def createPlot(name):
name2Emb = name
name2Sig = name
if isinstance(name, basestring):
name2Emb = analysisEmb+"/"+name
name2Sig = analysisSig+"/"+name
else:
name2Emb = name.clone(tree=analysisEmb+"/tree")
name2Sig = name.clone(tree=analysisSig+"/tree")
(emb, embVar) = datasetsEmb.getHistogram(datasetName, name2Emb)
sig = datasetsSig.getDataset(datasetName).getDatasetRootHisto(name2Sig)
sig.normalizeToLuminosity(lumi)
sig = sig.getHistogram()
emb.SetName("Embedded")
sig.SetName("Normal")
p = plots.ComparisonPlot(emb, sig)
p.histoMgr.forEachHisto(styles.generator())
legLabel = plots._legendLabels.get(datasetName, datasetName)+" MC"
p.histoMgr.setHistoLegendLabelMany({
"Embedded": "Embedded "+legLabel,
"Normal": "Normal "+legLabel
})
return p
def __init__(self, datasets, datasetsTau, directory, directoryTau, name):
h = datasets.getDatasetRootHistos(directory + "/" + name)[0]
h.setName(name + "Embedded")
hTau = datasetsTau.getDatasetRootHistos(directoryTau + "/" + name)[0]
hTau.setName(name + "Tau")
plots.ComparisonPlot.__init__(self, h, hTau, saveFormats=saveFormats)
self.histoMgr.normalizeToOne()
self.histoMgr.forEachHisto(styles.generator())
self.embeddingAnalysis = directory.replace("/", "_")
self.tauAnalysis = directoryTau.replace("/", "_")
self.plotName = mcSample + "_" + self.embeddingAnalysis + "_" + self.tauAnalysis + "_" + name
self.ratioLabel = "Embedded/true"
label = dataLabel
if not embeddingData:
label = mcLabel
self.histoMgr.setHistoLegendLabelMany({
name + "Embedded":
"Embedded #tau (%s)" % label,
name + "Tau":
"True #tau (%s)" % mcLabel
})
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 __init__(self, datasetsEmbSig, datasetsSig, path, normalizeToOne=False, **kwargs):
dataset = "Data"
if not useData:
dataset = "MC"
self.name = path.split("/")[-1]
hEmb = datasetsEmbSig.getDataset(dataset).getDatasetRootHisto(path)
hEmb.setName(self.name+"Embedded")
hSig = datasetsSig.getDataset(dataset).getDatasetRootHisto(path)
hSig.setName(self.name+"Normal")
if not useData:
hEmb.normalizeToLuminosity(datasetsEmbSig.getDataset("Data").getLuminosity())
hSig.normalizeToLuminosity(datasetsSig.getDataset("Data").getLuminosity())
plots.ComparisonPlot.__init__(self, hEmb, hSig, **kwargs)
self.normalizeToOne = normalizeToOne
if normalizeToOne:
self.histoMgr.normalizeToOne()
sg = styles.generator()
sg.reset(1)
self.histoMgr.forHisto(self.name+"Embedded", sg)
self.histoMgr.forHisto(self.name+"Normal", styles.dataStyle)
self.histoMgr.setHistoDrawStyleAll("EP")
self.histoMgr.setHistoLegendStyleAll("p")
s = "(Data)"
if not useData:
s = "(MC)"
self.histoMgr.setHistoLegendLabelMany({self.name+"Embedded": "Embedded "+s,
self.name+"Normal": "Normal "+s})
self.histoMgr.forHisto(self.name+"Embedded", lambda h: normaliseEmbedding(h.getRootHisto()))
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 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 manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.02, dy=+0.00)
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
return datasets
def createTransverseMassPlotInternal(dset, name, postfix, normalizeToOne, nominalSelection, compareSelection, nominalLegend="Nominal", compareLegend="Compare", moveLegend={}):
datasetName = dset.getName()
treeDraw = dataset.TreeDraw("tree", varexp="TauMETTransverseMass >>tmp(10,0,200)")
tdNominal = treeDraw.clone(selection=nominalSelection)#selection="Obj2Type == 3 && LeptonVetoStatus == 0 && (TauIDStatus == 1 || TauIDStatus == 2 || TauIDStatus == 3)") # FIXME
tdCompare = treeDraw.clone(selection=compareSelection)
drhNominal = dset.getDatasetRootHisto(tdNominal)
drhCompare = dset.getDatasetRootHisto(tdCompare)
integrate = lambda h: h.Integral(0, h.GetNbinsX()+1)
nNominal = integrate(drhNominal.getHistogram())
nCompare = integrate(drhCompare.getHistogram())
if normalizeToOne:
drhNominal.normalizeToOne()
drhCompare.normalizeToOne()
drhNominal.setName("Nominal")
drhCompare.setName("Compare")
p = plots.ComparisonPlot(drhNominal, drhCompare)
p.histoMgr.forEachHisto(styles.generator())
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
p.histoMgr.setHistoLegendLabelMany({
"Nominal": nominalLegend+ " (%d)" % int(nNominal),
"Compare": compareLegend+ " (%d)" % int(nCompare)
})
pfix = postfix
if normalizeToOne:
pfix += "_unit"
p.createFrame("mt_"+datasetName+"_"+name+"_"+pfix, createRatio=normalizeToOne, invertRatio=True, opts2={"ymin": 0, "ymax": 2})
p.frame.GetXaxis().SetTitle("Transverse mass (GeV/c^{2})")
if normalizeToOne:
p.frame.GetYaxis().SetTitle("Arbitrary units")
else:
p.frame.GetYaxis().SetTitle("MC events")
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.93, y2=0.80, x1=0.45, x2=0.85), **moveLegend))
nomErr = p.histoMgr.getHisto("Nominal").getRootHisto().Clone("Nominal_err")
nomErr.SetFillColor(ROOT.kBlue-7)
nomErr.SetFillStyle(3004)
nomErr.SetMarkerSize(0)
p.prependPlotObject(nomErr, "E2")
comErr = p.histoMgr.getHisto("Compare").getRootHisto().Clone("Compare_err")
comErr.SetFillColor(ROOT.kRed-7)
comErr.SetFillStyle(3013)
comErr.SetMarkerSize(0)
p.prependPlotObject(comErr, "E2")
if normalizeToOne:
p.appendPlotObject(histograms.PlotText(0.6, 0.75, "Normalized to unit area", size=17))
p.draw()
p.save()
def main():
# Get the ROOT files for all datasets, merge datasets and reorder them
datasets = dataset.getDatasetsFromMulticrabCfg(directory="/Volumes/disk/attikis/HIG-12-037/TreeAnalysis_v44_4_130113_105229/", dataEra="Run2011A")
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Merge desirable datasets
datasets.merge("EWK MC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles["EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Setup style
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.0, dy=+0.0)
# Merge signals into one histo
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Print desirable information here
print "*** Event weight applied: %s" % (EvtWeight)
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
print "*** WARNING! What about btag scale factor?"
