def jetPt(h, prefix="", rebin=10):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Jet p_{T} (GeV/c)"
ylabel = "Number of jets / %.0f GeV/c" % h.binWidth()
ptcut = 30
ymin = 1e-1
xmax = 400
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"jet_pt_log", xmax=xmax, ymin=ymin, yfactor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"jet_pt_log_cut%d"%ptcut, xmin=ptcut, xmax=xmax, ymin=ymin, yfactor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def tauCandPt(h, step="", rebin=2):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events /%.0f GeV/c" % h.binWidth()
xlabel = "p_{T}^{#tau candidate} (GeV/c)"
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "tauCandidatePt_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def transverseMass(h, prefix="", rebin=1, ratio=False):
xlabel = "m_{T}(#mu, E_{T}^{miss}) (GeV)"
ylabel = "Events / %.0f GeV"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"mt", opts=_optsLin)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"mt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def transverseMass(h, rebin=5, ratio=False, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = prefix+"_"+h.name
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name += "_log"
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix=""):
xlabel = "Number of vertices"
ylabel = "A.u."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {"xmax": 16}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10}
opts_log.update(opts)
h.createFrame(prefix + "vertices" + postfix, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
h.addStandardTexts()
h.save()
h.createFrame(prefix + "vertices" + postfix + "_log", opts=opts_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
h.addStandardTexts()
h.save()
def tauCandPt(h, step="", rebin=2):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events /%.0f GeV/c" % h.binWidth()
xlabel = "p_{T}^{#tau candidate} (GeV/c)"
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "tauCandidatePt_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def transverseMass(h, rebin=5, ratio=False, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = prefix + "_" + h.name
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name += "_log"
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix=""):
xlabel = "Number of vertices"
ylabel = "Number of events"
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"vertices"+postfix)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", ymin=0.1, factor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix + "vertices" + postfix,
opts=opts,
createRatio=ratio,
opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix + "vertices" + postfix + "_log",
opts=opts_log,
createRatio=ratio,
opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix+"vertices"+postfix, opts=opts, createRatio=ratio, opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", opts=opts_log, createRatio=ratio, opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def muonIsoQcd(plot, prefix=""):
plot.createFrame(prefix+"muon_qcdfraction")
plot.frame.GetXaxis().SetTitle("Cut on isolation")
plot.frame.GetYaxis().SetTitle("Fraction of QCD")
plot.setLegend(histograms.createLegend())
plot.draw()
plot.save()
plot.createFrame(prefix+"muon_qcdfraction_zoom", xmax=0.2, ymax=0.1)
plot.frame.GetXaxis().SetTitle("Cut on isolation")
plot.frame.GetYaxis().SetTitle("Fraction of QCD")
plot.setLegend(histograms.createLegend())
plot.draw()
plot.save()
def jetEta(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
particle = "jet"
if "bjet" in name:
particle = "b jet"
if "electron" in name:
particle = "electron"
if "muon" in name:
particle = "muon"
xlabel = "#eta^{%s}" % particle
# xlabel = "#eta^{muon}"
ylabel = "Events / %.2f" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.01,"xmin": -3.5,"xmax": 3.5, "ymaxfactor": 10}
opts2 = {"ymin": 0.05, "ymax": 1.5}
name = name+"_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.7, 0.9, 0.95))
# h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def jetPt(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
particle = "jet"
if "bjet" in name:
particle = "b jet"
if "electron" in name:
particle = "electron"
if "muon" in name:
particle = "muon"
# name = name.replace("jetPt", "bjetPt")
xlabel = "p_{T}^{%s} (GeV/c)" % particle
