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 39.8942 4
created -9.64 89.762 9
created -9.4 49.8678 5
created -9.16 99.7356 10
created -8.92 59.8413 6
created -8.68 19.9471 2
created -8.44 49.8678 5
created -8.2 49.8678 5
created -7.96 79.7885 8
created -7.72 29.9207 3
created -7.48 79.7885 8
created -7.24 39.8942 4
created -7 39.8942 4
created -6.76 49.8678 5
created -6.52 49.8678 5
created -6.28 29.9207 3
created -6.04 29.9207 3
created -5.8 69.8149 7
created -5.56 99.7356 10
created -5.32 49.8678 5
created -5.08 99.7356 10
created -4.84 29.9207 3
created -4.6 69.8149 7
created -4.36 19.9471 2
created -4.12 69.8149 7
created -3.88 49.8678 5
created -3.64 49.8678 5
created -3.4 89.762 9
created -3.16 19.9471 2
created -2.92 79.7885 8
created -2.68 19.9471 2
created -2.44 19.9471 2
created -2.2 89.762 9
created -1.96 9.97356 1
created -1.72 99.7356 10
created -1.48 19.9471 2
created -1.24 49.8678 5
created -1 99.7356 10
created -0.76 79.7885 8
created -0.52 89.762 9
created -0.28 99.7356 10
created -0.04 19.9471 2
created 0.2 39.8942 4
created 0.44 89.762 9
created 0.68 9.97356 1
created 0.92 79.7885 8
created 1.16 59.8413 6
created 1.4 39.8942 4
created 1.64 29.9207 3
created 1.88 89.762 9
created 2.12 39.8942 4
created 2.36 9.97356 1
created 2.6 19.9471 2
created 2.84 79.7885 8
created 3.08 69.8149 7
created 3.32 29.9207 3
created 3.56 69.8149 7
created 3.8 79.7885 8
created 4.04 99.7356 10
created 4.28 99.7356 10
created 4.52 69.8149 7
created 4.76 29.9207 3
created 5 29.9207 3
created 5.24 59.8413 6
created 5.48 39.8942 4
created 5.72 49.8678 5
created 5.96 89.762 9
created 6.2 9.97356 1
created 6.44 19.9471 2
created 6.68 59.8413 6
created 6.92 9.97356 1
created 7.16 39.8942 4
created 7.4 69.8149 7
created 7.64 29.9207 3
created 7.88 39.8942 4
created 8.12 69.8149 7
created 8.36 79.7885 8
created 8.6 29.9207 3
created 8.84 49.8678 5
created 9.08 79.7885 8
created 9.32 39.8942 4
created 9.56 39.8942 4
created 9.8 19.9471 2
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.0400021 (+-9.30924e-06)
fit chi^2 = 4.29062e-06
found -5.56 (+-0.000108619) 99.7336 (+-0.267066) 10.0003 (+-0.000927829)
found -0.280001 (+-0.000108456) 99.7333 (+-0.267023) 10.0003 (+-0.00092768)
found 4.04 (+-0.000108932) 99.7352 (+-0.267163) 10.0005 (+-0.000928165)
found 4.28 (+-0.000108881) 99.7349 (+-0.267147) 10.0004 (+-0.00092811)
found -9.16 (+-0.000108563) 99.7333 (+-0.267049) 10.0003 (+-0.000927769)
found -5.08 (+-0.000108347) 99.7326 (+-0.266985) 10.0002 (+-0.000927549)
found -1.72 (+-0.000107835) 99.7312 (+-0.266844) 10.0001 (+-0.000927059)
found -0.999999 (+-0.00010867) 99.7339 (+-0.267082) 10.0003 (+-0.000927884)
found -0.52 (+-0.000114919) 89.7622 (+-0.25348) 9.00048 (+-0.000880628)
