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 89.762 9
created -9.64 39.8942 4
created -9.4 49.8678 5
created -9.16 29.9207 3
created -8.92 89.762 9
created -8.68 9.97356 1
created -8.44 39.8942 4
created -8.2 9.97356 1
created -7.96 99.7356 10
created -7.72 59.8413 6
created -7.48 69.8149 7
created -7.24 29.9207 3
created -7 9.97356 1
created -6.76 9.97356 1
created -6.52 29.9207 3
created -6.28 49.8678 5
created -6.04 29.9207 3
created -5.8 29.9207 3
created -5.56 59.8413 6
created -5.32 69.8149 7
created -5.08 19.9471 2
created -4.84 49.8678 5
created -4.6 79.7885 8
created -4.36 9.97356 1
created -4.12 79.7885 8
created -3.88 99.7356 10
created -3.64 89.762 9
created -3.4 29.9207 3
created -3.16 89.762 9
created -2.92 19.9471 2
created -2.68 49.8678 5
created -2.44 39.8942 4
created -2.2 49.8678 5
created -1.96 19.9471 2
created -1.72 89.762 9
created -1.48 99.7356 10
created -1.24 79.7885 8
created -1 59.8413 6
created -0.76 89.762 9
created -0.52 59.8413 6
created -0.28 89.762 9
created -0.04 99.7356 10
created 0.2 59.8413 6
created 0.44 49.8678 5
created 0.68 39.8942 4
created 0.92 49.8678 5
created 1.16 69.8149 7
created 1.4 99.7356 10
created 1.64 49.8678 5
created 1.88 19.9471 2
created 2.12 79.7885 8
created 2.36 49.8678 5
created 2.6 69.8149 7
created 2.84 89.762 9
created 3.08 29.9207 3
created 3.32 59.8413 6
created 3.56 49.8678 5
created 3.8 89.762 9
created 4.04 39.8942 4
created 4.28 9.97356 1
created 4.52 9.97356 1
created 4.76 89.762 9
created 5 9.97356 1
created 5.24 9.97356 1
created 5.48 29.9207 3
created 5.72 59.8413 6
created 5.96 39.8942 4
created 6.2 99.7356 10
created 6.44 99.7356 10
created 6.68 79.7885 8
created 6.92 9.97356 1
created 7.16 69.8149 7
created 7.4 89.762 9
created 7.64 19.9471 2
created 7.88 29.9207 3
created 8.12 19.9471 2
created 8.36 29.9207 3
created 8.6 79.7885 8
created 8.84 79.7885 8
created 9.08 79.7885 8
created 9.32 19.9471 2
created 9.56 29.9207 3
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 (+-1.23147e-05)
fit chi^2 = 7.43782e-06
found -3.88 (+-0.000143365) 99.735 (+-0.351735) 10.0004 (+-0.00122198)
found -1.48 (+-0.000143365) 99.735 (+-0.351735) 10.0004 (+-0.00122198)
found -0.0400006 (+-0.000143223) 99.7345 (+-0.351691) 10.0004 (+-0.00122183)
found 1.4 (+-0.000143011) 99.7336 (+-0.351626) 10.0003 (+-0.0012216)
found 6.44 (+-0.000143423) 99.7352 (+-0.351753) 10.0005 (+-0.00122205)
found -7.96 (+-0.000142399) 99.7323 (+-0.351456) 10.0002 (+-0.00122101)
found 6.2 (+-0.000143105) 99.7342 (+-0.351658) 10.0004 (+-0.00122172)
found -3.64 (+-0.000150835) 89.7609 (+-0.333611) 9.00035 (+-0.00115902)
found -0.76 (+-0.000150861) 89.7606 (+-0.333613) 9.00032 (+-0.00115902)
