def GetDatasetsFromDir(mcrab, opts, **kwargs):
dataEra = kwargs.get("dataEra")
searchMode = kwargs.get("searchMode")
analysis = kwargs.get("analysis")
optMode = kwargs.get("optMode")
if opts.includeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs(
[mcrab],
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
includeOnlyTasks=opts.includeTasks,
optimizationMode=optMode)
elif opts.excludeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs(
[mcrab],
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
excludeTasks=opts.excludeTasks,
optimizationMode=optMode)
# excludeTasks="M_180|M_220|M_250"
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
[mcrab],
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
return datasets
def GetDatasetsFromDir(opts):
Verbose("Getting datasets")
if (not opts.includeOnlyTasks and not opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
optimizationMode=opts.optMode)
elif (opts.includeOnlyTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks=opts.includeOnlyTasks,
optimizationMode=opts.optMode)
elif (opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
excludeTasks=opts.excludeTasks,
optimizationMode=opts.optMode)
else:
raise Exception("This should never be reached")
return datasets
def GetDatasetsFromDir_secondDir(opts):
Verbose("Getting datasets")
usemcrab = "MyHplusAnalysis_180201_TrainingDRQuarks08"
if (not opts.includeOnlyTasks and not opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
optimizationMode=opts.optMode)
elif (opts.includeOnlyTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks=opts.includeOnlyTasks,
optimizationMode=opts.optMode)
elif (opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs([usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
excludeTasks=opts.excludeTasks,
optimizationMode=opts.optMode)
else:
raise Exception("This should never be reached")
return datasets
def GetDatasetsFromDir_secondDir(opts):
Verbose("Getting datasets")
usemcrab = "MyHplusAnalysis_180201_TrainingDRQuarks08"
if (not opts.includeOnlyTasks and not opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
optimizationMode=opts.optMode)
elif (opts.includeOnlyTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks=opts.includeOnlyTasks,
optimizationMode=opts.optMode)
elif (opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[usemcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
excludeTasks=opts.excludeTasks,
optimizationMode=opts.optMode)
else:
raise Exception("This should never be reached")
return datasets
def GetDatasetsFromDir(opts):
Verbose("Getting datasets")
if (not opts.includeOnlyTasks and not opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
optimizationMode=opts.optMode)
elif (opts.includeOnlyTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks=opts.includeOnlyTasks,
optimizationMode=opts.optMode)
elif (opts.excludeTasks):
datasets = dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
excludeTasks=opts.excludeTasks,
optimizationMode=opts.optMode)
else:
raise Exception("This should never be reached")
return datasets
def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "Hplus2tbAnalysis"
hName = "associatedTPt"
plotname = analysis + "_" + hName
datasetsHiggs = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTauB_M_")
datasetsTT = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis,
includeOnlyTasks="TT")
datasetsTT.merge("MC", ["TT", "TT_ext"], keepSources=True)
style = tdrstyle.TDRStyle()
dataset1 = datasetsHiggs.getDataset(
"ChargedHiggs_HplusTB_HplusToTauB_M_200").getDatasetRootHisto(hName)
dataset2 = datasetsTT.getDataset("MC").getDatasetRootHisto(hName)
# dataset1.normalizeToOne()
dataset2.normalizeToOne()
histo1 = dataset1.getHistogram()
histo1.SetMarkerColor(2)
histo1.SetMarkerStyle(20)
removeNegatives(histo1)
histo1.Scale(1. / histo1.Integral())
histo2 = dataset2.getHistogram()
histo2.SetMarkerColor(4)
histo2.SetMarkerStyle(21)
p = plots.ComparisonPlot(
histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "t#bar{t}", "p", "P"))
opts = {"ymin": 0, "ymax": 0.2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
p.createFrame(os.path.join(plotDir, plotname),
createRatio=True,
opts=opts,
opts2=opts2)
moveLegend = {"dx": -0.2, "dy": -0.1, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("Arbitrary units")
p.getFrame().GetXaxis().SetTitle("Top p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "Hplus2tbAnalysis"
hName = "associatedTPt"
plotname = analysis+"_"+hName
datasetsHiggs = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTauB_M_")
datasetsTT = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="TT")
datasetsTT.merge("MC", ["TT","TT_ext"], keepSources=True)
style = tdrstyle.TDRStyle()
dataset1 = datasetsHiggs.getDataset("ChargedHiggs_HplusTB_HplusToTauB_M_200").getDatasetRootHisto(hName)
dataset2 = datasetsTT.getDataset("MC").getDatasetRootHisto(hName)
# dataset1.normalizeToOne()
dataset2.normalizeToOne()
histo1 = dataset1.getHistogram()
histo1.SetMarkerColor(2)
histo1.SetMarkerStyle(20)
removeNegatives(histo1)
histo1.Scale(1./histo1.Integral())
histo2 = dataset2.getHistogram()
histo2.SetMarkerColor(4)
histo2.SetMarkerStyle(21)
p = plots.ComparisonPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "t#bar{t}", "p", "P"))
opts = {"ymin": 0, "ymax": 0.2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
p.createFrame(os.path.join(plotDir, plotname), createRatio=True, opts=opts, opts2=opts2)
moveLegend = {"dx": -0.2, "dy": -0.1, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("Arbitrary units")
p.getFrame().GetXaxis().SetTitle("Top p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
def GetDatasetsFromDir(mcrab, opts, **kwargs):
dataEra = kwargs.get("dataEra")
searchMode = kwargs.get("searchMode")
analysis = kwargs.get("analysis")
optMode = kwargs.get("optMode")
if opts.includeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, includeOnlyTasks=opts.includeTasks, optimizationMode=optMode)
elif opts.excludeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, excludeTasks=opts.excludeTasks, optimizationMode=optMode)
# excludeTasks="M_180|M_220|M_250"
else:
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
return datasets
def GetAllDatasetsFromDir(opts):
datasets = dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
optimizationMode=opts.optMode)
return datasets
def getDataSets(dirs, dataEra, searchMode, analysis, optMode):
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
return datasets
def getDataSets(dirs, dataEra, searchMode, analysis, optMode):
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
return datasets
def GetDatasetsFromDir(mcrab, opts, **kwargs):
dataEra = kwargs.get("dataEra")
searchMode = kwargs.get("searchMode")
analysis = kwargs.get("analysis")
optMode = kwargs.get("optMode")
# if opts.includeTasks != "":
# datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, includeOnlyTasks=opts.includeTasks, optimizationMode=optMode)
# elif opts.excludeTasks != "":
# datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, excludeTasks=opts.excludeTasks, optimizationMode=optMode)
# excludeTasks="M_180|M_220|M_250"
# else:
# datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab],
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
# Inform user of datasets retrieved
Verbose("Got the following datasets from multicrab dir \"%s\"" % mcrab)
for d in datasets.getAllDatasets():
Verbose("\t", d.getName(), False)
return datasets
def GetTTBackgroundDatasets(opts):
Verbose("Getting TT background datasets")
return dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks="TT|ST_t|ttbb|TTZToQQ|TTWJetsToQQ|TTTT",
optimizationMode=opts.optMode)
def GetSignalDatasets(opts):
Verbose("Getting signal datasets")
return dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_",
optimizationMode=opts.optMode)
def GetQCDBackgroundDatasets(opts):
Verbose("Getting QCD background datasets")
return dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=opts.analysisName,
includeOnlyTasks="QCD_HT",
optimizationMode=opts.optMode)
def GetDatasetsFromDir(mcrab, opts, **kwargs):
dataEra = kwargs.get("dataEra")
searchMode = kwargs.get("searchMode")
analysis = kwargs.get("analysis")
optMode = kwargs.get("optMode")
if opts.includeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, includeOnlyTasks=opts.includeTasks, optimizationMode=optMode)
elif opts.excludeTasks != "":
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, excludeTasks=opts.excludeTasks, optimizationMode=optMode)
# excludeTasks="M_180|M_220|M_250"
else:
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
# Inform user of datasets retrieved
Verbose("Got the following datasets from multicrab dir \"%s\"" % mcrab)
for d in datasets.getAllDatasets():
Verbose( "\t", d.getName(), False)
return datasets
def GetDatasetsFromDir(mcrab, analysis):
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab], analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="M_180|M_220|M_250")
# Inform user of datasets retrieved
Verbose("Got the following datasets from multicrab dir \"%s\"" % mcrab)
for d in datasets.getAllDatasets():
Verbose( "\t", d.getName(), False)
return datasets
def GetDatasetsFromDir(mcrab, analysis):
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab],
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="M_180|M_220|M_250")
# Inform user of datasets retrieved
Verbose("Got the following datasets from multicrab dir \"%s\"" % mcrab)
for d in datasets.getAllDatasets():
Verbose("\t", d.getName(), False)
return datasets
def GetDatasetsFromDir(opts, otherDir=False):
myDir = opts.mcrab1
analysis = opts.analysisName
if otherDir:
myDir = opts.mcrab2
#analysis = "FakeBMeasurement"
datasets = dataset.getDatasetsFromMulticrabDirs([myDir],
dataEra=opts.dataEra,
searchMode=opts.searchMode,
analysisName=analysis,
optimizationMode=opts.optMode)
return datasets
def GetDatasetsFromDir(opts, json):
Verbose("Getting datasets")
if opts.optMode == None:
optMode = json["optMode"]
else:
optMode = opts.optMode
if (opts.includeOnlyTasks):
return dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=json["dataEra"],
searchMode=json["searchMode"],
analysisName=json["analysis"],
includeOnlyTasks=opts.includeOnlyTasks,
optimizationMode=optMode)
elif (opts.excludeTasks):
return dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=json["dataEra"],
searchMode=json["searchMode"],
analysisName=json["analysis"],
excludeTasks=opts.excludeTasks,
optimizationMode=optMode)
else:
#return process.addDatasetsFromMulticrab(opts.mcrab)
if len(json["samples"]) < 1:
Print("No samples defined in the JSON file. Exit", True)
print __doc__
sys.exit()
else:
return dataset.getDatasetsFromMulticrabDirs(
[opts.mcrab],
dataEra=json["dataEra"],
searchMode=json["searchMode"],
analysisName=json["analysis"],
includeOnlyTasks="|".join(json["samples"]),
optimizationMode=optMode)
def GetDatasetsFromDir(opts, json):
Verbose("Getting datasets")
if len(json["samples"])<1:
Print("No samples defined in the JSON file. Exit", True)
print __doc__
sys.exit()
else:
return dataset.getDatasetsFromMulticrabDirs([opts.mcrab],
dataEra=json["dataEra"],
searchMode=json["searchMode"],
analysisName=json["analysis"],
includeOnlyTasks="|".join(json["samples"]),
optimizationMode=json["optMode"])
def GetSpecificDatasetsFromDir(mcrab, opts, RunEra, samples, **kwargs):
'''
'''
HasKeys(["dataEra", "searchMode", "analysis", "optMode"], **kwargs)
dataEra = kwargs.get("dataEra")
searchMode = kwargs.get("searchMode")
analysis = kwargs.get("analysis")
optMode = kwargs.get("optMode")
datasets = dataset.getDatasetsFromMulticrabDirs([mcrab],
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis +
"_" + RunEra,
includeOnlyTasks=samples,
optimizationMode=optMode)
return datasets
def plot(resultdir,jsonfile):
with open(os.path.abspath(jsonfile)) as jfile:
j = json.load(jfile)
print "Plotting",j["title"],"in",resultdir
if "outputdir" in j:
global plotDir
plotDir = j["outputdir"]
multicrabPaths = PathFinder.MulticrabPathFinder(resultdir)
print "multicrabPaths = ", multicrabPaths.
