void plotVBFVHReweighted(){
gROOT->ProcessLine(".x tdrstyle.cc");
gStyle->SetOptStat(0);
TString INPUT_NAME = "HtoZZ4l_Phantom_125p6_VHDistributions_GenLevel.root";
TString OUTPUT_NAME = "HtoZZ4l_Phantom_125p6_VHReweightingPlots_GenLevel.root";
double mPOLE = 125.6;
float ZZMass_PeakCut[2] ={ 125.1, 126.1 }; // Spin 0 analysis
float templateWeight = 1;
float GenDiJetMass;
float MC_weight;
float ZZMass = 0;
TString coutput_common = user_TemplateswithTrees_dir + "../VHContributions/Plots/GenLevel/";
gSystem->Exec("mkdir -p " + coutput_common);
TString coutput = coutput_common + OUTPUT_NAME;
TFile* foutput = new TFile(coutput, "recreate");
const int kNumTemplates = 3;
TString templatetitles[kNumTemplates] ={ "VBF Sig", "VBF Bkg", "VBF Int" };
TString templatenames[kNumTemplates] ={ "VBF_Sig", "VBF_Bkg", "VBF_Int" };
TH1F* hVBF_onshell_LC[kNumTemplates] ={ 0 };
TH1F* hVBF_offshell_LC[kNumTemplates] ={ 0 };
TH1F* hVBF_onshell_scaled[kNumTemplates] ={ 0 };
TH1F* hVBF_offshell_scaled[kNumTemplates] ={ 0 };
TH1F* hVBF_onshell_scaled_wVBF[kNumTemplates] ={ 0 };
TH1F* hVBF_offshell_scaled_wVBF[kNumTemplates] ={ 0 };
for (int erg_tev=7; erg_tev<=8; erg_tev++){
for (int folder=0; folder<3; folder++){
TString comstring;
comstring.Form("%i", erg_tev);
TString erg_dir;
erg_dir.Form("LHC_%iTeV/", erg_tev);
int EnergyIndex = 1;
if (erg_tev == 7) EnergyIndex = 0;
TString cinput_common = user_TemplateswithTrees_dir + "../VHContributions/" + erg_dir;
cinput_common += user_folder[folder] + "/";
TString cinput = cinput_common + INPUT_NAME;
TFile* finput = new TFile(cinput, "read");
if (finput->IsZombie()){
delete finput;
continue;
}
else if (finput==0) continue;
cout << "Opened file " << finput->GetName() << endl;
for (int tr=0; tr<kNumTemplates; tr++){
TString templatename = "VBF_";
templatename += templatenames[tr];
TH1F* hVBF_onshell_LC_temp = (TH1F*)finput->Get(Form("%s_onshell_LC", templatename.Data()));
TH1F* hVBF_offshell_LC_temp = (TH1F*)finput->Get(Form("%s_offshell_LC", templatename.Data()));
TH1F* hVBF_onshell_scaled_temp = (TH1F*)finput->Get(Form("%s_onshell_scaled", templatename.Data()));
TH1F* hVBF_offshell_scaled_temp = (TH1F*)finput->Get(Form("%s_offshell_scaled", templatename.Data()));
TH1F* hVBF_onshell_scaled_wVBF_temp = (TH1F*)finput->Get(Form("%s_onshell_scaled_wVBF", templatename.Data()));
TH1F* hVBF_offshell_scaled_wVBF_temp = (TH1F*)finput->Get(Form("%s_offshell_scaled_wVBF", templatename.Data()));
foutput->cd();
gStyle->SetOptStat(0);
if (hVBF_onshell_LC[tr]==0) hVBF_onshell_LC[tr] = (TH1F*)hVBF_onshell_LC_temp->Clone(Form("%s_clone", hVBF_onshell_LC_temp->GetName()));
else hVBF_onshell_LC[tr]->Add(hVBF_onshell_LC_temp);
delete hVBF_onshell_LC_temp;
if (hVBF_onshell_scaled[tr]==0) hVBF_onshell_scaled[tr] = (TH1F*)hVBF_onshell_scaled_temp->Clone(Form("%s_clone", hVBF_onshell_scaled_temp->GetName()));
else hVBF_onshell_scaled[tr]->Add(hVBF_onshell_scaled_temp);
delete hVBF_onshell_scaled_temp;
if (hVBF_onshell_scaled_wVBF[tr]==0) hVBF_onshell_scaled_wVBF[tr] = (TH1F*)hVBF_onshell_scaled_wVBF_temp->Clone(Form("%s_clone", hVBF_onshell_scaled_wVBF_temp->GetName()));
else hVBF_onshell_scaled_wVBF[tr]->Add(hVBF_onshell_scaled_wVBF_temp);
delete hVBF_onshell_scaled_wVBF_temp;
if (hVBF_offshell_LC[tr]==0) hVBF_offshell_LC[tr] = (TH1F*)hVBF_offshell_LC_temp->Clone(Form("%s_clone", hVBF_offshell_LC_temp->GetName()));
else hVBF_offshell_LC[tr]->Add(hVBF_offshell_LC_temp);
delete hVBF_offshell_LC_temp;
if (hVBF_offshell_scaled[tr]==0) hVBF_offshell_scaled[tr] = (TH1F*)hVBF_offshell_scaled_temp->Clone(Form("%s_clone", hVBF_offshell_scaled_temp->GetName()));
else hVBF_offshell_scaled[tr]->Add(hVBF_offshell_scaled_temp);
delete hVBF_offshell_scaled_temp;
if (hVBF_offshell_scaled_wVBF[tr]==0) hVBF_offshell_scaled_wVBF[tr] = (TH1F*)hVBF_offshell_scaled_wVBF_temp->Clone(Form("%s_clone", hVBF_offshell_scaled_wVBF_temp->GetName()));
else hVBF_offshell_scaled_wVBF[tr]->Add(hVBF_offshell_scaled_wVBF_temp);
delete hVBF_offshell_scaled_wVBF_temp;
}
finput->Close();
}
}
TH1F* hVBF_sig[2][3]={
{ hVBF_onshell_LC[0], hVBF_onshell_scaled[0], hVBF_onshell_scaled_wVBF[0] },
{ hVBF_offshell_LC[0], hVBF_offshell_scaled[0], hVBF_offshell_scaled_wVBF[0] }
};
TH1F* hVBF_bkg[2][3]={
{ hVBF_onshell_LC[1], hVBF_onshell_scaled[1], hVBF_onshell_scaled_wVBF[1] },
{ hVBF_offshell_LC[1], hVBF_offshell_scaled[1], hVBF_offshell_scaled_wVBF[1] }
};
TH1F* hVBF_int[2][3]={
{ hVBF_onshell_LC[2], hVBF_onshell_scaled[2], hVBF_onshell_scaled_wVBF[2] },
{ hVBF_offshell_LC[2], hVBF_offshell_scaled[2], hVBF_offshell_scaled_wVBF[2] }
};
double max_plot[2][3] ={ { 0 } };
double min_plot[2][3] ={ { 0 } };
// TString strmzztitle[2]={ "105.6<m_{4l}<140.6 GeV", "220<m_{4l}<1600 GeV" };
TString strmzztitle[2]={ "On-shell", "Off-shell" };
TString strmzzname[2]={ "Onshell", "Offshell" };
TString strBSItitle[3]={ "Signal", "Background", "Interference" };
TString strBSIname[3]={ "Signal", "Background", "Interference" };
TString strScaleSchemeTitle[3]={ " (Default Phantom)", " (VH Rescaling)", " (+VBF Rescaling)" };
cout << "Set up canvas gadgets" << endl;
for (int os=0; os<2; os++){
cout << strmzzname[os] << endl;
for (int sc=0; sc<3; sc++){
cout << strBSItitle[0] << strScaleSchemeTitle[sc] << " mJJ>=130 GeV / mJJ<130 GeV: " << hVBF_sig[os][sc]->GetBinContent(hVBF_sig[os][sc]->GetNbinsX()) << " / " << (hVBF_sig[os][sc]->Integral() - hVBF_sig[os][sc]->GetBinContent(hVBF_sig[os][sc]->GetNbinsX())) << " = " << hVBF_sig[os][sc]->GetBinContent(hVBF_sig[os][sc]->GetNbinsX())/(hVBF_sig[os][sc]->Integral() - hVBF_sig[os][sc]->GetBinContent(hVBF_sig[os][sc]->GetNbinsX())) << endl;
cout << strBSItitle[1] << strScaleSchemeTitle[sc] << " mJJ>=130 GeV / mJJ<130 GeV: " << hVBF_bkg[os][sc]->GetBinContent(hVBF_bkg[os][sc]->GetNbinsX()) << " / " << (hVBF_bkg[os][sc]->Integral() - hVBF_bkg[os][sc]->GetBinContent(hVBF_bkg[os][sc]->GetNbinsX())) << " = " << hVBF_bkg[os][sc]->GetBinContent(hVBF_bkg[os][sc]->GetNbinsX())/(hVBF_bkg[os][sc]->Integral() - hVBF_bkg[os][sc]->GetBinContent(hVBF_bkg[os][sc]->GetNbinsX())) << endl;
cout << strBSItitle[2] << strScaleSchemeTitle[sc] << " mJJ>=130 GeV / mJJ<130 GeV: " << hVBF_int[os][sc]->GetBinContent(hVBF_int[os][sc]->GetNbinsX()) << " / " << (hVBF_int[os][sc]->Integral() - hVBF_int[os][sc]->GetBinContent(hVBF_int[os][sc]->GetNbinsX())) << " = " << hVBF_int[os][sc]->GetBinContent(hVBF_int[os][sc]->GetNbinsX())/(hVBF_int[os][sc]->Integral() - hVBF_int[os][sc]->GetBinContent(hVBF_int[os][sc]->GetNbinsX())) << endl;
hVBF_sig[os][sc]->SetTitle("");
hVBF_bkg[os][sc]->SetTitle("");
hVBF_int[os][sc]->SetTitle("");
hVBF_sig[os][sc]->GetXaxis()->SetRangeUser(22, 129.9);
hVBF_bkg[os][sc]->GetXaxis()->SetRangeUser(22, 129.9);
hVBF_int[os][sc]->GetXaxis()->SetRangeUser(22, 129.9);
hVBF_sig[os][sc]->GetXaxis()->SetTitle("m^{true}_{jj} (GeV)");
hVBF_bkg[os][sc]->GetXaxis()->SetTitle("m^{true}_{jj} (GeV)");
hVBF_int[os][sc]->GetXaxis()->SetTitle("m^{true}_{jj} (GeV)");
double binwidth = hVBF_sig[os][sc]->GetBinWidth(1);
hVBF_sig[os][sc]->GetYaxis()->SetTitleOffset(1.5);
hVBF_bkg[os][sc]->GetYaxis()->SetTitleOffset(1.5);
hVBF_int[os][sc]->GetYaxis()->SetTitleOffset(1.5);
hVBF_sig[os][sc]->GetYaxis()->SetTitle(Form("Events / %.0f GeV", binwidth));
hVBF_bkg[os][sc]->GetYaxis()->SetTitle(Form("Events / %.0f GeV", binwidth));
hVBF_int[os][sc]->GetYaxis()->SetTitle(Form("Events / %.0f GeV", binwidth));
hVBF_sig[os][sc]->SetLineWidth(2);
hVBF_bkg[os][sc]->SetLineWidth(2);
hVBF_int[os][sc]->SetLineWidth(2);
hVBF_sig[os][sc]->SetLineStyle(1);
hVBF_bkg[os][sc]->SetLineStyle(1);
hVBF_int[os][sc]->SetLineStyle(1);
if (sc==0){
hVBF_sig[os][sc]->SetLineColor(kRed);
hVBF_bkg[os][sc]->SetLineColor(kRed);
hVBF_int[os][sc]->SetLineColor(kRed);
}
if (sc==1){
hVBF_sig[os][sc]->SetLineColor(kViolet);
hVBF_bkg[os][sc]->SetLineColor(kViolet);
hVBF_int[os][sc]->SetLineColor(kViolet);
}
else if (sc==2){
hVBF_sig[os][sc]->SetLineColor(kBlue);
hVBF_bkg[os][sc]->SetLineColor(kBlue);
hVBF_int[os][sc]->SetLineColor(kBlue);
}
for (int bin=1; bin<hVBF_sig[os][sc]->GetNbinsX(); bin++){
double bcsig = hVBF_sig[os][sc]->GetBinContent(bin);
double bcbkg = hVBF_bkg[os][sc]->GetBinContent(bin);
double bcint = hVBF_int[os][sc]->GetBinContent(bin);
max_plot[os][0] = max(max_plot[os][0], bcsig);
min_plot[os][0] = min(min_plot[os][0], bcsig);
max_plot[os][1] = max(max_plot[os][1], bcbkg);
min_plot[os][1] = min(min_plot[os][1], bcbkg);
max_plot[os][2] = max(max_plot[os][2], bcint);
min_plot[os][2] = min(min_plot[os][2], bcint);
}
cout << "Set up region " << os << " scheme " << sc << " complete." << endl;
}
for (int sc=0; sc<3; sc++){
hVBF_sig[os][sc]->GetYaxis()->SetRangeUser(min_plot[os][0]*1.5, max_plot[os][0]*1.5);
hVBF_bkg[os][sc]->GetYaxis()->SetRangeUser(min_plot[os][1]*1.5, max_plot[os][1]*1.5);
hVBF_int[os][sc]->GetYaxis()->SetRangeUser(min_plot[os][2]*1.5, max_plot[os][2]*1.5);
}
}
foutput->cd();
for (int os=0; os<2; os++){
for (int bsi=0; bsi<3; bsi++){
cout << "Begin plot of region " << os << endl;
TPaveText* pt = new TPaveText(0.15, 0.93, 0.85, 1, "brNDC");
pt->SetBorderSize(0);
pt->SetFillStyle(0);
pt->SetTextAlign(12);
pt->SetTextFont(42);
pt->SetTextSize(0.045);
TText* text = pt->AddText(0.025, 0.45, "#font[61]{CMS}");
text->SetTextSize(0.044);
text = pt->AddText(0.165, 0.42, "#font[52]{Simulation}");
text->SetTextSize(0.0315);
// TString cErgTev = "#font[42]{19.7 fb^{-1} (8 TeV) + 5.1 fb^{-1} (7 TeV)}";
TString cErgTev = "#font[42]{ 2e+2#mu 19.7 fb^{-1} (8 TeV)}";
text = pt->AddText(0.537, 0.45, cErgTev);
text->SetTextSize(0.0315);
TString appendName;
appendName = "_";
appendName += strmzzname[os];
appendName += strBSIname[bsi];
TString canvasname = "cCompareMJJ_GenLevel";
canvasname.Append(appendName);
TCanvas* cc = new TCanvas(canvasname, "", 8, 30, 800, 800);
gStyle->SetOptStat(0);
cc->cd();
gStyle->SetOptStat(0);
cc->SetFillColor(0);
cc->SetBorderMode(0);
cc->SetBorderSize(2);
cc->SetTickx(1);
cc->SetTicky(1);
cc->SetLeftMargin(0.17);
cc->SetRightMargin(0.05);
cc->SetTopMargin(0.07);
cc->SetBottomMargin(0.13);
cc->SetFrameFillStyle(0);
cc->SetFrameBorderMode(0);
cc->SetFrameFillStyle(0);
cc->SetFrameBorderMode(0);
TLegend *ll;
ll = new TLegend(0.22, 0.70, 0.60, 0.90);
ll->SetBorderSize(0);
ll->SetTextFont(42);
ll->SetTextSize(0.03);
ll->SetLineColor(1);
ll->SetLineStyle(1);
ll->SetLineWidth(1);
ll->SetFillColor(0);
ll->SetFillStyle(0);
for (int sc=0; sc<3; sc++){
if (bsi==0){
TString legendLabel = strBSItitle[bsi] + strScaleSchemeTitle[sc];
ll->AddEntry(hVBF_sig[os][sc], legendLabel, "l");
if (sc==0) hVBF_sig[os][sc]->Draw("hist");
else hVBF_sig[os][sc]->Draw("histsame");
}
else if (bsi==1){
TString legendLabel = strBSItitle[bsi] + strScaleSchemeTitle[sc];
ll->AddEntry(hVBF_bkg[os][sc], legendLabel, "l");
if (sc==0) hVBF_bkg[os][sc]->Draw("hist");
else hVBF_bkg[os][sc]->Draw("histsame");
}
else if (bsi==2){
TString legendLabel = strBSItitle[bsi] + strScaleSchemeTitle[sc];
ll->AddEntry(hVBF_int[os][sc], legendLabel, "l");
if (sc==0) hVBF_int[os][sc]->Draw("hist");
else hVBF_int[os][sc]->Draw("histsame");
}
}
ll->Draw("same");
pt->Draw();
TPaveText *pt10 = new TPaveText(0.80, 0.86, 0.90, 0.90, "brNDC");
pt10->SetBorderSize(0);
pt10->SetTextAlign(12);
pt10->SetTextSize(0.03);
pt10->SetFillStyle(0);
pt10->SetTextFont(42);
TText* text10;
text10 = pt10->AddText(0.01, 0.01, strmzztitle[os]);
pt10->Draw();
foutput->WriteTObject(cc);
delete pt10;
delete ll;
cc->Close();
delete pt;
cout << "End plot of region " << os << endl;
}
}
for (int tr = 0; tr < kNumTemplates; tr++){
delete hVBF_onshell_LC[tr];
delete hVBF_offshell_LC[tr];
delete hVBF_onshell_scaled[tr];
delete hVBF_offshell_scaled[tr];
delete hVBF_onshell_scaled_wVBF[tr];
delete hVBF_offshell_scaled_wVBF[tr];
}
foutput->Close();
}
//this makes Z(nunu) prediction vs. MC truth plots for the AN.