found -0.279999 (+-0.000151149) 89.7617 (+-0.333695) 9.00043 (+-0.00115931)
found 2.84 (+-0.000150628) 89.7601 (+-0.333552) 9.00026 (+-0.00115881)
found 7.4 (+-0.000150476) 89.7598 (+-0.333514) 9.00023 (+-0.00115868)
found -9.88 (+-0.000150488) 89.7585 (+-0.333483) 9.00011 (+-0.00115857)
found -8.92 (+-0.000149872) 89.7585 (+-0.333358) 9.0001 (+-0.00115814)
found -3.16 (+-0.000150086) 89.7587 (+-0.333409) 9.00013 (+-0.00115831)
found -1.72 (+-0.000150682) 89.7606 (+-0.333573) 9.00032 (+-0.00115889)
found 3.8 (+-0.000150579) 89.7598 (+-0.333536) 9.00024 (+-0.00115876)
found 4.76 (+-0.00014951) 89.7579 (+-0.333269) 9.00004 (+-0.00115783)
found -1.24 (+-0.000160464) 79.7887 (+-0.314647) 8.00043 (+-0.00109313)
found 6.68 (+-0.000159707) 79.7873 (+-0.314476) 8.00029 (+-0.00109254)
found 8.84 (+-0.000160488) 79.7886 (+-0.314652) 8.00042 (+-0.00109315)
found 9.08 (+-0.000159799) 79.787 (+-0.314488) 8.00026 (+-0.00109258)
found -4.6 (+-0.000159291) 79.786 (+-0.31437) 8.00016 (+-0.00109217)
found -4.12 (+-0.000159707) 79.7873 (+-0.314476) 8.00029 (+-0.00109254)
found 2.12 (+-0.000159529) 79.7862 (+-0.31442) 8.00018 (+-0.00109235)
found 8.6 (+-0.000159968) 79.7873 (+-0.314526) 8.00029 (+-0.00109271)
found -7.48 (+-0.000170976) 69.8137 (+-0.294202) 7.00024 (+-0.00102211)
found -5.32 (+-0.000170788) 69.8135 (+-0.294165) 7.00021 (+-0.00102198)
found 1.16 (+-0.000171616) 69.8153 (+-0.294343) 7.0004 (+-0.00102259)
found 2.6 (+-0.000171535) 69.8151 (+-0.294324) 7.00037 (+-0.00102253)
found 7.16 (+-0.000170799) 69.814 (+-0.294178) 7.00027 (+-0.00102202)
found -7.72 (+-0.000185854) 59.8428 (+-0.272602) 6.00045 (+-0.000947062)
found -0.52 (+-0.000185969) 59.8431 (+-0.272624) 6.00048 (+-0.000947139)
found 0.199998 (+-0.000185606) 59.8423 (+-0.272555) 6.0004 (+-0.000946898)
found -5.56 (+-0.000184986) 59.8409 (+-0.272437) 6.00026 (+-0.000946489)
found -1 (+-0.000185868) 59.8428 (+-0.272604) 6.00045 (+-0.00094707)
found 3.32 (+-0.000184741) 59.8404 (+-0.27239) 6.00021 (+-0.000946325)
found 5.72 (+-0.000184596) 59.8402 (+-0.272363) 6.00019 (+-0.000946231)
found 1.64 (+-0.000203025) 49.8685 (+-0.248767) 5.00032 (+-0.000864254)
found -9.4 (+-0.000202459) 49.8671 (+-0.248669) 5.00019 (+-0.000863917)
found -6.28 (+-0.00020226) 49.8668 (+-0.248639) 5.00016 (+-0.00086381)
found -2.2 (+-0.000202211) 49.8669 (+-0.248633) 5.00016 (+-0.000863789)
found 0.439999 (+-0.000202984) 49.8679 (+-0.248752) 5.00027 (+-0.000864203)
found 0.920001 (+-0.000203122) 49.8682 (+-0.248775) 5.00029 (+-0.000864282)
found 2.36 (+-0.000203717) 49.8692 (+-0.24887) 5.0004 (+-0.000864614)
found 3.56 (+-0.000203696) 49.8692 (+-0.248867) 5.0004 (+-0.000864604)
found -4.84 (+-0.000202797) 49.8679 (+-0.248728) 5.00027 (+-0.000864121)
