def createStyles():
st = [
styles.StyleCompound(styles=[s, styles.StyleLine(lineWidth=5)])
for s in styles.getStyles()
]
st[0] = styles.StyleCompound(styles=[st[0], styles.StyleLine(lineStyle=2)])
return st
def doPlotsWTauMuRatio(dsetMgr, selectionName, ratios, unweighted,
fitFunction):
prefix = era + "_" + selectionName + "_fitFunction%d" % fitFunction + unweighted + "_"
st = [s.clone() for s in styles.getStyles()]
st[0].marker = 33
st[1].marker = 26
st[2].marker = 32
st = [
styles.StyleCompound(styles=[s, styles.StyleMarker(markerSize=1.5)])
for s in st
]
# Do clone to get rid of statistics box
p = plots.PlotBase([x[0].Clone() for x in ratios])
p.setEnergy(dsetMgr.getEnergies())
p.histoMgr.setHistoDrawStyleAll("P")
p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.forEachHisto(styles.Generator(st))
for i, x in enumerate(ratios):
fitLine = x[1]
st[i].apply(fitLine)
p.appendPlotObject(fitLine)
def customize(plot):
xmin = plot.frame.GetXaxis().GetXmin()
xmax = plot.frame.GetXaxis().GetXmax()
val = 1
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
plot.prependPlotObject(l)
plot.appendPlotObject(
histograms.PlotText(0.18, 0.57, "0.038", size=18,
color=ROOT.kBlue))
plots.drawPlot(p,
prefix + "WMuFraction",
"Muon p_{T} (GeV/c)",
ylabel="W#rightarrow#mu fraction",
opts={
"ymin": 0.9,
"ymax": 1.02
},
moveLegend={
"dh": -0.15,
"dx": -0.5
},
customizeBeforeDraw=customize)
def MetComparison(datasets):
mt = plots.PlotBase(getHistos(datasets,"MetNoJetInHole", "MetJetInHole"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("MetNoJetInHole", st1)
mt.histoMgr.forHisto("MetJetInHole", st2)
mt.histoMgr.setHistoLegendLabelMany({
"MetNoJetInHole": "Jets outside dead cells",
"MetJetInHole": "Jets within dead cells"
})
# mt.histoMgr.setHistoDrawStyleAll("P")
mt.appendPlotObject(histograms.PlotText(300, 300, "p_{T}^{jet} > 50 GeV/c", size=20))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MetComparison", xlabel=xlabel, ylabel=ylabel, rebinX=2, log=True,
createLegend={"x1": 0.6, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 1.5})
def dashed(style):
return styles.StyleCompound([style, styles.StyleLine(lineStyle=3)])
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.dataset as dataset
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.histograms as histograms
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.plots as plots
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.counter as counter
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tdrstyle as tdrstyle
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.styles as styles
from HiggsAnalysis.HeavyChHiggsToTauNu.tools.cutstring import * # And, Not, Or
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.crosssection as xsect
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysisCaloMet60TEff"
analysisSig = "signalAnalysisGenuineTau" # require that the selected tau is genuine, valid comparison after njets
plotStyles = [
styles.StyleCompound([s, styles.StyleLine(lineWidth=4)])
for s in styles.styles[0:2]
]
plotStyles[0] = styles.StyleCompound(
[plotStyles[0],
styles.StyleMarker(markerStyle=21, markerSize=1.2)])
#taujet = "#tau jet"
#taujetH = "#tau-jet"
taujet = "#tau_{h}"
taujetH = "#tau_{h}"
def main():
# Adjust paths such that this script can be run inside the first embedding trial directory
dirEmbs = ["."] + [os.path.join("..", d) for d in tauEmbedding.dirEmbs[1:]]
return self._cache[dsetName].histogram(name)
systematicsSigMC = SystematicsSigMC()
systematicsEmbData = dataset.Systematics(shapes=[
"SystVarMuonIdDataEff",
"SystVarMuonTrgDataEff",
"SystVarWTauMu",
"SystVarEmbMTWeight",
], additionalNormalizations = {
"CaloMETApproximation": 0.12,
"QCDContamination": 0.02,
})
plotStyles = styles.styles[0:2]
plotStyles[0] = styles.StyleCompound([plotStyles[0], styles.StyleMarker(markerStyle=21, markerSize=1.2)])
def main():
parser = OptionParser(usage="Usage: %prog [options]")
parser.add_option("--dirSig", dest="dirSig", default=None,
help="Path to signalAnalysisGenTau multicrab directory")
(opts, args) = parser.parse_args()
if opts.dirSig is None:
parser.error("--dirSig missing")
dirEmb = "."
