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 99.7356 10
created -9.4 19.9471 2
created -9.16 59.8413 6
created -8.92 9.97356 1
created -8.68 79.7885 8
created -8.44 19.9471 2
created -8.2 39.8942 4
created -7.96 49.8678 5
created -7.72 49.8678 5
created -7.48 39.8942 4
created -7.24 29.9207 3
created -7 69.8149 7
created -6.76 39.8942 4
created -6.52 89.762 9
created -6.28 59.8413 6
created -6.04 49.8678 5
created -5.8 99.7356 10
created -5.56 9.97356 1
created -5.32 9.97356 1
created -5.08 49.8678 5
created -4.84 99.7356 10
created -4.6 99.7356 10
created -4.36 19.9471 2
created -4.12 29.9207 3
created -3.88 99.7356 10
created -3.64 99.7356 10
created -3.4 39.8942 4
created -3.16 49.8678 5
created -2.92 69.8149 7
created -2.68 29.9207 3
created -2.44 29.9207 3
created -2.2 49.8678 5
created -1.96 99.7356 10
created -1.72 89.762 9
created -1.48 9.97356 1
created -1.24 69.8149 7
created -1 89.762 9
created -0.76 99.7356 10
created -0.52 59.8413 6
created -0.28 49.8678 5
created -0.04 29.9207 3
created 0.2 99.7356 10
created 0.44 9.97356 1
created 0.68 9.97356 1
created 0.92 99.7356 10
created 1.16 49.8678 5
created 1.4 29.9207 3
created 1.64 39.8942 4
created 1.88 19.9471 2
created 2.12 49.8678 5
created 2.36 79.7885 8
created 2.6 69.8149 7
created 2.84 79.7885 8
created 3.08 39.8942 4
created 3.32 59.8413 6
created 3.56 59.8413 6
created 3.8 19.9471 2
created 4.04 89.762 9
created 4.28 99.7356 10
created 4.52 39.8942 4
created 4.76 49.8678 5
created 5 99.7356 10
created 5.24 9.97356 1
created 5.48 19.9471 2
created 5.72 59.8413 6
created 5.96 69.8149 7
created 6.2 29.9207 3
created 6.44 79.7885 8
created 6.68 89.762 9
created 6.92 59.8413 6
created 7.16 69.8149 7
created 7.4 59.8413 6
created 7.64 99.7356 10
created 7.88 89.762 9
created 8.12 49.8678 5
created 8.36 99.7356 10
created 8.6 79.7885 8
created 8.84 49.8678 5
created 9.08 39.8942 4
created 9.32 69.8149 7
created 9.56 89.762 9
created 9.8 9.97356 1
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.0400021 (+-7.31569e-06)
fit chi^2 = 2.80369e-06
found -4.6 (+-8.7707e-05) 99.7336 (+-0.215862) 10.0003 (+-0.000749939)
found -3.64 (+-8.78613e-05) 99.7341 (+-0.215905) 10.0004 (+-0.000750088)
found -0.760001 (+-8.79335e-05) 99.7344 (+-0.215925) 10.0004 (+-0.000750159)
found 4.28 (+-8.78255e-05) 99.7339 (+-0.215893) 10.0003 (+-0.000750048)
found 7.64 (+-8.79335e-05) 99.7344 (+-0.215925) 10.0004 (+-0.000750159)
found -9.64 (+-8.72886e-05) 99.7314 (+-0.215737) 10.0001 (+-0.000749505)
found -5.8 (+-8.73778e-05) 99.732 (+-0.215765) 10.0002 (+-0.000749603)
found -4.84 (+-8.79189e-05) 99.7344 (+-0.215922) 10.0004 (+-0.000750146)
found -3.88 (+-8.7793e-05) 99.7338 (+-0.215885) 10.0003 (+-0.00075002)
