void PlotParsDistr(TFile* f, TTree* tr, TString strMillepedeRes, TString strOutdir)
{
for (int isubd=PXB; isubd<=TEC; isubd++){
TString canvName="c_";
canvName+=strMillepedeRes;
canvName+="_";
canvName+=StrPlotType(PARS);
canvName+="_";
canvName+=subdLabels[isubd];
canvName.ReplaceAll(".res","");
f->cd();
TCanvas* canv = new TCanvas(canvName,canvName,600,600);
canv->Divide(3,3);
for (int parInd=1; parInd<=9; parInd++){
canv->cd(parInd);
TString strCut="((label%20-1)%9+1)==";
strCut+=parInd;
strCut+=" && label<700000 && ";
strCut+=StrCutSubd(isubd);
TString hName="hPars_";
hName+=subdLabels[isubd];
hName+="_";
hName+= StrPar(parInd);
TTree* trCut = tr->CopyTree(strCut);
float up = trCut->GetMaximum("parVal");
float low = trCut->GetMinimum("parVal");
std::cout<<"low="<<low<<", up="<<up<<", nent="<<trCut->GetEntries()<<std::endl;
TH1F* h = new TH1F(hName,hName,100,10000*low,10000*up);
TString strDraw="10000*parVal>>";
strDraw+=hName;
trCut->Draw(strDraw,strCut,"goff");
h->SetMarkerStyle(2);
h->Draw("EP");
}// end of loop over parInd
canvName+=".png";
TString saveName=strOutdir+canvName;
canv->SaveAs(saveName);
saveName.ReplaceAll(".png",".pdf");
canv->SaveAs(saveName);
}//end of loop over isubd
}// end of PlotParsDistr
TGraphErrors* PlotLightYieldGraph()
{
string filename;
vector<double> x,y,ex,ey;
while(1){
cout<<"\n\nEnter next file to process; <enter> to finish."<<endl;
getline(cin, filename);
if(filename=="")
break;
//load the tree
TTree* Events = GetEventsTree(filename.c_str());
if(!Events)
continue;
gROOT->ProcessLine(".x analysis/Aliases.C");
double start = Events->GetMinimum("timestamp");
double end = Events->GetMaximum("timestamp");
double error = 0;
TString title = TString("Na22 Spectrum, ") +
TString(filename)(TRegexp("Run......"));
TH1F* hist = new TH1F("Na22Spec",title,200,1000,2500);
Events->Draw("sumspec >> Na22Spec","min > 0","e");
double yield = Fit511Photopeak(hist,&error);
x.push_back((start+end)/2.);
ex.push_back((end-start)/2.);
y.push_back(yield);
ey.push_back(error);
}
if(x.size() == 0){
cerr<<"No valid points found!"<<endl;
return 0;
}
TGraphErrors* graph = new TGraphErrors(x.size(),&x[0],&y[0],&ex[0],&ey[0]);
graph->Draw("ape");
TAxis* xax = graph->GetXaxis();
xax->SetTitle("Run Time");
xax->SetTimeDisplay(1);
xax->SetTimeFormat("%Y/%m/%d");
xax->SetTimeOffset(1,"gmt");
TAxis* yax = graph->GetYaxis();
yax->SetTitle("511 keV Light Yield [pe/keV]");
graph->Draw("ape");
return graph;
}
void nbx_check_2(const char * filename="counts.root")
{
TFile * infile = new TFile(filename,"READ");
TTree * tr = (TTree*) infile->Get("sca");
Int_t NRUNS_tmp = tr->GetMaximum("i");
const Int_t NRUNS = NRUNS_tmp;
TH1D * h[NRUNS];
char h_n[NRUNS][256];
char h_t[NRUNS][256];
char cut[NRUNS][256];
TCanvas * c = new TCanvas("c","c",1400,1000);
Double_t max;
for(Int_t i=0; i<NRUNS; i++)
{
printf("i=%d\n",i);
sprintf(h_n[i],"nbx_bx_%d",i+1);
sprintf(h_t[i],"N_{bx} vs. bXing for i=%d",i+1);
h[i] = new TH1D(h_n[i],h_n[i],120,0,120);
sprintf(cut[i],"tot_bx*(i==%d)",i+1);
tr->Project(h_n[i],"bx",cut[i]);
max = h[i]->GetMaximum();
h[i]->Scale(1/max);
c->Clear();
c->SetGrid(1,1);
h[i]->Draw();
if(i==0) c->Print("nbx_check/nbx_vs_bxing.pdf(","pdf");
else if(i+1==NRUNS) c->Print("nbx_check/nbx_vs_bxing.pdf)","pdf");
else c->Print("nbx_check/nbx_vs_bxing.pdf");
};
};
void paracoor( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
{
// set style and remove existing canvas'
TMVAGlob::Initialize( useTMVAStyle );
// checks if file with name "fin" is already open, and if not opens one
TFile* file = TMVAGlob::OpenFile( fin );
TTree* tree = (TTree*)file->Get("TestTree");
if(!tree) {
cout << "--- No TestTree saved in ROOT file. Parallel coordinates will not be plotted" << endl;
return;
}
// first get list of leaves in tree
TObjArray* leafList = tree->GetListOfLeaves();
vector<TString> vars;
vector<TString> mvas;
for (Int_t iar=0; iar<leafList->GetSize(); iar++) {
TLeaf* leaf = (TLeaf*)leafList->At(iar);
if (leaf != 0) {
TString leafName = leaf->GetName();
if (leafName != "type" && leafName != "weight" && leafName != "boostweight" &&
leafName != "class" && leafName != "className" && leafName != "classID" &&
!leafName.Contains("prob_")) {
// is MVA ?
if (TMVAGlob::ExistMethodName( leafName )) {
mvas.push_back( leafName );
}
else {
vars.push_back( leafName );
}
}
}
}
cout << "--- Found: " << vars.size() << " variables" << endl;
cout << "--- Found: " << mvas.size() << " MVA(s)" << endl;
TString type[2] = { "Signal", "Background" };
const Int_t nmva = mvas.size();
TCanvas* csig[nmva];
TCanvas* cbkg[nmva];
for (Int_t imva=0; imva<mvas.size(); imva++) {
cout << "--- Plotting parallel coordinates for : " << mvas[imva] << " & input variables" << endl;
for (Int_t itype=0; itype<2; itype++) {
// create draw option
TString varstr = mvas[imva] + ":";
for (Int_t ivar=0; ivar<vars.size(); ivar++) varstr += vars[ivar] + ":";
varstr.Resize( varstr.Last( ':' ) );
// create canvas
TString mvashort = mvas[imva]; mvashort.ReplaceAll("MVA_","");
TCanvas* c1 = (itype == 0) ? csig[imva] : cbkg[imva];
c1 = new TCanvas( Form( "c1_%i",itype ),
Form( "Parallel coordinate representation for %s and input variables (%s events)",
mvashort.Data(), type[itype].Data() ),
50*(itype), 50*(itype), 750, 500 );
tree->Draw( varstr.Data(), Form("classID==%i",1-itype) , "para" );
c1->ToggleEditor();
gStyle->SetOptTitle(0);
TParallelCoord* para = (TParallelCoord*)gPad->GetListOfPrimitives()->FindObject( "ParaCoord" );
TParallelCoordVar* mvavar = (TParallelCoordVar*)para->GetVarList()->FindObject( mvas[imva] );
Double_t minrange = tree->GetMinimum( mvavar->GetName() );
Double_t maxrange = tree->GetMaximum( mvavar->GetName() );
Double_t width = 0.2*(maxrange - minrange);
Double_t x1 = minrange, x2 = x1 + width;
TParallelCoordRange* parrange = new TParallelCoordRange( mvavar, x1, x2 );
parrange->SetLineColor(4);
mvavar->AddRange( parrange );
para->AddSelection("-1");
for (Int_t ivar=1; ivar<TMath::Min(Int_t(vars.size()) + 1,3); ivar++) {
TParallelCoordVar* var = (TParallelCoordVar*)para->GetVarList()->FindObject( vars[ivar] );
minrange = tree->GetMinimum( var->GetName() );
maxrange = tree->GetMaximum( var->GetName() );
width = 0.2*(maxrange - minrange);
switch (ivar) {
case 0: { x1 = minrange; x2 = x1 + width; break; }
case 1: { x1 = 0.5*(maxrange + minrange - width)*0.02; x2 = x1 + width*0.02; break; }
case 2: { x1 = maxrange - width; x2 = x1 + width; break; }
}
parrange = new TParallelCoordRange( var, x1, x2 );
parrange->SetLineColor( ivar == 0 ? 2 : ivar == 1 ? 5 : 6 );
var->AddRange( parrange );
para->AddSelection( Form("%i",ivar) );
}
c1->Update();
TString fname = Form( "plots/paracoor_c%i_%s", imva, itype == 0 ? "S" : "B" );
TMVAGlob::imgconv( c1, fname );
}
}
}
//--------------------------------------
void FBI3D(TString namefile="", TString namefile2=""){
Int_t MaxNCoinc = 9e7;
Int_t* POS = (Int_t*)malloc(MaxNCoinc*sizeof(Int_t));
FILE* fichero;
FILE* fichero2;
cout << namefile<<" "<<namefile2<<endl;
// === STEP 1 === NORMALIZATION ================
if (namefile2=="normf.raw"){ // Allows reusing previous normalization image
cout << "Using precalculated Normalization file" << endl;
fichero2 = fopen(namefile2,"rb");
fread(SENS,sizeof(float),nvoxels,fichero2);
fclose(fichero2);
}else{ // Create normalization from ROOT file (filtered to reduce noise)
int nt = 10; // Number of filter iterations
SENS = normalization(namefile2,nt);
// Storing the normalization image so that it can be used for other reconstructions of this scanner
fichero2 = fopen("normf.raw","wb");
fwrite(SENS,sizeof(float),nvoxels,fichero2);
fclose(fichero2);
}
// === STEP 2 === READ ROOT FILE ======
int posic;
TFile *f1 = new TFile(namefile,"READ");
TTree *Coincidences = (TTree*)f1->Get("Coincidences");
ncoinc = Coincidences->GetEntries();
cout << "Simulated Coincidences = " << ncoinc << endl;
define_branches(Coincidences);
Scanner = (Coincidences->GetMaximum("globalPosX1")) - (Coincidences->GetMinimum("globalPosX1")); // Diameter of the scanner from the detected counts
FOV_Z = (Coincidences->GetMaximum("globalPosZ1")) - (Coincidences->GetMinimum("globalPosZ1"));
dz = float(NZS)/FOV_Z;
// === STEP 3 === REFERENCE IMAGE (OPTIONAL) ====
for(Int_t iV=0; iV<nvoxels ; iV++){IMG_N[iV]=0.;} // Initial Image
for(Int_t ic = 0; ic <= ncoinc; ic++){ //
Coincidences->GetEntry(ic);
ix = int(source_X*dxy + RESM);
iy = int(source_Y*dxy + RESM);
iz = int(source_Z*dz + NZM);
if (ix>=0 && ix<RES && iy>=0 && iy<RES && iz>=0 && iz<NZS){ // Check voxel valid
iV = iz*RES*RES+iy*RES+ix;
IMG_N[iV]++;
}
}
fichero = fopen("image_ref.raw","wb");
fwrite(IMG_N,sizeof(float),nvoxels,fichero);
fclose(fichero);
Float_t sens_corr = 0.;
for(Int_t iV=0; iV<nvoxels ; iV++){
sens_corr = SENS[iV];
if (sens_corr>0){sens_corr = 1.0/sens_corr;}
IMG_N[iV]*=sens_corr;
} // Reference image corrected by sensitivity
fichero = fopen("image_ref_senscor.raw","wb");
fwrite(IMG_N,sizeof(float),nvoxels,fichero);
fclose(fichero);
// === STEP 4 === INITIAL IMAGE ================
for(Int_t iV=0; iV<nvoxels ; iV++){IMG[iV]=0;} // Initial Image
for(Int_t iV=0; iV<ncoinc; iV++){POS[iV]=-1;} // Initial Position
// === STEP 5 === ITERATIONS ==================
for(Int_t iter=1; iter<=niter ; iter++){ // Iterations (1 iteration consists of a whole loop over all data)
for(Int_t ic = 0; ic<ncoinc; ic++){
Coincidences->GetEntry(ic);
if ( ic % 100000 == 0 ) cout << "Iter= " << iter << " Processing " << ic << " coincidences " << endl;
posic = POS[ic];
POS[ic] = Arek(posic,det1_X,det1_Y,det1_Z,det2_X,det2_Y,det2_Z,IMG,SENS);
} // COINCIDENCES
} // ITERATIONS
f1->Close();
fichero = fopen("image.raw","wb");
if(fichero){
fwrite(IMG,sizeof(int),nvoxels,fichero);
fclose(fichero);
}
// === STEP 6 === FINAL SENSITIVY NORMALIZATION ====
for(Int_t iV=0; iV<nvoxels ; iV++){
if (SENS[iV]>0){
IMG_N[iV] = float(IMG[iV])/SENS[iV];
}else{
IMG_N[iV] = 0.;
}
}
fichero = fopen("image_norm.raw","wb");
if(fichero){
fwrite(IMG_N,sizeof(float),nvoxels,fichero);
fclose(fichero);
}
IMG_N = MedianIMG(IMG_N);
fichero = fopen("image_norm_med.raw","wb");
if(fichero){
fwrite(IMG_N,sizeof(float),nvoxels,fichero);
fclose(fichero);
}
} // END
void PlotDecisionBoundary( TString weightFile = "weights/TMVAClassification_BDT.weights.xml",TString v0="var0", TString v1="var1", TString dataFileName = "/home/hvoss/TMVA/TMVA_data/data/data_circ.root")
{
//---------------------------------------------------------------
// default MVA methods to be trained + tested
// this loads the library
TMVA::Tools::Instance();
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
//
// create the Reader object
//
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
Float_t var0, var1;
reader->AddVariable( v0, &var0 );
reader->AddVariable( v1, &var1 );
//
// book the MVA method
//
reader->BookMVA( "M1", weightFile );
TFile *f = new TFile(dataFileName);
TTree *signal = (TTree*)f->Get("TreeS");
TTree *background = (TTree*)f->Get("TreeB");
//Declaration of leaves types
Float_t svar0;
Float_t svar1;
Float_t bvar0;
Float_t bvar1;
Float_t sWeight=1.0; // just in case you have weight defined, also set these branchaddresses
Float_t bWeight=1.0*signal->GetEntries()/background->GetEntries(); // just in case you have weight defined, also set these branchaddresses
// Set branch addresses.
