def plot_and_get_sig(sig_hist, bkg_hist, input_name, total_sig, total_bkg,
mva_name, prefix, out_name, lumi):
out_dir = input_name
if OUT_DIRECTORY:
out_dir = OUT_DIRECTORY
sig = get_sig_hist(sig_hist, bkg_hist, total_sig, total_bkg, lumi)
set_graph_style(sig, kBlack)
sig.SetTitle(";%s;significance" % mva_name)
canv = TCanvas("sig", "", 800, 600)
sig.Draw("a3")
sig.Draw("lx")
index_max = TMath.LocMax(sig.GetN(), sig.GetY())
sig_max = "%.1f #pm %.1f" % (sig.GetY()[index_max], sig.GetEY()[index_max])
leg = TLegend(0.2, 0.7, 0.5, 0.8)
PlotStyle.legend(leg)
leg.AddEntry(sig, "%s @%s/fb" % (sig_max, lumi), "l")
#ATLASLabel(0.20,0.75,"Work in progress")
#ATLASLumiLabel(0.20,0.895,str(lumi*0.001))
leg.Draw()
save_canv(canv, prefix + input_name + out_name,
out_dir + "_" + mva_name + out_name)
return sig_max
def plotEventsInAcceptance():
c = TCanvas("c","c",1200,1200)
gTdmiInGaNotConverted = file.Get("hoMuonAnalyzer/graphs/tdmiInGaNotDead")
gTdmiInGa = PlotStyle.convertToHcalCoords(gTdmiInGaNotConverted)
gTdmiInGa.GetXaxis().SetTitle("i#eta")
gTdmiInGa.GetYaxis().SetTitle("i#phi")
gTdmiInGa.SetMarkerStyle(6)
gTdmiInGa.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
gTdmiInGa.SetTitle("#eta #phi plot of HO geom. Acceptance and not dead channels")
gTdmiInGa.Draw("AP")
pText = TPaveText(0.7,0.85,0.9,0.9,'NDC')
pText.AddText('Total Events: %d' % (totalEvents))
pText.AddText('Events in Plot: %d' % (gTdmiInGa.GetN()))
pText.SetBorderSize(1)
pText.Draw()
chimney1 = PlotStyle.chimney1
chimney2 = PlotStyle.chimney2
labelCmsPrivateSimulation = PlotStyle.getLabelCmsPrivateSimulation()
chimney1Converted.Draw("same,l")
chimney2Converted.Draw("same,l")
labelCmsPrivateSimulation.Draw()
legend = TLegend(0.1,0.87,0.3,0.9)
legend.AddEntry(chimney2Converted,"chimney","l")
legend.Draw()
c.Update()
c.SaveAs("plots/graphsEtaPhi/gTdmiInGaNotDead.png")
def plotNoL1AndTdmiInAcceptance():
c4 = TCanvas("c4","c4",1200,1200)
gNoTrgNoL1TdmiInGa = file.Get("hoMuonAnalyzer/graphs/NoTrgNoL1TdmiInGA")
gConvertedToiEtaiPhi = PlotStyle.convertToHcalCoords(gNoTrgNoL1TdmiInGa)
gConvertedToiEtaiPhi.SetTitle('No Single #mu Trg, in HO Acceptance')
gConvertedToiEtaiPhi.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
gConvertedToiEtaiPhi.Draw("AP")
chimney1 = PlotStyle.chimney1
chimney2 = PlotStyle.chimney2
chimney1Converted = PlotStyle.convertToHcalCoords(chimney1)
chimney2Converted = PlotStyle.convertToHcalCoords(chimney2)
chimney1Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney2Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney1Converted.Draw('Same,l')
chimney2Converted.Draw('Same,l')
legend = TLegend(0.7,0.85,0.9,0.9)
legend.AddEntry(chimney2Converted,"chimneys","le")
legend.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c4.Update()
#boxes = PlotStyle.drawHcalBoxesHcalCoords(c4)
c4.SaveAs("plots/graphsEtaPhi/gNoTrgNoL1TdmiInGaHocoords.pdf")
c4.SaveAs("plots/graphsEtaPhi/gNoTrgNoL1TdmiInGaHocoords.png")
return c4,legend,chimney1Converted,chimney2Converted,gConvertedToiEtaiPhi
def plotHoAboveThr():
c5 = TCanvas("c5","c5",1200,1200)
noTrgTdmiAboveThrNotConverted = file.Get("hoMuonAnalyzer/graphs/NoTrgTdmiAboveThr")
noTrgTdmiAboveThr = PlotStyle.convertToHcalCoords(noTrgTdmiAboveThrNotConverted)
noTrgTdmiAboveThr.GetXaxis().SetTitle("i#eta / a.u.")
noTrgTdmiAboveThr.GetYaxis().SetTitle("i#phi / a.u.")
noTrgTdmiAboveThr.SetMarkerStyle(6)
noTrgTdmiAboveThr.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
noTrgTdmiAboveThr.SetTitle("#eta #phi plot of HO > 0.2 GeV in no Single #mu Trg. events")
noTrgTdmiAboveThr.Draw("AP")
pText = TPaveText(0.7,0.85,0.9,0.9,'NDC')
pText.AddText('No Single #mu in GA: %d' % (noSingleMuEventsInGa))
pText.AddText('Events in Plot: %d' % (noTrgTdmiAboveThr.GetN()))
pText.SetBorderSize(1)
pText.Draw()
chimney1 = PlotStyle.chimney1
chimney2 = PlotStyle.chimney2
labelCmsPrivateSimulation = PlotStyle.getLabelCmsPrivateSimulation()
chimney1Converted.Draw("same,l")
chimney2Converted.Draw("same,l")
labelCmsPrivateSimulation.Draw()
legend = TLegend(0.1,0.87,0.3,0.9)
legend.AddEntry(chimney2Converted,"chimney","l")
legend.Draw()
#c5.SetGridY(0)
#c5.SetGridX(0)
c5.Update()
c5.SaveAs("plots/graphsEtaPhi/gNoTrgTdmiHoAboveThr.png")
def doPlotPhiPtOfFailingMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtPhiHoMatchFail","PtPhiHoMatchFail",1200,1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiNotInGA_EtaPhiPt")
stack = THStack(hist,"zy","2dStack","",-1,-1,-1,-1,"zy","")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtEtaHoMatchFail","p_{T} vs. #phi distribution for events not in HO acceptance;#phi;p_{T} / 5 GeV;#",80, -3.2, 3.2,40,0,200)
histNew.GetYaxis().SetTitleOffset(1.2)
histNew.Sumw2()
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
PlotStyle.setupAxes(histNew)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1NotInHoAcceptancePhiPt.pdf')
canvas.SaveAs('plots/NoL1NotInHoAcceptancePhiPt.png')
return canvas,hist,stack,histNew,label,palette,file
def compare_results(tree_test, tree_train, lumi, mva_names, out_name,
total_sig_events, total_bkg_events, max_lines):
roc_dict = {}
for name, tree in tree_test.iteritems():
mva_name = mva_names[name]
roc = get_roc_curve(tree, name, mva_name)
roc_dict[name] = roc
rocs = roc_dict.values()
canv = TCanvas("comparison_roc", "", 800, 600)
leg = TLegend(0.17, 0.17, 0.62, 0.6)
PlotStyle.legend(leg)
sortedRocs = []
for name, roc in roc_dict.iteritems():
max_sig = get_max_sig(tree_test[name], name, mva_names[name],
total_sig_events[name], total_bkg_events[name],
lumi)
max_sig_train = get_max_sig(tree_train[name], name, mva_names[name],
total_sig_events[name],
total_bkg_events[name], lumi)
#sig_diff = abs(max_sig - max_sig_train)
area_test = get_area(tree_test[name], name, mva_names[name])
area_train = get_area(tree_train[name], name, mva_names[name])
diff_area = abs(area_test - area_train)
print name, area_test, area_train, diff_area, max_sig, max_sig_train
max_sig_error_string = get_max_sig_error(tree_test[name], name,
mva_names[name],
total_sig_events[name],
total_bkg_events[name], lumi)
sortedRocs.append((roc, name, diff_area, max_sig_error_string))
sortedRocs.sort(key=get_sig_from_tuple)
if max_lines > len(sortedRocs):
max_lines = len(sortedRocs)
set_graph_style(sortedRocs[0][0], get_color(0, max_lines))
sortedRocs[0][0].SetTitle("; sig. eff.; bkg. rej.")
sortedRocs[0][0].Draw("a l")
leg.AddEntry(sortedRocs[0][0],
"%s: %s %s/fb" % (sortedRocs[0][1], sortedRocs[0][3], lumi),
"l")
index = 1
for r in sortedRocs[1:max_lines]:
set_graph_style(r[0], get_color(index, max_lines))
r[0].Draw("same l")
leg.AddEntry(r[0], "%s: %s %s/fb" % (r[1], r[3], lumi), "l")
index += 1
leg.Draw()
save_canv(canv, out_name)
def plotPtCorrelation(folder,filename = 'L1MuonHistogram.root'):
if(DEBUG):
print prefix + 'was called'
if(folder == None):
print prefix + 'Error! Folder as first argument needed.'
return
if( not os.path.exists('plots')):
os.mkdir('plots')
if( not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
fullname = folder + '/' + filename
if( not os.path.exists(fullname)):
print 'Error! File ' + fullname + ' does not exist!'
return
print 'Opening file:',fullname
file = TFile.Open(fullname)
#Set plot style
PlotStyle.setPlotStyle()
l1Muon = file.Get("hoMuonAnalyzer/L1Muon_PtCorrelation")
canv = TCanvas("ptCorrelationCanvas",'ptCorrelationCanvas',1200,1200)
# frame = TH2D('frame','L1 Efficiency',1,5,150,1,0,1.1)
# frame.SetStats(0)
# frame.GetXaxis().SetTitle('p_{T} / GeV')
# frame.GetYaxis().SetTitle('Efficiency')
# frame.Draw()
l1Muon.SetMarkerStyle(20)
l1Muon.SetMarkerColor(ROOT.kBlack)
l1Muon.SetLineColor(ROOT.kBlack)
l1Muon.GetXaxis().SetRangeUser(97,103)
l1Muon.GetXaxis().SetTitle('p_{T} Gen / GeV')
l1Muon.GetYaxis().SetTitle('p_{T} L1Muon / GeV')
l1Muon.GetYaxis().SetRangeUser(0,150)
l1Muon.Draw('colz')
canv.Update()
pal = l1Muon.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
canv.SaveAs("plots/" + folder + "/ptCorrelation.png")
canv.SaveAs("plots/" + folder + "/ptCorrelation.pdf")
f = TFile.Open("plots/" + folder + "/Efficiency.root","RECREATE")
canv.Write()
f.Close()
return canv
def plotPtCorrelation(folder, filename='L1MuonHistogram.root'):
if (DEBUG):
print prefix + 'was called'
if (folder == None):
print prefix + 'Error! Folder as first argument needed.'
return
if (not os.path.exists('plots')):
os.mkdir('plots')
if (not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
fullname = folder + '/' + filename
if (not os.path.exists(fullname)):
print 'Error! File ' + fullname + ' does not exist!'
return
print 'Opening file:', fullname
file = TFile.Open(fullname)
#Set plot style
PlotStyle.setPlotStyle()
l1Muon = file.Get("hoMuonAnalyzer/L1Muon_PtCorrelation")
canv = TCanvas("ptCorrelationCanvas", 'ptCorrelationCanvas', 1200, 1200)
# frame = TH2D('frame','L1 Efficiency',1,5,150,1,0,1.1)
# frame.SetStats(0)
# frame.GetXaxis().SetTitle('p_{T} / GeV')
# frame.GetYaxis().SetTitle('Efficiency')
# frame.Draw()
l1Muon.SetMarkerStyle(20)
l1Muon.SetMarkerColor(ROOT.kBlack)
l1Muon.SetLineColor(ROOT.kBlack)
l1Muon.GetXaxis().SetRangeUser(97, 103)
l1Muon.GetXaxis().SetTitle('p_{T} Gen / GeV')
l1Muon.GetYaxis().SetTitle('p_{T} L1Muon / GeV')
l1Muon.GetYaxis().SetRangeUser(0, 150)
l1Muon.Draw('colz')
canv.Update()
pal = l1Muon.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
canv.SaveAs("plots/" + folder + "/ptCorrelation.png")
canv.SaveAs("plots/" + folder + "/ptCorrelation.pdf")
f = TFile.Open("plots/" + folder + "/Efficiency.root", "RECREATE")
canv.Write()
f.Close()
return canv
def plotDeltaEtaDeltaPhiEnergyProjection(folder,sourceHistogram = 'NoDoubleMuAboveThr_DeltaEtaDeltaPhiEnergy', sourceFile = 'L1MuonHistogram.root'):
if(DEBUG):
print prefix + 'was called'
if(folder == None):
print prefix + 'Error! Filename as first argument needed.'
return
if( not os.path.exists('plots')):
os.mkdir('plots')
if( not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
filename = folder + '/' + sourceFile
if( not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print prefix + 'Opening file:',filename
file = TFile.Open(filename)
PlotStyle.setPlotStyle()
sourceHisto = file.Get("hoMuonAnalyzer/etaPhi/" + sourceHistogram)
histo = sourceHisto.Project3DProfile('yx')
canv = TCanvas("canvasDeltaEtaDeltaPhiEnergy",'canvasDeltaEtaDeltaPhiEnergy',1200,1200)
histo.GetXaxis().SetRangeUser(-.45,.45)
histo.GetXaxis().SetTitle("#Delta#eta")
histo.GetYaxis().SetRangeUser(-.45,.45)
histo.GetYaxis().SetTitle("#Delta#phi")
histo.GetZaxis().SetTitle(sourceHisto.GetZaxis().GetTitle())
histo.Draw("colz")
boxList = drawHoBoxes(canv)
canv.Update()
pal = histo.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
stats = histo.GetListOfFunctions().FindObject("stats")
stats.SetX1NDC(.1)
stats.SetX2NDC(.2)
stats.SetY1NDC(.1)
stats.SetY2NDC(.25)
legend = TLegend(0.7,0.8,0.9,0.9)
legend.AddEntry(boxList[0],"HO tile dimensions","le")
legend.Draw()
canv.Update();
canv.SaveAs("plots/" + folder + "/" + sourceHistogram + ".png")
canv.SaveAs("plots/" + folder + "/" + sourceHistogram + ".pdf")
f = TFile.Open("plots/" + folder + "/" + sourceHistogram + ".root","RECREATE")
canv.Write()
f.Close()
return histo
def plotMultiplicity(folder):
if(folder == None):
print 'Error! Filename as first argument needed.'
