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 59.8413 6
created -9.64 99.7356 10
created -9.4 89.762 9
created -9.16 29.9207 3
created -8.92 9.97356 1
created -8.68 49.8678 5
created -8.44 99.7356 10
created -8.2 9.97356 1
created -7.96 19.9471 2
created -7.72 39.8942 4
created -7.48 59.8413 6
created -7.24 79.7885 8
created -7 29.9207 3
created -6.76 49.8678 5
created -6.52 39.8942 4
created -6.28 49.8678 5
created -6.04 29.9207 3
created -5.8 9.97356 1
created -5.56 29.9207 3
created -5.32 29.9207 3
created -5.08 99.7356 10
created -4.84 9.97356 1
created -4.6 79.7885 8
created -4.36 9.97356 1
created -4.12 69.8149 7
created -3.88 79.7885 8
created -3.64 79.7885 8
created -3.4 29.9207 3
created -3.16 49.8678 5
created -2.92 79.7885 8
created -2.68 99.7356 10
created -2.44 39.8942 4
created -2.2 39.8942 4
created -1.96 69.8149 7
created -1.72 89.762 9
created -1.48 29.9207 3
created -1.24 9.97356 1
created -1 9.97356 1
created -0.76 39.8942 4
created -0.52 79.7885 8
created -0.28 39.8942 4
created -0.04 39.8942 4
created 0.2 19.9471 2
created 0.44 79.7885 8
created 0.68 39.8942 4
created 0.92 79.7885 8
created 1.16 69.8149 7
created 1.4 99.7356 10
created 1.64 79.7885 8
created 1.88 59.8413 6
created 2.12 19.9471 2
created 2.36 9.97356 1
created 2.6 29.9207 3
created 2.84 29.9207 3
created 3.08 39.8942 4
created 3.32 39.8942 4
created 3.56 59.8413 6
created 3.8 79.7885 8
created 4.04 39.8942 4
created 4.28 19.9471 2
created 4.52 19.9471 2
created 4.76 29.9207 3
created 5 39.8942 4
created 5.24 49.8678 5
created 5.48 89.762 9
created 5.72 39.8942 4
created 5.96 89.762 9
created 6.2 29.9207 3
created 6.44 79.7885 8
created 6.68 99.7356 10
created 6.92 89.762 9
created 7.16 39.8942 4
created 7.4 9.97356 1
created 7.64 59.8413 6
created 7.88 9.97356 1
created 8.12 69.8149 7
created 8.36 69.8149 7
created 8.6 89.762 9
created 8.84 69.8149 7
created 9.08 49.8678 5
created 9.32 59.8413 6
created 9.56 19.9471 2
created 9.8 49.8678 5
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.37746e-05)
fit chi^2 = 8.91055e-06
found -9.64 (+-0.000156762) 99.7344 (+-0.384938) 10.0004 (+-0.00133734)
found -2.68 (+-0.000156502) 99.7336 (+-0.38486) 10.0003 (+-0.00133706)
found 1.4 (+-0.000156775) 99.7345 (+-0.384941) 10.0004 (+-0.00133735)
found 6.68 (+-0.000156918) 99.735 (+-0.384986) 10.0004 (+-0.0013375)
found -8.44 (+-0.000155772) 99.732 (+-0.384653) 10.0002 (+-0.00133635)
found -5.08 (+-0.000155551) 99.7315 (+-0.384589) 10.0001 (+-0.00133612)
found -9.4 (+-0.000165094) 89.7609 (+-0.365149) 9.00034 (+-0.00126859)
found -1.72 (+-0.000164868) 89.7601 (+-0.365084) 9.00026 (+-0.00126836)
found 6.92 (+-0.000165227) 89.7611 (+-0.365184) 9.00037 (+-0.00126871)