signal->SetBranchAddress(v0,&svar0);
signal->SetBranchAddress(v1,&svar1);
background->SetBranchAddress(v0,&bvar0);
background->SetBranchAddress(v1,&bvar1);
UInt_t nbin = 50;
Float_t xmax = signal->GetMaximum(v0.Data());
Float_t xmin = signal->GetMinimum(v0.Data());
Float_t ymax = signal->GetMaximum(v1.Data());
Float_t ymin = signal->GetMinimum(v1.Data());
xmax = TMath::Max(xmax,background->GetMaximum(v0.Data()));
xmin = TMath::Min(xmin,background->GetMinimum(v0.Data()));
ymax = TMath::Max(ymax,background->GetMaximum(v1.Data()));
ymin = TMath::Min(ymin,background->GetMinimum(v1.Data()));
TH2D *hs=new TH2D("hs","",nbin,xmin,xmax,nbin,ymin,ymax);
TH2D *hb=new TH2D("hb","",nbin,xmin,xmax,nbin,ymin,ymax);
hs->SetXTitle(v0);
hs->SetYTitle(v1);
hb->SetXTitle(v0);
hb->SetYTitle(v1);
hs->SetMarkerColor(4);
hb->SetMarkerColor(2);
TH2F * hist = new TH2F( "MVA", "MVA", nbin,xmin,xmax,nbin,ymin,ymax);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
Float_t MinMVA=10000, MaxMVA=-100000;
for (Int_t ibin=1; ibin<nbin+1; ibin++){
for (Int_t jbin=1; jbin<nbin+1; jbin++){
var0 = hs->GetXaxis()->GetBinCenter(ibin);
var1 = hs->GetYaxis()->GetBinCenter(jbin);
Float_t mvaVal=reader->EvaluateMVA( "M1" ) ;
if (MinMVA>mvaVal) MinMVA=mvaVal;
if (MaxMVA<mvaVal) MaxMVA=mvaVal;
hist->SetBinContent(ibin,jbin, mvaVal);
}
}
// creating a fine histograms containing the error rate
const Int_t nValBins=100;
Double_t sum = 0.;
TH1F *mvaS= new TH1F("mvaS","",nValBins,MinMVA,MaxMVA);
TH1F *mvaB= new TH1F("mvaB","",nValBins,MinMVA,MaxMVA);
TH1F *mvaSC= new TH1F("mvaSC","",nValBins,MinMVA,MaxMVA);
TH1F *mvaBC= new TH1F("mvaBC","",nValBins,MinMVA,MaxMVA);
Long64_t nentries;
nentries = TreeS->GetEntries();
for (Long64_t is=0; is<nentries;is++) {
signal->GetEntry(is);
sum +=sWeight;
var0 = svar0;
var1 = svar1;
Float_t mvaVal=reader->EvaluateMVA( "M1" ) ;
hs->Fill(svar0,svar1);
mvaS->Fill(mvaVal,sWeight);
}
nentries = TreeB->GetEntries();
for (Long64_t ib=0; ib<nentries;ib++) {
background->GetEntry(ib);
sum +=bWeight;
var0 = bvar0;
var1 = bvar1;
Float_t mvaVal=reader->EvaluateMVA( "M1" ) ;
hb->Fill(bvar0,bvar1);
mvaB->Fill(mvaVal,bWeight);
}
//SeparationBase *sepGain = new MisClassificationError();
//SeparationBase *sepGain = new GiniIndex();
SeparationBase *sepGain = new CrossEntropy();
Double_t sTot = mvaS->GetSum();
Double_t bTot = mvaB->GetSum();
mvaSC->SetBinContent(1,mvaS->GetBinContent(1));
mvaBC->SetBinContent(1,mvaB->GetBinContent(1));
Double_t sSel=mvaSC->GetBinContent(1);
Double_t bSel=mvaBC->GetBinContent(1);
Double_t separationGain=sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
Double_t mvaCut=mvaSC->GetBinCenter(1);
Double_t mvaCutOrientation=1; // 1 if mva > mvaCut --> Signal and -1 if mva < mvaCut (i.e. mva*-1 > mvaCut*-1) --> Signal
for (Int_t ibin=2;ibin<nValBins;ibin++){
mvaSC->SetBinContent(ibin,mvaS->GetBinContent(ibin)+mvaSC->GetBinContent(ibin-1));
mvaBC->SetBinContent(ibin,mvaB->GetBinContent(ibin)+mvaBC->GetBinContent(ibin-1));
sSel=mvaSC->GetBinContent(ibin);
bSel=mvaBC->GetBinContent(ibin);
if (separationGain < sepGain->GetSeparationGain(sSel,bSel,sTot,bTot) && mvaSC->GetBinCenter(ibin)<0){
separationGain = sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
mvaCut=mvaSC->GetBinCenter(ibin);
if (sSel/bSel > (sTot-sSel)/(bTot-bSel)) mvaCutOrientation=-1;
else mvaCutOrientation=1;
}
}
cout << "Min="<<MinMVA << " Max=" << MaxMVA
<< " sTot=" << sTot
<< " bTot=" << bTot
<< " sepGain="<<separationGain
<< " cut=" << mvaCut
<< " cutOrientation="<<mvaCutOrientation
<< endl;
delete reader;
gStyle->SetPalette(1);
plot(hs,hb,hist ,v0,v1,mvaCut);
TCanvas *cm=new TCanvas ("cm","",900,1200);
cm->cd();
cm->Divide(1,2);
cm->cd(1);
mvaS->SetLineColor(4);
mvaB->SetLineColor(2);
mvaS->Draw();
mvaB->Draw("same");
cm->cd(2);
mvaSC->SetLineColor(4);
mvaBC->SetLineColor(2);
mvaBC->Draw();
mvaSC->Draw("same");
// TH1F *add=(TH1F*)mvaBC->Clone("add");
// add->Add(mvaSC);
// add->Draw();
// errh->Draw("same");
//
// write histograms
//
TFile *target = new TFile( "TMVAPlotDecisionBoundary.root","RECREATE" );
hs->Write();
hb->Write();
hist->Write();
target->Close();
}
void DrawErrors(Int_t NBINS=50, const char * filename="spin.root")
{
TFile * infile = new TFile(filename,"READ");
TTree * tr = (TTree*) infile->Get("asy");
// set binning
// -- *_div = lower limit of each bin; last one is the upper limit
// -- *_width = bin width
Int_t phi_bins0, eta_bins0, pt_bins0, en_bins0;
if(gSystem->Getenv("PHI")==NULL){fprintf(stderr,"ERROR: source env vars\n"); return;};
sscanf(gSystem->Getenv("PHI"),"%d",&phi_bins0);
sscanf(gSystem->Getenv("ETA"),"%d",&eta_bins0);
sscanf(gSystem->Getenv("PT"),"%d",&pt_bins0);
sscanf(gSystem->Getenv("EN"),"%d",&en_bins0);
const Double_t pi=3.1415;
const Double_t phi_bins=phi_bins0;
const Double_t eta_bins=eta_bins0;
const Double_t eta_low=2.6; const Double_t eta_high=4.2;
const Double_t pt_bins=pt_bins0;
const Double_t pt_low=0; const Double_t pt_high=10;
const Double_t en_bins=en_bins0;
const Double_t en_low=0; const Double_t en_high=100;
const Double_t phi_low = (-1*pi)-0.1;
const Double_t phi_high = pi+0.1;
Double_t phi_div[phi_bins+1];
Double_t phi_width = (phi_high - phi_low)/phi_bins;
for(Int_t i=0; i<phi_bins; i++) phi_div[i] = phi_low + i * phi_width;
Double_t eta_div[eta_bins+1];
Double_t eta_width = (eta_high - eta_low)/eta_bins;
for(Int_t i=0; i<eta_bins; i++) eta_div[i] = eta_low + i * eta_width;
Double_t pt_div[pt_bins+1];
Double_t pt_width = (pt_high - pt_low)/pt_bins;
for(Int_t i=0; i<pt_bins; i++) pt_div[i] = pt_low + i * pt_width;
Double_t en_div[en_bins+1];
Double_t en_width = (en_high - en_low)/en_bins;
for(Int_t i=0; i<en_bins; i++) en_div[i] = en_low + i * en_width;
eta_div[eta_bins] = eta_high;
pt_div[pt_bins] = pt_high;
en_div[en_bins] = en_high;
Float_t R3_min, R3_max;
Float_t R3_err_min, R3_err_max;
Float_t PB_min, PB_max;
Float_t PB_err_min, PB_err_max;
Float_t PY_min, PY_max;
Float_t PY_err_min, PY_err_max;
Float_t A_LL_min, A_LL_max;
Float_t A_LL_err_min, A_LL_err_max;
R3_min = tr->GetMinimum("R3");
R3_max = tr->GetMaximum("R3");
R3_err_min = tr->GetMinimum("R3_err");
R3_err_max = tr->GetMaximum("R3_err");
PB_min = tr->GetMinimum("PB");
PB_max = tr->GetMaximum("PB");
PB_err_min = tr->GetMinimum("PB_err");
PB_err_max = tr->GetMaximum("PB_err");
PY_min = tr->GetMinimum("PY");
PY_max = tr->GetMaximum("PY");
PY_err_min = tr->GetMinimum("PY_err");
PY_err_max = tr->GetMaximum("PY_err");
A_LL_min = tr->GetMinimum("A_LL");
A_LL_max = tr->GetMaximum("A_LL");
A_LL_err_min = tr->GetMinimum("A_LL_err");
A_LL_err_max = tr->GetMaximum("A_LL_err");
TH2F * R3_dist = new TH2F("R3_dist","#sigma(R_{3}) vs. R_{3}",
NBINS,R3_min,R3_max,NBINS,R3_err_min,R3_err_max);
TH2F * PB_dist = new TH2F("PB_dist","#sigma(P_{B}) vs. P_{B}",
NBINS,PB_min,PB_max,NBINS,PB_err_min,PB_err_max);
TH2F * PY_dist = new TH2F("PY_dist","#sigma(P_{Y}) vs. P_{Y}",
NBINS,PY_min,PY_max,NBINS,PY_err_min,PY_err_max);
TH2F * A_LL_dist[eta_bins][pt_bins][en_bins];
TH2F * A_LL_dist_all = new TH2F("A_LL_dist_all","#sigma(A_{LL}) vs. A_{LL} :: all bins",
NBINS,A_LL_min,A_LL_max,NBINS,A_LL_err_min,A_LL_err_max);
char zero_bin[128];
strcpy(zero_bin,"phi_bin==1 && eta_bin==0 && pt_bin==0 && pt_bin==0");
tr->Project("R3_dist","R3_err:R3",zero_bin);
tr->Project("PB_dist","PB_err:PB",zero_bin);
tr->Project("PY_dist","PY_err:PY",zero_bin);
tr->Project("A_LL_dist_all","A_LL_err:A_LL");
TFile * outfile = new TFile("error.root","RECREATE");
R3_dist->Write();
PB_dist->Write();
PY_dist->Write();
A_LL_dist_all->Write();
char A_LL_dist_n[eta_bins][pt_bins][en_bins][256];
char A_LL_dist_t[eta_bins][pt_bins][en_bins][256];
char A_LL_cut[eta_bins][pt_bins][en_bins][256];
for(Int_t g=0; g<eta_bins; g++)
{
for(Int_t p=0; p<pt_bins; p++)
{
for(Int_t e=0; e<en_bins; e++)
{
sprintf(A_LL_cut[g][p][e],"eta_bin==%d && pt_bin==%d && en_bin==%d",g,p,e);
sprintf(A_LL_dist_n[g][p][e],"A_LL_dist_g%d_p%d_e%d",g,p,e);
sprintf(A_LL_dist_t[g][p][e],
"#sigma(A_{LL}) vs. A_{LL} :: #eta#in[%.2f,%.2f), p_{T}#in[%.2f,%.2f), E#in[%.2f,%.2f)",
eta_div[g],eta_div[g+1],pt_div[p],pt_div[p+1],en_div[e],en_div[e+1]);
A_LL_dist[g][p][e] = new TH2F(A_LL_dist_n[g][p][e],A_LL_dist_t[g][p][e],
NBINS,A_LL_min,A_LL_max,NBINS,A_LL_err_min,A_LL_err_max);
tr->Project(A_LL_dist_n[g][p][e],"A_LL_err:A_LL",A_LL_cut[g][p][e]);
A_LL_dist[g][p][e]->Write();
};
};
};
}
double QAnalysis::GetMaximum(string name) {
TTree* tree = ReadTree(file);
double max = tree->GetMaximum(name.c_str());
return max;
}
void nbx_step_check(const char * filename="counts.root")
{
TFile * infile = new TFile(filename,"READ");
TTree * tr = (TTree*) infile->Get("sca");
Int_t NRUNS_tmp = tr->GetMaximum("i");
const Int_t NRUNS = NRUNS_tmp;
TH1D * h[NRUNS];
char h_n[NRUNS][256];
char h_t[NRUNS][256];
char cut[NRUNS][256];
TCanvas * c = new TCanvas("c","c",1400,1000);
Double_t max;
Double_t step[NRUNS][120];
Double_t step_e[NRUNS][120];
Double_t bx_arr[120];
Double_t bx_arr_e[120];
for(Int_t b=0; b<120; b++)
{
bx_arr[b]=b;
bx_arr_e[b]=0;
};
TGraphErrors * tg[NRUNS];
for(Int_t i=0; i<NRUNS; i++)
{
printf("i=%d\n",i);
sprintf(h_n[i],"nbx_bx_%d",i+1);
sprintf(h_t[i],"N_{bx} vs. bXing for i=%d",i+1);
h[i] = new TH1D(h_n[i],h_n[i],120,0,120);
sprintf(cut[i],"tot_bx*(i==%d)",i+1);
tr->Project(h_n[i],"bx",cut[i]);
max = h[i]->GetMaximum();
//h[i]->Scale(1/max);
for(Int_t b=1; b<=119; b++)
{
step[i][b] = h[i]->GetBinContent(b) - h[i]->GetBinContent(b+1);
step_e[i][b] = sqrt(step[i][b]);
};
tg[i] = new TGraphErrors(120,bx_arr,step[i],bx_arr_e,step_e[i]);
c->Clear();
c->SetGrid(1,1);
tg[i]->SetMarkerStyle(kFullCircle);
tg[i]->Draw("ape");
if(i==0) c->Print("nbx_step_check.pdf(","pdf");
else if(i+1==NRUNS) c->Print("nbx_step_check.pdf)","pdf");
else c->Print("nbx_step_check.pdf");
};
};
void eff_IdHlt(const TString configFile, TString triggerSetString)
{
// ---------------------------------
// Preliminary checks
// ---------------------------------
// verify whether it was a compilation check
if (configFile.Contains("_DebugRun_") || triggerSetString.Contains("_DebugRun_")) {
std::cout << "eff_IdHlt: _DebugRun_ detected. Terminating the script\n";
return;
}
// fast check
TriggerConstantSet triggerSet=DetermineTriggerSet(triggerSetString);
assert ( triggerSet != TrigSet_UNDEFINED );
// ---------------------------------
// Normal execution
// ---------------------------------
gBenchmark->Start("eff_IdHlt");
//--------------------------------------------------------------------------------------------------------------
// Settings
//==============================================================================================================
Double_t massLow = 60;
Double_t massHigh = 120;
// Read in the configuratoin file
TString sampleTypeString = "";
TString effTypeString = "";
TString calcMethodString = "";
TString etBinningString = "";
TString etaBinningString = "";
TString dirTag;
vector<TString> ntupleFileNames;
ifstream ifs;
ifs.open(configFile.Data());
if (!ifs.is_open()) {
std::cout << "tried to open the configuration file <" << configFile << ">\n";
assert(ifs.is_open());
}
string line;
Int_t state=0;
while(getline(ifs,line)) {
if(line[0]=='#') continue;
if(line[0]=='%') break;
if(state==0){
// Read 1st line of content: data or MC?
