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 doPlots(datasetsEmb, datasetsSig, datasetName):
lumi = datasetsEmb.getLuminosity()
createPlot = tauEmbedding.PlotCreatorMany(analysisEmb, analysisSig,
datasetsEmb, datasetsSig,
datasetName, styles.getStyles())
def drawPlot(plot, name, *args, **kwargs):
drawPlotCommon(plot, "mcembsig_" + datasetName + "_" + name, *args,
**kwargs)
def createDrawPlot(name, *args, **kwargs):
p = createPlot(name)
drawPlot(plot, *args, **kwargs)
opts2def = {"ymin": 0, "ymax": 2}
def drawControlPlot(path, xlabel, rebin=None, opts2=None, **kwargs):
opts2_ = opts2def
if opts2 != None:
opts_ = opts2
cargs = {}
if rebin != None:
cargs["rebin"] = rebin
drawPlot(createPlot("ControlPlots/" + path, **cargs),
path,
xlabel,
opts2=opts2_,
**kwargs)
def update(d1, d2):
tmp = {}
tmp.update(d1)
tmp.update(d2)
return tmp
# Control plots
optsdef = {}
opts = optsdef
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawControlPlot("SelectedTau_pT_AfterStandardSelections",
"#tau-jet p_{T} (GeV/c)",
opts=update(opts, {"xmax": 250}),
rebin=2,
cutBox={
"cutValue": 40,
"greaterThan": 40
},
moveLegend=moveLegend)
opts = {"SingleTop": {"ymax": 1.8}}.get(datasetName, {"ymaxfactor": 1.4})
moveLegend = {
"TTJets": {
"dy": -0.6,
"dx": -0.2
},
}.get(datasetName, {"dx": -0.32})
drawControlPlot("SelectedTau_eta_AfterStandardSelections",
"#tau-jet #eta",
opts=update(opts, {
"xmin": -2.2,
"xmax": 2.2
}),
ylabel="Events / %.1f",
rebin=4,
log=False,
moveLegend=moveLegend)
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawControlPlot("SelectedTau_LeadingTrackPt_AfterStandardSelections",
"#tau-jet ldg. charged particle p_{T} (GeV/c)",
opts=update(opts, {"xmax": 300}),
rebin=2,
cutBox={
"cutValue": 20,
"greaterThan": True
},
moveLegend=moveLegend)
opts = {"ymin": 1e-1, "ymaxfactor": 5}
if datasetName == "Diboson":
opts["ymin"] = 1e-2
moveLegend = {"dx": -0.25}
drawControlPlot("SelectedTau_Rtau_AfterStandardSelections",
"R_{#tau} = p^{ldg. charged particle}/p^{#tau jet}",
opts=update(opts, {
"xmin": 0.65,
"xmax": 1.05
}),
rebin=5,
ylabel="Events / %.2f",
moveLegend=moveLegend,
cutBox={
"cutValue": 0.7,
"greaterThan": True
})
opts = optsdef
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawControlPlot("Njets_AfterStandardSelections",
"Number of jets",
ylabel="Events",
moveLegend=moveLegend)
# After Njets
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawControlPlot("MET",
"Uncorrected PF E_{T}^{miss} (GeV)",
rebin=5,
opts=update(opts, {"xmax": 400}),
cutLine=50,
moveLegend=moveLegend)
# after MET
moveLegend = {"dx": -0.23, "dy": -0.5}
moveLegend = {
"WJets": {},
"DYJetsToLL": {
"dx": -0.02
},
"SingleTop": {},
"Diboson": {}
}.get(datasetName, moveLegend)
drawControlPlot("NBjets",
"Number of selected b jets",
opts=update(opts, {"xmax": 6}),
ylabel="Events",
moveLegend=moveLegend,
cutLine=1)
# After BTag
xlabel = "#Delta#phi(#tau jet, E_{T}^{miss}) (^{o})"
def customDeltaPhi(h):
yaxis = h.getFrame().GetYaxis()
yaxis.SetTitleOffset(0.8 * yaxis.GetTitleOffset())
opts2 = opts2def
opts = {
# "WJets": {"ymax": 20},
# "DYJetsToLL": {"ymax": 5},
# "SingleTop": {"ymax": 2},
# "Diboson": {"ymax": 0.6},
}.get(datasetName, {"ymaxfactor": 1.2})
opts2 = {
# "WJets": {"ymin": 0, "ymax": 3}
}.get(datasetName, opts2def)
moveLegend = {"DYJetsToLL": {"dx": -0.24}}.get(datasetName, {"dx": -0.22})
drawPlot(createPlot("deltaPhi", rebin=10),
"deltaPhi_3AfterBTagging",
xlabel,
log=False,
opts=opts,
opts2=opts2,
ylabel="Events / %.0f^{o}",
function=customDeltaPhi,
moveLegend=moveLegend,
cutLine=[130, 160])
# After deltaPhi
opts = {
# "EWKMC": {"ymax": 40},
# "TTJets": {"ymax": 12},
# #"WJets": {"ymax": 35},
# "WJets": {"ymax": 25},
# "SingleTop": {"ymax": 2.2},
# "DYJetsToLL": {"ymax": 6.5},
# #"Diboson": {"ymax": 0.9},
# "Diboson": {"ymax": 0.8},
# "W3Jets": {"ymax": 5}
}.get(datasetName, {})
opts2 = {
# "TTJets": {"ymin": 0, "ymax": 1.2},
# "Diboson": {"ymin": 0, "ymax": 3.2},
}.get(datasetName, opts2)
