Grant Edwards wrote: >I've been looking for some way to approximate scattered 3D data >points in Python. The data doesn't seem to be amenable to >fitting functions like polymials, so I may have to use >something more like a spline surface.
>However, I can't find anything usable from Python, and my >Fortram skills are pretty rusty. I tried SciPy, but it's spline >fitting module doesn't work at all for my data. I've found >mentions of a Python port NURBS toolbox, but all the links I >can find are broken. NURBS is available in Matlab and Scilab at http://www.aria.uklinux.net/nurbs.php3 , and translating to Python with Numeric/Numarray should not be too hard. If you are trying to fit z = f(x,y) without having a particular functional form in mind, you can apply a nonparametric regression technique. One of the easiest approaches to code is Nadaraya-Watson kernel regression -- see for example http://www.quantlet.com/mdstat/scripts/spm/html/spmhtmlnode24.html , equation 4.68, where a Gaussian kernel can be used for K. PyML at http://pyml.sourceforge.net/doc/tutorial/tutorial.html may implement this (I have not tried it). LIBSVM at http://www.csie.ntu.edu.tw/~cjlin/libsvm/ has a Python interface for Support Vector Machines, a fairly popular and recent flexible regression method. -- http://mail.python.org/mailman/listinfo/python-list
