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 19.9471 2
created -9.4 79.7885 8
created -9.16 49.8678 5
created -8.92 29.9207 3
created -8.68 59.8413 6
created -8.44 59.8413 6
created -8.2 49.8678 5
created -7.96 39.8942 4
created -7.72 19.9471 2
created -7.48 39.8942 4
created -7.24 99.7356 10
created -7 79.7885 8
created -6.76 49.8678 5
created -6.52 79.7885 8
created -6.28 29.9207 3
created -6.04 9.97356 1
created -5.8 99.7356 10
created -5.56 79.7885 8
created -5.32 99.7356 10
created -5.08 39.8942 4
created -4.84 89.762 9
created -4.6 69.8149 7
created -4.36 59.8413 6
created -4.12 59.8413 6
created -3.88 39.8942 4
created -3.64 49.8678 5
created -3.4 99.7356 10
created -3.16 89.762 9
created -2.92 19.9471 2
created -2.68 99.7356 10
created -2.44 99.7356 10
created -2.2 19.9471 2
created -1.96 39.8942 4
created -1.72 19.9471 2
created -1.48 59.8413 6
created -1.24 49.8678 5
created -1 49.8678 5
created -0.76 9.97356 1
created -0.52 29.9207 3
created -0.28 99.7356 10
created -0.04 69.8149 7
created 0.2 99.7356 10
created 0.44 59.8413 6
created 0.68 39.8942 4
created 0.92 79.7885 8
created 1.16 89.762 9
created 1.4 49.8678 5
created 1.64 59.8413 6
created 1.88 49.8678 5
created 2.12 89.762 9
created 2.36 99.7356 10
created 2.6 29.9207 3
created 2.84 89.762 9
created 3.08 59.8413 6
created 3.32 49.8678 5
created 3.56 39.8942 4
created 3.8 19.9471 2
created 4.04 49.8678 5
created 4.28 99.7356 10
created 4.52 79.7885 8
created 4.76 49.8678 5
created 5 19.9471 2
created 5.24 59.8413 6
created 5.48 59.8413 6
created 5.72 79.7885 8
created 5.96 9.97356 1
created 6.2 89.762 9
created 6.44 59.8413 6
created 6.68 39.8942 4
created 6.92 39.8942 4
created 7.16 19.9471 2
created 7.4 29.9207 3
created 7.64 19.9471 2
created 7.88 59.8413 6
created 8.12 59.8413 6
created 8.36 29.9207 3
created 8.6 29.9207 3
created 8.84 29.9207 3
created 9.08 19.9471 2
created 9.32 79.7885 8
created 9.56 99.7356 10
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 (+-1.52867e-05)
fit chi^2 = 1.20343e-05
found -5.32 (+-0.000181877) 99.7337 (+-0.447261) 10.0003 (+-0.00155386)
found -2.44 (+-0.000181711) 99.7336 (+-0.447221) 10.0003 (+-0.00155372)
found 0.2 (+-0.000182015) 99.7339 (+-0.4473) 10.0003 (+-0.00155399)
found 2.36 (+-0.000181815) 99.7336 (+-0.447246) 10.0003 (+-0.0015538)
found 9.56 (+-0.000182194) 99.7345 (+-0.447356) 10.0004 (+-0.00155418)
found -7.24 (+-0.000181877) 99.7336 (+-0.447261) 10.0003 (+-0.00155386)
found -5.8 (+-0.000181309) 99.7328 (+-0.447108) 10.0002 (+-0.00155332)
found -3.4 (+-0.000182075) 99.7342 (+-0.447321) 10.0004 (+-0.00155406)
found -2.68 (+-0.000181711) 99.7336 (+-0.447221) 10.0003 (+-0.00155372)