//at the moment all numbers are hard-coded and this code needs major revision, the mc truth numbers are loaded from a root file created by ZnunuNumbers.C
//furthermore the stored root file is used for both producing the final result plots/tables and also interpretation
void ZinvVisualization(){
const int gNMT2bins_2j0b_hHT = 6;
double gMT2bins_2j0b_hHT[gNMT2bins_2j0b_hHT+1] = {120, 150, 200, 260, 350, 550, 900};
const int gNMT2bins_2j1b_hHT = 2;
double gMT2bins_2j1b_hHT[gNMT2bins_2j1b_hHT+1] = {100, 180, 350};
const int gNMT2bins_3j0b_hHT = 7;
double gMT2bins_3j0b_hHT[gNMT2bins_3j0b_hHT+1] = {160, 185, 220, 270, 350, 450, 650, 1000};
const int gNMT2bins_3j1b_hHT = 4;
double gMT2bins_3j1b_hHT[gNMT2bins_3j1b_hHT+1] = {150, 180, 230, 350, 550};
const int gNMT2bins_3j2b_hHT = 2;
double gMT2bins_3j2b_hHT[gNMT2bins_3j2b_hHT+1] = {130, 200, 350};
const int gNMT2bins_6j0b_hHT = 3;
double gMT2bins_6j0b_hHT[gNMT2bins_6j0b_hHT+1] = {160, 200, 300, 500};
const int gNMT2bins_6j1b_hHT = 3;
double gMT2bins_6j1b_hHT[gNMT2bins_6j1b_hHT+1] = {150, 200, 300, 500};
const int gNMT2bins_6j2b_hHT = 2;
double gMT2bins_6j2b_hHT[gNMT2bins_6j2b_hHT+1] = {130, 200, 350};
const int gNMT2bins_3b_hHT = 1;
double gMT2bins_3b_hHT[gNMT2bins_3b_hHT+1] = {125, 300};
// HT > 750 && HT < 1200
const int gNMT2bins_2j0b_mHT = 9;
double gMT2bins_2j0b_mHT[gNMT2bins_2j0b_mHT+1] = {125, 150, 180, 220, 270, 325, 425, 580, 780, 1000};
const int gNMT2bins_2j1b_mHT = 5;
double gMT2bins_2j1b_mHT[gNMT2bins_2j1b_mHT+1] = {100, 135, 170, 260, 450, 700};
const int gNMT2bins_3j0b_mHT = 9;
double gMT2bins_3j0b_mHT[gNMT2bins_3j0b_mHT+1] = {160, 185, 215, 250, 300, 370, 480, 640, 800, 1000};
const int gNMT2bins_3j1b_mHT = 6;
double gMT2bins_3j1b_mHT[gNMT2bins_3j1b_mHT+1] = {150, 175, 210, 270, 380, 600, 900};
const int gNMT2bins_3j2b_mHT = 5;
double gMT2bins_3j2b_mHT[gNMT2bins_3j2b_mHT+1] = {130, 160, 200, 270, 370, 500};
const int gNMT2bins_6j0b_mHT = 5;
double gMT2bins_6j0b_mHT[gNMT2bins_6j0b_mHT+1] = {160, 200, 250, 325, 425, 600};
const int gNMT2bins_6j1b_mHT = 4;
double gMT2bins_6j1b_mHT[gNMT2bins_6j1b_mHT+1] = {150, 190, 250, 350, 500};
const int gNMT2bins_6j2b_mHT = 4;
double gMT2bins_6j2b_mHT[gNMT2bins_6j2b_mHT+1] = {130, 170, 220, 300, 450};
const int gNMT2bins_3b_mHT = 3;
double gMT2bins_3b_mHT[gNMT2bins_3b_mHT+1] = {125, 175, 275, 450};
// HT > 450 && HT < 750
const int gNMT2bins_2j0b_lHT = 8;
double gMT2bins_2j0b_lHT[gNMT2bins_2j0b_lHT+1] = {200, 240, 290, 350, 420, 490, 570, 650, 750};
const int gNMT2bins_2j1b_lHT = 6;
double gMT2bins_2j1b_lHT[gNMT2bins_2j1b_lHT+1] = {200, 250, 310, 380, 450, 550, 700};
const int gNMT2bins_3j0b_lHT = 8;
double gMT2bins_3j0b_lHT[gNMT2bins_3j0b_lHT+1] = {200, 240, 290, 350, 420, 490, 570, 650, 750};
const int gNMT2bins_3j1b_lHT = 6;
double gMT2bins_3j1b_lHT[gNMT2bins_3j1b_lHT+1] = {200, 250, 310, 380, 460, 550, 700};
const int gNMT2bins_3j2b_lHT = 4;
double gMT2bins_3j2b_lHT[gNMT2bins_3j2b_lHT+1] = {200, 250, 325, 425, 550};
const int gNMT2bins_6j0b_lHT = 3;
double gMT2bins_6j0b_lHT[gNMT2bins_6j0b_lHT+1] = {200, 280, 380, 520};
const int gNMT2bins_6j1b_lHT = 3;
double gMT2bins_6j1b_lHT[gNMT2bins_6j1b_lHT+1] = {200, 250, 325, 450};
const int gNMT2bins_6j2b_lHT = 3;
double gMT2bins_6j2b_lHT[gNMT2bins_6j2b_lHT+1] = {200, 250, 300, 400};
const int gNMT2bins_3b_lHT = 2;
double gMT2bins_3b_lHT [gNMT2bins_3b_lHT+1] = {200, 280, 400};
const int signalregionsize = 9;
string signal_region[signalregionsize] = {"2j0b", "2j1to2b", "3to5j0b", "3to5j1b", "3to5j2b", "6j0b", "6j1b", "6j2b", "3b"};
const int HTbinsize = 3;
string HT_bin[HTbinsize] = {"lowHT", "mediumHT", "highHT"};
gROOT->SetStyle("Plain");
gStyle->SetPalette(1);
gStyle->SetOptStat(0);
TLegend *leg = new TLegend(0.6551724,0.7299578,0.8706897,0.8987342,NULL,"brNDC");
leg->SetBorderSize(0);
leg->SetTextFont(62);
leg->SetTextSize(0.04575163);
leg->SetLineColor(1);
leg->SetLineStyle(1);
leg->SetLineWidth(1);
leg->SetFillColor(0);
leg->SetFillStyle(1001);
//load MC truth numbers
TFile *fmctruth = TFile::Open("../Results/Filtered/GammaJetsPrediction/20130617_test/ZnunuNumbers.root");
//define all histograms
map<string, TH1D*> histos;
for(int i3 = 0; i3<HTbinsize; ++i3){
for(int i2 = 0; i2<signalregionsize; ++i2){
int NMT2bins;
if(i3==0){
if(signal_region[i2]=="2j0b") NMT2bins = gNMT2bins_2j0b_lHT;
if(signal_region[i2]=="2j1to2b") NMT2bins = gNMT2bins_2j1b_lHT;
if(signal_region[i2]=="3to5j0b") NMT2bins = gNMT2bins_3j0b_lHT;
if(signal_region[i2]=="3to5j1b") NMT2bins = gNMT2bins_3j1b_lHT;
if(signal_region[i2]=="3to5j2b") NMT2bins = gNMT2bins_3j2b_lHT;
if(signal_region[i2]=="6j0b") NMT2bins = gNMT2bins_6j0b_lHT;
if(signal_region[i2]=="6j1b") NMT2bins = gNMT2bins_6j1b_lHT;
if(signal_region[i2]=="6j2b") NMT2bins = gNMT2bins_6j2b_lHT;
if(signal_region[i2]=="3b") NMT2bins = gNMT2bins_3b_lHT;
} if(i3==1){
if(signal_region[i2]=="2j0b") NMT2bins = gNMT2bins_2j0b_mHT;
if(signal_region[i2]=="2j1to2b") NMT2bins = gNMT2bins_2j1b_mHT;
if(signal_region[i2]=="3to5j0b") NMT2bins = gNMT2bins_3j0b_mHT;
if(signal_region[i2]=="3to5j1b") NMT2bins = gNMT2bins_3j1b_mHT;
if(signal_region[i2]=="3to5j2b") NMT2bins = gNMT2bins_3j2b_mHT;
if(signal_region[i2]=="6j0b") NMT2bins = gNMT2bins_6j0b_mHT;
if(signal_region[i2]=="6j1b") NMT2bins = gNMT2bins_6j1b_mHT;
if(signal_region[i2]=="6j2b") NMT2bins = gNMT2bins_6j2b_mHT;
if(signal_region[i2]=="3b") NMT2bins = gNMT2bins_3b_mHT;
} if(i3==2){
if(signal_region[i2]=="2j0b") NMT2bins = gNMT2bins_2j0b_hHT;
if(signal_region[i2]=="2j1to2b") NMT2bins = gNMT2bins_2j1b_hHT;
if(signal_region[i2]=="3to5j0b") NMT2bins = gNMT2bins_3j0b_hHT;
if(signal_region[i2]=="3to5j1b") NMT2bins = gNMT2bins_3j1b_hHT;
if(signal_region[i2]=="3to5j2b") NMT2bins = gNMT2bins_3j2b_hHT;
if(signal_region[i2]=="6j0b") NMT2bins = gNMT2bins_6j0b_hHT;
if(signal_region[i2]=="6j1b") NMT2bins = gNMT2bins_6j1b_hHT;
if(signal_region[i2]=="6j2b") NMT2bins = gNMT2bins_6j2b_hHT;
if(signal_region[i2]=="3b") NMT2bins = gNMT2bins_3b_hHT;
}
double MT2bins[NMT2bins+1];
if(i3==0){
if(signal_region[i2]=="2j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j0b_lHT[i0]; }
if(signal_region[i2]=="2j1to2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j1b_lHT[i0]; }
if(signal_region[i2]=="3to5j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j0b_lHT[i0]; }
if(signal_region[i2]=="3to5j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j1b_lHT[i0]; }
if(signal_region[i2]=="3to5j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j2b_lHT[i0]; }
if(signal_region[i2]=="6j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j0b_lHT[i0]; }
if(signal_region[i2]=="6j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j1b_lHT[i0]; }
if(signal_region[i2]=="6j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j2b_lHT[i0]; }
if(signal_region[i2]=="3b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3b_lHT[i0]; }
} if(i3==1){
if(signal_region[i2]=="2j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j0b_mHT[i0]; }
if(signal_region[i2]=="2j1to2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j1b_mHT[i0]; }
if(signal_region[i2]=="3to5j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j0b_mHT[i0]; }
if(signal_region[i2]=="3to5j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j1b_mHT[i0]; }
if(signal_region[i2]=="3to5j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j2b_mHT[i0]; }
if(signal_region[i2]=="6j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j0b_mHT[i0]; }
if(signal_region[i2]=="6j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j1b_mHT[i0]; }
if(signal_region[i2]=="6j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j2b_mHT[i0]; }
if(signal_region[i2]=="3b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3b_mHT[i0]; }
} if(i3==2){
if(signal_region[i2]=="2j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j0b_hHT[i0]; }
if(signal_region[i2]=="2j1to2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_2j1b_hHT[i0]; }
if(signal_region[i2]=="3to5j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j0b_hHT[i0]; }
if(signal_region[i2]=="3to5j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j1b_hHT[i0]; }
if(signal_region[i2]=="3to5j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3j2b_hHT[i0]; }
if(signal_region[i2]=="6j0b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j0b_hHT[i0]; }
if(signal_region[i2]=="6j1b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j1b_hHT[i0]; }
if(signal_region[i2]=="6j2b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_6j2b_hHT[i0]; }
if(signal_region[i2]=="3b") { for(int i0 = 0; i0<=NMT2bins; ++i0) MT2bins[i0] = gMT2bins_3b_hHT[i0]; }
}
string hs = string("_") + HT_bin[i3] + string("_") + signal_region[i2];// + string("_") + sample_type[i1];
string mapname = "MT2" + hs;
TH1D *h = (TH1D*)fmctruth->Get(mapname.c_str());
mapname = "MT2truth" + hs;
if(histos.count(mapname) == 0 ) histos[mapname] = (TH1D*)h->Clone(mapname.c_str());
histos[mapname]->SetFillColor(kViolet-3); histos[mapname]->SetFillStyle(3001);
//get MC truth histo style
if(i3==0&&i2==0) leg->AddEntry(histos[mapname], "MC truth", "f");
histos[mapname]->GetXaxis()->SetTitle("M_{T2} [GeV]");
histos[mapname]->GetXaxis()->SetLabelFont(42);
histos[mapname]->GetXaxis()->SetLabelSize(0.06);
histos[mapname]->GetXaxis()->SetTitleSize(0.06);
histos[mapname]->GetXaxis()->SetLabelOffset(0.01);
histos[mapname]->GetXaxis()->SetTitleOffset(1.2);
histos[mapname]->GetXaxis()->SetTitleFont(42);
histos[mapname]->GetYaxis()->SetTitle("Z(#nu#nu) yield");
histos[mapname]->GetYaxis()->SetLabelFont(42);
histos[mapname]->GetYaxis()->SetLabelSize(0.06);
histos[mapname]->GetYaxis()->SetTitleSize(0.06);
histos[mapname]->GetYaxis()->SetLabelOffset(0.01);
histos[mapname]->GetYaxis()->SetTitleOffset(1.2);
histos[mapname]->GetYaxis()->SetTitleFont(42);
histos[mapname]->GetZaxis()->SetLabelFont(42);
histos[mapname]->GetZaxis()->SetLabelSize(0.035);
histos[mapname]->GetZaxis()->SetTitleSize(0.035);
histos[mapname]->GetZaxis()->SetTitleFont(42);
mapname = "MT2pred" + hs;
if(histos.count(mapname) == 0 ) histos[mapname] = new TH1D(mapname.c_str(), "", NMT2bins, MT2bins);
histos[mapname]->SetMarkerStyle(20), histos[mapname]->SetMarkerColor(kBlack); histos[mapname]->SetLineWidth(3); histos[mapname]->SetLineColor(kBlack);
//get prediction histo style
if(i3==0&&i2==0) leg->AddEntry(histos[mapname], "data prediction", "lp");
histos[mapname]->GetXaxis()->SetTitle("M_{T2} [GeV]");
histos[mapname]->GetXaxis()->SetLabelFont(42);
histos[mapname]->GetXaxis()->SetLabelSize(0.06);
histos[mapname]->GetXaxis()->SetTitleSize(0.06);
histos[mapname]->GetXaxis()->SetLabelOffset(0.01);
histos[mapname]->GetXaxis()->SetTitleOffset(1.2);
histos[mapname]->GetXaxis()->SetTitleFont(42);
histos[mapname]->GetYaxis()->SetTitle("Z(#nu#nu) yield");
histos[mapname]->GetYaxis()->SetLabelFont(42);
histos[mapname]->GetYaxis()->SetLabelSize(0.06);
histos[mapname]->GetYaxis()->SetTitleSize(0.06);
histos[mapname]->GetYaxis()->SetLabelOffset(0.01);
histos[mapname]->GetYaxis()->SetTitleOffset(1.2);
histos[mapname]->GetYaxis()->SetTitleFont(42);
histos[mapname]->GetZaxis()->SetLabelFont(42);
histos[mapname]->GetZaxis()->SetLabelSize(0.035);
histos[mapname]->GetZaxis()->SetTitleSize(0.035);
histos[mapname]->GetZaxis()->SetTitleFont(42);
}}
//hard-coded numbers
for(int i3 = 0; i3<HTbinsize; ++i3){
for(int i2 = 0; i2<signalregionsize; ++i2){
string hs = string("_") + HT_bin[i3] + string("_") + signal_region[i2];
string mapname = "MT2pred" + hs;
if(i3==0&&i2==0){//lHT,2j,0b
histos[mapname]->SetBinContent(1,304.27); histos[mapname]->SetBinError(1,sqrt(pow(11.84,2)+pow(62.55,2)));
histos[mapname]->SetBinContent(2,236.52); histos[mapname]->SetBinError(2,sqrt(pow(10.55,2)+pow(48.41,2)));
histos[mapname]->SetBinContent(3,182.98); histos[mapname]->SetBinError(3,sqrt(pow( 9.30,2)+pow(37.50,2)));
histos[mapname]->SetBinContent(4,160.92); histos[mapname]->SetBinError(4,sqrt(pow( 9.09,2)+pow(50.38,2)));
histos[mapname]->SetBinContent(5,116.69); histos[mapname]->SetBinError(5,sqrt(pow( 7.46,2)+pow(35.07,2)));
histos[mapname]->SetBinContent(6, 47.95); histos[mapname]->SetBinError(6,sqrt(pow( 4.86,2)+pow(14.57,2)));
histos[mapname]->SetBinContent(7, 11.74); histos[mapname]->SetBinError(7,sqrt(pow( 2.39,2)+pow( 3.56,2)));
histos[mapname]->SetBinContent(8, 3.28); histos[mapname]->SetBinError(8,sqrt(pow( 1.24,2)+pow( 0.98,2)));
} if(i3==0&&i2==2){//lHT,35j,0b
histos[mapname]->SetBinContent(1,458.79); histos[mapname]->SetBinError(1,sqrt(pow(16.03,2)+pow(97.64,2)));
histos[mapname]->SetBinContent(2,366.96); histos[mapname]->SetBinError(2,sqrt(pow(14.70,2)+pow(80.26,2)));
histos[mapname]->SetBinContent(3,301.04); histos[mapname]->SetBinError(3,sqrt(pow(12.94,2)+pow(62.16,2)));
histos[mapname]->SetBinContent(4,173.25); histos[mapname]->SetBinError(4,sqrt(pow(10.18,2)+pow(54.86,2)));
histos[mapname]->SetBinContent(5, 93.60); histos[mapname]->SetBinError(5,sqrt(pow( 7.29,2)+pow(28.28,2)));
histos[mapname]->SetBinContent(6, 41.36); histos[mapname]->SetBinError(6,sqrt(pow( 4.88,2)+pow(12.64,2)));
histos[mapname]->SetBinContent(7, 9.80); histos[mapname]->SetBinError(7,sqrt(pow( 2.38,2)+pow( 3.01,2)));
histos[mapname]->SetBinContent(8, 2.73); histos[mapname]->SetBinError(8,sqrt(pow( 1.22,2)+pow( 0.82,2)));
} if(i3==0&&i2==5){//lHT,6j,0b
histos[mapname]->SetBinContent(1, 12.02); histos[mapname]->SetBinError(1,sqrt(pow( 2.76,2)+pow( 2.58,2)));
histos[mapname]->SetBinContent(2, 5.13); histos[mapname]->SetBinError(2,sqrt(pow( 1.95,2)+pow( 1.29,2)));
histos[mapname]->SetBinContent(3, 3.53); histos[mapname]->SetBinError(3,sqrt(pow( 1.77,2)+pow( 1.24,2)));
} if(i3==1&&i2==0){//mHT,2j,0b
histos[mapname]->SetBinContent(1, 76.13); histos[mapname]->SetBinError(1,sqrt(pow( 5.38,2)+pow(15.32,2)));
histos[mapname]->SetBinContent(2, 62.24); histos[mapname]->SetBinError(2,sqrt(pow( 4.92,2)+pow(12.55,2)));
histos[mapname]->SetBinContent(3, 38.49); histos[mapname]->SetBinError(3,sqrt(pow( 3.95,2)+pow( 7.78,2)));
histos[mapname]->SetBinContent(4, 40.84); histos[mapname]->SetBinError(4,sqrt(pow( 4.19,2)+pow( 8.26,2)));
histos[mapname]->SetBinContent(5, 20.11); histos[mapname]->SetBinError(5,sqrt(pow( 3.00,2)+pow( 4.08,2)));
histos[mapname]->SetBinContent(6, 30.21); histos[mapname]->SetBinError(6,sqrt(pow( 3.82,2)+pow( 9.14,2)));
histos[mapname]->SetBinContent(7, 12.73); histos[mapname]->SetBinError(7,sqrt(pow( 2.44,2)+pow( 3.84,2)));
histos[mapname]->SetBinContent(8, 15.96); histos[mapname]->SetBinError(8,sqrt(pow( 2.79,2)+pow( 4.83,2)));
histos[mapname]->SetBinContent(9, 2.83); histos[mapname]->SetBinError(9,sqrt(pow( 1.15,2)+pow( 0.85,2)));
} if(i3==1&&i2==2){//mHT,35j,0b
histos[mapname]->SetBinContent(1, 88.17); histos[mapname]->SetBinError(1,sqrt(pow( 6.50,2)+pow(17.75,2)));
histos[mapname]->SetBinContent(2, 73.11); histos[mapname]->SetBinError(2,sqrt(pow( 6.38,2)+pow(14.78,2)));
histos[mapname]->SetBinContent(3, 63.34); histos[mapname]->SetBinError(3,sqrt(pow( 5.72,2)+pow(12.78,2)));
histos[mapname]->SetBinContent(4, 59.03); histos[mapname]->SetBinError(4,sqrt(pow( 5.71,2)+pow(11.95,2)));
histos[mapname]->SetBinContent(5, 50.85); histos[mapname]->SetBinError(5,sqrt(pow( 5.36,2)+pow(10.32,2)));
histos[mapname]->SetBinContent(6, 43.54); histos[mapname]->SetBinError(6,sqrt(pow( 4.88,2)+pow(13.11,2)));
histos[mapname]->SetBinContent(7, 23.40); histos[mapname]->SetBinError(7,sqrt(pow( 3.63,2)+pow( 7.07,2)));
histos[mapname]->SetBinContent(8, 6.43); histos[mapname]->SetBinError(8,sqrt(pow( 1.85,2)+pow( 1.93,2)));
histos[mapname]->SetBinContent(9, 2.68); histos[mapname]->SetBinError(9,sqrt(pow( 1.20,2)+pow( 0.81,2)));
} if(i3==1&&i2==5){//mHT,6j,0b
histos[mapname]->SetBinContent(1, 10.51); histos[mapname]->SetBinError(1,sqrt(pow( 2.84,2)+pow( 2.43,2)));
histos[mapname]->SetBinContent(2, 3.03); histos[mapname]->SetBinError(2,sqrt(pow( 1.35,2)+pow( 0.68,2)));
histos[mapname]->SetBinContent(3, 4.05); histos[mapname]->SetBinError(3,sqrt(pow( 1.65,2)+pow( 0.94,2)));
histos[mapname]->SetBinContent(4, 2.61); histos[mapname]->SetBinError(4,sqrt(pow( 1.51,2)+pow( 0.88,2)));
histos[mapname]->SetBinContent(5, 0.66); histos[mapname]->SetBinError(5,sqrt(pow( 0.66,2)+pow( 0.23,2)));
} if(i3==2&&i2==0){//hHT,2j,0b
histos[mapname]->SetBinContent(1, 10.05); histos[mapname]->SetBinError(1,sqrt(pow( 1.99,2)+pow( 2.09,2)));
histos[mapname]->SetBinContent(2, 10.34); histos[mapname]->SetBinError(2,sqrt(pow( 2.07,2)+pow( 2.14,2)));
histos[mapname]->SetBinContent(3, 9.22); histos[mapname]->SetBinError(3,sqrt(pow( 2.06,2)+pow( 1.97,2)));
histos[mapname]->SetBinContent(4, 3.52); histos[mapname]->SetBinError(4,sqrt(pow( 1.25,2)+pow( 0.76,2)));
histos[mapname]->SetBinContent(5, 2.94); histos[mapname]->SetBinError(5,sqrt(pow( 1.20,2)+pow( 0.91,2)));
histos[mapname]->SetBinContent(6, 2.45); histos[mapname]->SetBinError(6,sqrt(pow( 1.10,2)+pow( 0.76,2)));
} if(i3==2&&i2==2){//hHT,35j,0b
histos[mapname]->SetBinContent(1, 8.73); histos[mapname]->SetBinError(1,sqrt(pow( 2.13,2)+pow( 1.86,2)));
histos[mapname]->SetBinContent(2, 11.85); histos[mapname]->SetBinError(2,sqrt(pow( 2.42,2)+pow( 2.48,2)));
histos[mapname]->SetBinContent(3, 10.16); histos[mapname]->SetBinError(3,sqrt(pow( 2.33,2)+pow( 2.13,2)));
histos[mapname]->SetBinContent(4, 8.54); histos[mapname]->SetBinError(4,sqrt(pow( 2.20,2)+pow( 1.82,2)));
histos[mapname]->SetBinContent(5, 4.85); histos[mapname]->SetBinError(5,sqrt(pow( 1.73,2)+pow( 1.52,2)));
histos[mapname]->SetBinContent(6, 2.88); histos[mapname]->SetBinError(6,sqrt(pow( 1.29,2)+pow( 0.88,2)));
histos[mapname]->SetBinContent(7, 2.88); histos[mapname]->SetBinError(7,sqrt(pow( 1.29,2)+pow( 0.88,2)));
} if(i3==2&&i2==5){//hHT,6j,0b
histos[mapname]->SetBinContent(1, 2.80); histos[mapname]->SetBinError(1,sqrt(pow( 1.40,2)+pow( 0.80,2)));
histos[mapname]->SetBinContent(2, 3.43); histos[mapname]->SetBinError(2,sqrt(pow( 1.98,2)+pow( 0.93,2)));
// histos[mapname]->SetBinContent(3,); histos[mapname]->SetBinError(3,sqrt(pow(,2)+pow(,2)));
} if(i3==0&&i2==1){//lHT,2j,1b
histos[mapname]->SetBinContent(1, 33.99); histos[mapname]->SetBinError(1,sqrt(pow( 1.21,2)+pow(10.70,2)));
histos[mapname]->SetBinContent(2, 22.83); histos[mapname]->SetBinError(2,sqrt(pow( 1.00,2)+pow( 7.19,2)));
histos[mapname]->SetBinContent(3, 17.90); histos[mapname]->SetBinError(3,sqrt(pow( 0.91,2)+pow( 6.97,2)));
histos[mapname]->SetBinContent(4, 13.92); histos[mapname]->SetBinError(4,sqrt(pow( 0.82,2)+pow( 5.44,2)));
histos[mapname]->SetBinContent(5, 9.25); histos[mapname]->SetBinError(5,sqrt(pow( 0.65,2)+pow( 3.56,2)));
histos[mapname]->SetBinContent(6, 1.91); histos[mapname]->SetBinError(6,sqrt(pow( 0.30,2)+pow( 0.74,2)));
} if(i3==0&&i2==3){//lHT,35j,1b
histos[mapname]->SetBinContent(1, 87.24); histos[mapname]->SetBinError(1,sqrt(pow( 2.82,2)+pow(22.79,2)));
histos[mapname]->SetBinContent(2, 64.63); histos[mapname]->SetBinError(2,sqrt(pow( 2.45,2)+pow(16.77,2)));
histos[mapname]->SetBinContent(3, 42.61); histos[mapname]->SetBinError(3,sqrt(pow( 1.94,2)+pow(14.56,2)));
histos[mapname]->SetBinContent(4, 23.77); histos[mapname]->SetBinError(4,sqrt(pow( 1.52,2)+pow( 8.25,2)));
histos[mapname]->SetBinContent(5, 11.56); histos[mapname]->SetBinError(5,sqrt(pow( 1.04,2)+pow( 3.90,2)));
histos[mapname]->SetBinContent(6, 2.66); histos[mapname]->SetBinError(6,sqrt(pow( 0.50,2)+pow( 0.91,2)));
} if(i3==0&&i2==6){//lHT,6j,1b
histos[mapname]->SetBinContent(1, 2.12); histos[mapname]->SetBinError(1,sqrt(pow( 0.61,2)+pow( 1.40,2)));
histos[mapname]->SetBinContent(2, 1.84); histos[mapname]->SetBinError(2,sqrt(pow( 0.53,2)+pow( 1.22,2)));
histos[mapname]->SetBinContent(3, 1.55); histos[mapname]->SetBinError(3,sqrt(pow( 0.64,2)+pow( 1.11,2)));
} if(i3==1&&i2==1){//mHT,2j,1b
histos[mapname]->SetBinContent(1, 12.04); histos[mapname]->SetBinError(1,sqrt(pow( 0.61,2)+pow( 6.91,2)));
histos[mapname]->SetBinContent(2, 8.03); histos[mapname]->SetBinError(2,sqrt(pow( 0.54,2)+pow( 4.61,2)));
histos[mapname]->SetBinContent(3, 8.67); histos[mapname]->SetBinError(3,sqrt(pow( 0.58,2)+pow( 4.97,2)));
histos[mapname]->SetBinContent(4, 5.38); histos[mapname]->SetBinError(4,sqrt(pow( 0.49,2)+pow( 3.31,2)));
histos[mapname]->SetBinContent(5, 2.65); histos[mapname]->SetBinError(5,sqrt(pow( 0.34,2)+pow( 1.63,2)));
} if(i3==1&&i2==3){//mHT,35j,1b
histos[mapname]->SetBinContent(1, 16.86); histos[mapname]->SetBinError(1,sqrt(pow( 1.14,2)+pow( 3.89,2)));
histos[mapname]->SetBinContent(2, 15.66); histos[mapname]->SetBinError(2,sqrt(pow( 1.16,2)+pow( 3.62,2)));
histos[mapname]->SetBinContent(3, 16.99); histos[mapname]->SetBinError(3,sqrt(pow( 1.18,2)+pow( 3.92,2)));
histos[mapname]->SetBinContent(4, 13.70); histos[mapname]->SetBinError(4,sqrt(pow( 1.13,2)+pow( 3.17,2)));
histos[mapname]->SetBinContent(5, 8.91); histos[mapname]->SetBinError(5,sqrt(pow( 0.89,2)+pow( 2.87,2)));
histos[mapname]->SetBinContent(6, 2.27); histos[mapname]->SetBinError(6,sqrt(pow( 0.45,2)+pow( 0.73,2)));
} if(i3==1&&i2==6){//mHT,6j,1b
histos[mapname]->SetBinContent(1, 2.41); histos[mapname]->SetBinError(1,sqrt(pow( 0.70,2)+pow( 1.39,2)));
histos[mapname]->SetBinContent(2, 1.55); histos[mapname]->SetBinError(2,sqrt(pow( 0.52,2)+pow( 0.89,2)));
histos[mapname]->SetBinContent(3, 1.11); histos[mapname]->SetBinError(3,sqrt(pow( 0.45,2)+pow( 0.64,2)));
histos[mapname]->SetBinContent(4, 0.85); histos[mapname]->SetBinError(4,sqrt(pow( 0.42,2)+pow( 0.53,2)));
} if(i3==2&&i2==1){//hHT,2j,1b
histos[mapname]->SetBinContent(1, 2.69); histos[mapname]->SetBinError(1,sqrt(pow( 0.31,2)+pow( 1.80,2)));
histos[mapname]->SetBinContent(2, 2.25); histos[mapname]->SetBinError(2,sqrt(pow( 0.31,2)+pow( 1.51,2)));
} if(i3==2&&i2==3){//hHT,35j,1b
histos[mapname]->SetBinContent(1, 2.16); histos[mapname]->SetBinError(1,sqrt(pow( 0.41,2)+pow( 0.61,2)));
histos[mapname]->SetBinContent(2, 2.40); histos[mapname]->SetBinError(2,sqrt(pow( 0.43,2)+pow( 0.67,2)));
histos[mapname]->SetBinContent(3, 2.57); histos[mapname]->SetBinError(3,sqrt(pow( 0.49,2)+pow( 0.72,2)));
histos[mapname]->SetBinContent(4, 1.70); histos[mapname]->SetBinError(4,sqrt(pow( 0.40,2)+pow( 0.61,2)));
} if(i3==2&&i2==6){//hHT,6j,1b
histos[mapname]->SetBinContent(1, 1.06); histos[mapname]->SetBinError(1,sqrt(pow( 0.44,2)+pow( 0.84,2)));
histos[mapname]->SetBinContent(2, 0.92); histos[mapname]->SetBinError(2,sqrt(pow( 0.53,2)+pow( 0.73,2)));
// histos[mapname]->SetBinContent(3,); histos[mapname]->SetBinError(3,sqrt(pow(,2)+pow(,2)));
}
}}
cout << "Saving." << endl;
TFile *fsavefile = new TFile("../Results/Filtered/GammaJetsPrediction/20130617_test/ZinvPredictionNumbers.root","RECREATE");
fsavefile->cd();
for(map<string,TH1D*>::iterator h=histos.begin(); h!=histos.end();++h){
h->second->Write();
}
fsavefile->Close();
cout << "Saved histograms in " << fsavefile->GetName() << endl;
//make the plots - not TDR style
TLatex TitleBox;
TitleBox.SetNDC();
TitleBox.SetNDC();
TitleBox.SetTextAlign(12);
TitleBox.SetTextFont(42);
TitleBox.SetTextSize(0.04219409);
TitleBox.SetLineWidth(2);
TString text;
string outname;
double max = 0.;
double max1,max2;
string outputdir = "../Results/Filtered/GammaJetsPrediction/20130617_test/ZinvPredictionPlots/";
Util::MakeOutputDir(outputdir);
TCanvas *c1 = new TCanvas("c1", "c1",485,220,700,504);
c1->Range(82.71719,-0.4425771,532.9945,2.212885);
c1->SetFillColor(0);
c1->SetBorderMode(0);
c1->SetBorderSize(2);
c1->SetLeftMargin(0.1494253);
c1->SetRightMargin(0.07327586);
c1->SetTopMargin(0.08016878);
c1->SetBottomMargin(0.1666667);
c1->SetFrameBorderMode(0);
c1->SetFrameBorderMode(0);
for(int i3 = 0; i3<HTbinsize; ++i3){
for(int i2 = 0; i2<signalregionsize; ++i2){
string hs = string("_") + HT_bin[i3] + string("_") + signal_region[i2];// + string("_") + sample_type[i1];
string mapname = "MT2pred" + hs;
string mapnameMC = "MT2truth" + hs;
if(i3==0&&i2==0){//lHT,2j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==0&&i2==2){//lHT,35j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==0&&i2==5){//lHT,6j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "#geq6 jets, 0 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==0){//mHT,2j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==2){//mHT,35j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==5){//mHT,6j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.2);
histos[mapnameMC]->SetMinimum(0.2);
text = "#geq6 jets, 0 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==0){//hHT,2j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==2){//hHT,35j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==5){//hHT,6j,0b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
// max = 1.5*max;
max = 5.5;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "#geq6 jets, 0 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==0&&i2==1){//lHT,2j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, #geq1 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, #geq1 b-jets, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==0&&i2==3){//lHT,35j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 1 b-jet, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 1 b-jet, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==0&&i2==6){//lHT,6j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
// max = 1.5*max;
max = 3.5;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 1 b-jet, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.3);
histos[mapnameMC]->SetMinimum(0.3);
text = "#geq6 jets, 1 b-jet, low H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==1){//mHT,2j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, #geq1 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, #geq1 b-jets, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==3){//mHT,35j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 1 b-jet, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 1 b-jet, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==1&&i2==6){//mHT,6j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 1 b-jet, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.3);
histos[mapnameMC]->SetMinimum(0.3);
text = "#geq6 jets, 1 b-jet, medium H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==1){//hHT,2j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
// max = 1.5*max;
max = 6.5;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "2 jets, #geq1 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "2 jets, #geq1 b-jets, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==3){//hHT,35j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 1.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "3-5 jets, 1 b-jet, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(1.);
histos[mapnameMC]->SetMinimum(1.);
text = "3-5 jets, 1 b-jet, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
c1->Clear();
} if(i3==2&&i2==6){//hHT,6j,1b
c1->Clear();
c1->cd();
gPad->SetLogy(0);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
// max = 1.5*max;
max = 2.;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.);
histos[mapnameMC]->SetMinimum(0.);
text = "#geq6 jets, 1 b-jet, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + ".eps";
c1->SaveAs(outname.c_str());
c1->Clear();
c1->cd();
gPad->SetLogy(1);
max1=histos[mapname] ->GetMaximum();
max2=histos[mapnameMC]->GetMaximum();
max = (max1>max2)?max1:max2;
max = 2.5*max;
histos[mapname ]->SetMaximum(max);
histos[mapnameMC]->SetMaximum(max);
histos[mapname ]->SetMinimum(0.2);
histos[mapnameMC]->SetMinimum(0.2);
text = "#geq6 jets, 1 b-jet, high H_{T}";
histos[mapnameMC]->Draw("E2");
histos[mapname ]->Draw("sameE1");
leg->Draw();
TitleBox.DrawLatex(0.1494253,0.9493671,text.Data());
outname = histos[mapname ]->GetName();outname = outputdir + outname + "_log.eps";
c1->SaveAs(outname.c_str());
// c1->Clear();
}
}}
}
int main()
{
// this loads the library
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["Cuts"] =1;
Use["BDT"] =1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Create a new root output file.
