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 59.8413 6
created -9.4 9.97356 1
created -9.16 89.762 9
created -8.92 19.9471 2
created -8.68 49.8678 5
created -8.44 29.9207 3
created -8.2 29.9207 3
created -7.96 89.762 9
created -7.72 59.8413 6
created -7.48 39.8942 4
created -7.24 9.97356 1
created -7 79.7885 8
created -6.76 39.8942 4
created -6.52 69.8149 7
created -6.28 99.7356 10
created -6.04 69.8149 7
created -5.8 49.8678 5
created -5.56 69.8149 7
created -5.32 99.7356 10
created -5.08 19.9471 2
created -4.84 49.8678 5
created -4.6 69.8149 7
created -4.36 19.9471 2
created -4.12 79.7885 8
created -3.88 89.762 9
created -3.64 39.8942 4
created -3.4 49.8678 5
created -3.16 79.7885 8
created -2.92 9.97356 1
created -2.68 69.8149 7
created -2.44 39.8942 4
created -2.2 89.762 9
created -1.96 39.8942 4
created -1.72 79.7885 8
created -1.48 39.8942 4
created -1.24 49.8678 5
created -1 39.8942 4
created -0.76 29.9207 3
created -0.52 89.762 9
created -0.28 39.8942 4
created -0.04 19.9471 2
created 0.2 39.8942 4
created 0.44 19.9471 2
created 0.68 59.8413 6
created 0.92 19.9471 2
created 1.16 89.762 9
created 1.4 59.8413 6
created 1.64 19.9471 2
created 1.88 59.8413 6
created 2.12 19.9471 2
created 2.36 39.8942 4
created 2.6 89.762 9
created 2.84 49.8678 5
created 3.08 99.7356 10
created 3.32 29.9207 3
created 3.56 19.9471 2
created 3.8 9.97356 1
created 4.04 89.762 9
created 4.28 9.97356 1
created 4.52 59.8413 6
created 4.76 99.7356 10
created 5 9.97356 1
created 5.24 59.8413 6
created 5.48 29.9207 3
created 5.72 99.7356 10
created 5.96 59.8413 6
created 6.2 79.7885 8
created 6.44 99.7356 10
created 6.68 9.97356 1
created 6.92 9.97356 1
created 7.16 29.9207 3
created 7.4 49.8678 5
created 7.64 49.8678 5
created 7.88 89.762 9
created 8.12 79.7885 8
created 8.36 69.8149 7
created 8.6 39.8942 4
created 8.84 49.8678 5
created 9.08 19.9471 2
created 9.32 29.9207 3
created 9.56 19.9471 2
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 (+-1.89056e-05)
fit chi^2 = 1.70695e-05
found -6.28 (+-0.000216885) 99.7342 (+-0.532754) 10.0004 (+-0.00185087)
found -5.32 (+-0.000216128) 99.7329 (+-0.532535) 10.0002 (+-0.00185011)
found 4.76 (+-0.000215721) 99.7323 (+-0.532424) 10.0002 (+-0.00184972)
found 6.44 (+-0.000215932) 99.7329 (+-0.53249) 10.0002 (+-0.00184995)
found 3.08 (+-0.000216105) 99.7326 (+-0.532522) 10.0002 (+-0.00185006)
found 5.72 (+-0.000216228) 99.7329 (+-0.532559) 10.0002 (+-0.00185019)
found 9.8 (+-0.00021433) 99.7336 (+-0.532075) 10.0003 (+-0.00184851)
found -3.88 (+-0.000228484) 89.7606 (+-0.505381) 9.00032 (+-0.00175577)
found -9.16 (+-0.000226815) 89.7582 (+-0.50495) 9.00007 (+-0.00175428)
