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 39.8942 4
created -9.4 99.7356 10
created -9.16 69.8149 7
created -8.92 29.9207 3
created -8.68 79.7885 8
created -8.44 59.8413 6
created -8.2 29.9207 3
created -7.96 59.8413 6
created -7.72 49.8678 5
created -7.48 99.7356 10
created -7.24 59.8413 6
created -7 79.7885 8
created -6.76 39.8942 4
created -6.52 99.7356 10
created -6.28 59.8413 6
created -6.04 49.8678 5
created -5.8 89.762 9
created -5.56 49.8678 5
created -5.32 79.7885 8
created -5.08 39.8942 4
created -4.84 99.7356 10
created -4.6 19.9471 2
created -4.36 99.7356 10
created -4.12 89.762 9
created -3.88 39.8942 4
created -3.64 89.762 9
created -3.4 59.8413 6
created -3.16 59.8413 6
created -2.92 19.9471 2
created -2.68 49.8678 5
created -2.44 99.7356 10
created -2.2 49.8678 5
created -1.96 39.8942 4
created -1.72 29.9207 3
created -1.48 29.9207 3
created -1.24 19.9471 2
created -1 89.762 9
created -0.76 49.8678 5
created -0.52 19.9471 2
created -0.28 19.9471 2
created -0.04 9.97356 1
created 0.2 19.9471 2
created 0.44 89.762 9
created 0.68 79.7885 8
created 0.92 69.8149 7
created 1.16 99.7356 10
created 1.4 19.9471 2
created 1.64 89.762 9
created 1.88 19.9471 2
created 2.12 19.9471 2
created 2.36 79.7885 8
created 2.6 49.8678 5
created 2.84 19.9471 2
created 3.08 19.9471 2
created 3.32 19.9471 2
created 3.56 29.9207 3
created 3.8 99.7356 10
created 4.04 79.7885 8
created 4.28 79.7885 8
created 4.52 39.8942 4
created 4.76 39.8942 4
created 5 49.8678 5
created 5.24 39.8942 4
created 5.48 99.7356 10
created 5.72 49.8678 5
created 5.96 79.7885 8
created 6.2 69.8149 7
created 6.44 19.9471 2
created 6.68 69.8149 7
created 6.92 49.8678 5
created 7.16 99.7356 10
created 7.4 59.8413 6
created 7.64 69.8149 7
created 7.88 49.8678 5
created 8.12 19.9471 2
created 8.36 59.8413 6
created 8.6 39.8942 4
created 8.84 89.762 9
created 9.08 19.9471 2
created 9.32 69.8149 7
created 9.56 9.97356 1
created 9.8 29.9207 3
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.05451e-05)
fit chi^2 = 5.72294e-06
found 1.16 (+-0.000125144) 99.7329 (+-0.308353) 10.0002 (+-0.00107127)
found -9.4 (+-0.000125364) 99.7334 (+-0.308414) 10.0003 (+-0.00107148)
found -7.48 (+-0.000125381) 99.7334 (+-0.308418) 10.0003 (+-0.00107149)
found -6.52 (+-0.000125299) 99.7331 (+-0.308394) 10.0003 (+-0.00107141)
found -4.84 (+-0.000124928) 99.7321 (+-0.308287) 10.0002 (+-0.00107104)
found -4.36 (+-0.000125257) 99.7334 (+-0.308388) 10.0003 (+-0.00107139)
found -2.44 (+-0.00012531) 99.7331 (+-0.308397) 10.0003 (+-0.00107142)
found 3.8 (+-0.000125325) 99.7334 (+-0.308405) 10.0003 (+-0.00107145)
found 5.48 (+-0.000125228) 99.7328 (+-0.308373) 10.0002 (+-0.00107134)
