def _sanityChecks(self, dsetMgr, dirName, plotName):
'''
Check existence of histograms
'''
# Definitions
myStatus = True
myFoundStatus = True
# For-loop: All EWK datasets
for d in dsetMgr.getDataset("EWK").datasets:
if not d.hasRootHisto("%s/%s" % (dirName,plotName) ):
myFoundStatus = False
# If something is wrong
if not myFoundStatus:
myStatus = False
msg = "Skipping '%s', because it does not exist for all EWK datasets (you probably forgot to set histo level to Vital when producing the multicrab)!" % (plotName)
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), True)
else:
(myRootObject, myRootObjectName) = dsetMgr.getDataset("EWK").getFirstRootHisto("%s/%s" % (dirName,plotName) )
if isinstance(myRootObject, ROOT.TH2):
msg ="Skipping '%s', because it is not a TH1 object" % (plotName)
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), True)
myStatus = False
myRootObject.Delete()
return myStatus
def _sanityChecks(self, dsetMgr, dirName, plotName):
myStatus = True
myFoundStatus = True
for d in dsetMgr.getDataset("EWK").datasets:
if not d.hasRootHisto("%s/%s"%(dirName,plotName)):
myFoundStatus = False
if not myFoundStatus:
myStatus = False
print ShellStyles.WarningLabel()+"Skipping '%s', because it does not exist for all EWK datasets (you probably forgot to set histo level to Vital when producing the multicrab)!"%(plotName)+ShellStyles.NormalStyle()
else:
(myRootObject, myRootObjectName) = dsetMgr.getDataset("EWK").getFirstRootHisto("%s/%s"%(dirName,plotName))
if isinstance(myRootObject, ROOT.TH2):
print ShellStyles.WarningLabel()+"Skipping '%s', because it is not a TH1 object!"%(plotName)+ShellStyles.NormalStyle()
myStatus = False
myRootObject.Delete()
return myStatus
def __init__(
self,
dataPath,
ewkPath,
dsetMgr,
luminosity,
moduleInfoString,
normFactors,
#dataDrivenFakeTaus=False,
#shapeOnly=False,
#displayPurityBreakdown=False,
#optionUseInclusiveNorm=False,
optionCalculateQCDNormalizationSyst=True,
normDataSrc=None,
normEWKSrc=None,
optionUseInclusiveNorm=False):
self._shapePlots = []
self._shapePlotLabels = []
self._QCDNormalizationSystPlots = []
self._QCDNormalizationSystPlotLabels = []
self._moduleInfoString = moduleInfoString
self._useInclusiveNorm = optionUseInclusiveNorm
if len(normFactors.keys()) == 1 and normFactors.keys(
)[0] == "Inclusive":
self._useInclusiveNorm = True
print ShellStyles.HighlightStyle(
) + "...Obtaining final shape" + ShellStyles.NormalStyle()
# Determine list of plots to consider
myObjects = dsetMgr.getDataset("Data").getDirectoryContent(dataPath)
# Loop over plots to consider
i = 0
for plotName in myObjects:
i += 1
print ShellStyles.HighlightStyle(
) + "...Obtaining ctrl plot %d/%d: %s%s" % (
i, len(myObjects), plotName, ShellStyles.NormalStyle())
# Check that histograms exist
mySkipStatus = self._sanityChecks(dsetMgr, dataPath,
plotName) and self._sanityChecks(
dsetMgr, ewkPath, plotName)
if not mySkipStatus:
continue
# Obtain shape plots (the returned object is not owned)
# print "DEBUG: ewkPath: ", ewkPath
myShapeHisto = self._obtainShapeHistograms(i, dataPath, ewkPath,
dsetMgr, plotName,
luminosity, normFactors)
# Obtain plots for systematics coming from met shape difference for control plots
if optionCalculateQCDNormalizationSyst:
if isinstance(myShapeHisto, ROOT.TH2):
print ShellStyles.WarningLabel(
) + "Skipping met shape uncertainty because histogram has more than 1 dimensions!"
else:
self._obtainQCDNormalizationSystHistograms(
myShapeHisto, dsetMgr, plotName, luminosity,
normDataSrc, normEWKSrc)
def _obtainFinalShapeHistogram(self, histoName):
if histoName == None:
raise Exception(ShellStyles.ErrorLabel()+"You forgot to give final shape histo name or to cache the final shape histogram!")
print ShellStyles.WarningLabel()+"Final shape histo was not cached to QCDInvertedSystematics. Obtaining final shape from '%s'."%histoName
# Obtain final result
myFinalShape = DataDrivenQCDShape(self._dsetMgr, "Data", "EWK", histoName, self._luminosity, rebinList=self._myRebinList)
myFinalShapeResult = QCDInvertedShape(myFinalShape, self._moduleInfoString, self._normFactors, optionPrintPurityByBins=False)
self._hFinalShape = myFinalShapeResult.getResultShape().Clone()
def __init__(self,
dsetMgr,
dsetLabelData,
dsetLabelEwk,
histoName,
dataPath,
ewkPath,
luminosity,
optionUseInclusiveNorm,
verbose=False):
self._verbose = verbose
self._uniqueN = 0
self._splittedHistoReader = splittedHistoReader.SplittedHistoReader(
dsetMgr, dsetLabelData)
self._histoName = histoName
self._optionUseInclusiveNorm = optionUseInclusiveNorm #ALEX-NEW
dataFullName = os.path.join(dataPath, histoName)
ewkFullName = os.path.join(ewkPath, histoName)
# ALEX-NEW
if (self._optionUseInclusiveNorm):
msg = "Disabled call for getting splitted histograms. Getting \"Inclusive\" histogram only instead."
self.Verbose(ShellStyles.WarningLabel() + msg, self._verbose)
self._dataList = list(
self._getInclusiveHistogramsFromSingleSource(
dsetMgr, dsetLabelData, dataFullName,
luminosity)) # was called by default
self._ewkList = list(
self._getInclusiveHistogramsFromSingleSource(
dsetMgr, dsetLabelEwk, ewkFullName,
luminosity)) # was called by default
else:
msg = "This splitted histograms method is not validated! Use \"Inclusive\" histogram only instead."
self.Print(ShellStyles.WarningLabel() + msg, False)
self._dataList = list(
self._splittedHistoReader.getSplittedBinHistograms(
dsetMgr, dsetLabelData, dataFullName,
luminosity)) #FIXME: Does this work for Inclusive?
self._ewkList = list(
self._splittedHistoReader.getSplittedBinHistograms(
dsetMgr, dsetLabelEwk, ewkFullName,
luminosity)) #FIXME: Does this work for Inclusive?
return
def __init__(self, dataPath, ewkPath, dsetMgr, luminosity, moduleInfoString, normFactors,
optionDoFakeBNormalisationSyst=True, normDataSrc=None, normEWKSrc=None,
optionUseInclusiveNorm=False, keyList=[], verbose=False):
self._verbose = verbose
self._shapePlots = []
self._shapePlotLabels = []
self._QCDNormalizationSystPlots = []
self._QCDNormalizationSystPlotLabels = []
self._moduleInfoString = moduleInfoString
self._useInclusiveNorm = optionUseInclusiveNorm
if len(normFactors.keys()) == 1 and normFactors.keys()[0] == "Inclusive":
self._useInclusiveNorm = True
self._histoPathsData= self._GetHistoPaths(dsetMgr, "Data", dataPath, keyList)
if ewkPath == dataPath:
self._histoPathsEWK = self._histoPathsData
else:
self._histoPathsEWK = self._GetHistoPaths(dsetMgr, "EWK" , ewkPath , keyList)
# Sanity check
if len(self._histoPathsEWK) != len(self._histoPathsData):
msg = "List of histograms for EWK does not match in size that of Data"
raise Exception(ShellStyles.ErrorLabel() + msg + ShellStyles.NormalStyle())
# For-Loop: All plots to consider
for i, plotName in enumerate(self._histoPathsData, 1):
# Inform user of progress
msg = "{:<9} {:>3} {:<1} {:<3} {:<80}".format("Histogram", "%i" % i, "/", "%s:" % (len(self._histoPathsData)), os.path.join(dataPath, plotName) )
self.PrintFlushed(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), False)
if "JetEtaPhi_AfterAllSelections" in plotName:
continue
# Ensure that histograms exist && pass other sanity checks
dataOk = self._sanityChecks(dsetMgr, dataPath, plotName)
ewkOk = self._sanityChecks(dsetMgr, ewkPath , plotName)
if dataOk*ewkOk == False:
self.Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), i==1)
continue
self.Verbose("Obtaining shape plots (the returned object is not owned)", True)
myShapeHisto = self._obtainShapeHistograms(i, dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors)
# Obtain plots for systematics coming from invariant mass shape difference
if optionDoFakeBNormalisationSyst:
if isinstance(myShapeHisto, ROOT.TH2):
msg = "Skipping invariant mass shape uncertainty because histogram has more than 1 dimensions!"
