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 59.8413 6
created -9.4 9.97356 1
created -9.16 49.8678 5
created -8.92 79.7885 8
created -8.68 79.7885 8
created -8.44 39.8942 4
created -8.2 99.7356 10
created -7.96 79.7885 8
created -7.72 99.7356 10
created -7.48 9.97356 1
created -7.24 19.9471 2
created -7 59.8413 6
created -6.76 49.8678 5
created -6.52 69.8149 7
created -6.28 29.9207 3
created -6.04 9.97356 1
created -5.8 49.8678 5
created -5.56 19.9471 2
created -5.32 39.8942 4
created -5.08 9.97356 1
created -4.84 9.97356 1
created -4.6 79.7885 8
created -4.36 79.7885 8
created -4.12 59.8413 6
created -3.88 69.8149 7
created -3.64 19.9471 2
created -3.4 49.8678 5
created -3.16 89.762 9
created -2.92 29.9207 3
created -2.68 39.8942 4
created -2.44 29.9207 3
created -2.2 79.7885 8
created -1.96 69.8149 7
created -1.72 89.762 9
created -1.48 29.9207 3
created -1.24 9.97356 1
created -1 59.8413 6
created -0.76 89.762 9
created -0.52 99.7356 10
created -0.28 99.7356 10
created -0.04 79.7885 8
created 0.2 9.97356 1
created 0.44 9.97356 1
created 0.68 79.7885 8
created 0.92 59.8413 6
created 1.16 29.9207 3
created 1.4 59.8413 6
created 1.64 49.8678 5
created 1.88 9.97356 1
created 2.12 79.7885 8
created 2.36 29.9207 3
created 2.6 49.8678 5
created 2.84 89.762 9
created 3.08 9.97356 1
created 3.32 19.9471 2
created 3.56 39.8942 4
created 3.8 9.97356 1
created 4.04 9.97356 1
created 4.28 49.8678 5
created 4.52 69.8149 7
created 4.76 79.7885 8
created 5 39.8942 4
created 5.24 89.762 9
created 5.48 39.8942 4
created 5.72 39.8942 4
created 5.96 49.8678 5
created 6.2 89.762 9
created 6.44 99.7356 10
created 6.68 59.8413 6
created 6.92 49.8678 5
created 7.16 99.7356 10
created 7.4 39.8942 4
created 7.64 49.8678 5
created 7.88 69.8149 7
created 8.12 49.8678 5
created 8.36 39.8942 4
created 8.6 79.7885 8
created 8.84 79.7885 8
created 9.08 19.9471 2
created 9.32 19.9471 2
created 9.56 39.8942 4
created 9.8 19.9471 2
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.0400021 (+-9.42414e-06)
fit chi^2 = 4.18271e-06
found -7.72 (+-0.00010689) 99.7328 (+-0.26359) 10.0002 (+-0.000915755)
found -0.52 (+-0.000107601) 99.7355 (+-0.263796) 10.0005 (+-0.00091647)
found -0.28 (+-0.000107554) 99.7352 (+-0.263781) 10.0005 (+-0.000916418)
found 6.44 (+-0.000107403) 99.7344 (+-0.263735) 10.0004 (+-0.000916256)
found -8.2 (+-0.000107225) 99.7336 (+-0.263681) 10.0003 (+-0.00091607)
found 7.16 (+-0.000107058) 99.7328 (+-0.26363) 10.0002 (+-0.000915892)
found -1.72 (+-0.000112957) 89.76 (+-0.250132) 9.00026 (+-0.000868998)
found -0.759999 (+-0.000113348) 89.7616 (+-0.250239) 9.00042 (+-0.00086937)
found -3.16 (+-0.00011283) 89.7595 (+-0.250097) 9.00021 (+-0.000868876)