found 7.16 (+-0.000125381) 99.7334 (+-0.308418) 10.0003 (+-0.00107149)
found -4.12 (+-0.000132415) 89.7611 (+-0.292663) 9.00037 (+-0.00101676)
found -9.88 (+-0.000132004) 89.7585 (+-0.292523) 9.00011 (+-0.00101627)
found -5.8 (+-0.000132174) 89.7601 (+-0.292594) 9.00026 (+-0.00101652)
found -3.64 (+-0.000132163) 89.7601 (+-0.292591) 9.00027 (+-0.00101651)
found -0.999999 (+-0.000131845) 89.7592 (+-0.292509) 9.00018 (+-0.00101622)
found 0.440001 (+-0.000132058) 89.76 (+-0.292569) 9.00026 (+-0.00101643)
found 1.64 (+-0.000131519) 89.7585 (+-0.292425) 9.0001 (+-0.00101593)
found 8.84 (+-0.000131757) 89.759 (+-0.292485) 9.00016 (+-0.00101614)
found -7 (+-0.000140286) 79.787 (+-0.275884) 8.00027 (+-0.000958463)
found 0.68 (+-0.000140771) 79.7886 (+-0.276004) 8.00042 (+-0.000958882)
found 4.04 (+-0.000140907) 79.7891 (+-0.276039) 8.00048 (+-0.000959004)
found 4.28 (+-0.000140437) 79.7876 (+-0.275922) 8.00032 (+-0.000958596)
found -8.68 (+-0.000140169) 79.7868 (+-0.275856) 8.00024 (+-0.000958368)
found -5.32 (+-0.000140199) 79.7868 (+-0.275862) 8.00024 (+-0.000958389)
found 2.36 (+-0.000139935) 79.7862 (+-0.275802) 8.00018 (+-0.000958179)
found 5.96 (+-0.000140464) 79.7876 (+-0.275927) 8.00032 (+-0.000958615)
found -9.16 (+-0.000150293) 69.8148 (+-0.258139) 7.00035 (+-0.000896817)
found 0.920001 (+-0.000150809) 69.8161 (+-0.25825) 7.00048 (+-0.000897202)
found 6.2 (+-0.000149981) 69.814 (+-0.258073) 7.00027 (+-0.000896586)
found 7.64 (+-0.000150219) 69.8142 (+-0.258118) 7.00029 (+-0.000896744)
found 6.68 (+-0.000149714) 69.8132 (+-0.258013) 7.00019 (+-0.000896378)
found 9.32 (+-0.000149078) 69.8121 (+-0.257885) 7.00008 (+-0.000895935)
found -7.24 (+-0.000163122) 59.8431 (+-0.239138) 6.00048 (+-0.000830804)
found -6.28 (+-0.000162809) 59.8423 (+-0.239078) 6.0004 (+-0.000830596)
found -3.4 (+-0.000162841) 59.8423 (+-0.239083) 6.0004 (+-0.000830614)
found 7.4 (+-0.000163027) 59.8428 (+-0.23912) 6.00045 (+-0.00083074)
found -8.44 (+-0.000162359) 59.8412 (+-0.238994) 6.00029 (+-0.000830302)
found -7.96 (+-0.00016205) 59.8404 (+-0.238934) 6.00021 (+-0.000830094)
found -3.16 (+-0.000161975) 59.8404 (+-0.238923) 6.00021 (+-0.000830056)
found 8.36 (+-0.000161736) 59.8399 (+-0.238878) 6.00016 (+-0.000829898)
found -5.56 (+-0.000178913) 49.8697 (+-0.218339) 5.00045 (+-0.000758544)
found -2.2 (+-0.000178487) 49.869 (+-0.218271) 5.00037 (+-0.000758309)
found -0.760003 (+-0.000177992) 49.8681 (+-0.218196) 5.00029 (+-0.000758046)
found 5.72 (+-0.000179011) 49.87 (+-0.218355) 5.00048 (+-0.000758601)
found -7.72 (+-0.000178775) 49.8695 (+-0.218317) 5.00043 (+-0.000758467)
found -6.04 (+-0.000178677) 49.8692 (+-0.2183) 5.0004 (+-0.00075841)
found 2.6 (+-0.000177889) 49.8679 (+-0.218178) 5.00027 (+-0.000757986)
