created -9.76 44.881 9
created -9.28 9.97356 2
created -8.8 49.8678 10
created -8.32 9.97356 2
created -7.84 49.8678 10
created -7.36 14.9603 3
created -6.88 9.97356 2
created -6.4 24.9339 5
created -5.92 19.9471 4
created -5.44 34.9074 7
created -4.96 24.9339 5
created -4.48 9.97356 2
created -4 29.9207 6
created -3.52 49.8678 10
created -3.04 4.98678 1
created -2.56 49.8678 10
created -2.08 24.9339 5
created -1.6 19.9471 4
created -1.12 34.9074 7
created -0.64 29.9207 6
created -0.16 19.9471 4
created 0.32 34.9074 7
created 0.8 44.881 9
created 1.28 39.8942 8
created 1.76 14.9603 3
created 2.24 4.98678 1
created 2.72 29.9207 6
created 3.2 19.9471 4
created 3.68 4.98678 1
created 4.16 49.8678 10
created 4.64 29.9207 6
created 5.12 24.9339 5
created 5.6 24.9339 5
created 6.08 29.9207 6
created 6.56 49.8678 10
created 7.04 49.8678 10
created 7.52 49.8678 10
created 8 29.9207 6
created 8.48 14.9603 3
created 8.96 14.9603 3
created 9.44 9.97356 2
the total number of created peaks = 41 with sigma = 0.08
the total number of found peaks = 41 with sigma = 0.0800011 (+-1.44686e-05)
fit chi^2 = 1.2004e-06
found -8.8 (+-0.000119845) 49.8673 (+-0.0740576) 10 (+-0.000490423)
found -7.84 (+-0.000119961) 49.8674 (+-0.0740656) 10 (+-0.000490476)
found -3.52 (+-0.000120046) 49.8675 (+-0.0740731) 10.0001 (+-0.000490526)
found -2.56 (+-0.000119975) 49.8674 (+-0.0740678) 10.0001 (+-0.00049049)
found 4.16 (+-0.000120046) 49.8675 (+-0.0740731) 10.0001 (+-0.000490526)
found 6.56 (+-0.000120799) 49.8679 (+-0.0741273) 10.0002 (+-0.000490884)
found 7.04 (+-0.000121029) 49.8681 (+-0.0741449) 10.0002 (+-0.000491001)
found 7.52 (+-0.000120799) 49.8679 (+-0.0741273) 10.0002 (+-0.000490884)
found -9.76 (+-0.000126675) 44.8805 (+-0.0702736) 9.00002 (+-0.000465365)
found 0.8 (+-0.000127391) 44.8812 (+-0.0703271) 9.00015 (+-0.000465719)
found 1.28 (+-0.000134927) 39.8943 (+-0.0662941) 8.00012 (+-0.000439012)
found -5.44 (+-0.000144174) 34.9074 (+-0.062008) 7.00009 (+-0.000410628)
found -1.12 (+-0.000144271) 34.9075 (+-0.0620133) 7.0001 (+-0.000410663)
found 0.320001 (+-0.000144513) 34.9076 (+-0.0620267) 7.00013 (+-0.000410752)
found -0.640001 (+-0.000156108) 29.9208 (+-0.0574261) 6.00011 (+-0.000380286)
found 4.64 (+-0.000156491) 29.921 (+-0.0574443) 6.00015 (+-0.000380407)
found 8 (+-0.000156212) 29.9209 (+-0.0574318) 6.00013 (+-0.000380324)
found -4 (+-0.000156025) 29.9209 (+-0.0574238) 6.00012 (+-0.000380271)
found 2.72 (+-0.000155181) 29.9205 (+-0.0573851) 6.00005 (+-0.000380014)
found 6.08 (+-0.000156491) 29.921 (+-0.0574443) 6.00015 (+-0.000380407)
found -4.96 (+-0.000170865) 24.934 (+-0.0524179) 5.00009 (+-0.000347121)
found -2.08 (+-0.000171557) 24.9343 (+-0.0524448) 5.00014 (+-0.000347299)
found 5.12 (+-0.000171292) 24.9341 (+-0.0524335) 5.00011 (+-0.000347224)
found -6.4 (+-0.000170471) 24.9339 (+-0.0524022) 5.00006 (+-0.000347017)
found 5.6 (+-0.000171292) 24.9341 (+-0.0524335) 5.00011 (+-0.000347224)
found -0.16 (+-0.000192187) 19.9475 (+-0.0469198) 4.00013 (+-0.000310711)
found 3.2 (+-0.000190854) 19.9472 (+-0.0468793) 4.00007 (+-0.000310443)
found -5.92 (+-0.000192028) 19.9475 (+-0.0469146) 4.00012 (+-0.000310677)
found -1.6 (+-0.000192028) 19.9475 (+-0.0469146) 4.00012 (+-0.000310677)
found -7.36 (+-0.000221966) 14.9607 (+-0.0406367) 3.00012 (+-0.000269104)
found 1.76 (+-0.000221195) 14.9606 (+-0.0406187) 3.00009 (+-0.000268984)
found 8.48 (+-0.000221673) 14.9606 (+-0.040628) 3.00009 (+-0.000269046)
found 8.96 (+-0.000220629) 14.9604 (+-0.040603) 3.00005 (+-0.00026888)
found -9.28 (+-0.00027522) 9.97439 (+-0.0332363) 2.00019 (+-0.000220097)
found -8.32 (+-0.000275421) 9.97444 (+-0.0332399) 2.0002 (+-0.00022012)
found -6.88 (+-0.000272305) 9.97383 (+-0.0331859) 2.00008 (+-0.000219763)
found -4.48 (+-0.000273311) 9.97399 (+-0.0332027) 2.00011 (+-0.000219875)