found -9.64 (+-0.000114368) 89.7598 (+-0.253326) 9.00024 (+-0.000880096)
found -3.4 (+-0.00011416) 89.7592 (+-0.253274) 9.00018 (+-0.000879913)
found -2.2 (+-0.000113716) 89.7582 (+-0.253162) 9.00008 (+-0.000879525)
found 0.439999 (+-0.000113921) 89.7587 (+-0.253214) 9.00013 (+-0.000879707)
found 1.88 (+-0.000114199) 89.7592 (+-0.253282) 9.00018 (+-0.000879941)
found 5.96 (+-0.000113998) 89.7589 (+-0.253235) 9.00015 (+-0.000879779)
found -0.76 (+-0.000122066) 79.7894 (+-0.239028) 8.00051 (+-0.000830421)
found 3.8 (+-0.000121944) 79.7889 (+-0.238997) 8.00045 (+-0.000830313)
found 8.36 (+-0.000121436) 79.787 (+-0.238872) 8.00026 (+-0.000829878)
found -7.96 (+-0.000121292) 79.7865 (+-0.238836) 8.00021 (+-0.000829754)
found -7.48 (+-0.000121207) 79.7862 (+-0.238815) 8.00018 (+-0.000829682)
found -2.92 (+-0.000120853) 79.7854 (+-0.238735) 8.0001 (+-0.000829402)
found 0.920001 (+-0.000121059) 79.7862 (+-0.238787) 8.00019 (+-0.000829586)
found 2.84 (+-0.000121308) 79.7867 (+-0.238843) 8.00024 (+-0.000829779)
found 9.08 (+-0.000121394) 79.7867 (+-0.23886) 8.00024 (+-0.000829836)
found 3.08 (+-0.000130007) 69.8142 (+-0.223485) 7.00029 (+-0.000776421)
found 4.52 (+-0.000130134) 69.8148 (+-0.223514) 7.00035 (+-0.000776523)
found -5.8 (+-0.000130134) 69.8148 (+-0.223514) 7.00034 (+-0.000776523)
found -4.6 (+-0.000129424) 69.8127 (+-0.223361) 7.00013 (+-0.00077599)
found -4.12 (+-0.000129632) 69.8132 (+-0.223405) 7.00019 (+-0.000776143)
found 3.56 (+-0.000130007) 69.8142 (+-0.223485) 7.00029 (+-0.000776421)
found 7.4 (+-0.000129678) 69.8131 (+-0.223412) 7.00018 (+-0.00077617)
found 8.12 (+-0.000130121) 69.8145 (+-0.223508) 7.00032 (+-0.000776503)
found -8.92 (+-0.000140565) 59.8415 (+-0.206938) 6.00032 (+-0.000718934)
found 1.16 (+-0.000140712) 59.8414 (+-0.20696) 6.00032 (+-0.000719011)
found 5.24 (+-0.000140203) 59.8401 (+-0.206864) 6.00019 (+-0.000718678)
found 6.68 (+-0.000139523) 59.839 (+-0.206747) 6.00008 (+-0.00071827)
found -9.4 (+-0.00015509) 49.8703 (+-0.189081) 5.00051 (+-0.000656899)
found -5.32 (+-0.000155175) 49.8705 (+-0.189096) 5.00053 (+-0.000656948)
found -8.2 (+-0.000154503) 49.8687 (+-0.188985) 5.00035 (+-0.000656564)
found -6.76 (+-0.000154054) 49.8676 (+-0.188912) 5.00024 (+-0.000656312)
found -6.52 (+-0.000153901) 49.8673 (+-0.188889) 5.00021 (+-0.000656231)
found -3.88 (+-0.000154406) 49.8684 (+-0.188969) 5.00032 (+-0.000656508)
found -3.64 (+-0.000154593) 49.8689 (+-0.189) 5.00037 (+-0.000656616)
found 5.72 (+-0.000154461) 49.8687 (+-0.188979) 5.00035 (+-0.000656545)
found 8.84 (+-0.000154218) 49.8682 (+-0.188941) 5.00029 (+-0.000656411)
found -8.44 (+-0.000153712) 49.8671 (+-0.188861) 5.00019 (+-0.000656134)