self.Print(ShellStyles.WarningLabel() + msg, True)
else:
self._obtainQCDNormalizationSystHistograms(myShapeHisto, dsetMgr, plotName, luminosity, normDataSrc, normEWKSrc) #iro: fixme (missing plots)
msg = "Obtaining final shape from data path %s" % (ShellStyles.NoteStyle() + dataPath + ShellStyles.NormalStyle())
self.Verbose(msg, True)
return
def _findCommonAvailableModules(self, itemLabel, primaryLabel, primaryList,
otherLabel, otherList):
availableList = []
# Loop over first list to find common items
for item in primaryList:
if item in otherList:
availableList.append(item)
else:
if not item in otherList:
print ShellStyles.WarningLabel(
) + " %s selection: item '%s' is available in '%s', but missing from '%s'!" % (
itemLabel, item, primaryLabel, otherLabel)
# Return list of items available in both multicrab directories
return availableList
def getIntegratedPurityForShapeHisto(self):
'''
Return the QCD purity in bins of the final shape
'''
hData = self.getIntegratedDataHisto()
hEwk = self.getIntegratedEwkHisto()
# newName = ("_".join(hData.GetName().split("_", 2)[:2]) + "_IntegratedPurity_" + str(self._uniqueN) )
newName = hData.GetName() + "_Purity"
h = aux.Clone(hData, newName)
nameList = self._dataList[0].GetName().split("_")
newTitle = "PurityByFinalShapeBin_%s" % nameList[0][:len(nameList[0]) -
1]
h.SetTitle(newTitle)
self._uniqueN += 1
# For-loop: All bins
for i in range(1, h.GetNbinsX() + 1):
myPurity = 0.0
myUncert = 0.0
nData = hData.GetBinContent(i)
nEWK = hEwk.GetBinContent(i)
# Calculate the purity
if (nData > 0.0):
myPurity = (nData - nEWK) / nData
# Sanity check
if myPurity < 0.0:
myPurity = 0.0
myUncert = 0.0
else:
# Assume binomial error
myUncertSq = myPurity * (1.0 - myPurity) / nData
if myUncertSq >= 0.0:
myUncert = sqrt(myUncertSq)
else:
msg = "Purity is greater than 1 (%.4f) in bin %i of histogram %s" % (
myPurity, i, h.GetName())
self.Verbose(ShellStyles.WarningLabel() + msg, True)
myUncert = 0.0
# Set the purity value for the given bin
h.SetBinContent(i, myPurity)
h.SetBinError(i, myUncert)
return h
def getIntegratedPurityForShapeHisto(self):
'''
Return the QCD purity in bins of the final shape
'''
hData = self.getIntegratedDataHisto()
hEwk = self.getIntegratedEwkHisto()
#cloneName = "%s_purity_%d" % (hData, self._uniqueN) # original code
cloneName = ("_".join(hData.GetName().split("_", 2)[:2]) +
"_IntegratedPurity_" + str(self._uniqueN))
h = aux.Clone(hData, cloneName)
nameList = self._dataList[0].GetName().split("_")
h.SetTitle("PurityByFinalShapeBin_%s" %
nameList[0][:len(nameList[0]) - 1])
self._uniqueN += 1
# For-loop: All bins
for i in range(1, h.GetNbinsX() + 1):
myPurity = 0.0
myUncert = 0.0
if (hData.GetBinContent(i) > 0.0):
myPurity = (hData.GetBinContent(i) -
hEwk.GetBinContent(i)) / hData.GetBinContent(i)
if myPurity < 0.0:
myPurity = 0.0
myUncert = 0.0
else:
# Assume binomial error
myUncertSq = myPurity * (1.0 -
myPurity) / hData.GetBinContent(i)
if myUncertSq >= 0.0:
myUncert = sqrt(myUncertSq)
else:
msg = "Purity is greater than 1 (%.4f) in bin %i of histogram %s" % (
myPurity, i, h.GetName())
self.Verbose(ShellStyles.WarningLabel() + msg, True)
myUncert = 0.0
h.SetBinContent(i, myPurity)
h.SetBinError(i, myUncert)
return h
def main():
# Object for selecting data eras, search modes, and optimization modes
myModuleSelector = analysisModuleSelector.AnalysisModuleSelector()
# Obtain multicrab directory
myMulticrabDir = "."
if opts.mcrab != None:
myMulticrabDir = opts.mcrab
if not os.path.exists("%s/multicrab.cfg" % myMulticrabDir):
msg = "No multicrab directory found at path '%s'! Please check path or specify it with --mcrab!" % (
myMulticrabDir)
raise Exception(ShellStyles.ErrorLabel() + msg +
ShellStyles.NormalStyle())
if len(opts.shape) == 0:
raise Exception(
ShellStyles.ErrorLabel() +
"Provide a shape identifierwith --shape (for example MT)!" +
ShellStyles.NormalStyle())
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=myMulticrabDir)
# Obtain systematics names
mySystematicsNamesRaw = dsetMgrCreator.getSystematicVariationSources()
mySystematicsNames = []
for item in mySystematicsNamesRaw:
mySystematicsNames.append("%sPlus" % item)
mySystematicsNames.append("%sMinus" % item)
if opts.test:
mySystematicsNames = [] #[mySystematicsNames[0]] #FIXME
# Set the primary source
myModuleSelector.setPrimarySource(label=opts.analysisName,
dsetMgrCreator=dsetMgrCreator)
# Select modules
myModuleSelector.doSelect(opts=None) #FIXME: (opts=opts)
# Loop over era/searchMode/optimizationMode combos
myDisplayStatus = True
myTotalModules = myModuleSelector.getSelectedCombinationCount() * (
len(mySystematicsNames) + 1) * len(opts.shape)
Verbose("Found %s modules in total" % (myTotalModules), True)
count, nEras, nSearchModes, nOptModes, nSysVars = myModuleSelector.getSelectedCombinationCountIndividually(
)
if nSysVars > 0:
msg = "Will run over %d modules (%d eras x %d searchModes x %d optimizationModes x %d systematic variations)" % (
count, nEras, nSearchModes, nOptModes, nSysVars)
else:
msg = "Will run over %d modules (%d eras x %d searchModes x %d optimizationModes)" % (
count, nEras, nSearchModes, nOptModes)
Print(msg, True)
# Create pseudo-multicrab creator
myOutputCreator = pseudoMultiCrabCreator.PseudoMultiCrabCreator(
opts.analysisName, myMulticrabDir)
# Make time stamp for start time
myGlobalStartTime = time.time()
iModule = 0
# For-loop: All Shapes
for shapeType in opts.shape:
# Initialize
myOutputCreator.initialize(shapeType, prefix="")
msg = "Creating dataset for shape \"%s\"%s" % (
shapeType, ShellStyles.NormalStyle())
Verbose(ShellStyles.HighlightStyle() + msg, True)
# Get lists of settings
erasList = myModuleSelector.getSelectedEras()
modesList = myModuleSelector.getSelectedSearchModes()
optList = myModuleSelector.getSelectedOptimizationModes()
optList.append("") #append the default opt mode!
# For-Loop over era, searchMode, and optimizationMode options
for era in erasList:
for searchMode in modesList:
for optimizationMode in optList:
Verbose(
"era = %s, searchMode = %s, optMode = %s" %
(era, searchMode, optimizationMode), True)
# If an optimization mode is defined in options skip the rest
if opts.optMode != None:
if optimizationMode != opts.optMode:
continue
# Obtain normalization factors
myNormFactors = importNormFactors(era, searchMode,
optimizationMode,
opts.mcrab)
# Nominal module
myModuleInfoString = getModuleInfoString(
era, searchMode, optimizationMode)
iModule += 1
# Inform user of what is being processes
msg = "Module %d/%d:%s %s/%s" % (
iModule, myTotalModules, ShellStyles.NormalStyle(),
myModuleInfoString, shapeType)
Print(ShellStyles.CaptionStyle() + msg, True)
# Keep time
myStartTime = time.time()
Verbose("Create dataset manager with given settings", True)
nominalModule = ModuleBuilder(opts, myOutputCreator)
nominalModule.createDsetMgr(myMulticrabDir, era,
searchMode, optimizationMode)
if (iModule == 1):
if opts.verbose:
nominalModule.debug()
doQCDNormalizationSyst = False #FIXME
if not doQCDNormalizationSyst:
msg = "Disabling systematics"
Print(ShellStyles.WarningLabel() + msg, True)
nominalModule.buildModule(opts.dataSrc, opts.ewkSrc,
myNormFactors["nominal"],
doQCDNormalizationSyst,
opts.normDataSrc,
opts.normEwkSrc)
if len(mySystematicsNames) > 0:
Print(
"Adding QCD normalization systematics (iff also other systematics present) ",
True)
nominalModule.buildQCDNormalizationSystModule(
opts.dataSrc, opts.ewkSrc)
# FIXME: add quark gluon weighting systematics!
if 0:
Print("Adding Quark/Gluon weighting systematics", True)
nominalModule.buildQCDQuarkGluonWeightingSystModule(
opts.dataSrc, opts.ewkSrc,
myNormFactors["FakeWeightingUp"],
myNormFactors["FakeWeightingDown"], False,
opts.normDataSrc, opts.normEwkSrc)
Verbose("Deleting nominal module", True)
nominalModule.delete()
Verbose("Printing time estimate", True)
printTimeEstimate(myGlobalStartTime, myStartTime, iModule,
myTotalModules)
Verbose("Now do the rest of systematics variations", True)
for syst in mySystematicsNames:
iModule += 1
msg = "Analyzing systematics variations %d/%d: %s/%s/%s" % (
iModule, myTotalModules, myModuleInfoString, syst,
shapeType)
Print(
ShellStyles.CaptionStyle() + msg +
ShellStyles.NormalStyle(), True)
myStartTime = time.time()
systModule = ModuleBuilder(opts, myOutputCreator)
# Create dataset manager with given settings
systModule.createDsetMgr(myMulticrabDir,
era,
searchMode,
optimizationMode,
systematicVariation=syst)
# Build asystematics module
systModule.buildModule(opts.dataSrc, opts.ewkSrc,
myNormFactors["nominal"], False,
opts.normDataSrc,
opts.normEwkSrc)
printTimeEstimate(myGlobalStartTime, myStartTime,
iModule, myTotalModules)
systModule.delete()
Verbose("Pseudo-multicrab ready for %s" % shapeType, True)
# Create rest of pseudo multicrab directory
myOutputCreator.silentFinalize()
# Print some timing statistics
Print(
"Average processing time per module was %.1f s" %
getAvgProcessTimeForOneModule(myGlobalStartTime, myTotalModules), True)
Print(
"Total elapsed time was %.1f s" %
getTotalElapsedTime(myGlobalStartTime), False)
msg = "Created pseudo-multicrab %s for shape type \"%s\"" % (
myOutputCreator.getDirName(), shapeType)
Print(ShellStyles.SuccessLabel() + msg, True)
return
def importNormFactors(era, searchMode, optimizationMode, multicrabDirName):
'''
Imports the auto-generates QCDInvertedNormalizationFactors.py file, which is
created by the plotting/fitting templates script (plotQCD_Fit.py)
This containsthe results of fitting to the Baseline Data the templates m_{jjb}
shapes from the QCD (Inverted Data) and EWK (Baseline MC).