found -2.68 (+-0.000202211) 49.8669 (+-0.248633) 5.00016 (+-0.000863789)
found -9.64 (+-0.000228049) 39.8959 (+-0.222635) 4.00037 (+-0.00077347)
found 4.04 (+-0.00022685) 39.8949 (+-0.222492) 4.00027 (+-0.000772972)
found -2.44 (+-0.000227388) 39.8948 (+-0.222547) 4.00027 (+-0.000773164)
found 0.68 (+-0.000227388) 39.8949 (+-0.222547) 4.00027 (+-0.000773164)
found 5.96 (+-0.000228374) 39.8965 (+-0.222679) 4.00043 (+-0.00077362)
found 9.8 (+-0.000224524) 39.894 (+-0.222232) 4.00018 (+-0.000772069)
found -8.44 (+-0.000225027) 39.8928 (+-0.222261) 4.00006 (+-0.000772169)
found -3.4 (+-0.000264988) 29.9239 (+-0.192975) 3.00048 (+-0.000670427)
found 3.08 (+-0.000264372) 29.9231 (+-0.192913) 3.0004 (+-0.000670209)
found -9.16 (+-0.000264124) 29.9229 (+-0.192888) 3.00037 (+-0.000670124)
found -7.24 (+-0.00026219) 29.9213 (+-0.192705) 3.00021 (+-0.000669488)
found -6.04 (+-0.00026267) 29.9213 (+-0.192743) 3.00021 (+-0.000669618)
found -5.8 (+-0.000262914) 29.9216 (+-0.192767) 3.00024 (+-0.000669704)
found 7.88 (+-0.000261298) 29.9202 (+-0.192611) 3.00011 (+-0.000669162)
found 8.36 (+-0.00026294) 29.9218 (+-0.192774) 3.00027 (+-0.000669727)
found 9.56 (+-0.000262004) 29.9208 (+-0.192679) 3.00016 (+-0.000669396)
found -6.52 (+-0.00026173) 29.9207 (+-0.192658) 3.00016 (+-0.000669324)
found 5.48 (+-0.000261972) 29.921 (+-0.192683) 3.00019 (+-0.00066941)
found -2.92 (+-0.000325045) 19.9498 (+-0.1576) 2.00037 (+-0.000547528)
found 7.63999 (+-0.000324168) 19.9493 (+-0.157542) 2.00032 (+-0.000547326)
found 9.32 (+-0.000323911) 19.949 (+-0.157524) 2.00029 (+-0.000547262)
found -5.08 (+-0.000324503) 19.9493 (+-0.157562) 2.00032 (+-0.000547394)
found -1.96 (+-0.000325045) 19.9498 (+-0.1576) 2.00037 (+-0.000547528)
found 1.88 (+-0.000324787) 19.9496 (+-0.157582) 2.00035 (+-0.000547463)
found 8.12 (+-0.000322108) 19.9477 (+-0.1574) 2.00016 (+-0.000546831)
found -8.68001 (+-0.000463429) 9.97652 (+-0.111576) 1.00035 (+-0.000387634)
found -4.36 (+-0.000465355) 9.97732 (+-0.111645) 1.00043 (+-0.000387873)
found 4.99998 (+-0.000459965) 9.97575 (+-0.111462) 1.00027 (+-0.000387238)
found 6.92 (+-0.000464871) 9.97706 (+-0.111627) 1.0004 (+-0.00038781)
found -8.19999 (+-0.000463825) 9.97679 (+-0.111592) 1.00038 (+-0.000387688)
found 4.27999 (+-0.000457263) 9.97441 (+-0.11136) 1.00014 (+-0.000386882)
found -7 (+-0.00045643) 9.97413 (+-0.11133) 1.00011 (+-0.000386779)
found -6.76 (+-0.00045643) 9.97413 (+-0.11133) 1.00011 (+-0.000386779)
found 4.52002 (+-0.000459965) 9.97575 (+-0.111462) 1.00027 (+-0.000387238)
found 5.24 (+-0.000456429) 9.97414 (+-0.11133) 1.00011 (+-0.000386779)

 
#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.