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 osos.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 Delaunaydl = Delaunay(points)
convex = dl.convex_hullfrom 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

EDIT: plot also the convex hull

import numpy as np
from scipy.spatial import Delaunaypoints = 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 addedreturnedges.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 LineCollectionlines = 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 hulledges = 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.