Results include the fit details for each shape and the QCD NormFactor for moving
from the ControlRegion (CR) to the Signal Region (SR).
The aforementioned python file and a folder with the histogram ROOT files and the individual
fits. The foler name will be normalisationPlots/<OptsMode> and will be placed inside the
<pseudomulticrab_dir>. The autogenerated file file be place in the cwd (i.e. work/)
'''
# Find candidates for normalisation scripts
scriptList = getNormFactorFileList(dirName=multicrabDirName,
fileBaseName=opts.normFactorsSrc)
# Create a string with the module information used
moduleInfoString = getModuleInfoString(era, searchMode, optimizationMode)
# Construct source file name
src = getGetNormFactorsSrcFilename(multicrabDirName,
opts.normFactorsSrc % moduleInfoString)
# Check if normalization coefficients are suitable for the choses era
Verbose("Reading normalisation factors from:\n\t%s" % src, True)
# Split the path to get just the file name of src
pathList = src.replace(".py", "").split("/")
# Insert the directory where the normFactor files reside into the path so that they are found
if len(pathList) > 1:
cwd = os.getenv("PWD")
# Get directories to src in a list [i.e. remove the last entry (file-name) from the pathList]
dirList = map(str, pathList[:(len(pathList) - 1)])
srcDir = "/".join(dirList)
sys.path.insert(0, os.path.join(cwd, srcDir))
# Import the (normFactor) src file
normFactorsImport = __import__(os.path.basename("/".join(pathList)))
# Get the function definition
myNormFactorsSafetyCheck = getattr(normFactorsImport,
"QCDInvertedNormalizationSafetyCheck")
Verbose(
"Check that the era=%s, searchMode=%s, optimizationMode=%s info matches!"
% (era, searchMode, optimizationMode))
myNormFactorsSafetyCheck(era, searchMode, optimizationMode)
# Obtain normalization factors
myNormFactorsImport = getattr(normFactorsImport, "QCDNormalization")
msg = "Disabled NormFactors Syst Var Fake Weighting Up/Down"
Print(ShellStyles.WarningLabel() + msg, True)
# myNormFactorsImportSystVarFakeWeightingDown = getattr(normFactorsImport, "QCDPlusEWKFakeTausNormalizationSystFakeWeightingVarDown") #FIXME
# myNormFactorsImportSystVarFakeWeightingUp = getattr(normFactorsImport, "QCDPlusEWKFakeTausNormalizationSystFakeWeightingVarUp") #FIXME
myNormFactors = {}
myNormFactors["nominal"] = myNormFactorsImport
msg = "Obtained \"nominal\" QCD normalisation factors dictionary. The values are:\n"
for k in myNormFactors["nominal"]:
msg += "\t" + k + " = " + str(myNormFactors["nominal"][k])
Print(ShellStyles.NoteLabel() + msg, True)
msg = "Disabled NormFactors Weighting Up/Down"
Print(ShellStyles.WarningLabel() + msg, True)
# myNormFactors["FakeWeightingDown"] = myNormFactorsImportSystVarFakeWeightingDown # FIXME
# myNormFactors["FakeWeightingUp"] = myNormFactorsImportSystVarFakeWeightingUp # FIXME
return myNormFactors
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 "TetrajetPt" in h]
#folderList1 = [h for h in folderList if "TetrajetMass" in h]
#folderList1 = [h for h in folderList if "MET" in h]
#folderList1 = [h for h in folderList if "TetrajetBJetPt" in h]
folderList1 = [h for h in folderList if "QGLR" in h]
folderList2 = [
h for h in folderList1
if "CRtwo" in h or "VR" in h or "SR" in h or "CRone" 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):
# Object for selecting data eras, search modes, and optimization modes
myModuleSelector = analysisModuleSelector.AnalysisModuleSelector()
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Obtain systematics names
mySystematicsNamesRaw = dsetMgrCreator.getSystematicVariationSources()
mySystematicsNames = []
for i, item in enumerate(mySystematicsNamesRaw, 0):
Print(
"Using systematic %s" %
(ShellStyles.NoteStyle() + item + ShellStyles.NormalStyle()),
i == 0)
mySystematicsNames.append("%sPlus" % item)
mySystematicsNames.append("%sMinus" % item)
if opts.test:
mySystematicsNames = []
# Set the primary source
Verbose(
"Setting the primary source (label=%s)" %
(ShellStyles.NoteStyle() + opts.analysisName +
ShellStyles.NormalStyle()), True)
myModuleSelector.setPrimarySource(
label=opts.analysisName,
dsetMgrCreator=dsetMgrCreator) #fixme: what is label for?
# Select modules
myModuleSelector.doSelect(opts=None) #fixme: (opts=opts)
# Loop over era/searchMode/optimizationMode combos
myTotalModules = myModuleSelector.getSelectedCombinationCount() * (
len(mySystematicsNames) + 1) * len(opts.shape)
Verbose("Found %s modules in total" % (myTotalModules), True)
count, nEras, nSearchModes, nOptModes, nSysVars = myModuleSelector.getSelectedCombinationCountIndividually(
)
if nSysVars > 0:
msg = "Running over %d modules (%d eras x %d searchModes x %d optimizationModes x %d systematic variations)" % (
count, nEras, nSearchModes, nOptModes, nSysVars)
else:
msg = "Running over %d modules (%d eras x %d searchModes x %d optimizationModes)" % (
count, nEras, nSearchModes, nOptModes)
Verbose(msg, True)
# Create pseudo-multicrab creator
msg = "Will create pseudo-dataset %s inside the pseudo-multicrab directory" % (
ShellStyles.NoteStyle() + opts.analysisName +
ShellStyles.NormalStyle())
Verbose(msg, True)
myOutputCreator = pseudoMultiCrabCreator.PseudoMultiCrabCreator(
opts.analysisName, opts.mcrab, verbose=opts.verbose)
# Make time stamp for start time
myGlobalStartTime = time.time()
iModule = 0
# For-loop: All Shapes
for iShape, shapeType in enumerate(opts.shape, 1):
msg = "Shape %d/%d:%s %s" % (iShape, len(
opts.shape), ShellStyles.NormalStyle(), shapeType)
Print(ShellStyles.CaptionStyle() + msg, True)
# Initialize
myOutputCreator.initialize(
subTitle=shapeType,
prefix="") #fixeme: remove shapeType from sub-directory name?
# Get lists of settings
erasList = myModuleSelector.getSelectedEras()
modesList = myModuleSelector.getSelectedSearchModes()
optList = myModuleSelector.getSelectedOptimizationModes()
if 0:
optList.append(
""
) #append the default opt mode iff more optimization modes exist
# For-Loop over era, searchMode, and optimizationMode options
for era in erasList:
for searchMode in modesList:
for optimizationMode in optList:
Verbose(
"era = %s, searchMode = %s, optMode = %s" %
(era, searchMode, optimizationMode), True)
# If an optimization mode is defined in options skip the rest
if opts.optMode != None:
if optimizationMode != opts.optMode:
continue
# Obtain normalization factors
myNormFactors = importNormFactors(era, searchMode,
optimizationMode,
opts.mcrab)
# Nominal module
myModuleInfoString = getModuleInfoString(
era, searchMode, optimizationMode)
iModule += 1
# Inform user of what is being processes
msg = "Module %d/%d:%s %s/%s" % (
iModule, myTotalModules, ShellStyles.NormalStyle(),
myModuleInfoString, shapeType)
Print(ShellStyles.CaptionStyle() + msg, True)
# Keep time
myStartTime = time.time()
Verbose("Create dataset manager with given settings", True)
nominalModule = ModuleBuilder(opts, myOutputCreator,
opts.verbose)
nominalModule.createDsetMgr(opts.mcrab, era, searchMode,
optimizationMode)
if (iModule == 1):
if opts.verbose:
nominalModule.debug()
doQCDNormalizationSyst = False #FIXME
if not doQCDNormalizationSyst:
msg = "Disabling systematics"
Verbose(ShellStyles.WarningLabel() + msg, True) #fixme
# Build the module
nominalModule.buildModule(
opts.dataSrc, opts.ewkSrc,
myNormFactors[opts.normFactorKey],
doQCDNormalizationSyst, opts.normDataSrc,
opts.normEwkSrc)
if len(mySystematicsNames) > 0:
Print(
"Adding QCD normalization systematics (iff also other systematics present) ",
True)
nominalModule.buildQCDNormalizationSystModule(
opts.dataSrc, opts.ewkSrc)
