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 39.8942 4
created -9.64 89.762 9
created -9.4 39.8942 4
created -9.16 9.97356 1
created -8.92 29.9207 3
created -8.68 9.97356 1
created -8.44 59.8413 6
created -8.2 79.7885 8
created -7.96 89.762 9
created -7.72 99.7356 10
created -7.48 49.8678 5
created -7.24 49.8678 5
created -7 19.9471 2
created -6.76 39.8942 4
created -6.52 59.8413 6
created -6.28 99.7356 10
created -6.04 79.7885 8
created -5.8 19.9471 2
created -5.56 69.8149 7
created -5.32 99.7356 10
created -5.08 49.8678 5
created -4.84 59.8413 6
created -4.6 49.8678 5
created -4.36 69.8149 7
created -4.12 39.8942 4
created -3.88 69.8149 7
created -3.64 49.8678 5
created -3.4 99.7356 10
created -3.16 99.7356 10
created -2.92 69.8149 7
created -2.68 49.8678 5
created -2.44 9.97356 1
created -2.2 89.762 9
created -1.96 59.8413 6
created -1.72 59.8413 6
created -1.48 39.8942 4
created -1.24 59.8413 6
created -1 49.8678 5
created -0.76 9.97356 1
created -0.52 59.8413 6
created -0.28 49.8678 5
created -0.04 79.7885 8
created 0.2 9.97356 1
created 0.44 19.9471 2
created 0.68 49.8678 5
created 0.92 49.8678 5
created 1.16 59.8413 6
created 1.4 59.8413 6
created 1.64 9.97356 1
created 1.88 39.8942 4
created 2.12 39.8942 4
created 2.36 69.8149 7
created 2.6 79.7885 8
created 2.84 89.762 9
created 3.08 69.8149 7
created 3.32 59.8413 6
created 3.56 69.8149 7
created 3.8 89.762 9
created 4.04 99.7356 10
created 4.28 69.8149 7
created 4.52 9.97356 1
created 4.76 29.9207 3
created 5 79.7885 8
created 5.24 99.7356 10
created 5.48 99.7356 10
created 5.72 9.97356 1
created 5.96 89.762 9
created 6.2 9.97356 1
created 6.44 9.97356 1
created 6.68 49.8678 5
created 6.92 19.9471 2
created 7.16 9.97356 1
created 7.4 19.9471 2
created 7.64 69.8149 7
created 7.88 99.7356 10
created 8.12 79.7885 8
created 8.36 79.7885 8
created 8.6 69.8149 7
created 8.84 9.97356 1
created 9.08 29.9207 3
created 9.32 89.762 9
created 9.56 69.8149 7
created 9.8 29.9207 3
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.0400021 (+-1.06717e-05)
fit chi^2 = 5.81371e-06
found -7.72 (+-0.000126551) 99.7342 (+-0.31091) 10.0004 (+-0.00108015)
found -6.28 (+-0.000126569) 99.7341 (+-0.310914) 10.0004 (+-0.00108017)
found -5.32 (+-0.000126437) 99.7336 (+-0.310874) 10.0003 (+-0.00108003)
found -3.16 (+-0.000126741) 99.7349 (+-0.310968) 10.0004 (+-0.00108035)
found 4.04 (+-0.000126689) 99.7347 (+-0.310952) 10.0004 (+-0.0010803)
found 5.24 (+-0.000126801) 99.7352 (+-0.310987) 10.0005 (+-0.00108042)
found 5.48 (+-0.000126124) 99.7334 (+-0.310796) 10.0003 (+-0.00107975)
found 7.88 (+-0.000126634) 99.7344 (+-0.310934) 10.0004 (+-0.00108024)
found -3.4 (+-0.000126603) 99.7344 (+-0.310927) 10.0004 (+-0.00108021)
