h_ratio_hists[entry].GetYaxis().SetTitleSize(0.12)
#h_ratio_hists[entry].GetYaxis().SetTitleOffset(0.98)
h_ratio_hists[entry].GetYaxis().SetTitleOffset(0.25)
h_ratio_hists[entry].GetYaxis().SetRangeUser(0.5, 1.5)
h_ratio_hists[entry].GetYaxis().SetNdivisions(502, kFALSE)
h_ratio_hists[entry].GetXaxis().SetLabelSize(0.10)
h_ratio_hists[entry].GetXaxis().SetLabelOffset(0.03)
h_ratio_hists[entry].GetXaxis().SetTitleSize(0.12)
h_ratio_hists[entry].GetXaxis().SetTitleOffset(1.0)
h_ratio_hists[entry].GetYaxis().SetRangeUser(0., 2.)
line_on_one = TLine(
h_ratio_hists["h_ratio_2016_2017"].GetXaxis().GetXmin(), 1.,
h_ratio_hists["h_ratio_2016_2017"].GetXaxis().GetXmax(), 1.)
line_on_one.SetLineColor(4)
line_on_one.SetLineStyle(2)
h_ratio_hists["h_ratio_2016_2017"].Divide(
h_ratio_hists["h_ratio_2016_2017"], h_signal_2017, 1., 1., "B")
h_ratio_hists["h_ratio_2016_2018"].Divide(
h_ratio_hists["h_ratio_2016_2018"], h_signal_2018, 1., 1., "B")
h_ratio_hists["h_ratio_2017_2018"].Divide(
h_ratio_hists["h_ratio_2017_2018"], h_signal_2018, 1., 1., "B")
h_ratio_hists["h_ratio_2016_2017"].SetMarkerColor(1)
h_ratio_hists["h_ratio_2016_2018"].SetMarkerColor(1)
h_ratio_hists["h_ratio_2017_2018"].SetMarkerColor(1)
h_ratio_hists["h_ratio_2016_2017"].GetYaxis().SetRangeUser(0., 2.)
h_ratio_hists["h_ratio_2016_2018"].GetYaxis().SetRangeUser(0., 2.)
h_ratio_hists["h_ratio_2017_2018"].GetYaxis().SetRangeUser(0., 2.)
#h_ratio_hists["h_ratio_2016_2017"].Draw()
#h_ratio_hists["h_ratio_2016_2018"].Draw()
h_ratio_hists["h_ratio_2017_2018"].Draw()
gRaa[iFile].SetLineColor(colors[iFile])
gRaa[iFile].SetLineWidth(2)
gRaa[iFile].SetFillStyle(0)
leg.AddEntry(hRaa[iFile], legendnames[iFile], 'p')
hRaaRatio.append(hRaa[iFile].Clone("hRaa%d" % iFile))
hRaaRatio[iFile].SetDirectory(0)
hRaaRatio[iFile].Divide(hRaa[iFile], hRaa[0])
ptmin = hRaa[0].GetBinLowEdge(1)
ptmax = hRaa[0].GetBinLowEdge(hRaa[0].GetNbinsX()) + hRaa[0].GetBinWidth(
hRaa[0].GetNbinsX())
lineatone = TLine(ptmin, 1., ptmax, 1.)
lineatone.SetLineWidth(1)
lineatone.SetLineColor(kBlack)
lineatone.SetLineStyle(9)
cRaa = TCanvas('cRaa', '', 1000, 500)
cRaa.Divide(2, 1)
cRaa.cd(1).DrawFrame(ptmin, 0., ptmax, 2.,
';#it{p}_{T} (GeV/#it{c}); #it{R}_{AA}')
#cRaa.SetLogx()
lineatone.Draw('same')
for iFile in range(len(inputfilenames)):
# gRaa[iFile].Draw('2')
hRaa[iFile].Draw('same')
leg.Draw()
cRaa.cd(2).DrawFrame(ptmin, 0., ptmax, 2.,
';#it{p}_{T} (GeV/#it{c}); ratio #it{R}_{AA}')
for iFile in range(len(inputfilenames)):
if iFile == 0:
def makeLimitPlot(output,obs,exp,chan,interference,printStats=False,obs2="",ratioLabel=""):
fileObs=open(obs,'r')
fileExp=open(exp,'r')
observedx=[]
observedy=[]
obsLimits={}
#xSecs = getFittedXSecCurve("CI_%s"%interference,1.3)
for entry in fileObs:
massPoint=float(entry.split()[0])
limitEntry=float(entry.split()[1])
#limitEntry=float(entry.split()[1])*xSecs.Eval(int(float(entry.split()[0])))
if massPoint not in obsLimits: obsLimits[massPoint]=[]
obsLimits[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits)
for massPoint in sorted(obsLimits):
observedx.append(massPoint)
observedy.append(numpy.mean(obsLimits[massPoint]))
if (numpy.std(obsLimits[massPoint])/numpy.mean(obsLimits[massPoint])>0.05):
print massPoint," mean: ",numpy.mean(obsLimits[massPoint])," std dev: ",numpy.std(obsLimits[massPoint])," from: ",obsLimits[massPoint]
if not obs2 == "":
fileObs2=open(obs2,'r')
observedx2=[]
observedy2=[]
obsLimits2={}
for entry in fileObs2:
massPoint=float(entry.split()[0])
limitEntry=float(entry.split()[1])
#limitEntry=float(entry.split()[1])*xSecs.Eval(int(float(entry.split()[0])))
if massPoint not in obsLimits2: obsLimits2[massPoint]=[]
obsLimits2[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits2)
for massPoint in sorted(obsLimits2):
observedx2.append(massPoint)
observedy2.append(numpy.mean(obsLimits2[massPoint]))
if (numpy.std(obsLimits2[massPoint])/numpy.mean(obsLimits2[massPoint])>0.05):
print massPoint," mean: ",numpy.mean(obsLimits2[massPoint])," std dev: ",numpy.std(obsLimits2[massPoint])," from: ",obsLimits2[massPoint]
limits={}
expectedx=[]
expectedy=[]
expected1SigLow=[]
expected1SigHigh=[]
expected2SigLow=[]
expected2SigHigh=[]
for entry in fileExp:
massPoint=float(entry.split()[0])
#limitEntry=float(entry.split()[1])*xSecs.Eval(int(float(entry.split()[0])))
limitEntry=float(entry.split()[1])
if massPoint not in limits: limits[massPoint]=[]
limits[massPoint].append(limitEntry)
if printStats: print "len limits:", len(limits)
for massPoint in sorted(limits):
limits[massPoint].sort()
numLimits=len(limits[massPoint])
nrExpts=len(limits[massPoint])
medianNr=int(nrExpts*0.5)
#get indexes:
upper1Sig=int(nrExpts*(1-(1-0.68)*0.5))
lower1Sig=int(nrExpts*(1-0.68)*0.5)
upper2Sig=int(nrExpts*(1-(1-0.95)*0.5))
lower2Sig=int(nrExpts*(1-0.95)*0.5)
if printStats: print massPoint,":",limits[massPoint][lower2Sig],limits[massPoint][lower1Sig],limits[massPoint][medianNr],limits[massPoint][upper1Sig],limits[massPoint][upper2Sig]
#fill lists:
expectedx.append(massPoint)
print massPoint, limits[massPoint][medianNr]
expectedy.append(limits[massPoint][medianNr])
expected1SigLow.append(limits[massPoint][lower1Sig])
expected1SigHigh.append(limits[massPoint][upper1Sig])
expected2SigLow.append(limits[massPoint][lower2Sig])
expected2SigHigh.append(limits[massPoint][upper2Sig])
expX=numpy.array(expectedx)
expY=numpy.array(expectedy)
values2=[]
xPointsForValues2=[]
values=[]
xPointsForValues=[]
if printStats: print "length of expectedx: ", len(expectedx)
if printStats: print "length of expected1SigLow: ", len(expected1SigLow)
if printStats: print "length of expected1SigHigh: ", len(expected1SigHigh)
#Here is some Voodoo via Sam:
for x in range (0,len(expectedx)):
values2.append(expected2SigLow[x])
xPointsForValues2.append(expectedx[x])
for x in range (len(expectedx)-1,0-1,-1):
values2.append(expected2SigHigh[x])
xPointsForValues2.append(expectedx[x])
if printStats: print "length of values2: ", len(values2)
for x in range (0,len(expectedx)):
values.append(expected1SigLow[x])
xPointsForValues.append(expectedx[x])
for x in range (len(expectedx)-1,0-1,-1):
values.append(expected1SigHigh[x])
xPointsForValues.append(expectedx[x])
if printStats: print "length of values: ", len(values)
exp2Sig=numpy.array(values2)
xPoints2=numpy.array(xPointsForValues2)
exp1Sig=numpy.array(values)
xPoints=numpy.array(xPointsForValues)
if printStats: print "xPoints2: ",xPoints2
if printStats: print "exp2Sig: ",exp2Sig
if printStats: print "xPoints: ",xPoints
if printStats: print "exp1Sig: ",exp1Sig
GraphErr2Sig=TGraphAsymmErrors(len(xPoints),xPoints2,exp2Sig)
GraphErr2Sig.SetFillColor(ROOT.kOrange)
GraphErr1Sig=TGraphAsymmErrors(len(xPoints),xPoints,exp1Sig)
GraphErr1Sig.SetFillColor(ROOT.kGreen+1)
cCL=TCanvas("cCL", "cCL",0,0,800,500)
gStyle.SetOptStat(0)
if not obs2 == "":
plotPad = ROOT.TPad("plotPad","plotPad",0,0.3,1,1)
ratioPad = ROOT.TPad("ratioPad","ratioPad",0,0.,1,0.3)
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
else:
plotPad = ROOT.TPad("plotPad","plotPad",0,0,1,1)
plotPad.Draw()
plotPad.cd()
expX=numpy.array(expectedx)
expY=numpy.array(expectedy)
GraphExp=TGraph(len(expX),expX,expY)
GraphExp.SetLineWidth(3)
GraphExp.SetLineStyle(2)
GraphExp.SetLineColor(ROOT.kBlue)
obsX=numpy.array(observedx)
obsY=numpy.array(observedy)
if printStats: print "obsX: ",obsX
if printStats: print "obsY: ",obsY
if SMOOTH:
smooth_obs=TGraphSmooth("normal")
GraphObs_nonSmooth=TGraph(len(obsX),obsX,obsY)
GraphObs=smooth_obs.SmoothSuper(GraphObs_nonSmooth,"linear",0,0.005)
else:
GraphObs=TGraph(len(obsX),obsX,obsY)
GraphObs.SetLineWidth(3)
if not obs2 == "":
ratio = []
ratiox = []
for index,val in enumerate(observedy):
mass = observedx[index]
foundIndex = -1
for index2, mass2 in enumerate(observedx2):
if mass == mass2:
foundIndex = index2
if foundIndex > 0:
ratio.append(observedy2[foundIndex]/val)
ratiox.append(mass)
ratioA = numpy.array(ratio)
ratioX = numpy.array(ratiox)
obsX2=numpy.array(observedx2)
obsY2=numpy.array(observedy2)
ratioGraph = TGraph(len(ratioX),ratioX,ratioA)
if printStats: print "obsX2: ",obsX2
if printStats: print "obsY2: ",obsY2
if SMOOTH:
smooth_obs2=TGraphSmooth("normal")
GraphObs2_nonSmooth=TGraph(len(obsX2),obsX2,obsY2)
GraphObs2=smooth_obs2.SmoothSuper(GraphObs2_nonSmooth,"linear",0,0.005)
else:
GraphObs2=TGraph(len(obsX2),obsX2,obsY2)
GraphObs2.SetLineWidth(3)
#xSecCurves = []
#xSecCurves.append(getFittedXSecCurve("CI_%s"%interference,1.3))
#Draw the graphs:
#plotPad.SetLogy()
DummyGraph=TH1F("DummyGraph","",100,4000,7500)
DummyGraph.GetXaxis().SetTitle("#Lambda [GeV]")
if chan=="mumu":
DummyGraph.GetYaxis().SetTitle("95% CL limit on singal strength #mu")
elif chan=="elel":
DummyGraph.GetYaxis().SetTitle("95% CL limit on signal strength #mu")
elif chan=="elmu":
DummyGraph.GetYaxis().SetTitle("95% CL limit on signal strength #mu")
gStyle.SetOptStat(0)
#DummyGraph.GetXaxis().SetRangeUser(10,40)
DummyGraph.GetXaxis().SetRangeUser(4000, 7500)
DummyGraph.SetMinimum(0)
DummyGraph.SetMaximum(8)
DummyGraph.GetXaxis().SetLabelSize(0.04)
DummyGraph.GetXaxis().SetTitleSize(0.045)
DummyGraph.GetXaxis().SetTitleOffset(1.)
DummyGraph.GetYaxis().SetLabelSize(0.04)
DummyGraph.GetYaxis().SetTitleSize(0.045)
DummyGraph.GetYaxis().SetTitleOffset(1.)
DummyGraph.Draw()
if (FULL):
GraphErr2Sig.Draw("F")
GraphErr1Sig.Draw("F")
GraphExp.Draw("lpsame")
else:
if obs2 == "":
GraphExp.Draw("lp")
if not EXPONLY:
GraphObs.Draw("plsame")
if not obs2 == "":
GraphObs2.SetLineColor(ROOT.kRed)
GraphObs2.SetLineStyle(ROOT.kDashed)
GraphObs2.Draw("plsame")
#for curve in xSecCurves:
# print curve.Eval(28)
#curve.Draw()
#curve.Draw("sameR")
plCMS=TPaveLabel(.15,.81,.25,.88,"CMS","NBNDC")
#plCMS.SetTextSize(0.8)
plCMS.SetTextAlign(12)
plCMS.SetTextFont(62)
plCMS.SetFillColor(0)
plCMS.SetFillStyle(0)
plCMS.SetBorderSize(0)
plCMS.Draw()
plPrelim=TPaveLabel(.15,.76,.275,.82,"Supplementary","NBNDC")
plPrelim.SetTextSize(0.6)
plPrelim.SetTextAlign(12)
plPrelim.SetTextFont(52)
plPrelim.SetFillColor(0)
plPrelim.SetFillStyle(0)
plPrelim.SetBorderSize(0)
#plPrelim.Draw()
cCL.SetTickx(1)
cCL.SetTicky(1)
cCL.RedrawAxis()
cCL.Update()
#leg=TLegend(0.65,0.65,0.87,0.87,"","brNDC")
#leg=TLegend(0.540517,0.623051,0.834885,0.878644,"","brNDC")
leg=TLegend(0.15,0.473051,0.375,0.728644,"ADD model","brNDC")
leg.SetBorderSize(0)
# leg=TLegend(0.55,0.55,0.87,0.87,"","brNDC")
leg.SetTextSize(0.032)
if not obs2 == "":
if ratioLabel == "":
ratioLabel = "Variant/Default"
ratioLabels = ratioLabel.split("/")
print ratioLabels
leg.AddEntry(GraphObs, "%s Obs. 95%% CL limit"%ratioLabels[1],"l")
leg.AddEntry(GraphObs2,"%s Obs. 95%% CL limit"%ratioLabels[0],"l")
else:
if not EXPONLY:
leg.AddEntry(GraphObs,"Obs. 95% CL limit","l")
leg.AddEntry(GraphExp,"Exp. 95% CL limit, median","l")
if (FULL):
leg.AddEntry(GraphErr1Sig,"Exp. (68%)","f")
leg.AddEntry(GraphErr2Sig,"Exp. (95%)","f")
#leg1=TLegend(0.665517,0.483051,0.834885,0.623051,labels[interference],"brNDC")
#leg1.SetTextSize(0.032)
#leg1.AddEntry(xSecCurves[0],labels[interference],"l")
leg.SetLineWidth(0)
leg.SetLineStyle(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.Draw("hist")
#leg1.SetLineWidth(0)
#leg1.SetLineStyle(0)
#leg1.SetFillStyle(0)
#leg1.SetLineColor(0)
#leg1.Draw("hist")
if "Moriond" in output or "ADD" in output:
if (chan=="mumu"):
plLumi=TPaveLabel(.65,.905,.9,.99,"36.3 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
elif (chan=="elel"):
plLumi=TPaveLabel(.65,.905,.9,.99,"35.9 fb^{-1} (13 TeV, ee)","NBNDC")
elif (chan=="elmu"):
plLumi=TPaveLabel(.4,.905,.9,.99,"35.9 fb^{-1} (13 TeV, ee) + 36.3 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
elif "2017" in output:
if (chan=="mumu"):
plLumi=TPaveLabel(.65,.905,.9,.99,"42.1 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
elif (chan=="elel"):
plLumi=TPaveLabel(.65,.905,.9,.99,"41.5 fb^{-1} (13 TeV, ee)","NBNDC")
elif (chan=="elmu"):
plLumi=TPaveLabel(.4,.905,.9,.99,"41.5 fb^{-1} (13 TeV, ee) + 42.1 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
else:
if (chan=="mumu"):
plLumi=TPaveLabel(.65,.905,.9,.99,"13.0 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
elif (chan=="elel"):
plLumi=TPaveLabel(.65,.905,.9,.99,"2.7 fb^{-1} (13 TeV, ee)","NBNDC")
elif (chan=="elmu"):
plLumi=TPaveLabel(.4,.905,.9,.99,"12.4 fb^{-1} (13 TeV, ee) + 13.0 fb^{-1} (13 TeV, #mu#mu)","NBNDC")
plLumi.SetTextSize(0.5)
plLumi.SetTextFont(42)
plLumi.SetFillColor(0)
plLumi.SetBorderSize(0)
plLumi.Draw()
line = TLine(10,1,40,1)
line.SetLineColor(ROOT.kRed)
line.SetLineWidth(2)
line.Draw("same")
plotPad.RedrawAxis()
if not obs2 == "":
ratioPad.cd()
line = ROOT.TLine(200,1,5500,1)
line.SetLineStyle(ROOT.kDashed)
ROOT.gStyle.SetTitleSize(0.12, "Y")
ROOT.gStyle.SetTitleYOffset(0.35)
ROOT.gStyle.SetNdivisions(000, "Y")
ROOT.gStyle.SetNdivisions(408, "Y")
ratioPad.DrawFrame(200,0.8,5500,1.2, "; ; %s"%ratioLabel)
line.Draw("same")
ratioGraph.Draw("sameP")
cCL.Update()
printPlots(cCL,output)
def compare(trigger, leg, data, mc, axisTitle, canvas, dataLumi, metHLTFilters):
dataFile = TFile.Open("triggerEfficiency_" + data + ".root", "read")
mcFile = TFile.Open("triggerEfficiency_" + mc + ".root", "read")
dataEff = dataFile.Get(trigger + "_" + leg)
mcEff = mcFile.Get(trigger + "_" + leg)
SetStyle(dataEff)
SetStyle(mcEff)
mcEff.SetLineColor(600)
mcEff.SetMarkerColor(600)
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
backgroundHist.GetXaxis().SetTitle(axisTitle)
SetStyle(backgroundHist)
canvas.cd()
backgroundHist.Draw()
dataEff.Draw("CP same")
mcEff.Draw("CP same")
#oneLine.Draw("same")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
pt_cmsPrelim.AddText("CMS Preliminary")
pt_cmsPrelim.Draw("same")
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText("{:.2f}".format(dataLumi / 1000.0) + " fb^{-1}, 13 TeV")
pt_lumi.Draw("same")
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.025)
pt_leg.SetTextAlign(12)
if leg == "METLeg":
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1]
pt_leg.AddText(legLabel)
if leg == "TrackLeg":
pt_leg.AddText(trigger + "*")
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if leg == "METPath":
if trigger == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
if len(trigger) > 25 and len(trigger.split("_")) > 2:
firstLine = trigger.split("_")[0] + "_" + trigger.split("_")[1] + "_"
pt_leg.AddText(firstLine)
secondLine = ""
for line in trigger.split("_")[2:-1]:
secondLine += line + "_"
secondLine += trigger.split("_")[-1] + "*"
pt_leg.AddText(secondLine)
else:
pt_leg.AddText(trigger + "*")
pt_leg.Draw("same")
dataLabel = '2015 data'
if '2016BC' in data:
dataLabel = '2016 B+C data'
if '2016DEFGH' in data:
dataLabel = '2016 D-H data'
if '2017' in data:
dataLabel = '2017 data'
if '2018' in data:
dataLabel = '2018 data'
legendLabel = trigger
legend = TLegend(0.65, 0.75, 0.93, 0.88)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
if leg == 'METLeg':
legend.SetHeader('MET Leg')
elif leg == 'TrackLeg':
legend.SetHeader('Track Leg')
legend.AddEntry(dataEff, dataLabel, 'P')
legend.AddEntry(mcEff, 'W #rightarrow l#nu MC', 'P')
legend.Draw("same")
outputDirectory = 'plots_compare'
if 'BC' in data:
outputDirectory = 'plots_compareBC'
if 'DEFGH' in data:
outputDirectory = 'plots_compareDEFGH'
if not os.path.exists(outputDirectory):
os.mkdir(outputDirectory)
canvas.SaveAs(outputDirectory + '/' + trigger + '_' + leg + '.pdf')
mcFile.Close()
dataFile.Close()
return
c_phi.Update()
c_dist = TCanvas("c_dist")
h_dist_mc = TH1F("h_dist_mc", "", 60, 0, 60)
f = TF1("f", "landau", 0, 60)
f.SetParameters(10940, 3.8, 1.5)
h_dist_mc.FillRandom("f", int(h_dist.GetEntries()))
h_dist.Draw("ep")
gPad.SetLeftMargin(0.13)
h_dist.SetLineColor(1)
h_dist_mc.SetLineColor(kRed + 1)
h_dist_mc.Draw("same")
h_dist.SetMarkerStyle(20)
h_dist.GetYaxis().SetRangeUser(0.001, 2499)
limit = TLine(32, 0.001, 32, 2499)
limit.SetLineStyle(2)
limit.SetLineWidth(2)
limit.Draw()
leg = TLegend(0.61, 0.75, 0.84, 0.87)
leg.AddEntry(h_dist, "Data", "ep")
leg.AddEntry(h_dist_mc, "MuCS Monte Carlo", "l")
leg.Draw()
pt = TPaveText(0.1, 0.91, 0.39, 0.98, "ndc")
pt.AddText("MicroBooNE")
pt.SetFillColor(0)
pt.SetBorderSize(0)
pt.SetShadowColor(0)
pt.Draw()
c_dist.Update()
gStyle.SetCanvasPreferGL(1)
def plotter_triggereff(eff_type):
if "SLT" in args.trigger:
basepath = "$HOME/PhD/ttH_MultiLeptons/RUN2/HTopMultilepAnalysisCode/trunk/HTopMultilepAnalysis/PlotUtils/OutputPlots_TTbar_RealFakeLep_TriggerEff_SLT/EfficiencyPlots_TriggerEff/BasicPlots/"
elif "DLT" in args.trigger:
basepath = "$HOME/PhD/ttH_MultiLeptons/RUN2/HTopMultilepAnalysisCode/trunk/HTopMultilepAnalysis/PlotUtils/OutputPlots_TTbar_RealFakeLep_TriggerEff_DLT/EfficiencyPlots_TriggerEff/BasicPlots/"
filename_L = "RealFake_L_TriggerEfficiency.root"
filename_T = "RealFake_T_TriggerEfficiency.root"
filename_AntiT = "RealFake_AntiT_TriggerEfficiency.root"
file_L = TFile(basepath + filename_L)
file_T = TFile(basepath + filename_T)
file_AntiT = TFile(basepath + filename_AntiT)
if args.flavour == "mu": flavour = "Mu"
elif args.flavour == "el": flavour = "El"
if eff_type == "real": efficiency = "Real"
elif eff_type == "fake": efficiency = "Fake"
variable = "Pt"
append = ("_observed_sub", "_expectedbkg")[bool(args.closure)]
histname_L = efficiency + "_" + flavour + "_" + variable + "_L_TriggerEfficiency" + append
histname_T = efficiency + "_" + flavour + "_" + variable + "_T_TriggerEfficiency" + append
histname_AntiT = efficiency + "_" + flavour + "_" + variable + "_AntiT_TriggerEfficiency" + append
print("Try to get histogram:\n{0}\nfrom file:\n{1}\n".format(
histname_L, basepath + filename_L))
print("Try to get histogram:\n{0}\nfrom file:\n{1}\n".format(
histname_T, basepath + filename_T))
print("Try to get histogram:\n{0}\nfrom file:\n{1}\n".format(
histname_AntiT, basepath + filename_AntiT))
file_L.cd()
hist_L = file_L.Get(histname_L)
hist_L.SetDirectory(0)
file_T.cd()
hist_T = file_T.Get(histname_T)
hist_T.SetDirectory(0)
file_AntiT.cd()
hist_AntiT = file_AntiT.Get(histname_AntiT)
hist_AntiT.SetDirectory(0)
hist_L.SetLineStyle(1)
hist_L.SetLineColor(kBlack)
hist_T.SetLineStyle(2)
hist_T.SetLineColor(kBlack)
hist_T.SetMarkerStyle(24)
delta_eff = hist_L.Clone("DeltaEff")
delta_eff.SetXTitle(hist_L.GetXaxis().GetTitle())
delta_eff.SetYTitle("#Delta#varepsilon/#varepsilon [%]")
delta_eff.GetXaxis().SetTitleSize(0.15)
delta_eff.GetYaxis().SetTitleSize(0.15)
delta_eff.GetXaxis().SetTitleOffset(0.90)
delta_eff.GetYaxis().SetTitleOffset(0.35)
delta_eff.GetXaxis().SetLabelSize(0.15)
delta_eff.GetYaxis().SetLabelSize(0.12)
delta_eff.GetYaxis().SetRangeUser(-50.0, 20.0)
delta_eff.GetYaxis().SetNdivisions(505) #(5)
delta_eff.SetLineColor(kRed)
delta_eff.SetMarkerColor(kRed)
delta_eff.SetMarkerSize(1)
delta_eff.Add(hist_T, -1)
delta_eff.Divide(hist_T)
delta_eff.Scale(100.0)
legend = TLegend(
0.6, 0.4, 0.8, 0.55
) # (x1,y1 (--> bottom left corner), x2, y2 (--> top right corner) )
legend.SetHeader(flavour + " - " + efficiency)
legend.SetBorderSize(0) # no border
legend.SetFillStyle(0) # Legend transparent background
legend.SetTextSize(0.04) # Increase entry font size!