# xlabel = "p_{T}^{muon} (GeV/c)"
ylabel = "Events /%.0f GeV/c" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=False)
h.addMCUncertainty()
opts = {"ymin": 0.001,"xmax": 400.0, "ymaxfactor": 2}
opts2 = {"ymin": 0.05, "ymax": 1.5}
name = name+"_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
# h.createFrame(name, opts=opts)
#h.createFrameFraction(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.65, 0.9, 0.9))
common(h, xlabel, ylabel)
def transverseMass(h, rebin=20):
name = flipName(h.getRootHistoPath())
particle = ""
if "Original" in name:
particle = "#mu"
name = name.replace("TransverseMass", "Mt")
else:
particle = "#tau jet"
name = name.replace("TransverseMass", "Mt")
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=False)#stackSignal=True)
h.addMCUncertainty()
opts = {"xmax": 200}
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def deltaPhi(h, rebin=40, ratio=False):
name = flipName(h.getRootHistoPath())
particle = "#tau jet"
if "Original" in name:
particle = "#mu"
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#Delta#phi(%s, MET) (rad)" % particle
ylabel = "Events / %.2f rad" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.001, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
#h.createFrameFraction(name)
h.createFrame(name)
# h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.2, 0.6, 0.4, 0.9))
common(h, xlabel, ylabel)
def met2(h, name, rebin=30, ratio=True):
# name = h.getRootHistoPath()
# name = "met"
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
# xlabel = "MET (GeV)"
# if "embedding" in name:
# xlabel = "Embedded "+xlabel
# elif "original" in name:
# xlabel = "Original "+xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
xlabel = "E_{T}^{miss} (GeV)"
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.01, "ymaxfactor": 2}
opts2 = {"ymin": 0.0, "ymax": 2.5}
name = name+"_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.65, 0.55, 0.9, 0.9))
common(h, xlabel, ylabel)
def muonPhi(h, prefix="", plotAll=False, rebin=1):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Muon #phi"
ylabel = "Number of muons / %.1f" % h.binWidth()
h.stackMCHistograms()
if plotAll:
h.createFrame(prefix+"muon_phi", yfactor=1.4)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"muon_phi_log", yfactor=2, ymin=0.1)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy()
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def selectionFlow(h, name, rebin=1, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Cut"
ylabel = "Events"
#h.stackMCSignalHistograms()
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
njets = 5
lastSelection = njets
opts = {"xmax": lastSelection, "ymin": 0.1, "ymaxfactor": 2, "nbins": lastSelection}
opts2 = {"ymin": 0.5, "ymax": 1.5}
h.createFrame(name, opts=opts, createRatio=ratio, opts2=opts2)
xaxis = h.getFrame().GetXaxis()
xaxis.SetBinLabel(1, "Trigger")
xaxis.SetBinLabel(2, "#tau ID+R_{#tau}")
xaxis.SetBinLabel(3, "e veto")
xaxis.SetBinLabel(4, "#mu veto")
xaxis.SetBinLabel(5, "N_{jets}")
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
addMassBRText(x=0.4, y=0.87)
h.save()
def tauCandEta(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#eta^{#tau candidate}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
# opts = {"xmax": 2.5,"xmin":-2.5}
# opts["xmin"] = -2.7
# opts["xmax"] = 2.7
name = "tauCandidateEta_%s_log" % step
# h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.2, 0.7, 0.5))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def jetEta(h, step="", rebin=1):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#eta^{jet}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "jetEtaSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.3, 0.7, 0.6))
ROOT.gPad.SetLogy(True)
h.draw()
h.addStandardTexts()
h.save()
def rtauGen(h, name, rebin=5, ratio=False):
#h.setDefaultStyles()
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "R_{#tau}"
if "Mass" in name:
xlabel = "m (GeV/c^{2})"
elif "Pt" in name:
xlabel = "p_{T}(GeV/c)"
elif "vertices" in name:
xlabel = "N_{vertices}"
ylabel = "Events / %.2f" % h.binWidth()
if "gen" in name:
kwargs = {"ymin": 0.1, "xmax": 1.1}
elif "Pt" in name:
kwargs = {"ymin": 0.1, "xmax": 400}
elif "Mass" in name:
kwargs = {"ymin": 0.1, "xmax": 500}
