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 99.7356 10
created -9.64 59.8413 6
created -9.4 99.7356 10
created -9.16 19.9471 2
created -8.92 59.8413 6
created -8.68 29.9207 3
created -8.44 69.8149 7
created -8.2 79.7885 8
created -7.96 49.8678 5
created -7.72 59.8413 6
created -7.48 69.8149 7
created -7.24 69.8149 7
created -7 39.8942 4
created -6.76 89.762 9
created -6.52 9.97356 1
created -6.28 69.8149 7
created -6.04 89.762 9
created -5.8 39.8942 4
created -5.56 59.8413 6
created -5.32 9.97356 1
created -5.08 19.9471 2
created -4.84 79.7885 8
created -4.6 89.762 9
created -4.36 9.97356 1
created -4.12 39.8942 4
created -3.88 39.8942 4
created -3.64 29.9207 3
created -3.4 69.8149 7
created -3.16 9.97356 1
created -2.92 69.8149 7
created -2.68 89.762 9
created -2.44 29.9207 3
created -2.2 29.9207 3
created -1.96 59.8413 6
created -1.72 29.9207 3
created -1.48 59.8413 6
created -1.24 39.8942 4
created -1 39.8942 4
created -0.76 69.8149 7
created -0.52 39.8942 4
created -0.28 79.7885 8
created -0.04 49.8678 5
created 0.2 99.7356 10
created 0.44 49.8678 5
created 0.68 79.7885 8
created 0.92 89.762 9
created 1.16 39.8942 4
created 1.4 19.9471 2
created 1.64 49.8678 5
created 1.88 99.7356 10
created 2.12 89.762 9
created 2.36 49.8678 5
created 2.6 99.7356 10
created 2.84 89.762 9
created 3.08 29.9207 3
created 3.32 59.8413 6
created 3.56 9.97356 1
created 3.8 89.762 9
created 4.04 79.7885 8
created 4.28 9.97356 1
created 4.52 39.8942 4
created 4.76 99.7356 10
created 5 69.8149 7
created 5.24 49.8678 5
created 5.48 99.7356 10
created 5.72 29.9207 3
created 5.96 9.97356 1
created 6.2 39.8942 4
created 6.44 19.9471 2
created 6.68 39.8942 4
created 6.92 99.7356 10
created 7.16 39.8942 4
created 7.4 59.8413 6
created 7.64 79.7885 8
created 7.88 19.9471 2
created 8.12 99.7356 10
created 8.36 59.8413 6
created 8.6 69.8149 7
created 8.84 79.7885 8
created 9.08 79.7885 8
created 9.32 49.8678 5
created 9.56 9.97356 1
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 (+-1.62957e-05)
fit chi^2 = 1.38438e-05
found -9.4 (+-0.000194539) 99.7326 (+-0.479555) 10.0002 (+-0.00166605)
found -9.88 (+-0.000194952) 99.7322 (+-0.47963) 10.0002 (+-0.00166631)
found 0.2 (+-0.000194896) 99.7332 (+-0.479654) 10.0003 (+-0.00166639)
found 1.88 (+-0.000195285) 99.7342 (+-0.479774) 10.0004 (+-0.00166681)
found 2.6 (+-0.000195285) 99.7342 (+-0.479774) 10.0004 (+-0.00166681)
found 4.76 (+-0.00019498) 99.7334 (+-0.479681) 10.0003 (+-0.00166649)
found 5.48 (+-0.000194618) 99.7326 (+-0.479574) 10.0002 (+-0.00166611)
found 6.92 (+-0.000194642) 99.7326 (+-0.479579) 10.0002 (+-0.00166613)
found 8.12 (+-0.000194539) 99.7326 (+-0.479555) 10.0002 (+-0.00166605)