legend.SetTextFont(42) # Helvetica
legend.AddEntry(hist_T, "Trigger efficiency - T", "P")
legend.AddEntry(hist_L, "Trigger efficiency - L", "P")
leg_ATLAS = TLatex()
leg_lumi = TLatex()
leg_ATLAS.SetTextSize(0.04)
leg_ATLAS.SetNDC()
leg_lumi.SetTextSize(0.04)
leg_lumi.SetNDC()
# ---------------------------------------------------------------
c = TCanvas("c1", "Temp", 50, 50, 800, 600)
pad1 = TPad("pad1", "", 0, 0.25, 1, 1)
pad2 = TPad("pad2", "", 0, 0, 1, 0.25)
pad1.SetBottomMargin(0.02)
pad2.SetBottomMargin(0.4)
pad1.Draw()
pad2.Draw()
pad1.cd()
hist_L.GetXaxis().SetLabelSize(0)
hist_L.GetXaxis().SetLabelOffset(999)
hist_L.Draw("E0")
hist_T.Draw("E0 SAME")
if "SLT" in args.trigger:
if args.flavour == "el":
refl_vert0 = TLine(26.0, hist_L.GetMinimum(), 26.0,
hist_L.GetMaximum())
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
refl_vert1 = TLine(60.0, hist_L.GetMinimum(), 60.0,
hist_L.GetMaximum())
refl_vert1.SetLineStyle(2)
refl_vert1.SetLineWidth(2)
refl_vert1.Draw("SAME")
refl_vert2 = TLine(140.0, hist_L.GetMinimum(), 140.0,
hist_L.GetMaximum())
refl_vert2.SetLineStyle(2)
refl_vert2.SetLineWidth(2)
refl_vert2.Draw("SAME")
elif args.flavour == "mu":
refl_vert0 = TLine(26.0, hist_L.GetMinimum(), 26.0,
hist_L.GetMaximum())
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
refl_vert1 = TLine(50.0, hist_L.GetMinimum(), 50.0,
hist_L.GetMaximum())
refl_vert1.SetLineStyle(2)
refl_vert1.SetLineWidth(2)
refl_vert1.Draw("SAME")
elif "DLT" in args.trigger:
if args.flavour == "el":
refl_vert0 = TLine(17.0, hist_L.GetMinimum(), 17.0,
hist_L.GetMaximum())
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
elif args.flavour == "mu":
refl_vert0 = TLine(22.0, hist_L.GetMinimum(), 22.0,
hist_L.GetMaximum())
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
legend.Draw()
leg_ATLAS.DrawLatex(0.6, 0.27, "#bf{#it{ATLAS}} Work In Progress")
leg_lumi.DrawLatex(
0.6, 0.2,
"#sqrt{{s}} = 13 TeV, #int L dt = {0:.1f} fb^{{-1}}".format(args.lumi))
pad2.cd()
delta_eff.Draw("E0")
refl = TLine(delta_eff.GetBinLowEdge(1), 0.0,
delta_eff.GetBinLowEdge(delta_eff.GetNbinsX() + 1), 0.0)
refl.SetLineStyle(2)
refl.SetLineWidth(2)
refl.Draw("SAME")
outpath = basepath
if outpath[-1] == '/':
outpath = outpath[:-1]
c.SaveAs(outpath + "/TriggerEfficiencyRatio_" + efficiency + "_" +
flavour + "_" + variable + ".png")
if fit_s == None: continue
fpf_s = fit_s.floatParsFinal()
nuis_s = fpf_s.find('r')
r = nuis_s.getVal()
r_error = nuis_s.getError()
theHist.SetBinContent(i, r)
theHist.SetBinError(i, r_error)
theHist.GetXaxis().SetBinLabel(i, label)
theFile.Close()
if igno == "full":
y = r
y_error = r_error
i += 1
theLine = TLine(1, y, l, y)
theLine.SetLineStyle(2)
theLine.SetLineWidth(2)
theLine.SetLineColor(2)
theHist.GetXaxis().SetTickLength(0)
theHist.SetTitle("")
theHist.GetYaxis().SetRangeUser(low, hi)
theHist.Draw()
theLine.Draw()
tex.SetTextAlign(11)
tex.SetTextSize(0.06)
tex.DrawLatex(
0.1, 0.9,
"Stability of Fits (Wbb: %s, TTbar: %s)" % (wb, ttsample))
tex.SetTextAlign(13)
tex.SetTextSize(0.04)
tex.DrawLatex(0.15, 0.89,
def drawCut(cut, ymin, ymax):
line = TLine()
line.SetLineWidth(2)
line.SetLineStyle(7)
line.SetLineColor(1)
line.PaintLineNDC(cut, ymin, cut, ymax)
def compareMassRes(trackType):
fileEOYBB = open("MassResolutionVsMass_TunePNew_BB_EOY.pkl", "rb")
fileEOYBE = open("MassResolutionVsMass_TunePNew_BE_EOY.pkl", "rb")
fileULBB = open("default/MassResolutionVsMass_TunePNew_BB.pkl", "rb")
fileULBE = open("default/MassResolutionVsMass_TunePNew_BE.pkl", "rb")
resultsEOYBB = pickle.load(fileEOYBB)
resultsEOYBE = pickle.load(fileEOYBE)
resultsULBB = pickle.load(fileULBB)
resultsULBE = pickle.load(fileULBE)
graphEOYBB = getGraph(resultsEOYBB, "EOYBB")
graphEOYBE = getGraph(resultsEOYBE, "EOYBE")
graphULBB = getGraph(resultsULBB, "ULBB")
graphULBE = getGraph(resultsULBE, "ULBE")
ratioBB = getRatio(resultsEOYBB, resultsULBB, "ratioBB")
ratioBE = getRatio(resultsEOYBE, resultsULBE, "ratioBE")
canv = TCanvas("c1", "c1", 800, 800)
plotPad = TPad("plotPad", "plotPad", 0.01, 0.01, 0.99, 0.99)
ratioPad = TPad("ratioPad", "ratioPad", 0.01, 0.01, 0.99, 0.29)
style = setTDRStyle()
gStyle.SetOptStat(0)
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
plotPad.cd()
# ~ plotPad.SetGrid()
gStyle.SetTitleXOffset(1.45)
gStyle.SetTitleYOffset(1.55)
plotPad.SetTopMargin(0.05)
plotPad.SetLeftMargin(0.13)
plotPad.SetRightMargin(0.045)
plotPad.SetBottomMargin(0.3)
ratioPad.SetTopMargin(0)
ratioPad.SetTopMargin(0.05)
ratioPad.SetLeftMargin(0.13)
ratioPad.SetRightMargin(0.045)
ratioPad.SetBottomMargin(0.4)
#~ xMax = 0.08
#~ if trackType == "Inner":
#~ xMax = 0.2
#~ if trackType == "Outer":
#~ xMax = 0.4
graphEOYBB.SetMarkerStyle(22)
graphEOYBB.SetMarkerSize(2)
graphEOYBB.SetMarkerColor(kBlack)
graphEOYBB.SetLineColor(kBlack)
graphEOYBB.SetLineWidth(2)
graphEOYBB.SetFillColor(0)
graphEOYBB.SetTitle("Dimuon mass resolution vs pT for %s tracks" %
trackType)
graphEOYBB.GetYaxis().SetTitle("Mass resolution")
# ~ res_data.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
graphEOYBB.GetYaxis().SetTitleFont(42)
graphEOYBB.GetYaxis().SetTitleSize(0.05)
graphEOYBB.GetYaxis().SetTitleOffset(1.35)
graphEOYBB.GetYaxis().SetLabelFont(42)
graphEOYBB.GetYaxis().SetLabelSize(0.038)
graphEOYBB.GetYaxis().SetRangeUser(0, 0.1)
graphEOYBB.GetXaxis().SetTitleSize(0.0)
graphEOYBB.GetXaxis().SetLabelSize(0.0)
graphEOYBB.GetXaxis().SetRangeUser(0, 6500)
graphEOYBB.Draw("AP E0")
graphULBB.Draw("samepe")
graphEOYBB.SetMarkerSize(2)
graphULBB.SetMarkerSize(2)
graphEOYBB.SetLineWidth(2)
graphULBB.SetLineWidth(2)
graphEOYBB.SetMarkerStyle(20)
graphULBB.SetMarkerStyle(21)
graphULBB.SetLineColor(kRed)
graphULBB.SetMarkerColor(kRed)
latex = TLatex()
# ~ latex.SetTextFont(42)
# ~ latex.SetTextAlign(31)
# ~ latex.SetTextSize(0.04)
# ~ latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(.03 / 0.7)
latexCMSExtra.SetNDC(True)
latex.DrawLatexNDC(
0.50, 0.96,
"#scale[0.8]{#font[42]{ 2017, 42.1 fb^{-1} (13 TeV)}}")
cmsExtra = "Preliminary"
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
leg = TLegend(0.5, 0.65, 0.95, 0.90, "Both muons |#eta| < 1.2", "brNDC")
leg.SetFillColor(10)
leg.SetFillStyle(0)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(1)
leg.AddEntry(graphEOYBB, "EOY ReReco", "l")
leg.AddEntry(graphULBB, "Legacy ReReco", "l")
leg.Draw()
plotPad.RedrawAxis()
ratioPad.cd()
ratioBB.GetYaxis().SetTitle("#splitline{EOY ReReco/}{Legacy ReReco}")
ratioBB.GetXaxis().SetNoExponent(0)
ratioBB.GetXaxis().SetTitleFont(42)
ratioBB.GetXaxis().SetTitleOffset(0.85)
ratioBB.GetXaxis().SetTitleSize(0.2)
ratioBB.GetXaxis().SetLabelColor(1)
ratioBB.GetXaxis().SetLabelOffset(0.01)
ratioBB.GetXaxis().SetLabelFont(42)
ratioBB.GetXaxis().SetLabelSize(0.17)
ratioBB.GetXaxis().SetTitle("GEN dimuon mass (GeV)")
ratioBB.GetYaxis().SetRangeUser(0.5, 1.5)
ratioBB.GetXaxis().SetRangeUser(0, 6500)
ratioBB.GetYaxis().SetTitleOffset(0.475)
ratioBB.GetYaxis().SetTitleSize(0.12)
ratioBB.GetYaxis().SetTitleFont(42)
ratioBB.GetYaxis().SetLabelSize(0.14)
ratioBB.GetYaxis().SetLabelOffset(0.007)
ratioBB.GetYaxis().SetLabelFont(42)
ratioBB.GetYaxis().SetNdivisions(505)
ratioBB.SetMarkerColor(kRed)
ratioBB.SetLineColor(kRed)
ratioBB.SetLineWidth(2)
ratioBB.SetMarkerStyle(20)
ratioBB.SetMarkerSize(2)
line = TLine(10, 1, 6500, 1)
line.SetLineColor(kBlack)
line.SetLineStyle(kDashed)
line.SetLineWidth(2)
ratioBB.Draw("A P E")
ratioBB.GetXaxis().SetRangeUser(0, 6500)
line.Draw()
ratioBB.Draw("samePE")
ratioPad.RedrawAxis()
canv.Print("massResolutionCompareUL_%s_BB.pdf" % trackType)
canv = TCanvas("c1", "c1", 800, 800)
plotPad = TPad("plotPad", "plotPad", 0.01, 0.01, 0.99, 0.99)
ratioPad = TPad("ratioPad", "ratioPad", 0.01, 0.01, 0.99, 0.29)
style = setTDRStyle()
gStyle.SetOptStat(0)
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
plotPad.cd()
# ~ plotPad.SetGrid()
gStyle.SetTitleXOffset(1.45)
gStyle.SetTitleYOffset(1.55)
plotPad.SetTopMargin(0.05)
plotPad.SetLeftMargin(0.13)
plotPad.SetRightMargin(0.045)
plotPad.SetBottomMargin(0.3)
ratioPad.SetTopMargin(0)
ratioPad.SetTopMargin(0.05)
ratioPad.SetLeftMargin(0.13)
ratioPad.SetRightMargin(0.045)
ratioPad.SetBottomMargin(0.4)
#~ xMax = 0.08
#~ if trackType == "Inner":
#~ xMax = 0.2
#~ if trackType == "Outer":
#~ xMax = 0.4
graphEOYBE.SetMarkerStyle(22)
graphEOYBE.SetMarkerSize(2)
graphEOYBE.SetMarkerColor(kBlack)
graphEOYBE.SetLineColor(kBlack)
graphEOYBE.SetLineWidth(2)
graphEOYBE.SetFillColor(0)
graphEOYBE.SetTitle("Dimuon mass resolution vs pT for %s tracks" %
trackType)
graphEOYBE.GetYaxis().SetTitle("Mass resolution")
# ~ res_data.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
graphEOYBE.GetYaxis().SetTitleFont(42)
graphEOYBE.GetYaxis().SetTitleSize(0.05)
graphEOYBE.GetYaxis().SetTitleOffset(1.35)
graphEOYBE.GetYaxis().SetLabelFont(42)
graphEOYBE.GetYaxis().SetLabelSize(0.038)
graphEOYBE.GetYaxis().SetRangeUser(0, .2)
graphEOYBE.GetXaxis().SetTitleSize(0.0)
graphEOYBE.GetXaxis().SetLabelSize(0.0)
graphEOYBE.GetXaxis().SetRangeUser(0, 6500)
graphEOYBE.Draw("AP E0")
graphULBE.Draw("samepe")
graphEOYBE.SetMarkerSize(2)
graphULBE.SetMarkerSize(2)
graphEOYBE.SetLineWidth(2)
graphULBE.SetLineWidth(2)
graphEOYBE.SetMarkerStyle(20)
graphULBE.SetMarkerStyle(21)
graphULBE.SetLineColor(kRed)
graphULBE.SetMarkerColor(kRed)
latex = TLatex()
# ~ latex.SetTextFont(42)
# ~ latex.SetTextAlign(31)
# ~ latex.SetTextSize(0.04)
# ~ latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(.03 / 0.7)
latexCMSExtra.SetNDC(True)
latex.DrawLatexNDC(
0.50, 0.96,
"#scale[0.8]{#font[42]{ 2017, 42.1 fb^{-1} (13 TeV)}}")
cmsExtra = "Preliminary"
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
leg = TLegend(0.5, 0.65, 0.95, 0.90, "At least one muon |#eta| > 1.2",
"brNDC")
leg.SetFillColor(10)
leg.SetFillStyle(0)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(1)
leg.AddEntry(graphEOYBE, "EOY ReReco", "lp")
leg.AddEntry(graphULBE, "Legacy ReReco", "lp")
leg.Draw()
plotPad.RedrawAxis()
ratioPad.cd()
ratioBE.GetYaxis().SetTitle("#splitline{EOY ReReco/}{Legacy ReReco}")
ratioBE.GetXaxis().SetNoExponent(0)
ratioBE.GetXaxis().SetTitleFont(42)
ratioBE.GetXaxis().SetTitleOffset(0.85)
ratioBE.GetXaxis().SetTitleSize(0.2)
ratioBE.GetXaxis().SetLabelColor(1)
ratioBE.GetXaxis().SetLabelOffset(0.01)
ratioBE.GetXaxis().SetLabelFont(42)
ratioBE.GetXaxis().SetLabelSize(0.17)
ratioBE.GetXaxis().SetTitle("GEN dimuon mass (GeV)")
ratioBE.GetYaxis().SetRangeUser(0.5, 1.5)
ratioBE.GetXaxis().SetRangeUser(0, 6500)
ratioBE.GetYaxis().SetTitleOffset(0.475)
ratioBE.GetYaxis().SetTitleSize(0.12)
ratioBE.GetYaxis().SetTitleFont(42)
ratioBE.GetYaxis().SetLabelSize(0.14)
ratioBE.GetYaxis().SetLabelOffset(0.007)
ratioBE.GetYaxis().SetLabelFont(42)
ratioBE.GetYaxis().SetNdivisions(505)
ratioBE.SetMarkerColor(kRed)
ratioBE.SetLineColor(kRed)
ratioBE.SetLineWidth(2)
ratioBE.SetMarkerStyle(20)
ratioBE.SetMarkerSize(2)
line = TLine(10, 1, 6500, 1)
line.SetLineColor(kBlack)
line.SetLineStyle(kDashed)
line.SetLineWidth(2)
ratioBE.Draw("A P E")
ratioBE.GetXaxis().SetRangeUser(0, 6500)
line.Draw()
ratioBE.Draw("samePE")
ratioPad.RedrawAxis()
ratioBE.Draw("samepe")
canv.Print("massResolutionCompareUL_%s_BE.pdf" % trackType)
# set title and axis labels
hist.GetXaxis().SetLabelSize(0.03)
hist.GetXaxis().SetTitle("%s [GeV]" % (title))
hist.GetYaxis().SetLabelSize(0.03)
hist.GetYaxis().SetTitleOffset(1.5)
hist.GetYaxis().SetTitle("Events / %s [GeV]" % (hist.GetBinWidth(1)))
# fill legend
leg = TLegend(0.7, 0.75, 0.88, 0.88)
leg.AddEntry(hist, "TTbar", "f")
leg.SetFillColor(0)
leg.SetBorderSize(0)
# line at W mass
l = TLine(80.4, 0, 80.4, 1.05 * max_hist)
l.SetLineStyle(3)
# and draw
c.cd()
hist.Draw('hist')
leg.Draw('sames')
if (variable == "goodJ3J4_mass"): l.Draw()
cpr.prelim_alt(19700, 0.05)
tex.SetTextAlign(11) #left, bottom
tex.DrawLatex(0.1, 0.9, title)
c.Update()
c.Print(out_fname + ".png")
print("you just finished with %s.png" % out_fname)
fit.SetParameters(800, 100, 0.3)
fit.SetLineColor(ROOT.kBlue)
eff.Fit(fit, 'RQ')
line = TF1('line', '1', xmin, xmax)
line.GetXaxis().SetTitle('m_{jj} (GeV)')
line.GetYaxis().SetTitle('Trigger Efficiency')
line.SetLineColor(ROOT.kBlack)
line.SetLineStyle(2)
line.SetMinimum(0.3)
line.SetMaximum(1.1)
line.Draw()
