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 49.8678 5
created -9.4 29.9207 3
created -9.16 49.8678 5
created -8.92 59.8413 6
created -8.68 29.9207 3
created -8.44 99.7356 10
created -8.2 9.97356 1
created -7.96 59.8413 6
created -7.72 99.7356 10
created -7.48 19.9471 2
created -7.24 19.9471 2
created -7 69.8149 7
created -6.76 99.7356 10
created -6.52 99.7356 10
created -6.28 39.8942 4
created -6.04 49.8678 5
created -5.8 59.8413 6
created -5.56 29.9207 3
created -5.32 49.8678 5
created -5.08 99.7356 10
created -4.84 69.8149 7
created -4.6 79.7885 8
created -4.36 29.9207 3
created -4.12 19.9471 2
created -3.88 49.8678 5
created -3.64 99.7356 10
created -3.4 99.7356 10
created -3.16 69.8149 7
created -2.92 79.7885 8
created -2.68 69.8149 7
created -2.44 19.9471 2
created -2.2 39.8942 4
created -1.96 59.8413 6
created -1.72 19.9471 2
created -1.48 69.8149 7
created -1.24 29.9207 3
created -1 19.9471 2
created -0.76 59.8413 6
created -0.52 49.8678 5
created -0.28 29.9207 3
created -0.04 9.97356 1
created 0.2 99.7356 10
created 0.44 39.8942 4
created 0.68 19.9471 2
created 0.92 79.7885 8
created 1.16 79.7885 8
created 1.4 19.9471 2
created 1.64 39.8942 4
created 1.88 59.8413 6
created 2.12 39.8942 4
created 2.36 29.9207 3
created 2.6 39.8942 4
created 2.84 49.8678 5
created 3.08 39.8942 4
created 3.32 99.7356 10
created 3.56 39.8942 4
created 3.8 39.8942 4
created 4.04 29.9207 3
created 4.28 69.8149 7
created 4.52 49.8678 5
created 4.76 49.8678 5
created 5 59.8413 6
created 5.24 59.8413 6
created 5.48 9.97356 1
created 5.72 79.7885 8
created 5.96 89.762 9
created 6.2 19.9471 2
created 6.44 99.7356 10
created 6.68 59.8413 6
created 6.92 9.97356 1
created 7.16 69.8149 7
created 7.4 29.9207 3
created 7.64 39.8942 4
created 7.88 19.9471 2
created 8.12 59.8413 6
created 8.36 49.8678 5
created 8.6 59.8413 6
created 8.84 79.7885 8
created 9.08 69.8149 7
created 9.32 49.8678 5
created 9.56 59.8413 6
created 9.8 89.762 9
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.77244e-05)
fit chi^2 = 1.52816e-05
found -7.72 (+-0.000204391) 99.7326 (+-0.503842) 10.0002 (+-0.00175043)
found -6.76 (+-0.000205483) 99.735 (+-0.504166) 10.0004 (+-0.00175155)
found -6.52 (+-0.000205124) 99.7342 (+-0.504059) 10.0004 (+-0.00175118)
found -3.4 (+-0.000205483) 99.735 (+-0.504166) 10.0005 (+-0.00175155)
found -8.44 (+-0.000203706) 99.7315 (+-0.503649) 10.0001 (+-0.00174976)
found -5.08 (+-0.000204989) 99.7337 (+-0.504013) 10.0003 (+-0.00175102)
found -3.64 (+-0.000205259) 99.7344 (+-0.504098) 10.0004 (+-0.00175132)
found 0.200001 (+-0.000203863) 99.7317 (+-0.503695) 10.0001 (+-0.00174991)
found 3.32 (+-0.000204499) 99.7326 (+-0.503867) 10.0002 (+-0.00175051)
