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 79.7885 8
created -9.4 19.9471 2
created -9.16 19.9471 2
created -8.92 19.9471 2
created -8.68 9.97356 1
created -8.44 99.7356 10
created -8.2 79.7885 8
created -7.96 49.8678 5
created -7.72 49.8678 5
created -7.48 29.9207 3
created -7.24 69.8149 7
created -7 39.8942 4
created -6.76 29.9207 3
created -6.52 59.8413 6
created -6.28 9.97356 1
created -6.04 29.9207 3
created -5.8 99.7356 10
created -5.56 89.762 9
created -5.32 9.97356 1
created -5.08 39.8942 4
created -4.84 49.8678 5
created -4.6 79.7885 8
created -4.36 99.7356 10
created -4.12 19.9471 2
created -3.88 29.9207 3
created -3.64 89.762 9
created -3.4 19.9471 2
created -3.16 79.7885 8
created -2.92 79.7885 8
created -2.68 99.7356 10
created -2.44 59.8413 6
created -2.2 9.97356 1
created -1.96 79.7885 8
created -1.72 49.8678 5
created -1.48 69.8149 7
created -1.24 59.8413 6
created -1 49.8678 5
created -0.76 49.8678 5
created -0.52 59.8413 6
created -0.28 99.7356 10
created -0.04 29.9207 3
created 0.2 79.7885 8
created 0.44 39.8942 4
created 0.68 49.8678 5
created 0.92 29.9207 3
created 1.16 69.8149 7
created 1.4 49.8678 5
created 1.64 29.9207 3
created 1.88 59.8413 6
created 2.12 69.8149 7
created 2.36 29.9207 3
created 2.6 9.97356 1
created 2.84 39.8942 4
created 3.08 39.8942 4
created 3.32 29.9207 3
created 3.56 9.97356 1
created 3.8 99.7356 10
created 4.04 59.8413 6
created 4.28 49.8678 5
created 4.52 29.9207 3
created 4.76 89.762 9
created 5 99.7356 10
created 5.24 49.8678 5
created 5.48 9.97356 1
created 5.72 99.7356 10
created 5.96 89.762 9
created 6.2 99.7356 10
created 6.44 39.8942 4
created 6.68 99.7356 10
created 6.92 99.7356 10
created 7.16 89.762 9
created 7.4 9.97356 1
created 7.64 89.762 9
created 7.88 59.8413 6
created 8.12 19.9471 2
created 8.36 89.762 9
created 8.6 69.8149 7
created 8.84 29.9207 3
created 9.08 89.762 9
created 9.32 59.8413 6
created 9.56 99.7356 10
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 (+-1.80288e-05)
fit chi^2 = 1.70564e-05
found -4.36 (+-0.000216146) 99.7331 (+-0.532363) 10.0003 (+-0.00184951)
found -2.68 (+-0.000216792) 99.7342 (+-0.532548) 10.0004 (+-0.00185015)
found -0.280001 (+-0.000216146) 99.7329 (+-0.532355) 10.0002 (+-0.00184949)
found 5 (+-0.000216762) 99.7342 (+-0.53254) 10.0004 (+-0.00185013)
found 6.2 (+-0.00021662) 99.734 (+-0.532499) 10.0003 (+-0.00184998)
found 6.92 (+-0.000217286) 99.7355 (+-0.532701) 10.0005 (+-0.00185069)
found 9.56 (+-0.000216975) 99.7348 (+-0.532606) 10.0004 (+-0.00185036)
found 9.8 (+-0.000215265) 99.7358 (+-0.532179) 10.0005 (+-0.00184887)
found -8.44 (+-0.00021585) 99.7329 (+-0.532286) 10.0002 (+-0.00184925)
