void THSEventsPDF::adjustBinning(Int_t* offset1) const
{
RooRealVar* xvar = fx_off ;
if (!dynamic_cast<RooRealVar*>(xvar)) {
coutE(InputArguments) << "RooDataHist::adjustBinning(" << GetName() << ") ERROR: dimension " << xvar->GetName() << " must be real" << endl ;
assert(0) ;
}
Double_t xlo = xvar->getMin() ;
Double_t xhi = xvar->getMax() ;
//adjust bin range limits with new scale parameter
//cout<<scale<<" "<<fMean<<" "<<xlo<<" "<<xhi<<endl;
xlo=(xlo-fMean)/scale+fMean;
xhi=(xhi-fMean)/scale+fMean;
if(xvar->getBinning().lowBound()==xlo&&xvar->getBinning().highBound()==xhi) return;
xvar->setRange(xlo,xhi) ;
// Int_t xmin(0) ;
// cout<<"THSEventsPDF::adjustBinning( "<<xlo <<" "<<xhi<<endl;
//now adjust fitting range to bin limits??Possibly not
if (fRHist->GetXaxis()->GetXbins()->GetArray()) {
RooBinning xbins(fRHist->GetNbinsX(),fRHist->GetXaxis()->GetXbins()->GetArray()) ;
Double_t tolerance = 1e-6*xbins.averageBinWidth() ;
// Adjust xlo/xhi to nearest boundary
Double_t xloAdj = xbins.binLow(xbins.binNumber(xlo+tolerance)) ;
Double_t xhiAdj = xbins.binHigh(xbins.binNumber(xhi-tolerance)) ;
xbins.setRange(xloAdj,xhiAdj) ;
xvar->setBinning(xbins) ;
if (fabs(xloAdj-xlo)>tolerance||fabs(xhiAdj-xhi)<tolerance) {
coutI(DataHandling) << "RooDataHist::adjustBinning(" << GetName() << "): fit range of variable " << xvar->GetName() << " expanded to nearest bin boundaries: ["
<< xlo << "," << xhi << "] --> [" << xloAdj << "," << xhiAdj << "]" << endl ;
}
} else {
RooBinning xbins(fRHist->GetXaxis()->GetXmin(),fRHist->GetXaxis()->GetXmax()) ;
xbins.addUniform(fRHist->GetNbinsX(),fRHist->GetXaxis()->GetXmin(),fRHist->GetXaxis()->GetXmax()) ;
Double_t tolerance = 1e-6*xbins.averageBinWidth() ;
// Adjust xlo/xhi to nearest boundary
Double_t xloAdj = xbins.binLow(xbins.binNumber(xlo+tolerance)) ;
Double_t xhiAdj = xbins.binHigh(xbins.binNumber(xhi-tolerance)) ;
xbins.setRange(xloAdj,xhiAdj) ;
xvar->setRange(xloAdj,xhiAdj) ;
//xvar->setRange(xlo,xhi) ;
}
return;
}
RooHistN::RooHistN(const TH1 &data1, const TH1 &data2, Double_t nominalBinWidth, Double_t nSigma, Double_t xErrorFrac) :
TGraphAsymmErrors(), _nominalBinWidth(nominalBinWidth), _nSigma(nSigma), _rawEntries(-1)
{
// Create a histogram from the asymmetry between the specified TH1 objects
// which may have fixed or variable bin widths, but which must both have
// the same binning. The asymmetry is calculated as (1-2)/(1+2). Error bars are
// calculated using Binomial statistics. Prints a warning and rounds
// any bins with non-integer contents. Use the optional parameter to
// specify the confidence level in units of sigma to use for
// calculating error bars. The nominal bin width specifies the
// default used by addAsymmetryBin(), and is used to set the relative
// normalization of bins with different widths. If not set, the
// nominal bin width is calculated as range/nbins.
initialize();
// copy the first input histogram's name and title
SetName(data1.GetName());
SetTitle(data1.GetTitle());
// calculate our nominal bin width if necessary
if(_nominalBinWidth == 0) {
const TAxis *axis= ((TH1&)data1).GetXaxis();
if(axis->GetNbins() > 0) _nominalBinWidth= (axis->GetXmax() - axis->GetXmin())/axis->GetNbins();
}
setYAxisLabel(Form("Asymmetry (%s - %s)/(%s + %s)",
data1.GetName(),data2.GetName(),data1.GetName(),data2.GetName()));
// initialize our contents from the input histogram contents
Int_t nbin= data1.GetNbinsX();
if(data2.GetNbinsX() != nbin) {
coutE(InputArguments) << "RooHistN::RooHistN: histograms have different number of bins" << endl;
return;
}
for(Int_t bin= 1; bin <= nbin; bin++) {
Axis_t x= data1.GetBinCenter(bin);
if(fabs(data2.GetBinCenter(bin)-x)>1e-10) {
coutW(InputArguments) << "RooHistN::RooHistN: histograms have different centers for bin " << bin << endl;
}
Stat_t y1= data1.GetBinContent(bin);
Stat_t y2= data2.GetBinContent(bin);
addAsymmetryBin(x,roundBin(y1),roundBin(y2),data1.GetBinWidth(bin),xErrorFrac);
}
// we do not have a meaningful number of entries
_entries= -1;
}
void RooHistN::addAsymmetryBin(Axis_t binCenter, Int_t n1, Int_t n2, Double_t binWidth, Double_t xErrorFrac) {
// Add a bin to this histogram with the value (n1-n2)/(n1+n2)
// using an error bar calculated with Binomial statistics.
