[Numpy-discussion] Functions for finding the relative extrema of numeric data

Jonathan Helmus jjhelmus@gmail....
Thu Sep 15 10:03:23 CDT 2011


I've written some peak picking functions that work in N dimensions for a 
module for looking at NMR data in python, 
http://code.google.com/p/nmrglue/.  I'd be glad to polish up the code if 
people think it would be a useful addition to scipy.ndimage or 
scipy.interpolate?  The methods are not based on any formal algorithms I 
know of, just some fast and relatively simple methods that I found seem 
to work decently.  

The methods are contained in the peakpick.py and segmentation.py files 
in the analysis directory (specifically see the find_all_connected, 
find_all_ downward and find_all_thres):
http://code.google.com/p/nmrglue/source/browse/trunk/nmrglue/analysis/peakpick.py
http://code.google.com/p/nmrglue/source/browse/trunk/nmrglue/analysis/segmentation.py

Let me know if there is an interest in including these in scipy or numpy.

    -Jonathan Helmus



Jacob Silterra wrote:
> >What is your application?
>
> The most common case is looking at Fourier transforms and identifying 
> spectral peaks. I've also analyzed images looking at 1D slices 
> (usually very regular data) and looked for peaks there.
>
> That stackoverflow page had a nice link to a comparison of different 
> algorithms here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631518/. 
> That paper is focused on mass-spectrometry data, but the approach 
> would generalize to any 1D data set. Unless somebody feels otherwise, 
> I'll close this pull request and start working on an implementation of 
> peak finding via continuous wavelet transform (the best and most 
> computationally intensive approach of those described above).
>
> -Jacob
>
>     ------------------------------
>
>     Message: 4
>     Date: Tue, 13 Sep 2011 22:34:01 +0200
>     From: Ralf Gommers <ralf.gommers@googlemail.com
>     <mailto:ralf.gommers@googlemail.com>>
>     Subject: Re: [Numpy-discussion] Functions for finding the relative
>            extrema of numeric data
>     To: Discussion of Numerical Python <numpy-discussion@scipy.org
>     <mailto:numpy-discussion@scipy.org>>
>     Message-ID:
>          
>      <CABL7CQhxCX0LKFENMW6-4ZSbdieGxz04zbsrnY4bXYVxVL78Dw@mail.gmail.com
>     <mailto:CABL7CQhxCX0LKFENMW6-4ZSbdieGxz04zbsrnY4bXYVxVL78Dw@mail.gmail.com>>
>     Content-Type: text/plain; charset="iso-8859-1"
>
>     Hi Jacob,
>
>     On Fri, Sep 9, 2011 at 11:57 PM, Jacob Silterra <jsilter@gmail.com
>     <mailto:jsilter@gmail.com>> wrote:
>
>     > Hello all,
>     >
>     > I'd like to see functions for calculating the relative extrema
>     in a set of
>     > data included in numpy. I use that functionality frequently, and
>     always seem
>     > to be writing my own version. It seems like this functionality
>     would be
>     > useful to the community at large, as it's a fairly common operation.
>     >
>
>     What is your application?
>
>     >
>     > For numeric data (which is presumably noisy), the definition of
>     a relative
>     > extrema isn't completely obvious. The implementation I am
>     proposing finds a
>     > point in an ndarray along an axis which is larger (or smaller)
>     than it's
>     > `order` nearest neighbors (`order` being an optional parameter,
>     default 1).
>     > This is likely to find more points than may be desired,  which I
>     believe is
>     > preferable to the alternative. The output is formatted the same as
>     > numpy.where.
>     >
>     > Code available here: https://github.com/numpy/numpy/pull/154
>     >
>     > I'm not sure whether this belongs in numpy or scipy, that
>     question is
>     > somewhat debatable. More sophisticated peak-finding functions (in N
>     > dimensions, as opposed to 1) may also be useful to the
>     community, and those
>     > would definitely belong in scipy.
>     >
>
>     I have the feeling this belongs in scipy. Although if it's just
>     these two
>     functions I'm not sure where exactly to put them. Looking at the
