/*!
* ### Example usage:
\code
.L $AliRoot_SRC/STAT/Macros/aliExternalInfo.C+
drawLogbook("LHC15o","pass1","QA.TPC.meanTPCncl>0","runDuration:totalEventsPhysics:totalNumberOfFilesMigrated:MonALISA.RCT.tpc_value");
drawLogbook("LHC10h","pass2","totalEventsPhysics>1000&&totalNumberOfFilesMigrated>20","runDuration:totalEventsPhysics:totalNumberOfFilesMigrated:MonALISA.RCT.tpc_value");
drawLogbook("LHC11h","pass2","totalEventsPhysics>1000&&totalNumberOfFilesMigrated>20","runDuration:totalEventsPhysics:totalNumberOfFilesMigrated:MonALISA.RCT.tpc_value");
\endcode
*/
void drawLogbook(TString period, TString pass,TString runSelection="", TString varSelection="runDuration:totalEventsPhysics:totalNumberOfFilesMigrated"){
TTree * treeLogbook = info.GetTree("Logbook",period,pass,"QA.TPC;QA.TRD;QA.ITS;MonALISA.RCT");
TObjArray *varList=0;
if (varSelection[0]=='['){ //variable list using class selection
// Use class selection to select variables
varList=AliTreePlayer::selectMetadata(treeLogbook,varSelection,0);
Int_t nvars=varList->GetEntries();
for (Int_t i=0; i<nvars;i++){
TString vname=varList->At(i)->GetName();
vname.ReplaceAll(".class","");
}
// varList=AliTreePlayer::selectMetadata(treeLogbook, "[class==\"Logbook&&Time\"]",0);
}else{
varList=varSelection.Tokenize(":");
}
Int_t nvars=varList->GetEntries();
TCanvas * canvas = new TCanvas("drawLogbook","drawLogbook",1200,1000);
canvas->Divide(1,nvars);
for (Int_t i=0; i<nvars; i++){
canvas->cd(i+1);
TString var=TString::Format("%s:run",varList->At(i)->GetName());
TStatToolkit::MakeGraphSparse(treeLogbook,var,runSelection,25,1,1)->Draw("ap");
Double_t mean = TMath::Mean(treeLogbook->GetSelectedRows(),treeLogbook->GetV1());
Double_t sum = treeLogbook->GetSelectedRows()*mean;
latex.DrawLatexNDC(0.15,0.8,TString::Format("Mean=%0.0f",mean));
latex.DrawLatexNDC(0.15,0.7,TString::Format("Sum=%0.0f",sum));
}
canvas->SaveAs(TString::Format("%s_%s.png",pass.Data(),period.Data()).Data());
}
void treegraph(TString filename) {
gROOT->SetStyle("Plain");
gStyle->SetOptDate();
Double_t x, y;
Int_t nlines = 0;
TFile *f = new TFile("graph.root","RECREATE");
TCanvas *canvas_graph = new TCanvas("canvas_graph", "y vs x",467,89,400,700);
TTree t;
t.ReadFile(filename,"x:y");
t.Draw("x:y","","goff");
TGraph *g = new TGraph(t.GetSelectedRows(),t.GetV1(),t.GetV2());
g->SetTitle(filename+": Y vs X");
g->GetXaxis()->SetTitle("x[a.u.]");
g->GetYaxis()->SetTitle("y[a.u.]");
g->SetMarkerStyle(21);
g->Draw("AP");
f->Write();
}
void fillGraphFromTreeVar(std::string& treedrawspec,int index,wGraph_t *&pwg)
{
// Sample treedrawspec:
// mytree:"TDCwinstart[%d]:runnum","evtnum==1","P"
//
vector<string> v_tokens;
string tid;
TString drawspec;
Tokenize(treedrawspec,v_tokens,":",true);
if( (v_tokens.size() < 2) ||
(!v_tokens[0].size()) ||
(!v_tokens[2].size()) ) {
cerr << "malformed root tree draw spec treeid:\"varexp\",\"selection\",option: " << treedrawspec << endl;
return;
}
tid = v_tokens[0];
for (size_t i=2; i<v_tokens.size(); i++) {
drawspec += v_tokens[i];
}
int fmtcnt = drawspec.CountChar('%');
if (fmtcnt) { // use index for tree array var
switch(fmtcnt) {
case 1: drawspec = Form(drawspec,index); break;
