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 9.97356 1
created -9.64 59.8413 6
created -9.4 99.7356 10
created -9.16 59.8413 6
created -8.92 29.9207 3
created -8.68 9.97356 1
created -8.44 49.8678 5
created -8.2 79.7885 8
created -7.96 99.7356 10
created -7.72 89.762 9
created -7.48 79.7885 8
created -7.24 59.8413 6
created -7 9.97356 1
created -6.76 39.8942 4
created -6.52 19.9471 2
created -6.28 19.9471 2
created -6.04 29.9207 3
created -5.8 79.7885 8
created -5.56 39.8942 4
created -5.32 69.8149 7
created -5.08 29.9207 3
created -4.84 19.9471 2
created -4.6 39.8942 4
created -4.36 29.9207 3
created -4.12 9.97356 1
created -3.88 39.8942 4
created -3.64 9.97356 1
created -3.4 59.8413 6
created -3.16 69.8149 7
created -2.92 19.9471 2
created -2.68 49.8678 5
created -2.44 39.8942 4
created -2.2 89.762 9
created -1.96 99.7356 10
created -1.72 69.8149 7
created -1.48 79.7885 8
created -1.24 19.9471 2
created -1 59.8413 6
created -0.76 49.8678 5
created -0.52 89.762 9
created -0.28 19.9471 2
created -0.04 99.7356 10
created 0.2 19.9471 2
created 0.44 9.97356 1
created 0.68 9.97356 1
created 0.92 19.9471 2
created 1.16 9.97356 1
created 1.4 19.9471 2
created 1.64 19.9471 2
created 1.88 39.8942 4
created 2.12 39.8942 4
created 2.36 9.97356 1
created 2.6 39.8942 4
created 2.84 59.8413 6
created 3.08 39.8942 4
created 3.32 59.8413 6
created 3.56 9.97356 1
created 3.8 19.9471 2
created 4.04 49.8678 5
created 4.28 49.8678 5
created 4.52 79.7885 8
created 4.76 39.8942 4
created 5 9.97356 1
created 5.24 79.7885 8
created 5.48 9.97356 1
created 5.72 59.8413 6
created 5.96 29.9207 3
created 6.2 19.9471 2
created 6.44 79.7885 8
created 6.68 39.8942 4
created 6.92 19.9471 2
created 7.16 9.97356 1
created 7.4 59.8413 6
created 7.64 39.8942 4
created 7.88 69.8149 7
created 8.12 9.97356 1
created 8.36 19.9471 2
created 8.6 79.7885 8
created 8.84 99.7356 10
created 9.08 89.762 9
created 9.32 89.762 9
created 9.56 99.7356 10
created 9.8 39.8942 4
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.30857e-05)
fit chi^2 = 7.27167e-06
found -9.4 (+-0.000141413) 99.7336 (+-0.347678) 10.0003 (+-0.00120789)
found -7.96 (+-0.000141754) 99.735 (+-0.347784) 10.0005 (+-0.00120826)
found -1.96 (+-0.000141687) 99.7347 (+-0.347763) 10.0004 (+-0.00120818)
found 8.84 (+-0.000141754) 99.735 (+-0.347784) 10.0004 (+-0.00120826)
found 9.56 (+-0.00014144) 99.7339 (+-0.347689) 10.0003 (+-0.00120793)
found -0.04 (+-0.000140576) 99.7315 (+-0.347438) 10.0001 (+-0.00120705)
found -7.72 (+-0.000149606) 89.7622 (+-0.329989) 9.00048 (+-0.00114643)
found 9.08 (+-0.000149674) 89.7625 (+-0.330009) 9.00051 (+-0.0011465)
found 9.32 (+-0.000149674) 89.7625 (+-0.330009) 9.0005 (+-0.0011465)