return
if( not os.path.exists('plots')):
os.mkdir('plots')
if( not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
filename = folder + '/L1MuonHistogram.root'
if( not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print 'Opening file:',filename
file = TFile.Open(filename)
#Set plot style
PlotStyle.setPlotStyle()
L1Muon = file.Get('hoMuonAnalyzer/L1MuonPresent_Multiplicity')
ho = file.Get("hoMuonAnalyzer/horecoAboveThreshold_Multiplicity")
canv = TCanvas("multiplicityCanvas",'Multiplicity canvas',1200,1200)
canv.SetLogy()
ho.SetStats(0)
ho.SetTitle('Multiplicities')
ho.GetXaxis().SetTitle('Multiplicity')
ho.GetYaxis().SetTitle('N')
ho.GetXaxis().SetRangeUser(-2,6)
ho.SetLineColor(ROOT.kBlack)
L1Muon.SetLineColor(ROOT.kRed)
ho.SetLineWidth(3)
L1Muon.SetLineWidth(3)
ho.Draw()
L1Muon.Draw('same')
legend = TLegend(0.6,0.75,0.9,0.9)
legend.AddEntry(ho,'HO Rec hits > 0.2 GeV','l')
legend.AddEntry(L1Muon,'L1 Muons','l')
legend.Draw()
canv.SaveAs("plots/" + folder + "/Multiplicity.png")
canv.SaveAs("plots/" + folder + "/Multiplicity.pdf")
f = TFile.Open("plots/" + folder + "/Multiplicity.root","RECREATE")
canv.Write()
f.Close()
def plotMultiplicity(folder):
if (folder == None):
print 'Error! Filename as first argument needed.'
return
if (not os.path.exists('plots')):
os.mkdir('plots')
if (not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
filename = folder + '/L1MuonHistogram.root'
if (not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print 'Opening file:', filename
file = TFile.Open(filename)
#Set plot style
PlotStyle.setPlotStyle()
L1Muon = file.Get('hoMuonAnalyzer/L1MuonPresent_Multiplicity')
ho = file.Get("hoMuonAnalyzer/horecoAboveThreshold_Multiplicity")
canv = TCanvas("multiplicityCanvas", 'Multiplicity canvas', 1200, 1200)
canv.SetLogy()
ho.SetStats(0)
ho.SetTitle('Multiplicities')
ho.GetXaxis().SetTitle('Multiplicity')
ho.GetYaxis().SetTitle('N')
ho.GetXaxis().SetRangeUser(-2, 6)
ho.SetLineColor(ROOT.kBlack)
L1Muon.SetLineColor(ROOT.kRed)
ho.SetLineWidth(3)
L1Muon.SetLineWidth(3)
ho.Draw()
L1Muon.Draw('same')
legend = TLegend(0.6, 0.75, 0.9, 0.9)
legend.AddEntry(ho, 'HO Rec hits > 0.2 GeV', 'l')
legend.AddEntry(L1Muon, 'L1 Muons', 'l')
legend.Draw()
canv.SaveAs("plots/" + folder + "/Multiplicity.png")
canv.SaveAs("plots/" + folder + "/Multiplicity.pdf")
f = TFile.Open("plots/" + folder + "/Multiplicity.root", "RECREATE")
canv.Write()
f.Close()
def plot_and_save(hist, input_name, mva_name):
rho = hist.GetCorrelationFactor()
canv = TCanvas("corr_" + hist.GetName(), "", 800, 600)
PlotStyle.canv_2d(canv, hist)
hist.Draw("colz")
PlotStyle.string(.03, .03, "#rho = %.2f" % rho)
save_canv(canv, input_name, "corr_" + input_name + "_" + mva_name)
canv.SetLogz()
canv.Update()
save_canv(canv, input_name + "_log", "corr_" + input_name + "_" + mva_name)
def doPlotEtaPtOfSuccessfulMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtEtaHoMatch", "PtEtaHoMatch", 1200, 1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiAboveThr_EtaPhiPt")
stack = THStack(hist, "zx", "2dStack", "", -1, -1, -1, -1, "zx", "")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtEtaHoMatch",
"p_{T} vs. #eta distribution;#eta;p_{T} / 5 GeV;#", 40,
-1.6, 1.6, 40, 0, 200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1HoMatchPtEta.pdf')
canvas.SaveAs('plots/NoL1HoMatchPtEta.png')
return canvas, hist, stack, histNew, label, palette, file
def doPlotEtaPtOfSuccessfulMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtEtaHoMatch","PtEtaHoMatch",1200,1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiAboveThr_EtaPhiPt")
stack = THStack(hist,"zx","2dStack","",-1,-1,-1,-1,"zx","")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtEtaHoMatch","p_{T} vs. #eta distribution;#eta;p_{T} / 5 GeV;#",40,-1.6,1.6,40,0,200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1HoMatchPtEta.pdf')
canvas.SaveAs('plots/NoL1HoMatchPtEta.png')
return canvas,hist,stack,histNew,label,palette,file
def plotGenEtaPhi():
c = TCanvas("canvasGenEtaPhi","canvas Gen Eta Phi",1200,1200)
graph = file.Get('hoMuonAnalyzer/graphs/gen')
graph.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
graph.SetMarkerStyle(2)
graph.GetXaxis().SetTitleFont(62)
graph.GetYaxis().SetTitleFont(62)
graph.GetYaxis().SetLabelFont(62)
graph.GetYaxis().SetTitle('#phi')
graph.GetXaxis().SetTitle('#eta')
graph.SetTitle('#eta #phi distribution for GEN')
graph.Draw('AP')
#CMS label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
pText = TPaveText(0.7,0.85,0.9,0.9,'NDC')
pText.SetBorderSize(1)
pText.AddText('Total Events: %d' % (totalEvents))
pText.Draw()
c.SaveAs('plots/genControlPlots/genEtaPhi.pdf')
c.SaveAs('plots/genControlPlots/genEtaPhi.png')
#Prepare histograms for eta and phi alone
histPhi = TH1D('histGenPhi',"#phi GEN;#phi;#",80,-3.48,3.48)
histEta = TH1D('histGenEta',"#eta GEN;#eta;#",40,-1.74,1.74)
#Get the data from the graph
x = Double(0)
y = Double(0)
for i in range(0,graph.GetN()):
graph.GetPoint(i,x,y)
histPhi.Fill(y)
histEta.Fill(x)
#Draw phi histogram
cPhi = TCanvas("canvasGenPhi","canvas Gen Phi",1200,1200)
histPhi.SetStats(0)
histPhi.Draw()
label.Draw()
cPhi.SaveAs('plots/genControlPlots/genPhi.pdf')
cPhi.SaveAs('plots/genControlPlots/genPhi.pdf')
#Draw eta histogram
cEta = TCanvas("canvasGenEta","canvas Gen Eta",1200,1200)
histEta.SetStats(0)
histEta.Draw()
label.Draw()
cEta.SaveAs('plots/genControlPlots/genEta.pdf')
cEta.SaveAs('plots/genControlPlots/genEta.pdf')
return c,graph,label,histEta,histPhi,cEta,cPhi,pText
def plotGenEtaPhi():
c = TCanvas("canvasGenEtaPhi", "canvas Gen Eta Phi", 1200, 1200)
graph = file.Get('hoMuonAnalyzer/graphs/gen')
graph.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
graph.SetMarkerStyle(2)
graph.GetXaxis().SetTitleFont(62)
graph.GetYaxis().SetTitleFont(62)
graph.GetYaxis().SetLabelFont(62)
graph.GetYaxis().SetTitle('#phi')
graph.GetXaxis().SetTitle('#eta')
graph.SetTitle('#eta #phi distribution for GEN')
graph.Draw('AP')
#CMS label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
pText = TPaveText(0.7, 0.85, 0.9, 0.9, 'NDC')
pText.SetBorderSize(1)
pText.AddText('Total Events: %d' % (totalEvents))
pText.Draw()
c.SaveAs('plots/genControlPlots/genEtaPhi.pdf')
c.SaveAs('plots/genControlPlots/genEtaPhi.png')
#Prepare histograms for eta and phi alone
histPhi = TH1D('histGenPhi', "#phi GEN;#phi;#", 80, -3.48, 3.48)
histEta = TH1D('histGenEta', "#eta GEN;#eta;#", 40, -1.74, 1.74)
#Get the data from the graph
x = Double(0)
y = Double(0)
for i in range(0, graph.GetN()):
graph.GetPoint(i, x, y)
histPhi.Fill(y)
histEta.Fill(x)
#Draw phi histogram
cPhi = TCanvas("canvasGenPhi", "canvas Gen Phi", 1200, 1200)
histPhi.SetStats(0)
histPhi.Draw()
label.Draw()
cPhi.SaveAs('plots/genControlPlots/genPhi.pdf')
cPhi.SaveAs('plots/genControlPlots/genPhi.pdf')
#Draw eta histogram
cEta = TCanvas("canvasGenEta", "canvas Gen Eta", 1200, 1200)
histEta.SetStats(0)
histEta.Draw()
label.Draw()
cEta.SaveAs('plots/genControlPlots/genEta.pdf')
cEta.SaveAs('plots/genControlPlots/genEta.pdf')
return c, graph, label, histEta, histPhi, cEta, cPhi, pText
def doPlotGenPt(filename):
PlotStyle.setPlotStyle()
if (DEBUG):
print 'Opening file:', filename
file = TFile.Open(filename)
if (file == None):
print 'Error opening file:', filename
c = TCanvas('genPtCanvas', 'Gen Pt no Single #mu trg', 1200, 1200)
c.cd().SetLeftMargin(0.15)
genPtHist = file.Get("hoMuonAnalyzer/NoSingleMu_Pt")
genPtHist.SetLineColor(PlotStyle.colorRwthDarkBlue)
genPtHist.SetLineWidth(3)
genPtHist.Rebin(50)
genPtHist.Sumw2()
genPtHist.Scale(1 / genPtHist.Integral())
genPtHist.GetXaxis().SetRangeUser(0, 200)
genPtHist.GetYaxis().SetTitle('normalized entries / 5 GeV')
genPtHist.GetYaxis().SetTitleOffset(2)
genPtHist.GetXaxis().SetTitle('p_{T} Gen / GeV')
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
genPtHist.Draw("ehist")
c.Update()
stats = genPtHist.GetListOfFunctions().FindObject("stats")
stats.SetOptStat(10)
stats.SetX1NDC(.7)
stats.SetX2NDC(.9)
stats.SetY1NDC(.85)
stats.SetY2NDC(.9)
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c.SaveAs('plots/genPtNoSingleMuTrg.png')
c.SaveAs('plots/genPtNoSingleMuTrg.pdf')
return
def doPlotGenPt(filename):
PlotStyle.setPlotStyle()
if(DEBUG):
print 'Opening file:',filename
file = TFile.Open(filename)
if(file == None):
print 'Error opening file:',filename
c = TCanvas('genPtCanvas','Gen Pt no Single #mu trg',1200,1200)
c.cd().SetLeftMargin(0.15)
genPtHist = file.Get("hoMuonAnalyzer/NoSingleMu_Pt")
genPtHist.SetLineColor(PlotStyle.colorRwthDarkBlue)
genPtHist.SetLineWidth(3)
genPtHist.Rebin(50)
genPtHist.Sumw2()
genPtHist.Scale(1/genPtHist.Integral())
genPtHist.GetXaxis().SetRangeUser(0,200)
genPtHist.GetYaxis().SetTitle('normalized entries / 5 GeV')
genPtHist.GetYaxis().SetTitleOffset(2)
genPtHist.GetXaxis().SetTitle('p_{T} Gen / GeV')
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
genPtHist.Draw("ehist")
c.Update()
stats = genPtHist.GetListOfFunctions().FindObject("stats")
stats.SetOptStat(10)
stats.SetX1NDC(.7)
stats.SetX2NDC(.9)
stats.SetY1NDC(.85)
stats.SetY2NDC(.9)
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c.SaveAs('plots/genPtNoSingleMuTrg.png')
c.SaveAs('plots/genPtNoSingleMuTrg.pdf')
return
def plot_and_get_sig(train_tree, test_tree, input_name, total_sig, total_bkg,
nbins, min, max, mva_name, lumi, output):
sig_train = get_sig(train_tree, "train", mva_name, total_sig, total_bkg,
lumi, nbins, min, max)
sig_test = get_sig(test_tree, "test", mva_name, total_sig, total_bkg, lumi,
nbins, min, max)
set_graph_style(sig_train, kRed + 1)
set_graph_style(sig_test, kBlack)
sig_test.SetTitle(";%s;significance" % mva_name)
canv = TCanvas("sig", "", 800, 600)
sig_test.Draw("a3")
sig_train.Draw("3")
sig_test.Draw("lx")
sig_train.Draw("lx")
index_max_train = TMath.LocMax(sig_train.GetN(), sig_train.GetY())
sig_train_max = (sig_train.GetY()[index_max_train],
sig_train.GetEY()[index_max_train])
index_max_test = TMath.LocMax(sig_test.GetN(), sig_test.GetY())
sig_test_max = (sig_test.GetY()[index_max_test],
sig_test.GetEY()[index_max_test])
leg = TLegend(0.2, 0.75, 0.6, 0.9)
PlotStyle.legend(leg)
leg.AddEntry(
sig_train, "Train: %.1f #pm %.1f @%s/fb" %
(sig_train_max[0], sig_train_max[1], lumi), "l")
leg.AddEntry(
sig_test, "Test: %.1f #pm %.1f @%s/fb" %
(sig_test_max[0], sig_test_max[1], lumi), "l")
leg.Draw()
save_canv(canv, input_name, output)
return sig_train_max, sig_test_max
def plotAllEventsInAcceptance():
c = TCanvas("c","c",1200,1200)
gTdmiInGa = file.Get("hoMuonAnalyzer/graphs/tdmiInGA")
gTdmiInGa = PlotStyle.convertToHcalCoords(gTdmiInGa)
gTdmiInGa.GetXaxis().SetTitle("i#eta / a.u.")
gTdmiInGa.GetYaxis().SetTitle("i#phi / a.u.")