print "*** Integrated Luminosity: %s (pb)" % (datasets.getDataset("Data").getLuminosity())
# Define histogram name, expression, and binning here:
# { hName": "expression >> hNameTmp(nBins, xMin, xMax) }
histoDict1 = {
"sphericity": "sphericity >> sphericity(20, 0.0, 1.0)",
"aplanarity": "aplanarity >> aplanarity(20, 0.0, 0.5)",
"planarity": "planarity >> planarity(20, 0.0, 0.5)",
"circularity": "circularity >> circularity(20, 0.0, 1.0)",
"alphaT": "alphaT >> alphaT(20, 0.0, 1.0)",
"mT": "%s >> mT(30, 0.0, 600.0)" % (Mt)
}
# Do all plots defined in doPlots(datasets) function
# doPlots(datasets, histoDict1, "", SaveExtension = "JetSelection")
# doPlots(datasets, histoDict1, MetBtagDeltaPhiCuts, SaveExtension = "Met_Btag_DeltaPhi")
# doPlots(datasets, histoDict1, MetBtagDeltaPhiCircularityCuts, SaveExtension = "Met_Btag_DeltaPhi_Circularity")
# doPlots(datasets, histoDict1, TestCuts, SaveExtension = "Test")
# Do just mT plots
histoDict2 = {
"mT": "%s >> mT(30, 0.0, 600.0)" % (Mt)
}
for i in range(1, 8):
cutValue = (i+0.0)/(10.0)
doPlots(datasets, histoDict2, "circularity <= %f" % (cutValue), SaveExtension = "Circularity%s" % (i))
def main():
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining ROOT files from:\n %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabCfg(directory=multicrabPath, dataEra="Run2011A")
printPSet(bPrintPSet, folderName="signalAnalysisRun2011A")
print "*** Calling datasets.updateNAllEventsToPUWeighted():"
datasets.updateNAllEventsToPUWeighted()
print "*** Loading luminosities"
datasets.loadLuminosities()
print "*** Calling plots.mergeRenameReorderForDataMC(datasets):"
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
### Merge desirable datasets
if bMergeEwk:
print "*** Merging EWK MC"
datasets.merge("EWK MC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles["EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
### Remove signals other than M120
print "*** Removing all signal except M120"
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
print "*** Setting up style"
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.0, dy=+0.0)
### Merge signals into one histo
print "*** Merging WH and HH signals into one histogram"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Print desirable information here
print "*** Available datasets:\n %s" % (datasets.getAllDatasetNames())
if mcOnly:
print "*** Integrated Luminosity:\n %s (pb)" % (mcOnlyLumi)
else:
print "*** Integrated Luminosity:\n %s (pb)" % (datasets.getDataset("Data").getLuminosity())
### Do all plots defined in histoDict function
doPlots(datasets, histoDict, JetSelectionCuts, SaveExtension = "JetSelection")
doPlots(datasets, histoDict, MtCut, SaveExtension = "Mt")
doPlots(datasets, histoDict, MetBtagDeltaPhiCuts, SaveExtension = "MetBtagDeltaPhi")
doPlots(datasets, histoDict, MetBtagDeltaPhiMtCuts, SaveExtension = "MetBtagDeltaPhiMt")
### Keep session alive (otherwise canvases close automatically)
if bBatchMode == False:
raw_input("*** Press \"any\" key to exit pyROOT: ")
def __init__(self, datasets, directory, names):
PlotBase.__init__(self)
self.prefix = directory.replace("/", "_")+"_"
for name in names:
self.histoMgr.append(getHisto(datasets, directory+"/"+name, name))
self.histoMgr.forEachHisto(styles.generator())
def __init__(self, datasets, directory, names):
PlotBase.__init__(self)
self.prefix = directory.replace("/", "_") + "_"
for name in names:
self.histoMgr.append(
getHisto(datasets, directory + "/" + name, name))
self.histoMgr.forEachHisto(styles.generator())
def __init__(self, datasets, datasetsTau, directories, name):
PlotBase.__init__(self)
self.histoMgr.append(getHisto(datasets, directories[0]+"/"+name, name+"Embedded"))
self.histoMgr.append(getHisto(datasetsTau, directories[1]+"/"+name, name+"Tau"))
self.histoMgr.forEachHisto(styles.generator())
self.histoMgr.setHistoLegendLabelMany({name+"Embedded": "Embedded #tau",
name+"Tau": "Real #tau"})
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if getBool("bRemoveData"):
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
if getBool("bRemoveSignal"):
print "\n*** Removing all signal samples"
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
else:
print "\n*** Removing all signal samples, except m=%s GeV/cc" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
plots._legendLabels["TTToHplus_M"+signalMass] = "m_{H^{#pm}} = " + signalMass + " GeV/c^{2}"
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Now optionally remove datasets
if getBool("bRemoveEwk"):
mergeEwkMc(datasets)
datasets.remove(filter(lambda name: "EWK MC" in name, datasets.getAllDatasetNames()))
else:
if getBool("bMergeEwk"):
mergeEwkMc(datasets)
if getBool("bRemoveQcd"):
datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
return datasets
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly==True:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s GeV/cc" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
plots._legendLabels["TTToHplus_M"+signalMass] = "m_{H^{+}} = " + signalMass + " GeV/c^{2}"
style = tdrstyle.TDRStyle()
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Now optionally remove datasets
if bRemoveEwk:
mergeEwkMc(datasets)
datasets.remove(filter(lambda name: "EWK MC" in name, datasets.getAllDatasetNames()))
else:
if bMergeEwk==True:
mergeEwkMc(datasets)
if bRemoveQcd:
datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
#if bRemoveSignal: FIXME
# datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
return datasets
def __init__(self, datasets, datasetsTau, directories, name):
PlotBase.__init__(self)
self.histoMgr.append(
getHisto(datasets, directories[0] + "/" + name, name + "Embedded"))
self.histoMgr.append(
getHisto(datasetsTau, directories[1] + "/" + name, name + "Tau"))
self.histoMgr.forEachHisto(styles.generator())
self.histoMgr.setHistoLegendLabelMany({
name + "Embedded": "Embedded #tau",
name + "Tau": "Real #tau"
})
def __init__(self, datasets, prefix, directories, name, normalizeToOne):
PlotBase.__init__(self)
self.prefix = prefix+"_"
self.name = name
self.postfix = ""
for d in directories:
self.histoMgr.append(getHisto(datasets, d+"/"+name, d))
if normalizeToOne:
self.histoMgr.normalizeToOne()
self.postfix += "_one"
self.histoMgr.forEachHisto(styles.generator())
def __init__(self, datasets, prefix, directories, name, normalizeToOne):
PlotBase.__init__(self)