case 2: drawspec = Form(drawspec,index,index); break;
case 3: drawspec = Form(drawspec,index,index,index); break;
case 4: drawspec = Form(drawspec,index,index,index,index); break;
case 5: drawspec = Form(drawspec,index,index,index,index,index); break;
case 6: drawspec = Form(drawspec,index,index,index,index,index,index); break;
default:
cerr << "More than six fmt specifiers in drawspec found, fix me! " << drawspec <<endl;
exit(-1);
}
}
if( gl_verbose)
cout<<"drawspec="<<drawspec<<endl;
TTree *tree = findTree(tid);
assert (tree);
// can't use comma as delimiter since histo with binning spec may be supplied
TObjArray *tokens = drawspec.Tokenize("\"");
TString hname;
TString varexp = ((TObjString *)(*tokens)[0])->GetString();
if( gl_verbose)
cout<<"varexp="<<varexp<<endl;
switch(tokens->GetEntriesFast()) {
case 1:
tree->Draw(varexp,"","goff");
break;
case 3:
{
TString cut = ((TObjString *)(*tokens)[2])->GetString();
tree->Draw(varexp,cut,"goff");
}
break;
case 4: // assume the cut string is blank
{
TString gopt = ((TObjString *)(*tokens)[3])->GetString();
gopt = gopt + " goff";
tree->Draw(varexp,"",gopt);
}
break;
case 5:
{
TString cut = ((TObjString *)(*tokens)[2])->GetString();
TString gopt = ((TObjString *)(*tokens)[4])->GetString();
gopt = gopt + " goff";
tree->Draw(varexp,cut,gopt);
}
break;
default:
cerr << "malformed root tree draw spec treeid:varexp,selection,option";
for (int i=0; i<tokens->GetEntriesFast(); i++)
cerr << i<<": "<< ((TObjString *)(*tokens)[i])->GetString() << " ";
cerr << endl;
break;
}
assert(tree->GetSelectedRows());
if (!pwg)
pwg = new wGraph_t();
assert(pwg);
pwg->gr = new TGraph(tree->GetSelectedRows(),
tree->GetV2(), tree->GetV1());
} // fillGraphFromTreeVar
void
CalcLimit(bool useCLs=true, string inName="nEvents.txt", string outName="nLimit.txt"){
gErrorIgnoreLevel = kWarning;
gSystem->SetIncludePath( "-I$ROOFITSYS/include" );
gSystem->Load("libRooFit");
// gSystem->SetIncludePath( "-I/afs/hep.wisc.edu/cern/.root/root_v5.30.00.Linux-slc5_amd64-gcc4.3/include/RooStats" );
gROOT->ProcessLine(".L ../../../StatisticalTools/RooStatsRoutines/root/roostats_cl95.C+");
string outfile(outName.c_str());
ofstream out(outfile.c_str());
if(!out) {
cout << "Cannot open file " << outfile << endl;
abort();
}
out << setiosflags(ios::fixed) << setprecision(4) << setiosflags(ios::left);
out<<"SignalCode/F:"
<<"Mass/F:"
<<"Lumi/F:"
<<"sLumi/F:"
<<"Eff/F:"
<<"sEff/F:"
<<"DataEvts/F:"
<<"BkgEvts/F:"
<<"sBkgEvts/F:"
<<"ObsLimit/F:"
<<"ExpLimit/F:"
<<"ExpLimitP1/F:"
<<"ExpLimitM1/F:"
<<"ExpLimitP2/F:"
<<"ExpLimitM2/F"
<<endl;
TTree* tree = new TTree("tree", "Number of Events");
tree->ReadFile(inName.c_str());
tree->Draw("SignalCode:Mass:Lumi:DataEvts:BkgEvts:sBkgEvts:Eff:sEff",
"", "para goff");
float n = tree->GetSelectedRows();
for(int isample=0; isample<n; ++isample){
const Double_t SignalCode = tree->GetVal(0)[isample];
const Double_t mass = tree->GetVal(1)[isample];
const Double_t lumi = tree->GetVal(2)[isample];
const Double_t DataEvts = tree->GetVal(3)[isample];
const Double_t BkgEvts = tree->GetVal(4)[isample];
const Double_t sBkgEvts = tree->GetVal(5)[isample];
const Double_t Eff = tree->GetVal(6)[isample];
const Double_t sEff = tree->GetVal(7)[isample];
