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 19.9471 2
created -9.64 79.7885 8
created -9.4 89.762 9
created -9.16 39.8942 4
created -8.92 9.97356 1
created -8.68 39.8942 4
created -8.44 39.8942 4
created -8.2 19.9471 2
created -7.96 19.9471 2
created -7.72 99.7356 10
created -7.48 99.7356 10
created -7.24 19.9471 2
created -7 9.97356 1
created -6.76 79.7885 8
created -6.52 59.8413 6
created -6.28 49.8678 5
created -6.04 99.7356 10
created -5.8 29.9207 3
created -5.56 19.9471 2
created -5.32 89.762 9
created -5.08 69.8149 7
created -4.84 29.9207 3
created -4.6 79.7885 8
created -4.36 39.8942 4
created -4.12 19.9471 2
created -3.88 29.9207 3
created -3.64 59.8413 6
created -3.4 89.762 9
created -3.16 89.762 9
created -2.92 89.762 9
created -2.68 89.762 9
created -2.44 99.7356 10
created -2.2 39.8942 4
created -1.96 39.8942 4
created -1.72 69.8149 7
created -1.48 49.8678 5
created -1.24 49.8678 5
created -1 29.9207 3
created -0.76 59.8413 6
created -0.52 19.9471 2
created -0.28 69.8149 7
created -0.04 49.8678 5
created 0.2 89.762 9
created 0.44 59.8413 6
created 0.68 29.9207 3
created 0.92 89.762 9
created 1.16 19.9471 2
created 1.4 79.7885 8
created 1.64 79.7885 8
created 1.88 49.8678 5
created 2.12 99.7356 10
created 2.36 89.762 9
created 2.6 99.7356 10
created 2.84 19.9471 2
created 3.08 99.7356 10
created 3.32 39.8942 4
created 3.56 79.7885 8
created 3.8 9.97356 1
created 4.04 89.762 9
created 4.28 19.9471 2
created 4.52 99.7356 10
created 4.76 39.8942 4
created 5 19.9471 2
created 5.24 19.9471 2
created 5.48 39.8942 4
created 5.72 9.97356 1
created 5.96 49.8678 5
created 6.2 49.8678 5
created 6.44 59.8413 6
created 6.68 29.9207 3
created 6.92 9.97356 1
created 7.16 79.7885 8
created 7.4 19.9471 2
created 7.64 59.8413 6
created 7.88 79.7885 8
created 8.12 69.8149 7
created 8.36 29.9207 3
created 8.6 29.9207 3
created 8.84 19.9471 2
created 9.08 79.7885 8
created 9.32 29.9207 3
created 9.56 59.8413 6
created 9.8 89.762 9
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.75578e-05)
fit chi^2 = 1.53696e-05
found -7.48 (+-0.000205353) 99.7336 (+-0.505408) 10.0003 (+-0.00175587)
found -2.44 (+-0.00020563) 99.7339 (+-0.505482) 10.0003 (+-0.00175612)
found 2.6 (+-0.000205269) 99.7334 (+-0.505381) 10.0003 (+-0.00175577)
found -7.72 (+-0.000205353) 99.7336 (+-0.505408) 10.0003 (+-0.00175587)
found -6.04 (+-0.000205062) 99.7326 (+-0.50531) 10.0002 (+-0.00175553)
found 2.12 (+-0.000205765) 99.7342 (+-0.505521) 10.0004 (+-0.00175626)
found 3.08 (+-0.000204729) 99.7321 (+-0.505216) 10.0002 (+-0.0017552)
found 4.52 (+-0.000204729) 99.7321 (+-0.505216) 10.0002 (+-0.0017552)
found -9.4 (+-0.000216809) 89.7606 (+-0.479556) 9.00031 (+-0.00166605)
