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 9.97356 1
created -9.64 39.8942 4
created -9.4 89.762 9
created -9.16 79.7885 8
created -8.92 9.97356 1
created -8.68 59.8413 6
created -8.44 49.8678 5
created -8.2 39.8942 4
created -7.96 89.762 9
created -7.72 9.97356 1
created -7.48 89.762 9
created -7.24 69.8149 7
created -7 39.8942 4
created -6.76 79.7885 8
created -6.52 89.762 9
created -6.28 89.762 9
created -6.04 59.8413 6
created -5.8 69.8149 7
created -5.56 69.8149 7
created -5.32 29.9207 3
created -5.08 19.9471 2
created -4.84 9.97356 1
created -4.6 19.9471 2
created -4.36 89.762 9
created -4.12 99.7356 10
created -3.88 9.97356 1
created -3.64 39.8942 4
created -3.4 39.8942 4
created -3.16 99.7356 10
created -2.92 79.7885 8
created -2.68 49.8678 5
created -2.44 69.8149 7
created -2.2 99.7356 10
created -1.96 49.8678 5
created -1.72 79.7885 8
created -1.48 89.762 9
created -1.24 99.7356 10
created -1 29.9207 3
created -0.76 99.7356 10
created -0.52 69.8149 7
created -0.28 89.762 9
created -0.04 49.8678 5
created 0.2 39.8942 4
created 0.44 9.97356 1
created 0.68 29.9207 3
created 0.92 29.9207 3
created 1.16 79.7885 8
created 1.4 69.8149 7
created 1.64 39.8942 4
created 1.88 89.762 9
created 2.12 29.9207 3
created 2.36 99.7356 10
created 2.6 49.8678 5
created 2.84 9.97356 1
created 3.08 9.97356 1
created 3.32 79.7885 8
created 3.56 79.7885 8
created 3.8 29.9207 3
created 4.04 9.97356 1
created 4.28 59.8413 6
created 4.52 29.9207 3
created 4.76 59.8413 6
created 5 39.8942 4
created 5.24 99.7356 10
created 5.48 9.97356 1
created 5.72 99.7356 10
created 5.96 89.762 9
created 6.2 59.8413 6
created 6.44 39.8942 4
created 6.68 99.7356 10
created 6.92 99.7356 10
created 7.16 59.8413 6
created 7.4 69.8149 7
created 7.64 69.8149 7
created 7.88 49.8678 5
created 8.12 49.8678 5
created 8.36 79.7885 8
created 8.6 9.97356 1
created 8.84 89.762 9
created 9.08 39.8942 4
created 9.32 9.97356 1
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 = 0.0400021 (+-1.07485e-05)
fit chi^2 = 6.00203e-06
found -4.12 (+-0.000128099) 99.733 (+-0.315773) 10.0003 (+-0.00109704)
found -2.2 (+-0.000128468) 99.7336 (+-0.315869) 10.0003 (+-0.00109738)
found -1.24 (+-0.000128401) 99.7336 (+-0.315853) 10.0003 (+-0.00109732)
found 6.92 (+-0.000128711) 99.7347 (+-0.315944) 10.0004 (+-0.00109764)
found -3.16 (+-0.000128445) 99.7336 (+-0.315863) 10.0003 (+-0.00109736)
found -0.759999 (+-0.000128285) 99.7331 (+-0.315816) 10.0003 (+-0.0010972)
found 2.36 (+-0.000128146) 99.7325 (+-0.315774) 10.0002 (+-0.00109705)
found 5.24 (+-0.000127763) 99.7317 (+-0.315669) 10.0001 (+-0.00109669)
found 5.72 (+-0.000128099) 99.7331 (+-0.315773) 10.0003 (+-0.00109704)