found -7.96 (+-0.00013377) 89.7622 (+-0.295059) 9.00048 (+-0.00102508)
found 2.84 (+-0.000133586) 89.7614 (+-0.295007) 9.0004 (+-0.0010249)
found 3.8 (+-0.000133704) 89.7619 (+-0.295041) 9.00045 (+-0.00102502)
found -9.64 (+-0.000133038) 89.7595 (+-0.294857) 9.00021 (+-0.00102438)
found -2.2 (+-0.000132776) 89.7592 (+-0.294796) 9.00018 (+-0.00102417)
found 5.96 (+-0.000132182) 89.7579 (+-0.294645) 9.00005 (+-0.00102364)
found 9.32 (+-0.000133171) 89.76 (+-0.294895) 9.00026 (+-0.00102451)
found -8.2 (+-0.000141803) 79.7883 (+-0.278164) 8.0004 (+-0.000966387)
found -6.04 (+-0.00014141) 79.7875 (+-0.278074) 8.00032 (+-0.000966075)
found 2.6 (+-0.000141883) 79.7886 (+-0.278184) 8.00042 (+-0.000966457)
found 8.12 (+-0.000142021) 79.7891 (+-0.27822) 8.00048 (+-0.000966579)
found 8.36 (+-0.000141815) 79.7883 (+-0.278167) 8.0004 (+-0.000966396)
found -0.040001 (+-0.00014083) 79.7859 (+-0.277936) 8.00016 (+-0.000965592)
found 5 (+-0.000141559) 79.7878 (+-0.278108) 8.00034 (+-0.000966191)
found -2.92 (+-0.000151727) 69.8153 (+-0.26023) 7.0004 (+-0.00090408)
found 3.08 (+-0.000151755) 69.8153 (+-0.260235) 7.0004 (+-0.000904097)
found 3.56 (+-0.000151755) 69.8153 (+-0.260235) 7.0004 (+-0.000904097)
found 4.28 (+-0.000151075) 69.8142 (+-0.260101) 7.00029 (+-0.000903632)
found 8.6 (+-0.000150928) 69.8137 (+-0.260067) 7.00024 (+-0.000903513)
found 9.56 (+-0.000151409) 69.8145 (+-0.260162) 7.00032 (+-0.000903843)
found -5.56 (+-0.000151313) 69.8145 (+-0.260145) 7.00032 (+-0.000903786)
found -4.36 (+-0.000151194) 69.8137 (+-0.260111) 7.00024 (+-0.000903668)
found -3.88 (+-0.000151194) 69.8137 (+-0.260111) 7.00024 (+-0.000903668)
found 2.36 (+-0.000151465) 69.8145 (+-0.260172) 7.00032 (+-0.000903878)
found 7.64 (+-0.000151314) 69.8145 (+-0.260145) 7.00032 (+-0.000903786)
found -1.96 (+-0.000164127) 59.8422 (+-0.240972) 6.0004 (+-0.000837175)
found -6.52 (+-0.000163965) 59.842 (+-0.240943) 6.00037 (+-0.000837073)
found -4.84 (+-0.00016361) 59.8409 (+-0.240872) 6.00027 (+-0.000836829)
found -1.72 (+-0.000163595) 59.8409 (+-0.24087) 6.00027 (+-0.000836821)
found -1.24 (+-0.000163481) 59.8407 (+-0.240848) 6.00024 (+-0.000836745)
found 1.16 (+-0.000163724) 59.8412 (+-0.240894) 6.00029 (+-0.000836904)
found 1.4 (+-0.000162985) 59.8401 (+-0.240766) 6.00019 (+-0.000836459)
found 3.32 (+-0.000164046) 59.842 (+-0.240956) 6.00037 (+-0.000837118)
found -8.44 (+-0.000163181) 59.8407 (+-0.240805) 6.00024 (+-0.000836593)
found -0.519999 (+-0.000162872) 59.8399 (+-0.240744) 6.00016 (+-0.000836384)
found -7.48 (+-0.00018005) 49.8692 (+-0.220019) 5.0004 (+-0.000764383)
found -5.08 (+-0.000180187) 49.8695 (+-0.220041) 5.00043 (+-0.000764458)
found -7.24 (+-0.000178926) 49.8671 (+-0.219841) 5.00019 (+-0.000763763)
