I'm trying to port a program which currently uses a hand-rolled C++ interpolator (developed by a mathematician colleage) over to use the interpolators provided by scipy. I'd like to use or wrap the scipy interpolator so that it's behavior is as close as possible behavior to our old interpolator.
A key difference between the two functions is that in our original interpolator - if the input value is above or below the input range, our original interpolator will extrapolate the result. If you try this with the scipy interpolator it raises a ValueError. Consider this program as an example: # EXAMPLE import numpy as np from scipy import interpolate x = np.arange(0,10) y = np.exp(-x/3.0) f = interpolate.interp1d(x, y) print f(9) print f(11) ##### Causes ValueError, because it's greater than max(x) # END OF EXAMPLE In the example above, I'd like the last line not to raise a ValueError, but to return a value calculated from the gradient of the line between f(x[-2]) and f(x[-1]). Is there a sensible way to make it so that instead of crashing, the final line will simply do a linear extrapolate, continuing the gradients defined by the first and last pairs of input data-points to infinity? I know that this is a simple enough function to write myself, however I'd rather not re-invent the wheel, especially as if I wanted to introduce new basic math functions into our library they would need to be validated by a number of gate-keepers before they were permitted into our library! I'm on Python 2.4, scipy 0.7 on Windows XP, 32bit Incidentally, I have seen this tutorial which has a "left" and "right" argument on the interpolator. This does not seem to exist on any version of the interp1d function which I can use on Windows Python 2.4 - can anybody speculate which version of Scipy this tutorial is intended for? http://projects.scipy.org/scipy/browser/branches/Interpolate1D/docs/tuto rial.rst?rev=4591 Sal
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