FitAwmi.C File Reference

Detailed Description

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 -9.88 9.97356 1
created -9.64 99.7356 10
created -9.4 9.97356 1
created -9.16 29.9207 3
created -8.92 99.7356 10
created -8.68 39.8942 4
created -8.44 69.8149 7
created -8.2 69.8149 7
created -7.96 69.8149 7
created -7.72 19.9471 2
created -7.48 99.7356 10
created -7.24 19.9471 2
created -7 19.9471 2
created -6.76 9.97356 1
created -6.52 49.8678 5
created -6.28 59.8413 6
created -6.04 39.8942 4
created -5.8 79.7885 8
created -5.56 79.7885 8
created -5.32 29.9207 3
created -5.08 39.8942 4
created -4.84 29.9207 3
created -4.6 39.8942 4
created -4.36 49.8678 5
created -4.12 9.97356 1
created -3.88 69.8149 7
created -3.64 89.762 9
created -3.4 99.7356 10
created -3.16 59.8413 6
created -2.92 89.762 9
created -2.68 69.8149 7
created -2.44 69.8149 7
created -2.2 99.7356 10
created -1.96 69.8149 7
created -1.72 59.8413 6
created -1.48 49.8678 5
created -1.24 49.8678 5
created -1 9.97356 1
created -0.76 59.8413 6
created -0.52 79.7885 8
created -0.28 49.8678 5
created -0.04 89.762 9
created 0.2 9.97356 1
created 0.44 49.8678 5
created 0.68 79.7885 8
created 0.92 39.8942 4
created 1.16 89.762 9
created 1.4 49.8678 5
created 1.64 29.9207 3
created 1.88 29.9207 3
created 2.12 9.97356 1
created 2.36 29.9207 3
created 2.6 19.9471 2
created 2.84 69.8149 7
created 3.08 69.8149 7
created 3.32 69.8149 7
created 3.56 99.7356 10
created 3.8 79.7885 8
created 4.04 69.8149 7
created 4.28 39.8942 4
created 4.52 89.762 9
created 4.76 99.7356 10
created 5 89.762 9
created 5.24 49.8678 5
created 5.48 59.8413 6
created 5.72 29.9207 3
created 5.96 59.8413 6
created 6.2 29.9207 3
created 6.44 19.9471 2
created 6.68 29.9207 3
created 6.92 69.8149 7
created 7.16 19.9471 2
created 7.4 29.9207 3
created 7.64 69.8149 7
created 7.88 59.8413 6
created 8.12 9.97356 1
created 8.36 79.7885 8
created 8.6 9.97356 1
created 8.84 49.8678 5
created 9.08 79.7885 8
created 9.32 89.762 9
created 9.56 49.8678 5
created 9.8 39.8942 4
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.0400021 (+-1.22064e-05)
fit chi^2 = 7.41151e-06
found -3.4 (+-0.000142969) 99.7344 (+-0.351069) 10.0004 (+-0.00121967)
found -2.2 (+-0.000142913) 99.7341 (+-0.35105) 10.0004 (+-0.00121961)
found 3.56 (+-0.000142981) 99.7344 (+-0.351071) 10.0004 (+-0.00121968)
found 4.76 (+-0.000143173) 99.7352 (+-0.351132) 10.0005 (+-0.00121989)
found -9.64 (+-0.000141535) 99.7309 (+-0.350661) 10 (+-0.00121825)
found -8.92 (+-0.000142307) 99.7323 (+-0.35087) 10.0002 (+-0.00121898)
found -7.48 (+-0.000141921) 99.7315 (+-0.350763) 10.0001 (+-0.00121861)
found -3.64 (+-0.000150963) 89.7619 (+-0.333126) 9.00045 (+-0.00115733)
found -2.92 (+-0.000150674) 89.7609 (+-0.333044) 9.00035 (+-0.00115705)
