def main(opts):
Verbose("main function")
#optModes = ["", "OptChiSqrCutValue40", "OptChiSqrCutValue60", "OptChiSqrCutValue80", "OptChiSqrCutValue100", "OptChiSqrCutValue120", "OptChiSqrCutValue140"]
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100", "OptChiSqrCutValue150", "OptChiSqrCutValue200"]
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.Mode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
datasetsMgr.remove(filter(lambda name: "QCD" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "Charged" in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"] #, "TT", "DYJetsToQQHT", "TTWJetsToQQ", "WJetsToQQ_HT_600ToInf", "SingleTop", "Diboson", "TTZToQQ", "TTTT"]
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
else:
Print("Cannot draw the histograms without the option --mergeEWK. Exit", True)
sys.exit()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the fit
hName = "topSelection_Baseline/LdgTrijetMass_After" #"topSelection_Baseline/LdgTrijetMass_Before"
PlotAndFitTemplates(datasetsMgr, hName.split("/")[-1], True, opts)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "Charged" in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
if opts.signalMass != 0:
signal = "ChargedHiggs_HplusTB_HplusToTB_M_%.0f" % opts.signalMass
newOrder.extend([signal])
else:
newOrder.extend(["QCD-Data"])
newOrder.extend(["QCD"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do Data-MC histograms with DataDriven QCD
DataMCHistograms(datasetsMgr)
return
def main(opts):
Verbose("main function")
comparisonList = ["AfterStdSelections"]
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 1:
datasetsMgr.remove(filter(lambda name: "HplusTB" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Integrated Luminosity
if opts.mcOnly:
# Determine integrated lumi
if opts.intLumi < 0.0:
opts.intLumi = GetLumi(datasetsMgr)
# Remove data datasets
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"] #, "TT", "DYJetsToQQHT", "TTWJetsToQQ", "WJetsToQQ_HT_600ToInf", "SingleTop", "Diboson", "TTZToQQ", "TTTT"]
newOrder.extend(GetListOfEwkDatasets())
if opts.mcOnly:
newOrder.remove("Data")
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the Baseline Vs Inverted histograms
if opts.mergeEWK:
for hName in getTopSelectionHistos(opts.histoLevel):
BaselineVsInvertedComparison(datasetsMgr, hName.split("/")[-1])
else:
Print("Cannot draw the Baseline Vs Inverted histograms without the option --mergeEWK. Exit", True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue200p0"]
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
if 0:
newOrder = ["Data"]
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the purity plots
if not opts.mergeEWK:
Print("Cannot draw the Data/QCD/EWK histograms without the option --mergeEWK. Exit", True)
return
for hName in GetHistoList(analysisType="Inverted", bType=""):
PurityPlots(datasetsMgr, hName, "Inverted")
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(
1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Custom Filtering of datasets
removeList = ["Data", "QCD-b", "Charged", "EWK"]
for d in removeList:
datasetsMgr.remove(
filter(lambda name: d in name,
datasetsMgr.getAllDatasetNames()))
# Replace QCD MC with QCD-DataDriven
qcdDatasetName = "FakeBMeasurementTrijetMass"
qcdDatasetNameNew = "QCD-Data"
replaceQCDFromData(datasetsMgr, qcdDatasetName, qcdDatasetNameNew)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do Purity histograms with DataDriven QCD
PurityHistograms(datasetsMgr, qcdDatasetNameNew)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Custom Filtering of datasets
removeList = ["Data", "QCD-b", "Charged", "EWK"]
for d in removeList:
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
# Replace QCD MC with QCD-DataDriven
qcdDatasetName = "FakeBMeasurementTrijetMass"
qcdDatasetNameNew = "QCD-Data"
replaceQCDFromData(datasetsMgr, qcdDatasetName, qcdDatasetNameNew)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do Purity histograms with DataDriven QCD
PurityHistograms(datasetsMgr, qcdDatasetNameNew)
return
def main(opts, signalMass):
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr1 = GetDatasetsFromDir_secondDir(opts)
datasetsMgr1.updateNAllEventsToPUWeighted()
datasetsMgr1.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
for d in datasetsMgr1.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr1.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
plots.mergeRenameReorderForDataMC(datasetsMgr1)
# Determine integrated Lumi before removing data
#intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 0
# Remove datasets
if 1:
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "FakeBMeasurementTrijetMass" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "QCD" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr1.remove(filter(lambda name: "FakeBMeasurementTrijetMass" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# datasetsMgr.merge("QCD", GetListOfQCDatasets())
# plots._plotStyles["QCD"] = styles.getAltEWKStyle()
# Background1_Dataset = datasetsMgr.getDataset("QCD")
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
for m in signalMass:
datasetOrder.insert(0, m)
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr1.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoPaths1 = []
if folder != "":
histoList = datasetsMgr.getDataset(datasetOrder[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
numerators = [#"TopQuarkPt_InTopDirBDT",
#"AllTopQuarkPt_InTopDirBDT",
"AllTopQuarkPt_MatchedBDT",
#"TrijetNotInTopDirPt_BDT",
"TrijetFakePt_BDT",
#"AllTopQuarkPt_InTopDir",
"AllTopQuarkPt_Matched",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
#"AllTopQuarkPt_NotInTopDir",
#"EventTrijetPt_InTopDirBDT",
"AllTopQuarkPt_MatchedBDT",
#"TrijetPtMaxMVASameFakeObj_BjetPassCSV",
"SelectedTrijetsPt_BjetPassCSVdisc_afterCuts",
"TrijetPt_PassBDT_BJetPassCSV",
# "TopFromHiggsPt_isLdgMVATrijet_afterCuts",
# "TopFromHiggsPt_isSubldgMVATrijet_afterCuts",
# "TopFromHiggsPt_isMVATrijet_afterCuts",
# "LdgBjetPt_isLdgFreeBjet",
# "ChHiggsBjetPt_TetrajetBjetPt_Matched_afterCuts",
# "TopFromHiggsPt_notMVATrijet_afterCuts",
# "HiggsBjetPt_isTrijetSubjet_afterCuts",
]
denominators = [#"TrijetPt_BDT",
#"AllTopQuarkPt_InTopDir",
"AllTopQuarkPt_Matched",
#"TrijetNotInTopDirPt",
"TrijetFakePt",
#"TopQuarkPt",
"TopQuarkPt",
"EventTrijetPt2T_BDT",
"EventTrijetPt2T_Matched",
"EventTrijetPt2T",
#"TopQuarkPt",
#"EventTrijetPt_BDT",
"TopQuarkPt",
#"TrijetPtMaxMVASameFakeObj",
"SelectedTrijetsPt_afterCuts",
"TrijetPt_PassBDT",
# "TopFromHiggsPt_afterCuts",
# "TopFromHiggsPt_afterCuts",
# "TopFromHiggsPt_afterCuts",
# "LdgBjetPt",
# "ChHiggsBjetPt_foundTetrajetBjet_afterCuts",
# "TopFromHiggsPt_afterCuts",
# "HiggsBjetPt_afterCuts",
]
datasets = datasetsMgr.getAllDatasets()
datasets1 = datasetsMgr1.getAllDatasets()
for dataset in datasets1:
datasets.append(dataset)
for i in range(len(numerators)):
# PlotProb(datasetsMgr.getAllDatasets(), folder+"/"+numerators[i], folder+"/"+denominators[i])
PlotProb(datasets, folder+"/"+numerators[i], folder+"/"+denominators[i])
myHistoList = ["AllTopQuarkPt_MatchedBDT", "AllTopQuarkPt_Matched", "TrijetFakePt_BDT", "TrijetFakePt", "EventTrijetPt2T_MatchedBDT", "EventTrijetPt2T_BDT", "TopQuarkPt", "TrijetPt_BDT",
"LdgTrijetPt", "SubldgTrijetPt", "TopQuarkPt_BDT",]
for i in range(len(myHistoList)):
PlotMC(datasetsMgr, datasetsMgr1, folder+"/"+myHistoList[i], intLumi)
folder = "ForDataDrivenCtrlPlots"
histoList = datasetsMgr.getDataset(datasetOrder[0]).getDirectoryContent(folder)
hList0 = [x for x in histoList if "TrijetMass" in x]
hList1 = [x for x in histoList if "TetrajetMass" in x]
hList2 = [x for x in histoList if "TetrajetBjetPt" in x]
histoPaths2 = [os.path.join(folder, h) for h in hList0+hList1+hList2]
'''
histoPaths = histoPaths1 + histoPaths2
for h in histoPaths:
if "Vs" in h: # Skip TH2D
continue
PlotMC(datasetsMgr, h, intLumi)
'''
return
def main(opts, signalMass):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Determine integrated Lumi before removing data
#intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 5747.588 + 2573.399 + 4248.384 + 4008.663 + 2704.118 + 405.222 + 7539.457 + 8390.5 + 215.149
# Remove datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "QCD-b" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "QCD" in name,
datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "SingleTop" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQHT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "WJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "FakeBMeasurementTrijetMass" in name,
datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
if opts.mergeEWK:
newOrder = ["EWK", "QCD"]
else:
newOrder.extend(GetListOfEwkDatasets())
newOrder.append("QCD")
for d in datasetsMgr.getAllDatasetNames():
if "ChargedHiggs" in d:
newOrder.extend([d])
datasetsMgr.selectAndReorder(reversed(newOrder))
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoPaths = []
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
hList = [x for x in histoList if "_Vs_" not in x] # remove TH2
histoPaths = [os.path.join(folder, h) for h in hList]
skipMe = ["counters", "Weighting", "config", "configInfo"]
for h in histoPaths:
if h in skipMe:
continue
PlotMC(datasetsMgr, h, intLumi)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ZJetsToQQ_HT600toInf" in d.getName():
datasetsMgr.remove(d.getName())
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in opts.signal:
if not opts.acceptance:
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
# For acceptance plot use all available masses
if opts.acceptance:
opts.signal = []
opts.signalMasses = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
#dName = d.getName().replace("_ext1", "").split("M_")
dName = d.getName().split("M_")
m = int(dName[-1])
opts.signal.append(d.getName())
opts.signalMasses.append(m)
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if 1:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
datasetsMgr.PrintInfo()
# Get the efficiency graphs
hGraphList = []
histoName = os.path.join(opts.folder, "counter")
hGraphList, _kwargs = GetHistoGraphs(datasetsMgr, opts.folder,
histoName)
# Plot the histo graphs
PlotHistoGraphs(hGraphList, _kwargs)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab1)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Get the datasets from the directory
datasetsMgr1 = GetDatasetsFromDir(opts.mcrab1, opts)
datasetsMgr2 = GetDatasetsFromDir(opts.mcrab2, opts)
datasetsMgr3 = GetDatasetsFromDir(opts.mcrab3, opts)
# Setup the dataset managers
datasetsMgr1.updateNAllEventsToPUWeighted()
datasetsMgr1.loadLuminosities() # from lumi.json
datasetsMgr2.updateNAllEventsToPUWeighted()
datasetsMgr2.loadLuminosities() # from lumi.json
datasetsMgr3.updateNAllEventsToPUWeighted()
