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 29.9207 3
created -9.64 19.9471 2
created -9.4 29.9207 3
created -9.16 39.8942 4
created -8.92 39.8942 4
created -8.68 9.97356 1
created -8.44 9.97356 1
created -8.2 79.7885 8
created -7.96 19.9471 2
created -7.72 79.7885 8
created -7.48 39.8942 4
created -7.24 69.8149 7
created -7 29.9207 3
created -6.76 19.9471 2
created -6.52 49.8678 5
created -6.28 39.8942 4
created -6.04 89.762 9
created -5.8 29.9207 3
created -5.56 49.8678 5
created -5.32 89.762 9
created -5.08 49.8678 5
created -4.84 29.9207 3
created -4.6 49.8678 5
created -4.36 9.97356 1
created -4.12 49.8678 5
created -3.88 19.9471 2
created -3.64 69.8149 7
created -3.4 19.9471 2
created -3.16 39.8942 4
created -2.92 19.9471 2
created -2.68 19.9471 2
created -2.44 9.97356 1
created -2.2 29.9207 3
created -1.96 9.97356 1
created -1.72 39.8942 4
created -1.48 89.762 9
created -1.24 69.8149 7
created -1 19.9471 2
created -0.76 69.8149 7
created -0.52 39.8942 4
created -0.28 29.9207 3
created -0.04 69.8149 7
created 0.2 79.7885 8
created 0.44 39.8942 4
created 0.68 79.7885 8
created 0.92 39.8942 4
created 1.16 39.8942 4
created 1.4 49.8678 5
created 1.64 49.8678 5
created 1.88 49.8678 5
created 2.12 19.9471 2
created 2.36 59.8413 6
created 2.6 89.762 9
created 2.84 19.9471 2
created 3.08 39.8942 4
created 3.32 49.8678 5
created 3.56 39.8942 4
created 3.8 79.7885 8
created 4.04 19.9471 2
created 4.28 9.97356 1
created 4.52 29.9207 3
created 4.76 9.97356 1
created 5 39.8942 4
created 5.24 69.8149 7
created 5.48 49.8678 5
created 5.72 89.762 9
created 5.96 89.762 9
created 6.2 49.8678 5
created 6.44 19.9471 2
created 6.68 9.97356 1
created 6.92 79.7885 8
created 7.16 29.9207 3
created 7.4 39.8942 4
created 7.64 89.762 9
created 7.88 59.8413 6
created 8.12 39.8942 4
created 8.36 39.8942 4
created 8.6 49.8678 5
created 8.84 79.7885 8
created 9.08 69.8149 7
created 9.32 89.762 9
created 9.56 89.762 9
created 9.8 69.8149 7
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.66723e-05)
fit chi^2 = 1.18925e-05
found 2.6 (+-0.000190173) 89.7595 (+-0.421695) 9.00021 (+-0.00146503)
found 5.96 (+-0.00019093) 89.7611 (+-0.421897) 9.00037 (+-0.00146574)
found 9.32 (+-0.000191147) 89.7617 (+-0.421956) 9.00043 (+-0.00146594)
found 9.56 (+-0.000191147) 89.7617 (+-0.421956) 9.00043 (+-0.00146594)
found -6.04 (+-0.000190124) 89.7593 (+-0.421677) 9.00018 (+-0.00146497)
found -5.32 (+-0.000190534) 89.7601 (+-0.421786) 9.00026 (+-0.00146535)
found -1.48 (+-0.00019062) 89.7603 (+-0.421811) 9.00029 (+-0.00146544)
found 5.72 (+-0.00019093) 89.7611 (+-0.421897) 9.00037 (+-0.00146574)
found 7.64 (+-0.000190518) 89.7601 (+-0.421782) 9.00026 (+-0.00146534)
