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 49.8678 5
created -9.64 79.7885 8
created -9.4 79.7885 8
created -9.16 29.9207 3
created -8.92 39.8942 4
created -8.68 59.8413 6
created -8.44 59.8413 6
created -8.2 19.9471 2
created -7.96 19.9471 2
created -7.72 49.8678 5
created -7.48 49.8678 5
created -7.24 99.7356 10
created -7 69.8149 7
created -6.76 79.7885 8
created -6.52 29.9207 3
created -6.28 29.9207 3
created -6.04 19.9471 2
created -5.8 49.8678 5
created -5.56 59.8413 6
created -5.32 99.7356 10
created -5.08 29.9207 3
created -4.84 9.97356 1
created -4.6 9.97356 1
created -4.36 19.9471 2
created -4.12 49.8678 5
created -3.88 29.9207 3
created -3.64 19.9471 2
created -3.4 69.8149 7
created -3.16 29.9207 3
created -2.92 79.7885 8
created -2.68 69.8149 7
created -2.44 99.7356 10
created -2.2 39.8942 4
created -1.96 59.8413 6
created -1.72 79.7885 8
created -1.48 89.762 9
created -1.24 9.97356 1
created -1 49.8678 5
created -0.76 39.8942 4
created -0.52 19.9471 2
created -0.28 89.762 9
created -0.04 79.7885 8
created 0.2 59.8413 6
created 0.44 39.8942 4
created 0.68 99.7356 10
created 0.92 9.97356 1
created 1.16 19.9471 2
created 1.4 59.8413 6
created 1.64 79.7885 8
created 1.88 79.7885 8
created 2.12 19.9471 2
created 2.36 19.9471 2
created 2.6 49.8678 5
created 2.84 99.7356 10
created 3.08 9.97356 1
created 3.32 99.7356 10
created 3.56 29.9207 3
created 3.8 29.9207 3
created 4.04 69.8149 7
created 4.28 89.762 9
created 4.52 19.9471 2
created 4.76 59.8413 6
created 5 19.9471 2
created 5.24 49.8678 5
created 5.48 99.7356 10
created 5.72 29.9207 3
created 5.96 29.9207 3
created 6.2 79.7885 8
created 6.44 59.8413 6
created 6.68 79.7885 8
created 6.92 49.8678 5
created 7.16 39.8942 4
created 7.4 99.7356 10
created 7.64 19.9471 2
created 7.88 49.8678 5
created 8.12 59.8413 6
created 8.36 19.9471 2
created 8.6 9.97356 1
created 8.84 19.9471 2
created 9.08 99.7356 10
created 9.32 39.8942 4
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 (+-1.69918e-05)
fit chi^2 = 1.3721e-05
found -5.32 (+-0.000193863) 99.7329 (+-0.477475) 10.0002 (+-0.00165882)
found -2.44 (+-0.000194113) 99.7334 (+-0.477548) 10.0003 (+-0.00165908)
found -7.24 (+-0.00019424) 99.7337 (+-0.477586) 10.0003 (+-0.00165921)
found 0.679999 (+-0.000193174) 99.7318 (+-0.477283) 10.0001 (+-0.00165816)
found 2.84 (+-0.000193299) 99.7321 (+-0.477321) 10.0002 (+-0.00165829)
found 3.32 (+-0.000193025) 99.7315 (+-0.477241) 10.0001 (+-0.00165801)
found 5.48 (+-0.000193753) 99.7326 (+-0.477442) 10.0002 (+-0.00165871)
found 7.4 (+-0.000193438) 99.7321 (+-0.477353) 10.0002 (+-0.0016584)
found 9.08 (+-0.000193438) 99.732 (+-0.477353) 10.0002 (+-0.0016584)