>     functionality, this is quite a simple approach. For any data of
>     the type I'm
>     usually working with it will not be able to find the right local
>     extrema.
>     The same is true for your alternative definition below.
>
>     A more powerful peak detection function would be a very good
>     addition to
>     scipy imho (perhaps in scipy.interpolate?). See also
>     http://stackoverflow.com/questions/1713335/peak-finding-algorithm-for-python-scipy
>
>     Cheers,
>     Ralf
>
>
>     > An alternative implementation would be to require that function be
>     > continuously descending (or ascending) for `order` points, which
>     would
>     > enforce a minimum width on a peak.
>     >
>     > -Jacob Silterra
>     >
>     > _______________________________________________
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>     Message: 5
>     Date: Tue, 13 Sep 2011 15:44:03 -0500
>     From: Benjamin Root <ben.root@ou.edu <mailto:ben.root@ou.edu>>
>     Subject: Re: [Numpy-discussion] Functions for finding the relative
>            extrema of numeric data
>     To: Discussion of Numerical Python <numpy-discussion@scipy.org
>     <mailto:numpy-discussion@scipy.org>>
>     Message-ID:
>          
>      <CANNq6Fk973UWz7+uXWc55p3iRcUam36cUbFC_NUPxqdi0r7+Hg@mail.gmail.com
>     <mailto:CANNq6Fk973UWz7%2BuXWc55p3iRcUam36cUbFC_NUPxqdi0r7%2BHg@mail.gmail.com>>
>     Content-Type: text/plain; charset="iso-8859-1"
>
>     On Tue, Sep 13, 2011 at 3:34 PM, Ralf Gommers
>     <ralf.gommers@googlemail.com
>     <mailto:ralf.gommers@googlemail.com>>wrote:
>
>     > Hi Jacob,
>     >
>     > On Fri, Sep 9, 2011 at 11:57 PM, Jacob Silterra
>     <jsilter@gmail.com <mailto:jsilter@gmail.com>> wrote:
>     >
>     >> Hello all,
>     >>
>     >> I'd like to see functions for calculating the relative extrema
>     in a set of
>     >> data included in numpy. I use that functionality frequently,
>     and always seem
>     >> to be writing my own version. It seems like this functionality
>     would be
>     >> useful to the community at large, as it's a fairly common
>     operation.
>     >>
>     >
>     > What is your application?
>     >
>     >>
>     >> For numeric data (which is presumably noisy), the definition of
>     a relative
>     >> extrema isn't completely obvious. The implementation I am
>     proposing finds a
>     >> point in an ndarray along an axis which is larger (or smaller)
>     than it's
>     >> `order` nearest neighbors (`order` being an optional parameter,
>     default 1).
>     >> This is likely to find more points than may be desired,  which
>     I believe is
>     >> preferable to the alternative. The output is formatted the same as
>     >> numpy.where.
>     >>
>     >> Code available here: https://github.com/numpy/numpy/pull/154
>     >>
>     >> I'm not sure whether this belongs in numpy or scipy, that
>     question is
>     >> somewhat debatable. More sophisticated peak-finding functions (in N
>     >> dimensions, as opposed to 1) may also be useful to the
>     community, and those
>     >> would definitely belong in scipy.
>     >>
>     >
>     > I have the feeling this belongs in scipy. Although if it's just
>     these two
>     > functions I'm not sure where exactly to put them. Looking at the
>     > functionality, this is quite a simple approach. For any data of
>     the type I'm
>     > usually working with it will not be able to find the right local
>     extrema.
>     > The same is true for your alternative definition below.
>     >
>     > A more powerful peak detection function would be a very good
>     addition to
>     > scipy imho (perhaps in scipy.interpolate?). See also
>     >
>     http://stackoverflow.com/questions/1713335/peak-finding-algorithm-for-python-scipy
>     >
>     > Cheers,
>     > Ralf
>     >
>     >
>     Actually, such an algorithm would be great to partner with the
>     watershed
>     clustering implementation in ndimage.
>
>     Ben Root
>
> ------------------------------------------------------------------------
>
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