kwargs = {"ymin": 0.1, "xmax": 500}
# kwargs["opts"] = {"ymin": 0, "xmax": 14, "ymaxfactor": 1.1}}
if ratio:
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# name = name+"_log"
h.createFrame(name, **kwargs)
# h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.2, 0.75, 0.4, 0.9))
common(h, xlabel, ylabel, addLuminosityText=False)
def deltaPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "R_{#tau}"
ylabel = "Events / %.2f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "deltaPhiSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.3, 0.7, 0.6))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
plot = plots.DataMCPlot(datasets, analysis+"/MET/met")
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
plot.createFrame("MET", opts={"ymin": 1e-4, "ymaxfactor": 10})
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend())
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Number of events")
plot.draw()
plot.addLuminosityText()
plot.save()
def leadingTrack(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{leading track} (GeV/c)"
ylabel = "Events / %.0f GeV/c" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "leadingTrackSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
h.addStandardTexts()
h.save()
def ptTop(h, name, rebin=10, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
# particle = "jet"
# if "bjet" in name:
# particle = "bjet"
# name = name.replace("jetPt", "bjetPt")
xlabel = "p_{T}^{top} (GeV/c)"
ylabel = "Events /%.0f GeV/c" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.0001, "xmax": 500, "ymaxfactor": 1.1}
opts2 = {"ymin": 0.01, "ymax": 1.5}
name = name+"_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
# h.createFrame(name)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def doPlot(datasetsEmb, analyses, path, name, text):
histos = []
legends = {"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"}
for aname, analysis in analyses:
(rootHisto, tmp) = datasetsEmb.getHistogram("Data", analysis+"/"+path)
h = histograms.Histo(rootHisto, aname)
h.setLegendLabel(legends.get(aname, aname))
h.setDrawStyle("EP")
h.setLegendStyle("p")
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
styles.dataStyle(p.histoMgr.getHisto("Baseline"))
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasetsEmb.getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 35})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.appendPlotObject(histograms.PlotText(0.5, 0.55, text, size=20))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
p.addStandardTexts()
p.save()
def tauEta(h, name, rebin=5, ratio=False):
# name = flipName(h.getRootHistoPath())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#eta^{#tau jet}"
ylabel = "Events"
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
opts = {"ymin": 0.01, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# name = "selectedTauEta"
# name = name+"_log"
#h.createFrameFraction(name, opts=opts)
# h.createFrame(name, opts=opts)
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
common(h, xlabel, ylabel)
def met(h, rebin=20, ratio=False):
name = flipName(h.getRootHistoPath())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "MET (GeV)"
# if "embedding" in name:
# xlabel = "Embedded "+xlabel
# elif "original" in name:
# xlabel = "Original "+xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.001, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
name = "MET"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def drawPlotData(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, ratio=True, opts={}, opts2={}, moveLegend={}, **kwargs):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
#scaleNormalization(h)
#h.stackMCHistograms()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
if log:
name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
h.getPad().SetLogy(log)
if ratio:
h.getFrame2().GetYaxis().SetTitle("Ratio")
#yaxis = h.getFrame2().GetYaxis()
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.7)
#yaxis.SetTitleOffset(yaxis.GetTitleOffset()*1.5)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
common(h, xlabel, ylab, **kwargs)
def transverseMass(h, name="transverseMass", rebin=2, log=True):
# name = h.getRootHistoPath()
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, MET) (GeV/c^{2})"
ylabel = "A.u."