found -0.279999 (+-0.00018165) 99.7331 (+-0.447194) 10.0003 (+-0.00155362)
found 4.28 (+-0.000181996) 99.7339 (+-0.447296) 10.0003 (+-0.00155398)
found -3.16 (+-0.000191667) 89.7606 (+-0.424306) 9.00032 (+-0.00147411)
found 1.16 (+-0.000191978) 89.7609 (+-0.42438) 9.00034 (+-0.00147436)
found -4.84 (+-0.000191754) 89.7603 (+-0.424319) 9.00029 (+-0.00147415)
found 2.12 (+-0.000192148) 89.7614 (+-0.424429) 9.0004 (+-0.00147453)
found 2.84 (+-0.000191497) 89.7598 (+-0.42425) 9.00024 (+-0.00147391)
found 6.2 (+-0.00019103) 89.7593 (+-0.424137) 9.00018 (+-0.00147352)
found -7 (+-0.000203983) 79.7884 (+-0.400201) 8.0004 (+-0.00139036)
found -5.56 (+-0.000204522) 79.7897 (+-0.400336) 8.00053 (+-0.00139083)
found 4.52 (+-0.000203983) 79.7883 (+-0.400201) 8.0004 (+-0.00139036)
found 5.72 (+-0.000202744) 79.7862 (+-0.39991) 8.00019 (+-0.00138935)
found -9.4 (+-0.000202922) 79.7862 (+-0.399943) 8.00018 (+-0.00138947)
found -6.52 (+-0.000203135) 79.7865 (+-0.399991) 8.00021 (+-0.00138963)
found 0.920001 (+-0.000203747) 79.7878 (+-0.400142) 8.00034 (+-0.00139016)
found 9.32 (+-0.000203453) 79.7875 (+-0.400079) 8.00032 (+-0.00138994)
found -4.6 (+-0.000218336) 69.8153 (+-0.374412) 7.0004 (+-0.00130077)
found -0.04 (+-0.000218906) 69.8166 (+-0.37454) 7.00053 (+-0.00130121)
found 9.8 (+-0.000216443) 69.8158 (+-0.374077) 7.00045 (+-0.0012996)
found 0.439998 (+-0.000235904) 59.842 (+-0.346656) 6.00037 (+-0.00120434)
found 3.08 (+-0.000235971) 59.842 (+-0.346666) 6.00037 (+-0.00120437)
found 6.44 (+-0.000235785) 59.8417 (+-0.346632) 6.00034 (+-0.00120425)
found -8.68 (+-0.000235154) 59.8407 (+-0.346512) 6.00024 (+-0.00120384)
found -8.44 (+-0.000235558) 59.8412 (+-0.346585) 6.00029 (+-0.00120409)
found -4.36 (+-0.000235872) 59.8417 (+-0.346645) 6.00035 (+-0.0012043)
found -4.12 (+-0.000235372) 59.8409 (+-0.346551) 6.00027 (+-0.00120397)
found 1.64 (+-0.000235393) 59.841 (+-0.346554) 6.00027 (+-0.00120398)
found 5.48 (+-0.000236009) 59.842 (+-0.346672) 6.00037 (+-0.00120439)
found 8.12 (+-0.000235154) 59.8407 (+-0.346512) 6.00024 (+-0.00120384)
found -1.48 (+-0.000234719) 59.8401 (+-0.346433) 6.00019 (+-0.00120356)
found 5.24 (+-0.000234882) 59.8404 (+-0.346465) 6.00021 (+-0.00120367)
found 7.88 (+-0.000234882) 59.8404 (+-0.346465) 6.00021 (+-0.00120367)
found 1.4 (+-0.000259102) 49.8692 (+-0.31656) 5.0004 (+-0.00109978)
found -9.16 (+-0.000258278) 49.8682 (+-0.31643) 5.00029 (+-0.00109933)
found -8.2 (+-0.000258196) 49.8679 (+-0.316413) 5.00027 (+-0.00109927)
found -6.76 (+-0.000259292) 49.8695 (+-0.31659) 5.00043 (+-0.00109988)
found -3.64 (+-0.000258825) 49.869 (+-0.316518) 5.00037 (+-0.00109963)
found -1.24 (+-0.000258416) 49.8681 (+-0.316448) 5.00029 (+-0.00109939)
