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 9.97356 1
created -9.4 79.7885 8
created -9.16 79.7885 8
created -8.92 69.8149 7
created -8.68 69.8149 7
created -8.44 89.762 9
created -8.2 69.8149 7
created -7.96 69.8149 7
created -7.72 29.9207 3
created -7.48 9.97356 1
created -7.24 39.8942 4
created -7 99.7356 10
created -6.76 29.9207 3
created -6.52 19.9471 2
created -6.28 99.7356 10
created -6.04 99.7356 10
created -5.8 89.762 9
created -5.56 59.8413 6
created -5.32 49.8678 5
created -5.08 19.9471 2
created -4.84 69.8149 7
created -4.6 69.8149 7
created -4.36 69.8149 7
created -4.12 79.7885 8
created -3.88 59.8413 6
created -3.64 69.8149 7
created -3.4 79.7885 8
created -3.16 49.8678 5
created -2.92 89.762 9
created -2.68 49.8678 5
created -2.44 79.7885 8
created -2.2 49.8678 5
created -1.96 69.8149 7
created -1.72 89.762 9
created -1.48 89.762 9
created -1.24 29.9207 3
created -1 9.97356 1
created -0.76 79.7885 8
created -0.52 29.9207 3
created -0.28 19.9471 2
created -0.04 9.97356 1
created 0.2 19.9471 2
created 0.44 99.7356 10
created 0.68 69.8149 7
created 0.92 9.97356 1
created 1.16 89.762 9
created 1.4 79.7885 8
created 1.64 49.8678 5
created 1.88 99.7356 10
created 2.12 99.7356 10
created 2.36 19.9471 2
created 2.6 79.7885 8
created 2.84 9.97356 1
created 3.08 19.9471 2
created 3.32 49.8678 5
created 3.56 39.8942 4
created 3.8 19.9471 2
created 4.04 69.8149 7
created 4.28 49.8678 5
created 4.52 99.7356 10
created 4.76 39.8942 4
created 5 69.8149 7
created 5.24 49.8678 5
created 5.48 89.762 9
created 5.72 39.8942 4
created 5.96 89.762 9
created 6.2 49.8678 5
created 6.44 89.762 9
created 6.68 19.9471 2
created 6.92 19.9471 2
created 7.16 79.7885 8
created 7.4 59.8413 6
created 7.64 49.8678 5
created 7.88 29.9207 3
created 8.12 49.8678 5
created 8.36 49.8678 5
created 8.6 89.762 9
created 8.84 69.8149 7
created 9.08 9.97356 1
created 9.32 59.8413 6
created 9.56 19.9471 2
created 9.8 79.7885 8
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.61719e-05)
fit chi^2 = 1.37561e-05
found -6.04 (+-0.000195135) 99.7355 (+-0.478397) 10.0005 (+-0.00166203)
found 2.12 (+-0.000194275) 99.7337 (+-0.478145) 10.0003 (+-0.00166115)
found -7 (+-0.000193875) 99.7323 (+-0.478015) 10.0002 (+-0.0016607)
found -6.28 (+-0.000194275) 99.7337 (+-0.478145) 10.0003 (+-0.00166115)
found 0.440001 (+-0.000194021) 99.7328 (+-0.478065) 10.0002 (+-0.00166087)
found 1.88 (+-0.000194745) 99.7345 (+-0.478277) 10.0004 (+-0.00166161)
found 4.52 (+-0.000194151) 99.7329 (+-0.478095) 10.0002 (+-0.00166098)
found -8.44 (+-0.000205385) 89.7612 (+-0.45376) 9.00037 (+-0.00157644)
found -5.8 (+-0.000205557) 89.7617 (+-0.453811) 9.00043 (+-0.00157661)