sampleTypeString = TString(line);
state++;
}else if(state==1) {
// Read 2d content line: efficiency type string
effTypeString = TString(line);
state++;
}else if(state==2) {
// Read 3d content line: fitting mode
calcMethodString = TString(line);
state++;
}else if(state==3) {
// Read 4th content line: SC ET binning
etBinningString = TString(line);
state++;
}else if(state==4) {
// Read 5th content line: SC eta binning
etaBinningString = TString(line);
state++;
}else if(state==5) {
// Read 5th content line: SC eta binning
dirTag = TString(line);
state++;
}else if(state==6) {
ntupleFileNames.push_back(TString(line));
}
}
int calcMethod = 0;
if(calcMethodString == "COUNTnCOUNT")
calcMethod = COUNTnCOUNT;
else if(calcMethodString == "COUNTnFIT")
calcMethod = COUNTnFIT;
else if(calcMethodString == "FITnFIT")
calcMethod = FITnFIT;
else
assert(0);
printf("Efficiency calculation method: %s\n", calcMethodString.Data());
int effType = 0;
if(effTypeString == "ID")
effType = ID;
else if(effTypeString == "HLT")
effType = HLT;
else
assert(0);
printf("Efficiency type to measure: %s\n", effTypeString.Data());
int etBinning = 0;
if(etBinningString == "ETBINS1")
etBinning = ETBINS1;
else if(etBinningString == "ETBINS5")
etBinning = ETBINS5;
else
assert(0);
printf("SC ET binning: %s\n", etBinningString.Data());
int etaBinning = 0;
if(etaBinningString == "ETABINS1")
etaBinning = ETABINS1;
else if(etaBinningString == "ETABINS2")
etaBinning = ETABINS2;
else
assert(0);
printf("SC eta binning: %s\n", etaBinningString.Data());
int sample;
if(sampleTypeString == "DATA")
sample = DATA;
else if(sampleTypeString == "MC")
sample = MC;
else
assert(0);
printf("Sample: %s\n", sampleTypeString.Data());
// Construct the trigger object
TriggerSelection triggers(triggerSetString, (sample==DATA)?true:false, 0);
if (effType==HLT) std::cout << "\tHLT efficiency calculation method " << triggers.hltEffCalcName() << ", triggerSet=" << triggers.triggerSetName() << "\n";
else triggers.hltEffCalcMethod(HLTEffCalc_2011Old);
TRandom *rnd= new TRandom();
rnd->SetSeed(0);
// The label is a string that contains the fields that are passed to
// the function below, to be used to name files with the output later.
TString label = getLabel(sample, effType, calcMethod, etBinning, etaBinning, triggers);
//--------------------------------------------------------------------------------------------------------------
// Main analysis code
//==============================================================================================================
//
// Set up histograms
//
TH1F* hMass = new TH1F("hMass" ,"",30,massLow,massHigh);
TH1F* hMassTotal = new TH1F("hMassTotal","",30,massLow,massHigh);
TH1F* hMassPass = new TH1F("hMassPass" ,"",30,massLow,massHigh);
TH1F* hMassFail = new TH1F("hMassFail" ,"",30,massLow,massHigh);
// Save MC templates if sample is MC
TString tagAndProbeDir(TString("../root_files/tag_and_probe/")+dirTag);
gSystem->mkdir(tagAndProbeDir,kTRUE);
TFile *templatesFile = 0;
vector<TH1F*> hPassTemplateV;
vector<TH1F*> hFailTemplateV;
if( sample != DATA) {
// For simulation, we will be saving templates
TString labelMC = getLabel(-1111, effType, calcMethod, etBinning, etaBinning, triggers);
TString templatesLabel = tagAndProbeDir + TString("/mass_templates_")+labelMC+TString(".root");
templatesFile = new TFile(templatesLabel,"recreate");
for(int i=0; i<getNEtBins(etBinning); i++){
for(int j=0; j<getNEtaBins(etaBinning); j++){
TString hname = "hMassTemplate_Et";
hname += i;
hname += "_eta";
hname += j;
hPassTemplateV.push_back(new TH1F(hname+TString("_pass"),"",60,massLow,massHigh));
hFailTemplateV.push_back(new TH1F(hname+TString("_fail"),"",60,massLow,massHigh));
}
}
} else {
// For data, we will be using templates,
// however, if the request is COUNTnCOUNT, do nothing
if( calcMethod != COUNTnCOUNT ){
TString labelMC = getLabel(-1111, effType, calcMethod, etBinning, etaBinning, triggers);
TString templatesLabel = tagAndProbeDir+TString("/mass_templates_")+labelMC+TString(".root");
templatesFile = new TFile(templatesLabel);
if( ! templatesFile->IsOpen() )
assert(0);
}
}
// This file can be utilized in the future, but for now
// opening it just removes complaints about memory resident
// trees. No events are actually written.
TFile *selectedEventsFile = new TFile("selectedEventsFile.root","recreate");
if (!selectedEventsFile) {
assert(0);
}
TTree *passTree = new TTree("passTree","passTree");
Double_t storeMass, storeEt, storeEta;
passTree->Branch("mass",&storeMass,"mass/D");
passTree->Branch("et",&storeEt ,"et/D");
passTree->Branch("eta",&storeEta ,"eta/D");
TTree *failTree = new TTree("failTree","failTree");
failTree->Branch("mass",&storeMass,"mass/D");
failTree->Branch("et",&storeEt ,"et/D");
failTree->Branch("eta",&storeEta ,"eta/D");
int nDivisions = getNEtBins(etBinning)*getNEtaBins(etaBinning);
double ymax = 800;
if(nDivisions <4 )
ymax = nDivisions * 200;
TCanvas *c1 = MakeCanvas("c1","c1", 600, int(ymax));
c1->Divide(2,nDivisions);
int eventsInNtuple = 0;
int eventsAfterTrigger = 0;
int totalCand = 0;
int totalCandInMassWindow = 0;
int totalCandInEtaAcceptance = 0;
int totalCandEtAbove10GeV = 0;
int totalCandMatchedToGen = 0;
// Loop over files
for(UInt_t ifile=0; ifile<ntupleFileNames.size(); ifile++){
//
// Access samples and fill histograms
//
TFile *infile = 0;
TTree *eventTree = 0;
// Data structures to store info from TTrees
mithep::TEventInfo *info = new mithep::TEventInfo();
mithep::TGenInfo *gen = new mithep::TGenInfo();
TClonesArray *dielectronArr = new TClonesArray("mithep::TDielectron");
// Read input file
cout << "Processing " << ntupleFileNames[ifile] << "..." << endl;
infile = new TFile(ntupleFileNames[ifile]);
assert(infile);
// Get the TTrees
eventTree = (TTree*)infile->Get("Events"); assert(eventTree);
// Set branch address to structures that will store the info
eventTree->SetBranchAddress("Info",&info); TBranch *infoBr = eventTree->GetBranch("Info");
// check whether the file is suitable for the requested run range
UInt_t runNumMin = UInt_t(eventTree->GetMinimum("runNum"));
UInt_t runNumMax = UInt_t(eventTree->GetMaximum("runNum"));
std::cout << "runNumMin=" << runNumMin << ", runNumMax=" << runNumMax << "\n";
if (!triggers.validRunRange(runNumMin,runNumMax)) {
std::cout << "... file contains uninteresting run range\n";
continue;
}
// Define other branches
eventTree->SetBranchAddress("Dielectron",&dielectronArr); TBranch *dielectronBr = eventTree->GetBranch("Dielectron");
TBranch *genBr = 0;
if(sample != DATA){
eventTree->SetBranchAddress("Gen",&gen);
genBr = eventTree->GetBranch("Gen");
}
// loop over events
eventsInNtuple += eventTree->GetEntries();
for(UInt_t ientry=0; ientry<eventTree->GetEntries(); ientry++) {
// for(UInt_t ientry=0; ientry<200000; ientry++) { // This is for faster turn-around in testing
if(sample != DATA)
genBr->GetEntry(ientry);
// Check that the whole event has fired the appropriate trigger
infoBr->GetEntry(ientry);
/* Old code
// For EPS2011 for both data and MC (starting from Summer11 production)
// we use a special trigger for tag and probe that has second leg
// unbiased with cuts at HLT
ULong_t eventTriggerBit = kHLT_Ele17_CaloIdVT_CaloIsoVT_TrkIdT_TrkIsoVT_SC8_Mass30
| kHLT_Ele32_CaloIdL_CaloIsoVL_SC17;
// The tag trigger bit matches the "electron" of the trigger we
// use for this tag and probe study: electron+sc
ULong_t tagTriggerObjectBit = kHLT_Ele17_CaloIdVT_CaloIsoVT_TrkIdT_TrkIsoVT_SC8_Mass30_EleObj
| kHLT_Ele32_CaloIdL_CaloIsoVL_SC17_EleObj;
// The probe trigger, however, is any of possibilities used in
// the trigger that is used in the main analysis
ULong_t probeTriggerObjectBit = kHLT_Ele17_CaloIdL_CaloIsoVL_Ele8_CaloIdL_CaloIsoVL_Ele1Obj
| kHLT_Ele17_CaloIdT_TrkIdVL_CaloIsoVL_TrkIsoVL_Ele8_CaloIdT_TrkIdVL_CaloIsoVL_TrkIsoVL_Ele1Obj
| kHLT_Ele17_CaloIdL_CaloIsoVL_Ele8_CaloIdL_CaloIsoVL_Ele2Obj
| kHLT_Ele17_CaloIdT_TrkIdVL_CaloIsoVL_TrkIsoVL_Ele8_CaloIdT_TrkIdVL_CaloIsoVL_TrkIsoVL_Ele2Obj;
*/
ULong_t eventTriggerBit= triggers.getEventTriggerBit_TagProbe(info->runNum);
if(!(info->triggerBits & eventTriggerBit)) continue; // no trigger accept? Skip to next event...
eventsAfterTrigger++;
ULong_t tagTriggerObjectBit= triggers.getTagTriggerObjBit(info->runNum);
ULong_t probeTriggerObjectBit_Tight= triggers.getProbeTriggerObjBit_Tight(info->runNum);
ULong_t probeTriggerObjectBit_Loose= triggers.getProbeTriggerObjBit_Loose(info->runNum);
ULong_t probeTriggerObjectBit= probeTriggerObjectBit_Tight | probeTriggerObjectBit_Loose;
// loop through dielectrons
dielectronArr->Clear();
dielectronBr->GetEntry(ientry);
for(Int_t i=0; i<dielectronArr->GetEntriesFast(); i++) {
totalCand++;
const mithep::TDielectron *dielectron = (mithep::TDielectron*)((*dielectronArr)[i]);
// Tag and probe is done around the Z peak
if((dielectron->mass < massLow) || (dielectron->mass > massHigh)) continue;
totalCandInMassWindow++;
//
// Exclude ECAL gap region (should already be done for ntuple, but just to make sure...)
if((fabs(dielectron->scEta_1)>kGAP_LOW) && (fabs(dielectron->scEta_1)<kGAP_HIGH)) continue;
if((fabs(dielectron->scEta_2)>kGAP_LOW) && (fabs(dielectron->scEta_2)<kGAP_HIGH)) continue;
// ECAL acceptance cut on supercluster Et
if((fabs(dielectron->scEta_1) > 2.5) || (fabs(dielectron->scEta_2) > 2.5)) continue; // outside eta range? Skip to next event...
totalCandInEtaAcceptance++;
// None of the electrons should be below 10 GeV
if((dielectron->scEt_1 < 10) || (dielectron->scEt_2 < 10)) continue; // below supercluster ET cut? Skip to next event...
totalCandEtAbove10GeV++;
// Next, we will do a loose kinematic matching to generator level
// info.
