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 99.7356 10
created -9.64 29.9207 3
created -9.4 59.8413 6
created -9.16 9.97356 1
created -8.92 69.8149 7
created -8.68 79.7885 8
created -8.44 39.8942 4
created -8.2 49.8678 5
created -7.96 49.8678 5
created -7.72 19.9471 2
created -7.48 39.8942 4
created -7.24 59.8413 6
created -7 19.9471 2
created -6.76 99.7356 10
created -6.52 39.8942 4
created -6.28 99.7356 10
created -6.04 89.762 9
created -5.8 39.8942 4
created -5.56 69.8149 7
created -5.32 79.7885 8
created -5.08 29.9207 3
created -4.84 19.9471 2
created -4.6 49.8678 5
created -4.36 39.8942 4
created -4.12 39.8942 4
created -3.88 99.7356 10
created -3.64 29.9207 3
created -3.4 39.8942 4
created -3.16 79.7885 8
created -2.92 89.762 9
created -2.68 19.9471 2
created -2.44 39.8942 4
created -2.2 59.8413 6
created -1.96 9.97356 1
created -1.72 69.8149 7
created -1.48 39.8942 4
created -1.24 79.7885 8
created -1 69.8149 7
created -0.76 29.9207 3
created -0.52 99.7356 10
created -0.28 69.8149 7
created -0.04 49.8678 5
created 0.2 99.7356 10
created 0.44 89.762 9
created 0.68 69.8149 7
created 0.92 9.97356 1
created 1.16 19.9471 2
created 1.4 69.8149 7
created 1.64 59.8413 6
created 1.88 49.8678 5
created 2.12 59.8413 6
created 2.36 19.9471 2
created 2.6 79.7885 8
created 2.84 9.97356 1
created 3.08 49.8678 5
created 3.32 39.8942 4
created 3.56 89.762 9
created 3.8 89.762 9
created 4.04 59.8413 6
created 4.28 19.9471 2
created 4.52 39.8942 4
created 4.76 19.9471 2
created 5 29.9207 3
created 5.24 29.9207 3
created 5.48 79.7885 8
created 5.72 89.762 9
created 5.96 29.9207 3
created 6.2 69.8149 7
created 6.44 69.8149 7
created 6.68 59.8413 6
created 6.92 69.8149 7
created 7.16 29.9207 3
created 7.4 39.8942 4
created 7.64 19.9471 2
created 7.88 19.9471 2
created 8.12 69.8149 7
created 8.36 59.8413 6
created 8.6 19.9471 2
created 8.84 99.7356 10
created 9.08 19.9471 2
created 9.32 39.8942 4
created 9.56 79.7885 8
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 (+-1.75837e-05)
fit chi^2 = 1.52656e-05
found -9.88 (+-0.00020431) 99.7314 (+-0.503537) 10.0001 (+-0.00174937)
found -6.76 (+-0.000204035) 99.7321 (+-0.503504) 10.0002 (+-0.00174925)
found -6.28 (+-0.000204933) 99.734 (+-0.503769) 10.0003 (+-0.00175017)
found -3.88 (+-0.000204234) 99.7323 (+-0.503559) 10.0002 (+-0.00174944)
found -0.519999 (+-0.000204589) 99.7332 (+-0.503665) 10.0003 (+-0.00174981)
found 0.200001 (+-0.000205067) 99.7342 (+-0.503808) 10.0004 (+-0.00175031)
found 8.84 (+-0.00020368) 99.7315 (+-0.503404) 10.0001 (+-0.00174891)
found -6.04 (+-0.000216264) 89.7612 (+-0.477986) 9.00037 (+-0.0016606)
found -2.92 (+-0.000215681) 89.7601 (+-0.477832) 9.00026 (+-0.00166006)
