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slightly more general handling of data. Needed to implement musrview, ...

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nemu
2008-04-07 10:57:42 +00:00
parent c463cb2530
commit a5d079d0a1
14 changed files with 330 additions and 259 deletions
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134
src/classes/PRunSingleHisto.cpp
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@ -58,7 +58,7 @@ PRunSingleHisto::PRunSingleHisto() : PRunBase()
* \param msrInfo pointer to the msr info structure
* \param runNo number of the run of the msr-file
*/
PRunSingleHisto::PRunSingleHisto(PMsrHandler *msrInfo, PRunDataHandler *rawData, unsigned int runNo) : PRunBase(msrInfo, rawData, runNo)
PRunSingleHisto::PRunSingleHisto(PMsrHandler *msrInfo, PRunDataHandler *rawData, unsigned int runNo, EPMusrHandleTag tag) : PRunBase(msrInfo, rawData, runNo, tag)
{
if (!PrepareData()) {
cout << endl << "**SEVERE ERROR**: PRunSingleHisto::PRunSingleHisto: Couldn't prepare data for fitting!";
@ -127,11 +127,13 @@ double PRunSingleHisto::CalcChiSquare(const std::vector<double>& par)
}
// calculate chi square
for (unsigned int i=0; i<fFitData.fValue.size(); i++) {
if ((fFitData.fTime[i]>=fFitStartTime) && (fFitData.fTime[i]<=fFitStopTime)) {
diff = fFitData.fValue[i] -
(N0*TMath::Exp(-fFitData.fTime[i]/tau)*(1+fTheory->Func(fFitData.fTime[i], par, fFuncValues))+bkg);
chisq += diff*diff / (fFitData.fError[i]*fFitData.fError[i]);
double time;
for (unsigned int i=0; i<fData.fValue.size(); i++) {
time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
if ((time>=fFitStartTime) && (time<=fFitStopTime)) {
diff = fData.fValue[i] -
(N0*TMath::Exp(-time/tau)*(1+fTheory->Func(time, par, fFuncValues))+bkg);
chisq += diff*diff / (fData.fError[i]*fData.fError[i]);
}
}
@ -139,15 +141,17 @@ double PRunSingleHisto::CalcChiSquare(const std::vector<double>& par)
// static int counter = 0;
// TString fln=fRunInfo->fRunName+"_"+(Long_t)fRunInfo->fForwardHistoNo+"_data.dat";
// ofstream f(fln.Data(),ios_base::out);
// for (unsigned int i=0; i<fFitData.fValue.size(); i++) {
// f << endl << fFitData.fTime[i] << " " << fFitData.fValue[i] << " " << fFitData.fError[i];
// for (unsigned int i=0; i<fData.fValue.size(); i++) {
// time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
// f << endl << time << " " << fData.fValue[i] << " " << fData.fError[i];
// }
// f.close();
//
// fln=fRunInfo->fRunName+"_"+(Long_t)fRunInfo->fForwardHistoNo+"_theo.dat";
// ofstream ft(fln.Data(),ios_base::out);
// for (unsigned int i=0; i<fFitData.fValue.size(); i++) {
// ft << endl << fFitData.fTime[i] << " " << N0*TMath::Exp(-fFitData.fTime[i]/tau)*(1+fTheory->Func(fFitData.fTime[i], par))+bkg;
// for (unsigned int i=0; i<fData.fValue.size(); i++) {
// time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
// ft << endl << time << " " << N0*TMath::Exp(-time/tau)*(1.0+fTheory->Func(time, par, fFuncValues))+bkg;
// }
// ft.close();
// counter++;
@ -200,13 +204,15 @@ double PRunSingleHisto::CalcMaxLikelihood(const std::vector<double>& par)
// calculate maximum log likelihood
double theo;
double data;
for (unsigned int i=0; i<fFitData.fValue.size(); i++) {
if ((fFitData.fTime[i]>=fFitStartTime) && (fFitData.fTime[i]<=fFitStopTime)) {
double time;
for (unsigned int i=0; i<fData.fValue.size(); i++) {
time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
if ((time>=fFitStartTime) && (time<=fFitStopTime)) {
// calculate theory for the given parameter set
theo = N0*TMath::Exp(-fFitData.fTime[i]/tau)*(1+fTheory->Func(fFitData.fTime[i], par, fFuncValues))+bkg;
theo = N0*TMath::Exp(-time/tau)*(1+fTheory->Func(time, par, fFuncValues))+bkg;
// check if data value is not too small
if (fFitData.fValue[i] > 1.0e-9)
data = fFitData.fValue[i];
if (fData.fValue[i] > 1.0e-9)
data = fData.fValue[i];
else
data = 1.0e-9;
// add maximum log likelihood contribution of bin i
@ -251,8 +257,10 @@ void PRunSingleHisto::CalcTheory()
}
// calculate theory
for (unsigned int i=0; i<fFitData.fTime.size(); i++) {
fFitData.fTheory.push_back(N0*TMath::Exp(-fFitData.fTime[i]/tau)*(1+fTheory->Func(fFitData.fTime[i], par, fFuncValues))+bkg);
double time;
for (unsigned int i=0; i<fData.fValue.size(); i++) {
