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 99.7356 10
created -9.4 99.7356 10
created -9.16 29.9207 3
created -8.92 69.8149 7
created -8.68 59.8413 6
created -8.44 59.8413 6
created -8.2 39.8942 4
created -7.96 79.7885 8
created -7.72 49.8678 5
created -7.48 69.8149 7
created -7.24 59.8413 6
created -7 89.762 9
created -6.76 19.9471 2
created -6.52 69.8149 7
created -6.28 89.762 9
created -6.04 89.762 9
created -5.8 19.9471 2
created -5.56 79.7885 8
created -5.32 99.7356 10
created -5.08 59.8413 6
created -4.84 9.97356 1
created -4.6 49.8678 5
created -4.36 39.8942 4
created -4.12 49.8678 5
created -3.88 19.9471 2
created -3.64 9.97356 1
created -3.4 59.8413 6
created -3.16 99.7356 10
created -2.92 29.9207 3
created -2.68 59.8413 6
created -2.44 39.8942 4
created -2.2 9.97356 1
created -1.96 29.9207 3
created -1.72 19.9471 2
created -1.48 49.8678 5
created -1.24 79.7885 8
created -1 19.9471 2
created -0.76 79.7885 8
created -0.52 19.9471 2
created -0.28 19.9471 2
created -0.04 89.762 9
created 0.2 59.8413 6
created 0.44 29.9207 3
created 0.68 29.9207 3
created 0.92 39.8942 4
created 1.16 49.8678 5
created 1.4 29.9207 3
created 1.64 49.8678 5
created 1.88 79.7885 8
created 2.12 89.762 9
created 2.36 29.9207 3
created 2.6 29.9207 3
created 2.84 29.9207 3
created 3.08 19.9471 2
created 3.32 19.9471 2
created 3.56 9.97356 1
created 3.8 49.8678 5
created 4.04 99.7356 10
created 4.28 59.8413 6
created 4.52 9.97356 1
created 4.76 19.9471 2
created 5 29.9207 3
created 5.24 29.9207 3
created 5.48 29.9207 3
created 5.72 19.9471 2
created 5.96 99.7356 10
created 6.2 39.8942 4
created 6.44 59.8413 6
created 6.68 59.8413 6
created 6.92 49.8678 5
created 7.16 29.9207 3
created 7.4 29.9207 3
created 7.64 89.762 9
created 7.88 79.7885 8
created 8.12 69.8149 7
created 8.36 89.762 9
created 8.6 79.7885 8
created 8.84 59.8413 6
created 9.08 39.8942 4
created 9.32 19.9471 2
created 9.56 29.9207 3
created 9.8 29.9207 3
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.09635e-05)
fit chi^2 = 5.58978e-06
found -9.64 (+-0.000124335) 99.7352 (+-0.304939) 10.0005 (+-0.00105941)
found -9.4 (+-0.000123963) 99.7339 (+-0.304829) 10.0003 (+-0.00105902)
found -5.32 (+-0.000124107) 99.7341 (+-0.304868) 10.0004 (+-0.00105916)
found -3.16 (+-0.000123737) 99.7328 (+-0.304758) 10.0002 (+-0.00105878)
found 4.04 (+-0.000123914) 99.7333 (+-0.304809) 10.0003 (+-0.00105895)
found 5.96 (+-0.000123466) 99.732 (+-0.30468) 10.0002 (+-0.00105851)
found -7 (+-0.00013038) 89.7595 (+-0.289107) 9.00021 (+-0.0010044)
found -6.28 (+-0.000131048) 89.7616 (+-0.289287) 9.00042 (+-0.00100503)
found -6.04 (+-0.000130572) 89.7603 (+-0.289162) 9.00029 (+-0.0010046)
