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 59.8413 6
created -9.4 89.762 9
created -9.16 79.7885 8
created -8.92 79.7885 8
created -8.68 39.8942 4
created -8.44 39.8942 4
created -8.2 69.8149 7
created -7.96 79.7885 8
created -7.72 39.8942 4
created -7.48 69.8149 7
created -7.24 69.8149 7
created -7 59.8413 6
created -6.76 19.9471 2
created -6.52 39.8942 4
created -6.28 29.9207 3
created -6.04 59.8413 6
created -5.8 79.7885 8
created -5.56 79.7885 8
created -5.32 9.97356 1
created -5.08 49.8678 5
created -4.84 29.9207 3
created -4.6 29.9207 3
created -4.36 69.8149 7
created -4.12 69.8149 7
created -3.88 99.7356 10
created -3.64 69.8149 7
created -3.4 49.8678 5
created -3.16 89.762 9
created -2.92 29.9207 3
created -2.68 69.8149 7
created -2.44 9.97356 1
created -2.2 69.8149 7
created -1.96 39.8942 4
created -1.72 39.8942 4
created -1.48 79.7885 8
created -1.24 69.8149 7
created -1 79.7885 8
created -0.76 89.762 9
created -0.52 89.762 9
created -0.28 59.8413 6
created -0.04 59.8413 6
created 0.2 19.9471 2
created 0.44 79.7885 8
created 0.68 59.8413 6
created 0.92 39.8942 4
created 1.16 99.7356 10
created 1.4 59.8413 6
created 1.64 9.97356 1
created 1.88 69.8149 7
created 2.12 29.9207 3
created 2.36 49.8678 5
created 2.6 49.8678 5
created 2.84 89.762 9
created 3.08 9.97356 1
created 3.32 39.8942 4
created 3.56 59.8413 6
created 3.8 19.9471 2
created 4.04 59.8413 6
created 4.28 19.9471 2
created 4.52 39.8942 4
created 4.76 79.7885 8
created 5 79.7885 8
created 5.24 99.7356 10
created 5.48 39.8942 4
created 5.72 99.7356 10
created 5.96 69.8149 7
created 6.2 79.7885 8
created 6.44 29.9207 3
created 6.68 49.8678 5
created 6.92 49.8678 5
created 7.16 19.9471 2
created 7.4 89.762 9
created 7.64 49.8678 5
created 7.88 49.8678 5
created 8.12 99.7356 10
created 8.36 9.97356 1
created 8.6 69.8149 7
created 8.84 79.7885 8
created 9.08 29.9207 3
created 9.32 99.7356 10
created 9.56 49.8678 5
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 (+-7.09528e-06)
fit chi^2 = 2.6575e-06
found -3.88 (+-8.55768e-05) 99.7341 (+-0.21021) 10.0004 (+-0.000730302)
found 5.24 (+-8.54679e-05) 99.7336 (+-0.210178) 10.0003 (+-0.000730191)
found 1.16 (+-8.53839e-05) 99.7331 (+-0.210152) 10.0003 (+-0.000730101)
found 5.72 (+-8.54278e-05) 99.7334 (+-0.210165) 10.0003 (+-0.000730148)
found 8.12 (+-8.50693e-05) 99.732 (+-0.210065) 10.0002 (+-0.000729799)
found 9.32 (+-8.52691e-05) 99.7325 (+-0.210118) 10.0002 (+-0.000729984)
found -9.4 (+-9.02685e-05) 89.7611 (+-0.19944) 9.00037 (+-0.000692887)
found -0.76 (+-9.04029e-05) 89.7619 (+-0.199478) 9.00045 (+-0.000693019)
found -0.520001 (+-9.03097e-05) 89.7614 (+-0.199452) 9.0004 (+-0.000692928)
