def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# erik
# datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "WJetsToLNu" in name and not "WJetsToLNu_HT" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TT",
"WJetsHT",
"DYJetsToLLHT",
"SingleTop",
# "Diboson"
])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i,histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i,datasets,histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s"%i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s"%i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s"%i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s"%i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s"%i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s"%i] = "BackToBack cuts"
# plot.createFrame("purity_QCD_only", opts={"xmin": 40, "xmax": 160, "ymin": 0., "ymax": 1.05})
plot.createFrame("purity_QCD_only", opts={"xmin": 40, "xmax": 400, "ymin": 0., "ymax":1})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
# plot.histoMgr.appendHisto(histograms.Histo(histo,"InvertedTauPtAfterAllSelections"))
plot.histoMgr.setHistoLegendLabelMany(legends)
### plot.setLegend(histograms.createLegend(0.2, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
# histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.3, 0.35, "QCD only", 20)
histograms.addText(0.3, 0.3, "After all selection cuts", 20)
# histograms.addText(0.2, 0.3, "QCD only", 18)
histograms.addCmsPreliminaryText()
histograms.addEnergyText(s="%s TeV"%(datasets.getEnergies()[0]))
histograms.addLuminosityText(x=None, y=None, lumi=datasets.getDataset("Data").getLuminosity())
plot.draw()
plot.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
QCDInvertedNormalization = sort(
QCDInvertedNormalizationFactors.QCDInvertedNormalization)
labels, QCDInvertedNormalizationFilteredEWKFakeTaus = getSortedLabelsAndFactors(
QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.
QCDInvertedNormalization)
analysis_inverted = "signalAnalysisInvertedTau"
analysis = "signalAnalysis"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
varHistoName = "shapeEWKGenuineTausTransverseMass"
nomHistoName = "shapeTransverseMass"
signalHistoName = "shapeEWKFakeTausTransverseMass"
#Optimal: 0.8, 0.82, 0.9
###w_list = [0.65, 0.7, 0.76] #old baseline ft
w_list = [0.66, 0.67, 0.75]
#w_list = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 1]
#defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
defaultBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
defaultBinning_array = array.array("d", defaultBinning)
diff_opt = []
for optMode in optModes:
diff_list = []
for w in w_list:
var_values = []
nom_values = []
# baseline fake taus
mt_baseline_faketaus_data = getDataSets(dirs_signal, dataEra,
searchMode, analysis,
optMode)
mtplot_signalfaketaus = plots.DataMCPlot(mt_baseline_faketaus_data,
signalHistoName)
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(signalHistoName)
# inverted fake taus
mt_inverted_faketaus_data = getDataSets(dirs, dataEra, searchMode,
analysis_inverted, optMode)
histonames_var = mt_inverted_faketaus_data.getDataset(
"Data").getDirectoryContent(varHistoName)
histonames_nom = mt_inverted_faketaus_data.getDataset(
"Data").getDirectoryContent(nomHistoName)
bins_var = getBins(histonames_var, varHistoName)
bins_nom = getBins(histonames_nom, nomHistoName)
normalization_var_qg = getNormalization(
bins_var, w, QCDInvertedNormalizationFilteredEWKFakeTaus, True,
True)
normalization_var = getNormalization(
bins_var, w, QCDInvertedNormalizationFilteredEWKFakeTaus, True,
False)
normalization_nom = getNormalization(bins_nom, w,
QCDInvertedNormalization,
False, False)
mt_var_qg = getMt(mt_inverted_faketaus_data, bins_var,
varHistoName, normalization_var_qg)
mt_var = getMt(mt_inverted_faketaus_data, bins_var, varHistoName,
normalization_var)
mt_nom = getMt(mt_inverted_faketaus_data, bins_nom, nomHistoName,
normalization_nom)
mt_nom.Add(mt_signalfaketaus)
mt_var_qg.SetName(
"QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau), q-g bal.")
