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 19.9471 2
created -9.64 69.8149 7
created -9.4 89.762 9
created -9.16 19.9471 2
created -8.92 59.8413 6
created -8.68 29.9207 3
created -8.44 79.7885 8
created -8.2 99.7356 10
created -7.96 59.8413 6
created -7.72 9.97356 1
created -7.48 29.9207 3
created -7.24 79.7885 8
created -7 29.9207 3
created -6.76 79.7885 8
created -6.52 89.762 9
created -6.28 79.7885 8
created -6.04 19.9471 2
created -5.8 69.8149 7
created -5.56 19.9471 2
created -5.32 99.7356 10
created -5.08 19.9471 2
created -4.84 59.8413 6
created -4.6 69.8149 7
created -4.36 69.8149 7
created -4.12 89.762 9
created -3.88 79.7885 8
created -3.64 19.9471 2
created -3.4 79.7885 8
created -3.16 39.8942 4
created -2.92 79.7885 8
created -2.68 29.9207 3
created -2.44 9.97356 1
created -2.2 49.8678 5
created -1.96 49.8678 5
created -1.72 49.8678 5
created -1.48 49.8678 5
created -1.24 89.762 9
created -1 39.8942 4
created -0.76 69.8149 7
created -0.52 39.8942 4
created -0.28 79.7885 8
created -0.04 19.9471 2
created 0.2 9.97356 1
created 0.44 59.8413 6
created 0.68 69.8149 7
created 0.92 19.9471 2
created 1.16 89.762 9
created 1.4 59.8413 6
created 1.64 89.762 9
created 1.88 29.9207 3
created 2.12 19.9471 2
created 2.36 9.97356 1
created 2.6 9.97356 1
created 2.84 69.8149 7
created 3.08 49.8678 5
created 3.32 89.762 9
created 3.56 39.8942 4
created 3.8 29.9207 3
created 4.04 59.8413 6
created 4.28 49.8678 5
created 4.52 89.762 9
created 4.76 79.7885 8
created 5 99.7356 10
created 5.24 99.7356 10
created 5.48 59.8413 6
created 5.72 19.9471 2
created 5.96 9.97356 1
created 6.2 39.8942 4
created 6.44 79.7885 8
created 6.68 9.97356 1
created 6.92 9.97356 1
created 7.16 9.97356 1
created 7.4 99.7356 10
created 7.64 39.8942 4
created 7.88 99.7356 10
created 8.12 89.762 9
created 8.36 59.8413 6
created 8.6 99.7356 10
created 8.84 9.97356 1
created 9.08 19.9471 2
created 9.32 19.9471 2
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 (+-1.09904e-05)
fit chi^2 = 5.835e-06
found -8.2 (+-0.0001268) 99.7341 (+-0.311483) 10.0004 (+-0.00108214)
found 5 (+-0.000127033) 99.7352 (+-0.311556) 10.0005 (+-0.00108239)
found 5.24 (+-0.000126908) 99.7347 (+-0.311517) 10.0004 (+-0.00108226)
found 8.6 (+-0.000126126) 99.7323 (+-0.311292) 10.0002 (+-0.00108148)
found -5.32 (+-0.000125925) 99.7315 (+-0.31123) 10.0001 (+-0.00108126)
found 7.4 (+-0.000125972) 99.7317 (+-0.311246) 10.0001 (+-0.00108132)
found 7.88 (+-0.0001267) 99.7339 (+-0.311455) 10.0003 (+-0.00108204)
found -9.4 (+-0.00013328) 89.7598 (+-0.295401) 9.00024 (+-0.00102627)
found -6.52 (+-0.000133896) 89.7616 (+-0.295565) 9.00042 (+-0.00102684)
found -4.12 (+-0.00013383) 89.7614 (+-0.295547) 9.0004 (+-0.00102678)
