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 49.8678 5
created -9.64 49.8678 5
created -9.4 89.762 9
created -9.16 9.97356 1
created -8.92 39.8942 4
created -8.68 99.7356 10
created -8.44 39.8942 4
created -8.2 99.7356 10
created -7.96 89.762 9
created -7.72 79.7885 8
created -7.48 69.8149 7
created -7.24 9.97356 1
created -7 19.9471 2
created -6.76 79.7885 8
created -6.52 9.97356 1
created -6.28 59.8413 6
created -6.04 89.762 9
created -5.8 9.97356 1
created -5.56 79.7885 8
created -5.32 49.8678 5
created -5.08 79.7885 8
created -4.84 9.97356 1
created -4.6 19.9471 2
created -4.36 59.8413 6
created -4.12 29.9207 3
created -3.88 39.8942 4
created -3.64 89.762 9
created -3.4 49.8678 5
created -3.16 49.8678 5
created -2.92 19.9471 2
created -2.68 19.9471 2
created -2.44 69.8149 7
created -2.2 49.8678 5
created -1.96 89.762 9
created -1.72 9.97356 1
created -1.48 49.8678 5
created -1.24 29.9207 3
created -1 49.8678 5
created -0.76 89.762 9
created -0.52 99.7356 10
created -0.28 69.8149 7
created -0.04 79.7885 8
created 0.2 9.97356 1
created 0.44 9.97356 1
created 0.68 59.8413 6
created 0.92 99.7356 10
created 1.16 79.7885 8
created 1.4 69.8149 7
created 1.64 59.8413 6
created 1.88 99.7356 10
created 2.12 9.97356 1
created 2.36 9.97356 1
created 2.6 29.9207 3
created 2.84 19.9471 2
created 3.08 89.762 9
created 3.32 29.9207 3
created 3.56 99.7356 10
created 3.8 89.762 9
created 4.04 69.8149 7
created 4.28 19.9471 2
created 4.52 89.762 9
created 4.76 69.8149 7
created 5 39.8942 4
created 5.24 99.7356 10
created 5.48 79.7885 8
created 5.72 99.7356 10
created 5.96 59.8413 6
created 6.2 59.8413 6
created 6.44 89.762 9
created 6.68 19.9471 2
created 6.92 69.8149 7
created 7.16 99.7356 10
created 7.4 69.8149 7
created 7.64 29.9207 3
created 7.88 79.7885 8
created 8.12 9.97356 1
created 8.36 19.9471 2
created 8.6 79.7885 8
created 8.84 19.9471 2
created 9.08 29.9207 3
created 9.32 69.8149 7
created 9.56 79.7885 8
created 9.8 69.8149 7
the total number of created peaks = 83 with sigma = 0.04
the total number of found peaks = 83 with sigma = 0.040002 (+-1.54569e-05)
fit chi^2 = 1.23564e-05
found -0.52 (+-0.000184697) 99.7347 (+-0.453328) 10.0004 (+-0.00157493)
found 0.92 (+-0.000184521) 99.7342 (+-0.453273) 10.0004 (+-0.00157474)
found 1.88 (+-0.000183539) 99.7323 (+-0.452995) 10.0002 (+-0.00157378)
found 5.72 (+-0.000184521) 99.7342 (+-0.453273) 10.0004 (+-0.00157474)
found 7.16 (+-0.000184529) 99.7342 (+-0.453275) 10.0004 (+-0.00157475)
found -8.68 (+-0.000183888) 99.7326 (+-0.453084) 10.0002 (+-0.00157408)
found -8.2 (+-0.000184375) 99.7339 (+-0.453232) 10.0003 (+-0.0015746)
found 3.56 (+-0.000184231) 99.7337 (+-0.453191) 10.0003 (+-0.00157446)