found 0.199999 (+-0.000202342) 79.7873 (+-0.397726) 8.00029 (+-0.00138176)
found -8.2 (+-0.000200904) 79.7851 (+-0.397394) 8.00007 (+-0.00138061)
found -7.72 (+-0.000201581) 79.786 (+-0.397545) 8.00016 (+-0.00138113)
found 0.68 (+-0.00020196) 79.7865 (+-0.397632) 8.00021 (+-0.00138144)
found 3.8 (+-0.000201581) 79.7859 (+-0.397545) 8.00016 (+-0.00138113)
found 6.92 (+-0.000201114) 79.7854 (+-0.397442) 8.0001 (+-0.00138078)
found 8.84 (+-0.000202485) 79.7876 (+-0.39776) 8.00032 (+-0.00138188)
found -1.24 (+-0.000216313) 69.8143 (+-0.372047) 7.00029 (+-0.00129255)
found 9.08 (+-0.000217294) 69.8159 (+-0.372254) 7.00045 (+-0.00129327)
found 9.8 (+-0.000215063) 69.8156 (+-0.371843) 7.00042 (+-0.00129184)
found -7.24 (+-0.000215896) 69.8132 (+-0.371949) 7.00018 (+-0.00129221)
found -3.64 (+-0.000215237) 69.8124 (+-0.371816) 7.0001 (+-0.00129175)
found -0.759999 (+-0.000215659) 69.8129 (+-0.371902) 7.00016 (+-0.00129205)
found -0.0399987 (+-0.000216442) 69.8143 (+-0.37207) 7.00029 (+-0.00129263)
found 5.24 (+-0.000216244) 69.8137 (+-0.372023) 7.00024 (+-0.00129247)
found 7.88 (+-0.000234391) 59.8417 (+-0.344583) 6.00035 (+-0.00119714)
found 2.36 (+-0.000233902) 59.8412 (+-0.344497) 6.00029 (+-0.00119684)
found -5.08 (+-0.0002569) 49.8684 (+-0.314584) 5.00032 (+-0.00109292)
found 6.2 (+-0.000256583) 49.8682 (+-0.314538) 5.00029 (+-0.00109275)
found -5.56 (+-0.0002569) 49.8685 (+-0.314584) 5.00032 (+-0.00109292)
found -4.6 (+-0.000254994) 49.8663 (+-0.314291) 5.00011 (+-0.0010919)
found 1.4 (+-0.000256477) 49.8676 (+-0.314512) 5.00024 (+-0.00109266)
found 1.64 (+-0.000256695) 49.8679 (+-0.314546) 5.00027 (+-0.00109278)
found 1.88 (+-0.000255908) 49.8671 (+-0.314426) 5.00019 (+-0.00109237)
found 3.32 (+-0.00025626) 49.8674 (+-0.314477) 5.00021 (+-0.00109254)
found 5.48 (+-0.000257747) 49.8695 (+-0.314718) 5.00043 (+-0.00109338)
found 8.6 (+-0.000257006) 49.8684 (+-0.314598) 5.00032 (+-0.00109296)
found -6.52 (+-0.000255692) 49.8668 (+-0.314392) 5.00016 (+-0.00109225)
found -4.12 (+-0.000254683) 49.866 (+-0.314245) 5.00008 (+-0.00109174)
found -9.16 (+-0.000286683) 39.894 (+-0.2813) 4.00019 (+-0.000977279)
found -8.92 (+-0.000285761) 39.8935 (+-0.281192) 4.00013 (+-0.000976905)
found -7.48 (+-0.000288638) 39.8962 (+-0.281554) 4.0004 (+-0.000978163)
found -6.28 (+-0.000288364) 39.8959 (+-0.281519) 4.00037 (+-0.000978041)
found -0.520002 (+-0.000287401) 39.8949 (+-0.281394) 4.00027 (+-0.000977607)
found 0.44 (+-0.000288837) 39.8965 (+-0.281581) 4.00043 (+-0.000978255)
found 0.919998 (+-0.000287911) 39.8954 (+-0.28146) 4.00032 (+-0.000977835)
found 1.16 (+-0.00028726) 39.8946 (+-0.281373) 4.00024 (+-0.000977534)
found 3.56 (+-0.000288181) 39.8956 (+-0.281494) 4.00035 (+-0.000977955)
