python scipy Delaunay plotting point cloud

Question

I have a pointlist=[p1,p2,p3...] where p1 = [x1,y1],p2=[x2,y2] ...

I want to use scipy.spatial.Delaunay to do trianglation on these point clouds and then plot it

How can i do this ?

The documentation for the Delaunay is really scarce

so far i have this code

from subprocess import Popen, PIPE
import os


os.environ['point_num'] = "2000"

cmd = 'rbox $point_num D2 | tail -n $point_num'
sub_process = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE)
output = sub_process.communicate()
points = [line.split() for line in output[0].split('\n') if line]
x = [p[0] for p in points if p]
y = [p[1] for p in points if p]

import matplotlib.pyplot as plt
plt.plot(x,y,'bo')

from scipy.spatial import Delaunay

dl = Delaunay(points)
convex = dl.convex_hull

from numpy.core.numeric import reshape,shape
convex = reshape(convex,(shape(convex)[0]*shape(convex)[1],1))
convex_x = [x[i] for i in convex]
convex_y = [y[i] for i in convex]

plt.plot(convex_x,convex_y,'r')
plt.show()

Thanks

Style tip: replace `from numpy.core.numeric import ...` by `from numpy import ...` -- in Python there are usually no strictly private areas, but it's good practice to import from the topmost namespace possible. Also, what are you trying to plot -- the convex hull, or the delaunay triangulation (which my first answer was for, before your example code...) — pv., Jun 30 '11 at 22:20

pv. · Accepted Answer · 2011-06-30T22:43:08.710

EDIT: plot also the convex hull

import numpy as np
from scipy.spatial import Delaunay

points = np.random.rand(30, 2) # 30 points in 2-d
tri = Delaunay(points)

# Make a list of line segments: 
# edge_points = [ ((x1_1, y1_1), (x2_1, y2_1)),
#                 ((x1_2, y1_2), (x2_2, y2_2)),
#                 ... ]
edge_points = []
edges = set()

def add_edge(i, j):
    """Add a line between the i-th and j-th points, if not in the list already"""
    if (i, j) in edges or (j, i) in edges:
        # already added
        return
    edges.add( (i, j) )
    edge_points.append(points[ [i, j] ])

# loop over triangles: 
# ia, ib, ic = indices of corner points of the triangle
for ia, ib, ic in tri.vertices:
    add_edge(ia, ib)
    add_edge(ib, ic)
    add_edge(ic, ia)

# plot it: the LineCollection is just a (maybe) faster way to plot lots of
# lines at once
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

lines = LineCollection(edge_points)
plt.figure()
plt.title('Delaunay triangulation')
plt.gca().add_collection(lines)
plt.plot(points[:,0], points[:,1], 'o', hold=1)
plt.xlim(-1, 2)
plt.ylim(-1, 2)

# -- the same stuff for the convex hull

edges = set()
edge_points = []

for ia, ib in tri.convex_hull:
    add_edge(ia, ib)

lines = LineCollection(edge_points)
plt.figure()
plt.title('Convex hull')
plt.gca().add_collection(lines)
plt.plot(points[:,0], points[:,1], 'o', hold=1)
plt.xlim(-1, 2)
plt.ylim(-1, 2)
plt.show()

Note that using scipy.spatial.Delaunay just for computing the complex hull is probably overkill, because computing just the hull can in principle done faster than computing the triangulation. Unfortunately, there's no interface in Scipy yet for computing hulls directly with Qhull.

Thank you! Your answer contributed to a great [blog post regarding alpha shapes](http://sgillies.net/blog/1155/the-fading-shape-of-alpha/), which in turn laid the foundation for an [excellent tutorial on concave hulls in Python](http://blog.thehumangeo.com/2014/05/12/drawing-boundaries-in-python/), both of which has been extremely helpful for me! — joelostblom, Sep 07 '15 at 13:45
The tutorial is really bad. It's not a real concave hull. Real concave hull is a lot more difficult! — Micromega, Sep 18 '15 at 18:19
This answer is old, but in case anyone finds this via a search, scipy does have an interface to directly find the convex hull: https://scipy.github.io/devdocs/generated/scipy.spatial.ConvexHull.html — hazzey, Apr 11 '16 at 04:10

python scipy Delaunay plotting point cloud

1 Answers1

Linked