void patBJetTracks_efficiencies()
{
// define proper canvas style
setNiceStyle();
gStyle->SetOptStat(0);
// open file
TFile* file = new TFile("analyzePatBJetTracks.root");
TLegend *legend[3] = { 0, 0, 0 };
// draw canvas with efficiencies
TCanvas *canv;
canv = new TCanvas("canv0", "hand-crafted track counting efficiencies", 800, 300);
canv->Divide(3, 1);
TH1 *total = (TH1*)file->Get(Form("%s/flavours", directory));
TH1 *effVsCutB = 0;
unsigned int i = 0;
for(const char **flavour = flavours; *flavour; flavour++, i++) {
TH1 *h = (TH1*)file->Get(Form("%s/trackIPSig_%s", directory, *flavour));
TH1 *discrShape = (TH1*)h->Clone(Form("%s_discrShape", h->GetName()));
discrShape->Scale(1.0 / discrShape->Integral());
discrShape->SetMaximum(discrShape->GetMaximum() * 5);
TH1 *effVsCut = computeEffVsCut(h, total->GetBinContent(4 - i));
TH1 *effVsBEff = 0;
if (flavour == flavours) // b-jets
effVsCutB = effVsCut;
else
effVsBEff = computeEffVsBEff(effVsCut, effVsCutB);
discrShape->SetTitle("discriminator shape");
effVsCut->SetTitle("efficiency versus discriminator cut");
if (effVsBEff)
effVsBEff->SetTitle("mistag versus b efficiency");
setHistStyle(discrShape);
setHistStyle(effVsCut);
setHistStyle(effVsBEff);
canv->cd(1);
gPad->SetLogy(1);
gPad->SetGridy(1);
discrShape->SetLineColor(i + 1);
discrShape->SetMarkerColor(i + 1);
discrShape->Draw(i > 0 ? "same" : "");
if (!legend[0])
legend[0] = new TLegend(0.5, 0.7, 0.78, 0.88);
legend[0]->AddEntry(discrShape, *flavour);
canv->cd(2);
gPad->SetLogy(1);
gPad->SetGridy(1);
effVsCut->SetLineColor(i + 1);
effVsCut->SetMarkerColor(i + 1);
effVsCut->Draw(i > 0 ? "same" : "");
if (!legend[1])
legend[1] = new TLegend(0.12, 0.12, 0.40, 0.30);
legend[1]->AddEntry(effVsCut, *flavour);
if (!effVsBEff)
continue;
canv->cd(3);
gPad->SetLogy(1);
gPad->SetGridx(1);
gPad->SetGridy(1);
effVsBEff->SetLineColor(i + 1);
effVsBEff->SetMarkerColor(i + 1);
effVsBEff->Draw(i > 1 ? "same" : "");
if (!legend[2])
legend[2] = new TLegend(0.12, 0.7, 0.40, 0.88);
legend[2]->AddEntry(effVsBEff, *flavour);
}
canv->cd(1);
legend[0]->Draw();
canv->cd(2);
legend[1]->Draw();
canv->cd(3);
legend[2]->Draw();
////////////////////////////////////////////
// canvas to compare negative tagger with light flavour mistag
TCanvas *canv;
canv = new TCanvas("canv1", "comparing light flavour mistag with negative tagger", 530, 300);
canv->Divide(2, 1);
TH1 *h1 = (TH1*)file->Get(Form("%s/trackIPSig_udsg", directory));
TH1 *h2 = (TH1*)file->Get(Form("%s/negativeIPSig_all", directory));
h2 = invertHisto(h2); // invert x-axis
TH1 *discrShape1 = (TH1*)h1->Clone("discrShape1");
TH1 *discrShape2 = (TH1*)h2->Clone("discrShape2");
discrShape1->Scale(1.0 / discrShape1->Integral());
discrShape1->SetMaximum(discrShape1->GetMaximum() * 5);
discrShape2->Scale(1.0 / discrShape2->Integral());
TH1 *effVsCut1 = computeEffVsCut(h1, total->GetBinContent(2));
TH1 *effVsCut2 = computeEffVsCut(h2, total->GetBinContent(1));
discrShape1->SetTitle("discriminator shape");