gTdmiInGa.SetMarkerStyle(6)
gTdmiInGa.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
gTdmiInGa.Draw("AP")
pText = TPaveText(0.7,0.85,0.9,0.9,'NDC')
pText.AddText('Total Events: %d' % (totalEvents))
pText.AddText('Events in Plot: %d' % (gTdmiInGa.GetN()))
pText.SetBorderSize(1)
pText.Draw()
chimney1 = PlotStyle.chimney1
chimney2 = PlotStyle.chimney2
chimney1Converted = PlotStyle.convertToHcalCoords(chimney1)
chimney2Converted = PlotStyle.convertToHcalCoords(chimney2)
chimney1Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney2Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney1Converted.Draw("same,l")
chimney2Converted.Draw("same,l")
labelCmsPrivateSimulation = PlotStyle.getLabelCmsPrivateSimulation()
labelCmsPrivateSimulation.Draw()
legend = TLegend(0.1,0.87,0.3,0.9)
legend.AddEntry(chimney2Converted,"chimney","l")
legend.Draw()
c.Update()
c.SaveAs("plots/graphsEtaPhi/gTdmiInGa.pdf")
c.SaveAs("plots/graphsEtaPhi/gTdmiInGa.png")
return c,chimney1Converted,chimney2Converted,gTdmiInGa,pText
def doPlotPhiPtOfFailingMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtPhiHoMatchFail", "PtPhiHoMatchFail", 1200, 1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiNotInGA_EtaPhiPt")
stack = THStack(hist, "zy", "2dStack", "", -1, -1, -1, -1, "zy", "")
#Create new histogram and add the histograms from the stack
histNew = TH2D(
"histPtEtaHoMatchFail",
"p_{T} vs. #phi distribution for events not in HO acceptance;#phi;p_{T} / 5 GeV;#",
80, -3.2, 3.2, 40, 0, 200)
histNew.GetYaxis().SetTitleOffset(1.2)
histNew.Sumw2()
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
PlotStyle.setupAxes(histNew)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1NotInHoAcceptancePhiPt.pdf')
canvas.SaveAs('plots/NoL1NotInHoAcceptancePhiPt.png')
return canvas, hist, stack, histNew, label, palette, file
def plot_and_get_sig(sig_hist, bkg_hist, input_name, total_sig, total_bkg,
mva_name, k_fold, lumi):
sig = get_sig_hist(sig_hist, bkg_hist, total_sig, total_bkg, lumi)
set_graph_style(sig, kBlack)
sig.SetTitle(";%s;significance" % mva_name)
canv = TCanvas("sig", "", 800, 600)
sig.Draw("a3")
sig.Draw("lx")
sig_max = TMath.MaxElement(sig.GetN(), sig.GetY())
leg = TLegend(0.2, 0.75, 0.6, 0.9)
PlotStyle.legend(leg)
leg.AddEntry(sig, "%.1f @%s/fb" % (sig_max, lumi), "l")
leg.Draw()
save_canv(canv, input_name,
input_name + "_" + mva_name + "_" + str(k_fold))
return sig_max
sample["chain"].Draw(var["varStr"]+">>"+histos[sample["name"]][var["name"]].GetName(), "("+weightString+") * ("+cutString+")","goff")
for var in allVariables:
canv = ROOT.TCanvas(var['name']+'_Window',var['name']+'_Window',600,500)
if setRatioPlot:
pad1 = ROOT.TPad('pad1','pad1',0.,0.3,1.,1.)
pad1.SetBottomMargin(0.018)
pad1.SetRightMargin(0.08)
else:
pad1 = ROOT.TPad('pad1','pad1',0.,0.,1.,1.)
if setLogY:
pad1.SetLogy()
pad1.Draw()
pad1.cd()
legend = ROOT.TLegend(0.6,0.9-len(allBkg)*0.06,0.88,0.9)
PS.legend(legend)
first = True
nMax = 0.
for sample in allBkg:
print sample['name']
histos[sample['name']][var['name']].SetLineColor(sample['color'])
histos[sample['name']][var['name']].SetLineWidth(2)
#histos[sample['name']][var['name']].SetFillColor(sample['color'])
histos[sample['name']][var['name']].SetMarkerStyle(20)
histos[sample['name']][var['name']].SetMarkerColor(sample['color'])
histos[sample['name']][var['name']].SetMarkerSize(0.85)
histos[sample['name']][var['name']].GetXaxis().SetTitle(var['Xtitle'])
histos[sample['name']][var['name']].GetYaxis().SetTitle(var['Ytitle'])# / '+ str( (var['binning'][2] - var['binning'][1])/var['binning'][0])+'GeV')
if setRatioPlot:
histos[sample['name']][var['name']].GetXaxis().SetLabelSize(0.0)
def doPlotCutflow(filename='L1MuonHistogram.root'):
PlotStyle.setPlotStyle()
if (DEBUG):
print 'Opening file:', filename
file = TFile.Open(filename)
if (file == None):
print 'Error opening file:', filename
#Total event count
tagEvents = 'hoMuonAnalyzer/count/Events_Count'
tagL1Muons = 'hoMuonAnalyzer/count/L1MuonPresent_Count'
#Counters without energy threshold
tagHo1 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatch_Count'
tagHo2 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAcc_Count'
tagHo3 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAccNotDead_Count'
tagHo4 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAccThr_Count'
#Counters with energy threshold
tagThr1 = 'hoMuonAnalyzer/count/L1MuonAboveThr_Count'
tagThr2 = 'hoMuonAnalyzer/count/L1MuonAboveThrInAcc_Count'
tagThr3 = 'hoMuonAnalyzer/count/L1MuonAboveThrInAccNotDead_Count'
histEvents = file.Get(tagEvents)
histL1Muons = file.Get(tagL1Muons)
histHo1 = file.Get(tagHo1)
histHo2 = file.Get(tagHo2)
histHo3 = file.Get(tagHo3)
histHo4 = file.Get(tagHo4)
histThr1 = file.Get(tagThr1)
histThr2 = file.Get(tagThr2)
histThr3 = file.Get(tagThr3)
yValues = [
histEvents.GetBinContent(2),
histL1Muons.GetBinContent(2),
histHo1.GetBinContent(2),
# histHo2.GetBinContent(2),
# histHo3.GetBinContent(2),
histThr1.GetBinContent(2),
histThr2.GetBinContent(2),
histThr3.GetBinContent(2)
]
xLabels = [
'Event count',
'L1Muon objects',
'HO match (No Thr.)',
'HO match > 0.2 GeV',
# '+ HO acceptance'
]
norm = yValues[0]
for i, v in enumerate(yValues):
yValues[i] = v / norm
c = TCanvas('cutflowCanvas', 'PostLS1 Single #mu gun', 1200, 1200)
hist = TH1D("cutflow", "PostLS1 Single #mu gun", len(xLabels), 0,
len(xLabels))
for i, v in enumerate(xLabels):
hist.SetBinContent(i + 1, yValues[i])
hist.GetXaxis().SetBinLabel(i + 1, str(v))
hist.SetStats(0)
hist.GetYaxis().SetTitle('rel. Fraction')
hist.GetYaxis().SetRangeUser(0.7, 1.1)
hist.SetLineColor(PlotStyle.colorRwthDarkBlue)
hist.SetLabelFont(62)
hist.SetTitleFont(62)
hist.Draw("")
histTrgCount = file.Get('hoMuonAnalyzer/count/L1_SingleMu3_Count')
hist2 = TH1D("l1TrgCount", "PostLS1 Single #mu gun", len(xLabels), 0,
len(xLabels))
hist2.SetBinContent(1, histTrgCount.GetBinContent(2) / norm)
hist2.SetFillStyle(3002)
hist2.SetFillColor(PlotStyle.colorRwthMagenta)
hist2.SetLineColor(PlotStyle.colorRwthMagenta)
hist2.Draw("same")
paveText = TPaveText(0.51, 0.75, 0.9, 0.9, 'NDC')
paveText.AddText('%s: %.2f%% #pm %.2f%%' %
(xLabels[1], yValues[1] * 100,
calcSigma(yValues[1] * norm, norm) * 100))
paveText.AddText('%s: %.2f%% #pm %.2f%%' %
(xLabels[2], yValues[2] * 100,
calcSigma(yValues[2] * norm, norm) * 100))
paveText.AddText('%s: %.2f%% #pm %.2f%%' %
(xLabels[3], yValues[3] * 100,
calcSigma(yValues[3] * norm, norm) * 100))
paveText.SetBorderSize(1)
paveText.Draw()
legend = TLegend(0.1, 0.8, 0.45, 0.9)
legend.AddEntry(hist2, "Fraction with L1 Single #mu Trg.", "f")
legend.Draw()
PlotStyle.labelCmsPrivateSimulation.Draw()
c.Update()
c.SaveAs("cutflow.png")
c.SaveAs("cutflow.pdf")
c.SaveAs("cutflow.root")
nTotal = histEvents.GetBinContent(2)
nL1 = histL1Muons.GetBinContent(2)
nL1AndHo = histHo1.GetBinContent(2)
nL1AndHoAcc = histHo2.GetBinContent(2)
nL1AndHoAccNotDead = histHo3.GetBinContent(2)
nL1AndHoAccThr = histHo4.GetBinContent(2)
nL1AndHoThr = histThr1.GetBinContent(2)
nL1AndHoThrAcc = histThr2.GetBinContent(2)
nL1AndHoThrAccNotDead = histThr3.GetBinContent(2)
print '%s' % (80 * '#')
print '%s' % (80 * '#')
print '%25s%d' % ('nEvents:\t', nTotal)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('nL1:\t', nL1, nL1 / nTotal * 100,
calcSigma(nL1, nTotal) * 100)
print '%s' % (80 * '#')
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHo:\t', nL1AndHo,
nL1AndHo / nL1 * 100,
calcSigma(nL1AndHo, nL1) * 100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoAcc:\t', nL1AndHoAcc,
nL1AndHoAcc / nL1 * 100,
calcSigma(nL1AndHoAcc, nL1) * 100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % (
'L1AndHoAccNotDead:\t', nL1AndHoAccNotDead, nL1AndHoAccNotDead / nL1 *
100, calcSigma(nL1AndHoAccNotDead, nL1) * 100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % (
'L1AndHoAccThr:\t', nL1AndHoAccThr, nL1AndHoAccThr / nL1 * 100,
calcSigma(nL1AndHoAccThr, nL1) * 100)
print '%s' % (80 * '#')
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoThr:\t', nL1AndHoThr,
nL1AndHoThr / nL1 * 100,
calcSigma(nL1AndHoThr, nL1) * 100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % (
'L1AndHoThrAcc:\t', nL1AndHoThrAcc, nL1AndHoThrAcc / nL1 * 100,
calcSigma(nL1AndHoThrAcc, nL1) * 100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % (
'L1AndHoThrAccNotDead:\t', nL1AndHoThrAccNotDead, nL1AndHoThrAccNotDead
/ nL1 * 100, calcSigma(nL1AndHoThrAccNotDead, nL1) * 100)
print '%s' % (80 * '#')
print '%s' % (80 * '#')
return c
import os, sys
from math import sqrt
sys.path.append(os.path.abspath("../../python"))
from ROOT import TFile, TCanvas, TH1D, TLegend, TPaveText
from plotEnergy import plotEnergyVsEtaPhi
from plotDeltaEtaDeltaPhi import plotDeltaEtaDeltaPhiEnergyProjection, plotDeltaEtaDeltaPhi
DEBUG = 1
import PlotStyle
PlotStyle.setPlotStyle()
def calcSigma(num, denom):
return sqrt(num / (denom * denom) + num * num / (pow(denom, 3)))
def doPlotCutflow(filename='L1MuonHistogram.root'):
PlotStyle.setPlotStyle()
if (DEBUG):
print 'Opening file:', filename
file = TFile.Open(filename)
if (file == None):
print 'Error opening file:', filename
#Total event count
tagEvents = 'hoMuonAnalyzer/count/Events_Count'
def plotDeltaEtaDeltaPhi(folder,
sourceHistogram='L1MuonWithHoMatch_DeltaEtaDeltaPhi',
sourceFile='L1MuonHistogram.root',
storeSubdir='deltaEtaDeltaPhi'):
if (DEBUG):
print prefix + 'was called'
if (folder == None):
print prefix + 'Error! Filename as first argument needed.'
