LMU/musrfit
5
0
Fork 0
Code Issues 6 Pull Requests Actions Packages Projects Releases 4 Activity

start-/endTimeBin are now class members. This reduces the number of recalculations.

Browse Source
This commit is contained in:
suter_a
2016-04-26 12:39:27 +02:00
parent 03667f9dfb
commit af7b729a5a
7 changed files with 92 additions and 103 deletions
Download Patch File Download Diff File Expand all files Collapse all files

65
src/classes/PRunSingleHisto.cpp
View File

@ -64,6 +64,9 @@ PRunSingleHisto::PRunSingleHisto() : PRunBase()
// the fit range can be changed in the command block, these variables need to be accessible
fGoodBins[0] = -1;
fGoodBins[1] = -1;
fStartTimeBin = -1;
fEndTimeBin = -1;
}
//--------------------------------------------------------------------------
@ -81,6 +84,7 @@ PRunSingleHisto::PRunSingleHisto(PMsrHandler *msrInfo, PRunDataHandler *rawData,
{
fScaleN0AndBkg = IsScaleN0AndBkg();
fNoOfFitBins = 0;
fBackground = 0;
fPacking = fRunInfo->GetPacking();
if (fPacking == -1) { // i.e. packing is NOT given in the RUN-block, it must be given in the GLOBAL-block
@ -99,6 +103,9 @@ PRunSingleHisto::PRunSingleHisto(PMsrHandler *msrInfo, PRunDataHandler *rawData,
fGoodBins[0] = -1;
fGoodBins[1] = -1;
fStartTimeBin = -1;
fEndTimeBin = -1;
if (!PrepareData()) {
cerr << endl << ">> PRunSingleHisto::PRunSingleHisto: **SEVERE ERROR**: Couldn't prepare data for fitting!";
cerr << endl << ">> This is very bad :-(, will quit ...";
@ -173,15 +180,7 @@ Double_t PRunSingleHisto::CalcChiSquare(const std::vector<Double_t>& par)
// calculate chi square
Double_t time(1.0);
Int_t i, N(static_cast<Int_t>(fData.GetValue()->size()));
// In order not to have an IF in the next loop, determine the start and end bins for the fit range now
Int_t startTimeBin = static_cast<Int_t>(ceil((fFitStartTime - fData.GetDataTimeStart())/fData.GetDataTimeStep()));
if (startTimeBin < 0)
startTimeBin = 0;
Int_t endTimeBin = static_cast<Int_t>(floor((fFitEndTime - fData.GetDataTimeStart())/fData.GetDataTimeStep())) + 1;
if (endTimeBin > N)
endTimeBin = N;
Int_t i;
// Calculate the theory function once to ensure one function evaluation for the current set of parameters.
// This is needed for the LF and user functions where some non-thread-save calculations only need to be calculated once
@ -190,12 +189,12 @@ Double_t PRunSingleHisto::CalcChiSquare(const std::vector<Double_t>& par)
time = fTheory->Func(time, par, fFuncValues);
#ifdef HAVE_GOMP
Int_t chunk = (endTimeBin - startTimeBin)/omp_get_num_procs();
Int_t chunk = (fEndTimeBin - fStartTimeBin)/omp_get_num_procs();
if (chunk < 10)
chunk = 10;
#pragma omp parallel for default(shared) private(i,time,diff) schedule(dynamic,chunk) reduction(+:chisq)
#endif
for (i=startTimeBin; i < endTimeBin; ++i) {
for (i=fStartTimeBin; i<fEndTimeBin; ++i) {
time = fData.GetDataTimeStart() + (Double_t)i*fData.GetDataTimeStep();
diff = fData.GetValue()->at(i) -
(N0*TMath::Exp(-time/tau)*(1.0+fTheory->Func(time, par, fFuncValues))+bkg);
@ -266,15 +265,7 @@ Double_t PRunSingleHisto::CalcChiSquareExpected(const std::vector<Double_t>& par
// calculate chi square
Double_t time(1.0);
Int_t i, N(static_cast<Int_t>(fData.GetValue()->size()));
// In order not to have an IF in the next loop, determine the start and end bins for the fit range now
Int_t startTimeBin = static_cast<Int_t>(ceil((fFitStartTime - fData.GetDataTimeStart())/fData.GetDataTimeStep()));
if (startTimeBin < 0)
startTimeBin = 0;
Int_t endTimeBin = static_cast<Int_t>(floor((fFitEndTime - fData.GetDataTimeStart())/fData.GetDataTimeStep())) + 1;
if (endTimeBin > N)
endTimeBin = N;
Int_t i;
// Calculate the theory function once to ensure one function evaluation for the current set of parameters.
// This is needed for the LF and user functions where some non-thread-save calculations only need to be calculated once