found -3.4 (+-0.000217184) 89.7614 (+-0.479659) 9.0004 (+-0.00166641)
found -3.16 (+-0.000217508) 89.7622 (+-0.47975) 9.00048 (+-0.00166673)
found -2.92 (+-0.000217508) 89.7622 (+-0.47975) 9.00048 (+-0.00166673)
found -2.68 (+-0.000217601) 89.7625 (+-0.479777) 9.0005 (+-0.00166682)
found 2.36 (+-0.000217694) 89.7627 (+-0.479804) 9.00053 (+-0.00166691)
found 9.8 (+-0.000215082) 89.7614 (+-0.479163) 9.00039 (+-0.00166469)
found -5.32 (+-0.000216309) 89.7598 (+-0.479427) 9.00024 (+-0.0016656)
found 0.2 (+-0.000216733) 89.7604 (+-0.479533) 9.00029 (+-0.00166597)
found 0.92 (+-0.000215748) 89.7588 (+-0.479275) 9.00013 (+-0.00166508)
found 4.04 (+-0.000215225) 89.7582 (+-0.479147) 9.00008 (+-0.00166463)
found 1.64 (+-0.000230309) 79.7878 (+-0.452215) 8.00035 (+-0.00157107)
found 7.88 (+-0.000230334) 79.7878 (+-0.45222) 8.00034 (+-0.00157108)
found -9.64 (+-0.000229821) 79.7873 (+-0.452105) 8.00029 (+-0.00157068)
found -6.76 (+-0.000229123) 79.7862 (+-0.451942) 8.00018 (+-0.00157012)
found -4.6 (+-0.000229403) 79.7862 (+-0.451994) 8.00018 (+-0.0015703)
found 1.4 (+-0.000229711) 79.7871 (+-0.452077) 8.00027 (+-0.00157059)
found 3.56 (+-0.000228821) 79.7857 (+-0.451867) 8.00013 (+-0.00156986)
found 7.16 (+-0.000228394) 79.7852 (+-0.451769) 8.00008 (+-0.00156952)
found 9.08 (+-0.000228972) 79.7857 (+-0.451895) 8.00013 (+-0.00156996)
found -5.08 (+-0.000246181) 69.8145 (+-0.423007) 7.00032 (+-0.00146959)
found 8.12 (+-0.000246057) 69.8143 (+-0.422979) 7.00029 (+-0.00146949)
found -1.72 (+-0.000245832) 69.8137 (+-0.422925) 7.00024 (+-0.00146931)
found -0.279999 (+-0.000245348) 69.8132 (+-0.422827) 7.00019 (+-0.00146897)
found 0.439998 (+-0.000266215) 59.8415 (+-0.391687) 6.00032 (+-0.00136078)
found -6.52 (+-0.000266529) 59.8418 (+-0.391742) 6.00035 (+-0.00136097)
found -3.64 (+-0.000266215) 59.8415 (+-0.391687) 6.00032 (+-0.00136078)
found -0.76 (+-0.000264807) 59.8396 (+-0.391424) 6.00013 (+-0.00135987)
found 6.44 (+-0.000265565) 59.8404 (+-0.39156) 6.00021 (+-0.00136034)
found 9.56 (+-0.000266215) 59.8415 (+-0.391687) 6.00032 (+-0.00136078)
found 7.64 (+-0.000265763) 59.841 (+-0.391605) 6.00027 (+-0.0013605)
found -6.28 (+-0.000292973) 49.8695 (+-0.357774) 5.00043 (+-0.00124296)
found -1.48 (+-0.000292237) 49.8684 (+-0.357653) 5.00032 (+-0.00124254)
found -1.24 (+-0.000291281) 49.8674 (+-0.357501) 5.00021 (+-0.00124202)
found -0.0399992 (+-0.000293014) 49.8695 (+-0.357779) 5.00043 (+-0.00124298)
found 1.88 (+-0.000293359) 49.87 (+-0.357837) 5.00048 (+-0.00124318)
found 6.2 (+-0.000292037) 49.8682 (+-0.35762) 5.00029 (+-0.00124243)
found 5.96 (+-0.000290412) 49.8668 (+-0.357378) 5.00016 (+-0.00124159)
found -9.16 (+-0.000326096) 39.8949 (+-0.319832) 4.00027 (+-0.00111115)
