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 79.7885 8
created -9.64 89.762 9
created -9.4 69.8149 7
created -9.16 79.7885 8
created -8.92 19.9471 2
created -8.68 9.97356 1
created -8.44 79.7885 8
created -8.2 49.8678 5
created -7.96 99.7356 10
created -7.72 39.8942 4
created -7.48 79.7885 8
created -7.24 99.7356 10
created -7 89.762 9
created -6.76 19.9471 2
created -6.52 59.8413 6
created -6.28 99.7356 10
created -6.04 99.7356 10
created -5.8 99.7356 10
created -5.56 59.8413 6
created -5.32 39.8942 4
created -5.08 79.7885 8
created -4.84 89.762 9
created -4.6 89.762 9
created -4.36 79.7885 8
created -4.12 19.9471 2
created -3.88 29.9207 3
created -3.64 59.8413 6
created -3.4 9.97356 1
created -3.16 49.8678 5
created -2.92 69.8149 7
created -2.68 89.762 9
created -2.44 19.9471 2
created -2.2 39.8942 4
created -1.96 19.9471 2
created -1.72 49.8678 5
created -1.48 99.7356 10
created -1.24 39.8942 4
created -1 99.7356 10
created -0.76 19.9471 2
created -0.52 19.9471 2
created -0.28 79.7885 8
created -0.04 39.8942 4
created 0.2 49.8678 5
created 0.44 69.8149 7
created 0.68 59.8413 6
created 0.92 39.8942 4
created 1.16 89.762 9
created 1.4 89.762 9
created 1.64 69.8149 7
created 1.88 89.762 9
created 2.12 69.8149 7
created 2.36 49.8678 5
created 2.6 39.8942 4
created 2.84 49.8678 5
created 3.08 9.97356 1
created 3.32 39.8942 4
created 3.56 99.7356 10
created 3.8 59.8413 6
created 4.04 99.7356 10
created 4.28 39.8942 4
created 4.52 69.8149 7
created 4.76 79.7885 8
created 5 29.9207 3
created 5.24 69.8149 7
created 5.48 59.8413 6
created 5.72 19.9471 2
created 5.96 39.8942 4
created 6.2 69.8149 7
created 6.44 69.8149 7
created 6.68 9.97356 1
created 6.92 89.762 9
created 7.16 79.7885 8
created 7.4 39.8942 4
created 7.64 39.8942 4
created 7.88 29.9207 3
created 8.12 29.9207 3
created 8.36 49.8678 5
created 8.6 99.7356 10
created 8.84 79.7885 8
created 9.08 99.7356 10
created 9.32 29.9207 3
created 9.56 19.9471 2
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 (+-1.74859e-05)
fit chi^2 = 1.68512e-05
found -7.24 (+-0.000215791) 99.735 (+-0.529429) 10.0004 (+-0.00183932)
found -6.28 (+-0.000215666) 99.7347 (+-0.529392) 10.0004 (+-0.00183919)
found -6.04 (+-0.000216063) 99.7358 (+-0.529515) 10.0005 (+-0.00183962)
found -5.8 (+-0.000215666) 99.7348 (+-0.529392) 10.0004 (+-0.00183919)
found 4.04 (+-0.000215008) 99.7332 (+-0.52919) 10.0003 (+-0.00183849)
found 9.08 (+-0.000215052) 99.7334 (+-0.529208) 10.0003 (+-0.00183855)
found -7.96 (+-0.000214885) 99.7329 (+-0.529153) 10.0002 (+-0.00183836)
found -1.48 (+-0.000214885) 99.7329 (+-0.529153) 10.0002 (+-0.00183836)
found -1 (+-0.00021437) 99.7321 (+-0.529007) 10.0002 (+-0.00183785)