TString outfileName( "TMVA_output.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
// Add the variables you want to consider
//factory->AddVariable( "MT := MT", 'F' );
//factory->AddVariable( "nJets := nJets", 'F' );
factory->AddVariable( "MET := MET", 'F' );
factory->AddVariable( "MT2W := MT2W", 'F' );
factory->AddVariable( "dPhiMETjet := dPhiMETjet", 'F' );
factory->AddVariable( "HTratio := HTratio", 'F' );
factory->AddVariable( "HadronicChi2 := HadronicChi2", 'F' );
factory->AddVariable( "nWTag := nWTag", 'I' );
// Open samples
TFile* f_signal = TFile::Open((string(MICROTUPLES_FOLDER)+"signal.root").c_str());
TFile* f_ttbar = TFile::Open((string(MICROTUPLES_FOLDER)+"ttbar.root" ).c_str());
//TFile* f_W2Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W2Jets.root").c_str());
//TFile* f_W3Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W3Jets.root").c_str());
//TFile* f_W4Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W4Jets.root").c_str());
TTree* signal = (TTree*) f_signal->Get("microTuple");
TTree* ttbar = (TTree*) f_ttbar ->Get("microTuple");
//TTree* W2Jets = (TTree*) f_W2Jets->Get("microTuple");
//TTree* W3Jets = (TTree*) f_W3Jets->Get("microTuple");
//TTree* W4Jets = (TTree*) f_W4Jets->Get("microTuple");
// Register the trees
// float weightSignal = 1.0 * 20000.0 / getNumberOfEvent(signal);
// float weightBackground = 225.2 * 20000.0 / getNumberOfEvent(ttbar);
float weightSignal = 1.0;
float weightBackground = 1.0;
factory->AddSignalTree ( signal, weightSignal );
factory->AddBackgroundTree( ttbar, weightBackground);
/*
cout << " signal ; w = " << 1.0 * 20000.0 / getNumberOfEvent(signal) << endl;
factory->AddSignalTree ( signal, 1.0 * 20000.0 / getNumberOfEvent(signal));
cout << " ttbar ; w = " << 225.2 * 20000.0 / getNumberOfEvent(ttbar) << endl;
factory->AddBackgroundTree( ttbar, 234.0 * 20000.0 / getNumberOfEvent(ttbar));
cout << " W2Jets ; w = " << 2159 * 20000.0 / getNumberOfEvent(W2Jets) << endl;
factory->AddBackgroundTree( W2Jets, 2159 * 20000.0 / getNumberOfEvent(W2Jets));
cout << " W3Jets ; w = " << 640 * 20000.0 / getNumberOfEvent(W3Jets) << endl;
factory->AddBackgroundTree( W3Jets, 640 * 20000.0 / getNumberOfEvent(W3Jets));
cout << " W4Jets ; w = " << 264 * 20000.0 / getNumberOfEvent(W4Jets) << endl;
factory->AddBackgroundTree( W4Jets, 264 * 20000.0 / getNumberOfEvent(W4Jets));
*/
// Add preselection cuts
std::string preselectionCutsSig("nJets > 4 && MET > 80 && MT > 100");
std::string preselectionCutsBkg("nJets > 4 && MET > 80 && MT > 100");
// Prepare the training
factory->PrepareTrainingAndTestTree( preselectionCutsSig.c_str(), preselectionCutsBkg.c_str(),
"nTrain_Signal=40000:nTrain_Background=300000:nTest_Signal=40000:nTest_Background=300000:SplitMode=Random:NormMode=EqualNumEvents:!V" );
// Cut optimisation
//if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts",
// "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["BDT"]) factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning");
// --------------------------------------------------------------
// Train MVAs using the set of training events
factory->TrainAllMethodsForClassification();
// Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
//if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void createCorrectedBackgroundModel(std::string fileName, int nsidebands=6, bool makePlots=false, std::string defaultPrepend="CMS-HGG" ){
if (makePlots){
system("mkdir -p BMplots/ada");
system("mkdir -p BMplots/grad");
gStyle->SetPalette(1);
gStyle->SetOptStat(0);
}
global_nMassBins=nsidebands;
double massMin = 120;
double massMax = 130;
double dM = 0.1;
// Open the original Workspace
TFile *in = TFile::Open(fileName.c_str(),"UPDATE");
RooWorkspace *work = (RooWorkspace*)in->Get("cms_hgg_workspace");
std::string types[2]={"grad","ada"};
for (int ty=0;ty<2;ty++){
std::string type = types[ty];
// Create An output file for the TF1 Sideband Fits
std::string pathToFile=fileName.substr(0,fileName.find(defaultPrepend.c_str()));
std::string fName=fileName.substr(fileName.find(defaultPrepend.c_str()),fileName.size());
TFile *out = new TFile(Form("%sbdtSidebandFits_%s_%s",pathToFile.c_str(),type.c_str(),fName.c_str()),"RECREATE");
for (double mH=massMin;mH<=massMax;mH+=dM){
//TH1F *originalHist = (TH1F*) in->Get(Form("th1f_bkg_%s_%3.1f_cat0",type.c_str(),mH)); // This histogram is normalized to the inclusive fit (will not include VBF cat)
RooRealVar *nSignalVar = (RooRealVar*)work->var(Form("NBkgInSignal_mH%3.1f",mH));
TH1F *dataHist = (TH1F*) in->Get(Form("th1f_data_%s_%3.1f_cat0",type.c_str(),mH)); // Data Histogram, includes VBF category
int nBins = dataHist->GetNbinsX();
// Want to make a "corrected" histogram
TH1F *correctedHist = new TH1F(Form("th1f_bkg_%s_%3.1f_cat0_fitsb_biascorr",type.c_str(),mH),Form("th1f_bkg_%s_%3.1f_cat0_fitsb_biascorr",type.c_str(),mH),nBins,0,nBins);
TH1F *correctedHistFR = new TH1F(Form("th1f_bkg_%s_%3.1f_cat0_fitsb_biascorr_frac",type.c_str(),mH),Form("th1f_bkg_%s_%3.1f_cat0_fitsb_biascorr_frac",type.c_str(),mH),nBins,0,nBins);
TH2F *hFCovar=new TH2F(Form("fCovar_%3.1f",mH),
Form("Fraction covariance matrix m %3.1f",mH),
nBins,0.0,nBins,nBins,0.0,nBins);
TH2F *uCorrErr = new TH2F(Form("fUncorrErr_%s_%3.1f",type.c_str(),mH),
Form("Uncorrelated Errors m %3.1f",mH),
nBins,0.0,nBins,nBins,0.0,nBins);
TDirectory *mass_dir = out->mkdir(Form("mH_%3.1f",mH));
// For a given mass point, need to set up globals
global_nBdtBins=nBins;
global_mH=mH;
global_nSignalRegion=nSignalVar->getVal();
fillData(mH,in,type);
paulFit(mass_dir,correctedHistFR,correctedHist,hFCovar,makePlots,type);
// Finally Get the uncorrleated errors from the covariance matrix
diagonalizeMatrix(hFCovar,uCorrErr);
std::cout << "Final Check of Normalizations at mH="<<mH <<std::endl;
std::cout << "Original - "<< global_nSignalRegion << ", After - " <<correctedHist->Integral() <<std::endl;
// Write out the Hists into the original File
in->cd();
correctedHist->Write(correctedHist->GetName(),TObject::kOverwrite);
correctedHistFR->Write(correctedHistFR->GetName(),TObject::kOverwrite);
//hFCovar->Write();
uCorrErr->Write(uCorrErr->GetName(),TObject::kOverwrite);
out->cd();
mass_dir->cd();
uCorrErr->Write(uCorrErr->GetName(),TObject::kOverwrite);
if (makePlots){
TCanvas *canv = new TCanvas();
uCorrErr->SetMarkerColor(kGray);
gPad->SetRightMargin(2.);
uCorrErr->Draw("colz text");
canv->Print(Form("BMplots/%s/uncorrErr_m%3.1f.png",type.c_str(),mH));
canv->Print(Form("BMplots/%s/uncorrErr_m%3.1f.pdf",type.c_str(),mH));
}
}
std::cout << "Saving Fits to file -> " << out->GetName() << std::endl;
out->Close();
}
std::cout << "Updated (with corrected background model) -> " << in->GetName() << std::endl;
in->Close();
}
void combinePreFullTests( string rootFullName, string rootPreTest, string rootOutputName)
{
// open output file
TFile *_fileSave = TFile::Open( rootOutputName.c_str(), "new" );
// open existing files
TFile * fileFullTest = TFile::Open( rootFullName.c_str(),"read" );
TFile * filePreTest = TFile::Open( rootPreTest.c_str(),"read" );
fileFullTest->cd();
if( ! fileFullTest ) continue;
TIter nextkey( gDirectory->GetListOfKeys() );
TKey * key;
cout << "Copy Subdirectory/histograms from " << fileFullTest->GetName() << endl;
cout << "into the file: " << _fileSave->GetName() << endl;
while( ( key = (TKey*)nextkey() ) ) {
TObject * obj = key->ReadObj();
if (obj->IsA()->InheritsFrom( "TDirectory" )){
cout << "----- Subdir " << obj->GetName() << " " << obj->GetUniqueID() << endl;
// create same structure
_fileSave->mkdir(obj->GetName());
// loop over all histograms in subdirectory
fileFullTest->cd(obj->GetName());
TIter nextkey2( gDirectory->GetListOfKeys() );
TKey * key2;
string objName = obj->GetName();
if (objName.substr(0,3) == "BB2"){
cout << "for Subdirectory BB2 in file " << fileFullTest->GetName() << " do nothing" << endl;
}
else{
while( ( key2 = (TKey*)nextkey2() ) ) {
_fileSave->cd();
TObject * obj2 = key2->ReadObj();
if( obj2->IsA()->InheritsFrom( "TH2D" )) {
//cout << "2d histograms " << obj2->GetName() << endl;
TH2D * h2 = (TH2D*)obj2;
_fileSave->cd(obj->GetName());
h2->Write();
}
else if ( obj2->IsA()->InheritsFrom( "TH1D" )) {
//cout << "1d histograms " << obj2->GetName() << endl;
TH1D * h1 = (TH1D*)obj2;
_fileSave->cd(obj->GetName());
h1->Write();
}
}
} // do not save BB2 from Fulltest but from next file
// here please save BB2 to final file
}
else{
// store Histograms which are not in a subdirectory HA and HD
_fileSave->cd();
if ( obj->IsA()->InheritsFrom( "TH1D" )) {
//cout << "1d histograms " << obj->GetName() << endl;
TH1D * h1 = (TH1D*)obj;
_fileSave->cd();
h1->Write();
}
//cout << obj->GetName() << " this is a histogram " << endl;
}
}
// Now use BB2 from Pretest and store it in the outputfile _fileSave
if( ! filePreTest ) continue;
filePreTest->cd();
filePreTest->cd("BB2");
cout << "copy BB2 histograms from file " << filePreTest->GetName() << " into " << _fileSave->GetName() << endl;
TIter nextkey3( gDirectory->GetListOfKeys() );
TKey * key3;
while( ( key3 = (TKey*)nextkey3() ) ) {
TObject * obj3 = key3->ReadObj();
if( obj3->IsA()->InheritsFrom( "TH2D" )) {
//cout << "2d histograms " << obj3->GetName() << endl;
TH2D * h2 = (TH2D*)obj3;
_fileSave->cd("BB2");
h2->Write();
}
else if ( obj3->IsA()->InheritsFrom( "TH1D" )) {
//cout << "1d histograms " << obj3->GetName() << endl;
TH1D * h1 = (TH1D*)obj3;
_fileSave->cd("BB2");
h1->Write();
}
//cout << "----- " << obj3->GetName() << endl;
}
fileFullTest->Close();
filePreTest->Close();
_fileSave->Close();
}
int Ana(const char *filename, const char* outdir, TFile *fileout)
{
//TFile *file = new TFile("mc_KPI_22324.root");
TFile *file;
if (filename==0) file= new TFile("KK_22324.root");
else file = new TFile(filename);
std::cout<<"File name is "<<file->GetName()<<std::endl;
if (file==0) return -1;
TTree *tree = (TTree*)file->Get("TwoProng");
if (tree==0) return -2;
double ene[22];
double pcut[22];
double epcut[22];
double thecut[22];
double m_pcut;
double m_epcut;
double m_thecut;
ifstream cutin("cutpar");
char line[1000];
if (cutin.is_open()) cutin.getline(line,1000);
int iene=0;
// set E/p and p cut, mark: set cut
while (!cutin.eof()){
cutin.getline(line,1000);
istringstream iss;
iss.str(line);
iss>>ene[iene]>>pcut[iene]>>epcut[iene];
iene++;
if (iene==22) break;
}
for (int i=0;i<22;i++) epcut[i] = 1.64295 - 0.629622*ene[i] + 0.104755 *pow(ene[i],2);
//for (int i=0;i<21;i++) thecut[i] = 173.946 + 1.74736*ene[i];
for (int i=0;i<22;i++) thecut[i] = 179;
double kappx,kappy,kappz,kampx,kampy,kampz;
int nneu;
int run;
int idxmc;
int pdgid[100];
int motheridx[100];
int emcstatusInt;
short emcstatusShort;
double emctrk1;
double emctrk2;
double epratio1;
double epratio2;
int ntof1;
int ntof2;
int tofl1[5];
int tofl2[5];
double tof1[5];
double tof2[5];
tree->SetBranchAddress("run",&run);
tree->SetBranchAddress("indexmc",&idxmc);
tree->SetBranchAddress("pdgid",pdgid);
tree->SetBranchAddress("motheridx",motheridx);
tree->SetBranchAddress("kappx",&kappx);
tree->SetBranchAddress("kappy",&kappy);
tree->SetBranchAddress("kappz",&kappz);
tree->SetBranchAddress("kampx",&kampx);
tree->SetBranchAddress("kampy",&kampy);
tree->SetBranchAddress("kampz",&kampz);
//tree->SetBranchAddress("costheta",&costheta);
tree->SetBranchAddress("nneu",&nneu);
if (strncmp(tree->GetBranch("emcstatus")->GetLeaf("emcstatus")->GetTypeName(),"Short_t",7)==0)
tree->SetBranchAddress("emcstatus",&emcstatusShort);
else
tree->SetBranchAddress("emcstatus",&emcstatusInt);
tree->SetBranchAddress("epratio1",&epratio1);
tree->SetBranchAddress("epratio2",&epratio2);
tree->SetBranchAddress("emctrk1",&emctrk1);
tree->SetBranchAddress("emctrk2",&emctrk2);
tree->SetBranchAddress("ntof1",&ntof1);
tree->SetBranchAddress("toflayer1",tofl1);
tree->SetBranchAddress("tof1",tof1);
tree->SetBranchAddress("ntof2",&ntof2);
tree->SetBranchAddress("toflayer2",tofl2);
tree->SetBranchAddress("tof2",tof2);
double pi = TMath::Pi();
double mka = 0.493677;
//double mka = 0.13957;
//double mka = 0.1057;
tree->GetEntry(0);
double Ebeam = GetEnergy(run);
if (Ebeam<1.0) Ebeam = getEne(filename);
//Ebeam = 3.08;
for (int iene=0;iene<22;iene++){
if (fabs(Ebeam-ene[iene])<1e-4) {
m_pcut = pcut[iene];
m_epcut = epcut[iene];
m_thecut = thecut[iene];
break;
}
}
//m_epcut = m_epcut +0.05; // change E/p to determine uncertainty from this cut
//m_thecut = 179+0.2; // uncertainty from theta cut
//m_pcut = 0.956409;
m_thecut = 179;
m_epcut = 1.64295 - 0.629622*Ebeam + 0.104755 *pow(Ebeam,2);
double m_tofcut = 3;
//m_tofcut = 3-1;
std::cout<<"pcut "<<m_pcut<<", E/p cut "<<m_epcut<<std::endl;
//TFile *fileout = new TFile(name,"recreate");
const char *pureName = getPureName2(filename);
std::cout<<"Pure Name: "<< pureName <<std::endl;
char name1[1000];
//sprintf(name1,"output/%s.root",pureName);
sprintf(name1,"output/%s.root",pureName);
TLorentzVector kap,kam;
double mass;
double totp, costheta1, costheta2;
double costheta;
double p1,p2;
double tof11=-1000,tof21=-1000;
int tofid;
int isrtag=0;
int tof1tag=0;
int tof2tag=0;
int emctag=0;
fileout->cd();
int nentries = tree->GetEntries();
double pexp = sqrt(pow(Ebeam/2,2)-pow(mka,2));
std::cout<<"Total entries is " << nentries << std::endl;
std::cout<<"Beam energy is " << Ebeam << std::endl;
std::cout<<"Expected K momentum is " << pexp << std::endl;
int count0=0,count1=0,count2=0,count3=0;
int count[10]={0};
int tagmc=0;
double sumdeffp=0;
double sumdeffm=0;
double sumcntp=0;
double sumcntm=0;
for (int ien=0;ien<nentries;ien++){
tree->GetEntry(ien);
//if ( run == 39582||run == 39671||run == 39723||run == 39783||run == 40208||run == 40300||run == 40308||run == 40462||run == 40526||run == 40946||run == 41099||run == 41200||run == 41283||run == 41408||run == 41416||run == 41436||run == 41445||run == 41471||run == 41728||run == 41818||run == 41902) continue;
isrtag = 0;
for (int j=0;j<idxmc;j++){
if (pdgid[j] == 22 && motheridx[j]==j) { tagmc++; isrtag=1;}
}
if (emcstatusShort!=0x3 && emcstatusInt!=0x3) continue;
if (ntof1<1) continue;
if (ntof2<1) continue;
for (int i=0;i<ntof1;i++){
if (tofl1[i]==1) tof11=tof1[i];
}
for (int i=0;i<ntof2;i++){
if (tofl2[i]==1) tof21=tof2[i];
}
//if (tof11<-999 || tof21<-999) continue;
count0++;
kap.SetVectMag(TVector3(kappx,kappy,kappz),mka);
kam.SetVectMag(TVector3(kampx,kampy,kampz),mka);
// angular information in lab coordinate
costheta1 = kap.CosTheta();
costheta2 = kam.CosTheta();
// momentum information in cms coordinalte
kap.Boost(-0.011,0,0);
kam.Boost(-0.011,0,0);
costheta = kap.Vect().Dot(kam.Vect())/(kap.Vect().Mag()*kam.Vect().Mag());
double theta = kap.Vect().Angle(kam.Vect())/TMath::Pi()*180.;
mass = (kap+kam).M();
totp = (kap+kam).Rho();
p1 = kap.Rho();
p2 = kam.Rho();
// select candidate, p1 dis
// if (count0%100000==0) std::cout<<"epratio1 is "<< epratio1<<std::endl;
if (fabs(costheta1)<0.93 && costheta1 < 0.8) { count[4]++;
if (fabs(costheta2)<0.93 && costheta2 > -0.8){ count[5]++;
if (theta>m_thecut){ count[2]++;
if (epratio1<m_epcut)
{ count[0]++;
if (epratio2<m_epcut)
{ count[1]++;
if (fabs(tof11-tof21)<m_tofcut){
count3++;
if (p2<m_pcut && p1<m_pcut) {/*datasel2->Fill();*/ count2++; // p2 dis
//datasel1->Fill();
sumdeffp += getDeffp(kap.Pt())/pow(getDeffpErr(kap.Pt()),2);
sumdeffm += getDeffm(kam.Pt())/pow(getDeffmErr(kam.Pt()),2);
sumcntp += 1.0/pow(getDeffpErr(kap.Pt()),2);
sumcntm += 1.0/pow(getDeffmErr(kam.Pt()),2);
}
}
}
}
}
}
}
}
double avedeffp = sumdeffp/sumcntp;
double avedeffm = sumdeffm/sumcntm;
double deff = sqrt(avedeffp*avedeffp+avedeffm*avedeffm);
cout<< "for file: "<< filename<<endl;
cout<<"+ tracking error: "<< avedeffp<<endl;
cout<<"- tracking error: "<< avedeffm<<endl;
cout<<"tracking error: sqrt "<< deff<<endl;
cout<<"tracking error: e+ + e- "<< avedeffp+avedeffm<<endl;
cout<<"tracking error: abs+ + abs- "<< fabs(avedeffp)+fabs(avedeffm)<<endl;
ofstream cutflow("cutflow2",std::ios::app);
cutflow<<filename<<std::endl;
cutflow<<"Initial size :"<<nentries<<std::endl;
cutflow<<"Tagged ISR evt :"<< tagmc <<std::endl;
cutflow<<"Tagged no ISR evt :"<< nentries - tagmc <<std::endl;
cutflow<<"Valid tof and emc :"<<count0<<std::endl;
cutflow<<"After cos1<0.8 :"<<count[4]<<std::endl;
cutflow<<"After cos2>-0.8 :"<<count[5]<<std::endl;
cutflow<<"After theta cut :"<<count[2]<<std::endl;
cutflow<<"After ep1 :"<<count[0]<<std::endl;
cutflow<<"After ep2 :"<<count[1]<<std::endl;
//cutflow<<"After totp<0.05 :"<<count[3]<<std::endl;
cutflow<<"After dtof<3 :"<<count3<<std::endl;
//cutflow<<"p1-exp<0.08 for p1:"<<count1<<std::endl;
cutflow<<"p2-exp<3 sigma :"<<count2<<std::endl;
// finish program here
return 0;
}
void RunEbyEFlowAnalysisFWLite(
int ffrom, int fto,
double zvtxmin, double zvtxmax,
int nmin, int nmax,
int centmin, int centmax)
{
setup(ffrom, fto, zvtxmin, zvtxmax, nmin, nmax, centmin, centmax);
TStopwatch *watch = new TStopwatch();
// Run the analysis
TList* files = GetFileList(gFilesetname.Data());
if(!files) {
cerr << "Could not retrieve fileset: " << gFilesetname << endl;
return;
}
vector<string> fileNames = GetDataVector(files,gFfrom,gFto);
fwlite::ChainEvent event(fileNames);
//Initialize the analyzer
EbyEFlowAnalyzerFWLite* corr = new EbyEFlowAnalyzerFWLite(event);
corr->SetCutParameters(gCut);
TString strtrgid = "Track";
if(gSystem->Getenv("TRGID")) strtrgid = gSystem->Getenv("TRGID");
DiHadronCorrelationMultiBaseFWLite::ParticleType gTrgID = corr->GetParticleID(strtrgid);
TString strassid = "Track";
if(gSystem->Getenv("ASSID")) strassid = gSystem->Getenv("ASSID");
DiHadronCorrelationMultiBaseFWLite::ParticleType gAssID = corr->GetParticleID(strassid);
corr->SetTrgID(gTrgID);
corr->SetAssID(gAssID);
if(gCentfilename.Contains("root"))
{
cout<<"Running correlation analysis in Heavy Ion!"<<endl;
TFile* fcentfile = new TFile(gCentfilename.Data());
if(fcentfile->IsOpen()) corr->SetCentrality(fcentfile,gCenttablename,gNCentBins,gCentRunnum);
else cout<<"Centrality table cannot be opened!"<<endl;
}
else cout<<"Running correlation analysis in pp!"<<endl;
if(gEffhistname.Contains("root"))
{
TFile* feffhist = new TFile(gEffhistname.Data());
if(feffhist->IsOpen())
{
TH3D* htotaleff = 0;
if(!gCut.IsHI) htotaleff = (TH3D*)feffhist->Get("rTotalEff3D");
else
{
int histcentbin = -1;
if((gCut.centmin>=0 && gCut.centmax<=2) || gCut.centmin>=50) histcentbin = 0;
if(gCut.centmin>=2 && gCut.centmax<=4) histcentbin = 1;
if(gCut.centmin>=4 && gCut.centmax<=12) histcentbin = 2;
if(gCut.centmin>=12 && gCut.centmax<=20) histcentbin = 3;
if(gCut.centmin>=20 && gCut.centmax<=40) histcentbin = 4;
if(gCut.centmin==-1 && gCut.centmax==-1) histcentbin = 4;
htotaleff = (TH3D*)feffhist->Get(Form("Tot_%d",histcentbin));
}
corr->LoadEffWeight(htotaleff);
cout<<"Tracking efficiency weighting histogram is loaded!"<<endl;
}
else cout<<"Tracking efficiency weighting file cannot be opened!"<<endl;
}
else cout<<"No Tracking efficiency weighting histogram is found, or it's running MC!"<<endl;
if(gTrghistname.Contains("root"))
{
TFile* ftrghist = new TFile(gTrghistname.Data());
if(ftrghist->IsOpen())
{
corr->LoadTrgWeight((TH1D*)ftrghist->Get("trgEff"));
cout<<"Triggering efficiency weighting histogram is loaded!"<<endl;
}
else cout<<"Triggering efficiency weighting file cannot be opened!"<<endl;
}
else cout<<"No Triggering efficiency weighting histogram is found, or it's running MC!"<<endl;
if(gPileupdistfunchistname.Contains("root"))
{
TFile* fpileupdistfunchist = new TFile(gPileupdistfunchistname.Data());
if(fpileupdistfunchist->IsOpen())
