# Triangulation of a set of points: points = np.array([[0, 0], [0, 1.1], [1, 0], [1, 1]]) from scipy.spatial import Delaunay tri = Delaunay(points) # We can plot it: import matplotlib.pyplot as plt plt.triplot(points[:,0], points[:,1], tri.simplices.copy()) plt.plot(points[:,0], points[:,1], 'o') plt.show() # Point indices and coordinates for the two triangles forming the # triangulation: tri.simplices # array([[3, 2, 0], # [3, 1, 0]], dtype=int32) points[tri.simplices] # array([[[ 1. , 1. ], # [ 1. , 0. ], # [ 0. , 0. ]], # [[ 1. , 1. ], # [ 0. , 1.1], # [ 0. , 0. ]]]) # Triangle 0 is the only neighbor of triangle 1, and it's opposite to # vertex 1 of triangle 1: tri.neighbors[1] # array([-1, 0, -1], dtype=int32) points[tri.simplices[1,1]] # array([ 0. , 1.1]) # We can find out which triangle points are in: p = np.array([(0.1, 0.2), (1.5, 0.5)]) tri.find_simplex(p) # array([ 1, -1], dtype=int32) # We can also compute barycentric coordinates in triangle 1 for # these points: b = tri.transform[1,:2].dot(p - tri.transform[1,2]) np.c_[b, 1 - b.sum(axis=1)] # array([[ 0.1 , 0.2 , 0.7 ], # [ 1.27272727, 0.27272727, -0.54545455]]) # The coordinates for the first point are all positive, meaning it # is indeed inside the triangle.