import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = plt.axes(projection='3d')

u = np.linspace(0, 2*np.pi, endpoint=True, num=50)
v = np.linspace(-1, 1, endpoint=True, num=10)

u,v=np.meshgrid(u,v)
u=u.flatten()
v=v.flatten()
x = (1 + 0.5 * v * np.cos(u / 2.0)) * np.cos(u)
y = (1 + 0.5 * v * np.cos(u / 2.0)) * np.sin(u)
z = 0.5 * v * np.sin(u / 2.0)

ax.plot_trisurf(x,y,z,cmap="cool")

import matplotlib.tri as mtri
tri = mtri.Triangulation(u, v)

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(10,6))
ax = plt.axes(projection='3d')# u, v are parameterisation variables
u = np.linspace(0, 2*np.pi, endpoint=True, num=50)
v = np.linspace(-1, 1, endpoint=True, num=10)# This is the Mobius mapping, taking a u, v pair and returning an x, y, z
# triple
u,v=np.meshgrid(u,v) #用meshgrid函数来产生三维绘图时的矩阵
u=u.flatten() #把u展开，变成一维数组
v=v.flatten() #把v展开，变成一维数组
x = (1 + 0.5 * v * np.cos(u / 2.0)) * np.cos(u)
y = (1 + 0.5 * v * np.cos(u / 2.0)) * np.sin(u)
z = 0.5 * v * np.sin(u / 2.0)# Triangulate parameter space to determine the triangles
import matplotlib.tri as mtri
tri = mtri.Triangulation(u, v)# Plot the surface.  The triangles in parameter space determine which x, y, z
# points are connected by an edge.
ax.plot_trisurf(x,y,z,cmap="cool",triangles=tri.triangles)
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-1.1, 1.1)
ax.set_zlim(-1, 1)plt.show()