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 49.8678 5
created -9.64 79.7885 8
created -9.4 49.8678 5
created -9.16 69.8149 7
created -8.92 49.8678 5
created -8.68 19.9471 2
created -8.44 79.7885 8
created -8.2 99.7356 10
created -7.96 39.8942 4
created -7.72 79.7885 8
created -7.48 89.762 9
created -7.24 29.9207 3
created -7 29.9207 3
created -6.76 69.8149 7
created -6.52 59.8413 6
created -6.28 69.8149 7
created -6.04 99.7356 10
created -5.8 69.8149 7
created -5.56 19.9471 2
created -5.32 89.762 9
created -5.08 89.762 9
created -4.84 99.7356 10
created -4.6 49.8678 5
created -4.36 79.7885 8
created -4.12 9.97356 1
created -3.88 39.8942 4
created -3.64 99.7356 10
created -3.4 19.9471 2
created -3.16 59.8413 6
created -2.92 49.8678 5
created -2.68 89.762 9
created -2.44 49.8678 5
created -2.2 69.8149 7
created -1.96 59.8413 6
created -1.72 19.9471 2
created -1.48 59.8413 6
created -1.24 69.8149 7
created -1 9.97356 1
created -0.76 59.8413 6
created -0.52 9.97356 1
created -0.28 9.97356 1
created -0.04 9.97356 1
created 0.2 39.8942 4
created 0.44 19.9471 2
created 0.68 19.9471 2
created 0.92 49.8678 5
created 1.16 49.8678 5
created 1.4 39.8942 4
created 1.64 9.97356 1
created 1.88 59.8413 6
created 2.12 89.762 9
created 2.36 9.97356 1
created 2.6 59.8413 6
created 2.84 29.9207 3
created 3.08 29.9207 3
created 3.32 39.8942 4
created 3.56 39.8942 4
created 3.8 39.8942 4
created 4.04 89.762 9
created 4.28 69.8149 7
created 4.52 9.97356 1
created 4.76 69.8149 7
created 5 39.8942 4
created 5.24 9.97356 1
created 5.48 69.8149 7
created 5.72 79.7885 8
created 5.96 69.8149 7
created 6.2 59.8413 6
created 6.44 19.9471 2
created 6.68 19.9471 2
created 6.92 49.8678 5
created 7.16 39.8942 4
created 7.4 99.7356 10
created 7.64 29.9207 3
created 7.88 49.8678 5
created 8.12 69.8149 7
created 8.36 49.8678 5
created 8.6 39.8942 4
created 8.84 69.8149 7
created 9.08 89.762 9
created 9.32 39.8942 4
created 9.56 39.8942 4
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 (+-1.78043e-05)
fit chi^2 = 1.5274e-05
found -8.2 (+-0.000204901) 99.7337 (+-0.50388) 10.0003 (+-0.00175056)
found -6.04 (+-0.000205162) 99.7342 (+-0.503957) 10.0004 (+-0.00175083)
found -4.84 (+-0.000205124) 99.7342 (+-0.503948) 10.0004 (+-0.00175079)
found -3.64 (+-0.000204092) 99.7321 (+-0.503644) 10.0002 (+-0.00174974)
found 7.4 (+-0.000204291) 99.7323 (+-0.503699) 10.0002 (+-0.00174993)
found -7.48 (+-0.00021596) 89.7604 (+-0.478019) 9.00029 (+-0.00166071)
found -5.08 (+-0.000216924) 89.7625 (+-0.478284) 9.0005 (+-0.00166163)
found 2.12 (+-0.000215213) 89.7593 (+-0.47783) 9.00018 (+-0.00166005)
found 9.08 (+-0.000216028) 89.7604 (+-0.478034) 9.00029 (+-0.00166076)