found 5.24 (+-0.000184294) 99.7337 (+-0.453206) 10.0003 (+-0.00157451)
found -7.96 (+-0.000195019) 89.7622 (+-0.430159) 9.00048 (+-0.00149444)
found -6.04 (+-0.00019357) 89.7593 (+-0.429776) 9.00018 (+-0.00149311)
found 3.8 (+-0.000194923) 89.7619 (+-0.430132) 9.00045 (+-0.00149435)
found 6.44 (+-0.000193847) 89.7596 (+-0.429841) 9.00021 (+-0.00149334)
found -9.4 (+-0.000193456) 89.759 (+-0.429744) 9.00015 (+-0.001493)
found -3.64 (+-0.000194083) 89.7598 (+-0.429899) 9.00024 (+-0.00149354)
found -1.96 (+-0.000193456) 89.759 (+-0.429744) 9.00016 (+-0.001493)
found -0.759999 (+-0.000194702) 89.7614 (+-0.430071) 9.0004 (+-0.00149413)
found 3.08 (+-0.000193447) 89.7588 (+-0.429734) 9.00013 (+-0.00149296)
found 4.52 (+-0.00019395) 89.7598 (+-0.42987) 9.00024 (+-0.00149344)
found -7.72 (+-0.000206847) 79.7886 (+-0.405557) 8.00042 (+-0.00140897)
found -0.0400015 (+-0.000205553) 79.7865 (+-0.405255) 8.00021 (+-0.00140792)
found 1.16 (+-0.00020694) 79.7889 (+-0.405581) 8.00045 (+-0.00140905)
found 5.48 (+-0.000207241) 79.7897 (+-0.405658) 8.00053 (+-0.00140932)
found 9.56 (+-0.000206641) 79.7881 (+-0.405505) 8.00037 (+-0.00140879)
found -6.76 (+-0.000204785) 79.7851 (+-0.405071) 8.00007 (+-0.00140728)
found -5.56 (+-0.000205312) 79.786 (+-0.405195) 8.00016 (+-0.00140771)
found -5.08 (+-0.000205312) 79.786 (+-0.405195) 8.00016 (+-0.00140771)
found 7.88 (+-0.000204999) 79.7854 (+-0.405119) 8.0001 (+-0.00140745)
found 8.6 (+-0.00020509) 79.7854 (+-0.405136) 8.0001 (+-0.00140751)
found -7.48 (+-0.000220034) 69.8137 (+-0.379144) 7.00024 (+-0.00131721)
found -0.280001 (+-0.000221597) 69.8161 (+-0.37947) 7.00048 (+-0.00131834)
found 1.4 (+-0.000221127) 69.8151 (+-0.379364) 7.00037 (+-0.00131797)
found 4.04 (+-0.000220491) 69.8143 (+-0.379234) 7.00029 (+-0.00131752)
found 4.76 (+-0.000220928) 69.8148 (+-0.379322) 7.00035 (+-0.00131783)
found 7.4 (+-0.000220839) 69.8148 (+-0.379307) 7.00035 (+-0.00131777)
found 9.8 (+-0.000219108) 69.8153 (+-0.379) 7.00039 (+-0.00131671)
found -2.44 (+-0.000219988) 69.8132 (+-0.379121) 7.00018 (+-0.00131713)
found 6.92 (+-0.000220595) 69.8146 (+-0.379259) 7.00032 (+-0.0013176)
found 9.32 (+-0.000220623) 69.8143 (+-0.379257) 7.00029 (+-0.0013176)
found 5.96 (+-0.000239397) 59.8426 (+-0.35133) 6.00043 (+-0.00122058)
found 1.64 (+-0.000239549) 59.8428 (+-0.35136) 6.00045 (+-0.00122068)
found 6.2 (+-0.000239276) 59.8423 (+-0.351306) 6.0004 (+-0.00122049)
found -6.28 (+-0.000238024) 59.841 (+-0.351088) 6.00027 (+-0.00121973)
found -4.36 (+-0.000237435) 59.8396 (+-0.350964) 6.00013 (+-0.0012193)
found 0.680003 (+-0.000238144) 59.8412 (+-0.351112) 6.00029 (+-0.00121982)
found -3.4 (+-0.000262347) 49.869 (+-0.320736) 5.00037 (+-0.00111429)
found -9.64 (+-0.000262347) 49.869 (+-0.320736) 5.00037 (+-0.00111429)
