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FitAwmi.C File Reference
Tutorials » Spectrum tutorials

Detailed Description

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This macro fits the source spectrum using the AWMI algorithm from the "TSpectrumFit" class ("TSpectrum" class is used to find peaks).

To try this macro, in a ROOT (5 or 6) prompt, do:

root > .x FitAwmi.C

or:

root > .x FitAwmi.C++
root > FitAwmi(); // re-run with another random set of peaks
created -9.76 14.9603 3
created -9.28 9.97356 2
created -8.8 14.9603 3
created -8.32 49.8678 10
created -7.84 39.8942 8
created -7.36 39.8942 8
created -6.88 14.9603 3
created -6.4 44.881 9
created -5.92 44.881 9
created -5.44 19.9471 4
created -4.96 49.8678 10
created -4.48 9.97356 2
created -4 49.8678 10
created -3.52 39.8942 8
created -3.04 49.8678 10
created -2.56 9.97356 2
created -2.08 39.8942 8
created -1.6 4.98678 1
created -1.12 39.8942 8
created -0.64 34.9074 7
created -0.16 34.9074 7
created 0.32 44.881 9
created 0.8 49.8678 10
created 1.28 39.8942 8
created 1.76 19.9471 4
created 2.24 39.8942 8
created 2.72 14.9603 3
created 3.2 4.98678 1
created 3.68 9.97356 2
created 4.16 34.9074 7
created 4.64 24.9339 5
created 5.12 4.98678 1
created 5.6 34.9074 7
created 6.08 4.98678 1
created 6.56 4.98678 1
created 7.04 29.9207 6
created 7.52 4.98678 1
created 8 34.9074 7
created 8.48 4.98678 1
created 8.96 4.98678 1
created 9.44 34.9074 7
the total number of created peaks = 41 with sigma = 0.08
the total number of found peaks = 41 with sigma = 0.0800011 (+-2.67441e-05)
fit chi^2 = 3.98205e-06
found -8.32 (+-0.000219372) 49.8677 (+-0.134963) 10.0001 (+-0.000893748)
found -4.96 (+-0.00021866) 49.8674 (+-0.134911) 10.0001 (+-0.000893403)
found -4 (+-0.000219159) 49.8676 (+-0.134948) 10.0001 (+-0.000893651)
found -3.04 (+-0.000219159) 49.8676 (+-0.134948) 10.0001 (+-0.000893651)
found 0.8 (+-0.000220149) 49.868 (+-0.13502) 10.0002 (+-0.00089413)
found -6.4 (+-0.000231504) 44.881 (+-0.128055) 9.00012 (+-0.000848005)
found -5.92 (+-0.000231695) 44.8811 (+-0.128068) 9.00013 (+-0.000848086)
found 0.32 (+-0.000232232) 44.8813 (+-0.128104) 9.00017 (+-0.000848328)
found -7.84 (+-0.000246686) 39.8946 (+-0.120801) 8.00018 (+-0.000799963)
found -7.36 (+-0.000245627) 39.8943 (+-0.120736) 8.00011 (+-0.000799537)
found -3.52 (+-0.000246919) 39.8947 (+-0.120815) 8.0002 (+-0.000800061)
found -2.08 (+-0.000243938) 39.8938 (+-0.120638) 8.00003 (+-0.000798888)
found -1.12 (+-0.000244878) 39.8941 (+-0.120694) 8.00008 (+-0.000799259)
found 1.28 (+-0.00024607) 39.8944 (+-0.120763) 8.00014 (+-0.000799716)
found 2.24 (+-0.000245019) 39.894 (+-0.120699) 8.00007 (+-0.00079929)
found -0.64 (+-0.000263616) 34.9077 (+-0.112993) 7.00015 (+-0.000748259)
found -0.16 (+-0.000263751) 34.9078 (+-0.113) 7.00016 (+-0.000748309)
found 4.16 (+-0.000262059) 34.9073 (+-0.112911) 7.00007 (+-0.000747714)
found 5.6 (+-0.000260532) 34.9071 (+-0.112835) 7.00002 (+-0.000747213)
found 8 (+-0.000260532) 34.9071 (+-0.112835) 7.00002 (+-0.000747213)
found 9.44 (+-0.000259457) 34.9074 (+-0.112792) 7.00008 (+-0.000746931)
found 7.04 (+-0.000281578) 29.9204 (+-0.104472) 6.00002 (+-0.00069183)
found 4.64 (+-0.000310652) 24.934 (+-0.0954514) 5.00008 (+-0.000632096)
found -5.44 (+-0.000351417) 19.9478 (+-0.0855025) 4.00019 (+-0.000566213)
found 1.76 (+-0.000350774) 19.9477 (+-0.0854809) 4.00016 (+-0.00056607)
found -6.88 (+-0.000406594) 14.961 (+-0.0740682) 3.00017 (+-0.000490493)
found 2.72 (+-0.000402871) 14.9606 (+-0.0739802) 3.00009 (+-0.00048991)