found -5.32 (+-0.000215839) 89.7604 (+-0.477993) 9.00029 (+-0.00166062)
found -2.68 (+-0.00021593) 89.7601 (+-0.478006) 9.00026 (+-0.00166067)
found 4.04 (+-0.000216028) 89.7603 (+-0.478034) 9.00029 (+-0.00166076)
found 9.8 (+-0.000214145) 89.7608 (+-0.477598) 9.00034 (+-0.00165925)
found 5.72 (+-0.000229746) 79.7881 (+-0.450845) 8.00037 (+-0.00156631)
found -9.64 (+-0.000229203) 79.787 (+-0.45071) 8.00026 (+-0.00156584)
found -8.44 (+-0.000229208) 79.7876 (+-0.450725) 8.00032 (+-0.00156589)
found -7.72 (+-0.000229539) 79.7879 (+-0.450797) 8.00035 (+-0.00156614)
found -4.36 (+-0.000228269) 79.786 (+-0.4505) 8.00016 (+-0.00156511)
found -6.28 (+-0.000246093) 69.8156 (+-0.421836) 7.00043 (+-0.00146553)
found -5.8 (+-0.000245261) 69.8146 (+-0.421664) 7.00032 (+-0.00146493)
found -1.24 (+-0.000244361) 69.8132 (+-0.421475) 7.00018 (+-0.00146427)
found 4.28 (+-0.000244759) 69.814 (+-0.421565) 7.00027 (+-0.00146458)
found 5.96 (+-0.000245851) 69.8151 (+-0.421781) 7.00037 (+-0.00146533)
found -9.16 (+-0.000245249) 69.814 (+-0.421648) 7.00027 (+-0.00146487)
found -6.76 (+-0.000245013) 69.8137 (+-0.4216) 7.00024 (+-0.0014647)
found -2.2 (+-0.00024541) 69.8143 (+-0.421683) 7.00029 (+-0.00146499)
found 4.76 (+-0.000244022) 69.8127 (+-0.421401) 7.00013 (+-0.00146401)
found 5.48 (+-0.000244637) 69.8137 (+-0.421537) 7.00024 (+-0.00146449)
found 8.12 (+-0.000245249) 69.814 (+-0.421648) 7.00026 (+-0.00146487)
found 8.84 (+-0.000245631) 69.8148 (+-0.421735) 7.00035 (+-0.00146517)
found -6.52 (+-0.000265899) 59.842 (+-0.39056) 6.00037 (+-0.00135687)
found -1.96 (+-0.000264783) 59.8407 (+-0.390356) 6.00024 (+-0.00135616)
found 6.2 (+-0.000264783) 59.8407 (+-0.390356) 6.00024 (+-0.00135616)
found -3.16 (+-0.000264432) 59.8402 (+-0.390289) 6.00019 (+-0.00135592)
found -1.48 (+-0.000264783) 59.8407 (+-0.390356) 6.00024 (+-0.00135616)
found -0.76 (+-0.000262816) 59.8388 (+-0.39001) 6.00005 (+-0.00135496)
found 1.88 (+-0.000264639) 59.841 (+-0.390344) 6.00027 (+-0.00135611)
found 2.6 (+-0.000263548) 59.8394 (+-0.390135) 6.00011 (+-0.00135539)
found -4.6 (+-0.000292446) 49.87 (+-0.356723) 5.00048 (+-0.00123931)
found -2.44 (+-0.000292102) 49.8695 (+-0.356666) 5.00043 (+-0.00123911)
found -9.4 (+-0.000291931) 49.8692 (+-0.356638) 5.0004 (+-0.00123902)
found -8.92 (+-0.000290432) 49.8677 (+-0.356404) 5.00024 (+-0.0012382)
found -2.92 (+-0.000291902) 49.8692 (+-0.356634) 5.0004 (+-0.001239)
found 1.16 (+-0.000290662) 49.8676 (+-0.356433) 5.00024 (+-0.0012383)
found 7.88 (+-0.000290791) 49.8679 (+-0.356457) 5.00027 (+-0.00123839)
found 8.36 (+-0.00029108) 49.8682 (+-0.356501) 5.00029 (+-0.00123854)
found -9.88 (+-0.000290723) 49.8674 (+-0.356419) 5.00022 (+-0.00123826)