return
if (not os.path.exists('plots')):
os.mkdir('plots')
if (not os.path.exists('plots/' + storeSubdir)):
os.mkdir('plots/' + storeSubdir)
filename = folder + '/' + sourceFile
if (not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print prefix + 'Opening file:', filename
file = TFile.Open(filename)
PlotStyle.setPlotStyle()
h2dDeltaEtaDeltaPhi = file.Get("hoMuonAnalyzer/etaPhi/" + sourceHistogram)
hEventCount = file.Get("hoMuonAnalyzer/count/Events_Count")
hNoTrgCount = file.Get("hoMuonAnalyzer/count/NoSingleMu_Count")
if (h2dDeltaEtaDeltaPhi == None):
print 'Could not get histogram %s from file %s' % (
"hoMuonAnalyzer/etaPhi/" + sourceHistogram, filename)
canv = TCanvas("canvasDeltaEtaDeltaPhi" + sourceHistogram,
'canvasDeltaEtaDeltaPhi', 1200, 1200)
canv.SetLogz()
h2dDeltaEtaDeltaPhi.GetXaxis().SetRangeUser(-.45, .45)
h2dDeltaEtaDeltaPhi.GetXaxis().SetTitle("#Delta#eta")
h2dDeltaEtaDeltaPhi.GetYaxis().SetRangeUser(-.45, .45)
h2dDeltaEtaDeltaPhi.GetYaxis().SetTitle("#Delta#phi")
h2dDeltaEtaDeltaPhi.GetZaxis().SetTitle("N")
h2dDeltaEtaDeltaPhi.Draw("colz")
boxList = drawHoBoxes(canv)
canv.Update()
pal = h2dDeltaEtaDeltaPhi.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
stats = h2dDeltaEtaDeltaPhi.GetListOfFunctions().FindObject("stats")
stats.SetX1NDC(.1)
stats.SetX2NDC(.2)
stats.SetY1NDC(.1)
stats.SetY2NDC(.25)
paveText = TPaveText(0.2, 0.1, 0.65, 0.2, 'NDC')
paveText.AddText('Total Events: %d' % (hEventCount.GetBinContent(2)))
paveText.AddText('Events with no Single #mu Trg: %d' %
(hNoTrgCount.GetBinContent(2)))
paveText.SetBorderSize(1)
paveText.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
legend = TLegend(0.7, 0.8, 0.9, 0.9)
legend.AddEntry(boxList[0], "HO tile dimensions", "le")
legend.Draw()
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".png")
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".pdf")
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".root")
SetOwnership(h2dDeltaEtaDeltaPhi, False)
return [
h2dDeltaEtaDeltaPhi, canv, legend, boxList, stats, pal, paveText, label
]
clsexp = triwsmooth(exampleHarvest, "CLsexp", x, y, transformation=transformation, smooth=smooth, npx=npx, npy=npy)
clsexp_up = triwsmooth(exampleHarvest, "clsu1s", x, y, transformation=transformation, smooth=smooth, npx=npx, npy=npy)
clsexp_down = triwsmooth(exampleHarvest, "clsd1s", x, y, transformation=transformation, smooth=smooth, npx=npx, npy=npy)
fixedhist = FixAndSetBorders(clsexp, "cls", "cls")
fixedhist_up = FixAndSetBorders(clsexp_up, "cls_up", "cls_up")
fixedhist_down = FixAndSetBorders(clsexp_down, "cls_down", "cls_down")
#contours = getContours(fixedhist, level)
#contours_up = getContours(fixedhist_up, level)
#contours_down = getContours(fixedhist_down, level)
#errorband = myOneSigmaBand(contours_up[0], contours_down[0])
contours_5 = getContours(fixedhist, 5)
contours_3 = getContours(fixedhist, 3)
c = ROOT.TCanvas()
c.SetRightMargin(0.15)
leg = ROOT.TLegend(0.18, PS.ThirdLine-0.12, 0.4, PS.ThirdLine-0.03)
PS.legend(leg)
numbers = drawNumbers(exampleHarvest, "CLsexp", x, y, drawOnlyDots=True)
fixedhist.Draw("colz")
fixedhist.SetMinimum(0.01)
fixedhist.GetXaxis().SetTitle('m_{#tilde{t}_{1}}')
fixedhist.GetYaxis().SetTitle('m_{#tilde{#chi}_{1}}')
#errorband.SetFillStyle(3354)
#errorband.SetFillColor(ROOT.kBlack)
#errorband.Draw("f same")
#for contour in contours_up + contours_down:
for contour in contours_3:
contour.SetLineColor(ROOT.kBlack)
contour.SetLineStyle(7)
contour.Draw("l same")
#for contour in contours:
for contour in contours_5:
def plot_mlp_distribution(train_tree, test_tree, input_name, nbins, min, max,
mva_name, output):
mlp_train_sig, mlp_train_bkg = load_mlp(train_tree,
"train",
mva_name,
nbins,
min,
max,
norm=True)
mlp_test_sig, mlp_test_bkg = load_mlp(test_tree,
"test",
mva_name,
nbins,
min,
max,
norm=True)
# save all bdt distributions
if output:
try:
os.makedirs(os.path.join(output, "bdt_dist"))
except:
pass
outfile = TFile(os.path.join(output, "bdt_dist", input_name + ".root"),
"recreate")
outfile.WriteTObject(mlp_test_sig, "mlp_test_sig")
outfile.WriteTObject(mlp_train_sig, "mlp_train_sig")
outfile.WriteTObject(mlp_test_bkg, "mlp_test_bkg")
outfile.WriteTObject(mlp_train_bkg, "mlp_train_bkg")
outfile.Close()
set_style(mlp_test_sig, kRed)
set_style(mlp_train_sig, kRed)
set_style(mlp_test_bkg, kBlue)
set_style(mlp_train_bkg, kBlue)
# plot train and test
canv = TCanvas("train_test", "", 800, 600)
mlp_test_bkg.SetTitle("; %s; fraction of events" % mva_name)
mlp_test_bkg.GetYaxis().SetRangeUser(0, mlp_test_bkg.GetMaximum() * 1.8)
mlp_test_bkg.Draw("hist")
mlp_test_sig.Draw("same hist")
mlp_train_sig.Draw("same p")
mlp_train_bkg.Draw("same p")
leg = TLegend(0.2, 0.7, 0.5, 0.9)
leg.AddEntry(mlp_test_sig, "Signal Test", "f")
leg.AddEntry(mlp_test_bkg, "Background Test", "f")
leg.AddEntry(mlp_train_sig, "Signal Train", "pl")
leg.AddEntry(mlp_train_bkg, "Background Train", "pl")
leg.Draw("same")
kolmogorov_bkg = mlp_test_bkg.KolmogorovTest(mlp_train_bkg)
kolmogorov_sig = mlp_test_sig.KolmogorovTest(mlp_train_sig)
chi2_bkg = mlp_test_bkg.Chi2Test(mlp_train_bkg, "WW CHI2/NDF")
chi2_sig = mlp_test_sig.Chi2Test(mlp_train_sig, "WW CHI2/NDF")
PlotStyle.string(0.65,
0.85,
"Kolomogorov Bkg: %1.2f" % kolmogorov_bkg,
rel=0.7)
PlotStyle.string(0.65,
0.8,
"Kolomogorov Sig: %1.2f" % kolmogorov_sig,
rel=0.7)
PlotStyle.string(0.65,
0.75,
"red. #chi^{2} Bkg: %1.2f" % chi2_bkg,
rel=0.7)
PlotStyle.string(0.65, 0.7, "red. #chi^{2} Sig: %1.2f" % chi2_sig, rel=0.7)
save_canv(canv, input_name, output)
def main():
plotSig = True
filename = "SR_RNN0p86-1p0"
if plotSig:
filename = filename + "_Significance"
else:
filename = filename + "_signal_contamination"
wwwDir = "/project/etp5/dhandl/plots/Stop1L/FullRun2/SignifcanceScan/21.2.60/"
if not os.path.exists(wwwDir):
os.makedirs(wwwDir)
print('Creating Directory...')
bkgDir = '/project/etp3/dhandl/samples/SUSY/Stop1L/21.2.60_ML/'
#sigDir = '/project/etp5/dhandl/myFork-stop1l/export/default/'
sigDir = bkgDir
lumi = str(140500.)
cut = '(n_jet>=4) && (n_lep==1) && (lep_pt[0]>25e3) && (n_bjet>=1) && (mt>=110e3) && (met>=230e3) && (jet_pt[0]>=25e3) && (jet_pt[1]>=25e3) && (jet_pt[2]>=25e3) && (jet_pt[3]>=25e3) && (dphi_jet0_ptmiss > 0.4) && (dphi_jet1_ptmiss > 0.4) && !((mT2tauLooseTau_GeV > -0.5) && (mT2tauLooseTau_GeV < 80)) && (outputScore_RNN >= 0.86) && (outputScore_RNN<1.0)'
weight = 'weight * xs_weight * lumi_weight * sf_total'
sampleNames = [
{
'name':
'ttbar',
'target': [
bkgDir + 'mc16a_ttbar/*.root', bkgDir + 'mc16d_ttbar/*.root',
bkgDir + 'mc16e_ttbar/*.root'
],
'chain_name':
'ttbar_Nom',
'class':
'bkg'
},
{
'name':
'singletop',
'target': [
bkgDir + 'mc16a_singletop/*.root',
bkgDir + 'mc16d_singletop/*.root',
bkgDir + 'mc16e_singletop/*.root'
],
'chain_name':
'singletop_Nom',
'class':
'bkg'
},
{
'name':
'wjets',
'target': [
bkgDir + 'mc16a_wjets/*.root', bkgDir + 'mc16d_wjets/*.root',
bkgDir + 'mc16e_wjets/*.root'
],
'chain_name':
'wjets_Nom',
'class':
'bkg'
},
{
'name':
'ttV',
'target': [
bkgDir + 'mc16a_ttV/*.root', bkgDir + 'mc16d_ttV/*.root',
bkgDir + 'mc16e_ttV/*.root'
],
'chain_name':
'ttV_Nom',
'class':
'bkg'
},
{
'name':
'multiboson',
'target': [
bkgDir + 'mc16a_multiboson/*.root',
bkgDir + 'mc16d_multiboson/*.root',
bkgDir + 'mc16e_multiboson/*.root'
],
'chain_name':
'multiboson_Nom',
'class':
'bkg'
},
### bWN ###
{
'name':
'bWN_400_235',
'target': [
bkgDir + 'mc16a_bWN_400_235/*.root',
bkgDir + 'mc16d_bWN_400_235/*.root',
bkgDir + 'mc16e_bWN_400_235/*.root'
],
'chain_name':
'bWN_400_235_Nom',
'class':
'sig'
},
{
'name':
'bWN_400_250',
'target': [
bkgDir + 'mc16a_bWN_400_250/*.root',
bkgDir + 'mc16d_bWN_400_250/*.root',
bkgDir + 'mc16e_bWN_400_250/*.root'
],
'chain_name':
'bWN_400_250_Nom',
'class':
'sig'
},
{
'name':
'bWN_400_280',
'target': [
bkgDir + 'mc16a_bWN_400_280/*.root',
bkgDir + 'mc16d_bWN_400_280/*.root',
bkgDir + 'mc16e_bWN_400_280/*.root'
],
'chain_name':
'bWN_400_280_Nom',
'class':
'sig'
},
{
'name':
'bWN_400_310',
'target': [
bkgDir + 'mc16a_bWN_400_310/*.root',
bkgDir + 'mc16d_bWN_400_310/*.root',
bkgDir + 'mc16e_bWN_400_310/*.root'
],
'chain_name':
'bWN_400_310_Nom',
'class':
'sig'
},
{
'name':
'bWN_450_285',
'target': [
bkgDir + 'mc16a_bWN_450_285/*.root',
bkgDir + 'mc16d_bWN_450_285/*.root',
bkgDir + 'mc16e_bWN_450_285/*.root'
],
'chain_name':
'bWN_450_285_Nom',
'class':
'sig'
},
{
'name':
'bWN_450_300',
'target': [
bkgDir + 'mc16a_bWN_450_300/*.root',
bkgDir + 'mc16d_bWN_450_300/*.root',
bkgDir + 'mc16e_bWN_450_300/*.root'
],
'chain_name':
'bWN_450_300_Nom',
'class':
'sig'
},
{
'name':
'bWN_450_330',
'target': [
bkgDir + 'mc16a_bWN_450_330/*.root',
bkgDir + 'mc16d_bWN_450_330/*.root',
bkgDir + 'mc16e_bWN_450_330/*.root'
],
'chain_name':
'bWN_450_330_Nom',
'class':
'sig'
},
{
'name':
'bWN_450_360',
'target': [
bkgDir + 'mc16a_bWN_450_360/*.root',
bkgDir + 'mc16d_bWN_450_360/*.root',
bkgDir + 'mc16e_bWN_450_360/*.root'
],
'chain_name':
'bWN_450_360_Nom',
'class':
'sig'
},
{
'name':
'bWN_500_335',
'target': [
bkgDir + 'mc16a_bWN_500_335/*.root',
bkgDir + 'mc16d_bWN_500_335/*.root',
bkgDir + 'mc16e_bWN_500_335/*.root'
],
'chain_name':
'bWN_500_335_Nom',
'class':
'sig'
},
{
'name':
'bWN_500_350',
'target': [
bkgDir + 'mc16a_bWN_500_350/*.root',
bkgDir + 'mc16d_bWN_500_350/*.root',
bkgDir + 'mc16e_bWN_500_350/*.root'
],
'chain_name':
'bWN_500_350_Nom',
'class':
'sig'
},
{
'name':
'bWN_500_380',
'target': [
bkgDir + 'mc16a_bWN_500_380/*.root',
bkgDir + 'mc16d_bWN_500_380/*.root',
bkgDir + 'mc16e_bWN_500_380/*.root'
],
'chain_name':
'bWN_500_380_Nom',
'class':
'sig'
},
{
'name':
'bWN_500_410',
'target': [
bkgDir + 'mc16a_bWN_500_410/*.root',
bkgDir + 'mc16d_bWN_500_410/*.root',
bkgDir + 'mc16e_bWN_500_410/*.root'
],
'chain_name':
'bWN_500_410_Nom',
'class':
'sig'
},
{
'name':
'bWN_550_385',
'target': [
bkgDir + 'mc16a_bWN_550_385/*.root',
bkgDir + 'mc16d_bWN_550_385/*.root',
bkgDir + 'mc16e_bWN_550_385/*.root'
],
'chain_name':
'bWN_550_385_Nom',
'class':
'sig'
},
{
'name':
'bWN_550_400',
'target': [
bkgDir + 'mc16a_bWN_550_400/*.root',
bkgDir + 'mc16d_bWN_550_400/*.root',
bkgDir + 'mc16e_bWN_550_400/*.root'
],
'chain_name':
'bWN_550_400_Nom',
'class':
'sig'
},
{
'name':
'bWN_550_430',
'target': [
bkgDir + 'mc16a_bWN_550_430/*.root',
bkgDir + 'mc16d_bWN_550_430/*.root',
bkgDir + 'mc16e_bWN_550_430/*.root'
],
'chain_name':
'bWN_550_430_Nom',
'class':
'sig'
},
{
'name':
'bWN_550_460',
'target': [
bkgDir + 'mc16a_bWN_550_460/*.root',
bkgDir + 'mc16d_bWN_550_460/*.root',
bkgDir + 'mc16e_bWN_550_460/*.root'
],
'chain_name':
'bWN_550_460_Nom',
'class':
'sig'
},
{
'name':
'bWN_600_435',
'target': [
bkgDir + 'mc16a_bWN_600_435/*.root',
bkgDir + 'mc16d_bWN_600_435/*.root',
bkgDir + 'mc16e_bWN_600_435/*.root'
],
'chain_name':
'bWN_600_435_Nom',
'class':
'sig'
},
{
'name':
'bWN_600_450',
'target': [
bkgDir + 'mc16a_bWN_600_450/*.root',
bkgDir + 'mc16d_bWN_600_450/*.root',
bkgDir + 'mc16e_bWN_600_450/*.root'
],
'chain_name':
'bWN_600_450_Nom',
'class':
'sig'
},
{
'name':
'bWN_600_480',
'target': [
bkgDir + 'mc16a_bWN_600_480/*.root',
bkgDir + 'mc16d_bWN_600_480/*.root',
bkgDir + 'mc16e_bWN_600_480/*.root'
],
'chain_name':
'bWN_600_480_Nom',
'class':
'sig'
},
{
'name':
'bWN_600_510',
'target': [
bkgDir + 'mc16a_bWN_600_510/*.root',
bkgDir + 'mc16d_bWN_600_510/*.root',
bkgDir + 'mc16e_bWN_600_510/*.root'
],
'chain_name':
'bWN_600_510_Nom',
'class':
'sig'
},
{
'name':
'bWN_650_485',
'target': [
bkgDir + 'mc16a_bWN_650_485/*.root',
bkgDir + 'mc16d_bWN_650_485/*.root',
bkgDir + 'mc16e_bWN_650_485/*.root'
],
'chain_name':
'bWN_650_485_Nom',
'class':
'sig'
},
{
'name':
'bWN_650_500',
'target': [
bkgDir + 'mc16a_bWN_650_500/*.root',
bkgDir + 'mc16d_bWN_650_500/*.root',
bkgDir + 'mc16e_bWN_650_500/*.root'
],
'chain_name':
'bWN_650_500_Nom',
'class':
'sig'
},
{
