如何在Python中高效地计算两个高斯分布的热图？

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal

# 创建第一个高斯分布
mean1 = [0, 0]
cov1 = [[1, 0], [0, 1]]
gaussian1 = multivariate_normal(mean=mean1, cov=cov1)

# 创建第二个高斯分布
mean2 = [2, 2]
cov2 = [[1, 0], [0, 1]]
gaussian2 = multivariate_normal(mean=mean2, cov=cov2)

x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)

Z = gaussian1.pdf(np.dstack((X, Y))) + gaussian2.pdf(np.dstack((X, Y)))

plt.contourf(X, Y, Z, cmap='hot')
plt.colorbar()
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Gaussian Heatmap')
plt.show()