found 6.44 (+-0.000204391) 99.7326 (+-0.503842) 10.0002 (+-0.00175043)
found 5.96 (+-0.000215794) 89.7601 (+-0.478082) 9.00026 (+-0.00166093)
found 9.8 (+-0.000214466) 89.7614 (+-0.477789) 9.00039 (+-0.00165992)
found -4.6 (+-0.000229176) 79.787 (+-0.450805) 8.00026 (+-0.00156617)
found -2.92 (+-0.000229803) 79.7881 (+-0.450956) 8.00037 (+-0.00156669)
found 1.16 (+-0.000229053) 79.787 (+-0.450781) 8.00026 (+-0.00156608)
found 8.84 (+-0.000229674) 79.7878 (+-0.450923) 8.00034 (+-0.00156658)
found 0.920001 (+-0.000229053) 79.787 (+-0.450781) 8.00026 (+-0.00156608)
found 5.72 (+-0.000228818) 79.787 (+-0.450736) 8.00026 (+-0.00156593)
found -4.84 (+-0.000246435) 69.8161 (+-0.422002) 7.00048 (+-0.0014661)
found -3.16 (+-0.000246435) 69.8161 (+-0.422002) 7.00048 (+-0.0014661)
found -2.68 (+-0.000245082) 69.814 (+-0.421713) 7.00027 (+-0.0014651)
found 9.08 (+-0.00024575) 69.8148 (+-0.421849) 7.00035 (+-0.00146557)
found -7 (+-0.000245321) 69.8145 (+-0.421768) 7.00032 (+-0.00146529)
found -1.48 (+-0.000244252) 69.8127 (+-0.421532) 7.00013 (+-0.00146447)
found 4.28 (+-0.000244914) 69.8135 (+-0.421668) 7.00021 (+-0.00146494)
found 7.16 (+-0.000243871) 69.8124 (+-0.42146) 7.0001 (+-0.00146422)
found 6.68 (+-0.000264836) 59.8412 (+-0.390466) 6.00029 (+-0.00135654)
found -8.92 (+-0.000264803) 59.8404 (+-0.390438) 6.00021 (+-0.00135644)
found -5.8 (+-0.000264803) 59.8404 (+-0.390438) 6.00021 (+-0.00135644)
found -1.96 (+-0.00026429) 59.8399 (+-0.390346) 6.00016 (+-0.00135612)
found 1.88 (+-0.00026484) 59.8404 (+-0.390443) 6.00021 (+-0.00135646)
found 5 (+-0.000265442) 59.8412 (+-0.390556) 6.00029 (+-0.00135685)
found 5.24 (+-0.000264243) 59.8402 (+-0.390348) 6.00019 (+-0.00135613)
found 8.6 (+-0.000265765) 59.8418 (+-0.390619) 6.00035 (+-0.00135707)
found 9.56 (+-0.000265908) 59.8421 (+-0.390647) 6.00038 (+-0.00135717)
found -7.96 (+-0.000264836) 59.8412 (+-0.390466) 6.0003 (+-0.00135654)
found -0.759999 (+-0.000264498) 59.8401 (+-0.390385) 6.00018 (+-0.00135626)
found 8.12 (+-0.000264498) 59.8402 (+-0.390385) 6.00019 (+-0.00135626)
found -9.16 (+-0.000290664) 49.8676 (+-0.356512) 5.00024 (+-0.00123858)
found -6.04 (+-0.000290953) 49.8679 (+-0.356556) 5.00027 (+-0.00123873)
found -5.32 (+-0.000291372) 49.8687 (+-0.356629) 5.00035 (+-0.00123899)
found -0.520001 (+-0.000290665) 49.8676 (+-0.356512) 5.00024 (+-0.00123858)
found 2.84 (+-0.000290488) 49.8674 (+-0.356481) 5.00021 (+-0.00123847)
found 4.52 (+-0.000291399) 49.8684 (+-0.356627) 5.00032 (+-0.00123898)
found 4.76 (+-0.0002912) 49.8682 (+-0.356595) 5.00029 (+-0.00123887)
found 8.36 (+-0.00029142) 49.8684 (+-0.35663) 5.00032 (+-0.00123899)
found 9.32 (+-0.000291619) 49.8687 (+-0.356662) 5.00035 (+-0.0012391)