paths = []
if os.path.exists(multicrabPaths.getSignalPath()):
paths.append(multicrabPaths.getSignalPath())
if os.path.exists(multicrabPaths.getQCDInvertedPath()):
paths.append(multicrabPaths.getQCDInvertedPath())
if os.path.exists(multicrabPaths.getEWKPath()):
paths.append(multicrabPaths.getEWKPath())
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
#datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
plots.mergeRenameReorderForDataMC(datasets)
alldsets = datasets.getAllDatasets()
print "Merged datasets"
for d in alldsets:
print " ",d.getName()
# lumi = 0.0
# for d in datasets.getDataDatasets():
# print "luminosity",d.getName(),d.getLuminosity()
# lumi += d.getLuminosity()
# print "luminosity, sum",lumi
if len(j["samples"])>0:
for s in j["samples"]:
#print s
h = datasets.getDataset(s).getDatasetRootHisto(j["histogram"]).getHistogram()
name = j["histogram"]+s
plotgraph([h],lumi,j,name)
def plot(resultdir,jsonfile):
with open(os.path.abspath(jsonfile)) as jfile:
j = json.load(jfile)
print "Plotting",j["title"],"in",resultdir
if "outputdir" in j:
global plotDir
plotDir = j["outputdir"]
multicrabPaths = PathFinder.MulticrabPathFinder(resultdir)
paths = []
if os.path.exists(multicrabPaths.getSignalPath()):
paths.append(multicrabPaths.getSignalPath())
if os.path.exists(multicrabPaths.getQCDInvertedPath()):
paths.append(multicrabPaths.getQCDInvertedPath())
if os.path.exists(multicrabPaths.getEWKPath()):
paths.append(multicrabPaths.getEWKPath())
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
plots.mergeRenameReorderForDataMC(datasets)
alldsets = datasets.getAllDatasets()
print "Merged datasets"
for d in alldsets:
print " ",d.getName()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
if len(j["samples"])>0:
for s in j["samples"]:
h = datasets.getDataset(s).getDatasetRootHisto(j["histogram"]).getHistogram()
name = j["histogram"]+s
plotgraph([h],lumi,j,name)
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
#datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_111123_132128/multicrab.cfg", counters=counters)
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/afs/cern.ch/work/e/epekkari/DataDrivenFakeTaus/CMSSW_5_3_9_patch3/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_140526_122821/multicrab.cfg")
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
#for d in datasets.getAllDatasets():
# print d.getName()
#print "-------"
#plots.mergeRenameReorderForDataMC(datasets)
# print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", ["WJets", "DYJetsToLL", "SingleTop", "Diboson", "TTJets"], keepSources=True)
datasets.remove(filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_s-channel" in name, datasets.getAllDatasetNames()))
# Remove QCD
#datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
style = tdrstyle.TDRStyle()
hNames = getHistoNames(kinVar)
# hName = hNames[0]
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# Inform user of datasets retrieved
Print("Got following datasets from multicrab dir \"%s\"" % parseOpts.mcrab)
for d in datasets.getAllDatasets():
print "\t", d.getName()
# Determine Integrated Luminosity (If Data datasets present)
intLumi = 0.0
if len(datasets.getDataDatasets()) != 0:
# Load Luminosity JSON file
datasets.loadLuminosities(fname="lumi.json")
# Load RUN range
# runRange = datasets.loadRunRange(fname="runrange.json")
# Calculate Integrated Luminosity
intLumi = GetLumi(datasets)
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
plotName = hName #analysis + "_" + hName
saveName = os.path.join(savePath, plotName)
# Get Data or MC datasets
# dataDatasets = datasets.getDataDatasets()
# mcDatasets = datasets.getMCDatasets()
# Build ROOT histos from individual datasets
dataset1 = datasets.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_400").getDatasetRootHisto(hName)
dataset2 = datasets.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_300").getDatasetRootHisto(hName)
# dataset2 = datasets.getDataset("TT_ext3").getDatasetRootHisto(hName)
# datasets.getDataset("TT_ext3").setCrossSection(831.76)
# Normalise datasets
if normalizeTo == "One":
dataset1.normalizeToOne()
dataset2.normalizeToOne()
elif normalizeTo == "XSection":
dataset1.normalizeByCrossSection()
dataset2.normalizeByCrossSection()
elif normalizeTo == "Luminosity":
dataset1.normalizeToLumi(intLumi)
dataset2.normalizeToLumi(intLumi)
else:
isValidNorm(normalizeTo)
# Customise histos
histo1 = dataset1.getHistogram()
styles.signal200Style.apply(histo1)
# histo1.SetMarkerStyle(ROOT.kFullCircle)
# histo1.SetFillStyle(3001)
# histo1.SetFillColor(histo2.GetMarkerColor())
# removeNegatives(histo1)
# removeErrorBars(histo1)
histo1.Rebin(rebinFactor)
# Customise histos
histo2 = dataset2.getHistogram()
styles.ttStyle.apply(histo2)
# histo2.SetMarkerStyle(ROOT.kFullCross)
histo2.SetFillStyle(3001)
histo2.SetFillColor(styles.ttStyle.color)
# removeNegatives(histo2)
# removeErrorBars(histo2)
histo2.Rebin(rebinFactor)
# Create a comparison plot
p = plots.ComparisonPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 400 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST,E,9"))
# histograms.Histo(histo2, "t#bar{t}", "F", "HIST,E,9"))
# Create a comparison plot (One histogram is treated as a reference histogram, and all other histograms are compared with respect to that)
# p = plots.ComparisonManyPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
# [histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST9"),
# histograms.Histo(histo3, "t#bar{t}", "F", "HIST9")])
# Customise plots
opts = {"ymin": 0.0, "binWidthX": histo1.GetXaxis().GetBinWidth(0), "xUnits": getUnitsX(kinVar)}
ratioOpts = {"ymin": 0.0, "ymax": 2.0 , "binWidthX": histo1.GetXaxis().GetBinWidth(0), "xUnits": getUnitsX(kinVar)}
p.createFrame(os.path.join(savePath, plotName), createRatio=ratio, opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.2}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Customise text
if intLumi > 0.0:
histograms.addStandardTexts(lumi=intLumi)
else:
histograms.addStandardTexts()
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasets.loadRunRange(), 17)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetTitle( getTitleY(normalizeTo, kinVar, opts) )
p.getFrame().GetXaxis().SetTitle( getTitleX(kinVar, opts) )
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw plots
p.draw()
# Save canvas under custom dir
if counter == 0:
Print("Saving plots in %s format(s)" % (len(saveFormats)) )
SavePlotterCanvas(p, savePath, saveName, saveFormats)
return
def main(opts):
datasetsMgr = None
# Fixme: This options is currently invalid
if len(opts.files) > 0:
datasetsMgr = dataset.getDatasetsFromRootFiles( [(x,x) for x in opts.files], opts=opts, weightedCounters=opts.weighted)
else:
# Define the cwd as the multicrab dir containing the ROOT files with the counters
multicrabDir = os.getcwd()
# For-loop: All arguments passed during script execution
for d in sys.argv:
# Look for a directory that exists
if os.path.exists(d) and os.path.isdir(d):
multicrabDir = os.path.abspath(d)
Print("The multicrab directory to be used is %s" % (multicrabDir), True)
# Get the datasets
datasetsMgr = dataset.getDatasetsFromMulticrabDirs([multicrabDir],opts=opts, weightedCounters=opts.weighted)
# Optional: Apply include/exclude datasets
datasetsMgr = FilterDatasets(datasetsMgr, opts)
Print("The tasks to be included are:\n\t%s" % ("\n\t".join(d.getName() for d in datasetsMgr.getAllDatasets())), True)
# Optional: Print info on Data and MC samples
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Load the luminosities
if os.path.exists(opts.lumifile):
datasetsMgr.loadLuminosities(opts.lumifile)
# Optional: Apply PU-reweighting
if opts.weighted and opts.PUreweight:
Print("Updating all events to PU-weighted (opts.weighted=%s, opts.PUreweight=%s)\n" % (opts.weighted, opts.PUreweight), True)
datasetsMgr.updateNAllEventsToPUWeighted(era=opts.dataEra)
# Optional: Merge data
Verbose("Merging Data and/or MC datatets (opts.mergeData=%s, opts.mergeData=%s, opts.mergeData=%s)" % (opts.mergeData, opts.mergeMC, opts.mergeForDataMC), True)
if opts.mergeData:
datasetsMgr.mergeData()
if opts.mergeMC:
datasetsMgr.mergeMC()
if opts.mergeForDataMC:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Optional: Print dataset info
if opts.printInfo:
datasetsMgr.PrintInfo() #datasetsMgr.printInfo()
# Create the event counter
eventCounter = counter.EventCounter(datasetsMgr)
# Proceed differently depending on operation mode (opts.mode= 'events', 'xsect', 'eff')
quantity = "events"
if opts.mode == "events":
if opts.mergeForDataMC:
Print("Normalising the MC histograms to the data luminosity (opts.mergeForDataMC=%s)" % (opts.mergeForDataMC) )
eventCounter.normalizeMCByLuminosity()
else:
pass
elif opts.mode in ["xsect", "xsection", "crosssection", "crossSection", "eff"]:
if not opts.PUreweight:
Print("Mode '%s' works only with PU reweighting, which you disabled with --noPUreweight" % opts.mode)
return 1
Print("Normalising MC by cross-section (opt.mode=%s)" % opts.mode)
eventCounter.normalizeMCByCrossSection()
quantity = "MC by cross section, data by events"
else:
Print("Printing mode '%s' doesn't exist! The following ones are available 'events', 'xsect', 'eff'" % opts.mode)
