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SPLINEFIT

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SPLINEFIT

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31 Jan 2007 (Updated )

Fit a spline to noisy data.

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Description

Direct spline interpolation of noisy data may result in a curve with
unwanted oscillations. This is particularly bad if the slope of the
curve is important.

A better approach is to reduce the degrees of freedom for the spline
and use the method of least squares to fit the spline to the noisy data.
The deegres of freedom are connected to the number of breaks (knots),
so the smoothing effect is controlled by the selection of breaks.

SPLINEFIT:
- A curve fitting tool based on B-splines
- Splines on ppform (piecewise polynomial)
- Any spline order (cubic splines by default)
- Periodic boundary conditions
- Linear constraints on function values and derivatives
- Robust fitting scheme
- Operates on ND arrays in the same way as SPLINE
- Nonuniform distributions of breaks

M-FILES ALSO INCLUDED:
examples - Examples for splinefit
ppdiff - Differentiate piecewise polynomial
ppint - Integrate piecewise polynomial

Acknowledgements

This file inspired Clickfit Oh For Curve Fitting By Eye/Hand.

MATLAB release MATLAB 7.10 (R2010a)
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Comments and Ratings (49)
20 Feb 2014 francesco

great tool

07 Jan 2014 maryam  
23 Aug 2013 Maria

Thanks so much for the code. For my data works fine. Some questions though :
1. How can I retrieve the standard error (a posteriori sigma zero ) for the overall result?
2. How can I retrieve the standard errors (or Covariance matrix) for each coefficient

I need these std errors for evaluating the results.
Thanks

21 Aug 2013 Greg Fichter  
09 May 2013 Dang

It works well on my data. Thanks

26 Sep 2012 venky BS

Thanks for this useful code. It works really nice.

05 Jun 2012 Bradley Treeby

Perfect!

25 May 2012 Marc

How I should write the constrains to have an slope = 0 or a given value. With that you think that i can avoid the overshoot?

25 May 2012 Marc

well slope = 0 or a given value

27 Apr 2012 Maas

Jonas,

thank you for your quick reply. It took me some time and help from people at my university (TU Delft), but I got it right now. I developed a script to do what I asked you for. It solves the polynomial in a different way, by using mldivide. It uses the fact that overdetermined matrices are solved by a least squares fit.

If you are interested, I could mail my script to you.

25 Apr 2012 Jonas Lundgren

Maas, I admit that the splinebase function is a bit cryptic (also for the author). Unfortunatelly I can't see any quick fix to achieve the spline base you ask for.

23 Apr 2012 Maas

Jonas, thank you for this wonderful script!

I am currently searching to minimize influence of different pieces (between the breaks) on one another. I want to have a high degree polynomial (10 and up) but only have the total piecewise polynomial differentiable up to 3rd or 4th order.

I think I only need to change a line or two in the splinebase function, but I wouldn't know which, since I do not understand the fine details of it, in spite the very crisp and clear code you wrote.

I'd really appreciate your help, thank you in advance.

10 Apr 2012 Daniel Forsberg

Really nice piece of code.

Have a question though. Trying to use this to fit a curve to some 3D points and I have used the suggestion in http://www.mathworks.se/matlabcentral/answers/1717-3d-line-approximation-spline. However, I would like to find the appropriate x- and y-values for curve for given certain z-values. Any suggestions for how to do this?

The only thing that I can think of is to use 1D linear interpolation for the x- and y-values respectively based upon the z-values.

28 Mar 2012 Jonas Lundgren

Michael, splinefit fits a picewise polynomial curve to your data set by least squares. In the cubic case the curve has continuous second derivative. In other cases the regularity will follow the order of the spline. This is achieved by a base of spline curves with minimal support (B-splines). The method is straightforward and I have no specific reference. You can study B-splines and the method of least squares in the textbooks.

The smoothing effect on the noisy data is controlled by the degrees of freedom of the curve, i.e. the number of pieces. A cubic spline with P pieces has P+3 degrees of freedom. If the number of data points is greater than P+3 smoothing will happen.

Splinefit has no support for a desired tolerance or standard deviation. You have to select the number of pieces, see what you get and try again OR write a clever code for the task.

You can find the theory behind csaps/spaps in the documentation.

26 Mar 2012 Michael Lindholm Nielsen

I was wondering if you have any references to the method you use? How does it differ to the smoothing spline approach (csaps/spaps)?

13 Mar 2012 SHEIKH

hello,
Thanks to the author for such a beautiful piece of work.

I was wondering, how to achieve a monotonic spline? Any suggestions?

01 Feb 2012 Elena

Works very well and includes excellent documentation and examples

19 Jan 2012 Moncef

good guess

03 Dec 2011 Sander Aerts

Outstanding work with the splinefit function, this is exactely what i have been looking for. Very clean code and good documentation including the published examples.
I also like the ppdiff and the ppint function, how they work seamlessly with the standard piecewise functions.

This one certainly deserves a five star rating, congrats on the job

22 Nov 2011 Fer  
02 Aug 2011 K B  
27 Jul 2011 Naveen  
23 May 2011 Jonas Lundgren

Dani, I have no plans for more extensions of splinefit. If you have the Optimization Toolbox you should try SLM.