# FIXME: add quark gluon weighting systematics!
if 0:
Print("Adding Quark/Gluon weighting systematics", True)
nominalModule.buildQCDQuarkGluonWeightingSystModule(
opts.dataSrc, opts.ewkSrc,
myNormFactors["FakeWeightingUp"],
myNormFactors["FakeWeightingDown"], False,
opts.normDataSrc, opts.normEwkSrc)
Verbose("Deleting nominal module", True)
nominalModule.delete()
Verbose("Printing time estimate", True)
printTimeEstimate(myGlobalStartTime, myStartTime, iModule,
myTotalModules)
Verbose("Now do the rest of systematics variations", True)
for syst in mySystematicsNames:
iModule += 1
msg = "Analyzing systematics variations %d/%d: %s/%s/%s" % (
iModule, myTotalModules, myModuleInfoString, syst,
shapeType)
Print(
ShellStyles.CaptionStyle() + msg +
ShellStyles.NormalStyle(), True)
myStartTime = time.time()
systModule = ModuleBuilder(opts, myOutputCreator)
# Create dataset manager with given settings
systModule.createDsetMgr(opts.mcrab,
era,
searchMode,
optimizationMode,
systematicVariation=syst)
# Build asystematics module
systModule.buildModule(
opts.dataSrc, opts.ewkSrc,
myNormFactors[opts.normFactorKey], False,
opts.normDataSrc, opts.normEwkSrc)
printTimeEstimate(myGlobalStartTime, myStartTime,
iModule, myTotalModules)
systModule.delete()
Verbose("Pseudo-multicrab ready for %s" % shapeType, True)
# Print some timing statistics
Print(
"Average processing time per module was %.1f seconds" %
getAvgProcessTimeForOneModule(myGlobalStartTime, myTotalModules), True)
Print(
"Total elapsed time was %.1f seconds" %
getTotalElapsedTime(myGlobalStartTime), False)
# Create rest of pseudo multicrab directory
myOutputCreator.finalize(silent=False)
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 = [
"LdgTrijetPt_", "LdgTrijetMass_", "TetrajetBJetPt_",
"TetrajetBJetEta_", "LdgTetrajetPt_", "LdgTetrajetMass_"
]
region = ["CRone", "CRtwo"]
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):
# 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)
withCR2SF_datasetsMgr = GetDatasetsFromDir(opts, opts.withCR2SFcrab)
# Update all events to PU weighting
noSF_datasetsMgr.updateNAllEventsToPUWeighted()
withCR2SF_datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
noSF_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(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()))
# Print summary of datasets to be used
if 0:
noSF_datasetsMgr.PrintInfo()
withCR2SF_datasetsMgr.PrintInfo()
# Merge EWK samples
EwkDatasets = ["Diboson", "DYJetsToLL", "WJetsHT"]
noSF_datasetsMgr.merge("EWK", EwkDatasets)
withCR2SF_datasetsMgr.merge("EWK", EwkDatasets)
# Get histosgram names
folderListIncl = withCR2SF_datasetsMgr.getDataset(withCR2SF_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, withCR2SF_datasetsMgr, num_pathList, den_pathList, opts)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities(fname="lumi.json")
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = datasetsMgr.loadLumi()
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
if "M_%s" % (opts.signalMass) in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
else:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(
ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(),
i == 0)
datasetsMgr.remove(
filter(lambda name: d == name,
datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
keepList = ["LdgTetrajetMass_AfterAllSelections"]
#keepList = ["LdgTetrajetMass_AfterStandardSelections"]
myHistos = []
for h in histoPaths:
if h.split("/")[-1] not in keepList:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
PlotHistograms(datasetsMgr, h)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def _doCalculate2D(self, nSplitBins, shape, normFactors, optionPrintPurityByBins, optionDoNQCDByBinHistograms, myUncertaintyLabels):
'''
Calculates the result for 2D histograms
'''
# Intialize counters for purity calculation in final shape binning
myShapeDataSum = []
myShapeDataSumUncert = []
myShapeEwkSum = []
myShapeEwkSumUncert = []
myList = []
for k in range(1,self._resultShape.GetNbinsY()+1):
myList.append(0.0)
for j in range(1,self._resultShape.GetNbinsX()+1):
myShapeDataSum.append(myList[:])
myShapeDataSumUncert.append(myList[:])
myShapeEwkSum.append(myList[:])
myShapeEwkSumUncert.append(myList[:])
# Calculate results separately for each phase-space bin, and then combine them to get inclusive result
for i in range(0, nSplitBins):
# Get data-driven QCD, data, and MC EWK shape histogram for the phase space bin
h = shape.getDataDrivenQCDHistoForSplittedBin(i)
hData = shape.getDataHistoForSplittedBin(i)
hEwk = shape.getEwkHistoForSplittedBin(i)
# Get normalization factor
wQCDLabel = shape.getPhaseSpaceBinFileFriendlyTitle(i)
if self._optionUseInclusiveNorm:
wQCDLabel = "Inclusive"
wQCD = 0.0
if not wQCDLabel in normFactors.keys():
msg = "No normalization factors available for bin '%s' when accessing histogram %s! Ignoring this bin..." % (wQCDLabel, shape.getHistoName())
print ShellStyles.WarningLabel() + msg
else:
wQCD = normFactors[wQCDLabel]
# Loop over bins in the shape histogram
for j in range(1,h.GetNbinsX()+1):
for k in range(1,h.GetNbinsY()+1):
myResult = 0.0
myStatDataUncert = 0.0
myStatEwkUncert = 0.0
if abs(h.GetBinContent(j,k)) > 0.00001: # Ignore zero bins
# Calculate result
myResult = h.GetBinContent(j,k) * wQCD
# Calculate abs. stat. uncert. for data and for MC EWK
myStatDataUncert = hData.GetBinError(j,k) * wQCD
myStatEwkUncert = hEwk.GetBinError(j,k) * wQCD
#errorPropagation.errorPropagationForProduct(hLeg1.GetBinContent(j), hLeg1Data.GetBinError(j), myEffObject.value(), myEffObject.uncertainty("statData"))
# Do not calculate here MC EWK syst.
myCountObject = extendedCount.ExtendedCount(myResult, [myStatDataUncert, myStatEwkUncert], myUncertaintyLabels)
self._resultCountObject.add(myCountObject)
if optionDoNQCDByBinHistograms:
self._histogramsList[i].SetBinContent(j, k, myCountObject.value())
self._histogramsList[i].SetBinError(j, k, myCountObject.statUncertainty())
self._resultShape.SetBinContent(j, k, self._resultShape.GetBinContent(j, k) + myCountObject.value())
self._resultShape.SetBinError(j, k, self._resultShape.GetBinError(j, k) + myCountObject.statUncertainty()**2) # Sum squared
# Sum items for purity calculation
myShapeDataSum[j-1][k-1] += hData.GetBinContent(j,k)*wQCD
myShapeDataSumUncert[j-1][k-1] += (hData.GetBinError(j,k)*wQCD)**2
myShapeEwkSum[j-1][k-1] += hEwk.GetBinContent(j,k)*wQCD
myShapeEwkSumUncert[j-1][k-1] += (hEwk.GetBinError(j,k)*wQCD)**2
h.Delete()
hData.Delete()
hEwk.Delete()
# Take square root of uncertainties
for j in range(1,self._resultShape.GetNbinsX()+1):
for k in range(1,self._resultShape.GetNbinsY()+1):
self._resultShape.SetBinError(j, k, math.sqrt(self._resultShape.GetBinError(j, k)))
# Print result
print "NQCD Integral(%s) = %s "%(shape.getHistoName(), self._resultCountObject.getResultStringFull("%.1f"))
# Print purity as function of final shape bins
if optionPrintPurityByBins:
print "Purity of shape %s"%shape.getHistoName()
print "shapeBin purity purityUncert"
for j in range (1,self._resultShape.GetNbinsX()+1):
for k in range(1,self._resultShape.GetNbinsY()+1):
myPurity = 0.0
myPurityUncert = 0.0
if abs(myShapeDataSum[j-1][k-1]) > 0.000001:
myPurity = 1.0 - myShapeEwkSum[j-1][k-1] / myShapeDataSum[j-1][k-1]
myPurityUncert = errorPropagation.errorPropagationForDivision(myShapeEwkSum[j-1][k-1], math.sqrt(myShapeEwkSumUncert[j-1][k-1]), myShapeDataSum[j-1][k-1], math.sqrt(myShapeDataSumUncert[j-1][k-1]))
# Store MC EWK content
self._resultShapeEWK.SetBinContent(j, k, myShapeEwkSum[j-1][k-1])
self._resultShapeEWK.SetBinError(j, k, math.sqrt(myShapeEwkSumUncert[j-1][k-1]))
self._resultShapePurity.SetBinContent(j, k, myPurity)
self._resultShapePurity.SetBinError(j, k, myPurityUncert)
# Print purity info of final shape
if optionPrintPurityByBins:
myString = ""
if j < self._resultShape.GetNbinsX():
myString = "%d..%d, "%(self._resultShape.GetXaxis().GetBinLowEdge(j),self._resultShape.GetXaxis().GetBinUpEdge(j))
else:
myString = ">%d, "%(self._resultShape.GetXaxis().GetBinLowEdge(j))
if k < self._resultShape.GetNbinsY():
myString = "%d..%d"%(self._resultShape.GetYaxis().GetBinLowEdge(k),self._resultShape.GetYaxis().GetBinUpEdge(k))
else:
myString = ">%d"%(self._resultShape.GetYaxis().GetBinLowEdge(k))
myString += " %.3f %.3f"%(myPurity, myPurityUncert)
print myString
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
sys.exit()
# Define datasets to remove by default
QCD_list = ["QCD_HT700to1000", "QCD_HT50to100", "QCD_HT500to700", "QCD_HT300to500",
"QCD_HT200to300", "QCD_HT2000toInf", "QCD_HT1500to2000", "QCD_HT100to200", "QCD_HT1000to1500"]
QCDExt_list = [x+"_ext1" for x in QCD_list]
datasetsToRemove = ["QCD-b"]
# datasetsToRemove.extend(QCD_list)
# datasetsToRemove.extend(QCDExt_list)
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
if d.isData():
continue
else:
newOrder.append(d.getName())
