Thread Subject: Skewing a 2D grid to fit experimental data

I have a set of XY data points that should lie on a regular gird. But as the data is experimental data it suffers from skewing and squashing etc. I would like to to be able to take what should be a perfect grid and somehow fit it to my experimental data. Then I would be able make predictions about the locations of the next set of grid points outside my experimental data.

Can anybody point me in the direction of a function that may do this?

The data comes from scanning tunnelling microscope images of surface crystals. These images suffer from thermal and piezo drift which skews and squashes the image. Once I've located a few of the positions of the unit cells in the image I would like to be able to make predictions as to where the others may be. Hence the fitting of the 2D grid.

"Peter " <peter@nprl.ph.bham.ac.uk> wrote in message <glersh$89q$1@fred.mathworks.com>...
> I have a set of XY data points that should lie on a regular gird. But as the data is experimental data it suffers from skewing and squashing etc. I would like to to be able to take what should be a perfect grid and somehow fit it to my experimental data. Then I would be able make predictions about the locations of the next set of grid points outside my experimental data.
>
> Can anybody point me in the direction of a function that may do this?
>
> The data comes from scanning tunnelling microscope images of surface crystals. These images suffer from thermal and piezo drift which skews and squashes the image. Once I've located a few of the positions of the unit cells in the image I would like to be able to make predictions as to where the others may be. Hence the fitting of the 2D grid.

Try the interpolation:

help griddata

/Per