_y = 0sigma_y = np.sqrt(5)#Create grid and multivariate normalx = 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)# Create planex_p = 2y_p = np.linspace(-10,10,500)z_p = np.linspace(0,0.02,500)Y_p, Z_p = np.meshgrid(y_p, z_p)# ax = fig.gca(projection='3d')ax = fig.add_subplot(1,2,1, projection='3d')ax.plot_surface(X, Y, Z, cmap='viridis',linewidth=0)ax.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.5)plt.tight_layout()################ Second Plot ##############x_p = 2y_p = np.linspace(-10,10,500)z_p = np.linspace(0,0.02,500)Y_p, Z_p = np.meshgrid(y_p, z_p)# ax2 = fig.gca(projection='3d')ax2 = fig.add_subplot(1,2,2,projection='3d')ax2.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.3)plt.show()

Answer 1

例如，您可以尝试使用np.where获取与计划X公差范围内的最近坐标，然后使用生成的索引作为掩码来选择对应的和值。这将导致您进入以下代码：tolx_p = 2idx_x_pYZ

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.mlab import bivariate_normal

from mpl_toolkits.mplot3d import Axes3D

#Parameters to set for Gaussian distribution

mu_x = 0

sigma_x = np.sqrt(5)

mu_y = 0

sigma_y = np.sqrt(5)

#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)

# Create plane

x_p = 2

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

z_p = np.linspace(0,0.02,500)

Y_p, Z_p = np.meshgrid(y_p, z_p)

# Finding closest idx values of X mesh to x_p

tol = 1e-4

idx_x_p = (np.where(x < x_p+tol) and np.where(x > x_p-tol))[0][0]

# Select the corresponding values of X, Y, Z (carefully switch X and Y)

x_c, y_c, z_c = Y[idx_x_p], X[idx_x_p], Z[idx_x_p]

# Plot

fig = plt.figure(figsize=plt.figaspect(0.5))

ax = fig.add_subplot(1, 1, 1, projection='3d')

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

ax.plot_surface(x_p, Y_p, Z_p, color='r',linewidth=0, alpha=0.5,zorder=5)

ax.plot(x_c,y_c,z_c,zorder=10)

plt.tight_layout()

x_p它显示了不同值的高斯形重叠。让我们说x_p in np.linspace(-10,10,20)：