found -9.88 (+-9.00673e-05) 89.7591 (+-0.199368) 9.00017 (+-0.000692635)
found -3.16 (+-8.99355e-05) 89.7595 (+-0.19935) 9.00021 (+-0.000692573)
found 2.84 (+-8.97165e-05) 89.759 (+-0.199297) 9.00015 (+-0.000692389)
found 7.4 (+-8.98447e-05) 89.7592 (+-0.199327) 9.00018 (+-0.000692495)
found -9.16 (+-9.59767e-05) 79.7889 (+-0.188093) 8.00045 (+-0.000653464)
found -8.92 (+-9.56994e-05) 79.7875 (+-0.188024) 8.00032 (+-0.000653225)
found -7.96 (+-9.56502e-05) 79.7873 (+-0.188011) 8.00029 (+-0.000653181)
found -5.8 (+-9.58271e-05) 79.7881 (+-0.188055) 8.00037 (+-0.000653332)
found -5.56 (+-9.53757e-05) 79.7867 (+-0.187953) 8.00024 (+-0.000652977)
found -1 (+-9.5927e-05) 79.7886 (+-0.18808) 8.00043 (+-0.00065342)
found 5 (+-9.60199e-05) 79.7891 (+-0.188104) 8.00048 (+-0.000653503)
found 6.2 (+-9.55703e-05) 79.787 (+-0.187993) 8.00027 (+-0.000653117)
found 8.84 (+-9.55703e-05) 79.787 (+-0.187993) 8.00027 (+-0.000653117)
found -1.48 (+-9.56502e-05) 79.7873 (+-0.188011) 8.00029 (+-0.000653181)
found 0.440001 (+-9.54164e-05) 79.7865 (+-0.187957) 8.00021 (+-0.000652992)
found 4.76 (+-9.56994e-05) 79.7875 (+-0.188024) 8.00032 (+-0.000653225)
found -7.24 (+-0.000102493) 69.8148 (+-0.17592) 7.00034 (+-0.000611174)
found -4.12 (+-0.000102711) 69.8158 (+-0.175969) 7.00045 (+-0.000611345)
found -3.64 (+-0.000102582) 69.8153 (+-0.175941) 7.0004 (+-0.000611247)
found -1.24 (+-0.000102666) 69.8156 (+-0.175959) 7.00043 (+-0.000611309)
found 5.96 (+-0.000102767) 69.8161 (+-0.175982) 7.00048 (+-0.000611389)
found -8.2 (+-0.000102405) 69.8145 (+-0.175902) 7.00032 (+-0.000611111)
found -7.48 (+-0.00010235) 69.8142 (+-0.175889) 7.00029 (+-0.000611067)
found -4.36 (+-0.00010226) 69.814 (+-0.175871) 7.00027 (+-0.000611003)
found -2.68 (+-0.000101698) 69.8124 (+-0.175756) 7.00011 (+-0.000610603)
found -2.2 (+-0.000101786) 69.8126 (+-0.175774) 7.00013 (+-0.000610667)
found 1.88 (+-0.000101698) 69.8124 (+-0.175756) 7.0001 (+-0.000610603)
found 8.6 (+-0.000102042) 69.8137 (+-0.175831) 7.00024 (+-0.000610864)
found -9.64 (+-0.000111162) 59.843 (+-0.162959) 6.00048 (+-0.000566145)
found -0.280001 (+-0.000110966) 59.8423 (+-0.162921) 6.0004 (+-0.000566013)
found 1.4 (+-0.000110441) 59.8412 (+-0.162831) 6.00029 (+-0.0005657)
found -7 (+-0.000110446) 59.8407 (+-0.162825) 6.00024 (+-0.000565679)
found -6.04 (+-0.000110638) 59.8412 (+-0.16286) 6.00029 (+-0.0005658)
found -0.0400012 (+-0.000110376) 59.8404 (+-0.162811) 6.00021 (+-0.000565633)
found 0.679999 (+-0.000110741) 59.8415 (+-0.162878) 6.00032 (+-0.000565864)
found 3.56 (+-0.000110213) 59.8399 (+-0.162781) 6.00016 (+-0.000565525)
found 4.04 (+-0.000109986) 59.8393 (+-0.16274) 6.00011 (+-0.000565385)
found 7.64 (+-0.000121665) 49.869 (+-0.148744) 5.00037 (+-0.000516759)