Double_t scale= 1;
if(binWidth > 0) scale= _nominalBinWidth/binWidth;
Int_t index= GetN();
// calculate Binomial errors for this bin
Double_t ym,yp,dx(0.5*binWidth);
if(!RooHistError::instance().getBinomialInterval(n1,n2,ym,yp,_nSigma)) {
coutE(Plotting) << "RooHistN::addAsymmetryBin: unable to calculate binomial error for bin with " << n1 << "," << n2 << " events" << endl;
return;
}
Double_t a= (Double_t)(n1-n2)/(n1+n2);
SetPoint(index,binCenter,a);
SetPointError(index,dx*xErrorFrac,dx*xErrorFrac,(a-ym),(yp-a));
updateYAxisLimits(scale*yp);
updateYAxisLimits(scale*ym);
}
void RooHistN::addBin(Axis_t binCenter, Int_t n, Double_t binWidth, Double_t xErrorFrac) {
// Add a bin to this histogram with the specified integer bin contents
// and using an error bar calculated with Poisson statistics. The bin width
// is used to set the relative scale of bins with different widths.
Double_t scale= 1;
if(binWidth > 0) {
scale= _nominalBinWidth/binWidth;
}
_entries+= n;
Int_t index= GetN();
// calculate Poisson errors for this bin
Double_t ym,yp,dx(0.5*binWidth);
if(!RooHistError::instance().getPoissonInterval(n,ym,yp,_nSigma)) {
coutE(Plotting) << "RooHistN::addBin: unable to add bin with " << n << " events" << endl;
return;
}
SetPoint(index,binCenter,n*scale);
SetPointError(index,dx*xErrorFrac,dx*xErrorFrac,scale*(n-ym),scale*(yp-n));
updateYAxisLimits(scale*yp);
updateYAxisLimits(scale*ym);
}
RooHistN::RooHistN(const RooHistN& hist1, const RooHistN& hist2, Double_t wgt1, Double_t wgt2, RooAbsData::ErrorType etype, Double_t xErrorFrac) : _rawEntries(-1){
// Create histogram as sum of two existing histograms. If Poisson errors are selected the histograms are
// added and Poisson confidence intervals are calculated for the summed content. If wgt1 and wgt2 are not
// 1 in this mode, a warning message is printed. If SumW2 errors are selectd the histograms are added
// and the histograms errors are added in quadrature, taking the weights into account.
// Initialize the histogram
initialize() ;
// Copy all non-content properties from hist1
SetName(hist1.GetName()) ;
SetTitle(hist1.GetTitle()) ;
_nominalBinWidth=hist1._nominalBinWidth ;
_nSigma=hist1._nSigma ;
setYAxisLabel(hist1.getYAxisLabel()) ;
if (!hist1.hasIdenticalBinning(hist2)) {
coutE(InputArguments) << "RooHistN::RooHistN input histograms have incompatible binning, combined histogram will remain empty" << endl ;
return ;
}
if (etype==RooAbsData::Poisson) {
// Add histograms with Poisson errors
// Issue warning if weights are not 1
if (wgt1!=1.0 || wgt2 != 1.0) {
coutW(InputArguments) << "RooHistN::RooHistN: WARNING: Poisson errors of weighted sum of two histograms is not well defined! " << endl
<< " Summed histogram bins will rounded to nearest integer for Poisson confidence interval calculation" << endl ;
}
// Add histograms, calculate Poisson confidence interval on sum value
Int_t i,n=hist1.GetN() ;
for(i=0 ; i<n ; i++) {
Double_t x1,y1,x2,y2,dx1 ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
hist1.GetPoint(i,x1,y1) ;
#else
const_cast<RooHistN&>(hist1).GetPoint(i,x1,y1) ;
#endif
dx1 = hist1.GetErrorX(i) ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
hist2.GetPoint(i,x2,y2) ;
#else
const_cast<RooHistN&>(hist2).GetPoint(i,x2,y2) ;
#endif
addBin(x1,roundBin(wgt1*y1+wgt2*y2),2*dx1/xErrorFrac,xErrorFrac) ;
}
} else {
// Add histograms with SumW2 errors
// Add histograms, calculate combined sum-of-weights error
Int_t i,n=hist1.GetN() ;
for(i=0 ; i<n ; i++) {
Double_t x1,y1,x2,y2,dx1,dy1,dy2 ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
hist1.GetPoint(i,x1,y1) ;
#else
const_cast<RooHistN&>(hist1).GetPoint(i,x1,y1) ;
#endif
dx1 = hist1.GetErrorX(i) ;
dy1 = hist1.GetErrorY(i) ;
dy2 = hist2.GetErrorY(i) ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
hist2.GetPoint(i,x2,y2) ;
#else
const_cast<RooHistN&>(hist2).GetPoint(i,x2,y2) ;
#endif
Double_t dy = sqrt(wgt1*wgt1*dy1*dy1+wgt2*wgt2*dy2*dy2) ;
addBinWithError(x1,wgt1*y1+wgt2*y2,dy,dy,2*dx1/xErrorFrac,xErrorFrac) ;
}
}
}
