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 39.8942 4
created -9.4 29.9207 3
created -9.16 19.9471 2
created -8.92 89.762 9
created -8.68 49.8678 5
created -8.44 29.9207 3
created -8.2 89.762 9
created -7.96 99.7356 10
created -7.72 99.7356 10
created -7.48 79.7885 8
created -7.24 79.7885 8
created -7 39.8942 4
created -6.76 99.7356 10
created -6.52 89.762 9
created -6.28 99.7356 10
created -6.04 19.9471 2
created -5.8 89.762 9
created -5.56 49.8678 5
created -5.32 49.8678 5
created -5.08 89.762 9
created -4.84 59.8413 6
created -4.6 39.8942 4
created -4.36 59.8413 6
created -4.12 59.8413 6
created -3.88 69.8149 7
created -3.64 49.8678 5
created -3.4 19.9471 2
created -3.16 69.8149 7
created -2.92 79.7885 8
created -2.68 59.8413 6
created -2.44 79.7885 8
created -2.2 79.7885 8
created -1.96 39.8942 4
created -1.72 69.8149 7
created -1.48 29.9207 3
created -1.24 19.9471 2
created -1 89.762 9
created -0.76 29.9207 3
created -0.52 39.8942 4
created -0.28 59.8413 6
created -0.04 29.9207 3
created 0.2 49.8678 5
created 0.44 79.7885 8
created 0.68 79.7885 8
created 0.92 39.8942 4
created 1.16 9.97356 1
created 1.4 99.7356 10
created 1.64 59.8413 6
created 1.88 99.7356 10
created 2.12 19.9471 2
created 2.36 39.8942 4
created 2.6 19.9471 2
created 2.84 29.9207 3
created 3.08 69.8149 7
created 3.32 19.9471 2
created 3.56 59.8413 6
created 3.8 39.8942 4
created 4.04 59.8413 6
created 4.28 9.97356 1
created 4.52 99.7356 10
created 4.76 79.7885 8
created 5 9.97356 1
created 5.24 99.7356 10
created 5.48 9.97356 1
created 5.72 49.8678 5
created 5.96 9.97356 1
created 6.2 39.8942 4
created 6.44 59.8413 6
created 6.68 39.8942 4
created 6.92 59.8413 6
created 7.16 69.8149 7
created 7.4 49.8678 5
created 7.64 79.7885 8
created 7.88 9.97356 1
created 8.12 19.9471 2
created 8.36 59.8413 6
created 8.6 29.9207 3
created 8.84 49.8678 5
created 9.08 59.8413 6
created 9.32 99.7356 10
created 9.56 89.762 9
created 9.8 99.7356 10
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.81461e-05)
fit chi^2 = 1.69955e-05
found -7.96 (+-0.000216897) 99.7355 (+-0.531749) 10.0005 (+-0.00184738)
found -7.72 (+-0.000216802) 99.7353 (+-0.531719) 10.0005 (+-0.00184728)
found -6.28 (+-0.000215854) 99.7334 (+-0.531441) 10.0003 (+-0.00184631)
found 1.88 (+-0.000215548) 99.7326 (+-0.531346) 10.0002 (+-0.00184598)
found 9.32 (+-0.000216499) 99.7345 (+-0.531625) 10.0004 (+-0.00184695)
found 9.8 (+-0.000214793) 99.7355 (+-0.531199) 10.0005 (+-0.00184547)
found -6.76 (+-0.000216233) 99.734 (+-0.531546) 10.0003 (+-0.00184668)
found 1.4 (+-0.000215253) 99.7323 (+-0.531269) 10.0002 (+-0.00184571)
found 4.52 (+-0.000215464) 99.7329 (+-0.531334) 10.0002 (+-0.00184594)