{
corr->LoadPileUpDistFunc((TH1D*)fpileupdistfunchist->Get("distfunc"));
cout<<"Pileup distfunc histogram is loaded!"<<endl;
}
else cout<<"Pileup distfunc file cannot be opened!"<<endl;
}
corr->Process();
// open output file
TString outputfilename = Form("%s/%s/unmerged/%s_%s_ffrom%d_fto%d_vtxmin%.1f_vtxmax%.1f_nmin%d_nmax%d_etatrg%.1f-%.1f_etaass%.1f-%.1f_centmin%d_centmax%d.root",gOutputDir.Data(),gEventtype.Data(),gEventtype.Data(),gTag.Data(),gFfrom,gFto,gCut.zvtxmin,gCut.zvtxmax,gCut.nmin,gCut.nmax,gCut.etatrgmin,gCut.etatrgmax,gCut.etaassmin,gCut.etaassmax,gCut.centmin,gCut.centmax);
TFile* outf = new TFile(outputfilename.Data(),"recreate");
// Save the outputs
TList* output = corr->GetOutputs();
if(outf && output) {
outf->cd();
output->Write();
cout << "Output file " << outf->GetName()
<< " written: " << endl;
outf->ls();
outf->Close();
if(outf) delete outf;
}
delete output;
watch->Stop();
watch->Print();
}
void TMVAClassification( TString myMethodList = "" )
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
// (an example is given for using the BDT as plugin (see below),
// but of course the real application is when you write your own
// method based)
// this loads the library
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
// ---
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
// ---
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDERSkNN"] = 0; // depreciated until further notice
Use["PDEFoam"] = 0;
// --
Use["KNN"] = 0;
// ---
Use["HMatrix"] = 0;
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0;
Use["LD"] = 0;
// ---
Use["FDA_GA"] = 0;
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
// ---
Use["MLP"] = 0; // this is the recommended ANN
Use["MLPBFGS"] = 0; // recommended ANN with optional training method
Use["MLPBNN"] = 0; // recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // *** missing
Use["TMlpANN"] = 0;
// ---
Use["SVM"] = 0;
// ---
Use["BDT"] = 1;
Use["BDTD"] = 0;
Use["BDTG"] = 0;
Use["BDTB"] = 0;
// ---
Use["RuleFit"] = 0;
// ---
Use["Plugin"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 0;
}
}
// Create a new root output file.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory will
// then run the performance analysis for you.
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
// factory->AddVariable( "myvar1 := var1+var2", 'F' );
// factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
// factory->AddVariable( "var3", "Variable 3", "units", 'F' );
// factory->AddVariable( "var4", "Variable 4", "units", 'F' );
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
// factory->AddSpectator( "spec1:=var1*2", "Spectator 1", "units", 'F' );
// factory->AddSpectator( "spec2:=var1*3", "Spectator 2", "units", 'F' );
// read training and test data
factory->AddVariable("BDTZH105",'F');
factory->AddVariable("LikelihoodZH105",'F');
factory->AddVariable("BDTZZ",'F');
factory->AddVariable("LikelihoodZZ",'F');
if (ReadDataFromAsciiIFormat) {
// load the signal and background event samples from ascii files
// format in file must be:
// var1/F:var2/F:var3/F:var4/F
// 0.04551 0.59923 0.32400 -0.19170
// ...
TString datFileS = "tmva_example_sig.dat";
TString datFileB = "tmva_example_bkg.dat";
factory->SetInputTrees( datFileS, datFileB );
}
else {
// load the signal and background event samples from ROOT trees
TString fname = "./tmva_class_example.root";
TString fname_Data7TeV_DoubleElectron2011B_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleElectron2011B_0.root";
TString fname_Data7TeV_MuEG2011B_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_MuEG2011B_0.root";
TString fname_SingleT_tW = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleT_tW.root";
TString fname_SingleT_s = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleT_s.root";
TString fname_Data7TeV_DoubleMu2011B_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleMu2011B_0.root";
TString fname_F_ZH150 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH150.root";
TString fname_F_ZH105 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH105.root";
TString fname_Data7TeV_DoubleElectron2011A_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleElectron2011A_0.root";
TString fname_DYJetsToLL = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//DYJetsToLL.root";
TString fname_SingleTbar_t = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleTbar_t.root";
TString fname_Data7TeV_DoubleElectron2011B_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleElectron2011B_1.root";
TString fname_Data7TeV_DoubleMu2011A_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleMu2011A_1.root";
TString fname_Data7TeV_MuEG2011A_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_MuEG2011A_1.root";
TString fname_TTJets = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//TTJets.root";
TString fname_F_ZH145 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH145.root";
TString fname_SingleTbar_s = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleTbar_s.root";
TString fname_WJetsToLNu = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//WJetsToLNu.root";
TString fname_Data7TeV_DoubleElectron2011A_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleElectron2011A_1.root";
TString fname_F_ZH125 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH125.root";
TString fname_ZZ = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//ZZ.root";
TString fname_Data7TeV_MuEG2011B_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_MuEG2011B_1.root";
TString fname_F_ZH135 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH135.root";
TString fname_WW = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//WW.root";
TString fname_Data7TeV_DoubleMu2011A_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleMu2011A_0.root";
TString fname_SingleTbar_tW = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleTbar_tW.root";
TString fname_WZ = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//WZ.root";
TString fname_Data7TeV_MuEG2011A_0 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_MuEG2011A_0.root";
TString fname_Data7TeV_DoubleMu2011B_1 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//Data7TeV_DoubleMu2011B_1.root";
TString fname_F_ZH115 = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//F_ZH115.root";
TString fname_SingleT_t = "/tmp/chasco/INIT/HADD/TMVA/Trees_FUSION2/ZZ_vs_nonZZ/ZH105_vs_ZZ/ZH115_vs_ZZ/ZH125_vs_ZZ/ZH150_vs_ZZ/ZH135_vs_ZZ/ZH145_vs_ZZ//SingleT_t.root";
if (gSystem->AccessPathName( fname )) // file does not exist in local directory
gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
TFile *input_Data7TeV_DoubleElectron2011B_0 = TFile::Open( fname_Data7TeV_DoubleElectron2011B_0 );
TFile *input_Data7TeV_MuEG2011B_0 = TFile::Open( fname_Data7TeV_MuEG2011B_0 );
TFile *input_SingleT_tW = TFile::Open( fname_SingleT_tW );
TFile *input_SingleT_s = TFile::Open( fname_SingleT_s );
TFile *input_Data7TeV_DoubleMu2011B_0 = TFile::Open( fname_Data7TeV_DoubleMu2011B_0 );
TFile *input_F_ZH150 = TFile::Open( fname_F_ZH150 );
TFile *input_F_ZH105 = TFile::Open( fname_F_ZH105 );
TFile *input_Data7TeV_DoubleElectron2011A_0 = TFile::Open( fname_Data7TeV_DoubleElectron2011A_0 );
TFile *input_DYJetsToLL = TFile::Open( fname_DYJetsToLL );
TFile *input_SingleTbar_t = TFile::Open( fname_SingleTbar_t );
TFile *input_Data7TeV_DoubleElectron2011B_1 = TFile::Open( fname_Data7TeV_DoubleElectron2011B_1 );
TFile *input_Data7TeV_DoubleMu2011A_1 = TFile::Open( fname_Data7TeV_DoubleMu2011A_1 );
TFile *input_Data7TeV_MuEG2011A_1 = TFile::Open( fname_Data7TeV_MuEG2011A_1 );
TFile *input_TTJets = TFile::Open( fname_TTJets );
TFile *input_F_ZH145 = TFile::Open( fname_F_ZH145 );
TFile *input_SingleTbar_s = TFile::Open( fname_SingleTbar_s );
TFile *input_WJetsToLNu = TFile::Open( fname_WJetsToLNu );
TFile *input_Data7TeV_DoubleElectron2011A_1 = TFile::Open( fname_Data7TeV_DoubleElectron2011A_1 );
TFile *input_F_ZH125 = TFile::Open( fname_F_ZH125 );
TFile *input_ZZ = TFile::Open( fname_ZZ );
TFile *input_Data7TeV_MuEG2011B_1 = TFile::Open( fname_Data7TeV_MuEG2011B_1 );
TFile *input_F_ZH135 = TFile::Open( fname_F_ZH135 );
TFile *input_WW = TFile::Open( fname_WW );
TFile *input_Data7TeV_DoubleMu2011A_0 = TFile::Open( fname_Data7TeV_DoubleMu2011A_0 );
TFile *input_SingleTbar_tW = TFile::Open( fname_SingleTbar_tW );
TFile *input_WZ = TFile::Open( fname_WZ );
TFile *input_Data7TeV_MuEG2011A_0 = TFile::Open( fname_Data7TeV_MuEG2011A_0 );
TFile *input_Data7TeV_DoubleMu2011B_1 = TFile::Open( fname_Data7TeV_DoubleMu2011B_1 );
TFile *input_F_ZH115 = TFile::Open( fname_F_ZH115 );
TFile *input_SingleT_t = TFile::Open( fname_SingleT_t );
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleElectron2011B_0 file: " << input_Data7TeV_DoubleElectron2011B_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_MuEG2011B_0 file: " << input_Data7TeV_MuEG2011B_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleT_tW file: " << input_SingleT_tW->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleT_s file: " << input_SingleT_s->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleMu2011B_0 file: " << input_Data7TeV_DoubleMu2011B_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH150 file: " << input_F_ZH150->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH105 file: " << input_F_ZH105->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleElectron2011A_0 file: " << input_Data7TeV_DoubleElectron2011A_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_DYJetsToLL file: " << input_DYJetsToLL->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleTbar_t file: " << input_SingleTbar_t->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleElectron2011B_1 file: " << input_Data7TeV_DoubleElectron2011B_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleMu2011A_1 file: " << input_Data7TeV_DoubleMu2011A_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_MuEG2011A_1 file: " << input_Data7TeV_MuEG2011A_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_TTJets file: " << input_TTJets->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH145 file: " << input_F_ZH145->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleTbar_s file: " << input_SingleTbar_s->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_WJetsToLNu file: " << input_WJetsToLNu->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleElectron2011A_1 file: " << input_Data7TeV_DoubleElectron2011A_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH125 file: " << input_F_ZH125->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_ZZ file: " << input_ZZ->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_MuEG2011B_1 file: " << input_Data7TeV_MuEG2011B_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH135 file: " << input_F_ZH135->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_WW file: " << input_WW->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleMu2011A_0 file: " << input_Data7TeV_DoubleMu2011A_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleTbar_tW file: " << input_SingleTbar_tW->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_WZ file: " << input_WZ->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_MuEG2011A_0 file: " << input_Data7TeV_MuEG2011A_0->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_Data7TeV_DoubleMu2011B_1 file: " << input_Data7TeV_DoubleMu2011B_1->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_F_ZH115 file: " << input_F_ZH115->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input_SingleT_t file: " << input_SingleT_t->GetName() << std::endl;
TTree *signal_F_ZH105 = (TTree*)input_F_ZH105->Get("tmvatree");
TTree *background_SingleT_tW = (TTree*)input_SingleT_tW->Get("tmvatree");
TTree *background_DYJetsToLL = (TTree*)input_DYJetsToLL->Get("tmvatree");
TTree *background_SingleTbar_t = (TTree*)input_SingleTbar_t->Get("tmvatree");
TTree *background_TTJets = (TTree*)input_TTJets->Get("tmvatree");
TTree *background_ZZ = (TTree*)input_ZZ->Get("tmvatree");
TTree *background_WW = (TTree*)input_WW->Get("tmvatree");
TTree *background_SingleTbar_tW = (TTree*)input_SingleTbar_tW->Get("tmvatree");
TTree *background_WZ = (TTree*)input_WZ->Get("tmvatree");
TTree *background_SingleT_t = (TTree*)input_SingleT_t->Get("tmvatree");
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// ====== register trees ====================================================
//
// the following method is the prefered one:
// you can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal_F_ZH105, 1.0 );
factory->AddBackgroundTree( background_SingleT_tW, 1.0 );
factory->AddBackgroundTree( background_DYJetsToLL, 1.0 );
factory->AddBackgroundTree( background_SingleTbar_t, 1.0 );
factory->AddBackgroundTree( background_TTJets, 1.0 );
factory->AddBackgroundTree( background_ZZ, 1.0 );
factory->AddBackgroundTree( background_WW, 1.0 );
factory->AddBackgroundTree( background_SingleTbar_tW, 1.0 );
factory->AddBackgroundTree( background_WZ, 1.0 );
factory->AddBackgroundTree( background_SingleT_t, 1.0 );
// To give different trees for training and testing, do as follows:
// factory->AddSignalTree( signal_F_ZH105TrainingTree, signal_F_ZH105TrainWeight, "Training" );
// factory->AddSignalTree( signal_F_ZH105TestTree, signal_F_ZH105TestWeight, "Test" );
// Use the following code instead of the above two or four lines to add signal and background
// training and test events "by hand"
// NOTE that in this case one should not give expressions (such as "var1+var2") in the input
// variable definition, but simply compute the expression before adding the event
//
// // --- begin ----------------------------------------------------------
// std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
// Float_t treevars[4];
// for (Int_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<signal->GetEntries(); i++) {
// signal->GetEntry(i);
// for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < signal->GetEntries()/2) factory->AddSignalTrainingEvent( vars, signalWeight );
// else factory->AddSignalTestEvent ( vars, signalWeight );
// }
//
// for (Int_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<background->GetEntries(); i++) {
// background->GetEntry(i);
// for (Int_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight );
// else factory->AddBackgroundTestEvent ( vars, backgroundWeight );
// }
// // --- end ------------------------------------------------------------
//
// ====== end of register trees ==============================================
}
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// for signal : factory->SetSignalWeightExpression("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
factory->SetBackgroundWeightExpression("Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*CUT");
factory->SetSignalWeightExpression("Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*CUT");
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = "(CUT>2)";
TCut mycutb = "(CUT>2)";
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycuts, "SplitMode=random:!V" );
// "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
// If no numbers of events are given, half of the events in the tree are used for training, and
// the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// To also specify the number of testing events, use:
// factory->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
// ---- Book MVA methods
//
// please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:Seed=0:EffSel:Steps=50:Cycles=3:PopSize=1000:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// test the decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// test the new kernel density estimator
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// test the mixed splines and kernel density estimator (depending on which variable)
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSkNN"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSkNN",
"!H:!V:VolumeRangeMode=kNN:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:CutNmin=T:Nmin=100:Kernel=None:Compress=T" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
// Fisher discriminant
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=60:NsmoothMVAPdf=10" );
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2");
// Linear discriminant (same as Fisher)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
if (Use["MLPBNN"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=500:nEventsMin=400:MaxDepth=4:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=500:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=500:nEventsMin=400:MaxDepth=4:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// For an example of the category classifier, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// As an example how to use the ROOT plugin mechanism, book BDT via
// plugin mechanism
if (Use["Plugin"]) {
//
// first the plugin has to be defined, which can happen either through the following line in the local or global .rootrc:
//
// # plugin handler plugin name(regexp) class to be instanciated library constructor format
// Plugin.TMVA@@MethodBase: ^BDT TMVA::MethodBDT TMVA.1 "MethodBDT(TString,TString,DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("TMVA@@MethodBase", "BDT", "TMVA::MethodBDT", "TMVA.1", "MethodBDT(TString,TString,DataSet&,TString)");
factory->BookMethod( TMVA::Types::kPlugins, "BDT",
"!H:!V:NTrees=500:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=50" );
}
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
gROOT->ProcessLine(".q;");
}
void bfcread_tagsBranch(
const char *MainFile="/afs/rhic.bnl.gov/star/data/samples/gstar.tags.root",
Int_t printEvent=1,
const char *fname="qa_tags.out",
Int_t fullPrint=0)
{
// start timer
TStopwatch timer;
timer.Start();
cout << endl << endl;
cout << " bfcread_tagsBranch.C: input file = " << MainFile << endl;
cout << " bfcread_tagsBranch.C: print event # " << printEvent << endl;
cout << " bfcread_tagsBranch.C: output file = " << fname << endl;
cout << " bfcread_tagsBranch.C: full printout = " << fullPrint << endl;
cout << endl;
ofstream fout(fname);
fout << endl << endl;
fout << " bfcread_tagsBranch.C: input file = " << MainFile << endl;
fout << " bfcread_tagsBranch.C: print evt# = " << printEvent << endl;
fout << " bfcread_tagsBranch.C: output file = " << fname << endl;
fout << " bfcread_tagsBranch.C: full printout = " << fullPrint << endl;
fout << endl;
TFile *file = TFile::Open(MainFile);
TTree *tree = (TTree*)file->Get("Tag");
cout <<" read file: " << file->GetName() << endl << endl;
Int_t nEntries = tree->GetEntries();
cout << " Total # events = " << nEntries << endl;
TObjArray *leaves = tree->GetListOfLeaves();
Int_t nLeaves = leaves->GetEntriesFast();
cout << " Total # leaves = " << nLeaves << endl;
TString *tName = new TString(" ");
TNamed *tableName=0;
TObjArray *tagTable = new TObjArray;
Int_t tableCount = 0;
Int_t *tableIndex = new Int_t[nLeaves];
Int_t tagCount = 0;
TBranch *branch=0;
TLeaf *leaf=0;
Int_t ndim =0;
//count number of tag tables encoded in the TTree branch names
for (Int_t l=0;l<nLeaves;l++) {
leaf = (TLeaf*)leaves->UncheckedAt(l);
tagCount+=leaf->GetNdata();
branch = leaf->GetBranch();
cout << "leaf # " << l << " br name = " <<
branch->GetName() << endl;
//new tag table name
if ( strstr(branch->GetName(), tName->Data()) == 0 ) {
tName = new TString(branch->GetName());
tName->Resize(tName->Index("."));
//the tableName is encoded in the branch Name before the "."