// For the data, this is not needed and not done. We take all
// candidates, and take care of background by fitting.
// For MC, however, we do not fit, but count pass/fail events.
// So we need to make sure there is no background. However, even
// in the signal Z->ee MC sample there jets and therefore fake
// electrons. So we drop all candidates that do not have both leptons
// matched.
//
if( sample != DATA )
if( ! dielectronMatchedToGeneratorLevel(gen, dielectron) ) continue;
totalCandMatchedToGen++;
// ECAL driven: this condition is NOT applied
// Preliminary selection is complete. Now work on tags and probes.
TElectron *ele1 = extractElectron(dielectron, 1);
TElectron *ele2 = extractElectron(dielectron, 2);
bool isTag1 = isTag(ele1, tagTriggerObjectBit);
bool isTag2 = isTag(ele2, tagTriggerObjectBit);
// Any electron that made it here is eligible to be a probe
// for ID cuts.
bool isIDProbe1 = true;
bool isIDProbe2 = true;
bool isIDProbePass1 = passID(ele1);
bool isIDProbePass2 = passID(ele2);
// Probes for HLT cuts:
bool isHLTProbe1 = passID(ele1);
bool isHLTProbe2 = passID(ele2);
bool isHLTProbePass1 = ( isHLTProbe1 && (ele1 ->hltMatchBits & probeTriggerObjectBit) );
bool isHLTProbePass2 = ( isHLTProbe2 && (ele2 ->hltMatchBits & probeTriggerObjectBit) );
//
// Apply tag and probe, and accumulate counters or histograms
//
bool isProbe1 = false;
bool isProbe2 = false;
bool isProbePass1 = false;
bool isProbePass2 = false;
if( effType == ID ){
isProbe1 = isIDProbe1;
isProbe2 = isIDProbe2;
isProbePass1 = isIDProbePass1;
isProbePass2 = isIDProbePass2;
}else if( effType == HLT ){
isProbe1 = isHLTProbe1;
isProbe2 = isHLTProbe2;
isProbePass1 = isHLTProbePass1;
isProbePass2 = isHLTProbePass2;
if (triggers.useRandomTagTnPMethod(info->runNum)) {
if (rnd->Uniform() <= 0.5) {
// tag is 1st electron
if (!isTag1) continue;
isTag2=0; // ignore whether ele2 can be a tag
}
else {
if (!isTag2) continue;
isTag1=0; // ignore whether ele1 can be a tag
}
}
}else {
printf("ERROR: unknown efficiency type requested\n");
}
storeMass = dielectron->mass;
// First electron is the tag, second is the probe
if( isTag1 && isProbe2){
// total probes
hMassTotal->Fill(dielectron->mass);
storeEt = dielectron->scEt_2;
storeEta = dielectron->scEta_2;
int templateBin = getTemplateBin( findEtBin(storeEt,etBinning),
findEtaBin(storeEta,etaBinning),
etaBinning);
if( isProbePass2 ){
// passed
hMassPass->Fill(dielectron->mass);
passTree->Fill();
if(sample != DATA && templateBin != -1)
hPassTemplateV[templateBin]->Fill(dielectron->mass);
}else{
// fail
hMassFail->Fill(dielectron->mass);
failTree->Fill();
if(sample != DATA && templateBin != -1)
hFailTemplateV[templateBin]->Fill(dielectron->mass);
}
}
// Second electron is the tag, first is the probe
if( isTag2 && isProbe1 ){
// total probes
hMassTotal->Fill(dielectron->mass);
storeEt = dielectron->scEt_1;
storeEta = dielectron->scEta_1;
int templateBin = getTemplateBin( findEtBin(storeEt,etBinning),
findEtaBin(storeEta,etaBinning),
etaBinning);
if( isProbePass1 ){
// passed
hMassPass->Fill(dielectron->mass);
passTree->Fill();
if(sample != DATA && templateBin != -1)
hPassTemplateV[templateBin]->Fill(dielectron->mass);
}else{
// fail
hMassFail->Fill(dielectron->mass);
failTree->Fill();
if(sample != DATA && templateBin != -1)
hFailTemplateV[templateBin]->Fill(dielectron->mass);
}
}
// In case the full selection is applied:
// if( !(isTag1 && ele2_passID) && !(isTag2 && ele1_passID) ) continue;
if( !(isTag1 && isIDProbePass2) && !(isTag2 && isIDProbePass1) ) continue;
// if( !(isTag1) && !(isTag2) ) continue;
// Fill histogram
hMass->Fill(dielectron->mass);
} // end loop over dielectron candidates
} // end loop over events
delete infile;
infile=0;
eventTree=0;
delete gen;
delete info;
delete dielectronArr;
} // end loop over files
//
// Efficiency analysis
//
// printf("Number of regular candidates: %15.0f\n", hMass->GetSumOfWeights());
printf("Total events in ntuple %15d\n",eventsInNtuple);
printf(" events after event level trigger cut %15d\n",eventsAfterTrigger);
printf("\nTotal candidates (no cuts) %15d\n",totalCand);
printf(" candidates in 60-120 mass window %15d\n",totalCandInMassWindow);
printf(" candidates witheta 0-1.4442, 1.566-2.5 %15d\n",totalCandInEtaAcceptance);
printf(" candidates, both electrons above 10 GeV %15d\n",totalCandEtAbove10GeV);
printf(" candidates matched to GEN level (if MC) %15d\n",totalCandMatchedToGen);
printf("\nNumber of probes, total %15.0f\n", hMassTotal->GetSumOfWeights());
printf("Number of probes, passed %15.0f\n", hMassPass->GetSumOfWeights());
printf("Number of probes, failed %15.0f\n", hMassFail->GetSumOfWeights());
// Human-readbale text file to store measured efficiencies
TString reslog = tagAndProbeDir+TString("/efficiency_TnP_")+label+TString(".txt");
ofstream effOutput;
effOutput.open(reslog);
// Print into the results file the header.
effOutput << "Efficiency calculation method: " << calcMethodString.Data() << endl;
effOutput << "Efficiency type to measure: " << effTypeString.Data() << endl;
effOutput << "SC ET binning: " << etBinningString.Data() << endl;
effOutput << "SC eta binning: " << etaBinningString.Data() << endl;
effOutput << "Sample: " << sampleTypeString.Data() << endl;
effOutput << "Files processed: " << endl;
for(UInt_t i=0; i<ntupleFileNames.size(); i++)
effOutput << " " << ntupleFileNames[i].Data() << endl;
// ROOT file to store measured efficiencies in ROOT format
TString resroot = tagAndProbeDir+TString("/efficiency_TnP_")+label+TString(".root");
TFile *resultsRootFile = new TFile(resroot,"recreate");
// Fit log
TString fitlogname = tagAndProbeDir+TString("/efficiency_TnP_")+label+TString("_fitlog.dat");
ofstream fitLog;
fitLog.open(fitlogname);
//
// Find efficiency
//
bool useTemplates = false;
if(sample == DATA && effType == ID && (calcMethod == COUNTnFIT || FITnFIT) )
useTemplates = true;
int NsetBins=120;
bool isRECO=0;
const char* setBinsType="cache";
measureEfficiency(passTree, failTree,
calcMethod, etBinning, etaBinning, c1, effOutput, fitLog,
useTemplates, templatesFile, resultsRootFile,
NsetBins, isRECO, setBinsType,
dirTag, triggers.triggerSetName());
effOutput.close();
fitLog.close();
TString command = "cat ";
command += reslog;
system(command.Data());
TString fitpicname = tagAndProbeDir+TString("/efficiency_TnP_")+label+TString("_fit.png");
c1->SaveAs(fitpicname);
// Save MC templates
if(sample != DATA){
templatesFile->cd();
for(int i=0; i<getNEtBins(etBinning); i++){
for(int j=0; j<getNEtaBins(etaBinning); j++){
int templateBin = getTemplateBin( i, j, etaBinning);
hPassTemplateV[templateBin]->Write();
hFailTemplateV[templateBin]->Write();
}
}
templatesFile->Close();
}
gBenchmark->Show("eff_IdHlt");
}
void correlations(const char * filename="counts.root")
{
// read data tree
TFile * infile = new TFile(filename,"READ");
TTree * tr = (TTree*) infile->Get("sca");
Int_t index,t,runnum;
Int_t bbce,bbcw,bbcx;
Int_t zdce,zdcw,zdcx;
Int_t vpde,vpdw,vpdx;
tr->SetBranchAddress("t",&t);
tr->SetBranchAddress("i",&index);
tr->SetBranchAddress("runnum",&runnum);
tr->SetBranchAddress("bbce",&bbce);
tr->SetBranchAddress("bbcw",&bbcw);
tr->SetBranchAddress("bbcx",&bbcx);
tr->SetBranchAddress("zdce",&zdce);
tr->SetBranchAddress("zdcw",&zdcw);
tr->SetBranchAddress("zdcx",&zdcx);
tr->SetBranchAddress("vpde",&vpde);
tr->SetBranchAddress("vpdw",&vpdw);
tr->SetBranchAddress("vpdx",&vpdx);
// determine maximum trigger rates
/*
Float_t bbce_max_rate, bbcw_max_rate, bbcx_max_rate;
Float_t zdce_max_rate, zdcw_max_rate, zdcx_max_rate;
Float_t vpde_max_rate, vpdw_max_rate, vpdx_max_rate;
Float_t bbce_rate, bbcw_rate, bbcx_rate;
Float_t zdce_rate, zdcw_rate, zdcx_rate;
Float_t vpde_rate, vpdw_rate, vpdx_rate;
bbce_max_rate = bbcw_max_rate = bbcx_max_rate = 0;
zdce_max_rate = zdcw_max_rate = zdcx_max_rate = 0;
vpde_max_rate = vpdw_max_rate = vpdx_max_rate = 0;
for(Int_t i=0; i<tr->GetEntries(); i++)
{
tr->GetEntry(i);
bbce_rate = ((Float_t)bbce)/((Float_t)t);
bbcw_rate = ((Float_t)bbcw)/((Float_t)t);
bbcx_rate = ((Float_t)bbcx)/((Float_t)t);
zdce_rate = ((Float_t)zdce)/((Float_t)t);
zdcw_rate = ((Float_t)zdcw)/((Float_t)t);
zdcx_rate = ((Float_t)zdcx)/((Float_t)t);
vpde_rate = ((Float_t)vpde)/((Float_t)t);
vpdw_rate = ((Float_t)vpdw)/((Float_t)t);
vpdx_rate = ((Float_t)vpdx)/((Float_t)t);
bbce_max_rate = (bbce_rate > bbce_max_rate) ? bbce_rate : bbce_max_rate;
bbcw_max_rate = (bbcw_rate > bbcw_max_rate) ? bbcw_rate : bbcw_max_rate;
bbcx_max_rate = (bbcx_rate > bbcx_max_rate) ? bbcx_rate : bbcx_max_rate;
zdce_max_rate = (zdce_rate > zdce_max_rate) ? zdce_rate : zdce_max_rate;
zdcw_max_rate = (zdcw_rate > zdcw_max_rate) ? zdcw_rate : zdcw_max_rate;
zdcx_max_rate = (zdcx_rate > zdcx_max_rate) ? zdcx_rate : zdcx_max_rate;
vpde_max_rate = (vpde_rate > vpde_max_rate) ? vpde_rate : vpde_max_rate;
vpdw_max_rate = (vpdw_rate > vpdw_max_rate) ? vpdw_rate : vpdw_max_rate;
vpdx_max_rate = (vpdx_rate > vpdx_max_rate) ? vpdx_rate : vpdx_max_rate;
};
Float_t factor=0.1;
bbce_max_rate += factor*bbce_max_rate;