return 1
# Optional: Produce table in Text or LaTeX format?
if opts.latex:
cellFormat = counter.CellFormatTeX(valueOnly=opts.valueOnly, valueFormat=opts.format)
formatFunc = lambda table: table.format(counter.TableFormatLaTeX(cellFormat))
else:
cellFormat = counter.CellFormatText(valueOnly=opts.valueOnly, valueFormat=opts.format)
formatFunc = lambda table: table.format(counter.TableFormatText(cellFormat))
csvSplitter = counter.TableSplitter([" +- ", " +", " -"])
# Optional: Format as comma-separated-variables (csv), presubambly for exporting to a spreadsheet
if opts.csv:
formatFunc = lambda table: table.format(counter.TableFormatText(cellFormat, columnSeparator=","), csvSplitter)
# Optional: Convert to (relative) efficienies
if opts.mode == "eff":
if opts.weighted:
Print("Cannot operate in \"eff\" mode while using weighted counters (opts.mode=\'%s\', opts.weighted=%s)" % (opts.mode, opts.weighted) )
return 1
else:
Print("Converting to efficiencies (opts.mode=%s)" % (opts.mode) )
cellFormat = counter.CellFormatText(valueFormat="%.4f", valueOnly=opts.valueOnly)
if opts.latex:
formatFunc = lambda table: counter.counterEfficiency(table).format(counter.TableFormatLaTeX(cellFormat))
else:
formatFunc = lambda table: counter.counterEfficiency(table).format(counter.TableFormatText(cellFormat))
quantity = "Cut efficiencies"
# Optional: Format as comma-separated-variables (csv), presubambly for exporting to a spreadsheet
if opts.csv:
formatFunc = lambda table: counter.counterEfficiency(table).format(counter.TableFormatText(cellFormat, columnSeparator=","), csvSplitter)
# Optional: Print only this sub-counters
if opts.subCounter is not None:
msg = "Subcounter %s %s: " % (opts.subCounter, quantity)
Print(msg, True)
print formatFunc(eventCounter.getSubCounterTable(opts.subCounter))
print
return 0
# Print the main counters
hLine = "="*10
msg = " Main counter %s: " % quantity
print "\n" + hLine + msg + hLine
print formatFunc(eventCounter.getMainCounterTable())
print
# Optional: Print sub-counters (only if --mainCounterOnly is not called)
if not opts.mainCounterOnly:
names = eventCounter.getSubCounterNames()
names.sort()
for name in names:
hLine = "="*10
msg = " Subcounter %s %s: " % (name, quantity)
print "\n" + hLine + msg + hLine
print formatFunc(eventCounter.getSubCounterTable(name) )
return 0
def main(argv):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all dsetMgr from a multicrab task
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
#print dsetMgr
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
dsetMgr,
name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
dsetMgr.merge(
"EWK",
[
"TTJets",
"WJetsHT",
"DYJetsToLL",
"SingleTop",
#"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(
COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception(
"Error: Cannot find histogram or directory of name '%s'!" %
name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
bins.append(
hname.replace("NormalizationMETBaselineTau" + HISTONAME,
""))
title = dsetMgr.getDataset("Data").getDatasetRootHisto(
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + "/" +
hname).getHistogram().GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
binLabels.append(formatHistoTitle(title))
print "\nHistogram bins available", bins
print "Using bins ", bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
#===== Initialize normalization calculator
invertedQCD = InvertedTauID()
invertedQCD.setLumi(dsetMgr.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra, searchMode, HISTONAME])
#===== Loop over tau pT bins
for i, binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s" % binLabels[i]
print "********************************\n"
invertedQCD.resetBinResults()
invertedQCD.setLabel(binLabels[i])
#===== Obtain histograms for normalization
metBase = plots.DataMCPlot(
dsetMgr,
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver = plots.DataMCPlot(
dsetMgr,
COMBINEDHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
metBase_GenuineTaus = plots.DataMCPlot(
dsetMgr, GENUINEHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver_GenuineTaus = plots.DataMCPlot(
dsetMgr, GENUINEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
metBase_FakeTaus = plots.DataMCPlot(
dsetMgr, FAKEHISTODIR + "/" + BASELINETAUHISTONAME + binStr)
metInver_FakeTaus = plots.DataMCPlot(
dsetMgr, FAKEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr)
#===== Rebin histograms before subtracting
RebinFactor = 2 # Aim for 10 GeV binning
metBase.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_GenuineTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_GenuineTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_FakeTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_FakeTaus.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
#===== Obtain templates for data and EWK
metInverted_data = metInver.histoMgr.getHisto(
"Data").getRootHisto().Clone(COMBINEDHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_data, "Data, inverted")
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(GENUINEHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_EWK_GenuineTaus,
"EWK genuine taus, inverted")
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(FAKEHISTODIR + "/" +
INVERTEDTAUHISTONAME + binStr)
treatHistogram(metInverted_EWK_FakeTaus, "EWK fake taus, inverted")
metBase_data = metBase.histoMgr.getHisto(
"Data").getRootHisto().Clone(COMBINEDHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_data, "Data, baseline")
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(GENUINEHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_EWK_GenuineTaus,
"EWK genuine taus, baseline")
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(FAKEHISTODIR + "/" +
BASELINETAUHISTONAME + binStr)
treatHistogram(metBase_EWK_FakeTaus, "EWK fake taus, baseline")
#===== Obtain templates for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK_GenuineTaus,-1)
#metBase_QCD.Add(metBase_EWK_FakeTaus,-1)
#addLabels(metBase_QCD, "QCD, baseline")
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK_GenuineTaus, -1)
metInverted_QCD.Add(metInverted_EWK_FakeTaus, -1)
treatHistogram(metInverted_QCD, "QCD, inverted")
#===== Make plots of templates
print "\n*** Integrals of plotted templates"
#invertedQCD.plotHisto(metInverted_data,"template_Data_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_GenuineTaus,"template_EWKGenuineTaus_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"template_EWKFakeTaus_Inverted")
invertedQCD.plotHisto(metInverted_QCD, "template_QCD_Inverted")
invertedQCD.plotHisto(metBase_data, "template_Data_Baseline")
invertedQCD.plotHisto(metBase_EWK_GenuineTaus,
"template_EWKGenuineTaus_Baseline")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,
"template_EWKFakeTaus_Baseline")
#invertedQCD.plotHisto(metBase_QCD,"template_QCD_Baseline")
#===== Fit individual templates and
# Fit first templates for QCD, EWK_genuine_taus, and EWK_fake_taus
# Then fit the shape of those parametrizations to baseline data to obtain normalization coefficients
fitOptions = "RB"
# Strategy: take EWK templates from baseline and QCD template from inverted; then fit to baseline data
invertedQCD.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,
fitOptions)
invertedQCD.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus, fitOptions)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus, fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus, fitOptions)
invertedQCD.fitQCD(metInverted_QCD, fitOptions)
invertedQCD.fitData(metBase_data)
#===== Calculate normalization
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile(
"QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "METLeg_2015D_MET80"
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
styles.mcStyle.apply(eff2_MET80)
eff1_MET80.SetMarkerSize(1)
eff2_MET80.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_"+analysis+"_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1, "eff2_MET80": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
label = analysis.split("_")[1]
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs "+runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1_MET80)
pythonWriter.addMCParameters(label,eff2_MET80)
pythonWriter.writeJSON(os.path.join(plotDir,"metLegTriggerEfficiency2015.json"))
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
#### MET80
analysisc = "METLeg_2015D_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
eff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
opts = {"ymin": 0.001, "ymax": 0.1}
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=opts, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getPad1().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.25, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
histograms.addText(0.4, 0.18, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.11, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in",plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="SingleNeutrino")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="QCD")
# analysis = datasets.getAllDatasets()[0].getAnalysisName()
#datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="ZeroBias")
createRatio = False
# for d in datasets.getAllDatasets():
# print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
#### dataset1 = datasets.getMCDatasets()
rateETM120 = 5521.35 # Hz
#effETM120 = 0.000611208781402 #8.75017364672e-05
#effETM120 = 0.000619219298648
effETM120 = 0.000203698623826
#### effETM120 = 0.186701136914 # QCD
scale = rateETM120 / effETM120 * 0.001 #(Hz->kHz)
# for d in dataset1:
# d.scale(scale)
dataset2 = dataset1
createRatio = False
#if isinstance(datasetsMC,dataset.DatasetManager):
# dataset2 = datasetsMC.getMCDatasets()
# createRatio = True
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
scaleGraph(eff1PU, scale)
namePU = "TauMET_" + analysis + "_nVtx"
legend1 = "Data"
legend2 = "Simulation"
styles.dataStyle.apply(eff1PU)
eff1PU.SetMarkerSize(1)
#eff2PU.SetMarkerSize(1.5)
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
# Fit
# yval = fit("Data",pPU,eff1PU,30,59)
yval = fit("Data", pPU, eff1PU, 5, 59)
#### opts = {"ymin": 0, "ymax": 6, "xmax": 60}
opts = {"ymin": 0, "ymax": 20, "xmax": 60}
#### opts = {"ymin": 0, "ymax": 300, "xmax": 60}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.5, "dy": -0.1, "dh": -0.1}
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=opts,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getFrame().GetYaxis().SetTitle("L1 rate (kHz)")
pPU.getFrame().GetXaxis().SetTitle("n vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
pPU.draw()