cout<<"Calculating limit for mass: "<<mass<<" and lumi: "<<lumi<<endl;
float sLumi = sLumiFrac*lumi;
Double_t obs_limit, exp_limit;
Double_t exp_up, exp_down, exp_2up, exp_2down;
if(useCLs){////CLs Limits
//Does not work for bayesian, only works with cls
LimitResult limit = roostats_limit(lumi, sLumi, Eff, sEff, BkgEvts, sBkgEvts, DataEvts, false, 0, "cls", "", 12345);
cout<<"\nCompleted Limit Calc\n";
obs_limit = limit.GetObservedLimit();
exp_limit = limit.GetExpectedLimit();
exp_up = limit.GetOneSigmaHighRange();
exp_down = limit.GetOneSigmaLowRange();
exp_2up = limit.GetTwoSigmaHighRange();
exp_2down = limit.GetTwoSigmaLowRange();
}else{
////Bayesian Limits
LimitResult limit = roostats_clm (lumi, sLumi, Eff, sEff, BkgEvts, sBkgEvts);
//obs_limit = limit.GetObservedLimit();
obs_limit = roostats_cl95(lumi, sLumi, Eff, sEff, BkgEvts, sBkgEvts, DataEvts, false, 0, "bayesian", "");
exp_limit = limit.GetExpectedLimit();
exp_up = limit.GetOneSigmaHighRange();
exp_down = limit.GetOneSigmaLowRange();
exp_2up = limit.GetTwoSigmaHighRange();
exp_2down = limit.GetTwoSigmaLowRange();
}
out<<setprecision(1)
<<SignalCode<<"\t"
<<setprecision(0)
<<mass<<"\t"
<<lumi<<"\t"
<<sLumi<<"\t"
<<setprecision(4)
<<Eff<<"\t"
<<sEff<<"\t"
<<setprecision(0)
<<DataEvts<<"\t"
<<setprecision(4)
<<BkgEvts<<"\t"
<<sBkgEvts<<"\t";
// out<<Value(obs_limit,-4)<<"\t"
// <<Value(exp_limit,-4)<<"\t"
// <<Value(exp_up,-4)<<"\t"
// <<Value(exp_down,-4)<<"\t"
// <<Value(exp_2up,-4)<<"\t"
// <<Value(exp_2down,-4)
// <<endl;
out<<setprecision(8)
<<obs_limit<<"\t"
<<exp_limit<<"\t"
<<exp_up<<"\t"
<<exp_down<<"\t"
<<exp_2up<<"\t"
<<exp_2down
<<endl;
}
out.close();
cout<<"Done\n";
return;
}
void EffVsMass(string inName, int applyAnalysisCuts){
gErrorIgnoreLevel = kWarning;
CMSstyle();
gStyle->SetOptStat(0);
TFile *fin = TFile::Open(inName.c_str(), "read"); assert(fin);
TGraphAsymmErrors* g[4];
for(int ch=0; ch<4; ++ch){
g[ch] = new TGraphAsymmErrors();
}
int ipoint = 0;
for(int mass=200; mass <= 2000; mass += 100){
string name = Form("WprimeToWZTo3LNu_M-%i",mass);
float sample_tot(0), sample_pass(0);
TH1F* hInfo = (TH1F*) fin->Get(Form("%s/hFileInfo", name.c_str()));
if(!hInfo) continue;
float sample_weight = GetSampleInfo(hInfo, "Sample Weight");
sample_tot = GetSampleInfo(hInfo, "Number of Events Produced");
sample_tot *= (4./9.); //remove tau contribution from denom
//get tree from file
TTree* t = getTree(fin, name, "tEvts_MET"); assert(t);
string analysisCuts = applyAnalysisCuts ? (applyAnalysisCuts == 1 ? Form("Lt > %.0f", LtCut(mass)) : AnalysisCuts(mass)) : "1";
for(int ch=0; ch<4; ++ch){
float tot = sample_tot / 4;
float pass = 0;
float pass_alt = t->Draw("WZMass", Form("weight*(EvtType == %i)*(%s)",ch, analysisCuts.c_str()), "goff");
int n = t->GetSelectedRows();
for(int ientry=0; ientry<n; ++ientry){
float weight = t->GetW()[ientry];
pass += weight;
}
pass /= sample_weight;
sample_pass += pass;
float mean = tot>0 ? pass / tot : 0.;
float errUp = TEfficiency::ClopperPearson(tot, pass, 0.68, true) - mean;
float errDown = mean - TEfficiency::ClopperPearson(tot, pass, 0.68, false);
errDown = std::max(errDown, (float) 0.);
float err = (errUp + errDown)/2;
