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 29.9207 3
created -9.64 29.9207 3
created -9.4 9.97356 1
created -9.16 59.8413 6
created -8.92 69.8149 7
created -8.68 49.8678 5
created -8.44 49.8678 5
created -8.2 19.9471 2
created -7.96 19.9471 2
created -7.72 19.9471 2
created -7.48 9.97356 1
created -7.24 59.8413 6
created -7 29.9207 3
created -6.76 89.762 9
created -6.52 79.7885 8
created -6.28 59.8413 6
created -6.04 79.7885 8
created -5.8 39.8942 4
created -5.56 79.7885 8
created -5.32 39.8942 4
created -5.08 49.8678 5
created -4.84 99.7356 10
created -4.6 19.9471 2
created -4.36 19.9471 2
created -4.12 99.7356 10
created -3.88 69.8149 7
created -3.64 39.8942 4
created -3.4 29.9207 3
created -3.16 49.8678 5
created -2.92 49.8678 5
created -2.68 59.8413 6
created -2.44 39.8942 4
created -2.2 49.8678 5
created -1.96 99.7356 10
created -1.72 39.8942 4
created -1.48 79.7885 8
created -1.24 69.8149 7
created -1 89.762 9
created -0.76 79.7885 8
created -0.52 89.762 9
created -0.28 59.8413 6
created -0.04 79.7885 8
created 0.2 9.97356 1
created 0.44 9.97356 1
created 0.68 99.7356 10
created 0.92 59.8413 6
created 1.16 29.9207 3
created 1.4 29.9207 3
created 1.64 89.762 9
created 1.88 49.8678 5
created 2.12 69.8149 7
created 2.36 9.97356 1
created 2.6 19.9471 2
created 2.84 29.9207 3
created 3.08 19.9471 2
created 3.32 39.8942 4
created 3.56 89.762 9
created 3.8 69.8149 7
created 4.04 19.9471 2
created 4.28 79.7885 8
created 4.52 89.762 9
created 4.76 89.762 9
created 5 59.8413 6
created 5.24 9.97356 1
created 5.48 39.8942 4
created 5.72 89.762 9
created 5.96 89.762 9
created 6.2 39.8942 4
created 6.44 39.8942 4
created 6.68 9.97356 1
created 6.92 69.8149 7
created 7.16 29.9207 3
created 7.4 89.762 9
created 7.64 49.8678 5
created 7.88 39.8942 4
created 8.12 39.8942 4
created 8.36 19.9471 2
created 8.6 59.8413 6
created 8.84 19.9471 2
created 9.08 59.8413 6
created 9.32 59.8413 6
created 9.56 29.9207 3
created 9.8 39.8942 4
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.23777e-05)
fit chi^2 = 7.26426e-06
found -4.84 (+-0.00014084) 99.7323 (+-0.347357) 10.0002 (+-0.00120677)
found -4.12 (+-0.000140993) 99.7328 (+-0.347403) 10.0002 (+-0.00120693)
found -1.96 (+-0.000141087) 99.7328 (+-0.347426) 10.0002 (+-0.00120701)
found 0.680001 (+-0.000140727) 99.7322 (+-0.347331) 10.0002 (+-0.00120668)
found -1 (+-0.000149324) 89.7614 (+-0.329762) 9.0004 (+-0.00114565)
found -0.52 (+-0.000149244) 89.7611 (+-0.32974) 9.00037 (+-0.00114557)
found 4.52 (+-0.000149466) 89.7619 (+-0.329803) 9.00045 (+-0.00114579)
found 4.76 (+-0.000149312) 89.7614 (+-0.32976) 9.0004 (+-0.00114564)
found 5.96 (+-0.000149121) 89.7608 (+-0.329709) 9.00034 (+-0.00114546)