self.prefix = prefix + "_"
self.name = name
self.postfix = ""
for d in directories:
self.histoMgr.append(getHisto(datasets, d + "/" + name, d))
if normalizeToOne:
self.histoMgr.normalizeToOne()
self.postfix += "_one"
self.histoMgr.forEachHisto(styles.generator())
def __init__(self,
datasetsEmbSig,
datasetsSig,
path,
normalizeToOne=False,
**kwargs):
dataset = "Data"
if not useData:
dataset = "MC"
self.name = path.split("/")[-1]
hEmb = datasetsEmbSig.getDataset(dataset).getDatasetRootHisto(path)
hEmb.setName(self.name + "Embedded")
hSig = datasetsSig.getDataset(dataset).getDatasetRootHisto(path)
hSig.setName(self.name + "Normal")
if not useData:
hEmb.normalizeToLuminosity(
datasetsEmbSig.getDataset("Data").getLuminosity())
hSig.normalizeToLuminosity(
datasetsSig.getDataset("Data").getLuminosity())
plots.ComparisonPlot.__init__(self, hEmb, hSig, **kwargs)
self.normalizeToOne = normalizeToOne
if normalizeToOne:
self.histoMgr.normalizeToOne()
sg = styles.generator()
sg.reset(1)
self.histoMgr.forHisto(self.name + "Embedded", sg)
self.histoMgr.forHisto(self.name + "Normal", styles.dataStyle)
self.histoMgr.setHistoDrawStyleAll("EP")
self.histoMgr.setHistoLegendStyleAll("p")
s = "(Data)"
if not useData:
s = "(MC)"
self.histoMgr.setHistoLegendLabelMany({
self.name + "Embedded":
"Embedded " + s,
self.name + "Normal":
"Normal " + s
})
self.histoMgr.forHisto(self.name + "Embedded",
lambda h: normaliseEmbedding(h.getRootHisto()))
def createPlot(name):
if mtOnly and "shapeTransverseMass" not in name:
return None
drhEmb = dsetEmb.getDatasetRootHisto(syst.histogram(name))
drhSig = dsetSig.getDatasetRootHisto(name)
drhEmb.normalizeToLuminosity(lumi)
drhSig.normalizeToLuminosity(lumi)
drhEmb.setName("Embedded")
drhSig.setName("Normal")
if addData:
drhEmbData = dsetEmbData.getDatasetRootHisto(name)
drhEmbData.setName("Embedded data")
if addData:
p = plots.ComparisonManyPlot(drhSig, [drhEmb, drhEmbData])
else:
p = plots.ComparisonManyPlot(drhSig, [drhEmb])
p.setLuminosity(lumi)
if True:
# zero the stat uncertainty
th1 = p.histoMgr.getHisto("Embedded").getRootHistoWithUncertainties().getRootHisto()
for b in xrange(0, th1.GetNbinsX()+1):
th1.SetBinError(b, 0)
legLabel = plots._legendLabels.get(datasetName, datasetName)
#legEmb = "Embedded "+legLabel
#legSig = "Normal "+legLabel
legEmb = "W#rightarrow#tau#rightarrow#mu correction"
legSig = "W#rightarrow#mu truth"
if addEventCounts:
legEmb += " ("+strIntegral(drhEmb.getHistogram())+")"
legSig += " ("+strIntegral(drhSig.getHistogram())+")"
p.histoMgr.setHistoLegendLabelMany({
"Embedded": legEmb,
"Normal": legSig,
})
p.histoMgr.forEachHisto(styles.generator())
if addData:
p.histoMgr.setHistoLegendLabelMany({"Embedded data": "Embedded data ("+strIntegral(drhEmbData.getHistogram())+")"})
p.histoMgr.forHisto("Embedded data", styles.dataStyle)
p.histoMgr.setHistoDrawStyle("Embedded data", "EP")
p.histoMgr.setHistoLegendStyle("Embedded data", "P")
p.histoMgr.reorderDraw(["Embedded data"])
p.setDrawOptions(ratioYlabel="Corr./Truth")
return p
def main():
counters = analysis+"Counters"
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
tauEmbedding.updateAllEventsToWeighted(datasets)
#datasets.remove(filter(lambda name: name != "SingleMu_Mu_166374-167043_Prompt" and name != "TTJets_TuneZ2_Summer11", datasets.getAllDatasetNames()))
if era == "EPS":
datasets.remove([
"SingleMu_Mu_170722-172619_Aug05",
"SingleMu_Mu_172620-173198_Prompt",
"SingleMu_Mu_173236-173692_Prompt",
])
elif era == "Run2011A-EPS":
datasets.remove([
"SingleMu_Mu_160431-163261_May10",
"SingleMu_Mu_163270-163869_May10",
"SingleMu_Mu_165088-166150_Prompt",
"SingleMu_Mu_166161-166164_Prompt",
"SingleMu_Mu_166346-166346_Prompt",
"SingleMu_Mu_166374-167043_Prompt",
"SingleMu_Mu_167078-167913_Prompt",
# "SingleMu_Mu_170722-172619_Aug05",
# "SingleMu_Mu_172620-173198_Prompt",
# "SingleMu_Mu_173236-173692_Prompt",
])
elif era == "Run2011A":
pass
#datasets.remove(datasets.getMCDatasetNames())
datasets.loadLuminosities()
#datasetsMC = datasets.deepCopy()
#datasetsMC.remove(datasets.getDataDatasetNames())
plots.mergeRenameReorderForDataMC(datasets)
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
#histograms.createLegend.moveDefaults(dx=-0.15)
plots._legendLabels["QCD_Pt20_MuEnriched"] = "QCD"
histograms.createLegend.moveDefaults(dx=-0.02)
doPlots(datasets)
printCounters(datasets)
def genBquark_Pt(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{top} (GeV/c)"
ylabel = "Events /%.1f GeV/c" % h.binWidth()
opts = {"ymin": 0.0001, "xmax": 400, "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(False)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel, False)
def genBquark_Pt(h, name, rebin=5, ratio=False):
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "p_{T}^{top} (GeV/c)"
ylabel = "Events /%.1f GeV/c" % h.binWidth()
opts = {"ymin": 0.0001, "xmax": 400, "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(False)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel, False)
def genBquark_DeltaRTau(h, name, rebin=10, ratio=False):
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#Delta_{R} to #tau"
ylabel = "Events /%.2f" % h.binWidth()
# scaleMCfromWmunu(h)
opts = {"ymin": 0.0001, "xmax": 8, "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(False)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel, False)
def genBquark_DeltaRTau(h, name, rebin=10, ratio=False):
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#Delta_{R} to #tau"
ylabel = "Events /%.2f" % h.binWidth()
# scaleMCfromWmunu(h)
opts = {"ymin": 0.0001, "xmax": 8, "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(False)
h.setLegend(histograms.createLegend())
common(h, xlabel, ylabel, False)
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s" % (signalMass)
datasets.remove(
filter(
lambda name: "TTToHplus" in name and not "M" + signalMass in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.02, dy=+0.00)
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (
BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
return datasets
def __init__(self, passedPlots, names, legendLabels={}):
plots.PlotBase.__init__(self, [], passedPlots[0].saveFormats)
legends = {
"sumIsoRel": "Rel iso",
"pfSumIsoRel": "PF rel iso",
}
legends.update(legendLabels)
with SetTH1Directory(False):
rootHistos = []
for plot, name in zip(passedPlots, names):