eff.Draw('samePE1')
gPad.Update()
x0 = fit.GetX(0.99)
cut = TLine(x0, gPad.GetFrame().GetY1(), x0, gPad.GetFrame().GetY2())
cut.SetLineColor(ROOT.kRed)
cut.SetLineStyle(2)
cut.Draw()
print '99% efficiency point: ' + str(round(x0, 1)) + ' GeV'
#----- keep the GUI alive ------------
if __name__ == '__main__':
rep = ''
while not rep in ['q', 'Q']:
rep = raw_input('enter "q" to quit: ')
if 1 < len(rep):
rep = rep[0]
print "reco mass for n=15 :", reco_mass_n15
print " "
linex = TLine(145, 0, 205, 0)
linex.SetLineWidth(1)
linex.Draw()
liney = TLine(173, -0.00825, 173, 0.00725)
if mass == "167":
liney = TLine(173, -0.0096, 173, 0.0059)
if nlo:
liney = TLine(173, -0.01025, 173, 0.0055)
liney.SetLineWidth(1)
liney.SetLineStyle(2)
liney.Draw()
l = TLegend(0.6, 0.6, 0.8, 0.8)
if mass == "167":
l = TLegend(0.6, 0.62, 0.8, 0.82)
if nlo:
l = TLegend(0.6, 0.65, 0.80, 0.85)
l.AddEntry(final2, "n = 2", "L")
l.AddEntry(final3, "n = 3", "L")
l.AddEntry(final5, "n = 5", "L")
l.AddEntry(final15, "n = 15", "L")
l.SetTextSize(0.04)
l.SetLineColor(0)
l.SetFillColor(0)
def plot_qcd(
infile,
rebin=1,
bkg_file="../hh2bbbb_limit/classifier_reports/reports_no_bias_corr_SM_mixing_fix/BM0/20171120-160644-bm0.root"
):
f = TFile.Open(infile)
f2 = TFile.Open(bkg_file)
H_ref = 800
W_ref = 800
W = W_ref
H = H_ref
iPos = 11
iPeriod = 4
c1 = TCanvas("c1", "QCD MC distributions", H_ref, W_ref)
setTDRStyle()
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.12 * W_ref
R = 0.04 * W_ref
c1.SetFillColor(0)
c1.SetBorderMode(0)
c1.SetFrameFillStyle(0)
c1.SetFrameBorderMode(0)
c1.SetLeftMargin(L / W)
c1.SetRightMargin(R / W)
c1.SetTopMargin(T / H)
c1.SetBottomMargin(B / H)
#c1.SetBottomMargin( 0 )
pad1 = TPad("pad1", "pad1", 0, 0.4, 1, 1.0)
pad1.SetTopMargin(0.1)
pad1.SetBottomMargin(0.03)
pad1.Draw()
pad1.cd()
#pad1.SetLogy()
bkg = f2.Get("bkg_appl/classifier-20171120-160644-bm0_bkg_appl")
bkg_corr = get_bias_corrected_histo(bkg)
for b in range(1, bkg.GetNbinsX() + 1):
print bkg.GetBinContent(b), bkg_corr.GetBinContent(b)
bkg.Scale(1 / bkg.Integral())
bkg.Rebin(rebin)
bkg.SetMaximum(0.12)
#bkg.SetLineWidth(2)
#c.SetLogY()
bkg_corr.Scale(1 / bkg_corr.Integral())
bkg_corr.Rebin(rebin)
bkg.GetYaxis().SetTitleSize(20)
bkg.GetYaxis().SetTitleFont(43)
bkg.GetYaxis().SetTitleOffset(1.40)
bkg.GetYaxis().SetLabelFont(43)
bkg.GetYaxis().SetLabelSize(18)
#bkg.GetYaxis().SetTitle("Events")
pad1.SetTickx(1)
CMS_lumi(pad1, iPeriod, iPos)
legend = setLegend(0.7, 0.60, 0.90, 0.85)
#leg.SetTextSize(0.033);
#leg.SetFillColor(0);
#leg.SetNColumns(1);
#pl2.SetHeader(training);
bkg.Draw("e1")
bkg.GetXaxis().SetTitle("BDT")
legend.AddEntry(bkg, "Mixed data", "p")
total_mc = f.Get(
"QCD_HT2000toInf_m_%s/classifier-20171120-160644-bm0_QCD_HT2000toInf_m_%s"
% ("bbbb", "bbbb"))
total_mc.Reset()
assert total_mc.Integral() == 0
for state in ["bbcc", "bbll", "bbbb", "cccc"]:
hist = f.Get(
"QCD_HT2000toInf_m_%s/classifier-20171120-160644-bm0_QCD_HT2000toInf_m_%s"
% (state, state))
hist.Reset()
assert hist.Integral() == 0
for htrange in [
"700to1000", "1000to1500", "1500to2000", "2000toInf",
"200to300", "300to500", "500to700"
]:
sampname = "QCD_HT%s" % htrange
qcdname = "%s_m" % sampname
#print "QCD_all_%s/classifier-20171120-160644-bm0_QCD_all_%s" % (state, state)
myhist = f.Get("%s_%s/classifier-20171120-160644-bm0_%s_%s" %
(qcdname, state, qcdname, state))
samples_std = samples[sampname]
samples_ext = samples["%s_ext" % sampname]
n_events = samples_std["nevents"] + samples_ext["nevents"]
xs_br = samples_ext["xsec_br"]
scalef = xs_br / n_events
myhist.Scale(scalef)
hist.Add(myhist)
total_mc.Add(hist)
total_mc.Rebin(rebin)
total_mc.SetLineColor(ROOT.kBlue)
total_mc.SetLineWidth(1)
total_mc.SetMarkerStyle(8)
total_mc.SetMarkerColor(ROOT.kBlue)
total_mc.Scale(1 / total_mc.Integral())
total_mc.Draw("E1 SAME")
legend.AddEntry(total_mc, "QCD MC", "p")
for bin in range(1, bkg.GetNbinsX() + 1):
print bin, bkg.GetBinContent(bin), bkg_corr.GetBinContent(bin)
bkg_corr.SetLineColor(ROOT.kRed)
bkg_corr.SetMarkerColor(ROOT.kRed)
bkg_corr.Draw("E1 same")
legend.AddEntry(bkg_corr, "Mixed data bias corrected", "p")
#ks = hlist[0][0].KolmogorovTest(hlist[1][0])
# print("KS: ", ks)
# print("Chi2: ", hlist[0][0].Chi2Test(hlist[1][0], "UU NORM"))
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.035)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
legend.Draw("same")
#if(ymax > 1000): TGaxis.SetMaxDigits(3)
"""for i in range(len(hs)):
hs[i].SetMaximum(ymax)
herr[i].SetMaximum(ymax)
plotH(hlist[i], hs[i], herr[i], dofill[i], residuals)
if i == len(hs) - 1:
herr[i].Draw("Esameaxis")
"""
bkg.GetXaxis().SetLabelSize(0.)
legend.Draw("same")
c1.cd()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.4)
pad2.SetTopMargin(0.)
pad2.SetBottomMargin(0.2)
pad2.Draw()
pad2.cd()
ratio = bkg.Clone("ratio")
ratio.Divide(total_mc)
ratio.SetMinimum(0.)
ratio.SetMaximum(2.)
ratio_corr = bkg_corr.Clone("ratio_corr")
ratio_corr.Divide(total_mc)
"""h_err = total_mc.Clone("error_bar")
h_err.GetXaxis().SetRangeUser(0, 1)
#h_err.Reset()
#herr.Rebin(rebin)
h_err.GetXaxis().SetTitle("BDT classifier")
h_err.SetFillStyle(3005)
h_err.SetFillColor(ROOT.kBlue)
h_err.SetLineColor(922)
h_err.SetLineWidth(0)
h_err.SetMarkerSize(0)
h_err.SetMarkerColor(922)
#h_err.SetMinimum(0.)
#h_sig.SetLineStyle(1)
#h_sig.SetLineWidth(2)
#h_sig.SetLineColor(sam_opt["sig"]['linecolor'])
#Set error centered at zero as requested by ARC
for ibin in range(1, h_err.GetNbinsX()+1):
h_err.SetBinContent(ibin, 0. )
#If not loading already morphed fit results
if postfit_file == None:
for ibin in range(1, h_err.GetNbinsX()+1):
h_err.SetBinError(ibin, math.sqrt((err * h_err.GetBinContent(ibin))**2 + h_data_bkg.GetBinError(ibin)**2) )
else:
for ibin in range(1, h_err.GetNbinsX()+1):
if not only_bias_unc:
h_err.SetBinError(ibin, math.sqrt(h_sig.GetBinError(ibin)**2 + h_data_bkg.GetBinError(ibin)**2) )
else:
h_err.SetBinError(ibin, math.sqrt(h_data_bkg.GetBinError(ibin)**2) )
return h_data_bkg, h_sig, h_err
"""
ratio.Draw("e1")
ratio_corr.Draw("e1 same")
l = TLine(0., 1., 1., 1.)
l.SetLineStyle(3)
l.Draw("same")
"""leg_coords = 0.65,0.2,0.9,0.4
if "legpos" in hsOpt:
if hsOpt["legpos"] == "top":
leg_coords = 0.65,0.78,0.9,1.
elif hsOpt["legpos"] == "left" or hsOpt["legpos"] == "topleft":
leg_coords = 0.1,0.78,0.35,1.
elif hsOpt["legpos"] == "middle":
leg_coords = 0.47,0.0,0.63,0.25
leg = TLegend(*leg_coords)
leg.SetTextSize(0.05)
leg.AddEntry(h_data_bkg, "Data - fitted background", "p")
leg.AddEntry(h_sig, "HH4b fitted")
leg.AddEntry(hlist[0][-1], "HH4b fitted x5")
leg.AddEntry(h_error, "Total uncertainty")
leg.Draw("same")"""
c1.SaveAs("qcd_plot.png")
c1.SaveAs("qcd_plot.pdf")
c1.Clear()
f.Close()
class Property:
def __init__(self, name, bkg_hists, data_hist, signal_hists, sample_hists):
self.Name = name
self.Bkg = bkg_hists
self.Data = data_hist
self.Signal = signal_hists
self.Samples = sample_hists
@staticmethod
def AddOFUF(h):
UF = h.GetBinContent(0)
UF_E = h.GetBinError(0)
b1 = h.GetBinContent(1)
b1_e = h.GetBinError(1)
h.SetBinContent(1, UF + b1)
h.SetBinError(1, sqrt(UF_E * UF_E + b1_e * b1_e))
h.SetBinContent(0, 0.0)
h.SetBinError(0, 0.0)
lastBin = h.GetNbinsX()
OF = h.GetBinContent(lastBin + 1)
OF_E = h.GetBinError(lastBin + 1)
bL = h.GetBinContent(lastBin)
bL_e = h.GetBinError(lastBin)
h.SetBinContent(lastBin, OF + bL)
h.SetBinError(lastBin, sqrt(OF_E * OF_E + bL_e * bL_e))
h.SetBinContent(lastBin + 1, 0.0)
h.SetBinError(lastBin + 1, 0.0)
def AddOF_UF_Bins(self):
Property.AddOFUF(self.Data)
for s in self.Signal:
Property.AddOFUF(s)
for s in self.Bkg:
Property.AddOFUF(self.Bkg[s])
for s in self.Samples:
Property.AddOFUF(s)
def GetBkgFromCR(self, CRProp, Bkgs, replacement, yieldsMethod=1):
"""
extract the shape of bkg from CRProp using data minus all Bkgs
for the normalization in SR (which is the current property) several methods are foreseen :
yieldsMethod = 1 : sum of the integral of Bkgs in CR (so taken from the simulation and cross section of backgrounds, trusted in signal region)
yieldsMethod = 2 : data minus other MC's except Bkgs
yieldsMethod = 3 : template fit
replacement is a list with this format : [ NewName , NewColor ]
"""
notInBkg = [item for item in self.Bkg if item not in Bkgs]
template = CRProp.SubtractDataMC(notInBkg, self.Name)
if template.Integral() == 0:
return -1
nMCBkgsSR = 0
nDataMinusBkgsSR = self.Data.Integral()
for bkg in Bkgs:
nMCBkgsSR += self.Bkg.pop(bkg).Integral()
for bkg in self.Bkg:
nDataMinusBkgsSR -= self.Bkg[bkg].Integral()
nNormalization = 0
if yieldsMethod == 1:
nNormalization = nMCBkgsSR
elif yieldsMethod == 2:
nNormalization = nDataMinusBkgsSR
elif yieldsMethod == 3:
nFit = 1.0
var = RooRealVar(self.Name, self.Name,
self.Data.GetXaxis().GetXmin(),
self.Data.GetXaxis().GetXmax())
templatehist = RooDataHist("%s_bkg_templateHist" % (self.Name),
self.Name, RooArgList(var), template)
templatepdf = RooHistPdf("%s_bkg_templatePDF" % (self.Name),
self.Name, RooArgSet(var), templatehist)
SumNonBkg = self.Data.Clone("sum_%s_%s_th1" %
(self.Name, "_".join(notInBkg)))
SumNonBkg.Reset()
for bkg in notInBkg:
SumNonBkg.Add(self.Bkg[bkg])
SumNonBkgHist = RooDataHist(
"sum_%s_%s_Hist" % (self.Name, "_".join(notInBkg)), self.Name,
RooArgList(var), SumNonBkg)
SumNonBkgpdf = RooHistPdf(
"sum_%s_%s_PDF" % (self.Name, "_".join(notInBkg)), self.Name,
RooArgSet(var), SumNonBkgHist)
DataHist = RooDataHist("data_%s_Hist" % (self.Name), self.Name,
RooArgList(var), self.Data)
nBkgs = None
if nDataMinusBkgsSR > 0:
nBkgs = RooRealVar("nBkgs", "NBkgs", nDataMinusBkgsSR,
0.5 * nDataMinusBkgsSR,
2 * nDataMinusBkgsSR)
elif nDataMinusBkgsSR < 0:
nBkgs = RooRealVar("nBkgs", "NBkgs", nDataMinusBkgsSR,
2 * nDataMinusBkgsSR, -2 * nDataMinusBkgsSR)
else:
nBkgs = RooRealVar("nBkgs", "NBkgs", nDataMinusBkgsSR, -10, 10)
nFixed = RooRealVar("nFixed", "NFIXED", SumNonBkg.Integral(),
SumNonBkg.Integral(), SumNonBkg.Integral())
model = RooAddPdf("model", "model",
RooArgList(SumNonBkgpdf, templatepdf),
RooArgList(nFixed, nBkgs))
res = model.fitTo(DataHist, RooFit.Extended(True),
RooFit.Save(True), RooFit.SumW2Error(True))
nNormalization = nBkgs.getVal()
template.Scale(nNormalization / template.Integral())
template.SetLineWidth(2)
template.SetLineColor(1)
template.SetFillColor(replacement[1])
template.SetFillStyle(1001)
self.Bkg[replacement[0]] = template
return nNormalization
def Rebin(self, newbins):
bins = sorted(newbins)
runArray = array('d', bins)
self.Data = self.Data.Rebin(
len(newbins) - 1,
self.Data.GetName() + "_rebined", runArray)
if self.Signal:
for i in range(0, len(self.Signal)):
self.Signal[i] = self.Signal[i].Rebin(
len(newbins) - 1, self.Signal[i].GetName() + "_rebined",
runArray)
for bkg in self.Bkg:
self.Bkg[bkg] = self.Bkg[bkg].Rebin(
len(newbins) - 1, self.Bkg[bkg].GetName() + "_rebined",
runArray)
@staticmethod
def addLabels(histo, labels):
if not histo:
return
for i in range(1, histo.GetNbinsX() + 1):
if not i > len(labels):
histo.GetXaxis().SetBinLabel(i, labels[i - 1])
def SetLabels(self, labels):
Property.addLabels(self.Data, labels)
for s in self.Signal:
Property.addLabels(s, labels)
for bkg in self.Bkg:
Property.addLabels(self.Bkg[bkg], labels)
for smpl in self.Samples:
Property.addLabels(smpl, labels)
def Clone(self, newname, allsamples=False):
ret = Property(newname, {}, None, None, [])
ret.Data = self.Data.Clone(
string.replace(self.Data.GetName(), self.Name, newname))
if self.Signal:
ret.Signal = []
for i in range(0, len(self.Signal)):
ret.Signal.append(self.Signal[i].Clone(
string.replace(self.Signal[i].GetName(), self.Name,
newname)))
for bkg in self.Bkg:
ret.Bkg[bkg] = self.Bkg[bkg].Clone(
string.replace(self.Bkg[bkg].GetName(), self.Name, newname))
if allsamples:
ret.Samples = [
h.Clone(string.replace(h.GetName(), self.Name, newname))
for h in self.Samples
]
return ret
def SubtractDataMC(self, tosubtract, appendix=""):
tokeep = [item for item in self.Bkg if item not in tosubtract]
ret = self.Data.Clone("%s_%s_template_%s" %
(self.Name, "_".join(tokeep), appendix))
for bkg in tosubtract:
ret.Add(self.Bkg[bkg], -1)
return ret
def GetStack(self, normtodata=False):
if not hasattr(self, "Stack"):
stackname = "%s_stack" % (self.Name)
scale = 1.0
if normtodata:
totalmc = 0.