printf(" %s %ie%i\\mu & %4.2f & %4.2f or %4.2f & %.2f \\pm %.2f\\%% \\\\\n", name.c_str(), 3-ch, ch, tot, pass, pass_alt, mean*100, err*100);
g[ch]->SetPoint(ipoint, mass, mean);
g[ch]->SetPointError(ipoint, 0., 0., errDown, errUp);
}
ipoint++;
}
TCanvas* c1 = new TCanvas();
TMultiGraph* mg = new TMultiGraph("mg", ";M_{W'} (GeV); #varepsilon");
TLegend *legend = new TLegend(0.4,0.2,0.51,0.4,"");
prepLegend(legend);
for(int ch=0; ch<4; ++ch){
g[ch]->SetMarkerColor(ch+2);
g[ch]->SetLineColor(ch+2);
legend->AddEntry(g[ch], Form("%ie%i#mu", 3-ch, ch), "PE");
mg->Add(g[ch]);
}
mg->SetMaximum(1.1);
mg->SetMinimum(0. );
mg->Draw("AP");
legend->Draw();
TLatex latexLabel;
latexLabel.SetNDC();
latexLabel.SetTextSize(0.05);
latexLabel.SetTextFont(42);
latexLabel.DrawLatex(0.33, 0.96, "CMS Simulation 2012");
latexLabel.DrawLatex(0.21, 0.85, "#sqrt{s} = 8 TeV");
string outName = applyAnalysisCuts ? (applyAnalysisCuts==1 ? "EffVsMass_LtCut.png" : "EffVsMass_AnalysisCuts.png") : "EffVsMass.png";
c1->SaveAs(outName.c_str());
}
void
ModXSec(string inFile, string outFile, bool removeTaus=true, bool applyKFactors=true){
bool doTC = inFile.find("TC") != string::npos;
cout<<"doTC is "<<doTC<<endl;
TTree* tLimit = new TTree("tLimit", "Limits");
tLimit->ReadFile(inFile.c_str());
ofstream out(outFile.c_str());
if(doTC) out<<"Rho/F:";
else out<<"Mass/F:";
if(doTC){
out<<"Pi/F:";
out<<"SinX/F:";
}
out<<"Xsec/F:"
<<"percentError/F"
<<endl;
//kfactors
TGraph* gK = new TGraph(20);
gK->SetPoint( 0, 200,1.347);
gK->SetPoint( 1, 300,1.347);
gK->SetPoint( 2, 500,1.363);
gK->SetPoint( 3, 700,1.351);
gK->SetPoint( 4, 900,1.347);
gK->SetPoint( 5,1100,1.331);
gK->SetPoint( 6,1300,1.317);
gK->SetPoint( 7,1500,1.293);
gK->SetPoint( 8,1700,1.257);
gK->SetPoint( 9,1900,1.230);
gK->SetPoint( 10,2000,1.214);
//Signal pdf uncertainties
TGraph* gPerErr = new TGraph(20);
gPerErr->SetPoint( 0, 200,2.192);
gPerErr->SetPoint( 1, 300,2.192);
gPerErr->SetPoint( 2, 500,2.632);
gPerErr->SetPoint( 3, 700,3.070);
gPerErr->SetPoint( 4, 900,3.448);
gPerErr->SetPoint( 5,1100,3.771);
gPerErr->SetPoint( 6,1300,4.101);
gPerErr->SetPoint( 7,1500,4.339);
gPerErr->SetPoint( 8,1700,4.581);
gPerErr->SetPoint( 9,1900,4.846);
gPerErr->SetPoint( 10,2000,4.981);
if(doTC) tLimit->Draw("Rho:Xsec:Pi:SinX", "SinX > 0.32 && SinX<0.34", "para goff");
//if(doTC) tLimit->Draw("Rho:Xsec:Pi:SinX", "(280<=Rho && Rho<=300) && (150<=Pi && Pi<=160)", "para goff");
else tLimit->Draw("Mass:Xsec", "", "para goff");
float n = tLimit->GetSelectedRows();
for(int isample=0; isample<n; ++isample){
int idx=0;
Double_t mass = tLimit->GetVal(idx++)[isample];
Double_t xsec = tLimit->GetVal(idx++)[isample];
Double_t pi(-1), sinx(-1);
if(doTC){
pi = tLimit->GetVal(idx++)[isample];
sinx = tLimit->GetVal(idx++)[isample];
}
//cout<<"For rho: "<<rho<<" the kfactor is "<<gK->Eval(rho)<<endl;
//xsec *= 1e9; //convert from mb to pb
if(removeTaus) xsec *= 4./9.; //convert from emt to em
if(applyKFactors) xsec *= gK->Eval(mass);//apply k factor
Double_t perErr = gPerErr->Eval(mass);
out.precision(0) ;
out.setf ( ios::fixed, ios::floatfield);
out <<mass<<"\t";
if(doTC){
out.precision(0) ;
out.setf ( ios::fixed, ios::floatfield);