mcSum = histograms.sumRootHistos([getSumOrRootHisto(histo) for histo in filter(lambda h: h.isMC(), plot.histoMgr.getHistos())])
qcdHisto = plot.histoMgr.getHisto("QCD_Pt20_MuEnriched")
qcd = getSumOrRootHisto(qcdHisto).Clone(name+"_QCDfraction")
qcd.Divide(mcSum) # qcd/mcSum
h = histograms.Histo(qcdHisto.getDataset(), qcd, name)
h.setLegendLabel(legends.get(name, ""))
self.histoMgr.appendHisto(h)
self.histoMgr.forEachHisto(styles.generator())
self.histoMgr.setHistoLegendStyleAll("l")
def __init__(self, datasets, datasetsTau, directory, directoryTau, name, nameTau):
h = datasets.getDatasetRootHistos(directory+"/"+name)[0]
h.setName(name+"Embedded")
hTau = datasetsTau.getDatasetRootHistos(directoryTau+"/"+nameTau)[0]
hTau.setName(nameTau+"Tau")
plots.ComparisonPlot.__init__(self, h, hTau, saveFormats=saveFormats)
self.histoMgr.normalizeToOne()
self.histoMgr.forEachHisto(styles.generator())
self.embeddingAnalysis = directory.replace("/", "_")
self.tauAnalysis = directoryTau.replace("/", "_")
self.plotName = mcSample+"_"+self.embeddingAnalysis+"_"+name+"_"+self.tauAnalysis+"_"+nameTau
self.ratioLabel = "Selected #mu/gen #tau"
label = dataLabel
if not embeddingData:
label = mcLabel
self.histoMgr.setHistoLegendLabelMany({name+"Embedded": "Selected #mu (%s)"%label,
nameTau+"Tau": "Gen #tau (%s)"%mcLabel})
def createPlot(name):
name2Emb = name
name2Sig = name
if isinstance(name, basestring):
name2Emb = analysisEmb+"/"+name
name2Sig = analysisSig+"/"+name
else:
name2Emb = name.clone(tree=analysisEmb+"/tree")
name2Sig = name.clone(tree=analysisSig+"/tree")
emb = datasetsEmb.getDataset(datasetName).getDatasetRootHisto(name2Emb)
emb.setName("Embedded")
sig = datasetsSig.getDataset(datasetName).getDatasetRootHisto(name2Sig)
sig.setName("Normal")
p = plots.ComparisonPlot(emb, sig)
p.histoMgr.normalizeMCToLuminosity(lumi)
p.histoMgr.setHistoLegendLabelMany({
"Embedded": "Embedded "+plots._legendLabels[datasetName],
"Normal": "Normal "+plots._legendLabels[datasetName],
})
p.histoMgr.forEachHisto(styles.generator())
return p
def main():
counters = analysis+"Counters"
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
if era == "EPS":
datasets.remove([
"SingleMu_Mu_170722-172619_Aug05",
"SingleMu_Mu_172620-173198_Prompt",
"SingleMu_Mu_173236-173692_Prompt",
])
elif era == "Run2011A-EPS":
datasets.remove([
"SingleMu_Mu_160431-163261_May10",
"SingleMu_Mu_163270-163869_May10",
"SingleMu_Mu_165088-166150_Prompt",
"SingleMu_Mu_166161-166164_Prompt",
"SingleMu_Mu_166346-166346_Prompt",
"SingleMu_Mu_166374-167043_Prompt",
"SingleMu_Mu_167078-167913_Prompt",
])
elif era == "Run2011A":
pass
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
plots._legendLabels["QCD_Pt20_MuEnriched"] = "QCD multi-jet"
histograms.createLegend.moveDefaults(dx=-0.04)
doPlots(datasets)
def main():
counters = analysis + "Counters"
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
if era == "EPS":
datasets.remove([
"SingleMu_Mu_170722-172619_Aug05",
"SingleMu_Mu_172620-173198_Prompt",
"SingleMu_Mu_173236-173692_Prompt",
])
elif era == "Run2011A-EPS":
datasets.remove([
"SingleMu_Mu_160431-163261_May10",
"SingleMu_Mu_163270-163869_May10",
"SingleMu_Mu_165088-166150_Prompt",
"SingleMu_Mu_166161-166164_Prompt",
"SingleMu_Mu_166346-166346_Prompt",
"SingleMu_Mu_166374-167043_Prompt",
"SingleMu_Mu_167078-167913_Prompt",
])
elif era == "Run2011A":
pass
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
plots._legendLabels["QCD_Pt20_MuEnriched"] = "QCD multi-jet"
histograms.createLegend.moveDefaults(dx=-0.04)
doPlots(datasets)
def compare(step):
if step is None:
path = "shapeTransverseMass"
else:
path = "CommonPlots/AtEveryStep/%s/MET_MET" % step
#bins = range(0, 200, 20) + [200, 250, 300, 500]
bins = systematics.getBinningForPlot("shapeTransverseMass")
def getTH1(ds):
drh = ds.getDatasetRootHisto(path)
drh.normalizeToLuminosity(lumi)
th1 = drh.getHistogram()
#th1.Rebin(2)
#return th1
return th1.Rebin(len(bins)-1, th1.GetName(), array.array("d", bins))
th1NormalNum = getTH1(dsetNormalNum)
th1NormalDenom = getTH1(dsetNormalDenom)
#print th1NormalNum.Integral(0, th1NormalNum.GetNbinsX()+1), th1NormalDenom.Integral(0, th1NormalDenom.GetNbinsX()+1)
th1EmbNum = getTH1(dsetEmbNum)
th1EmbDenom = getTH1(dsetEmbDenom)
#print th1EmbNum.Integral(0, th1EmbNum.GetNbinsX()+1), th1EmbDenom.Integral(0, th1EmbDenom.GetNbinsX()+1)
effNormal = ROOT.TGraphAsymmErrors(th1NormalNum, th1NormalDenom, "n")
effEmb = ROOT.TGraphAsymmErrors(th1EmbNum, th1EmbDenom, "n")
effNormal.SetName("Normal ttbar")
effEmb.SetName("Embedded ttbar")
p = plots.ComparisonManyPlot(effNormal, [effEmb])
p.setLuminosity(lumi)
p.histoMgr.forEachHisto(styles.generator())
#p.histoMgr.forHisto("Normal ttbar", lambda h: doStyle(h, ROOT.kBlack, ROOT.kFullCircle))
#p.histoMgr.forHisto("Embedded ttbar", lambda h: doStyle(h, ROOT.kRed, ROOT.kFullSquare))
p.histoMgr.setHistoDrawStyleAll("EP")
p.histoMgr.setHistoLegendStyleAll("EPL")
unc1 = 1.12
unc2 = 0.88
p.prependPlotObjectToRatio(doLineStyle(ROOT.TLine(0, unc1, bins[-1], unc1)))
p.prependPlotObjectToRatio(doLineStyle(ROOT.TLine(0, unc2, bins[-1], unc2)))
p.appendPlotObject(histograms.PlotText(x=0.6, y=0.6, text="CaloMET > 70 GeV", size=20))
global ind
ind += 1
# plots.drawPlot(p, "calometComparison/eff_%02d_calomet_%s"%(ind, step), xlabel="Type I PF MET (GeV)", ylabel="CaloMET cut efficiency",
# ratio=True, ratioYlabel="Norm./emb.", ratioType="errorScale",
# opts={"xmin": 0, "xmax": 500},
# opts2={"ymin": 0.8, "ymax": 1.2},
# addLuminosityText=True, moveLegend={"dx": -0.2, "dy": -0.5})
plots.drawPlot(p, "calometComparison/eff_%d_calomet_shapeTransverseMass"%ind, xlabel="Transverse mass (GeV)", ylabel="CaloMET cut efficiency",
ratio=True, ratioYlabel="Emb./Norm.", ratioCreateLegend=True, ratioType="errorScale",
opts={"ymax": 1.2},
opts2={"ymin": 0.8, "ymax": 1.2},
addLuminosityText=True, moveLegend={"dx": -0.2, "dy": -0.5})
def doPlotsWTauMu(datasets,
name,
datasetName,
ntupleCache,
selectorName,
unweighted,
fitFunction,
rebin=None):
ds = datasets.getDataset(datasetName)
# Take first unweighted histograms for the fraction plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection" + unweighted,
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram(
"selectedMuonPt_AfterJetSelection_MuFromW" + unweighted,
selectorName))
def Rebin(th1, div=False):
if rebin is None:
return th1
else:
h = th1.Rebin(