found -5.8 (+-0.000216452) 99.7337 (+-0.532451) 10.0003 (+-0.00184982)
found 3.8 (+-0.000215639) 99.7323 (+-0.53222) 10.0002 (+-0.00184902)
found 5.72 (+-0.000215943) 99.7332 (+-0.532316) 10.0003 (+-0.00184935)
found 6.68 (+-0.000216709) 99.7343 (+-0.532527) 10.0004 (+-0.00185008)
found -5.56 (+-0.000227855) 89.7604 (+-0.505064) 9.00029 (+-0.00175467)
found 5.96 (+-0.00022933) 89.7628 (+-0.505448) 9.00053 (+-0.00175601)
found 7.16 (+-0.000227855) 89.7604 (+-0.505064) 9.00029 (+-0.00175467)
found -3.64 (+-0.00022728) 89.7588 (+-0.504891) 9.00013 (+-0.00175407)
found 4.76 (+-0.000228414) 89.7609 (+-0.505198) 9.00034 (+-0.00175514)
found 7.64 (+-0.000227424) 89.7593 (+-0.50494) 9.00018 (+-0.00175424)
found 8.36 (+-0.00022787) 89.7598 (+-0.505051) 9.00024 (+-0.00175463)
found 9.08 (+-0.000227979) 89.7599 (+-0.505074) 9.00024 (+-0.00175471)
found -9.64 (+-0.000241989) 79.787 (+-0.47624) 8.00026 (+-0.00165453)
found -8.2 (+-0.000242844) 79.7884 (+-0.476443) 8.0004 (+-0.00165524)
found -2.92 (+-0.000243259) 79.7892 (+-0.476546) 8.00048 (+-0.0016556)
found -9.88 (+-0.000242378) 79.7866 (+-0.476286) 8.00022 (+-0.00165469)
found -4.6 (+-0.000242844) 79.7884 (+-0.476443) 8.0004 (+-0.00165524)
found -3.16 (+-0.000241989) 79.7871 (+-0.47624) 8.00027 (+-0.00165453)
found -1.96 (+-0.00024122) 79.786 (+-0.47606) 8.00016 (+-0.00165391)
found 0.2 (+-0.000241664) 79.7863 (+-0.476152) 8.00019 (+-0.00165423)
found 2.12 (+-0.000258915) 69.8137 (+-0.44552) 7.00024 (+-0.00154781)
found 8.6 (+-0.000259339) 69.8146 (+-0.445616) 7.00032 (+-0.00154814)
found -7.24 (+-0.000258554) 69.8132 (+-0.445442) 7.00018 (+-0.00154754)
found -1.48 (+-0.000259334) 69.8143 (+-0.445608) 7.00029 (+-0.00154811)
found 1.16 (+-0.000258745) 69.8135 (+-0.445483) 7.00021 (+-0.00154768)
found -2.44 (+-0.000279793) 59.8413 (+-0.412519) 6.0003 (+-0.00143315)
found 4.04 (+-0.000281068) 59.8423 (+-0.412738) 6.0004 (+-0.00143392)
found 7.88 (+-0.000280118) 59.8413 (+-0.412566) 6.00029 (+-0.00143332)
found 9.32 (+-0.000281762) 59.8434 (+-0.412872) 6.00051 (+-0.00143438)
found -6.52 (+-0.000278501) 59.8394 (+-0.412269) 6.0001 (+-0.00143229)
found -1.24 (+-0.000280611) 59.8415 (+-0.412648) 6.00032 (+-0.0014336)
found -0.519998 (+-0.000281068) 59.8423 (+-0.412738) 6.0004 (+-0.00143392)
found 1.88 (+-0.00028013) 59.841 (+-0.41256) 6.00027 (+-0.0014333)
found 5.24 (+-0.000306903) 49.8682 (+-0.376641) 5.0003 (+-0.00130851)
found -7.96 (+-0.00030805) 49.8687 (+-0.3768) 5.00035 (+-0.00130906)
found -7.72 (+-0.00030685) 49.8674 (+-0.376609) 5.00021 (+-0.0013084)
found -4.84 (+-0.000307787) 49.8685 (+-0.376759) 5.00032 (+-0.00130892)
found -1.72 (+-0.000308495) 49.8692 (+-0.376872) 5.0004 (+-0.00130931)
