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 79.7885 8
created -9.16 89.762 9
created -8.92 79.7885 8
created -8.68 89.762 9
created -8.44 69.8149 7
created -8.2 39.8942 4
created -7.96 19.9471 2
created -7.72 39.8942 4
created -7.48 19.9471 2
created -7.24 19.9471 2
created -7 29.9207 3
created -6.76 19.9471 2
created -6.52 29.9207 3
created -6.28 59.8413 6
created -6.04 19.9471 2
created -5.8 29.9207 3
created -5.56 79.7885 8
created -5.32 49.8678 5
created -5.08 39.8942 4
created -4.84 69.8149 7
created -4.6 89.762 9
created -4.36 69.8149 7
created -4.12 79.7885 8
created -3.88 59.8413 6
created -3.64 99.7356 10
created -3.4 59.8413 6
created -3.16 99.7356 10
created -2.92 69.8149 7
created -2.68 19.9471 2
created -2.44 79.7885 8
created -2.2 19.9471 2
created -1.96 59.8413 6
created -1.72 79.7885 8
created -1.48 39.8942 4
created -1.24 79.7885 8
created -1 99.7356 10
created -0.76 79.7885 8
created -0.52 19.9471 2
created -0.28 99.7356 10
created -0.04 89.762 9
created 0.2 29.9207 3
created 0.44 9.97356 1
created 0.68 79.7885 8
created 0.92 59.8413 6
created 1.16 39.8942 4
created 1.4 69.8149 7
created 1.64 29.9207 3
created 1.88 89.762 9
created 2.12 69.8149 7
created 2.36 59.8413 6
created 2.6 39.8942 4
created 2.84 89.762 9
created 3.08 19.9471 2
created 3.32 59.8413 6
created 3.56 39.8942 4
created 3.8 49.8678 5
created 4.04 49.8678 5
created 4.28 99.7356 10
created 4.52 89.762 9
created 4.76 19.9471 2
created 5 49.8678 5
created 5.24 9.97356 1
created 5.48 79.7885 8
created 5.72 69.8149 7
created 5.96 69.8149 7
created 6.2 39.8942 4
created 6.44 19.9471 2
created 6.68 49.8678 5
created 6.92 29.9207 3
created 7.16 89.762 9
created 7.4 59.8413 6
created 7.64 19.9471 2
created 7.88 29.9207 3
created 8.12 49.8678 5
created 8.36 79.7885 8
created 8.6 49.8678 5
created 8.84 69.8149 7
created 9.08 49.8678 5
created 9.32 19.9471 2
created 9.56 69.8149 7
created 9.8 9.97356 1
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-7.03917e-06)
fit chi^2 = 2.52785e-06
found -3.64 (+-8.33771e-05) 99.7336 (+-0.204992) 10.0003 (+-0.000712173)
found -3.16 (+-8.34201e-05) 99.7339 (+-0.205005) 10.0003 (+-0.000712219)
found -1 (+-8.35419e-05) 99.7347 (+-0.205042) 10.0004 (+-0.000712349)
found -9.88 (+-8.31397e-05) 99.7313 (+-0.204904) 10.0001 (+-0.000711869)
found -0.279999 (+-8.32469e-05) 99.7334 (+-0.204957) 10.0003 (+-0.000712055)
found 4.28 (+-8.3448e-05) 99.7341 (+-0.205014) 10.0004 (+-0.000712251)
found -9.16 (+-8.81297e-05) 89.7616 (+-0.19454) 9.00042 (+-0.000675862)
found -8.68 (+-8.80863e-05) 89.7614 (+-0.194527) 9.0004 (+-0.000675819)
found -4.6 (+-8.8043e-05) 89.7611 (+-0.194515) 9.00037 (+-0.000675777)