effVsCut1->SetTitle("efficiency versus discriminator cut");
setHistStyle(discrShape1);
setHistStyle(discrShape2);
setHistStyle(effVsCut1);
setHistStyle(effVsCut2);
canv->cd(1);
gPad->SetLogy(1);
gPad->SetGridy(1);
discrShape1->SetLineColor(1);
discrShape1->SetMarkerColor(1);
discrShape2->SetLineColor(2);
discrShape2->SetMarkerColor(2);
discrShape1->Draw();
discrShape2->Draw("same");
TLegend *l = new TLegend(0.5, 0.7, 0.78, 0.88);
l->AddEntry(discrShape1, "udsg");
l->AddEntry(discrShape2, "inv. neg");
l->Draw();
canv->cd(2);
gPad->SetLogy(1);
gPad->SetGridy(1);
effVsCut1->SetLineColor(1);
effVsCut1->SetMarkerColor(1);
effVsCut2->SetLineColor(2);
effVsCut2->SetMarkerColor(2);
effVsCut1->Draw();
effVsCut2->Draw("same");
l = new TLegend(0.5, 0.7, 0.78, 0.88);
l->AddEntry(effVsCut1, "udsg");
l->AddEntry(effVsCut2, "inv. neg");
l->Draw();
}
//------------------------------------------------------------------------------
void PlotAlignmentValidation::plotDMR(const std::string& variable, Int_t minHits, const std::string& options)
{
// If several, comma-separated values are given,
// call plotDMR with each value separately.
// If a comma is found, the string is divided to two.
// (no space allowed)
std::size_t findres = variable.find(",");
if ( findres != std::string::npos) {
std::string substring1 = variable.substr(0, findres);
std::string substring2 = variable.substr(findres+1, std::string::npos);
plotDMR(substring1, minHits, options);
plotDMR(substring2, minHits, options);
return;
}
// Variable name should end with X or Y. If it doesn't, recursively calls plotDMR twice with
// X and Y added, respectively
if (variable == "mean" || variable == "median" || variable == "meanNorm" ||
variable == "rms" || variable == "rmsNorm") {
plotDMR(variable+"X", minHits, options);
plotDMR(variable+"Y", minHits, options);
return;
}
TRegexp layer_re("layer=[0-9]+");
bool plotPlain = false, plotSplits = false, plotLayers = false;
int plotLayerN = 0;
Ssiz_t index, len;
if (options.find("plain") != std::string::npos) { plotPlain = true; }
if (options.find("split") != std::string::npos) { plotSplits = true; }
if (options.find("layers") != std::string::npos) { plotLayers = true; }
if ((index = layer_re.Index(options, &len)) != -1) {
if (plotLayers) {
std::cerr << "Warning: option 'layers' overrides 'layer=N'" << std::endl;
} else {
std::string substr = options.substr(index+6, len-6);
plotLayerN = atoi(substr.c_str());
}
}
// Defaults to plotting only plain plot if empty (or invalid)
// option string is given
if (!plotPlain && !plotSplits) { plotPlain = true; }
// This boolean array tells for which detector modules to plot split DMR plots
// They are plotted for BPIX, FPIX, TIB and TOB
static bool plotSplitsFor[6] = { true, true, true, false, true, false };
// If layers are plotted, these are the numbers of layers for each subdetector
static int numberOfLayers[6] = { 3, 2, 4, 3, 6, 9 };
DMRPlotInfo plotinfo;