# Re-arrange dataset order?
if 0:
s = newOrder.pop( newOrder.index("noTop") )
newOrder.insert(len(newOrder), s) #after "Data"
# Move signal to top
if opts.signal in newOrder:
s = newOrder.pop( newOrder.index(opts.signal) )
newOrder.insert(1, s)
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
ignoreList = ["Aplanarity", "Planarity", "Sphericity", "FoxWolframMoment", "Circularity", "ThirdJetResolution", "Centrality", "_Vs_"]
myHistos = []
for h in histoPaths:
skip = False
# Skip unwanted histos
for i in ignoreList:
if i in h:
skip = True
if skip:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
# Plot the histograms!
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
DataMCHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#======================
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 PlotAndFitTemplates(datasetsMgr,
histoName,
folderName,
opts,
doFakeB=False):
Verbose("PlotAndFitTemplates()")
# Definitions
inclusiveFolder = folderName
genuineBFolder = folderName + "EWKGenuineB"
fakeBFolder = folderName + "EWKFakeB"
if doFakeB:
ewkFolder = genuineBFolder
bkgName = "FakeB"
else:
ewkFolder = inclusiveFolder
bkgName = "QCD"
# Create the plotters
p1 = plots.DataMCPlot(datasetsMgr, "%s/%s" % (inclusiveFolder, histoName))
p2 = plots.DataMCPlot(datasetsMgr, "%s/%s" % (ewkFolder, histoName))
p3 = plots.DataMCPlot(datasetsMgr, "%s/%s" % (inclusiveFolder, histoName))
p4 = plots.DataMCPlot(datasetsMgr, "%s/%s" % (ewkFolder, histoName))
if 0:
p1.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p2.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p3.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p4.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
# Get the histograms
Data_baseline = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline Data") #also legend entry name
FakeB_baseline = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline " + bkgName)
EWK_baseline = p2.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Baseline EWK")
Data_inverted = p3.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted Data")
FakeB_inverted = p3.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted " + bkgName)
EWK_inverted = p4.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Inverted EWK")
# Create FakeB histos: FakeB = (Data - EWK)
msg = "Disabled EWK subtraction (Use Case: Control Triggers)"
Print(ShellStyles.WarningLabel() + msg, True)
#FakeB_baseline.Add(EWK_baseline, -1)
#FakeB_inverted.Add(EWK_inverted, -1)
# Create the final plot object
compareHistos = [EWK_baseline]
p = plots.ComparisonManyPlot(FakeB_inverted, compareHistos, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Inverted " + bkgName, styles.getFakeBStyle())
p.histoMgr.forHisto("Baseline EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Inverted " + bkgName, "P")
p.histoMgr.setHistoDrawStyle("Baseline EWK", "AP")
# Set legend style
p.histoMgr.setHistoLegendStyle("Inverted " + bkgName, "P")
p.histoMgr.setHistoLegendStyle("Baseline EWK", "LP")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
if doFakeB:
p.histoMgr.setHistoLegendLabelMany({
"Baseline EWKGenuineB": "EWK (GenuineB)",
"Inverted FakeB": "Fake-b",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Baseline EWK": "EWK",
"Inverted " + bkgName: "QCD",
})
#=========================================================================================
# Set Minimizer Options
#=========================================================================================
'''
https://root.cern.ch/root/htmldoc/guides/users-guide/FittingHistograms.html#the-th1fit-method
https://root.cern.ch/root/html/src/ROOT__Math__MinimizerOptions.h.html#a14deB
'''
if 0:
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Migrad")
ROOT.Math.MinimizerOptions.SetDefaultStrategy(
2) # Speed = 0, Balance = 1, Robustness = 2
ROOT.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(
5000) # set maximum of function calls
ROOT.Math.MinimizerOptions.SetDefaultMaxIterations(
5000
) # set maximum iterations (one iteration can have many function calls)
if 0:
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Simplex")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Minimize")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit",
"MigradImproved")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Scan")
ROOT.Math.MinimizerOptions.SetDefaultMinimizer("Minuit", "Seek")
ROOT.Math.MinimizerOptions.SetDefaultErrorDef(
1
) # error definition (=1. for getting 1 sigma error for chi2 fits)
ROOT.Math.MinimizerOptions.SetDefaultMaxFunctionCalls(
1000000) # set maximum of function calls
ROOT.Math.MinimizerOptions.SetDefaultMaxIterations(
1000000
) # set maximum iterations (one iteration can have many function calls)
ROOT.Math.MinimizerOptions.SetDefaultPrecision(
-1
) # precision in the objective function calculation (value <= 0 means left to default)
ROOT.Math.MinimizerOptions.SetDefaultPrintLevel(
1
) # None = -1, Reduced = 0, Normal = 1, ExtraForProblem = 2, Maximum = 3
ROOT.Math.MinimizerOptions.SetDefaultTolerance(
1e-03
) # Minuit/Minuit2 converge when the EDM is less a given tolerance. (default 1e-03)
if 1:
hLine = "=" * 45
title = "{:^45}".format("Minimzer Options")
print "\t", hLine
print "\t", title
print "\t", hLine
minOpt = ROOT.Math.MinimizerOptions()
minOpt.Print()
print "\t", hLine, "\n"
#=========================================================================================
# Start fit process
#=========================================================================================
binLabels = ["Inclusive"]
FITMIN = 80
FITMAX = 1000
#moduleInfoString = opts.dataEra + "_" + opts.searchMode + "_" + opts.optMode
moduleInfoString = opts.optMode
#=========================================================================================
# Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
#=========================================================================================
manager = QCDNormalization.QCDNormalizationManagerDefault(
binLabels, opts.mcrab, moduleInfoString)
template_EWKFakeB_Baseline = manager.createTemplate("EWKFakeB_Baseline")
template_EWKFakeB_Inverted = manager.createTemplate("EWKFakeB_Inverted")
template_EWKInclusive_Baseline = manager.createTemplate(
"EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate(
"EWKInclusive_Inverted")
template_FakeB_Baseline = manager.createTemplate("QCD_Baseline")
template_FakeB_Inverted = manager.createTemplate("QCD_Inverted")
#======================================================================2==================
# EWK
#=========================================================================================
par0 = [+7.1817e-01, 0.0, 1.0] # cb_norm
par1 = [+1.7684e+02, 150.0, 200.0] # cb_mean
par2 = [+2.7287e+01, 20.0, 40.0] # cb_sigma (fixed for chiSq=2)
par3 = [-3.9174e-01, -0.5, 0.0] # cb_alpha (fixed for chiSq=2)
par4 = [+2.5104e+01, 0.0, 50.0] # cb_n
par5 = [+7.4724e-05, 0.0, 1.0] # expo_norm
par6 = [-4.6848e-02, -1.0, 0.0] # expo_a
par7 = [+2.1672e+02, 200.0, 250.0] # gaus_mean (fixed for chiSq=2)
par8 = [+6.3201e+01, 20.0, 80.0] # gaus_sigma
template_EWKInclusive_Baseline.setFitter(
QCDNormalization.FitFunction("EWKFunction",
boundary=0,
norm=1,
rejectPoints=0), FITMIN, FITMAX)
template_EWKInclusive_Baseline.setDefaultFitParam(
defaultInitialValue=None,
defaultLowerLimit=[
par0[1], par1[1], par2[0], par3[0], par4[1], par5[1], par6[1],
par7[0], par8[1]
],
defaultUpperLimit=[
par0[2], par1[2], par2[0], par3[0], par4[2], par5[2], par6[2],
par7[0], par8[2]
])
#=========================================================================================
# FakeB/QCD
#=========================================================================================
par0 = [8.9743e-01, 0.0, 1.0] # lognorm_norm
par1 = [2.3242e+02, 300.0, 1000.0] # lognorm_mean
par2 = [1.4300e+00, 0.5, 10.0] # lognorm_shape
par3 = [2.2589e+02, 100.0, 500.0] # gaus_mean