print "check frame min,max", pPU.getFrame().GetYaxis().GetXmin(
), pPU.getFrame().GetYaxis().GetXmax()
x = array.array('d', [55, 55, 0])
y = array.array('d', [0, yval, yval])
n = 3
vert = ROOT.TGraph(n, x, y)
vert.SetLineStyle(2)
vert.SetLineColor(2)
vert.SetLineWidth(2)
vert.Draw("L")
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
pPU.save(formats)
print "Output written in", plotDir
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 analyze(analysis=None):
paths = [sys.argv[1]]
if (len(sys.argv) == 3):
howAnalyse = sys.argv[2]
else:
howAnalyse = "--fit"
# howAnalyse = "--bin"
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL|Zprime")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
histeff1 = getEfficiency(dataset1)
histeff2 = getEfficiency(dataset2)
eff1 = convert2TGraph(histeff1)
eff2 = convert2TGraph(histeff2)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3 = getEfficiency(datasetsH125.getMCDatasets())
eff3 = convert2TGraph(histeff3)
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
## FIT FUNCTIONS: "Sigmoid", "Error", "Gompertz", "Richard","Crystal" ##
## FIT TYPES: binned max likelihood: "ML" , Chi2-fit: "Chi" ##
if (howAnalyse == "--fit"):
datafit = fitType("Data", p, histeff1, eff1, 20, 500, "Crystal", "ML")
mcfit = fitType("MC", p, histeff2, eff2, 20, 500, "Crystal", "ML")
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau120_Trk50_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau140_Trk50_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
## does the ratio of the fits
if (howAnalyse == "--fit"):
funcRatio = ROOT.TH1F("", "", 480, 20, 500)
for i in range(0, 480):
ratio = datafit.Eval(i + 20 - 1) / mcfit.Eval(i + 20 - 1)
funcRatio.SetBinContent(i, ratio)
p.getPad().GetCanvas().cd(2)
funcRatio.Draw("SAME")
p.getPad().GetCanvas().cd(1)
##
lumi = 0.0
for d in datasets.getDataDatasets():
if (d.getName() != "SingleMuon_Run2016F_03Feb2017_v1_277932_278800"
and d.getName() !=
"SingleMuon_Run2016C_03Feb2017_v1_275656_276283"):
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
if (howAnalyse == "--fit"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, datafit)
pythonWriter.addMCParameters(label, mcfit)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_fit.json"))
pythonWriter.__init__() #need to clear the Json arrays
# if (howAnalyse == "--bin"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_bin.json"))
# if not createRatio:
# sys.exit()
#########################################################################
histeff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
histeff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
eff1eta = convert2TGraph(histeff1eta)
eff2eta = convert2TGraph(histeff2eta)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3eta = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorEta", "DenominatorEta")
eff3eta = convert2TGraph(histeff3eta)
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
histeff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
histeff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
eff1phi = convert2TGraph(histeff1phi)
eff2phi = convert2TGraph(histeff2phi)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3phi = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorPhi", "DenominatorPhi")
eff3phi = convert2TGraph(histeff3phi)
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
histeff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
histeff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
eff1PU = convert2TGraph(histeff1PU)
eff2PU = convert2TGraph(histeff2PU)
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
#datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_111123_132128/multicrab.cfg", counters=counters)
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(
cfgfile=
"/afs/cern.ch/work/e/epekkari/DataDrivenFakeTaus/CMSSW_5_3_9_patch3/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_140526_122821/multicrab.cfg"
)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
#for d in datasets.getAllDatasets():
# print d.getName()
#print "-------"
#plots.mergeRenameReorderForDataMC(datasets)
# print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
datasets.remove(
filter(lambda name: "TTToHplus" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets.getAllDatasetNames()))
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()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["WJets", "DYJetsToLL", "SingleTop", "Diboson", "TTJets"],
keepSources=True)
datasets.remove(
filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_s-channel" in name,
datasets.getAllDatasetNames()))
# Remove QCD
#datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
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 analyze(analysis):
paths = [sys.argv[1]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Silver|GluGluHToTauTau_M125")
datasetsDY = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="DYJetsToLL")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
# dataset2 = datasets.getMCDatasets()
dataset2 = datasetsDY.getMCDatasets()
eff1 = getEfficiency(dataset1)
eff2 = getEfficiency(dataset2)
if isinstance(datasetsH125,dataset.DatasetManager):
eff3 = getEfficiency(datasetsH125.getMCDatasets())
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125,dataset.DatasetManager):
p = plots.ComparisonManyPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
[histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")])
else:
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
fit("Data",p,eff1,20,200)
fit("MC",p,eff2,20,200)
if isinstance(datasetsH125,dataset.DatasetManager):
fit("H125",p,eff3,20,200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_"+analysis+"_DataVsMC_PFTauPt"
legend1 = "Data"
legend2 = "MC (DY)"
legend3 = "MC (H125)"
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2, "eff3": legend3})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
label = analysis.split("_")[len(analysis.split("_")) -1]
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs "+runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1)
pythonWriter.addMCParameters(label,eff2)
pythonWriter.writeJSON(os.path.join(plotDir,"tauLegTriggerEfficiency2015.json"))
#########################################################################
eff1eta = getEfficiency(dataset1,"NumeratorEta","DenominatorEta")
eff2eta = getEfficiency(dataset2,"NumeratorEta","DenominatorEta")
if isinstance(datasetsH125,dataset.DatasetManager):
eff3eta = getEfficiency(datasetsH125.getMCDatasets(),"NumeratorEta","DenominatorEta")
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
[histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")])
else:
p_eta = plots.ComparisonPlot(histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1, "eff2eta": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1, "eff2eta": legend2, "eff3eta": legend3})
name = "TauMET_"+analysis+"_DataVsMC_PFTauEta"
p_eta.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.31, "Runs "+datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
eff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
optsPU = {"ymin": 0.001, "ymax": 0.1}
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=optsPU, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getPad1().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.5, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.5, 0.46, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TTJets","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
#########################################################################
print "Output written in",plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis, includeOnlyTasks="Tau|TT")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
analysis = datasets.getAllDatasets()[0].getAnalysisName()
analysisList = []
# analysisList.append("METLeg_2016MET80_MET80")
# analysisList.append("METLeg_2016ICHEP_MET90")
# analysisList.append("METLeg_2016_MET90")
met_re = re.compile("_(?P<met>MET\d+)$")
if True:
#for analysis in analysisList:
met = "METX"
met_match = met_re.search(analysis)
if met_match:
met = met_match.group("met")
print "Analysis", analysis
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
# datasetsMC = None
datasetsMC = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, excludeTasks="Tau_")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
createRatio = False
if isinstance(datasetsMC, dataset.DatasetManager):
dataset2 = datasetsMC.getMCDatasets()
createRatio = True
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
eff1_MET80.SetMarkerSize(1)
if createRatio:
styles.mcStyle.apply(eff2_MET80)
eff2_MET80.SetMarkerSize(1.5)
if createRatio:
p = plots.ComparisonPlot(
histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
else:
p = plots.PlotBase(
[histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P")])
#from plotTauLegEfficiency import fit
#fit("Data",p,eff1_MET80,20,300)
#fit("MC",p,eff2_MET80,20,300)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_" + analysis + "_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
#legend2 = "MC"
legend2 = "Simulation"
if createRatio:
p.histoMgr.setHistoLegendLabelMany({
"eff1_MET80": legend1,
"eff2_MET80": legend2
})
p.createFrame(os.path.join(plotDir, name),
createRatio=True,
opts=opts,
opts2=opts2)
else:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1})
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(
histograms.moveLegend(histograms.createLegend(y1=0.8),
**moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
histograms.addText(0.4, 0.87, "LooseIsoPFTau50_Trk30_eta2p1_" + met,
17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
# label = analysis.split("_")[1]
# label = "2016"
label = string.replace(analysis, "METLeg_", "")
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1_MET80)
pythonWriter.addMCParameters(label, eff2_MET80)
pythonWriter.writeJSON(
os.path.join(plotDir, "metLegTriggerEfficiency_" + label + ".json"))
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
opts = {"ymin": 0, "ymax": 0.3}
name = "TauMET_" + analysis + "_DataVsMC_PFMETPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
# moveLegendPhi = {"dx": -0.5, "dy": -0.6, "dh": -0.1}
moveLegendPhi = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p_phi.getFrame().GetXaxis().SetTitle("MET Type 1 #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.86, "LooseIsoPFTau50_Trk30_eta2p1_" + met, 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
"""
#### MET80
analysisc = "METLeg_2016B_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsMC, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
opts = {"ymin": 0.001, "ymax": 1.0}