setNiceStyle();
gStyle->SetOptStat(0);
TCanvas c("canv", "canv", 600, 600);
setCanvasStyle( c );
plotinfo.variable = variable;
plotinfo.minHits = minHits;
plotinfo.plotPlain = plotPlain;
plotinfo.plotLayers = plotLayers;
if (variable == "meanX") { plotinfo.nbins = 50; plotinfo.min = -0.001; plotinfo.max = 0.001; }
else if (variable == "meanY") { plotinfo.nbins = 50; plotinfo.min = -0.005; plotinfo.max = 0.005; }
else if (variable == "medianX") { plotinfo.nbins = 50; plotinfo.min = -0.005; plotinfo.max = 0.005; }
else if (variable == "medianY") { plotinfo.nbins = 50; plotinfo.min = -0.005; plotinfo.max = 0.005; }
else if (variable == "meanNormX") { plotinfo.nbins = 100; plotinfo.min = -2.0; plotinfo.max = 2.0; }
else if (variable == "meanNormY") { plotinfo.nbins = 100; plotinfo.min = -2.0; plotinfo.max = 2.0; }
else if (variable == "rmsX") { plotinfo.nbins = 100; plotinfo.min = 0.0; plotinfo.max = 0.1; }
else if (variable == "rmsY") { plotinfo.nbins = 100; plotinfo.min = 0.0; plotinfo.max = 0.1; }
else if (variable == "rmsNormX") { plotinfo.nbins = 100; plotinfo.min = 0.3; plotinfo.max = 1.8; }
else if (variable == "rmsNormY") { plotinfo.nbins = 100; plotinfo.min = 0.3; plotinfo.max = 1.8; }
else {
std::cerr << "Unknown variable " << variable << std::endl;
plotinfo.nbins = 100; plotinfo.min = -0.1; plotinfo.max = 0.1;
}
for (int i=1; i<=6; ++i) {
// Skip strip detectors if plotting any "Y" variable
if (i != 1 && i != 2 && variable.length() > 0 && variable[variable.length()-1] == 'Y') {
continue;
}
// Skips plotting too high layers
if (plotLayerN > numberOfLayers[i-1]) {
continue;
}
plotinfo.plotSplits = plotSplits && plotSplitsFor[i-1];
if (!plotinfo.plotPlain && !plotinfo.plotSplits) {
continue;
}
// Sets dimension of legend according to the number of plots
int nPlots = 1;
if (plotinfo.plotSplits) { nPlots = 3; }
if (plotinfo.plotLayers) { nPlots *= numberOfLayers[i-1]; }
nPlots *= sourceList.size();
double legendY = 0.80;
if (nPlots > 3) { legendY -= 0.01 * (nPlots - 3); }
if (legendY < 0.6) {
std::cerr << "Warning: Huge legend!" << std::endl;
legendY = 0.6;
}
THStack hstack("hstack", "hstack");
plotinfo.maxY = 0;
plotinfo.subDetId = i;
plotinfo.nLayers = numberOfLayers[i-1];
plotinfo.legend = new TLegend(0.17, legendY, 0.85, 0.88);
setLegendStyle(*plotinfo.legend);
plotinfo.hstack = &hstack;
plotinfo.h = plotinfo.h1 = plotinfo.h2 = 0;
plotinfo.firsthisto = true;
for(std::vector<TkOfflineVariables*>::iterator it = sourceList.begin();
it != sourceList.end(); ++it) {
int minlayer = plotLayers ? 1 : plotLayerN;
int maxlayer = plotLayers ? plotinfo.nLayers : plotLayerN;
plotinfo.vars = *it;
for (int layer = minlayer; layer <= maxlayer; layer++) {
if (plotinfo.plotPlain) {
plotDMRHistogram(plotinfo, 0, layer);
}
if (plotinfo.plotSplits) {
plotDMRHistogram(plotinfo, -1, layer);
plotDMRHistogram(plotinfo, 1, layer);
}
if (plotinfo.plotPlain) {
if (plotinfo.h) { setDMRHistStyleAndLegend(plotinfo.h, plotinfo, 0, layer); }