found -9.76 (+-0.000400875) 14.9602 (+-0.0739234) 3.00002 (+-0.000489534)
found -8.8 (+-0.000404274) 14.9608 (+-0.0740131) 3.00012 (+-0.000490128)
found -4.48 (+-0.000501634) 9.97445 (+-0.0605409) 2.0002 (+-0.000400913)
found -2.56 (+-0.000500873) 9.97434 (+-0.0605273) 2.00018 (+-0.000400823)
found -9.28 (+-0.00049466) 9.97373 (+-0.0604209) 2.00006 (+-0.000400118)
found 3.68 (+-0.0004948) 9.97383 (+-0.060427) 2.00008 (+-0.000400159)
found -1.6 (+-0.000714504) 4.98753 (+-0.0428558) 1.00016 (+-0.000283799)
found 5.12 (+-0.000711192) 4.98733 (+-0.0428252) 1.00012 (+-0.000283596)
found 7.52 (+-0.000712137) 4.98738 (+-0.0428338) 1.00013 (+-0.000283653)
found 6.07999 (+-0.000704894) 4.98713 (+-0.0427728) 1.00008 (+-0.000283249)
found 8.47999 (+-0.000704893) 4.98713 (+-0.0427728) 1.00008 (+-0.000283249)
found 3.2 (+-0.000703132) 4.98697 (+-0.042754) 1.00005 (+-0.000283124)
found 8.96001 (+-0.000704893) 4.98713 (+-0.0427728) 1.00008 (+-0.000283249)
found 6.56001 (+-0.00070408) 4.98707 (+-0.042765) 1.00007 (+-0.000283198)
#include "TROOT.h"
#include "TMath.h"
#include "TRandom.h"
#include "TH1.h"
#include "TF1.h"
#include "TCanvas.h"
#include "TSpectrum.h"
#include "TSpectrumFit.h"
#include "TPolyMarker.h"
#include "TList.h"
#include <iostream>
TH1F *FitAwmi_Create_Spectrum(void) {
Int_t nbins = 1000;
Double_t xmin = -10., xmax = 10.;
delete gROOT->FindObject("h"); // prevent "memory leak"
TH1F *h = new TH1F("h", "simulated spectrum", nbins, xmin, xmax);
h->SetStats(kFALSE);
TF1 f("f", "TMath::Gaus(x, [0], [1], 1)", xmin, xmax);
// f.SetParNames("mean", "sigma");
gRandom->SetSeed(0); // make it really random
// create well separated peaks with exactly known means and areas
// note: TSpectrumFit assumes that all peaks have the same sigma
Double_t sigma = (xmax - xmin) / Double_t(nbins) * Int_t(gRandom->Uniform(2., 6.));
Int_t npeaks = 0;
while (xmax > (xmin + 6. * sigma)) {
npeaks++;
xmin += 3. * sigma; // "mean"
f.SetParameters(xmin, sigma);
Double_t area = 1. * Int_t(gRandom->Uniform(1., 11.));
h->Add(&f, area, ""); // "" ... or ... "I"
std::cout << "created "
<< xmin << " "
<< (area / sigma / TMath::Sqrt(TMath::TwoPi())) << " "
<< area << std::endl;
xmin += 3. * sigma;
}
std::cout << "the total number of created peaks = " << npeaks
<< " with sigma = " << sigma << std::endl;
return h;
}
void FitAwmi(void) {
TH1F *h = FitAwmi_Create_Spectrum();
TCanvas *cFit = ((TCanvas *)(gROOT->GetListOfCanvases()->FindObject("cFit")));
if (!cFit) cFit = new TCanvas("cFit", "cFit", 10, 10, 1000, 700);
else cFit->Clear();
h->Draw("L");
Int_t i, nfound, bin;
Int_t nbins = h->GetNbinsX();
Double_t *source = new Double_t[nbins];
Double_t *dest = new Double_t[nbins];
for (i = 0; i < nbins; i++) source[i] = h->GetBinContent(i + 1);
TSpectrum *s = new TSpectrum(); // note: default maxpositions = 100
// searching for candidate peaks positions
nfound = s->SearchHighRes(source, dest, nbins, 2., 2., kFALSE, 10000, kFALSE, 0);
// filling in the initial estimates of the input parameters
Bool_t *FixPos = new Bool_t[nfound];
Bool_t *FixAmp = new Bool_t[nfound];
for(i = 0; i < nfound; i++) FixAmp[i] = FixPos[i] = kFALSE;
Double_t *Pos, *Amp = new Double_t[nfound]; // ROOT 6
Pos = s->GetPositionX(); // 0 ... (nbins - 1)
for (i = 0; i < nfound; i++) {
bin = 1 + Int_t(Pos[i] + 0.5); // the "nearest" bin
Amp[i] = h->GetBinContent(bin);
}
TSpectrumFit *pfit = new TSpectrumFit(nfound);
pfit->SetFitParameters(0, (nbins - 1), 1000, 0.1, pfit->kFitOptimChiCounts,
pfit->kFitAlphaHalving, pfit->kFitPower2,
pfit->kFitTaylorOrderFirst);
pfit->SetPeakParameters(2., kFALSE, Pos, FixPos, Amp, FixAmp);