datasetsMgr3.loadLuminosities() # from lumi.json
# Print dataset info?
if opts.verbose:
datasetsMgr1.PrintCrossSections()
datasetsMgr1.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr1)
plots.mergeRenameReorderForDataMC(datasetsMgr2)
plots.mergeRenameReorderForDataMC(datasetsMgr3)
# Get Luminosity
lumi1 = datasetsMgr1.getDataset("Data").getLuminosity()
lumi2 = datasetsMgr2.getDataset("Data").getLuminosity()
lumi3 = datasetsMgr3.getDataset("Data").getLuminosity()
if lumi1 != lumi2 != lumi3:
raise Exception("Lumi1 (=%.2f) != Lumi2 (=%.2f) != Lumi3 (=%.2f" %
(lumi1, lumi2, lumi3))
else:
opts.intLumi = datasetsMgr1.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr1.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr2.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr3.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr1.PrintInfo()
if 0:
datasetsMgr2.PrintInfo()
datasetsMgr3.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr1.getDataset(
datasetsMgr1.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create a smaller list with only histos of interest
hListS = []
for h in hList:
if "StandardSelections" in h:
continue
if "IsGenuineB" in h:
continue
if "_Bjet" in h:
continue
if "_Jet" in h:
continue
if "_SubLdg" in h:
continue
if "_Njets" in h:
continue
if "_NBjets" in h:
continue
if "_Delta" in h:
continue
if "Dijet" in h:
continue
if "Bdisc" in h:
continue
#if "MVA" in h:
# continue
if "MET" in h:
continue
if "HT" in h:
continue
# Otherwise keep the histogram
hListS.append(h)
hPathsS = [os.path.join(opts.folder, h) for h in hListS]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPathsS: #hPaths:
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
counter = 1
# For-loop: All histogram pairs
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections",
" (CR1 and R2)").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1,
hCR2, "CR1")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1,
hCR2, "CR2") #iro
counter += 1
# WARNING! This unblinds the Signal Region (SR)
for hSR, hVR in zip(path_SR, path_VR):
if "IsGenuineB" in hSR:
continue
if 1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_SRvVR").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (SR and VR)").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR,
"SR")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR,
"VR")
counter += 1
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities(fname="lumi.json")
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = datasetsMgr.loadLumi()
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
mass = "M_%s" % (opts.signalMass)
if mass in d.getName():
if ("%s" % opts.signalMass) != d.getName().split("M_")[-1]:
datasetsMgr.remove(d.getName())
else:
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
else:
#datasetsToRemove.append(d.getName())
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# # Custom Filtering of datasets
# for i, d in enumerate(datasetsToRemove, 0):
# msg = "Removing dataset %s" % d
# Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
# datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
keepList = ["LdgTetrajetMass_AfterAllSelections"]
#keepList = ["LdgTetrajetMass_AfterStandardSelections"]
myHistos = []
for h in histoPaths:
if h.split("/")[-1] not in keepList:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
PlotHistograms(datasetsMgr, h)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
Verbose("main function")
comparisonList = ["AfterStdSelections"]
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
print d.getName()
datasetsMgr.getDataset(d.getName()).setCrossSection(10.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Check multicrab consistency
if 0:
consistencyCheck.checkConsistencyStandalone(dirs[0],
datasets,
name="CorrelationAnalysis")
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "ST" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Integrated Luminosity
if opts.mcOnly:
# Determine integrated lumi
if opts.intLumi < 0.0:
opts.intLumi = GetLumi(datasetsMgr)
else:
pass
# Remove data datasets
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
newOrder.extend(["ChargedHiggs_HplusTB_HplusToTB_M_500"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
if opts.mcOnly:
newOrder.remove("Data")
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the standard top-selections
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
Print("Plotting Top Selection Histograms (%s)" % (analysis), True)
TopSelectionHistograms(opts, datasetsMgr, analysis)
# Do Data-MC histograms
#for analysis in analysisTypes:
# Print("Plotting Other Histograms (%s)" % (analysis), True)
# DataMCHistograms(datasetsMgr, analysis)
return
def PlotAndFitTemplates(datasetsMgr, histoName, inclusiveFolder, opts):
Verbose("PlotAndFitTemplates()")
# Variable definition
bkgName = "QCD"
genuineBFolder = inclusiveFolder + "EWKGenuineB"
fakeBFolder = inclusiveFolder + "EWKFakeB"
baselineHisto = "%s/%s" % (inclusiveFolder, "Baseline_" + histoName)
invertedHisto = "%s/%s" % (inclusiveFolder, "Inverted_" + histoName)
# Create the histograms
pBaseline = plots.DataMCPlot(datasetsMgr, baselineHisto)
pInverted = plots.DataMCPlot(datasetsMgr, invertedHisto)
# Get the desired histograms (Baseline)
Data_baseline = pBaseline.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline Data") #also legend entry name
FakeB_baseline = pBaseline.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline " + bkgName)
EWK_baseline = pBaseline.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Baseline EWK")
# Get the desired histograms (Inverted)
# Data_inverted = pInverted.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted Data") #not needed
FakeB_inverted = pInverted.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted " + bkgName)
EWK_inverted = pInverted.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Inverted EWK")
# Subtact EWK-M Cfrom Data (QCD = Data - EWK)
FakeB_baseline.Add(EWK_baseline, -1)
FakeB_inverted.Add(EWK_inverted, -1)
# Rebin the EWK-MC histo (significant fit improvement - opposite effect for QCD fit)
EWK_baseline.RebinX(2)
EWK_inverted.RebinX(2)
FakeB_inverted.RebinX(1)
if 0:
print "+" * 100
Print("FIXME: Optimal is 1 (i.e. bin width of 10 GeV/c^{2})")
print "+" * 100
FakeB_inverted.RebinX(2)
# Create the final plot object
compareHistos = [EWK_baseline]
p = plots.ComparisonManyPlot(FakeB_inverted, compareHistos, saveFormats=[])
# Apply styles
p.histoMgr.forHisto("Inverted " + bkgName, styles.getFakeBStyle())
p.histoMgr.forHisto("Baseline EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Inverted " + bkgName, "P")
p.histoMgr.setHistoDrawStyle("Baseline EWK", "AP")
# Set legend style
p.histoMgr.setHistoLegendStyle("Inverted " + bkgName, "P")
p.histoMgr.setHistoLegendStyle("Baseline EWK", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline EWK": "EWK",
"Inverted " + bkgName: "QCD",
})
#=========================================================================================
# Set Minimizer Options
#=========================================================================================
'''
https://root.cern.ch/root/htmldoc/guides/users-guide/FittingHistograms.html#the-th1fit-method
https://root.cern.ch/root/html/src/ROOT__Math__MinimizerOptions.h.html#a14deB
'''
if 0:
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Migrad")
ROOT.Math.MinimizerOptions.SetDefaultStrategy(
1) # Speed = 0, Balance = 1, Robustness = 2
ROOT.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(
5000) # set maximum of function calls
ROOT.Math.MinimizerOptions.SetDefaultMaxIterations(
5000
) # set maximum iterations (one iteration can have many function calls)
if 0:
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Simplex")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Minimize")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit",
"MigradImproved")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Scan")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Seek")
ROOT.Math.MinimizerOptions.SetDefaultErrorDef(
