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RAA_plot_finalpaper.C
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RAA_plot_finalpaper.C
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// Raghav Kunnawalkam Elayavalli
// April 14 2014
// Rutgers
// raghav.k.e at CERN dot CH
//
// Macro to plot the final paper plots.
//
#include <iostream>
#include <stdio.h>
#include <fstream>
#include <fstream>
#include <TH1F.h>
#include <TH1F.h>
#include <TH2F.h>
#include <TFile.h>
#include <TTree.h>
#include <TF1.h>
#include <TCanvas.h>
#include <TLegend.h>
#include <TGraphErrors.h>
#include <TGraphAsymmErrors.h>
#include <TH1.h>
#include <TH2.h>
#include <TH3.h>
#include <TFile.h>
#include <TStyle.h>
#include <TStopwatch.h>
#include <TRandom3.h>
#include <TChain.h>
#include <TProfile.h>
#include <TStopwatch.h>
#include <TCut.h>
#include <cstdlib>
#include <cmath>
#include "TLegend.h"
#include "TLatex.h"
#include "TMath.h"
#include "TLine.h"
#include "Headers/plot.h"
#include "Headers/utilities.h"
using namespace std;
void RAA_plot_finalpaper(Int_t unfoldingCut = 40 , char *algo = "Pu", char *jet_type = "PF"){
TStopwatch timer;
timer.Start();
TDatime date;
gStyle->SetOptStat(0);
TH1::SetDefaultSumw2();
TH2::SetDefaultSumw2();
char * etaWidth = (char*) "10_eta_10";
char * etaLable = (char*) "0.0 < |eta| < 1.0";
Float_t etaLow = 0;
Float_t etaHigh = 1.0;
Float_t deltaEta = 2.0;
TFile *fin_R2, *fin_R3, *fin_R4;
fin_R2 = TFile::Open(Form("/net/hisrv0001/home/rkunnawa/WORK/RAA/CMSSW_5_3_20/src/Output/Pawan_ntuple_PbPb_pp_calopfpt_ppNoJetidcut_R0p%d_noFakeWeight_unfold_mcclosure_oppside_trgMC_%s_%dGeVCut_ak%s_20150506.root",2,etaWidth,unfoldingCut,jet_type));
fin_R3 = TFile::Open(Form("/net/hisrv0001/home/rkunnawa/WORK/RAA/CMSSW_5_3_20/src/Output/Pawan_ntuple_PbPb_pp_calopfpt_ppNoJetidcut_R0p%d_noFakeWeight_unfold_mcclosure_oppside_trgMC_%s_%dGeVCut_ak%s_20150506.root",3,etaWidth,unfoldingCut,jet_type));
fin_R4 = TFile::Open(Form("/net/hisrv0001/home/rkunnawa/WORK/RAA/CMSSW_5_3_20/src/Output/Pawan_ntuple_PbPb_pp_calopfpt_ppNoJetidcut_R0p%d_noFakeWeight_unfold_mcclosure_oppside_trgMC_%s_%dGeVCut_ak%s_20150506.root",4,etaWidth,unfoldingCut,jet_type));
// get the histograms.