found 6.68 (+-0.000128553) 99.7341 (+-0.315898) 10.0004 (+-0.00109748)
found -6.52 (+-0.000135861) 89.7619 (+-0.299783) 9.00045 (+-0.0010415)
found -6.28 (+-0.000135721) 89.7614 (+-0.299744) 9.0004 (+-0.00104136)
found -1.48 (+-0.000135919) 89.7622 (+-0.2998) 9.00048 (+-0.00104155)
found -0.28 (+-0.000135512) 89.7606 (+-0.299686) 9.00032 (+-0.00104116)
found 5.96 (+-0.000135779) 89.7616 (+-0.299761) 9.00042 (+-0.00104142)
found -9.4 (+-0.000135486) 89.7605 (+-0.29968) 9.00031 (+-0.00104114)
found -7.96 (+-0.000134739) 89.7587 (+-0.299487) 9.00013 (+-0.00104046)
found -7.48 (+-0.000134981) 89.7595 (+-0.299553) 9.00021 (+-0.0010407)
found -4.36 (+-0.000135359) 89.7605 (+-0.299652) 9.00031 (+-0.00104104)
found 1.88 (+-0.000135067) 89.7593 (+-0.299566) 9.00019 (+-0.00104074)
found 8.84 (+-0.000134739) 89.7587 (+-0.299487) 9.00013 (+-0.00104046)
found -9.16 (+-0.000143402) 79.787 (+-0.28248) 8.00026 (+-0.000981381)
found -2.92 (+-0.000144056) 79.7883 (+-0.282629) 8.0004 (+-0.000981897)
found 3.56 (+-0.000143701) 79.7872 (+-0.282542) 8.00029 (+-0.000981595)
found -6.76 (+-0.00014389) 79.7878 (+-0.282587) 8.00035 (+-0.000981753)
found -1.72 (+-0.000143992) 79.7881 (+-0.282612) 8.00037 (+-0.000981838)
found 1.16 (+-0.000143627) 79.787 (+-0.282523) 8.00026 (+-0.000981529)
found 3.32 (+-0.000143334) 79.7867 (+-0.282462) 8.00023 (+-0.000981319)
found 8.36 (+-0.000143093) 79.7859 (+-0.282401) 8.00016 (+-0.000981107)
found -7.24 (+-0.000153977) 69.8148 (+-0.26437) 7.00035 (+-0.000918462)
found -5.8 (+-0.000154031) 69.8148 (+-0.264379) 7.00035 (+-0.000918496)
found -5.56 (+-0.00015368) 69.8139 (+-0.264305) 7.00026 (+-0.000918238)
found -0.52 (+-0.000154522) 69.8163 (+-0.26449) 7.0005 (+-0.00091888)
found 1.4 (+-0.000153899) 69.8145 (+-0.264352) 7.00032 (+-0.000918401)
found 7.4 (+-0.000154031) 69.8148 (+-0.264379) 7.00035 (+-0.000918496)
found 7.64 (+-0.00015393) 69.8145 (+-0.264357) 7.00032 (+-0.00091842)
found -2.44 (+-0.000154165) 69.8153 (+-0.264411) 7.0004 (+-0.000918606)
found -6.04 (+-0.00016687) 59.8425 (+-0.244864) 6.00043 (+-0.000850696)
found 6.2 (+-0.000166515) 59.8417 (+-0.244797) 6.00035 (+-0.000850465)
found 7.16 (+-0.000166955) 59.8428 (+-0.244881) 6.00045 (+-0.000850755)
found 4.76 (+-0.000165824) 59.8401 (+-0.244666) 6.00019 (+-0.000850009)
found -8.68 (+-0.000165489) 59.8399 (+-0.244612) 6.00016 (+-0.000849822)
found 4.28 (+-0.000165208) 59.8393 (+-0.24456) 6.0001 (+-0.000849642)
found -1.96 (+-0.000183324) 49.87 (+-0.223616) 5.00048 (+-0.000776878)
found -0.0400019 (+-0.000182688) 49.8687 (+-0.223513) 5.00035 (+-0.000776521)
found 2.6 (+-0.000182057) 49.8681 (+-0.223425) 5.00029 (+-0.000776215)
found -8.44 (+-0.000182342) 49.8679 (+-0.223456) 5.00027 (+-0.000776323)