# h.stackMCHistograms()
# h.addMCUncertainty()
opts = {"xmax": 200}
# opts = {"ymin": 0.0001,"xmax": 200, "ymaxfactor": 1.3}
# opts2 = {"ymin": 0.5, "ymax": 1.5}
# opts = {"xmax": 200}
if log:
name += "_log"
opts["ymin"] = 1e-4
opts["ymaxfactor"] = 2
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
legend = histograms.createLegend()
if log:
h.getPad().SetLogy(True)
histograms.moveLegend(legend, dx=-0.5, dy=-0.3)
h.setLegend(legend)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# h.addLuminosityText()
h.save()
def jetPt(h, step="", rebin=1):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events /%.0f GeV/c" % h.binWidth()
xlabel = "p_{T}^{jet} (GeV/c)"
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "jetPtSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
h.addStandardTexts()
h.save()
def jetPt(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
particle = "jet"
if "bjet" in name:
particle = "b jet"
if "electron" in name:
particle = "electron"
if "muon" in name:
particle = "muon"
# name = name.replace("jetPt", "bjetPt")
xlabel = "p_{T}^{%s} (GeV/c)" % particle
# xlabel = "p_{T}^{muon} (GeV/c)"
ylabel = "Events /%.0f GeV/c" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=False)
h.addMCUncertainty()
opts = {"ymin": 0.001, "xmax": 400.0, "ymaxfactor": 2}
opts2 = {"ymin": 0.05, "ymax": 1.5}
name = name + "_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
# h.createFrame(name, opts=opts)
#h.createFrameFraction(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.65, 0.9, 0.9))
common(h, xlabel, ylabel)
def leadingTrack(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{leading track} (GeV/c)"
ylabel = "Events / %.0f GeV/c" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "leadingTrackSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def rtauGen(h, name, rebin=5, ratio=False):
#h.setDefaultStyles()
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "R_{#tau}"
if "Mass" in name:
xlabel = "m (GeV/c^{2})"
elif "Pt" in name:
xlabel = "p_{T}(GeV/c)"
elif "vertices" in name:
xlabel = "N_{vertices}"
ylabel = "Events / %.2f" % h.binWidth()
if "gen" in name:
kwargs = {"ymin": 0.1, "xmax": 1.1}
elif "Pt" in name:
kwargs = {"ymin": 0.1, "xmax": 400}
elif "Mass" in name:
kwargs = {"ymin": 0.1, "xmax": 500}
kwargs = {"ymin": 0.1, "xmax": 500}
# kwargs["opts"] = {"ymin": 0, "xmax": 14, "ymaxfactor": 1.1}}
if ratio:
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# name = name+"_log"
h.createFrame(name, **kwargs)
# h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.2, 0.75, 0.4, 0.9))
common(h, xlabel, ylabel, addLuminosityText=False)
def tauCandPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#phi^{#tau candidate}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.01
name = "tauCandidatePhi_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
h.addStandardTexts()
h.save()
def tauPt(h, name, rebin=1, ratio=False, opts={}, opts2={}):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{#tau jet} (GeV/c)"
ylabel = "Events / %.0f GeV/c" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
# h.addMCUncertainty()
# if h.histoMgr.hasHisto("Data"):
# th1 = h.histoMgr.getHisto("Data").getRootHisto()
# print name
# for bin in xrange(1, th1.GetNbinsX()+1):
# print "Bin %d, low edge %.0f, content %.3f" % (bin, th1.GetXaxis().GetBinLowEdge(bin), th1.GetBinContent(bin))
# print
_opts = {"ymin": 0.01, "ymaxfactor": 2}
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
name = name+"_log"
#h.createFrameFraction(name, opts=opts)
# h.createFrame(name, opts=opts)
if ratio:
h.createFrameFraction(name, opts=_opts, opts2=_opts2)
else:
h.createFrame(name, opts=_opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def compare(raw, type1, quantity, name):
rawTh1 = raw.histoMgr.getHisto(name).getRootHisto().Clone("Raw")