}
if (plotinfo.plotSplits) {
// Add delta mu to the histogram
if (plotinfo.h1 != 0 && plotinfo.h2 != 0 && !plotinfo.plotPlain) {
std::ostringstream legend;
std::string unit = " #mum";
legend.precision(2);
float factor = 10000.0f;
if (plotinfo.variable == "meanNormX" || plotinfo.variable == "meanNormY" ||
plotinfo.variable == "rmsNormX" || plotinfo.variable == "rmsNormY") {
factor = 1.0f;
unit = "";
}
float deltamu = factor*(plotinfo.h2->GetMean(1) - plotinfo.h1->GetMean(1));
legend << plotinfo.vars->getName();
if (layer > 0) {
legend << ", layer " << layer;
}
legend << ": #Delta#mu = " << deltamu << unit;
plotinfo.legend->AddEntry(static_cast<TObject*>(0), legend.str().c_str(), "");
}
if (plotinfo.h1) { setDMRHistStyleAndLegend(plotinfo.h1, plotinfo, -1, layer); }
if (plotinfo.h2) { setDMRHistStyleAndLegend(plotinfo.h2, plotinfo, 1, layer); }
}
}
}
if (plotinfo.h != 0 || plotinfo.h1 != 0 || plotinfo.h2 != 0) {
hstack.Draw("nostack");
hstack.SetMaximum(plotinfo.maxY*1.3);
setTitleStyle(hstack, variable.c_str(), "#modules", plotinfo.subDetId);
setHistStyle(*hstack.GetHistogram(), variable.c_str(), "#modules", 1);
plotinfo.legend->Draw();
std::ostringstream plotName;
plotName << outputDir << "/D";
if (variable=="medianX") plotName << "medianR_";
else if (variable=="medianY") plotName << "medianYR_";
else if (variable=="meanX") plotName << "meanR_";
else if (variable=="meanY") plotName << "meanYR_";
else if (variable=="meanNormX") plotName << "meanNR_";
else if (variable=="meanNormY") plotName << "meanNYR_";
else if (variable=="rmsX") plotName << "rmsR_";
else if (variable=="rmsY") plotName << "rmsYR_";
else if (variable=="rmsNormX") plotName << "rmsNR_";
else if (variable=="rmsNormY") plotName << "rmsNYR_";
switch (i) {
case 1: plotName << "BPIX"; break;
case 2: plotName << "FPIX"; break;
case 3: plotName << "TIB"; break;
case 4: plotName << "TID"; break;
case 5: plotName << "TOB"; break;
case 6: plotName << "TEC"; break;
}
if (plotPlain && !plotSplits) { plotName << "_plain"; }
else if (!plotPlain && plotSplits) { plotName << "_split"; }
if (plotLayers) { plotName << "_layers"; }
if (plotLayerN > 0) { plotName << "_layer" << plotLayerN; }
plotName << ".eps";
c.Update();
c.Print(plotName.str().c_str());
}
}
}
void patBJetVertex_efficiencies()
{
// define proper canvas style
setNiceStyle();
gStyle->SetOptStat(0);
// open file
TFile* file = new TFile("analyzePatBJetVertex.root");
unsigned int j = 0;
for(const char **algo = algos; *algo; algo++, j++) {
TLegend *legend[3] = { 0, 0, 0 };
// draw canvas with efficiencies
TCanvas *canv;
canv = new TCanvas(*algo, Form("%s efficiencies", algoNames[j]), 800, 300);
canv->Divide(3, 1);
TH1 *total = (TH1*)file->Get(Form("%s/flavours", directory));
TH1 *effVsCutB = 0;
unsigned int i = 0;
for(const char **flavour = flavours; *flavour; flavour++, i++) {
TH1 *h = (TH1*)file->Get(Form("%s/%s_%s", directory, *algo, *flavour));
TH1 *discrShape = (TH1*)h->Clone(Form("%s_discrShape", h->GetName()));