found -5.32 (+-0.000262738) 49.8695 (+-0.320799) 5.00042 (+-0.00111451)
found -3.16 (+-0.000260852) 49.8671 (+-0.3205) 5.00019 (+-0.00111347)
found -2.2 (+-0.000262726) 49.8695 (+-0.320797) 5.00042 (+-0.0011145)
found -0.999998 (+-0.000261863) 49.8685 (+-0.320661) 5.00032 (+-0.00111403)
found -9.88 (+-0.000260949) 49.8666 (+-0.320487) 5.00014 (+-0.00111342)
found -1.48 (+-0.00025992) 49.8663 (+-0.320362) 5.00011 (+-0.00111299)
found -8.44 (+-0.000295146) 39.8975 (+-0.287119) 4.00053 (+-0.000997495)
found -3.88 (+-0.00029334) 39.8954 (+-0.286882) 4.00032 (+-0.000996674)
found 5 (+-0.000294576) 39.8967 (+-0.287042) 4.00045 (+-0.00099723)
found -8.92 (+-0.00029256) 39.8952 (+-0.286796) 4.0003 (+-0.000996375)
found 3.32 (+-0.00034177) 29.9242 (+-0.248752) 3.00051 (+-0.000864202)
found -4.12 (+-0.000339288) 29.9218 (+-0.2485) 3.00027 (+-0.000863327)
found -1.24 (+-0.000339329) 29.9218 (+-0.248503) 3.00027 (+-0.000863339)
found 7.64 (+-0.000340799) 29.9231 (+-0.248652) 3.0004 (+-0.000863854)
found 9.08 (+-0.000338648) 29.9216 (+-0.248442) 3.00024 (+-0.000863125)
found 2.6 (+-0.000336092) 29.92 (+-0.248197) 3.00008 (+-0.000862276)
found 6.68 (+-0.000419821) 19.9504 (+-0.203192) 2.00043 (+-0.000705921)
found 8.84 (+-0.000417492) 19.949 (+-0.203034) 2.00029 (+-0.000705372)
found -2.92 (+-0.000415542) 19.948 (+-0.202901) 2.00019 (+-0.000704911)
found 4.28 (+-0.000419821) 19.9504 (+-0.203192) 2.00043 (+-0.000705921)
found -2.68 (+-0.000416389) 19.9485 (+-0.202961) 2.00024 (+-0.000705117)
found 2.84001 (+-0.000417823) 19.9493 (+-0.203058) 2.00032 (+-0.000705455)
found -6.99999 (+-0.000415731) 19.9485 (+-0.202925) 2.00024 (+-0.000704993)
found -4.6 (+-0.000414976) 19.948 (+-0.202871) 2.00019 (+-0.000704806)
found 8.36001 (+-0.000415731) 19.9485 (+-0.202925) 2.00024 (+-0.000704993)
found 2.11998 (+-0.000593354) 9.97602 (+-0.143685) 1.0003 (+-0.000499186)
found -9.16001 (+-0.000597318) 9.97653 (+-0.143812) 1.00035 (+-0.000499625)
found -6.52 (+-0.000598481) 9.97679 (+-0.143852) 1.00037 (+-0.000499763)
found -5.8 (+-0.000600367) 9.97758 (+-0.143923) 1.00045 (+-0.00050001)
found -4.84001 (+-0.000594246) 9.97574 (+-0.143705) 1.00027 (+-0.000499255)
found -1.72001 (+-0.000598246) 9.97679 (+-0.143845) 1.00037 (+-0.000499739)
found 0.199987 (+-0.000592307) 9.97549 (+-0.143644) 1.00024 (+-0.00049904)
found 8.11999 (+-0.000594246) 9.97574 (+-0.143705) 1.00027 (+-0.000499255)
found -7.24001 (+-0.000593638) 9.97547 (+-0.143682) 1.00024 (+-0.000499173)
found 0.440009 (+-0.000591031) 9.97495 (+-0.143594) 1.00019 (+-0.000498869)
found 2.36 (+-0.000588297) 9.97414 (+-0.143495) 1.00011 (+-0.000498524)

 
#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.