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 59.8413 6
created -9.4 9.97356 1
created -9.16 49.8678 5
created -8.92 89.762 9
created -8.68 19.9471 2
created -8.44 19.9471 2
created -8.2 39.8942 4
created -7.96 79.7885 8
created -7.72 79.7885 8
created -7.48 29.9207 3
created -7.24 99.7356 10
created -7 39.8942 4
created -6.76 59.8413 6
created -6.52 49.8678 5
created -6.28 29.9207 3
created -6.04 39.8942 4
created -5.8 69.8149 7
created -5.56 79.7885 8
created -5.32 89.762 9
created -5.08 99.7356 10
created -4.84 9.97356 1
created -4.6 19.9471 2
created -4.36 79.7885 8
created -4.12 49.8678 5
created -3.88 99.7356 10
created -3.64 79.7885 8
created -3.4 9.97356 1
created -3.16 69.8149 7
created -2.92 79.7885 8
created -2.68 79.7885 8
created -2.44 29.9207 3
created -2.2 29.9207 3
created -1.96 29.9207 3
created -1.72 99.7356 10
created -1.48 69.8149 7
created -1.24 59.8413 6
created -1 69.8149 7
created -0.76 39.8942 4
created -0.52 79.7885 8
created -0.28 59.8413 6
created -0.04 19.9471 2
created 0.2 19.9471 2
created 0.44 79.7885 8
created 0.68 49.8678 5
created 0.92 9.97356 1
created 1.16 79.7885 8
created 1.4 99.7356 10
created 1.64 89.762 9
created 1.88 49.8678 5
created 2.12 29.9207 3
created 2.36 29.9207 3
created 2.6 19.9471 2
created 2.84 59.8413 6
created 3.08 59.8413 6
created 3.32 69.8149 7
created 3.56 59.8413 6
created 3.8 89.762 9
created 4.04 59.8413 6
created 4.28 89.762 9
created 4.52 89.762 9
created 4.76 79.7885 8
created 5 99.7356 10
created 5.24 39.8942 4
created 5.48 39.8942 4
created 5.72 19.9471 2
created 5.96 59.8413 6
created 6.2 69.8149 7
created 6.44 59.8413 6
created 6.68 79.7885 8
created 6.92 89.762 9
created 7.16 89.762 9
created 7.4 49.8678 5
created 7.64 79.7885 8
created 7.88 69.8149 7
created 8.12 9.97356 1
created 8.36 59.8413 6
created 8.6 9.97356 1
created 8.84 19.9471 2
created 9.08 39.8942 4
created 9.32 99.7356 10
created 9.56 59.8413 6
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 (+-1.61064e-05)
fit chi^2 = 1.36748e-05
found -5.08 (+-0.000193355) 99.7331 (+-0.476636) 10.0003 (+-0.00165591)
found 1.4 (+-0.000194393) 99.735 (+-0.476929) 10.0004 (+-0.00165693)
found 5 (+-0.000193877) 99.7336 (+-0.476773) 10.0003 (+-0.00165638)
found -7.24 (+-0.000193301) 99.7323 (+-0.476601) 10.0002 (+-0.00165579)
found -3.88 (+-0.000194004) 99.7339 (+-0.47681) 10.0003 (+-0.00165651)
found -1.72 (+-0.000193636) 99.7331 (+-0.476701) 10.0003 (+-0.00165614)
found 9.32 (+-0.000193687) 99.7331 (+-0.476714) 10.0003 (+-0.00165618)
found -5.32 (+-0.00020516) 89.7622 (+-0.452526) 9.00048 (+-0.00157215)
found 1.64 (+-0.000204826) 89.7614 (+-0.452434) 9.0004 (+-0.00157183)
