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first implementation of a RRF. So far, for single histo only.

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nemu
2009-12-23 13:43:23 +00:00
parent 19dee34c22
commit e0c893503f
10 changed files with 444 additions and 240 deletions
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136
src/classes/PRunSingleHisto.cpp
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@ -757,11 +757,15 @@ cout << endl << ">> data start time = " << fData.GetDataTimeStart();
*/
Bool_t PRunSingleHisto::PrepareViewData(PRawRunData* runData, const UInt_t histoNo)
{
// check if view_packing is wished
// check if view_packing is wished. This is a global option for all PLOT blocks!
Int_t packing = fRunInfo->GetPacking();
if (fMsrInfo->GetMsrPlotList()->at(0).fViewPacking > 0) {
packing = fMsrInfo->GetMsrPlotList()->at(0).fViewPacking;
}
// check if rrf_packing is present. This is a global option for all PLOT blocks, since operated on a single set of data.
if (fMsrInfo->GetMsrPlotList()->at(0).fRRFPacking > 0) {
packing = fMsrInfo->GetMsrPlotList()->at(0).fRRFPacking;
}
// transform raw histo data. This is done the following way (for details see the manual):
// for the single histo fit, just the rebinned raw data are copied
@ -855,9 +859,10 @@ Bool_t PRunSingleHisto::PrepareViewData(PRawRunData* runData, const UInt_t histo
bkg = par[fRunInfo->GetBkgFitParamNo()-1];
}
Double_t value = 0.0;
Double_t expval;
Double_t time;
Double_t value = 0.0;
Double_t expval = 0.0;
Double_t rrf_val = 0.0;
Double_t time = 0.0;
// data start at data_start-t0 shifted by (pack-1)/2
fData.SetDataTimeStart(fTimeResolution*((Double_t)start-(Double_t)t0+(Double_t)(packing-1)/2.0));
@ -870,21 +875,67 @@ cout << endl << "--------------------------------" << endl;
*/
Double_t normalizer = 1.0;
for (Int_t i=start; i<end; i++) {
if (((i-start) % packing == 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 1 nsec
normalizer = packing * (fTimeResolution * 1.0e3); // fTimeResolution us->ns
value /= normalizer;
time = (((Double_t)i-(Double_t)(packing-1)/2.0)-t0)*fTimeResolution;
expval = TMath::Exp(+time/tau)/N0;
fData.AppendValue(-1.0+expval*(value-bkg));
//cout << endl << ">> i=" << i << ",t0=" << t0 << ",time=" << time << ",expval=" << expval << ",value=" << value << ",bkg=" << bkg << ",expval*(value-bkg)-1=" << expval*(value-bkg)-1.0;
fData.AppendErrorValue(expval*TMath::Sqrt(value/normalizer));
//cout << endl << ">> " << time << ", " << expval << ", " << -1.0+expval*(value-bkg) << ", " << expval*TMath::Sqrt(value/packing);
value = 0.0;
Double_t gammaRRF = 0.0, wRRF = 0.0, phaseRRF = 0.0;
if (fMsrInfo->GetMsrPlotList()->at(0).fRRFFreq == 0.0) { // normal Data representation
for (Int_t i=start; i<end; i++) {
if (((i-start) % packing == 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 1 nsec
normalizer = packing * (fTimeResolution * 1.0e3); // fTimeResolution us->ns
value /= normalizer;
time = (((Double_t)i-(Double_t)(packing-1)/2.0)-t0)*fTimeResolution;
expval = TMath::Exp(+time/tau)/N0;
fData.AppendValue(-1.0+expval*(value-bkg));
//cout << endl << ">> i=" << i << ",t0=" << t0 << ",time=" << time << ",expval=" << expval << ",value=" << value << ",bkg=" << bkg << ",expval*(value-bkg)-1=" << expval*(value-bkg)-1.0;
fData.AppendErrorValue(expval*TMath::Sqrt(value/normalizer));
//cout << endl << ">> " << time << ", " << expval << ", " << -1.0+expval*(value-bkg) << ", " << expval*TMath::Sqrt(value/packing);
value = 0.0;
}
value += runData->GetDataBin(histoNo)->at(i);
}
} else { // RRF representation
// check which units shall be used
switch (fMsrInfo->GetMsrPlotList()->at(0).fRRFUnit) {
case RRF_UNIT_kHz:
gammaRRF = TMath::TwoPi()*1.0e-3;
break;
case RRF_UNIT_MHz:
gammaRRF = TMath::TwoPi();
break;
case RRF_UNIT_Mcs:
gammaRRF = 1.0;
break;
case RRF_UNIT_G:
