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 79.7885 8
created -9.64 39.8942 4
created -9.4 49.8678 5
created -9.16 39.8942 4
created -8.92 69.8149 7
created -8.68 19.9471 2
created -8.44 49.8678 5
created -8.2 69.8149 7
created -7.96 9.97356 1
created -7.72 99.7356 10
created -7.48 69.8149 7
created -7.24 69.8149 7
created -7 9.97356 1
created -6.76 9.97356 1
created -6.52 49.8678 5
created -6.28 29.9207 3
created -6.04 19.9471 2
created -5.8 29.9207 3
created -5.56 9.97356 1
created -5.32 9.97356 1
created -5.08 79.7885 8
created -4.84 69.8149 7
created -4.6 29.9207 3
created -4.36 69.8149 7
created -4.12 99.7356 10
created -3.88 29.9207 3
created -3.64 89.762 9
created -3.4 9.97356 1
created -3.16 49.8678 5
created -2.92 49.8678 5
created -2.68 69.8149 7
created -2.44 89.762 9
created -2.2 59.8413 6
created -1.96 39.8942 4
created -1.72 79.7885 8
created -1.48 9.97356 1
created -1.24 59.8413 6
created -1 49.8678 5
created -0.76 99.7356 10
created -0.52 69.8149 7
created -0.28 69.8149 7
created -0.04 9.97356 1
created 0.2 39.8942 4
created 0.44 49.8678 5
created 0.68 99.7356 10
created 0.92 79.7885 8
created 1.16 39.8942 4
created 1.4 79.7885 8
created 1.64 39.8942 4
created 1.88 9.97356 1
created 2.12 39.8942 4
created 2.36 79.7885 8
created 2.6 39.8942 4
created 2.84 89.762 9
created 3.08 29.9207 3
created 3.32 9.97356 1
created 3.56 49.8678 5
created 3.8 9.97356 1
created 4.04 69.8149 7
created 4.28 59.8413 6
created 4.52 9.97356 1
created 4.76 69.8149 7
created 5 19.9471 2
created 5.24 69.8149 7
created 5.48 89.762 9
created 5.72 9.97356 1
created 5.96 19.9471 2
created 6.2 79.7885 8
created 6.44 19.9471 2
created 6.68 99.7356 10
created 6.92 99.7356 10
created 7.16 9.97356 1
created 7.4 39.8942 4
created 7.64 69.8149 7
created 7.88 49.8678 5
created 8.12 89.762 9
created 8.36 79.7885 8
created 8.6 49.8678 5
created 8.84 19.9471 2
created 9.08 59.8413 6
created 9.32 89.762 9
created 9.56 59.8413 6
created 9.8 99.7356 10
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.88614e-05)
fit chi^2 = 1.71009e-05
found -4.12 (+-0.000216539) 99.7332 (+-0.533084) 10.0003 (+-0.00185202)
found 6.92 (+-0.000216313) 99.7334 (+-0.533039) 10.0003 (+-0.00185186)
found 9.8 (+-0.000215158) 99.7347 (+-0.53275) 10.0004 (+-0.00185085)
found -7.72 (+-0.00021603) 99.7326 (+-0.532949) 10.0002 (+-0.00185155)
found -0.76 (+-0.000216849) 99.7337 (+-0.533174) 10.0003 (+-0.00185233)
found 0.680001 (+-0.000216951) 99.734 (+-0.533205) 10.0003 (+-0.00185244)
found 6.68 (+-0.00021661) 99.7337 (+-0.533116) 10.0003 (+-0.00185213)
found -2.44 (+-0.000228874) 89.7609 (+-0.505894) 9.00034 (+-0.00175755)
found 5.48 (+-0.000227841) 89.7596 (+-0.505633) 9.00021 (+-0.00175665)