1
) # error definition (=1. for getting 1 sigma error for chi2 fits)
ROOT.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(
1000000) # set maximum of function calls
ROOT.Math.MinimizerOptions.SetDefaultMaxIterations(
1000000
) # set maximum iterations (one iteration can have many function calls)
ROOT.Math.MinimizerOptions.SetDefaultPrecision(
-1
) # precision in the objective function calculation (value <= 0 means left to default)
ROOT.Math.MinimizerOptions.SetDefaultPrintLevel(
1
) # None = -1, Reduced = 0, Normal = 1, ExtraForProblem = 2, Maximum = 3
ROOT.Math.MinimizerOptions.SetDefaultTolerance(
1e-03
) # Minuit/Minuit2 converge when the EDM is less a given tolerance. (default 1e-03)
if 1:
hLine = "=" * 45
title = "{:^45}".format("Minimizer Options")
print "\t", hLine
print "\t", title
print "\t", hLine
minOpt = ROOT.Math.MinimizerOptions()
minOpt.Print()
print "\t", hLine, "\n"
#=========================================================================================
# Start fit process
#=========================================================================================
binLabels = ["Inclusive"]
moduleInfoString = opts.optMode #opts.dataEra + "_" + opts.searchMode + "_" + opts.optMode
#=========================================================================================
# Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
#=========================================================================================
manager = QCDNormalization.QCDNormalizationManagerDefault(
binLabels, opts.mcrab, moduleInfoString)
Print(
"Creating the normalization templates with appropriate template names",
True)
template_EWKFakeB_Baseline = manager.createTemplate(
"EWKFakeB_Baseline") #fixme-unused
template_EWKFakeB_Inverted = manager.createTemplate(
"EWKFakeB_Inverted") #fixme-unused
template_EWKInclusive_Baseline = manager.createTemplate(
"EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate(
"EWKInclusive_Inverted")
template_FakeB_Baseline = manager.createTemplate("QCD_Baseline")
template_FakeB_Inverted = manager.createTemplate("QCD_Inverted")
#========================================================================================
# EWK
#=========================================================================================
'''
Optimal fit (Chi2/D.O.F = 3.2)
FITMIN ; 80
FITMAX : 800
BinWidth: 10 GeV/c^2
par3 : -3.9174e-01 (fixed)
'''
Print("Setting the fit-function to the EWK template", False)
FITMIN_EWK = 80
FITMAX_EWK = 800
par0 = [+7.1817e-01, 0.0, 1.0] # cb_norm
par1 = [+1.7684e+02, 150.0, 200.0] # cb_mean
par2 = [+2.7287e+01, 20.0, 40.0] # cb_sigma (fixed for chiSq=2)
par3 = [-3.9174e-01, -1.0, 0.0] # cb_alpha (fixed for chiSq=2)
par4 = [+2.5104e+01, 0.0, 50.0] # cb_n
par5 = [+7.4724e-05, 0.0, 1.0] # expo_norm
par6 = [-4.6848e-02, -1.0, 0.0] # expo_a
par7 = [+2.1672e+02, 200.0, 250.0] # gaus_mean (fixed for chiSq=2)
par8 = [+6.3201e+01, 20.0, 80.0] # gaus_sigma
template_EWKInclusive_Baseline.setFitter(
QCDNormalization.FitFunction("EWKFunction",
boundary=0,
norm=1,
rejectPoints=0), FITMIN_EWK, FITMAX_EWK)
template_EWKInclusive_Baseline.setDefaultFitParam(
defaultInitialValue=None,
defaultLowerLimit=[
par0[1], par1[1], par2[1], par3[0], par4[1], par5[1], par6[1],
par7[1], par8[1]
],
defaultUpperLimit=[
par0[2], par1[2], par2[2], par3[0], par4[2], par5[2], par6[2],
par7[2], par8[2]
])
#=========================================================================================
# QCD
#=========================================================================================
'''
Optimal fit (Chi2/D.O.F = 25.6)
FITMIN : 80
FITMAX : 1000
BinWidth: 5 GeV/c^2
f_{QCD} : ~0.75118 +/- 0.00740 (data-driven plots looked great with this value)
'''
Print("Setting the fit-function to the QCD template", False)
FITMIN_QCD = 90 # 120
FITMAX_QCD = 1000 # 1000
bPtochos = False
if bPtochos:
par0 = [0.92, 0.0, 1.0] # lognorm_norm
par1 = [220.00, 180.0, 500.0] # lognorm_mean
par2 = [1.44, 0.4, 10.0] # lognorm_shape
par3 = [0.0, 0.0, 1.0] # exp_const
par4 = [-0.01, -2.0, 0.0] # exp_coeff
par5 = [220.00, 100.0, 400.0] # gaus_mean
par6 = [40.00, 0.0, 60.0] # gaus_sigma
template_FakeB_Inverted.setFitter(
QCDNormalization.FitFunction("QCDFunction",
boundary=0,
norm=1,
rejectPoints=0), FITMIN_QCD,
FITMAX_QCD)
template_FakeB_Inverted.setDefaultFitParam(
defaultInitialValue=[
par0[0], par1[0], par2[0], par3[0], par4[0], par5[0], par6[0]
],
defaultLowerLimit=[
par0[1], par1[1], par2[1], par3[1], par4[1], par5[1], par6[1]
],
defaultUpperLimit=[
par0[2], par1[2], par2[2], par3[2], par4[2], par5[2], par6[2]
])
else:
# par0 = [9.2578e-01, 0.0 , 1.0] # lognorm_norm
# par1 = [2.3662e+02, 200.0 , 1000.0] # lognorm_mean
# par2 = [1.4436e+00, 0.5, 10.0] # lognorm_shape
# par3 = [2.2575e+02, 100.0 , 500.0] # gaus_mean
# par4 = [3.6716e+01, 0.0 , 60.0] # gaus_sigma
par0 = [0.92, 0.0, 1.0] # lognorm_norm
par1 = [220.00, 180.0, 500.0] # lognorm_mean
par2 = [1.44, 0.4, 10.0] # lognorm_shape
par3 = [220.00, 100.0, 400.0] # gaus_mean
par4 = [40.00, 0.0, 60.0] # gaus_sigma
template_FakeB_Inverted.setFitter(
QCDNormalization.FitFunction("QCDFunctionAlt",
boundary=0,
norm=1,
rejectPoints=0), FITMIN_QCD,
FITMAX_QCD)
template_FakeB_Inverted.setDefaultFitParam(
defaultInitialValue=[par0[0], par1[0], par2[0], par3[0], par4[0]],
defaultLowerLimit=[par0[1], par1[1], par2[1], par3[1], par4[1]],
defaultUpperLimit=[par0[2], par1[2], par2[2], par3[2], par4[2]])
#=========================================================================================
# Set histograms to the templates
#=========================================================================================
Print("Adding the appropriate histogram to each of the the templates",
False)
template_EWKFakeB_Baseline.setHistogram(EWK_baseline, "Inclusive") #fixme
template_EWKFakeB_Inverted.setHistogram(EWK_inverted, "Inclusive") #fixme
template_EWKInclusive_Baseline.setHistogram(EWK_baseline, "Inclusive")
template_EWKInclusive_Inverted.setHistogram(EWK_inverted, "Inclusive")
template_FakeB_Baseline.setHistogram(FakeB_baseline, "Inclusive")
template_FakeB_Inverted.setHistogram(FakeB_inverted, "Inclusive")
#=========================================================================================
# Fit individual templates to histogram "Data_baseline", with custom fit options
#=========================================================================================
fitOptions = "R L W 0 Q" #"R B L W 0 Q M"
FITMIN_DATA = 80
FITMAX_DATA = 1000
manager.calculateNormalizationCoefficients(Data_baseline, fitOptions,
FITMIN_DATA, FITMAX_DATA)
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(
opts.mcrab,
"QCDInvertedNormalizationFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
# Not really needed to plot the histograms again
if 1:
saveName = histoName
plots.drawPlot(
p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
SavePlot(p, saveName, os.path.join(opts.saveDir, "Fit", opts.optMode))
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
sys.exit()
# Define datasets to remove by default
QCD_list = ["QCD_HT700to1000", "QCD_HT50to100", "QCD_HT500to700", "QCD_HT300to500",
"QCD_HT200to300", "QCD_HT2000toInf", "QCD_HT1500to2000", "QCD_HT100to200", "QCD_HT1000to1500"]
QCDExt_list = [x+"_ext1" for x in QCD_list]
datasetsToRemove = ["QCD-b"]
# datasetsToRemove.extend(QCD_list)
# datasetsToRemove.extend(QCDExt_list)
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
if d.isData():
continue
else:
newOrder.append(d.getName())
# Re-arrange dataset order?
if 0:
s = newOrder.pop( newOrder.index("noTop") )
newOrder.insert(len(newOrder), s) #after "Data"
# Move signal to top
if opts.signal in newOrder:
s = newOrder.pop( newOrder.index(opts.signal) )
newOrder.insert(1, s)
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
ignoreList = ["Aplanarity", "Planarity", "Sphericity", "FoxWolframMoment", "Circularity", "ThirdJetResolution", "Centrality", "_Vs_"]
myHistos = []
for h in histoPaths:
skip = False
# Skip unwanted histos
for i in ignoreList:
if i in h:
skip = True
if skip:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
# Plot the histograms!
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
DataMCHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(True) #opts.gridX)
style.setGridY(True) #opts.gridY)
style.setLogX(False) #opts.logX)
style.setLogY(False) #opts.logY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all histogram names in the given ROOT folder
histoNames = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
histoList_ = [os.path.join(opts.folder, h) for h in histoNames if "_SR" in h]
histoList = [h for h in histoList_ if "Pt" in h] + [h for h in histoList_ if "Eta" in h]
# For-loop: All histos in SR
nHistos = len(histoList)
for i, h in enumerate(histoList, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i,"/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
# print "*** h = ", h
PlotHistograms(datasetsMgr, h)
#break
# Inform user where the plots where saved
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def EWKvQCD(datasetsMgr, histoName, analysisType=""):
Verbose("Plotting EWK Vs QCD unity-normalised histograms for %s" %
analysisType)
# Sanity check
IsBaselineOrInverted(analysisType)
p1 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "Data", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "EWK", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos
data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
" -Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histos: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Normalize histograms to unit area
QCD.Scale(1.0 / QCD.Integral())
EWK.Scale(1.0 / EWK.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(QCD, [EWK],
saveFormats=[]) #[".C", ".png", ".pdf"])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-QCD":
analysisType + " (QCD)",
analysisType + "-EWK":
analysisType + " (EWK)",
})
# Append analysisType to histogram name
saveName = histoName + "_" + analysisType
# Draw the histograms #alex
plots.drawPlot(p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "EWKvQCD", opts.optMode)
SavePlot(p, saveName, saveDir)
return
def main(opts):
optModes = [
"", "OptChiSqrCutValue40", "OptChiSqrCutValue60",
"OptChiSqrCutValue80", "OptChiSqrCutValue100", "OptChiSqrCutValue120",
"OptChiSqrCutValue140"
]
if opts.optMode != None:
optModes = [opts.Mode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
plots.mergeRenameReorderForDataMC(datasetsMgr)
if 0:
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = [
"Data"
] #, "TT", "DYJetsToQQHT", "TTWJetsToQQ", "WJetsToQQ_HT_600ToInf", "SingleTop", "Diboson", "TTZToQQ", "TTTT"]
newOrder.extend(GetListOfEwkDatasets())
# newOrder.extend("M_500")
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
else:
Print(
"Cannot draw the EWKvQCD histograms without the option --mergeEWK. Exit",
True)
sys.exit()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the template comparisons
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
for hName in getTopSelectionHistos(opts.histoLevel, analysis):
if "ldgtrijetmass_after" not in hName.lower():
continue
EWKvQCD(datasetsMgr, hName.split("/")[-1], analysis)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
PrintPSet("BJetSelection", datasetsMgr)
PrintPSet("TopSelectionBDT", datasetsMgr)
PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Filter the datasets
datasetsMgr.remove(filter(lambda name: "Charged" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Re-order datasets (different for inverted than default=baseline)
if 0:
newOrder = ["Data"]
newOrder.extend(aux.GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Print post-merged data dataset summary
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
if 1:
datasetsMgr.PrintInfo()
# Get all histograms from the in the selected folder inside the ROOT files
allHistos = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
hList = [h for h in allHistos if "CRSelections" in h and "_Vs" not in h]
hList.extend([h for h in allHistos if "AllSelections" in h and "_Vs" not in h])
# hList.extend([h for h in allHistos if "StandardSelections" in h and "_Vs" not in h])
# Create a list with strings included in the histogram names you want to plot
myHistos = ["LdgTrijetPt", "LdgTrijetMass", "TetrajetBJetPt", "TetrajetBJetEta", "LdgTetrajetPt", "LdgTetrajetMass", "MVAmax2", "MVAmax1", "HT", "MET"]
# For-loop: All histos
for i, h in enumerate(myHistos, 1):
hGraphList = []
for b in ["Baseline_", "Inverted_"]:
for r in [ "_AfterCRSelections", "_AfterAllSelections"]:
histoName = b + h + r
hgQCD, kwargs = GetPurityHistoGraph(datasetsMgr, opts.folder, histoName)
# Do not draw SR in multigraph plot!
if GetControlRegionLabel(histoName) != "SR":
hGraphList.append(hgQCD)
# Plot individual purity graphs?
if 0:
PlotHistoGraph(hgQCD, kwargs)
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistoGraphs(hGraphList, kwargs)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# PrintPSet("BJetSelection", datasetsMgr)
# PrintPSet("TopSelectionBDT", datasetsMgr)
# PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Print dataset info?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set signal cross-section
if 0:
datasetsMgr.getDataset(opts.signal).setCrossSection(1.0)
# Remove unwanted datasets
if 0:
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPaths:
if "AfterStandardSelections" in p:
continue
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
# For-loop: All histogram pairs
for hVR, hCR2, hCR1 in zip(path_VR, path_CR2, path_CR1):
break
if "IsGenuineB" in hVR:
continue
PlotComparison(datasetsMgr, hVR, hCR2, "VRvCR2")
# For-loop: All histogram pairs
counter = 1
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr, hCR1, hCR2, "CR1vCR2")
counter+=1
# For-loop: All histogram pairs
for hSR, hVR in zip(path_SR, path_VR):
break
# Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
if "IsGenuineB" in hSR:
continue
PlotComparison(datasetsMgr, hSR, hVR, "SRvVR")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Determine integrated Lumi before removing data
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
if 1:
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "SingleTop" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "DYJetsToQQHT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "WJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "FakeBMeasurementTrijetMass" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets
if 1:
#newOrder = ["QCD", "TT", "TTZToQQ", opts.signalMass]
newOrder = ["QCD", "TT", opts.signalMass]
newOrder.reverse()
#newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoList = datasetsMgr.getDataset("QCD").getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
for h in histoPaths:
#print h
if "Vs" in h: #skip TH2
continue
if "JetEtaPhi" in h: #skip TH2
continue
PlotMC(datasetsMgr, h, intLumi)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue40", "OptChiSqrCutValue60", "OptChiSqrCutValue80", "OptChiSqrCutValue100", "OptChiSqrCutValue120", "OptChiSqrCutValue140"]
optModes = [
"OptChiSqrCutValue250", "OptChiSqrCutValue150", "OptChiSqrCutValue200",
"OptChiSqrCutValue180", "OptChiSqrCutValue300"
]
if opts.optMode != None:
optModes = [opts.Mode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
if 0:
datasetsMgr.remove(
filter(lambda name: "ST" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Integrated Luminosity
if 0:
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
else:
Print(
"Cannot draw the histograms without the option --mergeEWK. Exit",
True)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the template comparisons
for hName in getTopSelectionHistos(opts.histoLevel, "Baseline"):
PlotBaselineVsInvertedTemplates(datasetsMgr,
hName.split("/")[-1],
addQcdBaseline=False)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Set/Overwrite cross-sections. Remove all but 1 signal mass
removeList = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() != opts.signal:
removeList.append(d.getName())
# Custom filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i==1)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
if 1:
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for i, h in enumerate(histoPaths, 1):
if "_Vs_" in h:
continue
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(histoPaths)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h)
break
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# PrintPSet("BJetSelection", datasetsMgr)
# PrintPSet("TopSelectionBDT", datasetsMgr)
# PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Print dataset info?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set signal cross-section
if 0:
datasetsMgr.getDataset(opts.signal).setCrossSection(1.0)
# Remove unwanted datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "QCD-b" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPaths:
if "AfterStandardSelections" in p:
#print p
continue
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
# For-loop: All histogram pairs
for hVR, hCR2, hCR1 in zip(path_VR, path_CR2, path_CR1):
break # not needed now
if "IsGenuineB" in hVR:
continue
PlotComparison(datasetsMgr, hVR, hCR2, "VRvCR2")
# For-loop: All histogram pairs
counter = 1
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr, hCR1, hCR2, "CR1vCR2")
counter += 1
# For-loop: All histogram pairs
for hSR, hVR in zip(path_SR, path_VR):
break
Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
if "IsGenuineB" in hSR:
continue
PlotComparison(datasetsMgr, hSR, hVR, "SRvVR")
# # For-loop: All histogram pairs
# for hSR, hCR1 in zip(path_SR, path_CR1):
# #break
# Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
# raw_input("Press any key to continue")
# if "IsGenuineB" in hSR:
# continue
# PlotComparison(datasetsMgr, hSR, hCR1, "SRvCR1")
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts, signalMass):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Determine integrated Lumi before removing data
#intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 5747.588 + 2573.399 + 4248.384 + 4008.663 + 2704.118 + 405.222 + 7539.457 + 8390.5 + 215.149
# Remove datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "SingleTop" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQHT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "WJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "FakeBMeasurementTrijetMass" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
if opts.mergeEWK:
newOrder = ["EWK", "QCD"]
else:
newOrder.extend(GetListOfEwkDatasets())
newOrder.append("QCD")
for d in datasetsMgr.getAllDatasetNames():
if "ChargedHiggs" in d:
newOrder.extend([d])
datasetsMgr.selectAndReorder( reversed(newOrder) )
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoPaths = []
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
hList = [x for x in histoList if "_Vs_" not in x] # remove TH2
histoPaths = [os.path.join(folder, h) for h in hList]
skipMe = ["counters", "Weighting", "config", "configInfo"]
for h in histoPaths:
if h in skipMe:
continue
PlotMC(datasetsMgr, h, intLumi)
return
def main(opts):
# optModes = [""]
# optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100", "OptChiSqrCutValue200"]
optModes = [""] #,
#"OptTriggerOR['HLT_PFHT400_SixJet30_DoubleBTagCSV_p056']",
#"OptTriggerOR['HLT_PFHT450_SixJet40_BTagCSV_p056']"]
#"OptInvertedBJetsDiscrMaxCutValue0p82",
#"OptInvertedBJetsDiscrMaxCutValue0p8",
#"OptInvertedBJetsDiscrMaxCutValue0p75",
#"OptInvertedBJetsDiscrMaxCutValue0p7"]
#"OptInvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeDescendingBDiscriminator",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeDescendingBDiscriminator"]
#"OptInvertedBJetsDiscrMaxCutValue0p85InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue0p85InvertedBJetsSortTypeDescendingBDiscriminator",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeDescendingBDiscriminator",
#"OptInvertedBJetsDiscrMaxCutValue0p75InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue0p75InvertedBJetsSortTypeDescendingBDiscriminator"]
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeRandom",
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeDescendingBDiscriminator",
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeRandom",
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeDescendingBDiscriminator",
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue0p75InvertedBJetsSortTypeRandom",
# "OptNumberOfInvertedBJetsCutValue1InvertedBJetsDiscrMaxCutValue0p75InvertedBJetsSortTypeDescendingBDiscriminator"]
#if opts.optMode != None:
# optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Remove dataset ?
datasetsMgr.remove(
filter(lambda name: "Charged" in name,
datasetsMgr.getAllDatasetNames()))
if not opts.useMC:
datasetsMgr.remove(
filter(lambda name: "QCD" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
newOrder.extend(GetListOfEwkDatasets())
if opts.useMC:
newOrder.append("QCD")
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# 1) Do the StandardSelections/AllSelections
bType = ""
folder = "ForFakeBMeasurement" + bType
hList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
hPaths = [os.path.join(folder, h) for h in hList]
baselinePaths = []
invertedPath = []
if 1:
for p in hPaths:
if "Baseline" in p:
baselinePaths.append(p)
if "Inverted" in p:
invertedPath.append(p)
for hBaseline, hInverted in zip(baselinePaths, invertedPath):
# print "--- Plotting histogram", hBaseline
PlotBaselineVsInverted(datasetsMgr, hBaseline, hInverted)
# 2) Do the topSelection histos
if 0:
analysisType = "Inverted"
folder = "topSelection_%s" % (analysisType)
invertedList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
folder)
invertedPaths = [os.path.join(folder, h) for h in invertedList]
baselinePaths = [
p.replace("Inverted", "Baseline") for p in invertedPaths
]
for hBaseline, hInverted in zip(baselinePaths, invertedPaths):
if "Vs" in hBaseline: #skip TH2
continue
PlotBaselineVsInverted(datasetsMgr, hBaseline, hInverted)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue150p0", "OptChiSqrCutValue200p0"]
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
print d.getName()
datasetsMgr.getDataset(d.getName()).setCrossSection(
1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Check multicrab consistency
if 0:
consistencyCheck.checkConsistencyStandalone(
dirs[0], datasets, name="CorrelationAnalysis")
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "ST" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
if 1:
newOrder = ["Data"]
newOrder.extend(["ChargedHiggs_HplusTB_HplusToTB_M_500"])
newOrder.extend(["QCD"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the standard top-selections
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
Print("Plotting Top Selection Histograms (%s)" % (analysis), True)
PlotSignal(datasetsMgr, analysis, opts)
# Do Data-MC histograms
# for analysis in analysisTypes:
# Print("Plotting Other Histograms (%s)" % (analysis), True)
# DataMCHistograms(datasetsMgr, analysis)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
style.setWide(True, 0.15)
# style.setPadRightMargin()#0.13)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
for d in datasetsMgr.getAllDatasets():
if d.getName() == opts.dataset:
newOrder.append(d.getName())
# Sanity check on selected dataset
nDatasets = len(newOrder)
if nDatasets < 1:
msg = "Please select a valid dataset. Dataset \"%s\" does not exist!" % (opts.dataset)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
sys.exit()
if nDatasets > 1:
msg = "Please select only 1 valid dataset. Requested %i datasets for plotting!" % (nDatasets)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
sys.exit()
# Select only given dataset
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information
msg = "Plotting for single dataset \"%s\". Integrated luminosity is %.2f 1/fb" % (opts.dataset, opts.intLumi)
Print(ShellStyles.NoteStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for h in histoPaths:
if "_vs_" not in h.lower():
continue
Plot2dHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setWide(True, 0.15)
# style.setPadRightMargin()#0.13)
# Do 2D histos
histoNames = []
saveFormats = [".png"] #[".C", ".png", ".pdf"]
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [opts.folder +"/" + h for h in histoList]
#histoKwargs = GetHistoKwargs(histoPaths)
# Axes divisions!
ROOT.gStyle.SetNdivisions(5, "X")
ROOT.gStyle.SetNdivisions(5, "Y")
# For-loop: All histogram
for histoName in histoPaths:
myKwargs = GetHistoKwargs(histoName)
# For-loop: All datasets
for d in datasetsMgr.getAllDatasetNames():
#if "M_500" not in d:
# continue
Plot2d(datasetsMgr, d, histoName, myKwargs, opts)
# Avoid replacing canvas with same name warning
for o in gROOT.GetListOfCanvases():
# print o.GetName()
o.SetName(o.GetName() + "_" + d)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
sys.exit()
# Define datasets to remove by default
QCD_list = ["QCD_HT700to1000", "QCD_HT50to100", "QCD_HT500to700", "QCD_HT300to500",
"QCD_HT200to300", "QCD_HT2000toInf", "QCD_HT1500to2000", "QCD_HT100to200", "QCD_HT1000to1500"]
QCDExt_list = [x+"_ext1" for x in QCD_list]
datasetsToRemove = ["QCD-b"]
# datasetsToRemove.extend(QCD_list)
# datasetsToRemove.extend(QCDExt_list)
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
if d.isData():
continue
else:
newOrder.append(d.getName())
# Re-arrange dataset order?
if 0:
s = newOrder.pop( newOrder.index("noTop") )
newOrder.insert(len(newOrder), s) #after "Data"
# Move signal to top
if opts.signal in newOrder:
s = newOrder.pop( newOrder.index(opts.signal) )
newOrder.insert(1, s)
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
ignoreList = ["Aplanarity", "Planarity", "Sphericity", "FoxWolframMoment", "Circularity", "ThirdJetResolution", "Centrality", "_Vs_"]
myHistos = []
for h in histoPaths:
skip = False
# Skip unwanted histos
for i in ignoreList:
if i in h:
skip = True
if skip:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
# Plot the histograms!
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
DataMCHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(True) #opts.gridX)
style.setGridY(True) #opts.gridY)
style.setLogX(False) #opts.logX)
style.setLogY(False) #opts.logY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all histogram names in the given ROOT folder
histoNames = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
# histoList = [os.path.join(opts.folder, h) for h in histoNames if "_" + opts.region in h and opts.refBdisc in h]
histoList = [os.path.join(opts.folder, h) for h in histoNames if opts.refBdisc in h]
# For-loop: All histos in CR of interest
nHistos = len(histoList)
for i, h in enumerate(histoList, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i,"/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h)
# Inform user where the plots where saved
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue200p0"]
optModes = [
"", "OptInvertedBJetsDiscrMaxCutValue0p82",
"OptInvertedBJetsDiscrMaxCutValue0p8",
"OptInvertedBJetsDiscrMaxCutValue0p75",
"OptInvertedBJetsDiscrMaxCutValue0p7"
]
#"OptInvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue1p0InvertedBJetsSortTypeDescendingBDiscriminator",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeRandom",
#"OptInvertedBJetsDiscrMaxCutValue0p8InvertedBJetsSortTypeDescendingBDiscriminator"]
#if opts.optMode != None:
# optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "Charged" in name and not "M_500" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
if 0:
newOrder = ["Data"]
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the Purity Triplets?
