def getDataSets(dirs, dataEra, searchMode, analysis, optMode):
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
return datasets
def 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):
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_HplusTB_HplusToTB_M_%s" % (opts.signalMass) in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
#else:
# datasetsMgr.remove(d.getName())
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 35920
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if str(opts.signalMass) not in d.getName():
#if d not in "%s" % (opts.signalMass):
continue
datasetOrder.append(d.getName())
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
for dName in ["TT", "QCD", "ChargedHiggs_HplusTB_HplusToTB_M_%s" % (opts.signalMass)]:
for var in ["M", "Pt", "BDT", "Eta"]:
# Do the histograms
PlotHistograms(datasetsMgr, dName, var)
return
def main():
style = tdrstyle.TDRStyle()
# Object for selecting data eras, search modes, and optimization modes
myModuleSelector = analysisModuleSelector.AnalysisModuleSelector()
parser = OptionParser(usage="Usage: %prog [options]",add_help_option=True,conflict_handler="resolve")
myModuleSelector.addParserOptions(parser)
parser.add_option("-m", "--mcrab", dest="mcrab", action="store", help="Path to the multicrab directory for input")
parser.add_option("-d", "--dataset", dest="dataset", action="store", help="Name of the dataset to be plotted")
parser.add_option("-e", "--error", dest="errorlevel", default=0.10, action="store", help="Maximum relative uncertainty per bin (default=10%%)")
(opts, args) = parser.parse_args()
if opts.mcrab == None:
raise Exception("Please provide input multicrab directory with -m")
if not os.path.exists(opts.mcrab):
raise Exception("The input root file '%s' does not exist!"%opts.mcrab)
if opts.dataset == None:
raise Exception("Please provide dataset name with -d")
# Find module names
#myNames = findModuleNames(opts.mcrab, "BTagEfficiency")
# Get dataset managers
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
myModuleSelector.setPrimarySource("analysis", dsetMgrCreator)
# Select modules
myModuleSelector.doSelect(opts)
myModuleSelector.printSelectedCombinationCount()
#for n in myNames:
results = []
for era in myModuleSelector.getSelectedEras():
for searchMode in myModuleSelector.getSelectedSearchModes():
for optimizationMode in myModuleSelector.getSelectedOptimizationModes():
dsetMgr = dsetMgrCreator.createDatasetManager(dataEra=era,searchMode=searchMode,optimizationMode=optimizationMode)
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs=[opts.mcrab], analysisName=n)
dsetMgr.loadLuminosities()
dsetMgr.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(dsetMgr)
lumi = dsetMgr.getDataset("Data").getLuminosity()
for dset in dsetMgr.getMCDatasets():
print dset.name
if dset.name.startswith(opts.dataset):
results.extend(doPlot("btageff_%s_%s_%s"%(era, searchMode, optimizationMode), dset, opts.errorlevel, optimizationMode, lumi))
#dsetMgr.close()
print "\nFigures generated"
for item in results:
print item
# Write results to a json file
filename = "btageff_%s.json"%opts.dataset
with open(filename, 'w') as outfile:
json.dump(results, outfile)
print "Written results to %s"%filename
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):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts, 0)
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)
if 1:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print datasets info summary
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["TetrajetMass"]
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)*opts.nDatasets
# For-loop: All datasets
for dataset in datasetsMgr.getAllDatasets():
# For-looop: All variables
for hName in VariableList:
hPath = os.path.join(opts.folder, hName)
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (dataset.getName()))
aux.Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotHistos(dataset.getName(), hPath) # For each dataset: Plot histos from different multicrabs on same canvas
aux.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")
optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue200p0"]
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()))
datasetsMgr.remove(filter(lambda name: "ST" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
# 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)
# Merge EWK samples (done later on)
# if opts.mergeEWK:
# datasetsMgr.merge("EWK", GetListOfEwkDatasets())
# Do default counters
doCounters(datasetsMgr)
return
def createDsetMgr(self, multicrabDir, era, searchMode, optimizationMode=None, systematicVariation=None):
self._era = era
self._searchMode = searchMode
self._optimizationMode = optimizationMode
self._systematicVariation = systematicVariation
# Construct info string of module
self._moduleInfoString = "%s_%s_%s"%(era, searchMode, optimizationMode)
# Obtain dataset manager
self._dsetMgrCreator = dataset.readFromMulticrabCfg(directory=multicrabDir)
self._dsetMgr = self._dsetMgrCreator.createDatasetManager(dataEra=era,searchMode=searchMode,optimizationMode=optimizationMode,systematicVariation=systematicVariation)
# Do the usual normalisation
self._dsetMgr.updateNAllEventsToPUWeighted()
self._dsetMgr.loadLuminosities()
plots.mergeRenameReorderForDataMC(self._dsetMgr)
self._dsetMgr.merge("EWK", _generalOptions["ewkDatasetsForMerging"])
# Obtain luminosity
self._luminosity = self._dsetMgr.getDataset("Data").getLuminosity()
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(True)
style.setGridY(True)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Do the plot
doPlot()
# Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def Plot(jsonfile, opts):
Verbose("Plotting")
with open(os.path.abspath(jsonfile)) as jfile:
j = json.load(jfile)
Print("Plotting %s in %s" % (j["title"], j["saveDir"]), True)
# Setup the style
style = tdrstyle.TDRStyle()
style.setGridX(j["gridX"]=="True")
style.setGridY(j["gridY"]=="True")
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts, j)
datasetsMgr.loadLuminosities()
datasetsMgr.updateNAllEventsToPUWeighted()
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)
# Print dataset information
datasetsMgr.PrintInfo()
# Get Integrated Luminosity
lumi = GetLumi(datasetsMgr)
# Plot the histogram
DataMCPlot(datasetsMgr, j)
return
def plot(resultdir,jsonfile):
with open(os.path.abspath(jsonfile)) as jfile:
j = json.load(jfile)
print "Plotting",j["title"],"in",resultdir
if "outputdir" in j:
global plotDir
plotDir = j["outputdir"]
multicrabPaths = PathFinder.MulticrabPathFinder(resultdir)
paths = []
if os.path.exists(multicrabPaths.getSignalPath()):
paths.append(multicrabPaths.getSignalPath())
if os.path.exists(multicrabPaths.getQCDInvertedPath()):
paths.append(multicrabPaths.getQCDInvertedPath())
if os.path.exists(multicrabPaths.getEWKPath()):
paths.append(multicrabPaths.getEWKPath())
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
plots.mergeRenameReorderForDataMC(datasets)
alldsets = datasets.getAllDatasets()
print "Merged datasets"
for d in alldsets:
print " ",d.getName()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
if len(j["samples"])>0:
for s in j["samples"]:
h = datasets.getDataset(s).getDatasetRootHisto(j["histogram"]).getHistogram()
name = j["histogram"]+s
plotgraph([h],lumi,j,name)
def main(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(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
FAKEHISTO = "OnlyEWKFakeTaus"
GENUINEHISTO = "PlusFilteredEWKFakeTaus"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="") #no collinear
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="OptQCDTailKillerLoosePlus") #collinear
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID(False)
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
invertedQCD_separatedFakes = InvertedTauID(True)
invertedQCD_separatedFakes.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD_separatedFakes.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
#binLabels = bins # for this data set
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
invertedQCD_separatedFakes.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
#metBase_EWK = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
#metInver_EWK = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_GenuineTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInver_GenuineTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_FakeTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metInver_FakeTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metBase_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
#metInver_EWK.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_GenuineTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK_GenuineTaus = metInver_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+GENUINEHISTO+"/METInverted"+HISTONAME+GENUINEHISTO+bin)
metBase_EWK_GenuineTaus = metBase_GenuineTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+GENUINEHISTO+"/METBaseline"+HISTONAME+GENUINEHISTO+bin)
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metBase_QCD_separatedFakes = metBase_data.Clone("QCD")
metBase_QCD_separatedFakes.Add(metBase_EWK_GenuineTaus,-1)
metBase_QCD_separatedFakes.Add(metBase_EWK_FakeTaus,-1)
metInverted_QCD_separatedFakes = metInverted_data.Clone("QCD")
metInverted_QCD_separatedFakes.Add(metInverted_EWK_GenuineTaus,-1)
metInverted_QCD_separatedFakes.Add(metInverted_EWK_FakeTaus,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metInverted_EWK_GenuineTaus = addlabels(metInverted_EWK_GenuineTaus)
metInverted_EWK_FakeTaus = addlabels(metInverted_EWK_FakeTaus)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metBase_EWK_GenuineTaus = addlabels(metBase_EWK_GenuineTaus)
metBase_EWK_FakeTaus = addlabels(metBase_EWK_FakeTaus)
metInverted_QCD = addlabels(metInverted_QCD)
metInverted_QCD_separatedFakes = addlabels(metInverted_QCD_separatedFakes)
#non-separated
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
fitOptions = "LRB"
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
#separated
invertedQCD_separatedFakes.plotHisto(metInverted_data,"inverted")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_GenuineTaus,"invertedEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metInverted_EWK_FakeTaus,"invertedEWKFakeTaus")
invertedQCD_separatedFakes.plotHisto(metBase_data,"baseline")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_GenuineTaus,"baselineEWKGenuineTaus")
invertedQCD_separatedFakes.plotHisto(metBase_EWK_FakeTaus,"baselineEWKFakeTaus")
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metInverted_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_GenuineTaus(metBase_EWK_GenuineTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD_separatedFakes.fitQCD(metInverted_QCD_separatedFakes,fitOptions)
invertedQCD_separatedFakes.fitData(metBase_data)
invertedQCD_separatedFakes.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
invertedQCD_separatedFakes.Summary()
invertedQCD_separatedFakes.WriteNormalizationToFile("QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
invertedQCD_separatedFakes.WriteLatexOutput("fits_separatedfaketaus.tex")
mergeNormalizations("QCDInvertedNormalizationFactors.py","QCDInvertedNormalizationFactorsSeparatedFakeTaus.py")
def main(opts):
# 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()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setOptStat(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# Define genuineB and fakeB datasets
mergeMap = {"TT": "GenuineB",
"QCD_HT50to100" : "FakeB", #v. poor stats!
"QCD_HT200to300" : "FakeB", #v. poor stats!
"QCD_HT100to200" : "FakeB", #v. poor stats!
"QCD_HT200to300" : "FakeB", #v. poor stats!