discrShape->Scale(1.0 / discrShape->Integral());
discrShape->SetMaximum(discrShape->GetMaximum() * 5);
TH1 *effVsCut = computeEffVsCut(h, total->GetBinContent(4 - i));
TH1 *effVsBEff = 0;
if (flavour == flavours) // b-jets
effVsCutB = effVsCut;
else
effVsBEff = computeEffVsBEff(effVsCut, effVsCutB);
discrShape->SetTitle("discriminator shape");
effVsCut->SetTitle("efficiency versus discriminator cut");
if (effVsBEff)
effVsBEff->SetTitle("mistag versus b efficiency");
setHistStyle(discrShape);
setHistStyle(effVsCut);
setHistStyle(effVsBEff);
canv->cd(1);
gPad->SetLogy(1);
gPad->SetGridy(1);
discrShape->SetLineColor(i + 1);
discrShape->SetMarkerColor(i + 1);
discrShape->Draw(i > 0 ? "same" : "");
if (!legend[0])
legend[0] = new TLegend(0.5, 0.7, 0.78, 0.88);
legend[0]->AddEntry(discrShape, *flavour);
canv->cd(2);
gPad->SetLogy(1);
gPad->SetGridy(1);
effVsCut->SetLineColor(i + 1);
effVsCut->SetMarkerColor(i + 1);
effVsCut->Draw(i > 0 ? "same" : "");
if (!legend[1])
legend[1] = new TLegend(0.3, 0.4, 0.58, 0.58);
legend[1]->AddEntry(effVsCut, *flavour);
if (!effVsBEff)
continue;
canv->cd(3);
gPad->SetLogy(1);
gPad->SetGridx(1);
gPad->SetGridy(1);
effVsBEff->SetLineColor(i + 1);
effVsBEff->SetMarkerColor(i + 1);
effVsBEff->Draw(i > 1 ? "same" : "");
if (!legend[2])
legend[2] = new TLegend(0.12, 0.7, 0.40, 0.88);
legend[2]->AddEntry(effVsBEff, *flavour);
}
canv->cd(1);
legend[0]->Draw();
canv->cd(2);
legend[1]->Draw();
canv->cd(3);
legend[2]->Draw();
}
}
void PlotAlignmentValidation::
setDMRHistStyleAndLegend(TH1F* h, PlotAlignmentValidation::DMRPlotInfo& plotinfo, int direction, int layer)
{
std::pair<float,float> fitResults(9999., 9999.);
h->SetDirectory(0);
// The whole DMR plot is plotted with wider line than the split plots
// If only split plots are plotted, they will be stronger too, though
h->SetLineWidth((direction == 0 || (plotinfo.plotSplits && !plotinfo.plotPlain)) ? 2 : 1);
// These lines determine the style of the plots according to rules:
// -If the plot is for direction != 0, +1 or +2 is added to the given style for distinction
// -However if only direction split plots are to be plotted, the additions should be 0 and +1 respectively
// -Modulo 4 arithmetic, because the styles run from 1..4
int linestyle = plotinfo.vars->getLineStyle() - 1, linestyleplus = 0;
if (direction == 1) { linestyleplus = 1; }
if (direction == -1) { linestyleplus = 2; }
if (direction != 0 && plotinfo.plotSplits && !plotinfo.plotPlain) { linestyleplus--; }
linestyle = (linestyle + linestyleplus) % 4 + 1;
int linecolor = plotinfo.vars->getLineColor();
if (plotinfo.plotLayers && layer > 0) { linecolor += layer - 1; }
if (plotinfo.firsthisto) {
setHistStyle(*h, plotinfo.variable.c_str(), "#modules", 1); //set color later
plotinfo.firsthisto = false;
}
h->SetLineColor( linecolor );
h->SetLineStyle( linestyle );
if (plotinfo.maxY<h->GetMaximum()){
plotinfo.maxY=h->GetMaximum();
}
//fit histogram for median and mean