found -9.64 (+-0.000289053) 49.8663 (+-0.35627) 5.0001 (+-0.00123774)
found -3.88 (+-0.000291011) 49.8684 (+-0.356577) 5.00032 (+-0.0012388)
found -6.28 (+-0.000327074) 39.8962 (+-0.319147) 4.0004 (+-0.00110877)
found 0.439996 (+-0.000325973) 39.8954 (+-0.319012) 4.00032 (+-0.0011083)
found 3.56 (+-0.000326767) 39.8959 (+-0.319108) 4.00037 (+-0.00110863)
found 2.12 (+-0.000325545) 39.8946 (+-0.318947) 4.00024 (+-0.00110807)
found 2.6 (+-0.000325277) 39.8943 (+-0.318911) 4.00021 (+-0.00110795)
found 3.08 (+-0.000327074) 39.8962 (+-0.319147) 4.0004 (+-0.00110877)
found 3.8 (+-0.000324975) 39.8941 (+-0.318872) 4.00019 (+-0.00110781)
found 7.64 (+-0.000324193) 39.8935 (+-0.318776) 4.00013 (+-0.00110748)
found -2.2 (+-0.00032511) 39.8943 (+-0.318894) 4.00021 (+-0.00110789)
found 1.64 (+-0.00032511) 39.8943 (+-0.318894) 4.00021 (+-0.00110789)
found -4.36 (+-0.000376893) 29.9218 (+-0.276318) 3.00027 (+-0.000959972)
found -9.4 (+-0.000377363) 29.9218 (+-0.276357) 3.00027 (+-0.000960106)
found -8.68 (+-0.000379193) 29.9234 (+-0.276543) 3.00043 (+-0.000960754)
found -5.56 (+-0.000377715) 29.9221 (+-0.276392) 3.00029 (+-0.000960229)
found -1.24 (+-0.000376606) 29.9216 (+-0.276288) 3.00024 (+-0.000959869)
found -0.280002 (+-0.000375158) 29.9208 (+-0.276152) 3.00016 (+-0.000959395)
found 7.4 (+-0.000377634) 29.9221 (+-0.276385) 3.00029 (+-0.000960205)
found 2.36 (+-0.00037657) 29.9213 (+-0.276278) 3.00021 (+-0.000959834)
found 4.04 (+-0.000377634) 29.9221 (+-0.276385) 3.00029 (+-0.000960205)
found -7.48001 (+-0.000464163) 19.9493 (+-0.225789) 2.00032 (+-0.000784428)
found 1.4 (+-0.000464963) 19.9493 (+-0.225835) 2.00032 (+-0.000784587)
found 6.2 (+-0.000468006) 19.9512 (+-0.226047) 2.00051 (+-0.000785322)
found -2.44 (+-0.000464559) 19.949 (+-0.225807) 2.00029 (+-0.000784488)
found -1.72 (+-0.000465643) 19.9496 (+-0.22588) 2.00035 (+-0.000784743)
found -7.24 (+-0.000463059) 19.9485 (+-0.225709) 2.00024 (+-0.00078415)
found -4.12 (+-0.000462938) 19.9482 (+-0.225696) 2.00022 (+-0.000784104)
found -0.999997 (+-0.000463438) 19.9485 (+-0.225731) 2.00024 (+-0.000784225)
found 0.680004 (+-0.000464963) 19.9493 (+-0.225835) 2.00032 (+-0.000784587)
found 7.88 (+-0.000464111) 19.9488 (+-0.225775) 2.00027 (+-0.000784379)
found -8.20001 (+-0.000666759) 9.97732 (+-0.160022) 1.00043 (+-0.000555941)
found 5.48 (+-0.000665561) 9.97679 (+-0.159975) 1.00037 (+-0.000555779)
found 6.92 (+-0.000664872) 9.97652 (+-0.159949) 1.00035 (+-0.000555689)
found -0.0399864 (+-0.000663593) 9.97653 (+-0.159911) 1.00035 (+-0.000555556)
found -9.87999 (+-0.000654629) 9.9744 (+-0.15959) 1.00014 (+-0.000554439)

 
#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.