found -2.2 (+-0.000149261) 89.7611 (+-0.329895) 9.00037 (+-0.00114611)
found -0.520001 (+-0.000148618) 89.7593 (+-0.329721) 9.00019 (+-0.0011455)
found -7.48 (+-0.00015859) 79.7883 (+-0.311094) 8.0004 (+-0.00108079)
found -1.48 (+-0.000157923) 79.7868 (+-0.310935) 8.00024 (+-0.00108024)
found -8.2 (+-0.000158562) 79.7883 (+-0.311089) 8.0004 (+-0.00108077)
found -5.8 (+-0.000157792) 79.7862 (+-0.310899) 8.00018 (+-0.00108011)
found 4.52 (+-0.000158035) 79.7867 (+-0.310956) 8.00024 (+-0.00108031)
found 5.24 (+-0.000156865) 79.7849 (+-0.310694) 8.00005 (+-0.0010794)
found 6.44 (+-0.000157627) 79.7859 (+-0.310862) 8.00016 (+-0.00107998)
found 8.6 (+-0.00015815) 79.7875 (+-0.310994) 8.00032 (+-0.00108044)
found -3.16 (+-0.00016887) 69.8134 (+-0.290861) 7.00021 (+-0.0010105)
found -1.72 (+-0.000169995) 69.8161 (+-0.291104) 7.00048 (+-0.00101134)
found -5.32 (+-0.00016882) 69.8132 (+-0.290847) 7.00018 (+-0.00101045)
found 7.88 (+-0.000168372) 69.8126 (+-0.29076) 7.00013 (+-0.00101015)
found -9.16 (+-0.000183205) 59.8417 (+-0.269436) 6.00035 (+-0.000936062)
found -7.24 (+-0.000182499) 59.8407 (+-0.269312) 6.00024 (+-0.000935631)
found 2.84 (+-0.000182691) 59.8404 (+-0.269334) 6.00021 (+-0.000935708)
found 3.32 (+-0.000182011) 59.8396 (+-0.269217) 6.00013 (+-0.000935304)
found -9.64 (+-0.000182688) 59.8412 (+-0.26935) 6.00029 (+-0.000935764)
found -3.4 (+-0.000182394) 59.8404 (+-0.269291) 6.00021 (+-0.000935559)
found -0.999999 (+-0.000182455) 59.8402 (+-0.269293) 6.00019 (+-0.000935568)
found 5.72 (+-0.000181844) 59.8393 (+-0.269187) 6.00011 (+-0.000935198)
found 7.4 (+-0.000182011) 59.8396 (+-0.269217) 6.00013 (+-0.000935304)
found -0.759999 (+-0.000201408) 49.8692 (+-0.246072) 5.0004 (+-0.000854893)
found 4.28 (+-0.000201138) 49.8687 (+-0.246028) 5.00035 (+-0.00085474)
found -8.44 (+-0.000200166) 49.8676 (+-0.245885) 5.00024 (+-0.000854245)
found -2.68 (+-0.000199939) 49.8668 (+-0.24584) 5.00016 (+-0.000854086)
found 4.04 (+-0.000200108) 49.8671 (+-0.245867) 5.00019 (+-0.00085418)
found 9.8 (+-0.000223203) 39.8959 (+-0.219898) 4.00037 (+-0.00076396)
found -5.56 (+-0.000225701) 39.8962 (+-0.220162) 4.0004 (+-0.000764877)
found -2.44 (+-0.000225487) 39.8959 (+-0.220134) 4.00037 (+-0.000764782)
found 2.12 (+-0.000223452) 39.8935 (+-0.219879) 4.00013 (+-0.000763894)
found 3.08 (+-0.000225207) 39.8954 (+-0.220096) 4.00032 (+-0.000764649)
found 4.76 (+-0.00022416) 39.8946 (+-0.219973) 4.00024 (+-0.000764222)
found 6.68 (+-0.000224587) 39.8948 (+-0.220021) 4.00027 (+-0.000764389)
found 7.64 (+-0.000225376) 39.8956 (+-0.220119) 4.00034 (+-0.000764727)
found -6.76 (+-0.000222918) 39.893 (+-0.219812) 4.00008 (+-0.000763663)
found -4.6 (+-0.000223634) 39.8935 (+-0.219896) 4.00013 (+-0.000763955)
