/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///
/// Likelihood and minimization: working with the profile likelihood estimator
///
/// \macro_image
/// \macro_output
/// \macro_code
///
/// \date 07/2008
/// \author Wouter Verkerke

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooAddPdf.h"
#include "RooMinimizer.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit;

void test_asTF_v2()
{
   int E_min = 20;
   int E_max = 145;
   RooRealVar mes("mes","Energy (keV)",E_min,E_max);

   TFile *file_sumBkg = new TFile("allBKG_twoGaus_lowXe133_set45.root","read");
   TH1D *xe127 = (TH1D *)file_sumBkg->Get("h_xe127");
	TH1D *xe133 = (TH1D *)file_sumBkg->Get("h_xe133");

   TH1D *h_all = new TH1D("h_all","h_all",3000,0,3000);
	h_all->Add(xe127,xe133);

   RooDataHist h_xe127("h_xe127","h_xe127",mes,xe127);
	RooDataHist h_xe133("h_xe133","h_xe133",mes,xe133);

   RooHistPdf pdf_Xe127("pdf_Xe127","pdf_Xe127",mes,h_xe127,0);
	RooHistPdf pdf_Xe133("pdf_Xe133","pdf_Xe133",mes,h_xe133,0);

   int input_xe127 = 36;
   int input_xe133 = 2000;
   RooRealVar nbkg_xe127("nbkg_xe127","#xe127 events",input_xe127, 0.1*input_xe127, input_xe127*10);
	RooRealVar nbkg_xe133("nbkg_xe133","#xe133 events",input_xe133, 0.1*input_xe133, input_xe133*10);

   RooAddPdf model("model","model",RooArgList(pdf_Xe127, pdf_Xe133), RooArgList(nbkg_xe127, nbkg_xe133));

   RooDataSet *data = model.generate(mes,2000);
   RooFitResult* fitresult = model.fitTo(*data, Save());

   // plot
	RooPlot* xframe = mes.frame(Title("try"));
	data->plotOn(xframe, Name("fake Data"), MarkerColor(kBlack), LineColor(kBlack));
	model.plotOn(xframe,Components("pdf_Xe127"),  Name("Xe127 bkg"), LineColor(kViolet-3));
	model.plotOn(xframe,Components("pdf_Xe133"),  Name("Xe133 bkg"), LineColor(kMagenta-3));
	model.plotOn(xframe, Name("Fit"), LineColor(4));

	//--------------//
	//     draw     //
   //--------------//
	gStyle->SetOptStat(0);
	TCanvas* c = new TCanvas("c","c",1);
   gPad->SetLogy();
	xframe->Draw();

   // from root forum
   TF1* normFunc = model.asTF(RooArgList(mes), RooArgList(nbkg_xe127, nbkg_xe133), RooArgList(mes));
   TF1 * func = new TF1("plotFunc",[&](double *x, double *p) { return p[0]* normFunc->EvalPar(x);},E_min,E_max,1);
   func->SetParameter(0, data->sumEntries() * mes.getBinWidth(1));

    cout << " 20~145: " << func->Integral(20,145) << endl;
   
   func->SetLineStyle(7);
   func->SetLineColor(2);
   func->DrawClone("same");

   // signal
   double xe127_value = ((RooRealVar*)fitresult->floatParsFinal().find("nbkg_xe127"))->getVal();
   cout << "xe127_value: " << xe127_value << endl;

   TCanvas* c1 = new TCanvas("c1","c1",1);
   cout << "---------  xe127  ----------" << endl;
   TF1* normFunc_127 = pdf_Xe127.asTF(RooArgList(mes), RooArgList(nbkg_xe127), RooArgList(mes));
   TF1 * func_127 = new TF1("plotFunc",[&](double *x, double *p) { return p[0]* normFunc_127->EvalPar(x);},E_min,E_max,1);
   func_127->SetParameter(0, xe127_value * mes.getBinWidth(1));

   cout << " 20~60: " << func_127->Integral(20,60) << endl;
   cout << " 35: " << func_127->Eval(35) << endl;
   cout << " max " << func_127->GetMaximum() << endl;

   func_127->DrawClone("");

}