%%cpp -d
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooFormulaVar.h"
#include "RooGenericPdf.h"
#include "RooPolynomial.h"
#include "RooMinimizer.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "RooFitResult.h"
using namespace RooFit;

RooRealVar x("x", "x", -10, 10);
x.setBins(40);

RooPolynomial p0("px", "px", x);

std::unique_ptr<RooDataSet> data{p0.generate(x, 1000)};

RooFormulaVar wFunc("w", "event weight", "(x*x+10)", x);

RooRealVar *w = (RooRealVar *)data->addColumn(wFunc);

data->Print();

RooDataSet wdata(data->GetName(), data->GetTitle(), data.get(), *data->get(), 0, w->GetName());

wdata.Print();

RooRealVar a0("a0", "a0", 1);
RooRealVar a1("a1", "a1", 0, -1, 1);
RooRealVar a2("a2", "a2", 1, 0, 10);
RooPolynomial p2("p2", "p2", x, RooArgList(a0, a1, a2), 0);

std::unique_ptr<RooFitResult> r_ml_wgt{p2.fitTo(wdata, Save(), PrintLevel(-1))};

std::unique_ptr<RooFitResult> r_ml_wgt_corr{p2.fitTo(wdata, Save(), SumW2Error(true), PrintLevel(-1))};

RooPlot *frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data"));

wdata.plotOn(frame, DataError(RooAbsData::SumW2));

p2.plotOn(frame);

RooGenericPdf genPdf("genPdf", "x*x+10", x);

std::unique_ptr<RooDataSet> data2{genPdf.generate(x, 1000)};

std::unique_ptr<RooDataSet> data3{genPdf.generate(x, 43000)};

std::unique_ptr<RooFitResult> r_ml_unw10{p2.fitTo(*data2, Save(), PrintLevel(-1))};
std::unique_ptr<RooFitResult> r_ml_unw43{p2.fitTo(*data3, Save(), PrintLevel(-1))};

std::unique_ptr<RooAbsData> binnedData{wdata.binnedClone()};
binnedData->Print("v");

std::unique_ptr<RooAbsReal> chi2{
   p2.createChi2(static_cast<RooDataHist &>(*binnedData), DataError(RooAbsData::SumW2))};
RooMinimizer m(*chi2);
m.migrad();
m.hesse();

std::unique_ptr<RooFitResult> r_chi2_wgt{m.save()};
p2.plotOn(frame, LineStyle(kDashed), LineColor(kRed));

cout << "==> ML Fit results on 1K unweighted events" << endl;
r_ml_unw10->Print();
cout << "==> ML Fit results on 43K unweighted events" << endl;
r_ml_unw43->Print();
cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_ml_wgt->Print();
cout << "==> Corrected ML Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_ml_wgt_corr->Print();
cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl;
r_chi2_wgt->Print();

new TCanvas("rf403_weightedevts", "rf403_weightedevts", 600, 600);
gPad->SetLeftMargin(0.15);
frame->GetYaxis()->SetTitleOffset(1.8);
frame->Draw();

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