Optimizing scipy.spatial.Delaunay.find_simplex
You can try point in location test, especially Kirkpatrick algorithm/data structure. Basically you subdivide the mesh in both axis and re-triangulate it. A better and simpler solution is to give each triangle a color and draw a bitmap then check the color of the bitmap with the point.
I have a set of points in a plane where each point has an associated altitude. I'm thinking of using the scipy.spatial library to compute the Delaunay triangulation of the point set and then use the result to interpolate for the points in between.
The library implements a nice function that, given a point, finds the triangle it lies in. This would be particularly useful when calculating the depth map from the mesh. I assume though (please do correct me if I'm wrong) that the search function searches from the same starting point every time it is called. Since the points I will be looking for will tend to lie either on the triangle the previous one lied on or on an adjacent one, I figure that's unneccessary, but can't seem to find a way to optimize the search, other than to implement it myself.
Is there a way to set the initial triangle for the search, or to optimize the depth map calculation otherwise?