found 9.56 (+-0.000121142) 49.8682 (+-0.148669) 5.00029 (+-0.000516499)
found -3.4 (+-0.000121841) 49.8695 (+-0.148772) 5.00043 (+-0.000516858)
found 2.36 (+-0.000121121) 49.8674 (+-0.148656) 5.00021 (+-0.000516456)
found 2.6 (+-0.000121665) 49.8689 (+-0.148744) 5.00037 (+-0.000516759)
found 6.68 (+-0.000121121) 49.8674 (+-0.148656) 5.00021 (+-0.000516456)
found 6.92 (+-0.000120972) 49.8671 (+-0.148634) 5.00019 (+-0.000516379)
found 7.88 (+-0.000121732) 49.8692 (+-0.148755) 5.0004 (+-0.000516798)
found -5.08 (+-0.00012054) 49.8663 (+-0.14857) 5.00011 (+-0.000516157)
found 5.48 (+-0.000136876) 39.8975 (+-0.133153) 4.00053 (+-0.000462596)
found -8.68 (+-0.0001361) 39.8954 (+-0.13305) 4.00032 (+-0.000462238)
found -8.44 (+-0.000136007) 39.8951 (+-0.133038) 4.00029 (+-0.000462195)
found -7.72 (+-0.000136444) 39.8962 (+-0.133095) 4.0004 (+-0.000462393)
found -1.96 (+-0.000136007) 39.8951 (+-0.133038) 4.00029 (+-0.000462195)
found -1.72 (+-0.0001361) 39.8954 (+-0.13305) 4.00032 (+-0.000462238)
found 0.920002 (+-0.000136509) 39.8964 (+-0.133104) 4.00043 (+-0.000462426)
found -6.52 (+-0.000135194) 39.8935 (+-0.132935) 4.00013 (+-0.000461836)
found 3.32 (+-0.000135319) 39.8941 (+-0.132955) 4.00019 (+-0.000461906)
found 4.52 (+-0.00013577) 39.8949 (+-0.13301) 4.00027 (+-0.000462098)
found -2.92 (+-0.00015816) 29.9234 (+-0.115326) 3.00043 (+-0.000400659)
found 6.44 (+-0.000157767) 29.9226 (+-0.115286) 3.00035 (+-0.000400521)
found 9.08 (+-0.000158386) 29.9239 (+-0.115349) 3.00048 (+-0.00040074)
found -4.84 (+-0.000157009) 29.9213 (+-0.11521) 3.00021 (+-0.000400259)
found 2.12 (+-0.000157646) 29.9223 (+-0.115273) 3.00032 (+-0.000400478)
found -6.28 (+-0.000157347) 29.9218 (+-0.115244) 3.00027 (+-0.000400374)
found -4.6 (+-0.000157287) 29.9218 (+-0.115238) 3.00027 (+-0.000400357)
found -6.76 (+-0.000193542) 19.9488 (+-0.0941519) 2.00027 (+-0.000327099)
found 0.200002 (+-0.000194351) 19.9498 (+-0.0942076) 2.00037 (+-0.000327292)
found 3.8 (+-0.000193993) 19.9493 (+-0.0941825) 2.00032 (+-0.000327205)
found 4.28 (+-0.000193542) 19.9488 (+-0.0941519) 2.00027 (+-0.000327099)
found 7.16 (+-0.000194293) 19.9498 (+-0.0942042) 2.00037 (+-0.00032728)
found 8.35999 (+-0.000278375) 9.97758 (+-0.0667436) 1.00045 (+-0.000231878)
found -5.32001 (+-0.000277182) 9.97652 (+-0.066699) 1.00035 (+-0.000231723)
found 3.07999 (+-0.000277011) 9.97652 (+-0.0666939) 1.00035 (+-0.000231705)
found -2.44 (+-0.000277585) 9.97678 (+-0.0667133) 1.00037 (+-0.000231773)
found 1.64 (+-0.000277263) 9.97652 (+-0.0667014) 1.00035 (+-0.000231731)
found 9.8 (+-0.000271038) 9.97466 (+-0.0665107) 1.00016 (+-0.000231069)

 
#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.