found 5 (+-0.000177768) 49.8674 (+-0.218154) 5.00021 (+-0.000757901)
found 6.92 (+-0.000178898) 49.8697 (+-0.218337) 5.00045 (+-0.000758536)
found 7.88 (+-0.000177778) 49.8676 (+-0.21816) 5.00024 (+-0.000757922)
found -2.68 (+-0.000178088) 49.8684 (+-0.218212) 5.00032 (+-0.000758103)
found -9.64 (+-0.000200743) 39.8972 (+-0.195384) 4.00051 (+-0.000678795)
found -3.88 (+-0.000200624) 39.897 (+-0.195368) 4.00048 (+-0.000678738)
found -6.76 (+-0.000200615) 39.897 (+-0.195367) 4.00048 (+-0.000678735)
found -5.08 (+-0.000200615) 39.897 (+-0.195367) 4.00048 (+-0.000678735)
found -1.96 (+-0.000199058) 39.8943 (+-0.195162) 4.00021 (+-0.000678024)
found 4.52 (+-0.000199725) 39.8954 (+-0.195249) 4.00032 (+-0.000678326)
found 4.76 (+-0.000199273) 39.8946 (+-0.195189) 4.00024 (+-0.000678118)
found 5.24 (+-0.000200158) 39.8962 (+-0.195307) 4.0004 (+-0.000678526)
found 8.6 (+-0.000200206) 39.8962 (+-0.195312) 4.0004 (+-0.000678545)
found -8.92 (+-0.000231933) 29.9231 (+-0.169221) 3.0004 (+-0.000587901)
found -8.2 (+-0.000231364) 29.9223 (+-0.169163) 3.00032 (+-0.000587701)
found -1.72 (+-0.000230167) 29.921 (+-0.169045) 3.00019 (+-0.00058729)
found -1.48 (+-0.000229545) 29.9205 (+-0.168986) 3.00014 (+-0.000587084)
found 3.56001 (+-0.000230956) 29.9224 (+-0.169129) 3.00032 (+-0.000587583)
found 9.8 (+-0.00022686) 29.9202 (+-0.168769) 3.00011 (+-0.000586331)
found -4.6 (+-0.000286614) 19.9514 (+-0.138348) 2.00053 (+-0.000480641)
found 1.4 (+-0.000286403) 19.9512 (+-0.138332) 2.00051 (+-0.000480589)
found 1.87999 (+-0.000283845) 19.949 (+-0.13816) 2.0003 (+-0.000479989)
found 9.08 (+-0.000285712) 19.9503 (+-0.138283) 2.00043 (+-0.000480419)
found -2.92 (+-0.000284371) 19.949 (+-0.13819) 2.00029 (+-0.000480094)
found -0.520003 (+-0.0002828) 19.948 (+-0.138086) 2.00019 (+-0.000479732)
found 2.84 (+-0.0002828) 19.948 (+-0.138086) 2.00019 (+-0.000479732)
found 6.44 (+-0.000285233) 19.9498 (+-0.13825) 2.00037 (+-0.000480301)
found 8.12 (+-0.000284371) 19.949 (+-0.13819) 2.00029 (+-0.000480094)
found -1.23999 (+-0.000284352) 19.9493 (+-0.138192) 2.00032 (+-0.000480101)
found -0.280001 (+-0.000280874) 19.9469 (+-0.137961) 2.00008 (+-0.000479298)
found 2.12001 (+-0.000283621) 19.9488 (+-0.138144) 2.00027 (+-0.000479933)
found 3.08 (+-0.000281553) 19.9472 (+-0.138003) 2.00011 (+-0.000479443)
found 3.32 (+-0.000282048) 19.9474 (+-0.138035) 2.00013 (+-0.000479555)
found 0.200008 (+-0.00028315) 19.9488 (+-0.138118) 2.00027 (+-0.000479844)
found 9.55999 (+-0.000404956) 9.97573 (+-0.0978153) 1.00027 (+-0.000339826)
found -0.0399999 (+-0.000400734) 9.97412 (+-0.0976666) 1.00011 (+-0.000339309)

 
#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.