par4 = [4.5060e+01, 0.0, 100.0] # gaus_sigma
template_FakeB_Inverted.setFitter(
QCDNormalization.FitFunction("QCDFunctionAlt",
boundary=0,
norm=1,
rejectPoints=0), FITMIN, FITMAX)
template_FakeB_Inverted.setDefaultFitParam(
defaultInitialValue=None,
defaultLowerLimit=[par0[1], par1[1], par2[1], par3[1], par4[1]],
defaultUpperLimit=[par0[2], par1[2], par2[2], par3[2], par4[2]])
#=========================================================================================
# Set histograms to the templates
#=========================================================================================
if doFakeB:
template_EWKFakeB_Baseline.setHistogram(EWKGenuineB_baseline,
"Inclusive")
template_EWKFakeB_Inverted.setHistogram(EWKGenuineB_inverted,
"Inclusive")
template_EWKInclusive_Baseline.setHistogram(EWKGenuineB_baseline,
"Inclusive")
template_EWKInclusive_Inverted.setHistogram(EWKGenuineB_inverted,
"Inclusive")
else:
template_EWKFakeB_Baseline.setHistogram(EWK_baseline, "Inclusive")
template_EWKFakeB_Inverted.setHistogram(EWK_inverted, "Inclusive")
template_EWKInclusive_Baseline.setHistogram(EWK_baseline, "Inclusive")
template_EWKInclusive_Inverted.setHistogram(EWK_inverted, "Inclusive")
template_FakeB_Baseline.setHistogram(FakeB_baseline, "Inclusive")
template_FakeB_Inverted.setHistogram(FakeB_inverted, "Inclusive")
#=========================================================================================
# Make plots of templates
#=========================================================================================
manager.plotTemplates()
#=========================================================================================
# Fit individual templates to histogram "data_baseline", with custom fit options
#=========================================================================================
fitOptions = "R B L W 0 Q M"
manager.calculateNormalizationCoefficients(Data_baseline, fitOptions,
FITMIN, FITMAX)
# Only for when the measurement is done in bins
fileName = os.path.join(
opts.mcrab,
"QCDInvertedNormalizationFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
if 1:
saveName = fileName.replace("/", "_")
# Draw the histograms
plots.drawPlot(
p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# Save plot in all formats
SavePlot(p, saveName, os.path.join(opts.saveDir, "Fit"))
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr_matched = GetDatasetsFromDir(opts)
datasetsMgr_matched.updateNAllEventsToPUWeighted()
datasetsMgr_matched.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames())) #soti
datasetsMgr_matched.remove(filter(lambda name: "QCD" in name, datasetsMgr_matched.getAllDatasetNames())) #soti
# Set/Overwrite cross-sections
datasetsToRemove = ["QCD-b"]#, "QCD_HT50to100", "QCD_HT100to200"]#, "QCD_HT200to300"]#, "QCD_HT300to500"]
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
#if d.getName() != opts.signal:
if "M_650" in d.getName(): #soti fixmi
datasetsToRemove.append(d.getName())
if "M_800" in d.getName():
datasetsToRemove.append(d.getName())
if "M_200" in d.getName():
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr_matched.remove(filter(lambda name: d in name, datasetsMgr_matched.getAllDatasetNames())) #soti
if opts.verbose:
datasetsMgr.PrintInfo()
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.merge("QCD", GetListOfQCDatasets())
#plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
# Get Luminosity
#intLumi = datasetsMgr.getDataset("Data").getLuminosity() Soti
intLumi = 35920
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
# For-loop: All MC datasets
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Move signal to top
# if opts.signal in newOrder:
# s = newOrder.pop( newOrder.index(opts.signal) )
# newOrder.insert(0, s)
print len(newOrder), "newOrder"
signalMass = ["M_300", "M_500", "M_1000"]
for d in datasetsMgr.getMCDatasets():
for m in signalMass:
if m in d.getName():
s = newOrder.pop( newOrder.index(d.getName()) )
newOrder.insert(0, s)
#datasetsMgr.selectAndReorder(newOrder)
print len(newOrder), "newOrder"
# Add Data to list of samples!
if not opts.onlyMC:
newOrder.insert(0, "Data")
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
histoPaths2 = [h for h in histoPaths1]# if "jet" not in h.lower()]
nHistos = len(histoPaths2)
# For-loop: All histograms
for i, h in enumerate(histoPaths2, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, datasetsMgr_matched, h, intLumi)
ROOT.gStyle.SetNdivisions(10, "X")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def _doCalculate(self, shape, moduleInfoString, normFactors, optionPrintPurityByBins, optionDoNQCDByBinHistograms):
'''
Calculates the result
'''
Verbose("Calculate final shape in signal region (shape * w_QCD) & initialize result containers", True)
nSplitBins = shape.getNumberOfPhaseSpaceSplitBins()
Verbose("Create Shape", True)
self._resultShape = aux.Clone(shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShape.Reset()
self._resultShape.SetTitle("NQCDFinal_Total_%s"%moduleInfoString)
self._resultShape.SetName("NQCDFinal_Total_%s"%moduleInfoString)
Verbose("Create EWK shape", True)
self._resultShapeEWK = aux.Clone(shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapeEWK.Reset()
self._resultShapeEWK.SetTitle("NQCDFinal_EWK_%s"%moduleInfoString)
self._resultShapeEWK.SetName("NQCDFinal_EWK_%s"%moduleInfoString)
Verbose("Create Purity shape", True)
self._resultShapePurity = aux.Clone(shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapePurity.Reset()
self._resultShapePurity.SetTitle("NQCDFinal_Purity_%s"%moduleInfoString)
self._resultShapePurity.SetName("NQCDFinal_Purity_%s"%moduleInfoString)
self._histogramsList = []
myUncertaintyLabels = ["statData", "statEWK"]
self._resultCountObject = extendedCount.ExtendedCount(0.0, [0.0, 0.0], myUncertaintyLabels)
if optionDoNQCDByBinHistograms:
for i in range(0, nSplitBins):
hBin = aux.Clone(self._resultShape)
hBin.SetTitle("NQCDFinal_%s_%s"%(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(" ",""), moduleInfoString))
hBin.SetName("NQCDFinal_%s_%s"%(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(" ",""), moduleInfoString))
self._histogramsList.append(hBin)
if isinstance(self._resultShape, ROOT.TH2):
self._doCalculate2D(nSplitBins, shape, normFactors, optionPrintPurityByBins, optionDoNQCDByBinHistograms, myUncertaintyLabels)
return
# Intialize counters for purity calculation in final shape binning
myShapeDataSum = []
myShapeDataSumUncert = []
myShapeEwkSum = []
myShapeEwkSumUncert = []
# For-loop: All Bins
for j in range(1,self._resultShape.GetNbinsX()+1):
myShapeDataSum.append(0.0)
myShapeDataSumUncert.append(0.0)
myShapeEwkSum.append(0.0)
myShapeEwkSumUncert.append(0.0)
Verbose("Calculate results separately for each phase-space bin and then combine", True)
# For-loop: All measurement bins (e.g. tau pT bins for HToTauNu)
for i in range(0, nSplitBins):
# N.B: The \"Inclusive\" value is in the zeroth bin
Verbose("Get data-driven QCD, data, and MC EWK shape histogram for the phase-space bin", True)
h = shape.getDataDrivenQCDHistoForSplittedBin(i)
hData = shape.getDataHistoForSplittedBin(i)
hEwk = shape.getEwkHistoForSplittedBin(i)
Verbose("Get normalization factor", True)
wQCDLabel = shape.getPhaseSpaceBinFileFriendlyTitle(i)
if self._optionUseInclusiveNorm:
wQCDLabel = "Inclusive"
wQCD = 0.0
if not wQCDLabel in normFactors.keys():
msg = "No normalization factors available for bin '%s' when accessing histogram %s! Ignoring this bin..." % (wQCDLabel,shape.getHistoName())
Print(ShellStyles.WarningLabel() + msg, True)
else:
wQCD = normFactors[wQCDLabel]
msg = "Weighting bin \"%i\" (label=\"%s\") with normFactor \"%s\"" % (i, wQCDLabel, wQCD)
Verbose(ShellStyles.NoteLabel() + msg, True)
# Construct info table (debugging)
table = []
align = "{:>6} {:^10} {:^15} {:>10} {:>10} {:>10} {:^3} {:^8} {:^3} {:^8}"
header = align.format("Bin", "Width", "Range", "Content", "NormFactor", "QCD", "+/-", "Data", "+/-", "EWK")
