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 79.7885 8
created -9.16 9.97356 1
created -8.92 69.8149 7
created -8.68 89.762 9
created -8.44 69.8149 7
created -8.2 99.7356 10
created -7.96 49.8678 5
created -7.72 49.8678 5
created -7.48 89.762 9
created -7.24 29.9207 3
created -7 29.9207 3
created -6.76 79.7885 8
created -6.52 9.97356 1
created -6.28 39.8942 4
created -6.04 19.9471 2
created -5.8 69.8149 7
created -5.56 59.8413 6
created -5.32 79.7885 8
created -5.08 9.97356 1
created -4.84 89.762 9
created -4.6 29.9207 3
created -4.36 19.9471 2
created -4.12 9.97356 1
created -3.88 39.8942 4
created -3.64 9.97356 1
created -3.4 69.8149 7
created -3.16 69.8149 7
created -2.92 99.7356 10
created -2.68 49.8678 5
created -2.44 19.9471 2
created -2.2 69.8149 7
created -1.96 89.762 9
created -1.72 19.9471 2
created -1.48 19.9471 2
created -1.24 59.8413 6
created -1 69.8149 7
created -0.76 99.7356 10
created -0.52 29.9207 3
created -0.28 99.7356 10
created -0.04 39.8942 4
created 0.2 29.9207 3
created 0.44 69.8149 7
created 0.68 99.7356 10
created 0.92 9.97356 1
created 1.16 99.7356 10
created 1.4 99.7356 10
created 1.64 29.9207 3
created 1.88 39.8942 4
created 2.12 9.97356 1
created 2.36 49.8678 5
created 2.6 79.7885 8
created 2.84 89.762 9
created 3.08 99.7356 10
created 3.32 49.8678 5
created 3.56 29.9207 3
created 3.8 79.7885 8
created 4.04 99.7356 10
created 4.28 99.7356 10
created 4.52 9.97356 1
created 4.76 49.8678 5
created 5 49.8678 5
created 5.24 19.9471 2
created 5.48 69.8149 7
created 5.72 49.8678 5
created 5.96 39.8942 4
created 6.2 19.9471 2
created 6.44 69.8149 7
created 6.68 89.762 9
created 6.92 29.9207 3
created 7.16 59.8413 6
created 7.4 9.97356 1
created 7.64 89.762 9
created 7.88 59.8413 6
created 8.12 39.8942 4
created 8.36 39.8942 4
created 8.6 89.762 9
created 8.84 49.8678 5
created 9.08 69.8149 7
created 9.32 29.9207 3
created 9.56 49.8678 5
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 (+-9.45161e-06)
fit chi^2 = 4.45371e-06
found -8.2 (+-0.000110664) 99.7336 (+-0.272094) 10.0003 (+-0.000945298)
found -2.92 (+-0.000110664) 99.7336 (+-0.272094) 10.0003 (+-0.000945298)
found -0.760001 (+-0.000110506) 99.7331 (+-0.272049) 10.0003 (+-0.00094514)
found 0.679999 (+-0.000110247) 99.7325 (+-0.27198) 10.0002 (+-0.0009449)
found 1.4 (+-0.000110651) 99.7339 (+-0.272094) 10.0003 (+-0.000945299)
found 3.08 (+-0.000110765) 99.7341 (+-0.272126) 10.0004 (+-0.000945408)
found 4.04 (+-0.000110983) 99.7352 (+-0.272193) 10.0005 (+-0.000945641)
found 4.28 (+-0.000110391) 99.7333 (+-0.272025) 10.0003 (+-0.00094506)
found -0.28 (+-0.000110315) 99.7323 (+-0.271991) 10.0002 (+-0.00094494)
found 1.16 (+-0.000110391) 99.7334 (+-0.272025) 10.0003 (+-0.00094506)
