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 49.8678 5
created -9.64 89.762 9
created -9.4 99.7356 10
created -9.16 79.7885 8
created -8.92 39.8942 4
created -8.68 89.762 9
created -8.44 39.8942 4
created -8.2 99.7356 10
created -7.96 29.9207 3
created -7.72 69.8149 7
created -7.48 89.762 9
created -7.24 39.8942 4
created -7 69.8149 7
created -6.76 39.8942 4
created -6.52 29.9207 3
created -6.28 79.7885 8
created -6.04 49.8678 5
created -5.8 69.8149 7
created -5.56 89.762 9
created -5.32 79.7885 8
created -5.08 29.9207 3
created -4.84 99.7356 10
created -4.6 69.8149 7
created -4.36 89.762 9
created -4.12 29.9207 3
created -3.88 69.8149 7
created -3.64 69.8149 7
created -3.4 9.97356 1
created -3.16 79.7885 8
created -2.92 29.9207 3
created -2.68 59.8413 6
created -2.44 99.7356 10
created -2.2 29.9207 3
created -1.96 9.97356 1
created -1.72 59.8413 6
created -1.48 29.9207 3
created -1.24 29.9207 3
created -1 69.8149 7
created -0.76 39.8942 4
created -0.52 29.9207 3
created -0.28 9.97356 1
created -0.04 49.8678 5
created 0.2 69.8149 7
created 0.44 39.8942 4
created 0.68 69.8149 7
created 0.92 9.97356 1
created 1.16 69.8149 7
created 1.4 19.9471 2
created 1.64 99.7356 10
created 1.88 9.97356 1
created 2.12 69.8149 7
created 2.36 79.7885 8
created 2.6 19.9471 2
created 2.84 49.8678 5
created 3.08 29.9207 3
created 3.32 99.7356 10
created 3.56 39.8942 4
created 3.8 9.97356 1
created 4.04 89.762 9
created 4.28 49.8678 5
created 4.52 79.7885 8
created 4.76 39.8942 4
created 5 49.8678 5
created 5.24 89.762 9
created 5.48 39.8942 4
created 5.72 49.8678 5
created 5.96 49.8678 5
created 6.2 29.9207 3
created 6.44 49.8678 5
created 6.68 89.762 9
created 6.92 89.762 9
created 7.16 59.8413 6
created 7.4 19.9471 2
created 7.64 39.8942 4
created 7.88 49.8678 5
created 8.12 9.97356 1
created 8.36 99.7356 10
created 8.6 79.7885 8
created 8.84 9.97356 1
created 9.08 79.7885 8
created 9.32 49.8678 5
created 9.56 59.8413 6
created 9.8 49.8678 5
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.33994e-05)
fit chi^2 = 9.16648e-06
found -9.4 (+-0.000159155) 99.735 (+-0.390475) 10.0004 (+-0.00135657)
found -2.44 (+-0.000158454) 99.7328 (+-0.390264) 10.0002 (+-0.00135584)
found -8.2 (+-0.000158261) 99.7323 (+-0.390207) 10.0002 (+-0.00135564)
found -4.84 (+-0.000158536) 99.7331 (+-0.390289) 10.0003 (+-0.00135593)
found 1.64 (+-0.000157616) 99.7312 (+-0.390031) 10.0001 (+-0.00135503)
found 3.32 (+-0.000158261) 99.7323 (+-0.390207) 10.0002 (+-0.00135564)
found 8.36 (+-0.000158237) 99.7328 (+-0.390214) 10.0002 (+-0.00135566)
found -7.48 (+-0.000167353) 89.7603 (+-0.370325) 9.00029 (+-0.00128657)
found -5.56 (+-0.000167739) 89.7614 (+-0.370431) 9.0004 (+-0.00128693)
