Book Image

matplotlib Plotting Cookbook

By : Alexandre Devert
Book Image

matplotlib Plotting Cookbook

By: Alexandre Devert

Overview of this book

Table of Contents (15 chapters)
matplotlib Plotting Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Plotting triangulations


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.

How to do it...

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.

The preceding script displays the following graph:

How it works...

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.