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 99.7356 10
created -9.64 99.7356 10
created -9.4 69.8149 7
created -9.16 99.7356 10
created -8.92 9.97356 1
created -8.68 59.8413 6
created -8.44 89.762 9
created -8.2 69.8149 7
created -7.96 99.7356 10
created -7.72 89.762 9
created -7.48 79.7885 8
created -7.24 89.762 9
created -7 9.97356 1
created -6.76 9.97356 1
created -6.52 9.97356 1
created -6.28 69.8149 7
created -6.04 69.8149 7
created -5.8 89.762 9
created -5.56 9.97356 1
created -5.32 19.9471 2
created -5.08 59.8413 6
created -4.84 19.9471 2
created -4.6 89.762 9
created -4.36 9.97356 1
created -4.12 89.762 9
created -3.88 59.8413 6
created -3.64 59.8413 6
created -3.4 59.8413 6
created -3.16 29.9207 3
created -2.92 39.8942 4
created -2.68 79.7885 8
created -2.44 39.8942 4
created -2.2 89.762 9
created -1.96 69.8149 7
created -1.72 59.8413 6
created -1.48 29.9207 3
created -1.24 69.8149 7
created -1 79.7885 8
created -0.76 29.9207 3
created -0.52 29.9207 3
created -0.28 29.9207 3
created -0.04 99.7356 10
created 0.2 29.9207 3
created 0.44 59.8413 6
created 0.68 9.97356 1
created 0.92 79.7885 8
created 1.16 49.8678 5
created 1.4 59.8413 6
created 1.64 49.8678 5
created 1.88 39.8942 4
created 2.12 59.8413 6
created 2.36 9.97356 1
created 2.6 69.8149 7
created 2.84 89.762 9
created 3.08 89.762 9
created 3.32 49.8678 5
created 3.56 59.8413 6
created 3.8 89.762 9
created 4.04 29.9207 3
created 4.28 69.8149 7
created 4.52 39.8942 4
created 4.76 69.8149 7
created 5 89.762 9
created 5.24 99.7356 10
created 5.48 19.9471 2
created 5.72 39.8942 4
created 5.96 49.8678 5
created 6.2 69.8149 7
created 6.44 9.97356 1
created 6.68 39.8942 4
created 6.92 19.9471 2
created 7.16 99.7356 10
created 7.4 29.9207 3
created 7.64 9.97356 1
created 7.88 19.9471 2
created 8.12 9.97356 1
created 8.36 39.8942 4
created 8.6 59.8413 6
created 8.84 9.97356 1
created 9.08 69.8149 7
created 9.32 89.762 9
created 9.56 19.9471 2
created 9.8 19.9471 2
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-9.40502e-06)
fit chi^2 = 4.39658e-06
found -9.64 (+-0.000110217) 99.735 (+-0.270425) 10.0005 (+-0.0009395)
found -9.16 (+-0.000109537) 99.7326 (+-0.27023) 10.0002 (+-0.00093882)
found -7.96 (+-0.000110172) 99.7347 (+-0.270411) 10.0004 (+-0.00093945)
found 5.24 (+-0.000109787) 99.7334 (+-0.2703) 10.0003 (+-0.000939065)
found -9.88 (+-0.000110066) 99.7332 (+-0.270356) 10.0003 (+-0.000939261)
found -0.04 (+-0.00010952) 99.732 (+-0.270217) 10.0002 (+-0.000938775)
found 7.16 (+-0.000109414) 99.7317 (+-0.270187) 10.0001 (+-0.000938674)
found -8.44 (+-0.00011605) 89.7609 (+-0.256511) 9.00034 (+-0.000891161)
found -7.72 (+-0.000116329) 89.7622 (+-0.25659) 9.00048 (+-0.000891436)