bbcw_max_rate += factor*bbcw_max_rate;
bbcx_max_rate += factor*bbcx_max_rate;
zdce_max_rate += factor*zdce_max_rate;
zdcw_max_rate += factor*zdcw_max_rate;
zdcx_max_rate += factor*zdcx_max_rate;
vpde_max_rate += factor*vpde_max_rate;
vpdw_max_rate += factor*vpdw_max_rate;
vpdx_max_rate += factor*vpdx_max_rate;
// initialise correlation histograms
const Int_t NBINS = 100;
TH2F * bbc_ew = new TH2F("bbc_ew","BBCE vs BBCW Rate Correlation",NBINS,0,bbcw_max_rate,NBINS,0,bbce_max_rate);
TH2F * bbc_ex = new TH2F("bbc_ex","BBCE vs BBCX Rate Correlation",NBINS,0,bbcx_max_rate,NBINS,0,bbce_max_rate);
TH2F * bbc_wx = new TH2F("bbc_wx","BBCW vs BBCX Rate Correlation",NBINS,0,bbcx_max_rate,NBINS,0,bbcw_max_rate);
TH2F * zdc_ew = new TH2F("zdc_ew","ZDCE vs ZDCW Rate Correlation",NBINS,0,zdcw_max_rate,NBINS,0,zdce_max_rate);
TH2F * zdc_ex = new TH2F("zdc_ex","ZDCE vs ZDCX Rate Correlation",NBINS,0,zdcx_max_rate,NBINS,0,zdce_max_rate);
TH2F * zdc_wx = new TH2F("zdc_wx","ZDCW vs ZDCX Rate Correlation",NBINS,0,zdcx_max_rate,NBINS,0,zdcw_max_rate);
TH2F * vpd_ew = new TH2F("vpd_ew","VPDE vs VPDW Rate Correlation",NBINS,0,vpdw_max_rate,NBINS,0,vpde_max_rate);
TH2F * vpd_ex = new TH2F("vpd_ex","VPDE vs VPDX Rate Correlation",NBINS,0,vpdx_max_rate,NBINS,0,vpde_max_rate);
TH2F * vpd_wx = new TH2F("vpd_wx","VPDW vs VPDX Rate Correlation",NBINS,0,vpdx_max_rate,NBINS,0,vpdw_max_rate);
TH2F * bz_e = new TH2F("bz_e","BBCE vs ZDCE Rate Correlation",NBINS,0,zdce_max_rate,NBINS,0,bbce_max_rate);
TH2F * bz_w = new TH2F("bz_w","BBCW vs ZDCW Rate Correlation",NBINS,0,zdcw_max_rate,NBINS,0,bbcw_max_rate);
TH2F * bz_x = new TH2F("bz_x","BBCX vs ZDCX Rate Correlation",NBINS,0,zdcx_max_rate,NBINS,0,bbcx_max_rate);
TH2F * bv_e = new TH2F("bv_e","BBCE vs VPDE Rate Correlation",NBINS,0,vpde_max_rate,NBINS,0,bbce_max_rate);
TH2F * bv_w = new TH2F("bv_w","BBCW vs VPDW Rate Correlation",NBINS,0,vpdw_max_rate,NBINS,0,bbcw_max_rate);
TH2F * bv_x = new TH2F("bv_x","BBCX vs VPDX Rate Correlation",NBINS,0,vpdx_max_rate,NBINS,0,bbcx_max_rate);
TH2F * vz_e = new TH2F("vz_e","VPDE vs ZDCE Rate Correlation",NBINS,0,zdce_max_rate,NBINS,0,vpde_max_rate);
TH2F * vz_w = new TH2F("vz_w","VPDW vs ZDCW Rate Correlation",NBINS,0,zdcw_max_rate,NBINS,0,vpdw_max_rate);
TH2F * vz_x = new TH2F("vz_x","VPDX vs ZDCX Rate Correlation",NBINS,0,zdcx_max_rate,NBINS,0,vpdx_max_rate);
// projections
tr->Project("bbc_ew","bbce/t:bbcw/t","bbce>0 && bbcw>0 && t>0");
tr->Project("bbc_ex","bbce/t:bbcx/t","bbce>0 && bbcx>0 && t>0");
tr->Project("bbc_wx","bbcw/t:bbcx/t","bbcw>0 && bbcx>0 && t>0");
tr->Project("zdc_ew","zdce/t:zdcw/t","zdce>0 && zdcw>0 && t>0");
tr->Project("zdc_ex","zdce/t:zdcx/t","zdce>0 && zdcx>0 && t>0");
tr->Project("zdc_wx","zdcw/t:zdcx/t","zdcw>0 && zdcx>0 && t>0");
tr->Project("vpd_ew","vpde/t:vpdw/t","vpde>0 && vpdw>0 && t>0");
tr->Project("vpd_ex","vpde/t:vpdx/t","vpde>0 && vpdx>0 && t>0");
tr->Project("vpd_wx","vpdw/t:vpdx/t","vpdw>0 && vpdx>0 && t>0");
tr->Project("bz_e","bbce/t:zdce/t","bbce>0 && zdce>0 && t>0");
tr->Project("bz_w","bbcw/t:zdcw/t","bbcw>0 && zdcw>0 && t>0");
tr->Project("bz_x","bbcx/t:zdcx/t","bbcx>0 && zdcx>0 && t>0");
tr->Project("bv_e","bbce/t:vpde/t","bbce>0 && vpde>0 && t>0");
tr->Project("bv_w","bbcw/t:vpdw/t","bbcw>0 && vpdw>0 && t>0");
tr->Project("bv_x","bbcx/t:vpdx/t","bbcx>0 && vpdx>0 && t>0");
tr->Project("vz_e","vpde/t:zdce/t","vpde>0 && zdce>0 && t>0");
tr->Project("vz_w","vpdw/t:zdcw/t","vpdw>0 && zdcw>0 && t>0");
tr->Project("vz_x","vpdx/t:zdcx/t","vpdx>0 && zdcx>0 && t>0");
*/
// define run total variables (total scaler counts in a single run)
Int_t IMAX_tmp = tr->GetMaximum("i");
const Int_t IMAX = IMAX_tmp;
Long_t bbce_total[IMAX];
Long_t bbcw_total[IMAX];
Long_t bbcx_total[IMAX];
Long_t zdce_total[IMAX];
Long_t zdcw_total[IMAX];
Long_t zdcx_total[IMAX];
Long_t vpde_total[IMAX];
Long_t vpdw_total[IMAX];
Long_t vpdx_total[IMAX];
Int_t time[IMAX];
for(Int_t i=0; i<IMAX; i++)
{
bbce_total[i] = 0;
bbcw_total[i] = 0;
bbcx_total[i] = 0;
zdce_total[i] = 0;
zdcw_total[i] = 0;
zdcx_total[i] = 0;
vpde_total[i] = 0;
vpdw_total[i] = 0;
vpdx_total[i] = 0;
};
// tree loop --> fills run totals arrays
Int_t ii;
for(Int_t i=0; i<tr->GetEntries(); i++)
{
tr->GetEntry(i);
ii = index-1; // run index starts at 1; arrays start at 0
time[ii] = t;
bbce_total[ii] += bbce;
bbcw_total[ii] += bbcw;
bbcx_total[ii] += bbcx;
zdce_total[ii] += zdce;
zdcw_total[ii] += zdcw;
zdcx_total[ii] += zdcx;
vpde_total[ii] += vpde;
vpdw_total[ii] += vpdw;
vpdx_total[ii] += vpdx;
};
// compute trigger rates & and max trigger rates
Float_t bbce_rate[IMAX], bbcw_rate[IMAX], bbcx_rate[IMAX];
Float_t zdce_rate[IMAX], zdcw_rate[IMAX], zdcx_rate[IMAX];
Float_t vpde_rate[IMAX], vpdw_rate[IMAX], vpdx_rate[IMAX];
Float_t bbce_rate_max, bbcw_rate_max, bbcx_rate_max;
Float_t zdce_rate_max, zdcw_rate_max, zdcx_rate_max;
Float_t vpde_rate_max, vpdw_rate_max, vpdx_rate_max;
bbce_rate_max = bbcw_rate_max = bbcx_rate_max = 0;
zdce_rate_max = zdcw_rate_max = zdcx_rate_max = 0;
vpde_rate_max = vpdw_rate_max = vpdx_rate_max = 0;
for(Int_t i=0; i<IMAX; i++)
{
bbce_rate[i] = ((Float_t) bbce_total[i])/((Float_t) time[i]);
bbcw_rate[i] = ((Float_t) bbcw_total[i])/((Float_t) time[i]);
bbcx_rate[i] = ((Float_t) bbcx_total[i])/((Float_t) time[i]);
zdce_rate[i] = ((Float_t) zdce_total[i])/((Float_t) time[i]);
zdcw_rate[i] = ((Float_t) zdcw_total[i])/((Float_t) time[i]);
zdcx_rate[i] = ((Float_t) zdcx_total[i])/((Float_t) time[i]);
vpde_rate[i] = ((Float_t) vpde_total[i])/((Float_t) time[i]);
vpdw_rate[i] = ((Float_t) vpdw_total[i])/((Float_t) time[i]);
vpdx_rate[i] = ((Float_t) vpdx_total[i])/((Float_t) time[i]);
bbce_rate_max = (bbce_rate[i] > bbce_rate_max) ? bbce_rate[i] : bbce_rate_max;
bbcw_rate_max = (bbcw_rate[i] > bbcw_rate_max) ? bbcw_rate[i] : bbcw_rate_max;
bbcx_rate_max = (bbcx_rate[i] > bbcx_rate_max) ? bbcx_rate[i] : bbcx_rate_max;
zdce_rate_max = (zdce_rate[i] > zdce_rate_max) ? zdce_rate[i] : zdce_rate_max;
zdcw_rate_max = (zdcw_rate[i] > zdcw_rate_max) ? zdcw_rate[i] : zdcw_rate_max;
zdcx_rate_max = (zdcx_rate[i] > zdcx_rate_max) ? zdcx_rate[i] : zdcx_rate_max;
zdce_rate_max = (zdce_rate[i] > zdce_rate_max) ? zdce_rate[i] : zdce_rate_max;
zdcw_rate_max = (zdcw_rate[i] > zdcw_rate_max) ? zdcw_rate[i] : zdcw_rate_max;
zdcx_rate_max = (zdcx_rate[i] > zdcx_rate_max) ? zdcx_rate[i] : zdcx_rate_max;
};
Float_t factor=0.1;
bbce_rate_max += factor*bbce_rate_max;
bbcw_rate_max += factor*bbcw_rate_max;
bbcx_rate_max += factor*bbcx_rate_max;
zdce_rate_max += factor*zdce_rate_max;
zdcw_rate_max += factor*zdcw_rate_max;
zdcx_rate_max += factor*zdcx_rate_max;
vpde_rate_max += factor*vpde_rate_max;
vpdw_rate_max += factor*vpdw_rate_max;
vpdx_rate_max += factor*vpdx_rate_max;
// initialise correlation histograms
const Int_t NBINS = 100;
TH2F * bbc_ew = new TH2F("bbc_ew","BBCE vs BBCW Rate Correlation",NBINS,0,bbcw_rate_max,NBINS,0,bbce_rate_max);
TH2F * bbc_ex = new TH2F("bbc_ex","BBCE vs BBCX Rate Correlation",NBINS,0,bbcx_rate_max,NBINS,0,bbce_rate_max);
TH2F * bbc_wx = new TH2F("bbc_wx","BBCW vs BBCX Rate Correlation",NBINS,0,bbcx_rate_max,NBINS,0,bbcw_rate_max);
TH2F * zdc_ew = new TH2F("zdc_ew","ZDCE vs ZDCW Rate Correlation",NBINS,0,zdcw_rate_max,NBINS,0,zdce_rate_max);
TH2F * zdc_ex = new TH2F("zdc_ex","ZDCE vs ZDCX Rate Correlation",NBINS,0,zdcx_rate_max,NBINS,0,zdce_rate_max);
TH2F * zdc_wx = new TH2F("zdc_wx","ZDCW vs ZDCX Rate Correlation",NBINS,0,zdcx_rate_max,NBINS,0,zdcw_rate_max);
TH2F * vpd_ew = new TH2F("vpd_ew","VPDE vs VPDW Rate Correlation",NBINS,0,vpdw_rate_max,NBINS,0,vpde_rate_max);
TH2F * vpd_ex = new TH2F("vpd_ex","VPDE vs VPDX Rate Correlation",NBINS,0,vpdx_rate_max,NBINS,0,vpde_rate_max);
TH2F * vpd_wx = new TH2F("vpd_wx","VPDW vs VPDX Rate Correlation",NBINS,0,vpdx_rate_max,NBINS,0,vpdw_rate_max);
TH2F * bz_e = new TH2F("bz_e","BBCE vs ZDCE Rate Correlation",NBINS,0,zdce_rate_max,NBINS,0,bbce_rate_max);
TH2F * bz_w = new TH2F("bz_w","BBCW vs ZDCW Rate Correlation",NBINS,0,zdcw_rate_max,NBINS,0,bbcw_rate_max);
TH2F * bz_x = new TH2F("bz_x","BBCX vs ZDCX Rate Correlation",NBINS,0,zdcx_rate_max,NBINS,0,bbcx_rate_max);
TH2F * bv_e = new TH2F("bv_e","BBCE vs VPDE Rate Correlation",NBINS,0,vpde_rate_max,NBINS,0,bbce_rate_max);
TH2F * bv_w = new TH2F("bv_w","BBCW vs VPDW Rate Correlation",NBINS,0,vpdw_rate_max,NBINS,0,bbcw_rate_max);
TH2F * bv_x = new TH2F("bv_x","BBCX vs VPDX Rate Correlation",NBINS,0,vpdx_rate_max,NBINS,0,bbcx_rate_max);
TH2F * vz_e = new TH2F("vz_e","VPDE vs ZDCE Rate Correlation",NBINS,0,zdce_rate_max,NBINS,0,vpde_rate_max);
TH2F * vz_w = new TH2F("vz_w","VPDW vs ZDCW Rate Correlation",NBINS,0,zdcw_rate_max,NBINS,0,vpdw_rate_max);
TH2F * vz_x = new TH2F("vz_x","VPDX vs ZDCX Rate Correlation",NBINS,0,zdcx_rate_max,NBINS,0,vpdx_rate_max);
// fill correlation histograms
for(Int_t i=0; i<IMAX; i++)
{
bbc_ew->Fill(bbcw_rate[i],bbce_rate[i]);
bbc_ex->Fill(bbcx_rate[i],bbce_rate[i]);
bbc_wx->Fill(bbcx_rate[i],bbcw_rate[i]);
zdc_ew->Fill(zdcw_rate[i],zdce_rate[i]);
