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 89.762 9
created -9.64 39.8942 4
created -9.4 39.8942 4
created -9.16 39.8942 4
created -8.92 29.9207 3
created -8.68 29.9207 3
created -8.44 49.8678 5
created -8.2 69.8149 7
created -7.96 79.7885 8
created -7.72 9.97356 1
created -7.48 19.9471 2
created -7.24 59.8413 6
created -7 29.9207 3
created -6.76 89.762 9
created -6.52 19.9471 2
created -6.28 39.8942 4
created -6.04 39.8942 4
created -5.8 39.8942 4
created -5.56 49.8678 5
created -5.32 79.7885 8
created -5.08 19.9471 2
created -4.84 89.762 9
created -4.6 49.8678 5
created -4.36 69.8149 7
created -4.12 49.8678 5
created -3.88 19.9471 2
created -3.64 9.97356 1
created -3.4 79.7885 8
created -3.16 49.8678 5
created -2.92 69.8149 7
created -2.68 19.9471 2
created -2.44 9.97356 1
created -2.2 9.97356 1
created -1.96 39.8942 4
created -1.72 99.7356 10
created -1.48 59.8413 6
created -1.24 9.97356 1
created -1 49.8678 5
created -0.76 69.8149 7
created -0.52 9.97356 1
created -0.28 39.8942 4
created -0.04 19.9471 2
created 0.2 9.97356 1
created 0.44 79.7885 8
created 0.68 89.762 9
created 0.92 39.8942 4
created 1.16 89.762 9
created 1.4 29.9207 3
created 1.64 89.762 9
created 1.88 19.9471 2
created 2.12 19.9471 2
created 2.36 49.8678 5
created 2.6 49.8678 5
created 2.84 59.8413 6
created 3.08 59.8413 6
created 3.32 29.9207 3
created 3.56 69.8149 7
created 3.8 19.9471 2
created 4.04 39.8942 4
created 4.28 89.762 9
created 4.52 49.8678 5
created 4.76 89.762 9
created 5 99.7356 10
created 5.24 29.9207 3
created 5.48 49.8678 5
created 5.72 49.8678 5
created 5.96 89.762 9
created 6.2 79.7885 8
created 6.44 9.97356 1
created 6.68 69.8149 7
created 6.92 79.7885 8
created 7.16 79.7885 8
created 7.4 19.9471 2
created 7.64 9.97356 1
created 7.88 59.8413 6
created 8.12 9.97356 1
created 8.36 29.9207 3
created 8.6 69.8149 7
created 8.84 49.8678 5
created 9.08 19.9471 2
created 9.32 39.8942 4
created 9.56 39.8942 4
created 9.8 59.8413 6
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.53777e-05)
fit chi^2 = 1.05032e-05
found 5 (+-0.000169855) 99.7337 (+-0.417828) 10.0003 (+-0.0014516)
found -1.72 (+-0.000169746) 99.7331 (+-0.41779) 10.0003 (+-0.00145147)
found 0.679999 (+-0.000179228) 89.7606 (+-0.396433) 9.00032 (+-0.00137727)
found -9.88 (+-0.00017883) 89.7586 (+-0.396289) 9.00011 (+-0.00137677)
found -6.76 (+-0.000178352) 89.7588 (+-0.396201) 9.00013 (+-0.00137646)
found -4.84 (+-0.000178615) 89.7593 (+-0.39627) 9.00018 (+-0.00137671)
found 1.16 (+-0.000178674) 89.7593 (+-0.396283) 9.00019 (+-0.00137675)
found 1.64 (+-0.000178352) 89.7588 (+-0.396201) 9.00013 (+-0.00137646)
found 4.28 (+-0.000178939) 89.7598 (+-0.396353) 9.00024 (+-0.00137699)
