def plotLimits():
mkdir(os.path.join(limitsDir, args.outDirName))
for mass in args.massPoints:
plotLimitsByMass(mass)
for category in sorted(categoriesToUse):
if names2RepsToUse[category] in args.categoriesToSkip:
continue
plotLimitsByCategory(category)
for combination in sorted(combinationsToUse):
if combination in args.categoriesToSkip:
continue
plotLimitsByCategory(combination)
def categories():
#
# prepare the submission/output dirs
#
outDir = os.path.join(combineoutputDir, args.outDirName)
mkdir(outDir)
submitDir = os.path.join(combinesubmissionsDir, args.outDirName)
mkdir(submitDir)
#
# Do each Category separately
#
import definitions as defs
cmds = []
for category in categoriesToUse:
if names2RepsToUse[category] in args.categoriesToSkip:
continue
datacard = os.path.join(
datacardsworkspacesDir, args.inDirName,
"datacard__{category}__{signalModel}.root".format(
category=names2RepsToUse[category],
signalModel=args.signalModel))
for massPoint in args.massPoints:
# set the mass for splines
physicsModelParametersToSet["MH"] = massPoint
outputModifier = names2RepsToUse[category] + "__" + \
str(massPoint) + "__" + args.signalModel
cmdToRun = "combine -M {method} -m {mass} -n {outputModifier} -d {datacard} --setPhysicsModelParameters {physicsModelParametersToSet} --freezeNuisances {nuisancesToFreeze}".format(
method=args.method,
mass=massPoint,
outputModifier=outputModifier,
datacard=datacard,
physicsModelParametersToSet=map2string(
physicsModelParametersToSet),
nuisancesToFreeze=",".join(nuisancesToFreeze))
cmd = defs.Command(category, [cmdToRun])
cmds.append(cmd)
label = "%s_%s_%s" % ("categories", args.method, args.signalModel)
createLaunchers(cmds, submitDir, label)
def biasScan():
biasScanResultsDir = os.path.join(biasScanDir, args.outDirName)
combineoutputPathDir = os.path.join(combineoutputDir, args.outDirName)
workspacesDir = os.path.join(datacardsworkspacesDir, args.workspacesDirName)
mkdir(biasScanResultsDir)
hMeans = {}
hMedians = {}
for category in categoriesToUse:
if names2RepsToUse[category] in args.categoriesToSkip:
continue
# extract the multi pdf
workspaceFileName = "workspace__{category}__{signalModel}.root".format(
category=names2RepsToUse[category], signalModel=args.signalModel)
refWorkspaceFile = R.TFile(os.path.join(workspacesDir, workspaceFileName))
higgsWorkspace = refWorkspaceFile.Get("higgs")
multipdf = higgsWorkspace.pdf("multipdf_{category}".format(
category=names2RepsToUse[category]))
for massPoint in args.massPoints:
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)] = R.TH2D("Means_{category}_{mass}".format(category=category, mass=massPoint), "Means", multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1,
multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)] = R.TH2D("Madians_{category}_{mass}".format(category=category, mass=massPoint), "Medians", multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1,
multipdf.getNumPdfs()+1, 0, multipdf.getNumPdfs()+1)
for iref in range(multipdf.getNumPdfs()):
refPdfName = multipdf.getPdf(iref).GetName()
q = False
for xxx in args.modelsToSkip:
if xxx==refPdfName.split("_")[0]:
q = True
if q==True: continue
for icurrent in range(multipdf.getNumPdfs()):
fitPdfName = multipdf.getPdf(icurrent).GetName()
q = False
for xxx in args.modelsToSkip:
if xxx==fitPdfName.split("_")[0]:
q = True
if q==True: continue
canvas = R.TCanvas("c1", "c1", 1000, 600)
try:
fileName = "mlfit{category}__{mass}__{iref}__{icurrent}__{signalModel}.root".format(category=names2RepsToUse[category], mass=massPoint, iref=iref, icurrent=icurrent, signalModel=args.signalModel)
f = R.TFile(os.path.join(combineoutputPathDir, fileName))
tree = f.Get("tree_fit_sb")
tree.Draw("(mu-1)/muErr>>h(500, -5,5)")
# get the histogram and perform some manipulations
hist = R.gFile.Get("h")
import array
probs = array.array("d", [0.5])
quantiles = array.array("d", [0])
hist.GetQuantiles(1, quantiles, probs)
latex = R.TLatex()
latex.SetNDC()
latex.SetTextSize(0.02)
latex.SetTextAlign(13) # align at top
latex.SetTextSize(0.03)
latex.DrawLatex(0.2, 0.8, "Median = " + str(quantiles[0]))
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].Fill(iref, icurrent, hist.GetMean()*100)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetBinLabel(iref+1, refPdfName)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetBinLabel(icurrent+1, fitPdfName)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].Fill(iref, icurrent, quantiles[0]*100)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetBinLabel(iref+1, refPdfName)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetBinLabel(icurrent+1, fitPdfName)
cfileName = "pull__{category}__{mass}__{iref}__{icurrent}__{signalModel}.png".format(category=names2RepsToUse[category], mass=massPoint, iref=iref, icurrent=icurrent, signalModel=args.signalModel)
canvas.SaveAs(os.path.join(biasScanResultsDir, cfileName))