for st in self.Bkg:
totalmc += self.Bkg[st].Integral()
if totalmc > 0.000001:
scale = self.Data.Integral() / totalmc
else:
print "\t%s was not normalized to data as the mc yield is %.2f" % (
self.Name, totalmc)
#print "in getStack, normtodata = %s and scale is %f" % (str(normtodata) , scale)
self.Stack = THStack(stackname, self.Name)
for st in self.Bkg:
if normtodata:
self.Bkg[st].Scale(scale)
self.Stack.Add(self.Bkg[st])
return self.Stack
def GetSignalCanvas(self):
canvasname = "%s_signal_canvas" % (self.Name)
if not hasattr(self, "SignalCanvas"):
self.SignalCanvas = TCanvas(canvasname)
if self.Signal:
for s in self.Signal:
s.DrawNormalized("E SAME HIST")
self.GetSLegend().Draw()
return self.SignalCanvas
def GetCanvas(self, padid, padOrCanvas=0):
canvasname = "%s_canvas" % (self.Name)
pad1name = "%s_pad1" % (self.Name)
pad2name = "%s_pad2" % (self.Name)
if not hasattr(self, "Canvas"):
#print canvasname
if padOrCanvas == 0:
self.Canvas = TCanvas(canvasname)
else:
self.Canvas = gPad
self.Canvas.cd()
self.Pad1 = TPad(pad1name, pad1name, 0, 0.25, 1, 1)
self.Pad1.SetBottomMargin(0.1)
self.Pad1.Draw()
self.Canvas.cd()
self.Pad2 = TPad(pad2name, pad2name, 0, 0, 1, 0.24)
self.Pad2.SetTopMargin(0.1)
self.Pad2.SetBottomMargin(0.1)
self.Pad2.Draw()
if padid == 0:
self.Canvas.cd()
elif padid == 1:
self.Pad1.cd()
if padid == 2:
self.Pad2.cd()
return self.Canvas
def GetLegend(self):
legendname = "%s_legend" % (self.Name)
if not hasattr(self, "Legend"):
self.Legend = TLegend(0.7, 0.6, 0.9, 0.9, "", "brNDC")
self.Legend.SetName(legendname)
self.Legend.AddEntry(self.Data, "Data", "lp")
for st in reversed(self.Bkg.keys()):
self.Legend.AddEntry(self.Bkg[st], st, "f")
return self.Legend
def GetSLegend(self):
legendname = "%s_Slegend" % (self.Name)
if not hasattr(self, "SLegend"):
self.SLegend = TLegend(0.6, 0.6, 0.7, 0.9, "", "brNDC")
self.SLegend.SetName(legendname)
for st in self.Signal:
self.SLegend.AddEntry(st, st.GetTitle(), "l")
return self.SLegend
def GetRatioPlot(self):
rationame = "%s_Ratio" % (self.Name)
if not hasattr(self, "Ratio"):
self.Ratio = self.Data.Clone(rationame)
self.Ratio.SetStats(0)
self.Ratio.Divide(self.GetStack().GetStack().Last())
for i in range(1, self.Data.GetNbinsX() + 1):
self.Ratio.GetXaxis().SetBinLabel(i, "")
self.Ratio.SetMarkerStyle(20)
self.Ratio.GetYaxis().SetRangeUser(0, 2)
self.Ratio.GetXaxis().SetLabelSize(0.)
self.Ratio.GetYaxis().SetTitle("Data / MC")
self.Ratio.GetXaxis().SetTitleSize(0.2)
self.Ratio.GetXaxis().SetTitleOffset(0.25)
self.Ratio.GetYaxis().SetLabelSize(0.1)
self.Ratio.GetXaxis().SetTickLength(0.09)
self.Ratio.GetYaxis().SetTitleSize(0.18)
self.Ratio.GetYaxis().SetNdivisions(509)
self.Ratio.GetYaxis().SetTitleOffset(0.25)
self.Ratio.SetFillStyle(3001)
return self.Ratio
def GetRatioUnc(self):
rationame = "%s_RatioUncert" % (self.Name)
if not hasattr(self, "RatioUncert"):
mc = self.GetStack().GetStack().Last()
self.RatioUncert = mc.Clone(rationame)
self.RatioUncert.SetStats(0)
self.RatioUncert.Divide(mc)
for i in range(1, self.Data.GetNbinsX() + 1):
self.RatioUncert.GetXaxis().SetBinLabel(i, "")
#self.RatioUncert.SetMarkerStyle(20)
self.RatioUncert.GetYaxis().SetRangeUser(0, 2)
#self.RatioUncert.GetXaxis().SetLabelSize( 0.)
self.RatioUncert.GetYaxis().SetTitle("Data / MC")
self.RatioUncert.GetXaxis().SetTitleSize(0.2)
self.RatioUncert.GetXaxis().SetTitleOffset(0.25)
self.RatioUncert.GetYaxis().SetLabelSize(0.1)
self.RatioUncert.GetXaxis().SetTickLength(0.09)
self.RatioUncert.GetYaxis().SetTitleSize(0.18)
self.RatioUncert.GetYaxis().SetNdivisions(509)
self.RatioUncert.GetYaxis().SetTitleOffset(0.25)
self.RatioUncert.SetFillStyle(3001)
self.RatioUncert.SetFillColor(1)
return self.RatioUncert
def GetLineOne(self):
linename = "%s_lineone" % (self.Name)
if not hasattr(self, "LineOne"):
self.LineOne = TLine(self.GetRatioPlot().GetXaxis().GetXmin(),
1.00,
self.GetRatioPlot().GetXaxis().GetXmax(),
1.00)
self.LineOne.SetLineWidth(2)
self.LineOne.SetLineStyle(7)
return self.LineOne
def Draw(self, normalizetodata=False, padOrCanvas=0):
gStyle.SetOptTitle(0)
#self.AddOF_UF_Bins()
self.GetCanvas(1, padOrCanvas)
self.Data.Draw("E")
#if normalizetodata:
# print "Norm to data"
self.GetStack(normalizetodata).Draw("HIST SAME")
self.Data.Draw("E SAME P")
if self.Signal:
for s in self.Signal:
s.Draw("E SAME HIST")
self.GetSLegend().Draw()
self.GetLegend().Draw()
self.GetCanvas(2)
self.GetRatioUnc().Draw("E2")
self.GetRatioPlot().Draw("ep same")
self.GetLineOne().Draw()
def Write(self, propdir, normtodata, mkdir=False):
if mkdir:
propdir = propdir.mkdir(self.Name)
propdir.cd()
catdir = propdir.mkdir("cats")
catdir.cd()
self.Data.Write()
for bkg in self.Bkg:
self.Bkg[bkg].Write()
self.GetStack(normtodata).GetStack().Last().Write("SumMC")
if self.Signal:
sigdir = propdir.mkdir("signals")
sigdir.cd()
for i in range(0, len(self.Signal)):
self.Signal[i].Write()
sampledir = propdir.mkdir("samples")
sampledir.cd()
for ss in self.Samples:
ss.Write()
propdir.cd()
self.Draw(normtodata)
self.GetCanvas(0).Write()
def plot_ratio(ih_,max_val_,xbins__,comb_ID_):
# // Define two gaussian histograms. Note the X and Y title are defined
# // at booking time using the convention "Hist_title ; X_title ; Y_title"
# TH1F *h1 = new TH1F("h1", "Two gaussian plots and their ratio;x title; h1 and h2 gaussian histograms", 100, -5, 5);
# TH1F *h2 = new TH1F("h2", "h2", 100, -5, 5);
# h1.FillRandom("gaus");
# h2.FillRandom("gaus");
# // Define the Canvas
#TCanvas *c = new TCanvas("c", "canvas", 800, 800);
cc = TCanvas("cc", "canvas", 800, 800)
h1 = ih_[0][0]
h2 = ih_[1][0]
#print 'h1 = ',h1
#print 'h2 = ',h2
#print'h1.GetMaximum() = ' ,h1.GetMaximum()
#// Upper plot will be in pad1
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0) # Upper and lower plot are joined
#pad1.SetGridx() #Vertical grid, dashed lines
pad1.Draw() #Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
h1.SetStats(0) # No statistics on upper plot
h1.GetXaxis().SetNdivisions(xbins__)
h1.Draw() # Draw h1
# pad1.Update()
# lline = TLine(pad1.GetUxmin(),20,pad1.GetUxmax(),20)
# #lline.SetNDC(1)
# lline.SetLineStyle(3)
# lline.Draw('same')
h2.Draw("same") # Draw h2 on top of h1
# // Do not draw the Y axis label on the upper plot and redraw a small
# // axis instead, in order to avoid the first label (0) to be clipped.
h1.GetYaxis().SetLabelSize(0.)
#TGaxis *axis = new TGaxis( -5, 20, -5, 220, 20,220,510,"");
#axis = TGaxis( -5, 20, -5, 220, 20,220,510,"") #xmin ymin xmax ymax
axis = TGaxis( 0, 0, 0, max_val_, 0.001,max_val_,510,"")
axis.SetLabelFont(43) #Absolute font size in pixel (precision 3)
axis.SetLabelSize(15)
axis.Draw()
#lower plot will be in pad
cc.cd() # Go back to the main canvas before defining pad2
#TPad *pad2 = new TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0) # can change to separate top and bottom
#pad2.SetBottomMargin(0.2)
#pad2.SetBottomMargin(0)
#pad2.SetGridx() # vertical grid, dashed lines
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
# Define the ratio plot
#TH1F *h3 = (TH1F*)h1.Clone("h3");
h3 = h2.Clone("h3")
h3.SetLineColor(1)
h3.SetMinimum(0.5) # Define Y ..
h3.SetMaximum(1.5) # .. range
h3.Sumw2()
h3.SetStats(0) # No statistics on lower plot
h3.Divide(h1)
h3.SetMarkerStyle(21)
#gPad.Modified()
#gPad.Update()
h3.Draw("ep") # Draw the ratio plot
pad2.Update()
lline = TLine(pad2.GetUxmin(),1,pad2.GetUxmax(),1)
#lline.SetNDC(1)
lline.SetLineStyle(1)
lline.Draw('same')
#// h1 settings
h1.SetLineColor(600+1)
h1.SetLineWidth(2)
#// Y axis h1 plot settings
h1.GetYaxis().SetTitleSize(20)
h1.GetYaxis().SetTitleFont(43)
h1.GetYaxis().SetTitleOffset(1.55)
#print 'xbins__ = ',xbins__
h1.GetXaxis().SetNdivisions(xbins__)
#h1.GetXaxis().SetNdivisions(0)
#// h2 settings
h2.SetLineColor(632)
h2.SetLineWidth(2)
# // Ratio plot (h3) settings
h3.SetTitle("") # Remove the ratio title
#// Y axis ratio plot settings
h3.GetYaxis().SetTitle("Reco/Gen")
h3.GetYaxis().SetNdivisions(505)
h3.GetYaxis().SetTitleSize(20)
h3.GetYaxis().SetTitleFont(43)
h3.GetYaxis().SetTitleOffset(1.55)
h3.GetYaxis().SetLabelFont(43) #Absolute font size in pixel (precision 3)
h3.GetYaxis().SetLabelSize(15)
# // X axis ratio plot settings
#h3.GetXaxis().SetNdivisions(xbins__)
h3.GetXaxis().SetNdivisions(xbins__)
#h3.GetXaxis().SetNdivisions(0)
h3.GetXaxis().SetTitleSize(20)
h3.GetXaxis().SetTitleFont(43)
h3.GetXaxis().SetTitleOffset(4.)
h3.GetXaxis().SetLabelFont(43) # Absolute font size in pixel (precision 3)
h3.GetXaxis().SetLabelSize(15)
pad1.cd()
leg_ = TLegend(0.6, 0.75, 0.89, 0.89)
#for hi_,h_ in enumerate(hists_):
a = ih_[:]
#print'a = ',a
#for i,hist_info_ in enumerate(a):
for hist_info_ in a:
this_h = hist_info_[0]
this_label = hist_info_[1]
leg_.AddEntry(this_h,this_label,'lf') # histo object, ID
#leg.SetTextSize(0.02)
leg_.Draw('same')
#cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".png")
cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".pdf")
cc.SaveAs(output_Loc + "Gen_Reco_" + comb_ID_ + ".png")
return 0
def draw(hist,
var_type,
log='',
plotdir=None,
plotname='foop',
more_hists=None,
write_csv=False,
stats=None,
bounds=None,
errors=False,
shift_overflows=False,
csv_fname=None,
scale_errors=None,
rebin=None,
plottitle='',
colors=None,
linestyles=None,
cwidth=None,
cheight=None,
imagetype='svg',
xtitle=None,
ytitle=None,
xline=None,
yline=None,
draw_str=None,
normalize=False,
normalization_bounds=None,
linewidths=None,
markersizes=None,
no_labels=False,
graphify=False,
translegend=(0.0, 0.0)):
assert os.path.exists(plotdir)
if not has_root:
return
if normalization_bounds is not None:
assert bounds is None
if bounds is not None:
assert normalization_bounds is None
cvn = TCanvas('cvn-' + plotname, '', 700 if cwidth is None else cwidth,
600 if cheight is None else cheight)
hists = [
hist,
]
if more_hists != None:
hists = hists + more_hists
xmin, xmax, ymax = None, None, None
ih = 0
for htmp in hists:
if rebin is not None:
htmp.Rebin(rebin)
if scale_errors is not None:
factor = float(
scale_errors[0]) if len(scale_errors) == 1 else float(
scale_errors[ih])
for ibin in range(htmp.GetNbinsX() + 2):
htmp.SetBinError(ibin, htmp.GetBinError(ibin) * factor)
if not normalize:
assert normalization_bounds is None
if bounds is not None:
ibin_start = htmp.FindBin(bounds[0])
ibin_end = htmp.FindBin(bounds[1])
this_y_max = GetMaximumWithBounds(
htmp,
htmp.GetXaxis().GetBinLowEdge(ibin_start),
htmp.GetXaxis().GetBinUpEdge(ibin_end))
else:
this_y_max = htmp.GetMaximum()
else: # renormalize the hist within these bounds
if normalization_bounds is None:
factor = 1. / htmp.Integral() if htmp.Integral() > 0.0 else 0.0
htmp.Scale(factor)
this_y_max = htmp.GetMaximum()
else:
ibin_start = 0 if normalization_bounds[
0] is None else htmp.FindBin(normalization_bounds[0])
ibin_end = htmp.GetNbinsX(
) if normalization_bounds[1] is None else htmp.FindBin(
normalization_bounds[1])
factor = htmp.Integral(ibin_start, ibin_end) if htmp.Integral(
ibin_start, ibin_end
) > 0.0 else 0.0 # NOTE this is inclusive, i.e. includes <ibin_end>
htmp.Scale(factor)
this_y_max = GetMaximumWithBounds(
htmp,
htmp.GetXaxis().GetBinLowEdge(ibin_start),
htmp.GetXaxis().GetBinUpEdge(ibin_end))
if ymax is None or this_y_max > ymax:
ymax = this_y_max
if xmin is None or htmp.GetBinLowEdge(1) < xmin:
xmin = htmp.GetBinLowEdge(1)
if xmax is None or htmp.GetXaxis().GetBinUpEdge(
htmp.GetNbinsX()) > xmax:
xmax = htmp.GetXaxis().GetBinUpEdge(htmp.GetNbinsX())
ih += 1
if bounds is not None:
xmin, xmax = bounds
hframe = TH1D('hframe', '', hist.GetNbinsX(), xmin, xmax)
if not no_labels and (var_type == 'string' or var_type == 'bool'):
for ib in range(1, hframe.GetNbinsX() + 1):
hframe.GetXaxis().SetBinLabel(ib, hist.GetXaxis().GetBinLabel(ib))
if 'y' in log:
hframe.SetMaximum(3 * ymax)
else:
hframe.SetMaximum(1.2 * ymax)
if var_type == 'bool':
hframe.GetXaxis().SetLabelSize(0.1)
if plottitle == '':
plottitle = plotname
if xtitle is None:
xtitle = hist.GetXaxis().GetTitle()
if ytitle is None:
ytitle = hframe.GetYaxis().GetTitle()
hframe.SetTitle(plottitle + ';' + xtitle + ';' + ytitle)
# gStyle.SetTitleFontSize(.075)
# gStyle.SetTitleY(gStyle.GetTitleY() + .0004)
if cwidth is not None:
gStyle.SetTitleOffset(0.99 * gStyle.GetTitleOffset('y'), 'y')
# gStyle.SetTitleFillStyle(0)
# hframe.SetTitleSize(gStyle.GetTitleSize())
# hframe.SetTitleFont(gStyle.GetTitleFont())
hframe.Draw('txt')
if shift_overflows:
for htmp in hists:
if htmp == None:
continue
underflows, overflows = 0.0, 0.0
first_shown_bin, last_shown_bin = -1, -1
for ib in range(0, htmp.GetXaxis().GetNbins() + 2):
if htmp.GetXaxis().GetBinCenter(ib) <= xmin:
underflows += htmp.GetBinContent(ib)
htmp.SetBinContent(ib, 0.0)
elif first_shown_bin == -1:
first_shown_bin = ib
else:
break
for ib in reversed(range(0, htmp.GetXaxis().GetNbins() + 2)):
if htmp.GetXaxis().GetBinCenter(ib) >= xmax:
overflows += htmp.GetBinContent(ib)
htmp.SetBinContent(ib, 0.0)
elif last_shown_bin == -1:
last_shown_bin = ib
else:
break
if 'd_hamming' in plotname:
print htmp.GetTitle()
print ' underflow', underflows, htmp.GetBinContent(
first_shown_bin)
print ' overflow', overflows, htmp.GetBinContent(
last_shown_bin)
print ' first', htmp.GetXaxis().GetBinCenter(first_shown_bin)
print ' last', htmp.GetXaxis().GetBinCenter(last_shown_bin)
htmp.SetBinContent(
first_shown_bin,
underflows + htmp.GetBinContent(first_shown_bin))
htmp.SetBinContent(last_shown_bin,
overflows + htmp.GetBinContent(last_shown_bin))
if colors is None:
assert len(hists) < 5
colors = (kRed, kBlue - 4, kGreen + 2, kOrange + 1
) # 632, 596, 418, 801
else:
assert len(hists) <= len(colors)
if linestyles is None:
# assert len(hists) < 5
linestyles = [1 for _ in range(len(hists))]
else:
assert len(hists) <= len(linestyles)
# legends
x0, y0, x1, y1 = 0.57 + translegend[0], 0.66 + translegend[
1], 0.99 + translegend[0], 0.88 + translegend[1]
if len(hists) < 5:
leg = TLegend(x0, y0, x1, y1)
else:
leg = TLegend(x0, y0 - 0.05, x1, y1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
# draw
if graphify:
graphs = []
for ih in range(len(hists)):
n_bins = hists[ih].GetNbinsX()
xvals, yvals, xerrs, yerrs = array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)]), array(
'f', [0 for i in range(n_bins)])
for ib in range(1, n_bins + 1): # NOTE ignoring overflows
xvals[ib - 1] = hists[ih].GetXaxis().GetBinCenter(ib)
xerrs[ib - 1] = 0.0
yvals[ib - 1] = hists[ih].GetBinContent(ib)
yerrs[ib - 1] = hists[ih].GetBinError(ib) if errors else 0.0
gr = TGraphErrors(n_bins, xvals, yvals, xerrs, yerrs)
if markersizes is not None:
imark = ih if len(markersizes) > 1 else 0
gr.SetMarkerSize(markersizes[imark])
gr.SetMarkerColor(colors[ih])
if linewidths is None:
if ih < 6: # and len(hists) < 5:
gr.SetLineWidth(6 - ih)
else:
ilw = ih if len(linewidths) > 1 else 0
gr.SetLineWidth(linewidths[ilw])
gr.SetLineColor(colors[ih])
# if int(linewidth) == 1:
# gr.SetLineColorAlpha(colors[ih], 0.4)
gr.SetLineStyle(linestyles[ih])
if draw_str is None:
draw_str = 'lpz'
if hists[ih].Integral() != 0.0:
gr.Draw(draw_str + ' same')
leg.AddEntry(gr, hists[ih].GetTitle(), 'pl')
graphs.append(
gr
) # yes, you really do have to do this to keep root from giving you only one graph
else:
if draw_str is None:
draw_str = 'hist same'
else:
draw_str += ' same'
if errors:
draw_str += ' e'
for ih in range(len(hists)):
htmp = hists[ih]
if stats is not None:
if 'rms' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetRMS()))
if 'mean' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetMean()))
if '0-bin' in stats:
htmp.SetTitle(htmp.GetTitle() +
(' (%.2f)' % htmp.GetBinContent(1)))
htmp.SetLineColor(colors[ih])
if markersizes is not None:
imark = ih if len(markersizes) > 1 else 0
htmp.SetMarkerSize(markersizes[imark])
htmp.SetMarkerColor(colors[ih])
htmp.SetLineStyle(linestyles[ih])
if linewidths is None:
if ih < 6: # and len(hists) < 5:
htmp.SetLineWidth(6 - ih)
else:
ilw = ih if len(linewidths) > 1 else 0
htmp.SetLineWidth(linewidths[ilw])
leg.AddEntry(htmp, htmp.GetTitle(), 'l')
htmp.Draw(draw_str)
leg.Draw()
if xline is not None:
if xline <= hframe.GetXaxis().GetXmin() or xline >= hframe.GetXaxis(
).GetXmax(): # make sure we got valid a x position for the line
print 'WARNING plotting x line at %f out of bounds (%f, %f)' % (
float(xmin), hframe.GetXaxis().GetXmin(),
hframe.GetXaxis().GetXmax())
# xl = TLine(xline, hframe.GetYaxis().GetXmin(), xline, 0.5*ymax)
xl = TLine(xline, -0.1 * ymax, xline, 0.5 * ymax)
xl.SetLineStyle(2)
xl.Draw()
if yline is not None:
if yline <= hframe.GetYaxis().GetXmin() or xline >= hframe.GetYaxis(
).GetXmax(): # make sure we got valid a x position for the line
print 'WARNING plotting y line at %f out of bounds (%f, %f)' % (
float(ymin), hframe.GetYaxis().GetXmin(),
hframe.GetYaxis().GetXmax())
yl = TLine(hframe.GetXaxis().GetXmin(), yline,
hframe.GetXaxis().GetXmax(), yline)
yl.Draw()
cvn.SetLogx('x' in log)
cvn.SetLogy('y' in log)
if not os.path.exists(plotdir + '/plots'):
print 'ERROR dir \'' + plotdir + '/plots\' d.n.e.'
