Bivariate Normal Trisurf

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# Scattered sampling
np.random.seed(11)
n_points = 600
x = np.random.uniform(-3, 3, n_points)
y = np.random.uniform(-3, 3, n_points)

# Correlated bivariate normal
rho = 0.6
sigma_x, sigma_y = 1.0, 1.2
z = (1 / (2*np.pi*sigma_x*sigma_y*np.sqrt(1-rho**2))) * np.exp(
    -1/(2*(1-rho**2)) * (x**2/sigma_x**2 - 2*rho*x*y/(sigma_x*sigma_y) + y**2/sigma_y**2)
)

# Custom colormap
colors = ['#ffffff', '#fce7f3', '#F527B0', '#4927F5', '#c4b5fd']
cmap = LinearSegmentedColormap.from_list('pink_purple', colors, N=256)

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

surf = ax.plot_trisurf(x, y, z, cmap=cmap, alpha=0.85, linewidth=0.1, edgecolor='#33333308')

ax.set_xlabel('X', fontsize=10, color='#374151', labelpad=10)
ax.set_ylabel('Y', fontsize=10, color='#374151', labelpad=10)
ax.set_zlabel('Density', fontsize=10, color='#374151', labelpad=10)
ax.set_title("Bivariate Normal Trisurf", fontsize=14, color='#1f2937', fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=30, azim=45)
plt.tight_layout()
plt.show()