388 lines
14 KiB
C++
388 lines
14 KiB
C++
/***************************************************************************
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PRunSingleHisto.cpp
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Author: Andreas Suter
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e-mail: andreas.suter@psi.ch
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$Id$
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***************************************************************************/
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/***************************************************************************
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* Copyright (C) 2007 by Andreas Suter *
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* andreas.suter@psi.c *
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* *
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* This program is free software; you can redistribute it and/or modify *
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* it under the terms of the GNU General Public License as published by *
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* the Free Software Foundation; either version 2 of the License, or *
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* (at your option) any later version. *
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* *
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* This program is distributed in the hope that it will be useful, *
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* but WITHOUT ANY WARRANTY; without even the implied warranty of *
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
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* GNU General Public License for more details. *
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* *
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* You should have received a copy of the GNU General Public License *
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* along with this program; if not, write to the *
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* Free Software Foundation, Inc., *
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* 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. *
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***************************************************************************/
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#include <iostream>
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#include <fstream>
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#include "PMusr.h"
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#include "PRunSingleHisto.h"
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//--------------------------------------------------------------------------
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// Constructor
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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*/
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PRunSingleHisto::PRunSingleHisto() : PRunBase()
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{
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fFitStartTime = 0.0;
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fFitStopTime = 0.0;
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fNoOfFitBins = 0;
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}
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//--------------------------------------------------------------------------
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// Constructor
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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* \param msrInfo pointer to the msr info structure
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* \param runNo number of the run of the msr-file
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*/
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PRunSingleHisto::PRunSingleHisto(PMsrHandler *msrInfo, PRunDataHandler *rawData, unsigned int runNo) : PRunBase(msrInfo, rawData, runNo)
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{
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if (!PrepareData()) {
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cout << endl << "**SEVERE ERROR**: PRunSingleHisto::PRunSingleHisto: Couldn't prepare data for fitting!";
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cout << endl << " This is very bad :-(, will quit ...";
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fValid = false;
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}
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}
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//--------------------------------------------------------------------------
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// Destructor
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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*/
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PRunSingleHisto::~PRunSingleHisto()
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{
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}
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//--------------------------------------------------------------------------
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// CalcChiSquare
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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* The return value is chisq * fRunInfo->fPacking, the reason is:
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* the data d_i and the theory t_i are scaled by the packing, i.e. d_i -> d_i / packing.
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* Since the error is 1/sqrt(d_i) and hence error^2 = d_i it follows that
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* (d_i - t_i)^2 ~ 1/packing^2 and error^2 ~ 1/packing, and hence the chisq needs
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* to be scaled by packing.
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*
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* \param par parameter vector iterated by minuit
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*/
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double PRunSingleHisto::CalcChiSquare(const std::vector<double>& par)
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{
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double chisq = 0.0;
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double diff = 0.0;
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double N0;
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// check if norm is a parameter or a function
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if (fRunInfo->fNormParamNo < MSR_PARAM_FUN_OFFSET) { // norm is a parameter
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N0 = par[fRunInfo->fNormParamNo-1];
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} else { // norm is a function
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// get function number
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unsigned int funNo = fRunInfo->fNormParamNo-MSR_PARAM_FUN_OFFSET;
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// evaluate function
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N0 = fMsrInfo->EvalFunc(funNo,fRunInfo->fMap,par);
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}
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// get tau
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double tau;
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if (fRunInfo->fLifetimeParamNo != -1)
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tau = par[fRunInfo->fLifetimeParamNo-1];
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else
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tau = PMUON_LIFETIME;
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// get background
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double bkg = par[fRunInfo->fBkgFitParamNo-1];
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// calculate functions
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for (int i=0; i<fMsrInfo->GetNoOfFuncs(); i++) {
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int funcNo = fMsrInfo->GetFuncNo(i);
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//cout << ">> i = " << i << ", funcNo = " << funcNo << endl;
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fFuncValues[i] = fMsrInfo->EvalFunc(funcNo, fRunInfo->fMap, par);
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}
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// calculate chi square
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for (unsigned int i=0; i<fData.fValue.size(); i++) {
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if ((fData.fTime[i]>=fFitStartTime) && (fData.fTime[i]<=fFitStopTime)) {
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diff = fData.fValue[i] -
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(N0*TMath::Exp(-fData.fTime[i]/tau)*(1+fTheory->Func(fData.fTime[i], par, fFuncValues))+bkg);
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chisq += diff*diff / (fData.fError[i]*fData.fError[i]);
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}
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}
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// static int counter = 0;
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// TString fln=fRunInfo->fRunName+"_"+(Long_t)fRunInfo->fForwardHistoNo+"_data.dat";
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// ofstream f(fln.Data(),ios_base::out);
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// for (unsigned int i=0; i<fData.fValue.size(); i++) {
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// f << endl << fData.fTime[i] << " " << fData.fValue[i] << " " << fData.fError[i];
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// }
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// f.close();
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//
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// fln=fRunInfo->fRunName+"_"+(Long_t)fRunInfo->fForwardHistoNo+"_theo.dat";
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// ofstream ft(fln.Data(),ios_base::out);