// pfit->SetBackgroundParameters(source[0], kFALSE, 0., kFALSE, 0., kFALSE);
pfit->FitAwmi(source);
Double_t *Positions = pfit->GetPositions();
Double_t *PositionsErrors = pfit->GetPositionsErrors();
Double_t *Amplitudes = pfit->GetAmplitudes();
Double_t *AmplitudesErrors = pfit->GetAmplitudesErrors();
Double_t *Areas = pfit->GetAreas();
Double_t *AreasErrors = pfit->GetAreasErrors();
delete gROOT->FindObject("d"); // prevent "memory leak"
TH1F *d = new TH1F(*h); d->SetNameTitle("d", ""); d->Reset("M");
for (i = 0; i < nbins; i++) d->SetBinContent(i + 1, source[i]);
Double_t x1 = d->GetBinCenter(1), dx = d->GetBinWidth(1);
Double_t sigma, sigmaErr;
pfit->GetSigma(sigma, sigmaErr);
// current TSpectrumFit needs a sqrt(2) correction factor for sigma
sigma /= TMath::Sqrt2(); sigmaErr /= TMath::Sqrt2();
// convert "bin numbers" into "x-axis values"
sigma *= dx; 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); // the "nearest" bin
Pos[i] = d->GetBinCenter(bin);
Amp[i] = d->GetBinContent(bin);
// convert "bin numbers" into "x-axis values"
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;
}
d->SetLineColor(kRed); d->SetLineWidth(1);
d->Draw("SAME L");
TPolyMarker *pm = ((TPolyMarker*)(h->GetListOfFunctions()->FindObject("TPolyMarker")));
if (pm) {
h->GetListOfFunctions()->Remove(pm);
delete pm;
}
pm = new TPolyMarker(nfound, Pos, Amp);
h->GetListOfFunctions()->Add(pm);
pm->SetMarkerStyle(23);
pm->SetMarkerColor(kRed);
pm->SetMarkerSize(1);
// cleanup
delete pfit;
delete [] Amp;
delete [] FixAmp;
delete [] FixPos;
delete s;
delete [] dest;
delete [] source;
return;
}
d
#define d(i)
Definition RSha256.hxx:102
f
#define f(i)
Definition RSha256.hxx:104
h
#define h(i)
Definition RSha256.hxx:106
Bool_t
bool Bool_t
Definition RtypesCore.h:63
Int_t
int Int_t
Definition RtypesCore.h:45
kFALSE
constexpr Bool_t kFALSE
Definition RtypesCore.h:101
Double_t
double Double_t
Definition RtypesCore.h:59
kRed
@ kRed
Definition Rtypes.h:66
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358
dest
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t dest
Definition TGWin32VirtualXProxy.cxx:164
x1
Option_t Option_t TPoint TPoint const char x1
Definition TGWin32VirtualXProxy.cxx:70
xmin
float xmin
Definition THbookFile.cxx:95
xmax
float xmax
Definition THbookFile.cxx:95
gROOT
#define gROOT
Definition TROOT.h:414
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62
TCanvas
The Canvas class.
Definition TCanvas.h:23
TF1
1-Dim function class
Definition TF1.h:233
TH1F
1-D histogram with a float per channel (see TH1 documentation)
Definition TH1.h:621
TPolyMarker
A PolyMarker is defined by an array on N points in a 2-D space.
Definition TPolyMarker.h:31
TRandom::SetSeed
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition TRandom.cxx:615
TRandom::Uniform
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Definition TRandom.cxx:682
TSpectrumFit
Advanced 1-dimensional spectra fitting functions.
Definition TSpectrumFit.h:18
TSpectrum
Advanced Spectra Processing.
Definition TSpectrum.h:18
TSpectrum::SearchHighRes
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.
Definition TSpectrum.cxx:2129
TSpectrum::GetPositionX
Double_t * GetPositionX() const
Definition TSpectrum.h:58
sigma
const Double_t sigma
Definition h1analysisProxy.h:11
TMath::Sqrt2
constexpr Double_t Sqrt2()
Definition TMath.h:86
TMath::Sqrt
Double_t Sqrt(Double_t x)
Returns the square root of x.
Definition TMath.h:662
TMath::TwoPi
constexpr Double_t TwoPi()
Definition TMath.h:44
Author

Definition in file FitAwmi.C.