tableName = new TNamed(tName->Data(),"Tag");
tagTable->AddLast(tableName);
tableCount++;
}
tableIndex[l]=tableCount-1;
}
cout << endl << " Total num tables(branches),tags = "
<< tableCount << " " << tagCount << endl << endl;
Int_t *countTagsTable = new Int_t[tableCount];
Int_t *countLeavesTable = new Int_t[tableCount];
Float_t *sumTagsLeaf = new Float_t[nLeaves];
Int_t *countTagsLeaf = new Int_t[nLeaves];
// Now loop over leaves (to get values of tags)
Int_t setBranch = -1;
Int_t nowBranch = -1;
for (Int_t l=0;l<nLeaves;l++) {
leaf = (TLeaf*)leaves->UncheckedAt(l);
branch = leaf->GetBranch();
ndim = leaf->GetNdata();
nowBranch = tableIndex[l];
//cout << " nowbranch, setBranch = " <<
// nowBranch << ", "<< setBranch << endl;
Float_t RtableIndex=tableIndex[l];
if (nowBranch != setBranch){
setBranch=nowBranch;
cout << " QAInfo: branch ";
cout.width(2);
cout << tableIndex[l] << " = ";
cout.width(10);
cout << ((TNamed*)tagTable->UncheckedAt(tableIndex[l]))->GetName();
cout << endl;
fout << " QAInfo: branch ";
fout.width(2);
fout << RtableIndex << " = ";
fout.width(10);
fout << ((TNamed*)tagTable->UncheckedAt(tableIndex[l]))->GetName();
fout << endl;
}
countTagsTable[tableIndex[l]]+=leaf->GetNdata();
countLeavesTable[tableIndex[l]]++;
countTagsLeaf[l]+=ndim;
Float_t Rl=l;
Float_t Rndim=ndim;
cout << " QAInfo: leaf ";
cout.width(3);
cout << l << " has ";
cout.width(1);
cout << ndim << " tags:";
cout << endl;
fout << " QAInfo: leaf ";
fout.width(3);
fout << Rl << " has ";
fout.width(1);
fout << Rndim << " tags:";
fout << endl;
// loop over all events in each leaf
for (Int_t nev=0; nev<nEntries; nev++) {
branch->GetEntry(nev);
// loop over all tags in each leaf for each event
for (Int_t itag=0;itag<ndim;itag++) {
Int_t ik = nev+1;
if (ik==printEvent) {
cout << " QAInfo: " << leaf->GetName();
if (ndim>1) cout << '['<<itag<<']';
cout << " = " << leaf->GetValue(itag) << endl;
fout << " QAInfo: " << leaf->GetName();
if (ndim>1) fout << '['<<itag<<']';
fout << " = " << leaf->GetValue(itag) << endl;
}
sumTagsLeaf[l]+=leaf->GetValue(itag);
}
}
}
cout << endl << endl;
fout << endl << endl;
if (fullPrint == 1){
// loop over leaves again for printout at end
for (Int_t m=0; m<nLeaves; m++){
leaf = (TLeaf*)leaves->UncheckedAt(m);
branch = leaf->GetBranch();
ndim = leaf->GetNdata();
cout << " QAInfo: branch ";
cout.width(2);
cout << tableIndex[m] << " = ";
cout.width(10);
cout << ((TNamed*)tagTable->UncheckedAt(tableIndex[m]))->GetName();
cout << ", leaf ";
cout.width(3);
cout << m << " = ";
cout.width(24);
cout << leaf->GetName();
cout << ", #tags = ";
cout.width(1);
cout << ndim;
cout << endl;
Float_t RtableIndex=tableIndex[m];
Float_t Rm=m;
Float_t Rndim=ndim;
fout << " QAInfo: branch ";
fout.width(2);
fout << RtableIndex << " = ";
fout.width(10);
fout << ((TNamed*)tagTable->UncheckedAt(tableIndex[m]))->GetName();
fout << ", leaf ";
fout.width(3);
fout << Rm << " = ";
fout.width(24);
fout << leaf->GetName();
fout << ", #tags = ";
fout.width(1);
fout << Rndim;
fout << endl;
}
}
cout << endl << endl;
fout << endl << endl;
// loop over leaves again for printout at end of averages
cout << " QAInfo: each leaf's avg over all tags,evts: " << endl;
fout << " QAInfo: each leaf's avg over all tags,evts: " << endl;
for (Int_t m=0; m<nLeaves; m++){
leaf = (TLeaf*)leaves->UncheckedAt(m);
sumTagsLeaf[m]/=countTagsLeaf[m]*nEntries;
cout << " QAInfo: avg leaf #";
cout.width(2);
cout << m << ", ";
cout.width(23);
cout << leaf->GetName() << " = ";
cout.width(12);
cout << sumTagsLeaf[m];
cout << endl;
Float_t Rm=m;
fout << " QAInfo: avg leaf #";
fout.width(2);
fout << Rm << ", ";
fout.width(23);
fout << leaf->GetName() << " = ";
fout.width(12);
fout << sumTagsLeaf[m];
fout << endl;
}
cout << endl << endl;
fout << endl << endl;
// loop over all tables
for (Int_t m=0; m<tableCount; m++){
cout << " QAInfo: branch(table) ";
cout.width(10);
cout << ((TNamed*)tagTable->UncheckedAt(m))->GetName() << " has ";
cout.width(4);
cout << countLeavesTable[m] << " leaves,";
cout.width(4);
cout << countTagsTable[m] << " tags" << endl;
Float_t RcountLeavesTable=countLeavesTable[m];
Float_t RcountTagsTable=countTagsTable[m];
fout << " QAInfo: branch(table) ";
fout.width(10);
fout << ((TNamed*)tagTable->UncheckedAt(m))->GetName() << " has ";
fout.width(4);
fout << RcountLeavesTable << " leaves,";
fout.width(4);
fout << RcountTagsTable << " tags" << endl;
}
cout << endl << endl;
fout << endl << endl;
Float_t RnEntries=nEntries;
Float_t RtableCount=tableCount;
Float_t RnLeaves=nLeaves;
Float_t RtagCount=tagCount;
cout << " QAInfo: tot num events = " << nEntries << endl;
fout << " QAInfo: tot num events = " << RnEntries << endl;
cout << " QAInfo: tot num branches = " << tableCount << endl;
fout << " QAInfo: tot num branches = " << RtableCount << endl;
cout << " QAInfo: tot num leaves = " << nLeaves << endl;
fout << " QAInfo: tot num leaves = " << RnLeaves << endl;
cout << " QAInfo: tot num tags = " << tagCount << endl;
fout << " QAInfo: tot num tags = " << RtagCount << endl;
// stop timer and print results
timer.Stop();
cout<< endl << endl <<"RealTime="<<timer.RealTime()<<
" seconds, CpuTime="<<timer.CpuTime()<<" seconds"<<endl;
//cleanup
file->Close();
fout.close();
}
int writentuple_pp(char *ksp="ppJet40")
{
timer.Start();
//LoadLib();
TString inname="";
if(strcmp(ksp,"ppJet40")==0)inname = "/hadoop/store/user/belt/hiForest2/PP2013_HiForest_PromptReco_JSon_Jet40Jet60_ppTrack_forestv84.root";
else if(strcmp(ksp,"ppJet80")==0)inname = "/hadoop/store/user/belt/hiForest2/PP2013_HiForest_PromptReco_JsonPP_Jet80_PPReco_forestv82.root";
//! Load Lib
//gSystem->Load("/afs/cern.ch/user/p/pawan/scratch0/CMSSW_6_2_0/src/work/pPb/HiForest/V3/hiForest_h.so");
//! Define the input file and HiForest
//! CMSSW_5_3_3
HiForest *c = new HiForest(inname,Form("Forest%s",ksp),cPP);
cout<<"Loaded the hiforest tree : "<<c->GetName()<<endl;
ShutoffBranches(c);
DuplicateEvents dupEvt(inname);
dupEvt.MakeList();
//! Output file
//! HIHighPt
TFile *fout = new TFile(Form("ntuple_2013_%s.root",ksp),"RECREATE");
std::cout<<"\t"<<std::endl;
std::cout<<"\t"<<std::endl;
std::cout<<"**************************************************** "<<std::endl;
std::cout<<Form("Running for %s ",ksp)<<std::endl;
std::cout<<Form("pT cut for %0.3f ",kptrecocut)<<std::endl;
std::cout<<Form("eta cut for %0.3f ",ketacut)<<std::endl;
std::cout<<"My hiForest Tree : " <<c->GetName()<<"\t Entries "<<c->GetEntries()<<std::endl;
std::cout<<"Output file "<<fout->GetName()<<std::endl;
std::cout<<"**************************************************** "<<std::endl;
std::cout<<"\t"<<std::endl;
std::cout<<"\t"<<std::endl;
//! shut off jet trees
//c->hasAk2CaloJetTree=0;
//c->hasAk4CaloJetTree=0;
//c->hasAk3CaloJetTree=0;
//c->hasAk5CaloJetTree=0;
c->hasAkPu2CaloJetTree=0;
c->hasAkPu4CaloJetTree=0;
c->hasAkPu3CaloJetTree=0;
//c->hasAkPu5CaloJetTree=0;
//c->hasAk2PFJetTree=0;
//c->hasAk4PFJetTree=0;
//c->hasAk5PFJetTree=0;
//c->hasAkPu2PFJetTree=0;
//c->hasAkPu4PFJetTree=0;
//c->hasAkPu5PFJetTree=0;
c->hasTrackTree=0;
//! For jets
Jets *mJets=0;
Long64_t nentries = c->GetEntries();
std::cout<<Form("# of entries in TTree for %s : ",ksp)<<nentries<<std::endl;
string jetVars = "";
jetVars += "evt:run:vz:trig:jet40:jet60:jet80:jet100:ntrk:nrefe:pt:raw:eta:phi:chMax:trkMax:chSum:phSum:neSum";
TNtuple *ntjet=0;
ntjet = new TNtuple("ntjet","",jetVars.data());
for (Long64_t ievt=0; ievt<nentries;ievt++) {//! event loop
//for (Long64_t ievt=0; ievt<100;ievt++) {//! event loop
//! load the hiForest event
c->GetEntry(ievt);
if(dupEvt.occurence[ievt] == 2)continue;
//! events with Single vertex
bool evSel = false;
float trig=-9;
if(strcmp(ksp,"ppJet40")==0){
evSel = fabs(c->evt.vz)<15. && c->skim.pHBHENoiseFilter && c->skim.pPAcollisionEventSelectionPA && (c->hlt.HLT_PAJet40_NoJetID_v1 || c->hlt.HLT_PAJet60_NoJetID_v1);
trig=1;
}
else if(strcmp(ksp,"ppJet80")==0){
evSel = fabs(c->evt.vz)<15. && c->skim.pHBHENoiseFilter && c->skim.pPAcollisionEventSelectionPA && (c->hlt.HLT_PAJet80_NoJetID_v1 || c->hlt.HLT_PAJet100_NoJetID_v1);
trig=2;
}
if(!evSel)continue;
float pt = -9,raw = -9,eta = -9,phi = -9,chMax = -9,trkMax=-9,chSum = -9,phSum = -9,neSum = -9,nrefe = 0;
float run = c->evt.run;
float evt = c->evt.evt;
float vz = c->evt.vz;
float jet40 = c->hlt.HLT_PAJet40_NoJetID_v1;
float jet60 = c->hlt.HLT_PAJet60_NoJetID_v1;
float jet80 = c->hlt.HLT_PAJet80_NoJetID_v1;
float jet100 = c->hlt.HLT_PAJet100_NoJetID_v1;
float ntrk = c->evt.hiNtracks;
if(ievt%10000==0)std::cout<<" ********** Event # " <<ievt<<"\t Run : "<<run<<std::endl;
mJets = &(c->ak3PF);
nrefe = mJets->nref;
/*
int *ljet = new int[3];
FindLeadSubLeadJets(mJets,ljet);
int jtLead = -1, jtSubLead = -1, jtThird = -1;
if(ljet[0] >=0 ) jtLead = ljet[0];
if(ljet[1] >=0 ) jtSubLead = ljet[1];
if(ljet[2] >=0 ) jtThird = ljet[2];
if(jtLead<0)continue;
if(jtLead > -1){
pt1 = mJets->jtpt[jtLead];
eta1 = mJets->jteta[jtLead];
phi1 = mJets->jtphi[jtLead];
raw1 = mJets->rawpt[jtLead];
chMax1 = mJets->chargedMax[jtLead];
chSum1 = mJets->chargedSum[jtLead];
phSum1 = mJets->photonSum[jtLead];
neSum1 = mJets->neutralSum[jtLead];
}
if(jtSubLead > -1){
pt2 = mJets->jtpt[jtSubLead];
eta2 = mJets->jteta[jtSubLead];
phi2 = mJets->jtphi[jtSubLead];
raw2 = mJets->rawpt[jtSubLead];
chMax2 = mJets->chargedMax[jtSubLead];
chSum2 = mJets->chargedSum[jtSubLead];
phSum2 = mJets->photonSum [jtSubLead];
neSum2 = mJets->neutralSum[jtSubLead];
}
if(jtThird > -1){
pt3 = mJets->jtpt[jtThird];
eta3 = mJets->jteta[jtThird];
phi3 = mJets->jtphi[jtThird];
raw3 = mJets->rawpt[jtThird];
chMax3 = mJets->chargedMax[jtThird];
chSum3 = mJets->chargedSum[jtThird];
phSum3 = mJets->photonSum [jtThird];
neSum3 = mJets->neutralSum[jtThird];
}
*/
for (int i = 0; i<mJets->nref; i++) {
pt = mJets->jtpt[i];
eta = mJets->jteta[i];
phi = mJets->jtphi[i];
raw = mJets->rawpt[i];
chMax = mJets->chargedMax[i];
trkMax = mJets->trackMax[i];
chSum = mJets->chargedSum[i];
phSum = mJets->photonSum[i];
neSum = mJets->neutralSum[i];
float jentry[] = {evt,run,vz,trig,jet40,jet60,jet80,jet100,ntrk,nrefe,
pt,raw,eta,phi,chMax,trkMax,chSum,phSum,neSum
};
ntjet->Fill(jentry);
}
//delete [] ljet;
}//! event loop ends
//! Write to output file
fout->cd();
fout->Write();
fout->Close();
//! Check
timer.Stop();
float rtime = timer.RealTime();
float ctime = timer.CpuTime();
std::cout<<"\t"<<std::endl;
std::cout<<Form("RealTime=%f seconds, CpuTime=%f seconds",rtime,ctime)<<std::endl;
std::cout<<"\t"<<std::endl;
std::cout<<"Good bye : " <<"\t"<<std::endl;
return 0;
}
//this makes shape plots of MT2 distributions for several VSPT bins for various SM types (W,Z,ttbar,susy,data)
void UTMplots(){
gROOT->cd();
gROOT->SetStyle("Plain");
gStyle->SetPalette(1);
gStyle->SetOptStat(0);
TChain *LM6 = new TChain("MassTree");
TChain *WJets = new TChain("MassTree");
TChain *ZJets = new TChain("MassTree");
TChain *TTbar = new TChain("MassTree");
TChain *Data = new TChain("MassTree");
//read in trees
LM6->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/mmasciov/SUSY/MassTrees/MT2_V02-03-02/20130430_8TeV/SUSY-LM6-sftsht-8TeV-pythia6-Summer12-DR53X-PU-S10-START53-V7A-v1/*.root");
WJets->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/mmasciov/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/WJetsToLNu-HT-400ToInf-8TeV-madgraph-Summer12-DR53X-PU-S10-START53-V7A-v1/*.root");
WJets->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/mmasciov/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/WJetsToLNu-HT-400ToInf-8TeV-madgraph-v2-Summer12-DR53X-PU-S10-START53-V7A-v1/*.root");
ZJets->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/haweber/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/ZJetsToNuNu-400-HT-inf-TuneZ2Star-8TeV-madgraph-Summer12-DR53X-PU-S10-START53-V7A-v1/*.root");
ZJets->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/haweber/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/ZJetsToNuNu-400-HT-inf-TuneZ2Star-8TeV-madgraph-ext-Summer12-DR53X-PU-S10-START53-V7A-v1/*.root");
TTbar->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130503_8TeV/TTJets-SemiLeptMGDecays-8TeV-madgraph-tauola-Summer12-DR53X-PU-S10-START53-V7C-v1/*.root");
int nd = Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/HT-Run2012A-13Jul2012-v1-2/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/HT-Run2012A-recover-06Aug2012-v1-2/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/JetHT-Run2012B-13Jul2012-v1-2/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/JetHT-Run2012C-24Aug2012-v2-2/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/JetHT-Run2012C-EcalRecover-11Dec2012-v1-2/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/JetHT-Run2012C-PromptReco-v2-3/*.root");
nd += Data->Add("dcap://t3se01.psi.ch:22125/pnfs/psi.ch/cms/trivcat/store/user/casal/SUSY/MassTrees/MT2_V02-03-02/20130318_8TeV/JetHT-Run2012D-PromptReco-v1-3/*.root");
cout << nd << endl;
//define histograms
TH1D *LM6VSPT0 = new TH1D("LM6VSPT0" ,"", 75, 0, 750); LM6VSPT0 ->Sumw2(); LM6VSPT0 ->SetLineColor(kBlack); LM6VSPT0 ->SetLineWidth(2);
TH1D *LM6VSPT20 = new TH1D("LM6VSPT20" ,"", 75, 0, 750); LM6VSPT20 ->Sumw2(); LM6VSPT20 ->SetLineColor(kRed); LM6VSPT20 ->SetLineWidth(2);
TH1D *LM6VSPT30 = new TH1D("LM6VSPT30" ,"", 75, 0, 750); LM6VSPT30 ->Sumw2(); LM6VSPT30 ->SetLineColor(kGreen); LM6VSPT30 ->SetLineWidth(2);
TH1D *LM6VSPT40 = new TH1D("LM6VSPT40" ,"", 75, 0, 750); LM6VSPT40 ->Sumw2(); LM6VSPT40 ->SetLineColor(kBlue); LM6VSPT40 ->SetLineWidth(2);
TH1D *LM6VSPT50 = new TH1D("LM6VSPT50" ,"", 75, 0, 750); LM6VSPT50 ->Sumw2(); LM6VSPT50 ->SetLineColor(kYellow); LM6VSPT50 ->SetLineWidth(2);
TH1D *LM6VSPT70 = new TH1D("LM6VSPT70" ,"", 75, 0, 750); LM6VSPT70 ->Sumw2(); LM6VSPT70 ->SetLineColor(kMagenta);LM6VSPT70 ->SetLineWidth(2);
TH1D *LM6VSPT100 = new TH1D("LM6VSPT100","", 75, 0, 750); LM6VSPT100->Sumw2(); LM6VSPT100->SetLineColor(kCyan); LM6VSPT100->SetLineWidth(2);
TH1D *WJetsVSPT0 = new TH1D("WJetsVSPT0" ,"", 75, 0, 750); WJetsVSPT0 ->Sumw2(); WJetsVSPT0 ->SetLineColor(kBlack); WJetsVSPT0 ->SetLineWidth(2);
TH1D *WJetsVSPT20 = new TH1D("WJetsVSPT20" ,"", 75, 0, 750); WJetsVSPT20 ->Sumw2(); WJetsVSPT20 ->SetLineColor(kRed); WJetsVSPT20 ->SetLineWidth(2);
TH1D *WJetsVSPT30 = new TH1D("WJetsVSPT30" ,"", 75, 0, 750); WJetsVSPT30 ->Sumw2(); WJetsVSPT30 ->SetLineColor(kGreen); WJetsVSPT30 ->SetLineWidth(2);
TH1D *WJetsVSPT40 = new TH1D("WJetsVSPT40" ,"", 75, 0, 750); WJetsVSPT40 ->Sumw2(); WJetsVSPT40 ->SetLineColor(kBlue); WJetsVSPT40 ->SetLineWidth(2);
TH1D *WJetsVSPT50 = new TH1D("WJetsVSPT50" ,"", 75, 0, 750); WJetsVSPT50 ->Sumw2(); WJetsVSPT50 ->SetLineColor(kYellow); WJetsVSPT50 ->SetLineWidth(2);
TH1D *WJetsVSPT70 = new TH1D("WJetsVSPT70" ,"", 75, 0, 750); WJetsVSPT70 ->Sumw2(); WJetsVSPT70 ->SetLineColor(kMagenta); WJetsVSPT70 ->SetLineWidth(2);
TH1D *WJetsVSPT100 = new TH1D("WJetsVSPT100","", 75, 0, 750); WJetsVSPT100->Sumw2(); WJetsVSPT100->SetLineColor(kCyan); WJetsVSPT100->SetLineWidth(2);
TH1D *ZJetsVSPT0 = new TH1D("ZJetsVSPT0" ,"", 75, 0, 750); ZJetsVSPT0 ->Sumw2(); ZJetsVSPT0 ->SetLineColor(kBlack); ZJetsVSPT0 ->SetLineWidth(2);
TH1D *ZJetsVSPT20 = new TH1D("ZJetsVSPT20" ,"", 75, 0, 750); ZJetsVSPT20 ->Sumw2(); ZJetsVSPT20 ->SetLineColor(kRed); ZJetsVSPT20 ->SetLineWidth(2);
TH1D *ZJetsVSPT30 = new TH1D("ZJetsVSPT30" ,"", 75, 0, 750); ZJetsVSPT30 ->Sumw2(); ZJetsVSPT30 ->SetLineColor(kGreen); ZJetsVSPT30 ->SetLineWidth(2);
TH1D *ZJetsVSPT40 = new TH1D("ZJetsVSPT40" ,"", 75, 0, 750); ZJetsVSPT40 ->Sumw2(); ZJetsVSPT40 ->SetLineColor(kBlue); ZJetsVSPT40 ->SetLineWidth(2);
TH1D *ZJetsVSPT50 = new TH1D("ZJetsVSPT50" ,"", 75, 0, 750); ZJetsVSPT50 ->Sumw2(); ZJetsVSPT50 ->SetLineColor(kYellow); ZJetsVSPT50 ->SetLineWidth(2);
TH1D *ZJetsVSPT70 = new TH1D("ZJetsVSPT70" ,"", 75, 0, 750); ZJetsVSPT70 ->Sumw2(); ZJetsVSPT70 ->SetLineColor(kMagenta); ZJetsVSPT70 ->SetLineWidth(2);
TH1D *ZJetsVSPT100 = new TH1D("ZJetsVSPT100","", 75, 0, 750); ZJetsVSPT100->Sumw2(); ZJetsVSPT100->SetLineColor(kCyan); ZJetsVSPT100->SetLineWidth(2);
TH1D *TTbarVSPT0 = new TH1D("TTbarVSPT0" ,"", 75, 0, 750); TTbarVSPT0 ->Sumw2(); TTbarVSPT0 ->SetLineColor(kBlack); TTbarVSPT0 ->SetLineWidth(2);
TH1D *TTbarVSPT20 = new TH1D("TTbarVSPT20" ,"", 75, 0, 750); TTbarVSPT20 ->Sumw2(); TTbarVSPT20 ->SetLineColor(kRed); TTbarVSPT20 ->SetLineWidth(2);
TH1D *TTbarVSPT30 = new TH1D("TTbarVSPT30" ,"", 75, 0, 750); TTbarVSPT30 ->Sumw2(); TTbarVSPT30 ->SetLineColor(kGreen); TTbarVSPT30 ->SetLineWidth(2);
TH1D *TTbarVSPT40 = new TH1D("TTbarVSPT40" ,"", 75, 0, 750); TTbarVSPT40 ->Sumw2(); TTbarVSPT40 ->SetLineColor(kBlue); TTbarVSPT40 ->SetLineWidth(2);
TH1D *TTbarVSPT50 = new TH1D("TTbarVSPT50" ,"", 75, 0, 750); TTbarVSPT50 ->Sumw2(); TTbarVSPT50 ->SetLineColor(kYellow); TTbarVSPT50 ->SetLineWidth(2);
TH1D *TTbarVSPT70 = new TH1D("TTbarVSPT70" ,"", 75, 0, 750); TTbarVSPT70 ->Sumw2(); TTbarVSPT70 ->SetLineColor(kMagenta); TTbarVSPT70 ->SetLineWidth(2);
TH1D *TTbarVSPT100 = new TH1D("TTbarVSPT100","", 75, 0, 750); TTbarVSPT100->Sumw2(); TTbarVSPT100->SetLineColor(kCyan); TTbarVSPT100->SetLineWidth(2);
//small MinDPhi
TH1D *DataQCDVSPT0 = new TH1D("DataQCDVSPT0" ,"", 75, 0, 750); DataQCDVSPT0 ->Sumw2(); DataQCDVSPT0 ->SetLineColor(kBlack); DataQCDVSPT0 ->SetLineWidth(2);
TH1D *DataQCDVSPT20 = new TH1D("DataQCDVSPT20" ,"", 75, 0, 750); DataQCDVSPT20 ->Sumw2(); DataQCDVSPT20 ->SetLineColor(kRed); DataQCDVSPT20 ->SetLineWidth(2);
TH1D *DataQCDVSPT30 = new TH1D("DataQCDVSPT30" ,"", 75, 0, 750); DataQCDVSPT30 ->Sumw2(); DataQCDVSPT30 ->SetLineColor(kGreen); DataQCDVSPT30 ->SetLineWidth(2);
TH1D *DataQCDVSPT40 = new TH1D("DataQCDVSPT40" ,"", 75, 0, 750); DataQCDVSPT40 ->Sumw2(); DataQCDVSPT40 ->SetLineColor(kBlue); DataQCDVSPT40 ->SetLineWidth(2);
TH1D *DataQCDVSPT50 = new TH1D("DataQCDVSPT50" ,"", 75, 0, 750); DataQCDVSPT50 ->Sumw2(); DataQCDVSPT50 ->SetLineColor(kYellow); DataQCDVSPT50 ->SetLineWidth(2);
TH1D *DataQCDVSPT70 = new TH1D("DataQCDVSPT70" ,"", 75, 0, 750); DataQCDVSPT70 ->Sumw2(); DataQCDVSPT70 ->SetLineColor(kMagenta);DataQCDVSPT70 ->SetLineWidth(2);
TH1D *DataQCDVSPT100 = new TH1D("DataQCDVSPT100","", 75, 0, 750); DataQCDVSPT100->Sumw2(); DataQCDVSPT100->SetLineColor(kCyan); DataQCDVSPT100->SetLineWidth(2);
//1lep,0b
TH1D *DataWVSPT0 = new TH1D("DataWVSPT0" ,"", 75, 0, 750); DataWVSPT0 ->Sumw2(); DataWVSPT0 ->SetLineColor(kBlack); DataWVSPT0 ->SetLineWidth(2);
TH1D *DataWVSPT20 = new TH1D("DataWVSPT20" ,"", 75, 0, 750); DataWVSPT20 ->Sumw2(); DataWVSPT20 ->SetLineColor(kRed); DataWVSPT20 ->SetLineWidth(2);
TH1D *DataWVSPT30 = new TH1D("DataWVSPT30" ,"", 75, 0, 750); DataWVSPT30 ->Sumw2(); DataWVSPT30 ->SetLineColor(kGreen); DataWVSPT30 ->SetLineWidth(2);
TH1D *DataWVSPT40 = new TH1D("DataWVSPT40" ,"", 75, 0, 750); DataWVSPT40 ->Sumw2(); DataWVSPT40 ->SetLineColor(kBlue); DataWVSPT40 ->SetLineWidth(2);
TH1D *DataWVSPT50 = new TH1D("DataWVSPT50" ,"", 75, 0, 750); DataWVSPT50 ->Sumw2(); DataWVSPT50 ->SetLineColor(kYellow); DataWVSPT50 ->SetLineWidth(2);
TH1D *DataWVSPT70 = new TH1D("DataWVSPT70" ,"", 75, 0, 750); DataWVSPT70 ->Sumw2(); DataWVSPT70 ->SetLineColor(kMagenta);DataWVSPT70 ->SetLineWidth(2);
TH1D *DataWVSPT100 = new TH1D("DataWVSPT100","", 75, 0, 750); DataWVSPT100->Sumw2(); DataWVSPT100->SetLineColor(kCyan); DataWVSPT100->SetLineWidth(2);
//1lep,1b
TH1D *DataTopVSPT0 = new TH1D("DataTopVSPT0" ,"", 75, 0, 750); DataTopVSPT0 ->Sumw2(); DataTopVSPT0 ->SetLineColor(kBlack); DataTopVSPT0 ->SetLineWidth(2);
TH1D *DataTopVSPT20 = new TH1D("DataTopVSPT20" ,"", 75, 0, 750); DataTopVSPT20 ->Sumw2(); DataTopVSPT20 ->SetLineColor(kRed); DataTopVSPT20 ->SetLineWidth(2);
TH1D *DataTopVSPT30 = new TH1D("DataTopVSPT30" ,"", 75, 0, 750); DataTopVSPT30 ->Sumw2(); DataTopVSPT30 ->SetLineColor(kGreen); DataTopVSPT30 ->SetLineWidth(2);
TH1D *DataTopVSPT40 = new TH1D("DataTopVSPT40" ,"", 75, 0, 750); DataTopVSPT40 ->Sumw2(); DataTopVSPT40 ->SetLineColor(kBlue); DataTopVSPT40 ->SetLineWidth(2);
TH1D *DataTopVSPT50 = new TH1D("DataTopVSPT50" ,"", 75, 0, 750); DataTopVSPT50 ->Sumw2(); DataTopVSPT50 ->SetLineColor(kYellow); DataTopVSPT50 ->SetLineWidth(2);
TH1D *DataTopVSPT70 = new TH1D("DataTopVSPT70" ,"", 75, 0, 750); DataTopVSPT70 ->Sumw2(); DataTopVSPT70 ->SetLineColor(kMagenta);DataTopVSPT70 ->SetLineWidth(2);
TH1D *DataTopVSPT100 = new TH1D("DataTopVSPT100","", 75, 0, 750); DataTopVSPT100->Sumw2(); DataTopVSPT100->SetLineColor(kCyan); DataTopVSPT100->SetLineWidth(2);
//highDeltaPhi,0b
TH1D *DataZVSPT0 = new TH1D("DataZVSPT0" ,"", 75, 0, 750); DataZVSPT0 ->Sumw2(); DataZVSPT0 ->SetLineColor(kBlack); DataZVSPT0 ->SetLineWidth(2);
TH1D *DataZVSPT20 = new TH1D("DataZVSPT20" ,"", 75, 0, 750); DataZVSPT20 ->Sumw2(); DataZVSPT20 ->SetLineColor(kRed); DataZVSPT20 ->SetLineWidth(2);
