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 79.7885 8
created -9.64 59.8413 6
created -9.4 29.9207 3
created -9.16 39.8942 4
created -8.92 89.762 9
created -8.68 49.8678 5
created -8.44 39.8942 4
created -8.2 39.8942 4
created -7.96 99.7356 10
created -7.72 89.762 9
created -7.48 9.97356 1
created -7.24 69.8149 7
created -7 19.9471 2
created -6.76 29.9207 3
created -6.52 9.97356 1
created -6.28 99.7356 10
created -6.04 39.8942 4
created -5.8 39.8942 4
created -5.56 29.9207 3
created -5.32 89.762 9
created -5.08 9.97356 1
created -4.84 89.762 9
created -4.6 99.7356 10
created -4.36 39.8942 4
created -4.12 39.8942 4
created -3.88 89.762 9
created -3.64 59.8413 6
created -3.4 9.97356 1
created -3.16 69.8149 7
created -2.92 19.9471 2
created -2.68 59.8413 6
created -2.44 99.7356 10
created -2.2 59.8413 6
created -1.96 79.7885 8
created -1.72 69.8149 7
created -1.48 29.9207 3
created -1.24 79.7885 8
created -1 49.8678 5
created -0.76 59.8413 6
created -0.52 99.7356 10
created -0.28 69.8149 7
created -0.04 79.7885 8
created 0.2 89.762 9
created 0.44 39.8942 4
created 0.68 89.762 9
created 0.92 9.97356 1
created 1.16 49.8678 5
created 1.4 39.8942 4
created 1.64 49.8678 5
created 1.88 9.97356 1
created 2.12 79.7885 8
created 2.36 79.7885 8
created 2.6 19.9471 2
created 2.84 89.762 9
created 3.08 49.8678 5
created 3.32 79.7885 8
created 3.56 99.7356 10
created 3.8 49.8678 5
created 4.04 9.97356 1
created 4.28 39.8942 4
created 4.52 19.9471 2
created 4.76 9.97356 1
created 5 69.8149 7
created 5.24 59.8413 6
created 5.48 29.9207 3
created 5.72 9.97356 1
created 5.96 69.8149 7
created 6.2 99.7356 10
created 6.44 69.8149 7
created 6.68 29.9207 3
created 6.92 89.762 9
created 7.16 49.8678 5
created 7.4 49.8678 5
created 7.64 89.762 9
created 7.88 59.8413 6
created 8.12 89.762 9
created 8.36 99.7356 10
created 8.6 69.8149 7
created 8.84 99.7356 10
created 9.08 79.7885 8
created 9.32 89.762 9
created 9.56 9.97356 1
created 9.8 9.97356 1
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-7.35476e-06)
fit chi^2 = 2.84415e-06
found -4.6 (+-8.8457e-05) 99.7339 (+-0.217446) 10.0003 (+-0.000755441)
found -2.44 (+-8.84399e-05) 99.7336 (+-0.217439) 10.0003 (+-0.000755417)
found -0.52 (+-8.84855e-05) 99.7339 (+-0.217453) 10.0003 (+-0.000755466)
found 3.56 (+-8.84764e-05) 99.7339 (+-0.217451) 10.0003 (+-0.000755458)
found 6.2 (+-8.85311e-05) 99.7341 (+-0.217467) 10.0004 (+-0.000755514)
found 8.36 (+-8.86116e-05) 99.7347 (+-0.217492) 10.0004 (+-0.000755601)
found 8.84 (+-8.85728e-05) 99.7344 (+-0.21748) 10.0004 (+-0.000755559)
found -7.96 (+-8.8457e-05) 99.7339 (+-0.217446) 10.0003 (+-0.000755441)
found -6.28 (+-8.79491e-05) 99.7317 (+-0.2173) 10.0001 (+-0.000754935)