found -2.68 (+-0.000183001) 49.8692 (+-0.223562) 5.0004 (+-0.000776692)
found 7.88 (+-0.000182622) 49.8684 (+-0.223501) 5.00032 (+-0.000776478)
found 8.12 (+-0.000182737) 49.8687 (+-0.22352) 5.00035 (+-0.000776544)
found 9.08 (+-0.000203781) 39.8948 (+-0.199867) 4.00027 (+-0.000694369)
found -8.2 (+-0.000204859) 39.8959 (+-0.199995) 4.00037 (+-0.000694816)
found -7 (+-0.000205053) 39.8962 (+-0.20002) 4.0004 (+-0.000694903)
found -3.4 (+-0.000204788) 39.8959 (+-0.199988) 4.00037 (+-0.000694789)
found 0.199998 (+-0.000203196) 39.8938 (+-0.199788) 4.00016 (+-0.000694094)
found 1.64 (+-0.000205184) 39.8964 (+-0.200038) 4.00043 (+-0.000694964)
found 5 (+-0.000205151) 39.8964 (+-0.200034) 4.00043 (+-0.000694951)
found 6.44 (+-0.000205151) 39.8964 (+-0.200034) 4.00043 (+-0.000694951)
found -9.64 (+-0.000203781) 39.8948 (+-0.199867) 4.00027 (+-0.000694369)
found -3.64 (+-0.000203009) 39.8935 (+-0.199763) 4.00014 (+-0.000694009)
found -1 (+-0.000238354) 29.9245 (+-0.173384) 3.00053 (+-0.000602365)
found 2.12 (+-0.000238198) 29.9242 (+-0.173368) 3.00051 (+-0.000602308)
found 3.8 (+-0.000235706) 29.9216 (+-0.173128) 3.00024 (+-0.000601473)
found -5.32 (+-0.000236022) 29.9215 (+-0.173152) 3.00024 (+-0.000601558)
found 4.52 (+-0.000236938) 29.9223 (+-0.173239) 3.00032 (+-0.00060186)
found 0.920003 (+-0.000236557) 29.9221 (+-0.173204) 3.00029 (+-0.000601737)
found 0.680001 (+-0.000234587) 29.9202 (+-0.173014) 3.00011 (+-0.000601079)
found 9.8 (+-0.000232326) 29.9202 (+-0.172836) 3.0001 (+-0.000600458)
found -5.08 (+-0.000288145) 19.9472 (+-0.141318) 2.00011 (+-0.00049096)
found -4.59999 (+-0.000289973) 19.9488 (+-0.141446) 2.00027 (+-0.000491405)
found -3.88001 (+-0.000416659) 9.97679 (+-0.100244) 1.00038 (+-0.000348264)
found 5.48 (+-0.000419548) 9.97838 (+-0.10035) 1.00053 (+-0.000348634)
found -8.92 (+-0.000417113) 9.97678 (+-0.100258) 1.00037 (+-0.000348311)
found -7.72 (+-0.000418823) 9.97784 (+-0.100322) 1.00048 (+-0.000348536)
found 8.6 (+-0.000418428) 9.97758 (+-0.100307) 1.00045 (+-0.000348483)
found 0.439998 (+-0.00041306) 9.97491 (+-0.100111) 1.00019 (+-0.000347803)
found 2.83999 (+-0.000411387) 9.97468 (+-0.100058) 1.00016 (+-0.000347619)
found 9.31999 (+-0.000410764) 9.97439 (+-0.100036) 1.00014 (+-0.00034754)
found 4.04001 (+-0.000414235) 9.97545 (+-0.100154) 1.00024 (+-0.000347951)
found -4.84 (+-0.000410389) 9.97412 (+-0.10002) 1.00011 (+-0.000347484)
found 3.08001 (+-0.00041281) 9.97548 (+-0.100113) 1.00024 (+-0.000347807)
found 9.56 (+-0.000410016) 9.97413 (+-0.100009) 1.00011 (+-0.000347448)
found -9.87999 (+-0.000409643) 9.97414 (+-0.0999934) 1.00011 (+-0.000347393)

 
#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.