TH1D *DataZVSPT30 = new TH1D("DataZVSPT30" ,"", 75, 0, 750); DataZVSPT30 ->Sumw2(); DataZVSPT30 ->SetLineColor(kGreen); DataZVSPT30 ->SetLineWidth(2);
TH1D *DataZVSPT40 = new TH1D("DataZVSPT40" ,"", 75, 0, 750); DataZVSPT40 ->Sumw2(); DataZVSPT40 ->SetLineColor(kBlue); DataZVSPT40 ->SetLineWidth(2);
TH1D *DataZVSPT50 = new TH1D("DataZVSPT50" ,"", 75, 0, 750); DataZVSPT50 ->Sumw2(); DataZVSPT50 ->SetLineColor(kYellow); DataZVSPT50 ->SetLineWidth(2);
TH1D *DataZVSPT70 = new TH1D("DataZVSPT70" ,"", 75, 0, 750); DataZVSPT70 ->Sumw2(); DataZVSPT70 ->SetLineColor(kMagenta);DataZVSPT70 ->SetLineWidth(2);
TH1D *DataZVSPT100 = new TH1D("DataZVSPT100","", 75, 0, 750); DataZVSPT100->Sumw2(); DataZVSPT100->SetLineColor(kCyan); DataZVSPT100->SetLineWidth(2);
//fill histograms
gROOT->cd();
cout << "LM6VSPT0" << endl;
LM6->Draw("misc.MT2>>LM6VSPT0", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "LM6VSPT20" << endl;
LM6->Draw("misc.MT2>>LM6VSPT20", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "LM6VSPT30" << endl;
LM6->Draw("misc.MT2>>LM6VSPT30", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "LM6VSPT40" << endl;
LM6->Draw("misc.MT2>>LM6VSPT40", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "LM6VSPT50" << endl;
LM6->Draw("misc.MT2>>LM6VSPT50", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "LM6VSPT70" << endl;
LM6->Draw("misc.MT2>>LM6VSPT70", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "LM6VSPT100" << endl;
LM6->Draw("misc.MT2>>LM6VSPT100","NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=100","goff");
cout << "WJetsVSPT0" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT0", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "WJetsVSPT20" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT20", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "WJetsVSPT30" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT30", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "WJetsVSPT40" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT40", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "WJetsVSPT50" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT50", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "WJetsVSPT70" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT70", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "WJetsVSPT100" << endl;
WJets->Draw("misc.MT2>>WJetsVSPT100","NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=100","goff");
cout << "ZJetsVSPT0" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT0", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "ZJetsVSPT20" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT20", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "ZJetsVSPT30" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT30", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "ZJetsVSPT40" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT40", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "ZJetsVSPT50" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT50", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "ZJetsVSPT70" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT70", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "ZJetsVSPT100" << endl;
ZJets->Draw("misc.MT2>>ZJetsVSPT100","NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=100","goff");
cout << "TTbarVSPT0" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT0", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "TTbarVSPT20" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT20", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "TTbarVSPT30" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT30", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "TTbarVSPT40" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT40", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "TTbarVSPT50" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT50", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "TTbarVSPT70" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT70", "NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "TTbarVSPT100" << endl;
TTbar->Draw("misc.MT2>>TTbarVSPT100","NJetsIDLoose40>=2&&misc.HT>450&&misc.MET>30&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.Vectorsumpt>=100","goff");
cout << "DataQCDVSPT0" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT0", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "DataQCDVSPT20" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT20", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "DataQCDVSPT30" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT30", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "DataQCDVSPT40" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT40", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "DataQCDVSPT50" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT50", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "DataQCDVSPT70" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT70", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "DataQCDVSPT100" << endl;
Data->Draw("misc.MT2>>DataQCDVSPT100","NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40<0.2&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=100","goff");
cout << "DataZVSPT0" << endl;
Data->Draw("misc.MT2>>DataZVSPT0", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "DataZVSPT20" << endl;
Data->Draw("misc.MT2>>DataZVSPT20", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "DataZVSPT30" << endl;
Data->Draw("misc.MT2>>DataZVSPT30", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "DataZVSPT40" << endl;
Data->Draw("misc.MT2>>DataZVSPT40", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "DataZVSPT50" << endl;
Data->Draw("misc.MT2>>DataZVSPT50", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "DataZVSPT70" << endl;
Data->Draw("misc.MT2>>DataZVSPT70", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "DataZVSPT100" << endl;
Data->Draw("misc.MT2>>DataZVSPT100","NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&NEles==0&&NMuons==0&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=100","goff");
cout << "DataWVSPT0" << endl;
Data->Draw("misc.MT2>>DataWVSPT0", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "DataWVSPT20" << endl;
Data->Draw("misc.MT2>>DataWVSPT20", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "DataWVSPT30" << endl;
Data->Draw("misc.MT2>>DataWVSPT30", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "DataWVSPT40" << endl;
Data->Draw("misc.MT2>>DataWVSPT40", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "DataWVSPT50" << endl;
Data->Draw("misc.MT2>>DataWVSPT50", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "DataWVSPT70" << endl;
Data->Draw("misc.MT2>>DataWVSPT70", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "DataWVSPT100" << endl;
Data->Draw("misc.MT2>>DataWVSPT100","NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM==0&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=100","goff");
cout << "DataWVSPT0" << endl;
Data->Draw("misc.MT2>>DataTopVSPT0", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=0&&misc.Vectorsumpt<20","goff");
cout << "DataTopVSPT20" << endl;
Data->Draw("misc.MT2>>DataTopVSPT20", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=20&&misc.Vectorsumpt<30","goff");
cout << "DataTopVSPT30" << endl;
Data->Draw("misc.MT2>>DataTopVSPT30", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=30&&misc.Vectorsumpt<40","goff");
cout << "DataTopVSPT40" << endl;
Data->Draw("misc.MT2>>DataTopVSPT40", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=40&&misc.Vectorsumpt<50","goff");
cout << "DataTopVSPT50" << endl;
Data->Draw("misc.MT2>>DataTopVSPT50", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=50&&misc.Vectorsumpt<70","goff");
cout << "DataTopVSPT70" << endl;
Data->Draw("misc.MT2>>DataTopVSPT70", "NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=70&&misc.Vectorsumpt<100","goff");
cout << "DataTopVSPT100" << endl;
Data->Draw("misc.MT2>>DataTopVSPT100","NJetsIDLoose40>=2&&misc.HT>750&&misc.MET>10&&(NEles+NMuons)==1&&NTausIDLoose3Hits==0&&misc.MinMetJetDPhi4Pt40>0.5&&NBJets40CSVM>=1&&misc.PassJet40ID ==1&&misc.HBHENoiseFlag == 0&&misc.CSCTightHaloIDFlag == 0&&misc.trackingFailureFlag==0&&misc.eeBadScFlag==0&&misc.EcalDeadCellTriggerPrimitiveFlag==0&&misc.TrackingManyStripClusFlag==0&&misc.TrackingTooManyStripClusFlag==0&&misc.TrackingLogErrorTooManyClustersFlag==0&&misc.CrazyHCAL==0&&(misc.MET>30||misc.MET/misc.CaloMETRaw<=2.)&&misc.Vectorsumpt>=100","goff");
TLegend *leg = new TLegend(.6,.6,.90,.90);
leg->SetName("leg");
leg -> SetFillColor(0);
leg -> SetBorderSize(0);
leg->AddEntry(LM6VSPT0,"0 #leq VSPT < 20 GeV", "l");
leg->AddEntry(LM6VSPT20,"20 #leq VSPT < 30 GeV", "l");
leg->AddEntry(LM6VSPT30,"30 #leq VSPT < 40 GeV", "l");
leg->AddEntry(LM6VSPT40,"40 #leq VSPT < 50 GeV", "l");
leg->AddEntry(LM6VSPT50,"50 #leq VSPT < 70 GeV", "l");
leg->AddEntry(LM6VSPT70,"70 #leq VSPT < 100 GeV", "l");
leg->AddEntry(LM6VSPT100,"100 GeV #leq VSPT", "l");
//store the file
TFile *file = new TFile("UTMfile.root","RECREATE");
file->cd();
LM6VSPT0 ->Write();
LM6VSPT20 ->Write();
LM6VSPT30 ->Write();
LM6VSPT40 ->Write();
LM6VSPT50 ->Write();
LM6VSPT70 ->Write();
LM6VSPT100->Write();
WJetsVSPT0 ->Write();
WJetsVSPT20 ->Write();
WJetsVSPT30 ->Write();
WJetsVSPT40 ->Write();
WJetsVSPT50 ->Write();
WJetsVSPT70 ->Write();
WJetsVSPT100->Write();
ZJetsVSPT0 ->Write();
ZJetsVSPT20 ->Write();
ZJetsVSPT30 ->Write();
ZJetsVSPT40 ->Write();
ZJetsVSPT50 ->Write();
ZJetsVSPT70 ->Write();
ZJetsVSPT100->Write();
TTbarVSPT0 ->Write();
TTbarVSPT20 ->Write();
TTbarVSPT30 ->Write();
TTbarVSPT40 ->Write();
TTbarVSPT50 ->Write();
TTbarVSPT70 ->Write();
TTbarVSPT100->Write();
DataQCDVSPT0 ->Write();
DataQCDVSPT20 ->Write();
DataQCDVSPT30 ->Write();
DataQCDVSPT40 ->Write();
DataQCDVSPT50 ->Write();
DataQCDVSPT70 ->Write();
DataQCDVSPT100->Write();
DataWVSPT0 ->Write();
DataWVSPT20 ->Write();
DataWVSPT30 ->Write();
DataWVSPT40 ->Write();
DataWVSPT50 ->Write();
DataWVSPT70 ->Write();
DataWVSPT100->Write();
DataZVSPT0 ->Write();
DataZVSPT20 ->Write();
DataZVSPT30 ->Write();
DataZVSPT40 ->Write();
DataZVSPT50 ->Write();
DataZVSPT70 ->Write();
DataZVSPT100->Write();
DataTopVSPT0 ->Write();
DataTopVSPT20 ->Write();
DataTopVSPT30 ->Write();
DataTopVSPT40 ->Write();
DataTopVSPT50 ->Write();
DataTopVSPT70 ->Write();
DataTopVSPT100->Write();
leg->Write();
file->Close();
cout << "File saved: " << file->GetName() << endl;
}
int main( int argc, char* argv[] ) {
std::string runName = "precalib_BGO_pedestal_noSource";
std::string tag = "default";
if( argc>2 ) {
std::string runName_str(argv[1]);
runName = runName_str;
std::string tag_str(argv[2]);
tag = tag_str;
}else{
std::cout<<"Usage:"<<std::endl;
std::cout<<"./makeSimulationToAnalysisTree BeamEnergy(in MeV) tag"<<std::endl;
exit(12345);
}
TString runName_tstr(runName);
bool isIdeal = false;
//run name = energy for now, might have to change this for different beams...
std::string fileName = "OriginalSimulationData/EEShash" + runName + ".root";
TFile* file = TFile::Open(fileName.c_str());
if( file==0 ) {
std::string fileName = "OriginalSimulationData/EEShashIdeal_" + runName + ".root";
TFile* file = TFile::Open(fileName.c_str());
isIdeal = true;
}
if( file==0 ) {
std::cout << "ERROR! Din't find file " << fileName << std::endl;
std::cout << "Exiting." << std::endl;
exit(11);
}
TChain* tree = new TChain("EEShash");
tree = (TChain*)file->Get("EEShash");
//std::string outdir = "analysisTrees_" + tag;
std::string outdir = "OriginalSimulationData";
system( Form("mkdir -p %s", outdir.c_str()) );
std::string outfileName;
if(isIdeal==0){ outfileName = outdir + "/Reco_Simulation" + runName + ".root";}
else { outfileName = outdir + "/Reco_Simulation_Ideal_" + runName + ".root";}
TFile* outfile = TFile::Open( outfileName.c_str(), "RECREATE" );
TTree* outTree = new TTree("recoTree","recoTree");
double Eact_0;
tree->SetBranchAddress("Eact_0", &Eact_0);
double Eact_1;
tree->SetBranchAddress("Eact_1", &Eact_1);
double Eact_2;
tree->SetBranchAddress("Eact_2", &Eact_2);
double Eact_3;
tree->SetBranchAddress("Eact_3", &Eact_3);
double Eact_4;
tree->SetBranchAddress("Eact_4", &Eact_4);
double Eact_5;
tree->SetBranchAddress("Eact_5", &Eact_5);
double Eact_6;
tree->SetBranchAddress("Eact_6", &Eact_6);
double Eact_7;
tree->SetBranchAddress("Eact_7", &Eact_7);
double Eact_8;
tree->SetBranchAddress("Eact_8", &Eact_8);
double Eact_9;
tree->SetBranchAddress("Eact_9", &Eact_9);
double Eact_10;
tree->SetBranchAddress("Eact_10", &Eact_10);
double Eact_11;
tree->SetBranchAddress("Eact_11", &Eact_11);
double Eact_12;
tree->SetBranchAddress("Eact_12", &Eact_12);
double Eact_13;
tree->SetBranchAddress("Eact_13", &Eact_13);
double Eact_14;
tree->SetBranchAddress("Eact_14", &Eact_14);
double Ebgo_0;
tree->SetBranchAddress( "Ebgo_0", &Ebgo_0);
double Ebgo_1;
tree->SetBranchAddress( "Ebgo_1", &Ebgo_1);
double Ebgo_2;
tree->SetBranchAddress( "Ebgo_2", &Ebgo_2);
double Ebgo_3;
tree->SetBranchAddress("Ebgo_3", &Ebgo_3);
double Ebgo_4;
tree->SetBranchAddress("Ebgo_4", &Ebgo_4);
double Ebgo_5;
tree->SetBranchAddress("Ebgo_5", &Ebgo_5);
double Ebgo_6;
tree->SetBranchAddress("Ebgo_6", &Ebgo_6);
double Ebgo_7;
tree->SetBranchAddress("Ebgo_7", &Ebgo_7);
double Ebgo;
tree->SetBranchAddress("Ebgo", &Ebgo);
double Eabs;
tree->SetBranchAddress("Eabs", &Eabs);
double Eact;
tree->SetBranchAddress("Eact", &Eact);
double Escint1;
tree->SetBranchAddress("Escint1", &Escint1);
double Ehodo11;
tree->SetBranchAddress("Ehodo11", &Ehodo11);
double Ehodo12;
tree->SetBranchAddress("Ehodo12", &Ehodo12);
double Escint;
tree->SetBranchAddress("Escint", &Escint);
double Ehodo;
tree->SetBranchAddress("Ehodo", &Ehodo);
double xPosition;
tree->SetBranchAddress("xPosition",&xPosition);
double yPosition;
tree->SetBranchAddress("yPosition",&yPosition);
float cef3_corr_[CEF3_CHANNELS],bgo_corr_[BGO_CHANNELS];
bool isSingleEle_scintFront_=1;
int nHodoClustersY=1; int nHodoClustersX=1;
int nHodoClusters1Y=1; int nHodoClusters1X=1;
float E_abs;
float xPos;
float yPos;
int bgo_chan=BGO_CHANNELS;
int cef3_chan=CEF3_CHANNELS;
outTree->Branch( "nHodoClustersX", &nHodoClustersX, "nHodoClustersX/I" );
outTree->Branch( "nHodoClustersY", &nHodoClustersY, "nHodoClustersY/I" );
outTree->Branch( "nHodoClusters1X", &nHodoClusters1X, "nHodoClusters1X/I" );
outTree->Branch( "nHodoClusters1Y", &nHodoClusters1Y, "nHodoClusters1Y/I" );
outTree->Branch( "cef3_chan", &cef3_chan, "cef3_chan/I" );
outTree->Branch( "bgo_chan", &bgo_chan, "bgo_chan/I" );
outTree->Branch( "bgo_corr", bgo_corr_, "bgo_corr_[bgo_chan]/F" );
outTree->Branch( "cef3_corr", cef3_corr_, "cef3_corr_[cef3_chan]/F" );
outTree->Branch( "isSingleEle_scintFront", &isSingleEle_scintFront_, "isSingleEle_scintFront_/O" );
outTree->Branch( "E_abs", &E_abs, "E_abs/F");
outTree->Branch( "xPos", &xPos, "xPos/F");
outTree->Branch( "yPos", &yPos, "yPos/F");
float LYSF[] = {0.85, 0.94, 0.95, 0.98, 1.00, 1.02, 1.05, 1.05, 1.05, 0.74};
// float LYSF[] = {0.85, 0.94, 0.95, 0.98, 1.00, 0.7, 1.05, 1.05, 1.05, 0.74};
// float LYSF[] = {0.87, 0.96, 0.97, 1.00, 1.02, 1.04, 1.07, 1.07, 1.08, 0.75};
//as a test with the best being the best one
// float LYSF[] = {0.81, 0.89, 0.90, 0.93, 0.95, 0.97, 1.00, 1.00, 1.00, 0.70};
//Test if 6.crystal fails
// float LYSFH[] = {0.87, 0.96, 0.97, 1.00, 1.02, 0.50, 1.07, 1.07, 1.08, 1.07, 1.08, 1.09, 1.09, 1.04, 0.75};
///
float LYSFH[] = {0.87, 0.96, 0.97, 1.00, 1.02, 1.04, 1.07, 1.07, 1.08, 1.07, 1.08, 1.09, 1.09, 1.04, 0.75};
/*
std::string fullVarName = "";
for( unsigned i=0; i<15; ++i ) {
std::string plusSign = (i==0) ? "" : " + ";
std::string thisPiece(Form("%s%f*Eact_%d", plusSign.c_str(), LYSFH[i], i));
fullVarName += thisPiece;
}
*/
unsigned nentries = tree->GetEntries();
double hodoEff;
double scintEff;
for( unsigned iEntry=0; iEntry<nentries; ++iEntry ) {
tree->GetEntry(iEntry);
if( iEntry % 5000 == 0 ) std::cout << "Entry: " << iEntry << " / " << nentries << std::endl;
xPos = xPosition;
yPos = yPosition;
if(Ehodo11>0.1){
nHodoClustersX=1;
hodoEff++;
} else{ nHodoClusters1X=0;}
if(Ehodo12>0.1){
nHodoClustersY=1;
hodoEff++;
} else{ nHodoClusters1Y=0; }
if(Escint1>0.25){
isSingleEle_scintFront_=1;
scintEff++;
} else{ isSingleEle_scintFront_=0;}
if(Ehodo>0.1){
nHodoClustersX=1; nHodoClustersY=1;
hodoEff++;
} else{ nHodoClustersX=0; nHodoClustersY=0;}
/*
if(Escint>0.25 && Escint< 1.5){
isSingleEle_scintFront_=1;
scintEff++;
} else{ isSingleEle_scintFront_=0;}
*/
E_abs= Eabs;
std::vector<float> cef3_corr;
for( int i=0; i<CEF3_CHANNELS; ++i ) cef3_corr.push_back(-1.);
std::vector<float> bgo_corr;
for( int i=0; i<BGO_CHANNELS; ++i ) bgo_corr.push_back(-1.);
/*
for(int i=0; i<10; ++i){
cef3_corr[0]= (LYSF[0]*Eact_0+LYSF[1]*Eact_1+LYSF[2]*Eact_2 +LYSF[3]*Eact_3 +LYSF[4]*Eact_4 +LYSF[5]*Eact_5 +LYSF[6]*Eact_6 +LYSF[7]*Eact_7 +LYSF[8]*Eact_8 +LYSF[9]*Eact_9)/4. ; //yes not very elegant, but it didn't want to work otherwise
// cef3_corr[0] = Eact /4.;
}
*/
for(int i=0; i<15; ++i){
cef3_corr[0]= (LYSFH[0]*Eact_0+LYSFH[1]*Eact_1+LYSFH[2]*Eact_2 +LYSFH[3]*Eact_3 +LYSFH[4]*Eact_4 +LYSFH[5]*Eact_5 +LYSFH[6]*Eact_6 +LYSFH[7]*Eact_7 +LYSFH[8]*Eact_8 +LYSFH[9]*Eact_9 +LYSFH[10]*Eact_10 +LYSFH[11]*Eact_11 +LYSFH[12]*Eact_12 +LYSFH[13]*Eact_13 +LYSFH[14]*Eact_14 )/4. ; //yes not very elegant, but it didn't want to work otherwise
// cef3_corr[0] = Eact /4.;
}
bgo_corr[0] = Ebgo/8.;
bgo_corr[1] = Ebgo/8.;
bgo_corr[2] = Ebgo/8.;
bgo_corr[3] = Ebgo/8.;
bgo_corr[4] = Ebgo/8.;
bgo_corr[5] = Ebgo/8.;
bgo_corr[6] = Ebgo/8.;
bgo_corr[7] = Ebgo/8.;
for(int k=0; k<CEF3_CHANNELS; ++k){
cef3_corr_[k] = cef3_corr[0];
}
for (int k=0; k<BGO_CHANNELS; ++k){
bgo_corr_[k] = bgo_corr[k];
}
outTree->Fill();
}
outfile->cd();
outTree->Write();
outfile->Close();
std::cout << "-> Simulated Analysis Tree saved in: " << outfile->GetName() << std::endl;
std::cout << "Scintillator efficiency = " << scintEff/nentries << std::endl;
std::cout << "Hodoscope efficiency = " << hodoEff/nentries << std::endl;
return 0;
}
void ZTMVAClassification( TString myMethodList = "" ) {
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 1; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" ); //
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// ADD IN OUR VARIABLES HERE
factory->AddVariable("log(kaon_IPCHI2_OWNPV)", "log(kaon_IPCHI2_OWNPV)", "", 'D');
//factory->AddVariable("kaon_TRACK_GhostProb", "kaon_TRACK_GhostProb", "", 'D');
factory->AddVariable("kaon_PT", "kaon_PT", "", 'D');
factory->AddVariable("log(proton_IPCHI2_OWNPV)", "log(proton_IPCHI2_OWNPV)", "", 'D');
//factory->AddVariable("proton_TRACK_GhostProb", "proton_TRACK_GhostProb", "", 'D');
factory->AddVariable("proton_PT", "proton_PT", "", 'D');
//factory->AddVariable("gamma_PT", "gamma_PT", "", 'D');
//factory->AddVariable("gamma_CL", "gamma_CL", "", 'D');
factory->AddVariable("muminus_ProbNNmu", "muminus_ProbNNmu", "", 'D');
//factory->AddVariable("muminus_TRACK_GhostProb", "muminus_TRACK_GhostProb", "", 'D');
factory->AddVariable("muplus_ProbNNmu", "muplus_ProbNNmu", "", 'D');
//factory->AddVariable("muplus_TRACK_GhostProb", "muplus_TRACK_GhostProb", "", 'D');
factory->AddVariable("Lambda_b0_DTF_CHI2NDOF", "Lambda_b0_DTF_CHI2NDOF", "", 'D');
//factory->AddVariable("log(Lambda_b0_IPCHI2_OWNPV)", "log(Lambda_b0_IPCHI2_OWNPV)", "", 'D');
factory->AddVariable("Lambda_b0_FDS", "Lambda_b0_FDS", "", 'D');
factory->AddVariable("Lambda_b0_PT", "Lambda_b0_PT", "", 'D');
// TFile * input_Background = new TFile("../back.root");
TFile * input_Signal = new TFile("/afs/cern.ch/work/a/apmorris/public/Lb2chicpK/signal_samples/reduced_Lb2chicpK_MC_2011_2012_signal.root"); //
TFile * input_Background = new TFile("/afs/cern.ch/work/a/apmorris/public/Lb2chicpK/signal_samples/background.root"); //
std::cout << "--- TMVAClassification : Using input file for signal : " << input_Signal->GetName() << std::endl;
std::cout << "--- TMVAClassification : Using input file for background : " << input_Background->GetName() << std::endl;
// --- Register the training and test trees
TTree *signal = (TTree*)input_Signal->Get("DecayTree");
TTree *background = (TTree*)input_Background->Get("DecayTree");
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// You can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
// ---- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood ("naive Bayes estimator")
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// Decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
// PCA-transformed likelihood
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// Use a kernel density estimator to approximate the PDFs
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// Use a variable-dependent mix of splines and kernel density estimator