found -1 (+-0.000256977) 49.8668 (+-0.316233) 5.00016 (+-0.00109864)
found 1.88 (+-0.000259102) 49.8692 (+-0.31656) 5.0004 (+-0.00109978)
found 3.32 (+-0.000258196) 49.8679 (+-0.316413) 5.00027 (+-0.00109927)
found 4.76 (+-0.000257959) 49.8679 (+-0.316383) 5.00027 (+-0.00109916)
found 4.04 (+-0.000258248) 49.8684 (+-0.316432) 5.00032 (+-0.00109933)
found -5.08 (+-0.0002911) 39.8972 (+-0.283328) 4.00051 (+-0.000984328)
found -7.96 (+-0.000288271) 39.8941 (+-0.28296) 4.00019 (+-0.000983049)
found -3.88 (+-0.000289477) 39.8951 (+-0.283112) 4.00029 (+-0.000983576)
found 0.680001 (+-0.000290135) 39.8959 (+-0.283199) 4.00037 (+-0.000983878)
found 3.56 (+-0.000288271) 39.8941 (+-0.28296) 4.00019 (+-0.000983049)
found 6.68 (+-0.000289207) 39.8948 (+-0.283077) 4.00027 (+-0.000983455)
found 6.92 (+-0.000288004) 39.8938 (+-0.282926) 4.00016 (+-0.000982928)
found -7.48 (+-0.000289274) 39.8954 (+-0.283096) 4.00032 (+-0.00098352)
found -1.96 (+-0.000287313) 39.8933 (+-0.28284) 4.00011 (+-0.000982631)
found -6.28 (+-0.000333758) 29.9216 (+-0.245148) 3.00024 (+-0.000851682)
found 2.6 (+-0.000337287) 29.9242 (+-0.245488) 3.00051 (+-0.000852865)
found -8.92 (+-0.00033519) 29.9221 (+-0.245274) 3.00029 (+-0.000852122)
found 8.36 (+-0.000334428) 29.9215 (+-0.2452) 3.00024 (+-0.000851865)
found 7.4 (+-0.000332372) 29.9202 (+-0.245002) 3.00011 (+-0.000851176)
found 8.6 (+-0.000333362) 29.9207 (+-0.245095) 3.00016 (+-0.000851499)
found 8.84 (+-0.000332866) 29.9205 (+-0.245048) 3.00013 (+-0.000851337)
found -0.519994 (+-0.000334207) 29.9221 (+-0.245195) 3.0003 (+-0.000851848)
found -2.92 (+-0.000415316) 19.9512 (+-0.200597) 2.00051 (+-0.000696907)
found -2.20001 (+-0.000413245) 19.9498 (+-0.200455) 2.00037 (+-0.000696413)
found 5 (+-0.00041237) 19.949 (+-0.200391) 2.00029 (+-0.00069619)
found -9.63999 (+-0.000411282) 19.9488 (+-0.200324) 2.00027 (+-0.000695956)
found -7.72 (+-0.000410906) 19.9482 (+-0.200291) 2.00021 (+-0.000695844)
found -1.72 (+-0.000411859) 19.9488 (+-0.200356) 2.00027 (+-0.00069607)
found 3.8 (+-0.000411414) 19.9485 (+-0.200326) 2.00024 (+-0.000695963)
found 7.16 (+-0.000410313) 19.948 (+-0.200252) 2.00019 (+-0.000695707)
found 7.64 (+-0.000411262) 19.9485 (+-0.200317) 2.00024 (+-0.000695933)
found 9.08 (+-0.000412016) 19.949 (+-0.20037) 2.00029 (+-0.000696118)
found -9.88 (+-0.000407184) 19.9467 (+-0.200034) 2.00006 (+-0.000694952)
found 5.96 (+-0.000592491) 9.97758 (+-0.142035) 1.00045 (+-0.00049345)
found -0.760004 (+-0.000585786) 9.97519 (+-0.141789) 1.00022 (+-0.000492599)
found -6.03999 (+-0.000588883) 9.97654 (+-0.141908) 1.00035 (+-0.000493009)

 
#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.