if 0:
bType = "" # ["", "EWKFakeB", "EWKGenuineB"]
folder = "FakeBPurity" + bType
hList = datasetsMgr.getDataset("EWK").getDirectoryContent(folder)
for hName in hList:
PlotPurity(datasetsMgr, os.path.join(folder, hName))
# Do the Std Selections Purity plots
folder = "ForDataDrivenCtrlPlots"
allHistos = datasetsMgr.getDataset("EWK").getDirectoryContent(folder)
hList = [
h for h in allHistos
if "StandardSelections" in h and "_Vs" not in h
]
hList.extend(
[h for h in allHistos if "AllSelections" in h and "_Vs" not in h])
# Only do these histos
myHistos = [
"Njets", "LdgTrijetMass", "TetrajetBjetPt", "LdgTetrajetMass",
"LdgTetrajetMass"
]
# For-loop: All histos
for h in hList:
if h.split("_")[0] not in myHistos:
continue
if "JetEtaPhi" in h:
continue
PlotPurity(datasetsMgr, os.path.join(folder, h))
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "Charged" in name, datasetsMgr.getAllDatasetNames()))
# Replace QCD MC with QCD-DataDriven
qcdDatasetName = "FakeBMeasurementTrijetMass"
qcdDatasetNameNew = "QCD-Data"
if opts.mcQCD:
pass
else:
replaceQCDFromData(datasetsMgr, qcdDatasetName, qcdDatasetNameNew)
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
if opts.signalMass != 0:
signal = "ChargedHiggs_HplusTB_HplusToTB_M_%.0f" % opts.signalMass
newOrder.extend([signal])
if opts.mcQCD:
newOrder.extend(["QCD"])
else:
newOrder.extend(["QCD-Data"])
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do Data-MC histograms with DataDriven QCD
DataMCHistograms(datasetsMgr, qcdDatasetNameNew)
return
def main(opts):
optModes = ["", "OptNumberOfBJetsCutValue0", "OptNumberOfBJetsCutValue1"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "HplusTB" in name and not "M_500" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets (different for inverted than default=baseline)
newOrder = [
"Data"
] #, "TT", "DYJetsToQQHT", "TTWJetsToQQ", "WJetsToQQ_HT_600ToInf", "SingleTop", "Diboson", "TTZToQQ", "TTTT"]
newOrder.extend(GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Sanity check
if not opts.mergeEWK:
Print(
"Cannot draw the Baseline Vs Inverted histograms without the option --mergeEWK. Exit",
True)
sys.exit()
# Merge EWK samples
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the Baseline Vs Inverted histograms
folder = "ForFakeBMeasurement"
hName = "%s/Inverted_TopMassReco_LdgTrijetM_AfterAllSelections" % folder
DoPlots(datasetsMgr, hName)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab1)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Get the datasets from the directory
datasetsMgr1 = GetDatasetsFromDir(opts.mcrab1, opts)
datasetsMgr2 = GetDatasetsFromDir(opts.mcrab2, opts)
datasetsMgr3 = GetDatasetsFromDir(opts.mcrab3, opts)
# Setup the dataset managers
datasetsMgr1.updateNAllEventsToPUWeighted()
datasetsMgr1.loadLuminosities() # from lumi.json
datasetsMgr2.updateNAllEventsToPUWeighted()
datasetsMgr2.loadLuminosities() # from lumi.json
datasetsMgr3.updateNAllEventsToPUWeighted()
datasetsMgr3.loadLuminosities() # from lumi.json
# Print dataset info?
if opts.verbose:
datasetsMgr1.PrintCrossSections()
datasetsMgr1.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr1)
plots.mergeRenameReorderForDataMC(datasetsMgr2)
plots.mergeRenameReorderForDataMC(datasetsMgr3)
# Get Luminosity
lumi1 = datasetsMgr1.getDataset("Data").getLuminosity()
lumi2 = datasetsMgr2.getDataset("Data").getLuminosity()
lumi3 = datasetsMgr3.getDataset("Data").getLuminosity()
if lumi1 != lumi2 != lumi3:
raise Exception("Lumi1 (=%.2f) != Lumi2 (=%.2f) != Lumi3 (=%.2f" % (lumi1, lumi2, lumi3))
else:
opts.intLumi = datasetsMgr1.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr1.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr2.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr3.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr1.PrintInfo()
if 0:
datasetsMgr2.PrintInfo()
datasetsMgr3.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr1.getDataset(datasetsMgr1.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create a smaller list with only histos of interest
hListS = []
for h in hList:
if "StandardSelections" in h:
continue
if "IsGenuineB" in h:
continue
if "_Bjet" in h:
continue
if "_Jet" in h:
continue
if "_SubLdg" in h:
continue
if "_Njets" in h:
continue
if "_NBjets" in h:
continue
if "_Delta" in h:
continue
if "Dijet" in h:
continue
if "Bdisc" in h:
continue
#if "MVA" in h:
# continue
if "MET" in h:
continue
if "HT" in h:
continue
# Otherwise keep the histogram
hListS.append(h)
hPathsS = [os.path.join(opts.folder, h) for h in hListS]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPathsS: #hPaths:
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
counter = 1
# For-loop: All histogram pairs
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (CR1 and R2)").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1, hCR2, "CR1")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1, hCR2, "CR2") #iro
counter+=1
# WARNING! This unblinds the Signal Region (SR)
for hSR, hVR in zip(path_SR, path_VR):
if "IsGenuineB" in hSR:
continue
if 1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_SRvVR").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (SR and VR)").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR, "SR")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR, "VR")
counter+=1
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts, signalMass):
print "Main Function"
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
# Get Luminosity
datasetsMgr.loadLuminosities()
# Print dataset cross sections
datasetsMgr.PrintCrossSections()
# Print luminosities
datasetsMgr.PrintLuminosities()
# Determine integrated Lumi before removing data
# intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 0
# Remove datasets
if 1:
datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD_bEnriched" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Set plot Styles
plots._plotStyles["QCD"] = styles.getAltEWKStyle()
plots._plotStyles["WJetsToQQ_HT_600ToInf"] = styles.getBaselineLineStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
JetTypes = ["Light", "Gluon"]
PtRange = ["30pt40", "40pt50", "50pt65", "65pt80", "80pt100", "100pt125", "125pt160", "160pt200", "200pt250",
"250pt320", "320pt400", "400pt630", "630pt800", "800ptInf"]
histos = ["LightJetsQGL_"+ pt for pt in PtRange]
histos.extend("GluonJetsQGL_"+ pt for pt in PtRange)
#histos.extend("JetsQGL")
#histos.extend("GluonJetsQGL")
#histos.extend("LightJetsQGL")
for h in histos:
# Produce & save the plots
PlotMC(datasetsMgr, h, intLumi)
# Dump the pdfs in a JSON file
for dataset in datasetsMgr.getAllDatasets():
for JetType in JetTypes:
jsonhistos = [JetType+"JetsQGL_"+ pt for pt in PtRange]
results = []
for h in jsonhistos:
dsetHisto = dataset.getDatasetRootHisto(h)
dsetHisto.normalizeToOne()
histo = dsetHisto.getHistogram()
ptbin = h.split("_")[-1]
minPt = ptbin.split("pt")[0]
maxPt = ptbin.split("pt")[-1]
if maxPt == "Inf":
maxPt = 9999999999.9
for k in range(1, histo.GetNbinsX()+1):
resultObject = {}
resultObject["Jet"] = JetType
resultObject["QGLmin"] = histo.GetBinLowEdge(k)
resultObject["QGLmax"] = histo.GetBinLowEdge(k)+histo.GetBinWidth(k)
resultObject["Ptmin"] = minPt
resultObject["Ptmax"] = maxPt
resultObject["prob"] = histo.GetBinContent(k)
resultObject["probError"] = histo.GetBinError(k)
results.append(resultObject)
filename = "QGLdiscriminator_%s_%sJets.json"%(dataset.name, JetType)
with open(filename, 'w') as outfile:
json.dump(results, outfile)
print "Written results to %s"%filename
return
def main(opts, signalMass):
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Determine integrated Lumi before removing data
# intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 35920
datasetsMgr.PrintInfo()
#datasetsMgr.merge("QCD", GetListOfQCDatasets())
#plots._plotStyles["QCD"] = styles.getQCDLineStyle()
#if opts.noQCD:
#datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "QCD_HT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "QCD" in name, datasetsMgr.getAllDatasetNames()))
# Remove datasets
if 1:
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "QCD_HT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "SingleTop" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "DYJetsToQQHT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTZToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTWJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "WJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "TTTT" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "TT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "FakeBMeasurementTrijetMass" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(filter(lambda name: "ZJets" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Determine integrated Lumi before removing data
intLumi = 35920
#intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