found 2.12 (+-0.000130645) 89.7603 (+-0.289178) 9.00029 (+-0.00100465)
found 8.36 (+-0.000130988) 89.7614 (+-0.28927) 9.0004 (+-0.00100497)
found -0.0399992 (+-0.00013038) 89.7595 (+-0.289107) 9.00021 (+-0.0010044)
found 7.64 (+-0.000130645) 89.7603 (+-0.289178) 9.00029 (+-0.00100465)
found 7.88 (+-0.000139124) 79.7886 (+-0.272774) 8.00043 (+-0.000947661)
found 8.6 (+-0.000139045) 79.7884 (+-0.272755) 8.0004 (+-0.000947593)
found -9.88 (+-0.000138883) 79.7871 (+-0.272693) 8.00027 (+-0.00094738)
found -7.96 (+-0.000138559) 79.7868 (+-0.272634) 8.00024 (+-0.000947173)
found -5.56 (+-0.000138659) 79.7876 (+-0.272667) 8.00032 (+-0.000947287)
found -1.24 (+-0.000138298) 79.7862 (+-0.272574) 8.00018 (+-0.000946966)
found -0.76 (+-0.000137942) 79.7854 (+-0.272491) 8.0001 (+-0.000946677)
found 1.88 (+-0.000138959) 79.7881 (+-0.272734) 8.00037 (+-0.000947519)
found 8.12 (+-0.000148974) 69.8159 (+-0.255212) 7.00045 (+-0.000886646)
found -8.92 (+-0.000148221) 69.8137 (+-0.255047) 7.00024 (+-0.000886075)
found -7.48 (+-0.000148461) 69.8143 (+-0.255098) 7.00029 (+-0.00088625)
found -6.52 (+-0.000148301) 69.8143 (+-0.25507) 7.00029 (+-0.000886153)
found -5.08 (+-0.000160174) 59.8412 (+-0.236155) 6.00029 (+-0.000820439)
found 0.199998 (+-0.000160546) 59.8415 (+-0.236214) 6.00032 (+-0.000820645)
found 4.28 (+-0.000160174) 59.8412 (+-0.236155) 6.00029 (+-0.000820439)
found -8.68 (+-0.000160754) 59.8417 (+-0.23625) 6.00034 (+-0.00082077)
found -8.44 (+-0.000160413) 59.841 (+-0.236186) 6.00027 (+-0.000820546)
found -7.24 (+-0.000161037) 59.8425 (+-0.236305) 6.00042 (+-0.000820962)
found -2.68 (+-0.000160028) 59.8401 (+-0.236114) 6.00019 (+-0.000820298)
found 6.44 (+-0.000160413) 59.841 (+-0.236186) 6.00027 (+-0.000820546)
found 6.68 (+-0.00016054) 59.8412 (+-0.236209) 6.00029 (+-0.000820628)
found 8.84 (+-0.000160608) 59.8415 (+-0.236224) 6.00032 (+-0.000820678)
found -3.4 (+-0.000160174) 59.8412 (+-0.236155) 6.00029 (+-0.000820439)
found -7.72 (+-0.000176604) 49.8692 (+-0.215748) 5.0004 (+-0.000749543)
found -4.12 (+-0.000175299) 49.8668 (+-0.215542) 5.00016 (+-0.000748828)
found 1.16 (+-0.000175514) 49.8671 (+-0.215574) 5.00019 (+-0.000748939)
found 1.64 (+-0.000176024) 49.8682 (+-0.215657) 5.00029 (+-0.000749226)
found 6.92 (+-0.000175795) 49.8676 (+-0.215619) 5.00024 (+-0.000749095)
found -4.6 (+-0.000174991) 49.8666 (+-0.2155) 5.00013 (+-0.00074868)
found -1.48 (+-0.000175807) 49.8679 (+-0.215625) 5.00027 (+-0.000749116)
found 3.8 (+-0.000175693) 49.8682 (+-0.215616) 5.00029 (+-0.000749083)
found 6.2 (+-0.00019798) 39.8964 (+-0.193042) 4.00043 (+-0.00067066)
found -8.2 (+-0.000197737) 39.8959 (+-0.193009) 4.00037 (+-0.000670545)
found -4.36 (+-0.000197125) 39.8948 (+-0.192929) 4.00027 (+-0.000670264)