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// Test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
if (Use["PDEFoamBoost"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );
// Linear discriminant (same as Fisher discriminant)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher discriminant (same as LD)
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"]){
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
//factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5" );
// factory->BookMethod( TMVA::Types::kMLP, "MLPCE", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5:EstimatorType=CE" );
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=9:TestRate=5:EstimatorType=CE" );
// factory->BookMethod( TMVA::Types::kMLP, "MLPCE83", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=8,3:TestRate=5:EstimatorType=CE" );
}
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
if (Use["MLPBNN"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) { // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" );
// factory->BookMethod( TMVA::Types::kBDT, "BDTGI",
// "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5:SeparationType=GiniIndexWithLaplace" );
// factory->BookMethod( TMVA::Types::kBDT, "BDTG6",
// "!H:!V:NTrees=600:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
//factory->BookMethod( TMVA::Types::kBDT, "BDTG2",
// "!H:!V:NTrees=800:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
factory->BookMethod( TMVA::Types::kBDT, "BDTG3",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
// factory->BookMethod( TMVA::Types::kBDT, "BDTG4",
// "!H:!V:NTrees=1200:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
// factory->BookMethod( TMVA::Types::kBDT, "BDTG5",
// "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=5" );
}
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
"!H:!V:NTrees=50:nEventsMin=150:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// For an example of the category classifier usage, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("ROCIntegral","GA");
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
// if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TMVAClassification_ttV( TString myMethodList = "" )
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
// (an example is given for using the BDT as plugin (see below),
// but of course the real application is when you write your own
// method based)
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// to get access to the GUI and all tmva macros
TString tmva_dir(TString(gRootDir) + "/tmva");
if(gSystem->Getenv("TMVASYS"))
tmva_dir = TString(gSystem->Getenv("TMVASYS"));
gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() );
gROOT->ProcessLine(".L TMVAGui.C");
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;//1
Use["CutsD"] = 0;//1
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;//1
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0;//1 // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;//1
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;//1
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0;//1 // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; //1// Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; //1// minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0;//1; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;//1;
//
// --- Boosted Decision Trees
Use["BDT"] = 0;//1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 1;//1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory->AddVariable(" Mqq := Mqq", 'F');
factory->AddVariable(" Pt_qq := Pt_qq", 'F');
//factory->AddVariable( "myvar1 := var1+var2", 'F' );
//factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
//factory->AddVariable( "var3", "Variable 3", "units", 'F' );
//factory->AddVariable( "var4", "Variable 4", "units", 'F' );
//factory->AddVariable( "Mqq", 'F' );
//factory->AddVariable( "Pt_qq", 'F' );
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
//factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
//factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
//Change the input files
/*
TString fname = "./tmva_class_example.root";
if (gSystem->AccessPathName( fname )) // file does not exist in local directory
gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
TFile *input = TFile::Open( fname );
std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// --- Register the training and test trees
TTree *signal = (TTree*)input->Get("TreeS");
TTree *background = (TTree*)input->Get("TreeB");
*/
VCandStruct vcand;
AsymetryStruct asym;
//TFile* fsignal = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof08-04-13_18-01-24/proof_ttW.root");
TFile* fsignal = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_ttW.root");
TTree *signal = (TTree*)fsignal->Get("theTree2");
//TTree *signal_orig = (TTree*)fsignal->Get("theTree2");
//TTree *signal = signal_orig->CloneTree(0);
//signal->SetDirectory(0);
TFile* fbackground = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_tt-dilepton.root");
TTree *background = (TTree*)fbackground->Get("theTree2");
//TTree *background_orig = (TTree*)fbackground->Get("theTree");
//TTree *background = background_orig->CloneTree(0);
//background->SetDirectory(0);
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 0.30*20/185338;
Double_t backgroundWeight = 222.*0.1*20/9982625;
// You can add an arbitrary number of signal or background trees
//Commented by Eric
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
// To give different trees for training and testing, do as follows:
//factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
//factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
// Use the following code instead of the above two or four lines to add signal and background
// training and test events "by hand"
// NOTE that in this case one should not give expressions (such as "var1+var2") in the input
// variable definition, but simply compute the expression before adding the event
//
// // --- begin ----------------------------------------------------------
/*
cout<<"begin"<<endl;
std::vector<Double_t> vars( 2 ); // vector has size of number of input variables
//Float_t treevars[4], weight;
//
// // Signal
//for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
signal->SetBranchAddress("Vcand",&vcand);
//signal->SetBranchAddress("VCand/Mqq",&vcand.Mqq);
//signal->SetBranchAddress("VCand/Pt_qq",&vcand.Pt_qq);
for (UInt_t i=0; i<signal->GetEntries(); i++) {
//cout<<"GetEntry "<<i<<endl;
signal->GetEntry(i);
//cout<<"done"<<endl;
//for (UInt_t ivar=0; ivar<2; ivar++) vars[ivar] = treevars[ivar];
vars[0] = vcand.Mqq;
vars[1] = vcand.Pt_qq;
//cout<<vars[0]<<" "<<vars[1]<<endl;
// add training and test events; here: first half is training, second is testing
// note that the weight can also be event-wise
//cout<<"there"<<endl;
if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight );
else factory->AddSignalTestEvent ( vars, signalWeight );
//return ;
}
//delete signal;
//fsignal->Close();
cout<<"begin"<<endl;
//TFile* fbackground = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof08-04-13_18-01-24/proof_tt-dilepton.root");
cout<<fsignal<<" "<<signal<<endl;
// Background (has event weights)
//background->SetBranchAddress( "weight", &weight );
//for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
background->SetBranchAddress("Vcand",&vcand);
//background->SetBranchAddress("VCand/Mqq",&vcand.Mqq);
//background->SetBranchAddress("VCand/Pt_qq",&vcand.Pt_qq);
cout<<"here"<<endl;
for (UInt_t i=0; i<background->GetEntries(); i++) {
//cout<<"GetEntry "<<i<<endl;
background->GetEntry(i);
//cout<<"done"<<endl;
//for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
vars[0] = vcand.Mqq;
vars[1] = vcand.Pt_qq;
//cout<<vars[0]<<" "<<vars[1]<<endl;
// add training and test events; here: first half is training, second is testing
// note that the weight can also be event-wise
//if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
//else factory->AddBackgroundTestEvent ( vars, backgroundWeight*weight );
//cout<<"toto"<<endl;
if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight);
else factory->AddBackgroundTestEvent ( vars, backgroundWeight);
//cout<<"toto"<<endl;
//return ;
}
*/
cout<<"eND"<<endl;
factory->EvaluateAllVariables();
factory->Print();
//return;
// --- end ------------------------------------------------------------
//
// --- end of tree registration
// Set individual event weights (the variables must exist in the original TTree)
// for signal : factory->SetSignalWeightExpression ("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
//Commented by Eric: factory->SetBackgroundWeightExpression( "weight" );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
// Tell the factory how to use the training and testing events
//
// If no numbers of events are given, half of the events in the tree are used
// for training, and the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// To also specify the number of testing events, use:
// factory->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
cout<<"Prepare it"<<endl;
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
cout<<"Prepare it : DONE"<<endl;
// ---- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
cout<<"Use cuts"<<endl;
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
cout<<"done"<<endl;
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood ("naive Bayes estimator")
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// Decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
// PCA-transformed likelihood
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// Use a kernel density estimator to approximate the PDFs
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// Use a variable-dependent mix of splines and kernel density estimator
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// Test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
if (Use["PDEFoamBoost"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );
// Linear discriminant (same as Fisher discriminant)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher discriminant (same as LD)
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
if (Use["MLPBNN"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
"!H:!V:NTrees=50:nEventsMin=150:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// For an example of the category classifier usage, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("ROCIntegral","GA");
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
cout<<"TrainAllMethods"<<endl;
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void MergeRootfile( TDirectory *target, TList *sourcelist, double crossArray[] ) {
cout << "Target path: " << target->GetPath() << endl;
TString path( (char*)strstr( target->GetPath(), ":" ) );
path.Remove( 0, 2 );
TFile *first_source = (TFile*)sourcelist->First();
first_source->cd( path );
TDirectory *current_sourcedir = gDirectory;
//gain time, do not add the objects in the list in memory
Bool_t status = TH1::AddDirectoryStatus();
TH1::AddDirectory(kFALSE);
// loop over all keys in this directory
TChain *globChain = 0;
TIter nextkey( current_sourcedir->GetListOfKeys() );
TKey *key, *oldkey=0;
while ( (key = (TKey*)nextkey())) {
//keep only the highest cycle number for each key
if (oldkey && !strcmp(oldkey->GetName(),key->GetName())) continue;
// read object from first source file
first_source->cd( path );
TObject *obj = key->ReadObj();
if ( obj->IsA()->InheritsFrom( "TH1" ) ) {
// descendant of TH1 -> merge it
cout << "Merging histogram " << obj->GetName() << endl;
TH1 *h1 = (TH1*)obj;
// Scale by the cross-section factor
h1->Scale(crossArray[0]);
h1->Sumw2();
// loop over all source files and add the content of the
// correspondant histogram to the one pointed to by "h1"
TFile *nextsource = (TFile*)sourcelist->After( first_source );
int q = 1; // This keeps track of which
// cross section factor to use
while ( nextsource ) {
// make sure we are at the correct directory level by cd'ing to path
nextsource->cd( path );
TKey *key2 = (TKey*)gDirectory->GetListOfKeys()->FindObject(h1->GetName());
if (key2) {
TH1 *h2 = (TH1*)key2->ReadObj();
// Scale by the cross section factor
// before adding.
h2->Scale(crossArray[q]);
h1->Add( h2 );
q++;
delete h2;
}
nextsource = (TFile*)sourcelist->After( nextsource );
}
}
else if ( obj->IsA()->InheritsFrom( "TTree" ) ) {
// loop over all source files create a chain of Trees "globChain"
const char* obj_name= obj->GetName();
globChain = new TChain(obj_name);
globChain->Add(first_source->GetName());
TFile *nextsource = (TFile*)sourcelist->After( first_source );
// const char* file_name = nextsource->GetName();
// cout << "file name " << file_name << endl;
while ( nextsource ) {
globChain->Add(nextsource->GetName());
nextsource = (TFile*)sourcelist->After( nextsource );
}
} else if ( obj->IsA()->InheritsFrom( "TDirectory" ) ) {
// it's a subdirectory
cout << "Found subdirectory " << obj->GetName() << endl;
// create a new subdir of same name and title in the target file
target->cd();
TDirectory *newdir = target->mkdir( obj->GetName(), obj->GetTitle() );
// newdir is now the starting point of another round of merging
// newdir still knows its depth within the target file via
// GetPath(), so we can still figure out where we are in the recursion
MergeRootfile( newdir, sourcelist, crossArray );
} else {
// object is of no type that we know or can handle
cout << "Unknown object type, name: "
<< obj->GetName() << " title: " << obj->GetTitle() << endl;
}
// now write the merged histogram (which is "in" obj) to the target file
// note that this will just store obj in the current directory level,
// which is not persistent until the complete directory itself is stored
// by "target->Write()" below
if ( obj ) {
target->cd();
//!!if the object is a tree, it is stored in globChain...
if(obj->IsA()->InheritsFrom( "TTree" ))
globChain->Merge(target->GetFile(),0,"keep");
else
obj->Write( key->GetName() );
}
} // while ( ( TKey *key = (TKey*)nextkey() ) )
// save modifications to target file
target->SaveSelf(kTRUE);
TH1::AddDirectory(status);
}
void TMVAtest1()
{
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString TrainName = "TMVA";
TString outfileName( TrainName+".root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( TrainName, outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory->AddVariable ("ptphoton", 'F');
// factory->AddVariable ("etaphoton", 'F');
factory->AddVariable ("ptmuon", 'F');
// factory->AddVariable ("etamuon", 'F');
factory->AddVariable ("ptjet", 'F');
// factory->AddVariable ("etajet", 'F');
// factory->AddVariable ("masstop", 'F');
// factory->AddVariable ("mtw", 'F');
factory->AddVariable ("deltaRphotonjet", 'F');
factory->AddVariable ("deltaRphotonmuon", 'F');
// factory->AddVariable ("ht", 'F');
// factory->AddVariable ("photonmuonmass", 'F');
factory->AddVariable ("costopphoton", 'F');
// factory->AddVariable ("topphotonmass", 'F');
//factory->AddVariable ("pttop", 'F');
//factory->AddVariable ("etatop", 'F');
factory->AddVariable ("jetmultiplicity", 'F');
// factory->AddVariable ("bjetmultiplicity", 'F');
factory->AddVariable ("deltaphiphotonmet", 'F');
factory->AddVariable ("cvsdiscriminant", 'F');
// factory->AddVariable ("leptoncharge", 'F');
//Load the signal and background event samples from ROOT trees
// TFile *inputSTrain(0);
// TFile *inputBTrain(0);
TString sigFileTrain = "SIGNALtGu.root";
TString bkgFileTrain = "WPHJET.root";
TString ttbarFileTrain1 ="TTBAR1.root";
TString ttbarFileTrain2 ="TTBAR2.root";
TString ttbarFileTrain3 ="TTBAR3.root";
TString TTGFileTrain ="TTG.root";
TString WWFileTrain ="WW.root";
TString WZFileTrain ="WZ.root";
TString ZZFileTrain ="ZZ.root";
TString ZGAMMAFileTrain ="ZGAMMA.root";
// TString bkgFileTrain = "TTG.root";
TFile *inputSTrain = TFile::Open( sigFileTrain );
if (!inputSTrain) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
TFile *inputBTrain = TFile::Open( bkgFileTrain );
TFile *inputBttbar1 = TFile::Open( ttbarFileTrain1);
TFile *inputBttbar2 = TFile::Open( ttbarFileTrain2);
TFile *inputBttbar3 = TFile::Open( ttbarFileTrain3);
TFile *inputTTG= TFile::Open(TTGFileTrain );
TFile *inputWW= TFile::Open(WWFileTrain );
TFile *inputWZ= TFile::Open(WZFileTrain );
TFile *inputZZ= TFile::Open(ZZFileTrain );
TFile *inputZGAMMA= TFile::Open( ZGAMMAFileTrain);
if (!inputBTrain) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAnalysis : Accessing Signal Train: " << sigFileTrain << std::endl;
std::cout << "--- TMVAnalysis : Accessing Background Train: " << bkgFileTrain << std::endl;
TTree *signalTrain = (TTree*)inputSTrain->FindObjectAny("atq");
TTree *backgroundTrain = (TTree*)inputBTrain->FindObjectAny("atq");
TTree *ttbarbackground1 = (TTree*)inputBttbar1->FindObjectAny("atq");
TTree *ttbarbackground2 = (TTree*)inputBttbar2->FindObjectAny("atq");
TTree *ttbarbackground3 = (TTree*)inputBttbar3->FindObjectAny("atq");
TTree *TTGbackground = (TTree*)inputTTG->FindObjectAny("atq");
TTree *WWbackground = (TTree*)inputWW->FindObjectAny("atq");
TTree *WZbackground = (TTree*)inputWZ->FindObjectAny("atq");
TTree *ZZbackground = (TTree*)inputZZ->FindObjectAny("atq");
TTree *ZGAMMAbackground = (TTree*)inputZGAMMA->FindObjectAny("atq");
// TTree *signalTrain = (TTree*)inputSTrain->FindObjectAny("insidetopmass");
// TTree *backgroundTrain = (TTree*)inputBTrain->FindObjectAny("insidetopmass");
// factory->AddSignalTree( signalTrain, 1);
//cout<<"reza1";
// factory->AddBackgroundTree( backgroundTrain, 1, );
//cout<<"reza2";
factory->SetInputTrees(signalTrain,backgroundTrain,1,1);
factory->AddBackgroundTree( ttbarbackground1, 1.1);
factory->AddBackgroundTree( ttbarbackground2, 1.1);
// factory->AddBackgroundTree( ttbarbackground3, 0.3);
factory->AddBackgroundTree( TTGbackground, 1);
factory->AddBackgroundTree( WWbackground, 1);
factory->AddBackgroundTree(WZbackground , 1);
factory->AddBackgroundTree(ZZbackground , 1);
factory->AddBackgroundTree(ZGAMMAbackground , 1);
// Set xs-weight
// factory->SetSignalWeightExpression ("weight");
//factory->SetBackgroundWeightExpression("weight");
// Apply additional cuts on the signal and background samples (can be different)
TCut mycut = "";
// TCut mycutb = "";
Int_t nSignalTrain = signalTrain->GetEntries();
Int_t nBackTrain = backgroundTrain->GetEntries();
factory->PrepareTrainingAndTestTree( "","", "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:SplitSeed=88!V" );
//factory->PrepareTrainingAndTestTree( "", "",
// "nTrain_Signal=100:nTrain_Background=100:nTest_Signal=100:nTest_Background=100:!V" );
// factory->PrepareTrainingAndTestTree( mycuts, mycutb,
//":NSigTrain=:NBkgTrain=:NSigTest=:NBkgTest=:SplitMode=Alternate:!V" );
//":nTrain_Signal=10000:nTest_Signal=2260:nTrain_Background=100000:nTest_Background=100000:SplitMode=Alternate:!V" );
// ":nTrain_Signal=nSignalTrain:nTrain_Background=nBackTrain:!V");
//":nTrain_Signal=nSignalTrain:nTest_Signal=nSignalTest:nTrain_Background=nBackTrain:nTest_Background=nBackTest:SplitMode=Block:!V");
//":SplitMode=Alternate:!V");
//":nTrain_Signal=:nTest_Signal=:nTrain_Background=:nTest_Background=:SplitMode=Block:!V");
//":SplitMode=Alternate:!V" );
// ---- Book MVA methods
// factory->BookMethod( TMVA::Types::kBDT, "BDT",
// "!H:!V:NTrees=300:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=-1:NNodesMax=5:UseNvars=4:PruneStrength=5:PruneMethod=CostComplexity:MaxDepth=4" );
//factory->BookMethod( TMVA::Types::kBDT, "BDT",
// "!H:!V:NTrees=400:BoostType=AdaBoost:AdaBoostBeta=0.5:MaxDepth=3:SeparationType=GiniIndex:PruneMethod=NoPruning" );
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:BoostType=AdaBoost:AdaBoostBeta=0.8:MaxDepth=3:SeparationType=GiniIndex:PruneMethod=NoPruning" );
// factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5");
// factory->BookMethod(TMVA::Types::kFisher, "Fisher", "H:!V:Fisher");
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
// factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001" );
// factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit","H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
// if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void mytmvaClass( TString myMethodList = "" )
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
//--------------------------------
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Select methods (Not of much interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
//Syntax --> "factory->AddVariable( "VarName", "UserDefinedLabel", "units", 'VarType' );" //Where VarType refers to float, int etc.
