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 29.9207 3
created -9.64 39.8942 4
created -9.4 29.9207 3
created -9.16 9.97356 1
created -8.92 39.8942 4
created -8.68 69.8149 7
created -8.44 9.97356 1
created -8.2 99.7356 10
created -7.96 69.8149 7
created -7.72 59.8413 6
created -7.48 89.762 9
created -7.24 69.8149 7
created -7 29.9207 3
created -6.76 9.97356 1
created -6.52 39.8942 4
created -6.28 19.9471 2
created -6.04 79.7885 8
created -5.8 39.8942 4
created -5.56 39.8942 4
created -5.32 39.8942 4
created -5.08 99.7356 10
created -4.84 69.8149 7
created -4.6 29.9207 3
created -4.36 79.7885 8
created -4.12 59.8413 6
created -3.88 89.762 9
created -3.64 59.8413 6
created -3.4 39.8942 4
created -3.16 29.9207 3
created -2.92 69.8149 7
created -2.68 49.8678 5
created -2.44 69.8149 7
created -2.2 49.8678 5
created -1.96 49.8678 5
created -1.72 49.8678 5
created -1.48 9.97356 1
created -1.24 79.7885 8
created -1 99.7356 10
created -0.76 29.9207 3
created -0.52 79.7885 8
created -0.28 69.8149 7
created -0.04 29.9207 3
created 0.2 9.97356 1
created 0.44 89.762 9
created 0.68 79.7885 8
created 0.92 69.8149 7
created 1.16 89.762 9
created 1.4 89.762 9
created 1.64 69.8149 7
created 1.88 9.97356 1
created 2.12 69.8149 7
created 2.36 79.7885 8
created 2.6 69.8149 7
created 2.84 79.7885 8
created 3.08 49.8678 5
created 3.32 59.8413 6
created 3.56 99.7356 10
created 3.8 39.8942 4
created 4.04 29.9207 3
created 4.28 49.8678 5
created 4.52 79.7885 8
created 4.76 89.762 9
created 5 29.9207 3
created 5.24 9.97356 1
created 5.48 29.9207 3
created 5.72 79.7885 8
created 5.96 69.8149 7
created 6.2 39.8942 4
created 6.44 89.762 9
created 6.68 49.8678 5
created 6.92 69.8149 7
created 7.16 19.9471 2
created 7.4 59.8413 6
created 7.64 39.8942 4
created 7.88 39.8942 4
created 8.12 69.8149 7
created 8.36 79.7885 8
created 8.6 79.7885 8
created 8.84 69.8149 7
created 9.08 69.8149 7
created 9.32 9.97356 1
created 9.56 9.97356 1
created 9.8 79.7885 8
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.63715e-05)
fit chi^2 = 1.36843e-05
found -1 (+-0.000193794) 99.7334 (+-0.476896) 10.0003 (+-0.00165681)
found 3.56 (+-0.000193754) 99.7331 (+-0.476879) 10.0003 (+-0.00165675)
found -8.2 (+-0.000193249) 99.7326 (+-0.476746) 10.0002 (+-0.00165629)
found -5.08 (+-0.000193854) 99.7334 (+-0.47691) 10.0003 (+-0.00165686)
found -7.48 (+-0.000204738) 89.7609 (+-0.452544) 9.00034 (+-0.00157221)
found -3.88 (+-0.000204628) 89.7606 (+-0.452514) 9.00032 (+-0.0015721)
found 1.16 (+-0.000205042) 89.7617 (+-0.45263) 9.00042 (+-0.00157251)
found 1.4 (+-0.000205042) 89.7617 (+-0.45263) 9.00042 (+-0.00157251)
found 4.76 (+-0.000204413) 89.7603 (+-0.45246) 9.00029 (+-0.00157192)
found 0.440001 (+-0.000203914) 89.7598 (+-0.452339) 9.00024 (+-0.0015715)
found 6.44 (+-0.000204246) 89.7598 (+-0.45241) 9.00024 (+-0.00157174)
found 0.68 (+-0.000217679) 79.7886 (+-0.426794) 8.00042 (+-0.00148275)
found 2.36 (+-0.000217462) 79.7881 (+-0.426739) 8.00037 (+-0.00148256)
found 2.84 (+-0.000217204) 79.7876 (+-0.426675) 8.00032 (+-0.00148234)
found 8.36 (+-0.000217574) 79.7884 (+-0.426767) 8.0004 (+-0.00148266)
found 8.6 (+-0.000217574) 79.7884 (+-0.426767) 8.0004 (+-0.00148266)
found -6.04 (+-0.000216234) 79.786 (+-0.426444) 8.00016 (+-0.00148153)
found -4.36 (+-0.000216748) 79.7868 (+-0.426565) 8.00024 (+-0.00148195)
found -1.24 (+-0.000216627) 79.7873 (+-0.426557) 8.00029 (+-0.00148193)
found -0.519999 (+-0.000216869) 79.787 (+-0.426596) 8.00027 (+-0.00148206)
found 4.52 (+-0.00021742) 79.7881 (+-0.42673) 8.00037 (+-0.00148253)
found 5.72 (+-0.000216869) 79.787 (+-0.426596) 8.00026 (+-0.00148206)
found 9.8 (+-0.000214351) 79.7867 (+-0.426064) 8.00023 (+-0.00148021)
found -7.96 (+-0.000232935) 69.8156 (+-0.399281) 7.00043 (+-0.00138717)
found -7.24 (+-0.000232293) 69.8145 (+-0.399144) 7.00032 (+-0.00138669)
found -4.84 (+-0.000232403) 69.8148 (+-0.399169) 7.00035 (+-0.00138678)
found -0.280001 (+-0.000232176) 69.8142 (+-0.399117) 7.00029 (+-0.00138659)
found 0.92 (+-0.00023309) 69.8159 (+-0.399315) 7.00045 (+-0.00138728)
found 1.64 (+-0.000231672) 69.814 (+-0.399025) 7.00027 (+-0.00138627)
found 2.6 (+-0.000232972) 69.8156 (+-0.399288) 7.00043 (+-0.00138719)
found 5.96 (+-0.00023238) 69.8145 (+-0.399159) 7.00032 (+-0.00138674)
found 8.84 (+-0.000232844) 69.8153 (+-0.399259) 7.0004 (+-0.00138709)
found 9.08 (+-0.000231431) 69.8134 (+-0.398969) 7.00021 (+-0.00138608)
found -8.68 (+-0.000230975) 69.8127 (+-0.398869) 7.00013 (+-0.00138573)
found -2.92 (+-0.000231761) 69.8135 (+-0.399024) 7.00021 (+-0.00138627)
found -2.44 (+-0.000232136) 69.814 (+-0.399103) 7.00027 (+-0.00138655)
found 2.12 (+-0.000231556) 69.8137 (+-0.398998) 7.00024 (+-0.00138618)
found 6.92 (+-0.000231507) 69.8132 (+-0.398974) 7.00019 (+-0.0013861)
found 8.12 (+-0.00023238) 69.8145 (+-0.399159) 7.00032 (+-0.00138674)
found -3.64 (+-0.00025143) 59.8417 (+-0.369632) 6.00035 (+-0.00128416)
found -7.72 (+-0.000251965) 59.8426 (+-0.369733) 6.00043 (+-0.00128451)
found -4.12 (+-0.000252113) 59.8428 (+-0.369761) 6.00045 (+-0.00128461)
found 3.32 (+-0.000251756) 59.8423 (+-0.369694) 6.0004 (+-0.00128438)
found 7.4 (+-0.000250098) 59.8399 (+-0.369384) 6.00016 (+-0.0012833)
found 6.68 (+-0.000276484) 49.8695 (+-0.337595) 5.00042 (+-0.00117286)
found -2.68 (+-0.000276148) 49.869 (+-0.33754) 5.00037 (+-0.00117267)
found -2.2 (+-0.000275751) 49.8684 (+-0.337476) 5.00032 (+-0.00117244)