found -7.24 (+-0.000115582) 89.7598 (+-0.256395) 9.00023 (+-0.000890757)
found -5.8 (+-0.000115526) 89.7595 (+-0.256379) 9.00021 (+-0.000890701)
found 2.84 (+-0.000116222) 89.7617 (+-0.25656) 9.00042 (+-0.000891329)
found 3.08 (+-0.00011609) 89.7611 (+-0.256524) 9.00037 (+-0.000891204)
found 3.8 (+-0.000115747) 89.7598 (+-0.25643) 9.00024 (+-0.000890878)
found 5 (+-0.000116272) 89.7619 (+-0.256574) 9.00045 (+-0.000891379)
found 9.32 (+-0.000115692) 89.7598 (+-0.256418) 9.00023 (+-0.000890837)
found -4.6 (+-0.000115112) 89.7582 (+-0.256269) 9.00008 (+-0.000890319)
found -4.12 (+-0.000115465) 89.7593 (+-0.256362) 9.00019 (+-0.000890641)
found -2.2 (+-0.000115902) 89.7603 (+-0.256471) 9.00029 (+-0.000891022)
found -7.48 (+-0.000123508) 79.7892 (+-0.241947) 8.00048 (+-0.000840562)
found -1 (+-0.000122926) 79.787 (+-0.241803) 8.00026 (+-0.000840062)
found -2.68 (+-0.000122797) 79.7865 (+-0.24177) 8.00021 (+-0.000839947)
found 0.920001 (+-0.000122469) 79.786 (+-0.241699) 8.00016 (+-0.000839701)
found -9.4 (+-0.000132314) 69.8166 (+-0.226384) 7.00053 (+-0.000786493)
found -8.2 (+-0.00013225) 69.8164 (+-0.226369) 7.00051 (+-0.000786443)
found -6.04 (+-0.000132048) 69.8156 (+-0.226324) 7.00042 (+-0.000786284)
found -1.96 (+-0.000131969) 69.8153 (+-0.226306) 7.0004 (+-0.000786224)
found -6.28 (+-0.00013118) 69.8134 (+-0.226144) 7.00021 (+-0.00078566)
found -1.24 (+-0.000131602) 69.8143 (+-0.226227) 7.00029 (+-0.00078595)
found 2.6 (+-0.000131317) 69.814 (+-0.226175) 7.00027 (+-0.000785769)
found 4.28 (+-0.00013127) 69.8132 (+-0.226154) 7.00019 (+-0.000785696)
found 4.76 (+-0.000131784) 69.8148 (+-0.226267) 7.00035 (+-0.000786086)
found 6.2 (+-0.000131018) 69.8129 (+-0.226108) 7.00016 (+-0.000785534)
found 9.08 (+-0.000131317) 69.814 (+-0.226175) 7.00027 (+-0.000785769)
found -3.88 (+-0.000142728) 59.8423 (+-0.209555) 6.0004 (+-0.000728026)
found -3.64 (+-0.000142478) 59.8415 (+-0.209506) 6.00032 (+-0.000727856)
found -3.4 (+-0.000142134) 59.8407 (+-0.209442) 6.00024 (+-0.000727636)
found -1.72 (+-0.000142224) 59.841 (+-0.20946) 6.00027 (+-0.000727696)
found 0.439999 (+-0.000141397) 59.8394 (+-0.209312) 6.00011 (+-0.000727184)
found 1.4 (+-0.000142279) 59.8409 (+-0.209468) 6.00027 (+-0.000727725)
found 2.12 (+-0.000141527) 59.8396 (+-0.209336) 6.00013 (+-0.000727266)
found 3.56 (+-0.000142628) 59.842 (+-0.209536) 6.00037 (+-0.000727961)
found 8.6 (+-0.000141527) 59.8396 (+-0.209336) 6.00013 (+-0.000727266)
found -8.68 (+-0.000141982) 59.841 (+-0.209424) 6.00027 (+-0.000727574)
found -5.08 (+-0.000141468) 59.8393 (+-0.209322) 6.00011 (+-0.000727218)
found 3.32 (+-0.000156609) 49.8692 (+-0.191339) 5.0004 (+-0.00066474)
found 1.16 (+-0.000156518) 49.869 (+-0.191323) 5.00037 (+-0.000664687)