found 5.24 (+-0.000214327) 99.731 (+-0.531008) 10 (+-0.00184481)
found -6.52 (+-0.00022892) 89.7627 (+-0.504545) 9.00053 (+-0.00175287)
found 9.56 (+-0.000228919) 89.7628 (+-0.504545) 9.00054 (+-0.00175287)
found -8.92 (+-0.000227207) 89.7593 (+-0.504077) 9.00018 (+-0.00175124)
found -8.2 (+-0.000228005) 89.7609 (+-0.504295) 9.00035 (+-0.001752)
found -5.8 (+-0.000227207) 89.7593 (+-0.504077) 9.00019 (+-0.00175124)
found -5.08 (+-0.000227909) 89.7604 (+-0.50426) 9.00029 (+-0.00175188)
found -1 (+-0.000226873) 89.7587 (+-0.503988) 9.00013 (+-0.00175093)
found -7.48 (+-0.000242824) 79.7892 (+-0.475694) 8.00048 (+-0.00165264)
found -7.24 (+-0.000242013) 79.7876 (+-0.475492) 8.00032 (+-0.00165193)
found -2.92 (+-0.000242211) 79.7878 (+-0.475538) 8.00034 (+-0.00165209)
found -2.44 (+-0.000242336) 79.7881 (+-0.47557) 8.00037 (+-0.0016522)
found -2.2 (+-0.000242013) 79.7876 (+-0.475492) 8.00032 (+-0.00165193)
found 0.679999 (+-0.000242013) 79.7876 (+-0.475492) 8.00032 (+-0.00165193)
found 4.76 (+-0.000241417) 79.7873 (+-0.47537) 8.00029 (+-0.00165151)
found 0.440001 (+-0.000242185) 79.7878 (+-0.475533) 8.00034 (+-0.00165208)
found 7.64 (+-0.000240789) 79.786 (+-0.475209) 8.00016 (+-0.00165095)
found -3.88 (+-0.00025887) 69.8143 (+-0.444811) 7.00029 (+-0.00154534)
found 7.16 (+-0.00025887) 69.8143 (+-0.444811) 7.00029 (+-0.00154534)
found -3.16 (+-0.00025846) 69.814 (+-0.444733) 7.00027 (+-0.00154507)
found -1.72 (+-0.000258092) 69.8132 (+-0.444646) 7.00018 (+-0.00154477)
found 3.08 (+-0.000257585) 69.8127 (+-0.444542) 7.00013 (+-0.00154441)
found -4.84 (+-0.000280202) 59.8418 (+-0.411931) 6.00035 (+-0.00143111)
found 1.64 (+-0.000281402) 59.8436 (+-0.412162) 6.00053 (+-0.00143191)
found -4.36 (+-0.000279711) 59.841 (+-0.411835) 6.00027 (+-0.00143078)
found -4.12 (+-0.000280305) 59.8418 (+-0.411947) 6.00035 (+-0.00143117)
found -2.68 (+-0.000280811) 59.8426 (+-0.412045) 6.00043 (+-0.00143151)
found -0.28 (+-0.000279039) 59.8402 (+-0.41171) 6.00019 (+-0.00143035)
found 4.04 (+-0.000278258) 59.8397 (+-0.411579) 6.00014 (+-0.00142989)
found 6.44 (+-0.000279297) 59.8404 (+-0.411757) 6.00021 (+-0.00143051)
found 6.92 (+-0.000279888) 59.8412 (+-0.411869) 6.00029 (+-0.0014309)
found 9.08 (+-0.000280566) 59.8423 (+-0.412) 6.0004 (+-0.00143135)
found 3.56 (+-0.000278717) 59.8399 (+-0.411654) 6.00016 (+-0.00143015)
found 8.36 (+-0.000278462) 59.8396 (+-0.411607) 6.00013 (+-0.00142999)
found -8.68 (+-0.00030711) 49.8685 (+-0.376069) 5.00032 (+-0.00130652)
found -5.56 (+-0.000307678) 49.869 (+-0.376157) 5.00037 (+-0.00130683)
found -5.32 (+-0.000307678) 49.869 (+-0.376157) 5.00037 (+-0.00130683)
found -3.64 (+-0.000306362) 49.8677 (+-0.375951) 5.00024 (+-0.00130611)