len(rebin) - 1, th1.GetName(), array.array("d", rebin))
if div:
h.Scale(1, "width")
return h
def createTEfficiency(drhAll, drhPure):
hallUn = Rebin(drhAll.getHistogram())
hpureUn = Rebin(drhPure.getHistogram())
teff = ROOT.TEfficiency(hpureUn, hallUn)
teff.SetDirectory(0)
teff.SetWeight(drhAll.getDataset().getCrossSection())
return teff
teffs = drh_all.forEach(createTEfficiency, drh_pure)
#coll = ROOT.TList()
#for o in teffs:
# coll.AddLast(o)
#ratio = ROOT.TEfficiency.Combine(coll)
ratio = teffs[0]
for e in teffs[1:]:
ratio.Add(e)
styles.getDataStyle().apply(ratio)
ratio.SetName(datasetName)
ROOT.gStyle.SetStatY(0.99)
ROOT.gStyle.SetStatX(0.52)
ROOT.gStyle.SetStatW(0.18)
ROOT.gStyle.SetStatH(0.23)
expFit = ROOT.TF1("purityFit", [
"1/(1+[0]*exp(-[1]*x))",
"1-[0]*exp(-[1]*x)",
"1-([0]/(x^[1]))",
][fitFunction], aux.th1Xmin(ratio.GetPassedHistogram()),
aux.th1Xmax(ratio.GetPassedHistogram()))
expFit.SetParameter(0, 0.05)
ratio.Fit(expFit, "N")
expFit.SetLineColor(ROOT.kRed)
expFit.SetLineWidth(2)
# expFit = None
# Then the correctly weighted for the main plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection",
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection_MuFromW",
selectorName))
if mcOnly:
lumi = mcLuminosity
else:
lumi = datasets.getDataset("Data").getLuminosity()
drh_all.normalizeToLuminosity(lumi)
drh_pure.normalizeToLuminosity(lumi)
hall = Rebin(drh_all.getHistogram(), div=True)
hpure = Rebin(drh_pure.getHistogram(), div=True)
hall.SetName("All")
hpure.SetName("Pure")
p = plots.ComparisonPlot(hall, hpure)
p.setLuminosity(lumi)
p.setEnergy(ds.getEnergy())
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
# "Pure": "W#rightarrow#tau#rightarrow#mu"
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(era + "_" + name +
"_selectedMuonPt_AFterJetSelection_MuFromW_%s_fit%d" %
(datasetName, fitFunction) + unweighted,
createRatio=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 2
},
opts2={
"ymin": 0.9,
"ymax": 1.05
})
p.setRatios([ratio])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
#p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.appendPlotObjectToRatio(
histograms.PlotText(0.18, 0.61, "0.038", size=18, color=ROOT.kBlue))
if expFit is not None:
p.appendPlotObjectToRatio(expFit)
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.getPad().SetLogy(True)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("Muon p_{T} (GeV/c)")
p.frame.GetYaxis().SetTitle("Events / #Deltap_{T} %.0f-%.0f GeV/c" %
(min(p.binWidths()), max(p.binWidths())))
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(datasetName, datasetName),
size=18))
p.draw()
p.addStandardTexts()
p.save()
# Clear list of functions
ratio.GetListOfFunctions().Delete()
return (ratio, expFit)
def main(opts):
datasets = dataset.getDatasetsFromMulticrabCfg(directory=muonDir,
weightedCounters=doWeighted,
dataEra=era,
analysisName=analysis,
useAnalysisNameOnly=True)
args = {}
if doTopPtReweighting:
args["topPtWeightType"] = PileupWeightType.NOMINAL
if topPtReweightingScheme != "":
ttjetsName = filter(lambda n: "TTJets" in n,
datasets.getMCDatasetNames())[0]
datasets.getDataset(ttjetsName).info[
"topPtReweightScheme"] = topPtReweightingScheme
datasets.updateNAllEventsToPUWeighted(era=era, **args)
#keepOnly = "SingleMu_Mu_160431-163261_2011A_Nov08"
#keepOnly = "SingleMu_Mu_163270-163869_2011A_Nov08"
#keepOnly = "SingleMu_Mu_165088-166150_2011A_Nov08"
#keepOnly = "SingleMu_Mu_166161-166164_2011A_Nov08"
#keepOnly = "SingleMu_Mu_166346-166346_2011A_Nov08"
#keepOnly = "SingleMu_Mu_166374-167043_2011A_Nov08"
#keepOnly = "SingleMu_Mu_167078-167913_2011A_Nov08"
#keepOnly = "SingleMu_Mu_170722-172619_2011A_Nov08"
#keepOnly = "SingleMu_Mu_172620-173198_2011A_Nov08"
#keepOnly = "SingleMu_Mu_173236-173692_2011A_Nov08"
#keepOnly = "SingleMu_Mu_173693-177452_2011B_Nov19"
#keepOnly = "SingleMu_Mu_177453-178380_2011B_Nov19"
#keepOnly = "SingleMu_Mu_178411-179889_2011B_Nov19"
#keepOnly = "SingleMu_Mu_179942-180371_2011B_Nov19"
#datasets.remove(filter(lambda name: name != keepOnly, datasets.getDataDatasetNames()))
#datasets.remove(datasets.getMCDatasetNames())
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
else:
datasets.loadLuminosities()
#datasetsMC = datasets.deepCopy()
#datasetsMC.remove(datasets.getDataDatasetNames())
# splitWJets(datasets)
# splitDYJets(datasets)
if mergeMC:
plots.mergeRenameReorderForDataMC(datasets)
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
#histograms.createLegend.moveDefaults(dx=-0.15)
plots._legendLabels["QCD_Pt20_MuEnriched"] = "QCD"
histograms.createLegend.moveDefaults(dx=-0.04)
wtaumuRatioFile = ROOT.TFile.Open("embedding-wtaumuRatio.root", "RECREATE")
# ROOT.TH1.AddDirectory(False)
selectorArgs = tauEmbedding.MuonAnalysisSelectorArgs(
era=era,
isolationMode="chargedHadrRel10",
topPtReweighting=doTopPtReweighting,
topPtReweightingScheme=topPtReweightingBranch)
args = {}
if opts.cache is not None:
args["cacheFileName"] = opts.cache
ntupleCache = dataset.NtupleCache(
"tree",
"MuonAnalysisSelector",
selectorArgs=selectorArgs,
process=opts.process,
#maxEvents=1000,
**args)
ntupleCache.addSelector("FullStandardIso", "MuonAnalysisSelector",
selectorArgs.clone(isolationMode="standard"))
variations = []
if doVariations:
def doVar(name, **kwargs):
ntupleCache.addSelector(name, "MuonAnalysisSelector",
selectorArgs.clone(**kwargs))
variations.append(name)
doVar("SystVarPUWeightPlus", puVariation="up")
doVar("SystVarPUWeightMinus", puVariation="down")
if doTopPtReweighting:
doVar("SystVarTopPtWeightPlus",
topPtReweightingScheme=topPtReweightingBranch + "Plus")
doVar("SystVarTopPtWeightMinus",
topPtReweightingScheme=topPtReweightingBranch + "Minus")
selections = [
# ("FullTauLike", "taulike"),
# ("FullNoIso", "disabled"),
# ("FullStandardIso", "standard"),
# ("FullChargedHadrRelIso10", "chargedHadrRel10"),
# ("FullChargedHadrRelIso15", "chargedHadrRel15"),
("FullStandardIso", "FullStandardIso"),
("FullChargedHadrRelIso10", None)
]
for v in variations:
selections.append(("FullChargedHadrRelIso10" + v, v))
print selections
# for name, isolation in selections:
allRatios = {}
for name, selectorName in selections:
# ntupleCache = dataset.NtupleCache("tree", "MuonAnalysisSelector",
# ,
# cacheFileName="histogramCache-%s.root" % name,
# process=opts.process,
# #maxEvents=10000,
# )
# ntupleCache.setDatasetSelectorArgs({
# "WJets_NoB": tauEmbedding.MuonAnalysisSelectorArgs(bquarkMode="breject"),
# "WJets_B": tauEmbedding.MuonAnalysisSelectorArgs(bquarkMode="baccept"),
# "DYJetsToLL_NoB": tauEmbedding.MuonAnalysisSelectorArgs(bquarkMode="breject"),
# "DYJetsToLL_B": tauEmbedding.MuonAnalysisSelectorArgs(bquarkMode="baccept"),
# })
# doPlots(datasets, name, ntupleCache, selectorName)
# printCounters(datasets, name, ntupleCache, selectorName)
# printCounters(datasets, name, ntupleCache, onlyDataset="TTJets")
if True and "FullChargedHadrRelIso10" in name:
def dop(unweighted, fitFunction):
directory = wtaumuRatioFile.mkdir(name)
ratios = []
rebin = {
"TTJets":
range(0, 350, 10) + [350, 400],
"WJets":
range(0, 260, 10) + range(260, 300, 20) + [300, 350, 400],
"SingleTop":
range(0, 200, 10) + range(200, 300, 20) + [300, 400]
}
for dname in ["TTJets", "WJets", "SingleTop"]:
#for dname in ["TTJets"]:
(ratio, fit) = doPlotsWTauMu(datasets,
name,
dname,
ntupleCache,
selectorName,
unweighted,
fitFunction,
rebin=rebin[dname])
cl = ratio.Clone()
cl.SetDirectory(directory)
ratios.append((ratio, fit, dname))
doPlotsWTauMuRatio(datasets, name, ratios, unweighted,
fitFunction)
return ratios
#dop("", 2)
if "SystVar" in name:
tmp = dop("", 2)
for ratio, fit, dname in tmp:
if dname == "TTJets":
allRatios[name] = (ratio, fit)
else:
for w in ["", "_Unweighted"]:
for fitFunction in [0, 1, 2]:
tmp = dop(w, fitFunction)
# break
if w == "":
n = name
if fitFunction != 2:
n += "SystFunc%d" % fitFunction
for ratio, fit, dname in tmp:
if dname == "TTJets":
allRatios[n] = (ratio, fit)
elif fitFunction == 2:
allRatios[name + "Syst" + dname] = (ratio,
fit)
# break
keys = allRatios.keys()
keys.sort()
print "\n".join(keys)
doPlotsWTauMuRatioUncertaintyEstimate(datasets, allRatios,
"FullChargedHadrRelIso10")
fitLine = allRatios["FullChargedHadrRelIso10"][1]
print "Fit result: p0 %f, p1 %f" % (fitLine.GetParameter(0),
fitLine.GetParameter(1))
wtaumuRatioFile.Write()
wtaumuRatioFile.Close()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(weightedCounters=False)
datasets.loadLuminosities()
#mc = "WJets"
mc = "QCD"
data = "2010"
#data = "2011"
# maxVtx = 15
maxVtx = 20
if data == "2010":
datasets.remove(filter(lambda name: "Prompt" in name, datasets.getAllDatasetNames()))
elif data == "2011":
datasets.remove(filter(lambda name: "Dec22" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
style = tdrstyle.TDRStyle()
if mc == "QCD":
datasets.remove(["WJets"])
elif mc == "WJets":
datasets.remove(["QCD"])
h = histograms.HistoManager(datasets, "signalAnalysis/verticesBeforeWeight")
h.normalizeToOne()
h.forEachMCHisto(styles.generator())
h.forHisto("Data", styles.getDataStyle())
h.setHistoDrawStyle("Data", "EP")
h.setHistoLegendStyle("Data", "p")
cf = histograms.CanvasFrame(h, "vertex_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("A.u.")
legend = histograms.createLegend()
h.addToLegend(legend)
h.draw()
legend.Draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Weight
dataHisto = h.getHisto("Data").getRootHisto()
mcHisto = h.getHisto(mc).getRootHisto()
# For normalization, see https://twiki.cern.ch/twiki/bin/view/CMS/PileupReweighting
weightHisto = dataHisto.Clone("weights")
weightHisto.Divide(mcHisto)
print "Weight histo integral", weightHisto.Integral()
#weightHisto.Scale(1/dataHisto.Integral())
#weightHisto.Scale(1/weightHisto.Integral())
print "Weight histo integral", weightHisto.Integral()
print "Sum of [weight*prob]", sum([weightHisto.GetBinContent(bin)*mcHisto.GetBinContent(bin) for bin in xrange(1, weightHisto.GetNbinsX())])
print "weights = cms.vdouble(%s)" % ", ".join(["%.8f" % weightHisto.GetBinContent(bin) for bin in xrange(1, min(maxVtx, weightHisto.GetNbinsX())+1)])
h = histograms.HistoManager(datasetRootHistos=[])
h.appendHisto(histograms.Histo(weightHisto, "Weight", "", "HIST"))
h.forEachHisto(styles.generator())
cf = histograms.CanvasFrame(h, "vertex_weight_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("Weight")
h.draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
def doPlotsWTauMu(datasets, name):
selection = And(muonSelection, muonVeto, electronVeto, jetSelection)
td = treeDraw.clone(varexp="muons_p4.Pt() >> tmp(40,0,400)")
ds = datasets.getDataset(name)
# Take first unweighted histograms for the fraction plot
drh_all = ds.getDatasetRootHisto(td.clone(selection=selection, weight=""))
drh_pure = ds.getDatasetRootHisto(td.clone(selection=And(selection, "abs(muons_mother_pdgid) == 24"), weight=""))
hallUn = drh_all.getHistogram()
hpureUn = drh_pure.getHistogram()
# Then the correctly weighted for the main plot
drh_all = ds.getDatasetRootHisto(td.clone(selection=selection))
drh_pure = ds.getDatasetRootHisto(td.clone(selection=And(selection, "abs(muons_mother_pdgid) == 24")))
lumi = datasets.getDataset("Data").getLuminosity()
drh_all.normalizeToLuminosity(lumi)
drh_pure.normalizeToLuminosity(lumi)
hall = drh_all.getHistogram()
hpure = drh_pure.getHistogram()
hall.SetName("All")
hpure.SetName("Pure")
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#tau#rightarrow#mu"
# "Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue-7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed-7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame("muonPt_wtaumu_"+name, createRatio=True, opts={"ymin": 1e-1, "ymaxfactor": 2}, opts2={"ymin": 0.9, "ymax": 1.05}
)
p.setRatios([plots._createRatio(hpureUn, hallUn, "", isBinomial=True)])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1-0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
#p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.getPad().SetLogy(True)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("Muon p_{T} (GeV/c)")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c" % p.binWidth())
p.appendPlotObject(histograms.PlotText(0.5, 0.9, plots._legendLabels.get(name, name), size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
drh_correctId.normalizeToLuminosity(mcOnlyLumi)
drh_incorrectId.normalizeToLuminosity(mcOnlyLumi)
h_correctId = drh_correctId.getHistogram() # returns TH1
h_incorrectId = drh_incorrectId.getHistogram()
#h_reco.SetName("Reco")
#h_gen.SetName("Gen")
h_correctId.SetName("#splitline{ID good}{#Delta R < 0.4}")
h_incorrectId.SetName("#splitline{ID bad}{#Delta R > 0.4}")
h = plots.ComparisonPlot(
h_correctId,
h_incorrectId) # QUESTION: how does rebinning work in a comparison plot?