found -7.72 (+-9.30446e-05) 89.7603 (+-0.206243) 9.00029 (+-0.00071652)
found 0.199999 (+-9.32658e-05) 89.7606 (+-0.206293) 9.00032 (+-0.000716696)
found 8.12 (+-9.34674e-05) 89.7617 (+-0.206349) 9.00043 (+-0.00071689)
found 9.32 (+-9.29632e-05) 89.7598 (+-0.206219) 9.00024 (+-0.000716438)
found -8.92 (+-9.31149e-05) 89.7598 (+-0.206251) 9.00024 (+-0.00071655)
found -5.32 (+-9.26776e-05) 89.7584 (+-0.206141) 9.0001 (+-0.000716166)
found -4.84 (+-9.30446e-05) 89.7603 (+-0.206243) 9.00029 (+-0.00071652)
found -3.88 (+-9.31702e-05) 89.76 (+-0.206266) 9.00026 (+-0.000716602)
found 0.679999 (+-9.27513e-05) 89.7587 (+-0.20616) 9.00013 (+-0.000716233)
found 2.84 (+-9.29463e-05) 89.7593 (+-0.206209) 9.00019 (+-0.000716401)
found 6.92 (+-9.30403e-05) 89.7595 (+-0.206232) 9.00021 (+-0.000716483)
found 7.64 (+-9.32334e-05) 89.7603 (+-0.206283) 9.00029 (+-0.000716661)
found -1.96 (+-9.90841e-05) 79.7878 (+-0.194534) 8.00035 (+-0.000675842)
found -0.0399996 (+-9.92386e-05) 79.7886 (+-0.194573) 8.00043 (+-0.000675978)
found 2.36 (+-9.88162e-05) 79.787 (+-0.194472) 8.00027 (+-0.000675628)
found 9.08 (+-9.93825e-05) 79.7894 (+-0.19461) 8.0005 (+-0.000676106)
found -9.88 (+-9.88689e-05) 79.786 (+-0.194465) 8.00016 (+-0.000675601)
found -1.24 (+-9.8753e-05) 79.7865 (+-0.194454) 8.00021 (+-0.000675563)
found 2.12 (+-9.86683e-05) 79.7867 (+-0.194441) 8.00024 (+-0.000675519)
found 3.32 (+-9.91653e-05) 79.7883 (+-0.194556) 8.0004 (+-0.000675917)
found -1.72 (+-0.000105848) 69.8142 (+-0.181955) 7.00029 (+-0.000632141)
found -0.280001 (+-0.000106315) 69.8161 (+-0.182057) 7.00048 (+-0.000632495)
found 6.44 (+-0.000105951) 69.8148 (+-0.181979) 7.00035 (+-0.000632224)
found 8.6 (+-0.00010642) 69.8166 (+-0.182081) 7.00053 (+-0.000632578)
found -7.24 (+-0.000105094) 69.8121 (+-0.1818) 7.00008 (+-0.000631602)
found -3.16 (+-0.000105094) 69.8121 (+-0.1818) 7.00008 (+-0.000631602)
found 5 (+-0.000105446) 69.8131 (+-0.181874) 7.00018 (+-0.000631859)
found 5.96 (+-0.000105668) 69.8142 (+-0.181925) 7.00029 (+-0.000632036)
found -3.64 (+-0.000114196) 59.8409 (+-0.16844) 6.00027 (+-0.000585189)
found -2.2 (+-0.000114995) 59.8431 (+-0.168584) 6.00048 (+-0.000585687)
found 7.88 (+-0.000114999) 59.8431 (+-0.168585) 6.00048 (+-0.000585689)
found -9.64 (+-0.000114457) 59.8412 (+-0.168482) 6.00029 (+-0.000585333)
found -0.759998 (+-0.000114774) 59.8423 (+-0.168542) 6.0004 (+-0.00058554)
found 5.24 (+-0.000114391) 59.8409 (+-0.168469) 6.00026 (+-0.000585288)
found -2.68 (+-0.000114444) 59.8415 (+-0.168483) 6.00032 (+-0.000585336)
found -8.68 (+-0.000125758) 49.8687 (+-0.153862) 5.00035 (+-0.000534541)
found 3.08 (+-0.000126127) 49.8697 (+-0.153921) 5.00045 (+-0.000534746)
found 3.8 (+-0.000125324) 49.8682 (+-0.153801) 5.00029 (+-0.00053433)
