In [24]:

    

import sys
import os
sys.path.append(os.path.abspath('..'))
import libpysal


    

In [25]:

    

from libpysal.cg.voronoi  import voronoi, voronoi_frames


    

In [26]:

    

points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]


    

In [27]:

    

regions, vertices = voronoi(points)


    

In [28]:

    

regions


    

    
Out[28]:





[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]

In [29]:

    

vertices


    

    
Out[29]:





array([[  4.21783296,   4.08408578],
       [  7.51956025,   3.51807539],
       [  9.4642193 ,  19.3994576 ],
       [ 14.98210684, -10.63503022],
       [ -9.22691341,  -4.58994414],
       [ 14.98210684, -10.63503022],
       [  1.78491801,  19.89803294],
       [  9.4642193 ,  19.3994576 ],
       [  1.78491801,  19.89803294],
       [ -9.22691341,  -4.58994414]])

In [30]:

    

region_df, point_df = voronoi_frames(points)


    

In [31]:

    

%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt


    

In [32]:

    

fig, ax = plt.subplots()
region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)
point_df.plot(ax=ax, color='red')


    

    
Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f85e38b94a8>






    

In [33]:

    

n_points = 200
np.random.seed(12345)
points = np.random.random((n_points,2))*10 + 10
results = voronoi(points)
mins = points.min(axis=0)
maxs = points.max(axis=0)


    

In [34]:

    

regions, vertices = voronoi(points)


    

In [35]:

    

regions_df, points_df = voronoi_frames(points)


    

In [36]:

    

fig, ax = plt.subplots()
points_df.plot(ax=ax, color='red')


    

    
Out[36]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f85e39dae10>






    

In [37]:

    

fig, ax = plt.subplots()
regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)
points_df.plot(ax=ax, color='red')


    

    
Out[37]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f85e1fbd240>






    

In [38]:

    

points = np.array(points)
maxs = points.max(axis=0)
mins = points.min(axis=0)
xr = maxs[0] - mins[0]
yr = maxs[1] - mins[1]
buff = 0.05
r = max(yr, xr) * buff
minx = mins[0] - r
miny = mins[1] - r
maxx = maxs[0] + r
maxy = maxs[1] + r


    

In [39]:

    

fig, ax = plt.subplots()
regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )
points_df.plot(ax=ax, color='red')
plt.xlim(minx, maxx)
plt.ylim(miny, maxy)
plt.title("buffer: %f, n: %d"%(r,n_points))
plt.show()


    

In [40]:

    

from libpysal.weights.contiguity import Voronoi as Vornoi_weights


    

In [41]:

    

w = Vornoi_weights(points)


    

In [42]:

    

w.n


    

    
Out[42]:





200

In [43]:

    

w.pct_nonzero


    

    
Out[43]:





2.915

In [44]:

    

w.histogram


    

    
Out[44]:





[(3, 3),
 (4, 28),
 (5, 52),
 (6, 65),
 (7, 34),
 (8, 10),
 (9, 5),
 (10, 2),
 (11, 0),
 (12, 1)]

In [45]:

    

idx = [i for i in range(w.n) if w.cardinalities[i]==12]


    

In [46]:

    

points[idx]


    

    
Out[46]:





array([[16.50851787, 13.12932895]])

In [ ]: