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 99.7356 10
created -9.64 79.7885 8
created -9.4 19.9471 2
created -9.16 99.7356 10
created -8.92 39.8942 4
created -8.68 19.9471 2
created -8.44 69.8149 7
created -8.2 19.9471 2
created -7.96 9.97356 1
created -7.72 19.9471 2
created -7.48 69.8149 7
created -7.24 89.762 9
created -7 49.8678 5
created -6.76 69.8149 7
created -6.52 59.8413 6
created -6.28 79.7885 8
created -6.04 19.9471 2
created -5.8 69.8149 7
created -5.56 59.8413 6
created -5.32 89.762 9
created -5.08 49.8678 5
created -4.84 49.8678 5
created -4.6 89.762 9
created -4.36 79.7885 8
created -4.12 79.7885 8
created -3.88 99.7356 10
created -3.64 39.8942 4
created -3.4 89.762 9
created -3.16 89.762 9
created -2.92 99.7356 10
created -2.68 19.9471 2
created -2.44 59.8413 6
created -2.2 59.8413 6
created -1.96 29.9207 3
created -1.72 79.7885 8
created -1.48 49.8678 5
created -1.24 79.7885 8
created -1 29.9207 3
created -0.76 59.8413 6
created -0.52 29.9207 3
created -0.28 39.8942 4
created -0.04 69.8149 7
created 0.2 59.8413 6
created 0.44 29.9207 3
created 0.68 39.8942 4
created 0.92 29.9207 3
created 1.16 49.8678 5
created 1.4 99.7356 10
created 1.64 19.9471 2
created 1.88 19.9471 2
created 2.12 39.8942 4
created 2.36 29.9207 3
created 2.6 39.8942 4
created 2.84 39.8942 4
created 3.08 59.8413 6
created 3.32 29.9207 3
created 3.56 59.8413 6
created 3.8 39.8942 4
created 4.04 69.8149 7
created 4.28 49.8678 5
created 4.52 79.7885 8
created 4.76 9.97356 1
created 5 99.7356 10
created 5.24 59.8413 6
created 5.48 89.762 9
created 5.72 79.7885 8
created 5.96 19.9471 2
created 6.2 89.762 9
created 6.44 59.8413 6
created 6.68 59.8413 6
created 6.92 19.9471 2
created 7.16 19.9471 2
created 7.4 89.762 9
created 7.64 99.7356 10
created 7.88 29.9207 3
created 8.12 89.762 9
created 8.36 99.7356 10
created 8.6 79.7885 8
created 8.84 99.7356 10
created 9.08 19.9471 2
created 9.32 39.8942 4
created 9.56 89.762 9
created 9.8 99.7356 10
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.77401e-05)
fit chi^2 = 1.68563e-05
found -3.88 (+-0.000215252) 99.7337 (+-0.529336) 10.0003 (+-0.001839)
found -2.92 (+-0.000214968) 99.7334 (+-0.529261) 10.0003 (+-0.00183874)
found 7.64 (+-0.000215179) 99.7337 (+-0.529318) 10.0003 (+-0.00183893)
found 8.36 (+-0.000215824) 99.735 (+-0.52951) 10.0005 (+-0.0018396)
found 8.84 (+-0.000214875) 99.7332 (+-0.529231) 10.0003 (+-0.00183863)
found 9.8 (+-0.000213912) 99.7355 (+-0.52902) 10.0005 (+-0.0018379)
found -9.88 (+-0.000215332) 99.7327 (+-0.529314) 10.0002 (+-0.00183892)
found -9.16 (+-0.000214403) 99.7321 (+-0.529088) 10.0002 (+-0.00183813)
found 1.4 (+-0.000214542) 99.7323 (+-0.529129) 10.0002 (+-0.00183828)
found 5 (+-0.00021437) 99.7324 (+-0.529089) 10.0002 (+-0.00183814)
found -7.24 (+-0.000227095) 89.7606 (+-0.502225) 9.00032 (+-0.00174481)
found -5.32 (+-0.000226974) 89.7604 (+-0.502191) 9.00029 (+-0.00174469)
found -3.16 (+-0.000227883) 89.7625 (+-0.502447) 9.0005 (+-0.00174558)
found 5.48 (+-0.000227343) 89.7612 (+-0.502293) 9.00037 (+-0.00174505)
found -4.6 (+-0.000227207) 89.7609 (+-0.502256) 9.00034 (+-0.00174492)
found -3.4 (+-0.000227156) 89.7609 (+-0.502245) 9.00035 (+-0.00174488)
found 6.2 (+-0.000226409) 89.7596 (+-0.502046) 9.00021 (+-0.00174419)
found 7.4 (+-0.00022684) 89.7606 (+-0.502169) 9.00032 (+-0.00174461)
found 8.12 (+-0.00022707) 89.7609 (+-0.502226) 9.00035 (+-0.00174481)
found 9.56 (+-0.000227253) 89.7612 (+-0.502273) 9.00037 (+-0.00174498)
found -9.64 (+-0.000240787) 79.7876 (+-0.473496) 8.00032 (+-0.001645)
found -6.28 (+-0.000240307) 79.7865 (+-0.473372) 8.00021 (+-0.00164457)
found -4.36 (+-0.000241719) 79.7889 (+-0.473714) 8.00045 (+-0.00164576)
found -4.12 (+-0.000241827) 79.7892 (+-0.473743) 8.00048 (+-0.00164586)
found 5.72 (+-0.00024068) 79.7874 (+-0.473467) 8.00029 (+-0.0016449)
found 8.6 (+-0.000242053) 79.7897 (+-0.473801) 8.00053 (+-0.00164606)
found -1.72 (+-0.000240411) 79.7865 (+-0.473392) 8.00021 (+-0.00164464)
found -1.24 (+-0.000240411) 79.7865 (+-0.473392) 8.00021 (+-0.00164464)
found 4.52 (+-0.0002398) 79.786 (+-0.473259) 8.00016 (+-0.00164418)
found -8.44 (+-0.000256249) 69.8124 (+-0.442663) 7.0001 (+-0.00153788)
found -7.48 (+-0.00025753) 69.8143 (+-0.442938) 7.00029 (+-0.00153884)
found -6.76 (+-0.000257808) 69.8143 (+-0.442986) 7.00029 (+-0.001539)
found -5.8 (+-0.000257108) 69.8135 (+-0.442843) 7.00021 (+-0.00153851)
found -0.0399995 (+-0.000257617) 69.814 (+-0.442946) 7.00026 (+-0.00153886)
found 4.04 (+-0.000257448) 69.8138 (+-0.442909) 7.00024 (+-0.00153873)
found 5.24 (+-0.000280105) 59.8434 (+-0.410443) 6.00051 (+-0.00142594)
found 6.44 (+-0.000279469) 59.8423 (+-0.410319) 6.0004 (+-0.00142551)
found -6.52 (+-0.000279495) 59.8423 (+-0.410323) 6.0004 (+-0.00142553)
found -5.56 (+-0.000279647) 59.8426 (+-0.410353) 6.00042 (+-0.00142563)
found -2.2 (+-0.000278306) 59.8407 (+-0.410099) 6.00024 (+-0.00142475)
found -0.76 (+-0.000277639) 59.8399 (+-0.409975) 6.00016 (+-0.00142432)
found 0.199999 (+-0.000278482) 59.841 (+-0.410133) 6.00027 (+-0.00142487)
found 3.08 (+-0.000277894) 59.8402 (+-0.410021) 6.00019 (+-0.00142448)
found 3.56 (+-0.000277894) 59.8402 (+-0.410021) 6.00019 (+-0.00142448)
found 6.68 (+-0.000277984) 59.8404 (+-0.410043) 6.00021 (+-0.00142455)
found -2.44 (+-0.000277984) 59.8405 (+-0.410043) 6.00021 (+-0.00142455)
found -7 (+-0.000306859) 49.8695 (+-0.374685) 5.00043 (+-0.00130171)