found 2.84 (+-0.000112555) 89.7589 (+-0.25003) 9.00015 (+-0.000868645)
found 5.24 (+-0.000112844) 89.7595 (+-0.2501) 9.00021 (+-0.000868886)
found 6.2 (+-0.00011328) 89.7614 (+-0.25022) 9.0004 (+-0.000869306)
found -8.68 (+-0.000120061) 79.7875 (+-0.235888) 8.00032 (+-0.000819511)
found -7.96 (+-0.000120575) 79.7896 (+-0.236017) 8.00053 (+-0.00081996)
found -4.36 (+-0.000120221) 79.7881 (+-0.235926) 8.00037 (+-0.000819646)
found -0.0400022 (+-0.000119765) 79.7872 (+-0.235827) 8.00029 (+-0.0008193)
found 4.76 (+-0.000119999) 79.7873 (+-0.235872) 8.00029 (+-0.000819456)
found 8.84 (+-0.000119834) 79.787 (+-0.235836) 8.00026 (+-0.000819332)
found -8.92 (+-0.000120146) 79.7878 (+-0.235908) 8.00034 (+-0.000819582)
found -4.6 (+-0.000119655) 79.7867 (+-0.235798) 8.00024 (+-0.0008192)
found -2.2 (+-0.000119899) 79.787 (+-0.235848) 8.00026 (+-0.000819375)
found 0.680001 (+-0.000119527) 79.7862 (+-0.235765) 8.00018 (+-0.000819087)
found 2.12 (+-0.000119271) 79.7854 (+-0.235703) 8.0001 (+-0.000818871)
found 8.6 (+-0.000120061) 79.7875 (+-0.235888) 8.00032 (+-0.000819511)
found -3.88 (+-0.000128075) 69.8134 (+-0.220596) 7.00021 (+-0.000766384)
found -1.96 (+-0.000128867) 69.8158 (+-0.220766) 7.00045 (+-0.000766976)
found -6.52 (+-0.000128132) 69.8134 (+-0.220605) 7.00021 (+-0.000766418)
found 4.52 (+-0.00012857) 69.8147 (+-0.2207) 7.00034 (+-0.000766747)
found 7.88 (+-0.000128339) 69.8139 (+-0.220649) 7.00026 (+-0.000766569)
found 6.68 (+-0.000139186) 59.8423 (+-0.20439) 6.0004 (+-0.000710083)
found -4.12 (+-0.000139227) 59.8422 (+-0.204396) 6.0004 (+-0.000710105)
found 0.919998 (+-0.000138802) 59.8412 (+-0.204317) 6.00029 (+-0.000709831)
found 1.4 (+-0.000138538) 59.8404 (+-0.204266) 6.00021 (+-0.000709653)
found -9.64 (+-0.000137532) 59.8388 (+-0.204093) 6.00005 (+-0.00070905)
found -7 (+-0.000138378) 59.8401 (+-0.204238) 6.00018 (+-0.000709557)
found -0.999997 (+-0.000138486) 59.8409 (+-0.204267) 6.00027 (+-0.000709656)
found -6.76 (+-0.000152567) 49.8687 (+-0.186596) 5.00034 (+-0.000648264)
found 1.64 (+-0.000151613) 49.8671 (+-0.186452) 5.00019 (+-0.000647765)
found 2.6 (+-0.000152355) 49.8684 (+-0.186565) 5.00032 (+-0.000648155)
found 5.96 (+-0.000152507) 49.8687 (+-0.186588) 5.00035 (+-0.000648236)
found 6.92 (+-0.000152836) 49.8695 (+-0.186641) 5.00043 (+-0.000648419)
found 7.64 (+-0.000152322) 49.8682 (+-0.186557) 5.00029 (+-0.000648129)
found 8.12 (+-0.000152322) 49.8681 (+-0.186557) 5.00029 (+-0.000648129)
found -9.16 (+-0.00015181) 49.8676 (+-0.186485) 5.00024 (+-0.000647879)
found -5.8 (+-0.00015104) 49.866 (+-0.186363) 5.00008 (+-0.000647455)
found -3.4 (+-0.000152167) 49.8682 (+-0.186537) 5.00029 (+-0.000648059)
found 4.28 (+-0.000151716) 49.8673 (+-0.186469) 5.00021 (+-0.000647824)
found 5.48 (+-0.000170855) 39.8956 (+-0.166935) 4.00035 (+-0.000579958)
found 7.4 (+-0.000171116) 39.8962 (+-0.166969) 4.0004 (+-0.000580077)
found -8.44 (+-0.000171507) 39.897 (+-0.16702) 4.00048 (+-0.000580255)
found -2.68 (+-0.000169835) 39.8938 (+-0.166802) 4.00016 (+-0.000579497)
found 5 (+-0.000171405) 39.8967 (+-0.167006) 4.00045 (+-0.000580207)
found 5.72 (+-0.00017036) 39.8946 (+-0.166869) 4.00024 (+-0.000579728)
found 8.36 (+-0.000170906) 39.8956 (+-0.16694) 4.00035 (+-0.000579977)
found -5.32 (+-0.000169066) 39.893 (+-0.166711) 4.00008 (+-0.00057918)
found 3.56 (+-0.000169066) 39.8929 (+-0.166711) 4.00008 (+-0.00057918)
found 9.56 (+-0.000169384) 39.8932 (+-0.166747) 4.00011 (+-0.000579306)
found -2.92 (+-0.000197858) 29.9226 (+-0.144627) 3.00035 (+-0.000502458)
found -1.48 (+-0.000196903) 29.9218 (+-0.14454) 3.00027 (+-0.000502156)
found 2.36 (+-0.000197928) 29.9226 (+-0.144633) 3.00035 (+-0.000502478)
found -6.28 (+-0.000196617) 29.9213 (+-0.14451) 3.00022 (+-0.000502052)
found 1.16 (+-0.000197795) 29.9223 (+-0.144619) 3.00032 (+-0.00050243)
found -2.44 (+-0.000197719) 29.9223 (+-0.144613) 3.00032 (+-0.000502407)
found 9.07999 (+-0.00024247) 19.9488 (+-0.1181) 2.00027 (+-0.000410298)
found -5.56 (+-0.000242548) 19.9485 (+-0.118101) 2.00024 (+-0.000410303)
found -3.64 (+-0.000243346) 19.9493 (+-0.118156) 2.00032 (+-0.000410493)
found 9.32 (+-0.000241471) 19.9477 (+-0.11803) 2.00016 (+-0.000410056)
found 9.8 (+-0.000239113) 19.9477 (+-0.117908) 2.00016 (+-0.000409631)
found -7.24 (+-0.000241438) 19.948 (+-0.118033) 2.00019 (+-0.000410065)
found 3.32 (+-0.000240886) 19.9474 (+-0.117994) 2.00014 (+-0.000409932)
found -7.48002 (+-0.000346354) 9.97628 (+-0.0836339) 1.00032 (+-0.000290557)
found 0.199987 (+-0.000344611) 9.97549 (+-0.0835735) 1.00024 (+-0.000290348)
found 3.07999 (+-0.00034606) 9.976 (+-0.0836221) 1.0003 (+-0.000290516)
found -9.4 (+-0.000346981) 9.97598 (+-0.0836493) 1.00029 (+-0.000290611)
found -5.08001 (+-0.000342904) 9.9744 (+-0.0835093) 1.00014 (+-0.000290125)
found 1.88001 (+-0.000347742) 9.97651 (+-0.0836779) 1.00035 (+-0.00029071)
found 3.79999 (+-0.000342904) 9.97439 (+-0.0835093) 1.00013 (+-0.000290125)
found -6.04 (+-0.000345348) 9.97518 (+-0.0835914) 1.00021 (+-0.00029041)
found -1.23999 (+-0.000345801) 9.97546 (+-0.083608) 1.00024 (+-0.000290468)
found -4.83999 (+-0.000344612) 9.97548 (+-0.0835735) 1.00024 (+-0.000290348)
found 0.440013 (+-0.000344611) 9.97548 (+-0.0835735) 1.00024 (+-0.000290348)
found 4.04001 (+-0.000343424) 9.97466 (+-0.0835283) 1.00016 (+-0.00029019)
found -9.87999 (+-0.000342927) 9.97467 (+-0.0835095) 1.00016 (+-0.000290125)

 
#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.