20 May 2011 Dani

Will it be possible in future versions to add monotonicity constrains to the spline?

17 Mar 2011 Jonas Lundgren

Stefan, only equality constraints so far.

14 Mar 2011 Stefan

Vey nice tool! Is there also a possibility to enter non-equalities in the constraints?
eg= slope at certain points should be greater then 2

18 Feb 2011 Jonas Lundgren

Gulcan, the smoothing spline is not intended to go through the knots/breaks. The curve fits to noisy data in the least square sense, also for knots. Use the constraint argument if you have exact data.

Example: If the curve must go through the points (0,5) and (2,3) use the constraint
con = struct('xc',[0 2],'yc',[5 3])

17 Feb 2011 Gulcan

Really appreciate the work, thank you. I have a question though. Cubic spline does not go through all knots that I've selected. Is it related to some constraints that I forget to indicate?

12 Nov 2010 Jonas Lundgren

Jonathan, SPLINEFIT is a curve fitting tool and deals with mappings from R to R^N. SPLINEFIT mimics SPLINE and the ND support can of course be replaced by a for-loop, but that will be less efficient.

If you are looking for a surface fitting tool i can recommend GRIDFIT by John D'Errico or SMOOTHN by Damien Garcia.

11 Nov 2010 Jonathan

Splinefit works great for 1D data, and I see ND support, but it looks like this simply facilitates batch processing several sets of 1D data. Is it possible to use this to generate 2D (an higher dimensional) splines? If so, some examples would be great!

02 Sep 2010 Jonas Lundgren

Sami, the robust fitting scheme uses weigthed regression where the weights are computed from previous residuals. It is close to the scheme described by John D'Errico in Optimization Tips and Tricks, section 33 (File ID: #8553).

The periodic condition forces endpoint derivatives to be equal. Example: A cubic spline with endpoints x=1 and x=4 (period length 3) satisfies the conditions y(1) = y(4), y'(1) = y'(4) and y"(1) = y"(4). This is perhaps not evident in the code where the B-splines at the endpoints are matched (pairwise) to have the same shape (and the same derivatives).

01 Sep 2010 Sami Aldalahmeh

That's a very useful code. Can you please provide some references for the robust spline fit and the periodic condition options?

09 Jul 2010 Vinesh Rajpaul

Useful...however, support for data containing NaNs would be helpful (spline interpolation using built-in MATLAB functions works fine for data containing NaNs)

09 Jul 2010 Vinesh Rajpaul  
25 Sep 2009 Jonas Lundgren

Non-uniform distributions of breaks/knots is ok. No problem.

12 Sep 2009 Ambrogio Manzino

If point distribution is not homogeneous, probably the software have some problem in L.S. parametres solution.
How cai I use the software in a multiresolution field data?

02 Sep 2009 Arthur Hebert

Easy to use, does what it says, saves me a ton of work. Thanks.

29 Jul 2009 Steffen Huber

Thank you very much - this code saved me a lot of time. Can you please send me references to the implemented method?

28 Apr 2009 David

Works very good to fit noisy 1D data, saved me a lot of effort, thank you!

22 Apr 2009 Maryam Ra

A very useful code in Matlab. I am wondering if anyone knows how to call this function from VC++?

01 Apr 2009 Amit Ailiani

A very useful code for cubic spline interpolation. Can you please give me the details of the method or any reference to read?

17 Mar 2009 Jonas Lundgren

That's true Chaos. This code was not intended for image processing and the figure shouldn't contain any image noise. How did you do to apply the code to a noisy image?

13 Mar 2009 Chaos

for images, it's too slow, use better steepest descent. this figure isn't 'noisy' at all. try to pluck an image out of the 'dirt' where the SN is about 12dB

06 Feb 2009 Husam Aldahiyat

Good I guess.

23 Jan 2009 Jonas Lundgren

Sorry, SPFIT don't support periodicity.

22 Jan 2009 P.S. Ramesh

I am trying to generate a smooth curve through noisy data that is periodic at the ends. Can I use this code or is there a version that enforces periodicty

03 Dec 2008 David Villeneuve

Thanks, this provides a simple method to put a smooth line through noisy data. You control the tightness by the number of break points.

05 Aug 2008 M C  
10 Mar 2007 Trevor Rayment

this is a useful piece of code. It does exactly what it says it does. Much appreciated.

Updates
07 Feb 2007

Code cleanup.

21 Feb 2007

Generalization to piecewise polynomial splines of arbitrary order.

11 Dec 2007

Exact conditions added.

17 Dec 2008

New polynomial base eliminating half the unknowns.
Short description of the numerical method added.

18 Dec 2008

Description update.

24 Feb 2009

Update of examples in help.

06 May 2009

A faster routine for cubic splines added.

15 May 2009

Bug fix for SPLINEFIT. Two utilities added.

28 Aug 2009

Description update

23 Jun 2010

New version of SPLINEFIT based on B-splines.

01 Sep 2010

Robust fitting scheme added. Support for data containing NaNs.

01 Jul 2011

Robust fitting parameter added.

22 Nov 2011

New contact info

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