"QCD_HT300to500" : "FakeB",
"QCD_HT500to700" : "FakeB",
"QCD_HT700to1000" : "FakeB",
"QCD_HT1000to1500": "FakeB",
"QCD_HT1500to2000": "FakeB",
"QCD_HT2000toInf" : "FakeB",
"WJetsToQQ_HT_600ToInf": "FakeB"
}
# Definitions
results = []
# For-loop: All optimisation modes
for index, opt in enumerate(optModes, 1):
opts.optMode = opt
# Definitions
genuineB = None
fakeB = None
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print datasets info ?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Remove unwanted datasets
removeList = ["QCD_HT50to100", "QCD_HT100to200", "QCD_HT200to300", "QCD_HT200to300_ext1", "WJetsToQQ_HT_600ToInf"]
for k, d in enumerate(datasetsMgr.getAllDatasets(), 1):
if d.getName() in removeList:
datasetsMgr.remove(d.getName())
Verbose(ShellStyles.ErrorStyle() + "Removing dataset %s" % d.getName() + ShellStyles.NormalStyle(), k==1)
if index == 1:
datasetsMgr.PrintInfo()
# Merge datasets into two groups: Genuine-B and Fake-B
datasetsMgr.mergeMany(mergeMap, addition=False)
for d in datasetsMgr.getAllDatasets():
if d.getName() == "GenuineB":
genuineB = d
if d.getName() == "FakeB":
fakeB = d
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get integrated luminosity
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
# Print dataset information
if index == 1:
datasetsMgr.PrintInfo()
# Do the plot
name = GetPlotName(opts)
myResults = doPlot(name, genuineB, fakeB, opts.errorlevel, opts.optMode, intLumi)
# Save results and msgs
results.extend(myResults)
# For-loop: All points
if opts.verbose:
for item in results:
print item
# Print path of all saved plots
msg = "All plots saved under directory %s" % (aux.convertToURL(opts.saveDir, opts.url))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), True)
# Write results to a json file
with open(opts.json, 'w') as outfile:
json.dump(results, outfile)
msg = "Wrote results to \"%s\"" % (opts.json)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), True)
return
def main():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# d.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY"):
opts = {"ymin": 1e-5, "ymaxfactor": 10}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# 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("")
optModes = optList
else:
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 opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Get the PSets:
if 0:
datasetsMgr.printSelections()
#PrintPSet("BJetSelection", datasetsMgr, depth=150)
# 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 a value is not specified
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = ["QCD-b", "Charged"]
if not opts.useMC:
removeList.append("QCD")
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Verbose(
ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(),
i == 0)
datasetsMgr.remove(
filter(lambda name: d in name,
datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
# Print dataset information
datasetsMgr.PrintInfo()
# Do the fit on the histo after ALL selections (incl. topology cuts)
folderList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
#folderList1 = [h for h in folderList if "TetrajetMass" in h]
#folderList1 = [h for h in folderList if "TetrajetBJetPt" in h]
#folderList1 = [h for h in folderList if "TetrajetBJetEta" in h]
folderList1 = [h for h in folderList if opts.histoKey in h]
folderList2 = [
h for h in folderList1 if "VR" in h or "SR" in h or "CRone" in h
or "CRtwo" in h or "CRthree" in h or "CRfour" in h
]
# For-loop: All folders
histoPaths = []
for f in folderList2:
folderPath = os.path.join(opts.folder, f)
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
folderPath)
pathList = [os.path.join(folderPath, h) for h in histoList]
histoPaths.extend(pathList)
binLabels = GetBinLabels("CRone", histoPaths)
PlotHistosAndCalculateTF(datasetsMgr, histoPaths, binLabels, opts)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
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)
if d.getName() not in opts.signalMass:
datasetsMgr.remove(d.getName())
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Print dataset information
datasetsMgr.PrintInfo()
# Do the topSelection histos
folder = opts.folder
histoList = datasetsMgr.getDataset(
opts.signalMass[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
myHistos = [
"Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark_dEta",
"Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark_dPhi",
"Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark_dR",
"Htb_tbW_BQuark_Htb_tbW_Wqq_Quark_dEta",
"Htb_tbW_BQuark_Htb_tbW_Wqq_Quark_dPhi",
"Htb_tbW_BQuark_Htb_tbW_Wqq_Quark_dR",
"Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark_dEta",
"Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark_dPhi",
"Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark_dR",
"Htb_tbW_WBoson_Htb_tbW_BQuark_dEta",
"Htb_tbW_WBoson_Htb_tbW_BQuark_dPhi",
"Htb_tbW_WBoson_Htb_tbW_BQuark_dR"
]
# For-loop: All histos in folder
counter = 0
for i, histo in enumerate(histoPaths, 1):
h = histo.split("/")[-1]
if h not in myHistos:
continue
counter += 1
# Print("Plotting histogram %s" % (histo), counter==1)
PlotMC(datasetsMgr, histo)
return
def main(hName, opts):
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
datasetsMgr.updateNAllEventsToPUWeighted()
# 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 datasets: All JetHT to "Data", QCD_Pt to "QCD", QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
# datasetsMgr.remove("QCD-b")
# Print dataset information
# datasetsMgr.PrintInfo()
# Create plot, with the default
s = {"normalizeToOne": True}
p = plots.MCPlot(datasetsMgr, hName, **s)
p.histoMgr.setHistoLegendStyleAll("LP")
p.histoMgr.setHistoDrawStyleAll("EP")
p.histoMgr.setHistoLegendStyle("ChargedHiggs_HplusTB_HplusToTB_M_500", "F")
p.histoMgr.setHistoDrawStyle ("ChargedHiggs_HplusTB_HplusToTB_M_500", "HIST")
# Create a comparison plot
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY")==True:
canvOpts = {"xmin": 0.0, "ymin": 1e-5, "ymaxfactor": 10}
else:
canvOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
# Draw a customised plot & Save it
plots.drawPlot(p,
os.path.join(kwargs.get("savePath"), hName.replace("/", "_").replace(" ", "_").replace("(", "_").replace(")", "") ),
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
rebinX=kwargs.get("rebinX"),
rebinY=kwargs.get("rebinY"),
xlabelsize=kwargs.get("xlabelsize"),
ratio=kwargs.get("ratio"),
stackMCHistograms=kwargs.get("stackMCHistograms"),
ratioYlabel=kwargs.get("ratioYlabel"),
ratioInvert=kwargs.get("ratioInvert"),
addMCUncertainty=kwargs.get("addMCUncertainty"),
addLuminosityText=kwargs.get("addLuminosityText"),
addCmsText=kwargs.get("addCmsText"),
opts=canvOpts, opts2=ratioOpts,
log=kwargs.get("logY"),
errorBarsX=kwargs.get("errorBarsX"),
cmsExtraText=kwargs.get("cmsExtraText"),
moveLegend=kwargs.get("moveLegend"),
drawStyle="P",
legendStyle="LP",
#cutLine=kwargs.get("cutValue"),
cutBox={"cutValue": kwargs.get("cutValue"), "fillColor": kwargs.get("cutFillColour"), "box": kwargs.get("cutBox"), "line": kwargs.get("cutLine"), "lessThan": kwargs.get("cutLessthan")},
)
# Remove legend?
if kwargs.get("removeLegend"):
p.removeLegend()
# Additional text
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
if not opts.batchMode:
raw_input("=== plotEventVars.py:\n\tPress any key to quit ROOT ...")
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Quick and dirty way to get total int lumi
allDatasetsMgr = GetAllDatasetsFromDir(opts)
allDatasetsMgr.updateNAllEventsToPUWeighted()
allDatasetsMgr.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(allDatasetsMgr)
opts.intLumi = GetLumi(allDatasetsMgr)
# 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 d != "TT":
# 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():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# d.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(
datasetsMgr
) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY"):
opts = {"ymin": 1e-5, "ymaxfactor": 10}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle(getTitleY(refHisto, **kwargs))
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = {"lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"),
fillColor=kwargs.get("cutFillColour"),
box=kwargs.get("cutBox"),
line=kwargs.get("cutLine"),
**_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(
filter(
lambda n: plots._legendLabels[kwargs.get("refDataset")] not in
n, histoNames))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
optModes = [""]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Numerator & Denominator dataset manager
noSF_datasetsMgr = GetDatasetsFromDir(opts, opts.noSFcrab)
withCR1SF_datasetsMgr = GetDatasetsFromDir(opts, opts.withCR1SFcrab)
withCR2SF_datasetsMgr = GetDatasetsFromDir(opts, opts.withCR2SFcrab)
# Update all events to PU weighting
noSF_datasetsMgr.updateNAllEventsToPUWeighted()
withCR1SF_datasetsMgr.updateNAllEventsToPUWeighted()
withCR2SF_datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
noSF_datasetsMgr.loadLuminosities()
withCR1SF_datasetsMgr.loadLuminosities()
withCR2SF_datasetsMgr.loadLuminosities()
if 0:
noSF_datasetsMgr.PrintCrossSections()
noSF_datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(noSF_datasetsMgr)
plots.mergeRenameReorderForDataMC(withCR1SF_datasetsMgr)
plots.mergeRenameReorderForDataMC(withCR2SF_datasetsMgr)
# Get luminosity if a value is not specified
if opts.intLumi < 0:
opts.intLumi = noSF_datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = []
#removeList = ["TTWJetsToLNu_", "TTWJetsToQQ"]
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
noSF_datasetsMgr.remove(filter(lambda name: d in name, noSF_datasetsMgr.getAllDatasetNames()))
withCR1SF_datasetsMgr.remove(filter(lambda name: d in name, withCR1SF_datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
noSF_datasetsMgr.PrintInfo()
withCR1SF_datasetsMgr.PrintInfo()
withCR2SF_datasetsMgr.PrintInfo()
# Merge EWK samples
EwkDatasets = ["Diboson", "DYJetsToLL", "WJetsHT"]
noSF_datasetsMgr.merge("EWK", EwkDatasets)
withCR1SF_datasetsMgr.merge("EWK", EwkDatasets)
withCR2SF_datasetsMgr.merge("EWK", EwkDatasets)
# Get histosgram names
folderListIncl = withCR1SF_datasetsMgr.getDataset(withCR1SF_datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
folderList = [h for h in folderListIncl if "AfterAllSelections_LeadingTrijet_Pt" in h ]
# For-loop: All histo paths
for h in folderList:
if "lowMET" in h:
folderList.remove(h)
folderPath = os.path.join(opts.folder, "")
folderPathGen = os.path.join(opts.folder + "Genuine")
folderPathFake =os.path.join(opts.folder + "Fake" )
histoList = folderList
num_pathList = [os.path.join(folderPath, h) for h in histoList]
num_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
num_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
# Denominator Histogram (To be used in the estimation of QCD Data-Driven)
histoList = [h for h in folderListIncl if "AfterStandardSelections_LeadingTrijet_Pt" in h]