found -7.96 (+-0.000228067) 89.7598 (+-0.505268) 9.00024 (+-0.00175538)
found -2.2 (+-0.00022796) 89.7596 (+-0.505237) 9.00021 (+-0.00175527)
found -0.52 (+-0.000227778) 89.7593 (+-0.505189) 9.00018 (+-0.00175511)
found 1.16 (+-0.000227836) 89.7596 (+-0.50521) 9.00021 (+-0.00175518)
found 2.6 (+-0.000228114) 89.7598 (+-0.505278) 9.00024 (+-0.00175541)
found 4.04 (+-0.000226494) 89.7579 (+-0.504873) 9.00005 (+-0.00175401)
found 7.88 (+-0.000228639) 89.7609 (+-0.505422) 9.00034 (+-0.00175592)
found 6.2 (+-0.000243088) 79.7887 (+-0.476663) 8.00043 (+-0.001656)
found 8.12 (+-0.000243116) 79.7887 (+-0.476668) 8.00042 (+-0.00165602)
found -7 (+-0.000241143) 79.7857 (+-0.476201) 8.00013 (+-0.0016544)
found -4.12 (+-0.000242197) 79.7873 (+-0.476452) 8.00029 (+-0.00165527)
found -3.16 (+-0.000241312) 79.786 (+-0.476242) 8.00016 (+-0.00165454)
found -1.72 (+-0.000241958) 79.7865 (+-0.476382) 8.00021 (+-0.00165502)
found -6.04 (+-0.000259983) 69.8154 (+-0.445903) 7.0004 (+-0.00154914)
found 8.36 (+-0.000259535) 69.8146 (+-0.445804) 7.00032 (+-0.00154879)
found -6.52 (+-0.000259789) 69.8151 (+-0.445862) 7.00037 (+-0.00154899)
found -5.56 (+-0.000259983) 69.8154 (+-0.445903) 7.0004 (+-0.00154914)
found -4.6 (+-0.00025856) 69.8132 (+-0.445597) 7.00019 (+-0.00154807)
found -2.68 (+-0.000257966) 69.8127 (+-0.44548) 7.00013 (+-0.00154767)
found -7.72 (+-0.000280811) 59.8418 (+-0.412827) 6.00034 (+-0.00143422)
found 1.4 (+-0.000280225) 59.8412 (+-0.412724) 6.00029 (+-0.00143387)
found 5.96 (+-0.000281717) 59.8431 (+-0.413) 6.00048 (+-0.00143483)
found -9.64 (+-0.000278863) 59.8396 (+-0.412474) 6.00013 (+-0.001433)
found 0.68 (+-0.000278747) 59.8394 (+-0.412447) 6.00011 (+-0.0014329)
found 1.88 (+-0.000278747) 59.8394 (+-0.412447) 6.00011 (+-0.0014329)
found 4.52 (+-0.0002799) 59.8413 (+-0.412677) 6.00029 (+-0.0014337)
found 5.24 (+-0.000278607) 59.8394 (+-0.412427) 6.00011 (+-0.00143284)
found 2.84 (+-0.000309338) 49.8703 (+-0.377137) 5.00051 (+-0.00131023)
found -5.8 (+-0.000308418) 49.869 (+-0.376984) 5.00037 (+-0.0013097)
found -3.4 (+-0.000307905) 49.8685 (+-0.376903) 5.00032 (+-0.00130942)
found -1.24 (+-0.000307011) 49.8674 (+-0.376759) 5.00021 (+-0.00130892)
found 7.4 (+-0.000306967) 49.8674 (+-0.376753) 5.00021 (+-0.0013089)
found 7.64 (+-0.000308347) 49.869 (+-0.376975) 5.00037 (+-0.00130967)
found 8.84 (+-0.000306331) 49.8669 (+-0.376656) 5.00016 (+-0.00130856)
found -8.68 (+-0.00030603) 49.8666 (+-0.37661) 5.00013 (+-0.0013084)
found -4.84 (+-0.000307028) 49.8677 (+-0.376769) 5.00024 (+-0.00130895)
found -3.64 (+-0.000345473) 39.8959 (+-0.337273) 4.00037 (+-0.00117174)
found -1.96 (+-0.000346262) 39.8967 (+-0.337377) 4.00045 (+-0.0011721)
