Triangulations arise when dealing with spatial locations. Apart from showing distances between points and neighborhood relationships, triangulation plots can be a convenient way to represent maps. matplotlib provides a fair amount of support for triangulations.
As in the preceding examples, the following few lines of code are enough:
import numpy as np import matplotlib.pyplot as plt import matplotlib.tri as tri data = np.random.rand(100, 2) triangles = tri.Triangulation(data[:,0], data[:,1]) plt.triplot(triangles) plt.show()
Every time the script is run, you will see a different triangulation as the cloud of points that is triangulated is generated randomly.
We import the matplotlib.tri
module, which provides helper functions to compute triangulations from points. In this example, for demonstration purpose, we generate a random cloud of points using the following code:
data = np.random.rand(100, 2)
We compute a triangulation and store it in the triangles' variable with the help of the following code:
triangles = tri.Triangulation(data[:,0], data[:,1])
The pyplot.triplot()
function simply takes triangles as inputs and displays the triangulation result.