mt_var.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)")
mt_nom.SetName("QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)")
mt_var_qg.SetLineWidth(4)
mt_var.SetLineColor(14)
mt_nom.SetLineColor(2)
mt_var_qg = mt_var_qg.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
mt_var = mt_var.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
mt_nom = mt_nom.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
for i in range(0, mt_nom.GetSize()):
var_values.append(mt_var_qg.GetBinContent(i))
nom_values.append(mt_nom.GetBinContent(i))
style = tdrstyle.TDRStyle()
varPlots = [mt_var, mt_var_qg]
plot = plots.ComparisonManyPlot(mt_nom, varPlots)
plot.createFrame(optMode.replace(
"Opt", "Mt_DataDrivenVsMC_" + "w=" + str(w) + "_"),
createRatio=True)
moveLegend = {"dx": -0.35, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.3,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = mt_inverted_faketaus_data.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
mt_var_qg.Delete()
mt_var.Delete()
mt_nom.Delete()
#TFile.CurrentFile().Close("R")
mt_baseline_faketaus_data.close()
mt_inverted_faketaus_data.close()
ROOT.gROOT.CloseFiles()
ROOT.gROOT.GetListOfCanvases().Delete()
ROOT.gDirectory.GetList().Delete()
#print var_values
#print nom_values
# difference metrics
num = 0
denom = 0
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
os.system("rm MtOptimal/*")
os.system("mkdir -p MtOptimal")
print "\nWeights:\t", w_list, '\n'
optimalWeights = {}
for i in range(0, len(diff_opt)):
print optModes[i]
print "Differences:\t", diff_opt[i], "- Optimal: w =", w_list[
diff_opt[i].index(min(diff_opt[i]))]
optimalWeights[optModes[i]] = w_list[diff_opt[i].index(min(
diff_opt[i]))]
command = "cp *" + str(w_list[diff_opt[i].index(min(
diff_opt[i]))]) + "*" + optModes[i].replace("Opt",
"") + ".eps MtOptimal"
os.system(command)
print optimalWeights
writeWeightsToFile("OptimalWeights.py", optimalWeights)
writeNormalizationToFile("QCDPlusEWKFakeTauNormalizationFactors.py",
normalization_var_qg, labels)
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
QCDInvertedNormalization = sort(QCDInvertedNormalizationFactors.QCDInvertedNormalization)
labels,QCDInvertedNormalizationFilteredEWKFakeTaus = getSortedLabelsAndFactors(QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization)
analysis_inverted = "signalAnalysisInvertedTau"
analysis = "signalAnalysis"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
varHistoName = "shapeEWKGenuineTausTransverseMass"
nomHistoName = "shapeTransverseMass"
signalHistoName = "shapeEWKFakeTausTransverseMass"
#Optimal: 0.8, 0.82, 0.9
###w_list = [0.65, 0.7, 0.76] #old baseline ft
w_list = [0.66, 0.67, 0.75]
#w_list = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 1]
#defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
defaultBinning = [0,20,40,60,80,100,120,140,160,200,400]
defaultBinning_array = array.array("d",defaultBinning)
diff_opt = []
for optMode in optModes:
diff_list = []
for w in w_list:
var_values = []
nom_values = []
# baseline fake taus
mt_baseline_faketaus_data = getDataSets(dirs_signal, dataEra, searchMode, analysis, optMode)
mtplot_signalfaketaus = plots.DataMCPlot(mt_baseline_faketaus_data, signalHistoName)
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto("EWK").getRootHisto().Clone(signalHistoName)
# inverted fake taus
mt_inverted_faketaus_data = getDataSets(dirs, dataEra, searchMode, analysis_inverted, optMode)
histonames_var = mt_inverted_faketaus_data.getDataset("Data").getDirectoryContent(varHistoName)
histonames_nom = mt_inverted_faketaus_data.getDataset("Data").getDirectoryContent(nomHistoName)
bins_var = getBins(histonames_var, varHistoName)
bins_nom = getBins(histonames_nom, nomHistoName)
normalization_var_qg = getNormalization(bins_var,w,QCDInvertedNormalizationFilteredEWKFakeTaus,True,True)
normalization_var = getNormalization(bins_var,w,QCDInvertedNormalizationFilteredEWKFakeTaus,True,False)
normalization_nom = getNormalization(bins_nom,w,QCDInvertedNormalization,False,False)
mt_var_qg = getMt(mt_inverted_faketaus_data,bins_var,varHistoName,normalization_var_qg)
mt_var = getMt(mt_inverted_faketaus_data,bins_var,varHistoName,normalization_var)
mt_nom = getMt(mt_inverted_faketaus_data,bins_nom,nomHistoName,normalization_nom)
mt_nom.Add(mt_signalfaketaus)
mt_var_qg.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau), q-g bal.")