found -4.36 (+-0.000167219) 89.7601 (+-0.37029) 9.00027 (+-0.00128645)
found 6.92 (+-0.000167726) 89.7614 (+-0.370427) 9.0004 (+-0.00128692)
found -9.64 (+-0.000167697) 89.7614 (+-0.370421) 9.0004 (+-0.0012869)
found -8.68 (+-0.000167051) 89.7596 (+-0.370242) 9.00021 (+-0.00128628)
found 4.04 (+-0.000166624) 89.759 (+-0.370139) 9.00016 (+-0.00128592)
found 5.24 (+-0.000167164) 89.7598 (+-0.370273) 9.00024 (+-0.00128639)
found 6.68 (+-0.000167625) 89.7611 (+-0.3704) 9.00037 (+-0.00128683)
found -9.16 (+-0.0001779) 79.7881 (+-0.349246) 8.00037 (+-0.00121333)
found -5.32 (+-0.000177671) 79.7876 (+-0.34919) 8.00032 (+-0.00121314)
found 2.36 (+-0.000177309) 79.7867 (+-0.349103) 8.00024 (+-0.00121284)
found 8.6 (+-0.000177297) 79.7873 (+-0.349113) 8.00029 (+-0.00121287)
found -6.28 (+-0.000177286) 79.7865 (+-0.349093) 8.00021 (+-0.0012128)
found -3.16 (+-0.000176566) 79.7854 (+-0.34893) 8.0001 (+-0.00121224)
found 4.52 (+-0.000177434) 79.7868 (+-0.349127) 8.00024 (+-0.00121292)
found 9.08 (+-0.000176836) 79.786 (+-0.348995) 8.00016 (+-0.00121246)
found -4.6 (+-0.000190959) 69.8164 (+-0.32686) 7.0005 (+-0.00113556)
found -3.64 (+-0.000189413) 69.8135 (+-0.326534) 7.00021 (+-0.00113443)
found -7.72 (+-0.000190119) 69.8145 (+-0.326677) 7.00032 (+-0.00113493)
found -7 (+-0.000189709) 69.8135 (+-0.326583) 7.00021 (+-0.0011346)
found -5.8 (+-0.000190428) 69.8151 (+-0.326741) 7.00037 (+-0.00113515)
found -3.88 (+-0.00018992) 69.814 (+-0.326631) 7.00027 (+-0.00113477)
found -1 (+-0.000189544) 69.8132 (+-0.326549) 7.00018 (+-0.00113448)
found 0.2 (+-0.00018985) 69.8137 (+-0.326614) 7.00024 (+-0.00113471)
found 0.679999 (+-0.00018904) 69.8127 (+-0.326452) 7.00013 (+-0.00113415)
found 1.16 (+-0.000188671) 69.8121 (+-0.326376) 7.00008 (+-0.00113388)
found 2.12 (+-0.000189516) 69.8137 (+-0.326558) 7.00024 (+-0.00113451)
found 7.16 (+-0.000205352) 59.8412 (+-0.302448) 6.00029 (+-0.00105075)
found -2.68 (+-0.000205694) 59.8417 (+-0.30251) 6.00035 (+-0.00105097)
found 9.56 (+-0.00020544) 59.841 (+-0.302456) 6.00027 (+-0.00105078)
found -1.72 (+-0.000204167) 59.8393 (+-0.302231) 6.0001 (+-0.00105)
found 4.28 (+-0.00022643) 49.8697 (+-0.276327) 5.00045 (+-0.000960002)
found -6.04 (+-0.000226154) 49.8692 (+-0.276281) 5.0004 (+-0.000959844)
found 5 (+-0.000225767) 49.8687 (+-0.27622) 5.00035 (+-0.000959634)
found 5.72 (+-0.000225171) 49.8676 (+-0.276123) 5.00024 (+-0.000959293)
found 5.96 (+-0.000224949) 49.8674 (+-0.276088) 5.00021 (+-0.000959175)
found 6.44 (+-0.000225543) 49.8684 (+-0.276186) 5.00032 (+-0.000959515)
found 7.88 (+-0.000224089) 49.8666 (+-0.275963) 5.00013 (+-0.000958738)
found 9.32 (+-0.000225999) 49.8689 (+-0.276256) 5.00037 (+-0.000959757)