found -6.76 (+-0.000148933) 89.7603 (+-0.329658) 9.00029 (+-0.00114528)
found 1.64 (+-0.000148693) 89.7595 (+-0.329591) 9.00021 (+-0.00114505)
found 3.56 (+-0.00014898) 89.7603 (+-0.329668) 9.00029 (+-0.00114532)
found 5.72 (+-0.000149121) 89.7608 (+-0.329709) 9.00034 (+-0.00114546)
found 7.4 (+-0.000148693) 89.7595 (+-0.329591) 9.00021 (+-0.00114505)
found -6.52 (+-0.000158509) 79.7883 (+-0.310936) 8.0004 (+-0.00108024)
found -6.04 (+-0.000158053) 79.787 (+-0.310822) 8.00027 (+-0.00107985)
found -0.76 (+-0.000158757) 79.7891 (+-0.310999) 8.00048 (+-0.00108046)
found -0.0400012 (+-0.000157519) 79.7862 (+-0.310704) 8.00018 (+-0.00107944)
found -5.56 (+-0.000157843) 79.7865 (+-0.310771) 8.00021 (+-0.00107967)
found -1.48 (+-0.000158141) 79.7873 (+-0.310845) 8.00029 (+-0.00107992)
found 4.28 (+-0.000157999) 79.7873 (+-0.310817) 8.00029 (+-0.00107983)
found -8.92 (+-0.000169243) 69.8142 (+-0.290807) 7.00029 (+-0.00101031)
found -3.88 (+-0.000169476) 69.815 (+-0.290861) 7.00037 (+-0.0010105)
found -1.24 (+-0.000169827) 69.8158 (+-0.290937) 7.00045 (+-0.00101076)
found 3.8 (+-0.00016906) 69.8142 (+-0.290775) 7.00029 (+-0.0010102)
found 2.12 (+-0.00016841) 69.8129 (+-0.290639) 7.00016 (+-0.00100973)
found 6.92 (+-0.00016814) 69.8124 (+-0.290582) 7.00011 (+-0.00100953)
found -0.28 (+-0.000183687) 59.8428 (+-0.269405) 6.00045 (+-0.000935955)
found 0.919998 (+-0.000183112) 59.8417 (+-0.269299) 6.00034 (+-0.000935586)
found 5 (+-0.000182504) 59.8409 (+-0.269194) 6.00027 (+-0.000935223)
found -6.28 (+-0.000183587) 59.8425 (+-0.269385) 6.00043 (+-0.000935887)
found -2.68 (+-0.000182741) 59.8407 (+-0.269223) 6.00024 (+-0.000935325)
found 9.32 (+-0.0001827) 59.8407 (+-0.269217) 6.00024 (+-0.000935303)
found -9.16 (+-0.000182301) 59.8404 (+-0.269154) 6.00021 (+-0.000935083)
found -7.24 (+-0.000181752) 59.8393 (+-0.26905) 6.00011 (+-0.000934722)
found 8.6 (+-0.000181843) 59.8393 (+-0.269063) 6.00011 (+-0.000934766)
found 9.08 (+-0.000182488) 59.8404 (+-0.269181) 6.00021 (+-0.000935176)
found 1.88 (+-0.000201444) 49.8695 (+-0.245969) 5.00042 (+-0.000854535)
found 7.64 (+-0.000200981) 49.8687 (+-0.245895) 5.00035 (+-0.000854279)
found -8.68 (+-0.000200909) 49.8684 (+-0.245882) 5.00032 (+-0.000854232)
found -8.44 (+-0.000200006) 49.8671 (+-0.245741) 5.00019 (+-0.000853745)
found -5.08 (+-0.000201091) 49.869 (+-0.245914) 5.00037 (+-0.000854344)
found -3.16 (+-0.000200252) 49.8674 (+-0.245778) 5.00021 (+-0.000853871)
found -2.92 (+-0.000200772) 49.8681 (+-0.245859) 5.00029 (+-0.000854154)
found -2.2 (+-0.000201091) 49.869 (+-0.245914) 5.00037 (+-0.000854344)
found -1.72 (+-0.000226021) 39.897 (+-0.220108) 4.00048 (+-0.000764692)
found 6.2 (+-0.000225161) 39.8956 (+-0.219995) 4.00035 (+-0.000764299)