found -0.0400015 (+-8.79335e-05) 89.7608 (+-0.194488) 9.00034 (+-0.000675683)
found 4.52 (+-8.78442e-05) 89.7606 (+-0.194466) 9.00032 (+-0.000675606)
found 1.88 (+-8.78131e-05) 89.7601 (+-0.194454) 9.00026 (+-0.000675563)
found 2.84 (+-8.75666e-05) 89.759 (+-0.194388) 9.00016 (+-0.000675335)
found 7.16 (+-8.77662e-05) 89.7598 (+-0.19444) 9.00024 (+-0.000675517)
found -8.92 (+-9.36512e-05) 79.7891 (+-0.183458) 8.00048 (+-0.000637364)
found -4.12 (+-9.3412e-05) 79.7878 (+-0.183398) 8.00035 (+-0.000637153)
found -1.72 (+-9.32354e-05) 79.787 (+-0.183355) 8.00026 (+-0.000637003)
found -0.760002 (+-9.32454e-05) 79.7876 (+-0.183362) 8.00032 (+-0.00063703)
found -9.4 (+-9.33022e-05) 79.7876 (+-0.183373) 8.00032 (+-0.000637068)
found -5.56 (+-9.30999e-05) 79.7865 (+-0.183322) 8.00021 (+-0.00063689)
found -2.44 (+-9.27627e-05) 79.7854 (+-0.183244) 8.0001 (+-0.00063662)
found -1.24 (+-9.34222e-05) 79.7881 (+-0.183402) 8.00037 (+-0.000637169)
found 0.680001 (+-9.29207e-05) 79.7862 (+-0.183285) 8.00018 (+-0.000636761)
found 5.48 (+-9.29725e-05) 79.7865 (+-0.183298) 8.00021 (+-0.000636807)
found 8.36 (+-9.32434e-05) 79.787 (+-0.183356) 8.00026 (+-0.000637008)
found -8.44 (+-9.99266e-05) 69.8148 (+-0.171569) 7.00035 (+-0.000596057)
found -4.36 (+-0.000100181) 69.8158 (+-0.171624) 7.00045 (+-0.00059625)
found -2.92 (+-9.9776e-05) 69.8145 (+-0.17154) 7.00032 (+-0.000595956)
found 2.12 (+-0.000100067) 69.8153 (+-0.171599) 7.0004 (+-0.000596161)
found 5.72 (+-0.000100076) 69.8153 (+-0.1716) 7.0004 (+-0.000596167)
found 5.96 (+-9.98217e-05) 69.8142 (+-0.171545) 7.00029 (+-0.000595974)
found -4.84 (+-9.99266e-05) 69.8148 (+-0.171569) 7.00035 (+-0.000596057)
found 1.4 (+-9.95366e-05) 69.8132 (+-0.171484) 7.00019 (+-0.000595761)
found 8.84 (+-9.97714e-05) 69.814 (+-0.171533) 7.00027 (+-0.000595934)
found 9.56 (+-9.90783e-05) 69.8121 (+-0.171393) 7.00008 (+-0.000595446)
found -3.4 (+-0.000108527) 59.8436 (+-0.158956) 6.00053 (+-0.000552238)
found 7.4 (+-0.000107838) 59.8412 (+-0.158827) 6.00029 (+-0.000551791)
found -6.28 (+-0.000107393) 59.8396 (+-0.158742) 6.00013 (+-0.000551495)
found -3.88 (+-0.000108412) 59.8431 (+-0.158933) 6.00048 (+-0.000552159)
found 0.919999 (+-0.000108005) 59.8415 (+-0.158855) 6.00032 (+-0.000551888)
found 2.36 (+-0.000107943) 59.8412 (+-0.158843) 6.00029 (+-0.000551845)
found -1.96 (+-0.00010778) 59.8409 (+-0.158816) 6.00027 (+-0.00055175)
found 3.32 (+-0.000107491) 59.8399 (+-0.15876) 6.00016 (+-0.000551557)
found -5.32 (+-0.00011849) 49.8684 (+-0.145042) 5.00032 (+-0.000503901)
found 3.8 (+-0.000118246) 49.8676 (+-0.145003) 5.00024 (+-0.000503762)
found 4.04 (+-0.000118725) 49.8692 (+-0.145081) 5.0004 (+-0.000504034)
found 8.12 (+-0.000118373) 49.8681 (+-0.145025) 5.00029 (+-0.000503838)