found 3.8 (+-0.000204557) 89.7606 (+-0.452356) 9.00032 (+-0.00157156)
found 4.28 (+-0.000204861) 89.7614 (+-0.452442) 9.0004 (+-0.00157185)
found 4.52 (+-0.000205072) 89.7619 (+-0.452501) 9.00045 (+-0.00157206)
found 6.92 (+-0.000205072) 89.7619 (+-0.452501) 9.00045 (+-0.00157206)
found 7.16 (+-0.000204738) 89.7611 (+-0.452409) 9.00037 (+-0.00157174)
found -8.92 (+-0.000203806) 89.7592 (+-0.452159) 9.00018 (+-0.00157087)
found -7.72 (+-0.000216905) 79.7873 (+-0.426476) 8.00029 (+-0.00148164)
found -5.56 (+-0.000217603) 79.7886 (+-0.426646) 8.00043 (+-0.00148223)
found -3.64 (+-0.000216552) 79.7873 (+-0.426408) 8.00029 (+-0.00148141)
found -2.92 (+-0.000217499) 79.7883 (+-0.426619) 8.0004 (+-0.00148214)
found -2.68 (+-0.000216905) 79.7873 (+-0.426476) 8.00029 (+-0.00148164)
found 4.76 (+-0.000217919) 79.7894 (+-0.426726) 8.00051 (+-0.00148251)
found 6.68 (+-0.000217481) 79.7884 (+-0.426615) 8.0004 (+-0.00148213)
found 9.8 (+-0.000215271) 79.7881 (+-0.426147) 8.00037 (+-0.0014805)
found -7.96 (+-0.000217087) 79.7875 (+-0.426518) 8.00032 (+-0.00148179)
found -4.36 (+-0.000216311) 79.7862 (+-0.426333) 8.00018 (+-0.00148115)
found -0.52 (+-0.000216854) 79.787 (+-0.426459) 8.00026 (+-0.00148159)
found 0.440001 (+-0.000216311) 79.7862 (+-0.426333) 8.00018 (+-0.00148115)
found 1.16 (+-0.000216552) 79.7873 (+-0.426408) 8.00029 (+-0.00148141)
found 7.64 (+-0.000217129) 79.7876 (+-0.426526) 8.00032 (+-0.00148182)
found -1.48 (+-0.000232854) 69.8156 (+-0.399142) 7.00043 (+-0.00138668)
found -1 (+-0.000232035) 69.814 (+-0.398961) 7.00027 (+-0.00138605)
found 3.32 (+-0.00023236) 69.8145 (+-0.399031) 7.00032 (+-0.0013863)
found 6.2 (+-0.00023236) 69.8145 (+-0.399031) 7.00032 (+-0.0013863)
found 7.88 (+-0.000231476) 69.8137 (+-0.398859) 7.00024 (+-0.0013857)
found -5.8 (+-0.000232299) 69.8145 (+-0.39902) 7.00032 (+-0.00138626)
found -3.16 (+-0.000231476) 69.8137 (+-0.398859) 7.00024 (+-0.0013857)
found 4.04 (+-0.000252162) 59.8431 (+-0.36966) 6.00048 (+-0.00128426)
found 9.56 (+-0.000252153) 59.8431 (+-0.369658) 6.00048 (+-0.00128425)
found -6.76 (+-0.000250727) 59.8407 (+-0.369384) 6.00024 (+-0.0012833)
found -1.24 (+-0.000251595) 59.842 (+-0.369549) 6.00037 (+-0.00128387)
found -0.280002 (+-0.000250683) 59.8409 (+-0.369385) 6.00026 (+-0.0012833)
found 3.08 (+-0.000251435) 59.8417 (+-0.369518) 6.00035 (+-0.00128376)
found 3.56 (+-0.000251878) 59.8425 (+-0.369604) 6.00043 (+-0.00128406)
found 6.44 (+-0.000251741) 59.8423 (+-0.369577) 6.0004 (+-0.00128397)
found -9.64 (+-0.000249085) 59.8391 (+-0.369096) 6.00008 (+-0.0012823)
found 2.84 (+-0.00025038) 59.8404 (+-0.369325) 6.00021 (+-0.00128309)
found 5.96 (+-0.000250538) 59.8407 (+-0.369356) 6.00024 (+-0.0012832)