found -6.04 (+-0.000205665) 89.7604 (+-0.455103) 9.00029 (+-0.0015811)
found -4.6 (+-0.000205098) 89.7598 (+-0.454968) 9.00023 (+-0.00158063)
found -2.68 (+-0.0002055) 89.7601 (+-0.45506) 9.00026 (+-0.00158095)
found 0.919999 (+-0.000205766) 89.7606 (+-0.455131) 9.00032 (+-0.0015812)
found 2.12 (+-0.000206088) 89.7615 (+-0.455221) 9.0004 (+-0.00158151)
found 2.84 (+-0.000205782) 89.7609 (+-0.455141) 9.00034 (+-0.00158123)
found -6.76 (+-0.000204631) 89.7588 (+-0.454838) 9.00013 (+-0.00158018)
found 3.8 (+-0.000205098) 89.7598 (+-0.454968) 9.00024 (+-0.00158063)
found -8.2 (+-0.000218466) 79.7876 (+-0.429154) 8.00032 (+-0.00149095)
found 4.04 (+-0.000217789) 79.787 (+-0.42901) 8.00026 (+-0.00149045)
found 7.64 (+-0.000217778) 79.7865 (+-0.428992) 8.00021 (+-0.00149039)
found 8.84 (+-0.000218839) 79.7884 (+-0.429247) 8.0004 (+-0.00149127)
found 9.08 (+-0.000218579) 79.7879 (+-0.429183) 8.00034 (+-0.00149105)
found -4.84 (+-0.000218115) 79.7873 (+-0.429079) 8.00029 (+-0.00149069)
found -0.28 (+-0.000218054) 79.7868 (+-0.429053) 8.00024 (+-0.0014906)
found 0.680001 (+-0.000218684) 79.7881 (+-0.42921) 8.00037 (+-0.00149114)
found 9.8 (+-0.000215596) 79.7868 (+-0.42854) 8.00023 (+-0.00148881)
found -7.48 (+-0.00023393) 69.8148 (+-0.40152) 7.00034 (+-0.00139494)
found -7.24 (+-0.000233603) 69.8143 (+-0.401449) 7.00029 (+-0.0013947)
found 5 (+-0.000234133) 69.8154 (+-0.401568) 7.0004 (+-0.00139511)
found 8.6 (+-0.000234058) 69.8151 (+-0.401548) 7.00037 (+-0.00139504)
found -8.44 (+-0.000233525) 69.8143 (+-0.401436) 7.00029 (+-0.00139465)
found -6.28 (+-0.000233018) 69.814 (+-0.401343) 7.00027 (+-0.00139433)
found -3.4 (+-0.000232115) 69.8124 (+-0.401144) 7.00011 (+-0.00139364)
found -2.92 (+-0.000233018) 69.814 (+-0.401343) 7.00027 (+-0.00139433)
found -0.76 (+-0.000233139) 69.8135 (+-0.401348) 7.00021 (+-0.00139435)
found -9.64 (+-0.000253974) 59.8436 (+-0.371988) 6.00053 (+-0.00129235)
found 8.36 (+-0.000253558) 59.8428 (+-0.371907) 6.00045 (+-0.00129206)
found -7.72 (+-0.000252807) 59.8415 (+-0.371761) 6.00032 (+-0.00129156)
found -5.56 (+-0.000251136) 59.8396 (+-0.371462) 6.00013 (+-0.00129052)
found -1.96 (+-0.000251609) 59.8399 (+-0.371538) 6.00016 (+-0.00129078)
found -1.48 (+-0.000251841) 59.8402 (+-0.37158) 6.00019 (+-0.00129093)
found 3.32 (+-0.000250906) 59.8394 (+-0.37142) 6.00011 (+-0.00129037)
found 7.4 (+-0.000252754) 59.8415 (+-0.371753) 6.00032 (+-0.00129153)
found -8.92 (+-0.00025132) 59.8397 (+-0.371488) 6.00013 (+-0.00129061)
found 0.439999 (+-0.000278418) 49.8701 (+-0.339611) 5.00048 (+-0.00117986)
found 2.36 (+-0.000278581) 49.8703 (+-0.339638) 5.00051 (+-0.00117996)
found -7.96 (+-0.000277737) 49.869 (+-0.339499) 5.00037 (+-0.00117947)