found -4.6 (+-0.00017987) 49.8687 (+-0.219989) 5.00035 (+-0.000764275)
found -3.64 (+-0.000180311) 49.8697 (+-0.220061) 5.00045 (+-0.000764528)
found -2.68 (+-0.000178866) 49.8674 (+-0.219839) 5.00022 (+-0.000763757)
found -1 (+-0.000178745) 49.8671 (+-0.219819) 5.00019 (+-0.000763688)
found -0.279999 (+-0.000179983) 49.8689 (+-0.220007) 5.00037 (+-0.00076434)
found 0.92 (+-0.000179612) 49.8681 (+-0.219947) 5.00029 (+-0.00076413)
found 0.680001 (+-0.000178926) 49.8671 (+-0.219841) 5.00019 (+-0.000763763)
found 6.68 (+-0.00017807) 49.866 (+-0.219714) 5.00008 (+-0.000763322)
found -9.4 (+-0.000200559) 39.8948 (+-0.196706) 4.00027 (+-0.000683388)
found -4.12 (+-0.000201671) 39.8959 (+-0.196839) 4.00037 (+-0.000683849)
found -1.48 (+-0.000201368) 39.8954 (+-0.196799) 4.00032 (+-0.00068371)
found 2.12 (+-0.000201164) 39.8951 (+-0.196773) 4.00029 (+-0.00068362)
found -9.88 (+-0.000200884) 39.8946 (+-0.196725) 4.00024 (+-0.000683456)
found -6.76 (+-0.000200527) 39.8943 (+-0.196693) 4.00021 (+-0.000683343)
found 1.88 (+-0.000199799) 39.8935 (+-0.196604) 4.00013 (+-0.000683035)
found 9.8 (+-0.000230363) 29.9218 (+-0.17027) 3.00027 (+-0.000591544)
found -8.92 (+-0.00023006) 29.9197 (+-0.170204) 3.00005 (+-0.000591315)
found 4.76 (+-0.000231979) 29.9216 (+-0.17039) 3.00024 (+-0.000591961)
found 9.08 (+-0.00023214) 29.9218 (+-0.170407) 3.00027 (+-0.000592021)
found -5.8 (+-0.000287738) 19.9501 (+-0.139359) 2.0004 (+-0.000484156)
found -7 (+-0.000285953) 19.9485 (+-0.139236) 2.00024 (+-0.000483729)
found 6.92 (+-0.000284341) 19.9477 (+-0.139134) 2.00016 (+-0.000483375)
found 0.440004 (+-0.000284341) 19.9477 (+-0.139134) 2.00016 (+-0.000483375)
found 7.40001 (+-0.000284917) 19.9482 (+-0.139175) 2.00022 (+-0.000483516)
found 5.72 (+-0.000412556) 9.97811 (+-0.0987497) 1.00051 (+-0.000343072)
found 0.199988 (+-0.000407612) 9.97573 (+-0.098572) 1.00027 (+-0.000342455)
found 6.19998 (+-0.000406657) 9.97575 (+-0.0985445) 1.00027 (+-0.000342359)
found 4.51999 (+-0.000408154) 9.97573 (+-0.0985879) 1.00027 (+-0.00034251)
found 8.83999 (+-0.000408154) 9.97573 (+-0.0985879) 1.00027 (+-0.00034251)
found 1.64 (+-0.000408444) 9.97572 (+-0.0985965) 1.00027 (+-0.00034254)
found -9.16 (+-0.000406528) 9.97492 (+-0.0985282) 1.00019 (+-0.000342303)
found -2.43999 (+-0.000410356) 9.97679 (+-0.0986676) 1.00038 (+-0.000342787)
found -0.759998 (+-0.000409075) 9.97598 (+-0.0986189) 1.00029 (+-0.000342618)
found -8.67999 (+-0.000407685) 9.97545 (+-0.0985703) 1.00024 (+-0.000342449)
found 6.44001 (+-0.000404881) 9.97467 (+-0.0984762) 1.00016 (+-0.000342122)
found 7.16 (+-0.000403899) 9.97412 (+-0.098438) 1.00011 (+-0.000341989)

 
#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.