found 8.6 (+-0.0001653) 89.7611 (+-0.3652) 9.00037 (+-0.00126876)
found 5.48 (+-0.000164814) 89.7598 (+-0.365067) 9.00024 (+-0.0012683)
found 5.96 (+-0.000164571) 89.7593 (+-0.365003) 9.00019 (+-0.00126808)
found -7.24 (+-0.000174902) 79.7868 (+-0.344212) 8.00024 (+-0.00119585)
found -3.88 (+-0.000175569) 79.7883 (+-0.344375) 8.0004 (+-0.00119641)
found -3.64 (+-0.00017509) 79.7873 (+-0.344259) 8.00029 (+-0.00119601)
found 1.64 (+-0.000175633) 79.7886 (+-0.344392) 8.00042 (+-0.00119647)
found 3.8 (+-0.000175048) 79.787 (+-0.344246) 8.00026 (+-0.00119596)
found -4.6 (+-0.000173645) 79.7849 (+-0.343928) 8.00005 (+-0.00119486)
found -2.92 (+-0.000175524) 79.7884 (+-0.344365) 8.0004 (+-0.00119638)
found -0.52 (+-0.000174816) 79.7865 (+-0.344189) 8.00021 (+-0.00119577)
found 0.44 (+-0.000174488) 79.786 (+-0.344114) 8.00016 (+-0.00119551)
found 0.920001 (+-0.000175146) 79.7873 (+-0.344271) 8.00029 (+-0.00119605)
found 6.44 (+-0.000175252) 79.7879 (+-0.344302) 8.00035 (+-0.00119616)
found 1.16 (+-0.000188179) 69.8161 (+-0.322243) 7.00048 (+-0.00111952)
found 8.36 (+-0.000187986) 69.8156 (+-0.322199) 7.00042 (+-0.00111937)
found 8.84 (+-0.000187751) 69.8151 (+-0.322148) 7.00037 (+-0.00111919)
found -4.12 (+-0.000186851) 69.8137 (+-0.321967) 7.00024 (+-0.00111856)
found -1.96 (+-0.000187611) 69.8148 (+-0.322118) 7.00034 (+-0.00111909)
found 8.12 (+-0.000186751) 69.8135 (+-0.321944) 7.00021 (+-0.00111848)
found -7.48 (+-0.000202779) 59.8415 (+-0.298249) 6.00032 (+-0.00103616)
found 1.88 (+-0.000202356) 59.8409 (+-0.298174) 6.00027 (+-0.0010359)
found 3.56 (+-0.000202779) 59.8415 (+-0.298249) 6.00032 (+-0.00103616)
found 9.32 (+-0.000201972) 59.8402 (+-0.2981) 6.00019 (+-0.00103564)
found -9.88 (+-0.000202745) 59.841 (+-0.298221) 6.00027 (+-0.00103607)
found 7.64 (+-0.000200737) 59.8388 (+-0.297887) 6.00005 (+-0.00103491)
found -6.76 (+-0.000221599) 49.8671 (+-0.272177) 5.00019 (+-0.000945587)
found -6.28 (+-0.000221599) 49.8671 (+-0.272177) 5.00019 (+-0.000945587)
found -3.16 (+-0.000222243) 49.8682 (+-0.272282) 5.00029 (+-0.00094595)
found 5.24 (+-0.000222593) 49.8687 (+-0.272337) 5.00035 (+-0.000946142)
found 9.08 (+-0.000222682) 49.8687 (+-0.272349) 5.00035 (+-0.000946184)
found -8.68 (+-0.000221825) 49.8682 (+-0.27223) 5.00029 (+-0.000945769)
found 9.8 (+-0.000219376) 49.8671 (+-0.271885) 5.00018 (+-0.000944573)
found -2.44 (+-0.000249521) 39.8959 (+-0.243673) 4.00037 (+-0.000846557)
found 5.72 (+-0.000250337) 39.897 (+-0.243779) 4.00048 (+-0.000846925)
found 7.16 (+-0.000248295) 39.8949 (+-0.243525) 4.00027 (+-0.000846045)
found -6.52 (+-0.000248884) 39.8948 (+-0.243586) 4.00027 (+-0.000846255)
found -2.2 (+-0.000249044) 39.8951 (+-0.243608) 4.0003 (+-0.000846331)