# if "M_" in d.getName() and "200" not in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
#newOrder = ["TT", "QCD"]
#newOrder = ["TT", "QCD"]
for m in signalMass:
#newOrder.insert(0, m)
datasetOrder.insert(0, m)
#datasetsMgr.selectAndReorder(newOrder)
datasetsMgr.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoPaths1 = []
if folder != "":
histoList = datasetsMgr.getDataset(datasetOrder[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
folder = ""
histoList = datasetsMgr.getDataset(datasetOrder[0]).getDirectoryContent(folder)
hList0 = [x for x in histoList if "TrijetMass" in x]
hList1 = [x for x in histoList if "TetrajetMass" in x]
hList2 = [x for x in histoList if "TetrajetBjetPt" in x]
histoPaths2 = [os.path.join(folder, h) for h in hList0+hList1+hList2]
histoPaths = histoPaths1 + histoPaths2
for h in histoPaths:
if "Vs" in h: # Skip TH2D
continue
if "VS" in h: # Skip TH2D
continue
PlotMC(datasetsMgr, h, intLumi)
ROOT.gStyle.SetNdivisions(10, "X")
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ZJetsToQQ_HT600toInf" in d.getName():
datasetsMgr.remove(d.getName())
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in opts.signal:
if not opts.acceptance:
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
# For acceptance plot use all available masses
if opts.acceptance:
opts.signal = []
opts.signalMasses = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
#dName = d.getName().replace("_ext1", "").split("M_")
dName = d.getName().split("M_")
m = int(dName[-1])
opts.signal.append(d.getName())
opts.signalMasses.append(m)
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if 1:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
datasetsMgr.PrintInfo()
# Get the efficiency graphs
hGraphList = []
histoName = os.path.join(opts.folder, "counter")
hGraphList, _kwargs = GetHistoGraphs(datasetsMgr, opts.folder, histoName)
# Plot the histo graphs
PlotHistoGraphs(hGraphList, _kwargs)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def DoPlots(datasetsMgr, histoName, analysisType="Inverted", bType="GenuineB"):
# Sanity checks
IsBaselineOrInverted(analysisType)
IsGenuineOrFake(bType)
# Definitions
defaultFolder = ""
if "FakeBPurity" in histoName:
defaultFolder = "FakeBPurity"
elif "ForFakeBMeasurement" + histoName:
defaultFolder = "ForFakeBMeasurement"
else:
raise Exception("This should never happen")
# Define folders for inclusive/genuine/fakes
inclusiveFolder = defaultFolder
genuineFolder = defaultFolder + "EWKGenuineB"
fakeFolder = defaultFolder + "EWKFakeB"
inclusiveHisto = histoName.replace(defaultFolder, inclusiveFolder)
genuineHisto = histoName.replace(defaultFolder, genuineFolder)
fakeHisto = histoName.replace(defaultFolder, fakeFolder)
# Get the inclusive histograms
p0 = plots.DataMCPlot(datasetsMgr, inclusiveHisto)
Data = p0.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
# Get the genuine-b histograms
p1 = plots.DataMCPlot(datasetsMgr, genuineHisto)
EWKGenuineB = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(
"EWKGenuineB")
QCDGenuineB = p1.histoMgr.getHisto("QCD").getRootHisto().Clone(
"QCDGenuineB")
# Get the fake-b histograms
p2 = plots.DataMCPlot(datasetsMgr, fakeHisto)
EWKFakeB = p2.histoMgr.getHisto("EWK").getRootHisto().Clone("EWKFakeB")
QCDFakeB = p2.histoMgr.getHisto("QCD").getRootHisto().Clone("QCDFakeB")
# Normalize histograms to unit area
if 0:
Data.Scale(1.0 / Data.Integral())
EWKGenuineB.Scale(1.0 / EWKGenuineB.Integral())
EWKFakeB.Scale(1.0 / EWKFakeB.Integral())
QCDGenuineB.Scale(1.0 / QCDGenuineB.Integral())
QCDFakeB.Scale(1.0 / EWKFakeB.Integral())
# Create the final plot object
comparisonList = [EWKGenuineB, QCDGenuineB, EWKFakeB, QCDFakeB]
p = plots.ComparisonManyPlot(Data, comparisonList, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Data", styles.getDataStyle())
p.histoMgr.forHisto("EWKGenuineB",
styles.getAltEWKStyle()) #GenuineBStyle()
p.histoMgr.forHisto("QCDGenuineB", styles.getQCDStyle())
p.histoMgr.forHisto("EWKFakeB", styles.getGenuineBStyle())
p.histoMgr.forHisto("QCDFakeB", styles.getFakeBStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Data", "AP")
p.histoMgr.setHistoDrawStyle("EWKGenuineB", "AP")
p.histoMgr.setHistoDrawStyle("QCDGenuineB", "AP")
p.histoMgr.setHistoDrawStyle("EWKFakeB", "AP")
p.histoMgr.setHistoDrawStyle("QCDFakeB", "AP")
# Set legend style
p.histoMgr.setHistoLegendStyle("Data", "P")
p.histoMgr.setHistoLegendStyle("EWKGenuineB", "P")
p.histoMgr.setHistoLegendStyle("QCDGenuineB", "P")
p.histoMgr.setHistoLegendStyle("EWKFakeB", "P")
p.histoMgr.setHistoLegendStyle("QCDFakeB", "P")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data": "Data",
"EWKGenuineB": "EWK-GenuineB",
"QCDGenuineB": "QCD-GenuineB",
"EWKFakeB": "EWK-FakeB",
"QCDFakeB": "QCD-FakeB",
#"Data" : "Data (%s)" % (analysisType),
#"EWKGenuineB": "EWK-GenuineB (%s)" % (analysisType),
#"QCDGenuineB": "QCD-GenuineB (%s)" % (analysisType),
#"EWKFakeB" : "EWK-FakeB (%s)" % (analysisType),
#"QCDFakeB" : "QCD-FakeB (%s)" % (analysisType),
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e0, "ymaxfactor": 2.0}
_format = "%0.0f"
_xlabel = None
if "dijetm" in histoName.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 400.0
if "trijetm" in histoName.lower():
_rebinX = 5
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500.0
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_rebinX = 10
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 3500.0
if "ancestry" in histoName.lower():
_rebinX = 1
_units = ""
_format = "%0.0f " + _units
_opts["xmax"] = 32.0
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.72,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.0,
"ymax": 1.5
}, #{"ymin": 0.6, "ymax": 1.4},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName,
os.path.join(opts.saveDir, "GenuineVsFake", opts.optMode))
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
global style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi == 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = GetLumi(datasetsMgr)
# Set/Overwrite cross-sections. Remove all but 1 signal mass
signalsList = []
for s in opts.signalList:
signalsList.append("ChargedHiggs_HplusTB_HplusToTB_M_%s" % s)
# Remove datasets with overlap?
removeList = ["QCD-b"]
dsetDY = "DYJetsToQQ_HT180"
dsetZJ = "ZJetsToQQ_HT600toInf"
dsetRM = dsetZJ # datasets with overlap
removeList.append(dsetRM)
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in signalsList:
removeList.append(d.getName())
# Custom filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i==1)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
if 1:
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList if "AllSelections" in h]
myHistos = []
skipStrings = ["StandardSelections", "JetPt", "JetEta", "JetEtaPhi", "BJetPt", "BJetEta",
"BtagDiscriminator", "Eta", "LdgTrijetTopMassWMassRatio", "Subldg", "Dijet",
"MET", "MHT", "MinDeltaPhiJetMHT", "MaxDeltaPhiJetMHT", "MinDeltaRJetMHT", "MinDeltaRJetMHTReversed"]
for h in histoPaths:
bSkip = False
for s in skipStrings:
if s in h:
bSkip = True
break
if bSkip:
continue
else:
myHistos.append(h)
# For-loop: All histograms
for i, h in enumerate(myHistos, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h, signalsList, opts.cutDir)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr_matched = GetDatasetsFromDir(opts)
datasetsMgr_matched.updateNAllEventsToPUWeighted()
datasetsMgr_matched.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames())) #soti
datasetsMgr_matched.remove(filter(lambda name: "QCD" in name, datasetsMgr_matched.getAllDatasetNames())) #soti
# Set/Overwrite cross-sections
datasetsToRemove = ["QCD-b"]#, "QCD_HT50to100", "QCD_HT100to200"]#, "QCD_HT200to300"]#, "QCD_HT300to500"]
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
#if d.getName() != opts.signal:
#if "M_650" in d.getName(): #soti fixmi
# datasetsToRemove.append(d.getName())
if "M_800" in d.getName():
datasetsToRemove.append(d.getName())
#if "M_200" in d.getName():
# datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr_matched.remove(filter(lambda name: d in name, datasetsMgr_matched.getAllDatasetNames())) #soti
if opts.verbose:
datasetsMgr.PrintInfo()
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.merge("QCD", GetListOfQCDatasets())
#plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
# Get Luminosity
#intLumi = datasetsMgr.getDataset("Data").getLuminosity() Soti
intLumi = 35920
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
# For-loop: All MC datasets
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Move signal to top
# if opts.signal in newOrder:
# s = newOrder.pop( newOrder.index(opts.signal) )
# newOrder.insert(0, s)
print len(newOrder), "newOrder"
signalMass = ["M_200", "M_500", "M_1000", "M_650"]
for d in datasetsMgr.getMCDatasets():
for m in signalMass:
if m in d.getName():
s = newOrder.pop( newOrder.index(d.getName()) )
newOrder.insert(0, s)
#datasetsMgr.selectAndReorder(newOrder)
print len(newOrder), "newOrder"
