%%cpp -d
std::unique_ptr<RooDataHist> generateBinnedAsimov(RooAbsPdf const &pdf, RooRealVar &x, int nEvents)
{
   auto dataH = std::make_unique<RooDataHist>("dataH", "dataH", RooArgSet{x});
   RooAbsBinning &xBinning = x.getBinning();
   for (int iBin = 0; iBin < x.numBins(); ++iBin) {
      x.setRange("bin", xBinning.binLow(iBin), xBinning.binHigh(iBin));
      std::unique_ptr<RooAbsReal> integ{pdf.createIntegral(x, RooFit::NormSet(x), RooFit::Range("bin"))};
      integ->getVal();
      dataH->set(iBin, nEvents * integ->getVal(), -1);
   }
   return dataH;
}

%%cpp -d
void enableBinIntegrator(RooAbsReal &func, int numBins)
{
   RooNumIntConfig customConfig(*func.getIntegratorConfig());
   customConfig.method1D().setLabel("RooBinIntegrator");
   customConfig.getConfigSection("RooBinIntegrator").setRealValue("numBins", numBins);
   func.setIntegratorConfig(customConfig);
   func.forceNumInt(true);
}

%%cpp -d
void disableBinIntegrator(RooAbsReal &func)
{
   func.setIntegratorConfig();
   func.forceNumInt(false);
}

using namespace RooFit;

RooMsgService::instance().getStream(1).removeTopic(Minimization);
RooMsgService::instance().getStream(1).removeTopic(Fitting);
RooMsgService::instance().getStream(1).removeTopic(Generation);

RooRealVar x{"x", "x", 0.1, 5.1};
x.setBins(10); // fewer bins so we have larger binning effects for this demo

RooRealVar c{"c", "c", -1.8, -5, 5};
RooExponential expo{"expo", "expo", x, c};

std::unique_ptr<RooAbsData> expoData{generateBinnedAsimov(expo, x, 10000)};

std::unique_ptr<RooFitResult> fit1{expo.fitTo(*expoData, Save(), PrintLevel(-1), SumW2Error(false))};
fit1->Print();

enableBinIntegrator(expo, x.numBins());
std::unique_ptr<RooFitResult> fit2{expo.fitTo(*expoData, Save(), PrintLevel(-1), SumW2Error(false))};
fit2->Print();
disableBinIntegrator(expo);

RooRealVar a{"a", "a", -0.3, -5.0, 5.0};
RooPower powerlaw{"powerlaw", "powerlaw", x, RooConst(1.0), a};
std::unique_ptr<RooAbsData> powerlawData{generateBinnedAsimov(powerlaw, x, 10000)};

std::unique_ptr<RooFitResult> fit3{powerlaw.fitTo(*powerlawData, Save(), PrintLevel(-1), SumW2Error(false))};
fit3->Print();

enableBinIntegrator(powerlaw, x.numBins());
std::unique_ptr<RooFitResult> fit4{powerlaw.fitTo(*powerlawData, Save(), PrintLevel(-1), SumW2Error(false))};
fit4->Print();
disableBinIntegrator(powerlaw);

std::unique_ptr<RooFitResult> fit5{
   powerlaw.fitTo(*powerlawData, IntegrateBins(1e-3), Save(), PrintLevel(-1), SumW2Error(false))};
fit5->Print();

x.setBins(100); // It's not about binning effects anymore, so reset the number of bins.

RooRealVar mu{"mu", "mu", 3.0, 0.1, 5.1};
RooRealVar sigma{"sigma", "sigma", 0.5, 0.01, 5.0};
RooGaussian gauss{"gauss", "gauss", x, mu, sigma};

RooRealVar nsig{"nsig", "nsig", 10000, 0, 1e9};
RooRealVar nbkg{"nbkg", "nbkg", 10000000, 0, 1e9};
RooRealVar frac{"frac", "frac", nsig.getVal() / (nsig.getVal() + nbkg.getVal()), 0.0, 1.0};

RooAddPdf model{"model", "model", {gauss, expo}, {nsig, nbkg}};

std::unique_ptr<RooAbsData> modelData{model.generateBinned(x)};

mu.setVal(2.0);
sigma.setVal(1.0);

std::unique_ptr<RooFitResult> fit6{model.fitTo(*modelData, Save(), PrintLevel(-1), SumW2Error(false))};
fit6->Print();

std::unique_ptr<RooFitResult> fit7{
   model.fitTo(*modelData, Offset("bin"), Save(), PrintLevel(-1), SumW2Error(false))};
fit7->Print();

%jsroot on
gROOT->GetListOfCanvases()->Draw()