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 19.9471 2
created -9.64 89.762 9
created -9.4 69.8149 7
created -9.16 79.7885 8
created -8.92 59.8413 6
created -8.68 59.8413 6
created -8.44 79.7885 8
created -8.2 39.8942 4
created -7.96 29.9207 3
created -7.72 69.8149 7
created -7.48 59.8413 6
created -7.24 99.7356 10
created -7 59.8413 6
created -6.76 79.7885 8
created -6.52 49.8678 5
created -6.28 79.7885 8
created -6.04 49.8678 5
created -5.8 79.7885 8
created -5.56 59.8413 6
created -5.32 29.9207 3
created -5.08 29.9207 3
created -4.84 39.8942 4
created -4.6 49.8678 5
created -4.36 59.8413 6
created -4.12 69.8149 7
created -3.88 19.9471 2
created -3.64 39.8942 4
created -3.4 9.97356 1
created -3.16 49.8678 5
created -2.92 39.8942 4
created -2.68 89.762 9
created -2.44 59.8413 6
created -2.2 59.8413 6
created -1.96 59.8413 6
created -1.72 69.8149 7
created -1.48 99.7356 10
created -1.24 39.8942 4
created -1 89.762 9
created -0.76 49.8678 5
created -0.52 39.8942 4
created -0.28 89.762 9
created -0.04 9.97356 1
created 0.2 89.762 9
created 0.44 39.8942 4
created 0.68 79.7885 8
created 0.92 39.8942 4
created 1.16 59.8413 6
created 1.4 79.7885 8
created 1.64 49.8678 5
created 1.88 9.97356 1
created 2.12 79.7885 8
created 2.36 9.97356 1
created 2.6 39.8942 4
created 2.84 19.9471 2
created 3.08 99.7356 10
created 3.32 69.8149 7
created 3.56 59.8413 6
created 3.8 9.97356 1
created 4.04 69.8149 7
created 4.28 49.8678 5
created 4.52 39.8942 4
created 4.76 79.7885 8
created 5 29.9207 3
created 5.24 9.97356 1
created 5.48 39.8942 4
created 5.72 9.97356 1
created 5.96 59.8413 6
created 6.2 59.8413 6
created 6.44 19.9471 2
created 6.68 99.7356 10
created 6.92 99.7356 10
created 7.16 39.8942 4
created 7.4 59.8413 6
created 7.64 99.7356 10
created 7.88 19.9471 2
created 8.12 89.762 9
created 8.36 29.9207 3
created 8.6 99.7356 10
created 8.84 99.7356 10
created 9.08 29.9207 3
created 9.32 49.8678 5
created 9.56 79.7885 8
created 9.8 89.762 9
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.71839e-05)
fit chi^2 = 1.53723e-05
found -7.24 (+-0.000205609) 99.7337 (+-0.505511) 10.0003 (+-0.00175622)
found -1.48 (+-0.000205462) 99.7334 (+-0.505469) 10.0003 (+-0.00175608)
found 6.92 (+-0.000205732) 99.7342 (+-0.505554) 10.0004 (+-0.00175637)
found 7.64 (+-0.000204997) 99.7326 (+-0.505336) 10.0002 (+-0.00175562)
found 8.84 (+-0.000205572) 99.7339 (+-0.505509) 10.0003 (+-0.00175622)
found 3.08 (+-0.000205102) 99.7328 (+-0.505369) 10.0002 (+-0.00175573)
found 6.68 (+-0.000205371) 99.7337 (+-0.505454) 10.0003 (+-0.00175603)
found 8.6 (+-0.000205572) 99.7339 (+-0.505509) 10.0003 (+-0.00175622)
found 9.8 (+-0.000215319) 89.7619 (+-0.479269) 9.00045 (+-0.00166506)