mt_var.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)")
mt_nom.SetName("QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)")
mt_var_qg.SetLineWidth(4)
mt_var.SetLineColor(14)
mt_nom.SetLineColor(2)
mt_var_qg = mt_var_qg.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
mt_var = mt_var.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
mt_nom = mt_nom.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
for i in range(0,mt_nom.GetSize()):
var_values.append(mt_var_qg.GetBinContent(i))
nom_values.append(mt_nom.GetBinContent(i))
style = tdrstyle.TDRStyle()
varPlots = [mt_var, mt_var_qg]
plot = plots.ComparisonManyPlot(mt_nom,varPlots)
plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"+"w="+str(w)+"_"), createRatio=True)
moveLegend={"dx": -0.35,"dy": 0.05}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(0.65, 0.3, optMode.replace("OptQCDTailKiller","R_{BB} ").replace("Plus",""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi=mt_inverted_faketaus_data.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
mt_var_qg.Delete()
mt_var.Delete()
mt_nom.Delete()
#TFile.CurrentFile().Close("R")
mt_baseline_faketaus_data.close()
mt_inverted_faketaus_data.close()
ROOT.gROOT.CloseFiles()
ROOT.gROOT.GetListOfCanvases().Delete()
ROOT.gDirectory.GetList().Delete()
#print var_values
#print nom_values
# difference metrics
num = 0
denom = 0
for i in range(0,len(nom_values)):
num += var_values[i]*(var_values[i]-nom_values[i])**2
denom += var_values[i]
diff = num/denom
diff_list.append(diff)
diff_opt.append(diff_list)
os.system("rm MtOptimal/*")
os.system("mkdir -p MtOptimal")
print "\nWeights:\t",w_list,'\n'
optimalWeights = {}
for i in range(0,len(diff_opt)):
print optModes[i]
print "Differences:\t",diff_opt[i],"- Optimal: w =",w_list[diff_opt[i].index(min(diff_opt[i]))]
optimalWeights[optModes[i]] = w_list[diff_opt[i].index(min(diff_opt[i]))]
command = "cp *" + str(w_list[diff_opt[i].index(min(diff_opt[i]))])+"*"+optModes[i].replace("Opt","") + ".eps MtOptimal"
os.system(command)
print optimalWeights
writeWeightsToFile("OptimalWeights.py",optimalWeights)
writeNormalizationToFile("QCDPlusEWKFakeTauNormalizationFactors.py",normalization_var_qg,labels)
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
#Optimal: 0.8, 0.82, 0.9
#w1_list = [0.8, 0.82, 0.84, 0.87]
#w1_list = [0.8, 0.82, 0.9, 1]
w1_list = [0.9]
defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
diff_opt = []
for optMode in optModes:
diff_list = []
for w1 in w1_list:
var_values = []
nom_values = []
w2 = 1 - w1
color = 1
#signal
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
mt_signalfaketaus.SetName("BaselineFakeTaus")
myBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
myArray = array.array("d", myBinning)
fitBinning = []
for i in range(0, 45):
fitBinning.append(i * 10)
fitArray = array.array("d", fitBinning)
mt_baseline = mt_signalfaketaus
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
for HISTONAME in histoNameList:
var = False
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
var = True
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(
datasets, HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if var:
legendName = "QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)"
else:
legendName = "QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
mtn.Add(mtn_ewk, -1)
mtn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
mt.Scale(scale)
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
hn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
hn.Add(mtn_ewk, -1)
hn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
h.Scale(scale)
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
mtn.Add(hn)
#mt = mt.Rebin(len(myBinning)-1,"",myArray)
#mt_corr = mt_corr.Rebin(len(myBinning)-1,"",myArray)
if not var:
mt.Add(mt_baseline)
#mt.Add(mt_corr)
#myBinning = []
#for i in range(0,11):
# myBinning.append(20*i)
#myBinning.append(400)
#myArray = array.array("d",defaultBinning)
mt = mt.Rebin(len(myBinning) - 1, "", myArray)
for i in range(0, mt.GetSize()):
if var:
var_values.append(mt.GetBinContent(i))
else:
nom_values.append(mt.GetBinContent(i))
if var:
#mt.SetLineStyle(2)
var_hist = mt
else:
#mt.SetLineStyle(2)
nom_hist = mt
style = tdrstyle.TDRStyle()
#gStyle.SetOptStat(1101)
#mt_data.SetStats(1)
#gPad.Update()
bins = [0, 390, 400]