found -5.08 (+-0.000306416) 49.869 (+-0.374613) 5.00037 (+-0.00130147)
found -4.84 (+-0.000306416) 49.869 (+-0.374613) 5.00037 (+-0.00130147)
found -1.48 (+-0.000306873) 49.8695 (+-0.374687) 5.00042 (+-0.00130172)
found 1.16 (+-0.000306017) 49.8687 (+-0.374554) 5.00035 (+-0.00130126)
found 4.28 (+-0.00030668) 49.8692 (+-0.374655) 5.0004 (+-0.00130161)
found -8.92 (+-0.000342357) 39.8954 (+-0.335046) 4.00032 (+-0.001164)
found -3.64 (+-0.000344518) 39.8973 (+-0.335321) 4.00051 (+-0.00116496)
found -0.279998 (+-0.000342163) 39.8949 (+-0.335011) 4.00027 (+-0.00116388)
found 0.68 (+-0.000340941) 39.8938 (+-0.334853) 4.00016 (+-0.00116333)
found 2.6 (+-0.000341309) 39.8941 (+-0.334899) 4.00019 (+-0.00116349)
found 2.84 (+-0.000342278) 39.8949 (+-0.335024) 4.00027 (+-0.00116392)
found 3.8 (+-0.00034314) 39.8957 (+-0.335136) 4.00035 (+-0.00116431)
found 2.12 (+-0.000340488) 39.8935 (+-0.334798) 4.00013 (+-0.00116314)
found 9.32 (+-0.000342155) 39.8952 (+-0.335018) 4.0003 (+-0.0011639)
found -1 (+-0.00039771) 29.9229 (+-0.290386) 3.00037 (+-0.00100885)
found 7.88 (+-0.00039918) 29.9242 (+-0.290537) 3.00051 (+-0.00100937)
found -1.96 (+-0.00039771) 29.9229 (+-0.290386) 3.00037 (+-0.00100885)
found -0.520001 (+-0.000396281) 29.9218 (+-0.290243) 3.00027 (+-0.00100835)
found 0.439999 (+-0.000396281) 29.9218 (+-0.290243) 3.00027 (+-0.00100835)
found 3.32 (+-0.00039707) 29.9223 (+-0.290321) 3.00032 (+-0.00100862)
found 0.920001 (+-0.000395912) 29.9216 (+-0.290206) 3.00024 (+-0.00100822)
found 2.36 (+-0.000395497) 29.9213 (+-0.290164) 3.00021 (+-0.00100808)
found -9.4 (+-0.000491134) 19.9509 (+-0.23738) 2.00048 (+-0.000824696)
found -6.04 (+-0.00048995) 19.9501 (+-0.237296) 2.0004 (+-0.000824404)
found -2.68 (+-0.000490224) 19.9504 (+-0.237317) 2.00043 (+-0.000824477)
found 1.63999 (+-0.000487493) 19.9493 (+-0.237138) 2.00032 (+-0.000823854)
found 5.96 (+-0.000490773) 19.9506 (+-0.237354) 2.00045 (+-0.000824606)
found 9.07999 (+-0.000489078) 19.9499 (+-0.23724) 2.00038 (+-0.000824209)
found -8.20001 (+-0.000485147) 19.9482 (+-0.236982) 2.00021 (+-0.000823313)
found 6.92 (+-0.000485867) 19.9483 (+-0.237021) 2.00022 (+-0.000823448)
found -8.68 (+-0.000487908) 19.949 (+-0.237156) 2.00029 (+-0.000823917)
found 1.88 (+-0.00048475) 19.9477 (+-0.236944) 2.00016 (+-0.000823181)
found 7.16001 (+-0.000487139) 19.9491 (+-0.237112) 2.0003 (+-0.000823764)
found -7.71999 (+-0.000485147) 19.9483 (+-0.236982) 2.00022 (+-0.000823313)
found 4.76 (+-0.000701822) 9.97785 (+-0.168122) 1.00048 (+-0.000584083)
found -7.96 (+-0.000687745) 9.97414 (+-0.167617) 1.00011 (+-0.000582327)

 
#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.