found 0.439999 (+-0.000216657) 89.762 (+-0.478093) 9.00045 (+-0.00166097)
found 3.8 (+-0.000216448) 89.7614 (+-0.478034) 9.0004 (+-0.00166076)
found 5.72 (+-0.0002159) 89.7604 (+-0.477886) 9.00029 (+-0.00166025)
found 9.8 (+-0.00021457) 89.7619 (+-0.477602) 9.00045 (+-0.00165926)
found 3.56 (+-0.000216172) 89.7609 (+-0.477959) 9.00034 (+-0.00166051)
found -8.68 (+-0.000229248) 79.7873 (+-0.450613) 8.00029 (+-0.0015655)
found -5.32 (+-0.000229056) 79.7871 (+-0.450569) 8.00026 (+-0.00156535)
found -3.16 (+-0.000229476) 79.7879 (+-0.450672) 8.00034 (+-0.0015657)
found -1.24 (+-0.000229248) 79.7873 (+-0.450613) 8.00029 (+-0.0015655)
found 2.6 (+-0.000227619) 79.7852 (+-0.450238) 8.00008 (+-0.0015642)
found 5.48 (+-0.000229284) 79.7876 (+-0.450627) 8.00032 (+-0.00156555)
found 9.56 (+-0.000229476) 79.7879 (+-0.450672) 8.00035 (+-0.0015657)
found -1 (+-0.000245223) 69.8143 (+-0.421545) 7.00029 (+-0.00146451)
found -0.280001 (+-0.000245862) 69.8154 (+-0.421684) 7.0004 (+-0.001465)
found 0.679998 (+-0.000244691) 69.814 (+-0.421448) 7.00026 (+-0.00146418)
found 6.44 (+-0.000245649) 69.8148 (+-0.421634) 7.00035 (+-0.00146482)
found 6.92 (+-0.000244945) 69.8138 (+-0.421483) 7.00024 (+-0.0014643)
found -8.92 (+-0.000244569) 69.8138 (+-0.42142) 7.00024 (+-0.00146408)
found -5.56 (+-0.000245438) 69.8146 (+-0.42159) 7.00032 (+-0.00146467)
found -1.72 (+-0.000243954) 69.8127 (+-0.421284) 7.00013 (+-0.0014636)
found 1.4 (+-0.000244676) 69.8135 (+-0.42143) 7.00021 (+-0.00146411)
found 6.2 (+-0.00024509) 69.814 (+-0.421515) 7.00027 (+-0.00146441)
found 8.12 (+-0.000244676) 69.8135 (+-0.42143) 7.00021 (+-0.00146411)
found 4.04 (+-0.000265005) 59.8413 (+-0.390307) 6.00029 (+-0.00135599)
found -9.4 (+-0.000263475) 59.8394 (+-0.390026) 6.00011 (+-0.00135501)
found -7.24 (+-0.000264152) 59.8399 (+-0.390142) 6.00016 (+-0.00135541)
found -2.2 (+-0.000263716) 59.8396 (+-0.39007) 6.00013 (+-0.00135517)
found 1.64 (+-0.000265471) 59.8415 (+-0.390384) 6.00032 (+-0.00135626)
found 2.12 (+-0.000264359) 59.8402 (+-0.39018) 6.00019 (+-0.00135555)
found 6.68 (+-0.000265825) 59.842 (+-0.390452) 6.00037 (+-0.00135649)
found 8.36 (+-0.000264709) 59.8407 (+-0.390248) 6.00024 (+-0.00135578)
found -8.2 (+-0.000290582) 49.8677 (+-0.356334) 5.00024 (+-0.00123796)
found -7.96 (+-0.000289937) 49.8671 (+-0.356237) 5.00019 (+-0.00123762)
found -0.0399989 (+-0.00029218) 49.8698 (+-0.356594) 5.00045 (+-0.00123886)
found 1.88 (+-0.000291267) 49.8685 (+-0.356443) 5.00032 (+-0.00123834)
found -4.6 (+-0.000289693) 49.8669 (+-0.356198) 5.00016 (+-0.00123749)
found 3.08 (+-0.000289185) 49.8666 (+-0.356128) 5.00014 (+-0.00123724)
found -6.52 (+-0.000328055) 39.8975 (+-0.319133) 4.00053 (+-0.00110872)
