MATLAB Interpolation Toolkit

· Read in about 2 min · (353 Words)
matlab interpolation toolkit

During the course of my PhD, I’ve spent quite a lot of time examining methods of interpolating scattered data. By scattered, I mean something that looks like this:

plasma_scatter
Uploaded with plasq's Skitch!

Or alternatively, like a very, very gappy image. This type of data is very common in geoscience, and so interpolating it so it looks more like this:

plasma_nn_surf
Uploaded with plasq's Skitch!

Is a common activity. As well as examining common methods of interpolating scattered data, such as cubic, linear and nearest neighbour methods, I’ve looked at natural neighbour interpolation, radial basis function interpolation, everyone’s favourite kriging and a less common methods known as adaptive normalised convolution (ANC).

In order to do meaningful comparisons, I needed working implementations of these, and so ended up writing my own versions of ANC, radial basis function interpolation and kriging. I also wrote a wrapper to Pavel Sakov’s natural neighbour interpolation.

I’ve just released these functions as a toolkit for Matlab, with a BSD license. You can find the download links on the toolkit’s homepage, which also gives the commands you need to clone the repository.

To build the toolkit, you’ll need a working build environment, with make, gcc and python 2.5 as well as a copy of Matlab. I’ve tested the kit on Debian/Ubuntu Linux with Matlab 2007a and Mac OS X (10.5). If you’re using a Mac, you’ll need XCode installed.

You’ll probably need to tell make where your mex binary is (MEX is Matlab’s external interface builder). To do this, run:

make MEX=/path/to/mex

You’ll find mex inside your Matlab installation, under the bin/ directory. On linux, it may be symlinked into /usr/local/bin.

Once make has run, you’ll find a directory called toolkit, which contains the interpolation toolkit. Copy this folder somewhere appropriate, and add it to your Matlab path, by running:

addpath('/path/to/toolkit')

Within Matlab. You can then run help toolkit, for some help and examples of how to test the methods.

I’d recommend using natural neighbour interpolation everywhere you would previously have used matlab’s griddata function. You can find more information on interpolation in my publications, which you can read and download here.

Comments