found -9.64 (+-0.000216329) 89.7598 (+-0.47947) 9.00024 (+-0.00166575)
found -2.68 (+-0.000216606) 89.7601 (+-0.479537) 9.00026 (+-0.00166599)
found -1 (+-0.000216477) 89.7598 (+-0.479502) 9.00024 (+-0.00166586)
found -0.280001 (+-0.000215632) 89.7588 (+-0.47929) 9.00013 (+-0.00166513)
found 0.200001 (+-0.000215632) 89.7588 (+-0.47929) 9.00013 (+-0.00166513)
found 8.12 (+-0.000215768) 89.7588 (+-0.479318) 9.00013 (+-0.00166523)
found -9.16 (+-0.000230355) 79.7878 (+-0.452261) 8.00035 (+-0.00157122)
found -8.44 (+-0.000229919) 79.787 (+-0.452154) 8.00026 (+-0.00157085)
found -6.76 (+-0.000230082) 79.7873 (+-0.452193) 8.00029 (+-0.00157099)
found 1.4 (+-0.000230082) 79.7873 (+-0.452193) 8.00029 (+-0.00157099)
found -6.28 (+-0.000229939) 79.787 (+-0.452158) 8.00027 (+-0.00157087)
found -5.8 (+-0.000230082) 79.7873 (+-0.452193) 8.00029 (+-0.00157099)
found 0.68 (+-0.000229615) 79.7865 (+-0.45208) 8.00021 (+-0.0015706)
found 2.12 (+-0.000228076) 79.7849 (+-0.451737) 8.00005 (+-0.0015694)
found 4.76 (+-0.000229424) 79.7862 (+-0.452035) 8.00018 (+-0.00157044)
found 9.56 (+-0.00023044) 79.7881 (+-0.452284) 8.00038 (+-0.00157131)
found -9.4 (+-0.000247048) 69.8158 (+-0.423227) 7.00045 (+-0.00147036)
found -4.12 (+-0.00024553) 69.8135 (+-0.422901) 7.00021 (+-0.00146922)
found -1.72 (+-0.000246883) 69.8156 (+-0.423191) 7.00043 (+-0.00147023)
found 3.32 (+-0.000246883) 69.8156 (+-0.423191) 7.00042 (+-0.00147023)
found -7.72 (+-0.0002458) 69.8137 (+-0.422954) 7.00024 (+-0.00146941)
found 4.04 (+-0.000244986) 69.8129 (+-0.422793) 7.00016 (+-0.00146885)
found -7 (+-0.000267346) 59.8431 (+-0.391931) 6.00048 (+-0.00136163)
found -2.44 (+-0.000266884) 59.8423 (+-0.391841) 6.0004 (+-0.00136132)
found -8.92 (+-0.00026674) 59.842 (+-0.391812) 6.00037 (+-0.00136122)
found -8.68 (+-0.00026674) 59.842 (+-0.391812) 6.00037 (+-0.00136122)
found -7.48 (+-0.000267189) 59.8428 (+-0.3919) 6.00045 (+-0.00136152)
found -5.56 (+-0.000266095) 59.8412 (+-0.391694) 6.00029 (+-0.00136081)
found -4.36 (+-0.000266398) 59.8415 (+-0.391747) 6.00032 (+-0.00136099)
found -2.2 (+-0.000266416) 59.8415 (+-0.39175) 6.00032 (+-0.001361)
found -1.96 (+-0.000266584) 59.8418 (+-0.391782) 6.00035 (+-0.00136111)
found 1.16 (+-0.000266342) 59.8415 (+-0.391738) 6.00032 (+-0.00136096)
found 3.56 (+-0.000265193) 59.8404 (+-0.391539) 6.00021 (+-0.00136027)
found 6.2 (+-0.000265466) 59.8404 (+-0.391578) 6.00021 (+-0.0013604)
found 7.4 (+-0.000266621) 59.842 (+-0.391794) 6.00037 (+-0.00136115)
found 5.96 (+-0.000265027) 59.8401 (+-0.391506) 6.00018 (+-0.00136015)
found -0.760002 (+-0.000292368) 49.8687 (+-0.357705) 5.00035 (+-0.00124272)