found -1.48 (+-0.000204186) 89.7598 (+-0.452945) 9.00024 (+-0.0015736)
found 4.28 (+-0.000204379) 89.7598 (+-0.452985) 9.00024 (+-0.00157374)
found -0.279999 (+-0.000204479) 89.7601 (+-0.453014) 9.00026 (+-0.00157384)
found -9.4 (+-0.000217271) 79.7873 (+-0.427195) 8.00029 (+-0.00148414)
found -6.76 (+-0.00021716) 79.7871 (+-0.427167) 8.00026 (+-0.00148404)
found -1.72 (+-0.000217847) 79.7884 (+-0.427335) 8.0004 (+-0.00148463)
found -0.0400007 (+-0.000217847) 79.7884 (+-0.427335) 8.0004 (+-0.00148463)
found 1.64 (+-0.000217743) 79.7881 (+-0.427308) 8.00037 (+-0.00148453)
found 1.88 (+-0.000217042) 79.787 (+-0.427144) 8.00026 (+-0.00148397)
found 6.68 (+-0.000217373) 79.7873 (+-0.427215) 8.00029 (+-0.00148421)
found 9.8 (+-0.000215634) 79.7881 (+-0.426866) 8.00037 (+-0.001483)
found -9.64 (+-0.000217607) 79.7878 (+-0.427274) 8.00034 (+-0.00148442)
found -2.92 (+-0.000217159) 79.7871 (+-0.427167) 8.00027 (+-0.00148404)
found 6.2 (+-0.000217038) 79.7868 (+-0.427136) 8.00024 (+-0.00148394)
found -7 (+-0.000233513) 69.8161 (+-0.399875) 7.00048 (+-0.00138923)
found -2.68 (+-0.000233513) 69.8161 (+-0.399875) 7.00048 (+-0.00138923)
found -3.4 (+-0.000231445) 69.8127 (+-0.399429) 7.00013 (+-0.00138768)
found 4.04 (+-0.000232604) 69.8145 (+-0.399678) 7.00032 (+-0.00138854)
found -8.68 (+-0.000251325) 59.8409 (+-0.37004) 6.00026 (+-0.00128558)
found -8.44 (+-0.000250803) 59.8404 (+-0.369948) 6.00021 (+-0.00128526)
found -5.56 (+-0.000252093) 59.8423 (+-0.370189) 6.0004 (+-0.00128609)
found -1.96 (+-0.00025163) 59.8415 (+-0.3701) 6.00032 (+-0.00128578)
found 0.199999 (+-0.00025163) 59.8415 (+-0.3701) 6.00032 (+-0.00128578)
found 6.44 (+-0.000252313) 59.8425 (+-0.370229) 6.00042 (+-0.00128624)
found 8.12 (+-0.000250629) 59.8401 (+-0.369915) 6.00018 (+-0.00128514)
found 9.56 (+-0.00025163) 59.8415 (+-0.3701) 6.00032 (+-0.00128578)
found 1.4 (+-0.000251106) 59.841 (+-0.370008) 6.00027 (+-0.00128546)
found 4.76 (+-0.000249915) 59.8394 (+-0.369786) 6.00011 (+-0.00128469)
found -7.48 (+-0.000276605) 49.8692 (+-0.338008) 5.0004 (+-0.00117429)
found 6.92 (+-0.000276058) 49.8685 (+-0.337919) 5.00032 (+-0.00117398)
found -9.88 (+-0.000275546) 49.8674 (+-0.337813) 5.00022 (+-0.00117362)
found -7.72 (+-0.000274878) 49.8671 (+-0.337735) 5.00019 (+-0.00117334)
found -5.8 (+-0.000275084) 49.8674 (+-0.337768) 5.00021 (+-0.00117346)
found -4.12 (+-0.000274377) 49.8666 (+-0.337656) 5.00013 (+-0.00117307)
found -0.999999 (+-0.000274165) 49.8666 (+-0.337631) 5.00013 (+-0.00117298)
found 2.6 (+-0.000275752) 49.8684 (+-0.33788) 5.00032 (+-0.00117385)
found 5.24 (+-0.000275752) 49.8685 (+-0.33788) 5.00032 (+-0.00117385)
found 7.88 (+-0.000275084) 49.8674 (+-0.337768) 5.00021 (+-0.00117346)
