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 69.8149 7
created -9.16 19.9471 2
created -8.92 19.9471 2
created -8.68 79.7885 8
created -8.44 69.8149 7
created -8.2 39.8942 4
created -7.96 89.762 9
created -7.72 9.97356 1
created -7.48 99.7356 10
created -7.24 39.8942 4
created -7 19.9471 2
created -6.76 29.9207 3
created -6.52 89.762 9
created -6.28 89.762 9
created -6.04 19.9471 2
created -5.8 49.8678 5
created -5.56 29.9207 3
created -5.32 9.97356 1
created -5.08 89.762 9
created -4.84 69.8149 7
created -4.6 69.8149 7
created -4.36 99.7356 10
created -4.12 19.9471 2
created -3.88 39.8942 4
created -3.64 99.7356 10
created -3.4 59.8413 6
created -3.16 29.9207 3
created -2.92 99.7356 10
created -2.68 99.7356 10
created -2.44 59.8413 6
created -2.2 39.8942 4
created -1.96 19.9471 2
created -1.72 49.8678 5
created -1.48 59.8413 6
created -1.24 99.7356 10
created -1 99.7356 10
created -0.76 9.97356 1
created -0.52 29.9207 3
created -0.28 59.8413 6
created -0.04 29.9207 3
created 0.2 69.8149 7
created 0.44 59.8413 6
created 0.68 79.7885 8
created 0.92 99.7356 10
created 1.16 69.8149 7
created 1.4 49.8678 5
created 1.64 19.9471 2
created 1.88 39.8942 4
created 2.12 39.8942 4
created 2.36 59.8413 6
created 2.6 9.97356 1
created 2.84 69.8149 7
created 3.08 79.7885 8
created 3.32 99.7356 10
created 3.56 69.8149 7
created 3.8 39.8942 4
created 4.04 49.8678 5
created 4.28 99.7356 10
created 4.52 49.8678 5
created 4.76 19.9471 2
created 5 69.8149 7
created 5.24 9.97356 1
created 5.48 69.8149 7
created 5.72 49.8678 5
created 5.96 99.7356 10
created 6.2 59.8413 6
created 6.44 29.9207 3
created 6.68 69.8149 7
created 6.92 99.7356 10
created 7.16 39.8942 4
created 7.4 39.8942 4
created 7.64 9.97356 1
created 7.88 9.97356 1
created 8.12 79.7885 8
created 8.36 9.97356 1
created 8.6 29.9207 3
created 8.84 49.8678 5
created 9.08 99.7356 10
created 9.32 9.97356 1
created 9.56 69.8149 7
created 9.8 99.7356 10
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.83348e-05)
fit chi^2 = 1.70948e-05
found -4.36 (+-0.000216288) 99.7329 (+-0.53293) 10.0002 (+-0.00185148)
found -2.68 (+-0.00021722) 99.7347 (+-0.533205) 10.0004 (+-0.00185244)
found -1.24 (+-0.00021722) 99.7347 (+-0.533205) 10.0004 (+-0.00185244)
found -1 (+-0.000216274) 99.7334 (+-0.532943) 10.0003 (+-0.00185153)
found 0.92 (+-0.000217148) 99.7345 (+-0.533181) 10.0004 (+-0.00185235)
found 3.32 (+-0.000217148) 99.7345 (+-0.533181) 10.0004 (+-0.00185235)
found 6.92 (+-0.000216668) 99.7334 (+-0.533036) 10.0003 (+-0.00185185)
found 9.8 (+-0.000215228) 99.7349 (+-0.532687) 10.0004 (+-0.00185064)
found -7.48 (+-0.000215619) 99.7318 (+-0.53274) 10.0001 (+-0.00185082)