found -0.280003 (+-0.000344312) 39.8951 (+-0.337129) 4.00029 (+-0.00117124)
found -7.48 (+-0.000342952) 39.8941 (+-0.33696) 4.00019 (+-0.00117065)
found -6.76 (+-0.000345801) 39.8962 (+-0.337315) 4.0004 (+-0.00117188)
found -2.44 (+-0.000346022) 39.8965 (+-0.337345) 4.00043 (+-0.00117199)
found -1.48 (+-0.000345254) 39.8957 (+-0.337243) 4.00035 (+-0.00117163)
found -1 (+-0.000343779) 39.8944 (+-0.337051) 4.00021 (+-0.00117097)
found 8.6 (+-0.000345016) 39.8954 (+-0.337211) 4.00032 (+-0.00117152)
found -9.88 (+-0.000343506) 39.8938 (+-0.336993) 4.00016 (+-0.00117077)
found 0.2 (+-0.00034218) 39.8933 (+-0.336853) 4.00011 (+-0.00117028)
found 2.36 (+-0.000344312) 39.8952 (+-0.337129) 4.00029 (+-0.00117124)
found 3.31999 (+-0.000398869) 29.9224 (+-0.292092) 3.00032 (+-0.00101477)
found -8.44 (+-0.000397922) 29.9213 (+-0.291988) 3.00021 (+-0.00101441)
found 5.48 (+-0.000400762) 29.9234 (+-0.292273) 3.00043 (+-0.0010154)
found -8.2 (+-0.00039921) 29.9224 (+-0.29212) 3.00032 (+-0.00101487)
found -0.759997 (+-0.000399701) 29.9226 (+-0.292167) 3.00035 (+-0.00101503)
found 9.32 (+-0.000395843) 29.9202 (+-0.291789) 3.00011 (+-0.00101372)
found 7.16 (+-0.000396498) 29.9208 (+-0.29186) 3.00016 (+-0.00101397)
found -8.92 (+-0.000492415) 19.9498 (+-0.23875) 2.00037 (+-0.000829456)
found -5.08 (+-0.000492775) 19.9501 (+-0.238776) 2.0004 (+-0.000829547)
found -4.36 (+-0.000493039) 19.9501 (+-0.238792) 2.0004 (+-0.0008296)
found 0.920003 (+-0.000492953) 19.9501 (+-0.238787) 2.0004 (+-0.000829583)
found 1.64 (+-0.000491653) 19.9493 (+-0.238695) 2.00032 (+-0.000829265)
found 2.12 (+-0.00049051) 19.9488 (+-0.238618) 2.00027 (+-0.000828995)
found 9.08 (+-0.000489271) 19.9482 (+-0.238534) 2.00021 (+-0.000828705)
found -0.0400001 (+-0.000489375) 19.9482 (+-0.23854) 2.00021 (+-0.000828726)
found 0.440002 (+-0.00049051) 19.9488 (+-0.238618) 2.00027 (+-0.000828995)
found 3.56 (+-0.000485927) 19.9472 (+-0.238319) 2.00011 (+-0.000827956)
found 9.56001 (+-0.000491442) 19.9496 (+-0.238688) 2.00035 (+-0.000829241)
found 4.99999 (+-0.000704685) 9.97732 (+-0.169124) 1.00043 (+-0.000587564)
found 6.67998 (+-0.000697393) 9.97603 (+-0.168879) 1.0003 (+-0.000586714)
found -2.92 (+-0.000704239) 9.97705 (+-0.169105) 1.0004 (+-0.000587498)
found 4.27999 (+-0.00070408) 9.97706 (+-0.1691) 1.0004 (+-0.000587481)
found -9.39999 (+-0.00070408) 9.97705 (+-0.1691) 1.0004 (+-0.000587481)
found -7.23999 (+-0.000701398) 9.97626 (+-0.169003) 1.00032 (+-0.000587142)
found 3.80001 (+-0.00069909) 9.976 (+-0.168928) 1.0003 (+-0.000586884)
found 6.92 (+-0.00069145) 9.97415 (+-0.168656) 1.00011 (+-0.000585936)

 
#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.