den_pathList = [os.path.join(folderPath, h) for h in histoList]
den_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
den_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
# For-loop: All histo paths
for h in den_pathList:
if "lowMET" in h:
den_pathList.remove(h)
# Do the histograms
PlotHistos(noSF_datasetsMgr, withCR1SF_datasetsMgr, withCR2SF_datasetsMgr, num_pathList, den_pathList, opts)
return
def main(count, runRange, dsetList, opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# 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("")
optModes = optList
else:
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 opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Remove datasets
removeList = ["QCD-b", "Charged", "QCD", "ZJetsToQQ_HT600toInf"]
opts.intLumi = 0.0
# For-loop: All datasets in the manager
for d in datasetsMgr.getAllDatasets():
if d.isMC():
continue
# Inclusive data
if len(dsetList) == 1 and dsetList[0] == "Run2016":
Verbose("Inclusive data. Will not remove anything", True)
opts.intLumi += GetLumi(datasetsMgr)
break
# Special combinations
for rr in dsetList:
if rr not in d.getName():
Verbose(
"\"%s\" is not in dataset name \"%s\"" %
(rr, d.getName()), False)
if d.getName() not in removeList:
# Ensure dataset to be removed is not in the dsetList
if not any(rr in d.getName() for rr in dsetList):
removeList.append(d.getName())
else:
Verbose(
"\"%s\" is in dataset name \"%s\"" % (rr, d.getName()),
False)
# Get luminosity if a value is not specified
opts.intLumi += d.getLuminosity()
# For-loop: All dataset names to be removed
for i, d in enumerate(removeList, 0):
Verbose(
ShellStyles.HighlightAltStyle() + "Removing dataset %s" % d +
ShellStyles.NormalStyle(), False)
datasetsMgr.remove(
filter(lambda name: d in name,
datasetsMgr.getAllDatasetNames()))
# Inform user of dataset and corresponding integrated lumi
Print("%d) %s (%.1f 1/pb)" % (count, runRange, opts.intLumi),
count == 1)
#Print("%d) %s (%.1f 1/pb)" % (count, ", ".join(dsetList), opts.intLumi), count==1)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
# datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
# Do the fit on the histo after ALL selections (incl. topology cuts)
folderList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
folderList1 = [h for h in folderList if opts.histoKey in h]
folderList2 = [
h for h in folderList1 if "VR" in h or "SR" in h or "CRone" in h
or "CRtwo" in h or "CRthree" in h or "CRfour" in h
]
# For-loop: All folders
histoPaths = []
for f in folderList2:
folderPath = os.path.join(opts.folder, f)
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
folderPath)
pathList = [os.path.join(folderPath, h) for h in histoList]
histoPaths.extend(pathList)
binLabels = GetBinLabels("CRone", histoPaths)
# Calculate TF and plot histo (Data Vs Fake b + Genuine b)
stackListForRunRange = PlotHistosAndCalculateTF(
runRange, datasetsMgr, histoPaths, binLabels, opts)
return stackListForRunRange
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):
# 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 main(opts):
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Get all datasets from the mcrab dir
# def GetDatasetsFromDir(mcrab, opts, **kwargs): #iro
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs) #kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
#datasetsMgr.getDataset("QCD_bEnriched_HT1000to1500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT1500to2000").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT2000toInf").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT300to500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT500to700").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT700to1000").setCrossSection(1.0)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# Print the cross
datasetsMgr.PrintCrossSections()
for h_prefix in ["reco"]:
# Get ref histo here and the fixed histos (TT, QCD, QCD-b
inclusiveHisto, fixedHistos = GetHistosForPlotter(datasetsMgr, h_prefix + "MET_Et", **kwargs)
inclusiveHisto.setName("Inclusive")
inclusiveHisto.setLegendLabel("Inclusive")
#for hi in fixedHistos:
# print(type(hi), hi.getName())
#return
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(h_prefix + hName[0], **kwargs)
saveName = savePath + h_prefix + hSaveNames[counter]
# Get Histos for Plotter
refHisto_, otherHistos_ = GetHistosForPlotter(datasetsMgr, h_prefix + hName[0], **kwargs)
refHisto1, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[1], **kwargs)
refHisto2, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[2], **kwargs)
refHisto3, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[3], **kwargs)
refHisto4, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[4], **kwargs)
refHisto5, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[5], **kwargs)
refHisto = inclusiveHisto
# customize histos
refHisto_.setName(hLegends[counter][0])
refHisto_.setLegendLabel(hLegends[counter][0])
refHisto_.setDrawStyle("P")
refHisto_.setLegendStyle("P")
styleDict[hStyles[0]].apply(refHisto_.getRootHisto())
refHisto1.setName(hLegends[counter][1])
refHisto1.setLegendLabel(hLegends[counter][1])
refHisto1.setDrawStyle("P")
refHisto1.setLegendStyle("P")
styleDict[hStyles[1]].apply(refHisto1.getRootHisto())
refHisto2.setName(hLegends[counter][2])
refHisto2.setLegendLabel(hLegends[counter][2])
refHisto2.setDrawStyle("P")
refHisto2.setLegendStyle("P")
styleDict[hStyles[2]].apply(refHisto2.getRootHisto())
refHisto3.setName(hLegends[counter][3])
refHisto3.setLegendLabel(hLegends[counter][3])
refHisto3.setDrawStyle("P")
refHisto3.setLegendStyle("P")
styleDict[hStyles[3]].apply(refHisto3.getRootHisto())
refHisto4.setName(hLegends[counter][4])
refHisto4.setLegendLabel(hLegends[counter][4])
refHisto4.setDrawStyle("P")
refHisto4.setLegendStyle("P")
styleDict[hStyles[4]].apply(refHisto4.getRootHisto())
refHisto5.setName(hLegends[counter][5])
refHisto5.setLegendLabel(hLegends[counter][5])
refHisto5.setDrawStyle("P")
refHisto5.setLegendStyle("P")
styleDict[hStyles[5]].apply(refHisto5.getRootHisto())
otherHistos = [refHisto_, refHisto1, refHisto2, refHisto3, refHisto4, refHisto5]
#for hi in otherHistos:
# print hi.getName()
#return
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY")==True:
opts = {"ymin": 8e-5, "ymax": 2}
#opts = {"ymin": 1e-3, "ymax": 1}
else:
opts = {"ymin": 8.e-5, "ymax": 2}
ratioOpts = {"ymin": 0.1, "ymax": 10.0}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.2, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.7)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(2))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
histograms.addText(0.2, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
#datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_111123_132128/multicrab.cfg", counters=counters)
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(
cfgfile=
"/afs/cern.ch/work/e/epekkari/DataDrivenFakeTaus/CMSSW_5_3_9_patch3/src/HiggsAnalysis/NtupleAnalysis/test/multicrab_140526_122821/multicrab.cfg"
)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
#for d in datasets.getAllDatasets():
# print d.getName()
#print "-------"
#plots.mergeRenameReorderForDataMC(datasets)
# print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
datasets.remove(
filter(lambda name: "TTToHplus" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK",
["WJets", "DYJetsToLL", "SingleTop", "Diboson", "TTJets"],
keepSources=True)
datasets.remove(
filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_s-channel" in name,
datasets.getAllDatasetNames()))
# Remove QCD
#datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main(opts):
style = tdrstyle.TDRStyle()
#style.setWide(True)
style.setPaletteMy()
ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# ========================================
# Datasets
# ========================================
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
intLumi = GetLumi(datasetsMgr)
datasetsMgr.updateNAllEventsToPUWeighted()
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 datasts (Note: Merged MC histograms must be normalized to something)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
if 0:
datasetsMgr.remove("TTJets")
datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# Print dataset information
datasetsMgr.PrintInfo()
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(2))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(0.0015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(0.0045))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle( getTitleX(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.17, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.17, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter==0)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50p0", "OptChiSqrCutValue100p0", "OptChiSqrCutValue200p0"]
optModes = [
"", "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)
# Only do these histos
myHistos = [
"FailedBJet1BDisc",
"FailedBJet1Pt",
"FailedBJet1Eta",
#"FailedBJet1PdgId",
#"FailedBJet1PartonFlavour",
#"FailedBJet1HadronFlavour",
#"FailedBJet1Ancestry"
]
# For-loop: All histos
folders = ["", "FakeB", "GenuineB"]
for f in folders:
folder = "FailedBJet" + f
hList = datasetsMgr.getDataset("EWK").getDirectoryContent(folder)
for hName in hList:
if hName.split("_")[-2] not in myHistos:
continue
PlotHisto(datasetsMgr, os.path.join(folder, hName))
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Quick and dirty way to get total int lumi
opts2 = copy.deepcopy(opts)
opts2.includeOnlyTasks = ""
allDatasetsMgr = GetDatasetsFromDir(opts2)
allDatasetsMgr.updateNAllEventsToPUWeighted()
allDatasetsMgr.loadLuminosities() # from lumi.json
opts.intLumi = GetLumi(allDatasetsMgr)/2
# 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"]
allHistos = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoList = [h for h in allHistos if "_Vs_" in h]
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 histograms in list
for histoName in histoPaths:
Plot2d(datasetsMgr, histoName, histoKwargs[histoName], opts)
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
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs_HplusTB_HplusToTB_M_%s" % (
opts.signalMass) in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
#else:
# datasetsMgr.remove(d.getName())
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 35920
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if str(opts.signalMass) not in d.getName():
#if d not in "%s" % (opts.signalMass):
continue
datasetOrder.append(d.getName())
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
for dName in [
"TT", "QCD",
"ChargedHiggs_HplusTB_HplusToTB_M_%s" % (opts.signalMass)
]:
for var in ["M", "Pt", "BDT", "Eta"]:
# Do the histograms
PlotHistograms(datasetsMgr, dName, var)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# 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()
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Print dataset information before removing anything?