TH1F * uPbPb_R2_Bayes[nbins_cent], * uPP_R2_Bayes, * uPbPb_R3_Bayes[nbins_cent], * uPP_R3_Bayes, * uPbPb_R4_Bayes[nbins_cent], * uPP_R4_Bayes;
TH1F * mPbPb_R2[nbins_cent], * mPP_R2, * mPbPb_R3[nbins_cent], * mPP_R3, * mPbPb_R4[nbins_cent], * mPP_R4;
TH1F * RAA_R2_Bayes[nbins_cent], * RAA_R3_Bayes[nbins_cent], * RAA_R4_Bayes[nbins_cent];
TH1F * RAA_R2_BinByBin[nbins_cent], * RAA_R3_BinByBin[nbins_cent], * RAA_R4_BinByBin[nbins_cent];
TH1F * RAA_R2_Meas[nbins_cent], * RAA_R3_Meas[nbins_cent], * RAA_R4_Meas[nbins_cent];
uPP_R2_Bayes = (TH1F*)fin_R2->Get("PP_bayesian_unfolded_spectra");
uPP_R2_Bayes->Print("base");
uPP_R3_Bayes = (TH1F*)fin_R3->Get("PP_bayesian_unfolded_spectra");
uPP_R3_Bayes->Print("base");
uPP_R4_Bayes = (TH1F*)fin_R4->Get("PP_bayesian_unfolded_spectra");
uPP_R4_Bayes->Print("base");
mPP_R2 = (TH1F*)fin_R2->Get("PP_Gen_spectra_refpt");
mPP_R2->Print("base");
mPP_R3 = (TH1F*)fin_R3->Get("PP_Gen_spectra_refpt");
mPP_R3->Print("base");
mPP_R4 = (TH1F*)fin_R4->Get("PP_Gen_spectra_refpt");
mPP_R4->Print("base");
for(int i = 0; i<nbins_cent; ++i){
uPbPb_R2_Bayes[i] = (TH1F*)fin_R2->Get(Form("PbPb_bayesian_unfolded_spectra_combined_cent%d",i));
uPbPb_R2_Bayes[i]->Print("base");
uPbPb_R3_Bayes[i] = (TH1F*)fin_R3->Get(Form("PbPb_bayesian_unfolded_spectra_combined_cent%d",i));
uPbPb_R3_Bayes[i]->Print("base");
uPbPb_R4_Bayes[i] = (TH1F*)fin_R4->Get(Form("PbPb_bayesian_unfolded_spectra_combined_cent%d",i));
uPbPb_R3_Bayes[i]->Print("base");
mPbPb_R2[i] = (TH1F*)fin_R2->Get(Form("PbPb_Gen_spectra_refpt_cent%d",i));
mPbPb_R2[i]->Print("base");
mPbPb_R3[i] = (TH1F*)fin_R3->Get(Form("PbPb_Gen_spectra_refpt_cent%d",i));
mPbPb_R3[i]->Print("base");
mPbPb_R4[i] = (TH1F*)fin_R4->Get(Form("PbPb_Gen_spectra_refpt_cent%d",i));
mPbPb_R4[i]->Print("base");
RAA_R2_Bayes[i] = (TH1F*)fin_R2->Get(Form("RAA_bayesian_cent%d",i));
RAA_R2_Bayes[i]->Print("base");
RAA_R3_Bayes[i] = (TH1F*)fin_R3->Get(Form("RAA_bayesian_cent%d",i));
RAA_R3_Bayes[i]->Print("base");
RAA_R4_Bayes[i] = (TH1F*)fin_R4->Get(Form("RAA_bayesian_cent%d",i));
RAA_R4_Bayes[i]->Print("base");
RAA_R2_BinByBin[i] = (TH1F*)fin_R2->Get(Form("RAA_binbybin_cent%d",i));
RAA_R3_BinByBin[i] = (TH1F*)fin_R3->Get(Form("RAA_binbybin_cent%d",i));
RAA_R4_BinByBin[i] = (TH1F*)fin_R4->Get(Form("RAA_binbybin_cent%d",i));
RAA_R2_Meas[i] = (TH1F*)fin_R2->Get(Form("RAA_measured_cent%d",i));
RAA_R3_Meas[i] = (TH1F*)fin_R3->Get(Form("RAA_measured_cent%d",i));
RAA_R4_Meas[i] = (TH1F*)fin_R4->Get(Form("RAA_measured_cent%d",i));
}
// plot 1 - spectra plot showing pp and 6 different centrality classes PbPb spectra
// - have a 3 panel plot for the different radii, with each of them scaled by two orders of magnitude
