[SciPy-Dev] Savitzky-Golay and other smoothing filters

[Jonathan Stickel]

Thomas

Thank you for the email. It looks like the new savitzky-golay functions 
went into scipy.signal. Already in scipy.signal are other types of 
smoothing capabilities, including splines, Fourier, and Wavelet, but 
these are not grouped together nor are all the user interfaces obvious 
for smoothing use.

I have come across other smoothing methods and discussions of methods 
outside of scipy (but written with python/scipy), including "lowess" in 
statsmodels:

http://statsmodels.sourceforge.net/devel/generated/statsmodels.nonparametric.smoothers_lowess.lowess.html

a discussion of a faster lowess method here:

http://slendrmeans.wordpress.com/2012/05/14/how-do-you-speed-up-40000-weighted-least-squares-calculations-skip-36000-of-them/

my regularization method:

https://github.com/jjstickel/scikit-datasmooth/

and discussion of smoothing by regularization in N-dimensions:

http://pav.iki.fi/blog/2010-09-19/nd-smoothing.html

I think it would be great to group all of these together in scipy. To do 
this will take a champion, though, and I personally can't afford the 
time. Without one, perhaps I can work to put my regularization-smoothing 
method into scipy.signal alongside savitzky-golay.

Regards,
Jonathan



On 12/4/13 03:59 , Thomas Haslwanter wrote:
> Hi Jonathan,
> adding your smoothing module to scipy would be good. As for the location, I
> am not sure where the savitzky-golay filter is located. The same module
> would be good for your contribution, alternatively it could perhaps be
> placed in filter. I am not sure if a "smoothing" module is justified for two
> or so functions.
>
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