'name':
'bWN_650_530',
'target': [
bkgDir + 'mc16a_bWN_650_530/*.root',
bkgDir + 'mc16d_bWN_650_530/*.root',
bkgDir + 'mc16e_bWN_650_530/*.root'
],
'chain_name':
'bWN_650_530_Nom',
'class':
'sig'
},
{
'name':
'bWN_650_560',
'target': [
bkgDir + 'mc16a_bWN_650_560/*.root',
bkgDir + 'mc16d_bWN_650_560/*.root',
bkgDir + 'mc16e_bWN_650_560/*.root'
],
'chain_name':
'bWN_650_560_Nom',
'class':
'sig'
},
{
'name':
'bWN_700_535',
'target': [
bkgDir + 'mc16a_bWN_700_535/*.root',
bkgDir + 'mc16d_bWN_700_535/*.root',
bkgDir + 'mc16e_bWN_700_535/*.root'
],
'chain_name':
'bWN_700_535_Nom',
'class':
'sig'
},
{
'name':
'bWN_700_550',
'target': [
bkgDir + 'mc16a_bWN_700_550/*.root',
bkgDir + 'mc16d_bWN_700_550/*.root',
bkgDir + 'mc16e_bWN_700_550/*.root'
],
'chain_name':
'bWN_700_550_Nom',
'class':
'sig'
},
{
'name':
'bWN_700_580',
'target': [
bkgDir + 'mc16a_bWN_700_580/*.root',
bkgDir + 'mc16d_bWN_700_580/*.root',
bkgDir + 'mc16e_bWN_700_580/*.root'
],
'chain_name':
'bWN_700_580_Nom',
'class':
'sig'
},
{
'name':
'bWN_700_610',
'target': [
bkgDir + 'mc16a_bWN_700_610/*.root',
bkgDir + 'mc16d_bWN_700_610/*.root',
bkgDir + 'mc16e_bWN_700_610/*.root'
],
'chain_name':
'bWN_700_610_Nom',
'class':
'sig'
},
{
'name':
'bWN_750_585',
'target': [
bkgDir + 'mc16a_bWN_750_585/*.root',
bkgDir + 'mc16d_bWN_750_585/*.root',
bkgDir + 'mc16e_bWN_750_585/*.root'
],
'chain_name':
'bWN_750_585_Nom',
'class':
'sig'
},
{
'name':
'bWN_750_600',
'target': [
bkgDir + 'mc16a_bWN_750_600/*.root',
bkgDir + 'mc16d_bWN_750_600/*.root',
bkgDir + 'mc16e_bWN_750_600/*.root'
],
'chain_name':
'bWN_750_600_Nom',
'class':
'sig'
},
{
'name':
'bWN_750_630',
'target': [
bkgDir + 'mc16a_bWN_750_630/*.root',
bkgDir + 'mc16d_bWN_750_630/*.root',
bkgDir + 'mc16e_bWN_750_630/*.root'
],
'chain_name':
'bWN_750_630_Nom',
'class':
'sig'
},
{
'name':
'bWN_750_660',
'target': [
bkgDir + 'mc16a_bWN_750_660/*.root',
bkgDir + 'mc16d_bWN_750_660/*.root',
bkgDir + 'mc16e_bWN_750_660/*.root'
],
'chain_name':
'bWN_750_660_Nom',
'class':
'sig'
},
### bffN ###
{
'name':
'bffN_300_220',
'target': [
bkgDir + 'mc16a_bffN_300_220/*.root',
bkgDir + 'mc16d_bffN_300_220/*.root',
bkgDir + 'mc16e_bffN_300_220/*.root'
],
'chain_name':
'bffN_300_220_Nom',
'class':
'sig'
},
{
'name':
'bffN_300_250',
'target': [
bkgDir + 'mc16a_bffN_300_250/*.root',
bkgDir + 'mc16d_bffN_300_250/*.root',
bkgDir + 'mc16e_bffN_300_250/*.root'
],
'chain_name':
'bffN_300_250_Nom',
'class':
'sig'
},
{
'name':
'bffN_300_280',
'target': [
bkgDir + 'mc16a_bffN_300_280/*.root',
bkgDir + 'mc16d_bffN_300_280/*.root',
bkgDir + 'mc16e_bffN_300_280/*.root'
],
'chain_name':
'bffN_300_280_Nom',
'class':
'sig'
},
{
'name':
'bffN_300_293',
'target': [
bkgDir + 'mc16a_bffN_300_293/*.root',
bkgDir + 'mc16d_bffN_300_293/*.root',
bkgDir + 'mc16e_bffN_300_293/*.root'
],
'chain_name':
'bffN_300_293_Nom',
'class':
'sig'
},
{
'name':
'bffN_350_270',
'target': [
bkgDir + 'mc16a_bffN_350_270/*.root',
bkgDir + 'mc16d_bffN_350_270/*.root',
bkgDir + 'mc16e_bffN_350_270/*.root'
],
'chain_name':
'bffN_350_270_Nom',
'class':
'sig'
},
{
'name':
'bffN_350_300',
'target': [
bkgDir + 'mc16a_bffN_350_300/*.root',
bkgDir + 'mc16d_bffN_350_300/*.root',
bkgDir + 'mc16e_bffN_350_300/*.root'
],
'chain_name':
'bffN_350_300_Nom',
'class':
'sig'
},
{
'name':
'bffN_350_330',
'target': [
bkgDir + 'mc16a_bffN_350_330/*.root',
bkgDir + 'mc16d_bffN_350_330/*.root',
bkgDir + 'mc16e_bffN_350_330/*.root'
],
'chain_name':
'bffN_350_330_Nom',
'class':
'sig'
},
{
'name':
'bffN_350_243',
'target': [
bkgDir + 'mc16a_bffN_350_243/*.root',
bkgDir + 'mc16d_bffN_350_243/*.root',
bkgDir + 'mc16e_bffN_350_243/*.root'
],
'chain_name':
'bffN_350_243_Nom',
'class':
'sig'
},
{
'name':
'bffN_400_320',
'target': [
bkgDir + 'mc16a_bffN_400_320/*.root',
bkgDir + 'mc16d_bffN_400_320/*.root',
bkgDir + 'mc16e_bffN_400_320/*.root'
],
'chain_name':
'bffN_400_320_Nom',
'class':
'sig'
},
{
'name':
'bffN_400_350',
'target': [
bkgDir + 'mc16a_bffN_400_350/*.root',
bkgDir + 'mc16d_bffN_400_350/*.root',
bkgDir + 'mc16e_bffN_400_350/*.root'
],
'chain_name':
'bffN_400_350_Nom',
'class':
'sig'
},
{
'name':
'bffN_400_380',
'target': [
bkgDir + 'mc16a_bffN_400_380/*.root',
bkgDir + 'mc16d_bffN_400_380/*.root',
bkgDir + 'mc16e_bffN_400_380/*.root'
],
'chain_name':
'bffN_400_380_Nom',
'class':
'sig'
},
{
'name':
'bffN_400_393',
'target': [
bkgDir + 'mc16a_bffN_400_393/*.root',
bkgDir + 'mc16d_bffN_400_393/*.root',
bkgDir + 'mc16e_bffN_400_393/*.root'
],
'chain_name':
'bffN_400_393_Nom',
'class':
'sig'
},
{
'name':
'bffN_450_370',
'target': [
bkgDir + 'mc16a_bffN_450_370/*.root',
bkgDir + 'mc16d_bffN_450_370/*.root',
bkgDir + 'mc16e_bffN_450_370/*.root'
],
'chain_name':
'bffN_450_370_Nom',
'class':
'sig'
},
{
'name':
'bffN_450_400',
'target': [
bkgDir + 'mc16a_bffN_450_400/*.root',
bkgDir + 'mc16d_bffN_450_400/*.root',
bkgDir + 'mc16e_bffN_450_400/*.root'
],
'chain_name':
'bffN_450_400_Nom',
'class':
'sig'
},
{
'name':
'bffN_450_430',
'target': [
bkgDir + 'mc16a_bffN_450_430/*.root',
bkgDir + 'mc16d_bffN_450_430/*.root',
bkgDir + 'mc16e_bffN_450_430/*.root'
],
'chain_name':
'bffN_450_430_Nom',
'class':
'sig'
},
{
'name':
'bffN_450_443',
'target': [
bkgDir + 'mc16a_bffN_450_443/*.root',
bkgDir + 'mc16d_bffN_450_443/*.root',
bkgDir + 'mc16e_bffN_450_443/*.root'
],
'chain_name':
'bffN_450_443_Nom',
'class':
'sig'
},
{
'name':
'bffN_500_420',
'target': [
bkgDir + 'mc16a_bffN_500_420/*.root',
bkgDir + 'mc16d_bffN_500_420/*.root',
bkgDir + 'mc16e_bffN_500_420/*.root'
],
'chain_name':
'bffN_500_420_Nom',
'class':
'sig'
},
{
'name':
'bffN_500_450',
'target': [
bkgDir + 'mc16a_bffN_500_450/*.root',
bkgDir + 'mc16d_bffN_500_450/*.root',
bkgDir + 'mc16e_bffN_500_450/*.root'
],
'chain_name':
'bffN_500_450_Nom',
'class':
'sig'
},
{
'name':
'bffN_500_480',
'target': [
bkgDir + 'mc16a_bffN_500_480/*.root',
bkgDir + 'mc16d_bffN_500_480/*.root',
bkgDir + 'mc16e_bffN_500_480/*.root'
],
'chain_name':
'bffN_500_480_Nom',
'class':
'sig'
},
{
'name':
'bffN_500_493',
'target': [
bkgDir + 'mc16a_bffN_500_493/*.root',
bkgDir + 'mc16d_bffN_500_493/*.root',
bkgDir + 'mc16e_bffN_500_493/*.root'
],
'chain_name':
'bffN_500_493_Nom',
'class':
'sig'
},
{
'name':
'bffN_550_470',
'target': [
bkgDir + 'mc16a_bffN_550_470/*.root',
bkgDir + 'mc16d_bffN_550_470/*.root',
bkgDir + 'mc16e_bffN_550_470/*.root'
],
'chain_name':
'bffN_550_470_Nom',
'class':
'sig'
},
{
'name':
'bffN_550_500',
'target': [
bkgDir + 'mc16a_bffN_550_500/*.root',
bkgDir + 'mc16d_bffN_550_500/*.root',
bkgDir + 'mc16e_bffN_550_500/*.root'
],
'chain_name':
'bffN_550_500_Nom',
'class':
'sig'
},
{
'name':
'bffN_550_530',
'target': [
bkgDir + 'mc16a_bffN_550_530/*.root',
bkgDir + 'mc16d_bffN_550_530/*.root',
bkgDir + 'mc16e_bffN_550_530/*.root'
],
'chain_name':
'bffN_550_530_Nom',
'class':
'sig'
},
{
'name':
'bffN_550_543',
'target': [
bkgDir + 'mc16a_bffN_550_543/*.root',
bkgDir + 'mc16d_bffN_550_543/*.root',
bkgDir + 'mc16e_bffN_550_543/*.root'
],
'chain_name':
'bffN_550_543_Nom',
'class':
'sig'
},
{
'name':
'bffN_600_520',
'target': [
bkgDir + 'mc16a_bffN_600_520/*.root',
bkgDir + 'mc16d_bffN_600_520/*.root',
bkgDir + 'mc16e_bffN_600_520/*.root'
],
'chain_name':
'bffN_600_520_Nom',
'class':
'sig'
},
{
'name':
'bffN_600_550',
'target': [
bkgDir + 'mc16a_bffN_600_550/*.root',
bkgDir + 'mc16d_bffN_600_550/*.root',
bkgDir + 'mc16e_bffN_600_550/*.root'
],
'chain_name':
'bffN_600_550_Nom',
'class':
'sig'
},
{
'name':
'bffN_600_580',
'target': [
bkgDir + 'mc16a_bffN_600_580/*.root',
bkgDir + 'mc16d_bffN_600_580/*.root',
bkgDir + 'mc16e_bffN_600_580/*.root'
],
'chain_name':
'bffN_600_580_Nom',
'class':
'sig'
},
{
'name':
'bffN_600_593',
'target': [
bkgDir + 'mc16a_bffN_600_593/*.root',
bkgDir + 'mc16d_bffN_600_593/*.root',
bkgDir + 'mc16e_bffN_600_593/*.root'
],
'chain_name':
'bffN_600_593_Nom',
'class':
'sig'
},
{
'name':
'bffN_650_570',
'target': [
bkgDir + 'mc16a_bffN_650_570/*.root',
bkgDir + 'mc16d_bffN_650_570/*.root',
bkgDir + 'mc16e_bffN_650_570/*.root'
],
'chain_name':
'bffN_650_570_Nom',
'class':
'sig'
},
{
'name':
'bffN_650_600',
'target': [
bkgDir + 'mc16a_bffN_650_600/*.root',
bkgDir + 'mc16d_bffN_650_600/*.root',
bkgDir + 'mc16e_bffN_650_600/*.root'
],
'chain_name':
'bffN_650_600_Nom',
'class':
'sig'
},
{
'name':
'bffN_650_630',
'target': [
bkgDir + 'mc16a_bffN_650_630/*.root',
bkgDir + 'mc16d_bffN_650_630/*.root',
bkgDir + 'mc16e_bffN_650_630/*.root'
],
'chain_name':
'bffN_650_630_Nom',
'class':
'sig'
},
{
'name':
'bffN_650_643',
'target': [
bkgDir + 'mc16a_bffN_650_643/*.root',
bkgDir + 'mc16d_bffN_650_643/*.root',
bkgDir + 'mc16e_bffN_650_643/*.root'
],
'chain_name':
'bffN_650_643_Nom',
'class':
'sig'
},
### tN ###
{
'name':
'tN_190_17',
'target': [
bkgDir + 'mc16a_tN_190_17/*.root',
bkgDir + 'mc16d_tN_190_17/*.root',
bkgDir + 'mc16e_tN_190_17/*.root'
],
'chain_name':
'tN_190_17_Nom',
'class':
'sig'
},
{
'name':
'tN_400_200',
'target': [
bkgDir + 'mc16a_tN_400_200/*.root',
bkgDir + 'mc16d_tN_400_200/*.root',
bkgDir + 'mc16e_tN_400_200/*.root'
],
'chain_name':
'tN_400_200_Nom',
'class':
'sig'
},
{
'name':
'tN_500_300',
'target': [
bkgDir + 'mc16a_tN_500_300/*.root',
bkgDir + 'mc16d_tN_500_300/*.root',
bkgDir + 'mc16e_tN_500_300/*.root'
],
'chain_name':
'tN_500_300_Nom',
'class':
'sig'
},
{
'name':
'tN_500_312',
'target': [
bkgDir + 'mc16a_tN_500_312/*.root',
bkgDir + 'mc16d_tN_500_312/*.root',
bkgDir + 'mc16e_tN_500_312/*.root'
],
'chain_name':
'tN_500_312_Nom',
'class':
'sig'
},
{
'name':
'tN_500_327',
'target': [
bkgDir + 'mc16a_tN_500_327/*.root',
bkgDir + 'mc16d_tN_500_327/*.root',
bkgDir + 'mc16e_tN_500_327/*.root'
],
'chain_name':
'tN_500_327_Nom',
'class':
'sig'
},
{
'name':
'tN_550_350',
'target': [
bkgDir + 'mc16a_tN_550_350/*.root',
bkgDir + 'mc16d_tN_550_350/*.root',
bkgDir + 'mc16e_tN_550_350/*.root'
],
'chain_name':
'tN_550_350_Nom',
'class':
'sig'
},
{
'name':
'tN_550_362',
'target': [
bkgDir + 'mc16a_tN_550_362/*.root',
bkgDir + 'mc16d_tN_550_362/*.root',
bkgDir + 'mc16e_tN_550_362/*.root'
],
'chain_name':
'tN_550_362_Nom',
'class':
'sig'
},
{
'name':
'tN_550_377',
'target': [
bkgDir + 'mc16a_tN_550_377/*.root',
bkgDir + 'mc16d_tN_550_377/*.root',
bkgDir + 'mc16e_tN_550_377/*.root'
],
'chain_name':
'tN_550_377_Nom',
'class':
'sig'
},
{
'name':
'tN_600_400',
'target': [
bkgDir + 'mc16a_tN_600_400/*.root',
bkgDir + 'mc16d_tN_600_400/*.root',
bkgDir + 'mc16e_tN_600_400/*.root'
],
'chain_name':
'tN_600_400_Nom',
'class':
'sig'
},
{
'name':
'tN_600_412',
'target': [
bkgDir + 'mc16a_tN_600_412/*.root',
bkgDir + 'mc16d_tN_600_412/*.root',
bkgDir + 'mc16e_tN_600_412/*.root'
],
'chain_name':
'tN_600_412_Nom',
'class':
'sig'
},
{
'name':
'tN_600_427',
'target': [
bkgDir + 'mc16a_tN_600_427/*.root',
bkgDir + 'mc16d_tN_600_427/*.root',
bkgDir + 'mc16e_tN_600_427/*.root'
],
'chain_name':
'tN_600_427_Nom',
'class':
'sig'
},
{
'name':
'tN_650_450',
'target': [
bkgDir + 'mc16a_tN_650_450/*.root',
bkgDir + 'mc16d_tN_650_450/*.root',
bkgDir + 'mc16e_tN_650_450/*.root'
],
'chain_name':
'tN_650_450_Nom',
'class':
'sig'
},
{
'name':
'tN_650_462',
'target': [
bkgDir + 'mc16a_tN_650_462/*.root',
bkgDir + 'mc16d_tN_650_462/*.root',
bkgDir + 'mc16e_tN_650_462/*.root'
],
'chain_name':
'tN_650_462_Nom',
'class':
'sig'
},
{
'name':
'tN_650_477',
'target': [
bkgDir + 'mc16a_tN_650_477/*.root',
bkgDir + 'mc16d_tN_650_477/*.root',
bkgDir + 'mc16e_tN_650_477/*.root'
],
'chain_name':
'tN_650_477_Nom',
'class':
'sig'
},
{
'name':
'tN_700_500',
'target': [
bkgDir + 'mc16a_tN_700_500/*.root',
bkgDir + 'mc16d_tN_700_500/*.root',
bkgDir + 'mc16e_tN_700_500/*.root'
],
'chain_name':
'tN_700_500_Nom',
'class':
'sig'
},
]
harvest = [
### bWN ###
{
'mT': 400,
