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 89.762 9
created -9.64 19.9471 2
created -9.4 9.97356 1
created -9.16 89.762 9
created -8.92 69.8149 7
created -8.68 39.8942 4
created -8.44 79.7885 8
created -8.2 39.8942 4
created -7.96 59.8413 6
created -7.72 59.8413 6
created -7.48 39.8942 4
created -7.24 9.97356 1
created -7 79.7885 8
created -6.76 29.9207 3
created -6.52 99.7356 10
created -6.28 59.8413 6
created -6.04 29.9207 3
created -5.8 19.9471 2
created -5.56 99.7356 10
created -5.32 29.9207 3
created -5.08 69.8149 7
created -4.84 19.9471 2
created -4.6 29.9207 3
created -4.36 79.7885 8
created -4.12 99.7356 10
created -3.88 39.8942 4
created -3.64 79.7885 8
created -3.4 19.9471 2
created -3.16 89.762 9
created -2.92 99.7356 10
created -2.68 79.7885 8
created -2.44 19.9471 2
created -2.2 69.8149 7
created -1.96 89.762 9
created -1.72 9.97356 1
created -1.48 99.7356 10
created -1.24 19.9471 2
created -1 89.762 9
created -0.76 99.7356 10
created -0.52 9.97356 1
created -0.28 39.8942 4
created -0.04 39.8942 4
created 0.2 69.8149 7
created 0.44 79.7885 8
created 0.68 79.7885 8
created 0.92 99.7356 10
created 1.16 79.7885 8
created 1.4 19.9471 2
created 1.64 79.7885 8
created 1.88 49.8678 5
created 2.12 89.762 9
created 2.36 89.762 9
created 2.6 59.8413 6
created 2.84 19.9471 2
created 3.08 79.7885 8
created 3.32 79.7885 8
created 3.56 29.9207 3
created 3.8 89.762 9
created 4.04 29.9207 3
created 4.28 89.762 9
created 4.52 29.9207 3
created 4.76 19.9471 2
created 5 19.9471 2
created 5.24 99.7356 10
created 5.48 69.8149 7
created 5.72 49.8678 5
created 5.96 99.7356 10
created 6.2 39.8942 4
created 6.44 99.7356 10
created 6.68 39.8942 4
created 6.92 99.7356 10
created 7.16 59.8413 6
created 7.4 59.8413 6
created 7.64 99.7356 10
created 7.88 79.7885 8
created 8.12 9.97356 1
created 8.36 69.8149 7
created 8.6 39.8942 4
created 8.84 79.7885 8
created 9.08 79.7885 8
created 9.32 69.8149 7
created 9.56 59.8413 6
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 (+-1.67364e-05)
fit chi^2 = 1.55941e-05
found -4.12 (+-0.000207036) 99.7337 (+-0.509132) 10.0003 (+-0.0017688)
found -2.92 (+-0.000207586) 99.735 (+-0.509299) 10.0004 (+-0.00176938)
found -0.760002 (+-0.000206479) 99.7331 (+-0.508986) 10.0003 (+-0.0017683)
found 0.92 (+-0.000207495) 99.7347 (+-0.50927) 10.0004 (+-0.00176928)
found 7.64 (+-0.000207291) 99.7342 (+-0.509207) 10.0004 (+-0.00176906)
found -6.52 (+-0.000206672) 99.7329 (+-0.509023) 10.0002 (+-0.00176843)
found -5.56 (+-0.00020606) 99.7318 (+-0.508847) 10.0001 (+-0.00176782)
found -1.48 (+-0.00020558) 99.7313 (+-0.508718) 10.0001 (+-0.00176737)
found 5.24 (+-0.000206576) 99.7329 (+-0.509001) 10.0002 (+-0.00176835)