found -2.2 (+-0.000310182) 39.8965 (+-0.302446) 4.00043 (+-0.00105075)
found 9.32 (+-0.000310182) 39.8965 (+-0.302446) 4.00043 (+-0.00105075)
found -8.92 (+-0.000308475) 39.8946 (+-0.302223) 4.00024 (+-0.00104997)
found -0.760001 (+-0.00030781) 39.8941 (+-0.30214) 4.00019 (+-0.00104968)
found 0.440002 (+-0.000310182) 39.8965 (+-0.302446) 4.00043 (+-0.00105075)
found 7.16 (+-0.000309924) 39.8962 (+-0.302413) 4.0004 (+-0.00105063)
found -9.16 (+-0.000358106) 29.9224 (+-0.261921) 3.00032 (+-0.000909955)
found -6.52 (+-0.000357667) 29.9221 (+-0.261879) 3.00029 (+-0.000909809)
found -5.08001 (+-0.00035686) 29.9221 (+-0.261815) 3.00029 (+-0.000909586)
found 3.56 (+-0.000358151) 29.9226 (+-0.26193) 3.00035 (+-0.000909986)
found 5.72 (+-0.000358151) 29.9226 (+-0.26193) 3.00035 (+-0.000909986)
found -3.88 (+-0.000356231) 29.921 (+-0.261737) 3.00019 (+-0.000909316)
found -3.16 (+-0.000359125) 29.9231 (+-0.262022) 3.0004 (+-0.000910307)
found -6.28 (+-0.000355427) 29.9205 (+-0.261658) 3.00013 (+-0.000909041)
found 3.8 (+-0.000357394) 29.9218 (+-0.261851) 3.00027 (+-0.000909711)
found 5.96 (+-0.000357667) 29.9221 (+-0.261879) 3.0003 (+-0.000909809)
found 2.11999 (+-0.000439159) 19.9488 (+-0.213902) 2.00027 (+-0.000743128)
found 4.52 (+-0.000441965) 19.9501 (+-0.214088) 2.0004 (+-0.000743777)
found 7.64 (+-0.000441806) 19.9501 (+-0.214079) 2.0004 (+-0.000743744)
found -8.2 (+-0.000438359) 19.9482 (+-0.213845) 2.00021 (+-0.00074293)
found 5 (+-0.000440321) 19.949 (+-0.213974) 2.00029 (+-0.000743378)
found 8.36 (+-0.000437291) 19.948 (+-0.21378) 2.00019 (+-0.000742705)
found -7.96 (+-0.000437887) 19.948 (+-0.213812) 2.00019 (+-0.000742817)
found -6.04 (+-0.000438664) 19.9482 (+-0.213862) 2.00021 (+-0.00074299)
found -3.64 (+-0.00043956) 19.9488 (+-0.213924) 2.00027 (+-0.000743207)
found -0.519995 (+-0.000440933) 19.9496 (+-0.214019) 2.00035 (+-0.000743535)
found 2.36 (+-0.000437887) 19.948 (+-0.213812) 2.00019 (+-0.000742817)
found -4.36 (+-0.000436823) 19.9477 (+-0.213747) 2.00016 (+-0.000742592)
found 1.16 (+-0.00043729) 19.948 (+-0.21378) 2.00019 (+-0.000742705)
found 8.84001 (+-0.000438747) 19.9491 (+-0.213885) 2.0003 (+-0.000743072)
found 0.919985 (+-0.000627313) 9.97628 (+-0.151477) 1.00032 (+-0.000526255)
found 3.08 (+-0.000634344) 9.97838 (+-0.151727) 1.00053 (+-0.000527124)
found -1.24001 (+-0.000630416) 9.97679 (+-0.15158) 1.00037 (+-0.000526611)
found -4.84 (+-0.000619932) 9.97413 (+-0.151211) 1.00011 (+-0.000525331)
found -4.6 (+-0.000618506) 9.97386 (+-0.151162) 1.00008 (+-0.00052516)
found 8.6 (+-0.000620496) 9.97414 (+-0.151227) 1.00011 (+-0.000525386)

 
#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.