found 8.6 (+-0.000118762) 49.8692 (+-0.145086) 5.0004 (+-0.000504051)
found 9.08 (+-0.000118153) 49.8676 (+-0.144991) 5.00024 (+-0.000503721)
found 5 (+-0.000117419) 49.8661 (+-0.144879) 5.00008 (+-0.000503334)
found 6.68 (+-0.000117769) 49.8666 (+-0.14493) 5.00013 (+-0.000503508)
found -8.2 (+-0.000132326) 39.8946 (+-0.129713) 4.00024 (+-0.000450642)
found -5.08 (+-0.000132772) 39.8954 (+-0.129768) 4.00032 (+-0.000450834)
found -1.48 (+-0.000133166) 39.8964 (+-0.12982) 4.00043 (+-0.000451015)
found 1.16 (+-0.000132882) 39.8956 (+-0.129782) 4.00035 (+-0.000450884)
found 2.6 (+-0.000133058) 39.8962 (+-0.129806) 4.0004 (+-0.000450967)
found 3.56 (+-0.000132672) 39.8951 (+-0.129755) 4.00029 (+-0.000450788)
found 6.2 (+-0.000132326) 39.8946 (+-0.129713) 4.00024 (+-0.000450642)
found -7.72 (+-0.00013168) 39.8932 (+-0.12963) 4.00011 (+-0.000450355)
found -9.64 (+-0.000154474) 29.9239 (+-0.1125) 3.00048 (+-0.000390842)
found 0.199995 (+-0.000153073) 29.9218 (+-0.112366) 3.00027 (+-0.000390378)
found 1.64 (+-0.000154254) 29.9234 (+-0.112477) 3.00043 (+-0.000390763)
found 6.92 (+-0.000153979) 29.9229 (+-0.11245) 3.00037 (+-0.000390668)
found -7 (+-0.000152331) 29.9202 (+-0.112288) 3.00011 (+-0.000390107)
found -6.52 (+-0.000153044) 29.9213 (+-0.112358) 3.00021 (+-0.000390349)
found -5.8 (+-0.000153288) 29.9218 (+-0.112383) 3.00027 (+-0.000390436)
found 7.88 (+-0.000152902) 29.921 (+-0.112343) 3.00019 (+-0.000390299)
found -2.2 (+-0.00018955) 19.9498 (+-0.0918807) 2.00037 (+-0.000319208)
found -0.519998 (+-0.000190193) 19.9509 (+-0.0919261) 2.00048 (+-0.000319366)
found 3.08 (+-0.000189702) 19.9501 (+-0.0918915) 2.0004 (+-0.000319246)
found 4.76 (+-0.000189494) 19.9498 (+-0.0918775) 2.00037 (+-0.000319197)
found -6.04 (+-0.000188488) 19.9485 (+-0.0918084) 2.00024 (+-0.000318957)
found -2.68 (+-0.000189735) 19.9501 (+-0.0918935) 2.0004 (+-0.000319253)
found 7.64 (+-0.000188488) 19.9485 (+-0.0918084) 2.00024 (+-0.000318957)
found 9.32 (+-0.000189179) 19.9493 (+-0.091855) 2.00032 (+-0.000319119)
found -7.96 (+-0.000188325) 19.9482 (+-0.0917966) 2.00021 (+-0.000318916)
found -7.48 (+-0.000187721) 19.9477 (+-0.0917571) 2.00016 (+-0.000318779)
found -7.24 (+-0.000187451) 19.9474 (+-0.0917391) 2.00013 (+-0.000318716)
found -6.76 (+-0.000187782) 19.9477 (+-0.0917605) 2.00016 (+-0.000318791)
found 6.44 (+-0.000188557) 19.9485 (+-0.0918124) 2.00024 (+-0.000318971)
found 9.79999 (+-0.000264977) 9.9752 (+-0.0648925) 1.00022 (+-0.000225447)
found 5.24001 (+-0.000270336) 9.97651 (+-0.0650516) 1.00035 (+-0.000225999)
found 0.44001 (+-0.000269413) 9.976 (+-0.0650195) 1.0003 (+-0.000225888)

 
#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.