type1Th1 = type1.histoMgr.getHisto(name).getRootHisto().Clone("Type1")
styles.dataStyle.apply(rawTh1)
styles.mcStyle.apply(type1Th1)
plot = plots.ComparisonPlot(
histograms.Histo(rawTh1, "Raw"),
histograms.Histo(type1Th1, "Type 1")
)
q = " "+quantity
if quantity == "Et":
q = ""
plot.setLegend(histograms.moveLegend(histograms.createLegend(), dh=0.15))
plot.createFrame("metres_%s_%s" % (name, quantity))
plot.frame.GetXaxis().SetTitle("MET%s/genMET%s" % (q, q))
plot.frame.GetYaxis().SetTitle("Events / %.2f" % raw.binWidth())
plot.draw()
l = ROOT.TLatex()
l.SetNDC()
x = 0.5
y = 0.7
dy = 0.05
l.DrawLatex(x, y, name); y -= dy
l.SetTextSize(l.GetTextSize()*0.7)
l.SetTextColor(rawTh1.GetLineColor())
l.DrawLatex(x, y, "Raw mean %.2f, stddev %.2f" % (rawTh1.GetMean(), rawTh1.GetRMS())); y -= dy
l.SetTextColor(type1Th1.GetLineColor())
l.DrawLatex(x, y, "Type 1 mean %.2f, stddev %.2f" % (type1Th1.GetMean(), type1Th1.GetRMS())); y -= dy
plot.save()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
plot = plots.DataMCPlot(datasets, analysis + "/MET/met")
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
plot.createFrame("MET", opts={"ymin": 1e-4, "ymaxfactor": 10})
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend())
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Number of events")
plot.draw()
plot.addLuminosityText()
plot.save()
def deltaPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "R_{#tau}"
ylabel = "Events / %.2f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "deltaPhiSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.3, 0.7, 0.6))
ROOT.gPad.SetLogy(True)
h.draw()
h.addStandardTexts()
h.save()
def transverseMass(h, name="transverseMass", rebin=2, log=True):
# name = h.getRootHistoPath()
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, MET) (GeV/c^{2})"
ylabel = "A.u."
# h.stackMCHistograms()
# h.addMCUncertainty()
opts = {"xmax": 200}
# opts = {"ymin": 0.0001,"xmax": 200, "ymaxfactor": 1.3}
# opts2 = {"ymin": 0.5, "ymax": 1.5}
# opts = {"xmax": 200}
if log:
name += "_log"
opts["ymin"] = 1e-4
opts["ymaxfactor"] = 2
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
legend = histograms.createLegend()
if log:
h.getPad().SetLogy(True)
histograms.moveLegend(legend, dx=-0.5, dy=-0.3)
h.setLegend(legend)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# h.addLuminosityText()
h.save()
def compare(raw, type1, quantity, name):
rawTh1 = raw.histoMgr.getHisto(name).getRootHisto().Clone("Raw")
type1Th1 = type1.histoMgr.getHisto(name).getRootHisto().Clone("Type1")
styles.dataStyle.apply(rawTh1)
styles.mcStyle.apply(type1Th1)
plot = plots.ComparisonPlot(
histograms.Histo(rawTh1, "Raw"),
histograms.Histo(type1Th1, "Type 1")
)
q = " "+quantity
if quantity == "Et":
q = ""
plot.setLegend(histograms.moveLegend(histograms.createLegend(), dh=0.15))
plot.createFrame("metres_%s_%s" % (name, quantity))
plot.frame.GetXaxis().SetTitle("MET%s/genMET%s" % (q, q))
plot.frame.GetYaxis().SetTitle("Events / %.2f" % raw.binWidth())
plot.draw()
l = ROOT.TLatex()
l.SetNDC()
x = 0.5
y = 0.7
dy = 0.05
l.DrawLatex(x, y, name); y -= dy
l.SetTextSize(l.GetTextSize()*0.7)
l.SetTextColor(rawTh1.GetLineColor())
l.DrawLatex(x, y, "Raw mean %.2f, stddev %.2f" % (rawTh1.GetMean(), rawTh1.GetRMS())); y -= dy
l.SetTextColor(type1Th1.GetLineColor())
l.DrawLatex(x, y, "Type 1 mean %.2f, stddev %.2f" % (type1Th1.GetMean(), type1Th1.GetRMS())); y -= dy
plot.save()
def rtauGen(h, name, rebin=2, ratio=False, defaultStyles=True):
if defaultStyles:
h.setDefaultStyles()
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f" % h.binWidth()
if "LeptonsInMt" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "NoLeptonsRealTau" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "Mass" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
kwargs = {"ymin": 0.1, "ymax": 1000}