if 1:
datasetsMgr.PrintInfo()
# Remove datasets
filterKeys = ["TTW"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
datasetsMgr.selectAndReorder(datasetOrder)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Merge EWK datasets
EWKlist = ["WJetsHT", "DYJetsToLL", "Diboson"]
datasetsMgr.merge("EWK", EWKlist)
# Print dataset information after merging
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
intLumi = datasetsMgr.getDataset('Data').getLuminosity()
# Define Numerator & Denominator Histograms
numerator_name = "AfterAllSelections_LeadingTrijet_Pt"
denominator_name = "AfterStandardSelections_LeadingTrijet_Pt"
# Do the fit on the histo after ALL selections (incl. topology cuts)
folderListIncl = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
folderList = [h for h in folderListIncl if "AfterAllSelections_LeadingTrijet_Pt" in h ]
folderPath = os.path.join(opts.folder, "")
folderPathGen = os.path.join(opts.folder+"Genuine", "")
folderPathFake =os.path.join(opts.folder+"Fake", "")
histoList = folderList
num_pathList = [os.path.join(folderPath, h) for h in histoList]
num_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
num_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
for h in num_pathList:
if "lowMET" in h:
num_pathList.remove(h)
histoList = [h for h in folderListIncl if "AfterStandardSelections_LeadingTrijet_Pt" in h]
den_pathList = [os.path.join(folderPath, h) for h in histoList]
den_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
den_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
for h in den_pathList:
if "lowMET" in h:
den_pathList.remove(h)
# Calculate Scale Factors
GetScaleFactors(datasetsMgr, num_pathList, den_pathList, intLumi)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# 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 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)
# Print dataset information before removing anything?
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
filterKeys = ["TTW"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
else:
intLumi = 35920
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = [
"LdgTop_Pt",
]
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasets_ = datasetsMgr.getAllDatasets()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Print dataset information
datasetsMgr.PrintInfo()
# For-loop: All numerator-denominator pairs
counter = 0
nPlots = len(VariableList)
for var in VariableList:
histoN = "AfterAllSelections_"+var
histoD = "AfterStandardSelections_"+var
numerator = os.path.join(opts.folder, histoN)
denominator = os.path.join(opts.folder, histoD)
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
for dataset in datasets_:
if dataset.isMC():
n = dataset.getDatasetRootHisto(numerator)
n.normalizeToLuminosity(intLumi)
num = n.getHistogram()
d = dataset.getDatasetRootHisto(denominator)
d.normalizeToLuminosity(intLumi)
den = d.getHistogram()
else:
num = dataset.getDatasetRootHisto(numerator).getHistogram()
den = dataset.getDatasetRootHisto(denominator).getHistogram()
x_bins = num.GetXaxis().GetNbins()
i = 1
while i < x_bins:
xvalue = num.GetXaxis().GetBinLowEdge(i)+0.5*num.GetXaxis().GetBinWidth(i)
if xvalue < 20:
my_bin = i
i+=1
my_xvalue = num.GetXaxis().GetBinUpEdge(my_bin)+0.5*num.GetXaxis().GetBinWidth(my_bin)
total = den.Integral(0, x_bins) #my_bin
selected = num.Integral(0, x_bins) #my_bin
plotName = "Eff_%s" % (var)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(var, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, numerator, denominator, intLumi)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, numerator, denominator, intLumi)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.qcdStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC" , "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany(
{
"eff_Data": "Data",
"eff_MC" : "QCD"
}
)
# Append in list
myList = []
myList.append(histograms.HistoGraph(eff_Data, plots._legendLabels["Data"], "lp", "P"))
p.setLuminosity(intLumi)
_kwargs["ratio"] = True
_kwargs["opts"] = {"xmin": 0.0, "xmax": 600.0, "ymin": 0.0, "ymax": 0.16, "ymaxfactor": 1.8}
_kwargs["cutBoxY"] = {"cutValue": 1.10, "fillColor": ROOT.kGray+1, "fillStyle": 3001, "box": False, "line": True, "greaterThan": True, "mainCanvas": False,
"ratioCanvas": True, "mirror": True}
plots.drawPlot(p, plotName, **_kwargs)
# Draw
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, plotName, savePath, saveFormats = [".png", ".pdf", ".C"])
# Save results in JSON
name = opts.mcrab.split("_")[-3]
name = name.replace(opts.analysisName, "")
jsonName = "topMisID_"+ name +"_TopMassCut400.json"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi, eff_Data)
pythonWriter.addMCParameters(label, eff_MC)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def main(opts):
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# ========================================
# Datasets
# ========================================
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
intLumi = GetLumi(datasetsMgr)
datasetsMgr.updateNAllEventsToPUWeighted()
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 datasets:All JetHT to "Data", QCD_Pt to "QCD",
# QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
if 0:
datasetsMgr.remove("TTJets")
datasetsMgr.remove(
filter(lambda name: not "QCD" in name,
datasetsMgr.getAllDatasetNames()))
# Print dataset information
datasetsMgr.PrintInfo()
# ========================================
# Histograms
# ========================================
for counter, hName in enumerate(hNames):
# Get the save path and name, Get Histos for Plotter
savePath, saveName = GetSavePathAndName(hName, **kwargs)
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a comparison plot
#p = plots.ComparisonManyPlot(refHisto, otherHistos)
p = plots.DataMCPlot(datasetsMgr, hName)
# Remove negative contributions
if 0:
RemoveNegativeBins(datasetsMgr, hName, p)
# Y-axis
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY") == True:
opts = {"ymin": 1e-2, "ymaxfactor": 100}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
# ========================================
# Frame
# ========================================
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Legend
moveLegend = {"dx": -0.11, "dy": +0.0, "dh": +0.2}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(
filter(
lambda n: plots._legendLabels[kwargs.get("refDataset")] not in
n, histoNames))
if 0:
p.removeLegend()
# Axes
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(p.histoMgr.getHistos()[0], **kwargs) )
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Set Log and Grid
SetLogAndGrid(p, **kwargs)
# Cut line / Cut box
_kwargs = {"lessThan": kwargs.get("cutLessthan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"),
fillColor=kwargs.get("cutFillColour"),
box=kwargs.get("cutBox"),
line=kwargs.get("cutLine"),
**_kwargs)
# Draw the final plot
p.draw()
# ========================================
# Add Text
# ========================================
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save the canvas to a file
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter == 0)
return
def main(opts):
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# ========================================
# Datasets
# ========================================
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
intLumi = GetLumi(datasetsMgr)
datasetsMgr.updateNAllEventsToPUWeighted()
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 datasets:All JetHT to "Data", QCD_Pt to "QCD",
# QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
if 0:
datasetsMgr.remove("TTJets")
datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# Print dataset information
datasetsMgr.PrintInfo()
# ========================================
# Histograms
# ========================================
for counter, hName in enumerate(hNames):
# Get the save path and name, Get Histos for Plotter
savePath, saveName = GetSavePathAndName(hName, **kwargs)
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a comparison plot
#p = plots.ComparisonManyPlot(refHisto, otherHistos)
p = plots.DataMCPlot(datasetsMgr, hName)
# Remove negative contributions
if 0:
RemoveNegativeBins(datasetsMgr, hName, p)
# Y-axis
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY")==True:
opts = {"ymin": 1e-2, "ymaxfactor": 100}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
# ========================================
# Frame
# ========================================
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Legend
moveLegend = {"dx": -0.11, "dy": +0.0, "dh": +0.2}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
if 0:
p.removeLegend()
# Axes
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(p.histoMgr.getHistos()[0], **kwargs) )
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Set Log and Grid
SetLogAndGrid(p, **kwargs)
# Cut line / Cut box
_kwargs = {"lessThan": kwargs.get("cutLessthan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Draw the final plot
p.draw()
# ========================================
# Add Text
# ========================================
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save the canvas to a file
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter==0)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
# 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
datasetsMgr40 = GetDatasetsFromDir_second(
opts, opts.mcrab.replace("_BDT85", "_BDT40"))
datasetsMgr40.updateNAllEventsToPUWeighted()
datasetsMgr40.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr40]:
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)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(
filter(lambda name: key in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr40.remove(
filter(lambda name: key in name,
datasetsMgr40.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
datasetOrder.append(d.getName())
# Select and re-order
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr40.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# QCD multijet
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr40.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
PlotEfficiency(datasetsMgr, datasetsMgr40, intLumi)
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() # from lumi.json
# Set/Overwrite cross-sections
datasetsToRemove = ["QCD-b", "TTTT"]#, "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
# 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)
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()))
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()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", GetListOfEwkDatasets(datasetsMgr))
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)
# Get histogram list
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)
# Calculate Signal Significance for all histograms
for h in histoList:
PlotCutFlowEfficiency(h, datasetsMgr, intLumi)
return
def main(opts, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# 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 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)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "QCD", "TTZToQQ", "TTWJets", "TTTT", "ZJetsToQQ_HT600toInf", "DYJetsToQQHT", "SingleTop", "WJetsToQQ_HT_600ToInf", "Diboson"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
# Append signal datasets
for m in signalMass:
datasetOrder.insert(0, m)
datasetsMgr.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
HistoMap = {
"AllTopQuarkPt_MatchedBDT" : "AllTopQuarkPt_Matched",
"TrijetFakePt_BDT" : "TrijetFakePt",
"AllTopQuarkPt_Matched" : "TopQuarkPt",
"EventTrijetPt2T_MatchedBDT": "EventTrijetPt2T_BDT",
"EventTrijetPt2T_MatchedBDT": "EventTrijetPt2T_Matched",
"EventTrijetPt2T_MatchedBDT": "EventTrijetPt2T",
"AllTopQuarkPt_MatchedBDT" : "TopQuarkPt",
#"SelectedTrijetsPt_BjetPassCSVdisc_afterCuts": "SelectedTrijetsPt_afterCuts",
#"TrijetPt_PassBDT_BJetPassCSV": "TrijetPt_PassBDT",
}
# For-loop: All numerator-denominator pairs
for key in HistoMap:
numerator = os.path.join(opts.folder, key)
denominator = os.path.join(opts.folder, HistoMap[key])
PlotEfficiency(datasetsMgr, numerator, denominator, intLumi)
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():
if len(sys.argv) < 2 and not os.path.isdir(sys.argv[1]):
usage()
multicrabdir = sys.argv[1]
signalAnalysisDir = ""
QCDAnalysisDir = os.path.join(multicrabdir,"pseudoMulticrab_QCDMeasurement")
candDirs = execute("ls %s"%multicrabdir)
for d in candDirs:
if "SignalAnalysis_" in d:
signalAnalysisDir = os.path.join(multicrabdir,d)
print "Using",signalAnalysisDir
print " ",QCDAnalysisDir
style = tdrstyle.TDRStyle()
ROOT.gROOT.SetBatch(True)
datasets = dataset.getDatasetsFromMulticrabDirs([signalAnalysisDir], analysisName=analysis)
datasetsQCD = dataset.getDatasetsFromMulticrabDirs([QCDAnalysisDir], analysisName="signalAnalysis")
datasets.extend(datasetsQCD)
plots.mergeRenameReorderForDataMC(datasets)
luminosity = datasets.getDataset("Data").getLuminosity()
# Semi-ugly hack for approval homework, remember improve for the next round
totalErrorFile = ROOT.TFile("outputLight.root")
#totalErrorFile = ROOT.TFile("outputHeavy.root")
## Remove superfluous shape variation uncertainties
totalErrorHistoUp = totalErrorFile.Get("total_background")
rebin = [0,20,40,60,80,100,120,140,160,180,200,220,240,260,280,300,320,340,360,380,400,420,440,460,480,500,600,700,800]
import array
#totalErrorHistoUp.Sumw2()
totalErrorHistoUp = totalErrorHistoUp.Rebin(len(rebin)-1,"foo",array.array("d",rebin))
totalErrorHistoDown = totalErrorHistoUp.Clone("foo2")
for i in xrange(1, totalErrorHistoUp.GetNbinsX()+1):
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoUp.GetBinError(i)
totalErrorHistoUp.SetBinContent(i, totalErrorHistoUp.GetBinError(i))
totalErrorHistoDown.SetBinContent(i, -totalErrorHistoDown.GetBinError(i))
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoDown.GetBinContent(i)
# semi-ugly hack continues below in QCD
myStackList = []
h_data = datasets.getDataset("Data").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_data)
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "Data")
myHisto.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, myHisto)
h_FakeTau = datasets.getDataset("QCDMeasurementMT").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_FakeTau)
# semi-ugly hack continues
htemp=myRHWU.getRootHisto()
myRHWU.addAbsoluteShapeUncertainty("toterr", totalErrorHistoUp, totalErrorHistoDown)
#myRHWU.addShapeUncertaintyFromVariation("toterr", totalErrorHistoUp, totalErrorHistoDown)
# semi-ugly hack ends
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "QCDdata")
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, myHisto)
expectedList = []
expectedList.append("TT")
expectedList.append("WJets")
expectedList.append("SingleTop")
expectedList.append("DYJetsToLL")
expectedList.append("Diboson")
for i in range(0,len(expectedList)):
drh = datasets.getDataset(expectedList[i]).getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT")
h_bgr = drh.getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_bgr)
# myRHWU.addShapeUncertaintyRelative("syst", th1Plus=self._expectedListSystUp[i], th1Minus=self._expectedListSystDown[i])
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, expectedList[i])
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
#myStackList = divideByBinWidth(myStackList)
# no, ugly hack continues here
histograms.uncertaintyMode.set(histograms.Uncertainty.SystOnly)
plots._legendLabels["MCSystError"] = "Bkg. stat.#oplus syst. unc."