found -1.24 (+-0.000154201) 49.8684 (+-0.188942) 5.00032 (+-0.000656416)
found 2.12 (+-0.000172296) 39.8948 (+-0.168986) 4.00027 (+-0.000587085)
found -7.24 (+-0.000172935) 39.8954 (+-0.16906) 4.00032 (+-0.00058734)
found -7 (+-0.000172544) 39.8946 (+-0.169008) 4.00024 (+-0.000587159)
found 1.4 (+-0.000172499) 39.8946 (+-0.169003) 4.00024 (+-0.000587143)
found 5.48 (+-0.000172848) 39.8951 (+-0.169047) 4.00029 (+-0.000587296)
found 7.88 (+-0.000172628) 39.8949 (+-0.16902) 4.00027 (+-0.000587203)
found 9.32 (+-0.000172935) 39.8954 (+-0.16906) 4.00032 (+-0.00058734)
found 9.56 (+-0.000171968) 39.8938 (+-0.168936) 4.00016 (+-0.00058691)
found -9.88 (+-0.000172575) 39.8946 (+-0.169003) 4.00024 (+-0.000587143)
found 0.200003 (+-0.000172624) 39.8951 (+-0.169023) 4.0003 (+-0.000587214)
found 7.16 (+-0.00017207) 39.8943 (+-0.168955) 4.00021 (+-0.000586978)
found -7.72 (+-0.000200977) 29.9234 (+-0.146539) 3.00043 (+-0.000509099)
found -4.84 (+-0.000201097) 29.9237 (+-0.146551) 3.00045 (+-0.000509143)
found 8.6 (+-0.000200465) 29.9226 (+-0.146487) 3.00035 (+-0.000508919)
found -6.28 (+-0.000199502) 29.9213 (+-0.146391) 3.00021 (+-0.000508586)
found 3.32 (+-0.000200668) 29.9229 (+-0.146507) 3.00037 (+-0.000508989)
found 4.76 (+-0.000199855) 29.9218 (+-0.146427) 3.00027 (+-0.00050871)
found 7.64 (+-0.0002001) 29.9221 (+-0.14645) 3.00029 (+-0.000508792)
found -6.04 (+-0.000199855) 29.9218 (+-0.146427) 3.00027 (+-0.00050871)
found 1.64 (+-0.000200394) 29.9226 (+-0.146481) 3.00035 (+-0.000508898)
found 5 (+-0.000199688) 29.9215 (+-0.14641) 3.00024 (+-0.000508651)
found -3.16 (+-0.000247605) 19.9506 (+-0.11975) 2.00045 (+-0.000416031)
found -2.68001 (+-0.000245578) 19.9488 (+-0.119614) 2.00027 (+-0.000415557)
found -1.48 (+-0.000247058) 19.9501 (+-0.119713) 2.0004 (+-0.000415902)
found -0.0400057 (+-0.00024675) 19.9498 (+-0.119692) 2.00038 (+-0.000415831)
found -8.68 (+-0.000246227) 19.949 (+-0.119654) 2.00029 (+-0.000415697)
found -4.36 (+-0.000246974) 19.9498 (+-0.119706) 2.00037 (+-0.000415876)
found -2.43999 (+-0.000245772) 19.949 (+-0.119628) 2.0003 (+-0.000415606)
found 9.8 (+-0.000242178) 19.9477 (+-0.119419) 2.00016 (+-0.000414882)
found 2.60001 (+-0.000244978) 19.9485 (+-0.119578) 2.00024 (+-0.000415431)
found 6.44 (+-0.000244533) 19.948 (+-0.119546) 2.00019 (+-0.000415321)
found -1.96 (+-0.000354419) 9.97811 (+-0.0848339) 1.00051 (+-0.000294727)
found 0.679998 (+-0.000353778) 9.97758 (+-0.0848092) 1.00045 (+-0.000294641)
found 6.19999 (+-0.000350496) 9.976 (+-0.084694) 1.0003 (+-0.00029424)
found 6.92 (+-0.000350886) 9.97571 (+-0.0847023) 1.00027 (+-0.000294269)
found 2.36 (+-0.000348425) 9.97466 (+-0.0846161) 1.00016 (+-0.00029397)

 
#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.