Moving from Mayavi to Matplotlib

import numpy as np
np.set_printoptions(precision=3, suppress=True)
import pylab
import matplotlib.pyplot as plt
%matplotlib inline

from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)
ax.clabel(cset, fontsize=9, inline=1)

X, Y, Z

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np

n_angles = 36
n_radii = 8

# An array of radii
# Does not include radius r=0, this is to eliminate duplicate points
radii = np.linspace(0.125, 1.0, n_radii)

# An array of angles
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)

# Repeat all angles for each radius
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)

# Convert polar (radii, angles) coords to cartesian (x, y) coords
# (0, 0) is added here. There are no duplicate points in the (x, y) plane
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())

# Pringle surface
z = np.sin(-x*y)

fig = plt.figure()
ax = fig.gca(projection='3d')

ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2, alpha=.6)

"""
Contour plots of unstructured triangular grids.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.tri as tri
import numpy as np
import math

# First create the x and y coordinates of the points.
n_angles = 48
n_radii = 8
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)

angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)
angles[:,1::2] += math.pi/n_angles

x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
z = (np.cos(radii)*np.cos(angles*3.0)).flatten()

# Create a custom triangulation
triang = tri.Triangulation(x, y)

# Mask off unwanted triangles.
#xmid = x[triang.triangles].mean(axis=1)
#ymid = y[triang.triangles].mean(axis=1)
#mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
#triang.set_mask(mask)

plt.figure()
plt.gca(projection='3d')
plt.tricontour(triang, z)

from __future__ import print_function
"""
A very simple 'animation' of a 3D plot
"""
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import time

def generate(X, Y, phi):
    R = 1 - np.sqrt(X**2 + Y**2)
    return np.cos(2 * np.pi * X + phi) * R

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

xs = np.linspace(-1, 1, 50)
ys = np.linspace(-1, 1, 50)
X, Y = np.meshgrid(xs, ys)
Z = generate(X, Y, 0.0)

wframe = None
tstart = time.time()
for phi in np.linspace(0, 360 / 2 / np.pi, 100):

    oldcol = wframe

    Z = generate(X, Y, phi)
    wframe = ax.plot_wireframe(X, Y, Z, rstride=2, cstride=2)

    # Remove old line collection before drawing
    if oldcol is not None:
        ax.collections.remove(oldcol)

    plt.pause(.001)

print ('FPS: %f' % (100 / (time.time() - tstart)))