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 9.97356 1
created -9.4 59.8413 6
created -9.16 29.9207 3
created -8.92 29.9207 3
created -8.68 99.7356 10
created -8.44 89.762 9
created -8.2 59.8413 6
created -7.96 19.9471 2
created -7.72 19.9471 2
created -7.48 79.7885 8
created -7.24 99.7356 10
created -7 89.762 9
created -6.76 9.97356 1
created -6.52 69.8149 7
created -6.28 59.8413 6
created -6.04 89.762 9
created -5.8 9.97356 1
created -5.56 89.762 9
created -5.32 59.8413 6
created -5.08 59.8413 6
created -4.84 49.8678 5
created -4.6 89.762 9
created -4.36 49.8678 5
created -4.12 19.9471 2
created -3.88 29.9207 3
created -3.64 19.9471 2
created -3.4 9.97356 1
created -3.16 29.9207 3
created -2.92 49.8678 5
created -2.68 29.9207 3
created -2.44 29.9207 3
created -2.2 29.9207 3
created -1.96 89.762 9
created -1.72 59.8413 6
created -1.48 9.97356 1
created -1.24 79.7885 8
created -1 79.7885 8
created -0.76 39.8942 4
created -0.52 99.7356 10
created -0.28 69.8149 7
created -0.04 49.8678 5
created 0.2 49.8678 5
created 0.44 59.8413 6
created 0.68 89.762 9
created 0.92 19.9471 2
created 1.16 29.9207 3
created 1.4 9.97356 1
created 1.64 39.8942 4
created 1.88 89.762 9
created 2.12 19.9471 2
created 2.36 29.9207 3
created 2.6 19.9471 2
created 2.84 49.8678 5
created 3.08 59.8413 6
created 3.32 9.97356 1
created 3.56 39.8942 4
created 3.8 39.8942 4
created 4.04 89.762 9
created 4.28 9.97356 1
created 4.52 59.8413 6
created 4.76 49.8678 5
created 5 29.9207 3
created 5.24 39.8942 4
created 5.48 89.762 9
created 5.72 9.97356 1
created 5.96 19.9471 2
created 6.2 99.7356 10
created 6.44 89.762 9
created 6.68 59.8413 6
created 6.92 89.762 9
created 7.16 79.7885 8
created 7.4 9.97356 1
created 7.64 39.8942 4
created 7.88 9.97356 1
created 8.12 39.8942 4
created 8.36 29.9207 3
created 8.6 29.9207 3
created 8.84 79.7885 8
created 9.08 99.7356 10
created 9.32 9.97356 1
created 9.56 99.7356 10
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 (+-1.73037e-05)
fit chi^2 = 1.39344e-05
found -7.24 (+-0.000196229) 99.735 (+-0.481434) 10.0004 (+-0.00167258)
found 9.08 (+-0.000195097) 99.7329 (+-0.481111) 10.0002 (+-0.00167146)
found -8.68 (+-0.000195642) 99.7337 (+-0.481261) 10.0003 (+-0.00167197)
found -0.519999 (+-0.000195617) 99.7334 (+-0.481248) 10.0003 (+-0.00167193)
found 6.2 (+-0.000195451) 99.7334 (+-0.481208) 10.0003 (+-0.00167179)
found 9.56 (+-0.000195097) 99.7329 (+-0.481111) 10.0002 (+-0.00167146)
found -8.44 (+-0.000206884) 89.7617 (+-0.456742) 9.00042 (+-0.00158679)
found -7 (+-0.000205949) 89.7604 (+-0.456506) 9.00029 (+-0.00158597)
found -6.04 (+-0.000205559) 89.7593 (+-0.456394) 9.00019 (+-0.00158559)
