In [1]:

    

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


    

In [2]:

    

w = h = 360


    

In [3]:

    

n = 6
np.random.seed(0)
pts = np.random.randint(0, w, (n, 2))


    

In [4]:

    

print(pts)


    

    




[[172  47]
 [117 192]
 [323 251]
 [195 359]
 [  9 211]
 [277 242]]

In [5]:

    

print(type(pts))


    

    




<class 'numpy.ndarray'>

In [6]:

    

print(pts.shape)


    

    




(6, 2)

In [7]:

    

tri = Delaunay(pts)


    

In [8]:

    

print(type(tri))


    

    




<class 'scipy.spatial.qhull.Delaunay'>

In [9]:

    

fig = delaunay_plot_2d(tri)
fig.savefig('data/dst/scipy_matplotlib_delaunay.png')
plt.close()


    

In [10]:

    

print(tri.points)


    

    




[[172.  47.]
 [117. 192.]
 [323. 251.]
 [195. 359.]
 [  9. 211.]
 [277. 242.]]

In [11]:

    

print(tri.points == pts)


    

    




[[ True  True]
 [ True  True]
 [ True  True]
 [ True  True]
 [ True  True]
 [ True  True]]

In [12]:

    

print(tri.simplices)


    

    




[[0 1 4]
 [1 3 4]
 [5 0 2]
 [5 1 0]
 [3 5 2]
 [5 3 1]]

In [13]:

    

print(pts[tri.simplices])


    

    




[[[172  47]
  [117 192]
  [  9 211]]

 [[117 192]
  [195 359]
  [  9 211]]

 [[277 242]
  [172  47]
  [323 251]]

 [[277 242]
  [117 192]
  [172  47]]

 [[195 359]
  [277 242]
  [323 251]]

 [[277 242]
  [195 359]
  [117 192]]]

In [14]:

    

vor = Voronoi(pts)


    

In [15]:

    

print(type(vor))


    

    




<class 'scipy.spatial.qhull.Voronoi'>

In [16]:

    

fig = voronoi_plot_2d(vor)
fig.savefig('data/dst/scipy_matplotlib_voronoi.png')
plt.close()


    

In [17]:

    

print(vor.vertices)


    

    




[[ 41.71518987  80.51265823]
 [ 82.09150528 310.02013526]
 [331.19719626  87.04766355]
 [218.67630058 147.63583815]
 [282.99117647 333.43398693]
 [182.6014461  263.07537248]]

In [18]:

    

print(vor.regions)


    

    




[[-1, 0, 1], [], [5, 3, 2, 4], [3, 0, -1, 2], [4, -1, 2], [5, 1, 0, 3], [5, 1, -1, 4]]

In [19]:

    

print([r for r in vor.regions if -1 not in r and r])


    

    




[[5, 3, 2, 4], [5, 1, 0, 3]]

In [20]:

    

for region in [r for r in vor.regions if -1 not in r and r]:
    print(vor.vertices[region])


    

    




[[182.6014461  263.07537248]
 [218.67630058 147.63583815]
 [331.19719626  87.04766355]
 [282.99117647 333.43398693]]
[[182.6014461  263.07537248]
 [ 82.09150528 310.02013526]
 [ 41.71518987  80.51265823]
 [218.67630058 147.63583815]]

In [21]:

    

fig, ax = plt.subplots()
voronoi_plot_2d(vor, ax)

for region, c in zip([r for r in vor.regions if -1 not in r and r], ['yellow', 'pink']):
    ax.fill(vor.vertices[region][:, 0],
            vor.vertices[region][:, 1],
            color=c)

fig.savefig('data/dst/scipy_matplotlib_voronoi_fill.png')
plt.close()


    

    




/usr/local/lib/python3.7/site-packages/scipy/spatial/_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.
  was_held = ax.ishold()

In [22]:

    

fig, ax = plt.subplots(figsize=(4, 4))
delaunay_plot_2d(tri, ax)
voronoi_plot_2d(vor, ax, show_vertices=False)

ax.set_xlim(0, w)
ax.set_ylim(0, h)
ax.grid(linestyle='--')

fig.savefig('data/dst/scipy_matplotlib_delaunay_voronoi.png')
plt.close()


    

    




/usr/local/lib/python3.7/site-packages/scipy/spatial/_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.
  was_held = ax.ishold()