found -2.2 (+-0.000327707) 39.8959 (+-0.320026) 4.00037 (+-0.00111182)
found 3.32 (+-0.000328764) 39.897 (+-0.320163) 4.00048 (+-0.0011123)
found 4.76 (+-0.000326911) 39.8954 (+-0.319929) 4.00032 (+-0.00111148)
found -8.44 (+-0.000325476) 39.8938 (+-0.319737) 4.00016 (+-0.00111081)
found -4.36 (+-0.000326511) 39.8949 (+-0.319874) 4.00027 (+-0.00111129)
found -1.96 (+-0.00032708) 39.8951 (+-0.319941) 4.00029 (+-0.00111152)
found -8.68 (+-0.000324861) 39.8935 (+-0.319667) 4.00013 (+-0.00111057)
found 5.48 (+-0.000324084) 39.893 (+-0.31957) 4.00008 (+-0.00111024)
found -5.8 (+-0.000378486) 29.9224 (+-0.277166) 3.00032 (+-0.00096292)
found 9.32 (+-0.000379766) 29.9229 (+-0.277284) 3.00037 (+-0.000963329)
found -4.84 (+-0.000380087) 29.9231 (+-0.277317) 3.0004 (+-0.000963442)
found -0.999999 (+-0.000378801) 29.9221 (+-0.277187) 3.00029 (+-0.00096299)
found 0.680002 (+-0.000380035) 29.9231 (+-0.277312) 3.0004 (+-0.000963426)
found 6.68 (+-0.000376584) 29.921 (+-0.276981) 3.00019 (+-0.000962276)
found 8.36 (+-0.000378256) 29.9218 (+-0.277135) 3.00027 (+-0.000962811)
found -3.88 (+-0.000377374) 29.9213 (+-0.27705) 3.00021 (+-0.000962516)
found 8.6 (+-0.000376174) 29.9205 (+-0.276931) 3.00014 (+-0.000962103)
found -7.24001 (+-0.000464357) 19.9491 (+-0.22637) 2.0003 (+-0.000786445)
found 1.16 (+-0.00046863) 19.9506 (+-0.226645) 2.00045 (+-0.000787401)
found 2.84 (+-0.000469698) 19.9514 (+-0.226722) 2.00053 (+-0.000787666)
found 4.28 (+-0.000469352) 19.9512 (+-0.226697) 2.00051 (+-0.00078758)
found 7.4 (+-0.00046739) 19.9498 (+-0.226558) 2.00037 (+-0.000787099)
found -0.519999 (+-0.000466982) 19.9496 (+-0.226529) 2.00035 (+-0.000786999)
found -8.2 (+-0.000462879) 19.9477 (+-0.226254) 2.00016 (+-0.00078604)
found -7.95999 (+-0.000465498) 19.9493 (+-0.226439) 2.00032 (+-0.000786684)
found -5.55999 (+-0.000465991) 19.9493 (+-0.226467) 2.00032 (+-0.000786781)
found -4.12 (+-0.000463698) 19.948 (+-0.226306) 2.00019 (+-0.000786224)
found 5 (+-0.000462879) 19.9477 (+-0.226254) 2.00016 (+-0.00078604)
found 5.24 (+-0.000462879) 19.9477 (+-0.226254) 2.00016 (+-0.00078604)
found 8.84 (+-0.000465622) 19.949 (+-0.22644) 2.00029 (+-0.000786689)
found -9.87999 (+-0.000463524) 19.9483 (+-0.226291) 2.00022 (+-0.000786172)
found 3.8 (+-0.000669579) 9.97758 (+-0.160514) 1.00045 (+-0.000557652)
found -8.92 (+-0.000662213) 9.97519 (+-0.160244) 1.00021 (+-0.000556711)
found 5.72 (+-0.000663231) 9.97545 (+-0.16028) 1.00024 (+-0.000556838)
found 6.92001 (+-0.000664316) 9.97599 (+-0.160324) 1.0003 (+-0.000556991)
found -6.99999 (+-0.000662751) 9.97575 (+-0.160272) 1.00027 (+-0.00055681)

 
#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.