found -2.44 (+-0.000196255) 39.894 (+-0.192826) 4.00019 (+-0.000669907)
found 0.920001 (+-0.000196728) 39.8943 (+-0.192878) 4.00021 (+-0.000670089)
found 9.08 (+-0.000196628) 39.8943 (+-0.192868) 4.00021 (+-0.000670054)
found -9.16 (+-0.000229531) 29.9237 (+-0.167273) 3.00045 (+-0.000581134)
found -2.92 (+-0.000229337) 29.9234 (+-0.167254) 3.00043 (+-0.000581066)
found 2.36 (+-0.000228449) 29.9223 (+-0.167166) 3.00032 (+-0.000580762)
found 0.439998 (+-0.000227923) 29.9215 (+-0.167112) 3.00024 (+-0.000580573)
found 1.4 (+-0.00022823) 29.9218 (+-0.167141) 3.00027 (+-0.000580675)
found 7.16 (+-0.000227712) 29.9213 (+-0.167091) 3.00021 (+-0.000580499)
found 0.680001 (+-0.000227473) 29.921 (+-0.167067) 3.00019 (+-0.000580417)
found 2.6 (+-0.000227197) 29.9207 (+-0.16704) 3.00016 (+-0.000580324)
found 2.84 (+-0.000226859) 29.9205 (+-0.167008) 3.00013 (+-0.000580214)
found 5 (+-0.000226859) 29.9205 (+-0.167008) 3.00013 (+-0.000580214)
found 5.24 (+-0.000227197) 29.9207 (+-0.16704) 3.00016 (+-0.000580324)
found 5.48 (+-0.000226859) 29.9205 (+-0.167008) 3.00014 (+-0.000580214)
found 7.4 (+-0.000228449) 29.9224 (+-0.167166) 3.00032 (+-0.000580762)
found 9.56 (+-0.000226859) 29.9205 (+-0.167008) 3.00013 (+-0.000580214)
found 9.8 (+-0.000224991) 29.9207 (+-0.166867) 3.00016 (+-0.000579724)
found -1.96 (+-0.000226053) 29.9199 (+-0.166935) 3.00008 (+-0.000579959)
found -6.76 (+-0.000282368) 19.9504 (+-0.136665) 2.00043 (+-0.000474796)
found -5.8 (+-0.000282616) 19.9506 (+-0.136683) 2.00045 (+-0.000474857)
found -1 (+-0.00028239) 19.9504 (+-0.136666) 2.00043 (+-0.000474801)
found -0.520006 (+-0.000280302) 19.9488 (+-0.136527) 2.00027 (+-0.000474316)
found -3.88 (+-0.000278811) 19.9477 (+-0.136428) 2.00016 (+-0.000473974)
found -1.72 (+-0.000279986) 19.9482 (+-0.136502) 2.00021 (+-0.000474228)
found -0.279993 (+-0.000280523) 19.9491 (+-0.136543) 2.0003 (+-0.000474372)
found 3.08 (+-0.000278747) 19.9474 (+-0.13642) 2.00013 (+-0.000473943)
found 3.32 (+-0.000277587) 19.9469 (+-0.136346) 2.00008 (+-0.000473689)
found 5.72001 (+-0.000281229) 19.9496 (+-0.13659) 2.00035 (+-0.000474535)
found 9.32 (+-0.000279642) 19.948 (+-0.136478) 2.00019 (+-0.000474147)
found 4.76 (+-0.000278073) 19.9472 (+-0.136378) 2.00011 (+-0.000473799)
found -4.84 (+-0.000401119) 9.97599 (+-0.096701) 1.00029 (+-0.000335955)
found 4.51999 (+-0.000398819) 9.97519 (+-0.0966219) 1.00022 (+-0.00033568)
found -2.2 (+-0.000398622) 9.97492 (+-0.0966121) 1.00019 (+-0.000335646)
found -3.63999 (+-0.000398819) 9.9752 (+-0.0966219) 1.00022 (+-0.00033568)
found 3.56001 (+-0.000398299) 9.97492 (+-0.0966026) 1.00019 (+-0.000335613)

 
#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.