found 0.679999 (+-0.000205853) 89.7596 (+-0.456464) 9.00021 (+-0.00158583)
found 6.44 (+-0.000206884) 89.7617 (+-0.456742) 9.00042 (+-0.00158679)
found 6.92 (+-0.000206702) 89.7612 (+-0.456689) 9.00037 (+-0.00158661)
found -5.56 (+-0.000205559) 89.7593 (+-0.456394) 9.00019 (+-0.00158559)
found -4.6 (+-0.000206244) 89.7601 (+-0.456562) 9.00026 (+-0.00158617)
found -1.96 (+-0.000206061) 89.7598 (+-0.456516) 9.00023 (+-0.00158601)
found 1.88 (+-0.000205593) 89.759 (+-0.456393) 9.00015 (+-0.00158558)
found 4.04 (+-0.0002053) 89.7588 (+-0.456324) 9.00013 (+-0.00158534)
found 5.48 (+-0.0002053) 89.7587 (+-0.456324) 9.00013 (+-0.00158534)
found -1 (+-0.000219138) 79.7876 (+-0.430547) 8.00032 (+-0.00149579)
found 7.16 (+-0.0002185) 79.7871 (+-0.430411) 8.00026 (+-0.00149532)
found 9.8 (+-0.000217733) 79.7891 (+-0.430285) 8.00047 (+-0.00149488)
found -7.48 (+-0.000218926) 79.7876 (+-0.430506) 8.00032 (+-0.00149565)
found -1.24 (+-0.000218396) 79.7868 (+-0.430384) 8.00024 (+-0.00149522)
found 8.84 (+-0.000219157) 79.7878 (+-0.430557) 8.00034 (+-0.00149582)
found -0.280001 (+-0.000234898) 69.8153 (+-0.402879) 7.0004 (+-0.00139967)
found -6.52 (+-0.000233399) 69.8132 (+-0.402567) 7.00019 (+-0.00139858)
found -8.2 (+-0.000253187) 59.8412 (+-0.372901) 6.00029 (+-0.00129552)
found -5.32 (+-0.000254096) 59.8423 (+-0.373065) 6.0004 (+-0.00129609)
found -1.72 (+-0.000252767) 59.841 (+-0.372833) 6.00027 (+-0.00129528)
found 6.68 (+-0.000254544) 59.8431 (+-0.373152) 6.00048 (+-0.00129639)
found -6.28 (+-0.000254257) 59.8425 (+-0.373096) 6.00042 (+-0.00129619)
found -5.08 (+-0.000253472) 59.8412 (+-0.372944) 6.00029 (+-0.00129567)
found 0.440001 (+-0.000253918) 59.842 (+-0.373031) 6.00037 (+-0.00129597)
found 3.08 (+-0.000252153) 59.8399 (+-0.372713) 6.00016 (+-0.00129486)
found -9.4 (+-0.000251726) 59.8394 (+-0.372633) 6.0001 (+-0.00129459)
found 4.52 (+-0.000252153) 59.8399 (+-0.372713) 6.00016 (+-0.00129486)
found -4.36 (+-0.000277738) 49.8682 (+-0.340472) 5.00029 (+-0.00118285)
found -4.84 (+-0.000278807) 49.8692 (+-0.340635) 5.0004 (+-0.00118342)
found -2.92 (+-0.000276841) 49.8668 (+-0.340322) 5.00016 (+-0.00118233)
found -0.0400008 (+-0.000278259) 49.8685 (+-0.340545) 5.00032 (+-0.00118311)
found 0.2 (+-0.000278069) 49.8682 (+-0.340514) 5.00029 (+-0.001183)
found 4.76 (+-0.000277557) 49.8676 (+-0.340435) 5.00024 (+-0.00118272)
found -9.88 (+-0.000275784) 49.8656 (+-0.34014) 5.00003 (+-0.0011817)
found 2.84 (+-0.000277215) 49.8674 (+-0.340385) 5.00021 (+-0.00118255)
found -0.759999 (+-0.000313038) 39.897 (+-0.304849) 4.00048 (+-0.00105909)
found 3.8 (+-0.000311848) 39.8957 (+-0.304693) 4.00035 (+-0.00105855)
found 5.24 (+-0.000311508) 39.8954 (+-0.304651) 4.00032 (+-0.0010584)