found 5.96 (+-0.000206714) 99.7329 (+-0.509033) 10.0002 (+-0.00176846)
found 6.44 (+-0.00020658) 99.7326 (+-0.508994) 10.0002 (+-0.00176832)
found 6.92 (+-0.000206833) 99.7332 (+-0.509069) 10.0003 (+-0.00176858)
found -1.96 (+-0.000217572) 89.7596 (+-0.482842) 9.00021 (+-0.00167747)
found 2.36 (+-0.000218765) 89.7614 (+-0.48315) 9.0004 (+-0.00167854)
found 9.8 (+-0.000216648) 89.7614 (+-0.48265) 9.00039 (+-0.0016768)
found -9.88 (+-0.000217507) 89.758 (+-0.48277) 9.00005 (+-0.00167722)
found -9.16 (+-0.000217572) 89.7595 (+-0.482842) 9.00021 (+-0.00167747)
found -3.16 (+-0.000218181) 89.7607 (+-0.483002) 9.00032 (+-0.00167802)
found -0.999998 (+-0.000218181) 89.7607 (+-0.483002) 9.00032 (+-0.00167802)
found 2.12 (+-0.000218634) 89.7612 (+-0.483114) 9.00037 (+-0.00167841)
found 3.8 (+-0.000217537) 89.7591 (+-0.482818) 9.00016 (+-0.00167738)
found 4.28 (+-0.000217537) 89.759 (+-0.482818) 9.00016 (+-0.00167738)
found -2.68 (+-0.000231596) 79.7876 (+-0.455423) 8.00032 (+-0.00158221)
found 0.44 (+-0.000232261) 79.7884 (+-0.455574) 8.0004 (+-0.00158274)
found 0.68 (+-0.000232597) 79.7892 (+-0.45566) 8.00048 (+-0.00158303)
found 1.16 (+-0.000231596) 79.7876 (+-0.455423) 8.00032 (+-0.00158221)
found 3.32 (+-0.000231627) 79.7873 (+-0.455421) 8.00029 (+-0.00158221)
found 7.88 (+-0.000231249) 79.7873 (+-0.455349) 8.00029 (+-0.00158195)
found 9.08 (+-0.000232261) 79.7884 (+-0.455574) 8.0004 (+-0.00158274)
found -8.44 (+-0.000231265) 79.7865 (+-0.455329) 8.00021 (+-0.00158188)
found -7 (+-0.000230295) 79.7855 (+-0.455111) 8.0001 (+-0.00158113)
found -4.36 (+-0.000231841) 79.7878 (+-0.455477) 8.00034 (+-0.0015824)
found -3.64 (+-0.00023083) 79.786 (+-0.455229) 8.00016 (+-0.00158154)
found 1.64 (+-0.000230992) 79.7863 (+-0.455268) 8.00019 (+-0.00158167)
found 3.08 (+-0.000231383) 79.7871 (+-0.455367) 8.00026 (+-0.00158202)
found 8.84 (+-0.000231821) 79.7876 (+-0.455466) 8.00032 (+-0.00158236)
found -8.92 (+-0.000248191) 69.8148 (+-0.426131) 7.00035 (+-0.00148044)
found 5.48 (+-0.000248494) 69.8154 (+-0.426197) 7.0004 (+-0.00148068)
found 9.32 (+-0.000248413) 69.8151 (+-0.426177) 7.00037 (+-0.0014806)
found -5.08 (+-0.000246737) 69.8127 (+-0.42582) 7.00013 (+-0.00147937)
found -2.2 (+-0.0002477) 69.8143 (+-0.426032) 7.00029 (+-0.0014801)
found 0.200001 (+-0.000248065) 69.8146 (+-0.426102) 7.00032 (+-0.00148034)
found 8.36 (+-0.000246565) 69.8127 (+-0.425792) 7.00013 (+-0.00147927)
found -6.28 (+-0.000268287) 59.8418 (+-0.394565) 6.00035 (+-0.00137078)
found 2.6 (+-0.000267841) 59.8413 (+-0.394484) 6.00029 (+-0.0013705)
found 7.16 (+-0.000268938) 59.8426 (+-0.394684) 6.00043 (+-0.00137119)
found -7.96 (+-0.000267931) 59.841 (+-0.39449) 6.00027 (+-0.00137052)
