def PrepareWeightsAndHistos(self,
regionString="",
normString="",
normCuts="",
abstract=None,
topLvl=None,
chan=None,
sam=None):
nomName = "h%sNom_%s_obs_%s" % (sam.name, regionString,
replaceSymbols(chan.variableName))
for lowhigh in ["High_", "Low_"]:
lowhighName = "h%s%s%s%s_obs_%s" % (
sam.name, self.name, lowhigh, regionString,
replaceSymbols(chan.variableName))
if abstract.hists[lowhighName] is None:
if lowhigh == "High_":
abstract.hists[
lowhighName] = histMgr.buildUserHistoSysFromHist(
lowhighName, self.high, abstract.hists[nomName])
elif lowhigh == "Low_":
abstract.hists[
lowhighName] = histMgr.buildUserHistoSysFromHist(
lowhighName, self.low, abstract.hists[nomName])
return
def PrepareGlobalNormalization(self, normString, abstract, topLvl, chan,
sam):
for lowhigh in ["Nom_", self.name + "High_", self.name + "Low_"]:
histName = "h%s%s%sNorm" % (sam.name, lowhigh, normString)
if not histName in abstract.hists.keys():
if sam.normRegions:
if not abstract.readFromTree:
abstract.hists[histName] = None
abstract.prepare.addHisto(histName)
else:
abstract.hists[histName] = TH1F(
histName, histName, 1, 0.5, 1.5)
totNorm = 0.0
for normReg in sam.normRegions:
nameTmp = "h" + sam.name + lowhigh + normReg[
0] + "_obs_" + replaceSymbols(
chan.variableName)
try:
totNorm += abstract.hists[
nameTmp].GetSumOfWeights()
except:
log.warning(
"could get histogram %s for normalization"
% nameTmp)
abstract.hists[histName].SetBinContent(1, totNorm)
return
def PrepareWeightsAndHistos(self, regionString="", normString="",
normCuts="", abstract=None,
topLvl=None, chan=None, sam=None):
nomName = "h%sNom_%s_obs_%s" % (sam.name, regionString, replaceSymbols(chan.variableName) )
for lowhigh in ["High_","Low_"]:
lowhighName = "h%s%s%s%s_obs_%s" % (sam.name, self.name, lowhigh, regionString, replaceSymbols(chan.variableName))
if abstract.hists[lowhighName] is None:
if lowhigh=="High_":
abstract.hists[lowhighName] = histMgr.buildUserHistoSysFromHist(lowhighName, self.high, abstract.hists[nomName])
elif lowhigh=="Low_":
abstract.hists[lowhighName] = histMgr.buildUserHistoSysFromHist(lowhighName, self.low, abstract.hists[nomName])
return
def tryAddHistos(self,
highorlow="",
regionString="",
normString="",
normCuts="",
abstract=None,
chan=None,
sam=None):
histName = "h%s%s%s%s_obs_%s" % (sam.name, self.name, highorlow,
regionString,
replaceSymbols(chan.variableName))
log.debug(" adding histo %s" % histName)
try:
abstract.prepare.addHisto(histName,
useOverflow=chan.useOverflowBin,
useUnderflow=chan.useUnderflowBin)
except:
pass
def PrepareGlobalNormalization(self,normString,abstract,topLvl,chan,sam):
for lowhigh in ["Nom_",self.name+"High_",self.name+"Low_"]:
histName = "h%s%s%sNorm" % (sam.name, lowhigh, normString)
if not histName in abstract.hists.keys():
if sam.normRegions:
if not abstract.readFromTree:
abstract.hists[histName] = None
abstract.prepare.addHisto(histName)
else:
abstract.hists[histName] = TH1F(histName, histName, 1, 0.5, 1.5)
totNorm=0.0
for normReg in sam.normRegions:
nameTmp = "h" + sam.name + lowhigh + normReg[0] + "_obs_" + replaceSymbols(chan.variableName)
try:
totNorm += abstract.hists[nameTmp].GetSumOfWeights()
except:
log.warning("could get histogram %s for normalization" % nameTmp)
abstract.hists[histName].SetBinContent(1,totNorm)
return
def tryAddHistos(self, highorlow="", regionString="", normString="",
normCuts="", abstract=None, chan=None, sam=None):
histName = "h%s%s%s%s_obs_%s" % (sam.name, self.name, highorlow, regionString, replaceSymbols(chan.variableName) )
log.debug(" adding histo %s" % histName)
try:
abstract.prepare.addHisto(histName,
useOverflow=chan.useOverflowBin,
useUnderflow=chan.useUnderflowBin)
except:
pass
def __init__(self,
variableName,
regions,
prefix,
nBins,
binLow,
binHigh,
statErrorThreshold=None,
hasB=False):
"""
Store configuration, set unique channel name from variable,
define cut region, binning and open file
@param variableName The name of the variable to bin in
@param regions All regions used in the channel
@param prefix A prefix for XML files for the channel
@param nBins The number of bins to use
@param binLow Lower edge of the first bin
@param binHigh Upper edge of the last bin
@param statErrorThreshold Threshold for statistical errors
"""
regions.sort()
self.regionString = "".join(regions)
self.variableName = variableName
self.niceVarName = replaceSymbols(variableName)
self.name = self.niceVarName + "_" + self.regionString
self.channelName = self.regionString + "_" + self.niceVarName
self.ConstructorInit(prefix) # shared method with Clone or deepcopy
self.regions = regions
self.nBins = nBins
self.binHigh = binHigh
self.binLow = binLow
self.sampleList = []
self.dataList = []
self.weights = []
self.systDict = {}
self.infoDict = {}
self.hasBQCD = False
self.useOverflowBin = False
self.useUnderflowBin = False
self.hasStatConfig = False
self.hasDiscovery = False
if statErrorThreshold is not None:
self.hasStatConfig = True
self.statErrorThreshold = statErrorThreshold
self.statErrorType = "Gaussian" #"Gaussian" "Poisson"
self.files = []
self.treeName = ''
self.parentTopLvl = None
# Plot cosmetics
self.minY = None
self.maxY = None
self.title = None
self.titleX = None
self.titleY = None
self.logY = None
self.ATLASLabelX = None
self.ATLASLabelY = None
self.ATLASLabelText = None
self.showLumi = None
self.lumi = None
self.remapSystChanName = ""
self.blind = False
self.text1 = ''
self.text2 = ''
self.textsize1 = 0.03
self.textsize2 = 0.03
log.debug("Defining new channel '{0}'".format(self.name))
return
