def loadHistsForProcess(self,
processName,
scaleNorm=1,
expandedTheory=True):
plotsToRead = self.listOfHistsByProcess(processName,
nameReplace=("unrolled", "2D"))
group = HistTools.makeCompositeHists(
self.inputFile,
processName, {
proc: self.crossSectionMap[proc]
for proc in self.processes[processName]
},
self.lumi,
plotsToRead,
rebin=self.rebin,
overflow=False)
if self.isUnrolledFit:
for hist in group:
if not "TH2" in hist.ClassName():
continue
hist = HistTools.makeUnrolledHist(hist, self.unrolledBinsX,
self.unrolledBinsY)
histName = hist.GetName()
group.Add(hist)
fitVariable = self.getFitVariable(processName)
#TODO:Make optional
processedHists = []
for chan in self.channels:
histName = "_".join([fitVariable, chan])
hist = group.FindObject(histName)
if not hist:
logging.warning("Failed to produce hist %s for process %s" %
(histName, processName))
continue
if self.removeZeros and "data" not in processName.lower():
HistTools.removeZeros(hist)
if self.addOverflow:
HistTools.addOverflow(hist)
processedHists.append(histName)
self.yields[chan].update({
processName:
round(hist.Integral(), 3) if hist.Integral() > 0 else 0.0001
})
if chan == self.channels[0]:
self.yields["all"][processName] = self.yields[chan][
processName]
else:
self.yields["all"][processName] += self.yields[chan][
processName]
if processName in self.perbinVariations:
for varName, var, corr in self.perbinVariations[processName]:
map(
lambda x: group.Add(x),
self.perBinVariationHists(hist, varName, var, corr,
chan == self.channels[0]))
scaleHists = []
if processName in self.theoryVariations:
theoryVars = self.theoryVariations[processName]
try:
scaleHists.extend(
self.scaleHistsForProcess(group, processName, chan,
expandedTheory))
if 'theoryBasedVars' in theoryVars['scale']:
for theoryBasedVar in theoryVars['scale'][
'theoryBasedVars']:
scaleHists.extend(
self.scaleHistsForProcess(
group, processName, chan, expandedTheory,
theoryBasedVar))
except ValueError as e:
logging.warning(e)
continue
pdfHists = []
pdfVars = filter(lambda x: 'pdf' in x, theoryVars.keys())
weightHist = group.FindObject(
self.weightHistName(chan, processName))
for var in pdfVars:
pdfVar = theoryVars[var]
pdfType = "MC"
if "hessian" in pdfVar['combine']:
pdfType = "Hessian" if "assym" not in pdfVar[
'combine'] else "AssymHessian"
pdfFunction = "get%sPDFVarHists" % pdfType
args = [
weightHist, pdfVar['entries'], processName, self.rebin,
pdfVar['central'], pdfVar['name']
]
if self.isUnrolledFit:
pdfFunction = pdfFunction.replace(
"get", "getTransformed3D")
args = args[0:1] + [
HistTools.makeUnrolledHist,
[self.unrolledBinsX, self.unrolledBinsY]
] + args[1:]
updatePdfs = getattr(HistTools, pdfFunction)(*args)
pdfHists += updatePdfs
if expandedTheory and pdfVar['name'] == 'NNPDF31':
args.pop(len(args) - 1)
pdfFunction = HistTools.getAllSymHessianHists if not self.isUnrolledFit else HistTools.getTransformed3DAllSymHessianHists
allPdfHists = pdfFunction(*args)
pdfHists.extend(allPdfHists)
if not self.isUnrolledFit:
cenHist, _ = HistTools.getLHEWeightHists(
weightHist, pdfVar['entries'][:1], processName,
"pdf", self.rebin)
else:
cenHist = HistTools.getAllTransformed3DHists(
weightHist, HistTools.makeUnrolledHist,
[self.unrolledBinsX, self.unrolledBinsY],
processName, pdfVar['entries'][:1])
if len(cenHist) and cenHist[0]:
pdfHists.append(cenHist[0].Clone())
group.extend(scaleHists + pdfHists)
if chan == self.channels[0]:
theoryVarLabels = []
for h in group:
fitvar = self.getFitVariable(processName)
theoryVarLabels.extend(
re.findall("_".join([fitvar, "(.*pdf.*)", chan]),
h.GetName()))
theoryVarLabels.extend(
re.findall(
"_".join([fitvar, "(.*QCDscale.*)", chan]),
h.GetName()))
self.variations[processName].extend(set(theoryVarLabels))