assert False
if write_csv:
assert more_hists == None
if csv_fname == None:
write_hist_to_file(plotdir + '/plots/' + plotname + '.csv', hist)
else:
write_hist_to_file(csv_fname, hist)
cvn.SaveAs(plotdir + '/plots/' + plotname + '.' + imagetype)
def plotter_flavours(eff_type):
if eff_type == "real":
hist_eff_type = "Real"
elif eff_type == "fake":
hist_eff_type = "Fake"
basepath = "$HOME/PhD/ttH_MultiLeptons/RUN2/HTopMultilepAnalysisCode/trunk/HTopMultilepAnalysis/PlotUtils/"
# ---------------------------
# HTop Tag & Probe efficiency
# ---------------------------
file_TP_path = basepath + "blahblah"
if args.closure:
if "SLT" in args.trigger:
file_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_HTopTP/OutputPlots_MMClosureRates_HTopTagProbe_NoCorr_SLT_SFmuSFel_25ns_v23/LeptonEfficiencies.root"
elif "DLT" in args.trigger:
#file_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_HTopTP/OutputPlots_MMClosureRates_HTopTagProbe_NoCorr_DLT_SFmuSFel_25ns_v23/LeptonEfficiencies.root"
file_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_DLT_SFmuSFel_Pt_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
print(
"------------------------------------------------------------------------------------------"
)
print("HTop Tag & Probe efficiency - Opening file:\n{0}".format(
file_TP_path))
print(
"------------------------------------------------------------------------------------------"
)
file_TP = TFile(file_TP_path)
if args.closure:
hist_TP = (file_TP.Get(hist_eff_type +
"_El_Pt_Efficiency_expectedbkg"),
file_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_expectedbkg"))[bool(
args.flavour == "mu")]
else:
hist_TP = (file_TP.Get(hist_eff_type +
"_El_Pt_Efficiency_observed_sub"),
file_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_observed_sub"))[bool(
args.flavour == "mu")]
print("Reading histogram {0} from file {1}".format(hist_TP.GetName(),
file_TP_path))
hist_TP.SetLineWidth(2)
hist_TP.SetMarkerSize(1.0)
hist_TP.GetXaxis().SetTitleOffset(1.0)
hist_TP.GetYaxis().SetTitleOffset(1.0)
lepton = ("e", "#mu")[bool(args.flavour == "mu")]
hist_TP.GetXaxis().SetTitle("p_{T}^{" + lepton + "} [GeV]")
hist_TP.GetYaxis().SetTitle("efficiency")
hist_TP.GetYaxis().SetRangeUser(0.0, 1.0)
hist_TP.SetLineColor(kRed)
hist_TP.SetMarkerColor(kRed)
#c = TCanvas("c1","Temp",50,50,1000,900)
c = TCanvas("c1", "Temp", 50, 50, 1300, 800)
legend = TLegend(
0.45, 0.5, 0.925, 0.8
) # (x1,y1 (--> bottom left corner), x2, y2 (--> top right corner) )
legend.SetHeader("#epsilon_{" + eff_type + "}")
legend.SetBorderSize(0) # no border
legend.SetFillStyle(0) # Legend transparent background
legend.SetTextSize(0.04) # Increase entry font size!
legend.SetTextFont(42) # Helvetica
leg_ATLAS = TLatex()
leg_lumi = TLatex()
leg_ATLAS.SetTextSize(0.03)
leg_ATLAS.SetNDC()
leg_lumi.SetTextSize(0.03)
leg_lumi.SetNDC()
#hist_TP.Draw("E0")
#legend.AddEntry(hist_TP, "HTop Tag & Probe (ICHEP)", "P")
# ---------------------------
# New Tag & Probe efficiency
# ---------------------------
file_SUSY_TP_path = basepath + "blahblah"
if args.closure:
if "SLT" in args.trigger:
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_SFmuSFel_Pt_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_OFmuOFel_Pt_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_SFmuSFel_massClosestBJet_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_SFmuSFel_deltaRClosestBJet_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v23_OLD_EL_BINNING/LeptonEfficiencies.root"
#
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v24/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v24_ForceProbeToBeFake/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v24_TightTagIso/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v24_TightTagIsoTagPt30/LeptonEfficiencies.root"
file_SUSY_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_OF_AMBISOLVING_25ns_v24_TightTagIsoTagPt30_ForceProbeToBeFake/LeptonEfficiencies.root"
elif "DLT" in args.trigger:
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_DLT_SFmuSFel_Pt_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
#file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_DLT_SFmuSFel_massClosestBJet_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
file_SUSY_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_SUSYTP/OutputPlots_MMClosureRates_SUSYTagProbe_NoCorr_DLT_SFmuSFel_deltaRClosestBJet_AMBISOLVING_25ns_v23/LeptonEfficiencies.root"
print(
"------------------------------------------------------------------------------------------"
)
print("New Tag & Probe efficiency - Opening file:\n{0}".format(
file_SUSY_TP_path))
print(
"------------------------------------------------------------------------------------------"
)
file_SUSY_TP = TFile(file_SUSY_TP_path)
if args.closure:
hist_SUSY_TP = (
file_SUSY_TP.Get(hist_eff_type + "_El_Pt_Efficiency_expectedbkg"),
file_SUSY_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_expectedbkg"))[bool(
args.flavour == "mu")]
else:
hist_SUSY_TP = (
file_SUSY_TP.Get(hist_eff_type + "_El_Pt_Efficiency_observed_sub"),
file_SUSY_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_observed_sub"))[bool(
args.flavour == "mu")]
print("Reading histogram {0} from file {1}".format(hist_SUSY_TP.GetName(),
file_SUSY_TP_path))
hist_SUSY_TP.SetLineColor(kMagenta)
hist_SUSY_TP.SetMarkerColor(kMagenta)
hist_SUSY_TP.GetXaxis().SetTitle("p_{T}^{" + lepton + "} [GeV]")
hist_SUSY_TP.GetYaxis().SetTitle("#varepsilon")
if eff_type == "real":
hist_SUSY_TP.GetYaxis().SetRangeUser(0.5, 1.0)
elif eff_type == "fake":
if args.flavour == "el":
hist_SUSY_TP.GetYaxis().SetRangeUser(0.0, 0.7)
if args.flavour == "mu":
hist_SUSY_TP.GetYaxis().SetRangeUser(0.0, 0.5)
hist_SUSY_TP.Draw("E0")
legend.AddEntry(hist_SUSY_TP, "Tag & Probe", "P")
# ----------------------------
# TRUTH Tag & Probe efficiency
# ----------------------------
file_TRUTH_TP_path = basepath + "blahblah"
if args.closure:
if "SLT" in args.trigger:
#file_TRUTH_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_SLT_SFmuSFel_25ns_v23/LeptonEfficiencies.root"
#file_TRUTH_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_SLT_OFmuOFel_25ns_v23/LeptonEfficiencies.root"
#file_TRUTH_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_25ns_v23/LeptonEfficiencies.root"
#file_TRUTH_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_25ns_v23_OLD_EL_BINNING/LeptonEfficiencies.root"
#
file_TRUTH_TP_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_SLT_RealOFmuOFel_FakeSFmuOFel_25ns_v24/LeptonEfficiencies.root"
elif "DLT" in args.trigger:
file_TRUTH_TP_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_TruthTP/OutputPlots_MMClosureRates_TruthTagProbe_NoCorr_DLT_SFmuSFel_25ns_v23/LeptonEfficiencies.root"
print(
"------------------------------------------------------------------------------------------"
)
print("TRUTH Tag & Probe efficiency - Opening file:\n{0}".format(
file_TRUTH_TP_path))
print(
"------------------------------------------------------------------------------------------"
)
file_TRUTH_TP = TFile(file_TRUTH_TP_path)
if args.closure:
hist_TRUTH_TP = (
file_TRUTH_TP.Get(hist_eff_type + "_El_Pt_Efficiency_expectedbkg"),
file_TRUTH_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_expectedbkg"))[bool(
args.flavour == "mu")]
else:
hist_TRUTH_TP = (
file_TRUTH_TP.Get(hist_eff_type +
"_El_Pt_Efficiency_observed_sub"),
file_TRUTH_TP.Get(hist_eff_type +
"_Mu_Pt_Efficiency_observed_sub"))[bool(
args.flavour == "mu")]
print("Reading histogram {0} from file {1}".format(hist_TRUTH_TP.GetName(),
file_TRUTH_TP_path))
hist_TRUTH_TP.SetLineWidth(2)
hist_TRUTH_TP.SetLineColor(kBlack)
hist_TRUTH_TP.SetLineStyle(2)
hist_TRUTH_TP.SetMarkerSize(1.0)
hist_TRUTH_TP.SetMarkerColor(kBlack)
hist_TRUTH_TP.SetMarkerStyle(24)
hist_TRUTH_TP.Draw("E0 SAME")
legend.AddEntry(hist_TRUTH_TP, "Truth", "P")
# ---------------------
# Likelihood efficiency
# ---------------------
#"""
LH_init_path = basepath + "blahblah"
if args.closure:
if "SLT" in args.trigger:
#LH_init_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_LikelihoodFit/OutputPlots_MMClosureRates_LHFit_NoCorr_SFmuSFel_SLT_25ns_v21/LeptonEfficiencies_LH/"
#LH_init_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_LikelihoodFit/OutputPlots_MMClosureRates_LHFit_NoCorr_INCLUSIVE_FLAV_SLT_25ns_v24/LeptonEfficiencies_LH/"
LH_init_path = basepath + "PLOTS_25ns_v24/MMClosure_v24_LikelihoodFit/OutputPlots_MMClosureRates_LHFit_NoCorr_INCLUSIVE_FLAV_DLT_25ns_v24/LeptonEfficiencies_LH/"
elif "DLT" in args.trigger:
LH_init_path = basepath + "PLOTS_25ns_v23/MMClosure_v23_LikelihoodFit/OutputPlots_MMClosureRates_LHFit_NoCorr_SFmuSFel_DLT_25ns_v21/LeptonEfficiencies_LH/"
hist_LH_list = []
if eff_type == "real":
flav_comp_list = args.flavRealCR
elif eff_type == "fake":
flav_comp_list = args.flavFakeCR
if (args.flavour == "mu" and "elel" in flav_comp_list):
flav_comp_list.remove("elel")
if (args.flavour == "el" and "mumu" in flav_comp_list):
flav_comp_list.remove("mumu")
for idx, flavcomp in enumerate(flav_comp_list, start=0):
file_LH_path = LH_init_path + "LH_" + flavcomp + "/LH_efficiencies_" + eff_type + "_" + args.flavour + "_" + flavcomp + ".root"
print(
"\t------------------------------------------------------------------------------------------"
)
print("\tLikelihood efficiency - Opening file:\n{0}".format(
file_LH_path))
print(
"\t------------------------------------------------------------------------------------------"
)
file_LH = TFile(file_LH_path)
if eff_type == "real":
hist_LH_name = "r_hist"
elif eff_type == "fake":
hist_LH_name = "f_hist"
hist_LH = file_LH.Get(hist_LH_name)
print("Reading histogram {0} from file {1} - index : {2}".format(
hist_LH.GetName(), file_LH_path, idx))
# A dirty hack to make multiple plots superimposed with a small offset
offsets = []
for ibin in range(0, hist_LH.GetNbinsX() + 1):
offset = (idx + 1) * 0.15 * hist_LH.GetBinWidth(ibin)
# hardcoded...so ugly, any better ideas?
if ibin == 4:
offset = (idx + 1) * 0.05 * hist_LH.GetBinWidth(ibin)
if ibin == 5:
offset = (idx + 1) * 0.01 * hist_LH.GetBinWidth(ibin)
offsets.append(offset)
print("offsets per bin: {0}".format(offsets))
binlist = []
for ibin in range(0, hist_LH.GetNbinsX() + 1):
binlist.append(hist_LH.GetXaxis().GetBinUpEdge(ibin) +
offsets[ibin])
print("shifted bins: {0}".format(binlist))
binlist_arr = array.array("d", binlist)
shiftedhist = TH1D(hist_LH.GetName() + "_shift", hist_LH.GetTitle(),
len(binlist) - 1, binlist_arr)
for ibin in range(1, hist_LH.GetNbinsX() + 1):
shiftedhist.SetBinContent(ibin, hist_LH.GetBinContent(ibin))
shiftedhist.SetBinError(ibin, hist_LH.GetBinError(ibin))
shiftedhist.SetLineWidth(2)
shiftedhist.SetMarkerSize(1.0)
lepton = ("e", "#mu")[bool(args.flavour == "mu")]
shiftedhist.GetXaxis().SetTitle("p_{T}^{" + lepton + "} [GeV]")
shiftedhist.GetYaxis().SetTitle("efficiency")
shiftedhist.GetXaxis().SetTitleOffset(1.0)
shiftedhist.GetYaxis().SetTitleOffset(1.0)
shiftedhist.GetYaxis().SetRangeUser(0.0, 1.0)
if idx == 0:
shiftedhist.SetLineColor(kBlue)
shiftedhist.SetMarkerColor(kBlue)
elif idx == 1:
shiftedhist.SetLineColor(kBlue + 3)
shiftedhist.SetMarkerColor(kBlue + 3)
elif idx == 2:
shiftedhist.SetLineColor(kAzure + 1)
shiftedhist.SetMarkerColor(kAzure + 1)
elif idx == 3:
shiftedhist.SetLineColor(kCyan)
shiftedhist.SetMarkerColor(kCyan)
shiftedhist.SetDirectory(0)
pair = (flavcomp, shiftedhist)
hist_LH_list.append(pair)
#gPad.SetLogx()
#hist_TP.GetXaxis().SetMoreLogLabels()
for idx, histpair in enumerate(hist_LH_list, start=0):
#histpair[1].GetXaxis().SetMoreLogLabels()
#if idx == 0:
# histpair[1].Draw("E0")
#else:
# histpair[1].Draw("E0,SAME")
# TEMP: plot LH efficiency only for fake muon
if not (eff_type == "fake" and args.flavour == "mu"): continue
histpair[1].Draw("E0,SAME")
if histpair[0] == "mumu": flag = "#mu#mu"
else: flag = histpair[0]
legend.AddEntry(histpair[1], "Likelihood - " + flag, "P")
#"""
legend.Draw()
leg_ATLAS.DrawLatex(0.6, 0.35, "#bf{#it{ATLAS}} Work In Progress")
leg_lumi.DrawLatex(
0.6, 0.27,
"#sqrt{{s}} = 13 TeV, #int L dt = {0:.1f} fb^{{-1}}".format(args.lumi))
# Add a vertical line to highlight relevant bins
if eff_type == "real":
low_y_edge = 0.5
up_y_edge = 1.0
elif eff_type == "fake":
low_y_edge = 0.0
if args.flavour == "el":
up_y_edge = 0.7
elif args.flavour == "mu":
up_y_edge = 0.5
if "SLT" in args.trigger:
if args.flavour == "el":
refl_vert0 = TLine(26.0, 0, 26.0, up_y_edge)
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
refl_vert1 = TLine(60.0, low_y_edge, 60.0, up_y_edge)
refl_vert1.SetLineStyle(2)
refl_vert1.SetLineWidth(2)
refl_vert1.Draw("SAME")
refl_vert2 = TLine(140.0, low_y_edge, 140.0, up_y_edge)
refl_vert2.SetLineStyle(2)
refl_vert2.SetLineWidth(2)
refl_vert2.Draw("SAME")
elif args.flavour == "mu":
refl_vert0 = TLine(26.0, low_y_edge, 26.0, up_y_edge)
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
refl_vert1 = TLine(50.0, low_y_edge, 50.0, up_y_edge)
refl_vert1.SetLineStyle(2)
refl_vert1.SetLineWidth(2)
refl_vert1.Draw("SAME")
elif "DLT" in args.trigger:
if args.flavour == "el":
refl_vert0 = TLine(17.0, low_y_edge, 17.0, up_y_edge)
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
elif args.flavour == "mu":
refl_vert0 = TLine(22.0, low_y_edge, 22.0, up_y_edge)
refl_vert0.SetLineStyle(2)
refl_vert0.SetLineWidth(2)
refl_vert0.Draw("SAME")
canvasname = (eff_type + "_eff_el_TP_LH",
eff_type + "_eff_mu_TP_LH")[bool(args.flavour == "mu")]
c.SaveAs(canvasname + "_" + args.trigger[0] + ".png")
def drawHistsWithRatio(hists, name, **kwargs):
"""Draw histograms with ratios."""