found -8.68 (+-0.000116864) 89.7611 (+-0.25819) 9.00037 (+-0.000896993)
found -1.96 (+-0.000116441) 89.7597 (+-0.258079) 9.00023 (+-0.000896607)
found 2.84 (+-0.000117083) 89.7622 (+-0.258252) 9.00048 (+-0.000897209)
found 6.68 (+-0.000116559) 89.76 (+-0.258108) 9.00026 (+-0.000896709)
found -7.48 (+-0.000116428) 89.7595 (+-0.258072) 9.00021 (+-0.000896582)
found -4.84 (+-0.000115974) 89.7584 (+-0.257958) 9.0001 (+-0.000896186)
found 7.64 (+-0.000116212) 89.7592 (+-0.258022) 9.00018 (+-0.000896409)
found 8.6 (+-0.000116521) 89.7598 (+-0.258096) 9.00024 (+-0.000896666)
found -9.4 (+-0.000123407) 79.7865 (+-0.243301) 8.00021 (+-0.000845266)
found -5.32 (+-0.000123338) 79.7862 (+-0.243283) 8.00019 (+-0.000845205)
found -6.76 (+-0.000123074) 79.7854 (+-0.243219) 8.0001 (+-0.000844983)
found 2.6 (+-0.000124036) 79.7881 (+-0.243446) 8.00037 (+-0.000845769)
found 3.8 (+-0.0001239) 79.7878 (+-0.243415) 8.00035 (+-0.000845663)
found -8.44 (+-0.000133107) 69.8164 (+-0.227835) 7.00051 (+-0.000791536)
found -3.16 (+-0.000132966) 69.8158 (+-0.227804) 7.00045 (+-0.000791427)
found -0.999999 (+-0.000132887) 69.8155 (+-0.227786) 7.00042 (+-0.000791366)
found -9.64 (+-0.000132309) 69.8139 (+-0.227664) 7.00026 (+-0.00079094)
found -8.92 (+-0.000132167) 69.814 (+-0.22764) 7.00027 (+-0.000790858)
found -5.8 (+-0.000132159) 69.8134 (+-0.22763) 7.00021 (+-0.000790822)
found -3.4 (+-0.000132029) 69.8134 (+-0.227608) 7.00021 (+-0.000790748)
found -2.2 (+-0.000132375) 69.8142 (+-0.227679) 7.00029 (+-0.000790993)
found 0.440002 (+-0.000132584) 69.8147 (+-0.227722) 7.00034 (+-0.000791144)
found 5.48 (+-0.000132073) 69.8132 (+-0.227611) 7.00018 (+-0.000790757)
found 6.44 (+-0.000132375) 69.8142 (+-0.227679) 7.00029 (+-0.000790993)
found 9.08 (+-0.000132218) 69.8134 (+-0.22764) 7.00021 (+-0.000790857)
found 7.88 (+-0.000143438) 59.8417 (+-0.210872) 6.00034 (+-0.000732602)
found -5.56 (+-0.000143666) 59.8422 (+-0.210914) 6.0004 (+-0.000732749)
found 7.16 (+-0.000142313) 59.8394 (+-0.210668) 6.00011 (+-0.000731893)
found -1.24 (+-0.000142979) 59.8407 (+-0.210788) 6.00024 (+-0.00073231)
found -7.96 (+-0.00015759) 49.8692 (+-0.192573) 5.0004 (+-0.000669029)
found -2.68 (+-0.000157104) 49.8684 (+-0.1925) 5.00032 (+-0.000668775)
found 3.32 (+-0.000157299) 49.8687 (+-0.192528) 5.00035 (+-0.000668874)
found 8.84 (+-0.000157732) 49.8695 (+-0.192595) 5.00043 (+-0.000669106)
found -7.72 (+-0.000157504) 49.8689 (+-0.192559) 5.00037 (+-0.000668979)
found 5 (+-0.000156606) 49.8671 (+-0.192417) 5.00019 (+-0.000668488)
found 5.72 (+-0.00015718) 49.8681 (+-0.192506) 5.00029 (+-0.000668796)
found 9.56 (+-0.00015632) 49.8666 (+-0.192372) 5.00013 (+-0.000668332)
