How to plot a 3D Surface Triangulation Plot in Python?

In this tutorial, we’ll try to understand how to plot a 3D surface triangulation plot in Python using the matplotlib library.

What is a Surface Triangulation Plot?

A triangulation of a compact surface is a finite collection of triangles that cover the surface in such a way that every point on the surface is in a triangle, and the intersection of any two triangles is either void, a common edge, or a common vertex. A triangulated surface is called a tri-surface.

To plot a surface triangulation plot in Python, use the matplotlib Axes.plot_trisurf method.

Using the matplotlib.Axes.plot_trisurf() method

To plot a 3D surface triangulation plot, use the following steps –

1. Create a matplotlib Axes object with a 3D project.
2. For the above axes above, plot the surface triangulation plot using the plot_trisurf() method.

The following is the syntax –

BasicSyntax:

Axes.plot_trisurf(*args, color=None, norm=None, vmin=None, vmax=None, lightsource=None, **kwargs)

The (optional) triangulation can be specified in one of two ways; either:

plot_trisurf(triangulation, ...)

where triangulation is a Triangulation object, or:

plot_trisurf(X, Y, ...)
plot_trisurf(X, Y, triangles, ...)
plot_trisurf(X, Y, triangles=triangles, ...)

Parameters:

• X, Y, Z: Data values as 1D arrays.
• color: Color of the surface patches.

For more details about the parameters refer this.

Now let us understand the above method with some examples.

Example 1 – 3D surface triangulation plot

import numpy as np
import matplotlib.pyplot as plt

# Defining variables
z = np.linspace(-10000, 10000, 200)
x = np.sin(z)
y = np.cos(z)

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# Creating plot
ax.plot_trisurf(x, y, z)
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.

Example 2 – 3D surface triangulation plot along with colorbar

import numpy as np
import matplotlib.pyplot as plt

# Defining variables
# Creating radii and angles
r = np.linspace(0.125, 1.0, 100) 
a = np.linspace(0, 2 * np.pi,
                100,
                endpoint = False) 

# Repeating all angles for every radius 
a = np.repeat(a[..., np.newaxis], 100, axis = 1) 

# Creating dataset
x = np.append(0, (r * np.cos(a))) 
y = np.append(0, (r * np.sin(a)))   
z = (np.sin(x ** 4) + np.cos(y ** 4))

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# plot the 3D Surface triangulation
trisurf=ax.plot_trisurf(x, y, z,cmap='hot')
fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 5)
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.
• Add the colorabar usinf fig.colorbar(refer this).

Example 3 – 3D surface triangulation plot

import numpy as np
import matplotlib.pyplot as plt

# Defining variables
radii = np.linspace(0.125, 1.0, 60)
angles = np.linspace(0, 2*np.pi, 60, endpoint=False)
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())
z = np.sin(-x*y)

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# Creating plot
trisurf=ax.plot_trisurf(x, y, z,cmap='hot', linewidth=0.2, antialiased=True)
fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 5)
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.
• Add the colorabar usinf fig.colorbar(refer this).

Example 4 – 3D surface triangulation by setting triangles parameter

import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay

# Defining variables
radii = np.linspace(0.125, 1.0, 60)
angles = np.linspace(0, 2*np.pi, 60, endpoint=False)
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())
z = np.sin(-x*y)

triangle = [[x[i],y[i]] for i in range(len(x))]

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# Creating plot
trisurf=ax.plot_trisurf(x, y, z,cmap='hot',triangles=triangle)
fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 5)
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.
• Add the colorabar usinf fig.colorbar(refer this).
• To understand more about triangulation, refer this.
• To understand more about Delaunay triangulation, refer this.

Example 5 – Sphere using surface triangulation

import numpy as np
import matplotlib.pyplot as plt

from scipy.spatial import Delaunay

# Defining variables
a = np.linspace(0, 2 * np.pi,25)
b = np.linspace(0, np.pi, 25)
[X, Y] = np.meshgrid(a, b)
x = np.outer(np.cos(a), np.sin(b))
y = np.outer(np.sin(a), np.sin(b))
z = np.outer(np.ones_like(a), np.cos(b))

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# Creating plot
ax.plot_trisurf(x.flatten(), y.flatten(), z.flatten(),
                triangles=Delaunay(np.array([X.flatten(), Y.flatten()]).T).simplices,
                cmap='winter')
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot.
  • List a and b contains 25 evenly spaced number generated using numpy.linspace().
  • Then create a mesh grid of a and b using numpy.meshgrid().
  • List x, y, and z is the outer product of the vectors (cos(a) and sin(b)) , (sin(a) and sin(b)) and (array of ones and cos(b)) generated using numpy.outer() and numpy.ones()
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.

Example 6 – Ellipsoid using surface triangulation

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

from scipy.spatial import Delaunay

# Defining variables
#covariance matrix
A = np.array([
    [2.2, 0.4,  0.2],
    [0.4, 2.8, 0.3],
    [0.2,  0.3, 2.4]
])

a = np.linspace(0, 2 * np.pi, 25)
b = np.linspace(0, np.pi, 25)
[X, Y] = np.meshgrid(a, b)

x = np.outer(np.cos(a), np.sin(b))
y = np.outer(np.sin(a), np.sin(b))
z = np.outer(np.ones_like(a), np.cos(b))

E1 = np.zeros((25, 25))
E2 = np.zeros((25, 25))
E3 = np.zeros((25, 25))
for i in range(25):
        for j in range(25):
            [E1[i, j], E2[i, j], E3[i, j]] = A @ np.array([x[i, j], y[i, j], z[i, j]])

# Creating figure and 3D axes
fig = plt.figure(figsize =(9, 5))
ax = plt.axes(projection ='3d')

# Creating plot
ax.plot_trisurf(
        E2.flatten(), E3.flatten(), E1.flatten(),
        triangles=Delaunay(np.array([X.flatten(), Y.flatten()]).T).simplices, 
        cmap='summer')

# fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 5)
plt.show()

Output:

The steps followed in the above example are:

• Import required modules
• Define the variables to plot
  • List a and b contains 25 evenly spaced number generated using numpy.linspace().
  • List x,y, and z is the outer product of the vectors (cos(a) and sin(b)), (sin(a) and sin(b)), and (array of ones and cos(b)) respectively as these arrays are multiplied by covariance matrix A to get the coordinate for plotting the ellipsoid.
• Generate the figure and 3D axes
• Plot the 3D surface triangulation plot using Axes.plot_trisurf.

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