found 1.64 (+-0.000156062) 49.8679 (+-0.19125) 5.00027 (+-0.000664432)
found 5.96 (+-0.000156168) 49.8681 (+-0.191267) 5.00029 (+-0.000664493)
found -2.92 (+-0.000174867) 39.8951 (+-0.171111) 4.00029 (+-0.000594465)
found -2.44 (+-0.000175733) 39.8967 (+-0.171223) 4.00045 (+-0.000594856)
found 1.88 (+-0.000174969) 39.8951 (+-0.171122) 4.00029 (+-0.000594504)
found 4.52 (+-0.000175378) 39.8959 (+-0.171176) 4.00037 (+-0.000594691)
found 5.72 (+-0.00017424) 39.8941 (+-0.17103) 4.00019 (+-0.000594185)
found 6.68 (+-0.000173334) 39.893 (+-0.17092) 4.00008 (+-0.000593803)
found 8.36 (+-0.000174053) 39.894 (+-0.171011) 4.00019 (+-0.000594121)
found -0.760003 (+-0.000202462) 29.9221 (+-0.14824) 3.00029 (+-0.000515009)
found 0.199997 (+-0.000203392) 29.9234 (+-0.148333) 3.00043 (+-0.000515331)
found 4.04 (+-0.000203431) 29.9234 (+-0.148336) 3.00043 (+-0.000515342)
found 7.39999 (+-0.000202005) 29.9221 (+-0.148204) 3.00029 (+-0.000514883)
found -3.16 (+-0.000202386) 29.9218 (+-0.14823) 3.00027 (+-0.000514976)
found -1.48 (+-0.00020296) 29.9226 (+-0.148288) 3.00035 (+-0.000515175)
found -0.52 (+-0.000201494) 29.9208 (+-0.148143) 3.00016 (+-0.000514672)
found -0.279996 (+-0.000202736) 29.9226 (+-0.148269) 3.00035 (+-0.000515109)
found 5.47999 (+-0.000249778) 19.9498 (+-0.121161) 2.00038 (+-0.000420934)
found 9.55999 (+-0.000248788) 19.9491 (+-0.121096) 2.0003 (+-0.000420707)
found -4.84 (+-0.00025018) 19.9501 (+-0.121187) 2.0004 (+-0.000421025)
found 6.92001 (+-0.000249778) 19.9498 (+-0.121161) 2.00037 (+-0.000420934)
found 9.8 (+-0.000244383) 19.9472 (+-0.120833) 2.00011 (+-0.000419793)
found -5.32 (+-0.000247534) 19.948 (+-0.121013) 2.00019 (+-0.000420418)
found 7.88 (+-0.000245593) 19.9466 (+-0.120885) 2.00005 (+-0.000419974)
found -8.92001 (+-0.000357637) 9.97732 (+-0.0858328) 1.00043 (+-0.000298197)
found -7.00002 (+-0.000353638) 9.97575 (+-0.0856965) 1.00027 (+-0.000297723)
found -5.56001 (+-0.000354797) 9.97601 (+-0.0857334) 1.0003 (+-0.000297851)
found -4.36 (+-0.000358458) 9.97785 (+-0.085863) 1.00048 (+-0.000298302)
found 6.43999 (+-0.000355602) 9.97598 (+-0.0857572) 1.00029 (+-0.000297934)
found 0.680004 (+-0.000356995) 9.97678 (+-0.0858078) 1.00037 (+-0.00029811)
found 2.36 (+-0.000356625) 9.97652 (+-0.0857938) 1.00035 (+-0.000298061)
found 8.84 (+-0.000356625) 9.97652 (+-0.0857938) 1.00035 (+-0.000298061)
found 7.64 (+-0.000352055) 9.97439 (+-0.0856318) 1.00013 (+-0.000297498)
found -6.76 (+-0.000348997) 9.97361 (+-0.0855304) 1.00006 (+-0.000297146)
found -6.51999 (+-0.000352954) 9.9752 (+-0.0856696) 1.00022 (+-0.00029763)
found 8.12 (+-0.000352702) 9.97465 (+-0.0856546) 1.00016 (+-0.000297578)

 
#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.