except Exception as exc:
print "There was a problem with file: {file}\n".format(file=fileName)
print type(exc)
print exc.args
print exc
finally:
f.Close()
# plot the 2D
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].SetTitle("Mean (#mu_{fit} - #mu_{0})/#sigma #mu_{fit} (%%), %s, %d GeV" % (category, massPoint))
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].SetStats(0)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetTitle("Fit Model")
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetTitle("Reference Model")
# hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetRangeUser(0,5)
# hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetRangeUser(0,5)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].GetZaxis().SetRangeUser(-100,+100)
hMeans["Mean_{category}_{mass}".format(category=category, mass=massPoint)].Draw("COLZTEXT")
canvas.SaveAs(os.path.join(biasScanResultsDir, "pullMeans2D__{category}__{mass}__{signalModel}.png".format(
category=names2RepsToUse[category], mass=massPoint, signalModel=args.signalModel)))
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].SetTitle("Median (#mu_{fit} - #mu_{0})/#sigma #mu_{fit} (%%), %s, %d GeV" % (category, massPoint))
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].SetStats(0)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetTitle("Fit Model")
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetTitle("Reference Model")
# hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetXaxis().SetRangeUser(0,5)
# hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetYaxis().SetRangeUser(0,5)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].GetZaxis().SetRangeUser(-100,+100)
hMedians["Median_{category}_{mass}".format(category=category, mass=massPoint)].Draw("COLZTEXT")
canvas.SaveAs(os.path.join(biasScanResultsDir, "pullMedians2D__{category}__{mass}__{signalModel}.png".format(
category=names2RepsToUse[category], mass=massPoint, signalModel=args.signalModel)))
def map2string(m, sep=","):
return ",".join(["{key}={value}".format(key=x, value=m[x]) for x in m])
#######################
### Directory Setup ###
#######################
projectDirLocal = "/Users/vk/software/Analysis/files/analysis_results/"
projectDirLxplus = "/afs/cern.ch/work/v/vkhriste/Projects/HiggsAnalysis/analysis_results"
cmsswDir = "/afs/cern.ch/work/v/vkhriste/Projects/HiggsAnalysis/CMSSW_7_4_9/src"
projectDirToUse = projectDirLxplus
histDir = os.path.join(projectDirToUse, "results", jobLabel)
distributionsDir = os.path.join(projectDirToUse, "distributions", jobLabel)
aux.mkdir(distributionsDir)
backgroundfitsDir = os.path.join(projectDirToUse, "backgroundfits", jobLabel)
aux.mkdir(backgroundfitsDir)
signalfitsDir = os.path.join(projectDirToUse, "signalfits", jobLabel)
aux.mkdir(signalfitsDir)
singalfitinterpolationsDir = os.path.join(projectDirToUse,
"signalfitinterpolations", jobLabel)
aux.mkdir(singalfitinterpolationsDir)
signalfitinterpolationswithsplineDir = os.path.join(
projectDirToUse, "signalfitinterpolationswithspline", jobLabel)
aux.mkdir(signalfitinterpolationswithsplineDir)
backgroundfitswithroomultipdfDir = os.path.join(
projectDirToUse, "backgroundfitswithroomultipdf", jobLabel)
aux.mkdir(backgroundfitswithroomultipdfDir)
datacardsworkspacesDir = os.path.join(projectDirToUse, "datacardsworkspaces",
jobLabel)
def biasScan():
R.gSystem.Load("libHiggsAnalysisCombinedLimit.so")
#
# prepare the submission/output dirs
#
outDir = os.path.join(combineoutputDir, args.outDirName)
mkdir(outDir)
submitDir = os.path.join(combinesubmissionsDir, args.outDirName)
mkdir(submitDir)
workspacesDir = os.path.join(datacardsworkspacesDir, args.inDirName)
#
# perform the scan
#
import definitions as defs
cmds = []
ourNuisances = nuisancesToFreeze
for category in categoriesToUse:
if names2RepsToUse[category] in args.categoriesToSkip:
continue
datacard = os.path.join(
datacardsworkspacesDir, args.inDirName,
"datacard__{category}__{signalModel}.root".format(
category=names2RepsToUse[category],
signalModel=args.signalModel))
# add the current pdfindex to the list of nuisances to be frozen
ourNuisances.append(
"pdfindex_{category}".format(category=names2RepsToUse[category]))
# get the current workspace
workspaceFileName = "workspace__{category}__{signalModel}.root".format(
category=names2RepsToUse[category], signalModel=args.signalModel)
currentWorkspaceFile = R.TFile(
os.path.join(workspacesDir, workspaceFileName))
higgsWorkspace = currentWorkspaceFile.Get("higgs")
# get all the variables
allVariables = higgsWorkspace.allVars().contentsString().split(",")
# get the multipdf
multipdf = higgsWorkspace.pdf(
"multipdf_{category}".format(category=names2RepsToUse[category]))
for massPoint in args.massPoints:
physicsModelParametersToSet["MH"] = massPoint
# iterate thru all of the pdfs in our envelope
for iref in range(multipdf.getNumPdfs()):