zdc_ex->Fill(zdcx_rate[i],zdce_rate[i]);
zdc_wx->Fill(zdcx_rate[i],zdcw_rate[i]);
vpd_ew->Fill(vpdw_rate[i],vpde_rate[i]);
vpd_ex->Fill(vpdx_rate[i],vpde_rate[i]);
vpd_wx->Fill(vpdx_rate[i],vpdw_rate[i]);
bz_e->Fill(zdce_rate[i],bbce_rate[i]);
bz_w->Fill(zdcw_rate[i],bbcw_rate[i]);
bz_x->Fill(zdcx_rate[i],bbcx_rate[i]);
bv_e->Fill(vpde_rate[i],bbce_rate[i]);
bv_w->Fill(vpdw_rate[i],bbcw_rate[i]);
bv_x->Fill(vpdx_rate[i],bbcx_rate[i]);
vz_e->Fill(zdce_rate[i],vpde_rate[i]);
vz_w->Fill(zdcw_rate[i],vpdw_rate[i]);
vz_x->Fill(zdcx_rate[i],vpdx_rate[i]);
};
// compute correlation coefficients
char bbc_ew_text[64];
char bbc_ex_text[64];
char bbc_wx_text[64];
char zdc_ew_text[64];
char zdc_ex_text[64];
char zdc_wx_text[64];
char vpd_ew_text[64];
char vpd_ex_text[64];
char vpd_wx_text[64];
char bz_e_text[64];
char bz_w_text[64];
char bz_x_text[64];
char bv_e_text[64];
char bv_w_text[64];
char bv_x_text[64];
char vz_e_text[64];
char vz_w_text[64];
char vz_x_text[64];
sprintf(bbc_ew_text,"BBC E:W -- C=%0.5f",bbc_ew->GetCorrelationFactor());
sprintf(bbc_ex_text,"BBC E:X -- C=%0.5f",bbc_ex->GetCorrelationFactor());
sprintf(bbc_wx_text,"BBC W:X -- C=%0.5f",bbc_wx->GetCorrelationFactor());
sprintf(zdc_ew_text,"ZDC E:W -- C=%0.5f",zdc_ew->GetCorrelationFactor());
sprintf(zdc_ex_text,"ZDC E:X -- C=%0.5f",zdc_ex->GetCorrelationFactor());
sprintf(zdc_wx_text,"ZDC W:X -- C=%0.5f",zdc_wx->GetCorrelationFactor());
sprintf(vpd_ew_text,"VPD E:W -- C=%0.5f",vpd_ew->GetCorrelationFactor());
sprintf(vpd_ex_text,"VPD E:X -- C=%0.5f",vpd_ex->GetCorrelationFactor());
sprintf(vpd_wx_text,"VPD W:X -- C=%0.5f",vpd_wx->GetCorrelationFactor());
sprintf(bz_e_text,"BBCE:ZDCE -- C=%0.5f",bz_e->GetCorrelationFactor());
sprintf(bz_w_text,"BBCW:ZDCW -- C=%0.5f",bz_w->GetCorrelationFactor());
sprintf(bz_x_text,"BBCX:ZDCX -- C=%0.5f",bz_x->GetCorrelationFactor());
sprintf(bv_e_text,"BBCE:VPDE -- C=%0.5f",bv_e->GetCorrelationFactor());
sprintf(bv_w_text,"BBCW:VPDW -- C=%0.5f",bv_w->GetCorrelationFactor());
sprintf(bv_x_text,"BBCX:VPDX -- C=%0.5f",bv_x->GetCorrelationFactor());
sprintf(vz_e_text,"VPDE:ZDCE -- C=%0.5f",vz_e->GetCorrelationFactor());
sprintf(vz_w_text,"VPDW:ZDCW -- C=%0.5f",vz_w->GetCorrelationFactor());
sprintf(vz_x_text,"VPDX:ZDCX -- C=%0.5f",vz_x->GetCorrelationFactor());
Float_t starty=0.8;
Float_t startx=0.3;
Float_t interval=0.1;
TLatex * corr_latex = new TLatex(startx-0.05,starty,"Correlation Coefficients");
TLatex * bbc_ew_latex = new TLatex(startx,starty-1*interval,bbc_ew_text);
TLatex * bbc_ex_latex = new TLatex(startx,starty-2*interval,bbc_ex_text);
TLatex * bbc_wx_latex = new TLatex(startx,starty-3*interval,bbc_wx_text);
TLatex * zdc_ew_latex = new TLatex(startx,starty-1*interval,zdc_ew_text);
TLatex * zdc_ex_latex = new TLatex(startx,starty-2*interval,zdc_ex_text);
TLatex * zdc_wx_latex = new TLatex(startx,starty-3*interval,zdc_wx_text);
TLatex * vpd_ew_latex = new TLatex(startx,starty-1*interval,vpd_ew_text);
TLatex * vpd_ex_latex = new TLatex(startx,starty-2*interval,vpd_ex_text);
TLatex * vpd_wx_latex = new TLatex(startx,starty-3*interval,vpd_wx_text);
TLatex * bz_e_latex = new TLatex(startx,starty-1*interval,bz_e_text);
TLatex * bz_w_latex = new TLatex(startx,starty-2*interval,bz_w_text);
TLatex * bz_x_latex = new TLatex(startx,starty-3*interval,bz_x_text);
TLatex * bv_e_latex = new TLatex(startx,starty-1*interval,bv_e_text);
TLatex * bv_w_latex = new TLatex(startx,starty-2*interval,bv_w_text);
TLatex * bv_x_latex = new TLatex(startx,starty-3*interval,bv_x_text);
TLatex * vz_e_latex = new TLatex(startx,starty-1*interval,vz_e_text);
TLatex * vz_w_latex = new TLatex(startx,starty-2*interval,vz_w_text);
TLatex * vz_x_latex = new TLatex(startx,starty-3*interval,vz_x_text);
// draw correlation histograms
gStyle->SetOptStat(0);
TCanvas * canv_corr_bbc = new TCanvas("canv_corr_bbc","canv_corr_bbc",1100,700); canv_corr_bbc->Divide(2,2);
TCanvas * canv_corr_zdc = new TCanvas("canv_corr_zdc","canv_corr_zdc",1100,700); canv_corr_zdc->Divide(2,2);
TCanvas * canv_corr_vpd = new TCanvas("canv_corr_vpd","canv_corr_vpd",1100,700); canv_corr_vpd->Divide(2,2);
TCanvas * canv_corr_bz = new TCanvas("canv_corr_bz","canv_corr_bz",1100,700); canv_corr_bz->Divide(2,2);
TCanvas * canv_corr_bv = new TCanvas("canv_corr_bv","canv_corr_bv",1100,700); canv_corr_bv->Divide(2,2);
TCanvas * canv_corr_vz = new TCanvas("canv_corr_vz","canv_corr_vz",1100,700); canv_corr_vz->Divide(2,2);
for(Int_t i=1; i<=4; i++)
{
canv_corr_bbc->GetPad(i)->SetGrid(1,1);
canv_corr_zdc->GetPad(i)->SetGrid(1,1);
canv_corr_vpd->GetPad(i)->SetGrid(1,1);
canv_corr_bz->GetPad(i)->SetGrid(1,1);
canv_corr_bv->GetPad(i)->SetGrid(1,1);
canv_corr_vz->GetPad(i)->SetGrid(1,1);
};
canv_corr_bbc->cd(1); bbc_ew->Draw("colz");
canv_corr_bbc->cd(3); bbc_ex->Draw("colz");
canv_corr_bbc->cd(4); bbc_wx->Draw("colz");
canv_corr_zdc->cd(1); zdc_ew->Draw("colz");
canv_corr_zdc->cd(3); zdc_ex->Draw("colz");
canv_corr_zdc->cd(4); zdc_wx->Draw("colz");
canv_corr_vpd->cd(1); vpd_ew->Draw("colz");
canv_corr_vpd->cd(3); vpd_ex->Draw("colz");
canv_corr_vpd->cd(4); vpd_wx->Draw("colz");
canv_corr_bz->cd(1); bz_e->Draw("colz");
canv_corr_bz->cd(3); bz_w->Draw("colz");
canv_corr_bz->cd(4); bz_x->Draw("colz");
canv_corr_bv->cd(1); bv_e->Draw("colz");
canv_corr_bv->cd(3); bv_w->Draw("colz");
canv_corr_bv->cd(4); bv_x->Draw("colz");
canv_corr_vz->cd(1); vz_e->Draw("colz");
canv_corr_vz->cd(3); vz_w->Draw("colz");
canv_corr_vz->cd(4); vz_x->Draw("colz");
canv_corr_bbc->cd(2); corr_latex->Draw(); bbc_ew_latex->Draw(); bbc_ex_latex->Draw(); bbc_wx_latex->Draw();
canv_corr_zdc->cd(2); corr_latex->Draw(); zdc_ew_latex->Draw(); zdc_ex_latex->Draw(); zdc_wx_latex->Draw();
canv_corr_vpd->cd(2); corr_latex->Draw(); vpd_ew_latex->Draw(); vpd_ex_latex->Draw(); vpd_wx_latex->Draw();
canv_corr_bz->cd(2); corr_latex->Draw(); bz_e_latex->Draw(); bz_w_latex->Draw(); bz_x_latex->Draw();
canv_corr_bv->cd(2); corr_latex->Draw(); bv_e_latex->Draw(); bv_w_latex->Draw(); bv_x_latex->Draw();
canv_corr_vz->cd(2); corr_latex->Draw(); vz_e_latex->Draw(); vz_w_latex->Draw(); vz_x_latex->Draw();
};
void csv(TString input="tmva.csvoutput.txt", TString par1="par2", TString par2="par3", TString par3="", TString value="eventEffScaled_5") {
std::cout << "Usage:" << std::endl
<< ".x scripts/csv.C with default arguments" << std::endl
<< ".x scripts/csv.C(filename, par1, par2, value)" << std::endl
<< std::endl
<< " Optional arguments:" << std::endl
<< " filename path to CSV file" << std::endl
<< " par1 name of X-parameter branch" << std::endl
<< " par2 name of Y-parameter branch (if empty, efficiency is drawn as a function of par1)" << std::endl
<< " value name of result (efficiency) branch" << std::endl
<< std::endl;
TTree *tree = new TTree("data", "data");
tree->ReadFile(input);
gStyle->SetPalette(1);
gStyle->SetPadRightMargin(0.14);
TCanvas *canvas = new TCanvas("csvoutput", "CSV Output", 1200, 900);
tree->SetMarkerStyle(kFullDotMedium);
tree->SetMarkerColor(kRed);
if(par2.Length() > 0) {
//tree->Draw(Form("%s:%s", par2.Data(), par1.Data()));
if(par3.Length() > 0)
tree->Draw(Form("%s:%s:%s:%s", par1.Data(), par2.Data(), par3.Data(), value.Data()), "", "COLZ"); //, "", "Z");
else
tree->Draw(Form("%s:%s:%s", par2.Data(), par1.Data(), value.Data()), "", "COLZ"); //, "", "Z");
TH1 *histo = tree->GetHistogram();
if(!histo)
return;
histo->SetTitle(Form("%s with different classifier parameters", value.Data()));
histo->GetXaxis()->SetTitle(Form("Classifier parameter %s", par1.Data()));
histo->GetYaxis()->SetTitle(Form("Classifier parameter %s", par2.Data()));
if(par3.Length() > 0)
histo->GetZaxis()->SetTitle(Form("Classifier parameter %s", par3.Data()));
else
histo->GetZaxis()->SetTitle("");
if(par3.Length() == 0) {
float x = 0;
float y = 0;
float val = 0;
double maxVal = tree->GetMaximum(value);
double minVal = tree->GetMinimum(value);
tree->SetBranchAddress(par1, &x);
tree->SetBranchAddress(par2, &y);
tree->SetBranchAddress(value, &val);
TLatex l;
l.SetTextSize(0.03);
Long64_t nentries = tree->GetEntries();
for(Long64_t entry=0; entry < nentries; ++entry) {
tree->GetEntry(entry);
l.SetTextColor(textColor(val, maxVal, minVal));
l.DrawLatex(x, y, Form("%.3f", val*100));
}
}
}
else {
tree->Draw(Form("%s:%s", value.Data(), par1.Data()));
TH1 *histo = tree->GetHistogram();
if(!histo)
return;
histo->SetTitle(Form("%s with different classifier parameters", value.Data()));
histo->GetXaxis()->SetTitle(Form("Classifier parameter %s", par1.Data()));
histo->GetYaxis()->SetTitle(value);
}
}
void mk_tree(const char * acc_file="datfiles/acc.dat")
{
// read acc file into tree
TTree * acc = new TTree("acc","counts tree from acc.dat");
char cols[2048];
char bbc_cols[256];
char zdc_cols[256];
char vpd_cols[256];
for(Int_t i=0; i<=7; i++)
{
if(i==0)
{
sprintf(bbc_cols,"bbc_%d/D",i);
sprintf(zdc_cols,"zdc_%d/D",i);
sprintf(vpd_cols,"vpd_%d/D",i);
}
else
{
sprintf(bbc_cols,"%s:bbc_%d/D",bbc_cols,i);
sprintf(zdc_cols,"%s:zdc_%d/D",zdc_cols,i);
sprintf(vpd_cols,"%s:vpd_%d/D",vpd_cols,i);
};
};