plots._legendLabels["BackgroundSystError"] = "Bkg. stat.#oplus syst. unc."
# and stops again here
myStackPlot = plots.DataMCPlot2(myStackList)
myStackPlot.setLuminosity(luminosity)
myStackPlot.setDefaultStyles()
myParams = {}
myParams["ylabel"] = "Events / bin"
myParams["ratioYlabel"] = "Data/Bkg."
myParams["xlabel"] = "m_{T} (GeV)"
myParams["log"] = True
myParams["ratio"] = True
myParams["cmsTextPosition"] = "outframe"
myParams["opts"] = {"ymin": 0.0001, "ymax": 3000.0}
myParams["opts2"] = {"ymin": 0., "ymax":1.99}
myParams["moveLegend"] = {"dx": -0.15, "dy": 0., "dh":0.05} # for data-driven
myParams["ratioMoveLegend"] = {"dx": -0.51, "dy": 0.03}
myParams["stackMCHistograms"] = True
myParams["divideByBinWidth"] = True
myParams["addMCUncertainty"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioCreateLegend"] = True
myParams["ratioMoveLegend"] = dict(dy=-0.45, dh=-0.1, dx=-0.5)
plots.drawPlot(myStackPlot, "TransVerseMassPOSTFIT", **myParams)
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# 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 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)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Print datasets info summary
datasetsMgr.PrintInfo()
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
# Append signal datasets
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["LeadingTrijet_Pt"]
# VariableList = ["LeadingTrijet_Pt", "LeadingTrijet_Eta", "LeadingTrijet_Phi"]
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)
# For-loop: All numerator-denominator pairs
for var in VariableList:
hNumerator = "AfterAllSelections_" + var + "_SR"
hDenominator = "AfterStandardSelections_" + var + "_SR"
numerator = os.path.join(opts.folder, hNumerator)
denFolder = opts.folder
#denFolder = denFolder.replace("Genuine", "")
#print "denFolder", denFolder
denominator = os.path.join(denFolder, hDenominator)
counter += 1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (nPlots),
"%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotEfficiency(datasetsMgr, numerator, denominator)
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(argv, dsetMgr, moduleInfoString):
COMBINEDHISTODIR = "ForQCDNormalization"
FAKEHISTODIR = "ForQCDNormalizationEWKFakeTaus"
GENUINEHISTODIR = "ForQCDNormalizationEWKGenuineTaus"
comparisonList = ["AfterStdSelections"]
dirs = []
dirs.append(sys.argv[1])
# Check multicrab consistency
# consistencyCheck.checkConsistencyStandalone(dirs[0],dsetMgr,name="QCD inverted") #FIXME needs to be updated
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
dsetMgr.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data dsetMgr from lumi.json
dsetMgr.loadLuminosities()
if verbose:
print "Datasets list (initial):"
print dsetMgr.getMCDatasetNames()
# Include only 120 mass bin of HW and HH dsetMgr
dsetMgr.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "HplusTB" in name, dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "DY2JetsToLL" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "DY3JetsToLL" in name,
dsetMgr.getAllDatasetNames()))
dsetMgr.remove(
filter(lambda name: "DY4JetsToLL" in name,
dsetMgr.getAllDatasetNames()))
# Ignore DY dataset with HERWIG hadronization (it's only for testing)
dsetMgr.remove(filter(lambda name: "DYJetsToLL_M_50_HERWIGPP" in name,
dsetMgr.getAllDatasetNames()),
close=False)
if verbose:
print "Datasets after filter removals:"
print dsetMgr.getMCDatasetNames()
# Default merging nad ordering of data and MC dsetMgr
# All data dsetMgr to "Data"
# All QCD dsetMgr to "QCD"
# All single top dsetMgr to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(dsetMgr)
if verbose:
print "Datasets after mergeRenameReorderForDataMC:"
print dsetMgr.getMCDatasetNames()
# Only WJets/WJetsToLNu or WJetsToLNu_HT_* should be used (not both)
if useWJetsHT:
dsetMgr.remove(filter(lambda name: "WJets" == name,
dsetMgr.getAllDatasetNames()),
close=False)
else:
dsetMgr.remove(filter(lambda name: "WJetsHT" in name,
dsetMgr.getAllDatasetNames()),
close=False)
print "Datasets used for EWK (after choosing between WJets or WJetsHT sample):"
print dsetMgr.getMCDatasetNames()
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH dsetMgr to one (for each mass bin)
plots.mergeWHandHH(dsetMgr)
# Merge MC EWK samples as one EWK sample
myMergeList = []
# Always use TT (or TTJets) as a part of the EWK background
if "TT" in dsetMgr.getMCDatasetNames():
myMergeList.append("TT") # Powheg, no neg. weights -> large stats.
else:
myMergeList.append("TTJets") # Madgraph with negative weights
print "Warning: using TTJets as input, but this is suboptimal. Please switch to the TT sample (much more stats.)."
# Always use WJets as a part of the EWK background
if useWJetsHT:
myMergeList.append("WJetsHT")
else:
myMergeList.append("WJets")
# For SY, single top and diboson, use only if available:
if "DYJetsToQQHT" in dsetMgr.getMCDatasetNames():
myMergeList.append("DYJetsToQQHT")
if "DYJetsToLL" in dsetMgr.getMCDatasetNames():
myMergeList.append("DYJetsToLL")
else:
print "Warning: ignoring DYJetsToLL sample (since merged sample does not exist) ..."
if "SingleTop" in dsetMgr.getMCDatasetNames():
myMergeList.append("SingleTop")
else:
print "Warning: ignoring single top sample (since merged sample does not exist) ..."
if "Diboson" in dsetMgr.getMCDatasetNames():
myMergeList.append("Diboson")
else:
print "Warning: ignoring diboson sample (since merged sample does not exist) ..."
for item in myMergeList:
if not item in dsetMgr.getMCDatasetNames():
raise Exception(
"Error: tried to use dataset '%s' as part of the merged EWK dataset, but the dataset '%s' does not exist!"