found -0.280002 (+-0.000249215) 39.8954 (+-0.243631) 4.00032 (+-0.000846411)
found -0.0400009 (+-0.000247822) 39.8938 (+-0.243452) 4.00016 (+-0.000845792)
found 0.68 (+-0.000250017) 39.8964 (+-0.243735) 4.00043 (+-0.000846775)
found 3.08 (+-0.000248152) 39.8941 (+-0.243492) 4.00019 (+-0.000845931)
found 3.32 (+-0.000248858) 39.8949 (+-0.243583) 4.00027 (+-0.000846245)
found 4.04 (+-0.000248611) 39.8949 (+-0.243557) 4.00027 (+-0.000846155)
found 5 (+-0.000248383) 39.8943 (+-0.243522) 4.00021 (+-0.000846034)
found -7.72 (+-0.000248256) 39.8943 (+-0.243509) 4.00021 (+-0.000845989)
found -0.759997 (+-0.000248138) 39.8946 (+-0.243504) 4.00024 (+-0.00084597)
found -9.16001 (+-0.000287393) 29.9218 (+-0.210966) 3.00027 (+-0.00073293)
found -7 (+-0.000288889) 29.9226 (+-0.211102) 3.00035 (+-0.0007334)
found -3.4 (+-0.000288889) 29.9226 (+-0.211102) 3.00035 (+-0.0007334)
found -1.48001 (+-0.000287394) 29.9218 (+-0.210966) 3.00027 (+-0.00073293)
found 6.2 (+-0.000289832) 29.9237 (+-0.211197) 3.00045 (+-0.000733732)
found -6.04 (+-0.000286473) 29.9207 (+-0.210871) 3.00016 (+-0.000732599)
found -5.32 (+-0.00028862) 29.9226 (+-0.211079) 3.00035 (+-0.000733321)
found 2.84 (+-0.000287201) 29.921 (+-0.210934) 3.00019 (+-0.000732816)
found 4.76 (+-0.000286772) 29.9207 (+-0.210894) 3.00016 (+-0.000732678)
found -5.56 (+-0.000285829) 29.9202 (+-0.210807) 3.00011 (+-0.000732378)
found 2.6 (+-0.000285829) 29.9202 (+-0.210807) 3.00011 (+-0.000732378)
found 2.12 (+-0.000352395) 19.948 (+-0.172277) 2.00019 (+-0.000598516)
found 9.56 (+-0.000354837) 19.949 (+-0.172433) 2.0003 (+-0.000599058)
found 0.200004 (+-0.000355048) 19.9493 (+-0.172449) 2.00032 (+-0.000599115)
found 4.28 (+-0.000352443) 19.9477 (+-0.172273) 2.00016 (+-0.000598503)
found 4.52 (+-0.000351938) 19.9474 (+-0.172239) 2.00013 (+-0.000598386)
found -7.96 (+-0.000351588) 19.9475 (+-0.172221) 2.00014 (+-0.000598322)
found -8.20002 (+-0.000505527) 9.97628 (+-0.122069) 1.00032 (+-0.000424088)
found -4.84 (+-0.000510268) 9.97784 (+-0.122235) 1.00048 (+-0.000424665)
found -4.36 (+-0.000508818) 9.97705 (+-0.12218) 1.0004 (+-0.000424472)
found 7.88 (+-0.0005077) 9.97651 (+-0.122138) 1.00035 (+-0.000424326)
found 7.4 (+-0.000505659) 9.97572 (+-0.122064) 1.00027 (+-0.00042407)
found -8.92 (+-0.000504058) 9.9752 (+-0.122007) 1.00022 (+-0.000423872)
found -5.8 (+-0.000502361) 9.97465 (+-0.121947) 1.00016 (+-0.000423663)
found -1.24 (+-0.000499578) 9.97413 (+-0.121855) 1.00011 (+-0.000423343)
found -0.999994 (+-0.000500491) 9.9744 (+-0.121887) 1.00014 (+-0.000423456)
found 2.36 (+-0.000501193) 9.97439 (+-0.121907) 1.00013 (+-0.000423525)

 
#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.