found 7.16 (+-0.000125866) 49.869 (+-0.153879) 5.00037 (+-0.000534599)
found -1 (+-0.000125887) 49.869 (+-0.153882) 5.00037 (+-0.000534609)
found 1.64 (+-0.000124823) 49.8666 (+-0.153718) 5.00013 (+-0.000534042)
found 7.4 (+-0.000125866) 49.869 (+-0.153879) 5.00037 (+-0.000534599)
found 1.16 (+-0.000124823) 49.8666 (+-0.153718) 5.00013 (+-0.000534042)
found -6.04 (+-0.000140972) 39.8959 (+-0.137667) 4.00037 (+-0.000478278)
found -4.36 (+-0.000140971) 39.8959 (+-0.137667) 4.00038 (+-0.000478278)
found 0.44 (+-0.000141432) 39.897 (+-0.137727) 4.00048 (+-0.000478486)
found -9.16 (+-0.000140735) 39.8954 (+-0.137637) 4.00032 (+-0.000478172)
found -8.44 (+-0.00014048) 39.8946 (+-0.137601) 4.00024 (+-0.000478049)
found -8.2 (+-0.000140972) 39.8959 (+-0.137667) 4.00037 (+-0.000478278)
found -5.8 (+-0.000140198) 39.8941 (+-0.137565) 4.00019 (+-0.000477924)
found -4.12 (+-0.000140888) 39.8957 (+-0.137656) 4.00035 (+-0.000478238)
found 1.4 (+-0.000140612) 39.8948 (+-0.137618) 4.00026 (+-0.000478107)
found 4.28 (+-0.000139413) 39.893 (+-0.137471) 4.00008 (+-0.000477597)
found -9.4 (+-0.000162779) 29.9218 (+-0.119222) 3.00027 (+-0.000414196)
found -1.48 (+-0.000163504) 29.9231 (+-0.119295) 3.0004 (+-0.000414449)
found 5.48 (+-0.000161997) 29.921 (+-0.119151) 3.00019 (+-0.000413948)
found 6.68 (+-0.00016362) 29.9234 (+-0.119307) 3.00043 (+-0.000414491)
found -6.76 (+-0.000161246) 29.92 (+-0.119077) 3.00008 (+-0.000413692)
found -5.56 (+-0.000163155) 29.9226 (+-0.119261) 3.00035 (+-0.000414331)
found 2.6 (+-0.000201593) 19.9506 (+-0.0974972) 2.00045 (+-0.000338721)
found -7 (+-0.000200126) 19.9488 (+-0.0973966) 2.00027 (+-0.000338371)
found -2.92 (+-0.000200884) 19.9496 (+-0.0974474) 2.00035 (+-0.000338548)
found 4.52 (+-0.000198636) 19.9474 (+-0.0972993) 2.00013 (+-0.000338033)
found -7.48 (+-0.000287737) 9.97731 (+-0.0690381) 1.00043 (+-0.000239849)
found -5.08 (+-0.000288308) 9.97785 (+-0.0690597) 1.00048 (+-0.000239925)
found 0.919992 (+-0.000287019) 9.97678 (+-0.069012) 1.00037 (+-0.000239759)
found 9.55998 (+-0.000284432) 9.97575 (+-0.0689259) 1.00027 (+-0.000239459)
found -3.4 (+-0.000286834) 9.97651 (+-0.0690041) 1.00035 (+-0.000239731)
found 1.88001 (+-0.000286751) 9.97652 (+-0.0690016) 1.00035 (+-0.000239722)
found 4.04 (+-0.000285306) 9.97545 (+-0.0689485) 1.00024 (+-0.000239538)
found -6.51999 (+-0.000286283) 9.97653 (+-0.0689876) 1.00035 (+-0.000239674)
found 5.72001 (+-0.000285479) 9.97573 (+-0.0689563) 1.00027 (+-0.000239565)
found 4.76001 (+-0.000284809) 9.97546 (+-0.0689339) 1.00024 (+-0.000239487)
found 9.8 (+-0.000277975) 9.97359 (+-0.0687211) 1.00005 (+-0.000238748)

 
#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.