// first we need to scale the MC to the level of Data:
// PbPb Data scaling:
// uPbPb_Bayes[i]->Scale(1./deltaEta);// delta eta
// //uPbPb_Bayes[i]->Scale(1./145.156/1e6);// Jet 80 luminosity
// //uPbPb_Bayes[i]->Scale(1./1.1153/1e6);// equivalent no of minbias events
// uPbPb_Bayes[i]->Scale(1./(0.025*(boundaries_cent[i+1] - boundaries_cent[i])));
// //uPbPb_Bayes[i]->Scale(1./145.156);
// //uPbPb_Bayes[i]->Scale(1./161.939);
// uPbPb_Bayes[i]->Scale(1./(7.65*1e6));
// uPbPb_Bayes[i]->Scale(64.*1e9/(ncoll[i]*1e3));
// uPbPb_Bayes[i] = (TH1F*)uPbPb_Bayes[i]->Rebin(nbins_pt,Form("PbPb_bayesian_unfolded_spectra_combined_cent%d",i),boundaries_pt);
// divideBinWidth(uPbPb_Bayes[i]);
// uPbPb_Bayes[i]->Write();
// So finally PbPb is in
// PbPb MC scaling: is already in sigma (mb) / (dEta dpT)
// mPbPb_Reco[i]->Scale(1./deltaEta);// delta eta
// mPbPb_Reco[i] = (TH1F*)mPbPb_Reco[i]->Rebin(nbins_pt,Form("PbPb_Reco_spectra_refpt_cent%d",i),boundaries_pt);
// divideBinWidth(mPbPb_Reco[i]);
// mPbPb_Reco[i]->Write();
// take MC to nano barns from milli barns
//mPP_R2->Scale(1e6);
//mPP_R3->Scale(1e6);
//mPP_R4->Scale(1e6);
for(int i = 0; i<nbins_cent; ++i){
mPbPb_R2[i]->Scale(1./(0.025*(boundaries_cent[i+1] - boundaries_cent[i])));
mPbPb_R2[i]->Scale(64.*1e9/(ncoll[i]*1e3));
mPbPb_R2[i]->Scale(1./(7.65));
mPbPb_R3[i]->Scale(1./(0.025*(boundaries_cent[i+1] - boundaries_cent[i])));
mPbPb_R3[i]->Scale(64.*1e9/(ncoll[i]*1e3));
mPbPb_R3[i]->Scale(1./(7.65));
mPbPb_R4[i]->Scale(1./(0.025*(boundaries_cent[i+1] - boundaries_cent[i])));
mPbPb_R4[i]->Scale(64.*1e9/(ncoll[i]*1e3));
mPbPb_R4[i]->Scale(1./(7.65));
}
Double_t ScaleFactor[nbins_cent+2] = {1, 1e2, 1e4, 1e6, 1e8, 1e10, 1e12, 1e14};
TCanvas * cSpectra_R2 = new TCanvas("cSpectra_R2","",1200,1000);
//makeMultiPanelCanvas(cSpectra_R2,3,1,0.0,0.0,0.2,0.15,0.07);
cSpectra_R2->SetLogy();
cSpectra_R2->SetGridy();
cSpectra_R2->SetLogx();
uPP_R2_Bayes->Scale(ScaleFactor[0]);
uPP_R2_Bayes->SetMarkerStyle(20);
uPP_R2_Bayes->SetMarkerColor(kBlack);
makeHistTitle(uPP_R2_Bayes," "," Jet p_{T} (GeV/c)","#frac{d #sigma}{T_{AA} dp_{T} d#eta} nb");
uPP_R2_Bayes->SetAxisRange(unfoldingCut, 299, "X");
uPP_R2_Bayes->SetAxisRange(1e-4, 1e14, "Y");
uPP_R2_Bayes->Draw();
// draw the MC
mPP_R2->Scale(ScaleFactor[0]);
mPP_R2->SetLineColor(kBlack);
mPP_R2->SetAxisRange(unfoldingCut, 299, "X");
mPP_R2->Draw("same Lhist");
for(int i = 0; i<nbins_cent; ++i){
uPbPb_R2_Bayes[i]->Scale(ScaleFactor[i+1]);
uPbPb_R2_Bayes[i]->SetMarkerStyle(33);
uPbPb_R2_Bayes[i]->SetMarkerColor(kRed);
uPbPb_R2_Bayes[i]->SetAxisRange(unfoldingCut, 299, "X");
uPbPb_R2_Bayes[i]->Draw("same");