arr = array.array("d", bins)
mtn = mtn.Rebin(len(bins) - 1, "", arr)
plot_data = plots.PlotBase()
plot_data.histoMgr.appendHisto(histograms.Histo(mtn, "Data"))
plot_data.createFrame("Data_" + HISTONAME + "_" + optMode +
"_" + str(w1))
plot_data.draw()
plot_data.save()
plot = plots.ComparisonPlot(nom_hist, var_hist)
plot.createFrame(optMode.replace(
"Opt", "Mt_" + "w1=" + str(w1) + "_w2=" + str(w2) +
"_DataDrivenVsMC_"),
createRatio=True)
moveLegend = {"dx": -0.295, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
num = 0
denom = 0
#print var_values
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
print w1_list, '\n'
for i in range(0, len(diff_opt)):
print diff_opt[i]
print w1_list[diff_opt[i].index(min(diff_opt[i]))]
mt_baseline = mt_baseline.Rebin(len(bins) - 1, "", arr)
plot_bft = plots.PlotBase()
plot_bft.histoMgr.appendHisto(histograms.Histo(mt_baseline, "baseline"))
#mt_corr.Scale(2)
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#plot_bft.histoMgr.appendHisto(histograms.Histo(theFit,"theFit"))
plot_bft.createFrame('BaselineFakeTaus')
plot_bft.draw()
plot_bft.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
for optMode in optModes:
plot = plots.PlotBase()
color = 1
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK", ["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
for HISTONAME in histoNameList:
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
invjet_name = "MTInvertedTauIdAfterAllSelectionsPlusJetFakeTau"
basejet_name = "MTBaselineTauIdAfterAllSelectionsPlusJetFakeTau"
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets,
HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(
HISTONAME + "/" + HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
legendName = "Data-driven Fake Taus"
else:
legendName = "Simulated Fake Taus"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
mt.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(
HISTONAME + "/" + HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
h.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
if HISTONAME == "shapeTransverseMass":
mt.Add(mt_signalfaketaus)
plot.histoMgr.appendHisto(histograms.Histo(mt, mt.GetName()))
style = tdrstyle.TDRStyle()
#plot.createFrame("mt")
#plot.createFrame(HISTONAME.replace("shape","final"))
plot.createFrame(optMode.replace("Opt", "Mt_DataDrivenVsMC_"))
moveLegend = {"dx": -0.3, "dy": 0.}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller", "R_{BB} ").replace("Plus", ""),
25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
for optMode in optModes:
plot = plots.PlotBase()
color = 1
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(dirs_signal,dataEra=dataEra, searchMode=searchMode, analysisName=analysis.replace("InvertedTau",""), optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(filter(lambda name: "TTToHplus" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "HplusTB" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_FullLept" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
mtplot_signalfaketaus = plots.DataMCPlot(datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto("EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
for HISTONAME in histoNameList:
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
histonames = datasets.getDataset("Data").getDirectoryContent(HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME,"")
if not binname == "Inclusive":
bins.append(binname)
invjet_name = "MTInvertedTauIdAfterAllSelectionsPlusJetFakeTau"
basejet_name = "MTBaselineTauIdAfterAllSelectionsPlusJetFakeTau"
for i,bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets, HISTONAME+"/"+HISTONAME+bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mt_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
legendName = "Data-driven Fake Taus"
else:
legendName = "Simulated Fake Taus"
legendName = legendName.replace("Plus","")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk,-1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
mt.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)])
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mt_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
h.Add(mt_ewk,-1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
h.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)])
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