moveLegend = {"dx": -0.33, "dy": -0.25, "dh": -0.1}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=opts,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.85, "LooseIsoPFTau50_Trk30_eta2p1_MET90", 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in", plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="SingleNeutrino")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="QCD")
# analysis = datasets.getAllDatasets()[0].getAnalysisName()
#datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="ZeroBias")
createRatio = False
# for d in datasets.getAllDatasets():
# print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
#### dataset1 = datasets.getMCDatasets()
rateETM120 = 5521.35 # Hz
#effETM120 = 0.000611208781402 #8.75017364672e-05
#effETM120 = 0.000619219298648
effETM120 = 0.000203698623826
#### effETM120 = 0.186701136914 # QCD
scale = rateETM120/effETM120*0.001 #(Hz->kHz)
# for d in dataset1:
# d.scale(scale)
dataset2 = dataset1
createRatio = False
#if isinstance(datasetsMC,dataset.DatasetManager):
# dataset2 = datasetsMC.getMCDatasets()
# createRatio = True
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
scaleGraph(eff1PU,scale)
namePU = "TauMET_"+analysis+"_nVtx"
legend1 = "Data"
legend2 = "Simulation"
styles.dataStyle.apply(eff1PU)
eff1PU.SetMarkerSize(1)
#eff2PU.SetMarkerSize(1.5)
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
# Fit
# yval = fit("Data",pPU,eff1PU,30,59)
yval = fit("Data",pPU,eff1PU,5,59)
#### opts = {"ymin": 0, "ymax": 6, "xmax": 60}
opts = {"ymin": 0, "ymax": 20, "xmax": 60}
#### opts = {"ymin": 0, "ymax": 300, "xmax": 60}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.5, "dy": -0.1, "dh": -0.1}
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=opts, opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getFrame().GetYaxis().SetTitle("L1 rate (kHz)")
pPU.getFrame().GetXaxis().SetTitle("n vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
pPU.draw()
print "check frame min,max",pPU.getFrame().GetYaxis().GetXmin(),pPU.getFrame().GetYaxis().GetXmax()
x = array.array('d',[55,55,0])
y = array.array('d',[0,yval,yval])
n = 3
vert = ROOT.TGraph(n,x,y)
vert.SetLineStyle(2)
vert.SetLineColor(2)
vert.SetLineWidth(2)
vert.Draw("L")
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
pPU.save(formats)
print "Output written in",plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
if (len(sys.argv) == 3):
howAnalyse = sys.argv[2]
else:
howAnalyse = "--fit"
# howAnalyse = "--bin"
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths,excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="DYJetsToLL")
datasetsDY = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="DYJetsToLL|Zprime")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
histeff1 = getEfficiency(dataset1)
histeff2 = getEfficiency(dataset2)
eff1 = convert2TGraph(histeff1)
eff2 = convert2TGraph(histeff2)
if isinstance(datasetsH125,dataset.DatasetManager):
histeff3 = getEfficiency(datasetsH125.getMCDatasets())
eff3 = convert2TGraph(histeff3)
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125,dataset.DatasetManager):
p = plots.ComparisonManyPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
[histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")])
elif isinstance(datasetsDY,dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
## FIT FUNCTIONS: "Sigmoid", "Error", "Gompertz", "Richard","Crystal" ##
## FIT TYPES: binned max likelihood: "ML" , Chi2-fit: "Chi" ##
if (howAnalyse == "--fit" ):
datafit = fitType("Data",p,histeff1,eff1,20,500,"Crystal","ML")
mcfit = fitType("MC",p,histeff2,eff2,20,500,"Crystal","ML")
if isinstance(datasetsH125,dataset.DatasetManager):
fit("H125",p,eff3,20,200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_"+analysis+"_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY,dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125,dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2, "eff3": legend3})
if createRatio:
p.createFrame(os.path.join(plotDir, name), createRatio=createRatio, opts=opts, opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau120_Trk50_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau140_Trk50_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs "+runRange, 17)
p.draw()
## does the ratio of the fits
if (howAnalyse=="--fit"):
funcRatio = ROOT.TH1F("","",480,20,500)
for i in range(0,480):
ratio = datafit.Eval(i+20-1)/mcfit.Eval(i+20-1)
funcRatio.SetBinContent(i,ratio)
p.getPad().GetCanvas().cd(2)
funcRatio.Draw("SAME")
p.getPad().GetCanvas().cd(1)
##
lumi = 0.0
for d in datasets.getDataDatasets():
if(d.getName() != "SingleMuon_Run2016F_03Feb2017_v1_277932_278800" and d.getName() != "SingleMuon_Run2016C_03Feb2017_v1_275656_276283"):
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
if (howAnalyse == "--fit"):
pythonWriter.addParameters(plotDir,label,runRange,lumi,datafit)
pythonWriter.addMCParameters(label,mcfit)
pythonWriter.writeJSON(os.path.join(plotDir,"tauLegTriggerEfficiency_"+label+"_fit.json"))
pythonWriter.__init__() #need to clear the Json arrays
# if (howAnalyse == "--bin"):
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1)
pythonWriter.addMCParameters(label,eff2)
pythonWriter.writeJSON(os.path.join(plotDir,"tauLegTriggerEfficiency_"+label+"_bin.json"))
# if not createRatio:
# sys.exit()
#########################################################################
histeff1eta = getEfficiency(dataset1,"NumeratorEta","DenominatorEta")
histeff2eta = getEfficiency(dataset2,"NumeratorEta","DenominatorEta")
eff1eta = convert2TGraph(histeff1eta)
eff2eta = convert2TGraph(histeff2eta)
if isinstance(datasetsH125,dataset.DatasetManager):
histeff3eta = getEfficiency(datasetsH125.getMCDatasets(),"NumeratorEta","DenominatorEta")
eff3eta = convert2TGraph(histeff3eta)
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
[histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")])
elif isinstance(datasetsDY,dataset.DatasetManager):
p_eta = plots.ComparisonPlot(histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase([histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY,dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1, "eff2eta": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1, "eff2eta": legend2, "eff3eta": legend3})
name = "TauMET_"+analysis+"_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name), createRatio=createRatio, opts=opts, opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs "+datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
histeff1phi = getEfficiency(dataset1,"NumeratorPhi","DenominatorPhi")
histeff2phi = getEfficiency(dataset2,"NumeratorPhi","DenominatorPhi")
eff1phi = convert2TGraph(histeff1phi)
eff2phi = convert2TGraph(histeff2phi)
if isinstance(datasetsH125,dataset.DatasetManager):
histeff3phi = getEfficiency(datasetsH125.getMCDatasets(),"NumeratorPhi","DenominatorPhi")
eff3phi = convert2TGraph(histeff3phi)
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
[histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")])
elif isinstance(datasetsDY,dataset.DatasetManager):
p_phi = plots.ComparisonPlot(histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase([histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY,dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1, "eff2phi": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1, "eff2phi": legend2, "eff3phi": legend3})
name = "TauMET_"+analysis+"_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name), createRatio=createRatio, opts=opts, opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs "+datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
histeff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
histeff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
eff1PU = convert2TGraph(histeff1PU)
eff2PU = convert2TGraph(histeff2PU)
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY,dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=optsPU, opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=optsPU, opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in",plotDir
def main():
style = tdrstyle.TDRStyle()
hNames = getHistoNames(kinVar)
# hName = hNames[0]
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab],
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# Inform user of datasets retrieved
Print("Got following datasets from multicrab dir \"%s\"" % parseOpts.mcrab)
for d in datasets.getAllDatasets():
print "\t", d.getName()
# Determine Integrated Luminosity (If Data datasets present)
intLumi = 0.0
if len(datasets.getDataDatasets()) != 0:
# Load Luminosity JSON file
datasets.loadLuminosities(fname="lumi.json")
# Load RUN range
# runRange = datasets.loadRunRange(fname="runrange.json")
# Calculate Integrated Luminosity
intLumi = GetLumi(datasets)
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
plotName = hName #analysis + "_" + hName
saveName = os.path.join(savePath, plotName)
# Get Data or MC datasets
# dataDatasets = datasets.getDataDatasets()
# mcDatasets = datasets.getMCDatasets()
# Build ROOT histos from individual datasets
dataset1 = datasets.getDataset(
"ChargedHiggs_HplusTB_HplusToTB_M_400").getDatasetRootHisto(hName)
dataset2 = datasets.getDataset(
"ChargedHiggs_HplusTB_HplusToTB_M_300").getDatasetRootHisto(hName)
# dataset2 = datasets.getDataset("TT_ext3").getDatasetRootHisto(hName)
# datasets.getDataset("TT_ext3").setCrossSection(831.76)
# Normalise datasets
if normalizeTo == "One":
dataset1.normalizeToOne()
dataset2.normalizeToOne()
elif normalizeTo == "XSection":
dataset1.normalizeByCrossSection()
dataset2.normalizeByCrossSection()
elif normalizeTo == "Luminosity":
dataset1.normalizeToLumi(intLumi)
dataset2.normalizeToLumi(intLumi)
else:
isValidNorm(normalizeTo)
# Customise histos
histo1 = dataset1.getHistogram()
styles.signal200Style.apply(histo1)
# histo1.SetMarkerStyle(ROOT.kFullCircle)
# histo1.SetFillStyle(3001)
# histo1.SetFillColor(histo2.GetMarkerColor())
# removeNegatives(histo1)
# removeErrorBars(histo1)
histo1.Rebin(rebinFactor)
# Customise histos
histo2 = dataset2.getHistogram()
styles.ttStyle.apply(histo2)
# histo2.SetMarkerStyle(ROOT.kFullCross)
histo2.SetFillStyle(3001)
histo2.SetFillColor(styles.ttStyle.color)
# removeNegatives(histo2)
# removeErrorBars(histo2)
histo2.Rebin(rebinFactor)
# Create a comparison plot
p = plots.ComparisonPlot(
histograms.Histo(histo1, "m_{H^{#pm}} = 400 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F",
"HIST,E,9"))
