Thanks Clément.
Interested users can readily download the files if they want to test the
implementation even if it has not been reviewed. Particularly, it has
not been discussed if we want to stick to the Matlab's implementation
and API for this particular feature. Comments are welcome.
S.
Le 17/03/2021 à 10:19, Clément David a écrit :
Hello all,
I take your question as a way to explain / remind how we validate user
contributions into the Scilab source code. Any change to the source
code should be pushed to the codereview.scilab.org website (this is a
gerrit instant, a git server that help reviewing changes). This help
testing on multiple machines/OS/compilers and review the content ; any
user can comment and give +1/-1 on a change. After there is no
disagreement, we merge it into the Scilab source code.
The “Cannot merge” error is an alert to the reviewer, this commit need
to be rebase (refreshed) against the latest source code ; this is not
a blocker for the review but rather for a one-click merge 😊.
Regards,
Clément
*From:* users <users-boun...@lists.scilab.org> *On Behalf Of *Claus
Futtrup
*Sent:* Tuesday, March 16, 2021 7:40 PM
*To:* users@lists.scilab.org
*Subject:* Re: [Scilab-users] find and locate local maxima
Hi Stéphane
It looks very nice and I hope it will be added to Scilab as proposed
by your code review. Why does it say in red print "Cannot Merge" ?
/Claus
On 16-03-2021 17:45, Stéphane Mottelet wrote:
Hi
For real life signals you should rather use something like this
(Savitsky-Golay filters)
https://codereview.scilab.org/#/c/21499/
<https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/codereview.scilab.org/#/c/21499/>
S.
Le 16/03/2021 à 17:09, CHEZE David 227480 a écrit :
Hi Clément,
Thank you for your quick reply and solution ! Actually it’s
working for simple data but with noisy experimental
timeseries, some filtering is required to get perfect regular
signal (between the ‘true’ extrema) that could be then managed
by the routine. I suppose this is something the Matlab/Octave
is handling internally, with some parameters as function’s
argument to tune it, maybe it’s not the case .
Regards,
David
*De :* users <users-boun...@lists.scilab.org>
<mailto:users-boun...@lists.scilab.org> *De la part de*
Clément David
*Envoyé :* mardi 16 mars 2021 16:27
*À :* Users mailing list for Scilab <users@lists.scilab.org>
<mailto:users@lists.scilab.org>
*Objet :* Re: [Scilab-users] find and locate local maxima
Hello David,
After reading the Matlab documentation page, it seems pretty
simple to implement using Scilab : and $ symbols:
function[*pks*, *locs*]=_findpeaks_(*data*)
ii = find(d(1:$-2) < d(2:$-1) & d(2:$-1) >= d(3:$));
*pks* = *data*(ii+2);
*locs* = ii + 2;
endfunction
data= [25 8 15 5 6 10 10 3 1 20 7];
_plot_(data)
[pks,locs]= _findpeaks_(data);
_plot_(locs,pks, 'xr');
Note: using oct2py and pims might also be an option for simple
cases but these wrappers are complex to use and data need to
be copied at language boundaries.
Regards,
Clément
*From:* users <users-boun...@lists.scilab.org
<mailto:users-boun...@lists.scilab.org>> *On Behalf Of *CHEZE
David 227480
*Sent:* Tuesday, March 16, 2021 2:53 PM
*To:* Users mailing list for Scilab <users@lists.scilab.org
<mailto:users@lists.scilab.org>>
*Subject:* [Scilab-users] find and locate local maxima
Hi all,
I’m looking for function that could find and locate every
local maxima of any discrete time signal (timeseries), similar
to Matlab or Octave function findpeaks(), scipy find_peaks().
Is anyone aware if something similar is already available in
Scilab ? (I already browsed a little bit and it don’t seem so…)
If not in Scilab macros, any hint to use the Octave or scipy
function directly from Scilab?
More globally it seems that Octave Forge could be linked with
Python (from oct2py import octave
# Load the Octage-Forge signal package.
octave.eval("pkg load signal")), does someone ever tried to
bridge similarly in Scilab ? oct2sci
Kind regards,
David
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Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
http://www.utc.fr/~mottelet
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Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
http://www.utc.fr/~mottelet
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