[R] R: Morlet wavelet not supportd by wavCWTPeaks

[mauede at alice.it]

Sure. Now it works ... but it is a different signal !
I could not understand how the signal I sent was modified. So I plotted yours version of the signal and mine (see attachments).
My signal represent a single breathing cycle extracted from a breathing signal. It shows that the individual completed one respiratory cycle
in about 8 seconds, so he/she is breathing very deeply and slowly.
Whereas you transformed it into the breathing pattern of someone who can complete almost 5 respiratory cycles within 8  seconds, therefore breathing
at a much higher rate ... maybe short of breath.
The whole original signal can be seen on my web site:
http://trusoftdev.za.net/maura/Breathing-Signals-Dir/Signals-Dir/3570-Dir/Plots-Dir/3570-Original-PureSignal.pdf

Bottomline: I need a package that helps me shed light onto the original signals I was given. I cannot alter the signals in order to make the package work.
I need to analyze true signals not fake ones.
If wmTSA cannot work on non-periodic signals then it should be stated in the on-line documentation.
Any suggestion ?

Thank you very much.
Maura

-----Messaggio originale-----
Da: stephen sefick [mailto:ssefick at gmail.com]
Inviato: sab 06/12/2008 8.25
A: mauede at alice.it
Cc: R-help
Oggetto: Re: Morlet wavelet not supportd by wavCWTPeaks
 
aa <- (structure(list(X.0.85 = c(-1.02, -1.17, -1.29, -1.39, -1.46,
-1.5, -1.52, -1.5, -1.46, -1.39, -1.3, -1.19, -1.07, -0.93, -0.79,
-0.65, -0.5, -0.36, -0.22, -0.08, 0.05, 0.18, 0.3, 0.41, 0.52,
0.62, 0.72, 0.81, 0.89, 0.98, 1.05, 1.13, 1.19, 1.25, 1.29, 1.31,
1.31, 1.29, 1.24, 1.16, 1.06, 0.93, 0.77, 0.58, 0.38, 0.16, -0.07,
-0.31, -0.89, -1.05, -1.19, -1.31, -1.41, -1.47, -1.51, -1.51,
-1.49, -1.44, -1.37, -1.28, -1.17, -1.04, -0.91, -0.76, -0.62,
-0.47, -0.33, -0.19, -0.06, 0.08, 0.2, 0.32, 0.43, 0.54, 0.64,
0.73, 0.82, 0.91, 0.99, 1.07, 1.14, 1.2, 1.25, 1.29, 1.31, 1.31,
1.28, 1.23, 1.15, 1.04, 0.9, 0.73, 0.55, 0.34, 0.11, -0.12, -0.92,
-1.08, -1.22, -1.33, -1.42, -1.48, -1.51, -1.51, -1.48, -1.43,
-1.35, -1.26, -1.14, -1.01, -0.88, -0.74, -0.59, -0.45, -0.3,
-0.16, -0.03, 0.1, 0.22, 0.34, 0.45, 0.56, 0.66, 0.75, 0.84,
0.93, 1.01, 1.08, 1.15, 1.21, 1.26, 1.3, 1.31, 1.3, 1.27, 1.21,
1.13, 1.01, 0.87, 0.7, 0.5, 0.29, 0.07, -0.17, -0.95, -1.11,
-1.24, -1.35, -1.44, -1.49, -1.51, -1.51, -1.48, -1.42, -1.33,
-1.23, -1.12, -0.99, -0.85, -0.71, -0.56, -0.42, -0.27, -0.14,
0, 0.13, 0.25, 0.36, 0.47, 0.58, 0.68, 0.77, 0.86, 0.94, 1.02,
1.1, 1.17, 1.23, 1.27, 1.3, 1.31, 1.3, 1.26, 1.2, 1.11, 0.98,
0.83, 0.66, 0.46, 0.25, 0.02, -0.22, -0.99, -1.14, -1.27, -1.37,
-1.45, -1.5, -1.52, -1.5, -1.47, -1.4, -1.32, -1.21, -1.09, -0.96,
-0.82, -0.68, -0.53, -0.39, -0.25, -0.11, 0.02, 0.15, 0.27, 0.39,
0.5, 0.6, 0.7, 0.79, 0.87, 0.96, 1.04, 1.11, 1.18, 1.24, 1.28,
1.31, 1.31, 1.29, 1.25, 1.18, 1.08, 0.96, 0.8, 0.62, 0.42, 0.2,
-0.03, -0.27)), .Names = "X.0.85", class = "data.frame", row.names = c(NA,
-240L)))
library(wmtsa)
library(fields)
aats <- ts (aa, deltat =1/30, start = 0.0)
aa.cwt <-  wavCWT(aats)
aa.tree <- wavCWTTree (aa.cwt)
aa.peak <- wavCWTPeaks(aa.tree)

sorry I didn't realize that only the mexican hat wavelet could be used
for the peak function.  This should work fine.  and Use the tim.colors
argument in the plot() function.  I hope this helps....  This reason I
suggested the morlet wavelet is that it is compactly suppoted

On Sat, Dec 6, 2008 at 1:39 AM,  <mauede at alice.it> wrote:
> aa.cwt <-  wavCWT(aats,wavelet="morlet")
>> aa.cwt
> Continuous Wavelet Transform of aats
> ------------------------------------
> Wavelet                 : Morlet
> Wavelet frequency shift : 5 (rad/sec)
> Length of series        : 241
> Sampling interval       : 0.03333333
> Number of scales        : 65
> Range of scales         : 0.0333333333333333 to 8.03333333333333
>>  aa.cwt.coef <- as.matrix (aa.cwt)
>>    plot (aa.cwt, series=TRUE)
>> plot (aa.cwt, type="persp")
>
>>   aa.tree <- wavCWTTree (aa.cwt)
>>  aa.peak <- wavCWTPeaks (aa.tree)
> Error in wavCWTPeaks(aa.tree) :
>   Only CWT developed using the Mexican hat (gaussian2) filter are supported
>>
>
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-- 
Stephen Sefick

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K. Mullis




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