found 0.200002 (+-0.000306932) 49.8682 (+-0.376039) 5.00029 (+-0.00130642)
found 7.4 (+-0.000307943) 49.8692 (+-0.376198) 5.0004 (+-0.00130697)
found 8.84 (+-0.000306531) 49.8677 (+-0.375973) 5.00024 (+-0.00130619)
found 5.72 (+-0.00030393) 49.8658 (+-0.375588) 5.00005 (+-0.00130485)
found -9.64 (+-0.000342346) 39.8938 (+-0.336232) 4.00016 (+-0.00116812)
found -7 (+-0.000345716) 39.897 (+-0.336672) 4.00048 (+-0.00116965)
found -4.6 (+-0.000344295) 39.8954 (+-0.336482) 4.00032 (+-0.00116899)
found -1.96 (+-0.000345051) 39.8962 (+-0.336583) 4.0004 (+-0.00116934)
found -0.519999 (+-0.000343316) 39.8946 (+-0.336357) 4.00024 (+-0.00116856)
found 0.919997 (+-0.000342695) 39.8946 (+-0.336294) 4.00024 (+-0.00116834)
found 3.8 (+-0.000344295) 39.8954 (+-0.336482) 4.00032 (+-0.00116899)
found 6.68 (+-0.000344295) 39.8954 (+-0.336482) 4.00032 (+-0.00116899)
found 2.36 (+-0.000341437) 39.8933 (+-0.336122) 4.00011 (+-0.00116774)
found 6.2 (+-0.000342208) 39.8941 (+-0.336228) 4.00019 (+-0.00116811)
found -0.760003 (+-0.000398833) 29.9226 (+-0.291533) 3.00035 (+-0.00101283)
found -8.44 (+-0.000399256) 29.9229 (+-0.291574) 3.00038 (+-0.00101297)
found -1.48 (+-0.000397164) 29.9216 (+-0.29137) 3.00024 (+-0.00101227)
found -0.0400006 (+-0.000398334) 29.9221 (+-0.29148) 3.00029 (+-0.00101265)
found 8.6 (+-0.000398334) 29.9221 (+-0.29148) 3.00029 (+-0.00101265)
found -9.4 (+-0.000396051) 29.9208 (+-0.291258) 3.00016 (+-0.00101188)
found 2.84 (+-0.000397164) 29.9216 (+-0.29137) 3.00024 (+-0.00101227)
found -9.88 (+-0.000395572) 29.9202 (+-0.29119) 3.00011 (+-0.00101164)
found -6.04 (+-0.000493554) 19.9512 (+-0.238386) 2.00051 (+-0.000828191)
found 2.11999 (+-0.000491093) 19.9498 (+-0.238217) 2.00037 (+-0.000827604)
found -3.4 (+-0.000490527) 19.9493 (+-0.238174) 2.00032 (+-0.000827453)
found 3.32 (+-0.000491061) 19.9496 (+-0.23821) 2.00035 (+-0.00082758)
found -9.15999 (+-0.000490019) 19.9493 (+-0.238144) 2.00032 (+-0.000827351)
found -1.23999 (+-0.000490019) 19.9493 (+-0.238144) 2.00032 (+-0.000827351)
found 2.6 (+-0.000487608) 19.948 (+-0.237976) 2.00019 (+-0.000826765)
found 8.12 (+-0.000486681) 19.948 (+-0.237925) 2.00019 (+-0.00082659)
found 5.47999 (+-0.000702221) 9.97705 (+-0.168723) 1.0004 (+-0.000586172)
found 5 (+-0.000704713) 9.97785 (+-0.168815) 1.00048 (+-0.000586489)
found 7.87999 (+-0.000696926) 9.97574 (+-0.168536) 1.00027 (+-0.000585522)
found 4.28001 (+-0.000703156) 9.97732 (+-0.168757) 1.00043 (+-0.000586289)
found 1.16001 (+-0.000701129) 9.9768 (+-0.168685) 1.00038 (+-0.000586039)
found 5.96 (+-0.00069743) 9.97545 (+-0.168545) 1.00024 (+-0.000585551)

 
#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.