if "LeptonsInMt" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "NoLeptonsRealTau" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "Rtau" in name:
kwargs = {"ymin": 0.0001, "xmax": 1.1}
kwargs = {"ymin": 0.1, "xmax": 1.1}
h.getPad().SetLogy(True)
# kwargs["opts"] = {"ymin": 0, "xmax": 14, "ymaxfactor": 1.1}}
if ratio:
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# name = name+"_log"
h.createFrame(name, **kwargs)
# histograms.addText(0.65, 0.7, "BR(t #rightarrow bH^{#pm})=0.05", 20)
h.getPad().SetLogy(True)
leg = histograms.createLegend(0.6, 0.75, 0.8, 0.9)
if "LeptonsInMt" in name:
h.getPad().SetLogy(False)
leg = histograms.moveLegend(leg, dx=-0.18)
histograms.addText(0.5, 0.65, "TailKiller cut: Tight", 20)
h.setLegend(leg)
plots._legendLabels["MetNoJetInHole"] = "Jets outside dead cells"
plots._legendLabels["MetJetInHole"] = "Jets within dead cells"
histograms.addText(300, 300, "p_{T}^{jet} > 50 GeV/c", 20)
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# if ratio:
# h.createFrameFraction(name, opts=opts, opts2=opts2)
# h.setLegend(leg)
common(h, xlabel, ylabel)
def met(h, rebin=5, ratio=True, opts={}, opts2={}):
name = flipName(h.getRootHistoPath())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "MET (GeV)"
if "embedding" in name:
xlabel = "Embedded " + xlabel
elif "original" in name:
xlabel = "Original " + xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
h.addMCUncertainty()
_opts = {"ymin": 0.001, "ymaxfactor": 2}
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
name = name + "_log"
if ratio:
h.createFrameFraction(name, opts=_opts, opts2=_opts2)
else:
h.createFrame(name, opts=_opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def tauCandEta(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#eta^{#tau candidate}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
# opts = {"xmax": 2.5,"xmin":-2.5}
# opts["xmin"] = -2.7
# opts["xmax"] = 2.7
name = "tauCandidateEta_%s_log" % step
# h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.2, 0.7, 0.5))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def ptTop(h, name, rebin=10, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
# particle = "jet"
# if "bjet" in name:
# particle = "bjet"
# name = name.replace("jetPt", "bjetPt")
xlabel = "p_{T}^{top} (GeV/c)"
ylabel = "Events /%.0f GeV/c" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.0001, "xmax": 500, "ymaxfactor": 1.1}
opts2 = {"ymin": 0.01, "ymax": 1.5}
name = name + "_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
# h.createFrame(name)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def tauPt(h, name, rebin=1, ratio=False, opts={}, opts2={}):
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{#tau jet} (GeV/c)"
ylabel = "Events / %.0f GeV/c" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
# h.addMCUncertainty()
# if h.histoMgr.hasHisto("Data"):
# th1 = h.histoMgr.getHisto("Data").getRootHisto()
# print name
# for bin in xrange(1, th1.GetNbinsX()+1):
# print "Bin %d, low edge %.0f, content %.3f" % (bin, th1.GetXaxis().GetBinLowEdge(bin), th1.GetBinContent(bin))
# print
_opts = {"ymin": 0.01, "ymaxfactor": 2}
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
name = name + "_log"
#h.createFrameFraction(name, opts=opts)
# h.createFrame(name, opts=opts)
if ratio:
h.createFrameFraction(name, opts=_opts, opts2=_opts2)
else:
h.createFrame(name, opts=_opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def transverseMass(h, rebin=20):
name = flipName(h.getRootHistoPath())
particle = ""
if "Original" in name:
particle = "#mu"
name = name.replace("TransverseMass", "Mt")
else:
particle = "#tau jet"
name = name.replace("TransverseMass", "Mt")
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=False) #stackSignal=True)
h.addMCUncertainty()
opts = {"xmax": 200}
