[SciPy-User] Cubic splines - MATLAB vs Scipy.interpolate

[Zachary Pincus]

scipy.signal has some cubic and quadratic spline functions:
cspline1d
cspline1d_eval
cspline2d

(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.

Zach



On Sep 27, 2011, at 3:32 PM, Charles R Harris wrote:

> 
> 
> On Tue, Sep 27, 2011 at 1:04 PM, Jaidev Deshpande <deshpande.jaidev at gmail.com> wrote:
> Hi
> 
> 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.
> 
> Chuck
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