out <<pi<<"\t";
out.precision(4) ;
out.setf ( ios::fixed, ios::floatfield);
out <<sinx<<"\t";
}
out.precision(4) ;
out.setf ( ios::scientific, ios::floatfield);
out <<xsec<<"\t";
out.precision(3);
out.setf ( ios::fixed, ios::floatfield);
out<<perErr<<"\t";
out<<endl;
}
}
void PlotMacro(char *inputFile, char *outputFile){
TFile *out = new TFile(outputFile,"recreate");
TFile *f = new TFile(inputFile);
TObjArray DetList(0);
TTree *t = (TTree*) f->Get("DataTree");
Double_t errx[t->GetSelectedRows()];
for(int i=0;i<t->GetSelectedRows();i++){
errx[i]=0;
}
//=== S/N Corr MPV
TCanvas *C1=new TCanvas("SN_MPV","SN_MPV",10,10,600,400);
DetList.Add(C1);
C1->cd();
TLegend *legend1 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(1,t,errx,"Run.number:TOB.StoNCorr.On.FitPar.mp:TOB.StoNCorr.On.FitPar.emp","",2,22,"MPV S/N Corrected for the track angle","Run Number","MPV",legend1,"TOB",5,35);
DrawSame(1,t,errx,"Run.number:TIB.StoNCorr.On.FitPar.mp:TIB.StoNCorr.On.FitPar.emp","",3,23,legend1,"TIB");
DrawSame(1,t,errx,"Run.number:TID.StoNCorr.On.FitPar.mp:TID.StoNCorr.On.FitPar.emp","",4,24,legend1,"TID");
//=== Charge off Track mean
TCanvas *C2=new TCanvas("chOffmean","chOffmean",10,10,600,400);
DetList.Add(C2);
C2->cd();
TLegend *legend2 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TOB.Charge.Off.HistoPar.mean","",2,22,"Charge of the clusters off track - Mean","Run Number","Mean Charge [ADC]",legend2,"TOB",50,250);
DrawSame(0,t,errx,"Run.number:TIB.Charge.Off.HistoPar.mean","",3,23,legend2,"TIB");
DrawSame(0,t,errx,"Run.number:TID.Charge.Off.HistoPar.mean","",4,24,legend2,"TID");
//=== Charge off Track RMS
TCanvas *C3=new TCanvas("chOffRms","chOffRms",10,10,600,400);
DetList.Add(C3);
C3->cd();
TLegend *legend3 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TOB.Charge.Off.HistoPar.rms","",2,22,"Charge of the clusters off track - RMS","Run Number","RMS [ADC]",legend3,"TOB",50,110);
DrawSame(0,t,errx,"Run.number:TIB.Charge.Off.HistoPar.rms","",3,23,legend3,"TIB");
DrawSame(0,t,errx,"Run.number:TID.Charge.Off.HistoPar.rms","",4,24,legend3,"TID");
//=== Noise on Track Gauss Fit
TCanvas *C4=new TCanvas("NoiseOn","NoiseOn",10,10,600,400);
DetList.Add(C4);
C4->cd();
TLegend *legend4 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(1,t,errx,"Run.number:TOB.Noise.On.FitNoisePar.fitmean:TOB.Noise.On.FitNoisePar.efitmean","",2,22,"Mean Noise of the clusters on track","Run Number","Mean Noise [ADC]",legend4,"TOB",2,6);
DrawSame(1,t,errx,"Run.number:TIB.Noise.On.FitNoisePar.fitmean:TIB.Noise.On.FitNoisePar.efitmean","",3,23,legend4,"TIB");
DrawSame(1,t,errx,"Run.number:TID.Noise.On.FitNoisePar.fitmean:TID.Noise.On.FitNoisePar.efitmean","",4,24,legend4,"TID");
//=== Noise off Track mean
TCanvas *C5=new TCanvas("NoiseOff","NoiseOff",10,10,600,400);
DetList.Add(C5);
C5->cd();
TLegend *legend5 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TOB.Noise.Off.HistoPar.mean","",2,22,"Mean noise of the clusters off track","Run Number","Mean noise [ADC]",legend5,"TOB",1,6);
DrawSame(0,t,errx,"Run.number:TIB.Noise.Off.HistoPar.mean","",3,23,legend5,"TIB");
DrawSame(0,t,errx,"Run.number:TID.Noise.Off.HistoPar.mean","",4,24,legend5,"TID");