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawPlot(createPlot("transverseMass", rebin=10),
"transverseMass_4AfterDeltaPhi160",
"m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})",
opts=opts,
opts2=opts2,
ylabel="Events / %.0f GeV/c^{2}",
log=False,
moveLegend=moveLegend)
return
# Tree cut definitions
treeDraw = dataset.TreeDraw(
"dummy", weight=tauEmbedding.signalNtuple.weightBTagging)
tdDeltaPhi = treeDraw.clone(varexp="%s >>tmp(18, 0, 180)" %
tauEmbedding.signalNtuple.deltaPhiExpression)
tdMt = treeDraw.clone(varexp="%s >>tmp(15,0,300)" %
tauEmbedding.signalNtuple.mtExpression)
# DeltapPhi
xlabel = "#Delta#phi(#tau jet, E_{T}^{miss}) (^{o})"
def customDeltaPhi(h):
yaxis = h.getFrame().GetYaxis()
yaxis.SetTitleOffset(0.8 * yaxis.GetTitleOffset())
opts2 = opts2def
opts = {
"WJets": {
"ymax": 20
},
"DYJetsToLL": {
"ymax": 5
},
"SingleTop": {
"ymax": 2
},
"Diboson": {
"ymax": 0.6
},
}.get(datasetName, {"ymaxfactor": 1.2})
opts2 = {"WJets": {"ymin": 0, "ymax": 3}}.get(datasetName, opts2def)
moveLegend = {"DYJetsToLL": {"dx": -0.24}}.get(datasetName, {"dx": -0.22})
drawPlot(createPlot(
tdDeltaPhi.clone(
selection=And(tauEmbedding.signalNtuple.metCut,
tauEmbedding.signalNtuple.bTaggingCut))),
"deltaPhi_3AfterBTagging",
xlabel,
log=False,
opts=opts,
opts2=opts2,
ylabel="Events / %.0f^{o}",
function=customDeltaPhi,
moveLegend=moveLegend,
cutLine=[160])
# Transverse mass
selection = And(*[
tauEmbedding.signalNtuple.metCut, tauEmbedding.signalNtuple.
bTaggingCut, tauEmbedding.signalNtuple.deltaPhi160Cut
])
opts = {
"EWKMC": {
"ymax": 40
},
"TTJets": {
"ymax": 12
},
#"WJets": {"ymax": 35},
"WJets": {
"ymax": 25
},
"SingleTop": {
"ymax": 2.2
},
"DYJetsToLL": {
"ymax": 6.5
},
#"Diboson": {"ymax": 0.9},
"Diboson": {
"ymax": 0.8
},
"W3Jets": {
"ymax": 5
}
}.get(datasetName, {})
opts2 = {
"TTJets": {
"ymin": 0,
"ymax": 1.2
},
"Diboson": {
"ymin": 0,
"ymax": 3.2
},
}.get(datasetName, opts2)
p = createPlot(tdMt.clone(selection=selection))
p.appendPlotObject(
histograms.PlotText(0.6,
0.7,
"#Delta#phi(#tau jet, E_{T}^{miss}) < 160^{o}",
size=20))
moveLegend = {"DYJetsToLL": {"dx": -0.02}}.get(datasetName, {})
drawPlot(p,
"transverseMass_4AfterDeltaPhi160",
"m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})",
opts=opts,
opts2=opts2,
ylabel="Events / %.0f GeV/c^{2}",
log=False,
moveLegend=moveLegend)
def __init__(self, datasets, datasetName, ntupleCache):
self.datasets = datasets
self.datasetName = datasetName
self.ntupleCache = ntupleCache
self.styles = styles.getStyles()
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
ROOT.gROOT.SetBatch(True)
ROOT.PyConfig.IgnoreCommandLineOptions = True
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
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysis"
dataEra = "Run2012ABCD"
myStyles = [styles.getDataStyle()]
for st in styles.getStyles():
myStyles.append(st)
#myStyles.append(styles.StyleCompound([st, styles.StyleLine(lineStyle=2)]))
def main():
dirAve = "."
# dirSeeds = []
# inputInfoPath = os.path.join(dirAve, "inputInfo.txt")
# f = open(inputInfoPath)
# input_re = re.compile("Embedded input directory: (?P<dir>\S+)")
# for line in f:
# m = input_re.search(line)
# if m:
# dirSeeds.append(os.path.join("..", m.group("dir")))
# f.close()
# if len(dirSeeds) == 0:
ROOT.gROOT.SetBatch(True)
ROOT.PyConfig.IgnoreCommandLineOptions = True
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
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysis"
dataEra = "Run2012ABCD"
myStyles = [styles.getDataStyle()]
for st in styles.getStyles():
myStyles.append(st)
#myStyles.append(styles.StyleCompound([st, styles.StyleLine(lineStyle=2)]))
def main():
dirAve = "."
# dirSeeds = []
# inputInfoPath = os.path.join(dirAve, "inputInfo.txt")
# f = open(inputInfoPath)
# input_re = re.compile("Embedded input directory: (?P<dir>\S+)")
# for line in f:
# m = input_re.search(line)
# if m:
# dirSeeds.append(os.path.join("..", m.group("dir")))
# f.close()
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)