gammaRRF = 0.0135538817*TMath::TwoPi();
break;
case RRF_UNIT_T:
gammaRRF = 0.0135538817*TMath::TwoPi()*1.0e4;
break;
default:
gammaRRF = TMath::TwoPi();
break;
}
wRRF = gammaRRF * fMsrInfo->GetMsrPlotList()->at(0).fRRFFreq;
phaseRRF = fMsrInfo->GetMsrPlotList()->at(0).fRRFPhase / 180.0 * TMath::Pi();
// in order that after rebinning the fit does not need to be redone (important for plots)
// the value is normalize to per 1 nsec
normalizer = (fTimeResolution * 1.0e3); // fTimeResolution us->ns
Double_t error = 0.0;
for (Int_t i=start; i<end; i++) {
if (((i-start) % packing == 0) && (i != start)) { // fill data
fData.AppendValue(2.0*value/packing); // factor 2 needed because cos(a)cos(b) = 1/2(cos(a+b)+cos(a-b))
fData.AppendErrorValue(expval*TMath::Sqrt(error)/normalizer/packing);
value = 0.0;
error = 0.0;
}
time = ((Double_t)i-t0)*fTimeResolution;
expval = TMath::Exp(+time/tau)/N0;
rrf_val = (-1.0+expval*(runData->GetDataBin(histoNo)->at(i)/normalizer-bkg))*TMath::Cos(wRRF * time + phaseRRF);
value += rrf_val;
error += runData->GetDataBin(histoNo)->at(i);
}
value += runData->GetDataBin(histoNo)->at(i);
}
// count the number of bins to be fitted
@ -904,23 +955,58 @@ cout << endl << "--------------------------------" << endl;
Double_t theoryValue;
UInt_t size = runData->GetDataBin(histoNo)->size();
Double_t factor = 1.0;
if (fData.GetValue()->size() * 10 > runData->GetDataBin(histoNo)->size()) {
size = fData.GetValue()->size() * 10;
factor = (Double_t)runData->GetDataBin(histoNo)->size() / (Double_t)size;
UInt_t rebinRRF = 0;
if (wRRF == 0) { // no RRF
// check if a finer binning for the theory is needed
if (fData.GetValue()->size() * 10 > runData->GetDataBin(histoNo)->size()) {
size = fData.GetValue()->size() * 10;
factor = (Double_t)runData->GetDataBin(histoNo)->size() / (Double_t)size;
}
fData.SetTheoryTimeStart(fData.GetDataTimeStart());
fData.SetTheoryTimeStep(fTimeResolution*factor);
} else { // RRF
rebinRRF = static_cast<UInt_t>((TMath::Pi()/2.0/wRRF)/fTimeResolution); // RRF time resolution / data time resolution
fData.SetTheoryTimeStart(fData.GetDataTimeStart());
fData.SetTheoryTimeStep(TMath::Pi()/2.0/wRRF/rebinRRF); // = theory time resolution as close as possible to the data time resolution compatible with wRRF
}
//cout << endl << ">> runData->GetDataBin(histoNo).size() = " << runData->GetDataBin(histoNo).size() << ", fData.GetValue()->size() * 10 = " << fData.GetValue()->size() * 10 << ", size = " << size << ", factor = " << factor << endl;
fData.SetTheoryTimeStart(fData.GetDataTimeStart());
fData.SetTheoryTimeStep(fTimeResolution*factor);
//cout << endl << ">> size=" << size << ", startTime=" << startTime << ", fTimeResolution=" << fTimeResolution;
for (UInt_t i=0; i<size; i++) {
time = fData.GetTheoryTimeStart() + (Double_t)i*fData.GetTheoryTimeStep();
theoryValue = fTheory->Func(time, par, fFuncValues);
if (wRRF != 0.0) {
theoryValue *= 2.0*TMath::Cos(wRRF * time + phaseRRF);
}
if (fabs(theoryValue) > 10.0) { // dirty hack needs to be fixed!!
theoryValue = 0.0;
}
fData.AppendTheoryValue(theoryValue);
}
// if RRF filter the theory with a FIR Kaiser low pass filter
if (wRRF != 0.0) {
// rebin theory to the RRF frequency
if (rebinRRF != 0) {
Double_t dval = 0.0;
PDoubleVector theo;
for (UInt_t i=0; i<fData.GetTheory()->size(); i++) {
if ((i % rebinRRF == 0) && (i != 0)) {
theo.push_back(dval/rebinRRF);
dval = 0.0;
}
dval += fData.GetTheory()->at(i);
}
fData.SetTheoryTimeStart(fData.GetTheoryTimeStart()+static_cast<Double_t>(rebinRRF-1)*fData.GetTheoryTimeStep()/2.0);
fData.SetTheoryTimeStep(rebinRRF*fData.GetTheoryTimeStep());
fData.ReplaceTheory(theo);
theo.clear();
}
// filter data
CalculateKaiserFilterCoeff(wRRF, 60.0, 0.2); // w_c = wRRF, A = -20 log_10(delta), Delta w / w_c = (w_s - w_p) / (2 w_c)
FilterData();
}
// clean up
par.clear();