sprintf(cols,"i/I:runnum/I:fi/I:fill/I:t/D:bx/I:%s:%s:%s:tot_bx/D:blue/I:yell/I",bbc_cols,zdc_cols,vpd_cols);
printf("%s\n",cols);
acc->ReadFile(acc_file,cols);
acc->Print();
Int_t IMAX_tmp = acc->GetMaximum("i");
const Int_t IMAX = IMAX_tmp;
// set branch addresses to read through acc tree
Int_t index,runnum,fill_index,fill,bx;
Double_t bbc[8];
Double_t zdc[8];
Double_t vpd[8];
Double_t time;
Double_t tot_bx;
Int_t blue,yell;
acc->SetBranchAddress("i",&index);
acc->SetBranchAddress("runnum",&runnum);
acc->SetBranchAddress("fi",&fill_index);
acc->SetBranchAddress("fill",&fill);
acc->SetBranchAddress("t",&time);
acc->SetBranchAddress("bx",&bx);
char str[16];
for(Int_t i=0; i<8; i++) { sprintf(str,"bbc_%d",i); acc->SetBranchAddress(str,&bbc[i]); };
for(Int_t i=0; i<8; i++) { sprintf(str,"zdc_%d",i); acc->SetBranchAddress(str,&zdc[i]); };
for(Int_t i=0; i<8; i++) { sprintf(str,"vpd_%d",i); acc->SetBranchAddress(str,&vpd[i]); };
acc->SetBranchAddress("tot_bx",&tot_bx);
acc->SetBranchAddress("blue",&blue);
acc->SetBranchAddress("yell",&yell);
// build arrays for restructuring; arrays are needed so that
// we can implement bXing shift corrections
Double_t bbce_arr[IMAX][120];
Double_t bbcw_arr[IMAX][120];
Double_t bbcx_arr[IMAX][120];
Double_t zdce_arr[IMAX][120];
Double_t zdcw_arr[IMAX][120];
Double_t zdcx_arr[IMAX][120];
Double_t vpde_arr[IMAX][120];
Double_t vpdw_arr[IMAX][120];
Double_t vpdx_arr[IMAX][120];
Int_t runnum_arr[IMAX];
Int_t fi_arr[IMAX];
Int_t fill_arr[IMAX];
Double_t time_arr[IMAX];
Double_t tot_bx_arr[IMAX][120];
Int_t blue_arr[IMAX][120];
Int_t yell_arr[IMAX][120];
Bool_t kicked_arr[IMAX][120];
// restructure tree into one suitable for analysis
TTree * sca = new TTree("sca","restructured tree");
Double_t bbce,bbcw,bbcx; // e=east, w=west, x=coincidence
Double_t zdce,zdcw,zdcx;
Double_t vpde,vpdw,vpdx;
Bool_t okEntry,kicked;
sca->Branch("i",&index,"i/I");
sca->Branch("runnum",&runnum,"runnum/I");
sca->Branch("fi",&fill_index,"fi/I");
sca->Branch("fill",&fill,"fill/I");
sca->Branch("t",&time,"t/D");
sca->Branch("bx",&bx,"bx/I");
sca->Branch("bbce",&bbce,"bbce/D");
sca->Branch("bbcw",&bbcw,"bbcw/D");
sca->Branch("bbcx",&bbcx,"bbcx/D");
sca->Branch("zdce",&zdce,"zdce/D");
sca->Branch("zdcw",&zdcw,"zdcw/D");
sca->Branch("zdcx",&zdcx,"zdcx/D");
sca->Branch("vpde",&vpde,"vpde/D");
sca->Branch("vpdw",&vpdw,"vpdw/D");
sca->Branch("vpdx",&vpdx,"vpdx/D");
sca->Branch("tot_bx",&tot_bx,"tot_bx/D");
sca->Branch("blue",&blue,"blue/I");
sca->Branch("yell",&yell,"yell/I");
sca->Branch("kicked",&kicked,"kicked/O");
// read kicked bunches tree from "kicked" file
TTree * kicked_tr = new TTree();
kicked_tr->ReadFile("kicked","fill/I:bx/I:spinbit/I");
Int_t kicked_fill,kicked_bx,kicked_spinbit;
kicked_tr->SetBranchAddress("fill",&kicked_fill);
kicked_tr->SetBranchAddress("bx",&kicked_bx);
kicked_tr->SetBranchAddress("spinbit",&kicked_spinbit);
for(Int_t q=0; q<acc->GetEntries(); q++)
{
acc->GetEntry(q);
// -- see doc for bit details
// BBC, ZDC, VPD bits: [ x w e ]
bbce = bbc[1] + bbc[3] + bbc[5] + bbc[7]; // e + we + xe + xwe
bbcw = bbc[2] + bbc[3] + bbc[6] + bbc[7]; // w + we + xw + xwe
bbcx = bbc[3] + bbc[7]; // we + xwe
zdce = zdc[1] + zdc[3] + zdc[5] + zdc[7]; // e + we + xe + xwe
zdcw = zdc[2] + zdc[3] + zdc[6] + zdc[7]; // w + we + xw + xwe
zdcx = zdc[3] + zdc[7]; // we + xwe
vpde = vpd[1] + vpd[3] + vpd[5] + vpd[7]; // e + we + xe + xwe
vpdw = vpd[2] + vpd[3] + vpd[6] + vpd[7]; // w + we + xw + xwe
vpdx = vpd[3] + vpd[7]; // we + xwe
// KICKED BUNCHES
// manually omit empty bunches documented in pathologies.dat -- CLEAN UP PROCEDURE
// (see 09.01.14 log entry)
okEntry=true;
// kicked bunches (presumably empty)
/*
if(fill==17384 && (bx==29 || bx==30 || bx==117)) okEntry=false;
if(fill==17416 && bx==79) okEntry=false;
if(fill==17491 && bx==105) okEntry=false;
if(fill==17519 && (bx==94 || bx==109)) okEntry=false;
if(fill==17520 && bx==0) okEntry=false;
if(fill==17529 && bx==97) okEntry=false;
if(fill==17534 && bx==112) okEntry=false;
if(fill==17553 && bx==73) okEntry=false;
if(fill==17554 && (bx==7 || bx==14)) okEntry=false;
if(fill==17555 && bx==61) okEntry=false;
if(fill==17576 && bx==94) okEntry=false;
// afterpulse-like bunches -- remove 1st 2 bunches after abort gaps
//if(fill==17512 && (bx>=40 && bx<=59)) okEntry=false;
//if((fill>=17513 && fill<=17520) && ((bx>=0 && bx<=19) || (bx>=40 && bx<=59))) okEntry=false;
*/
for(Int_t kk=0; kk<kicked_tr->GetEntries(); kk++)
{
kicked_tr->GetEntry(kk);
if(fill==kicked_fill && bx==kicked_bx) okEntry=false;
};
kicked=!okEntry; // cleaned up analysis
//kicked=0; // take all bXings
// store data into arrays, implementing bXing shift corrections on scalers
if(fill==16570 ||
fill==16567)
{
bbce_arr[index-1][(bx+113)%120] = bbce; // shift down 7 bXings
bbcw_arr[index-1][(bx+113)%120] = bbcw;
bbcx_arr[index-1][(bx+113)%120] = bbcx;
zdce_arr[index-1][(bx+113)%120] = zdce; // shift down 7 bXings
zdcw_arr[index-1][(bx+113)%120] = zdcw;
zdcx_arr[index-1][(bx+113)%120] = zdcx;
vpde_arr[index-1][(bx+113)%120] = vpde; // shift down 7 bXings
vpdw_arr[index-1][(bx+113)%120] = vpdw;
vpdx_arr[index-1][(bx+113)%120] = vpdx;
}
else if(fill == 16582 ||
fill == 16586 ||
fill == 16587 ||
fill == 16592 ||
fill == 16593 ||
fill == 16594 ||
fill == 16597 ||
fill == 16602)
{
bbce_arr[index-1][bx] = bbce; // no shift
bbcw_arr[index-1][bx] = bbcw;
bbcx_arr[index-1][bx] = bbcx;
zdce_arr[index-1][bx] = zdce; // no shift
zdcw_arr[index-1][bx] = zdcw;
zdcx_arr[index-1][bx] = zdcx;
vpde_arr[index-1][(bx+1)%120] = vpde; // shift up 1 bXings
vpdw_arr[index-1][(bx+1)%120] = vpdw;
vpdx_arr[index-1][(bx+1)%120] = vpdx;
}
else
{
bbce_arr[index-1][bx] = bbce;
bbcw_arr[index-1][bx] = bbcw;
bbcx_arr[index-1][bx] = bbcx;
zdce_arr[index-1][bx] = zdce;
zdcw_arr[index-1][bx] = zdcw;
zdcx_arr[index-1][bx] = zdcx;
vpde_arr[index-1][bx] = vpde;
vpdw_arr[index-1][bx] = vpdw;
vpdx_arr[index-1][bx] = vpdx;
};
runnum_arr[index-1] = runnum;
fi_arr[index-1] = fill_index;
fill_arr[index-1] = fill;
time_arr[index-1] = time;
tot_bx_arr[index-1][bx] = tot_bx;
blue_arr[index-1][bx] = blue;
yell_arr[index-1][bx] = yell;
kicked_arr[index-1][bx] = kicked;
};
// fill restructured tree
for(Int_t i=0; i<IMAX; i++)
{
index = i+1;
runnum = runnum_arr[i];
fill_index = fi_arr[i];
fill = fill_arr[i];
time = time_arr[i];
for(Int_t b=0; b<120; b++)
{
bx = b;
bbce = bbce_arr[i][b];
bbcw = bbcw_arr[i][b];
bbcx = bbcx_arr[i][b];
zdce = zdce_arr[i][b];
zdcw = zdcw_arr[i][b];
zdcx = zdcx_arr[i][b];
vpde = vpde_arr[i][b];
vpdw = vpdw_arr[i][b];
vpdx = vpdx_arr[i][b];
tot_bx = tot_bx_arr[i][b];
blue = blue_arr[i][b];
yell = yell_arr[i][b];
kicked = kicked_arr[i][b];
sca->Fill();
};
};
TFile * outfile = new TFile("counts.root","RECREATE");
acc->Write("acc");
sca->Write("sca");
printf("counts.root written\n");
};
void bit_combinations(const char * filename="counts.root")
{
TFile * infile = new TFile(filename,"READ");
TTree * acc = (TTree*) infile->Get("acc");
// set branch addresses
Int_t index,runnum,fill_index,fill;
Double_t t;
Double_t bbc[8];
Double_t zdc[8];
//Double_t vpd[4];
Double_t vpd[8];
Double_t tot_bx;
Int_t blue,yell;
acc->SetBranchAddress("i",&index);
acc->SetBranchAddress("runnum",&runnum);
acc->SetBranchAddress("fi",&fill_index);
acc->SetBranchAddress("fill",&fill);
acc->SetBranchAddress("t",&t);
acc->SetBranchAddress("tot_bx",&tot_bx);
acc->SetBranchAddress("blue",&blue);
acc->SetBranchAddress("yell",&yell);
char bbc_br[8][16];
char zdc_br[8][16];
//char vpd_br[4][16];
char vpd_br[8][16];
for(Int_t i=0; i<8; i++)
{
sprintf(bbc_br[i],"bbc_%d",i);
sprintf(zdc_br[i],"zdc_%d",i);
acc->SetBranchAddress(bbc_br[i],&(bbc[i]));
acc->SetBranchAddress(zdc_br[i],&(zdc[i]));
//if(i<4)
//{
sprintf(vpd_br[i],"vpd_%d",i);
acc->SetBranchAddress(vpd_br[i],&(vpd[i]));
//};
};
// ----------------------------------------------
// total counts vs. scaler bit bar charts
TH1F * ntot_vs_bits_bbc = new TH1F();
TH1F * ntot_vs_bits_zdc = new TH1F();
TH1F * ntot_vs_bits_vpd = new TH1F();
// scaler bit combination names
char comb[8][16];
strcpy(comb[0],"none");
strcpy(comb[1],"e");
strcpy(comb[2],"w");
strcpy(comb[3],"w+e");
strcpy(comb[4],"x");
strcpy(comb[5],"x+e");
strcpy(comb[6],"x+w");
strcpy(comb[7],"x+w+e");
// fill bar charts
for(Int_t i=0; i<acc->GetEntries(); i++)
{
acc->GetEntry(i);
for(Int_t j=0; j<8; j++)
{
ntot_vs_bits_bbc->Fill(comb[j],bbc[j]);
ntot_vs_bits_zdc->Fill(comb[j],zdc[j]);
//if(j<4) ntot_vs_bits_vpd->Fill(comb[j],vpd[j]);
ntot_vs_bits_vpd->Fill(comb[j],vpd[j]);
};
};
ntot_vs_bits_bbc->SetStats(0);
ntot_vs_bits_bbc->SetTitle("total bbc counts vs. scaler bits");
ntot_vs_bits_bbc->SetBarWidth(0.4);
ntot_vs_bits_bbc->SetBarOffset(0.55);
ntot_vs_bits_bbc->SetFillColor(50);
TCanvas * c_bbc_bits = new TCanvas("c_bbc_bits","c_bbc_bits",700,500);
c_bbc_bits->SetGrid(0,1);
c_bbc_bits->SetLogy();
ntot_vs_bits_bbc->Draw("bar2");
ntot_vs_bits_zdc->SetStats(0);
ntot_vs_bits_zdc->SetTitle("total zdc counts vs. scaler bits");
ntot_vs_bits_zdc->SetBarWidth(0.4);
ntot_vs_bits_zdc->SetBarOffset(0.55);
