void
Convolution::Construct(){
if (!fFunction || !fHasAxes)
throw LogicError("Convolution::Construct() : Tried to construct convolution without axes or function, or both!!");
if(!fCachedCompatibleBins)
CacheCompatibleBins();
size_t nBins = fPdfMapping.GetNBins();
size_t nDims = fPdfMapping.GetAxes().GetNDimensions();
const AxisCollection& axes = fPdfMapping.GetAxes();
std::vector<size_t> relativeIndices = fDataRep.GetRelativeIndices(fPdfDataRep);
// Work out the transition probabilitites within this sub set of the bins
std::vector<double> binCentres(fSysAxes.GetNDimensions());
std::vector<double> lowEdges(fSysAxes.GetNDimensions());
std::vector<double> highEdges(fSysAxes.GetNDimensions());
PdfMapping subMap;
subMap.SetAxes(fSysAxes);
for (size_t origBin = 0; origBin < fSysAxes.GetNBins(); origBin++){
// get the centre of the bin. Need to offset by this for a convolution
fSysAxes.GetBinCentres(origBin, binCentres);
// loop over the bins it can be smeared into
for(size_t destBin = 0; destBin < fSysAxes.GetNBins(); destBin++){
fSysAxes.GetBinLowEdges(destBin, lowEdges);
fSysAxes.GetBinHighEdges(destBin, highEdges);
for(size_t i = 0; i < fSysAxes.GetNDimensions(); i++){
lowEdges[i] -= binCentres.at(i);
highEdges[i] -= binCentres.at(i);
}
subMap.SetComponent(destBin, origBin, fFunction -> Integral(lowEdges, highEdges));
}
}
// Now expand to the full size matrix. Elements are zero by default
// compatible bins are cached, values must match the smaller matrix above
size_t destBin = -1;
std::vector<unsigned> nonZeroRowIndices;
std::vector<unsigned> nonZeroColIndices;
std::vector<double> values;
nonZeroRowIndices.reserve(fCompatibleBins.at(0).size());
nonZeroColIndices.reserve(fCompatibleBins.at(0).size());
for(size_t origBin = 0; origBin < axes.GetNBins(); origBin++){
for(size_t i = 0; i < fCompatibleBins.at(origBin).size(); i++){
destBin = fCompatibleBins.at(origBin).at(i);
nonZeroRowIndices.push_back(origBin);
nonZeroColIndices.push_back(destBin);
values.push_back( subMap.GetComponent(fSysBins.at(origBin),
fSysBins.at(destBin)));
}
}
fPdfMapping.SetComponents(nonZeroRowIndices, nonZeroColIndices, values);
}
void Convolution::Construct(){
if (!fPdf || !fHasAxes)
throw InitialisationError("Tried to construct convolution without axes and pdf!");
if(!fCachedCompatibleBins)
CacheCompatibleBins();
Reset();
size_t nBins = fPdfMapping.GetNBins();
size_t nDims = fPdfMapping.GetAxes().GetNDimensions();
const AxisCollection& axes = fPdfMapping.GetAxes();
std::vector<size_t> relativeIndicies = fDataRep.GetRelativeIndicies(fPdfDataRep);
std::vector<double> binCentre(relativeIndicies.size());
std::vector<double> lowEdges(relativeIndicies.size());
std::vector<double> highEdges(relativeIndicies.size());
for(size_t i = 0; i < nBins; i++){
for(size_t k = 0; k < relativeIndicies.size(); k++)
binCentre[k] = axes.GetBinCentre(i, relativeIndicies.at(k));
// Loop over compatible bins and integrate over bin j to work out the response from i -> j
// others are zero from reset
for(size_t j = 0; j < fCompatibleBins.at(i).size(); j++){
size_t mappedBin = fCompatibleBins.at(i).at(j);
for(size_t k = 0; k < relativeIndicies.size(); k++){
lowEdges[k] = axes.GetBinLowEdge(mappedBin, relativeIndicies.at(k));
highEdges[k] = axes.GetBinHighEdge(mappedBin, relativeIndicies.at(k));
}
// Move the pdf origin to the centre of bin i
for(size_t k = 0; k < lowEdges.size(); k++){
lowEdges[k] -= binCentre[k];
highEdges[k] -= binCentre[k];
}
double Rij = fPdf -> Integral(lowEdges, highEdges);
fPdfMapping.SetComponent(mappedBin, i, Rij);
}
}
}
