Hi, I'm rewriting some IDL code in Python. The original code uses IDL's spline function to interpolate a 1D ungridded data series onto a regular grid. Unfortunately, I can't find a close enough equivalent in scipy. I'm guessing the IDL spline function implements a "tensioned spline." The signature of the IDL spline function is Result = SPLINE(X, Y, T [, Sigma]) where Sigma is 'The amount of "tension" that is applied to the curve. If sigma is close to 0, (e.g., .01), then effectively there is a cubic spline fit. If sigma is large, (e.g., greater than 10), then the fit will be like a polynomial interpolation.' In the code I'm trying to reproduce, the author has set sigma=15 so the result is quite different to a simple cubic spline. Scipy's pchip algorithm gives a similar result, but is unfortunately too slow for my application. Can anyone confirm that IDL's spline is a tensioned spline routine? Does anyone know of a BSD-licensed Python module that implements a tensioned spline. Failing that any suggestions for a good routine I should look at wrapping (maybe Alan Cline's fitpack on netlib?) thanks, Gary