% (item, item))
dsetMgr.merge("EWK", myMergeList)
if verbose:
print "\nFinal merged dataset list:\n"
print dsetMgr.getMCDatasetNames()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
for HISTONAME in comparisonList:
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME + "/NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME + "/NormalizationMETInvertedTau" + HISTONAME
FITMIN = None
FITMAX = None
#===== Infer binning information and labels
histonames = dsetMgr.getDataset("Data").getDirectoryContent(
COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME)
bins = []
binLabels = []
if histonames == None:
# Assume that only inclusive bin exists
name = COMBINEDHISTODIR + "/NormalizationMETBaselineTau" + HISTONAME
if not dsetMgr.getDataset("Data").hasRootHisto(name):
raise Exception(
"Error: Cannot find histogram or directory of name '%s'!" %
name)
BASELINETAUHISTONAME = "NormalizationMETBaselineTau" + HISTONAME
INVERTEDTAUHISTONAME = "NormalizationMETInvertedTau" + HISTONAME
bins = [""]
binLabels = ["Inclusive"]
else:
for hname in histonames:
binIndex = hname.replace(
"NormalizationMETBaselineTau" + HISTONAME, "")
# print "DEBUG: We are looking for hisrogram "+COMBINEDHISTODIR+"/"+BASELINETAUHISTONAME+binIndex
hDummy = dsetMgr.getDataset("Data").getDatasetRootHisto(
COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME +
binIndex).getHistogram()
title = hDummy.GetTitle()
title = title.replace("METBaseline" + HISTONAME, "")
if hDummy.Integral() > 0.0:
bins.append(binIndex)
if binIndex == "Inclusive":
binLabels.append(binIndex)
else:
binLabels.append(
QCDNormalization.getModifiedBinLabelString(title))
if FITMIN == None:
FITMIN = hDummy.GetXaxis().GetXmin()
FITMAX = hDummy.GetXaxis().GetXmax()
hDummy.Delete()
else:
print "Skipping bin '%s' (%s) because it has no entries" % (
binIndex,
QCDNormalization.getModifiedBinLabelString(title))
print "\nHistogram bins available", bins
# Select bins by filter
if len(selectOnlyBins) > 0:
oldBinLabels = binLabels[:]
oldBins = bins[:]
binLabels = []
bins = []
for k in selectOnlyBins:
for i in range(len(oldBinLabels)):
if k == oldBinLabels[i] or k == oldBins[i]:
binLabels.append(oldBinLabels[i])
bins.append(oldBins[i])
print "Using bins ", bins
print "\nBin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": " + binLabels[i]
print line
print
#===== Initialize normalization calculator
#manager = QCDNormalization.QCDNormalizationManagerExperimental1(binLabels)
manager = QCDNormalization.QCDNormalizationManagerDefault(
binLabels, dirs[0], moduleInfoString)
#===== Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
template_EWKFakeTaus_Baseline = manager.createTemplate(
"EWKFakeTaus_Baseline")
template_EWKFakeTaus_Inverted = manager.createTemplate(
"EWKFakeTaus_Inverted")
template_EWKGenuineTaus_Baseline = manager.createTemplate(
"EWKGenuineTaus_Baseline")
template_EWKGenuineTaus_Inverted = manager.createTemplate(
"EWKGenuineTaus_Inverted")
template_EWKInclusive_Baseline = manager.createTemplate(
"EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate(
"EWKInclusive_Inverted")
template_QCD_Baseline = manager.createTemplate("QCD_Baseline")
template_QCD_Inverted = manager.createTemplate("QCD_Inverted")
#===== Define fit functions and fit parameters
# The available functions are defined in the FitFunction class in the QCDMeasurement/python/QCDNormalization.py file
# commented out fitter for EWK fake taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 100
#template_EWKFakeTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunctionInv", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKFakeTaus_Baseline.setDefaultFitParam(defaultInitialValue=[10.0, 100, 45, 0.02],
#defaultLowerLimit= [ 0.1, 70, 10, 0.001],
#defaultUpperLimit= [ 30, 300, 100, 0.1])
# commented out fitter for EWK genuine taus, since only the fit on inclusive EWK is used to obtain w_QCD
#boundary = 150
#template_EWKGenuineTaus_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=boundary, norm=1, rejectPoints=1),
#FITMIN, FITMAX)
#template_EWKGenuineTaus_Baseline.setDefaultFitParam(defaultLowerLimit=[0.5, 90, 30, 0.0001],
#defaultUpperLimit=[ 20, 150, 50, 1.0])
# Inclusive EWK
boundary = 150
template_EWKInclusive_Baseline.setFitter(
QCDNormalization.FitFunction("EWKFunction",
boundary=boundary,
norm=1,
rejectPoints=1), FITMIN, FITMAX)
template_EWKInclusive_Baseline.setDefaultFitParam(
defaultLowerLimit=[0.5, 90, 30, 0.0001],
defaultUpperLimit=[20, 150, 60, 1.0])
# Fake tau and QCD
# Note that the same function is used for QCD only and QCD+EWK fakes (=Fake Tau)
# Old function, used until May 2017
# template_QCD_Inverted.setFitter(QCDNormalization.FitFunction("QCDFunction", norm=1), FITMIN, FITMAX)
# template_QCD_Inverted.setDefaultFitParam(defaultLowerLimit=[0.0001, 0.001, 0.1, 0.0, 10, 0.0001, 0.001],
# defaultUpperLimit=[ 200, 10, 10, 150, 100, 1, 0.05])
# template_QCD_Inverted.setDefaultFitParam(defaultLowerLimit=[ 30, 0.1, 0.1, 0, 10, 0.0, 0.0001], # new default limits to make fits more stable,
# defaultUpperLimit=[ 130, 20, 20, 200, 200, 1.0, 1.0]) # corresponding to the 7 free param. of the fit function
# New dunction with one more d.o.f in Rayleigh distribution WARNING!!! UNSTABLE!!!
# template_QCD_Inverted.setFitter(QCDNormalization.FitFunction("QCDFunctionWithPeakShift", norm=1), FITMIN, FITMAX)
# template_QCD_Inverted.setDefaultFitParam(defaultLowerLimit=[ 30, 0.1, -10, 0, -20, 10, 0.0001, 0.0001],
# defaultUpperLimit=[ 130, 20, 10, 20, 200, 100, 1.0, 0.05])
# As the factor multiplicative for exponential function (p6 in QCDFunctionWithPeakShift tends to 0 in fitting,
# we drop that term and use only this:
template_QCD_Inverted.setFitter(
QCDNormalization.FitFunction("RayleighShiftedPlusGaussian",
norm=1), FITMIN, FITMAX)
template_QCD_Inverted.setDefaultFitParam(
defaultLowerLimit=[30, 0.1, -10, 0, -20, 10],
defaultUpperLimit=[130, 20, 10, 20, 200, 100])
#===== Loop over tau pT bins
for i, binStr in enumerate(bins):
print "\n********************************"
print "*** Fitting bin %s" % binLabels[i]
print "********************************\n"
#===== Reset bin results
manager.resetBinResults()
#===== Obtain histograms for normalization
# Data
histoName = COMBINEDHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_data = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
histoName = COMBINEDHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_data = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
# EWK genuine taus
histoName = GENUINEHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_EWK_GenuineTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
histoName = GENUINEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_EWK_GenuineTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
# EWK fake taus
histoName = FAKEHISTODIR + "/" + BASELINETAUHISTONAME + binStr
hmetBase_EWK_FakeTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
histoName = FAKEHISTODIR + "/" + INVERTEDTAUHISTONAME + binStr
hmetInverted_EWK_FakeTaus = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
#===== Obtain inclusive EWK histograms
hmetBase_EWKinclusive = hmetBase_EWK_GenuineTaus.Clone(
"EWKinclusiveBase")
hmetBase_EWKinclusive.Add(hmetBase_EWK_FakeTaus, 1.0)
hmetInverted_EWKinclusive = hmetInverted_EWK_GenuineTaus.Clone(
"EWKinclusiveInv")
hmetInverted_EWKinclusive.Add(hmetInverted_EWK_FakeTaus, 1.0)
# Finalize histograms by rebinning
for histogram in [
hmetBase_data, hmetInverted_data, hmetBase_EWK_GenuineTaus,
hmetInverted_EWK_GenuineTaus, hmetBase_EWKinclusive,
hmetBase_EWK_FakeTaus, hmetInverted_EWK_FakeTaus,
hmetInverted_EWKinclusive
]:
histogram.Rebin(_rebinFactor)
#===== Obtain histograms for QCD (subtract MC EWK events from data)
# QCD from baseline is usable only as a cross check
hmetBase_QCD = hmetBase_data.Clone("QCDbase")
hmetBase_QCD.Add(hmetBase_EWKinclusive, -1)
hmetInverted_QCD = hmetInverted_data.Clone("QCDinv")
hmetInverted_QCD.Add(hmetInverted_EWKinclusive, -1)
#===== Set histograms to the templates
template_EWKFakeTaus_Inverted.setHistogram(
hmetInverted_EWK_FakeTaus, binLabels[i])
template_EWKGenuineTaus_Inverted.setHistogram(
hmetInverted_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Inverted.setHistogram(
hmetInverted_EWKinclusive, binLabels[i])
template_QCD_Inverted.setHistogram(hmetInverted_QCD, binLabels[i])
template_EWKFakeTaus_Baseline.setHistogram(hmetBase_EWK_FakeTaus,
binLabels[i])
template_EWKGenuineTaus_Baseline.setHistogram(
hmetBase_EWK_GenuineTaus, binLabels[i])
template_EWKInclusive_Baseline.setHistogram(
hmetBase_EWKinclusive, binLabels[i])
template_QCD_Baseline.setHistogram(hmetBase_QCD, binLabels[i])
#===== Make plots of templates
manager.plotTemplates()
#===== Fit individual templates to data
fitOptions = "R B L W M" # RBLWM
manager.calculateNormalizationCoefficients(hmetBase_data,
fitOptions, FITMIN,
FITMAX)
#===== Calculate combined normalisation coefficient (f_fakes = w*f_QCD + (1-w)*f_EWKfakes)
# Obtain histograms
histoName = "ForDataDrivenCtrlPlots/shapeTransverseMass/shapeTransverseMass" + binStr
dataMt = plots.DataMCPlot(dsetMgr, histoName).histoMgr.getHisto(
"Data").getRootHisto().Clone(histoName)
treatNegativeBins(dataMt, "Data_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKFakeTaus/shapeTransverseMass/shapeTransverseMass" + binStr
ewkFakeTausMt = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkFakeTausMt, "ewkFakeTaus_inverted mT")
histoName = "ForDataDrivenCtrlPlotsEWKGenuineTaus/shapeTransverseMass/shapeTransverseMass" + binStr
ewkGenuineTausMt = plots.DataMCPlot(
dsetMgr, histoName).histoMgr.getHisto(
"EWK").getRootHisto().Clone(histoName)
treatNegativeBins(ewkGenuineTausMt, "ewkGenuineTaus_inverted mT")
qcdMt = dataMt.Clone("QCD")
qcdMt.Add(ewkFakeTausMt, -1)
qcdMt.Add(ewkGenuineTausMt, -1)
treatNegativeBins(qcdMt, "QCD_inverted mT")
# Do calculation
manager.calculateCombinedNormalizationCoefficient(
qcdMt, ewkFakeTausMt)
#===== Save normalization
outFileName = "QCDNormalizationFactors_%s_%s.py" % (HISTONAME,
moduleInfoString)
outFileFullName = os.path.join(argv[1], outFileName)
manager.writeScaleFactorFile(outFileFullName, moduleInfoString)
def main(opts):
# Suppress warnings about weight being re-applied
ROOT.gErrorIgnoreLevel = ROOT.kError
# 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]
opts.optMode = ""
mcrabName = opts.mcrab
RunEra = mcrabName.split("_")[1]
# Setup ROOT and style
ROOT.gROOT.SetBatch(opts.batchMode)
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
if opts.paper:
style.setGridX(False)
style.setGridY(False)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
# Remove some QCD samples (the cross sections of these samples are not calculated)
if 0:
msg = "Removing following samples:"
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
for d in getDatasetsToExclude():
Print(d, False)
datasetsMgr.remove(d)
# Get run-range
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Get int lumi
intLumi = GetLumi(datasetsMgr)
# Update to PU & load luminosities
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities()
#datasetsMgr.normalizeMCByLuminosity()
# Print luminisoties and cross-sections
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintCrossSections()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get datasets
datasetsMgr.mergeMC()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Define lists of Triggers to be plotted and Variables