found 1.64 (+-0.000310499) 39.8949 (+-0.304534) 4.00027 (+-0.001058)
found 3.56 (+-0.000309322) 39.8936 (+-0.304376) 4.00014 (+-0.00105745)
found 7.64 (+-0.000308005) 39.8928 (+-0.304218) 4.00005 (+-0.0010569)
found 8.12 (+-0.000308991) 39.8933 (+-0.304334) 4.00011 (+-0.00105731)
found -9.16 (+-0.000359862) 29.9216 (+-0.263849) 3.00024 (+-0.000916651)
found -2.68 (+-0.000359528) 29.9213 (+-0.263815) 3.00021 (+-0.000916534)
found 5 (+-0.000359967) 29.9216 (+-0.263857) 3.00024 (+-0.000916681)
found -8.92 (+-0.000360926) 29.9226 (+-0.263959) 3.00035 (+-0.000917035)
found -3.88 (+-0.00035765) 29.9202 (+-0.263635) 3.00011 (+-0.00091591)
found -2.44 (+-0.000358714) 29.9208 (+-0.263735) 3.00016 (+-0.000916257)
found -2.2 (+-0.000360691) 29.9224 (+-0.263934) 3.00032 (+-0.000916949)
found 1.16 (+-0.000356908) 29.92 (+-0.26357) 3.00008 (+-0.000915682)
found 2.36 (+-0.00035765) 29.9202 (+-0.263635) 3.00011 (+-0.00091591)
found 8.36 (+-0.000359151) 29.921 (+-0.263778) 3.00019 (+-0.000916404)
found 8.6 (+-0.000360438) 29.9221 (+-0.263908) 3.00029 (+-0.000916857)
found -3.16 (+-0.000358241) 29.9208 (+-0.263699) 3.00016 (+-0.000916132)
found 0.919994 (+-0.000443702) 19.9493 (+-0.215634) 2.00032 (+-0.000749148)
found 2.11999 (+-0.000443702) 19.9493 (+-0.215634) 2.00032 (+-0.000749148)
found -7.96 (+-0.000441754) 19.9483 (+-0.215501) 2.00022 (+-0.000748686)
found -4.12 (+-0.000442062) 19.9482 (+-0.215519) 2.00021 (+-0.000748746)
found -7.71999 (+-0.000442561) 19.9488 (+-0.215559) 2.00027 (+-0.000748885)
found -3.64 (+-0.000439042) 19.9472 (+-0.215324) 2.00011 (+-0.000748069)
found 2.6 (+-0.000442062) 19.9482 (+-0.215519) 2.00021 (+-0.000748746)
found 5.96001 (+-0.000442145) 19.9491 (+-0.215542) 2.0003 (+-0.000748828)
found 9.32 (+-0.000639258) 9.97839 (+-0.152903) 1.00054 (+-0.000531208)
found -6.76 (+-0.000636888) 9.97732 (+-0.152812) 1.00043 (+-0.000530892)
found -5.8 (+-0.000638153) 9.97785 (+-0.15286) 1.00048 (+-0.000531059)
found 4.27999 (+-0.000636145) 9.97705 (+-0.152784) 1.0004 (+-0.000530797)
found 5.71999 (+-0.000631636) 9.97602 (+-0.152629) 1.0003 (+-0.000530257)
found 7.39999 (+-0.000633722) 9.97626 (+-0.152696) 1.00032 (+-0.000530491)
found -1.48 (+-0.000635548) 9.97679 (+-0.152761) 1.00038 (+-0.000530717)
found 3.32 (+-0.000632339) 9.97572 (+-0.152644) 1.00027 (+-0.000530309)
found -9.64 (+-0.000633317) 9.97598 (+-0.152679) 1.00029 (+-0.000530429)
found 7.88 (+-0.000630539) 9.97518 (+-0.152579) 1.00021 (+-0.000530083)
found 1.4 (+-0.000629373) 9.97492 (+-0.152538) 1.00019 (+-0.000529942)
found -3.4 (+-0.000626754) 9.97439 (+-0.152448) 1.00013 (+-0.000529629)

 
#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.