found -1.96 (+-0.000275355) 49.8679 (+-0.337412) 5.00027 (+-0.00117222)
found -1.72 (+-0.000274029) 49.8669 (+-0.337216) 5.00016 (+-0.00117154)
found 3.08 (+-0.000276133) 49.869 (+-0.337538) 5.00037 (+-0.00117266)
found 4.28 (+-0.000275415) 49.8682 (+-0.337426) 5.00029 (+-0.00117227)
found 3.8 (+-0.000308883) 39.8957 (+-0.30193) 4.00035 (+-0.00104895)
found -9.64 (+-0.000307192) 39.8938 (+-0.301707) 4.00016 (+-0.00104818)
found -5.8 (+-0.00030884) 39.8954 (+-0.30192) 4.00032 (+-0.00104892)
found -5.56 (+-0.000307856) 39.8943 (+-0.30179) 4.00022 (+-0.00104847)
found -5.32 (+-0.000309219) 39.8959 (+-0.301972) 4.00037 (+-0.0010491)
found -3.4 (+-0.000308063) 39.8946 (+-0.301819) 4.00024 (+-0.00104857)
found 6.2 (+-0.000309818) 39.8965 (+-0.302048) 4.00043 (+-0.00104936)
found 7.64 (+-0.000308397) 39.8949 (+-0.301861) 4.00027 (+-0.00104871)
found 7.88 (+-0.000308628) 39.8951 (+-0.301891) 4.00029 (+-0.00104882)
found -8.92 (+-0.000307297) 39.8943 (+-0.301734) 4.00021 (+-0.00104827)
found -6.52 (+-0.000305801) 39.893 (+-0.301542) 4.00008 (+-0.0010476)
found -0.760001 (+-0.000359411) 29.9239 (+-0.261751) 3.00048 (+-0.000909365)
found 4.99999 (+-0.000356153) 29.9218 (+-0.26144) 3.00027 (+-0.000908285)
found -7 (+-0.000355635) 29.9213 (+-0.261386) 3.00022 (+-0.000908096)
found -4.6 (+-0.000358645) 29.9231 (+-0.261672) 3.0004 (+-0.00090909)
found -0.0400037 (+-0.000355635) 29.9213 (+-0.261386) 3.00022 (+-0.000908096)
found -9.4 (+-0.000354642) 29.9205 (+-0.261285) 3.00013 (+-0.000907746)
found -3.16 (+-0.000357354) 29.9221 (+-0.261543) 3.00029 (+-0.000908641)
found 4.04 (+-0.000356722) 29.9216 (+-0.261479) 3.00024 (+-0.000908419)
found -9.88 (+-0.000354953) 29.9202 (+-0.261289) 3.00011 (+-0.00090776)
found 5.48 (+-0.000355905) 29.9216 (+-0.261414) 3.00024 (+-0.000908194)
found 7.16 (+-0.000440637) 19.9496 (+-0.21375) 2.00035 (+-0.000742601)
found -6.28 (+-0.000439994) 19.9493 (+-0.213708) 2.00032 (+-0.000742454)
found -8.43999 (+-0.000631691) 9.97758 (+-0.151456) 1.00045 (+-0.000526181)
found 1.88 (+-0.0006299) 9.97679 (+-0.151387) 1.00037 (+-0.000525942)
found 9.31999 (+-0.000622689) 9.97522 (+-0.151141) 1.00022 (+-0.000525087)
found -1.47999 (+-0.000628984) 9.97652 (+-0.151354) 1.00035 (+-0.000525828)
found -9.16 (+-0.0006237) 9.97492 (+-0.151163) 1.00019 (+-0.000525165)
found -6.76 (+-0.0006237) 9.97492 (+-0.151163) 1.00019 (+-0.000525165)
found 0.200012 (+-0.000627422) 9.97627 (+-0.151302) 1.00032 (+-0.000525648)
found 5.24 (+-0.000622551) 9.97466 (+-0.151123) 1.00016 (+-0.000525025)
found 9.56001 (+-0.000623322) 9.9755 (+-0.151165) 1.00025 (+-0.000525172)

 
#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.