hLine = "="*90
table.append("{:^90}".format(shape.getHistoName()))
table.append(hLine)
table.append(header)
table.append(hLine)
binSum = 0.0
nBins = h.GetNbinsX()
binWidth = hData.GetBinWidth(0)
xMin = hData.GetXaxis().GetBinCenter(0)
xMax = hData.GetXaxis().GetBinCenter(nBins+1)
# For-Loop (nested): All bins in the shape histogram
for j in range(1, nBins+1):
# Initialise values
myResult = 0.0
myStatDataUncert = 0.0
myStatEwkUncert = 0.0
# Ignore zero bins
if abs(h.GetBinContent(j)) > 0.00001:
Verbose("Calculating the result")
binContent = h.GetBinContent(j)
binRange = "%.1f -> %.1f" % (h.GetXaxis().GetBinLowEdge(j), h.GetXaxis().GetBinUpEdge(j) )
binWidth = GetTH1BinWidthString(h, j)
binSum += binContent
myResult = binContent * wQCD #apply normalisation factor (transfer from CR to SR))
Verbose("Calculate abs. stat. uncert. for data and for MC EWK (Do not calculate here MC EWK syst.)", True)
myStatDataUncert = hData.GetBinError(j) * wQCD
myStatEwkUncert = hEwk.GetBinError(j) * wQCD
table.append(align.format(j, binWidth, binRange, "%0.1f" % binContent, wQCD, "%.1f" % myResult, "+/-", "%.1f" % myStatDataUncert, "+/-", "%.1f" % myStatEwkUncert))
# Get count object
myCountObject = extendedCount.ExtendedCount(myResult, [myStatDataUncert, myStatEwkUncert], myUncertaintyLabels)
self._resultCountObject.add(myCountObject)
if optionDoNQCDByBinHistograms:
Verbose("Setting bin content \"%i\"" % (j), True)
self._histogramsList[i].SetBinContent(j, myCountObject.value())
self._histogramsList[i].SetBinError(j, myCountObject.statUncertainty())
binContent = self._resultShape.GetBinContent(j) + myCountObject.value()
binError = self._resultShape.GetBinError(j) + myCountObject.statUncertainty()**2
Verbose("Setting bin %i to content %0.1f +/- %0.1f" % (j, binContent, binError), j==0)
self._resultShape.SetBinContent(j, binContent)
self._resultShape.SetBinError(j, binError) # Sum squared (take sqrt outside loop on final squared sum)
Verbose("Sum items for purity calculation", True)
myShapeDataSum[j-1] += hData.GetBinContent(j)*wQCD
myShapeDataSumUncert[j-1] += (hData.GetBinError(j)*wQCD)**2
myShapeEwkSum[j-1] += hEwk.GetBinContent(j)*wQCD
myShapeEwkSumUncert[j-1] += (hEwk.GetBinError(j)*wQCD)**2
# Delete the shape histograms
h.Delete()
hData.Delete()
hEwk.Delete()
# For-loop: All shape bins
for j in range(1,self._resultShape.GetNbinsX()+1):
# Take square root of uncertainties
self._resultShape.SetBinError(j, math.sqrt(self._resultShape.GetBinError(j)))
# Print detailed results in a formatted table
qcdResults = self._resultCountObject.getResultAndStatErrorsDict()
bins = "%0.f-%.0f" % (1, nBins)
binRange = "%.1f -> %.1f" % (xMin, xMax)
binSum = "%.1f" % binSum
nQCD = "%.1f" % qcdResults["value"]
dataStat = "%.1f" % qcdResults["statData"]
ewkStat = "%.1f" % qcdResults["statEWK"]
table.append(align.format(bins, binWidth, binRange, binSum, wQCD, nQCD, "+/-", dataStat, "+/-", ewkStat))
table.append(hLine)
for i, line in enumerate(table):
if i == len(table)-2:
Verbose(ShellStyles.TestPassedStyle()+line+ShellStyles.NormalStyle(), i==0)
else:
Verbose(line, i==0)
if optionPrintPurityByBins:
Verbose("Printing Shape Purity bin-by-bin.", True)
self.PrintPurityByBins(nBins, shape, myShapeDataSum, myShapeDataSumUncert, myShapeEwkSum, myShapeEwkSumUncert)
return
def _initialize(self, h):
if len(self._binLabels) > 0:
return
myTitle = h.GetTitle()
myList = myTitle.split(self._separator)
myFactorisationBins = int(
len(myList) / 2
) # allows for the title of the histogram to be placed after the last separator
myOutput = ""
for i in range(0, myFactorisationBins):
self._binLabels.append(myList[i * 2])
if myList[i * 2 + 1].isdigit():
self._binCount.append(int(myList[i * 2 + 1]))
else:
# try a bug fix by taking first character only
if myList[i * 2 + 1][0].isdigit():
print ShellStyles.WarningLabel(
) + "UnfoldedHistogramReader::_initialize(): tried naive bug fix for last factorisation bin dimension (guessed dimension: %s, histo: %s)" % (
myList[i * 2 + 1][0], myList[i * 2 + 1][1:])
self._binCount.append(int(myList[i * 2 + 1][0]))
else:
raise Exception(
ShellStyles.ErrorLabel() +
"UnfoldedHistogramReader: failed to decompose histogram title (it should contain the bin label and nbins information for n bins separated with '%s'\nHistogram title was: %s"
% (self._separator, myTitle))
myOutput += "%s nbins=%d " % (self._binLabels[i],
self._binCount[i])
if self._debugStatus:
print "UnfoldedHistogramReader: Histogram binning determined as : %s" % myOutput
if len(self._binLabels) == 0:
raise Exception(
ShellStyles.ErrorLabel() +
"UnfoldedHistogramReader: failed to decompose histogram title (it should contain the bin label and nbins information for n bins separated with '%s'\nHistogram title was: %s"
% (self._separator, myTitle))
self._unfoldedBinCount = h.GetNbinsY()
# Loop over y axis to find axis values
myBinCaptions = []
myBinRanges = []
for i in range(1, h.GetNbinsY() + 1):
mySplitBin = h.GetYaxis().GetBinLabel(i).split("/")
# Obtain bin captions
if len(self._factorisationCaptions) == 0:
for s in mySplitBin:
myCaption = ""
if "=" in s:
myCaption = s.split("=")[0]
elif ">" in s:
myCaption = s.split(">")[0]
elif "<" in s:
myCaption = s.split("<")[0]
self._factorisationFullBinLabels.append([])
self._factorisationCaptions.append(myCaption)
self._factorisationRanges.append([])
# Obtain range information
for k in range(0, len(mySplitBin)):
if not mySplitBin[k] in self._factorisationFullBinLabels[k]:
self._factorisationFullBinLabels[k].append(mySplitBin[k])
# Remove label and equal signs
s = mySplitBin[k].replace(self._factorisationCaptions[k],
"").replace("=", "")
if not s in self._factorisationRanges[k]:
self._factorisationRanges[k].append(s)
def __init__(self,
dataPath,
ewkPath,
dsetMgr,
luminosity,
moduleInfoString,
normFactors,
optionCalculateQCDNormalizationSyst=True,
normDataSrc = None,
normEWKSrc = None,
optionUseInclusiveNorm=False,
verbose=False):
self._shapePlots = []
self._shapePlotLabels = []
self._QCDNormalizationSystPlots = []
self._QCDNormalizationSystPlotLabels = []
self._moduleInfoString = moduleInfoString
self._useInclusiveNorm = optionUseInclusiveNorm
if len(normFactors.keys()) == 1 and normFactors.keys()[0] == "Inclusive":
self._useInclusiveNorm = True
self._verbose = verbose
msg = "Obtaining final shape from data path \"%s\"" % (dataPath)
Verbose(ShellStyles.HighlightStyle() + msg + ShellStyles.NormalStyle(), True)
# Determine list of plots to consider
myObjects = dsetMgr.getDataset("Data").getDirectoryContent(dataPath)
# Ignore unwanted histograms and those designed for HToTauNu
keywordList = ["JetEtaPhi"]
ignoreList = []
for k in keywordList:
ignoreList.extend(filter(lambda name: k in name, myObjects))
msg = "Ignoring a total of %s histograms:" % (len(ignoreList))
Print(ShellStyles.WarningLabel() + msg, True)
for hName in ignoreList:
print "\t", os.path.join(dataPath, hName)
# Update myObjects list with filtered results
myObjects = list(x for x in myObjects if x not in ignoreList)
# For-Loop: All plots to consider
for i, plotName in enumerate(myObjects, 1):
# For testing
#if "LdgTrijetMass_AfterAllSelections" not in plotName:
# continue
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myObjects)), os.path.join(dataPath, plotName) )
Print(ShellStyles.HighlightAltStyle() + msg + ShellStyles.NormalStyle(), i==1)
# Ensure that histograms exist
dataOk = self._sanityChecks(dsetMgr, dataPath, plotName)
ewkOk = self._sanityChecks(dsetMgr, ewkPath, plotName)
Verbose("Obtaining shape plots (the returned object is not owned)", True)
myShapeHisto = self._obtainShapeHistograms(i, dataPath, ewkPath, dsetMgr, plotName, luminosity, normFactors)
# Obtain plots for systematics coming from met shape difference for control plots #FIXME-Systematics
if optionCalculateQCDNormalizationSyst:
if isinstance(myShapeHisto, ROOT.TH2):
msg = "Skipping met shape uncertainty because histogram has more than 1 dimensions!"