if HISTONAME == "shapeTransverseMass":
mt.Add(mt_signalfaketaus)
plot.histoMgr.appendHisto(histograms.Histo(mt,mt.GetName()))
style = tdrstyle.TDRStyle()
#plot.createFrame("mt")
#plot.createFrame(HISTONAME.replace("shape","final"))
plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"))
moveLegend={"dx": -0.3,"dy": 0.}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(0.65, 0.20, optMode.replace("OptQCDTailKiller","R_{BB} ").replace("Plus",""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi=datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
#Optimal: 0.8, 0.82, 0.9
#w1_list = [0.8, 0.82, 0.84, 0.87]
#w1_list = [0.8, 0.82, 0.9, 1]
w1_list = [0.9]
defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
diff_opt = []
for optMode in optModes:
diff_list = []
for w1 in w1_list:
var_values = []
nom_values = []
w2 = 1 - w1
color = 1
#signal
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(dirs_signal,dataEra=dataEra, searchMode=searchMode, analysisName=analysis.replace("InvertedTau",""), optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(filter(lambda name: "TTToHplus" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "HplusTB" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_FullLept" in name, datasets_signal.getAllDatasetNames()))
datasets_signal.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
mtplot_signalfaketaus = plots.DataMCPlot(datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto("EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
mt_signalfaketaus.SetName("BaselineFakeTaus")
myBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
myArray = array.array("d",myBinning)
fitBinning = []
for i in range(0,45):
fitBinning.append(i*10)
fitArray = array.array("d",fitBinning)
mt_baseline = mt_signalfaketaus
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
for HISTONAME in histoNameList:
var = False
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
var = True
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
histonames = datasets.getDataset("Data").getDirectoryContent(HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME,"")
if not binname == "Inclusive":
bins.append(binname)
for i,bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets, HISTONAME+"/"+HISTONAME+bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mt_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mtn = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mtn_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
if var:
legendName = "QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)"
else:
legendName = "QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)"
legendName = legendName.replace("Plus","")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk,-1)
mtn.Add(mtn_ewk,-1)
mtn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1*QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)] + w2*QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)+"EWK_FakeTaus"]
mt.Scale(scale)
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mt_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
hn = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
mtn_ewk = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME+"/"+HISTONAME+bin)
h.Add(mt_ewk,-1)
hn.Add(mtn_ewk,-1)
hn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1*QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)] + w2*QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)+"EWK_FakeTaus"]
h.Scale(scale)
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
mtn.Add(hn)
#mt = mt.Rebin(len(myBinning)-1,"",myArray)
#mt_corr = mt_corr.Rebin(len(myBinning)-1,"",myArray)
if not var:
mt.Add(mt_baseline)
#mt.Add(mt_corr)
#myBinning = []
#for i in range(0,11):
# myBinning.append(20*i)
#myBinning.append(400)
#myArray = array.array("d",defaultBinning)
mt = mt.Rebin(len(myBinning)-1,"",myArray)
for i in range(0,mt.GetSize()):
if var:
var_values.append(mt.GetBinContent(i))
else:
nom_values.append(mt.GetBinContent(i))
if var:
#mt.SetLineStyle(2)
var_hist = mt
else:
#mt.SetLineStyle(2)
nom_hist = mt
style = tdrstyle.TDRStyle()
#gStyle.SetOptStat(1101)
#mt_data.SetStats(1)
#gPad.Update()
bins = [0, 390, 400]
arr = array.array("d",bins)
mtn = mtn.Rebin(len(bins)-1,"",arr)
plot_data = plots.PlotBase()
plot_data.histoMgr.appendHisto(histograms.Histo(mtn,"Data"))
