Sign in to follow this  

Distance Weighted Interpolation: Gradient Plane Nodal Functions

This topic is 3408 days old which is more than the 365 day threshold we allow for new replies. Please post a new topic.

If you intended to correct an error in the post then please contact us.

Recommended Posts

Hello everyone, I implemented shepard's method for a distance weighted interpolation. Basically the resource that helped me most was: http://www.ems-i.com/gmshelp/Interpolation/Interpolation_Schemes/Inverse_Distance_Weighted/Shepards_Method.htm To sum it up: f_i are my values I want to interpolate (my scatter points). w_i are the interpolation coefficients. (how much of a scatter point is used to compute the currentInterpolationPoint?) The sum of w_i is 1. Now there seems to be a way which ameliorates this method. The formula can be extended using Gradient Plane Nodal Functions Q_i(x, y, z) in the original formula: f(x, y, z): sum i = 1 to n (w_i * f_i) It is extended to: f(x, y, z): sum i = 1 to n (w_i * Q_i(x, y, z)) where: Q_i(x, y, z) = f_x(x - x_i) + f_y(y - y_i) + f_z(z - z_i) + f_i f_x, f_y and f_z are the partial derivatives at the scatter point. How could I get these? The surrounding scatter points in my data structure are easily to access. x_i and y_i and z_i are the positional values of each scatter point. x, y, z seems to be the positional values of the currentInterpolationPoint, am I right? f_i is simple, this is like before. So basically I am searching for a way the get the partial derivatives. Anyone?

Share this post


Link to post
Share on other sites

This topic is 3408 days old which is more than the 365 day threshold we allow for new replies. Please post a new topic.

If you intended to correct an error in the post then please contact us.

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!

Register a new account

Sign in

Already have an account? Sign in here.

Sign In Now

Sign in to follow this