'mX': 235,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 250,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 280,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 310,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 285,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 300,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 330,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 360,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 335,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 350,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 380,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 410,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 385,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 400,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 430,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 460,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 435,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 450,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 480,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 510,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 485,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 500,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 530,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 560,
'yield': 0,
'var': 0
},
{
'mT': 700,
'mX': 535,
'yield': 0,
'var': 0
},
{
'mT': 700,
'mX': 550,
'yield': 0,
'var': 0
},
{
'mT': 700,
'mX': 580,
'yield': 0,
'var': 0
},
{
'mT': 700,
'mX': 610,
'yield': 0,
'var': 0
},
{
'mT': 750,
'mX': 585,
'yield': 0,
'var': 0
},
{
'mT': 750,
'mX': 600,
'yield': 0,
'var': 0
},
{
'mT': 750,
'mX': 630,
'yield': 0,
'var': 0
},
{
'mT': 750,
'mX': 660,
'yield': 0,
'var': 0
},
### bffN ###
{
'mT': 300,
'mX': 220,
'yield': 0,
'var': 0
},
{
'mT': 300,
'mX': 250,
'yield': 0,
'var': 0
},
{
'mT': 300,
'mX': 280,
'yield': 0,
'var': 0
},
{
'mT': 300,
'mX': 293,
'yield': 0,
'var': 0
},
{
'mT': 350,
'mX': 270,
'yield': 0,
'var': 0
},
{
'mT': 350,
'mX': 300,
'yield': 0,
'var': 0
},
{
'mT': 350,
'mX': 330,
'yield': 0,
'var': 0
},
{
'mT': 350,
'mX': 343,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 320,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 350,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 380,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 393,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 370,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 400,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 430,
'yield': 0,
'var': 0
},
{
'mT': 450,
'mX': 443,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 420,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 450,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 480,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 493,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 470,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 500,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 530,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 543,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 520,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 550,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 580,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 593,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 570,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 600,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 630,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 643,
'yield': 0,
'var': 0
},
### tN ###
{
'mT': 190,
'mX': 17,
'yield': 0,
'var': 0
},
{
'mT': 400,
'mX': 200,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 300,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 312,
'yield': 0,
'var': 0
},
{
'mT': 500,
'mX': 327,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 350,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 362,
'yield': 0,
'var': 0
},
{
'mT': 550,
'mX': 377,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 400,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 412,
'yield': 0,
'var': 0
},
{
'mT': 600,
'mX': 427,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 450,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 462,
'yield': 0,
'var': 0
},
{
'mT': 650,
'mX': 477,
'yield': 0,
'var': 0
},
{
'mT': 700,
'mX': 500,
'yield': 0,
'var': 0
},
]
for i, sample in enumerate(sampleNames):
sample['chain'] = ROOT.TChain(sample['chain_name'])
for target in sample['target']:
sample['chain'].Add(target)
totBkg = 0
totBkgVar = 0
for sample in sampleNames:
sample['yield'], sample['error'] = getYieldFromChain(sample['chain'],
cut,
lumi,
weight=weight,
returnError=True)
if sample['class'] == 'bkg':
totBkg += sample['yield']
totBkgVar += sample['error']**2
elif sample['class'] == 'sig':
for point in harvest:
if (str(point['mT']) in sample['name']) and (str(
point['mX']) in sample['name']):
point['yield'] += sample['yield']
point['var'] += sample['error']**2
print totBkg, totBkgVar
relBkg = math.sqrt(totBkgVar / (totBkg**2) + 0.25 * 0.25)
xs = []
ys = []
zs = []
for point in harvest:
print "Signal point: mT {}, mX {}".format(point['mT'], point['mX'])
point['Z'] = ROOT.RooStats.NumberCountingUtils.BinomialExpZ(
point['yield'], totBkg, relBkg)
if point['Z'] < 0.:
point['Z'] = 0.
try:
point['A'] = asimovZ(point['yield'],
totBkg,
math.sqrt(totBkgVar),
syst=True)
except ValueError:
point['A'] = 0.
point['C'] = point['yield'] / (point['yield'] + totBkg)
xs.append(point['mT'])
ys.append(point['mX'])
if plotSig:
zs.append(point['Z'])
else:
zs.append(point['C'])
print("X" * 50)
print(len(xs))
for x in xs:
print("{:.5f},".format(x))
print("Y" * 50)
print(len(ys))
for y in ys:
print("{:.5f},".format(y))
print("Z" * 50)
print(len(zs))
for z in zs:
print("{:.5f},".format(z))
if plotSig:
sig = triwsmooth(harvest,
'A',
'mT',
'mX',
transformation=None,
smooth=True,
npx=100,
npy=100)
fixedhist = FixAndSetBorders(sig, "significance", "significance")
contours = getContours(fixedhist, 3.)
else:
sig = triwsmooth(harvest,
'C',
'mT',
'mX',
transformation=None,
smooth=True,
npx=100,
npy=100)
fixedhist = FixAndSetBorders(sig, "signal_contamination",
"signal_contamination")
contours = getContours(fixedhist, .05)
#contours = getContours(fixedhist, .10)
c = ROOT.TCanvas()
c.SetRightMargin(0.15)
leg = ROOT.TLegend(0.18, PS.ThirdLine - 0.12, 0.4, PS.ThirdLine - 0.03)
PS.legend(leg)
fixedhist.GetXaxis().SetTitle("m_{#tilde{t}} [GeV]")
fixedhist.GetYaxis().SetTitle("m_{#tilde{#chi}} [GeV]")
#if plotSig:
# numbers = drawNumbers(harvest, "Z", x, y)
#else:
# numbers = drawNumbers(harvest, "C", x, y)
fixedhist.Draw("colz")
#for contour in contours + contours2:
for contour in contours:
contour.SetLineStyle(1)
contour.SetLineColor(ROOT.kBlack)
contour.Draw("l same")
if plotSig:
leg.AddEntry(contours[0], '3 #sigma', 'l')
else:
leg.AddEntry(contours[0], '5% sig. cont.', 'l')
#if plotSig:
# numbers = drawNumbers(exampleHarvest, "Z", x, y)
#else:
# numbers = drawNumbers(exampleHarvest, "Z", x, y)
leg.Draw()
PS.atlas('Work in progress')
PS.sqrts_lumi(13, 140.5, x=0.18)
PS.string(x=0.18, y=PS.ThirdLine, text="Simulation")
c.SaveAs(wwwDir + filename + ".pdf")
c.SaveAs(wwwDir + filename + ".png")
c.SaveAs(wwwDir + filename + ".root")
def plotDigiTest():
#Prepare canvas
PlotStyle.setPlotStyle()
canvas = TCanvas("canvasBxId","BXID",1200,1200)
canvas.SetLogy()
histBx = file.Get("hoMuonAnalyzer/hoDigi_BxId")
histBx.GetXaxis().SetRangeUser(-3,3)
histBx.SetLineWidth(3)
histBx.Scale(1/histBx.Integral())
histBx.SetStats(0)
histBx.SetLineColor(PlotStyle.colorRwthDarkBlue)
histBx.Draw('')
#Add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
# #Add legend
# legend = TLegend(0.7,0.65,0.9,0.8)
# legend.AddEntry(histBx,"DT Only","l")
# legend.AddEntry(histNew,"DT shifted with HO","l")
# legend.SetBorderSize(1)
# legend.Draw()
# #Add text object
# pText = TPaveText(0.52,0.8,0.9,0.9,'NDC')
# pText.AddText('Fraction in BX ID 0: %6.3f%% #pm %6.3f%%' % (dtBx0/float(dtBxTotal)*100,calcSigma(dtBx0, dtBxTotal)))
# pText.AddText('Fraction in BX ID 0 (HO corr.): %6.3f%% #pm %6.3f%%' % (correctedRightFraction*100,calcSigma(correctedBxId0, correctedTotal)))
# pText.SetBorderSize(1)
# pText.SetFillColor(0)
# pText.Draw()
#
pText2 = TPaveText(0.7,0.85,0.9,0.9,'NDC')
pText2.AddText('Entries: %d' % (histBx.GetEntries()))
pText2.SetBorderSize(1)
pText2.SetFillColor(0)
pText2.Draw()
canvas.Update()
canvas.SaveAs('plots/timing/digiBxId.pdf')
canvas.SaveAs('plots/timing/digiBxId.png')
canvasAdcSum = TCanvas("cavasAdcSum","ADC Sum",1200,1200)
canvasAdcSum.SetLogy()
histAdcSum = file.Get("hoMuonAnalyzer/hoDigiAdcSum_Multiplicity")
histAdcSum.GetXaxis().SetRangeUser(-0.5,500)
histAdcSum.GetXaxis().SetTitle('ADC counts')
histAdcSum.GetYaxis().SetTitle('#')
histAdcSum.SetLineWidth(3)
histAdcSum.SetStats(0)
histAdcSum.SetLineColor(PlotStyle.colorRwthDarkBlue)
histAdcSum.Draw()
canvasAdcSum.Update()
canvasAdcSum.SaveAs('plots/timing/digiAdcSum.pdf')
canvasAdcSum.SaveAs('plots/timing/digiAdcSum.png')
canvasTS4 = TCanvas("cavasTS4","TS4",1200,1200)
canvasTS4.SetLogy()
histAdcTS4 = file.Get("hoMuonAnalyzer/hoDigiAdcTS4_Multiplicity")
histAdcTS4.GetXaxis().SetRangeUser(-0.5,500)
histAdcTS4.GetXaxis().SetTitle('ADC counts')
histAdcTS4.GetYaxis().SetTitle('#')
histAdcTS4.SetLineWidth(3)
histAdcTS4.SetStats(0)
histAdcTS4.SetLineColor(PlotStyle.colorRwthDarkBlue)
histAdcTS4.Draw()
canvasTS4.Update()
canvasTS4.SaveAs('plots/timing/digiAdcTs4.pdf')
canvasTS4.SaveAs('plots/timing/digiAdcTs4.png')
print histAdcSum.Integral(histAdcSum.FindBin(17),histAdcSum.FindBin(500))
return canvas, histBx,label,canvasAdcSum,histAdcSum,canvasTS4,histAdcTS4
def evaluate_k_fold_test_train(sig_tree, bkg_tree, total_sig, total_bkg,
mva_name, name, reader_list, var_store_list,
nbins, min, max, k_fold, lumi):
bkg_mva_test, bkg_mva_train = evaluate_mva_k_fold_test_train(
"bkg", bkg_tree, mva_name, reader_list, var_store_list, nbins, min,
max, k_fold)
sig_mva_test, sig_mva_train = evaluate_mva_k_fold_test_train(
"sig", sig_tree, mva_name, reader_list, var_store_list, nbins, min,
max, k_fold)
sig_train = get_max_sig_hist(sig_mva_train, bkg_mva_train, total_sig,
total_bkg, lumi)
sig_test = get_max_sig_hist(sig_mva_test, bkg_mva_test, total_sig,
total_bkg, lumi)
print sig_train, sig_test
set_style(sig_mva_test, kRed, norm=True)
set_style(sig_mva_train, kRed, norm=True)
set_style(bkg_mva_test, kBlue, norm=True)
set_style(bkg_mva_train, kBlue, norm=True)
# plot train and test
canv = TCanvas("mva_dist", "", 800, 600)
bkg_mva_test.SetTitle("; %s; fraction of events" % mva_name)
bkg_mva_test.GetYaxis().SetRangeUser(0, bkg_mva_test.GetMaximum() * 1.7)
bkg_mva_test.Draw("hist")
sig_mva_test.Draw("same hist")
bkg_mva_train.Draw("same p")
sig_mva_train.Draw("same p")
leg = TLegend(0.3, 0.7, 0.6, 0.9)
leg.AddEntry(sig_mva_test, "Signal Test", "f")
leg.AddEntry(bkg_mva_test, "Background Test", "f")
leg.AddEntry(sig_mva_train, "Signal Train", "pl")
leg.AddEntry(bkg_mva_train, "Background Train", "pl")
leg.Draw("same")
kolmogorov_bkg = bkg_mva_test.KolmogorovTest(bkg_mva_train)
kolmogorov_sig = sig_mva_test.KolmogorovTest(sig_mva_train)
chi2_bkg = bkg_mva_test.Chi2Test(bkg_mva_train, "WW CHI2/NDF")
chi2_sig = sig_mva_test.Chi2Test(sig_mva_train, "WW CHI2/NDF")
PlotStyle.string(0.65,
0.85,
"Kolomogorov Bkg: %1.2f" % kolmogorov_bkg,
rel=0.7)
PlotStyle.string(0.65,
0.8,
"Kolomogorov Sig: %1.2f" % kolmogorov_sig,
rel=0.7)
PlotStyle.string(0.65,
0.75,
"red. #chi^{2} Bkg: %1.2f" % chi2_bkg,
rel=0.7)
PlotStyle.string(0.65, 0.7, "red. #chi^{2} Sig: %1.2f" % chi2_sig, rel=0.7)
save_canv(canv, name + "_full", name + "_" + mva_name + "_" + str(k_fold))
# plot roc curve
roc_test = get_roc_curve_hist(sig_mva_test, bkg_mva_test)
roc_train = get_roc_curve_hist(sig_mva_train, bkg_mva_train)
set_style(roc_train, kCyan + 2)
set_style(roc_test, kBlack)
canv = TCanvas("roc", "", 800, 600)
roc_test.SetTitle("; sig. eff.; bkg. rej.")