found 5 (+-0.000150792) 89.7614 (+-0.333079) 9.0004 (+-0.00115717)
found 9.32 (+-0.000150659) 89.7608 (+-0.333041) 9.00034 (+-0.00115704)
found -0.0400009 (+-0.000149827) 89.759 (+-0.332826) 9.00016 (+-0.00115629)
found 1.16 (+-0.000150313) 89.7598 (+-0.332946) 9.00024 (+-0.00115671)
found 4.52 (+-0.000150689) 89.7611 (+-0.333052) 9.00037 (+-0.00115708)
found -5.56 (+-0.000159684) 79.7873 (+-0.313969) 8.00029 (+-0.00109078)
found -0.52 (+-0.000159759) 79.7873 (+-0.313984) 8.00029 (+-0.00109083)
found 3.8 (+-0.00016027) 79.7889 (+-0.314113) 8.00045 (+-0.00109128)
found -5.8 (+-0.000159818) 79.7875 (+-0.314) 8.00032 (+-0.00109089)
found 0.68 (+-0.000159547) 79.7867 (+-0.313932) 8.00024 (+-0.00109065)
found 8.36 (+-0.000158366) 79.7849 (+-0.313667) 8.00005 (+-0.00108973)
found 9.08 (+-0.000160008) 79.7881 (+-0.314047) 8.00037 (+-0.00109105)
found -8.2 (+-0.000171265) 69.815 (+-0.293809) 7.00037 (+-0.00102074)
found -7.96 (+-0.000170587) 69.8137 (+-0.293667) 7.00024 (+-0.00102025)
found -2.68 (+-0.000171446) 69.8156 (+-0.29385) 7.00043 (+-0.00102088)
found -2.44 (+-0.000171528) 69.8158 (+-0.293869) 7.00045 (+-0.00102095)
found -1.96 (+-0.000171425) 69.8156 (+-0.293846) 7.00043 (+-0.00102087)
found 3.08 (+-0.000171265) 69.815 (+-0.293809) 7.00037 (+-0.00102074)
found 3.32 (+-0.000171528) 69.8158 (+-0.293869) 7.00045 (+-0.00102095)
found 4.04 (+-0.000171017) 69.8145 (+-0.293756) 7.00032 (+-0.00102056)
found -8.44 (+-0.000170924) 69.8142 (+-0.293735) 7.00029 (+-0.00102048)
found -3.88 (+-0.000170497) 69.814 (+-0.293657) 7.00027 (+-0.00102021)
found 2.84 (+-0.000170587) 69.8137 (+-0.293667) 7.00024 (+-0.00102025)
found 6.92 (+-0.000170101) 69.8126 (+-0.293562) 7.00013 (+-0.00101988)
found 7.64 (+-0.000170674) 69.8137 (+-0.293682) 7.00024 (+-0.0010203)
found -3.16 (+-0.000185735) 59.8433 (+-0.27216) 6.00051 (+-0.000945527)
found -6.28 (+-0.000184584) 59.8407 (+-0.271938) 6.00024 (+-0.000944757)
found -1.72 (+-0.000184975) 59.8415 (+-0.272012) 6.00032 (+-0.000945014)
found 5.48 (+-0.000184414) 59.8404 (+-0.271907) 6.00021 (+-0.00094465)
found 5.96 (+-0.000184099) 59.8399 (+-0.27185) 6.00016 (+-0.000944449)
found 7.88 (+-0.000184139) 59.8404 (+-0.271868) 6.00021 (+-0.000944512)
found -0.759998 (+-0.000184245) 59.8407 (+-0.271889) 6.00024 (+-0.000944585)
found 1.4 (+-0.000202806) 49.8684 (+-0.248344) 5.00032 (+-0.000862787)
found 5.24 (+-0.000203335) 49.8692 (+-0.248427) 5.0004 (+-0.000863074)
found 9.56 (+-0.000203008) 49.8687 (+-0.248375) 5.00035 (+-0.000862894)
found -4.36 (+-0.000201499) 49.8666 (+-0.248143) 5.00013 (+-0.000862089)
found -1.48 (+-0.000202797) 49.8681 (+-0.248339) 5.00029 (+-0.000862768)
found -1.24 (+-0.000201668) 49.8668 (+-0.24817) 5.00016 (+-0.000862183)