found 9.32 (+-0.000228751) 89.7607 (+-0.50586) 9.00032 (+-0.00175743)
found -3.64 (+-0.000227252) 89.7585 (+-0.505473) 9.0001 (+-0.00175609)
found 2.84 (+-0.000227988) 89.7593 (+-0.505655) 9.00018 (+-0.00175672)
found 8.12 (+-0.00022885) 89.7609 (+-0.505888) 9.00034 (+-0.00175753)
found 0.919999 (+-0.000242988) 79.7882 (+-0.477024) 8.00037 (+-0.00165725)
found 8.36 (+-0.000243052) 79.7881 (+-0.477037) 8.00037 (+-0.0016573)
found -9.88 (+-0.000242113) 79.7855 (+-0.476764) 8.00011 (+-0.00165635)
found -5.08 (+-0.000241818) 79.7865 (+-0.476753) 8.00021 (+-0.00165631)
found -1.72 (+-0.000241365) 79.7857 (+-0.47664) 8.00013 (+-0.00165592)
found 1.4 (+-0.000242181) 79.7865 (+-0.476821) 8.00021 (+-0.00165655)
found 2.36 (+-0.000242181) 79.7865 (+-0.476821) 8.00021 (+-0.00165655)
found 6.2 (+-0.000241273) 79.7855 (+-0.476613) 8.0001 (+-0.00165583)
found -7.48 (+-0.00026055) 69.8159 (+-0.446386) 7.00045 (+-0.00155081)
found -7.24 (+-0.000258714) 69.8135 (+-0.446003) 7.00021 (+-0.00154948)
found -4.84 (+-0.000259547) 69.8143 (+-0.446168) 7.00029 (+-0.00155006)
found -0.520001 (+-0.00026055) 69.8159 (+-0.446386) 7.00045 (+-0.00155081)
found -0.280002 (+-0.000258714) 69.8135 (+-0.446003) 7.00021 (+-0.00154948)
found -8.92 (+-0.000258608) 69.813 (+-0.445967) 7.00016 (+-0.00154936)
found -8.2 (+-0.000258394) 69.813 (+-0.445932) 7.00016 (+-0.00154924)
found -4.36 (+-0.000259801) 69.8148 (+-0.446226) 7.00035 (+-0.00155026)
found -2.68 (+-0.0002601) 69.8151 (+-0.446286) 7.00037 (+-0.00155047)
found 4.04 (+-0.000258562) 69.8132 (+-0.445969) 7.00018 (+-0.00154937)
found 4.76 (+-0.000257699) 69.8122 (+-0.445787) 7.00008 (+-0.00154873)
found 5.24 (+-0.000259392) 69.8143 (+-0.446141) 7.00029 (+-0.00154996)
found 7.64 (+-0.000259309) 69.8138 (+-0.446112) 7.00024 (+-0.00154986)
found -2.2 (+-0.00028107) 59.8418 (+-0.413207) 6.00035 (+-0.00143555)
found 9.56 (+-0.00028213) 59.8434 (+-0.413411) 6.00051 (+-0.00143625)
found 4.28 (+-0.000279707) 59.8405 (+-0.412967) 6.00021 (+-0.00143471)
found -1.24 (+-0.000279338) 59.8399 (+-0.412895) 6.00016 (+-0.00143446)
found 9.08 (+-0.000280484) 59.8413 (+-0.413104) 6.00029 (+-0.00143519)
found -9.4 (+-0.000307294) 49.8674 (+-0.377106) 5.00021 (+-0.00131013)
found -2.92 (+-0.000308258) 49.8685 (+-0.37726) 5.00032 (+-0.00131066)
found -0.999998 (+-0.000309034) 49.8695 (+-0.377388) 5.00043 (+-0.0013111)
found 0.440002 (+-0.000308536) 49.869 (+-0.377309) 5.00037 (+-0.00131083)
found 7.88 (+-0.000309078) 49.8695 (+-0.377394) 5.00043 (+-0.00131113)
found 8.6 (+-0.000307503) 49.8679 (+-0.377149) 5.00027 (+-0.00131027)
found -8.44 (+-0.000307311) 49.8677 (+-0.377117) 5.00024 (+-0.00131016)
found -6.52 (+-0.000305776) 49.8663 (+-0.376882) 5.0001 (+-0.00130935)