found -3.88 (+-0.0002225) 39.8927 (+-0.219764) 4.00005 (+-0.000763496)
found 1.88 (+-0.000223875) 39.8938 (+-0.219927) 4.00016 (+-0.000764061)
found 2.6 (+-0.000223841) 39.894 (+-0.21993) 4.00019 (+-0.000764072)
found -8.92 (+-0.000259029) 29.921 (+-0.190518) 3.00019 (+-0.000661891)
found -5.08 (+-0.000259789) 29.9215 (+-0.190588) 3.00024 (+-0.000662133)
found 5.96 (+-0.000259572) 29.9213 (+-0.190566) 3.00021 (+-0.000662056)
found -6.04 (+-0.000259987) 29.9218 (+-0.190609) 3.00027 (+-0.000662204)
found -4.36 (+-0.000258521) 29.9205 (+-0.190467) 3.00013 (+-0.000661712)
found -1.24 (+-0.000321489) 19.9498 (+-0.155835) 2.00037 (+-0.000541397)
found -0.279999 (+-0.000322838) 19.9511 (+-0.155931) 2.00051 (+-0.000541728)
found 0.199991 (+-0.000319403) 19.949 (+-0.155706) 2.0003 (+-0.000540947)
found -2.92 (+-0.000320858) 19.9493 (+-0.155792) 2.00032 (+-0.000541245)
found -6.52 (+-0.000318386) 19.9477 (+-0.155626) 2.00016 (+-0.000540669)
found -6.28 (+-0.000317929) 19.9474 (+-0.155595) 2.00013 (+-0.000540562)
found -4.84 (+-0.000318949) 19.948 (+-0.155662) 2.00019 (+-0.000540795)
found 1.64 (+-0.000318386) 19.9477 (+-0.155626) 2.00016 (+-0.000540669)
found 6.2 (+-0.000320273) 19.949 (+-0.155754) 2.00029 (+-0.000541115)
found 6.92 (+-0.000317614) 19.9474 (+-0.155579) 2.00014 (+-0.000540505)
found 0.92 (+-0.000315847) 19.9466 (+-0.155465) 2.00005 (+-0.000540109)
found 1.4 (+-0.000316606) 19.9469 (+-0.155512) 2.00008 (+-0.000540273)
found 3.8 (+-0.000318002) 19.9477 (+-0.155605) 2.00016 (+-0.000540598)
found 8.36001 (+-0.000318921) 19.9485 (+-0.155671) 2.00024 (+-0.000540824)
found 5.48 (+-0.000459115) 9.97677 (+-0.110353) 1.00037 (+-0.000383385)
found 8.11999 (+-0.0004554) 9.97547 (+-0.110223) 1.00024 (+-0.000382933)
found -7 (+-0.000456797) 9.97572 (+-0.110269) 1.00027 (+-0.000383091)
found 3.55999 (+-0.000454879) 9.97519 (+-0.110203) 1.00022 (+-0.000382864)
found -3.64 (+-0.000456797) 9.97571 (+-0.110269) 1.00027 (+-0.000383091)
found 2.36 (+-0.000455496) 9.97518 (+-0.110222) 1.00021 (+-0.000382927)
found 5.00001 (+-0.000457796) 9.97625 (+-0.110306) 1.00032 (+-0.000383222)
found -8.68 (+-0.00045535) 9.97519 (+-0.110217) 1.00021 (+-0.000382912)
found -4.12 (+-0.000454654) 9.97491 (+-0.110192) 1.00019 (+-0.000382825)
found 0.439998 (+-0.000450265) 9.97386 (+-0.110044) 1.00008 (+-0.00038231)
found 0.680002 (+-0.000450266) 9.97385 (+-0.110044) 1.00008 (+-0.00038231)
found 1.16 (+-0.000451714) 9.97411 (+-0.110091) 1.00011 (+-0.000382475)
found 7.16001 (+-0.000454879) 9.97519 (+-0.110203) 1.00021 (+-0.000382864)
found -9.87999 (+-0.000452157) 9.97468 (+-0.110109) 1.00016 (+-0.000382538)

 
#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.