found 3.56 (+-0.000215008) 99.7331 (+-0.52919) 10.0003 (+-0.00183849)
found 8.6 (+-0.00021536) 99.734 (+-0.529297) 10.0003 (+-0.00183886)
found 9.8 (+-0.000212955) 99.7336 (+-0.528662) 10.0003 (+-0.00183665)
found -9.64 (+-0.00022743) 89.7614 (+-0.502251) 9.0004 (+-0.0017449)
found -7 (+-0.000226805) 89.7606 (+-0.502093) 9.00032 (+-0.00174435)
found -4.84 (+-0.000227646) 89.762 (+-0.502312) 9.00045 (+-0.00174511)
found -4.6 (+-0.000227646) 89.762 (+-0.502312) 9.00045 (+-0.00174511)
found -2.68 (+-0.000226495) 89.7598 (+-0.502003) 9.00024 (+-0.00174404)
found 1.4 (+-0.000227534) 89.7617 (+-0.502281) 9.00042 (+-0.001745)
found 1.88 (+-0.000227318) 89.7612 (+-0.502219) 9.00037 (+-0.00174479)
found 1.16 (+-0.000227122) 89.7609 (+-0.502169) 9.00034 (+-0.00174461)
found 6.92 (+-0.000226282) 89.7598 (+-0.501958) 9.00024 (+-0.00174388)
found -9.16 (+-0.000240405) 79.7868 (+-0.473334) 8.00024 (+-0.00164444)
found -4.36 (+-0.000240643) 79.7873 (+-0.473396) 8.00029 (+-0.00164465)
found 4.76 (+-0.000240659) 79.7871 (+-0.473391) 8.00026 (+-0.00164463)
found 7.16 (+-0.000241099) 79.7878 (+-0.473499) 8.00034 (+-0.00164501)
found 8.84 (+-0.000242016) 79.7897 (+-0.473729) 8.00053 (+-0.00164581)
found -9.88 (+-0.000241031) 79.7869 (+-0.473442) 8.00025 (+-0.00164481)
found -8.44 (+-0.000239764) 79.786 (+-0.473187) 8.00016 (+-0.00164393)
found -7.48 (+-0.000241207) 79.7881 (+-0.473527) 8.00037 (+-0.00164511)
found -5.08 (+-0.000241099) 79.7879 (+-0.473499) 8.00035 (+-0.00164501)
found -0.28 (+-0.000239954) 79.786 (+-0.473223) 8.00016 (+-0.00164405)
found -9.4 (+-0.000258659) 69.8159 (+-0.443117) 7.00045 (+-0.00153946)
found 1.64 (+-0.00025879) 69.8161 (+-0.443147) 7.00048 (+-0.00153956)
found 2.12 (+-0.000258193) 69.8151 (+-0.443015) 7.00037 (+-0.0015391)
found 6.44 (+-0.000256818) 69.8135 (+-0.442734) 7.00021 (+-0.00153813)
found -2.92 (+-0.000258193) 69.8151 (+-0.443015) 7.00037 (+-0.0015391)
found 0.44 (+-0.000257769) 69.8143 (+-0.442919) 7.00029 (+-0.00153877)
found 4.52 (+-0.00025787) 69.8146 (+-0.442944) 7.00032 (+-0.00153886)
found 5.24 (+-0.000257352) 69.8137 (+-0.442832) 7.00024 (+-0.00153847)
found 6.2 (+-0.00025773) 69.8143 (+-0.442912) 7.00029 (+-0.00153875)
found -5.56 (+-0.000279151) 59.8421 (+-0.410207) 6.00037 (+-0.00142512)
found 3.8 (+-0.000280205) 59.8436 (+-0.410409) 6.00053 (+-0.00142582)
found -3.64 (+-0.000276821) 59.8394 (+-0.409782) 6.0001 (+-0.00142365)
found 0.679999 (+-0.000278697) 59.8412 (+-0.410117) 6.00029 (+-0.00142481)
found 5.48 (+-0.000278117) 59.8407 (+-0.410015) 6.00024 (+-0.00142446)
found -6.52 (+-0.000278568) 59.8415 (+-0.410105) 6.00032 (+-0.00142477)
found -8.2 (+-0.000307174) 49.87 (+-0.374688) 5.00048 (+-0.00130172)