found 0.920001 (+-0.000290018) 49.8671 (+-0.356336) 5.00019 (+-0.00123797)
found 6.92 (+-0.000289773) 49.8669 (+-0.356297) 5.00016 (+-0.00123783)
found -7.96 (+-0.00032774) 39.897 (+-0.319167) 4.00048 (+-0.00110884)
found 9.32 (+-0.000326494) 39.8957 (+-0.319003) 4.00035 (+-0.00110827)
found 1.4 (+-0.000324149) 39.8938 (+-0.318711) 4.00016 (+-0.00110725)
found 3.32 (+-0.000324895) 39.8941 (+-0.318794) 4.00019 (+-0.00110754)
found 3.56 (+-0.000325246) 39.8943 (+-0.318838) 4.00021 (+-0.00110769)
found 3.8 (+-0.000326494) 39.8957 (+-0.319003) 4.00035 (+-0.00110827)
found 5 (+-0.000324655) 39.8943 (+-0.318779) 4.00021 (+-0.00110749)
found 7.16 (+-0.000326994) 39.8962 (+-0.319069) 4.0004 (+-0.0011085)
found 8.6 (+-0.000326367) 39.8954 (+-0.318984) 4.00032 (+-0.0011082)
found 9.56 (+-0.000326493) 39.8957 (+-0.319003) 4.00035 (+-0.00110827)
found -3.88 (+-0.000325272) 39.8952 (+-0.318864) 4.0003 (+-0.00110778)
found 0.2 (+-0.000323076) 39.893 (+-0.318575) 4.00008 (+-0.00110678)
found -7.24 (+-0.000377631) 29.9224 (+-0.27633) 3.00032 (+-0.000960015)
found 7.64 (+-0.000378744) 29.9232 (+-0.27644) 3.0004 (+-0.000960395)
found 2.84 (+-0.000376763) 29.9215 (+-0.276241) 3.00024 (+-0.000959703)
found -7 (+-0.000377078) 29.9218 (+-0.276273) 3.00027 (+-0.000959815)
found 3.08 (+-0.000376019) 29.921 (+-0.276166) 3.00019 (+-0.000959445)
found -3.4 (+-0.00046665) 19.9504 (+-0.225905) 2.00043 (+-0.000784828)
found -8.68 (+-0.000465428) 19.9496 (+-0.225819) 2.00035 (+-0.00078453)
found -5.56 (+-0.000466761) 19.9504 (+-0.225911) 2.00043 (+-0.000784851)
found -1.72 (+-0.000465078) 19.9493 (+-0.225793) 2.00032 (+-0.000784441)
found 6.44 (+-0.000462502) 19.9482 (+-0.225623) 2.00022 (+-0.000783849)
found 0.439998 (+-0.000461439) 19.9477 (+-0.225549) 2.00016 (+-0.000783594)
found 0.680003 (+-0.000462005) 19.948 (+-0.225588) 2.00019 (+-0.000783729)
found 6.68 (+-0.000462005) 19.948 (+-0.225588) 2.00019 (+-0.000783729)
found -4.12001 (+-0.000663485) 9.97626 (+-0.159868) 1.00032 (+-0.000555406)
found 2.35999 (+-0.000666023) 9.97705 (+-0.15996) 1.0004 (+-0.000555727)
found -1 (+-0.00066471) 9.97652 (+-0.15991) 1.00035 (+-0.000555552)
found 4.52 (+-0.000665482) 9.97679 (+-0.159939) 1.00037 (+-0.000555652)
found -0.52001 (+-0.000657117) 9.97493 (+-0.15965) 1.00019 (+-0.000554649)
found 1.64 (+-0.000662038) 9.97573 (+-0.159813) 1.00027 (+-0.000555216)
found 5.24001 (+-0.000662802) 9.976 (+-0.159842) 1.0003 (+-0.000555315)
found -0.28 (+-0.000650491) 9.9736 (+-0.159419) 1.00006 (+-0.000553847)
found -0.0399943 (+-0.000655271) 9.97439 (+-0.159582) 1.00013 (+-0.000554412)

 
#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.