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// for (unsigned int i=0; i<fData.fValue.size(); i++) {
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// ft << endl << fData.fTime[i] << " " << N0*TMath::Exp(-fData.fTime[i]/tau)*(1+fTheory->Func(fData.fTime[i], par))+bkg;
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// }
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// ft.close();
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// counter++;
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// if (counter == 4) exit(0);
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return chisq*fRunInfo->fPacking;
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}
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//--------------------------------------------------------------------------
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// CalcMaxLikelihood
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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* \param par parameter vector iterated by minuit
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*/
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double PRunSingleHisto::CalcMaxLikelihood(const std::vector<double>& par)
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{
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double mllh = 0.0; // maximum log likelihood assuming poisson distribution for the single bin
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double N0;
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// check if norm is a parameter or a function
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if (fRunInfo->fNormParamNo < MSR_PARAM_FUN_OFFSET) { // norm is a parameter
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N0 = par[fRunInfo->fNormParamNo-1];
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} else { // norm is a function
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// get function number
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unsigned int funNo = fRunInfo->fNormParamNo-MSR_PARAM_FUN_OFFSET;
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// evaluate function
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N0 = fMsrInfo->EvalFunc(funNo,fRunInfo->fMap,par);
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}
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// get tau
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double tau;
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if (fRunInfo->fLifetimeParamNo != -1)
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tau = par[fRunInfo->fLifetimeParamNo-1];
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else
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tau = PMUON_LIFETIME;
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// get background
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double bkg = par[fRunInfo->fBkgFitParamNo-1];
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// calculate functions
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for (int i=0; i<fMsrInfo->GetNoOfFuncs(); i++) {
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int funcNo = fMsrInfo->GetFuncNo(i);
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fFuncValues[i] = fMsrInfo->EvalFunc(funcNo, fRunInfo->fMap, par);
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}
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// calculate maximum log likelihood
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double theo;
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double data;
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for (unsigned int i=0; i<fData.fValue.size(); i++) {
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if ((fData.fTime[i]>=fFitStartTime) && (fData.fTime[i]<=fFitStopTime)) {
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// calculate theory for the given parameter set
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theo = N0*TMath::Exp(-fData.fTime[i]/tau)*(1+fTheory->Func(fData.fTime[i], par, fFuncValues))+bkg;
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// check if data value is not too small
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if (fData.fValue[i] > 1.0e-9)
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data = fData.fValue[i];
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else
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data = 1.0e-9;
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// add maximum log likelihood contribution of bin i
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mllh -= data*TMath::Log(theo) - theo - TMath::LnGamma(data+1);
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}
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}
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return mllh;
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}
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//--------------------------------------------------------------------------
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// CalcTheory
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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*/
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void PRunSingleHisto::CalcTheory()
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{
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// feed the parameter vector
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std::vector<double> par;
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PMsrParamList *paramList = fMsrInfo->GetMsrParamList();
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for (unsigned int i=0; i<paramList->size(); i++)
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par.push_back((*paramList)[i].fValue);
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// calculate asymmetry
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double N0 = par[fRunInfo->fNormParamNo-1];
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// get tau
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double tau;
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if (fRunInfo->fLifetimeParamNo != -1)
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tau = par[fRunInfo->fLifetimeParamNo-1];
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else
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tau = PMUON_LIFETIME;
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// get background
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double bkg = par[fRunInfo->fBkgFitParamNo-1];
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// calculate functions
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for (int i=0; i<fMsrInfo->GetNoOfFuncs(); i++) {
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fFuncValues[i] = fMsrInfo->EvalFunc(fMsrInfo->GetFuncNo(i), fRunInfo->fMap, par);
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}
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// calculate theory
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for (unsigned int i=0; i<fData.fTime.size(); i++) {
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fData.fTheory.push_back(N0*TMath::Exp(-fData.fTime[i]/tau)*(1+fTheory->Func(fData.fTime[i], par, fFuncValues))+bkg);
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}
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// clean up
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par.clear();
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}
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//--------------------------------------------------------------------------
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// PrepareData
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//--------------------------------------------------------------------------
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/**
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* <p>
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*
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*/
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bool PRunSingleHisto::PrepareData()
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{
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// cout << endl << "in PRunSingleHisto::PrepareData(): will feed fData";
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// get the proper run
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PRawRunData* runData = fRawData->GetRunData(fRunInfo->fRunName);
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if (!runData) { // couldn't get run
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cout << endl << "PRunSingleHisto::PrepareData(): Couldn't get run " << fRunInfo->fRunName.Data() << "!";
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return false;
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}
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// keep the time resolution in (us)
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fTimeResolution = runData->fTimeResolution/1.0e3;
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// keep start/stop time for fit
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fFitStartTime = fRunInfo->fFitRange[0];
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fFitStopTime = fRunInfo->fFitRange[1];
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//cout << endl << "start/stop (fit): " << fFitStartTime << ", " << fFitStopTime;
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// check if the t0's are given in the msr-file
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if (fRunInfo->fT0[0] == -1) { // t0's are NOT in the msr-file
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// check if the t0's are in the data file
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if (runData->fT0s.size() != 0) { // t0's in the run data
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// keep the proper t0's. For single histo runs, forward is holding the histo no
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// fForwardHistoNo starts with 1 not with 0 ;-)
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fT0s.push_back(runData->fT0s[fRunInfo->fForwardHistoNo-1]);
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} else { // t0's are neither in the run data nor in the msr-file -> not acceptable!