found 2.36 (+-0.000156651) 49.8676 (+-0.192432) 5.00024 (+-0.000668539)
found 4.76 (+-0.000156331) 49.8668 (+-0.192379) 5.00016 (+-0.000668355)
found -0.0400033 (+-0.000176215) 39.8956 (+-0.172248) 4.00035 (+-0.000598418)
found 1.88 (+-0.000174688) 39.8933 (+-0.172055) 4.00011 (+-0.000597747)
found 5.96 (+-0.000175368) 39.8941 (+-0.172138) 4.00019 (+-0.000598033)
found 8.12 (+-0.000175938) 39.8949 (+-0.172209) 4.00027 (+-0.000598281)
found 8.36 (+-0.000176303) 39.8956 (+-0.172258) 4.00035 (+-0.000598451)
found -6.28 (+-0.000174457) 39.893 (+-0.172027) 4.00008 (+-0.000597648)
found -3.88 (+-0.00017413) 39.8927 (+-0.171989) 4.00005 (+-0.000597518)
found -7.24 (+-0.000203916) 29.9223 (+-0.149215) 3.00032 (+-0.000518396)
found -4.6 (+-0.00020361) 29.9221 (+-0.149187) 3.00029 (+-0.000518298)
found -0.52 (+-0.000205322) 29.9245 (+-0.149356) 3.00053 (+-0.000518886)
found 1.64 (+-0.0002043) 29.9229 (+-0.149253) 3.00037 (+-0.000518528)
found 6.92 (+-0.000204576) 29.9231 (+-0.149279) 3.0004 (+-0.000518619)
found 3.56 (+-0.000204239) 29.9226 (+-0.149245) 3.00035 (+-0.000518501)
found 9.32 (+-0.000204083) 29.9223 (+-0.149229) 3.00032 (+-0.000518445)
found -7 (+-0.000203773) 29.9221 (+-0.1492) 3.00029 (+-0.000518344)
found 0.200002 (+-0.000203867) 29.9221 (+-0.149208) 3.0003 (+-0.000518372)
found -1.72001 (+-0.000250399) 19.949 (+-0.12188) 2.0003 (+-0.000423431)
found -2.44 (+-0.000251106) 19.9493 (+-0.121924) 2.00032 (+-0.000423583)
found 5.24 (+-0.000251106) 19.9493 (+-0.121924) 2.00032 (+-0.000423583)
found 9.8 (+-0.000247035) 19.9479 (+-0.121689) 2.00019 (+-0.000422766)
found -6.04 (+-0.000250795) 19.949 (+-0.121903) 2.00029 (+-0.00042351)
found -4.36 (+-0.000248212) 19.9472 (+-0.121733) 2.00011 (+-0.00042292)
found -1.48 (+-0.000249745) 19.9482 (+-0.121833) 2.00022 (+-0.000423269)
found 6.2 (+-0.000250794) 19.949 (+-0.121903) 2.00029 (+-0.00042351)
found -9.87999 (+-0.000249304) 19.948 (+-0.121799) 2.00019 (+-0.000423148)
found 0.92 (+-0.000361404) 9.97838 (+-0.0864433) 1.00054 (+-0.000300318)
found 4.51999 (+-0.000359474) 9.97706 (+-0.0863714) 1.0004 (+-0.000300068)
found -9.16 (+-0.000359726) 9.97705 (+-0.0863789) 1.0004 (+-0.000300094)
found -6.52001 (+-0.000358275) 9.97625 (+-0.0863266) 1.00032 (+-0.000299912)
found -5.08 (+-0.000360439) 9.97758 (+-0.086406) 1.00045 (+-0.000300188)
found 7.40001 (+-0.000359644) 9.97705 (+-0.0863765) 1.0004 (+-0.000300086)
found -3.63999 (+-0.000357905) 9.97598 (+-0.0863125) 1.00029 (+-0.000299863)
found 2.12 (+-0.000357022) 9.97545 (+-0.0862798) 1.00024 (+-0.00029975)
found -4.12 (+-0.000354986) 9.97464 (+-0.0862093) 1.00016 (+-0.000299505)

 
#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.