found -3.64 (+-0.000216556) 99.7331 (+-0.533002) 10.0003 (+-0.00185173)
found -2.92 (+-0.000216784) 99.7339 (+-0.533078) 10.0003 (+-0.001852)
found 4.28 (+-0.000216574) 99.7331 (+-0.533006) 10.0003 (+-0.00185175)
found 5.96 (+-0.000216698) 99.7334 (+-0.533043) 10.0003 (+-0.00185188)
found 9.08 (+-0.000215759) 99.7321 (+-0.532782) 10.0002 (+-0.00185097)
found -6.28 (+-0.000228342) 89.7604 (+-0.505681) 9.00029 (+-0.00175681)
found -7.96 (+-0.000227392) 89.7588 (+-0.505429) 9.00013 (+-0.00175594)
found -6.52 (+-0.000228573) 89.7606 (+-0.505738) 9.00032 (+-0.00175701)
found -5.08 (+-0.0002278) 89.7595 (+-0.505542) 9.00021 (+-0.00175633)
found 0.680001 (+-0.000243269) 79.7887 (+-0.477016) 8.00043 (+-0.00165723)
found 3.08 (+-0.000243406) 79.7889 (+-0.47705) 8.00045 (+-0.00165735)
found -9.64 (+-0.000242137) 79.7868 (+-0.476743) 8.00023 (+-0.00165628)
found -8.68 (+-0.000242137) 79.7868 (+-0.476743) 8.00024 (+-0.00165628)
found 8.12 (+-0.000240515) 79.7849 (+-0.476373) 8.00004 (+-0.001655)
found -9.4 (+-0.000259214) 69.814 (+-0.446031) 7.00026 (+-0.00154958)
found -8.44 (+-0.000259727) 69.8145 (+-0.446134) 7.00032 (+-0.00154994)
found -4.84 (+-0.000260379) 69.8156 (+-0.446277) 7.00043 (+-0.00155044)
found -4.6 (+-0.000260503) 69.8159 (+-0.446305) 7.00045 (+-0.00155053)
found 1.16 (+-0.000260176) 69.8153 (+-0.446234) 7.0004 (+-0.00155029)
found 3.56 (+-0.000259982) 69.8151 (+-0.446193) 7.00037 (+-0.00155014)
found 0.200001 (+-0.000259206) 69.8138 (+-0.446021) 7.00024 (+-0.00154955)
found 2.84 (+-0.000258807) 69.8138 (+-0.445954) 7.00024 (+-0.00154932)
found 5 (+-0.000257653) 69.8121 (+-0.445707) 7.00008 (+-0.00154846)
found 5.48 (+-0.000258347) 69.813 (+-0.445851) 7.00016 (+-0.00154896)
found 6.68 (+-0.000259754) 69.8148 (+-0.446146) 7.00035 (+-0.00154998)
found 9.56 (+-0.000259059) 69.8144 (+-0.446013) 7.0003 (+-0.00154952)
found -3.4 (+-0.0002809) 59.8418 (+-0.413114) 6.00035 (+-0.00143522)
found -2.44 (+-0.000281161) 59.842 (+-0.413161) 6.00037 (+-0.00143539)
found 6.2 (+-0.0002809) 59.8418 (+-0.413114) 6.00035 (+-0.00143522)
found -1.48 (+-0.000281384) 59.8423 (+-0.413202) 6.0004 (+-0.00143553)
found -0.28 (+-0.000279596) 59.8399 (+-0.412865) 6.00016 (+-0.00143436)
found 0.44 (+-0.000281466) 59.8423 (+-0.413215) 6.0004 (+-0.00143557)
found 2.36 (+-0.00027907) 59.8396 (+-0.41278) 6.00013 (+-0.00143406)
found 4.52 (+-0.000307792) 49.8685 (+-0.377139) 5.00032 (+-0.00131024)
found 1.4 (+-0.000307256) 49.8677 (+-0.377048) 5.00024 (+-0.00130993)
found 4.04 (+-0.000308481) 49.869 (+-0.377241) 5.00037 (+-0.0013106)
found 5.72 (+-0.000309191) 49.8698 (+-0.377354) 5.00045 (+-0.00131099)
found 8.84 (+-0.000308174) 49.8687 (+-0.377195) 5.00035 (+-0.00131043)