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cout << endl << "PRunSingleHisto::PrepareData(): NO t0's found, neither in the run data nor in the msr-file!";
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return false;
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}
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} else { // t0's in the msr-file
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// check if t0's are given in the data file
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if (runData->fT0s.size() != 0) {
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// compare t0's of the msr-file with the one in the data file
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if (fabs(fRunInfo->fT0[0]-runData->fT0s[fRunInfo->fForwardHistoNo-1])>5.0) { // given in bins!!
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cout << endl << "PRunSingleHisto::PrepareData(): **WARNING**:";
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cout << endl << " t0 from the msr-file is " << fRunInfo->fT0[0];
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cout << endl << " t0 from the data file is " << runData->fT0s[fRunInfo->fForwardHistoNo-1];
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cout << endl << " This is quite a deviation! Is this done intentionally??";
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cout << endl;
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}
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}
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fT0s.push_back(fRunInfo->fT0[0]);
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}
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// check if post pile up data shall be used
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unsigned int histoNo;
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if (fRunInfo->fFileFormat.Contains("ppc")) {
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histoNo = runData->fDataBin.size()/2 + fRunInfo->fForwardHistoNo-1;
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} else {
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histoNo = fRunInfo->fForwardHistoNo-1;
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}
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if ((runData->fDataBin.size() < histoNo) || (histoNo < 0)) {
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cout << endl << "PRunSingleHisto::PrepareData(): PANIC ERROR:";
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cout << endl << " histoNo found = " << histoNo << ", but there are only " << runData->fDataBin.size() << " runs!?!?";
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cout << endl << " Will quite :-(";
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cout << endl;
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return false;
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}
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// transform raw histo data. This is done the following way (for details see the manual):
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// for the single histo fit, just the rebinned raw data are copied
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// first get start data, end data, and t0
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unsigned int start = fRunInfo->fDataRange[0];
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unsigned int end = fRunInfo->fDataRange[1];
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unsigned int t0 = fT0s[0];
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// check if start, end, and t0 make any sense
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// 1st check if start and end are in proper order
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if (end < start) { // need to swap them
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int keep = end;
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end = start;
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start = keep;
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}
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// 2nd check if start is within proper bounds
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if ((start < 0) || (start > runData->fDataBin[histoNo].size())) {
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cout << endl << "PRunSingleHisto::PrepareData(): start data bin doesn't make any sense!";
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return false;
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}
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// 3rd check if end is within proper bounds
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if ((end < 0) || (end > runData->fDataBin[histoNo].size())) {
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cout << endl << "PRunSingleHisto::PrepareData(): end data bin doesn't make any sense!";
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return false;
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}
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// 4th check if t0 is within proper bounds
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if ((t0 < 0) || (t0 > runData->fDataBin[histoNo].size())) {
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cout << endl << "PRunSingleHisto::PrepareData(): t0 data bin doesn't make any sense!";
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return false;
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}
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// everything looks fine, hence fill data set
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double value = 0.0;
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for (unsigned i=start; i<end; i++) {
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if (((i-start) % fRunInfo->fPacking == 0) && (i != start)) { // fill data
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// in order that after rebinning the fit does not need to be redone (important for plots)
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// the value is normalize to per bin
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value /= fRunInfo->fPacking;
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// time shifted so that packing is included correctly, i.e. t0 == t0 after packing
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fData.fTime.push_back(fTimeResolution*((double)i-(double)t0-(double)fRunInfo->fPacking));
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fData.fValue.push_back(value);
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if (value == 0.0)
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fData.fError.push_back(1.0);
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else
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fData.fError.push_back(TMath::Sqrt(value));
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value = 0.0;
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}
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value += runData->fDataBin[histoNo][i];
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}
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// count the number of bins to be fitted
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fNoOfFitBins=0;
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for (unsigned int i=0; i<fData.fValue.size(); i++) {
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if ((fData.fTime[i] >= fFitStartTime) && (fData.fTime[i] <= fFitStopTime))
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fNoOfFitBins++;
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}
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return true;
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}
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