found -5.8 (+-0.000225742) 39.8964 (+-0.220071) 4.00043 (+-0.00076456)
found -5.32 (+-0.000225229) 39.8957 (+-0.220003) 4.00035 (+-0.000764325)
found -3.64 (+-0.00022462) 39.8949 (+-0.219925) 4.00027 (+-0.000764053)
found -2.44 (+-0.000224905) 39.8951 (+-0.21996) 4.00029 (+-0.000764175)
found 6.44 (+-0.000223338) 39.8935 (+-0.219767) 4.00014 (+-0.000763505)
found 7.88 (+-0.00022451) 39.8946 (+-0.219908) 4.00024 (+-0.000763997)
found 8.12 (+-0.00022376) 39.8938 (+-0.219815) 4.00016 (+-0.000763672)
found 9.8 (+-0.000221889) 39.894 (+-0.219624) 4.00018 (+-0.000763008)
found 3.32 (+-0.000224615) 39.8951 (+-0.219929) 4.00029 (+-0.000764068)
found 5.48 (+-0.000224187) 39.8949 (+-0.219881) 4.00027 (+-0.000763901)
found -7 (+-0.000261269) 29.9231 (+-0.190649) 3.0004 (+-0.000662344)
found 1.16 (+-0.000259829) 29.9216 (+-0.190505) 3.00024 (+-0.000661844)
found 7.16 (+-0.00026149) 29.9234 (+-0.190671) 3.00043 (+-0.000662421)
found 9.56 (+-0.000260147) 29.9218 (+-0.190535) 3.00027 (+-0.00066195)
found -9.64 (+-0.000258078) 29.9202 (+-0.190339) 3.00011 (+-0.000661269)
found -3.4 (+-0.000259905) 29.9215 (+-0.190511) 3.00024 (+-0.000661866)
found 1.4 (+-0.000260428) 29.9223 (+-0.190567) 3.00032 (+-0.000662059)
found 2.84 (+-0.000258232) 29.9202 (+-0.190351) 3.00011 (+-0.000661309)
found -9.88 (+-0.000258303) 29.92 (+-0.190342) 3.00008 (+-0.00066128)
found -4.60001 (+-0.000320024) 19.9493 (+-0.155674) 2.00032 (+-0.000540836)
found -8.2 (+-0.000318615) 19.948 (+-0.155573) 2.00019 (+-0.000540486)
found 4.04 (+-0.000321638) 19.9501 (+-0.155778) 2.0004 (+-0.000541196)
found 8.84 (+-0.000320734) 19.9493 (+-0.155715) 2.00032 (+-0.000540977)
found -7.96 (+-0.000317209) 19.9472 (+-0.15548) 2.00011 (+-0.000540161)
found -7.72 (+-0.000316445) 19.9469 (+-0.155433) 2.00008 (+-0.000539998)
found -4.35999 (+-0.000320024) 19.9493 (+-0.155674) 2.00032 (+-0.000540836)
found 3.08 (+-0.000318787) 19.948 (+-0.155583) 2.00019 (+-0.000540519)
found 8.36 (+-0.000319988) 19.9488 (+-0.155664) 2.00027 (+-0.000540801)
found 2.6 (+-0.000316999) 19.9472 (+-0.155469) 2.00011 (+-0.000540124)
found 0.199987 (+-0.000454147) 9.97549 (+-0.110138) 1.00025 (+-0.000382636)
found 2.35999 (+-0.000455169) 9.97546 (+-0.110167) 1.00024 (+-0.000382738)
found 5.24 (+-0.000456564) 9.97572 (+-0.110213) 1.00027 (+-0.000382896)
found 6.68001 (+-0.000457091) 9.97598 (+-0.110232) 1.00029 (+-0.000382964)
found -9.39999 (+-0.000455716) 9.97545 (+-0.110183) 1.00024 (+-0.000382794)
found -7.47999 (+-0.000454648) 9.97518 (+-0.110147) 1.00021 (+-0.000382669)
found 0.440017 (+-0.00045495) 9.97603 (+-0.11017) 1.0003 (+-0.000382747)

 
#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.