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 59.8413 6
created -9.64 39.8942 4
created -9.4 59.8413 6
created -9.16 59.8413 6
created -8.92 59.8413 6
created -8.68 9.97356 1
created -8.44 59.8413 6
created -8.2 9.97356 1
created -7.96 39.8942 4
created -7.72 39.8942 4
created -7.48 19.9471 2
created -7.24 39.8942 4
created -7 59.8413 6
created -6.76 19.9471 2
created -6.52 9.97356 1
created -6.28 59.8413 6
created -6.04 79.7885 8
created -5.8 29.9207 3
created -5.56 69.8149 7
created -5.32 89.762 9
created -5.08 59.8413 6
created -4.84 89.762 9
created -4.6 19.9471 2
created -4.36 79.7885 8
created -4.12 89.762 9
created -3.88 79.7885 8
created -3.64 99.7356 10
created -3.4 79.7885 8
created -3.16 99.7356 10
created -2.92 49.8678 5
created -2.68 69.8149 7
created -2.44 89.762 9
created -2.2 9.97356 1
created -1.96 19.9471 2
created -1.72 39.8942 4
created -1.48 99.7356 10
created -1.24 99.7356 10
created -1 69.8149 7
created -0.76 9.97356 1
created -0.52 19.9471 2
created -0.28 49.8678 5
created -0.04 39.8942 4
created 0.2 19.9471 2
created 0.44 39.8942 4
created 0.68 59.8413 6
created 0.92 79.7885 8
created 1.16 99.7356 10
created 1.4 69.8149 7
created 1.64 29.9207 3
created 1.88 19.9471 2
created 2.12 49.8678 5
created 2.36 89.762 9
created 2.6 49.8678 5
created 2.84 59.8413 6
created 3.08 59.8413 6
created 3.32 49.8678 5
created 3.56 59.8413 6
created 3.8 69.8149 7
created 4.04 89.762 9
created 4.28 29.9207 3
created 4.52 69.8149 7
created 4.76 9.97356 1
created 5 59.8413 6
created 5.24 29.9207 3
created 5.48 69.8149 7
created 5.72 69.8149 7
created 5.96 79.7885 8
created 6.2 59.8413 6
created 6.44 49.8678 5
created 6.68 19.9471 2
created 6.92 49.8678 5
created 7.16 99.7356 10
created 7.4 79.7885 8
created 7.64 69.8149 7
created 7.88 69.8149 7
created 8.12 19.9471 2
created 8.36 49.8678 5
created 8.6 39.8942 4
created 8.84 79.7885 8
created 9.08 69.8149 7
created 9.32 79.7885 8
created 9.56 9.97356 1
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 (+-1.5365e-05)
fit chi^2 = 1.20496e-05
found -3.64 (+-0.000182396) 99.7347 (+-0.447666) 10.0004 (+-0.00155526)
found -3.16 (+-0.000182111) 99.7339 (+-0.447579) 10.0003 (+-0.00155496)
found -1.24 (+-0.000182464) 99.735 (+-0.447688) 10.0004 (+-0.00155534)
found 1.16 (+-0.00018231) 99.7344 (+-0.447639) 10.0004 (+-0.00155517)
found -1.48 (+-0.000182146) 99.7342 (+-0.447593) 10.0004 (+-0.00155501)
found 7.16 (+-0.000182111) 99.7339 (+-0.447579) 10.0003 (+-0.00155496)
found -5.32 (+-0.00019212) 89.7609 (+-0.424653) 9.00034 (+-0.00147531)
found -4.84 (+-0.000191425) 89.7596 (+-0.424471) 9.00021 (+-0.00147468)
found -4.12 (+-0.000192412) 89.7617 (+-0.424736) 9.00042 (+-0.0014756)