found 0.200001 (+-0.000305738) 49.8682 (+-0.374454) 5.00029 (+-0.00130091)
found 2.36 (+-0.000305738) 49.8682 (+-0.374454) 5.00029 (+-0.00130091)
found 2.84 (+-0.000303832) 49.8666 (+-0.374166) 5.00013 (+-0.00129991)
found 8.36 (+-0.00030597) 49.8687 (+-0.374497) 5.00035 (+-0.00130106)
found -3.16 (+-0.000304521) 49.8674 (+-0.374278) 5.00021 (+-0.0013003)
found -1.72 (+-0.000305592) 49.8685 (+-0.374442) 5.00032 (+-0.00130087)
found -7.72 (+-0.000344245) 39.897 (+-0.33524) 4.00048 (+-0.00116468)
found -1.24 (+-0.000344672) 39.8975 (+-0.335298) 4.00053 (+-0.00116488)
found 4.28 (+-0.000344007) 39.8967 (+-0.335209) 4.00045 (+-0.00116457)
found -5.32 (+-0.000343324) 39.8959 (+-0.335117) 4.00037 (+-0.00116425)
found -0.0400014 (+-0.000343039) 39.8957 (+-0.33508) 4.00035 (+-0.00116412)
found 0.920001 (+-0.000343543) 39.8962 (+-0.335147) 4.0004 (+-0.00116435)
found 2.6 (+-0.000342263) 39.8949 (+-0.334977) 4.00027 (+-0.00116376)
found 7.4 (+-0.000342718) 39.8954 (+-0.335039) 4.00032 (+-0.00116398)
found 7.64 (+-0.000341257) 39.8941 (+-0.334848) 4.00019 (+-0.00116332)
found -2.2 (+-0.000339985) 39.8933 (+-0.334692) 4.00011 (+-0.00116277)
found 3.32 (+-0.000341653) 39.8952 (+-0.334921) 4.0003 (+-0.00116357)
found 5.96 (+-0.000341654) 39.8946 (+-0.334906) 4.00024 (+-0.00116351)
found 5 (+-0.000397986) 29.9232 (+-0.290376) 3.0004 (+-0.00100881)
found 9.32 (+-0.00039631) 29.9224 (+-0.290219) 3.00032 (+-0.00100826)
found -3.88 (+-0.000395145) 29.9213 (+-0.290097) 3.00021 (+-0.00100784)
found 7.88 (+-0.000394955) 29.921 (+-0.290074) 3.00019 (+-0.00100776)
found 8.12 (+-0.000395369) 29.9213 (+-0.290115) 3.00021 (+-0.0010079)
found -8.92001 (+-0.000485491) 19.9485 (+-0.236976) 2.00024 (+-0.000823293)
found -6.76 (+-0.00048979) 19.9501 (+-0.237255) 2.0004 (+-0.000824262)
found -4.12 (+-0.000487549) 19.9491 (+-0.237104) 2.0003 (+-0.000823735)
found -2.44 (+-0.000488647) 19.9496 (+-0.237178) 2.00035 (+-0.000823994)
found -0.760008 (+-0.000487418) 19.9493 (+-0.237102) 2.00032 (+-0.00082373)
found 5.72 (+-0.000487363) 19.9488 (+-0.237087) 2.00027 (+-0.000823678)
found -1.96 (+-0.000486836) 19.9485 (+-0.237051) 2.00024 (+-0.000823551)
found -0.519994 (+-0.00048668) 19.9488 (+-0.237048) 2.00027 (+-0.000823542)
found 9.56001 (+-0.00048829) 19.9496 (+-0.237157) 2.00035 (+-0.000823922)
found 6.68 (+-0.00070038) 9.97732 (+-0.168046) 1.00043 (+-0.000583817)
found -3.4 (+-0.000696452) 9.97599 (+-0.167899) 1.00029 (+-0.000583309)
found 3.08 (+-0.000694463) 9.97546 (+-0.167828) 1.00024 (+-0.00058306)
found -8.67999 (+-0.000693962) 9.97574 (+-0.167819) 1.00027 (+-0.000583031)

 
#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.