found -1 (+-0.000307646) 49.8682 (+-0.376734) 5.00029 (+-0.00130883)
found -0.76 (+-0.000307646) 49.8682 (+-0.376734) 5.00029 (+-0.00130883)
found 0.68 (+-0.00030659) 49.8671 (+-0.376568) 5.00019 (+-0.00130826)
found 1.4 (+-0.000307289) 49.8679 (+-0.376681) 5.00027 (+-0.00130865)
found 4.28 (+-0.00030708) 49.8677 (+-0.376646) 5.00024 (+-0.00130853)
found 6.44 (+-0.000346764) 39.8975 (+-0.337333) 4.00053 (+-0.00117195)
found -7 (+-0.000344188) 39.8949 (+-0.336994) 4.00027 (+-0.00117077)
found 0.439999 (+-0.000345122) 39.8957 (+-0.337114) 4.00035 (+-0.00117119)
found 3.08 (+-0.000343329) 39.8941 (+-0.336881) 4.00018 (+-0.00117038)
found -5.08 (+-0.000342539) 39.8938 (+-0.336793) 4.00016 (+-0.00117007)
found 2.84 (+-0.000342224) 39.8935 (+-0.336752) 4.00013 (+-0.00116993)
found -0.0400013 (+-0.000401258) 29.9239 (+-0.292227) 3.00048 (+-0.00101524)
found -7.48 (+-0.000399383) 29.9224 (+-0.292036) 3.00032 (+-0.00101458)
found 0.920001 (+-0.000399383) 29.9224 (+-0.292036) 3.00032 (+-0.00101458)
found 1.64 (+-0.000399047) 29.9221 (+-0.292002) 3.00029 (+-0.00101446)
found 2.36 (+-0.000397042) 29.9213 (+-0.291819) 3.00021 (+-0.00101383)
found 4.52 (+-0.000399971) 29.9229 (+-0.292097) 3.00037 (+-0.00101479)
found 8.84 (+-0.000400685) 29.9234 (+-0.292168) 3.00043 (+-0.00101504)
found -6.76 (+-0.000398627) 29.9218 (+-0.29196) 3.00027 (+-0.00101432)
found -3.88 (+-0.000398457) 29.9221 (+-0.291953) 3.0003 (+-0.00101429)
found 3.32 (+-0.000395933) 29.9205 (+-0.291707) 3.00014 (+-0.00101344)
found -6.03999 (+-0.000397877) 29.9221 (+-0.291908) 3.0003 (+-0.00101413)
found -9.40001 (+-0.000489635) 19.9488 (+-0.238487) 2.00027 (+-0.000828542)
found -4.12001 (+-0.000491254) 19.9496 (+-0.238597) 2.00035 (+-0.000828924)
found -3.4 (+-0.000493678) 19.9506 (+-0.238759) 2.00045 (+-0.000829486)
found 8.12 (+-0.000492764) 19.9501 (+-0.238696) 2.0004 (+-0.000829265)
found -9.16 (+-0.000486064) 19.9472 (+-0.238244) 2.00011 (+-0.000827697)
found -8.92 (+-0.000484893) 19.9469 (+-0.238172) 2.00008 (+-0.000827446)
found -5.32001 (+-0.000701784) 9.97653 (+-0.168964) 1.00035 (+-0.000587006)
found 7.4 (+-0.000706032) 9.97786 (+-0.169119) 1.00048 (+-0.000587546)
found -6.28001 (+-0.000698299) 9.97547 (+-0.168835) 1.00024 (+-0.000586561)
found -2.2 (+-0.00070315) 9.97679 (+-0.16901) 1.00037 (+-0.000587168)
found 5.48001 (+-0.000703477) 9.97707 (+-0.169026) 1.0004 (+-0.000587222)
found 2.6 (+-0.000696318) 9.97493 (+-0.168763) 1.00019 (+-0.00058631)
found 3.56001 (+-0.000701071) 9.97654 (+-0.168942) 1.00035 (+-0.000586932)
found -8.67998 (+-0.000699416) 9.97628 (+-0.168888) 1.00032 (+-0.000586742)

 
#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.