# Add Data to list of samples!
if not opts.onlyMC:
newOrder.insert(0, "Data")
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
histoPaths2 = [h for h in histoPaths1]# if "jet" not in h.lower()]
nHistos = len(histoPaths2)
# For-loop: All histograms
for i, h in enumerate(histoPaths2, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, datasetsMgr_matched, h, intLumi)
ROOT.gStyle.SetNdivisions(10, "X")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities(fname="lumi.json")
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = datasetsMgr.loadLumi()
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
if "M_%s" % (opts.signalMass) in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
else:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
keepList = ["LdgTetrajetMass_AfterAllSelections"]
#keepList = ["LdgTetrajetMass_AfterStandardSelections"]
myHistos = []
for h in histoPaths:
if h.split("/")[-1] not in keepList:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
PlotHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def DoPlots(datasetsMgr, histoName):
analysisType = "Inverted"
# Get the Inclusive (Data, EWK)
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, analysisType))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos
inverted_Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-Data")
inverted_EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Inverted-EWK")
inverted_QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-QCD")
# Normalize histograms to unit area
inverted_Data.Scale(1.0 / inverted_QCD.Integral())
inverted_QCD.Scale(1.0 / inverted_QCD.Integral())
inverted_EWK.Scale(1.0 / inverted_EWK.Integral())
# Create the final plot object (Cannot subtract EWK from Data since the trigger in the MC samples is NOT prescaled. In data it is)
p = plots.ComparisonManyPlot(inverted_Data, [inverted_EWK], saveFormats=[])
#p = plots.PlotBase([inverted_Data], saveFormats=[])
# Apply styles
p.histoMgr.forHisto("Inverted-Data", styles.getDataStyle())
#p.histoMgr.forHisto("Inverted-QCD" , styles.getInvertedLineStyle() )
p.histoMgr.forHisto("Inverted-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Inverted-Data", "AP")
#p.histoMgr.setHistoDrawStyle("Inverted-QCD" , "HIST")
p.histoMgr.setHistoDrawStyle("Inverted-EWK", "AP")
# Set legend style
p.histoMgr.setHistoLegendStyle("Inverted-Data", "P")
#p.histoMgr.setHistoLegendStyle("Inverted-QCD" , "L")
p.histoMgr.setHistoLegendStyle("Inverted-EWK", "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Inverted-Data": "Data",
"Inverted-EWK": "EWK",
#"Inverted-QCD" : "QCD=Data-EWK",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
#_opts = {"ymin": 1e-4, "ymaxfactor": 2.0}
_opts = {"ymin": 1e-4, "ymax": 1e-1}
_xlabel = None
_units = "GeV/c^{2}"
_xlabel = "m_{jjb} (%s)" % _units
_ylabel = "Arbitrary Units / %0.0f %s " % (inverted_QCD.GetBinWidth(0),
_units)
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500.0
# Complains (SysError in <TFile::TFile>: file ...)
if 0:
plots.drawPlot(p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts)
if 1:
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.0,
"ymax": 2.0
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save to ROOT file
inverted_QCD.SaveAs("Inverted_QCD.root")
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "Test", opts.optMode))
return
def main(opts):
Verbose("main function")
comparisonList = ["AfterStdSelections"]
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Check multicrab consistency
# consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="CorrelationAnalysis")
# Custom Filtering of datasets
# datasetsMgr.remove(filter(lambda name: "HplusTB" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "ST" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Integrated Luminosity
if opts.mcOnly:
# Determine integrated lumi
if opts.intLumi < 0.0:
opts.intLumi = GetLumi(datasetsMgr)
else:
pass
# Remove data datasets
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets (different for inverted than default=baseline)
newOrder = [
"Data"
] #, "TT", "DYJetsToQQHT", "TTWJetsToQQ", "WJetsToQQ_HT_600ToInf", "SingleTop", "Diboson", "TTZToQQ", "TTTT"]
newOrder.extend(GetListOfEwkDatasets())
if opts.mcOnly:
newOrder.remove("Data")
datasetsMgr.selectAndReorder(newOrder)
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
# Do the Purity Triples analysisTypes = ["Baseline", "Inverted"]
analysisTypes = ["", "EWKFakeB", "EWKGenuineB"]
for analysis in analysisTypes:
Print("Plotting Purity Triplet Histograms (%s)" % (analysis), True)
PurityTripletPlots(datasetsMgr, analysisType=analysis)
return
# Do the Data/QCD/EWK histograms
if opts.mergeEWK:
analysisTypes = ["Baseline", "Inverted"]
for analysis in analysisTypes:
Print("Plotting DataEWkQcd Histograms (%s)" % (analysis), True)
for hName in getTopSelectionHistos(opts.histoLevel, analysis):
DataEwkQcd(datasetsMgr, hName.split("/")[-1], analysis)
else:
Print(
"Cannot draw the Data/QCD/EWK histograms without the option --mergeEWK. Exit",
True)
return
def main(opts, signalMass):
optModes = ["OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Determine integrated Lumi before removing data
#intLumi = datasetsMgr.getDataset("Data").getLuminosity()
intLumi = 35800
# Remove datasets
if 1:
datasetsMgr.remove(
filter(lambda name: "Data" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "QCD-b" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "QCD" in name,
datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "SingleTop" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQHT" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "TTZToQQ" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "TTWJetsToQQ" in name,
datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "WJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "Diboson" in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "TTTT" in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr.remove(
filter(lambda name: "FakeBMeasurementTrijetMass" in name,
datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "M_" in name and "M_" + str(opts.signalMass) not in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
#newOrder = ["TT", "QCD"]
#newOrder = ["TT", "QCD"]
for m in signalMass:
#newOrder.insert(0, m)
datasetOrder.insert(0, m)
#datasetsMgr.selectAndReorder(newOrder)
datasetsMgr.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(False)
# Do the topSelection histos
folder = opts.folder
histoPaths1 = []
if folder != "":
histoList = datasetsMgr.getDataset(
datasetOrder[0]).getDirectoryContent(folder)
# hList0 = [x for x in histoList if "TrijetMass" in x]
# hList1 = [x for x in histoList if "TetrajetMass" in x]
# hList2 = [x for x in histoList if "TetrajetBJetPt" in x]
# histoPaths1 = [os.path.join(folder, h) for h in hList0+hList1+hList2]
histoPaths1 = [os.path.join(folder, h) for h in histoList]
folder = "TrijetCandidate"
histoList = datasetsMgr.getDataset(
datasetOrder[0]).getDirectoryContent(folder)
hList0 = [x for x in histoList if "TrijetMass" in x]
hList1 = [x for x in histoList if "TetrajetMass" in x]
hList2 = [x for x in histoList if "TetrajetBjetPt" in x]
histoPaths2 = [
os.path.join(folder, h) for h in hList0 + hList1 + hList2
]
histoPaths = histoPaths1 + histoPaths2
for h in histoPaths:
if "Vs" in h: # Skip TH2D
continue
PlotMC(datasetsMgr, h, intLumi)
HistoFit = [
"TrijetDPtOverGenPt",
]
for i in range(len(HistoFit)):
Fit(datasetsMgr.getAllDatasets(), folder + "/" + HistoFit[i],
"gaus")
return
def DataEwkQcd(datasetsMgr, histoName, analysisType):
'''
Create plots with "Data", "QCD=Data-EWK", and "EWK" on the same canvas
Mostly for sanity checks and visualisation purposes
'''
Verbose(
"Plotting histogram %s for Data, EWK, QCD for %s" %
(histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
# Define histogram names (full path)
h1 = "topSelection_Baseline/%s" % (histoName)
h2 = "topSelection_Inverted/%s" % (histoName)
# Create plot object for Data
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, "Data", h1, h2))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Create plot object for EWK
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, "EWK", h1, h2))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get Data and EWK histos
Data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histo: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Create the final plot object. The Data is treated as the reference histo.
# All other histograms are compared with respect to that.
p = plots.ComparisonManyPlot(Data, [QCD, EWK], saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply histo styles
p.histoMgr.forHisto(analysisType + "-Data", styles.getDataStyle())
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoLegendStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "HIST")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "LFP")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-Data": "Data",
analysisType + "-QCD": "QCD", #=Data-EWK",
analysisType + "-EWK": "EWK",
# analysisType + "-Data": "%s (Data)" % (analysisType),
# analysisType + "-QCD" : "%s (QCD)" % (analysisType),
# analysisType + "-EWK" : "%s (EWK)" % (analysisType),
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e0, "ymaxfactor": 10}
_format = "%0.0f"
if "mass" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c^{2}"
if "TrijetMass" in histoName:
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "DijetMass" in histoName:
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_rebinX = 2
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "bdisc" in histoName.lower():
_format = "%0.2f"
_rebinX = 1
if "chisqr" in histoName.lower():
_format = "%0.0f"
_rebinX = 10
if "after" in histoName.lower():
_rebinX = 1
_opts["xmax"] = 20.0
if "tetrajet" in histoName.lower():
if "mass" in histoName.lower():
_rebinX = 10
_opts["xmax"] = 3500.0
else:
pass
histoName += "_" + analysisType
plots.drawPlot(
p,
histoName,
ylabel="Events / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.75,
"y1": 0.76,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p,
histoName,
os.path.join(opts.saveDir, "DataEwkQcd"),
saveFormats=[".png"])
return