title = kwargs.get('title', "")
xtitle = kwargs.get('xtitle', "")
ytitle = kwargs.get('ytitle', "")
rtitle = kwargs.get('rtitle', "Ratio")
xmin = kwargs.get('xmin', hists[0].GetXaxis().GetXmin())
xmax = kwargs.get('xmax', hists[0].GetXaxis().GetXmax())
ymin = kwargs.get('ymin', None)
ymax = kwargs.get('ymax', None)
rmin = kwargs.get('rmin', 0.45)
rmax = kwargs.get('rmax', 1.55)
logx = kwargs.get('logx', False)
logy = kwargs.get('logy', False)
denom = kwargs.get('denom', 1) - 1 # denominator for ratio
textsize = kwargs.get('textsize', 0.045)
texts = kwargs.get('text', [])
width = kwargs.get('width', 0.26)
#textheight = kwargs.get('textheight', 1.09 )
#ctext = kwargs.get('ctext', [ ] ) # corner text
#cposition = kwargs.get('cposition', 'topleft' ).lower() # cornertext
#ctextsize = kwargs.get('ctextsize', 1.4*legendtextsize )
colors = kwargs.get('colors', linecolors)
if not isinstance(texts, list) or isinstance(texts, tuple):
texts = [texts]
if ymax == None:
ymax = 1.12 * max(h.GetMaximum() for h in hists)
# MAIN plot
canvas = TCanvas('canvas', 'canvas', 100, 100, 800, 800)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameBorderMode(0)
canvas.Divide(2)
canvas.SetMargin(0.0, 0.0, 0.0, 0.0)
canvas.cd(1)
gPad.SetPad('pad1', 'pad1', 0, 0.33, 1, 1)
gPad.SetMargin(0.12, 0.04, 0.02, 0.08)
gPad.SetFillColor(0)
gPad.SetBorderMode(0)
gPad.SetTickx(0)
gPad.SetTicky(0)
gPad.SetGrid()
gPad.Draw()
canvas.cd(2)
gPad.SetPad('pad2', 'pad2', 0, 0, 1, 0.33)
gPad.SetMargin(0.12, 0.04, 0.30, 0.03)
gPad.SetFillColor(0)
gPad.SetFillStyle(4000)
gPad.SetFrameFillStyle(0)
gPad.SetBorderMode(0)
gPad.Draw()
# MAIN plot
canvas.cd(1)
for i, hist in enumerate(hists):
color = colors[i % len(colors)]
hist.SetLineColor(color)
hist.SetLineWidth(2)
hist.Draw('HIST SAME')
frame = hists[0]
frame.GetYaxis().SetTitleSize(0.060)
frame.GetXaxis().SetTitleSize(0)
frame.GetXaxis().SetLabelSize(0)
frame.GetYaxis().SetLabelSize(0.052)
frame.GetXaxis().SetLabelOffset(0.010)
frame.GetXaxis().SetTitleOffset(0.98)
frame.GetYaxis().SetTitleOffset(1.05)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().SetTitle(ytitle)
frame.GetXaxis().SetTitle(xtitle)
if logx:
gPad.Update()
gPad.SetLogx()
if logy:
gPad.Update()
gPad.SetLogy()
if ymin: frame.SetMinimum(ymin)
if ymax: frame.SetMaximum(ymax)
height = 1.1 * textsize * len([l for l in texts + hists if l])
x1, y1 = 0.90, 0.88
x2, y2 = x1 - width, y1 - height
legend = TLegend(x1, y1, x2, y2)
legend.SetTextSize(textsize)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.SetFillColor(0)
legend.SetMargin(0.05 / width)
if title:
legend.SetTextFont(62)
legend.SetHeader(title)
legend.SetTextFont(42)
for hist in hists:
legend.AddEntry(hist, hist.GetTitle(), 'l')
for text in texts:
legend.AddEntry(0, text, '')
legend.Draw()
gPad.SetTicks(1, 1)
gPad.Modified()
frame.Draw('AXIS SAME')
CMS_style.CMS_lumi(gPad, 13, 0)
# RATIO plot
canvas.cd(2)
ratios = []
for i, hist in enumerate(hists):
if i == denom: continue
ratio = hist.Clone(hist.GetName() + "_ratio")
ratio.Divide(hists[denom])
for ibin in xrange(1, hist.GetXaxis().GetNbins() + 1):
if hists[denom].GetBinContent(ibin) == 0:
if hist.GetBinContent(ibin) == 0:
ratio.SetBinContent(ibin, 1)
else:
ratio.SetBinContent(ibin, rmax + 1e5)
ratio.Draw('HIST SAME')
ratios.append(ratio)
frame_ratio = ratios[0]
frame_ratio.GetYaxis().SetRangeUser(rmin, rmax)
frame_ratio.GetYaxis().CenterTitle()
frame_ratio.GetYaxis().SetTitleSize(0.13)
frame_ratio.GetXaxis().SetTitleSize(0.13)
frame_ratio.GetXaxis().SetLabelSize(0.12)
frame_ratio.GetYaxis().SetLabelSize(0.11)
frame_ratio.GetXaxis().SetLabelOffset(0.012)
frame_ratio.GetXaxis().SetTitleOffset(1.02)
frame_ratio.GetYaxis().SetTitleOffset(0.48)
frame_ratio.GetXaxis().SetNdivisions(508)
frame_ratio.GetYaxis().CenterTitle(True)
frame_ratio.GetYaxis().SetTitle(rtitle)
frame_ratio.GetXaxis().SetTitle(xtitle)
frame_ratio.GetYaxis().SetNdivisions(505)
if logx:
gPad.Update()
gPad.SetLogx()
line = TLine(xmin, 1., xmax, 1.)
line.SetLineColor(hists[denom].GetLineColor())
line.SetLineWidth(hists[denom].GetLineWidth())
line.SetLineStyle(1)
line.Draw('SAME')
gPad.SetTicks(1, 1)
gPad.Update()
gPad.SetGrid()
gPad.Modified()
frame_ratio.Draw('SAME AXIS')
canvas.SaveAs(name + ".png")
canvas.SaveAs(name + ".pdf")
canvas.Close()
def plot(self, **kw):
from ROOT import kRed
dirname = retrieve_kw(kw, 'dirname', 'Distribution')
basecolor = retrieve_kw(kw, 'basecolor', kRed - 7)
pdftitle = retrieve_kw(kw, 'pdftitle', 'Distributions')
pdfoutput = retrieve_kw(kw, 'pdfoutput', 'distributions')
import os
# Organize outputs (.py and .pdf)
prefix = self._basepath.split('/')[-1]
localpath = os.getcwd() + '/' + dirname + '/' + prefix
try:
if not os.path.exists(localpath):
os.makedirs(localpath)
except:
self._logger.warning('The director %s exist.', localpath)
hist_names = [
'et', 'eta', 'mu', 'nvtx', 'reta', 'eratio', 'weta2', 'rhad',
'rphi', 'f1', 'f3'
]
hist_labels = [
'E_{T}', "#eta", "<#mu>", 'N_{vtx}', 'R_{eta}', 'E_{ratio}',
'W_{eta2}', 'R_{had}', 'R_{phi}', 'f_{1}', 'f_{3}'
]
from ROOT import TCanvas, TH1F, gStyle, TLegend, TPad
from ROOT import kGreen, kRed, kBlue, kBlack, kGray, gPad, kAzure
from TrigEgammaDevelopments.plots.AtlasStyle import AtlasStyle, atlas_template, setLegend1
canvas1 = TCanvas('canvas1', 'canvas1', 2500, 1600)
canvas1.Divide(4, 3)
# Concatenate distributions for all regions
def sumAllRegions(histname):
h = None
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx, etaBinIdx)
path = self._basepath + '/' + self.currentDir(
) + '/' + binningname
if h:
h += self.storeSvc().histogram(path + '/' + histname)
else:
h = self.storeSvc().histogram(path + '/' +
histname).Clone()
return h
collector = []
figures = {
'rings': [],
'rnnOutput': [],
'ringer_profile': str(),
'shower_shapes': str()
}
"""
Plot all shower shapes distributins
"""
for idx, histname in enumerate(hist_names):
self.setDir('Data')
h_data = sumAllRegions(histname)
self.setDir('MonteCarlo')
h_mc = sumAllRegions(histname)
#h_mc, h_data = self.__scale_histograms(h_mc, h_data, 100, 0.01, 0.01)
pad = canvas1.cd(idx + 1)
gStyle.SetOptStat(110011)
collector.append(pad)
h_mc.SetFillColor(basecolor)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.2, 0.75, 0.5, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
collector[-1].Update()
collector.append(h_mc)
collector.append(h_data)
collector.append(leg1)
canvas1.SaveAs(localpath + '/shower_shapes_distributions.pdf')
figures[
'shower_shapes'] = localpath + '/shower_shapes_distributions.pdf'
"""
Plot all shower ringer shapes for each ring
"""
ratio_size_as_fraction = 0.35
from RingerCore import progressbar
rings_localpath = []
for r in progressbar(range(100),
100,
step=1,
logger=self._logger,
prefix="Looping over rings (Plotting...) "):
canvas2 = TCanvas('canvas2', 'canvas2', 2500, 1600)
drawopt = 'pE1'
canvas2.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas2.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
self.setDir('MonteCarlo')
h_mc = sumAllRegions('rings/ring_' + str(r))
self.setDir('Data')
h_data = sumAllRegions('rings/ring_' + str(r))
gStyle.SetOptStat(000000)
h_mc, h_data = self.__scale_histograms(h_mc, h_data, 100, 0.0001,
0.025)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mc.Clone()
h = h_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle('Ring #' + str(r + 1) + ' [MeV]')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_data.GetNbinsX()
xmin = h_data.GetXaxis().GetBinLowEdge(1)
xmax = h_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mc.SetFillColor(basecolor)
h_mc.SetLineWidth(1)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_data.SetLineWidth(1)
h_mc.GetYaxis().SetTitle('Count')
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas2.SaveAs(localpath + '/distribution_ring_' + str(r + 1) +
'.pdf')
figures['rings'].append(localpath + '/distribution_ring_' +
str(r + 1) + '.pdf')
"""
Plot ringer mean shapes
"""
h_mean_data = TH1F('h_mean_data', '', 100, 0, 100)
h_mean_mc = TH1F('h_mean_mc', '', 100, 0, 100)
for bin in range(100):
self.setDir('MonteCarlo')
h_mc = sumAllRegions('rings/ring_' + str(bin))
self.setDir('Data')
h_data = sumAllRegions('rings/ring_' + str(bin))
h_mean_data.SetBinContent(bin + 1, h_data.GetMean())
h_mean_mc.SetBinContent(bin + 1, h_mc.GetMean())
canvas3 = TCanvas('canvas3', 'canvas3', 2500, 1600)
drawopt = 'pE1'
canvas3.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas3.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
gStyle.SetOptStat(000000)
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mean_mc.Clone()
h = h_mean_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle('Rings')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_mean_data.GetNbinsX()
xmin = h_mean_data.GetXaxis().GetBinLowEdge(1)
xmax = h_mean_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mean_mc.SetFillColor(basecolor)
h_mean_mc.SetLineWidth(1)
h_mean_mc.SetLineColor(basecolor)
h_mean_data.SetLineColor(kBlack)
h_mean_data.SetLineWidth(1)
#h_mean_mc.Scale( 1./h_mean_mc.GetEntries() )
#h_mean_data.Scale( 1./h_mean_data.GetEntries() )
if h_mean_mc.GetMaximum() > h_mean_data.GetMaximum():
ymin = h_mean_mc.GetMinimum()
ymax = h_mean_mc.GetMaximum()
h_mean_mc.Draw()
h_mean_mc.GetYaxis().SetTitle('E[Ring] MeV')
h_mean_data.Draw('same')
else:
ymin = h_mean_data.GetMinimum()
ymax = h_mean_data.GetMaximum()
h_mean_data.GetYaxis().SetTitle('E[Ring] MeV')
h_mean_data.Draw()
h_mean_mc.Draw('same')
h_mean_data.Draw('same')
# prepare ringer lines
def gen_line_90(x, ymin, ymax, text):
from ROOT import TLine, TLatex
ymax = 1.05 * ymax
l = TLine(x, ymin, x, ymax)
l.SetLineStyle(2)
l.Draw()
txt = TLatex()
txt.SetTextFont(12)
txt.SetTextAngle(90)
txt.SetTextSize(0.04)
txt.DrawLatex(x - 1, (ymax - ymin) / 2., text)
return l, txt
l_ps, t_ps = gen_line_90(8, ymin, ymax, 'presampler')
l_em1, t_em1 = gen_line_90(72, ymin, ymax, 'EM.1')
l_em2, t_em2 = gen_line_90(80, ymin, ymax, 'EM.2')
l_em3, t_em3 = gen_line_90(88, ymin, ymax, 'EM.3')
l_had1, t_had1 = gen_line_90(92, ymin, ymax, 'Had.1')
l_had2, t_had2 = gen_line_90(96, ymin, ymax, 'Had.2')
l_had3, t_had3 = gen_line_90(100, ymin, ymax, 'Had.3')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mean_mc, 'MC')
leg1.AddEntry(h_mean_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas3.SaveAs(localpath + '/ringer_profile.pdf')
figures['ringer_profile'] = localpath + '/ringer_profile.pdf'
"""
Plot all NN distributions for each calo region
"""
for algname in self._discrList:
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx, etaBinIdx)
path = self._basepath + '/MonteCarlo/' + binningname
h_mc = self.storeSvc().histogram(
path + '/' + algname +
'/discriminantVsMu').ProjectionX().Clone()
path = self._basepath + '/Data/' + binningname
h_data = self.storeSvc().histogram(
path + '/' + algname +
'/discriminantVsMu').ProjectionX().Clone()
h_mc.Rebin(10)
h_data.Rebin(10)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
canvas4 = TCanvas('canvas4', 'canvas4', 2500, 1600)
drawopt = 'pE1'
canvas4.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
canvas4.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0,
1.0, ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
gStyle.SetOptStat(000000)
from ROOT import TH1, kGray
divide = ""
drawopt = 'pE1'
bot.cd()
ref = h_mc.Clone()
h = h_data.Clone()
ref.Sumw2()
h.Sumw2()
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kGray)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(-1.6, 3.7)
ratioplot.GetYaxis().SetTitleOffset(0.7)
ratioplot.GetYaxis().SetTitle('Data/MC')
ratioplot.GetXaxis().SetTitle(
'Neural Network (Discriminant)')
ratioplot.Draw(drawopt)
from ROOT import TLine
nbins = h_data.GetNbinsX()
xmin = h_data.GetXaxis().GetBinLowEdge(1)
xmax = h_data.GetXaxis().GetBinLowEdge(nbins + 1)
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
bot.Update()
top.cd()
h_mc.SetFillColor(basecolor)
h_mc.SetLineWidth(1)
h_mc.SetLineColor(basecolor)
h_data.SetLineColor(kBlack)
h_data.SetLineWidth(1)
h_mc.Scale(1. / h_mc.GetMaximum())
h_data.Scale(1. / h_data.GetMaximum())
h_mc.GetYaxis().SetTitle(
('Counts (%s)') % (binningname.replace('_', ',')))
h_mc.Draw()
h_data.Draw('same')
leg1 = TLegend(0.8, 0.70, 0.95, 0.95)
setLegend1(leg1)
leg1.AddEntry(h_mc, 'MC')
leg1.AddEntry(h_data, 'Data')
leg1.Draw()
atlas_template(top)
top.Update()
canvas4.SaveAs(localpath + '/' + algname + '_rnnOutput_' +
binningname + '.pdf')
figures['rnnOutput'].append(localpath + '/' + algname +
'_rnnOutput_' + binningname +
'.pdf')
#from RingerCore.tex.TexAPI import *
from RingerCore.tex.BeamerAPI import BeamerTexReportTemplate1, BeamerSection, BeamerMultiFigureSlide, BeamerFigureSlide
with BeamerTexReportTemplate1(theme='Berlin',
_toPDF=True,
title=pdftitle,
outputFile=pdfoutput,
font='structurebold'):
with BeamerSection(name='Shower Shapes'):
BeamerMultiFigureSlide(
title='Shower Shapes (MC and Data)',
paths=[figures['shower_shapes']],
nDivWidth=1 # x
,
nDivHeight=1 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
with BeamerSection(name='Ringer Shapes Profile'):
BeamerMultiFigureSlide(
title='Ringer Profile (MC and Data)',
paths=[figures['ringer_profile']],
nDivWidth=1 # x
,
nDivHeight=1 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
with BeamerSection(name='Ringer Shapes'):
for s in range(4):
paths1 = [
path for path in figures['rings'][s * 25:s * 25 + 25]
]
BeamerMultiFigureSlide(
title='Ringer Shapes (MC and Data)',
paths=paths1,
nDivWidth=5 # x
,
nDivHeight=5 # y
,
texts=None,
fortran=False,
usedHeight=0.8,
usedWidth=1.1)
for algname in self._discrList:
with BeamerSection(name=('%s Neural Network Output') %
(algname.replace('_', '\_'))):
paths2 = []
for etBinIdx in range(len(self._etBins) - 1):
for etaBinIdx in range(len(self._etaBins) - 1):
binningname = ('et%d_eta%d') % (etBinIdx,
etaBinIdx)
paths2.append(localpath + '/' + algname +
'_rnnOutput_' + binningname + '.pdf')
BeamerMultiFigureSlide(
title=algname.replace('_', '\_'),
paths=paths2,
nDivWidth=len(self._etaBins) # x
,
nDivHeight=len(self._etBins) # y
,
texts=None,
fortran=False,
usedHeight=1.0,
usedWidth=1.1)
return StatusCode.SUCCESS
def plot_2D(sign,
var,
nbins=50,
minimum=0,
maximum=2000,
bins=np.array([]),
filename="",
string="",
part_var="GenBquarks",
particle="#pi",
norm=False):
chain = {}
hist = {}
r_ecal = 129
r_hcal = 179
r_magnet = 295
r_mb1 = 402
r_mb4 = 738
z_ecal = 300
z_hcal = 376
z_magnet = 0
z_mb1 = 560
if var == "radius2D":
v_ecal = TLine(r_ecal, minimum, r_ecal, maximum)
v_hcal = TLine(r_hcal, minimum, r_hcal, maximum)
v_magnet = TLine(r_magnet, minimum, r_magnet, maximum)
v_mb1 = TLine(r_mb1, minimum, r_mb1, maximum)
v_mb4 = TLine(r_mb4, minimum, r_mb4, maximum)
h_ecal = TLine(minimum, r_ecal, maximum, r_ecal)
h_hcal = TLine(minimum, r_hcal, maximum, r_hcal)
h_magnet = TLine(minimum, r_magnet, maximum, r_magnet)
h_mb1 = TLine(minimum, r_mb1, maximum, r_mb1)
h_mb4 = TLine(minimum, r_mb4, maximum, r_mb4)
elif var == "z":
v_ecal = TLine(z_ecal, minimum, z_ecal, maximum)
v_hcal = TLine(z_hcal, minimum, z_hcal, maximum)
v_magnet = TLine(z_magnet, minimum, z_magnet, maximum)
v_mb1 = TLine(z_mb1, minimum, z_mb1, maximum)
h_ecal = TLine(minimum, z_ecal, maximum, z_ecal)
h_hcal = TLine(minimum, z_hcal, maximum, z_hcal)
h_magnet = TLine(minimum, z_magnet, maximum, z_magnet)
h_mb1 = TLine(minimum, z_mb1, maximum, z_mb1)
else:
v_ecal = TLine(r_ecal, minimum, r_ecal, maximum)
v_hcal = TLine(r_hcal, minimum, r_hcal, maximum)
v_magnet = TLine(r_magnet, minimum, r_magnet, maximum)
v_mb1 = TLine(r_mb1, minimum, r_mb1, maximum)
v_mb4 = TLine(r_mb4, minimum, r_mb4, maximum)
h_ecal = TLine(minimum, r_ecal, maximum, r_ecal)
h_hcal = TLine(minimum, r_hcal, maximum, r_hcal)
h_magnet = TLine(minimum, r_magnet, maximum, r_magnet)
h_mb1 = TLine(minimum, r_mb1, maximum, r_mb1)
h_mb4 = TLine(minimum, r_mb4, maximum, r_mb4)
v_ecal.SetLineColor(2)
h_ecal.SetLineColor(2)
v_hcal.SetLineColor(881)
h_hcal.SetLineColor(881)
v_magnet.SetLineColor(1)
h_magnet.SetLineColor(1)
v_mb1.SetLineColor(801)
v_mb4.SetLineColor(4)
h_mb1.SetLineColor(801)
h_mb4.SetLineColor(4)
v_ecal.SetLineWidth(4)
h_ecal.SetLineWidth(4)
v_hcal.SetLineWidth(4)
h_hcal.SetLineWidth(4)
v_magnet.SetLineWidth(4)
h_magnet.SetLineWidth(4)
v_mb1.SetLineWidth(4)
h_mb1.SetLineWidth(4)
v_mb4.SetLineWidth(3)
h_mb4.SetLineWidth(3)
v_ecal.SetLineStyle(3)
h_ecal.SetLineStyle(3)
v_hcal.SetLineStyle(2)
h_hcal.SetLineStyle(2)
v_magnet.SetLineStyle(4)
h_magnet.SetLineStyle(4)
v_mb1.SetLineStyle(8)
h_mb1.SetLineStyle(8)
v_mb4.SetLineStyle(9)
h_mb4.SetLineStyle(9)
leg = TLegend(1 - 0.9, 0.75, 1 - 0.75, 0.9)
leg.AddEntry(v_ecal, "ECAL", "L")
leg.AddEntry(v_hcal, "HCAL", "L")
leg.AddEntry(v_magnet, "solenoid", "L")
leg.AddEntry(v_mb1, "MB1", "L")
leg.AddEntry(v_mb4, "MB4", "L")
#pal= 68 #kAvocado
#pal= 64 #kAquamarine, very readable
#pal= 75 #kCherry 75, awful
#pal= 85 #kIsland 85, not beautiful but readable
#pal= 86 #kLake 86, too violet
#pal= 87 #kLightTemperature 87, used for trigger
#pal= 91 #kPastel 91, too purple
#pal= 100 #kSolar 100, very red and orange
pal = 98 #kSandyTerrain 98, quite fine
#pal= 99 #kSienna 99, a bit hard to read
gStyle.SetPalette(pal)
gStyle.SetPaintTextFormat(".f")
if part_var == "GenBquarks":
cutstring = "isMC"
cutstring = "(EventWeight * PUReWeight) * ( (HLT_PFMETNoMu120_PFMHTNoMu120_IDTight_v) && nMuonsPassing==0 && nElectronsPassing==0 && nPhotonsPassing==0 && nTausPassing==0 && MEt.pt>200 )"
#cutstring = "(EventWeight * PUReWeight) * (HLT_PFMETNoMu120_PFMHTNoMu120_IDTight_v && MEt.pt>200 && nMuonsPassing==0 && nElectronsPassing==0 && nPhotonsPassing==0 && nTausPassing==0)"
#cutstring = "(EventWeight * PUReWeight)"
#else:
# cutstring = "(EventWeight * PUReWeight) * ( (HLT_PFMETNoMu120_PFMHTNoMu120_IDTight_v && Flag2_globalSuperTightHalo2016Filter && Flag2_goodVertices && Flag2_EcalDeadCellTriggerPrimitiveFilter && Flag2_HBHENoiseFilter && Flag2_HBHEIsoNoiseFilter && Flag2_ecalBadCalibFilter && Flag2_eeBadScFilter && Flag2_BadPFMuonFilter) && nMuonsPassing==0 && nElectronsPassing==0 && nPhotonsPassing==0 && nTausPassing==0 && MEt.pt>200 )"
for i, s in enumerate(sign):
chain[s] = TChain("tree")
if filename == "":
for p, ss in enumerate(samples[s]['files']):
chain[s].Add(NTUPLEDIR + ss + ".root")
else:
chain[s].Add(NTUPLEDIR + filename + ".root")
print "Entries: ", chain[s].GetEntries()
#filename[s] = TFile("VBFH_HToSSTobbbb_MH-125_MS-30_ctauS-1000.root", "READ")
if len(bins) == 0:
hist[s] = TH2F(s, "", nbins, minimum, maximum, nbins, minimum,
maximum)
else:
hist[s] = TH2F(s, "", len(bins) - 1, bins, len(bins) - 1, bins)
hist[s].Sumw2()
if var == "z":
#sign of eta for getting the right z value!