dirSig = opts.dirSig
# Apply TDR style
style = tdrstyle.TDRStyle()
def main():
parser = OptionParser(usage="Usage: %prog [options]")
parser.add_option("--dirSig",
dest="dirSig",
default=None,
help="Path to signalAnalysisGenTau multicrab directory")
parser.add_option("--nortau",
dest="nortau",
default=False,
action="store_true",
help="Is Rtau cut disabled?")
parser.add_option("--notrigger",
dest="notrigger",
default=False,
action="store_true",
help="Is tau+MET trigger disabled?")
parser.add_option("--dofit",
dest="dofit",
default=False,
action="store_true",
help="Do the fit on mT slope on ttbar?")
(opts, args) = parser.parse_args()
if opts.dirSig is None:
parser.error("--dirSig missing")
dirEmb = "."
dirSig = opts.dirSig
global analysisSig, systematicsSigMC
if opts.notrigger:
analysisSig = analysisSig.replace("Triggered", "")
systematicsSigMC = dataset.Systematics()
# Apply TDR style
style = tdrstyle.TDRStyle()
styles.styles[1] = styles.StyleCompound(
[styles.styles[1],
styles.StyleMarker(markerStyle=25)])
histograms.cmsTextMode = histograms.CMSMode.SIMULATION_PRELIMINARY
# histograms.cmsTextMode = histograms.CMSMode.SIMULATION
# histograms.cmsTextMode = histograms.CMSMode.SIMULATION_UNPUBLISHED
# histograms.cmsTextMode = histograms.CMSMode.UNPUBLISHED
#histograms.createLegend.setDefaults(y1=0.93, y2=0.75, x1=0.52, x2=0.93)
histograms.createLegend.setDefaults(textSize=0.04)
histograms.createLegend.moveDefaults(dx=-0.25, dh=-0.2, dy=-0.12)
histograms.createLegendRatio.setDefaults(ncolumns=2,
textSize=0.08,
columnSeparation=0.6)
histograms.createLegendRatio.moveDefaults(dx=-0.48, dh=-0.1, dw=0.25)
if opts.dofit:
histograms.uncertaintyMode.set(histograms.uncertaintyMode.StatOnly)
histograms.createLegendRatio.moveDefaults(dw=-0.25)
plots._legendLabels["BackgroundStatError"] = "Emb. stat. unc."
plots._legendLabels[
"BackgroundStatSystError"] = "Emb. stat.#oplussyst. unc."
else:
histograms.uncertaintyMode.set(histograms.uncertaintyMode.StatAndSyst)
# plots._legendLabels["BackgroundStatError"] = "Norm. stat. unc."
# plots._legendLabels["BackgroundStatSystError"] = "Norm. stat.#oplussyst. unc."
plots._legendLabels["BackgroundStatError"] = "Non-emb. stat. unc."
plots._legendLabels[
"BackgroundStatSystError"] = "Non-emb. stat.#oplussyst. unc."
plots._legendLabels["Data"] = "Embedded data"
plots._legendLabels["EWKMC"] = "EWK+t#bar{t}"
# plots._legendLabels["EWKMC"] = "Non-emb. EWK+t#bar{t} with ^{}#tau_{h}"
postfix = ""
if opts.dofit:
postfix = "_fit"
for optMode in [
# "OptQCDTailKillerNoCuts",
"OptQCDTailKillerLoosePlus",
# "OptQCDTailKillerMediumPlus",
# "OptQCDTailKillerTightPlus",
# None
]:
datasetsEmb = dataset.getDatasetsFromMulticrabCfg(
directory=dirEmb,
dataEra=dataEra,
analysisName=analysisEmb,
optimizationMode=optMode)
datasetsSig = dataset.getDatasetsFromMulticrabCfg(
directory=dirSig,
dataEra=dataEra,
analysisName=analysisSig,
optimizationMode=optMode)
doDataset(datasetsEmb, datasetsSig, optMode + postfix, opts)
datasetsEmb.close()
datasetsSig.close()
tauEmbedding.writeToFile(
optMode + postfix, "input.txt",
"Embedded: %s\nSignal analysis (GenTau): %s\n" %
(os.getcwd(), dirSig))
def doPlotsWTauMuRatioUncertaintyEstimate(dsetMgr, ratios, analysisName):
prefix = era + "_"
st = [s.clone() for s in styles.getStyles()]
st[0].marker = 33
st = [
styles.StyleCompound(styles=[s, styles.StyleMarker(markerSize=1.5)])
for s in st
]