xVars = ["pt6thJet", "eta6thJet", "phi6thJet", "Ht", "nBTagJets", "pu", "JetMulti", "BJetMulti"]
trgList = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
if opts.fast:
trgList = ["OR_PFJet450"]
xVars = ["pt6thJet", "Ht"]
nPlots = len(trgList)*len(xVars)
counter = 0
# For-loop: All signal triggers
for i, trg in enumerate(trgList, 1):
# For-loop: All x-variables
for j, xVar in enumerate(xVars, 1):
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s Vs %s" % (trg, xVar))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
# Define names
hNumerator = "hNum_%s_RefTrg_OfflineSel_Signal%s" % (xVar, trg)
hDenominator = "hDen_%s_RefTrg_OfflineSel" % (xVar)
plotName = "Eff_%s_%s" % (xVar, trg)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(xVar, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, hNumerator, hDenominator , **_kwargs)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, hNumerator, hDenominator, **_kwargs)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC" , "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany(
{
"eff_Data": "Data",
"eff_MC" : "Simulation"
}
)
# Draw and save the plot
p.setLuminosity(intLumi)
plots.drawPlot(p, plotName, **_kwargs)
# Draw
histograms.addText(0.65, 0.06, "Runs "+ runRange, 17)
histograms.addText(0.65, 0.10, "2016", 17)
# Save the canvas to a file
SavePlot(p, plotName, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".pdf", ".png", ".C"] )
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), 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
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():
style = tdrstyle.TDRStyle()
#style.setWide(True)
style.setPaletteMy()
ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
#datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, parseOpts, **kwargs)
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
#datasetsMgr.remove(kwargs.get("rmDataset"))
datasetsMgr.remove(filter(lambda name: not kwargs.get("refDataset") in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# d.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
# datasetsMgr.remove(kwargs.get("rmDataset"))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle( getTitleX(refHisto, **kwargs) )
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
# p.getFrame().GetZaxis().SetTitle( getTitleZ(refHisto, **kwargs) ) #does not work
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
#histograms.addText(0.16, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.20, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main(hName, opts):
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
datasetsMgr.updateNAllEventsToPUWeighted()
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 datasets: All JetHT to "Data", QCD_Pt to "QCD", QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
# datasetsMgr.remove("QCD-b")
# datasetsMgr.remove("QCD")
# Print dataset information
datasetsMgr.PrintInfo()
# Create data-MC comparison plot, with the default
p = plots.DataMCPlot(datasetsMgr, hName)
# Create a comparison plot
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY") == True:
canvOpts = {"xmin": 0.0, "ymin": 1e-1, "ymaxfactor": 10}
else:
canvOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
# Draw a customised plot & Save it
plots.drawPlot(
p,
"Plots/" + hName.replace("/", "_").replace(" ", "_").replace(
"(", "_").replace(")", ""),
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
rebinX=kwargs.get("rebinX"),
rebinY=kwargs.get("rebinY"),
xlabelsize=kwargs.get("xlabelsize"),
ratio=kwargs.get("ratio"),
stackMCHistograms=kwargs.get("stackMCHistograms"),
ratioYlabel=kwargs.get("ratioYlabel"),
ratioInvert=kwargs.get("ratioInvert"),
addMCUncertainty=kwargs.get("addMCUncertainty"),
addLuminosityText=kwargs.get("addLuminosityText"),
addCmsText=kwargs.get("addCmsText"),
opts=canvOpts,
opts2=ratioOpts,
log=kwargs.get("logY"),
errorBarsX=kwargs.get("errorBarsX"),
cmsExtraText=kwargs.get("cmsExtraText"),
moveLegend=kwargs.get("moveLegend"),
#cutLine=kwargs.get("cutValue"),
cutBox={
"cutValue": kwargs.get("cutValue"),
"fillColor": kwargs.get("cutFillColour"),
"box": kwargs.get("cutBox"),
"line": kwargs.get("cutLine"),
"lessThan": kwargs.get("cutLessthan")
},
)
# Remove legend?
if kwargs.get("removeLegend"):
p.removeLegend()
# Additional text
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
if not opts.batchMode:
raw_input("=== plotCounters.py:\n\tPress any key to quit ROOT ...")
return
myModuleSelector.doSelect(opts)
myDisplayStatus = True
# Loop over era/searchMode/optimizationMode options
for era in myModuleSelector.getSelectedEras():
for searchMode in myModuleSelector.getSelectedSearchModes():
for optimizationMode in myModuleSelector.getSelectedOptimizationModes():
# Construct info string of module
myModuleInfoString = "%s_%s_%s"%(era, searchMode, optimizationMode)
print HighlightStyle()+"Module:",myModuleInfoString,NormalStyle()
# Obtain dataset manager
dsetMgr = dsetMgrCreator.createDatasetManager(dataEra=era,searchMode=searchMode,optimizationMode=optimizationMode)
# Do the usual normalisation
dsetMgr.updateNAllEventsToPUWeighted()
dsetMgr.loadLuminosities()
plots.mergeRenameReorderForDataMC(dsetMgr)
dsetMgr.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Remove MC QCD
dsetMgr.remove("QCD")
# Make a directory for output
myDir = ""
#myDir = "plots_%s"%myModuleInfoString
#createOutputdirectory(myDir)
# Obtain luminosity
myLuminosity = dsetMgr.getDataset("Data").getLuminosity()
def main(opts):
# 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)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Do the plot
moduleDict = {}
moduleNames = list(
filter(
lambda x: "FakeBTransferFactors" in x and x.endswith(".py") and
not x.startswith("#"), os.listdir(opts.mcrab)))
sKeys = []
Verbose(
"Found %d import files:%s%s%s" %
(len(moduleNames), hs, "\n\t" + "\n\t".join(moduleNames), ns), True)
# For-loop: All module files to be imported
for i, m in enumerate(moduleNames, 1):
if not opts.verbose:
Print(
"Importing module %s" %
(hs + os.path.join(opts.mcrab, m) + ns), i == 1)
else:
Print(
"Importing module %s" %
(hs + os.path.join(opts.mcrab, m) + ns), True)
# Define the path to the .py file (module Path) and the name to be imported as (module name)
mPath = os.path.join(os.getcwd(), opts.mcrab, m)
mName = m.split("_")[-1].replace(".py", "")
sKeys.append(mName)
mObj = imp.load_source(mName, mPath)
moduleDict[mName] = mObj
# Debugging
Verbose(moduleDict[mName].FakeBNormalisation_Value.keys(), False)
Verbose(moduleDict[mName].FakeBNormalisation_Value.values(), False)
gList = []
gListR = []
# For-loop: All modules imported
for i, k in enumerate(sorted(sKeys, key=natural_keys), 0):
g = PlotTFs(i, k, moduleDict[k])
gList.extend(g)
gListR.extend(g)
# Make sure you use correct graph as reference
refPos = 0
for i, g in enumerate(gList, 0):
if g.getName() == opts.refHisto:
refPos = i
break
# Reference histograph
gList.insert(0, gList.pop(refPos))
gListR.insert(0, gListR.pop(refPos))
# Create comparison plot
PlotTFsCompare("transferFactors", sKeys, gList)
# Create comparison ratio plot
gListR = [
divideGraph(g, gListR[0], errorY=True, invRatio=False) for g in gListR
]
PlotTFsCompare("transferFactorsR", sKeys, gListR, isRatio=True)
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)
optModes = [""]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Numerator & Denominator dataset manager
noSF_datasetsMgr = GetDatasetsFromDir(opts, opts.noSFcrab)
withCR1SF_datasetsMgr = GetDatasetsFromDir(opts, opts.withCR1SFcrab)
withCR2SF_datasetsMgr = GetDatasetsFromDir(opts, opts.withCR2SFcrab)
# Update all events to PU weighting
noSF_datasetsMgr.updateNAllEventsToPUWeighted()
withCR1SF_datasetsMgr.updateNAllEventsToPUWeighted()
withCR2SF_datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
noSF_datasetsMgr.loadLuminosities()
withCR1SF_datasetsMgr.loadLuminosities()
withCR2SF_datasetsMgr.loadLuminosities()
if 0:
noSF_datasetsMgr.PrintCrossSections()
noSF_datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(noSF_datasetsMgr)
plots.mergeRenameReorderForDataMC(withCR1SF_datasetsMgr)
plots.mergeRenameReorderForDataMC(withCR2SF_datasetsMgr)
# Get luminosity if a value is not specified
if opts.intLumi < 0:
opts.intLumi = noSF_datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = []
#removeList = ["TTWJetsToLNu_", "TTWJetsToQQ"]
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
noSF_datasetsMgr.remove(filter(lambda name: d in name, noSF_datasetsMgr.getAllDatasetNames()))
withCR1SF_datasetsMgr.remove(filter(lambda name: d in name, withCR1SF_datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
noSF_datasetsMgr.PrintInfo()
withCR1SF_datasetsMgr.PrintInfo()
withCR2SF_datasetsMgr.PrintInfo()
# Merge EWK samples
EwkDatasets = ["Diboson", "DYJetsToLL", "WJetsHT"]
noSF_datasetsMgr.merge("EWK", EwkDatasets)
withCR1SF_datasetsMgr.merge("EWK", EwkDatasets)
withCR2SF_datasetsMgr.merge("EWK", EwkDatasets)
# Get histosgram names
folderListIncl = withCR1SF_datasetsMgr.getDataset(withCR1SF_datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
folderList = [h for h in folderListIncl if "AfterAllSelections_LeadingTrijet_Pt" in h ]
# For-loop: All histo paths
for h in folderList:
if "lowMET" in h:
folderList.remove(h)
folderPath = os.path.join(opts.folder, "")
folderPathGen = os.path.join(opts.folder + "Genuine")
folderPathFake =os.path.join(opts.folder + "Fake" )
histoList = folderList
num_pathList = [os.path.join(folderPath, h) for h in histoList]
num_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
num_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
# Denominator Histogram (To be used in the estimation of QCD Data-Driven)
histoList = [h for h in folderListIncl if "AfterStandardSelections_LeadingTrijet_Pt" in h]
den_pathList = [os.path.join(folderPath, h) for h in histoList]
den_pathList.extend([os.path.join(folderPathGen, h) for h in histoList])
den_pathList.extend([os.path.join(folderPathFake, h) for h in histoList])
# For-loop: All histo paths
for h in den_pathList:
if "lowMET" in h:
den_pathList.remove(h)
# Do the histograms
PlotHistos(noSF_datasetsMgr, withCR1SF_datasetsMgr, withCR2SF_datasetsMgr, num_pathList, den_pathList, opts)
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, 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, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# 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
dir = opts.mcrab.replace("_BDT85", "_BDT")
print dir
datasetsMgr30 = GetDatasetsFromDir_second(opts, dir+"30")
datasetsMgr30.updateNAllEventsToPUWeighted()
datasetsMgr30.loadLuminosities() # from lumi.json
datasetsMgr40 = GetDatasetsFromDir_second(opts, dir+"40")
datasetsMgr40.updateNAllEventsToPUWeighted()
datasetsMgr40.loadLuminosities() # from lumi.json
datasetsMgr50 = GetDatasetsFromDir_second(opts, dir+"50")
datasetsMgr50.updateNAllEventsToPUWeighted()
datasetsMgr50.loadLuminosities() # from lumi.json
datasetsMgr60 = GetDatasetsFromDir_second(opts, dir+"60")
datasetsMgr60.updateNAllEventsToPUWeighted()
datasetsMgr60.loadLuminosities() # from lumi.json
datasetsMgr70 = GetDatasetsFromDir_second(opts, dir+"70")
datasetsMgr70.updateNAllEventsToPUWeighted()
datasetsMgr70.loadLuminosities() # from lumi.json
datasetsMgr80 = GetDatasetsFromDir_second(opts, dir+"80")
datasetsMgr80.updateNAllEventsToPUWeighted()
datasetsMgr80.loadLuminosities() # from lumi.json
datasetsMgr90 = GetDatasetsFromDir_second(opts, dir+"90")