plot_data.createFrame("Data_"+HISTONAME+"_"+optMode+"_"+str(w1))
plot_data.draw()
plot_data.save()
plot = plots.ComparisonPlot(nom_hist,var_hist)
plot.createFrame(optMode.replace("Opt","Mt_"+"w1="+str(w1)+"_w2="+str(w2)+"_DataDrivenVsMC_"), createRatio=True)
moveLegend={"dx": -0.295,"dy": 0.05}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(0.65, 0.20, optMode.replace("OptQCDTailKiller","R_{BB} ").replace("Plus",""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi=datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
num = 0
denom = 0
#print var_values
for i in range(0,len(nom_values)):
num += var_values[i]*(var_values[i]-nom_values[i])**2
denom += var_values[i]
diff = num/denom
diff_list.append(diff)
diff_opt.append(diff_list)
print w1_list,'\n'
for i in range(0,len(diff_opt)):
print diff_opt[i]
print w1_list[diff_opt[i].index(min(diff_opt[i]))]
mt_baseline = mt_baseline.Rebin(len(bins)-1,"",arr)
plot_bft = plots.PlotBase()
plot_bft.histoMgr.appendHisto(histograms.Histo(mt_baseline,"baseline"))
#mt_corr.Scale(2)
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#plot_bft.histoMgr.appendHisto(histograms.Histo(theFit,"theFit"))
plot_bft.createFrame('BaselineFakeTaus')
plot_bft.draw()
plot_bft.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("")
#optModes.append("OptQCDTailKillerLoosePlus")
#optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
color = 1
#plot = plots.PlotBase()
jetRatios = []
for HISTONAME in histonameList:
for optMode in optModes:
plot = plots.PlotBase()
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("EWK").getDirectoryContent(
HISTONAME)
mtplot = plots.DataMCPlot(datasets, HISTONAME)
mt = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(
HISTONAME)
#legendName = legendName.replace("Plus","")
mt.SetName("JetBalance")
mt.SetLineColor(color)
if HISTONAME == "InvertedAllCutsJetBalance":
qinv = mt.GetBinContent(1)
ginv = mt.GetBinContent(3)
else:
qbase = mt.GetBinContent(1)
gbase = mt.GetBinContent(3)
jetRatios.append(
mt.GetBinContent(1) /
(mt.GetBinContent(1) + mt.GetBinContent(3)))
plot.histoMgr.appendHisto(histograms.Histo(mt, mt.GetName()))
color = color + 1
style = tdrstyle.TDRStyle()
plot.createFrame(HISTONAME)
#plot.createFrame(HISTONAME.replace("shape","final"))
#plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"))
#moveLegend={"dx": -0.3,"dy": 0.}
#plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
print "Baseline All Cuts", qbase + gbase
print "Inverted All Cuts", qinv + ginv
gsf = qinv * gbase / (ginv * qbase)
#print "Gluon jet SF:", gsf
#print "Corrected Inverted Jet Balance:", qinv/(qinv+gsf*ginv), ", Baseline Jet Balance:", qbase/(qbase+gbase)
for i in range(0, len(jetRatios)):
print histonameList[i], ":", jetRatios[i]
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("")
#optModes.append("OptQCDTailKillerLoosePlus")
#optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
color = 1
#plot = plots.PlotBase()
jetRatios = []
for HISTONAME in histonameList:
for optMode in optModes:
plot = plots.PlotBase()
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
histonames = datasets.getDataset("EWK").getDirectoryContent(HISTONAME)
mtplot = plots.DataMCPlot(datasets, HISTONAME)
mt = mtplot.histoMgr.getHisto("EWK").getRootHisto().Clone(HISTONAME)
#legendName = legendName.replace("Plus","")
mt.SetName("JetBalance")
mt.SetLineColor(color)
if HISTONAME == "InvertedAllCutsJetBalance":
qinv = mt.GetBinContent(1)
ginv = mt.GetBinContent(3)
else:
qbase = mt.GetBinContent(1)
gbase = mt.GetBinContent(3)
jetRatios.append(mt.GetBinContent(1)/(mt.GetBinContent(1)+mt.GetBinContent(3)))
plot.histoMgr.appendHisto(histograms.Histo(mt,mt.GetName()))
color = color + 1
style = tdrstyle.TDRStyle()
plot.createFrame(HISTONAME)
#plot.createFrame(HISTONAME.replace("shape","final"))
#plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"))
#moveLegend={"dx": -0.3,"dy": 0.}
#plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi=datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
print "Baseline All Cuts",qbase+gbase
print "Inverted All Cuts",qinv+ginv
gsf = qinv*gbase/(ginv*qbase)
#print "Gluon jet SF:", gsf
#print "Corrected Inverted Jet Balance:", qinv/(qinv+gsf*ginv), ", Baseline Jet Balance:", qbase/(qbase+gbase)
for i in range(0,len(jetRatios)):
print histonameList[i],":",jetRatios[i]