Is it possible to plot a Voronoi tessellation directly from a previous Delaunay triangulation?

I have a small Python code plotting a small Delaunay triangulation:

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

points = np.array([[0, 0], 
                   [1, 0], 
                   [0.5, 0.5],
                   [1 , 0.5]])
tri = Delaunay(points) 
fig, ax = plt.subplots(figsize=(8, 4))
ax.set_aspect('equal', 'box')
plt.triplot(points[:,0], points[:,1], tri.simplices.copy()) 
plt.plot(points[:,0], points[:,1], 'o') 
plt.show() 

Do you know if it’s now possible in Python to directly superimpose on the previous plot the corresponding Voronoi tessellation?

I am using Python ‘3.9.7’ and a Matplotlib ‘3.8.4’

>Solution :

You can use the delauney_plot_2d and voronoi_plot_2d helper functions and just pass the Matplotlib axes object to them both. E.g.,

import numpy as np 
import matplotlib.pyplot as plt 
from scipy.spatial import Delaunay, Voronoi, voronoi_plot_2d, delaunay_plot_2d

points = np.array([[0, 0], 
                   [1, 0], 
                   [0.5, 0.5],
                   [1 , 0.5]])
tri = Delaunay(points) 
fig, ax = plt.subplots(figsize=(8, 4))

vor = Voronoi(points)

_ = delaunay_plot_2d(tri, ax=ax)
_ = voronoi_plot_2d(vor, ax=ax)

ax.set_aspect('equal', 'box')

You can customise the points/line styles/etc in those functions.