[SciPy-Dev] Signal Smooth

[Paul Kuin]

I just like the old boxcar average (astronomical spectral data) and bypass
scipy for that for stsci.convolve.  The thing is that with real data it is
often useful to bring out the signal in the noisy parts. Sometimes its just
handy because of instrumental noise being present and you want to get rid
of (most of ) it.  By the time you need more sophisticated filtering, you
usually have more control over your system/experiment or gotten enough
understanding to know you can/should use this or that method.  I think it
depends a lot on the field or application you're using.

Probably it makes more sense to write your own basic filter and use that
consistently, rather then adding such functions to the toolkit. However,
having good examples for different applications somewhere (with perhaps
example filters) would be very useful.  It still find the ones in the
documentation a bit to far removed from my field (to my taste), and there
is a lot that actually can be done with filtering. (I must admit that I do
not look into textbooks anymore, they are just to difficult to get access
to for my taste).

On Sat, Jun 20, 2015 at 12:58 AM, Sturla Molden <sturla.molden at gmail.com>
wrote:

> Nicolas Petitclerc <npetitclerc at gmail.com> wrote:
>
> > I think it would be worth it, for the cases when you plot a 1D array, it
> > looks very messy(noisy) and you just want to quickly see the general
> trend.
> > A quick way to apply the most common methods: flat and Gaussian filters.
> A
> > few more like Savitzky-Golay, lowess would be nice, and simple to do, but
> > the idea would be to add convenience for the most basic operations.
>
> Lowess (aka loess) is a scatterplot smoother, not a signal smoother. It
> more properly belongs to the realm of statsmodels (which actually has it).
> Smoothing splines and kernel regression are other alternatives to lowess. I
> am not sure scipy.signal should implement a method to deal with unevenly
> sampled data.
>
> "Smoothing" is also sometimes used errorneously for denoising, which
> includes methods such as Wiener filtering and wavelet shrinkage.
>
> Sturla
>
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-- 

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Dr. N.P.M. Kuin      (n.kuin at ucl.ac.uk)
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