found -2.44 (+-0.000191253) 89.7595 (+-0.424435) 9.00021 (+-0.00147455)
found 4.04 (+-0.000191721) 89.7601 (+-0.424548) 9.00026 (+-0.00147495)
found 2.36 (+-0.000191788) 89.7601 (+-0.424562) 9.00026 (+-0.001475)
found -6.04 (+-0.00020339) 79.7868 (+-0.400276) 8.00024 (+-0.00139062)
found -3.88 (+-0.000204559) 79.7894 (+-0.400566) 8.00051 (+-0.00139163)
found -3.4 (+-0.000204652) 79.7897 (+-0.40059) 8.00053 (+-0.00139171)
found 0.920001 (+-0.00020424) 79.7886 (+-0.400485) 8.00042 (+-0.00139135)
found 5.96 (+-0.000203945) 79.7878 (+-0.40041) 8.00034 (+-0.00139109)
found 7.4 (+-0.000204355) 79.7889 (+-0.400514) 8.00045 (+-0.00139145)
found 9.32 (+-0.000202985) 79.7865 (+-0.400192) 8.00021 (+-0.00139033)
found -4.36 (+-0.00020349) 79.7873 (+-0.400308) 8.00029 (+-0.00139073)
found 8.84 (+-0.000203674) 79.7873 (+-0.400344) 8.00029 (+-0.00139086)
found -1 (+-0.000217497) 69.8143 (+-0.374456) 7.00029 (+-0.00130092)
found 1.4 (+-0.00021808) 69.8148 (+-0.374568) 7.00035 (+-0.00130131)
found 3.8 (+-0.000218475) 69.8153 (+-0.374649) 7.0004 (+-0.00130159)
found 5.72 (+-0.000218494) 69.8153 (+-0.374653) 7.0004 (+-0.0013016)
found 7.64 (+-0.000218494) 69.8153 (+-0.374653) 7.0004 (+-0.0013016)
found 7.88 (+-0.000217509) 69.8137 (+-0.374445) 7.00024 (+-0.00130088)
found 9.08 (+-0.000218614) 69.8156 (+-0.37468) 7.00042 (+-0.0013017)
found -5.56 (+-0.000217977) 69.8145 (+-0.374544) 7.00032 (+-0.00130122)
found -2.68 (+-0.000218331) 69.8151 (+-0.374618) 7.00037 (+-0.00130148)
found 4.52 (+-0.000216552) 69.8124 (+-0.374247) 7.00011 (+-0.00130019)
found 5.48 (+-0.000217749) 69.814 (+-0.374492) 7.00027 (+-0.00130104)
found 9.8 (+-0.000215083) 69.8134 (+-0.373994) 7.00021 (+-0.00129931)
found -5.08 (+-0.000236703) 59.8431 (+-0.346998) 6.00048 (+-0.00120553)
found -9.4 (+-0.000235521) 59.841 (+-0.34677) 6.00027 (+-0.00120474)
found -9.16 (+-0.000235872) 59.8415 (+-0.346836) 6.00032 (+-0.00120496)
found -8.92 (+-0.000234642) 59.8402 (+-0.346621) 6.00019 (+-0.00120421)
found -7 (+-0.000234684) 59.8399 (+-0.346618) 6.00016 (+-0.00120421)
found 0.680001 (+-0.000235806) 59.8415 (+-0.346826) 6.00032 (+-0.00120493)
found 2.84 (+-0.000235707) 59.8412 (+-0.346805) 6.00029 (+-0.00120486)
found 3.08 (+-0.000235707) 59.8412 (+-0.346805) 6.00029 (+-0.00120486)
found 3.56 (+-0.000235856) 59.8415 (+-0.346834) 6.00032 (+-0.00120496)
found 6.2 (+-0.000235993) 59.8417 (+-0.346861) 6.00035 (+-0.00120505)
found -9.88 (+-0.000234889) 59.8394 (+-0.346624) 6.00011 (+-0.00120423)
found -8.44 (+-0.000233432) 59.8388 (+-0.346405) 6.00005 (+-0.00120347)
found -6.28 (+-0.000234925) 59.8407 (+-0.346676) 6.00024 (+-0.00120441)
found 5 (+-0.000234082) 59.8394 (+-0.346516) 6.00011 (+-0.00120385)
