/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///
/// Plot a PDF in disjunct ranges, and get normalisation right.
///
/// Usually, when comparing a fit to data, one should first plot the data, and then the PDF.
/// In this case, the PDF is automatically normalised to match the number of data events in the plot.
/// However, when plotting only a sub-range, when e.g. a signal region has to be blinded,
/// one has to exclude the blinded region from the computation of the normalisation.
///
/// In this tutorial, we show how to explicitly choose the normalisation when plotting using `NormRange()`.
///
/// Thanks to Marc Escalier for asking how to do this correctly.
///
/// \macro_image
/// \macro_code
/// \macro_output
///
/// \date 03/2020
/// \author Stephan Hageboeck

#include <RooDataSet.h>
#include <RooExponential.h>
#include <RooPlot.h>
#include <RooRealVar.h>
#include <TCanvas.h>

using namespace RooFit;

void rf212_plottingInRanges_blinding()
{
  // Make a fit model
  RooRealVar x("x", "The observable", 1, 30);
  RooRealVar tau("tau", "The exponent", -0.1337, -10., -0.1);
  RooExponential exp("exp", "A falling exponential function", x, tau);

  // Define the sidebands (e.g. background regions)
  x.setRange("full", 1, 30);
  x.setRange("left", 1, 10);
  x.setRange("right", 20, 30);

  // Generate toy data, and cut out the blinded region.
  RooDataSet* data = exp.generate(x, 1000);
  auto blindedData = data->reduce(CutRange("left,right"));

  // Kick tau a bit, and run an unbinned fit where the blinded data are missing.
  // ----------------------------------------------------------------------------------------------------------
  tau.setVal(-2.);
  exp.fitTo(*blindedData);


  // Here we will plot the results
  TCanvas *canvas=new TCanvas("canvas","canvas",800,600);
  canvas->Divide(2,1);


  // Wrong:
  // Plotting each slice on its own normalises the PDF over its plotting range. For the full curve, that means
  // that the blinded region where data is missing is included in the normalisation calculation. The PDF therefore
  // comes out too low, and doesn't match up with the slices in the side bands, which are normalised to "their" data.
  // ----------------------------------------------------------------------------------------------------------

  std::cout << "Now plotting with unique normalisation for each slice." << std::endl;
  canvas->cd(1);
  RooPlot* plotFrame = x.frame(RooFit::Title("Wrong: Each slice normalised over its plotting range"));

  // Plot only the blinded data, and then plot the PDF over the full range as well as both sidebands
  blindedData->plotOn(plotFrame);
  exp.plotOn(plotFrame, LineColor(kRed),   Range("full"));
  exp.plotOn(plotFrame, LineColor(kBlue),  Range("left"));
  exp.plotOn(plotFrame, LineColor(kGreen), Range("right"));

  plotFrame->Draw();

  // Right:
  // Make the same plot, but normalise each piece with respect to the regions "left" AND "right". This requires setting
  // a "NormRange", which tells RooFit over which range the PDF has to be integrated to normalise.
  // This is means that the normalisation of the blue and green curves is slightly different from the left plot,
  // because they get a common scale factor.
  // ----------------------------------------------------------------------------------------------------------

  std::cout << "\n\nNow plotting with correct norm ranges:" << std::endl;
  canvas->cd(2);
  RooPlot* plotFrameWithNormRange = x.frame(RooFit::Title("Right: All slices have common normalisation"));

  // Plot only the blinded data, and then plot the PDF over the full range as well as both sidebands
  blindedData->plotOn(plotFrameWithNormRange);
  exp.plotOn(plotFrameWithNormRange, LineColor(kBlue),  Range("left"),  RooFit::NormRange("left,right"));
  exp.plotOn(plotFrameWithNormRange, LineColor(kGreen), Range("right"), RooFit::NormRange("left,right"));
  exp.plotOn(plotFrameWithNormRange, LineColor(kRed),   Range("full"),  RooFit::NormRange("left,right"), LineStyle(10));

  plotFrameWithNormRange->Draw();

  canvas->Draw();

}