found 4.76 (+-0.000179509) 89.7614 (+-0.396512) 9.0004 (+-0.00137754)
found 5.96 (+-0.00017935) 89.7609 (+-0.396466) 9.00034 (+-0.00137738)
found -7.96 (+-0.000189514) 79.7865 (+-0.373632) 8.00021 (+-0.00129806)
found 6.2 (+-0.000189701) 79.7871 (+-0.37368) 8.00027 (+-0.00129822)
found 6.92 (+-0.000190615) 79.7884 (+-0.373887) 8.0004 (+-0.00129894)
found 7.16 (+-0.000189895) 79.787 (+-0.373717) 8.00026 (+-0.00129835)
found -5.32 (+-0.000189574) 79.7863 (+-0.373636) 8.00019 (+-0.00129807)
found -3.4 (+-0.000189291) 79.786 (+-0.373576) 8.00016 (+-0.00129786)
found 0.440002 (+-0.000189701) 79.787 (+-0.37368) 8.00026 (+-0.00129822)
found -8.2 (+-0.000203738) 69.8148 (+-0.349732) 7.00034 (+-0.00121502)
found -4.36 (+-0.000203372) 69.814 (+-0.349651) 7.00027 (+-0.00121474)
found -2.92 (+-0.000202821) 69.8132 (+-0.349537) 7.00018 (+-0.00121435)
found -0.760001 (+-0.000202504) 69.8129 (+-0.349478) 7.00016 (+-0.00121414)
found 3.56 (+-0.000202496) 69.8127 (+-0.349469) 7.00013 (+-0.00121411)
found 6.68 (+-0.000202864) 69.8138 (+-0.349559) 7.00024 (+-0.00121442)
found 8.6 (+-0.000203044) 69.8135 (+-0.349582) 7.00021 (+-0.0012145)
found -1.48 (+-0.000219561) 59.8412 (+-0.323714) 6.00029 (+-0.00112463)
found 2.84 (+-0.000220063) 59.8412 (+-0.323788) 6.00029 (+-0.00112489)
found 3.08 (+-0.000219686) 59.8407 (+-0.323719) 6.00024 (+-0.00112465)
found 9.8 (+-0.000217762) 59.8409 (+-0.323421) 6.00026 (+-0.00112362)
found -7.24 (+-0.000218907) 59.8396 (+-0.323577) 6.00013 (+-0.00112416)
found 7.88 (+-0.000217939) 59.8388 (+-0.323415) 6.00005 (+-0.00112359)
found -4.6 (+-0.000242225) 49.8695 (+-0.295765) 5.00043 (+-0.00102753)
found 4.52 (+-0.00024252) 49.87 (+-0.295813) 5.00048 (+-0.0010277)
found -8.44 (+-0.000241138) 49.8679 (+-0.295591) 5.00027 (+-0.00102693)
found -5.56 (+-0.000241529) 49.8684 (+-0.295652) 5.00032 (+-0.00102714)
found -4.12 (+-0.000240841) 49.8676 (+-0.295547) 5.00024 (+-0.00102678)
found -3.16 (+-0.000242084) 49.8692 (+-0.295741) 5.0004 (+-0.00102745)
found 2.6 (+-0.000241418) 49.8682 (+-0.295633) 5.00029 (+-0.00102707)
found 5.48 (+-0.000240793) 49.8674 (+-0.295535) 5.00022 (+-0.00102673)
found 5.72 (+-0.000241875) 49.869 (+-0.295708) 5.00037 (+-0.00102734)
found 8.84 (+-0.00024084) 49.8676 (+-0.295547) 5.00024 (+-0.00102678)
found -0.999998 (+-0.000240416) 49.8674 (+-0.295489) 5.00021 (+-0.00102657)
found 2.36 (+-0.000240497) 49.8671 (+-0.295491) 5.00019 (+-0.00102658)
found -9.64 (+-0.000270744) 39.8957 (+-0.264533) 4.00035 (+-0.000919028)
found 0.92 (+-0.00027179) 39.897 (+-0.26467) 4.00048 (+-0.000919505)
found -9.4 (+-0.00026971) 39.8943 (+-0.264396) 4.00021 (+-0.000918552)
found -9.16 (+-0.000269419) 39.8941 (+-0.264359) 4.00019 (+-0.000918425)