# histograms.Histo(histo2, "t#bar{t}", "F", "HIST,E,9"))
# Create a comparison plot (One histogram is treated as a reference histogram, and all other histograms are compared with respect to that)
# p = plots.ComparisonManyPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
# [histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST9"),
# histograms.Histo(histo3, "t#bar{t}", "F", "HIST9")])
# Customise plots
opts = {
"ymin": 0.0,
"binWidthX": histo1.GetXaxis().GetBinWidth(0),
"xUnits": getUnitsX(kinVar)
}
ratioOpts = {
"ymin": 0.0,
"ymax": 2.0,
"binWidthX": histo1.GetXaxis().GetBinWidth(0),
"xUnits": getUnitsX(kinVar)
}
p.createFrame(os.path.join(savePath, plotName),
createRatio=ratio,
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.2}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Customise text
if intLumi > 0.0:
histograms.addStandardTexts(lumi=intLumi)
else:
histograms.addStandardTexts()
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasets.loadRunRange(), 17)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetTitle(getTitleY(normalizeTo, kinVar, opts))
p.getFrame().GetXaxis().SetTitle(getTitleX(kinVar, opts))
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw plots
p.draw()
# Save canvas under custom dir
if counter == 0:
Print("Saving plots in %s format(s)" % (len(saveFormats)))
SavePlotterCanvas(p, savePath, saveName, saveFormats)
return
def main():
if len(sys.argv) < 2:
usage()
analysis = _analysis
if "--QCD" in sys.argv:
analysis = "QCDMeasurement"
dirs = []
dirs.append(sys.argv[1])
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
dsetMgr.loadLuminosities()
dsetMgr.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(dsetMgr)
#dsetMgr.normalizeToLuminosity()
lumi = dsetMgr.getDataset("Data").getLuminosity()
# Apply TDR style
style = tdrstyle.TDRStyle()
# Format: list of [denominator, numerator] pairs
plotSources = OrderedDict()
plotSources["trg_vs_vtx"] = ["PUDependency/NvtxTrg","PUDependency/NvtxVtx"]
plotSources["vtx_vs_antiIsolTau"] = ["PUDependency/NvtxVtx","PUDependency/NvtxAntiIsolatedTau"]
plotSources["vtx_vs_tau"] = ["PUDependency/NvtxVtx","PUDependency/NvtxTau"]
if not "--QCD" in sys.argv:
plotSources["tau_vs_eveto"] = ["PUDependency/NvtxTau","PUDependency/NvtxElectronVeto"]
else:
plotSources["tau_vs_eveto"] = ["PUDependency/NvtxAntiIsolatedTau","PUDependency/NvtxElectronVeto"]
plotSources["eveto_vs_muveto"] = ["PUDependency/NvtxElectronVeto","PUDependency/NvtxMuonVeto"]
plotSources["jet_vs_muveto"] = ["PUDependency/NvtxMuonVeto","PUDependency/NvtxJetSelection"]
plotSources["rcoll_vs_jet"] = ["PUDependency/NvtxJetSelection","PUDependency/NvtxAngularCutsCollinear"]
plotSources["btag_vs_rcoll"] = ["PUDependency/NvtxAngularCutsCollinear","PUDependency/NvtxBtagging"]
plotSources["met_vs_btag"] = ["PUDependency/NvtxBtagging","PUDependency/NvtxMETSelection"]
plotSources["rbb_vs_met"] = ["PUDependency/NvtxMETSelection","PUDependency/NvtxAngularCutsBackToBack"]
plotSources["allsel_vs_rbb"] = ["PUDependency/NvtxAngularCutsBackToBack","PUDependency/NvtxAllSelections"]
if not "--QCD" in sys.argv:
plotSources["propbtag_vs_btag"] = ["PUDependency/NvtxBtagging","PUDependency/NvtxAllSelectionsWithProbabilisticBtag"]
plotSources["allsel_vs_trg"] = ["PUDependency/NvtxTrg","PUDependency/NvtxAllSelections"]
#plotSources["tau_isol_pt"] = ["tauSelection_/IsolPtBefore","tauSelection_/IsolPtAfter"]
#plotSources["tau_isol_eta"] = ["tauSelection_/IsolEtaBefore","tauSelection_/IsolEtaAfter"]
#plotSources["tau_isol_vtx"] = ["tauSelection_/IsolVtxBefore","tauSelection_/IsolVtxAfter"]
#plotSources["e_isol_pt"] = ["eSelection_Veto/IsolPtBefore","eSelection_Veto/IsolPtAfter"]
#plotSources["e_isol_eta"] = ["eSelection_Veto/IsolEtaBefore","eSelection_Veto/IsolEtaAfter"]
#plotSources["e_isol_vtx"] = ["eSelection_Veto/IsolVtxBefore","eSelection_Veto/IsolVtxAfter"]
#plotSources["mu_isol_pt"] = ["muSelection_Veto/IsolPtBefore","muSelection_Veto/IsolPtAfter"]
#plotSources["mu_isol_eta"] = ["muSelection_Veto/IsolEtaBefore","muSelection_Veto/IsolEtaAfter"]
#plotSources["mu_isol_vtx"] = ["muSelection_Veto/IsolVtxBefore","muSelection_Veto/IsolVtxAfter"]
dsetInputs = {
#"TTJets": ["TTJets"], # Madgraph with negative weights
"TT": ["TT"], # Powheg, no neg. weights -> large stats.
"TTJets": ["TTJets"],
"WJets": ["WJetsHT"],
"EWK": ["TTJets", "WJetsHT", "DYJetsToLL", "SingleTop"],
"QCD": ["QCD"],
"Data": ["Data"],
}
summarySources = ["vtx_vs_antiIsolTau",
"vtx_vs_tau",
"tau_vs_eveto",
"eveto_vs_muveto",
"jet_vs_muveto",
"btag_vs_rcoll",
"met_vs_btag",
"allsel_vs_trg"]
# Create plots (MC vs. MC)
doPlots(dsetMgr, lumi, plotSources, dsetInputs, summarySources)
# Create plots (data vs. MC)
doPlots(dsetMgr, lumi, plotSources, dsetInputs, summarySources, "Data")
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
FAKEHISTO = "OnlyEWKFakeTaus"
GENUINEHISTO = "PlusFilteredEWKFakeTaus"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="") #no collinear
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="OptQCDTailKillerLoosePlus") #collinear
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
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: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
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()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID(False)
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
invertedQCD_separatedFakes = InvertedTauID(True)
invertedQCD_separatedFakes.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD_separatedFakes.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
#binLabels = bins # for this data set
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
invertedQCD_separatedFakes.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
#metBase_EWK = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
#metInver_EWK = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_GenuineTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInver_GenuineTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_FakeTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metInver_FakeTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metBase_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metInver_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metBase_QCD_separatedFakes = metBase_data.Clone("QCD")
metBase_QCD_separatedFakes.Add(metBase_EWK_GenuineTaus,-1)
metBase_QCD_separatedFakes.Add(metBase_EWK_FakeTaus,-1)
metInverted_QCD_separatedFakes = metInverted_data.Clone("QCD")
metInverted_QCD_separatedFakes.Add(metInverted_EWK_GenuineTaus,-1)
metInverted_QCD_separatedFakes.Add(metInverted_EWK_FakeTaus,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metInverted_EWK_GenuineTaus = addlabels(metInverted_EWK_GenuineTaus)
metInverted_EWK_FakeTaus = addlabels(metInverted_EWK_FakeTaus)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metBase_EWK_GenuineTaus = addlabels(metBase_EWK_GenuineTaus)
metBase_EWK_FakeTaus = addlabels(metBase_EWK_FakeTaus)
metInverted_QCD = addlabels(metInverted_QCD)
metInverted_QCD_separatedFakes = addlabels(metInverted_QCD_separatedFakes)
#non-separated
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
fitOptions = "LRB"
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
#separated
invertedQCD_separatedFakes.plotHisto(metInverted_data,"inverted")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_GenuineTaus,"invertedEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_FakeTaus,"invertedEWKFakeTaus")
invertedQCD_separatedFakes.plotHisto(metBase_data,"baseline")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_GenuineTaus,"baselineEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_FakeTaus,"baselineEWKFakeTaus")
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitQCD(metInverted_QCD_separatedFakes,fitOptions)
invertedQCD_separatedFakes.fitData(metBase_data)
invertedQCD_separatedFakes.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
invertedQCD_separatedFakes.Summary()
invertedQCD_separatedFakes.WriteNormalizationToFile("QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
invertedQCD_separatedFakes.WriteLatexOutput("fits_separatedfaketaus.tex")
mergeNormalizations("QCDInvertedNormalizationFactors.py","QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
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")
#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():
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):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all dsetMgr from a multicrab task
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
#print dsetMgr
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],dsetMgr,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, dsetMgr.getAllDatasetNames()))
dsetMgr.remove(filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
dsetMgr.merge("EWK", [
"TTJets",
"WJetsHT",
"DYJetsToLL",
"SingleTop",
#"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME+"/NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME+"/NormalizationMETInvertedTau"+HISTONAME
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR+"/NormalizationMETBaselineTau"+HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception("Error: Cannot find histogram or directory of name '%s'!"%name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau"+HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau"+HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
bins.append(hname.replace("NormalizationMETBaselineTau"+HISTONAME,""))
title = dsetMgr.getDataset("Data").getDatasetRootHisto(COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+"/"+hname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
binLabels.append(formatHistoTitle(title))
print "\nHistogram bins available",bins
print "Using bins ",bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
#===== Initialize normalization calculator
invertedQCD = InvertedTauID()
invertedQCD.setLumi(dsetMgr.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
#===== Loop over tau pT bins
for i,binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s"%binLabels[i]
print "********************************\n"
invertedQCD.resetBinResults()
invertedQCD.setLabel(binLabels[i])
#===== Obtain histograms for normalization
metBase = plots.DataMCPlot(dsetMgr, COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver = plots.DataMCPlot(dsetMgr, COMBINEDHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
metBase_GenuineTaus = plots.DataMCPlot(dsetMgr, GENUINEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver_GenuineTaus = plots.DataMCPlot(dsetMgr, GENUINEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
metBase_FakeTaus = plots.DataMCPlot(dsetMgr, FAKEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
metInver_FakeTaus = plots.DataMCPlot(dsetMgr, FAKEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