found -5.8 (+-0.000306257) 49.8666 (+-0.376889) 5.00013 (+-0.00130937)
found -1.72 (+-0.000307047) 49.8674 (+-0.377014) 5.00021 (+-0.00130981)
found -7.24 (+-0.00034477) 39.8954 (+-0.337407) 4.00032 (+-0.00117221)
found 7.16 (+-0.00034561) 39.8959 (+-0.33751) 4.00037 (+-0.00117256)
found -8.2 (+-0.000346279) 39.8965 (+-0.337595) 4.00043 (+-0.00117286)
found -2.2 (+-0.000343858) 39.8943 (+-0.337283) 4.00021 (+-0.00117177)
found 2.12 (+-0.000344691) 39.8949 (+-0.337385) 4.00027 (+-0.00117213)
found 3.8 (+-0.000345272) 39.8954 (+-0.337461) 4.00032 (+-0.00117239)
found 7.4 (+-0.000342609) 39.8935 (+-0.337131) 4.00013 (+-0.00117125)
found -3.88 (+-0.00034477) 39.8954 (+-0.337407) 4.00032 (+-0.00117221)
found 1.88 (+-0.000343257) 39.8938 (+-0.337205) 4.00016 (+-0.0011715)
found -5.56 (+-0.000396792) 29.9208 (+-0.292076) 3.00016 (+-0.00101472)
found -3.16 (+-0.000401059) 29.9234 (+-0.29249) 3.00043 (+-0.00101616)
found -0.0399993 (+-0.000400206) 29.9226 (+-0.292402) 3.00035 (+-0.00101585)
found 6.44 (+-0.000400206) 29.9226 (+-0.292402) 3.00035 (+-0.00101585)
found -6.76 (+-0.000398905) 29.9221 (+-0.292281) 3.0003 (+-0.00101543)
found -0.519997 (+-0.000397158) 29.921 (+-0.292114) 3.00019 (+-0.00101485)
found 8.6 (+-0.000396791) 29.9208 (+-0.292076) 3.00016 (+-0.00101472)
found -6.04 (+-0.00049278) 19.9498 (+-0.238927) 2.00037 (+-0.000830071)
found -4.12001 (+-0.000492525) 19.9499 (+-0.238912) 2.00038 (+-0.000830019)
found -9.16 (+-0.000489762) 19.9485 (+-0.238725) 2.00024 (+-0.000829368)
found 1.64 (+-0.000490343) 19.9485 (+-0.238758) 2.00024 (+-0.000829483)
found 4.76 (+-0.000491958) 19.9493 (+-0.238869) 2.00032 (+-0.000829868)
found -8.91999 (+-0.000490186) 19.9488 (+-0.238755) 2.00027 (+-0.000829474)
found -7 (+-0.000489031) 19.948 (+-0.23867) 2.00019 (+-0.000829177)
found -1.96 (+-0.000490343) 19.9485 (+-0.238758) 2.00024 (+-0.000829483)
found -9.87999 (+-0.000488848) 19.9483 (+-0.238654) 2.00022 (+-0.000829122)
found -0.760014 (+-0.000701859) 9.97654 (+-0.169132) 1.00035 (+-0.000587592)
found 9.31999 (+-0.000706032) 9.97759 (+-0.16928) 1.00046 (+-0.000588105)
found -7.72 (+-0.000707438) 9.97812 (+-0.169333) 1.00051 (+-0.000588289)
found 8.35999 (+-0.000700609) 9.97599 (+-0.169083) 1.00029 (+-0.000587421)
found 5.24 (+-0.000704031) 9.97678 (+-0.169203) 1.00037 (+-0.000587838)
found 2.6 (+-0.000703212) 9.97652 (+-0.169173) 1.00035 (+-0.000587732)
found 7.63999 (+-0.000693226) 9.97441 (+-0.168826) 1.00014 (+-0.000586527)
found -5.31999 (+-0.000701261) 9.97627 (+-0.169109) 1.00032 (+-0.00058751)
found 7.88001 (+-0.00069668) 9.97548 (+-0.168955) 1.00024 (+-0.000586978)

 
#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.