chain[s].Project(
s, "sqrt(pow(" + part_var + "[0].radius,2) - pow(" + part_var +
"[0].radius2D,2)):sqrt(pow(" + part_var +
"[2].radius,2) - pow(" + part_var + "[2].radius2D,2))",
cutstring)
else:
if part_var == "GenBquarks":
chain[s].Project(
s, "" + part_var + "[0]." + var + ":" + part_var + "[2]." +
var + "", cutstring)
else:
chain[s].Project(
s, "" + part_var + "[0]." + var + ":" + part_var + "[1]." +
var + "", cutstring)
hist[s].SetOption("%s" % chain[s].GetTree().GetEntriesFast())
if norm:
hist[s].Scale(100. / hist[s].Integral())
gStyle.SetPaintTextFormat('5.1f')
c1 = TCanvas("c1", "c1", 1000, 1000)
c1.cd()
#c1.SetGrid()
#c1.SetLogz()
c1.SetLogx()
c1.SetLogy()
hist[s].GetYaxis().SetTitle(
"Leading " + particle +
" transverse decay length (cm)") #(""+part_var+"[0] "+var+" (cm)")
hist[s].GetYaxis().SetTitleOffset(1.4)
hist[s].GetXaxis().SetTitle(
"Sub-leading " + particle +
" transverse decay length (cm)") #(""+part_var+"[2] "+var+" (cm)")
hist[s].SetTitle(samples[s]['label'] if filename == "" else filename)
hist[s].SetMarkerColor(0) #(2)#
hist[s].Draw("colz") #()#
v_ecal.Draw("sames")
h_ecal.Draw("sames")
v_hcal.Draw("sames")
h_hcal.Draw("sames")
v_magnet.Draw("sames")
h_magnet.Draw("sames")
v_mb1.Draw("sames")
h_mb1.Draw("sames")
v_mb4.Draw("sames")
h_mb4.Draw("sames")
hist[s].SetMarkerSize(1.2) #(2)#
hist[s].Draw("text,sames") #()#
leg.Draw("sames")
c1.Print(OUTPUTDIR + "2D_gen_b_quark_" + var + "_" +
(s if filename == "" else filename) + string + ".png")
c1.Print(OUTPUTDIR + "2D_gen_b_quark_" + var + "_" +
(s if filename == "" else filename) + string + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Close()
def compareDatasets(trigger, leg, datasets, lineColors, legendLabels, axisTitle, canvas, dataLumi, metHLTFilters, outputSuffix = None):
inputFiles = [TFile.Open("triggerEfficiency_" + x + ".root") for x in datasets]
efficiencies = [x.Get(trigger + "_" + leg) for x in inputFiles]
for iEff in range(len(efficiencies)):
SetStyle(efficiencies[iEff])
efficiencies[iEff].SetLineColor(lineColors[iEff])
efficiencies[iEff].SetMarkerColor(lineColors[iEff])
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
backgroundHist.GetXaxis().SetTitle(axisTitle)
SetStyle(backgroundHist)
canvas.cd()
backgroundHist.Draw()
for eff in efficiencies:
eff.Draw("CP same")
#oneLine.Draw("same")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
if dataLumi > 0:
pt_cmsPrelim.AddText("CMS Preliminary")
else:
pt_cmsPrelim.AddText("CMS Simulation")
pt_cmsPrelim.Draw("same")
if dataLumi > 0:
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText("{:.2f}".format(dataLumi / 1000.0) + " fb^{-1}, 13 TeV")
pt_lumi.Draw("same")
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.025)
pt_leg.SetTextAlign(12)
if leg == "METLeg":
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1]
pt_leg.AddText(legLabel)
if leg == "TrackLeg":
pt_leg.AddText(trigger + "*")
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if leg == "METPath":
if trigger == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
if len(trigger) > 25 and len(trigger.split("_")) > 2:
firstLine = trigger.split("_")[0] + "_" + trigger.split("_")[1] + "_"
pt_leg.AddText(firstLine)
secondLine = ""
for line in trigger.split("_")[2:-1]:
secondLine += line + "_"
secondLine += trigger.split("_")[-1] + "*"
pt_leg.AddText(secondLine)
else:
pt_leg.AddText(trigger + "*")
pt_leg.Draw("same")
legendLabel = trigger
legend = TLegend(0.65, 0.75, 0.93, 0.88)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
if leg == 'METLeg':
legend.SetHeader('MET Leg')
elif leg == 'TrackLeg':
legend.SetHeader('Track Leg')
for iEff in range(len(efficiencies)):
legend.AddEntry(efficiencies[iEff], legendLabels[iEff], 'P')
legend.Draw("same")
if outputSuffix is not None:
canvas.SaveAs('compareDatasets_' + trigger + '_' + leg + '_' + outputSuffix + '.pdf')
else:
canvas.SaveAs('compareDatasets_' + trigger + '_' + leg + '_.pdf')
for inputFile in inputFiles:
inputFile.Close()
return
def limit_canvas(limits_, signal_, oname_):
m = [mp.mass for mp in limits_]
exp = [mp.exp for mp in limits_]
exp68up = [mp.exp68up for mp in limits_]
exp68dn = [mp.exp68dn for mp in limits_]
exp95up = [mp.exp95up for mp in limits_]
exp95dn = [mp.exp95dn for mp in limits_]
obs = [mp.obs for mp in limits_]
N = len(limits_)
gExp = TGraph()
g68 = TGraph(2*N)
g95 = TGraph(2*N)
gObs = TGraph()
gTH = get_theory_XsecBR_graph(signal_)
for a in range(0,N):
gExp.SetPoint(a,m[a],exp[a])
gObs.SetPoint(a,m[a],obs[a])
g68.SetPoint(a,m[a],exp68dn[a])
g95.SetPoint(a,m[a],exp95dn[a])
g68.SetPoint(N+a,m[N-a-1],exp68up[N-a-1])
g95.SetPoint(N+a,m[N-a-1],exp95up[N-a-1])
trans = 0
up = 0
if signal_ == 'n':
trans = 0.770776
up = 3
if signal_ == 'w':
trans = 0.836432
up = 10
if signal_ == 'r':
trans = 0.899902
up = 4
gExp.SetLineStyle(2)
gExp.SetLineWidth(4)
gExp.SetLineColor(TColor.GetColor('#112288'))
#gExp.Print()
g68.SetLineStyle(1)
g68.SetLineWidth(0)
g68.SetLineColor(kBlack)
g68.SetFillColor(TColor.GetColor('#4488dd'))
g95.SetLineStyle(1)
g95.SetLineWidth(0)
g95.SetLineColor(kBlack)
g95.SetFillColor(TColor.GetColor('#99bbff'))
gObs.SetLineStyle(1)
gObs.SetLineWidth(4)
gObs.SetLineColor(kBlack)
gObs.SetMarkerStyle(21)
gObs.SetMarkerSize(0.8)
gTH.SetLineStyle(7)
gTH.SetLineWidth(4)
gTH.SetMarkerSize(0)
gTH.SetMarkerColor(kRed+1)
gTH.SetLineColor(kRed+1)
leg = TLegend(0.58,0.633,0.969,0.908)
leg.SetFillColor(0)
leg.SetBorderSize(0)
leg.AddEntry(gExp,'Expected (95% CL)','l')
leg.AddEntry(gObs,'Observed (95% CL)','l')
leg.AddEntry(gTH,signal_dict[signal_][1],'l')
leg.AddEntry(g68,'#pm1#sigma Expected','f')
leg.AddEntry(g95,'#pm2#sigma Expected','f')
text_TL = TPaveText(0.18,0.830,0.44,0.900,'NDC')
text_TL.AddText(label_TL)
text_TL.SetFillColor(0)
text_TL.SetTextAlign(12)
text_TL.SetTextSize(0.06)
text_TL.SetTextFont(42)
text_TR = TPaveText(0.586,0.923,0.999,0.997,'NDC')
text_TR.AddText(label_TR)
text_TR.SetFillColor(0)
text_TR.SetTextAlign(32)
text_TR.SetTextSize(0.055)
text_TR.SetTextFont(42)
c = TCanvas('c','c',950,750)
c.SetTopMargin(0.08)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.135)
c.SetLeftMargin(0.11)
#c.SetGrid()
c.SetLogy()
#hr = c.DrawFrame(0.401,0.001,3.999,1000)
#hr = c.DrawFrame(0.401,0.001,4.199,1000)
hr = c.DrawFrame(0.401,0.001,5.199,1000)
gExp.Sort()
gTH.Print()
g95.Draw('f')
g95.Print()
g68.Draw('f')
gTH.Draw('L')
gExp.Draw('L')
#Don't draw observed for blind analysis
# gObs.Sort()
# gObs.Draw('L')
hr.GetXaxis().SetTitle('M_{'+signal_dict[signal_][0]+'} [TeV]')
hr.GetYaxis().SetTitle('Upper limit on #sigma_{'+signal_dict[signal_][0]+'} #times B('+signal_dict[signal_][0]+' #rightarrow t#bar{t}) [pb]')
#hr.GetYaxis().SetTitle('\\mathrm{Upper~limit~on~}\\sigma_{'+signal_dict[signal_][0]+'}\\times\\mathscr{B}('+signal_dict[signal_][0]+' \\rightarrow t\\bar{t}) [pb]')
hr.GetXaxis().SetTitleSize(0.055)
hr.GetYaxis().SetTitleSize(0.055)
hr.GetXaxis().SetTitleFont(42)
hr.GetYaxis().SetTitleFont(42)
hr.GetXaxis().SetTitleOffset(1.00)
hr.GetYaxis().SetTitleOffset(0.98)
hr.GetXaxis().SetLabelSize(0.045)
hr.GetYaxis().SetLabelSize(0.045)
name = TString(oname_)
if name.Contains("com"):
tl = TLine(trans, 1e-3, trans, up)
tl.SetLineStyle(ROOT.kDashed)
tl.SetLineColor(kGray+1)
tl.SetLineWidth(3)
tl.Draw()
c.Update()
text_TL.Draw('same')
text_TR.Draw('same')
leg.Draw()
c.SaveAs(oname_+'.pdf')
c.Close()
def plotEfficiency(self, doFit = False):
if hasattr(self, "Denominator") and hasattr(self, "Numerator"):
if self._rebinFactor != 1:
self.Denominator["hist"].Rebin(self._rebinFactor)
self.Numerator["hist"].Rebin(self._rebinFactor)
for ibin in range(-1, self.Numerator["hist"].GetNbinsX()+1):
if self.Numerator["hist"].GetBinContent(ibin+1) > self.Denominator["hist"].GetBinContent(ibin+1):
print('Fixing bad bin:', (ibin+1))
self.Numerator["hist"].SetBinContent(ibin+1, self.Denominator["hist"].GetBinContent(ibin+1))
self.Numerator["hist"].SetBinError(ibin+1, self.Denominator["hist"].GetBinError(ibin+1))
efficiencyGraph = TGraphAsymmErrors(self.Numerator["hist"], self.Denominator["hist"], "cp")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
pt_cmsPrelim.AddText("CMS Preliminary")
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText(self._luminosityLabel)
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.0349127)
pt_leg.SetTextAlign(12)
if self._leg == "METLeg":
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1]
pt_leg.AddText(legLabel)
pt_leg.AddText(self._datasetLabel)
if self._leg == "TrackLeg":
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if self._leg == "METPath":
if self._path == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
lumiLabel = TPaveText(0.66416, 0.937339, 0.962406, 0.992894, "brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str (self._luminosityLabel))
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
if self._leg == "METLeg" or self._leg == "METPath":
backgroundHist.GetXaxis().SetTitle(self._metLegAxisTitle)
elif self._leg == "TrackLeg":
backgroundHist.GetXaxis().SetTitle(self._trackLegAxisTitle)
SetStyle(backgroundHist)
SetStyle(efficiencyGraph)
self._canvas.cd()
backgroundHist.Draw()
efficiencyGraph.Draw("P")
pt_cmsPrelim.Draw("same")
pt_lumi.Draw("same")
pt_leg.Draw("same")
oneLine.Draw("same")
if doFit:
(fitFunc, fitText) = self.PlotFit(efficiencyGraph)
fitFunc.Draw("same")
fitText.Draw("same")
if not os.path.exists('plots_' + self.Denominator["sample"]):
os.mkdir('plots_' + self.Denominator["sample"])
if self._leg == "METPath":
self._canvas.SaveAs('plots_' + self.Denominator["sample"] + '/' + self._path + "_Efficiency.pdf")
else:
self._canvas.SaveAs('plots_' + self.Denominator["sample"] + '/' + self._path + "_" + self._leg + ".pdf")
self._fout.cd()
if self._tgraphSuffix is not None:
efficiencyGraph.Write(self._path + "_" + self._leg + "_" + self._tgraphSuffix)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_" + self._tgraphSuffix + "_fitResult")
else:
efficiencyGraph.Write(self._path + "_" + self._leg)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_fitResult")
else:
print("Denominator and Numerator must be defined for path ", self._path, ", leg ", self._leg)
return 0.0
def printSystematic(self):
fakeRate3, fakeRate3Error, ratio3, ratio3Error = self.printFakeRateRatio(
3)
fakeRate4, fakeRate4Error, ratio4, ratio4Error = self.printFakeRateRatio(
4)
fakeRate5, fakeRate5Error, ratio5, ratio5Error = self.printFakeRateRatio(
5)
fakeRate6, fakeRate6Error, ratio6, ratio6Error = self.printFakeRateRatio(
6)
print("systematic uncertainty: " +
str((abs(ratio5 - 1.0) + ratio5Error) * 100.0) + "%")
basicFakeRate = TH1D("basicFakeRate", ";number of hits", 400, 2.5, 6.5)
zToMuMuFakeRate = TH1D("zToMuMuFakeRate", ";number of hits", 400, 2.5,
6.5)
ratio = TH1D("ratio", ";number of hits", 400, 2.5, 6.5)
basicFakeRate.SetBinContent(basicFakeRate.FindBin(3.0), fakeRate3[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(3.0),
fakeRate3Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(4.0), fakeRate4[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(4.0),
fakeRate4Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(5.0), fakeRate5[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(5.0),
fakeRate5Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(6.0), fakeRate6[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(6.0),
fakeRate6Error[0])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(3.0),
fakeRate3[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(3.0),
fakeRate3Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(4.0),
fakeRate4[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(4.0),
fakeRate4Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(5.0),
fakeRate5[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(5.0),
fakeRate5Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(6.0),
fakeRate6[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(6.0),
fakeRate6Error[1])
ratio.SetBinContent(ratio.FindBin(3.0), ratio3)
ratio.SetBinError(ratio.FindBin(3.0), ratio3Error)
ratio.SetBinContent(ratio.FindBin(4.0), ratio4)
ratio.SetBinError(ratio.FindBin(4.0), ratio4Error)
ratio.SetBinContent(ratio.FindBin(5.0), ratio5)
ratio.SetBinError(ratio.FindBin(5.0), ratio5Error)
ratio.SetBinContent(ratio.FindBin(6.0), ratio6)
ratio.SetBinError(ratio.FindBin(6.0), ratio6Error)
cmsLabel = TPaveText(0.134085, 0.937984, 0.418546, 0.984496, "brNDC")
cmsLabel.SetBorderSize(0)
cmsLabel.SetFillStyle(0)
cmsLabel.SetTextFont(62)
cmsLabel.SetTextSize(0.0387597)
cmsLabel.AddText("CMS Preliminary")
lumiLabel = TPaveText(0.66416, 0.937339, 0.962406, 0.992894, "brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str(self._luminosityLabel))
leg = TLegend(0.7550251, 0.7759067, 0.9459799, 0.8795337)
leg.SetBorderSize(0)
leg.SetTextSize(0.0388601)
leg.SetTextFont(42)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.AddEntry(zToMuMuFakeRate, "Z#rightarrow#mu#mu", "p")
leg.AddEntry(basicFakeRate, "basic", "p")
setStyle(basicFakeRate, 632)
setStyle(zToMuMuFakeRate, 600)
setAxisStyle(basicFakeRate, yTitle="P_{fake}")
setAxisStyle(zToMuMuFakeRate, yTitle="P_{fake}")
self._canvas.cd()
self._canvas.SetLogy()
zToMuMuFakeRate.Draw()
basicFakeRate.Draw("same")
leg.Draw("same")
cmsLabel.Draw("same")
lumiLabel.Draw("same")
self._fout.cd()
self._canvas.Write("fakeRate")
line = TLine(2.5, 1.0, 6.5, 1.0)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.SetLineColor(1)
setStyle(ratio)
setAxisStyle(
ratio,
yTitle=("P_{fake}^{Z#rightarrow#mu#mu} / P_{fake}^{basic}"
if not self._plotDiff else
"P_{fake}^{Z#rightarrow#mu#mu} - P_{fake}^{basic}"))
self._canvas.cd()
self._canvas.SetLogy(False)
ratio.Draw()
line.Draw("same")
cmsLabel.Draw("same")
lumiLabel.Draw("same")
self._fout.cd()
self._canvas.Write("fakeRateRatio")
class vertex_displayer(object):
"""Debug class for displaying vertices, tracks and impact parameters on the transverse plane, based on ROOT classes.
"""
def __init__(self,
name,
title,
lenght,
time_min,
time_max,
helix,
n_points=100,
ip_algorithm='complex'):
"""
Constructor providing:
name: string used to identify ROOT objects
title: string used as title of canvases and graphs
lenght: half of the axis dimension
time_min and time_max: defining the time interval for which the track is shown
helix: helix object to be shown; having the ip attributes is mandatory
n_points: number of points that show the track
ip_algorithm: if 'complex' it shows also the distance of
closest approach to the jet direction, and the linearized track at
that point. It requires to have run the function compute_IP_wrt_direction
on the helix object.