found -7.72 (+-0.000267931) 59.841 (+-0.39449) 6.00026 (+-0.00137052)
found 7.4 (+-0.000268938) 59.8426 (+-0.394684) 6.00043 (+-0.00137119)
found 9.56 (+-0.000268973) 59.8426 (+-0.39469) 6.00043 (+-0.00137121)
found 1.88 (+-0.000295333) 49.8698 (+-0.360414) 5.00045 (+-0.00125213)
found 5.72 (+-0.000295307) 49.8698 (+-0.36041) 5.00045 (+-0.00125212)
found -3.88 (+-0.000331156) 39.897 (+-0.322493) 4.00048 (+-0.00112039)
found 6.2 (+-0.000331566) 39.8975 (+-0.322549) 4.00053 (+-0.00112059)
found 6.68 (+-0.000331566) 39.8975 (+-0.322549) 4.00053 (+-0.00112059)
found -8.68 (+-0.000330519) 39.8962 (+-0.322408) 4.0004 (+-0.00112009)
found -8.2 (+-0.00033027) 39.8959 (+-0.322375) 4.00037 (+-0.00111998)
found -7.48 (+-0.000327795) 39.8941 (+-0.322068) 4.00019 (+-0.00111891)
found -0.0399986 (+-0.000329461) 39.8951 (+-0.322269) 4.00029 (+-0.00111961)
found 8.6 (+-0.000330519) 39.8962 (+-0.322408) 4.0004 (+-0.00112009)
found -0.279999 (+-0.000327224) 39.8936 (+-0.321993) 4.00014 (+-0.00111865)
found -6.76 (+-0.000383672) 29.9239 (+-0.279419) 3.00048 (+-0.000970747)
found -5.32 (+-0.000383375) 29.9237 (+-0.279389) 3.00045 (+-0.000970642)
found 3.56 (+-0.00038342) 29.9237 (+-0.279393) 3.00045 (+-0.000970655)
found 4.04 (+-0.000383692) 29.9239 (+-0.279421) 3.00048 (+-0.000970753)
found 4.52 (+-0.000380994) 29.9221 (+-0.279157) 3.00029 (+-0.000969836)
found -6.04 (+-0.00038012) 29.9213 (+-0.279067) 3.00022 (+-0.000969521)
found -4.6 (+-0.000380727) 29.9218 (+-0.279129) 3.00027 (+-0.000969739)
found -9.64001 (+-0.000467398) 19.9488 (+-0.227993) 2.00027 (+-0.000792082)
found -3.4 (+-0.000472041) 19.9506 (+-0.228295) 2.00045 (+-0.000793131)
found -2.44 (+-0.000471249) 19.9501 (+-0.228239) 2.0004 (+-0.000792937)
found -1.24 (+-0.000472767) 19.9512 (+-0.228347) 2.00051 (+-0.000793312)
found 1.4 (+-0.000471663) 19.9504 (+-0.228268) 2.00043 (+-0.000793038)
found -4.84 (+-0.000468603) 19.9488 (+-0.228059) 2.00027 (+-0.000792313)
found 2.84 (+-0.000470791) 19.9498 (+-0.228207) 2.00037 (+-0.000792827)
found -5.79999 (+-0.000469724) 19.9496 (+-0.22814) 2.00035 (+-0.000792594)
found 4.76 (+-0.000465578) 19.9475 (+-0.227855) 2.00014 (+-0.000791605)
found 5.00001 (+-0.000468885) 19.9493 (+-0.228087) 2.00032 (+-0.000792409)
found -0.520012 (+-0.000671601) 9.9768 (+-0.161581) 1.00038 (+-0.000561358)
found -1.72 (+-0.000675672) 9.97812 (+-0.16173) 1.00051 (+-0.000561874)
found 8.12 (+-0.000673116) 9.97706 (+-0.161632) 1.0004 (+-0.000561534)
found -7.23999 (+-0.0006704) 9.97626 (+-0.161534) 1.00032 (+-0.000561194)
found -9.39999 (+-0.000668193) 9.97602 (+-0.161463) 1.0003 (+-0.000560947)

 
#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.