found -1.96 (+-8.7883e-05) 99.7341 (+-0.21591) 10.0004 (+-0.000750106)
found 0.2 (+-8.72541e-05) 99.7314 (+-0.215729) 10.0001 (+-0.000749478)
found 0.920001 (+-8.73778e-05) 99.7319 (+-0.215765) 10.0002 (+-0.000749603)
found 5 (+-8.73778e-05) 99.732 (+-0.215765) 10.0002 (+-0.000749603)
found 8.36 (+-8.78447e-05) 99.7339 (+-0.215898) 10.0003 (+-0.000750064)
found -1.72 (+-9.23803e-05) 89.7603 (+-0.20477) 9.00029 (+-0.000711405)
found -0.999999 (+-9.285e-05) 89.7619 (+-0.20489) 9.00045 (+-0.000711819)
found 6.68 (+-9.27181e-05) 89.7611 (+-0.204852) 9.00037 (+-0.000711689)
found 7.88 (+-9.27446e-05) 89.7614 (+-0.204861) 9.0004 (+-0.000711719)
found 9.56 (+-9.22545e-05) 89.7595 (+-0.204734) 9.00021 (+-0.000711278)
found -6.52 (+-9.2505e-05) 89.76 (+-0.204794) 9.00026 (+-0.000711486)
found 4.04 (+-9.25129e-05) 89.7606 (+-0.204802) 9.00032 (+-0.000711513)
found 2.84 (+-9.82458e-05) 79.7873 (+-0.193113) 8.00029 (+-0.000670906)
found 8.6 (+-9.84573e-05) 79.7883 (+-0.193166) 8.0004 (+-0.000671091)
found -8.68 (+-9.75479e-05) 79.7851 (+-0.192952) 8.00008 (+-0.000670347)
found 2.36 (+-9.83153e-05) 79.7875 (+-0.19313) 8.00032 (+-0.000670964)
found 6.44 (+-9.8261e-05) 79.7875 (+-0.193119) 8.00032 (+-0.000670927)
found 2.6 (+-0.000105452) 69.8155 (+-0.180734) 7.00042 (+-0.000627897)
found 5.96 (+-0.000104973) 69.8137 (+-0.180629) 7.00024 (+-0.000627535)
found 7.16 (+-0.000105212) 69.8145 (+-0.18068) 7.00032 (+-0.000627711)
found -7 (+-0.000104827) 69.8132 (+-0.180598) 7.00019 (+-0.000627425)
found -2.92 (+-0.000104904) 69.8134 (+-0.180614) 7.00021 (+-0.000627482)
found -1.24 (+-0.000104864) 69.814 (+-0.180614) 7.00027 (+-0.000627483)
found 9.32 (+-0.000105238) 69.8147 (+-0.180687) 7.00034 (+-0.000627736)
found -6.28 (+-0.000113897) 59.842 (+-0.167327) 6.00037 (+-0.00058132)
found -0.520002 (+-0.000113955) 59.8422 (+-0.167338) 6.0004 (+-0.00058136)
found 6.92 (+-0.00011405) 59.8425 (+-0.167356) 6.00043 (+-0.00058142)
found 3.32 (+-0.000113608) 59.8409 (+-0.167271) 6.00027 (+-0.000581126)
found 3.56 (+-0.000113372) 59.8404 (+-0.167229) 6.00021 (+-0.000580982)
found 7.4 (+-0.000114107) 59.8428 (+-0.167367) 6.00045 (+-0.00058146)
found -9.16 (+-0.000112785) 59.8391 (+-0.167126) 6.00008 (+-0.000580621)
found 5.72 (+-0.000113443) 59.8406 (+-0.167243) 6.00024 (+-0.00058103)
found 1.16 (+-0.000124804) 49.8687 (+-0.152756) 5.00035 (+-0.000530698)
found 8.12 (+-0.000125369) 49.8703 (+-0.152846) 5.00051 (+-0.00053101)
found -7.96 (+-0.000124531) 49.8676 (+-0.152709) 5.00024 (+-0.000530536)
found -7.72 (+-0.000124531) 49.8676 (+-0.152709) 5.00024 (+-0.000530536)
found -6.04 (+-0.00012513) 49.8695 (+-0.152807) 5.00043 (+-0.000530875)
found -3.16 (+-0.000124709) 49.8681 (+-0.152738) 5.00029 (+-0.000530637)
