How to plot 3d gaussian distribution with matplotlib?

This is based on documentation of mpl_toolkits and an answer on SO based on scipy multinormal pdf:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

import numpy as np
from scipy.stats import multivariate_normal

x, y = np.mgrid[-1.0:1.0:30j, -1.0:1.0:30j]

# Need an (N, 2) array of (x, y) pairs.
xy = np.column_stack([x.flat, y.flat])

mu = np.array([0.0, 0.0])

sigma = np.array([.5, .5])
covariance = np.diag(sigma**2)

z = multivariate_normal.pdf(xy, mean=mu, cov=covariance)

# Reshape back to a (30, 30) grid.
z = z.reshape(x.shape)





fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')



ax.plot_surface(x,y,z)
#ax.plot_wireframe(x,y,z)

plt.show()

reference:-

1. Generating 3D Gaussian distribution in Python

2. https://matplotlib.org/tutorials/toolkits/mplot3d.html#sphx-glr-tutorials-toolkits-mplot3d-py

Another example without using scipi:

import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes(projection="3d")

mesh_size=100
max=mesh_size**2
xyz = np.zeros((3,max))
nx, ny = (mesh_size,mesh_size)
x = np.linspace(-1, 1, nx)
y = np.linspace(-1, 1, ny)
xv, yv = np.meshgrid(x, y)
xyz[0]=xv.reshape(max,)
xyz[1]=yv.reshape(max,)
xyz[2]=np.exp(-(xyz[0]**2+xyz[1]**2))

ax.scatter(xyz[0],xyz[1],xyz[2])
ax.set_title("3D Plot")

plt.show()