I started to look at this and think some extra work is required. The problem is that regrid requires the data to be evenly spaced on a rectangular grid, rather than just a rectangular grid (which could have, say, increasing spacing in the y direction). I think this may limit its use more than users of interp2d would expect. The problem then is: 1. The currently used function (dierckx surfit) is not suitable for interpolation and needs to be replaced (it segfaults my simple test). 2. regrid works very well (fast, low mem etc.) for regular rectangular grids, but can't handle varying rectangular grids, let alone scattered data. 3. uses probably expect that at least varying rectangular grids are handled Solutions: a) just use regrid and say we can't work with no regular data. b) use a regridding method to get regular data if required, then use regrid I favour the second. As it happens, this is what MATLAB does which I guess is a reasonable example to follow. The are several regridding methods. MATLAB uses the freely available (I think scipy compatible) Qhull code. So I'll have a look at packaging the qhull code into scipy, then we can move on from there. In the mean time, do we use regrid over the restricted domain, or use the current surfit (which only works in very few cases). Regards, John On 12/10/06, Stefan van der Walt <stefan@sun.ac.za> wrote:

On Thu, Oct 12, 2006 at 01:42:44PM +0100, John Travers wrote:

Thanks! Sorry for the long lines, I'll be stricter on the format next time. I can send a patch for interp2d this evening (London time) if you want.

That's great, I'll keep an eye out for it. If anyone on the list currently uses interp2d on irregularly gridded data, now is the time to speak up.

Cheers StÃ©fan _______________________________________________ Scipy-dev mailing list Scipy-dev@scipy.org http://projects.scipy.org/mailman/listinfo/scipy-dev

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