found -2.2 (+-0.000124804) 49.8687 (+-0.152756) 5.00035 (+-0.000530698)
found -0.280001 (+-0.000124501) 49.8676 (+-0.152705) 5.00024 (+-0.000530523)
found 4.76 (+-0.000124928) 49.8689 (+-0.152775) 5.00037 (+-0.000530764)
found 8.84 (+-0.000124788) 49.8684 (+-0.152751) 5.00032 (+-0.000530682)
found -5.08 (+-0.000124429) 49.8681 (+-0.152703) 5.00029 (+-0.000530515)
found 2.12 (+-0.00012451) 49.8679 (+-0.15271) 5.00027 (+-0.000530538)
found -3.4 (+-0.000140096) 39.8962 (+-0.136701) 4.0004 (+-0.000474921)
found 4.52 (+-0.000140096) 39.8962 (+-0.136701) 4.0004 (+-0.000474921)
found -7.48 (+-0.000139327) 39.8943 (+-0.1366) 4.00021 (+-0.00047457)
found -6.76 (+-0.000140236) 39.8964 (+-0.136719) 4.00043 (+-0.000474983)
found 1.64 (+-0.000138863) 39.8935 (+-0.136542) 4.00013 (+-0.000474368)
found 3.08 (+-0.000140041) 39.8959 (+-0.136693) 4.00037 (+-0.000474892)
found 9.08 (+-0.000139828) 39.8954 (+-0.136665) 4.00032 (+-0.000474795)
found -8.2 (+-0.000139141) 39.8941 (+-0.136577) 4.00019 (+-0.000474492)
found -2.68 (+-0.000161555) 29.9218 (+-0.118366) 3.00027 (+-0.000411221)
found -0.0399969 (+-0.000162268) 29.9231 (+-0.118437) 3.0004 (+-0.00041147)
found 1.4 (+-0.000161467) 29.9215 (+-0.118356) 3.00024 (+-0.000411186)
found 6.2 (+-0.000162337) 29.9231 (+-0.118443) 3.0004 (+-0.00041149)
found -7.24 (+-0.000161753) 29.9221 (+-0.118385) 3.00029 (+-0.000411287)
found -4.12 (+-0.000161653) 29.9224 (+-0.118379) 3.00032 (+-0.000411267)
found -2.44 (+-0.00016127) 29.9213 (+-0.118337) 3.00022 (+-0.00041112)
found -9.4 (+-0.00019993) 19.9503 (+-0.096786) 2.00043 (+-0.00033625)
found -8.44 (+-0.000199159) 19.9493 (+-0.0967325) 2.00032 (+-0.000336064)
found -4.36001 (+-0.000199172) 19.9496 (+-0.0967355) 2.00035 (+-0.000336074)
found 3.8 (+-0.000199784) 19.9501 (+-0.0967754) 2.0004 (+-0.000336213)
found 1.88 (+-0.000198579) 19.9485 (+-0.096692) 2.00024 (+-0.000335923)
found -9.87999 (+-0.000198272) 19.9488 (+-0.0966719) 2.00027 (+-0.000335854)
found 5.48 (+-0.000197671) 19.948 (+-0.0966359) 2.00019 (+-0.000335729)
found -5.56002 (+-0.000282639) 9.97602 (+-0.0684434) 1.0003 (+-0.000237783)
found 0.439983 (+-0.000282639) 9.97603 (+-0.0684434) 1.0003 (+-0.000237783)
found 5.23998 (+-0.000283567) 9.97627 (+-0.0684728) 1.00032 (+-0.000237885)
found -1.48 (+-0.000285682) 9.97732 (+-0.0685452) 1.00043 (+-0.000238137)
found 9.79999 (+-0.000279591) 9.97574 (+-0.0683628) 1.00027 (+-0.000237503)
found -8.92 (+-0.000285082) 9.97677 (+-0.0685226) 1.00037 (+-0.000238058)
found -5.31999 (+-0.000281168) 9.97468 (+-0.0683863) 1.00016 (+-0.000237585)
found 0.680017 (+-0.000282639) 9.97603 (+-0.0684434) 1.0003 (+-0.000237783)

 
#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.