found 9.8 (+-0.00022334) 49.8681 (+-0.275894) 5.00029 (+-0.0009585)
found -9.88 (+-0.000225349) 49.8677 (+-0.276134) 5.00024 (+-0.000959332)
found -0.0399977 (+-0.000224597) 49.8674 (+-0.276045) 5.00021 (+-0.000959025)
found 2.84 (+-0.000224262) 49.8666 (+-0.275983) 5.00014 (+-0.00095881)
found -8.44 (+-0.000254058) 39.8972 (+-0.247275) 4.00051 (+-0.000859073)
found -7.24 (+-0.000253569) 39.8964 (+-0.247209) 4.00043 (+-0.000858844)
found 3.56 (+-0.000251983) 39.8952 (+-0.247018) 4.0003 (+-0.00085818)
found 5.48 (+-0.000253166) 39.8959 (+-0.247157) 4.00037 (+-0.000858662)
found -8.92 (+-0.000253744) 39.8967 (+-0.247233) 4.00045 (+-0.000858926)
found -6.76 (+-0.000252321) 39.8949 (+-0.247047) 4.00027 (+-0.00085828)
found -0.760002 (+-0.000252321) 39.8948 (+-0.247047) 4.00027 (+-0.00085828)
found 0.44 (+-0.000253232) 39.8959 (+-0.247164) 4.00037 (+-0.000858687)
found 4.76 (+-0.000253005) 39.8957 (+-0.247135) 4.00035 (+-0.000858586)
found 7.64 (+-0.000251589) 39.8941 (+-0.246954) 4.00019 (+-0.000857957)
found -7.96 (+-0.000293931) 29.9237 (+-0.214206) 3.00045 (+-0.000744184)
found -5.08 (+-0.000294158) 29.9239 (+-0.214229) 3.00048 (+-0.000744265)
found -4.12 (+-0.000293739) 29.9234 (+-0.214186) 3.00043 (+-0.000744114)
found -2.92 (+-0.000293283) 29.9229 (+-0.214139) 3.00037 (+-0.000743953)
found -2.20001 (+-0.00029168) 29.9221 (+-0.213995) 3.0003 (+-0.000743451)
found -1.48 (+-0.000291872) 29.9215 (+-0.213999) 3.00024 (+-0.000743466)
found 3.08 (+-0.000293407) 29.9231 (+-0.214153) 3.0004 (+-0.000744002)
found 6.2 (+-0.000292265) 29.9218 (+-0.214036) 3.00027 (+-0.000743596)
found -6.52 (+-0.000292698) 29.9223 (+-0.214081) 3.00032 (+-0.000743752)
found -1.24 (+-0.000292116) 29.9218 (+-0.214024) 3.00027 (+-0.000743553)
found -0.520002 (+-0.000290255) 29.9205 (+-0.213847) 3.00013 (+-0.00074294)
found 2.6 (+-0.000360559) 19.9496 (+-0.174938) 2.00035 (+-0.000607762)
found 1.4 (+-0.000361858) 19.9506 (+-0.175029) 2.00045 (+-0.000608078)
found 7.4 (+-0.000359451) 19.9488 (+-0.174861) 2.00027 (+-0.000607496)
found 1.87999 (+-0.000517004) 9.97758 (+-0.123958) 1.00045 (+-0.00043065)
found 8.84 (+-0.00051661) 9.97732 (+-0.123942) 1.00043 (+-0.000430595)
found -3.4 (+-0.000516074) 9.97705 (+-0.123922) 1.0004 (+-0.000430524)
found 0.92 (+-0.000515539) 9.97678 (+-0.123902) 1.00037 (+-0.000430454)
found 3.80001 (+-0.000514471) 9.97653 (+-0.123866) 1.00035 (+-0.000430328)
found 8.12001 (+-0.000515713) 9.97706 (+-0.123911) 1.0004 (+-0.000430487)
found -1.95999 (+-0.000511917) 9.97546 (+-0.123772) 1.00024 (+-0.000430002)
found -0.279996 (+-0.000511246) 9.97519 (+-0.123747) 1.00021 (+-0.000429916)

 
#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.