[SciPy-User] Cubic splines - MATLAB vs Scipy.interpolate
Tue Sep 27 15:27:27 CDT 2011
scipy.signal has some cubic and quadratic spline functions:
(and replace the c with q for the quadratic versions).
I have no idea how fast they are, or if they're at all drop-in replacements for the matlab ones. The stuff in scipy.interpolate is powerful, but the fitpack spline-fitting operations can be a bit input-sensitive and prone to strange ringing.
On Sep 27, 2011, at 3:32 PM, Charles R Harris wrote:
> On Tue, Sep 27, 2011 at 1:04 PM, Jaidev Deshpande <firstname.lastname@example.org> wrote:
> The big question: Why does the MATLAB function spline operate faster than the cubic spline alternatives in Scipy, especially splrep and splev ?
> The context: I'm working on an algorithm that bottlenecks on spline interpolation.
> Some functions in Scipy return an interpolation object function depending on the input data which needs to be evaluated independently over the whole range.
> So I used 'lower order' functions like splrep and splev. Even that was too slow.
> Then I tried to write my own code for cubic splines, generating and solving a system of 4N simultaneous equations for interpolation between N+1 points.
> No matter what I do, the code is quite slow. How come the MATLAB function spline operate so fast? What am I missing? What can I do to speed it up?
> I suspect it is because the scipy routines you reference are based on b-splines, which are needed for least squares fits. Simple cubic spline interpolation through a give set of points tends to be faster and I believe that is what the Matlab spline function does. To get b-splines in Matlab you need one of the toolboxes, it doesn't come with the core. I don't think scipy has a simple cubic spline interpolation, but I may be wrong.
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