if (plotinfo.variable == "medianX" || plotinfo.variable == "meanX") {
fitResults = fitGauss(h, linecolor );
}
plotinfo.hstack->Add(h);
std::ostringstream legend;
legend.precision(2);
// Legend: header part
if (direction == -1 && plotinfo.subDetId != 2) { legend << "rDirection < 0: "; }
else if (direction == 1 && plotinfo.subDetId != 2) { legend << "rDirection > 0: "; }
else if (direction == -1 && plotinfo.subDetId == 2) { legend << "zDirection < 0: "; }
else if (direction == 1 && plotinfo.subDetId == 2) { legend << "zDirection > 0: "; }
else {
legend << plotinfo.vars->getName();
if (layer > 0) {
legend << ", layer " << layer << "";
}
legend << ":";
}
// Legend: Statistics
if (plotinfo.variable == "medianX" || plotinfo.variable == "meanX" ||
plotinfo.variable == "medianY" || plotinfo.variable == "meanY") {
if (useFit_) {
legend << "#mu = " << fitResults.first << " #mum, #sigma = " << fitResults.second << " #mum";
} else {
legend << "#mu = " << h->GetMean(1)*10000 << " #mum, rms = " << h->GetRMS(1)*10000 << " #mum";
}
} else if (plotinfo.variable == "rmsX" || plotinfo.variable == "rmsY") {
legend << "#mu = " << h->GetMean(1)*10000 << " #mum, rms = " << h->GetRMS(1)*10000 << " #mum";
} else if (plotinfo.variable == "meanNormX" || plotinfo.variable == "meanNormY" ||
plotinfo.variable == "rmsNormX" || plotinfo.variable == "rmsNormY") {
legend << "#mu = " << h->GetMean(1) << ", rms = " << h->GetRMS(1);
}
// Legend: Delta mu for split plots
if (plotinfo.h1 != 0 && plotinfo.h2 != 0 && plotinfo.plotSplits &&
plotinfo.plotPlain && direction == 0) {
std::string unit = " #mum";
float factor = 10000.0f;
if (plotinfo.variable == "meanNormX" || plotinfo.variable == "meanNormY" ||
plotinfo.variable == "rmsNormX" || plotinfo.variable == "rmsNormY") {
factor = 1.0f;
unit = "";
}
float deltamu = factor*(plotinfo.h2->GetMean(1) - plotinfo.h1->GetMean(1));
legend << ", #Delta#mu = " << deltamu << unit;
}
plotinfo.legend->AddEntry(h, legend.str().c_str(), "l");
}
void PlotAlignmentValidation::plotDMR(const std::string variable, Int_t minHits )
{
setNiceStyle();
gStyle->SetOptStat(0);
// TList treeList=getTreeList();
TCanvas *c = new TCanvas("canv", "canv", 600, 600);
setCanvasStyle( *c );
//loop over sub-detectors
for (int i=1;i<7;++i){
int histo_Counter=1;
TLegend *leg_hist = new TLegend(0.17,0.8,0.85,0.88);
setLegendStyle(*leg_hist);
//loop over file list
//TTree *tree= (TTree*)treeList.First();
//binning
int nbinsX=100;
double xmin=0;
double xmax=0;
float maxY=0;
bool isHisto = false;
std::string plotVar=variable;
THStack *hstack=new THStack("hstack","hstack");
for(std::vector<TkOfflineVariables*>::iterator it = sourceList.begin();
it != sourceList.end(); ++it){
//while ( tree ){
plotVar=variable;
TString subdet = "entries>=";
subdet+=minHits;
subdet+=" && subDetId==";
subdet+=i;
char binning [50]="";
sprintf (binning, ">>myhisto(%d, %f , %f)", nbinsX, xmin, xmax);
TH1F *h = 0;