// mPbPb_R2[i]->Scale(ScaleFactor[i+2]);
// mPbPb_R2[i]->SetLineColor(kRed);
// mPbPb_R2[i]->SetAxisRange(unfoldingCut, 299, "X");
// mPbPb_R2[i]->Draw("same Lhist");
}
TLegend * leg1_R2 = myLegend(0.15,0.1,0.25,0.2);
leg1_R2->AddEntry(uPP_R2_Bayes,"PP Data","pl");
leg1_R2->AddEntry(mPP_R2,"PYTHIA","pl");
leg1_R2->SetTextSize(0.02);
leg1_R2->Draw();
TLegend * leg2_R2 = myLegend(0.75,0.8,0.85,0.9);
leg2_R2->AddEntry(uPbPb_R2_Bayes[0],"PbPb Data","pl");
//leg2_R2->AddEntry(mPbPb_R2[0],"PYTHIA+HYDJET","pl");
leg2_R2->SetTextSize(0.02);
leg2_R2->Draw();
drawText("R=0.2, anti k_{T} PF Jets", 0.15,0.2,16);
drawText("R=0.2, anti k_{T} Pu PF Jets", 0.75,0.78,16);
drawText(Form("%s", etaLable),0.15,0.25,16);
putCMSPrel();
putPbPbLumi();
putPPLumi();
//drawText("pp", 0.7,0.15,16);
drawText("0-5% x 10^{2}", 0.8,0.20,16);
drawText("5-10% x 10^{4}", 0.8,0.28,16);
drawText("10-30% x 10^{6}", 0.8,0.38,16);
drawText("30-50% x 10^{8}", 0.8,0.47,16);
drawText("50-70% x 10^{10}", 0.8,0.54,16);
drawText("70-90% x 10^{12}", 0.8,0.63,16);
cSpectra_R2->SaveAs(Form("../../Plots/Final_paper_plots_spectra_akR2_%s_%d.pdf",etaWidth,date.GetDate()),"RECREATE");
TCanvas * cSpectra_R3 = new TCanvas("cSpectra_R3","",1200,1000);
//makeMultiPanelCanvas(cSpectra_R3,3,1,0.0,0.0,0.2,0.15,0.07);
cSpectra_R3->SetLogy();
cSpectra_R3->SetGridy();
cSpectra_R3->SetLogx();
uPP_R3_Bayes->Scale(ScaleFactor[0]);
uPP_R3_Bayes->SetMarkerStyle(20);
uPP_R3_Bayes->SetMarkerColor(kBlack);
makeHistTitle(uPP_R3_Bayes," "," Jet p_{T} (GeV/c)","#frac{d #sigma}{T_{AA} dp_{T} d#eta} nb");
uPP_R3_Bayes->SetAxisRange(unfoldingCut, 299, "X");
uPP_R3_Bayes->SetAxisRange(1e-4, 1e14, "Y");
uPP_R3_Bayes->Draw();
// draw the MC
mPP_R3->Scale(ScaleFactor[0]);
mPP_R3->SetLineColor(kBlack);
mPP_R3->SetAxisRange(unfoldingCut, 299, "X");
mPP_R3->Draw("same Lhist");
for(int i = 0; i<nbins_cent; ++i){
uPbPb_R3_Bayes[i]->Scale(ScaleFactor[i+1]);
uPbPb_R3_Bayes[i]->SetMarkerStyle(33);
uPbPb_R3_Bayes[i]->SetMarkerColor(kRed);
uPbPb_R3_Bayes[i]->SetAxisRange(unfoldingCut, 299, "X");
uPbPb_R3_Bayes[i]->Draw("same");
// mPbPb_R3[i]->Scale(ScaleFactor[i+2]);
// mPbPb_R3[i]->SetLineColor(kRed);
// mPbPb_R3[i]->SetAxisRange(unfoldingCut, 299, "X");
// mPbPb_R3[i]->Draw("same Lhist");
}
TLegend * leg1_R3 = myLegend(0.15,0.1,0.25,0.2);
leg1_R3->AddEntry(uPP_R3_Bayes,"PP Data","pl");
leg1_R3->AddEntry(mPP_R3,"PYTHIA","pl");
leg1_R3->SetTextSize(0.02);
leg1_R3->Draw();
TLegend * leg2_R3 = myLegend(0.75,0.8,0.85,0.9);
leg2_R3->AddEntry(uPbPb_R3_Bayes[0],"PbPb Data","pl");
// leg2_R3->AddEntry(mPbPb_R3[0],"PYTHIA+HYDJET","pl");
leg2_R3->SetTextSize(0.02);
leg2_R3->Draw();