ntot_vs_bits_zdc->SetFillColor(50);
TCanvas * c_zdc_bits = new TCanvas("c_zdc_bits","c_zdc_bits",700,500);
c_zdc_bits->SetGrid(0,1);
c_zdc_bits->SetLogy();
ntot_vs_bits_zdc->Draw("bar2");
ntot_vs_bits_vpd->SetStats(0);
ntot_vs_bits_vpd->SetTitle("total vpd counts vs. scaler bits");
ntot_vs_bits_vpd->SetBarWidth(0.4);
ntot_vs_bits_vpd->SetBarOffset(0.55);
ntot_vs_bits_vpd->SetFillColor(50);
TCanvas * c_vpd_bits = new TCanvas("c_vpd_bits","c_vpd_bits",700,500);
c_vpd_bits->SetGrid(0,1);
c_vpd_bits->SetLogy();
ntot_vs_bits_vpd->Draw("bar2");
char outdir[32];
char bbc_outfile[64];
char zdc_outfile[64];
char vpd_outfile[64];
strcpy(outdir,"bit_combos");
sprintf(bbc_outfile,"%s/bbc_bit_combos.png",outdir);
sprintf(zdc_outfile,"%s/zdc_bit_combos.png",outdir);
sprintf(vpd_outfile,"%s/vpd_bit_combos.png",outdir);
c_bbc_bits->Print(bbc_outfile,"png");
c_zdc_bits->Print(zdc_outfile,"png");
c_vpd_bits->Print(vpd_outfile,"png");
printf("%s created\n",bbc_outfile);
printf("%s created\n",zdc_outfile);
printf("%s created\n",vpd_outfile);
// ----------------------------------------------
// get maximum number of runs = IMAX
Int_t IMAX_tmp = acc->GetMaximum("i");
const Int_t IMAX = IMAX_tmp;
// compute no. bXings with possible interaction in a given run
Int_t nbx[IMAX];
for(Int_t i=0; i<IMAX; i++) nbx[i]=0;
for(Int_t i=0; i<acc->GetEntries(); i++)
{
acc->GetEntry(i);
if(blue*yell != 0) nbx[index-1]++;
};
};
void makePlotWithSelection(TTree* tr, TString varToPlot, TString canvName, TString xAxisName, TCut cutLoose, TCut cutAdditional)
{
TTree* trCutted = tr->CopyTree(cutLoose);
TH1D* histLoose = new TH1D("hist",varToPlot,50,trCutted->GetMinimum(varToPlot)-0.1*fabs(trCutted->GetMinimum(varToPlot)),trCutted->GetMaximum(varToPlot)+0.1*fabs(trCutted->GetMaximum(varToPlot)));
trCutted->Draw(varToPlot+TString(">>hist"),"1","goff");
TCanvas* canv = new TCanvas(canvName,canvName);
//TAxis* xAxis = histLoose->GetXaxis();
histLoose->GetXaxis()->SetTitle(xAxisName);
histLoose->GetYaxis()->SetRangeUser(0,histLoose->GetMaximumStored());
histLoose->SetFillStyle(3004);
histLoose->SetFillColor(2);
histLoose->Draw();
trCutted->SetFillStyle(1001);
trCutted->SetFillColor(3);
trCutted->Draw(varToPlot,cutAdditional,"same");
canv->SaveAs(canvName+".png");
delete histLoose;
histLoose=0;
trCutted=0;
//xAxis=0;
}
void PlotDecisionBoundary( TString weightFile = "weights/Zprime_vs_QCD_TMVAClassification_BDT.weights.xml",TString v0="lep_pt_ljet", TString v1="met_pt", TString dataFileNameS = "/nfs/dust/cms/user/karavdia/ttbar_semilep_13TeV/RunII_25ns_v1/test_03/uhh2.AnalysisModuleRunner.MC.Zp01w3000.root", TString dataFileNameB = "/nfs/dust/cms/user/karavdia/ttbar_semilep_13TeV/RunII_25ns_v1/test_03/uhh2.AnalysisModuleRunner.MC.QCD_EMEnriched.root")
{
//---------------------------------------------------------------
// default MVA methods to be trained + tested
// this loads the library
TMVA::Tools::Instance();
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
//
// create the Reader object
//
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
// Float_t var0, var1;
// reader->AddVariable( v0, &var0 );
// reader->AddVariable( v1, &var1 );
Float_t lep_pt, lep_fbrem, MwT;
Float_t log_ljet_pt, log_met_pt, log_lep_pt_ljet;
Float_t log_dR_lep_cljet, log_dR_cljet_ljet;
Float_t dPhi_lep_cljet;
reader->AddVariable("lep_pt", &lep_pt);
reader->AddVariable("lep_fbrem", & lep_fbrem);
reader->AddVariable("MwT", &MwT);
reader->AddVariable("log(ljet_pt)", &log_ljet_pt);
reader->AddVariable("log(met_pt)",&log_met_pt);
reader->AddVariable("log(lep_pt_ljet)",&log_lep_pt_ljet);
reader->AddVariable("log(dR_lep_cljet)",&log_dR_lep_cljet_trans);
reader->AddVariable("log(fabs((dR_cljet_ljet-3.14)/3.14))", &log_dR_cljet_ljet);
reader->AddSpectator("dPhi_lep_cljet", &dPhi_lep_cljet);
//
// book the MVA method
//
reader->BookMVA( "BDT", weightFile );
TFile *fS = new TFile(dataFileNameS);
TTree *signal = (TTree*)fS->Get("AnalysisTree");
TFile *fB = new TFile(dataFileNameS);
TTree *background = (TTree*)fB->Get("AnalysisTree");
//Declaration of leaves types
Float_t svar0;
Float_t svar1;
Float_t bvar0;
Float_t bvar1;
Float_t sWeight=1.0; // just in case you have weight defined, also set these branchaddresses
Float_t bWeight=1.0*signal->GetEntries()/background->GetEntries(); // just in case you have weight defined, also set these branchaddresses
// Set branch addresses.
signal->SetBranchAddress(v0,&svar0);
signal->SetBranchAddress(v1,&svar1);
background->SetBranchAddress(v0,&bvar0);
background->SetBranchAddress(v1,&bvar1);
UInt_t nbin = 50;
Float_t xmax = signal->GetMaximum(v0.Data());
Float_t xmin = signal->GetMinimum(v0.Data());
Float_t ymax = signal->GetMaximum(v1.Data());
Float_t ymin = signal->GetMinimum(v1.Data());
xmax = TMath::Max(xmax,(Float_t)background->GetMaximum(v0.Data()));
xmin = TMath::Min(xmin,(Float_t)background->GetMinimum(v0.Data()));
ymax = TMath::Max(ymax,(Float_t)background->GetMaximum(v1.Data()));
ymin = TMath::Min(ymin,(Float_t)background->GetMinimum(v1.Data()));
TH2D *hs=new TH2D("hs","",nbin,xmin,xmax,nbin,ymin,ymax);
TH2D *hb=new TH2D("hb","",nbin,xmin,xmax,nbin,ymin,ymax);
hs->SetXTitle(v0);
hs->SetYTitle(v1);
hb->SetXTitle(v0);
hb->SetYTitle(v1);
hs->SetMarkerColor(4);
hb->SetMarkerColor(2);
TH2F * hist = new TH2F( "MVA", "MVA", nbin,xmin,xmax,nbin,ymin,ymax);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
Float_t MinMVA=10000, MaxMVA=-100000;
for (UInt_t ibin=1; ibin<nbin+1; ibin++){
for (UInt_t jbin=1; jbin<nbin+1; jbin++){
var0 = hs->GetXaxis()->GetBinCenter(ibin);
var1 = hs->GetYaxis()->GetBinCenter(jbin);
Float_t mvaVal=reader->EvaluateMVA( "BDT" ) ;
if (MinMVA>mvaVal) MinMVA=mvaVal;
if (MaxMVA<mvaVal) MaxMVA=mvaVal;
hist->SetBinContent(ibin,jbin, mvaVal);
}
}
// now you need to try to find the MVA-value at which you cut for the plotting of the decision boundary
// (Use the smallest number of misclassifications as criterion)
const Int_t nValBins=100;
Double_t sum = 0.;
TH1F *mvaS= new TH1F("mvaS","",nValBins,MinMVA,MaxMVA); mvaS->SetXTitle("MVA-ouput"); mvaS->SetYTitle("#entries");
TH1F *mvaB= new TH1F("mvaB","",nValBins,MinMVA,MaxMVA); mvaB->SetXTitle("MVA-ouput"); mvaB->SetYTitle("#entries");
TH1F *mvaSC= new TH1F("mvaSC","",nValBins,MinMVA,MaxMVA); mvaSC->SetXTitle("MVA-ouput"); mvaSC->SetYTitle("cummulation");
TH1F *mvaBC= new TH1F("mvaBC","",nValBins,MinMVA,MaxMVA); mvaBC->SetXTitle("MVA-ouput"); mvaBC->SetYTitle("cummulation");
Long64_t nentries;
nentries = signal->GetEntries();
for (Long64_t is=0; is<nentries;is++) {
signal->GetEntry(is);
sum +=sWeight;
var0 = svar0;
var1 = svar1;
Float_t mvaVal=reader->EvaluateMVA( "BDT" ) ;
hs->Fill(svar0,svar1);
mvaS->Fill(mvaVal,sWeight);
}
nentries = background->GetEntries();
for (Long64_t ib=0; ib<nentries;ib++) {
background->GetEntry(ib);
sum +=bWeight;
var0 = bvar0;
var1 = bvar1;
Float_t mvaVal=reader->EvaluateMVA( "BDT" ) ;
hb->Fill(bvar0,bvar1);
mvaB->Fill(mvaVal,bWeight);
}
SeparationBase *sepGain = new MisClassificationError();
//SeparationBase *sepGain = new GiniIndex();
//SeparationBase *sepGain = new CrossEntropy();
Double_t sTot = mvaS->GetSum();
Double_t bTot = mvaB->GetSum();
mvaSC->SetBinContent(1,mvaS->GetBinContent(1));
mvaBC->SetBinContent(1,mvaB->GetBinContent(1));
Double_t sSel=mvaSC->GetBinContent(1);
Double_t bSel=mvaBC->GetBinContent(1);
Double_t sSelBest=0;
Double_t bSelBest=0;
Double_t separationGain=sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
Double_t mvaCut=mvaSC->GetBinCenter(1);
Double_t mvaCutOrientation=1; // 1 if mva > mvaCut --> Signal and -1 if mva < mvaCut (i.e. mva*-1 > mvaCut*-1) --> Signal
for (UInt_t ibin=2;ibin<nValBins;ibin++){
mvaSC->SetBinContent(ibin,mvaS->GetBinContent(ibin)+mvaSC->GetBinContent(ibin-1));
mvaBC->SetBinContent(ibin,mvaB->GetBinContent(ibin)+mvaBC->GetBinContent(ibin-1));
sSel=mvaSC->GetBinContent(ibin);
bSel=mvaBC->GetBinContent(ibin);
if (separationGain < sepGain->GetSeparationGain(sSel,bSel,sTot,bTot)){
separationGain = sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
mvaCut=mvaSC->GetBinCenter(ibin);
if (sSel/bSel > (sTot-sSel)/(bTot-bSel)) mvaCutOrientation=-1;
else mvaCutOrientation=1;
sSelBest=sSel;
bSelBest=bSel;
}
}
cout << "Min="<<MinMVA << " Max=" << MaxMVA
<< " sTot=" << sTot
<< " bTot=" << bTot
<< " sSel=" << sSelBest
<< " bSel=" << bSelBest
<< " sepGain="<<separationGain
<< " cut=" << mvaCut
<< " cutOrientation="<<mvaCutOrientation
<< endl;
delete reader;
gStyle->SetPalette(1);
plot(hs,hb,hist ,v0,v1,mvaCut);
TCanvas *cm=new TCanvas ("cm","",900,1200);
cm->cd();
cm->Divide(1,2);
cm->cd(1);
mvaS->SetLineColor(4);
mvaB->SetLineColor(2);
mvaS->Draw();
mvaB->Draw("same");
cm->cd(2);
mvaSC->SetLineColor(4);
mvaBC->SetLineColor(2);
mvaBC->Draw();
mvaSC->Draw("same");
// TH1F *add=(TH1F*)mvaBC->Clone("add");
// add->Add(mvaSC);
// add->Draw();
// errh->Draw("same");
//
// write histograms
//
TFile *target = new TFile( "TMVAPlotDecisionBoundary.root","RECREATE" );
hs->Write();
hb->Write();
hist->Write();
target->Close();
}