datasetsMgr90.updateNAllEventsToPUWeighted()
datasetsMgr90.loadLuminosities() # from lumi.json
datasetsMgr95 = GetDatasetsFromDir_second(opts, dir+"95")
datasetsMgr95.updateNAllEventsToPUWeighted()
datasetsMgr95.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr30, datasetsMgr40, datasetsMgr50, datasetsMgr60, datasetsMgr70, datasetsMgr80, datasetsMgr90, datasetsMgr95]:
for d in datasetsMgr_.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr_.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr50.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr60.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr70.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr80.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr90.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr95.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
#plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr30.remove(filter(lambda name: key in name, datasetsMgr30.getAllDatasetNames()))
datasetsMgr40.remove(filter(lambda name: key in name, datasetsMgr40.getAllDatasetNames()))
datasetsMgr50.remove(filter(lambda name: key in name, datasetsMgr50.getAllDatasetNames()))
datasetsMgr60.remove(filter(lambda name: key in name, datasetsMgr60.getAllDatasetNames()))
datasetsMgr70.remove(filter(lambda name: key in name, datasetsMgr70.getAllDatasetNames()))
datasetsMgr80.remove(filter(lambda name: key in name, datasetsMgr80.getAllDatasetNames()))
datasetsMgr90.remove(filter(lambda name: key in name, datasetsMgr90.getAllDatasetNames()))
datasetsMgr95.remove(filter(lambda name: key in name, datasetsMgr95.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
# Append signal datasets
for m in signalMass:
datasetOrder.insert(0, m)
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr30.selectAndReorder(datasetOrder)
datasetsMgr40.selectAndReorder(datasetOrder)
datasetsMgr50.selectAndReorder(datasetOrder)
datasetsMgr60.selectAndReorder(datasetOrder)
datasetsMgr70.selectAndReorder(datasetOrder)
datasetsMgr80.selectAndReorder(datasetOrder)
datasetsMgr90.selectAndReorder(datasetOrder)
datasetsMgr95.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
Numerators = ["AllTopQuarkPt_MatchedBDT",
"AllTopQuarkPt_Matched",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"AllTopQuarkPt_MatchedBDT",
"SelectedTrijetsPt_BjetPassCSVdisc_afterCuts",
"TrijetFakePt_BDT",
]
Denominators = ["AllTopQuarkPt_Matched",
"TopQuarkPt",
"EventTrijetPt2T_BDT",
"EventTrijetPt2T_Matched",
"EventTrijetPt2T",
"TopQuarkPt",
"SelectedTrijetsPt_afterCuts",
"TrijetFakePt",
]
if 1:
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr30.merge("QCD", GetListOfQCDatasets())
datasetsMgr40.merge("QCD", GetListOfQCDatasets())
datasetsMgr50.merge("QCD", GetListOfQCDatasets())
datasetsMgr60.merge("QCD", GetListOfQCDatasets())
datasetsMgr70.merge("QCD", GetListOfQCDatasets())
datasetsMgr80.merge("QCD", GetListOfQCDatasets())
datasetsMgr90.merge("QCD", GetListOfQCDatasets())
datasetsMgr95.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
#Background1_Dataset = datasetsMgr.getDataset("QCD")
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
for i in range(len(Numerators)):
numerator = os.path.join(opts.folder, Numerators[i])
denominator = os.path.join(opts.folder, Denominators[i])
PlotEfficiency_comparison(datasetsMgr, datasetsMgr30, datasetsMgr40, datasetsMgr50, datasetsMgr60, datasetsMgr70, datasetsMgr80, datasetsMgr90, datasetsMgr95, numerator, denominator, intLumi)
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 = [""]
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():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
ROOT.gErrorIgnoreLevel = 3000
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_200")
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), excludeTasks="M_200")
# Determine Integrated Luminosity
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# datasetsMgr.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get efficiency histos
refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFCP", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFFC", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kBJeffrey", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFNormal", **kwargs)
# Plot the efficiencies
p = plots.ComparisonManyPlot(refEff, otherEff)
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.2} #"ymax": 5e-1}
ratioOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1 , "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
# p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
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("")
optModes = optList
else:
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 opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Get the PSets:
if 0:
datasetsMgr.printSelections()
#PrintPSet("BJetSelection", datasetsMgr, depth=150)
#PrintPSet("fakeBMeasurement", datasetsMgr, depth=150)
sys.exit()
# 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 a value is not specified
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = ["QCD-b", "Charged"]
if not opts.useMC:
removeList.append("QCD")
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Verbose(
ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(),
i == 0)
datasetsMgr.remove(
filter(lambda name: d in name,
datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
# Print dataset information
datasetsMgr.PrintInfo()
# List of TDirectoryFile (_CRone, _CRtwo, _VR, _SR)
tdirs = [
"LdgTetrajetMass_", "LdgTetrajetPt_", "LdgTrijetPt_",
"LdgTrijetMass_", "TetrajetBJetPt_", "TetrajetBJetEta_"
]
region = ["CRone", "CRtwo", "SR", "VR"]
hList = []
for d in tdirs:
for r in region:
hList.append(d + r)
# Get the folders with the binned histograms
folderList_ = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
folderList = [h for h in folderList_ if h in hList]
# For-loop: All folders
histoPaths = []
for f in folderList:
folderPath = os.path.join(opts.folder, f)
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
folderPath)
pathList = [os.path.join(folderPath, h) for h in histoList]
histoPaths.extend(pathList)
# Get all the bin labels
binLabels = GetBinLabels("CRone", histoPaths)
for i, t in enumerate(tdirs, 1):
myList = []
for p in histoPaths:
if t in p:
myList.append(p)
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % i, "/", "%s:" % (len(tdirs)),
t.replace("_", ""))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
i == 1)
PlotHistograms(datasetsMgr, myList, binLabels, opts)
# Save the plots
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(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
for optMode in optModes:
plot = plots.PlotBase()
color = 1
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK", ["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
for HISTONAME in histoNameList:
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
invjet_name = "MTInvertedTauIdAfterAllSelectionsPlusJetFakeTau"
basejet_name = "MTBaselineTauIdAfterAllSelectionsPlusJetFakeTau"
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(datasets,
HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(
HISTONAME + "/" + HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
legendName = "Data-driven Fake Taus"
else:
legendName = "Simulated Fake Taus"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
mt.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto("Data").getRootHisto().Clone(
HISTONAME + "/" + HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
h.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
if HISTONAME == "shapeTransverseMass":
mt.Add(mt_signalfaketaus)
plot.histoMgr.appendHisto(histograms.Histo(mt, mt.GetName()))
style = tdrstyle.TDRStyle()
#plot.createFrame("mt")
#plot.createFrame(HISTONAME.replace("shape","final"))
plot.createFrame(optMode.replace("Opt", "Mt_DataDrivenVsMC_"))
moveLegend = {"dx": -0.3, "dy": 0.}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller", "R_{BB} ").replace("Plus", ""),
25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# 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 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)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Print datasets info summary
datasetsMgr.PrintInfo()
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
# Append signal datasets
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["LeadingTrijet_Pt"]
# VariableList = ["LeadingTrijet_Pt", "LeadingTrijet_Eta", "LeadingTrijet_Phi"]
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)
# For-loop: All numerator-denominator pairs
for var in VariableList:
hNumerator = "AfterAllSelections_" + var + "_SR"
hDenominator = "AfterStandardSelections_" +var + "_SR"
numerator = os.path.join(opts.folder, hNumerator)
denFolder = opts.folder
#denFolder = denFolder.replace("Genuine", "")
#print "denFolder", denFolder
denominator = os.path.join(denFolder, hDenominator)
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotEfficiency(datasetsMgr, numerator, denominator)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Get all datasets from the mcrab dir
# def GetDatasetsFromDir(mcrab, opts, **kwargs): #iro
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs) #kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
#datasetsMgr.getDataset("QCD_bEnriched_HT1000to1500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT1500to2000").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT2000toInf").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT300to500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT500to700").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT700to1000").setCrossSection(1.0)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# Print the cross
datasetsMgr.PrintCrossSections()
for h_prefix in ["reco", "gen"]:
# Get ref histo here and the fixed histos (TT, QCD, QCD-b
inclusiveHisto, fixedHistos = GetHistosForPlotter(datasetsMgr, h_prefix + "MET_Et", **kwargs)
inclusiveHisto.setName("Inclusive")
inclusiveHisto.setLegendLabel("Inclusive")
#for hi in fixedHistos:
# print(type(hi), hi.getName())
#return
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(h_prefix + hName[0], **kwargs)
saveName = savePath + h_prefix + hSaveNames[counter]
# Get Histos for Plotter
refHisto_, otherHistos_ = GetHistosForPlotter(datasetsMgr, h_prefix + hName[0], **kwargs)
refHisto2, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[1], **kwargs)
refHisto = inclusiveHisto
# customize histos
refHisto_.setName(hLegends[counter][0])
refHisto_.setLegendLabel(hLegends[counter][0])
refHisto_.setDrawStyle("P")
refHisto_.setLegendStyle("P")
styleDict[hStyles[0]].apply(refHisto_.getRootHisto())
refHisto2.setName(hLegends[counter][1])
refHisto2.setLegendLabel(hLegends[counter][1])
refHisto2.setDrawStyle("P")
refHisto2.setLegendStyle("P")
styleDict[hStyles[1]].apply(refHisto2.getRootHisto())
otherHistos = [refHisto_, refHisto2]
#for hi in otherHistos:
# print hi.getName()
#return
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY")==True:
opts = {"ymin": 8e-5, "ymax": 2}
#opts = {"ymin": 1e-3, "ymax": 1}
else:
opts = {"ymin": 8.e-5, "ymax": 2}
ratioOpts = {"ymin": 0.1, "ymax": 10.0}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.2, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.7)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
histograms.addText(0.2, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