found 1.64 (+-0.000133344) 89.7598 (+-0.295414) 9.00024 (+-0.00102632)
found 8.12 (+-0.000133876) 89.7617 (+-0.295561) 9.00043 (+-0.00102682)
found -1.24 (+-0.000133372) 89.7598 (+-0.29542) 9.00024 (+-0.00102634)
found 1.16 (+-0.000133209) 89.7595 (+-0.295381) 9.00021 (+-0.0010262)
found 3.32 (+-0.000133372) 89.7598 (+-0.29542) 9.00024 (+-0.00102634)
found 4.52 (+-0.000133678) 89.7609 (+-0.295505) 9.00035 (+-0.00102663)
found -6.28 (+-0.000141605) 79.7873 (+-0.278567) 8.00029 (+-0.000967785)
found -3.88 (+-0.000141605) 79.7873 (+-0.278567) 8.00029 (+-0.000967785)
found 4.76 (+-0.000142349) 79.7894 (+-0.278746) 8.00051 (+-0.000968408)
found -8.44 (+-0.000141818) 79.7878 (+-0.278617) 8.00035 (+-0.000967958)
found -7.24 (+-0.00014123) 79.7859 (+-0.278471) 8.00016 (+-0.000967451)
found -6.76 (+-0.000141755) 79.7876 (+-0.2786) 8.00032 (+-0.000967901)
found -3.4 (+-0.000141199) 79.786 (+-0.278465) 8.00016 (+-0.000967431)
found -2.92 (+-0.000141348) 79.7862 (+-0.278498) 8.00018 (+-0.000967547)
found -0.280001 (+-0.000141199) 79.7859 (+-0.278465) 8.00016 (+-0.000967431)
found 6.44 (+-0.000140989) 79.7856 (+-0.27842) 8.00013 (+-0.000967276)
found -4.6 (+-0.000151872) 69.8148 (+-0.260675) 7.00035 (+-0.000905626)
found -4.36 (+-0.000152123) 69.8156 (+-0.260731) 7.00043 (+-0.000905821)
found 0.679999 (+-0.000151271) 69.8134 (+-0.260549) 7.00021 (+-0.000905187)
found -9.64 (+-0.000151519) 69.8142 (+-0.260605) 7.00029 (+-0.000905382)
found -5.8 (+-0.000150765) 69.8124 (+-0.260442) 7.00011 (+-0.000904819)
found -0.76 (+-0.000151359) 69.8135 (+-0.260563) 7.00021 (+-0.000905239)
found 2.84 (+-0.000150936) 69.8129 (+-0.260482) 7.00016 (+-0.000904957)
found -7.96 (+-0.000163649) 59.8412 (+-0.241279) 6.00029 (+-0.000838243)
found 1.4 (+-0.000164717) 59.843 (+-0.241469) 6.00048 (+-0.000838902)
found 5.48 (+-0.000163922) 59.8415 (+-0.241323) 6.00032 (+-0.000838396)
found 8.36 (+-0.000164801) 59.8433 (+-0.241486) 6.00051 (+-0.00083896)
found 4.04 (+-0.000163629) 59.8404 (+-0.241262) 6.00021 (+-0.000838181)
found -8.92 (+-0.000163162) 59.8396 (+-0.241178) 6.00013 (+-0.000837889)
found -4.84 (+-0.000163656) 59.8407 (+-0.241271) 6.00024 (+-0.000838213)
found 0.440002 (+-0.000163385) 59.8404 (+-0.241226) 6.00021 (+-0.000838059)
found -1.96 (+-0.000179805) 49.8679 (+-0.220327) 5.00027 (+-0.000765452)
found -1.72 (+-0.000179805) 49.8679 (+-0.220327) 5.00027 (+-0.000765452)
found -1.48 (+-0.000180281) 49.8689 (+-0.220405) 5.00037 (+-0.000765724)
found 3.08 (+-0.000180542) 49.8695 (+-0.220447) 5.00042 (+-0.000765869)
found 4.28 (+-0.000180418) 49.8692 (+-0.220427) 5.0004 (+-0.000765799)
found -2.2 (+-0.000178939) 49.8668 (+-0.2202) 5.00016 (+-0.000765009)
found -1 (+-0.000202309) 39.8964 (+-0.197235) 4.00043 (+-0.000685226)
found 3.56 (+-0.000201579) 39.8954 (+-0.197142) 4.00032 (+-0.000684901)
found 7.64 (+-0.00020282) 39.8975 (+-0.197304) 4.00053 (+-0.000685465)
found -3.16 (+-0.000202319) 39.8964 (+-0.197236) 4.00043 (+-0.00068523)
found -0.52 (+-0.00020218) 39.8962 (+-0.197218) 4.0004 (+-0.000685165)
found 6.2 (+-0.000200799) 39.8946 (+-0.197049) 4.00024 (+-0.000684578)
found -7 (+-0.000234373) 29.9234 (+-0.170888) 3.00043 (+-0.000593693)
found -2.68 (+-0.000232403) 29.9216 (+-0.170702) 3.00024 (+-0.000593044)
found 1.88 (+-0.000233055) 29.9221 (+-0.170761) 3.00029 (+-0.000593252)
found -8.68 (+-0.000233995) 29.9229 (+-0.17085) 3.00037 (+-0.00059356)
found 3.8 (+-0.000233154) 29.9218 (+-0.170766) 3.00027 (+-0.000593267)
found -7.48 (+-0.000232403) 29.9216 (+-0.170702) 3.00024 (+-0.000593044)
found 9.8 (+-0.00022907) 29.9202 (+-0.170414) 3.0001 (+-0.000592044)
found -9.16 (+-0.000288215) 19.9501 (+-0.139611) 2.0004 (+-0.000485032)
found -6.04 (+-0.000288265) 19.9501 (+-0.139614) 2.0004 (+-0.000485042)
found -5.08 (+-0.000288426) 19.9504 (+-0.139627) 2.00043 (+-0.000485085)
found -3.64 (+-0.000288518) 19.9504 (+-0.139632) 2.00043 (+-0.000485104)
found -0.0400065 (+-0.000285685) 19.9485 (+-0.139447) 2.00024 (+-0.000484462)
found -5.56 (+-0.000288707) 19.9506 (+-0.139646) 2.00045 (+-0.000485152)
found 0.920002 (+-0.000288495) 19.9504 (+-0.139631) 2.00043 (+-0.000485099)
found 5.72 (+-0.000285166) 19.948 (+-0.13941) 2.00019 (+-0.000484333)
found 2.12 (+-0.000284107) 19.9472 (+-0.139338) 2.00011 (+-0.000484081)
found 9.32 (+-0.000283611) 19.9469 (+-0.139305) 2.00008 (+-0.000483968)
found -9.87999 (+-0.000285357) 19.948 (+-0.139413) 2.00019 (+-0.000484342)
found 9.08 (+-0.000283611) 19.9469 (+-0.139305) 2.00008 (+-0.000483968)
found 8.83998 (+-0.000409084) 9.97627 (+-0.0987811) 1.00032 (+-0.000343181)
found 6.67999 (+-0.000407026) 9.97547 (+-0.0987099) 1.00024 (+-0.000342934)
found -7.72001 (+-0.000408431) 9.97546 (+-0.0987506) 1.00024 (+-0.000343075)
found -2.44 (+-0.000407896) 9.97519 (+-0.0987309) 1.00021 (+-0.000343007)
found 0.200008 (+-0.000407473) 9.9752 (+-0.0987185) 1.00022 (+-0.000342964)
found 2.36 (+-0.00040334) 9.97386 (+-0.0985757) 1.00008 (+-0.000342468)
found 2.60001 (+-0.000406612) 9.9752 (+-0.0986938) 1.00022 (+-0.000342878)
found 5.96 (+-0.000406322) 9.97466 (+-0.0986764) 1.00016 (+-0.000342818)
found 6.92 (+-0.000402054) 9.97361 (+-0.0985334) 1.00006 (+-0.000342321)
found 7.16002 (+-0.000407745) 9.97601 (+-0.0987387) 1.0003 (+-0.000343034)
found 9.56 (+-0.000405577) 9.97439 (+-0.0986502) 1.00013 (+-0.000342726)

 
#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.