Print(ShellStyles.WarningLabel() + msg, True)
else:
self._obtainQCDNormalizationSystHistograms(myShapeHisto, dsetMgr, plotName, luminosity, normDataSrc, normEWKSrc)
return
def _doCalculate(self, shape, moduleInfoString, normFactors,
optionPrintPurityByBins, optionDoNQCDByBinHistograms):
# Calculate final shape in signal region (shape * w_QCD)
nSplitBins = shape.getNumberOfPhaseSpaceSplitBins()
# Initialize result containers
self._resultShape = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShape.Reset()
self._resultShape.SetTitle("NQCDFinal_Total_%s" % moduleInfoString)
self._resultShape.SetName("NQCDFinal_Total_%s" % moduleInfoString)
self._resultShapeEWK = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapeEWK.Reset()
self._resultShapeEWK.SetTitle("NQCDFinal_EWK_%s" % moduleInfoString)
self._resultShapeEWK.SetName("NQCDFinal_EWK_%s" % moduleInfoString)
self._resultShapePurity = aux.Clone(
shape.getDataDrivenQCDHistoForSplittedBin(0))
self._resultShapePurity.Reset()
self._resultShapePurity.SetTitle("NQCDFinal_Purity_%s" %
moduleInfoString)
self._resultShapePurity.SetName("NQCDFinal_Purity_%s" %
moduleInfoString)
self._histogramsList = []
myUncertaintyLabels = ["statData", "statEWK"]
self._resultCountObject = extendedCount.ExtendedCount(
0.0, [0.0, 0.0], myUncertaintyLabels)
if optionDoNQCDByBinHistograms:
for i in range(0, nSplitBins):
hBin = aux.Clone(self._resultShape)
hBin.SetTitle(
"NQCDFinal_%s_%s" %
(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(
" ", ""), moduleInfoString))
hBin.SetName(
"NQCDFinal_%s_%s" %
(shape.getPhaseSpaceBinFileFriendlyTitle(i).replace(
" ", ""), moduleInfoString))
self._histogramsList.append(hBin)
if isinstance(self._resultShape, ROOT.TH2):
self._doCalculate2D(nSplitBins, shape, normFactors,
optionPrintPurityByBins,
optionDoNQCDByBinHistograms,
myUncertaintyLabels)
return
# Intialize counters for purity calculation in final shape binning
myShapeDataSum = []
myShapeDataSumUncert = []
myShapeEwkSum = []
myShapeEwkSumUncert = []
for j in range(1, self._resultShape.GetNbinsX() + 1):
myShapeDataSum.append(0.0)
myShapeDataSumUncert.append(0.0)
myShapeEwkSum.append(0.0)
myShapeEwkSumUncert.append(0.0)
# Calculate results separately for each phase space bin and then combine
for i in range(0, nSplitBins):
# Get data-driven QCD, data, and MC EWK shape histogram for the phase space bin
h = shape.getDataDrivenQCDHistoForSplittedBin(i)
hData = shape.getDataHistoForSplittedBin(i)
hEwk = shape.getEwkHistoForSplittedBin(i)
# Get normalization factor
wQCDLabel = shape.getPhaseSpaceBinFileFriendlyTitle(i)
if self._optionUseInclusiveNorm:
wQCDLabel = "Inclusive"
wQCD = 0.0
if not wQCDLabel in normFactors.keys():
print ShellStyles.WarningLabel(
) + "No normalization factors available for bin '%s' when accessing histogram %s! Ignoring this bin..." % (
wQCDLabel, shape.getHistoName())
else:
wQCD = normFactors[wQCDLabel]
# Loop over bins in the shape histogram
for j in range(1, h.GetNbinsX() + 1):
myResult = 0.0
myStatDataUncert = 0.0
myStatEwkUncert = 0.0
if abs(h.GetBinContent(j)) > 0.00001: # Ignore zero bins
# Calculate result
myResult = h.GetBinContent(j) * wQCD
# Calculate abs. stat. uncert. for data and for MC EWK
myStatDataUncert = hData.GetBinError(j) * wQCD
myStatEwkUncert = hEwk.GetBinError(j) * wQCD
#errorPropagation.errorPropagationForProduct(hLeg1.GetBinContent(j), hLeg1Data.GetBinError(j), myEffObject.value(), myEffObject.uncertainty("statData"))
# Do not calculate here MC EWK syst.
myCountObject = extendedCount.ExtendedCount(
myResult, [myStatDataUncert, myStatEwkUncert],
myUncertaintyLabels)
self._resultCountObject.add(myCountObject)
if optionDoNQCDByBinHistograms:
self._histogramsList[i].SetBinContent(
j, myCountObject.value())
self._histogramsList[i].SetBinError(
j, myCountObject.statUncertainty())
self._resultShape.SetBinContent(
j,
self._resultShape.GetBinContent(j) + myCountObject.value())
self._resultShape.SetBinError(
j,
self._resultShape.GetBinError(j) +
myCountObject.statUncertainty()**2) # Sum squared
# Sum items for purity calculation
myShapeDataSum[j - 1] += hData.GetBinContent(j) * wQCD
myShapeDataSumUncert[j - 1] += (hData.GetBinError(j) * wQCD)**2
myShapeEwkSum[j - 1] += hEwk.GetBinContent(j) * wQCD
myShapeEwkSumUncert[j - 1] += (hEwk.GetBinError(j) * wQCD)**2
h.Delete()
hData.Delete()
hEwk.Delete()
# Take square root of uncertainties
for j in range(1, self._resultShape.GetNbinsX() + 1):
self._resultShape.SetBinError(
j, math.sqrt(self._resultShape.GetBinError(j)))
# Print result
print "NQCD Integral(%s) = %s " % (
shape.getHistoName(),
self._resultCountObject.getResultStringFull("%.1f"))
# Print purity as function of final shape bins
if optionPrintPurityByBins:
print "Purity of shape %s" % shape.getHistoName()
print "shapeBin purity purityUncert"
for j in range(1, self._resultShape.GetNbinsX() + 1):
myPurity = 0.0
myPurityUncert = 0.0
if abs(myShapeDataSum[j - 1]) > 0.000001:
myPurity = 1.0 - myShapeEwkSum[j - 1] / myShapeDataSum[j - 1]
myPurityUncert = errorPropagation.errorPropagationForDivision(
myShapeEwkSum[j - 1],
math.sqrt(myShapeEwkSumUncert[j - 1]),
myShapeDataSum[j - 1],
math.sqrt(myShapeDataSumUncert[j - 1]))
# Store MC EWK content
self._resultShapeEWK.SetBinContent(j, myShapeEwkSum[j - 1])
self._resultShapeEWK.SetBinError(
j, math.sqrt(myShapeEwkSumUncert[j - 1]))
self._resultShapePurity.SetBinContent(j, myPurity)
self._resultShapePurity.SetBinError(j, myPurityUncert)
# Print purity info of final shape
if optionPrintPurityByBins:
myString = ""
if j < self._resultShape.GetNbinsX():
myString = "%d..%d" % (
self._resultShape.GetXaxis().GetBinLowEdge(j),
self._resultShape.GetXaxis().GetBinUpEdge(j))
else:
myString = ">%d" % (
self._resultShape.GetXaxis().GetBinLowEdge(j))
myString += " %.3f %.3f" % (myPurity, myPurityUncert)
print myString
def importNormFactors(era, searchMode, optimizationMode, multicrabDirName):
'''
Imports the auto-generates FakeBTranserFactors.py file, which is
created by the plotting/fitting templates script (plotQCD_Fit.py)
This containsthe results of fitting to the Baseline Data the templates m_{jjb}
shapes from the QCD (Inverted Data) and EWK (Baseline MC).
Results include the fit details for each shape and the QCD NormFactor for moving
from the ControlRegion (CR) to the Signal Region (SR).
The aforementioned python file and a folder with the histogram ROOT files and the individual
fits. The foler name will be normalisationPlots/<OptsMode> and will be placed inside the
<pseudomulticrab_dir>. The autogenerated file file be place in the cwd (i.e. work/)
'''
# Find candidates for normalisation scripts
scriptList = getNormFactorFileList(dirName=multicrabDirName,
fileBaseName=opts.normFactorsSrc)
# Create a string with the module information used
moduleInfoString = getModuleInfoString(era, searchMode, optimizationMode)
# Construct source file name
src = getGetNormFactorsSrcFilename(multicrabDirName,
opts.normFactorsSrc % moduleInfoString)
# Check if normalization coefficients are suitable for the choses era
Verbose("Reading normalisation factors from:\n\t%s" % src, True)
# Split the path to get just the file name of src
pathList = src.replace(".py", "").split("/")
# Insert the directory where the normFactor files reside into the path so that they are found
if len(pathList) > 1:
cwd = os.getenv("PWD")
# Get directories to src in a list [i.e. remove the last entry (file-name) from the pathList]
dirList = map(str, pathList[:(len(pathList) - 1)])
srcDir = "/".join(dirList)
sys.path.insert(0, os.path.join(cwd, srcDir))
# Import the (normFactor) src file
Print(
"Importing the transfer factors from src file %s" %
(ShellStyles.NoteStyle() + src + ShellStyles.NormalStyle()), True)
srcBase = os.path.basename("/".join(pathList))
normFactorsImport = __import__(srcBase)
# Get the function definition
myNormFactorsSafetyCheck = getattr(normFactorsImport,
"QCDInvertedNormalizationSafetyCheck")
Verbose(
"Check that the era=%s, searchMode=%s, optimizationMode=%s info matches!"
% (era, searchMode, optimizationMode))
myNormFactorsSafetyCheck(era, searchMode, optimizationMode)
# Obtain normalization factors
myNormFactorsImport = getattr(normFactorsImport, "QCDNormalization")
# Systematic Variations
msg = "Disabled NormFactors SystVar Fake Weighting Up/Down"
Print(ShellStyles.WarningLabel() + msg, True)
# myNormFactorsImportSystVarFakeWeightingDown = getattr(normFactorsImport, "QCDPlusEWKFakeTausNormalizationSystFakeWeightingVarDown") #FIXME
# myNormFactorsImportSystVarFakeWeightingUp = getattr(normFactorsImport, "QCDPlusEWKFakeTausNormalizationSystFakeWeightingVarUp") #FIXME
# Import the normalisation factors and inform user
myNormFactors = {}
if "FakeB" in opts.analysisName:
myNormFactors[opts.normFactorKey] = myNormFactorsImport
elif "GenuineB" in opts.analysisName:
myNormFactors[opts.normFactorKey] = {'Inclusive': 1.0}
else:
raise Exception("This should not be reached!")
# Inform user of normalisation factors
msg = "Obtained %s normalisation factor dictionary. The values are:" % (
ShellStyles.NoteStyle() + opts.normFactorKey +
ShellStyles.NormalStyle())
Print(msg, True)
for i, k in enumerate(myNormFactors[opts.normFactorKey], 1):
keyName = k
keyValue = myNormFactors[opts.normFactorKey][k]
#msg += "%s = %s" % (keyName, keyValue)
msg = "%s = %s" % (keyName, keyValue)
Print(msg, i == 0)
# Inform user of weighting up/down
msg = "Disabled NormFactors Weighting Up/Down"
Verbose(ShellStyles.WarningLabel() + msg, True) #fixme
# myNormFactors["FakeWeightingDown"] = myNormFactorsImportSystVarFakeWeightingDown # FIXME
# myNormFactors["FakeWeightingUp"] = myNormFactorsImportSystVarFakeWeightingUp # FIXME
return myNormFactors