"""
self.name = name
self.title = title
self.helix = helix
self.ip_algorithm = ip_algorithm
self.gr_transverse = TGraph()
self.gr_transverse.SetNameTitle("g_tr_" + name, title)
self.gr_transverse.SetPoint(0, 0., 0.)
self.gr_transverse.SetPoint(1, 0., lenght)
self.gr_transverse.SetPoint(2, 0., -lenght)
self.gr_transverse.SetPoint(3, lenght, 0.)
self.gr_transverse.SetPoint(4, -lenght, 0.)
self.gr_transverse.SetMarkerStyle(22)
self.gr_transverse.SetMarkerColor(3)
self.gr_transverse.GetXaxis().SetTitle("X axis")
self.gr_transverse.GetYaxis().SetTitle("Y axis")
# Origin
self.gr_transverse.SetPoint(5, helix.origin.X(), helix.origin.Y())
# Track
self.ell_transverse = TGraph()
self.ell_transverse.SetNameTitle("g_ellipse_" + name, title)
self.ell_transverse.SetMarkerStyle(7)
self.ell_transverse.SetMarkerColor(1)
for i in range(n_points):
time_coord = time_min + i * (time_max - time_min) / n_points
point = helix.point_at_time(time_coord)
self.ell_transverse.SetPoint(i, point.X(), point.Y())
# Jet direction
jet_dir_x1 = self.helix.primary_vertex.X(
) - lenght * self.helix.jet_direction.X()
jet_dir_x2 = self.helix.primary_vertex.X(
) + lenght * self.helix.jet_direction.X()
jet_dir_y1 = self.helix.primary_vertex.Y(
) - lenght * self.helix.jet_direction.Y()
jet_dir_y2 = self.helix.primary_vertex.Y(
) + lenght * self.helix.jet_direction.Y()
self.jet_dir = TLine(jet_dir_x1, jet_dir_y1, jet_dir_x2, jet_dir_y2)
self.jet_dir.SetLineColor(6)
self.jet_dir.SetLineStyle(1)
self.jet_dir.SetLineWidth(1)
# Impact Parameter D
impact_parameter_x1 = self.helix.primary_vertex.X()
impact_parameter_y1 = self.helix.primary_vertex.Y()
impact_parameter_x2 = self.helix.vector_impact_parameter.X()
impact_parameter_y2 = self.helix.vector_impact_parameter.Y()
self.impact_parameter = TLine(impact_parameter_x1, impact_parameter_y1,
impact_parameter_x2, impact_parameter_y2)
self.impact_parameter.SetLineColor(4)
self.impact_parameter.SetLineStyle(6)
self.impact_parameter.SetLineWidth(4)
if self.ip_algorithm == 'complex':
# Linearized_track
lin_track_x1 = self.helix.linearized_track.origin.X() \
- lenght * self.helix.linearized_track.direction.X()
lin_track_x2 = self.helix.linearized_track.origin.X() \
+ lenght * self.helix.linearized_track.direction.X()
lin_track_y1 = self.helix.linearized_track.origin.Y() \
- lenght * self.helix.linearized_track.direction.Y()
lin_track_y2 = self.helix.linearized_track.origin.Y() \
+ lenght * self.helix.linearized_track.direction.Y()
self.lin_track = TLine(lin_track_x1, lin_track_y1, lin_track_x2,
lin_track_y2)
self.lin_track.SetLineColor(2)
self.lin_track.SetLineStyle(9)
self.lin_track.SetLineWidth(2)
# Jet-track distance D_j: vector from S_j to S_t
jet_track_distance_x1 = self.helix.jet_point_min_approach.X()
jet_track_distance_y1 = self.helix.jet_point_min_approach.Y()
jet_track_distance_x2 = self.helix.point_min_approach.X()
jet_track_distance_y2 = self.helix.point_min_approach.Y()
self.jet_track_distance = TLine(jet_track_distance_x1,
jet_track_distance_y1,
jet_track_distance_x2,
jet_track_distance_y2)
self.jet_track_distance.SetLineColor(3)
self.jet_track_distance.SetLineStyle(3)
self.jet_track_distance.SetLineWidth(4)
def draw(self):
"""Draw the canvas showing the transverse plane to the beam axis.
In particular it shows the x-y axis, the track, the jet direction and
the impact parameter.
If the ip_algorithm 'complex' is enabled it also shows the distance of
closest approach to the jet direction, and the linearized track at
that point.
"""
self.c_transverse = TCanvas("c_tr_" + self.name, self.title, 800, 600)
self.c_transverse.cd()
self.c_transverse.SetGrid()
self.gr_transverse.Draw("AP")
self.ell_transverse.Draw("same LP")
self.jet_dir.Draw("same")
self.impact_parameter.Draw("same")
if self.ip_algorithm == 'complex':
self.lin_track.Draw("same")
self.jet_track_distance.Draw("same")
def plot_dir(dlist,
binfo,
outfile,
prefix,
infix,
color=[kBlack],
legend=[],
norm=False):
c = TCanvas()
c.SetGrid(0)
bnarr = binfo.keys()
bnarr.sort()
bncode = reduce(lambda x, y: x + y, bnarr)
namex = bnarr[-2]
namey = bnarr[-1]
xarr = binfo[namex]
yarr = binfo[namey]
c.GetPad(0).SetMargin(0.2, 0.15, 0.2, 0.15)
c.Divide(len(xarr) - 1, len(yarr) - 1, 0.0, 0.0)
#CanvasPartition(c,len(xarr)-1,len(yarr)-1)
Ntot = 1
for k in range(len(bnarr) - 2):
Ntot *= len(binfo[bnarr[k]]) - 1
for k in range(Ntot):
indarr = []
fact = 1
for n in range(len(bnarr) - 2):
edg = binfo[bnarr[n]]
indarr.append((k / fact) % (len(edg) - 1))
fact *= len(binfo[bnarr[n]]) - 1
for j in range(len(yarr) - 1, 0, -1):
YMAX = -100000000.
YMIN = 100000000.
row = j
for i in range(len(xarr) - 1):
col = i + 1
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
bcode = str(bcode) + str(col - 1) + str(row - 1)
cnt = 0
for d in dlist:
h = d.Get(prefix[cnt] + bncode + "_" + bcode)
maxb = h.GetMaximumBin()
minb = h.GetMinimumBin()
ymaxtest = h.GetBinContent(maxb)
ymintest = h.GetBinContent(minb)
# if True:
# print "before::" + d.GetPath() + "/" + h.GetName() + "::row::" + str(row) + ":: ymax / integral / bin / content(bin): " + str(ymaxtest) + " / " + str(h.Integral()) + " / " + str( maxb) + " / " + str( h.GetBinContent(maxb))
if norm and h.Integral() != 0:
ymaxtest = ymaxtest / h.Integral()
ymintest = ymintest / h.Integral()
if norm and h.Integral() == 0:
ymaxtest = 0
ymintest = 0
if ymaxtest > YMAX:
YMAX = ymaxtest
if ymintest < YMIN:
YMIN = ymintest
cnt += 1
# if norm:
# print bncode + "::row::" + str(row) + "::ymax: " + str(YMAX)
for i in range(len(xarr) - 1):
col = i + 1
pad = c.cd(col + (len(yarr) - 1 - row) * (len(xarr) - 1))
pad.SetGridy(0)
#pad.SetGridx(0)
pad.SetTicks(0, 0)
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
bcode = str(bcode) + str(col - 1) + str(row - 1)
cnt = 0
leg = TLegend(0.77, 0.64, 0.95, 0.89)
leg.SetTextSize(0.15)
for d in dlist:
h = d.Get(prefix[cnt] + bncode + "_" + bcode)
h.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
if "ac" in prefix[cnt] or "Acceptance" in d.GetPath():
h.GetYaxis().SetRangeUser(0, 1)
h.GetYaxis().SetLabelSize(0.15)
h.GetXaxis().SetLabelSize(0.1)
h.GetYaxis().SetLabelSize(0.15)
xtitle = str(
binfo[namex][col - 1]) + '<' + namex + '<' + str(
binfo[namex][col]) + " " + prefix[cnt].split(
'_')[0][1:]
h.GetXaxis().SetTitle(xtitle)
h.GetXaxis().CenterTitle()
h.GetXaxis().SetTitleSize(0.12)
ytitle = str(
binfo[namey][row - 1]) + '<' + namey + '<' + str(
binfo[namey][row])
h.GetXaxis().SetNdivisions(505)
h.GetYaxis().SetNdivisions(505)
h.SetLineWidth(3)
h.SetLineColor(color[cnt])
h.SetMarkerColor(color[cnt])
if (legend != []):
le = leg.AddEntry(h, legend[cnt], 'lp')
le.SetTextColor(color[cnt])
if cnt == 0:
if not norm: h.DrawCopy()
elif (h.Integral() != 0):
hnorm = h.DrawNormalized("e")
hnorm.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
else:
h.DrawCopy()
if 'hM_' in h.GetName():
rhopeak = TLine(0.77, 0, 0.77, YMAX)
rhopeak.SetLineStyle(kDashed)
rhopeak.SetLineWidth(1)
rhopeak.SetLineColor(kRed)
rhopeak.DrawLine(0.77, 0, 0.77, YMAX)
pad.Update()
pt = pad.FindObject("title")
pt.SetX1NDC(0.15)
pt.SetTextSizePixels(12)
if col == len(xarr) - 1:
yaxis = TGaxis(pad.GetUxmax(), pad.GetUymin(),
pad.GetUxmax(), pad.GetUymax(), 0,
1, 1, "U+-")
yaxis.SetTitle(ytitle + " ")
yaxis.SetTitleSize(0.12)
yaxis.SetTitleOffset(-0.25)
yaxis.SetName(h.GetName() + "_ax_y")
yaxis.Draw("")
pad.GetListOfPrimitives().Add(yaxis)
pad.Update()
else:
if not norm: h.DrawCopy("same")
elif (h.Integral() != 0):
hnorm = h.DrawNormalized("samee")
hnorm.GetYaxis().SetRangeUser(YMIN, YMAX * 1.1)
else:
h.DrawCopy("samee")
cnt += 1
if (legend != []):
leg.Draw()
pad.GetListOfPrimitives().Add(leg)
outfile.cd()
bcode = reduce(lambda x, y: str(x) + str(y), indarr)
cncode = reduce(lambda x, y: x + y, bnarr[-3])
c.Write("can_" + infix + "_" + str(cncode) + "_" + str(bcode),
TObject.kOverwrite)
def runMCStudy():
# load workspace
f = TFile("./data/fitWorkspace.root")
fitWorkspace = f.Get("fitWorkspace")
fEnergy = fitWorkspace.var("energy_keV")
model = fitWorkspace.pdf("model")
fitResult = fitWorkspace.allGenericObjects().front()
fitValsFinal = getRooArgDict(fitResult.floatParsFinal())
pdfList = fitWorkspace.allPdfs() # RooArgSet
nExpected = int(model.expectedEvents(pdfList))
# thank you, lindsey gray. http://www.hep.wisc.edu/~lgray/toyMC.py
nSamples = 10
args = [
# RF.Binned(True),
RF.Silence(),
RF.FitOptions(RF.PrintEvalErrors(0),
RF.NumCPU(2)) # not sure numCPU is doing anything
]
mcStudy = ROOT.RooMCStudy(model, ROOT.RooArgSet(fEnergy), *args)
start = time.time()
mcStudy.generateAndFit(
nSamples, nExpected) # nSamples (nToys), nEvtPerSample (nEvents)
print "MC Study Complete. %i samples, %.2f seconds." % (
nSamples, time.time() - start)
parName = "amp-36Cl"
parVal = fitResult.floatParsFinal().find(parName).getValV()
parErr = fitResult.floatParsFinal().find(parName).getError()
parVar = fitWorkspace.var(parName)
# make 4 rooplot frames
fParam = mcStudy.plotParam(parVar, RF.Bins(50))
fError = mcStudy.plotError(
parVar, RF.FrameRange(parErr - 0.5 * parErr, parErr + 0.5 * parErr),
RF.Bins(50))
fPull = mcStudy.plotPull(parVar, RF.FrameRange(-5, 5), RF.Bins(50))
fNLL = mcStudy.plotNLL(RF.Bins(50))
line = TLine()
line.SetLineColor(ROOT.kBlue)
line.SetLineWidth(2)
line.SetLineStyle(3)
c = TCanvas("c", "c", 1100, 800)
c.Divide(2, 2)
c.cd(1)
fParam.SetTitle(parName)
fParam.Draw()
line.DrawLine(parVal, fParam.GetMinimum(), parVal,
fParam.GetMaximum()) # best fit
c.cd(2)
fError.SetTitle(parName + " Error")
fError.Draw()
line.DrawLine(parErr, fError.GetMinimum(), parErr, fError.GetMaximum())
c.cd(3)
fPull.SetTitle(parName + " Pull")
fPull.Draw()
c.cd(4)
fNLL.SetTitle("-log(likelihood)")
fNLL.Draw()
line.DrawLine(fitResult.minNll(), fNLL.GetMinimum(), fitResult.minNll(),
fNLL.GetMaximum())
c.Print("plots/mcStudy.pdf")
"""
def drawLine(x1, y1, x2, y2):
line = TLine(x1, y1, x2, y2)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.Draw()
return line
# ____________________________________________________________________________
# Setup basic drawer
gROOT.LoadMacro("tdrstyle.C")
gROOT.ProcessLine("setTDRStyle();")
#gROOT.SetBatch(True)
gStyle.SetPadGridX(True)
gStyle.SetPadGridY(True)
#gStyle.SetPadRightMargin(0.05)
#gStyle.SetTitleOffset(0.9, "Y")
#gStyle.SetTitleOffset(1.2, "Y")
gROOT.ForceStyle()
tline = TLine()
tline.SetLineColor(920 + 2) # kGray+2
tline.SetLineStyle(2)
tlatexCMS1 = TLatex()
tlatexCMS1.SetNDC()
tlatexCMS1.SetTextFont(61)
tlatexCMS1.SetTextSize(0.75 * 0.05)
tlatexCMS2 = TLatex()
tlatexCMS2.SetNDC()
tlatexCMS2.SetTextFont(52)
tlatexCMS2.SetTextSize(0.60 * 0.05)
tlatexCMS3 = TLatex()
tlatexCMS3.SetNDC()
tlatexCMS3.SetTextFont(42)
tlatexCMS3.SetTextSize(0.60 * 0.05)
def __plot_profiles_in_same_canvas(self, hist_objs, hist_names, legends,
y_limits, **kwargs):
legend_position = retrieve_kw(kwargs, 'legend_position',
(0.36, 0.20, 0.66, 0.36))
legend_prefix = retrieve_kw(kwargs, 'legend_prefix',
'Z#rightarrowee, ')
legend_header = retrieve_kw(kwargs, 'legend_header', 'Trigger step')
ylabel = retrieve_kw(kwargs, 'ylabel', 'Trigger Efficiency')
title = retrieve_kw(kwargs, 'title', 'Trigger Efficiency')
oname = retrieve_kw(kwargs, 'oname', 'plot_efficiencys')
column = retrieve_kw(kwargs, 'column', 2)
doRatio = retrieve_kw(kwargs, 'doRatio', False)
canvas_size = retrieve_kw(kwargs, 'canvas_size', (1800, 1500))
# FIXME: This must be disable for now, there is some problem into the xaxis scale
# The top and bot axis must be match!
tobject_collector = []
ratio_size_as_fraction = 0.35
from ROOT import TCanvas, TLegend, TProfile, TPad
rows = int(round(len(hist_objs) / float(column)))
canvas = TCanvas('canvas', 'canvas', canvas_size[0], canvas_size[1])
canvas.Divide(rows, column)
leg_holder = []
for index, hist_str in enumerate(hist_names):
hists = hist_objs[hist_str]
pad = canvas.cd(index + 1)
# Force disable if != of 2
if len(hists) != 2:
doRatio = False
if doRatio:
drawopt = 'pE1'
pad.cd()
top = TPad("pad_top", "This is the top pad", 0.0,
ratio_size_as_fraction, 1.0, 1.0)
top.SetBottomMargin(0.0)
top.SetBottomMargin(0.06 / float(top.GetHNDC()))
#top.SetTopMargin (0.04/float(top.GetHNDC()))
top.SetRightMargin(0.05)
top.SetLeftMargin(0.16)
top.SetFillColor(0)
top.Draw(drawopt)
tobject_collector.append(top)
pad.cd()
bot = TPad("pad_bot", "This is the bottom pad", 0.0, 0.0, 1.0,
ratio_size_as_fraction)
bot.SetBottomMargin(0.10 / float(bot.GetHNDC()))
#bot.SetTopMargin (0.02/float(bot.GetHNDC()))
bot.SetTopMargin(0.0)
bot.SetRightMargin(0.05)
bot.SetLeftMargin(0.16)
bot.SetFillColor(0)
bot.Draw(drawopt)
tobject_collector.append(bot)
if type(legend_prefix) is not list:
legend_prefix = [legend_prefix] * len(legends)
if doRatio:
top.cd()
leg = TLegend(legend_position[0], legend_position[1],
legend_position[2], legend_position[3])
from AtlasStyle import setLegend1, atlas_template
setLegend1(leg)
leg.SetHeader(legend_header)
for i, eff in enumerate(hists):
eff.SetLineColor(self._curve_color[i])
eff.SetMarkerColor(self._curve_color[i])
eff.SetMarkerStyle(self._marker_style[i])
if type(eff) is TProfile: eff.SetStats(0)
leg.AddEntry(eff, legend_prefix[i] + legends[i], 'p')
if i is 0: eff.Draw()
else: eff.Draw('same')
leg.SetTextSize(0.03)
leg.SetBorderSize(0)
leg.Draw()
if doRatio:
top.Modified()
top.Update
pad.Modified()
pad.Update()
from ROOT import TProfile, TEfficiency, kFALSE
xmin = 0
xmax = 999
if type(hists[0]) is TProfile:
hists[0].GetYaxis().SetRangeUser(y_limits[index][0],
y_limits[index][1])
elif type(hists[0]) is TEfficiency:
hists[0].GetPaintedGraph().GetYaxis().SetRangeUser(
y_limits[index][0], y_limits[index][1])
hists[0].GetPaintedGraph().GetYaxis().SetTitle('A')
# Fix the X axis
nbins = hists[0].GetTotalHistogram().GetNbinsX()
xmin = hists[0].GetTotalHistogram().GetXaxis().GetBinLowEdge(1)
xmax = hists[0].GetTotalHistogram().GetXaxis().GetBinLowEdge(
nbins + 1)
hists[0].GetPaintedGraph().GetXaxis().SetLimits(xmin, xmax)
else:
hists[0].GetYaxis().SetRangeUser(y_limits[index][0],
y_limits[index][1])
pad.Modified()
pad.Update()
tobject_collector.append(leg)
if doRatio:
atlas_template(top, **kwargs)
top.Update()
from ROOT import TH1
divide = ""
drawopt = 'pE1'
bot.cd()
ref = hists[0].GetPassedHistogram().Clone()
h = hists[1].GetPassedHistogram().Clone()
#ref = hists[0].Clone()
#h = hists[1].Clone()
ref.Sumw2()
h.Sumw2()
ref.Divide(hists[0].GetTotalHistogram().Clone())
h.Divide(hists[1].GetTotalHistogram().Clone())
#ratioplot = TEfficiency(h,ref)
ratioplot = h.Clone()
ratioplot.Sumw2()
ratioplot.SetName(h.GetName() + '_ratio')
ratioplot.Divide(h, ref, 1., 1., '')
#ratioplot.Divide(ref)
ratioplot.SetFillColor(0)
ratioplot.SetFillStyle(0)
ratioplot.SetMarkerColor(1)
ratioplot.SetLineColor(kBlack)
ratioplot.SetMarkerStyle(24)
ratioplot.SetMarkerSize(1.2)
ratioplot.GetYaxis().SetTitle('trigger / ref')
ratioplot.GetYaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetTitleSize(0.10)
ratioplot.GetXaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetLabelSize(0.10)
ratioplot.GetYaxis().SetRangeUser(0.9, 1.07)
ratioplot.Draw(drawopt)
tobject_collector.append(ratioplot)
#atlas_template(bot, **kwargs)
from ROOT import TLine
l1 = TLine(xmin, 1, xmax, 1)
l1.SetLineColor(kRed)
l1.SetLineStyle(2)
l1.Draw()
tobject_collector.append(l1)
bot.Update()
else:
atlas_template(pad, **kwargs)
pad.Update()
canvas.SaveAs(oname + '.pdf')