found 9.44 (+-0.000268451) 9.97368 (+-0.0331318) 2.00005 (+-0.000219404)
found -3.04 (+-0.00039378) 4.98774 (+-0.023544) 1.00021 (+-0.000155912)
found 3.68001 (+-0.000391024) 4.98743 (+-0.0235188) 1.00014 (+-0.000155745)
found 2.24 (+-0.00038872) 4.98717 (+-0.0234974) 1.00009 (+-0.000155604)
#include <iostream>
TH1F *FitAwmi_Create_Spectrum(
void)
{
npeaks++;
<< std::endl;
}
std::cout <<
"the total number of created peaks = " << npeaks <<
" with sigma = " <<
sigma << std::endl;
}
void FitAwmi(void)
{
TH1F *
h = FitAwmi_Create_Spectrum();
if (!cFit)
cFit =
new TCanvas(
"cFit",
"cFit", 10, 10, 1000, 700);
else
for (i = 0; i < nbins; i++)
source[i] =
h->GetBinContent(i + 1);
for (i = 0; i < nfound; i++)
FixAmp[i] = FixPos[i] =
kFALSE;
for (i = 0; i < nfound; i++) {
bin = 1 +
Int_t(Pos[i] + 0.5);
Amp[i] =
h->GetBinContent(bin);
}
delete gROOT->FindObject(
"d");
d->SetNameTitle(
"d",
"");
for (i = 0; i < nbins; i++)
d->SetBinContent(i + 1, source[i]);
sigmaErr *= dx;
std::cout <<
"the total number of found peaks = " << nfound <<
" with sigma = " <<
sigma <<
" (+-" << sigmaErr <<
")"
<< std::endl;
std::cout <<
"fit chi^2 = " << pfit->
GetChi() << std::endl;
for (i = 0; i < nfound; i++) {
bin = 1 +
Int_t(Positions[i] + 0.5);
Pos[i] =
d->GetBinCenter(bin);
Amp[i] =
d->GetBinContent(bin);
Positions[i] =
x1 + Positions[i] * dx;
PositionsErrors[i] *= dx;
Areas[i] *= dx;
AreasErrors[i] *= dx;
std::cout << "found " << Positions[i] << " (+-" << PositionsErrors[i] << ") " << Amplitudes[i] << " (+-"
<< AmplitudesErrors[i] << ") " << Areas[i] << " (+-" << AreasErrors[i] << ")" << std::endl;
}
if (pm) {
h->GetListOfFunctions()->Remove(pm);
delete pm;
}
h->GetListOfFunctions()->Add(pm);
delete pfit;
delete[] Amp;
delete[] FixAmp;
delete[] FixPos;
delete s;
delete[] source;
return;
}
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t dest
Option_t Option_t TPoint TPoint const char x1
R__EXTERN TRandom * gRandom
virtual void SetMarkerColor(Color_t mcolor=1)
Set the marker color.
virtual void SetMarkerStyle(Style_t mstyle=1)
Set the marker style.
virtual void SetMarkerSize(Size_t msize=1)
Set the marker size.
void Clear(Option_t *option="") override
Remove all primitives from the canvas.
1-D histogram with a float per channel (see TH1 documentation)
TObject * FindObject(const char *name) const override
Search object named name in the list of functions.
A PolyMarker is defined by an array on N points in a 2-D space.
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Advanced 1-dimensional spectra fitting functions.
void SetPeakParameters(Double_t sigma, Bool_t fixSigma, const Double_t *positionInit, const Bool_t *fixPosition, const Double_t *ampInit, const Bool_t *fixAmp)
This function sets the following fitting parameters of peaks:
Double_t * GetAmplitudesErrors() const
void FitAwmi(Double_t *source)
This function fits the source spectrum.
Double_t * GetAreasErrors() const
void GetSigma(Double_t &sigma, Double_t &sigmaErr)
This function gets the sigma parameter and its error.
Double_t * GetAreas() const
Double_t * GetAmplitudes() const
void SetFitParameters(Int_t xmin, Int_t xmax, Int_t numberIterations, Double_t alpha, Int_t statisticType, Int_t alphaOptim, Int_t power, Int_t fitTaylor)
This function sets the following fitting parameters:
Double_t * GetPositionsErrors() const
Double_t * GetPositions() const
Advanced Spectra Processing.
Int_t SearchHighRes(Double_t *source, Double_t *destVector, Int_t ssize, Double_t sigma, Double_t threshold, bool backgroundRemove, Int_t deconIterations, bool markov, Int_t averWindow)
One-dimensional high-resolution peak search function.
Double_t * GetPositionX() const
constexpr Double_t Sqrt2()
Double_t Sqrt(Double_t x)
Returns the square root of x.
constexpr Double_t TwoPi()