delaunay

Is it possible to plot a list of triangles generated by scipy.spatial.Delaunay using pyplot's triplot function so that each triangle can be drawn and filled with an individual color? The basic python script I've created is import numpy as np import matplotlib.pyplot as plt from scipy.spatial import Delaunay import matplotlib.image as mpimg h = 300 w = 1000 npts = 30 pts = np.zeros((npts,2)) pts[:,0] = np.random.randint(0,w,npts) pts[:,1] = np.random.randint(0,h,npts) tri = Delaunay(pts) plt.xlim(0, w) plt.ylim(0, h) # Determine the color used for each triangle based upon the orthocenter # of

Is there a straight forward way to plot a non-convex surface in R? I have used something like the following for convex surfaces and it works fine: xyz <- cbind(y,x,z) tbr <- t(surf.tri(xyz, delaunayn(xyz))) rgl.triangles(xyz[tbr,1], xyz[tbr,2], xyz[tbr,3]) However, for non-convex surfaces, concave areas become filled. I think this is a problem with the function delaunayn() as it uses the Qhull library which does not support constrained Delaunay triangulations or mesh generation of non-convex objects. Any suggestions appreciated. P.S. I have data as an ascii file but it has 3 columns and is 225

From a set of points, I'm getting the convex hull with scipy.spatial , either with Delaunay or ConvexHull (from the qhull library). Now I would like to get the projection of a point outside this convex hull onto the hull (i.e. the point on the hull that is the smallest distance from the point outside). This is the code I have so far: from scipy.spatial import Delaunay, ConvexHull import numpy as np hu = np.random.rand(10, 2) ## the set of points to get the hull from pt = np.array([1.1, 0.5]) ## a point outside pt2 = np.array([0.4, 0.4]) ## a point inside hull = ConvexHull(hu) ## get only the