if (histo_Counter==1&&plotVar=="meanX")(*it)->getTree()->Draw( (plotVar+=">>myhisto(50,-0.005,0.005)").c_str(),subdet,"goff");
else if (histo_Counter==1&&plotVar=="meanY")(*it)->getTree()->Draw( (plotVar+=">>myhisto(50,-0.005,0.005)").c_str(),subdet,"goff");
else if (histo_Counter==1&&plotVar=="medianX")(*it)->getTree()->Draw( (plotVar+=">>myhisto(50,-0.005,0.005)").c_str(),subdet,"goff");
else if (histo_Counter==1&&plotVar=="meanNormX")(*it)->getTree()->Draw( (plotVar+=">>myhisto(100,-2,2)").c_str(),subdet,"goff");
else if (histo_Counter==1&&plotVar=="rmsX")(*it)->getTree()->Draw( (plotVar+=">>myhisto(100,0.,0.1)").c_str(),subdet,"goff");
else if (histo_Counter!=1)(*it)->getTree()->Draw( (plotVar+=binning).c_str(),subdet,"goff");
if (gDirectory) gDirectory->GetObject("myhisto", h);
std::pair<float,float> fitResults(9999., 9999.);
if (h){
if (h->GetEntries()>0) {
isHisto = true;
h->SetDirectory(0);
//general draw options
h->SetLineWidth(2);
//first histo only, setting optStat...
if (histo_Counter==1)
setHistStyle(*h,plotVar.c_str() ,"#modules", 1 ); //set color later
h->SetLineColor( (*it)->getLineColor() );
h->SetLineStyle( (*it)->getLineStyle() );
//h->SetMarkerStyle(20+file_Counter);
//draw options
if (maxY<h->GetMaximum()){
maxY=h->GetMaximum();
}
//fit histogram for median and mean
if (variable=="medianX"||variable =="meanX")fitResults=fitGauss(h, (*it)->getLineColor() );
if (histo_Counter==1){
//get mean and sigma from fit: gauss for 2sigma range
hstack->Add(h);
nbinsX=h->GetXaxis()->GetNbins();
xmin=h->GetXaxis()->GetXmin();
xmax=h->GetXaxis()->GetXmax();
}else if (histo_Counter!=1 && h->GetEntries()>0)hstack->Add(h);
char legend [50]="";
std::string legEntry = (*it)->getName();
if (variable=="medianX"||variable =="meanX")sprintf (legend, "%s: #mu = %4.2f#mum, #sigma = %4.2f#mum ",legEntry.c_str(),fitResults.first ,fitResults.second);
else sprintf (legend, "%s ",legEntry.c_str());
if(h)
leg_hist->AddEntry(h,legend,"l");
else
std::cerr<< "histogram did not exist!";
}
}
// tree= (TTree*)treeList.After( tree );
// file_Counter++;
histo_Counter++;
}
if (isHisto){
hstack->Draw("nostack");
hstack->SetMaximum(maxY*1.3);
setTitleStyle(*hstack,plotVar.c_str() ,"#modules",i);
setHistStyle(*hstack->GetHistogram(),plotVar.c_str() ,"#modules", 1 );
leg_hist->Draw();
std::string histName="D";
if (variable=="medianX") histName+="medianR_";
else if (variable=="meanX") histName+="meanR_";
else if (variable=="meanY") histName+="meanYR_";
else if (variable=="rmsX") histName+="rmsR_";
std::string subDetector ="";
switch (i){
case 1 : subDetector+="TPB";break;
case 2 : subDetector+="TPE";break;
case 3 : subDetector+="TIB";break;
case 4 : subDetector+="TID";break;
case 5 : subDetector+="TOB";break;
case 6 : subDetector+="TEC";break;
}
char PlotName[100];
sprintf( PlotName, "%s/%s%s.eps",outputDir.c_str(), histName.c_str(), subDetector.c_str() );
c->Update();
c->Print(PlotName);
//c->Update();
//c->Close();
}
delete hstack;
hstack=0;
}
delete c;
c=0;
}
