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 9.97356 1
created -9.64 49.8678 5
created -9.4 39.8942 4
created -9.16 89.762 9
created -8.92 89.762 9
created -8.68 99.7356 10
created -8.44 79.7885 8
created -8.2 69.8149 7
created -7.96 9.97356 1
created -7.72 49.8678 5
created -7.48 9.97356 1
created -7.24 89.762 9
created -7 89.762 9
created -6.76 89.762 9
created -6.52 69.8149 7
created -6.28 49.8678 5
created -6.04 39.8942 4
created -5.8 49.8678 5
created -5.56 79.7885 8
created -5.32 59.8413 6
created -5.08 29.9207 3
created -4.84 59.8413 6
created -4.6 99.7356 10
created -4.36 9.97356 1
created -4.12 9.97356 1
created -3.88 69.8149 7
created -3.64 69.8149 7
created -3.4 99.7356 10
created -3.16 49.8678 5
created -2.92 9.97356 1
created -2.68 19.9471 2
created -2.44 39.8942 4
created -2.2 89.762 9
created -1.96 49.8678 5
created -1.72 39.8942 4
created -1.48 19.9471 2
created -1.24 19.9471 2
created -1 69.8149 7
created -0.76 29.9207 3
created -0.52 99.7356 10
created -0.28 69.8149 7
created -0.04 39.8942 4
created 0.2 59.8413 6
created 0.44 29.9207 3
created 0.68 89.762 9
created 0.92 79.7885 8
created 1.16 19.9471 2
created 1.4 49.8678 5
created 1.64 19.9471 2
created 1.88 89.762 9
created 2.12 9.97356 1
created 2.36 39.8942 4
created 2.6 89.762 9
created 2.84 89.762 9
created 3.08 99.7356 10
created 3.32 9.97356 1
created 3.56 39.8942 4
created 3.8 39.8942 4
created 4.04 19.9471 2
created 4.28 29.9207 3
created 4.52 59.8413 6
created 4.76 49.8678 5
created 5 39.8942 4
created 5.24 69.8149 7
created 5.48 59.8413 6
created 5.72 19.9471 2
created 5.96 79.7885 8
created 6.2 79.7885 8
created 6.44 29.9207 3
created 6.68 39.8942 4
created 6.92 39.8942 4
created 7.16 69.8149 7
created 7.4 99.7356 10
created 7.64 9.97356 1
created 7.88 39.8942 4
created 8.12 79.7885 8
created 8.36 59.8413 6
created 8.6 19.9471 2
created 8.84 69.8149 7
created 9.08 79.7885 8
created 9.32 79.7885 8
created 9.56 79.7885 8
created 9.8 69.8149 7
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.55681e-05)
fit chi^2 = 1.21903e-05
found -8.68 (+-0.000183538) 99.735 (+-0.450298) 10.0005 (+-0.00156441)
found -4.6 (+-0.000182302) 99.7323 (+-0.449941) 10.0002 (+-0.00156317)
found -3.4 (+-0.000183086) 99.7336 (+-0.450159) 10.0003 (+-0.00156392)
found 3.08 (+-0.000182559) 99.7331 (+-0.450022) 10.0003 (+-0.00156345)
found 7.4 (+-0.000182395) 99.7325 (+-0.44997) 10.0002 (+-0.00156327)
found -0.519999 (+-0.000182824) 99.7331 (+-0.450084) 10.0003 (+-0.00156366)
found -8.92 (+-0.000193793) 89.7625 (+-0.427284) 9.0005 (+-0.00148445)
found -7 (+-0.00019371) 89.7622 (+-0.42726) 9.00048 (+-0.00148437)
found -6.76 (+-0.000193526) 89.7617 (+-0.427208) 9.00042 (+-0.00148419)