roc_test.Draw("a l")
roc_train.Draw("same l")
leg = TLegend(0.2, 0.2, 0.5, 0.4)
leg.AddEntry(roc_train, "Train: %.1f @%s/fb" % (sig_train, lumi), "l")
leg.AddEntry(roc_test, "Test: %.1f @%s/fb" % (sig_test, lumi), "l")
leg.Draw("same")
save_canv(canv, name + "_full", name + "_" + mva_name + "_" + str(k_fold))
def plotDigiTest():
#Prepare canvas
PlotStyle.setPlotStyle()
canvas = TCanvas("canvasBxId", "BXID", 1200, 1200)
canvas.SetLogy()
histBx = file.Get("hoMuonAnalyzer/hoDigi_BxId")
histBx.GetXaxis().SetRangeUser(-3, 3)
histBx.SetLineWidth(3)
histBx.Scale(1 / histBx.Integral())
histBx.SetStats(0)
histBx.SetLineColor(PlotStyle.colorRwthDarkBlue)
histBx.Draw('')
#Add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
# #Add legend
# legend = TLegend(0.7,0.65,0.9,0.8)
# legend.AddEntry(histBx,"DT Only","l")
# legend.AddEntry(histNew,"DT shifted with HO","l")
# legend.SetBorderSize(1)
# legend.Draw()
# #Add text object
# pText = TPaveText(0.52,0.8,0.9,0.9,'NDC')
# pText.AddText('Fraction in BX ID 0: %6.3f%% #pm %6.3f%%' % (dtBx0/float(dtBxTotal)*100,calcSigma(dtBx0, dtBxTotal)))
# pText.AddText('Fraction in BX ID 0 (HO corr.): %6.3f%% #pm %6.3f%%' % (correctedRightFraction*100,calcSigma(correctedBxId0, correctedTotal)))
# pText.SetBorderSize(1)
# pText.SetFillColor(0)
# pText.Draw()
#
pText2 = TPaveText(0.7, 0.85, 0.9, 0.9, 'NDC')
pText2.AddText('Entries: %d' % (histBx.GetEntries()))
pText2.SetBorderSize(1)
pText2.SetFillColor(0)
pText2.Draw()
canvas.Update()
canvas.SaveAs('plots/timing/digiBxId.pdf')
canvas.SaveAs('plots/timing/digiBxId.png')
canvasAdcSum = TCanvas("cavasAdcSum", "ADC Sum", 1200, 1200)
canvasAdcSum.SetLogy()
histAdcSum = file.Get("hoMuonAnalyzer/hoDigiAdcSum_Multiplicity")
histAdcSum.GetXaxis().SetRangeUser(-0.5, 500)
histAdcSum.GetXaxis().SetTitle('ADC counts')
histAdcSum.GetYaxis().SetTitle('#')
histAdcSum.SetLineWidth(3)
histAdcSum.SetStats(0)
histAdcSum.SetLineColor(PlotStyle.colorRwthDarkBlue)
histAdcSum.Draw()
canvasAdcSum.Update()
canvasAdcSum.SaveAs('plots/timing/digiAdcSum.pdf')
canvasAdcSum.SaveAs('plots/timing/digiAdcSum.png')
canvasTS4 = TCanvas("cavasTS4", "TS4", 1200, 1200)
canvasTS4.SetLogy()
histAdcTS4 = file.Get("hoMuonAnalyzer/hoDigiAdcTS4_Multiplicity")
histAdcTS4.GetXaxis().SetRangeUser(-0.5, 500)
histAdcTS4.GetXaxis().SetTitle('ADC counts')
histAdcTS4.GetYaxis().SetTitle('#')
histAdcTS4.SetLineWidth(3)
histAdcTS4.SetStats(0)
histAdcTS4.SetLineColor(PlotStyle.colorRwthDarkBlue)
histAdcTS4.Draw()
canvasTS4.Update()
canvasTS4.SaveAs('plots/timing/digiAdcTs4.pdf')
canvasTS4.SaveAs('plots/timing/digiAdcTs4.png')
print histAdcSum.Integral(histAdcSum.FindBin(17), histAdcSum.FindBin(500))
return canvas, histBx, label, canvasAdcSum, histAdcSum, canvasTS4, histAdcTS4
def doPlotCutflowNoL1(filename = 'L1MuonHistogram.root'):
if(DEBUG):
print 'Opening file:',filename
file = TFile.Open(filename)
if(file == None):
print 'Error opening file:',filename
histoNames = [
'hoMuonAnalyzer/count/NoSingleMu_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa5x5_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa3x3_Count',
'hoMuonAnalyzer/count/NoSingleMuInGaCentral_Count',
'hoMuonAnalyzer/count/Events_Count'
]
histograms = []
yValues = []
for s in histoNames:
histograms.append(file.Get(s))
yValues.append(histograms[-1].GetBinContent(2))
nEvents = histograms[-1].GetEntries()
xLabels = [
'No Single #mu trigger',
'TDMI in GA',
'HO > 0.2 GeV in 5x5',
'HO > 0.2 GeV in 3x3',
'HO > 0.2 GeV in Central'
]
c = TCanvas('eventCountCanvas','PostLS1 Single #mu gun',1200,1200)
hist = TH1D("eventCount","PostLS1 Single #mu gun",len(xLabels)-1,0,len(xLabels))
paveText = TPaveText(0.51,0.75,0.9,0.9,'NDC')
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' % (xLabels[0],yValues[0],yValues[0]/nEvents*100,calcSigma(yValues[0], nEvents)*100))
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' % (xLabels[2],yValues[2],yValues[2]/yValues[1]*100,calcSigma(yValues[2], yValues[1])*100))
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' % (xLabels[4],yValues[4],yValues[4]/yValues[1]*100,calcSigma(yValues[4], yValues[1])*100))
paveText.SetBorderSize(1)
print yValues[1]
norm = yValues[1]
for i,v in enumerate(xLabels):
if(i == 0):
continue
hist.SetBinContent(i,yValues[i]/norm)
hist.GetXaxis().SetBinLabel(i,str(v))
hist.SetStats(0)
hist.SetLineColor(PlotStyle.colorRwthDarkBlue)
hist.GetYaxis().SetTitle('#')
# hist.GetYaxis().SetRangeUser(0.5,1.1)
hist.SetLabelFont(62)
hist.SetTitleFont(62)
hist.SetMinimum(0)
print yValues
hist.Draw("")
paveText.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c.Update()
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.png")
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.pdf")
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.root")
return c,hist
def doPlotCutflow(filename='L1MuonHistogram.root'):
PlotStyle.setPlotStyle()
if(DEBUG):
print 'Opening file:',filename
file = TFile.Open(filename)
if(file == None):
print 'Error opening file:',filename
#Total event count
tagEvents = 'hoMuonAnalyzer/count/Events_Count'
tagL1Muons = 'hoMuonAnalyzer/count/L1MuonPresent_Count'
#Counters without energy threshold
tagHo1 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatch_Count'
tagHo2 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAcc_Count'
tagHo3 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAccNotDead_Count'
tagHo4 = 'hoMuonAnalyzer/count/L1MuonPresentHoMatchInAccThr_Count'
#Counters with energy threshold
tagThr1 = 'hoMuonAnalyzer/count/L1MuonAboveThr_Count'
tagThr2 = 'hoMuonAnalyzer/count/L1MuonAboveThrInAcc_Count'
tagThr3 = 'hoMuonAnalyzer/count/L1MuonAboveThrInAccNotDead_Count'
histEvents = file.Get(tagEvents)
histL1Muons = file.Get(tagL1Muons)
histHo1 = file.Get(tagHo1)
histHo2 = file.Get(tagHo2)
histHo3 = file.Get(tagHo3)
histHo4 = file.Get(tagHo4)
histThr1 = file.Get(tagThr1)
histThr2 = file.Get(tagThr2)
histThr3 = file.Get(tagThr3)
yValues = [
histEvents.GetBinContent(2),
histL1Muons.GetBinContent(2),
histHo1.GetBinContent(2),
# histHo2.GetBinContent(2),
# histHo3.GetBinContent(2),
histThr1.GetBinContent(2),
histThr2.GetBinContent(2),
histThr3.GetBinContent(2)
]
xLabels = [
'Event count',
'L1Muon objects',
'HO match (No Thr.)',
'HO match > 0.2 GeV',
# '+ HO acceptance'
]
norm = yValues[0]
for i,v in enumerate(yValues):
yValues[i] = v/norm
c = TCanvas('cutflowCanvas','PostLS1 Single #mu gun',1200,1200)
hist = TH1D("cutflow","PostLS1 Single #mu gun",len(xLabels),0,len(xLabels))
for i,v in enumerate(xLabels):
hist.SetBinContent(i+1,yValues[i])
hist.GetXaxis().SetBinLabel(i+1,str(v))
hist.SetStats(0)
hist.GetYaxis().SetTitle('rel. Fraction')
hist.GetYaxis().SetRangeUser(0.7,1.1)
hist.SetLineColor(PlotStyle.colorRwthDarkBlue)
hist.SetLabelFont(62)
hist.SetTitleFont(62)
hist.Draw("")
histTrgCount = file.Get('hoMuonAnalyzer/count/L1_SingleMuOpen_Count')
hist2 = TH1D("l1TrgCount","PostLS1 Single #mu gun",len(xLabels),0,len(xLabels))
hist2.SetBinContent(1,histTrgCount.GetBinContent(2)/norm)
hist2.SetFillStyle(3002)
hist2.SetFillColor(PlotStyle.colorRwthMagenta)
hist2.SetLineColor(PlotStyle.colorRwthMagenta)
hist2.Draw("same")
paveText = TPaveText(0.51,0.75,0.9,0.9,'NDC')
paveText.AddText('%s: %.2f%% #pm %.2f%%' % (xLabels[1],yValues[1]*100,calcSigma(yValues[1]*norm,norm)*100))
paveText.AddText('%s: %.2f%% #pm %.2f%%' % (xLabels[2],yValues[2]*100,calcSigma(yValues[2]*norm,norm)*100))
paveText.AddText('%s: %.2f%% #pm %.2f%%' % (xLabels[3],yValues[3]*100,calcSigma(yValues[3]*norm,norm)*100))
paveText.SetBorderSize(1)
paveText.Draw()
legend = TLegend(0.1,0.8,0.45,0.9)
legend.AddEntry(hist2,"Fraction with L1 Single #mu Trg.","f")
legend.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c.Update()
c.SaveAs("plots/cutflow/cutflowL1.png")
c.SaveAs("plots/cutflow/cutflowL1.pdf")
c.SaveAs("plots/cutflow/cutflowL1.root")
nTotal = histEvents.GetBinContent(2)
nL1 = histL1Muons.GetBinContent(2)
nL1AndHo = histHo1.GetBinContent(2)
nL1AndHoAcc = histHo2.GetBinContent(2)
nL1AndHoAccNotDead = histHo3.GetBinContent(2)
nL1AndHoAccThr = histHo4.GetBinContent(2)
nL1AndHoThr = histThr1.GetBinContent(2)
nL1AndHoThrAcc = histThr2.GetBinContent(2)
nL1AndHoThrAccNotDead = histThr3.GetBinContent(2)
print '%s' % (80*'#')
print '%s' % (80*'#')
print '%25s%d' % ('nEvents:\t',nTotal)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('nL1:\t',nL1,nL1/nTotal*100,calcSigma(nL1, nTotal)*100)
print '%s' % (80*'#')
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHo:\t',nL1AndHo,nL1AndHo/nL1*100,calcSigma(nL1AndHo, nL1)*100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoAcc:\t',nL1AndHoAcc,nL1AndHoAcc/nL1*100,calcSigma(nL1AndHoAcc, nL1)*100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoAccNotDead:\t',nL1AndHoAccNotDead,nL1AndHoAccNotDead/nL1*100,calcSigma(nL1AndHoAccNotDead, nL1)*100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoAccThr:\t',nL1AndHoAccThr,nL1AndHoAccThr/nL1*100,calcSigma(nL1AndHoAccThr, nL1)*100)
print '%s' % (80*'#')
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoThr:\t',nL1AndHoThr,nL1AndHoThr/nL1*100,calcSigma(nL1AndHoThr, nL1)*100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoThrAcc:\t',nL1AndHoThrAcc,nL1AndHoThrAcc/nL1*100,calcSigma(nL1AndHoThrAcc,nL1)*100)
print '%25s%d\t=>\t%.2f%% +/- %.2f%%' % ('L1AndHoThrAccNotDead:\t',nL1AndHoThrAccNotDead,nL1AndHoThrAccNotDead/nL1*100,calcSigma(nL1AndHoThrAccNotDead, nL1)*100)
print '%s' % (80*'#')
print '%s' % (80*'#')
return c
def plotFailedHoMatchesNoTrg():
c = TCanvas("cFailedHoMatchesNoTrg","cFailedHoMatchesNoTrg",1200,1200)
c.cd().SetRightMargin(0.25)
c.cd().SetLeftMargin(0.08)
#Graph for in events not in geometric acceptance
grNotInGaNC = file.Get("hoMuonAnalyzer/graphs/NoTrgTdmiNotInGA")
grNotInGa = PlotStyle.convertToHcalCoords(grNotInGaNC)
grNotInGa.GetYaxis().SetTitle("i#phi / a.u.")