//=== Width on Track mean
TCanvas *C6=new TCanvas("WidthOn","WidthOn",10,10,600,400);
DetList.Add(C6);
C6->cd();
TLegend *legend6 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TOB.Width.On.HistoPar.mean","",2,22,"Mean width of the clusters on track","Run Number","Mean width [#strips]",legend6,"TOB",0,7);
DrawSame(0,t,errx,"Run.number:TIB.Width.On.HistoPar.mean","",3,23,legend6,"TIB");
DrawSame(0,t,errx,"Run.number:TID.Width.On.HistoPar.mean","",4,24,legend6,"TID");
//=== Width off Track mean
TCanvas *C7=new TCanvas("WidthOff","WidthOff",10,10,600,400);
DetList.Add(C7);
C7->cd();
TLegend *legend7 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TOB.Width.Off.HistoPar.mean","",2,22,"Mean width of the clusters off track","Run Number","Mean width [#strips]",legend7,"TOB",0,7);
DrawSame(0,t,errx,"Run.number:TIB.Width.Off.HistoPar.mean","",3,23,legend7,"TIB");
DrawSame(0,t,errx,"Run.number:TID.Width.Off.HistoPar.mean","",4,24,legend7,"TID");
//=== Number of Events
TCanvas *C8= new TCanvas("nEvents","nEvents",10,10,600,400);
DetList.Add(C8);
C8->cd();
TLegend *legend8 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TrAndClus.nTracks.entries","",1,22,"Number of processed Events","Run Number", "# Events",legend8,"",40000,1000000);
//=== Number of Events with at least one track
TCanvas *C9= new TCanvas("nTracks","nTracks",10,10,600,400);
DetList.Add(C9);
C9->cd();
TLegend *legend9 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:(TrAndClus.nTracks.nonzero/TrAndClus.nTracks.entries)","",1,22,"Fraction of Events with at least one track","Run Number", "# Events",legend9,"",0,1);
//=== Number of clusters on track
TCanvas *C10= new TCanvas("nClusOn","nClusOn",10,10,600,400);
DetList.Add(C10);
C10->cd();
TLegend *legend10 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:(TIB.nClusters.On.HistoPar.nonzero/TrAndClus.nTracks.nonzero)","",2,22,"Fraction of clusters associated to a track","Run Number", "Fraction of Cluster On Track",legend10,"TIB",0,1);
DrawSame(0,t,errx,"Run.number:(TOB.nClusters.On.HistoPar.nonzero/TrAndClus.nTracks.nonzero)","",3,23,legend10,"TOB");
DrawSame(0,t,errx,"Run.number:(TID.nClusters.On.HistoPar.nonzero/TrAndClus.nTracks.nonzero)","",4,24,legend10,"TID");
//=== Mean number of clusters off track
TCanvas *C12= new TCanvas("nClusOff","nClusOff",10,10,600,400);
DetList.Add(C12);
C12->cd();
TLegend *legend12 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TrAndClus.OffClus.mean","",1,21,"Mean number of clusters off track","Run Number", "# Cluster Off Track",legend12,"TOT",0,25);
DrawSame(0,t,errx,"Run.number:TIB.nClusters.Off.HistoPar.mean","",2,22,legend12,"TIB");
DrawSame(0,t,errx,"Run.number:TOB.nClusters.Off.HistoPar.mean","",3,23,legend12,"TOB");
DrawSame(0,t,errx,"Run.number:TID.nClusters.Off.HistoPar.mean","",4,24,legend12,"TID");
//=== Mean Number of Tracks
TCanvas *C11= new TCanvas("MeanTracks","MeanTracks",10,10,600,400);
DetList.Add(C11);
C11->cd();
TLegend *legend11 = new TLegend(0.851008,0.437882,0.995954,1.00035);
DrawClus(0,t,errx,"Run.number:TrAndClus.nTracks.mean","",1,22,"Mean number of tracks per event","Run Number", "# Tracks",legend11,"",0.4,0.7);
out->cd();
DetList.Write();
f->cd();
}