found -6.52 (+-0.000293054) 49.8695 (+-0.357814) 5.00043 (+-0.0012431)
found -6.04 (+-0.000293054) 49.8695 (+-0.357814) 5.00043 (+-0.0012431)
found -4.6 (+-0.000291816) 49.8679 (+-0.357613) 5.00027 (+-0.0012424)
found 1.64 (+-0.000291033) 49.8677 (+-0.357508) 5.00024 (+-0.00124204)
found 4.28 (+-0.000292015) 49.8682 (+-0.357646) 5.00029 (+-0.00124252)
found 9.32 (+-0.000291908) 49.8682 (+-0.357632) 5.0003 (+-0.00124247)
found -3.16 (+-0.000290194) 49.8666 (+-0.357371) 5.00013 (+-0.00124156)
found -1.24 (+-0.000329004) 39.8972 (+-0.320221) 4.00051 (+-0.0011125)
found 0.44 (+-0.000328598) 39.8967 (+-0.320166) 4.00045 (+-0.00111231)
found 7.16 (+-0.000328318) 39.8965 (+-0.320129) 4.00043 (+-0.00111218)
found -8.2 (+-0.000326979) 39.8951 (+-0.319955) 4.00029 (+-0.00111158)
found -4.84 (+-0.000326242) 39.8943 (+-0.319857) 4.00021 (+-0.00111123)
found -2.92 (+-0.00032785) 39.8959 (+-0.320067) 4.00037 (+-0.00111196)
found -0.519998 (+-0.00032785) 39.8959 (+-0.320067) 4.00037 (+-0.00111196)
found 0.919999 (+-0.000327914) 39.8959 (+-0.320074) 4.00037 (+-0.00111199)
found 4.52 (+-0.000327641) 39.8957 (+-0.320039) 4.00035 (+-0.00111187)
found -3.64 (+-0.000324113) 39.893 (+-0.319599) 4.00008 (+-0.00111034)
found 2.6 (+-0.000324113) 39.893 (+-0.319599) 4.00008 (+-0.00111034)
found 5.48 (+-0.000323506) 39.8927 (+-0.319529) 4.00005 (+-0.00111009)
found 5 (+-0.000377217) 29.9216 (+-0.277068) 3.00024 (+-0.000962579)
found 8.36 (+-0.000381205) 29.9242 (+-0.277453) 3.00051 (+-0.000963917)
found 9.08 (+-0.00037996) 29.9232 (+-0.277328) 3.0004 (+-0.00096348)
found -5.32 (+-0.000377973) 29.9216 (+-0.277128) 3.00024 (+-0.000962786)
found -7.96 (+-0.000378753) 29.9221 (+-0.277205) 3.00029 (+-0.000963053)
found -5.08 (+-0.000377227) 29.921 (+-0.277053) 3.00019 (+-0.000962527)
found 7.88 (+-0.000469394) 19.9512 (+-0.226717) 2.00051 (+-0.000787651)
found -3.88 (+-0.000465937) 19.949 (+-0.226476) 2.00029 (+-0.000786814)
found 6.44 (+-0.000468148) 19.9504 (+-0.22663) 2.00043 (+-0.000787349)
found 2.84001 (+-0.000467054) 19.9498 (+-0.226557) 2.00037 (+-0.000787093)
found -9.87999 (+-0.00046393) 19.9485 (+-0.226338) 2.00024 (+-0.000786335)
found -0.04 (+-0.000670271) 9.97785 (+-0.160553) 1.00048 (+-0.000557786)
found 2.35999 (+-0.000665618) 9.97625 (+-0.160381) 1.00032 (+-0.00055719)
found 3.8 (+-0.000666844) 9.97652 (+-0.160424) 1.00035 (+-0.000557337)
found -3.4 (+-0.000663291) 9.97545 (+-0.160294) 1.00024 (+-0.000556888)
found 1.88001 (+-0.00066665) 9.97652 (+-0.160418) 1.00035 (+-0.000557316)
found 5.72 (+-0.000664165) 9.97572 (+-0.160326) 1.00027 (+-0.000556999)
found 5.24 (+-0.000661049) 9.97492 (+-0.160215) 1.00019 (+-0.000556613)

 
#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.