found 2.84 (+-0.000193793) 89.7624 (+-0.427284) 9.0005 (+-0.00148445)
found -9.16 (+-0.000193175) 89.7609 (+-0.427113) 9.00034 (+-0.00148386)
found -7.24 (+-0.000192548) 89.7601 (+-0.426959) 9.00026 (+-0.00148332)
found -2.2 (+-0.000192775) 89.7598 (+-0.427) 9.00024 (+-0.00148347)
found 0.680001 (+-0.000192932) 89.7603 (+-0.427047) 9.00029 (+-0.00148363)
found 1.88 (+-0.000191677) 89.7582 (+-0.426723) 9.00007 (+-0.0014825)
found 2.6 (+-0.000193175) 89.7609 (+-0.427113) 9.00034 (+-0.00148386)
found -8.44 (+-0.000205545) 79.7889 (+-0.402847) 8.00045 (+-0.00139955)
found 0.919998 (+-0.000204676) 79.7873 (+-0.40264) 8.00029 (+-0.00139883)
found 6.2 (+-0.000204794) 79.7873 (+-0.402663) 8.00029 (+-0.00139891)
found 9.08 (+-0.000205354) 79.7884 (+-0.402798) 8.0004 (+-0.00139938)
found 9.32 (+-0.000205461) 79.7886 (+-0.402825) 8.00043 (+-0.00139948)
found 9.56 (+-0.000205354) 79.7884 (+-0.402798) 8.0004 (+-0.00139938)
found -5.56 (+-0.00020489) 79.7873 (+-0.402682) 8.00029 (+-0.00139898)
found 5.96 (+-0.000204578) 79.787 (+-0.402615) 8.00026 (+-0.00139875)
found 8.12 (+-0.000204745) 79.787 (+-0.402647) 8.00026 (+-0.00139886)
found -8.2 (+-0.000218551) 69.8137 (+-0.376588) 7.00024 (+-0.00130833)
found -6.52 (+-0.000219603) 69.8151 (+-0.3768) 7.00037 (+-0.00130906)
found -3.64 (+-0.000219983) 69.8158 (+-0.376884) 7.00045 (+-0.00130936)
found -0.280002 (+-0.000219543) 69.815 (+-0.376789) 7.00037 (+-0.00130902)
found 9.8 (+-0.000217631) 69.8153 (+-0.376445) 7.0004 (+-0.00130783)
found -3.88 (+-0.000218433) 69.8134 (+-0.376561) 7.00021 (+-0.00130823)
found -1 (+-0.000218154) 69.8127 (+-0.376491) 7.00013 (+-0.00130799)
found 5.24 (+-0.000219079) 69.814 (+-0.376684) 7.00026 (+-0.00130866)
found 7.16 (+-0.000219543) 69.815 (+-0.376789) 7.00037 (+-0.00130902)
found 8.84 (+-0.000218895) 69.814 (+-0.376652) 7.00027 (+-0.00130855)
found -5.32 (+-0.00023696) 59.8412 (+-0.348807) 6.00029 (+-0.00121181)
found -4.84 (+-0.000237208) 59.8417 (+-0.348856) 6.00035 (+-0.00121198)
found 0.2 (+-0.000236323) 59.8402 (+-0.348685) 6.00019 (+-0.00121139)
found 4.52 (+-0.000236509) 59.8404 (+-0.348719) 6.00021 (+-0.00121151)
found 5.48 (+-0.000236549) 59.8407 (+-0.348732) 6.00024 (+-0.00121155)
found 8.36 (+-0.000236686) 59.8409 (+-0.348759) 6.00027 (+-0.00121164)
found -3.16 (+-0.000259457) 49.8682 (+-0.318413) 5.00029 (+-0.00110622)
found -1.96 (+-0.000260356) 49.8687 (+-0.318539) 5.00035 (+-0.00110665)
found -6.28 (+-0.000260042) 49.8682 (+-0.318487) 5.00029 (+-0.00110647)
found -5.8 (+-0.000260205) 49.8684 (+-0.318514) 5.00032 (+-0.00110657)
found 4.76 (+-0.000259864) 49.8679 (+-0.318457) 5.00027 (+-0.00110637)
found -9.64 (+-0.000258421) 49.8666 (+-0.318242) 5.00013 (+-0.00110562)