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def jetEta(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
particle = "jet"
if "bjet" in name:
particle = "b jet"
if "electron" in name:
particle = "electron"
if "muon" in name:
particle = "muon"
xlabel = "#eta^{%s}" % particle
# xlabel = "#eta^{muon}"
ylabel = "Events / %.2f" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.01, "xmin": -5, "xmax": 5, "ymaxfactor": 10}
opts2 = {"ymin": 0.05, "ymax": 1.5}
name = name + "_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.7, 0.9, 0.95))
# h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def deltaPhi(h, rebin=40, ratio=False):
name = flipName(h.getRootHistoPath())
particle = "#tau jet"
if "Original" in name:
particle = "#mu"
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#Delta#phi(%s, MET) (rad)" % particle
ylabel = "Events / %.2f rad" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.001, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
#h.createFrameFraction(name)
h.createFrame(name)
# h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.2, 0.6, 0.4, 0.9))
common(h, xlabel, ylabel)
def met2(h, name, rebin=10, ratio=True):
# name = h.getRootHistoPath()
# name = "met"
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
# xlabel = "MET (GeV)"
# if "embedding" in name:
# xlabel = "Embedded "+xlabel
# elif "original" in name:
# xlabel = "Original "+xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
xlabel = "E_{T}^{miss} (GeV)"
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.01, "ymaxfactor": 2}
opts2 = {"ymin": 0.0, "ymax": 2.5}
name = name + "_log"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.65, 0.65, 0.9, 0.93))
common(h, xlabel, ylabel)
def met(h, rebin=20, ratio=False):
name = flipName(h.getRootHistoPath())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "MET (GeV)"
# if "embedding" in name:
# xlabel = "Embedded "+xlabel
# elif "original" in name:
# xlabel = "Original "+xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
scaleMCfromWmunu(h)
h.stackMCHistograms()
h.addMCUncertainty()
opts = {"ymin": 0.001, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
name = "MET"
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def met(h, rebin=5, ratio=True, opts={}, opts2={}):
name = flipName(h.getRootHistoPath())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "MET (GeV)"
if "embedding" in name:
xlabel = "Embedded "+xlabel
elif "original" in name:
xlabel = "Original "+xlabel
ylabel = "Events / %.0f GeV" % h.binWidth()
tauEmbedding.scaleNormalization(h)
h.stackMCHistograms()
h.addMCUncertainty()
_opts = {"ymin": 0.001, "ymaxfactor": 2}
_opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts.update(opts)
_opts2.update(opts2)
name = name+"_log"
if ratio:
h.createFrameFraction(name, opts=_opts, opts2=_opts2)
else:
h.createFrame(name, opts=_opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel)
def muonPt(h, prefix="", rebin=1, ratio=False, cutBox=None):
xlabel = "Muon p_{T} (GeV/c)"
ylabel = "Events / %.0f GeV/c"
#ylabel = "Number of events / 5.0 GeV/c"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
# tmp = h.histoMgr.getHisto("Data").getRootHisto()
# dataEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# tmp = h.histoMgr.getHisto("StackedMC").getSumRootHisto()
# mcEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# print "Muon pt Data/MC = %f/%f = %f" % (dataEvents, mcEvents, dataEvents/mcEvents)
h.createFrame(prefix+"muon_pt", opts=_optsLin)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"muon_pt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def plotEff(h, prefix):
h.createFrame(prefix + "eff_%s" % h.name)
h.frame.GetXaxis().SetTitle("Cut on isolation")
h.frame.GetYaxis().SetTitle(h.ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend(), dx=0.55))
h.draw()
h.save()
return