time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
fData.fTheory.push_back(N0*TMath::Exp(-time/tau)*(1+fTheory->Func(time, par, fFuncValues))+bkg);
}
// clean up
@ -268,7 +276,7 @@ void PRunSingleHisto::CalcTheory()
*/
bool PRunSingleHisto::PrepareData()
{
// cout << endl << "in PRunSingleHisto::PrepareData(): will feed fFitData";
// cout << endl << "in PRunSingleHisto::PrepareData(): will feed fData";
// get the proper run
PRawRunData* runData = fRawData->GetRunData(fRunInfo->fRunName);
@ -357,20 +365,51 @@ bool PRunSingleHisto::PrepareData()
return false;
}
// everything looks fine, hence fill fit data set
// everything looks fine, hence fill data set
bool status;
switch(fHandleTag) {
case kFit:
status = PrepareFitData(start, end, t0, runData, histoNo);
break;
case kView:
status = PrepareViewData(start, end, t0, runData, histoNo);
break;
default:
status = false;
break;
}
return status;
}
//--------------------------------------------------------------------------
// PrepareFitData
//--------------------------------------------------------------------------
/**
* <p>
*
* \param start
* \param end
* \param runData
* \param histoNo
*/
bool PRunSingleHisto::PrepareFitData(unsigned int start, unsigned int end, double t0,
PRawRunData* runData, unsigned int histoNo)
{
double value = 0.0;
// time shifted so that packing is included correctly, i.e. t0 == t0 after packing
fData.fDataTimeStart = fTimeResolution*((double)fRunInfo->fPacking/2.0);
fData.fDataTimeStep = fTimeResolution*fRunInfo->fPacking;
for (unsigned i=start; i<end; i++) {
if (((i-start) % fRunInfo->fPacking == 0) && (i != start)) { // fill data
// in order that after rebinning the fit does not need to be redone (important for plots)
// the value is normalize to per bin
value /= fRunInfo->fPacking;
// time shifted so that packing is included correctly, i.e. t0 == t0 after packing
fFitData.fTime.push_back(fTimeResolution*((double)i-(double)t0-(double)fRunInfo->fPacking));
fFitData.fValue.push_back(value);
fData.fValue.push_back(value);
if (value == 0.0)
fFitData.fError.push_back(1.0);
fData.fError.push_back(1.0);
else
fFitData.fError.push_back(TMath::Sqrt(value));
fData.fError.push_back(TMath::Sqrt(value));
value = 0.0;
}
value += runData->fDataBin[histoNo][i];
@ -378,35 +417,30 @@ bool PRunSingleHisto::PrepareData()
// count the number of bins to be fitted
fNoOfFitBins=0;
for (unsigned int i=0; i<fFitData.fValue.size(); i++) {
if ((fFitData.fTime[i] >= fFitStartTime) && (fFitData.fTime[i] <= fFitStopTime))
double time;
for (unsigned int i=0; i<fData.fValue.size(); i++) {
time = fData.fDataTimeStart + (double)i*fData.fDataTimeStep;
if ((time >= fFitStartTime) && (time <= fFitStopTime))
fNoOfFitBins++;
}
// fill the bin data set (used for plots and t0's search
start = (unsigned int)t0-fRunInfo->fPacking*((unsigned int)t0/fRunInfo->fPacking);
end = (unsigned int)t0+fRunInfo->fPacking*((runData->fDataBin[histoNo].size()-1-(unsigned int)t0)/fRunInfo->fPacking);
value = 0.0;
//cout << endl << ">> start = " << start << ", end = " << end << ", " << runData->fDataBin[histoNo].size();
for (unsigned i=start; i<end; i++) {
if (((i-start) % fRunInfo->fPacking == 0) && (i != start)) { // fill data
// in order that after rebinning the fit does not need to be redone (important for plots)
// the value is normalize to per bin
value /= fRunInfo->fPacking;
// time shifted so that packing is included correctly, i.e. t0 == t0 after packing
fBinData.fTime.push_back(fTimeResolution*((double)i-(double)t0-(double)fRunInfo->fPacking));
fBinData.fValue.push_back(value);
if (value == 0.0)
fBinData.fError.push_back(1.0);
else
fBinData.fError.push_back(TMath::Sqrt(value));
value = 0.0;
}
value += runData->fDataBin[histoNo][i];
}
//cout << endl << "fBinData.fValue.size() = " << fBinData.fValue.size();
return true;
}
//--------------------------------------------------------------------------
// PrepareViewData
//--------------------------------------------------------------------------
/**
* <p>
*
* \param start
* \param end
* \param runData
* \param histoNo
*/
bool PRunSingleHisto::PrepareViewData(unsigned int start, unsigned int end, double t0,
PRawRunData* runData, unsigned int histoNo)
{
// to be implemented yet
return true;
}