h.histoMgr.forEachHisto(styles.generator())
# Stack MC histograms
#h.stackMCHistograms()
#h.stackMCHistograms(stackSignal=True)
#h.addMCUncertainty()
# # Create canvas and frame for only the distributions
h.createFrame("Full_Higgs_mass")
h.frame.GetXaxis().SetTitle("m(b, #tau, #nu_{#tau}) (GeV)")
h.frame.GetYaxis().SetTitle("Events")
# # Create legend
h.setLegend(histograms.createLegend())
h.addEnergyText()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg()
datasets.loadLuminosities()
#mc = "WJets"
mc = "QCD"
data = "2010"
#data = "2011"
# maxVtx = 15
maxVtx = 20
if data == "2010":
datasets.remove(
filter(lambda name: "Prompt" in name,
datasets.getAllDatasetNames()))
elif data == "2011":
datasets.remove(
filter(lambda name: "Dec22" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
style = tdrstyle.TDRStyle()
if mc == "QCD":
datasets.remove(["WJets"])
elif mc == "WJets":
datasets.remove(["QCD"])
h = histograms.HistoManager(datasets,
"signalAnalysis/verticesBeforeWeight")
h.normalizeToOne()
h.forEachMCHisto(styles.generator())
h.forHisto("Data", styles.getDataStyle())
h.setHistoDrawStyle("Data", "EP")
h.setHistoLegendStyle("Data", "p")
cf = histograms.CanvasFrame(h, "vertex_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("A.u.")
legend = histograms.createLegend()
h.addToLegend(legend)
h.draw()
legend.Draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Weight
dataHisto = h.getHisto("Data").getRootHisto()
mcHisto = h.getHisto(mc).getRootHisto()
# For normalization, see https://twiki.cern.ch/twiki/bin/view/CMS/PileupReweighting
weightHisto = dataHisto.Clone("weights")
weightHisto.Divide(mcHisto)
print "Weight histo integral", weightHisto.Integral()
#weightHisto.Scale(1/dataHisto.Integral())
#weightHisto.Scale(1/weightHisto.Integral())
print "Weight histo integral", weightHisto.Integral()
print "Sum of [weight*prob]", sum([
weightHisto.GetBinContent(bin) * mcHisto.GetBinContent(bin)
for bin in xrange(1, weightHisto.GetNbinsX())
])
print "weights = cms.vdouble(%s)" % ", ".join([
"%.8f" % weightHisto.GetBinContent(bin)
for bin in xrange(1,
min(maxVtx, weightHisto.GetNbinsX()) + 1)
])
h = histograms.HistoManager(datasetRootHistos=[])
h.appendHisto(histograms.Histo(weightHisto, "Weight", "", "HIST"))
h.forEachHisto(styles.generator())
cf = histograms.CanvasFrame(h,
"vertex_weight_%s_%s" % (data, mc),
xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("Weight")
h.draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
### Normalize the area of *all* histograms to 1
#TauIsoMaxlTrkPt.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauIsoMaxlTrkPt.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauIsoMaxlTrkPt.setHistoLegendStyleAll("F")
TauIsoMaxlTrkPt.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauIsoMaxlTrkPt.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauIsoMaxlTrkPt.forHisto("Data", styles.getDataStyle())
#TauIsoMaxlTrkPt.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauIsoMaxlTrkPt.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauIsoMaxlTrkPt,
"TauIsoMaxlTrkPt",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=20.0)
### Set the frame options, e.g. axis labels
def compare(step):
if step is None:
path = "shapeTransverseMass"
else:
path = "CommonPlots/AtEveryStep/%s/MET_MET" % step
#bins = range(0, 200, 20) + [200, 250, 300, 500]
bins = systematics.getBinningForPlot("shapeTransverseMass")
def getTH1(ds):
drh = ds.getDatasetRootHisto(path)
drh.normalizeToLuminosity(lumi)
th1 = drh.getHistogram()
#th1.Rebin(2)
#return th1
return th1.Rebin(
len(bins) - 1, th1.GetName(), array.array("d", bins))
th1NormalNum = getTH1(dsetNormalNum)
th1NormalDenom = getTH1(dsetNormalDenom)
#print th1NormalNum.Integral(0, th1NormalNum.GetNbinsX()+1), th1NormalDenom.Integral(0, th1NormalDenom.GetNbinsX()+1)
th1EmbNum = getTH1(dsetEmbNum)
th1EmbDenom = getTH1(dsetEmbDenom)
#print th1EmbNum.Integral(0, th1EmbNum.GetNbinsX()+1), th1EmbDenom.Integral(0, th1EmbDenom.GetNbinsX()+1)
effNormal = ROOT.TGraphAsymmErrors(th1NormalNum, th1NormalDenom, "n")
effEmb = ROOT.TGraphAsymmErrors(th1EmbNum, th1EmbDenom, "n")
effNormal.SetName("Normal ttbar")
effEmb.SetName("Embedded ttbar")
p = plots.ComparisonManyPlot(effNormal, [effEmb])
p.setLuminosity(lumi)
p.histoMgr.forEachHisto(styles.generator())
#p.histoMgr.forHisto("Normal ttbar", lambda h: doStyle(h, ROOT.kBlack, ROOT.kFullCircle))
#p.histoMgr.forHisto("Embedded ttbar", lambda h: doStyle(h, ROOT.kRed, ROOT.kFullSquare))
p.histoMgr.setHistoDrawStyleAll("EP")
p.histoMgr.setHistoLegendStyleAll("EPL")
unc1 = 1.12
unc2 = 0.88
p.prependPlotObjectToRatio(
doLineStyle(ROOT.TLine(0, unc1, bins[-1], unc1)))
p.prependPlotObjectToRatio(
doLineStyle(ROOT.TLine(0, unc2, bins[-1], unc2)))
p.appendPlotObject(
histograms.PlotText(x=0.6, y=0.6, text="CaloMET > 70 GeV",
size=20))
global ind
ind += 1
# plots.drawPlot(p, "calometComparison/eff_%02d_calomet_%s"%(ind, step), xlabel="Type I PF MET (GeV)", ylabel="CaloMET cut efficiency",
# ratio=True, ratioYlabel="Norm./emb.", ratioType="errorScale",
# opts={"xmin": 0, "xmax": 500},
# opts2={"ymin": 0.8, "ymax": 1.2},
# addLuminosityText=True, moveLegend={"dx": -0.2, "dy": -0.5})
plots.drawPlot(p,
"calometComparison/eff_%d_calomet_shapeTransverseMass" %
ind,
xlabel="Transverse mass (GeV)",
ylabel="CaloMET cut efficiency",
ratio=True,
ratioYlabel="Emb./Norm.",
ratioCreateLegend=True,
ratioType="errorScale",
opts={"ymax": 1.2},
opts2={
"ymin": 0.8,
"ymax": 1.2
},
addLuminosityText=True,
moveLegend={
"dx": -0.2,
"dy": -0.5
})
ylabel = "Events"
### Normalize the area of *all* histograms to 1
#NSelJets.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
NSelJets.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
NSelJets.setHistoLegendStyleAll("F")
NSelJets.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
NSelJets.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
NSelJets.forHisto("Data", styles.getDataStyle())
#NSelJets.setHistoDrawStyle("Data", "EP")
NSelJets.forHisto("BTau_146240-147116", styles.getDataStyle())
NSelJets.setHistoDrawStyle("BTau_146240-147116", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
NSelJets.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(NSelJets, "NSelJets", ymin=0.01, ymax=None, xmin=0.0, xmax=10.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Number of Selected Jets")
cf.frame.GetYaxis().SetTitle(ylabel)
# TTJets
drh_reco = datasets.getDataset("TTToHplusBWB_M120").getDatasetRootHisto(analysis+"/FullHiggsMass/HiggsMass")
drh_gen = datasets.getDataset("TTToHplusBWB_M120").getDatasetRootHisto(analysis+"/GenParticleAnalysis/genFullHiggsMass")
drh_reco.normalizeToLuminosity(mcOnlyLumi)
drh_gen.normalizeToLuminosity(mcOnlyLumi)
h_reco = drh_reco.getHistogram() # returns TH1
h_gen = drh_gen.getHistogram()
h_reco.SetName("Reco")
h_gen.SetName("Gen")
h = plots.ComparisonPlot(h_reco, h_gen)
h.histoMgr.forEachHisto(styles.generator())
# Stack MC histograms
#h.stackMCHistograms()
#h.stackMCHistograms(stackSignal=True)
#h.addMCUncertainty()
# # Create canvas and frame for only the distributions
h.createFrame("Full_Higgs_mass")
# h.frame.GetXaxis().SetTitle("m_H (GeV)")
# h.frame.GetYaxis().SetTitle("events / ? GeV")
# # Create legend
h.setLegend(histograms.createLegend())
ylabel = "Events"
### Normalize the area of *all* histograms to unity. Canno be used with mergeDatasets
#transverseMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
transverseMass.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
transverseMass.setHistoLegendStyleAll("F")
transverseMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
transverseMass.forEachMCHisto(styles.generator(fill=True))
transverseMass.forHisto("Data", styles.getDataStyle())
#transverseMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#transverseMass.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(transverseMass, "transverseMass", ymin=0.01, ymax=None, xmin=0.0, xmax=300.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("M_{T} GeV/c^{2}")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
def doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)")
(hall, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(name, tdEff.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")), tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {True: "Btagging", False: "NoBTag"}[btag], npure/nall, (1-npure/nall)*100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_"+name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_"+name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue-7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed-7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1-0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(histograms.PlotText(0.5, 0.9, plots._legendLabels.get(name, name), size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
selectionsAco = muonJetMetAoc
datasets = getDatasetsFromMulticrabCfg(counters=None)
#datasets.getDataset("Mu_135821-144114").setLuminosity(3051760.115/1e6) # ub^-1 -> pb^-1
#datasets.getDataset("Mu_146240-147116").setLuminosity(4390660.197/1e6)
#datasets.getDataset("Mu_147196-149442").setLuminosity(27384630.974/1e6)
#datasets.getDataset("Mu_135821-144114").setLuminosity(2863224.758/1e6) # ub^-1 -> pb^-1
datasets.getDataset("Mu_146240-147116").setLuminosity(3977060.866/1e6)
#datasets.getDataset("Mu_147196-149442").setLuminosity(27907588.871/1e6)
#datasets.loadLuminosities("lumis.txt")
datasets.mergeData()
styleGenerator = styles.generator(fill=True)
#textDefaults.setCmsPreliminaryDefaults()
textDefaults.setEnergyDefaults(x=0.17)
textDefaults.setLuminosityDefaults(x=0.4, size=0.04)
createLegend.setDefaults(x1=0.65,y1=0.7)
style = TDRStyle()
class Histo:
def __init__(self, datasets, name, lumi=None):
self.histos = HistoManager(datasets, name)
#print "\n".join(histos.getDatasetNames())
if lumi == None:
self.histos.normalizeMCByLuminosity()
### Normalize the area of *all* histograms to 1
#TauNTrksVSPtcut.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauNTrksVSPtcut.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauNTrksVSPtcut.setHistoLegendStyleAll("F")
TauNTrksVSPtcut.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauNTrksVSPtcut.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauNTrksVSPtcut.forHisto("Data", styles.getDataStyle())
#TauNTrksVSPtcut.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauNTrksVSPtcut.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauNTrksVSPtcut,
"TauNTrksVSPtcut",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=20.0)
### Set the frame options, e.g. axis labels
ylabel = "??"
### Normalize the area of *all* histograms to 1
#TauIsoltrkPtsumVSPtcut.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauIsoltrkPtsumVSPtcut.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauIsoltrkPtsumVSPtcut.setHistoLegendStyleAll("F")
TauIsoltrkPtsumVSPtcut.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauIsoltrkPtsumVSPtcut.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauIsoltrkPtsumVSPtcut.forHisto("Data", styles.getDataStyle())
#TauIsoltrkPtsumVSPtcut.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauIsoltrkPtsumVSPtcut.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauIsoltrkPtsumVSPtcut, "TauIsoltrkPtsumVSPtcut", ymin=0.01, ymax=None, xmin=0.0, xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("???")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
### Normalize the area of *all* histograms to unity. Canno be used with mergeDatasets
#transverseMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
transverseMass.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
transverseMass.setHistoLegendStyleAll("F")
transverseMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
transverseMass.forEachMCHisto(styles.generator(fill=True))
transverseMass.forHisto("Data", styles.getDataStyle())
#transverseMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#transverseMass.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(transverseMass,
"transverseMass",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=300.0)
### Set the frame options, e.g. axis labels
def doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(
analysisEmb + "/tree",
weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb + "/tree",
weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(
varexp=
"sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)"
)
(hall, tmp) = datasetsEmb.getHistogram(name,
tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(
name,
tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(
name,
tdEff.clone(
selection=And(selection, "abs(temuon_mother_pdgid) == 24")),
tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {
True: "Btagging",
False: "NoBTag"
}[btag], npure / nall, (1 - npure / nall) * 100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_" + name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_" + name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(
histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(name, name),
size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
# Normalize the area of *all* histograms to 1
# tauPts.normalizeToOne()
# ylabel = "A.u."
# Example how to set legend labels from defaults
# tauPts.setHistoLegendLabel("TTbar_Htaunu_M80", "H^{#pm} M=80") # one dataset at a time
tauPts.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
# Example how to modify legend styles
tauPts.setHistoLegendStyleAll("F")
tauPts.setHistoLegendStyle("Data", "p")
# Apply the default styles (for all histograms, for MC histograms, for a single histogram)
# tauPts.forEachHisto(styles.generator())
# tauPts.forEachMCHisto(styles.generator(fill=True, fillStyle=3002)) # Example how to set non-default fill style
tauPts.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
tauPts.forHisto("Data", styles.getDataStyle())
tauPts.setHistoDrawStyle("Data", "EP")
# Example how to stack all MC datasets
# Note: this MUST be done after all legend/style manipulation
tauPts.stackMCHistograms()
# Example how to add MC uncertainty
tauPts.addMCUncertainty(styles.getErrorStyle())
# tauPts.addMCUncertainty(styles.getErrorStyle(), "MC uncertainty")
# Create TCanvas and TH1F such that they cover all histograms
cf = histograms.CanvasFrame(tauPts, "taupt")
# cf = histograms.CanvasFrame(tauPts, "taupt", ymin=10, ymax=1e9) # for logy
selections = muonJetMet
selectionsAco = muonJetMetAoc
datasets = getDatasetsFromMulticrabCfg(counters=None)
#datasets.getDataset("Mu_135821-144114").setLuminosity(3051760.115/1e6) # ub^-1 -> pb^-1
#datasets.getDataset("Mu_146240-147116").setLuminosity(4390660.197/1e6)
#datasets.getDataset("Mu_147196-149442").setLuminosity(27384630.974/1e6)
#datasets.getDataset("Mu_135821-144114").setLuminosity(2863224.758/1e6) # ub^-1 -> pb^-1
datasets.getDataset("Mu_146240-147116").setLuminosity(3977060.866 / 1e6)
#datasets.getDataset("Mu_147196-149442").setLuminosity(27907588.871/1e6)
#datasets.loadLuminosities("lumis.txt")
datasets.mergeData()
styleGenerator = styles.generator(fill=True)
#textDefaults.setCmsPreliminaryDefaults()
textDefaults.setEnergyDefaults(x=0.17)
textDefaults.setLuminosityDefaults(x=0.4, size=0.04)
createLegend.setDefaults(x1=0.65, y1=0.7)
style = TDRStyle()
class Histo:
def __init__(self, datasets, name, lumi=None):
self.histos = HistoManager(datasets, name)
#print "\n".join(histos.getDatasetNames())
if lumi == None: