I did have lines over 80 chars after all... Zachary Pincus <zachary.pincus <at> yale.edu> writes:
This doesn't answer your specific question, but look at scipy.ndimage.map_coordinates() for general-purpose spline interpolation of regularly-spaced (e.g. image) data. If you want to repeatedly interpolate the same data, you can get the spline coefficients with: scipy.ndimage.spline_filter() and pass them to map_coordinates() with the "prefilter=False" option.
Thank you very much for your helpful hint. I tried out the code you suggested and it seems to do just what I want. It seems to me that I can also process the coefficient matrix I get from signal.cspline2d() with ndimage.map_coordinates() as well as being able to have ndimage.spline_filter() generate the coefficients, though the coefficient matrices the two routines provide aren't identical. The resulting program is fast; both the calculation of the coefficient matrix for a 512X512 image and the interpolation of an equally-sized output took under a second on my system.
It is curious that while scipy.signal has cspline1d() and cspline1d_eval(), there is no cspline2d_eval() function... hopefully someone else can weight in on what's going on here.
curious indeed, but ndimage seems to solve the problem. Still signal.cspline2d_eval() should be put on the wish-list :-)