found -0.0399992 (+-0.000278418) 49.87 (+-0.339611) 5.00048 (+-0.00117986)
found 5.24 (+-0.000278242) 49.8698 (+-0.339583) 5.00045 (+-0.00117976)
found 9.32 (+-0.000276185) 49.8677 (+-0.339269) 5.00024 (+-0.00117867)
found 1.64 (+-0.000276984) 49.8685 (+-0.339389) 5.00032 (+-0.00117909)
found -5.8 (+-0.000311383) 39.8962 (+-0.303772) 4.0004 (+-0.00105535)
found 1.16 (+-0.000310077) 39.8952 (+-0.303609) 4.00029 (+-0.00105479)
found 7.16 (+-0.000311568) 39.8965 (+-0.303797) 4.00043 (+-0.00105544)
found -7 (+-0.000311618) 39.8965 (+-0.303803) 4.00043 (+-0.00105546)
found -3.88 (+-0.00030931) 39.8941 (+-0.303502) 4.00019 (+-0.00105441)
found -1.24 (+-0.000310189) 39.8949 (+-0.303614) 4.00027 (+-0.0010548)
found -0.999999 (+-0.000310421) 39.8951 (+-0.303645) 4.00029 (+-0.00105491)
found -0.52 (+-0.000311419) 39.8962 (+-0.303776) 4.0004 (+-0.00105537)
found -4.12 (+-0.000308315) 39.8936 (+-0.303385) 4.00014 (+-0.00105401)
found 4.52 (+-0.000309669) 39.8952 (+-0.303568) 4.0003 (+-0.00105464)
found 6.2 (+-0.000307578) 39.893 (+-0.303294) 4.00008 (+-0.00105369)
found 6.68 (+-0.00031026) 39.8954 (+-0.303634) 4.00032 (+-0.00105487)
found -2.44 (+-0.000359517) 29.9224 (+-0.263075) 3.00032 (+-0.000913964)
found 3.08 (+-0.000360679) 29.9232 (+-0.263188) 3.0004 (+-0.000914357)
found 5.71999 (+-0.000358454) 29.9221 (+-0.262984) 3.0003 (+-0.000913648)
found -8.68 (+-0.000360147) 29.9226 (+-0.263133) 3.00035 (+-0.000914166)
found -1.72 (+-0.000359844) 29.9223 (+-0.263103) 3.00032 (+-0.000914059)
found -3.64 (+-0.00035943) 29.9221 (+-0.263062) 3.00029 (+-0.00091392)
found -2.2 (+-0.00035869) 29.9216 (+-0.262989) 3.00024 (+-0.000913667)
found -9.16 (+-0.000444265) 19.9504 (+-0.215068) 2.00043 (+-0.00074718)
found 7.88 (+-0.000445089) 19.9509 (+-0.215125) 2.00048 (+-0.000747379)
found 1.4 (+-0.000441261) 19.9485 (+-0.214859) 2.00024 (+-0.000746455)
found 6.44 (+-0.000440717) 19.9482 (+-0.214822) 2.00021 (+-0.000746326)
found -5.07999 (+-0.000440042) 19.9485 (+-0.214792) 2.00024 (+-0.00074622)
found -6.52 (+-0.000634814) 9.97732 (+-0.152314) 1.00043 (+-0.000529163)
found -4.36001 (+-0.000632249) 9.97653 (+-0.152222) 1.00035 (+-0.000528843)
found 4.27999 (+-0.000631658) 9.97627 (+-0.152199) 1.00032 (+-0.000528763)
found -3.16 (+-0.00063356) 9.97679 (+-0.152266) 1.00037 (+-0.000528997)
found -5.32001 (+-0.000627634) 9.9752 (+-0.152057) 1.00022 (+-0.00052827)
found 3.56001 (+-0.000634074) 9.97706 (+-0.152287) 1.0004 (+-0.000529069)
found 9.56001 (+-0.000632637) 9.97654 (+-0.152234) 1.00035 (+-0.000528883)
found 5.96 (+-0.000627323) 9.97493 (+-0.152042) 1.00019 (+-0.000528216)

 
#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.