#===== Rebin histograms before subtracting
RebinFactor = 2 # Aim for 10 GeV binning
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(RebinFactor))
#===== Obtain templates for data and EWK
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(COMBINEDHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_data, "Data, inverted")
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(GENUINEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_EWK_GenuineTaus, "EWK genuine taus, inverted")
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(FAKEHISTODIR+"/"+INVERTEDTAUHISTONAME+binStr)
treatHistogram(metInverted_EWK_FakeTaus, "EWK fake taus, inverted")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_data, "Data, baseline")
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(GENUINEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_EWK_GenuineTaus, "EWK genuine taus, baseline")
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone(FAKEHISTODIR+"/"+BASELINETAUHISTONAME+binStr)
treatHistogram(metBase_EWK_FakeTaus, "EWK fake taus, baseline")
#===== Obtain templates for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK_GenuineTaus,-1)
#metBase_QCD.Add(metBase_EWK_FakeTaus,-1)
#addLabels(metBase_QCD, "QCD, baseline")
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK_GenuineTaus,-1)
metInverted_QCD.Add(metInverted_EWK_FakeTaus,-1)
treatHistogram(metInverted_QCD, "QCD, inverted")
#===== Make plots of templates
print "\n*** Integrals of plotted templates"
#invertedQCD.plotHisto(metInverted_data,"template_Data_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_GenuineTaus,"template_EWKGenuineTaus_Inverted")
#invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"template_EWKFakeTaus_Inverted")
invertedQCD.plotHisto(metInverted_QCD,"template_QCD_Inverted")
invertedQCD.plotHisto(metBase_data,"template_Data_Baseline")
invertedQCD.plotHisto(metBase_EWK_GenuineTaus,"template_EWKGenuineTaus_Baseline")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,"template_EWKFakeTaus_Baseline")
#invertedQCD.plotHisto(metBase_QCD,"template_QCD_Baseline")
#===== Fit individual templates and
# Fit first templates for QCD, EWK_genuine_taus, and EWK_fake_taus
# Then fit the shape of those parametrizations to baseline data to obtain normalization coefficients
fitOptions = "RB"
# Strategy: take EWK templates from baseline and QCD template from inverted; then fit to baseline data
invertedQCD.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,fitOptions)
invertedQCD.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
#===== Calculate normalization
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.py")
invertedQCD.WriteLatexOutput("fits.tex")
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis, includeOnlyTasks="Tau|TT")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
analysis = datasets.getAllDatasets()[0].getAnalysisName()
analysisList = []
# analysisList.append("METLeg_2016MET80_MET80")
# analysisList.append("METLeg_2016ICHEP_MET90")
# analysisList.append("METLeg_2016_MET90")
met_re = re.compile("_(?P<met>MET\d+)$")
if True:
#for analysis in analysisList:
met = "METX"
met_match = met_re.search(analysis)
if met_match:
met = met_match.group("met")
print "Analysis",analysis
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
# datasetsMC = None
datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
createRatio = False
if isinstance(datasetsMC,dataset.DatasetManager):
dataset2 = datasetsMC.getMCDatasets()
createRatio = True
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
eff1_MET80.SetMarkerSize(1)
if createRatio:
styles.mcStyle.apply(eff2_MET80)
eff2_MET80.SetMarkerSize(1.5)
if createRatio:
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P")])
#from plotTauLegEfficiency import fit
#fit("Data",p,eff1_MET80,20,300)
#fit("MC",p,eff2_MET80,20,300)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_"+analysis+"_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
#legend2 = "MC"
legend2 = "Simulation"
if createRatio:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1, "eff2_MET80": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
else:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1})
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
histograms.addText(0.4, 0.87, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
# label = analysis.split("_")[1]
# label = "2016"
label = string.replace(analysis,"METLeg_","")
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs "+runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1_MET80)
pythonWriter.addMCParameters(label,eff2_MET80)
pythonWriter.writeJSON(os.path.join(plotDir,"metLegTriggerEfficiency_"+label+".json"))
#########################################################################
eff1phi = getEfficiency(dataset1,"NumeratorPhi","DenominatorPhi")
eff2phi = getEfficiency(dataset2,"NumeratorPhi","DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsMC,dataset.DatasetManager):
p_phi = plots.ComparisonPlot(histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase([histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsMC,dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1, "eff2phi": legend2})
opts = {"ymin": 0, "ymax": 0.3}
name = "TauMET_"+analysis+"_DataVsMC_PFMETPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name), createRatio=createRatio, opts=opts, opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
# moveLegendPhi = {"dx": -0.5, "dy": -0.6, "dh": -0.1}
moveLegendPhi = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
p_phi.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p_phi.getFrame().GetXaxis().SetTitle("MET Type 1 #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.86, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs "+datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
"""
#### MET80
analysisc = "METLeg_2016B_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
eff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsMC,dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
opts = {"ymin": 0.001, "ymax": 1.0}
moveLegend = {"dx": -0.33, "dy": -0.25, "dh": -0.1}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=opts, opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.85, "LooseIsoPFTau50_Trk30_eta2p1_MET90", 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in",plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
eff1 = getEfficiency(dataset1)
eff2 = getEfficiency(dataset2)
if isinstance(datasetsH125, dataset.DatasetManager):
eff3 = getEfficiency(datasetsH125.getMCDatasets())
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
fit("Data", p, eff1, 20, 200)
fit("MC", p, eff2, 20, 200)
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir, "tauLegTriggerEfficiency_" + label + ".json"))
# if not createRatio:
# sys.exit()
#########################################################################
eff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
eff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3eta = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorEta",
"DenominatorEta")
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3phi = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorPhi",
"DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def main():
if len(sys.argv) < 2 and not os.path.isdir(sys.argv[1]):
usage()
multicrabdir = sys.argv[1]
signalAnalysisDir = ""
QCDAnalysisDir = os.path.join(multicrabdir,"pseudoMulticrab_QCDMeasurement")
candDirs = execute("ls %s"%multicrabdir)
for d in candDirs:
if "SignalAnalysis_" in d:
signalAnalysisDir = os.path.join(multicrabdir,d)
print "Using",signalAnalysisDir
print " ",QCDAnalysisDir
style = tdrstyle.TDRStyle()
ROOT.gROOT.SetBatch(True)
datasets = dataset.getDatasetsFromMulticrabDirs([signalAnalysisDir], analysisName=analysis)
datasetsQCD = dataset.getDatasetsFromMulticrabDirs([QCDAnalysisDir], analysisName="signalAnalysis")
datasets.extend(datasetsQCD)
plots.mergeRenameReorderForDataMC(datasets)
luminosity = datasets.getDataset("Data").getLuminosity()
# Semi-ugly hack for approval homework, remember improve for the next round
totalErrorFile = ROOT.TFile("outputLight.root")
#totalErrorFile = ROOT.TFile("outputHeavy.root")
## Remove superfluous shape variation uncertainties
totalErrorHistoUp = totalErrorFile.Get("total_background")
rebin = [0,20,40,60,80,100,120,140,160,180,200,220,240,260,280,300,320,340,360,380,400,420,440,460,480,500,600,700,800]
import array
#totalErrorHistoUp.Sumw2()
totalErrorHistoUp = totalErrorHistoUp.Rebin(len(rebin)-1,"foo",array.array("d",rebin))
totalErrorHistoDown = totalErrorHistoUp.Clone("foo2")
for i in xrange(1, totalErrorHistoUp.GetNbinsX()+1):
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoUp.GetBinError(i)
totalErrorHistoUp.SetBinContent(i, totalErrorHistoUp.GetBinError(i))
totalErrorHistoDown.SetBinContent(i, -totalErrorHistoDown.GetBinError(i))
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoDown.GetBinContent(i)
# semi-ugly hack continues below in QCD
myStackList = []
h_data = datasets.getDataset("Data").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_data)
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "Data")
myHisto.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, myHisto)
h_FakeTau = datasets.getDataset("QCDMeasurementMT").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_FakeTau)
# semi-ugly hack continues
htemp=myRHWU.getRootHisto()
myRHWU.addAbsoluteShapeUncertainty("toterr", totalErrorHistoUp, totalErrorHistoDown)
#myRHWU.addShapeUncertaintyFromVariation("toterr", totalErrorHistoUp, totalErrorHistoDown)
# semi-ugly hack ends
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "QCDdata")
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, myHisto)
expectedList = []
expectedList.append("TT")
expectedList.append("WJets")
expectedList.append("SingleTop")
expectedList.append("DYJetsToLL")
expectedList.append("Diboson")
for i in range(0,len(expectedList)):
drh = datasets.getDataset(expectedList[i]).getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT")
h_bgr = drh.getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_bgr)
# myRHWU.addShapeUncertaintyRelative("syst", th1Plus=self._expectedListSystUp[i], th1Minus=self._expectedListSystDown[i])
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, expectedList[i])
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
#myStackList = divideByBinWidth(myStackList)
# no, ugly hack continues here
histograms.uncertaintyMode.set(histograms.Uncertainty.SystOnly)
plots._legendLabels["MCSystError"] = "Bkg. stat.#oplus syst. unc."
plots._legendLabels["BackgroundSystError"] = "Bkg. stat.#oplus syst. unc."