if self.addOverflow:
map(HistTools.addOverflow,
filter(lambda x: (x.GetName() not in processedHists), group))
if self.removeZeros and "data" not in group.GetName().lower():
map(HistTools.removeZeros,
filter(lambda x: (x.GetName() not in processedHists), group))
#TODO: You may want to combine channels before removing zeros
if self.channelsToCombine.keys():
self.combineChannels(group, processName)
self.histData[processName] = group
def loadHistsForProcess(self, processName, scaleNorm=1, expandedTheory=True):
plotsToRead = self.listOfHistsByProcess(processName)
group = HistTools.makeCompositeHists(self.inputFile, processName,
{proc : self.crossSectionMap[proc] for proc in self.processes[processName]},
self.lumi, plotsToRead, rebin=self.rebin, overflow=False)
fitVariable = self.getFitVariable(processName)
#TODO:Make optional
processedHists = []
for chan in self.channels:
histName = "_".join([fitVariable, chan]) if chan != "all" else fitVariable
hist = group.FindObject(histName)
if not hist:
raise RuntimeError("Failed to produce hist %s for process %s" % (histName, processName))
#TODO: Make optional
if "data" not in processName.lower():
HistTools.removeZeros(hist)
HistTools.addOverflow(hist)
processedHists.append(histName)
self.yields[chan].update({processName : round(hist.Integral(), 3) if hist.Integral() > 0 else 0.0001})
if chan == self.channels[0]:
self.yields["all"][processName] = self.yields[chan][processName]
else:
self.yields["all"][processName] += self.yields[chan][processName]
if processName in self.theoryVariations:
weightHist = group.FindObject(self.weightHistName(chan, processName))
if not weightHist:
logging.warning("Failed to find %s. Skipping" % self.weightHistName(chan, processName))
continue
theoryVars = self.theoryVariations[processName]
scaleHists = HistTools.getScaleHists(weightHist, processName, self.rebin,
entries=theoryVars['scale']['entries'],
central=(theoryVars['scale']['central'] if 'scale' in theoryVars else -1))
if expandedTheory:
expandedScaleHists = HistTools.getExpandedScaleHists(weightHist, processName, self.rebin,
entries=theoryVars['scale']['entries'],
central=(theoryVars['scale']['central'] if 'scale' in theoryVars else -1),
)
scaleHists.extend(expandedScaleHists)
pdfHists = []
pdfVars = filter(lambda x: 'pdf' in x, theoryVars.keys())
for var in pdfVars:
pdfVar = theoryVars[var]
pdfType = "SymmMC"
if "hessian" in pdfVar['combine']:
pdfType = "Hessian" if "assym" not in pdfVar['combine'] else "AssymHessian"
pdfFunction = "get%sPDFVariationHists" % pdfType
pdfHists += getattr(HistTools, pdfFunction)(weightHist, pdfVar['entries'], processName,
self.rebin, central=pdfVar['central'],
pdfName=pdfVar['name'])
if expandedTheory and "hessian" in pdfVar['combine']:
allPdfHists = HistTools.getAllSymmetricHessianVariationHists(weightHist, pdfVar['entries'], processName,
self.rebin, central=pdfVar['central'])
pdfHists.extend(allPdfHists)
cenHist, _ = HistTools.getLHEWeightHists(weightHist, pdfVar['entries'][:1], processName, "pdf", self.rebin)
if len(cenHist) and cenHist[0]:
pdfHists.append(cenHist[0].Clone())
group.extend(scaleHists+pdfHists)
if chan == self.channels[0]:
theoryVarLabels = []
for h in group:
theoryVarLabels.extend(re.findall("_".join([self.fitVariable, "(.*)", chan]), h.GetName()))
self.variations[processName].extend(set(theoryVarLabels))
#TODO: Make optional
map(HistTools.addOverflow, filter(lambda x: (x.GetName() not in processedHists), group))
if "data" not in group.GetName().lower():
map(HistTools.removeZeros, filter(lambda x: (x.GetName() not in processedHists), group))
#TODO: You may want to combine channels before removing zeros
self.combineChannels(group, processName)
self.histData[processName] = group