found 7.4 (+-0.000287781) 39.8954 (+-0.281445) 4.00032 (+-0.000977786)
found 8.12 (+-0.000287498) 39.8949 (+-0.281404) 4.00027 (+-0.000977643)
found 8.36 (+-0.00028726) 39.8946 (+-0.281373) 4.00024 (+-0.000977534)
found -3.16 (+-0.000285615) 39.8933 (+-0.281168) 4.00011 (+-0.000976823)
found -1.72 (+-0.000286848) 39.8949 (+-0.281338) 4.00027 (+-0.000977411)
found 3.08 (+-0.000286567) 39.8941 (+-0.281288) 4.00019 (+-0.000977238)
found 5 (+-0.000286472) 39.8943 (+-0.281286) 4.00021 (+-0.000977232)
found -5.8 (+-0.00033398) 29.9229 (+-0.243904) 3.00037 (+-0.000847361)
found 7.16 (+-0.000333392) 29.9223 (+-0.243845) 3.00032 (+-0.000847156)
found -7 (+-0.000332231) 29.9216 (+-0.243733) 3.00024 (+-0.000846768)
found -4.84 (+-0.000332898) 29.9218 (+-0.243794) 3.00027 (+-0.000846978)
found -9.4 (+-0.0003313) 29.9207 (+-0.243639) 3.00016 (+-0.000846441)
found -0.279998 (+-0.000333137) 29.9221 (+-0.243819) 3.00029 (+-0.000847065)
found -9.88 (+-0.00033001) 29.9197 (+-0.243497) 3.00006 (+-0.000845947)
found -2.2 (+-0.000329042) 29.9197 (+-0.243433) 3.00005 (+-0.000845724)
found 4.52 (+-0.000329042) 29.9197 (+-0.243433) 3.00005 (+-0.000845724)
found -7.96 (+-0.000411897) 19.9504 (+-0.199343) 2.00043 (+-0.000692549)
found 2.84 (+-0.000410503) 19.9496 (+-0.199249) 2.00035 (+-0.000692221)
found 4.03999 (+-0.000407852) 19.9485 (+-0.199079) 2.00024 (+-0.000691633)
found -3.88 (+-0.00041033) 19.9493 (+-0.199234) 2.00032 (+-0.000692171)
found -3.4 (+-0.00040982) 19.949 (+-0.1992) 2.00029 (+-0.000692052)
found -1 (+-0.000411175) 19.9498 (+-0.199292) 2.00037 (+-0.000692373)
found 2.12 (+-0.000409933) 19.949 (+-0.199206) 2.00029 (+-0.000692075)
found 6.44 (+-0.000406676) 19.9477 (+-0.198996) 2.00016 (+-0.000691343)
found -9.64 (+-0.000407301) 19.9477 (+-0.199029) 2.00016 (+-0.000691459)
found -6.76 (+-0.00040839) 19.9482 (+-0.199103) 2.00021 (+-0.000691714)
found -2.92 (+-0.000407168) 19.9477 (+-0.199022) 2.00016 (+-0.000691434)
found -2.68 (+-0.000404891) 19.9469 (+-0.198876) 2.00008 (+-0.000690927)
found -8.68001 (+-0.000578202) 9.9744 (+-0.140813) 1.00014 (+-0.000489206)
found -4.36 (+-0.000584305) 9.97571 (+-0.141021) 1.00027 (+-0.000489929)
found -1.96 (+-0.000581434) 9.97492 (+-0.140919) 1.00019 (+-0.000489576)
found 4.76 (+-0.000581434) 9.97492 (+-0.140919) 1.00019 (+-0.000489576)
found -8.43999 (+-0.000581083) 9.97548 (+-0.140921) 1.00024 (+-0.000489583)
found -2.44 (+-0.000579014) 9.97438 (+-0.140836) 1.00013 (+-0.000489287)
found 4.28 (+-0.000579014) 9.97439 (+-0.140836) 1.00013 (+-0.000489287)
found 6.68001 (+-0.000582984) 9.97573 (+-0.140982) 1.00027 (+-0.000489793)

 
#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.