drawText("R=0.3, anti k_{T} PF Jets", 0.15,0.2,16);
drawText("R=0.3, anti k_{T} Pu PF Jets", 0.75,0.78,16);
drawText(Form("%s", etaLable),0.15,0.25,16);
putCMSPrel();
putPbPbLumi();
putPPLumi();
//drawText("pp", 0.7,0.15,16);
drawText("0-5% x 10^{2}", 0.8,0.20,16);
drawText("5-10% x 10^{4}", 0.8,0.28,16);
drawText("10-30% x 10^{6}", 0.8,0.38,16);
drawText("30-50% x 10^{8}", 0.8,0.47,16);
drawText("50-70% x 10^{10}", 0.8,0.54,16);
drawText("70-90% x 10^{12}", 0.8,0.63,16);
cSpectra_R3->SaveAs(Form("../../Plots/Final_paper_plots_spectra_akR3_%s_%d.pdf",etaWidth, date.GetDate()),"RECREATE");
TCanvas * cSpectra_R4 = new TCanvas("cSpectra_R4","",1200,1000);
//makeMultiPanelCanvas(cSpectra_R4,3,1,0.0,0.0,0.2,0.15,0.07);
cSpectra_R4->SetLogy();
cSpectra_R4->SetGridy();
cSpectra_R4->SetLogx();
uPP_R4_Bayes->Scale(ScaleFactor[0]);
uPP_R4_Bayes->SetMarkerStyle(20);
uPP_R4_Bayes->SetMarkerColor(kBlack);
makeHistTitle(uPP_R4_Bayes," "," Jet p_{T} (GeV/c)","#frac{d #sigma}{T_{AA} dp_{T} d#eta} nb");
uPP_R4_Bayes->SetAxisRange(unfoldingCut, 299, "X");
uPP_R4_Bayes->SetAxisRange(1e-4, 1e14, "Y");
uPP_R4_Bayes->Draw();
// draw the MC
mPP_R4->Scale(ScaleFactor[0]);
mPP_R4->SetLineColor(kBlack);
mPP_R4->SetAxisRange(unfoldingCut, 299, "X");
mPP_R4->Draw("same Lhist");
for(int i = 0; i<nbins_cent; ++i){
uPbPb_R4_Bayes[i]->Scale(ScaleFactor[i+1]);
uPbPb_R4_Bayes[i]->SetMarkerStyle(33);
uPbPb_R4_Bayes[i]->SetMarkerColor(kRed);
uPbPb_R4_Bayes[i]->SetAxisRange(unfoldingCut, 299, "X");
uPbPb_R4_Bayes[i]->Draw("same");
// mPbPb_R4[i]->Scale(ScaleFactor[i+2]);
// mPbPb_R4[i]->SetLineColor(kRed);
// mPbPb_R4[i]->SetAxisRange(unfoldingCut, 299, "X");
// mPbPb_R4[i]->Draw("same Lhist");
}
TLegend * leg1_R4 = myLegend(0.15,0.1,0.25,0.2);
leg1_R4->AddEntry(uPP_R4_Bayes,"PP Data","pl");
leg1_R4->AddEntry(mPP_R4,"PYTHIA","pl");
leg1_R4->SetTextSize(0.02);
leg1_R4->Draw();
TLegend * leg2_R4 = myLegend(0.75,0.8,0.85,0.9);
leg2_R4->AddEntry(uPbPb_R4_Bayes[0],"PbPb Data","pl");
// leg2_R4->AddEntry(mPbPb_R4[0],"PYTHIA+HYDJET","pl");
leg2_R4->SetTextSize(0.02);
leg2_R4->Draw();
drawText("R=0.4, anti k_{T} PF Jets", 0.15,0.2,16);
drawText("R=0.4, anti k_{T} Pu PF Jets", 0.75,0.78,16);
drawText(Form("%s", etaLable),0.15,0.25,16);
putCMSPrel();
putPbPbLumi();
putPPLumi();
//drawText("pp", 0.7,0.15,16);
drawText("0-5% x 10^{2}", 0.8,0.20,16);
drawText("5-10% x 10^{4}", 0.8,0.28,16);
drawText("10-30% x 10^{6}", 0.8,0.38,16);
drawText("30-50% x 10^{8}", 0.8,0.47,16);
drawText("50-70% x 10^{10}", 0.8,0.54,16);
drawText("70-90% x 10^{12}", 0.8,0.63,16);
cSpectra_R4->SaveAs(Form("../../Plots/Final_paper_plots_spectra_akR4_%s_%d.pdf",etaWidth, date.GetDate()),"RECREATE");
}