# and stops again here
myStackPlot = plots.DataMCPlot2(myStackList)
myStackPlot.setLuminosity(luminosity)
myStackPlot.setDefaultStyles()
myParams = {}
myParams["ylabel"] = "Events / bin"
myParams["ratioYlabel"] = "Data/Bkg."
myParams["xlabel"] = "m_{T} (GeV)"
myParams["log"] = True
myParams["ratio"] = True
myParams["cmsTextPosition"] = "outframe"
myParams["opts"] = {"ymin": 0.0001, "ymax": 3000.0}
myParams["opts2"] = {"ymin": 0., "ymax":1.99}
myParams["moveLegend"] = {"dx": -0.15, "dy": 0., "dh":0.05} # for data-driven
myParams["ratioMoveLegend"] = {"dx": -0.51, "dy": 0.03}
myParams["stackMCHistograms"] = True
myParams["divideByBinWidth"] = True
myParams["addMCUncertainty"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioCreateLegend"] = True
myParams["ratioMoveLegend"] = dict(dy=-0.45, dh=-0.1, dx=-0.5)
plots.drawPlot(myStackPlot, "TransVerseMassPOSTFIT", **myParams)
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
comparisonList = ["AfterStdSelections"]
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
#print datasets.getDatasetNames()
print " dirs ",dirs[0]
# Check multicrab consistency
# consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="CorrelationAnalysis")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
#datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTToHplusBWB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name and not "M_500" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "DYJetsToLL_M_50_HT" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "WJetsToLNu" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: ("DYJetsToLL_M_10to50" in name or "DYJetsToLL_M_50" in name) and not "DYJetsToLL_M_50_HT" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "WJetsToLNu" in name and not "WJetsToLNu_HT" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "DY2JetsToLL" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "DY3JetsToLL" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "DY4JetsToLL" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "ST" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# datasets.getDataset("TTbar_HBWB_HToTauNu_M_160_13TeV_pythia6").setCrossSection(0.336902*2*0.955592) # pb
# At the moment the collision energy must be set by hand
# for dset in datasets.getMCDatasets():
# dset.setEnergy("13")
# At the moment the cross sections must be set by hand
#xsect.setBackgroundCrossSections(datasets)
# datasets.merge("EWK", [
# "TT",
# "WJetsHT",
# "DYJetsToLLHT",
# "SingleTop",
# "Diboson"
# ])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
dataMCExample(datasets)
# MtComparison(datasets)
MetComparisonBaselineVsInverted(datasets)
# MetComparison(datasets)
# TauPtComparison(datasets)
# Print counters
doCounters(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
comparisonList = ["AfterStdSelections"]
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
#print datasets.getDatasetNames()
#print datasets
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],
datasets,
name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
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: "Hplus_taunu_t-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets.getAllDatasetNames()))
#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()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge(
"EWK",
[
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
# "Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra, searchMode, HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent(
"ForQCDNormalization/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(
histoname.replace("NormalizationMETBaselineTau" + HISTONAME,
""))
title = datasets.getDataset("Data").getDatasetRootHisto(
"ForQCDNormalization/NormalizationMETBaselineTau" + HISTONAME +
"/" + histoname).getHistogram().GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
title = title.replace("#tau p_{T}", "taup_T")
title = title.replace("#tau eta", "taueta")
title = title.replace("<", "lt")
title = title.replace(">", "gt")
title = title.replace("=", "eq")
title = title.replace("..", "to")
title = title.replace(".", "p")
title = title.replace("/", "_")
binLabels.append(title)
binLabels = bins # for this data set
print
print "Histogram bins available", bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ", bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
for i, bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
metBase = plots.DataMCPlot(
datasets, "ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME + bin)
metInver = plots.DataMCPlot(
datasets, "ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME + bin)
# Rebin before subtracting
RebinFactor = 10
metBase.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInver.histoMgr.forEachHisto(
lambda h: h.getRootHisto().Rebin(RebinFactor))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME +
bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETInvertedTau" +
HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME +
bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME +
bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto(
).Clone("ForQCDNormalization/NormalizationMETBaselineTau" +
HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME +
bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK, -1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK, -1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metInverted_QCD = addlabels(metInverted_QCD)
invertedQCD.plotHisto(metInverted_data, "inverted")
invertedQCD.plotHisto(metInverted_EWK, "invertedEWK")
invertedQCD.plotHisto(metBase_data, "baseline")
invertedQCD.plotHisto(metBase_EWK, "baselineEWK")
fitOptions = "RB"
invertedQCD.fitEWK(metInverted_EWK, fitOptions)
invertedQCD.fitEWK(metBase_EWK, fitOptions)
invertedQCD.fitQCD(metInverted_QCD, fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
normalizationFileName = HISTONAME #.replace("TauIdAfterCollinearCuts","")
if HISTONAME == "TauIdAfterCollinearCutsPlusFilteredEWKFakeTaus":
normalizationFileName = normalizationFileName.replace("Plus", "")
invertedQCD.WriteNormalizationToFile(
"QCDInvertedNormalizationFactors" + normalizationFileName + ".py")
invertedQCD.WriteLatexOutput("fits" + normalizationFileName + ".tex")
def main():
if len(sys.argv) < 2:
usage()
analysis = _analysis
if "--QCD" in sys.argv:
analysis = "QCDMeasurement"
dirs = []
dirs.append(sys.argv[1])
dsetMgr = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
dsetMgr.loadLuminosities()
dsetMgr.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(dsetMgr)
#dsetMgr.normalizeToLuminosity()
lumi = dsetMgr.getDataset("Data").getLuminosity()
# Apply TDR style
style = tdrstyle.TDRStyle()
# Format: list of [denominator, numerator] pairs
plotSources = OrderedDict()
plotSources["trg_vs_vtx"] = [
"PUDependency/NvtxTrg", "PUDependency/NvtxVtx"
]
plotSources["vtx_vs_antiIsolTau"] = [
"PUDependency/NvtxVtx", "PUDependency/NvtxAntiIsolatedTau"
]
plotSources["vtx_vs_tau"] = [
"PUDependency/NvtxVtx", "PUDependency/NvtxTau"
]
if not "--QCD" in sys.argv:
plotSources["tau_vs_eveto"] = [
"PUDependency/NvtxTau", "PUDependency/NvtxElectronVeto"
]
else:
plotSources["tau_vs_eveto"] = [
"PUDependency/NvtxAntiIsolatedTau", "PUDependency/NvtxElectronVeto"
]
plotSources["eveto_vs_muveto"] = [
"PUDependency/NvtxElectronVeto", "PUDependency/NvtxMuonVeto"
]
plotSources["jet_vs_muveto"] = [
"PUDependency/NvtxMuonVeto", "PUDependency/NvtxJetSelection"
]
plotSources["rcoll_vs_jet"] = [
"PUDependency/NvtxJetSelection",
"PUDependency/NvtxAngularCutsCollinear"
]
plotSources["btag_vs_rcoll"] = [
"PUDependency/NvtxAngularCutsCollinear", "PUDependency/NvtxBtagging"
]
plotSources["met_vs_btag"] = [
"PUDependency/NvtxBtagging", "PUDependency/NvtxMETSelection"
]
plotSources["rbb_vs_met"] = [
"PUDependency/NvtxMETSelection",
"PUDependency/NvtxAngularCutsBackToBack"
]
plotSources["allsel_vs_rbb"] = [
"PUDependency/NvtxAngularCutsBackToBack",
"PUDependency/NvtxAllSelections"
]
if not "--QCD" in sys.argv:
plotSources["propbtag_vs_btag"] = [
"PUDependency/NvtxBtagging",
"PUDependency/NvtxAllSelectionsWithProbabilisticBtag"
]
plotSources["allsel_vs_trg"] = [
"PUDependency/NvtxTrg", "PUDependency/NvtxAllSelections"
]
#plotSources["tau_isol_pt"] = ["tauSelection_/IsolPtBefore","tauSelection_/IsolPtAfter"]
#plotSources["tau_isol_eta"] = ["tauSelection_/IsolEtaBefore","tauSelection_/IsolEtaAfter"]
#plotSources["tau_isol_vtx"] = ["tauSelection_/IsolVtxBefore","tauSelection_/IsolVtxAfter"]
#plotSources["e_isol_pt"] = ["eSelection_Veto/IsolPtBefore","eSelection_Veto/IsolPtAfter"]
#plotSources["e_isol_eta"] = ["eSelection_Veto/IsolEtaBefore","eSelection_Veto/IsolEtaAfter"]
#plotSources["e_isol_vtx"] = ["eSelection_Veto/IsolVtxBefore","eSelection_Veto/IsolVtxAfter"]
#plotSources["mu_isol_pt"] = ["muSelection_Veto/IsolPtBefore","muSelection_Veto/IsolPtAfter"]
#plotSources["mu_isol_eta"] = ["muSelection_Veto/IsolEtaBefore","muSelection_Veto/IsolEtaAfter"]
#plotSources["mu_isol_vtx"] = ["muSelection_Veto/IsolVtxBefore","muSelection_Veto/IsolVtxAfter"]
dsetInputs = {
#"TTJets": ["TTJets"], # Madgraph with negative weights
"TT": ["TT"], # Powheg, no neg. weights -> large stats.
"TTJets": ["TTJets"],
"WJets": ["WJetsHT"],
"EWK": ["TTJets", "WJetsHT", "DYJetsToLL", "SingleTop"],
"QCD": ["QCD"],
"Data": ["Data"],
}
summarySources = [
"vtx_vs_antiIsolTau", "vtx_vs_tau", "tau_vs_eveto", "eveto_vs_muveto",
"jet_vs_muveto", "btag_vs_rcoll", "met_vs_btag", "allsel_vs_trg"
]
# Create plots (MC vs. MC)
doPlots(dsetMgr, lumi, plotSources, dsetInputs, summarySources)
# Create plots (data vs. MC)
doPlots(dsetMgr, lumi, plotSources, dsetInputs, summarySources, "Data")
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]