# Nominal
p = plots.PlotBase([ratios[analysisName][0].Clone()])
p.setEnergy(dsetMgr.getEnergies())
p.histoMgr.setHistoDrawStyleAll("P")
p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.forEachHisto(styles.Generator(st))
fitLine = ratios[analysisName][1]
st[0].apply(fitLine)
p.appendPlotObject(fitLine)
# Stat. unc. from parameters
pars = [fitLine.GetParameter(i) for i in xrange(0, fitLine.GetNpar())]
parErrors = [fitLine.GetParError(i) for i in xrange(0, fitLine.GetNpar())]
def evalPar(x, i, shift):
pars_ = pars[:]
pars_[i] += shift
return fitLine.EvalPar(x, array.array("d", pars_))
# Fit function
fits = [
ratios[analysisName + "SystFunc0"][1],
ratios[analysisName + "SystFunc1"][1]
]
def maxDiff(x, tf1s, nominal):
return max([abs(tf1.Eval(x[0]) - nominal) for tf1 in tf1s])
# Sample
samples = [
ratios[analysisName + "SystWJets"][1],
ratios[analysisName + "SystSingleTop"][1]
]
# Weight systematic variations
keys = filter(lambda n: "SystVar" in n, ratios.keys())
weightSources = {}
for key in keys:
src = key[key.find("SystVar") + 7:].replace("Plus",
"").replace("Minus", "")
# if src != "TopPtWeight":
# continue
# if src != "PUWeight":
# continue
# print src
if not src in weightSources:
weightSources[src] = [ratios[key][1]]
else:
weightSources[src].append(ratios[key][1])
def comb(plus, x, p):
nominal = fitLine.Eval(x[0])
diff = 0
# stat unc
uncStat = 0.0
for i, parErr in enumerate(parErrors):
diffI = max(abs(evalPar(x, i, parErr) - nominal),
abs(evalPar(x, i, -parErr)) - nominal)
uncStat += diffI**2
diff += uncStat
# Fit function
diffFit = maxDiff(x, fits, nominal)
diff += diffFit**2
# Sample
diffSample = maxDiff(x, samples, nominal)
diff += diffSample**2
# Weights
for src, tf1s in weightSources.iteritems():
diffWeight = maxDiff(x, tf1s, nominal)
diff += diffWeight**2
diff = math.sqrt(diff)
if plus:
ret = nominal + diff
else:
ret = nominal - diff
if ret > 1:
ret = 1.0
if ret < 0:
ret = 0.0
# if ret > 0:
# print x[0], (ret-nominal)/ret
return ret
p.appendPlotObject(
ROOT.TF1("combPlus", lambda x, p: comb(True, x, p), fitLine.GetXmin(),
fitLine.GetXmax(), fitLine.GetNpar()))
p.appendPlotObject(
ROOT.TF1("combMinus", lambda x, p: comb(False, x, p),
fitLine.GetXmin(), fitLine.GetXmax(), fitLine.GetNpar()))
# Simple relative uncertainty
def rel(x):
return 0.012
def relUnc(plus, x, p):
nominal = fitLine.Eval(x[0])
if plus:
f = 1 + rel(x[0])
else:
f = 1 - rel(x[0])
return nominal * f
relPlus = ROOT.TF1("relPlus", lambda x, p: relUnc(True, x, p),
fitLine.GetXmin(), fitLine.GetXmax(), fitLine.GetNpar())
relMinus = ROOT.TF1("relMinus", lambda x, p: relUnc(False, x, p),
fitLine.GetXmin(), fitLine.GetXmax(),
fitLine.GetNpar())
relPlus.SetLineColor(ROOT.kGreen)
relMinus.SetLineColor(ROOT.kGreen)
p.appendPlotObject(relPlus)
p.appendPlotObject(relMinus)
def customize(plot):
xmin = plot.frame.GetXaxis().GetXmin()
xmax = plot.frame.GetXaxis().GetXmax()
ymin = plot.frame.GetYaxis().GetXmin()
ymax = plot.frame.GetYaxis().GetXmax()
val = 1
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
l = ROOT.TLine(41, ymin, 41, ymax)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kRed)
l.SetLineStyle(2)
plot.prependPlotObject(l)
plots.drawPlot(
p,
prefix + "WMuFractionUncertainty",
"Muon p_{T} (GeV/c)",
ylabel="W#rightarrow#mu fraction",
opts={
"ymin": 0.9,
"ymax": 1.02
},
createLegend=None,
#moveLegend={"dh": -0.15, "dx": -0.5},
customizeBeforeDraw=customize)