Float_t nhiggsJetSubjets;
Float_t njets;
Float_t deltaPhiWH;
Float_t jetPt[11];
Float_t jetEta[11];
Float_t jetPhi[11];
Float_t jetE[11];
Float_t higgsJetSubjetEta[3];
Float_t higgsJetSubjetPhi[3];
Float_t higgsJetSubjetPt[3];
Float_t higgsJetSubjetE[3];
Float_t Wphi;
Float_t fatjetPt[15];
Float_t fatjetEta[15];
Float_t fatjetPhi[15];
Float_t fatjetE[15];
Float_t DeltaPhiWH;
TLorentzVector fatjet(0,0,0,0);
TLorentzVector subjet1(0,0,0,0);
TLorentzVector subjet2(0,0,0,0);
TLorentzVector subjet3(0,0,0,0);
TLorentzVector akt[15];
fatjet.SetPtEtaPhiE(fatjetPt[0],fatjetEta[0],fatjetPhi[0], fatjetE[0]);
// deltaPhiWH = std::fabs(Wphi-fatjetPhi[0]);
// if(deltaPhiWH>TMath::Pi()){ std::fabs(deltaPhiWH = 2*TMath::Pi()-deltaPhiWH);}
factory->AddVariable( "deltaPhiWH", "deltaPhiWH", "", 'F' );
factory->AddVariable( "jetPt.array[0]", "jetPt.array[0]", "", 'F' );
factory->AddVariable( "fatjetPt[0].array[0]", "fatjetPt.array[0]", "", 'F' );
//factory->AddVariable( "DeltaR[3][13]", "DeltaR[3][13]", "", 'F' );
// factory->AddSpectator( "nhiggsJetSubjets", "nhiggsJetSubjets", "", 'F');
// factory->AddSpectator( "njets", "", "njets", 'I');
// factory->AddSpectator( "jetPt[0]", "jetPt[0]", "", 'F');
// factory->AddSpectator( "jetPt[1]", "jetPt[1]", "", 'F');
// factory->AddSpectator( "jetPt[2]", "jetPt[2]", "", 'F');
// factory->AddSpectator( "jetPt[3]", "jetPt[3]", "", 'F');
// factory->AddSpectator( "jetPt[4]", "jetPt[4]", "", 'F');
// factory->AddSpectator( "jetPt[5]", "jetPt[5]", "", 'F');
// factory->AddSpectator( "jetPt[6]", "jetPt[6]", "", 'F');
// factory->AddSpectator( "jetPt[7]", "jetPt[7]", "", 'F');
// factory->AddSpectator( "jetPt[8]", "jetPt[8]", "", 'F');
// factory->AddSpectator( "jetPt[9]", "jetPt[9]", "", 'F');
// factory->AddSpectator( "jetPt[10]", "jetPt[10]", "", 'F');
// factory->AddSpectator( "jetEta[0]", "jetEta[0]", "", 'F');
// factory->AddSpectator( "jetEta[1]", "jetEta[1]", "", 'F');
// factory->AddSpectator( "jetEta[2]", "jetEta[2]", "", 'F');
// factory->AddSpectator( "jetEta[3]", "jetEta[3]", "", 'F');
// factory->AddSpectator( "jetEta[4]", "jetEta[4]", "", 'F');
// factory->AddSpectator( "jetEta[5]", "jetEta[5]", "", 'F');
// factory->AddSpectator( "jetEta[6]", "jetEta[6]", "", 'F');
// factory->AddSpectator( "jetEta[7]", "jetEta[7]", "", 'F');
// factory->AddSpectator( "jetEta[8]", "jetEta[8]", "", 'F');
// factory->AddSpectator( "jetEta[9]", "jetEta[9]", "", 'F');
// factory->AddSpectator( "jetEta[10]", "jetEta[10]", "", 'F');
// factory->AddSpectator( "jetPhi[0]", "jetPhi[0]", "", 'F');
// factory->AddSpectator( "jetPhi[1]", "jetPhi[1]", "", 'F');
// factory->AddSpectator( "jetPhi[2]", "jetPhi[2]", "", 'F');
// factory->AddSpectator( "jetPhi[3]", "jetPhi[3]", "", 'F');
// factory->AddSpectator( "jetPhi[4]", "jetPhi[4]", "", 'F');
// factory->AddSpectator( "jetPhi[5]", "jetPhi[5]", "", 'F');
// factory->AddSpectator( "jetPhi[6]", "jetPhi[6]", "", 'F');
// factory->AddSpectator( "jetPhi[7]", "jetPhi[7]", "", 'F');
// factory->AddSpectator( "jetPhi[8]", "jetPhi[8]", "", 'F');
// factory->AddSpectator( "jetPhi[9]", "jetPhi[9]", "", 'F');
// factory->AddSpectator( "jetPhi[10]", "jetPhi[10]", "", 'F');
// factory->AddSpectator("jetE[0]", "jetE[0]", "", 'F');
// factory->AddSpectator("jetE[1]", "jetE[1]", "", 'F');
// factory->AddSpectator("jetE[2]", "jetE[2]", "", 'F');
// factory->AddSpectator("higgsJetSubjetEta[0]","higgsJetSubjetEta[0]", "", 'F');
// factory->AddSpectator("higgsJetSubjetEta[1]","higgsJetSubjetEta[1]", "", 'F');
// factory->AddSpectator("higgsJetSubjetEta[2]","higgsJetSubjetEta[2]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPhi[0]","higgsJetSubjetPhi[0]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPhi[1]","higgsJetSubjetPhi[1]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPhi[2]","higgsJetSubjetPhi[2]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPt[0]","higgsJetSubjetPt[0]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPt[1]","higgsJetSubjetPt[1]", "", 'F');
// factory->AddSpectator("higgsJetSubjetPt[2]","higgsJetSubjetPt[2]", "", 'F');
// factory->AddSpectator("higgsJetSubjetE[0]","higgsJetSubjetE[0]", "", 'F');
// factory->AddSpectator("higgsJetSubjetE[1]","higgsJetSubjetE[1]", "", 'F');
// factory->AddSpectator("higgsJetSubjetE[2]","higgsJetSubjetE[2]", "", 'F');
// factory->AddSpectator("Wphi", "Wphi", "" , 'F');
// factory->AddVariable("fatjetPt[0]", "fatjetPt[0]", "", 'F');
// factory->AddSpectator("fatjetPhi[0]", "fatjetPhi[0]", "", 'F');
// factory->AddSpectator("fatjetEta[0]", "fatjetEta[0]", "", 'F');
// factory->AddSpectator("fatjetE[0]", "fatjetE[0]", "", 'F');
// //if(std::fabs(Wphi-fatjetPhi[0])<TMath::Pi()){
// factory->AddVariable("abs(Wphi-fatjetPhi[0])", "DeltaPhiWH", "", 'F');
// //}
// if ((abs(Wphi-fatjetPhi[0])>TMath::Pi())){
// factory->AddVariable("(abs(DeltaPhiWH = 2*(TMath::Pi())-DeltaPhiWH))", "DeltaPhiWH", "", 'F');
// }
// Here, we add the lepton flag as a spectator so we can cut (further down) on lep == 0 for muons only or lep == 1 for electrons only.
//factory->AddSpectator( "lepType[0]", "lepType[0]", "units", 'I');
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
// factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
// factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
// --- Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
TString fsig_name_WH = "/Disk/speyside7/Grid/grid-files/rsmith/SummerProject/OverlapperInputFiles/PreCutsApplied/mc11_7TeV.128034.HerwigppPowHeg_Boosted_WH125lnubb.merge.NTUP_HSG5WH.Output_PreCuts-Original.root";
TString fback_name_TT = "/Disk/speyside7/Grid/grid-files/rsmith/SummerProject/OverlapperInputFiles/PreCutsApplied/TT_NewBranch2.output_MERGE_PreCuts-Original.root";
TString fback_name_Wbb = "/Disk/speyside7/Grid/grid-files/rsmith/SummerProject/OverlapperInputFiles/PreCutsApplied/mc11_7TeV.128861.SherpaWbb2J_lnu_Wpt100GeV_PreCuts-Original.root";
TFile *sig_input_WH = TFile::Open( fsig_name_WH );
TFile *back_input_TT = TFile::Open( fback_name_TT );
TFile *back_input_Wbb = TFile::Open( fback_name_Wbb);
// --- Register the training and test trees
TTree *signal_WH = (TTree*)sig_input_WH->Get("OutputTree");
TTree *background_TT = (TTree*)back_input_TT->Get("OutputTree");
TTree * background_Wbb = (TTree*)back_input_Wbb->Get("OutputTree");
// --- global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight_WH = 1;
Double_t backgroundWeight_TT = 1;
Double_t backgroundWeight_Wbb= 1;
// --- Here can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal_WH, signalWeight_WH );
factory->AddBackgroundTree( background_TT, backgroundWeight_TT );
factory->AddBackgroundTree( background_Wbb, backgroundWeight_Wbb);
Float_t nhiggsJetSubjets;
Float_t njets;
Float_t deltaPhiWH;
Float_t jetPt[11];
Float_t jetEta[11];
Float_t jetPhi[11];
Float_t jetE[11];
Float_t higgsJetSubjetEta[3];
Float_t higgsJetSubjetPhi[3];
Float_t higgsJetSubjetPt[3];
Float_t higgsJetSubjetE[3];
Float_t Wphi;
Float_t fatjetPt[15];
Float_t fatjetEta[15];
Float_t fatjetPhi[15];
Float_t fatjetE[15];
TLorentzVector fatjet(0,0,0,0);
TLorentzVector subjet1(0,0,0,0);
TLorentzVector subjet2(0,0,0,0);
TLorentzVector subjet3(0,0,0,0);
TLorentzVector akt[15];
fatjet.SetPtEtaPhiE(fatjetPt[0],fatjetEta[0],fatjetPhi[0], fatjetE[0]);
deltaPhiWH = std::fabs(Wphi-fatjetPhi[0]);
if(deltaPhiWH>TMath::Pi()){ std::fabs(deltaPhiWH = 2*TMath::Pi()-deltaPhiWH);}
float deltaEta[3][11];
float deltaPhi[3][11];
float DeltaR[3][13];
for(int j = 0; j<nhiggsJetSubjets; j++){
for(int i = 0; i<njets; i++){
deltaEta[j][i] = std::fabs(higgsJetSubjetEta[j]-jetEta[i]);
deltaPhi[j][i] = std::fabs(higgsJetSubjetPhi[j]-jetPhi[i]);
DeltaR[j][i] = std::sqrt(deltaEta[j][i]*deltaEta[j][i]+deltaPhi[j][i]*deltaPhi[j][i]);
}
}
for (int i=0; i < njets; i++) {
akt[i].SetPtEtaPhiE(jetPt[i],jetEta[i],jetPhi[i], jetE[i]);
}
subjet1.SetPtEtaPhiE(higgsJetSubjetPt[0],higgsJetSubjetEta[0],higgsJetSubjetPhi[0], higgsJetSubjetE[0]);
subjet2.SetPtEtaPhiE(higgsJetSubjetPt[1],higgsJetSubjetEta[1],higgsJetSubjetPhi[1], higgsJetSubjetE[1]);
FatHiggs = subjet1 + subjet2;
if (nhiggsJetSubjets>2){
subjet3.SetPtEtaPhiE(higgsJetSubjetPt[2],higgsJetSubjetEta[2],higgsJetSubjetPhi[2], higgsJetSubjetE[2]);
}
// To give different trees for training and testing, do:
// factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
// factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
// Set individual event weights (the variables must exist in the original TTree)
// for signal : factory->SetSignalWeightExpression ("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("wei
// --- Apply additional cuts on the signal and background samples (can be different)
// PLEASE NOTE: If no cuts are required, do NOT leave any spaces between quotation marks, otherwise an error will occur.
TCut mycuts = "deltaPhiWH>-800"; //Signal Tree Cuts
TCut mycutb = "deltaPhiWH>-800"; //Background Tree Cuts
//cout << "DeltaPhiWH: " << deltaPhiWH << endl;
// Tell the factory how to use the training and testing events
//
// If no numbers of events are given, half of the events in the tree are used
// for training, and the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// To also specify the number of testing events, use:
// factory->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:!V:NormMode=NumEvents");
// ---- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// --- Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
"H:!V:FitMethod=GA:CutRangeMin[1]=0:CutRangeMax[1]=450:VarProp[1]=FMax:EffSel:Steps=60:Cycles=3:PopSize=2000:SC_steps=10:SC_rate=5:SC_factor=0.95" );
if (Use["CutsSA"])
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
// Likelihood ("naive Bayes estimator")
if (Use["Likelihood"])
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
// Decorrelated likelihood
if (Use["LikelihoodD"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
// PCA-transformed likelihood
if (Use["LikelihoodPCA"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
// Use a kernel density estimator to approximate the PDFs
if (Use["LikelihoodKDE"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
// Use a variable-dependent mix of splines and kernel density estimator
if (Use["LikelihoodMIX"])
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
// Test the multi-dimensional probability density estimator
// here are the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
if (Use["PDEFoamBoost"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// H-Matrix (chi2-squared) method
if (Use["HMatrix"])
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
// Linear discriminant (same as Fisher discriminant)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher discriminant (same as LD)
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
// Fisher with Gauss-transformed input variables
if (Use["FisherG"])
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
if (Use["FDA_MCMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
if (Use["MLPBNN"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=350:nEventsMin=100:MaxDepth=3:BoostType=Grad:Shrinkage=0.05:UseBaggedGrad:GradBaggingFraction=0.51:nCuts=65:PruneStrength=2:PruneMethod=ExpectedError:NNodesMax=7" );
//"!H:!V:NTrees=350:nEventsMin=100:MaxDepth=3:BoostType=Grad:Shrinkage=0.05:UseBaggedGrad:GradBaggingFraction=0.55:nCuts=50:PruneStrength=2:PruneMethod=ExpectedError:NNodesMax=10" ); //nCuts=20 && no pruning originally
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"NTrees=550:nEventsMin=110:MaxDepth=5.5:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=10:PruneStrength=6:PruneMethod=ExpectedError");
if (Use["BDTB"]) // Bagging
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=950:nEventsMin=20:MaxDepth=10:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
"!H:!V:NTrees=300:nEventsMin=20:MaxDepth=10:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=10:PruneStrength=6:PruneMethod=ExpectedError:VarTransform=Decorrelate" );
// RuleFit -- TMVA implementation of Friedman's method
if (Use["RuleFit"])
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );
// --------------------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("SigEffAt001","FitGA");
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TMVAClassification( )
{
// this loads the library
TMVA::Tools::Instance();
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Create a new root output file.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object.
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
// ---------- input variables
factory->AddVariable("iso12x12", 'F');
factory->AddVariable("iso4x4", 'F');
factory->AddVariable("isoLshaped", 'F');
factory->AddVariable("PUM0", 'F');
// ---------- spectators ----------------
factory->AddSpectator("pt", "F");
factory->AddSpectator("eta", "F");
factory->AddSpectator("phi", "F");
factory->AddSpectator("e", "F");
// read training and test data
TString fSig = "DrellYan_Zee.root";
TString fBkg = "MinBias.root";
TFile *fileSig = TFile::Open( fSig );
TFile *fileBkg = TFile::Open( fBkg );
TTree *signal = (TTree*)fileSig->Get("tree");
TTree *background = (TTree*)fileBkg->Get("tree");
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// ====== register trees ====================================================
// you can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
TCut mycuts = "pt > 10";
TCut mycutb = "pt > 10";
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
// If no numbers of events are given, half of the events in the tree are used for training, and
// the other half for testing:
// ---- Book MVA methods
// Boosted Decision Trees
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
// ---- Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TAnalysisOfAlignment::DoEtaCorrection(UInt_t correctionStep){
cout<<"****************************************"<<endl;
cout<<"Do Eta correction for all silicon planes "<<correctionStep<< endl;
cout<<"****************************************"<<endl;
vector<TH1F*> histoStripDistribution,histoStripDistributionFlattned;
vector<TH1F *>vecHEta;
// stringstream fileName;
// fileName<< "etaIntegral_Step"<<correctionStep<<"."<<settings->getRunNumber()<<".root";
TFile* correctedEtaFile = new TFile(settings->getEtaDistributionPath(correctionStep).c_str(),"RECREATE");
cout<<"create Histos..."<<endl;
for(UInt_t det=0; det<TPlaneProperties::getNSiliconDetectors();det++){
stringstream histoTitle;
histoTitle<<"hPredictedStripPosition"<<"_step"<<correctionStep<<"_"<<TPlaneProperties::getStringForDetector(det);
histoStripDistribution.push_back(new TH1F(histoTitle.str().c_str(),histoTitle.str().c_str(),128,-0.501,0.501));
histoTitle<<"_flattend";
histoStripDistributionFlattned.push_back(new TH1F(histoTitle.str().c_str(),histoTitle.str().c_str(),128,-0.501,0.501));
histoTitle.str("");
histoTitle.clear();
histoTitle<<"hCorrectedEtaDistribution"<<"_step"<<correctionStep<<"_"<<TPlaneProperties::getStringForDetector(det);
vecHEta.push_back(new TH1F(histoTitle.str().c_str(),histoTitle.str().c_str(),128,0,1));
}
cout<<"fill first strip hit histo"<<eventReader->GetEntries()<<endl;
for( nEvent=0;nEvent<eventReader->GetEntries();nEvent++){
TRawEventSaver::showStatusBar(nEvent,eventReader->GetEntries());
eventReader->LoadEvent(nEvent);
if(!eventReader->useForAnalysis()&&!eventReader->useForAlignment())
continue;
for(UInt_t subjectPlane =0; subjectPlane<TPlaneProperties::getNSiliconPlanes();subjectPlane++){
// if(!eventReader->useForAlignment()&&!eventReader->useForAnalysis())
// continue;
vector<UInt_t>vecRefPlanes;
for(UInt_t refPlane=0;refPlane<TPlaneProperties::getNSiliconPlanes();refPlane++)
if(subjectPlane!=refPlane)vecRefPlanes.push_back(refPlane);
TPositionPrediction* pred = eventReader->predictPosition(subjectPlane,vecRefPlanes,false);
Float_t predictedStripPositionX = eventReader->getPositionInDetSystem(subjectPlane*2,pred->getPositionX(),pred->getPositionY());
Float_t predictedStripPositionY = eventReader->getPositionInDetSystem(subjectPlane*2+1,pred->getPositionX(),pred->getPositionY());
UInt_t stripMiddleX=(UInt_t) (predictedStripPositionX+0.5);
Float_t deltaX = predictedStripPositionX-stripMiddleX;
UInt_t stripMiddleY=(UInt_t) (predictedStripPositionY+0.5);
Float_t deltaY = predictedStripPositionY-stripMiddleY;
// cout<<nEvent<<": "<<subjectPlane<<"Fill "<<deltaX<<" "<<deltaY<<endl;
histoStripDistribution.at(subjectPlane*2)->Fill(deltaX);
histoStripDistribution.at(subjectPlane*2+1)->Fill(deltaY);
chi2Distribution();
}
}
saveHistos();
vector<UInt_t> vecMinEntries;
cout<<"Minimal Entries in a bin of historgram:"<<endl;
for(UInt_t det=0;det<TPlaneProperties::getNSiliconDetectors();det++){
TH1F* histo=histoStripDistribution.at(det);
Int_t minBin = histo->GetMinimumBin();
Int_t nMinEntries = histo->GetBinContent(minBin);
cout<<endl<< det<< ": "<<minBin<<" "<<nMinEntries<<"\t";
vecMinEntries.push_back(nMinEntries);
}
cout<<"\n\n"<<endl;
vector<UInt_t>vecEventNo[9];
cout<<"create flattened strip hit histo "<<eventReader->GetEntries()<<endl;
for( nEvent=0;nEvent<eventReader->GetEntries();nEvent++){
TRawEventSaver::showStatusBar(nEvent,eventReader->GetEntries());
eventReader->LoadEvent(nEvent);
if(!eventReader->useForAnalysis()&&!eventReader->useForAlignment())
continue;
for(UInt_t subjectPlane=0; subjectPlane<TPlaneProperties::getNSiliconPlanes();subjectPlane++){
// if(!eventReader->useForAlignment()&&!eventReader->useForAnalysis())
// continue;
vector<UInt_t>refPlanes;
for(UInt_t refPlane=0;refPlane<TPlaneProperties::getNSiliconPlanes();refPlane++)
if(subjectPlane!=refPlane)refPlanes.push_back(refPlane);
TPositionPrediction* pred = eventReader->predictPosition(subjectPlane,refPlanes,false);
Float_t predictedStripPositionX = eventReader->getPositionInDetSystem(subjectPlane*2,pred->getPositionX(),pred->getPositionY());
Float_t predictedStripPositionY = eventReader->getPositionInDetSystem(subjectPlane*2+1,pred->getPositionX(),pred->getPositionY());
UInt_t stripMiddleX=(UInt_t) (predictedStripPositionX+0.5);
Float_t deltaX = predictedStripPositionX-stripMiddleX;
// histoStripDistribution.at(subjectPlane*2)->Fill(deltaX);
UInt_t stripMiddleY=(UInt_t) (predictedStripPositionY+0.5);
Float_t deltaY = predictedStripPositionY-stripMiddleY;
// histoStripDistribution.at(subjectPlane*2)->Fill(deltaY);
Int_t binX=histoStripDistributionFlattned.at(subjectPlane)->FindBin(deltaX);
Int_t binY=histoStripDistributionFlattned.at(subjectPlane)->FindBin(deltaY);
if(histoStripDistributionFlattned.at(subjectPlane*2)->GetBinContent(binX)<vecMinEntries.at(subjectPlane*2)){
vecEventNo[subjectPlane*2].push_back(nEvent);
histoStripDistributionFlattned.at(subjectPlane*2)->Fill(deltaX);
}
if(histoStripDistributionFlattned.at(subjectPlane*2+1)->GetBinContent(binY)<vecMinEntries.at(subjectPlane*2+1)){
vecEventNo[subjectPlane*2+1].push_back(nEvent);
histoStripDistributionFlattned.at(subjectPlane*2+1)->Fill(deltaY);
}
}
}
for(UInt_t det =0; det<TPlaneProperties::getNSiliconDetectors();det++){
cout<<"save histogram: "<<det<<" "<<histoStripDistributionFlattned.at(det)->GetTitle()<<" "<<histoStripDistribution.at(det)->GetTitle()<<endl;
histSaver->SaveHistogram(histoStripDistributionFlattned.at(det));
correctedEtaFile->Add(histoStripDistributionFlattned.at(det));
histSaver->SaveHistogram(histoStripDistribution.at(det));
correctedEtaFile->Add(histoStripDistribution.at(det));
}
cout<<"\n\ncreate eta correction histo"<<endl;
for(UInt_t det =0; det<TPlaneProperties::getNSiliconDetectors();det++){
for(UInt_t i=0;i<vecEventNo[det].size();i++){
nEvent= vecEventNo[det].at(i);
eventReader->LoadEvent(nEvent);
if(!eventReader->useForAlignment()&&!eventReader->useForAnalysis())
continue;
Float_t eta = eventReader->getCluster(det,0).getEta();
vecHEta.at(det)->Fill(eta);
}
stringstream histName;
histName<<"hEtaIntegral"<<"_step"<<correctionStep<<"_"<<TPlaneProperties::getStringForDetector(det);;
// UInt_t nBins = vecHEta.at(det)->GetNbinsX();
TH1F *histo= TClustering::createEtaIntegral(vecHEta.at(det),histName.str());
cout<<"save "<<vecHEta.at(det)->GetTitle()<<" "<<vecHEta.at(det)->GetEntries()<<endl;
histSaver->SaveHistogram(vecHEta.at(det));
correctedEtaFile->Add(vecHEta.at(det));
cout<<"save "<<histo->GetTitle()<<" "<<histo->GetEntries()<<endl;
histSaver->SaveHistogram(histo);
correctedEtaFile->Add((TH1F*)histo->Clone());
}
correctedEtaFile->Write();
cout<<"Closing "<<correctedEtaFile->GetName()<<endl;
for(UInt_t i=0;i<histoStripDistribution.size();i++)
cout<<histoStripDistribution.at(i)<<" "<<flush;
cout<<endl;
for(UInt_t i =0;i<histoStripDistribution.size();i++)
cout<<histoStripDistributionFlattned.at(i)<<" "<<flush;
cout<<endl;
for(UInt_t i =0;i<histoStripDistribution.size();i++)
cout<<vecHEta.at(i)<<" "<<flush;
}