found -5.8 (+-0.000327228) 39.8965 (+-0.319022) 4.00043 (+-0.00110833)
found -8.44 (+-0.000326501) 39.8957 (+-0.318926) 4.00035 (+-0.001108)
found -4.36 (+-0.000325458) 39.8946 (+-0.318789) 4.00024 (+-0.00110752)
found -4.12 (+-0.000326596) 39.8959 (+-0.318941) 4.00037 (+-0.00110805)
found -3.4 (+-0.000325841) 39.8952 (+-0.318842) 4.0003 (+-0.00110771)
found -1.48 (+-0.000327019) 39.8962 (+-0.318993) 4.0004 (+-0.00110823)
found 3.32 (+-0.000326709) 39.8959 (+-0.318954) 4.00037 (+-0.0011081)
found 7.4 (+-0.000324024) 39.8935 (+-0.318609) 4.00013 (+-0.0011069)
found -7.48 (+-0.00032494) 39.8943 (+-0.318727) 4.00021 (+-0.00110731)
found -2.44 (+-0.00032494) 39.8944 (+-0.318727) 4.00021 (+-0.00110731)
found 4.52 (+-0.000323594) 39.8933 (+-0.318557) 4.00011 (+-0.00110672)
found 9.32 (+-0.000325405) 39.8949 (+-0.31879) 4.00027 (+-0.00110753)
found -9.64 (+-0.000378995) 29.9234 (+-0.276398) 3.00043 (+-0.000960251)
found -5.08 (+-0.000376695) 29.9218 (+-0.276173) 3.00027 (+-0.00095947)
found -3.64 (+-0.000378237) 29.9229 (+-0.276324) 3.00037 (+-0.000959992)
found 5.96 (+-0.000379067) 29.9234 (+-0.276405) 3.00043 (+-0.000960273)
found -0.759998 (+-0.000379316) 29.9237 (+-0.276431) 3.00045 (+-0.000960363)
found 7.16 (+-0.000377436) 29.9221 (+-0.27624) 3.00029 (+-0.000959702)
found 5 (+-0.000374899) 29.9205 (+-0.275993) 3.00013 (+-0.000958842)
found 5.24 (+-0.000377262) 29.9221 (+-0.276226) 3.00029 (+-0.000959652)
found -2.68 (+-0.000465089) 19.9496 (+-0.225744) 2.00035 (+-0.000784269)
found 9.07999 (+-0.000465429) 19.9498 (+-0.225768) 2.00038 (+-0.000784355)
found -7.72 (+-0.000463366) 19.9485 (+-0.225623) 2.00024 (+-0.000783848)
found -7 (+-0.00046652) 19.9504 (+-0.225842) 2.00043 (+-0.00078461)
found 2.36 (+-0.000465806) 19.9498 (+-0.225791) 2.00037 (+-0.000784432)
found 4.28 (+-0.000463868) 19.9488 (+-0.225657) 2.00027 (+-0.000783968)
found 8.6 (+-0.00046652) 19.9504 (+-0.225842) 2.00043 (+-0.00078461)
found -4.84 (+-0.000462696) 19.9482 (+-0.225578) 2.00021 (+-0.000783694)
found 4.76 (+-0.000462127) 19.948 (+-0.225539) 2.00019 (+-0.000783559)
found 7.64 (+-0.00046131) 19.9477 (+-0.225487) 2.00016 (+-0.000783376)
found 7.88 (+-0.000462817) 19.9485 (+-0.225591) 2.00024 (+-0.00078374)
found 1.16001 (+-0.000461688) 19.9483 (+-0.225523) 2.00022 (+-0.000783502)
found 2.83999 (+-0.000664331) 9.97652 (+-0.15986) 1.00035 (+-0.000555378)
found 0.91999 (+-0.00065983) 9.97548 (+-0.159703) 1.00024 (+-0.000554833)
found -9.16 (+-0.000664524) 9.97652 (+-0.159865) 1.00035 (+-0.000555398)
found -1.96 (+-0.000664525) 9.97652 (+-0.159865) 1.00035 (+-0.000555398)

 
#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.