@ -283,12 +274,12 @@ Double_t PRunSingleHisto::CalcChiSquareExpected(const std::vector<Double_t>& par
time = fTheory->Func(time, par, fFuncValues);
#ifdef HAVE_GOMP
Int_t chunk = (endTimeBin - startTimeBin)/omp_get_num_procs();
Int_t chunk = (fEndTimeBin - fStartTimeBin)/omp_get_num_procs();
if (chunk < 10)
chunk = 10;
#pragma omp parallel for default(shared) private(i,time,diff) schedule(dynamic,chunk) reduction(+:chisq)
#endif
for (i=startTimeBin; i < endTimeBin; ++i) {
for (i=fStartTimeBin; i < fEndTimeBin; ++i) {
time = fData.GetDataTimeStart() + (Double_t)i*fData.GetDataTimeStep();
theo = N0*TMath::Exp(-time/tau)*(1.0+fTheory->Func(time, par, fFuncValues))+bkg;
diff = fData.GetValue()->at(i) - theo;
@ -359,7 +350,7 @@ Double_t PRunSingleHisto::CalcMaxLikelihood(const std::vector<Double_t>& par)
Double_t theo;
Double_t data;
Double_t time(1.0);
Int_t i, N(static_cast<Int_t>(fData.GetValue()->size()));
Int_t i;
// norm is needed since there is no simple scaling like in chisq case to get the correct Max.Log.Likelihood value when normlizing N(t) to 1/ns
Double_t normalizer = 1.0;
@ -367,14 +358,6 @@ Double_t PRunSingleHisto::CalcMaxLikelihood(const std::vector<Double_t>& par)
if (fScaleN0AndBkg)
normalizer = fPacking * (fTimeResolution * 1.0e3);
// In order not to have an IF in the next loop, determine the start and end bins for the fit range now
Int_t startTimeBin = static_cast<Int_t>(ceil((fFitStartTime - fData.GetDataTimeStart())/fData.GetDataTimeStep()));
if (startTimeBin < 0)
startTimeBin = 0;
Int_t endTimeBin = static_cast<Int_t>(floor((fFitEndTime - fData.GetDataTimeStart())/fData.GetDataTimeStep())) + 1;
if (endTimeBin > N)
endTimeBin = N;
// Calculate the theory function once to ensure one function evaluation for the current set of parameters.
// This is needed for the LF and user functions where some non-thread-save calculations only need to be calculated once
// for a given set of parameters---which should be done outside of the parallelized loop.
@ -382,12 +365,12 @@ Double_t PRunSingleHisto::CalcMaxLikelihood(const std::vector<Double_t>& par)
time = fTheory->Func(time, par, fFuncValues);
#ifdef HAVE_GOMP
Int_t chunk = (endTimeBin - startTimeBin)/omp_get_num_procs();
Int_t chunk = (fEndTimeBin - fStartTimeBin)/omp_get_num_procs();
if (chunk < 10)
chunk = 10;
#pragma omp parallel for default(shared) private(i,time,theo,data) schedule(dynamic,chunk) reduction(-:mllh)
#endif
for (i=startTimeBin; i<endTimeBin; ++i) {
for (i=fStartTimeBin; i<fEndTimeBin; ++i) {
time = fData.GetDataTimeStart() + (Double_t)i*fData.GetDataTimeStep();
// calculate theory for the given parameter set
theo = N0*TMath::Exp(-time/tau)*(1.0+fTheory->Func(time, par, fFuncValues))+bkg;
@ -588,15 +571,15 @@ void PRunSingleHisto::SetFitRangeBin(const TString fitRange)
void PRunSingleHisto::CalcNoOfFitBins()
{
// In order not having to loop over all bins and to stay consistent with the chisq method, calculate the start and end bins explicitly
Int_t startTimeBin = static_cast<Int_t>(ceil((fFitStartTime - fData.GetDataTimeStart())/fData.GetDataTimeStep()));
if (startTimeBin < 0)
startTimeBin = 0;
Int_t endTimeBin = static_cast<Int_t>(floor((fFitEndTime - fData.GetDataTimeStart())/fData.GetDataTimeStep())) + 1;
if (endTimeBin > static_cast<Int_t>(fData.GetValue()->size()))
endTimeBin = fData.GetValue()->size();
fStartTimeBin = static_cast<Int_t>(ceil((fFitStartTime - fData.GetDataTimeStart())/fData.GetDataTimeStep()));
if (fStartTimeBin < 0)
fStartTimeBin = 0;
fEndTimeBin = static_cast<Int_t>(floor((fFitEndTime - fData.GetDataTimeStart())/fData.GetDataTimeStep())) + 1;
if (fEndTimeBin > static_cast<Int_t>(fData.GetValue()->size()))
fEndTimeBin = fData.GetValue()->size();
if (endTimeBin > startTimeBin)
fNoOfFitBins = endTimeBin - startTimeBin;
if (fEndTimeBin > fStartTimeBin)
fNoOfFitBins = fEndTimeBin - fStartTimeBin;
else
fNoOfFitBins = 0;
}