found -1.72 (+-0.000205579) 89.7617 (+-0.453816) 9.00042 (+-0.00157663)
found -1.48 (+-0.000205042) 89.7606 (+-0.453672) 9.00032 (+-0.00157613)
found -2.92 (+-0.00020492) 89.7601 (+-0.453632) 9.00027 (+-0.00157599)
found 1.16 (+-0.000204448) 89.7598 (+-0.453525) 9.00024 (+-0.00157562)
found 5.48 (+-0.000204782) 89.7598 (+-0.453595) 9.00024 (+-0.00157586)
found 5.96 (+-0.000204782) 89.7598 (+-0.453595) 9.00024 (+-0.00157586)
found 6.44 (+-0.000204411) 89.7593 (+-0.453501) 9.00018 (+-0.00157553)
found 8.6 (+-0.000205152) 89.7606 (+-0.453696) 9.00032 (+-0.00157621)
found -9.16 (+-0.000218145) 79.7884 (+-0.427885) 8.0004 (+-0.00148654)
found -4.12 (+-0.000217909) 79.7878 (+-0.427826) 8.00035 (+-0.00148634)
found -3.4 (+-0.000217773) 79.7876 (+-0.427793) 8.00032 (+-0.00148622)
found 1.4 (+-0.00021799) 79.7881 (+-0.427848) 8.00037 (+-0.00148641)
found -9.4 (+-0.000216995) 79.7868 (+-0.427622) 8.00024 (+-0.00148563)
found -2.44 (+-0.000217516) 79.7871 (+-0.427729) 8.00027 (+-0.001486)
found -0.76 (+-0.000216299) 79.7854 (+-0.42745) 8.0001 (+-0.00148503)
found 2.6 (+-0.000216073) 79.7852 (+-0.427399) 8.00008 (+-0.00148485)
found 7.16 (+-0.000217087) 79.7865 (+-0.427632) 8.00021 (+-0.00148566)
found 9.8 (+-0.000215228) 79.787 (+-0.427249) 8.00026 (+-0.00148433)
found -8.92 (+-0.000233454) 69.8153 (+-0.400305) 7.0004 (+-0.00139072)
found -8.68 (+-0.000233573) 69.8156 (+-0.400332) 7.00043 (+-0.00139082)
found -8.2 (+-0.000233573) 69.8156 (+-0.400332) 7.00043 (+-0.00139082)
found -7.96 (+-0.000232658) 69.814 (+-0.400134) 7.00026 (+-0.00139013)
found -4.6 (+-0.000233327) 69.8151 (+-0.400277) 7.00037 (+-0.00139062)
found -4.36 (+-0.000233454) 69.8153 (+-0.400305) 7.0004 (+-0.00139072)
found -3.64 (+-0.000233315) 69.8151 (+-0.400275) 7.00037 (+-0.00139062)
found 0.679998 (+-0.000232389) 69.8143 (+-0.400096) 7.00029 (+-0.00139)
found 8.84 (+-0.000232279) 69.814 (+-0.40007) 7.00027 (+-0.00138991)
found -4.84 (+-0.000232402) 69.8137 (+-0.400083) 7.00024 (+-0.00138995)
found -1.96 (+-0.00023328) 69.8151 (+-0.400268) 7.00037 (+-0.0013906)
found 4.04 (+-0.000232114) 69.8132 (+-0.400019) 7.00019 (+-0.00138973)
found 5 (+-0.000232572) 69.8138 (+-0.400112) 7.00024 (+-0.00139005)
found -5.56 (+-0.000252288) 59.842 (+-0.370637) 6.00037 (+-0.00128765)
found -3.88 (+-0.000252489) 59.8423 (+-0.370675) 6.0004 (+-0.00128778)
found 7.4 (+-0.000252152) 59.8417 (+-0.37061) 6.00034 (+-0.00128756)
found 9.32 (+-0.000249824) 59.8391 (+-0.370192) 6.00008 (+-0.0012861)
found -2.68 (+-0.000277383) 49.8698 (+-0.338509) 5.00045 (+-0.00117603)
found 6.2 (+-0.000277546) 49.87 (+-0.338536) 5.00048 (+-0.00117613)
found -5.32 (+-0.000275436) 49.8674 (+-0.3382) 5.00021 (+-0.00117496)