found -3.16 (+-0.000306333) 49.8669 (+-0.37697) 5.00016 (+-0.00130965)
found 3.56 (+-0.000304872) 49.8658 (+-0.376752) 5.00005 (+-0.0013089)
found -9.64 (+-0.000345572) 39.8957 (+-0.337554) 4.00035 (+-0.00117271)
found -9.16 (+-0.000345334) 39.8954 (+-0.337522) 4.00032 (+-0.0011726)
found -1.96 (+-0.00034586) 39.8959 (+-0.337591) 4.00037 (+-0.00117285)
found 1.16 (+-0.000346359) 39.8965 (+-0.337658) 4.00043 (+-0.00117308)
found 1.64 (+-0.000343757) 39.8946 (+-0.337337) 4.00024 (+-0.00117196)
found 2.6 (+-0.000346581) 39.8967 (+-0.337688) 4.00045 (+-0.00117318)
found 0.200002 (+-0.000342986) 39.8938 (+-0.337233) 4.00016 (+-0.0011716)
found 2.12 (+-0.000343757) 39.8946 (+-0.337337) 4.00024 (+-0.00117196)
found 7.4 (+-0.000343523) 39.8944 (+-0.337305) 4.00022 (+-0.00117185)
found -3.88 (+-0.000402067) 29.9242 (+-0.292638) 3.00051 (+-0.00101667)
found 3.08 (+-0.000398139) 29.9219 (+-0.292261) 3.00027 (+-0.00101536)
found -6.28 (+-0.000397694) 29.921 (+-0.292202) 3.00019 (+-0.00101515)
found -4.6 (+-0.000400616) 29.9229 (+-0.292489) 3.00037 (+-0.00101615)
found -5.8 (+-0.000395387) 29.92 (+-0.291985) 3.00008 (+-0.0010144)
found 6.44 (+-0.000494686) 19.9509 (+-0.239097) 2.00048 (+-0.00083066)
found -8.68 (+-0.000492047) 19.9493 (+-0.238912) 2.00032 (+-0.000830017)
found 5 (+-0.000493061) 19.9498 (+-0.238982) 2.00037 (+-0.00083026)
found 8.84 (+-0.000491571) 19.9491 (+-0.238879) 2.00029 (+-0.000829902)
found -6.04 (+-0.000488415) 19.9477 (+-0.238666) 2.00016 (+-0.000829164)
found 5.96001 (+-0.000489076) 19.9485 (+-0.238726) 2.00024 (+-0.000829372)
found 7.15999 (+-0.000703301) 9.97681 (+-0.169208) 1.00038 (+-0.000587855)
found -3.40001 (+-0.000703792) 9.97679 (+-0.169223) 1.00037 (+-0.000587906)
found -1.48 (+-0.000704068) 9.97679 (+-0.169231) 1.00037 (+-0.000587934)
found 5.71999 (+-0.000699733) 9.97602 (+-0.169084) 1.0003 (+-0.000587425)
found -7.95999 (+-0.000706159) 9.97759 (+-0.16931) 1.00045 (+-0.000588211)
found -7.00001 (+-0.000696097) 9.97522 (+-0.168958) 1.00022 (+-0.000586988)
found -0.0400058 (+-0.000701321) 9.976 (+-0.169131) 1.00029 (+-0.000587588)
found 4.52 (+-0.000703339) 9.97652 (+-0.169203) 1.00035 (+-0.000587838)
found 1.88 (+-0.000698517) 9.9752 (+-0.169029) 1.00021 (+-0.000587232)
found 3.8 (+-0.000702408) 9.97625 (+-0.169169) 1.00032 (+-0.000587721)
found -5.56 (+-0.000692088) 9.97414 (+-0.168811) 1.00011 (+-0.000586476)
found 3.32 (+-0.000698294) 9.9752 (+-0.169022) 1.00021 (+-0.000587209)
found -6.75999 (+-0.000694403) 9.97468 (+-0.168894) 1.00016 (+-0.000586765)
found -5.31999 (+-0.000696805) 9.97548 (+-0.168986) 1.00024 (+-0.000587084)

 
#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.