found -7.72 (+-0.000257404) 49.8658 (+-0.318092) 5.00006 (+-0.0011051)
found 1.4 (+-0.000258304) 49.8663 (+-0.318218) 5.00011 (+-0.00110554)
found -9.4 (+-0.000291953) 39.8959 (+-0.285023) 4.00037 (+-0.000990213)
found -6.04 (+-0.000291107) 39.8949 (+-0.28491) 4.00027 (+-0.000989821)
found -1.72 (+-0.000290133) 39.8941 (+-0.284789) 4.00019 (+-0.0009894)
found -0.0400005 (+-0.000291809) 39.8957 (+-0.285002) 4.00035 (+-0.000990142)
found 3.8 (+-0.000289865) 39.8938 (+-0.284754) 4.00016 (+-0.000989279)
found 5 (+-0.000291566) 39.8954 (+-0.28497) 4.00032 (+-0.000990032)
found 6.68 (+-0.000290251) 39.8941 (+-0.284801) 4.00019 (+-0.000989442)
found 6.92 (+-0.000291294) 39.8951 (+-0.284935) 4.00029 (+-0.000989911)
found -2.44 (+-0.000290971) 39.8951 (+-0.284901) 4.00029 (+-0.000989792)
found 2.36 (+-0.000290417) 39.8949 (+-0.284839) 4.00027 (+-0.000989576)
found 3.56 (+-0.000289317) 39.8935 (+-0.284691) 4.00014 (+-0.000989063)
found 7.88 (+-0.000290235) 39.8946 (+-0.284814) 4.00024 (+-0.000989488)
found 6.44 (+-0.000337541) 29.9223 (+-0.24688) 3.00032 (+-0.000857699)
found -5.08 (+-0.000337671) 29.9224 (+-0.246891) 3.00032 (+-0.000857738)
found -0.759998 (+-0.000338963) 29.9237 (+-0.247023) 3.00045 (+-0.000858197)
found 0.440002 (+-0.000338455) 29.9231 (+-0.246971) 3.0004 (+-0.000858017)
found 4.28 (+-0.000336085) 29.9213 (+-0.246738) 3.00021 (+-0.000857206)
found 1.16 (+-0.000415799) 19.9496 (+-0.201739) 2.00035 (+-0.000700874)
found 1.64 (+-0.00041613) 19.9498 (+-0.201763) 2.00037 (+-0.000700957)
found 5.72 (+-0.000416253) 19.9498 (+-0.20177) 2.00037 (+-0.000700982)
found 8.6 (+-0.000415889) 19.9496 (+-0.201745) 2.00035 (+-0.000700893)
found -1.48 (+-0.000412235) 19.9477 (+-0.201499) 2.00016 (+-0.000700039)
found -1.24 (+-0.000413582) 19.9485 (+-0.201592) 2.00024 (+-0.000700364)
found 4.04 (+-0.000412964) 19.948 (+-0.201546) 2.00019 (+-0.000700202)
found -2.68 (+-0.000411235) 19.9474 (+-0.201438) 2.00014 (+-0.000699827)
found -4.36002 (+-0.000589353) 9.97603 (+-0.142717) 1.0003 (+-0.00049582)
found 3.31999 (+-0.000593799) 9.9768 (+-0.142863) 1.00038 (+-0.000496327)
found 7.63999 (+-0.000593799) 9.9768 (+-0.142862) 1.00038 (+-0.000496327)
found 2.11999 (+-0.000593291) 9.97653 (+-0.142842) 1.00035 (+-0.000496257)
found -7.96 (+-0.000593044) 9.97625 (+-0.14283) 1.00032 (+-0.000496214)
found -7.47999 (+-0.000594213) 9.97679 (+-0.142875) 1.00037 (+-0.00049637)
found -2.92001 (+-0.000588192) 9.97493 (+-0.142659) 1.00019 (+-0.00049562)
found -4.11999 (+-0.000587715) 9.97522 (+-0.142652) 1.00022 (+-0.000495595)
found -9.87999 (+-0.000584682) 9.9744 (+-0.142537) 1.00014 (+-0.000495197)

 
#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.