found -2.92 (+-0.000259586) 49.8698 (+-0.316813) 5.00045 (+-0.00110066)
found 2.6 (+-0.000259266) 49.8692 (+-0.31676) 5.0004 (+-0.00110048)
found 3.32 (+-0.000258774) 49.8684 (+-0.316679) 5.00032 (+-0.00110019)
found 6.44 (+-0.000257786) 49.8674 (+-0.316527) 5.00021 (+-0.00109967)
found -0.279999 (+-0.000257376) 49.8668 (+-0.316462) 5.00016 (+-0.00109944)
found 2.12 (+-0.000258272) 49.8682 (+-0.316608) 5.00029 (+-0.00109995)
found 6.92 (+-0.000258411) 49.8684 (+-0.316632) 5.00032 (+-0.00110003)
found 8.36 (+-0.000257375) 49.8669 (+-0.316462) 5.00016 (+-0.00109944)
found -9.64 (+-0.000289901) 39.8954 (+-0.283323) 4.00032 (+-0.000984308)
found -7.72 (+-0.000288186) 39.8938 (+-0.283105) 4.00016 (+-0.000983551)
found -0.0400014 (+-0.000288453) 39.8941 (+-0.283139) 4.00019 (+-0.000983671)
found 8.6 (+-0.000290078) 39.8957 (+-0.283347) 4.00035 (+-0.000984392)
found -7.96 (+-0.000287642) 39.8935 (+-0.283043) 4.00013 (+-0.000983336)
found -7.24 (+-0.000288691) 39.8943 (+-0.283171) 4.00021 (+-0.00098378)
found -1.72 (+-0.000289457) 39.8954 (+-0.283275) 4.00032 (+-0.000984142)
found 0.440002 (+-0.000288691) 39.8943 (+-0.283171) 4.00021 (+-0.00098378)
found -5.8 (+-0.000336541) 29.9231 (+-0.245545) 3.0004 (+-0.000853062)
found 4.28 (+-0.000336779) 29.9234 (+-0.24557) 3.00043 (+-0.000853148)
found 1.64 (+-0.000334418) 29.9216 (+-0.245338) 3.00024 (+-0.000852342)
found 5.24 (+-0.000335999) 29.9226 (+-0.24549) 3.00035 (+-0.00085287)
found -4.6 (+-0.00041494) 19.9506 (+-0.200679) 2.00045 (+-0.00069719)
found -6.76 (+-0.000409792) 19.948 (+-0.200336) 2.00019 (+-0.000696)
found 6.68 (+-0.000412184) 19.9488 (+-0.200487) 2.00027 (+-0.000696524)
found 8.12 (+-0.00041303) 19.9493 (+-0.200546) 2.00032 (+-0.000696727)
found -7.48 (+-0.000411166) 19.9482 (+-0.200418) 2.00021 (+-0.000696284)
found 0.2 (+-0.000411166) 19.9482 (+-0.200418) 2.00021 (+-0.000696284)
found 1.88 (+-0.000411079) 19.9482 (+-0.200413) 2.00022 (+-0.000696267)
found -1.96 (+-0.000408853) 19.9475 (+-0.200271) 2.00014 (+-0.000695774)
found -0.519996 (+-0.000409353) 19.9477 (+-0.200306) 2.00016 (+-0.000695894)
found -2.20001 (+-0.000587366) 9.97601 (+-0.141931) 1.0003 (+-0.000493092)
found 9.56 (+-0.000591692) 9.97706 (+-0.14208) 1.0004 (+-0.000493608)
found -0.76001 (+-0.000586222) 9.97547 (+-0.141887) 1.00024 (+-0.000492937)
found 4.76 (+-0.000590392) 9.97651 (+-0.142031) 1.00035 (+-0.000493439)
found -8.68 (+-0.000589708) 9.97625 (+-0.142006) 1.00032 (+-0.000493351)
found -8.2 (+-0.00058802) 9.97571 (+-0.141945) 1.00027 (+-0.00049314)
found -6.51999 (+-0.000585551) 9.9752 (+-0.141861) 1.00022 (+-0.000492849)

 
#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.