found -3.16 (+-0.000277383) 49.8698 (+-0.338509) 5.00045 (+-0.00117603)
found -2.2 (+-0.000277046) 49.8692 (+-0.338453) 5.0004 (+-0.00117584)
found 1.64 (+-0.000277535) 49.87 (+-0.338534) 5.00048 (+-0.00117612)
found 4.28 (+-0.00027736) 49.8698 (+-0.338505) 5.00045 (+-0.00117602)
found 5.24 (+-0.000277208) 49.8695 (+-0.33848) 5.00043 (+-0.00117593)
found 7.64 (+-0.000275776) 49.8676 (+-0.33825) 5.00024 (+-0.00117513)
found 8.12 (+-0.000275569) 49.8674 (+-0.338217) 5.00021 (+-0.00117502)
found 8.36 (+-0.000276808) 49.869 (+-0.338416) 5.00037 (+-0.00117571)
found 3.32 (+-0.000274998) 49.8668 (+-0.33813) 5.00016 (+-0.00117472)
found 4.76 (+-0.000310814) 39.8967 (+-0.302864) 4.00045 (+-0.0010522)
found 5.72 (+-0.000311043) 39.897 (+-0.302895) 4.00048 (+-0.0010523)
found 3.56 (+-0.000308204) 39.8941 (+-0.302526) 4.00019 (+-0.00105102)
found -9.88 (+-0.000306516) 39.8925 (+-0.302295) 4.00003 (+-0.00105022)
found -7.24 (+-0.000308687) 39.8951 (+-0.302605) 4.00029 (+-0.0010513)
found -6.76 (+-0.00035807) 29.9224 (+-0.262215) 3.00032 (+-0.000910978)
found -1.24 (+-0.000357086) 29.9219 (+-0.262125) 3.00027 (+-0.000910665)
found -0.520004 (+-0.000357588) 29.9218 (+-0.262164) 3.00027 (+-0.000910801)
found -7.72 (+-0.000356567) 29.9213 (+-0.262071) 3.00022 (+-0.000910475)
found 7.88 (+-0.000358034) 29.9218 (+-0.262201) 3.00027 (+-0.000910928)
found 2.36 (+-0.000443678) 19.9509 (+-0.214443) 2.00048 (+-0.000745009)
found 6.67999 (+-0.000440068) 19.9491 (+-0.214201) 2.0003 (+-0.000744167)
found -5.08 (+-0.000441311) 19.9493 (+-0.214277) 2.00032 (+-0.000744433)
found 9.56 (+-0.000442178) 19.9498 (+-0.214337) 2.00038 (+-0.000744641)
found -6.51999 (+-0.000441175) 19.9496 (+-0.214274) 2.00035 (+-0.000744422)
found -0.280002 (+-0.000436225) 19.9472 (+-0.213942) 2.00011 (+-0.000743268)
found 3.8 (+-0.000440763) 19.949 (+-0.21424) 2.00029 (+-0.000744305)
found 6.92001 (+-0.000439721) 19.9488 (+-0.214175) 2.00027 (+-0.000744079)
found 0.200009 (+-0.000439309) 19.9491 (+-0.214159) 2.0003 (+-0.000744023)
found 3.08 (+-0.000437382) 19.9477 (+-0.214021) 2.00016 (+-0.000743542)
found 2.83999 (+-0.000627002) 9.97573 (+-0.151627) 1.00027 (+-0.000526775)
found 0.920004 (+-0.0006328) 9.97732 (+-0.151831) 1.00043 (+-0.000527485)
found 9.08 (+-0.000630817) 9.97652 (+-0.151756) 1.00035 (+-0.000527225)
found -9.63999 (+-0.000629656) 9.97625 (+-0.151716) 1.00032 (+-0.000527086)
found -7.48 (+-0.000625334) 9.97493 (+-0.151559) 1.00019 (+-0.000526541)
found -0.99999 (+-0.000628481) 9.976 (+-0.151676) 1.0003 (+-0.000526946)
found -0.0399999 (+-0.00062129) 9.97413 (+-0.151421) 1.00011 (+-0.000526059)

 
#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.