grNotInGa.GetXaxis().SetTitle("i#eta / a.u.")
grNotInGa.GetYaxis().SetTitleFont(62)
grNotInGa.GetYaxis().SetLabelFont(62)
grNotInGa.SetMarkerStyle(6)
grNotInGa.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
grNotInGa.SetTitle("#eta #phi plot failed HO matches in no Single #mu Trg. events")
grNotInGa.Draw("AP")
#Graph of events with HO match below threshold
grHoBelowThrNC = file.Get("hoMuonAnalyzer/graphs/NoTrgTdmiBelowThr")
grHoBelowThr = PlotStyle.convertToHcalCoords(grHoBelowThrNC)
grHoBelowThr.SetMarkerStyle(20)
grHoBelowThr.SetMarkerSize(1.2)
grHoBelowThr.SetMarkerColor( PlotStyle.colorRwthGruen )
grHoBelowThr.Draw("samep")
#Graph for events where HO matching failed
grHoMatchFailNC = file.Get("hoMuonAnalyzer/graphs/NoTrgHoMatchFail")
grHoMatchFail = PlotStyle.convertToHcalCoords(grHoMatchFailNC)
grHoMatchFail.SetMarkerStyle(21)
grHoMatchFail.SetMarkerSize(1)
grHoMatchFail.SetMarkerColor( PlotStyle.colorRwthRot )
grHoMatchFail.Draw("samep")
#Draw chimneys
chimney1Converted = PlotStyle.convertToHcalCoords(PlotStyle.chimney1)
chimney2Converted = PlotStyle.convertToHcalCoords(PlotStyle.chimney2)
chimney1Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney2Converted.SetLineColor(PlotStyle.colorRwthMagenta)
chimney1Converted.Draw('same')
chimney2Converted.Draw('same')
#cms private label
label = TPaveText(PlotStyle.getLabelCmsPrivateSimulation(x1ndc=0.5,x2ndc=0.75))
label.Draw()
#create extra marker for the legend
marker = TMarker(1,1,2)
marker.SetMarkerSize(3)
marker.SetMarkerColor(PlotStyle.colorRwthDarkBlue)
#legend
legend = TLegend(0.75,0.8,0.99,0.9)
legend.AddEntry(chimney2Converted,"chimney","l")
legend.AddEntry(marker,'Not in GA','p')
legend.AddEntry(grHoMatchFail,'HO match fail','p')
legend.AddEntry(grHoBelowThr,'HO match < 0.2 GeV','p')
legend.Draw()
nNotMatching = grHoMatchFail.GetN()
nNotInGa = grNotInGa.GetN()
nBelowThr = grHoBelowThr.GetN()
nTotal = nNotMatching + nNotInGa + nBelowThr
print 80*'#'
print 'Not Matching:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nNotMatching,nTotal,nNotMatching/float(nTotal)*100,PlotStyle.calcSigma(nNotMatching,float(nTotal)))
print 'Not in GA:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nNotInGa,nTotal,nNotInGa/float(nTotal)*100,PlotStyle.calcSigma(nNotInGa,float(nTotal)))
print 'Below Thr:\t%5d/%d\t=> %5.2f%% +- %f%%' % (nBelowThr,nTotal,nBelowThr/float(nTotal)*100,PlotStyle.calcSigma(nBelowThr,float(nTotal)))
print 80*'#'
c.Update()
c.SaveAs('plots/graphsEtaPhi/gNoTrgHoMatchingFailed.png')
c.SaveAs('plots/graphsEtaPhi/gNoTrgHoMatchingFailed.pdf')
return c,grNotInGa,label,chimney1Converted,chimney2Converted,legend,grHoMatchFail,grHoBelowThr
def plotDeltaEtaDeltaPhiEnergyProjection(
folder,
sourceHistogram='NoDoubleMuAboveThr_DeltaEtaDeltaPhiEnergy',
sourceFile='L1MuonHistogram.root'):
if (DEBUG):
print prefix + 'was called'
if (folder == None):
print prefix + 'Error! Filename as first argument needed.'
return
if (not os.path.exists('plots')):
os.mkdir('plots')
if (not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
filename = folder + '/' + sourceFile
if (not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print prefix + 'Opening file:', filename
file = TFile.Open(filename)
PlotStyle.setPlotStyle()
sourceHisto = file.Get("hoMuonAnalyzer/etaPhi/" + sourceHistogram)
histo = sourceHisto.Project3DProfile('yx')
canv = TCanvas("canvasDeltaEtaDeltaPhiEnergy",
'canvasDeltaEtaDeltaPhiEnergy', 1200, 1200)
histo.GetXaxis().SetRangeUser(-.45, .45)
histo.GetXaxis().SetTitle("#Delta#eta")
histo.GetYaxis().SetRangeUser(-.45, .45)
histo.GetYaxis().SetTitle("#Delta#phi")
histo.GetZaxis().SetTitle(sourceHisto.GetZaxis().GetTitle())
histo.Draw("colz")
boxList = drawHoBoxes(canv)
canv.Update()
pal = histo.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
stats = histo.GetListOfFunctions().FindObject("stats")
stats.SetX1NDC(.1)
stats.SetX2NDC(.2)
stats.SetY1NDC(.1)
stats.SetY2NDC(.25)
legend = TLegend(0.7, 0.8, 0.9, 0.9)
legend.AddEntry(boxList[0], "HO tile dimensions", "le")
legend.Draw()
canv.Update()
canv.SaveAs("plots/" + folder + "/" + sourceHistogram + ".png")
canv.SaveAs("plots/" + folder + "/" + sourceHistogram + ".pdf")
f = TFile.Open("plots/" + folder + "/" + sourceHistogram + ".root",
"RECREATE")
canv.Write()
f.Close()
return histo
def doPlotCutflowNoL1(filename='L1MuonHistogram.root'):
if (DEBUG):
print 'Opening file:', filename
file = TFile.Open(filename)
if (file == None):
print 'Error opening file:', filename
histoNames = [
'hoMuonAnalyzer/count/NoSingleMu_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa5x5_Count',
'hoMuonAnalyzer/count/NoSingleMuInGa3x3_Count',
'hoMuonAnalyzer/count/NoSingleMuInGaCentral_Count',
'hoMuonAnalyzer/count/Events_Count'
]
histograms = []
yValues = []
for s in histoNames:
histograms.append(file.Get(s))
yValues.append(histograms[-1].GetBinContent(2))
nEvents = histograms[-1].GetEntries()
xLabels = [
'No Single #mu trigger', 'TDMI in GA', 'HO > 0.2 GeV in 5x5',
'HO > 0.2 GeV in 3x3', 'HO > 0.2 GeV in Central'
]
c = TCanvas('eventCountCanvas', 'PostLS1 Single #mu gun', 1200, 1200)
hist = TH1D("eventCount", "PostLS1 Single #mu gun",
len(xLabels) - 1, 0, len(xLabels))
paveText = TPaveText(0.51, 0.75, 0.9, 0.9, 'NDC')
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' %
(xLabels[0], yValues[0], yValues[0] / nEvents * 100,
calcSigma(yValues[0], nEvents) * 100))
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' %
(xLabels[2], yValues[2], yValues[2] / yValues[1] * 100,
calcSigma(yValues[2], yValues[1]) * 100))
paveText.AddText('%s: %d => %.2f%% #pm %.2f%%' %
(xLabels[4], yValues[4], yValues[4] / yValues[1] * 100,
calcSigma(yValues[4], yValues[1]) * 100))
paveText.SetBorderSize(1)
print yValues[1]
norm = yValues[1]
for i, v in enumerate(xLabels):
if (i == 0):
continue
hist.SetBinContent(i, yValues[i] / norm)
hist.GetXaxis().SetBinLabel(i, str(v))
hist.SetStats(0)
hist.SetLineColor(PlotStyle.colorRwthDarkBlue)
hist.GetYaxis().SetTitle('#')
# hist.GetYaxis().SetRangeUser(0.5,1.1)
hist.SetLabelFont(62)
hist.SetTitleFont(62)
hist.SetMinimum(0)
hist.Draw("")
paveText.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
c.Update()
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.png")
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.pdf")
c.SaveAs("plots/cutflow/cutflowTdmiNoTrg.root")
return c, hist
#!/usr/bin/python
from ROOT import ROOT,gROOT,gStyle,TCanvas,TFile,TH1D,TH2D,TLegend,THStack,TPaveText,TBox
import sys
import os
import PlotStyle
import numpy as np
DEBUG = 1
prefix = '[plotL1Phi] '
PlotStyle.setPlotStyle()
gStyle.SetPalette(1)
def plotPhi(folder):
if(DEBUG):
print prefix + 'was called'
if(folder == None):
print prefix + 'Error! Filename as first argument needed.'
return
if( not os.path.exists('plots')):
os.mkdir('plots')
if( not os.path.exists('plots/' + folder)):
os.mkdir('plots/' + folder)
filename = folder + '/L1MuonHistogram.root'
if( not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
def plotDeltaEtaDeltaPhi(folder,sourceHistogram = 'L1MuonWithHoMatch_DeltaEtaDeltaPhi', sourceFile = 'L1MuonHistogram.root',storeSubdir = 'deltaEtaDeltaPhi'):
if(DEBUG):
print prefix + 'was called'
if(folder == None):
print prefix + 'Error! Filename as first argument needed.'
return
if( not os.path.exists('plots')):
os.mkdir('plots')
if( not os.path.exists('plots/' + storeSubdir)):
os.mkdir('plots/' + storeSubdir)
filename = folder + '/' + sourceFile
if( not os.path.exists(filename)):
print 'Error! File ' + filename + ' does not exist!'
return
print prefix + 'Opening file:',filename
file = TFile.Open(filename)
PlotStyle.setPlotStyle()
h2dDeltaEtaDeltaPhi = file.Get("hoMuonAnalyzer/etaPhi/" + sourceHistogram)
hEventCount = file.Get("hoMuonAnalyzer/count/Events_Count")
hNoTrgCount = file.Get("hoMuonAnalyzer/count/NoSingleMu_Count")
if(h2dDeltaEtaDeltaPhi == None):
print 'Could not get histogram %s from file %s'%("hoMuonAnalyzer/etaPhi/" + sourceHistogram,filename)
canv = TCanvas("canvasDeltaEtaDeltaPhi" + sourceHistogram,'canvasDeltaEtaDeltaPhi',1200,1200)
canv.SetLogz()
h2dDeltaEtaDeltaPhi.GetXaxis().SetRangeUser(-.45,.45)
h2dDeltaEtaDeltaPhi.GetXaxis().SetTitle("#Delta#eta")
h2dDeltaEtaDeltaPhi.GetYaxis().SetRangeUser(-.45,.45)
h2dDeltaEtaDeltaPhi.GetYaxis().SetTitle("#Delta#phi")
h2dDeltaEtaDeltaPhi.GetZaxis().SetTitle("N")
h2dDeltaEtaDeltaPhi.Draw("colz")
boxList = drawHoBoxes(canv)
canv.Update()
pal = h2dDeltaEtaDeltaPhi.GetListOfFunctions().FindObject("palette")
pal.SetX2NDC(0.92)
stats = h2dDeltaEtaDeltaPhi.GetListOfFunctions().FindObject("stats")
stats.SetX1NDC(.1)
stats.SetX2NDC(.2)
stats.SetY1NDC(.1)
stats.SetY2NDC(.25)
paveText = TPaveText(0.2,0.1,0.65,0.2,'NDC')
paveText.AddText('Total Events: %d' % (hEventCount.GetBinContent(2)))
paveText.AddText('Events with no Single #mu Trg: %d' % (hNoTrgCount.GetBinContent(2)))
paveText.SetBorderSize(1)
paveText.Draw()
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
legend = TLegend(0.7,0.8,0.9,0.9)
legend.AddEntry(boxList[0],"HO tile dimensions","le")
legend.Draw()
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".png")
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".pdf")
canv.SaveAs("plots/" + storeSubdir + "/" + sourceHistogram + ".root")
SetOwnership(h2dDeltaEtaDeltaPhi,False)
return [h2dDeltaEtaDeltaPhi,canv,legend,boxList,stats,pal,paveText,label]
