Int_t RooHistN::roundBin(Double_t y) {
// Return the nearest positive integer to the input value
// and print a warning if an adjustment is required.
if(y < 0) {
coutW(Plotting) << fName << "::roundBin: rounding negative bin contents to zero: " << y << endl;
return 0;
}
Int_t n= (Int_t)(y+0.5);
if(fabs(y-n)>1e-6) {
coutW(Plotting) << fName << "::roundBin: rounding non-integer bin contents: " << y << endl;
}
return n;
}
Double_t RooHistN::getFitRangeNEvt(Double_t xlo, Double_t xhi) const
{
// Calculate integral of histogram in given range
Double_t sum(0) ;
for (int i=0 ; i<GetN() ; i++) {
Double_t x,y ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
GetPoint(i,x,y) ;
#else
const_cast<RooHistN*>(this)->GetPoint(i,x,y) ;
#endif
if (x>=xlo && x<=xhi) {
sum += y ;
}
}
if (_rawEntries!=-1) {
coutW(Plotting) << "RooHistN::getFitRangeNEvt() WARNING: Number of normalization events associated to histogram is not equal to number of events in histogram" << endl
<< " due cut made in RooAbsData::plotOn() call. Automatic normalization over sub-range of plot variable assumes" << endl
<< " that the effect of that cut is uniform across the plot, which may be an incorrect assumption. To be sure of" << endl
<< " correct normalization explicit pass normalization information to RooAbsPdf::plotOn() call using Normalization()" << endl ;
sum *= _rawEntries / _entries ;
}
return sum ;
}
RooHistN* RooHistN::makeResidHist(const RooCurve& curve,bool normalize) const {
// Make histogram of (normalized) residuals w.r.t to given curve
// Copy all non-content properties from hist1
RooHistN* hist = new RooHistN(_nominalBinWidth) ;
hist->SetName(Form(normalize?"pull_%s_s":"resid_%s_s",GetName(),curve.GetName())) ;
hist->SetTitle(Form(normalize?"Pull of %s and %s":"Residual of %s and %s",GetTitle(),curve.GetTitle())) ;
// Determine range of curve
Double_t xstart,xstop,y ;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
curve.GetPoint(0,xstart,y) ;
curve.GetPoint(curve.GetN()-1,xstop,y) ;
#else
const_cast<RooCurve&>(curve).GetPoint(0,xstart,y) ;
const_cast<RooCurve&>(curve).GetPoint(curve.GetN()-1,xstop,y) ;
#endif
// Add histograms, calculate Poisson confidence interval on sum value
for(Int_t i=0 ; i<GetN() ; i++) {
Double_t x,point;
#if ROOT_VERSION_CODE >= ROOT_VERSION(4,0,1)
GetPoint(i,x,point) ;
#else
const_cast<RooHistN&>(*this).GetPoint(i,x,point) ;
#endif
// Only calculate pull for bins inside curve range
if (x<xstart || x>xstop) continue ;
Double_t y = point - curve.interpolate(x) ;
Double_t dyl = GetErrorYlow(i) ;
Double_t dyh = GetErrorYhigh(i) ;
if (normalize) {
Double_t norm = (y>0?dyh:dyl);
if (norm==0.) {
coutW(Plotting) << "RooHistN::makeResisHist(" << GetName() << ") WARNING: point " << i << " has zero error, setting residual to zero" << endl ;
y=0 ;
dyh=0 ;
dyl=0 ;
} else {
y /= norm;
dyh /= norm;
dyl /= norm;
}
}
hist->addBinWithError(x,y,dyl,dyh);
}
return hist ;
}
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 coutW(const std::wstring &str)
{
coutW((const wchar_t *)str.c_str(), (int)str.size());
}
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) ;
}
}
}