found -6.04 (+-0.00026971) 39.8943 (+-0.264396) 4.00021 (+-0.000918552)
found -5.8 (+-0.000269961) 39.8946 (+-0.264428) 4.00024 (+-0.000918665)
found 9.56 (+-0.000270184) 39.8949 (+-0.264457) 4.00027 (+-0.000918767)
found -6.28 (+-0.00026906) 39.8938 (+-0.264316) 4.00016 (+-0.000918274)
found -1.96 (+-0.000269731) 39.8951 (+-0.264417) 4.00029 (+-0.000918626)
found -0.28 (+-0.00026791) 39.893 (+-0.264178) 4.00008 (+-0.000917796)
found 4.04 (+-0.000270087) 39.8952 (+-0.264453) 4.0003 (+-0.00091875)
found 9.32 (+-0.00026906) 39.8938 (+-0.264316) 4.00016 (+-0.000918274)
found 1.4 (+-0.000314894) 29.9239 (+-0.229319) 3.00048 (+-0.000796692)
found 5.24 (+-0.000314072) 29.9232 (+-0.229237) 3.0004 (+-0.000796406)
found -7 (+-0.000314162) 29.9231 (+-0.229245) 3.0004 (+-0.000796433)
found 3.32 (+-0.000313699) 29.9226 (+-0.229197) 3.00035 (+-0.000796267)
found -8.92 (+-0.000311813) 29.921 (+-0.22901) 3.00019 (+-0.000795617)
found -8.68 (+-0.00031214) 29.9213 (+-0.229043) 3.00021 (+-0.00079573)
found 8.36 (+-0.000311569) 29.9213 (+-0.228998) 3.00022 (+-0.000795575)
found -6.52 (+-0.000385781) 19.9496 (+-0.187249) 2.00035 (+-0.000650534)
found -5.08 (+-0.000387401) 19.9506 (+-0.18736) 2.00045 (+-0.000650919)
found 1.87999 (+-0.000384533) 19.9491 (+-0.187169) 2.0003 (+-0.000650254)
found 7.39999 (+-0.00038329) 19.9485 (+-0.18709) 2.00024 (+-0.000649981)
found -3.88 (+-0.000382185) 19.9477 (+-0.187012) 2.00016 (+-0.000649708)
found -2.68001 (+-0.00038296) 19.9482 (+-0.187066) 2.00021 (+-0.000649898)
found 3.8 (+-0.00038514) 19.949 (+-0.187204) 2.00029 (+-0.000650375)
found 9.08 (+-0.000384352) 19.9485 (+-0.187149) 2.00024 (+-0.000650185)
found -0.0400027 (+-0.000381719) 19.9475 (+-0.18698) 2.00014 (+-0.000649597)
found 2.12 (+-0.000383116) 19.948 (+-0.187069) 2.00019 (+-0.000649905)
found -7.48 (+-0.000382595) 19.948 (+-0.187041) 2.00019 (+-0.000649808)
found -7.72001 (+-0.000547875) 9.97574 (+-0.132492) 1.00027 (+-0.000460297)
found 6.44 (+-0.000552423) 9.97706 (+-0.13265) 1.0004 (+-0.000460848)
found -0.520006 (+-0.000549628) 9.97599 (+-0.132548) 1.00029 (+-0.000460494)
found -1.24 (+-0.000549841) 9.97599 (+-0.132555) 1.00029 (+-0.000460516)
found 8.11999 (+-0.000547974) 9.97546 (+-0.132489) 1.00024 (+-0.000460289)
found -3.63999 (+-0.000547875) 9.97574 (+-0.132492) 1.00027 (+-0.000460297)
found -2.44 (+-0.000541144) 9.97387 (+-0.132255) 1.00008 (+-0.000459474)
found -2.19999 (+-0.000543382) 9.9744 (+-0.132333) 1.00014 (+-0.000459745)
found 0.200012 (+-0.000547875) 9.97574 (+-0.132492) 1.00027 (+-0.000460297)
found 7.64001 (+-0.000546689) 9.9752 (+-0.132446) 1.00022 (+-0.000460139)

 
#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.