found -0.28 (+-0.000203603) 49.8697 (+-0.24847) 5.00045 (+-0.000863226)
found -6.52 (+-0.000201819) 49.8671 (+-0.248195) 5.00018 (+-0.000862268)
found 0.440003 (+-0.000202081) 49.8676 (+-0.248238) 5.00024 (+-0.00086242)
found 8.84 (+-0.000202081) 49.8676 (+-0.248238) 5.00024 (+-0.00086242)
found -8.68 (+-0.000228142) 39.8967 (+-0.222307) 4.00045 (+-0.00077233)
found -6.04 (+-0.00022769) 39.8959 (+-0.222246) 4.00037 (+-0.000772118)
found -5.08 (+-0.000226074) 39.8938 (+-0.222037) 4.00016 (+-0.000771393)
found -4.6 (+-0.000226528) 39.8943 (+-0.222095) 4.00021 (+-0.000771594)
found 0.92 (+-0.000228164) 39.8967 (+-0.222309) 4.00045 (+-0.000772338)
found 4.28 (+-0.000228007) 39.8964 (+-0.222288) 4.00043 (+-0.000772265)
found 9.8 (+-0.000224569) 39.8946 (+-0.221896) 4.00024 (+-0.000770903)
found -5.32 (+-0.000263192) 29.9223 (+-0.1925) 3.00032 (+-0.000668776)
found 1.64 (+-0.000262205) 29.9213 (+-0.192401) 3.00021 (+-0.000668433)
found 5.72 (+-0.000263293) 29.9223 (+-0.192509) 3.00032 (+-0.000668806)
found 6.2 (+-0.000262056) 29.9213 (+-0.192389) 3.00021 (+-0.000668391)
found -4.84 (+-0.00026225) 29.9213 (+-0.192405) 3.00021 (+-0.000668445)
found 1.88 (+-0.00026068) 29.9202 (+-0.192259) 3.00011 (+-0.000667938)
found 6.68 (+-0.000262275) 29.9215 (+-0.192412) 3.00024 (+-0.000668469)
found 7.4 (+-0.000262275) 29.9215 (+-0.192412) 3.00024 (+-0.000668469)
found -9.15999 (+-0.000262276) 29.9221 (+-0.192422) 3.0003 (+-0.000668505)
found 2.36 (+-0.000260295) 29.9199 (+-0.192222) 3.00008 (+-0.000667811)
found -7.24001 (+-0.000323251) 19.9493 (+-0.157244) 2.00032 (+-0.000546289)
found -7.72 (+-0.00032538) 19.9506 (+-0.157384) 2.00045 (+-0.000546778)
found 7.16 (+-0.000323057) 19.9488 (+-0.157225) 2.00027 (+-0.000546223)
found -7 (+-0.000319636) 19.9469 (+-0.157) 2.00008 (+-0.000545443)
found 2.6 (+-0.000323057) 19.9488 (+-0.157225) 2.00027 (+-0.000546223)
found 6.44 (+-0.000321538) 19.9477 (+-0.157121) 2.00016 (+-0.000545863)
found -9.40001 (+-0.000462138) 9.97653 (+-0.111365) 1.00035 (+-0.000386899)
found 0.199992 (+-0.000463327) 9.97679 (+-0.111404) 1.00038 (+-0.000387035)
found 8.59999 (+-0.000462894) 9.97651 (+-0.111387) 1.00035 (+-0.000386977)
found 8.12 (+-0.000463509) 9.97678 (+-0.11141) 1.00037 (+-0.000387054)
found -4.12 (+-0.000462415) 9.97625 (+-0.111369) 1.00032 (+-0.000386914)
found -0.999998 (+-0.00046188) 9.97598 (+-0.111349) 1.00029 (+-0.000386845)
found 2.12 (+-0.00045816) 9.97464 (+-0.111217) 1.00016 (+-0.000386386)
found -6.75999 (+-0.000458633) 9.97492 (+-0.111236) 1.00019 (+-0.000386451)
found -9.87998 (+-0.000458268) 9.97576 (+-0.111234) 1.00027 (+-0.000386443)

 
#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;
}

Author

Definition in file FitAwmi.C.