found 8.36 (+-0.000248677) 59.8388 (+-0.369028) 6.00005 (+-0.00128206)
found 1.88 (+-0.000275479) 49.8684 (+-0.337335) 5.00032 (+-0.00117196)
found 7.4 (+-0.000276562) 49.8698 (+-0.337507) 5.00045 (+-0.00117255)
found -6.52 (+-0.00027496) 49.8676 (+-0.337249) 5.00024 (+-0.00117166)
found -4.12 (+-0.000276713) 49.87 (+-0.337532) 5.00048 (+-0.00117264)
found 0.679997 (+-0.000274494) 49.8676 (+-0.337192) 5.00024 (+-0.00117146)
found -9.16 (+-0.000274653) 49.8679 (+-0.337219) 5.00027 (+-0.00117155)
found -7 (+-0.00030966) 39.8964 (+-0.301937) 4.00043 (+-0.00104898)
found 5.24 (+-0.000309112) 39.8959 (+-0.301867) 4.00037 (+-0.00104873)
found -6.04 (+-0.000308186) 39.8949 (+-0.301744) 4.00027 (+-0.00104831)
found -0.76 (+-0.000309512) 39.8962 (+-0.301916) 4.0004 (+-0.0010489)
found 5.48 (+-0.000307007) 39.8938 (+-0.301594) 4.00016 (+-0.00104778)
found -8.2 (+-0.000307984) 39.8949 (+-0.301723) 4.00027 (+-0.00104823)
found 9.08 (+-0.000308361) 39.8954 (+-0.301775) 4.00032 (+-0.00104841)
found -7.48 (+-0.000359286) 29.9239 (+-0.26166) 3.00048 (+-0.000909049)
found -2.44 (+-0.000357065) 29.9221 (+-0.261438) 3.00029 (+-0.000908277)
found -6.28 (+-0.000356598) 29.9215 (+-0.261388) 3.00024 (+-0.000908103)
found 2.12 (+-0.000356163) 29.9213 (+-0.261346) 3.00021 (+-0.000907957)
found -2.2 (+-0.000355357) 29.9208 (+-0.261267) 3.00016 (+-0.000907683)
found -1.96 (+-0.000357548) 29.9226 (+-0.261489) 3.00035 (+-0.000908454)
found 2.36 (+-0.000354829) 29.9205 (+-0.261217) 3.00013 (+-0.000907511)
found -8.68001 (+-0.000438766) 19.949 (+-0.213567) 2.0003 (+-0.000741964)
found -0.0400037 (+-0.00043762) 19.9482 (+-0.213485) 2.00022 (+-0.00074168)
found -8.44 (+-0.000436614) 19.9477 (+-0.213415) 2.00016 (+-0.000741439)
found 0.200006 (+-0.00043842) 19.9488 (+-0.213541) 2.00027 (+-0.000741877)
found 2.6 (+-0.000438398) 19.9485 (+-0.213535) 2.00024 (+-0.000741853)
found 5.72 (+-0.000439035) 19.9488 (+-0.213576) 2.00027 (+-0.000741999)
found -9.87999 (+-0.00043643) 19.9477 (+-0.213394) 2.00016 (+-0.000741365)
found -4.59999 (+-0.000437348) 19.9485 (+-0.213477) 2.00024 (+-0.000741652)
found 8.84 (+-0.000435555) 19.9475 (+-0.213351) 2.00014 (+-0.000741214)
found -4.84002 (+-0.000626256) 9.97628 (+-0.151222) 1.00032 (+-0.000525369)
found -3.4 (+-0.000630335) 9.97705 (+-0.151359) 1.0004 (+-0.000525844)
found 8.12 (+-0.00062895) 9.97651 (+-0.151307) 1.00035 (+-0.000525665)
found -9.4 (+-0.000627389) 9.97598 (+-0.15125) 1.00029 (+-0.000525466)
found 8.59999 (+-0.000623792) 9.97519 (+-0.151126) 1.00021 (+-0.000525036)
found 0.920006 (+-0.000628765) 9.97653 (+-0.151302) 1.00035 (+-0.000525646)

 
#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.