# this is list of cmd strings that have to go as a single Command!
cmdStrings = []
# set the pdfindex_category to the iref
physicsModelParametersToSet["pdfindex_{category}".format(
category=names2RepsToUse[category])] = iref
outputModifierGenerateOnly = names2RepsToUse[category] + "__" + \
str(massPoint) + "__" + str(iref) + "__" + args.signalModel
# retrieve the names of all other parameters
# that we have to fix. Given that we fit with ibkg
# parameters of other models must be frozen
# for the fit to be executed 100% correctly!!!
otherModelsParametersToFreezeForGenOnly = []
for var in allVariables:
for ipdf in range(multipdf.getNumPdfs()):
if iref == ipdf: continue
currentPdfName = multipdf.getPdf(ipdf).GetName()
if currentPdfName in var:
otherModelsParametersToFreezeForGenOnly.append(var)
break
# generate the toys command
cmdStrings.append("# GenerateOnly\n")
cmdGenerateOnly = "combine -d {datacard} -n {outputModifier} -M GenerateOnly --setPhysicsModelParameters {physicsModelParametersToSet} --toysFrequentist -t {nToys} -s {seed} --expectSignal {injectSignal} --saveToys -m {mass} --freezeNuisances {nuisancesToFreeze}".format(
datacard=datacard,
mass=massPoint,
outputModifier=outputModifierGenerateOnly,
physicsModelParametersToSet=map2string(
physicsModelParametersToSet),
nuisancesToFreeze=",".join(
ourNuisances +
otherModelsParametersToFreezeForGenOnly),
nToys=nToys,
seed="$SEED",
injectSignal=args.injectSig)
cmdStrings.append(cmdGenerateOnly)
for ibkg in range(multipdf.getNumPdfs()):
currentPdfName = multipdf.getPdf(ibkg).GetName()
# retrieve the names of all other parameters
# that we have to fix. Given that we fit with ibkg
# parameters of other models must be frozen
# for the fit to be executed 100% correctly!!!
otherModelsParametersToFreeze = []
for var in allVariables:
for ipdf in range(multipdf.getNumPdfs()):
if ibkg == ipdf: continue
currentPdfName = multipdf.getPdf(ipdf).GetName()
if currentPdfName in var:
otherModelsParametersToFreeze.append(var)
break
# set the pdfindex_category to the model that
# we are going to fit our toy's reference with!
physicsModelParametersToSet["pdfindex_{category}".format(
category=names2RepsToUse[category])] = ibkg
outputModifier = names2RepsToUse[category] + "__" + \
str(massPoint) + "__" + str(iref) + "__" + str(ibkg) + \
"__" + args.signalModel
# perform the fit for that function
cmdStrings.append("# MaxLikelihoodFit\n")
cmdMaxLikelihoodFit = "combine -d {datacard} -M MaxLikelihoodFit --setPhysicsModelParameters {physicsModelParametersToSet} --toysFile higgsCombine{outputModifierGenerateOnly}.GenerateOnly.mH{mass}.{seed}.root -t {nToys} -s {seed} --rMin -20 --rMax 20 --freezeNuisances {nuisancesToFreeze} -m {mass} -n {outputModifier} --saveShapes --robustFit 1 --initFromBonly --maxFailedSteps 100 --stepSize 0.01".format(
datacard=datacard, ## --saveShapes --plots
physicsModelParametersToSet=map2string(
physicsModelParametersToSet),
outputModifierGenerateOnly=outputModifierGenerateOnly,
mass=massPoint,
nuisancesToFreeze=",".join(
ourNuisances + otherModelsParametersToFreeze),
outputModifier=outputModifier,
seed="$SEED",
nToys=nToys)
cmdStrings.append(cmdMaxLikelihoodFit)
if nIter != 1:
cmdStrings.append(
"rename mlfit saveShapes_${SEED} mlfit*.root")
# create a single Command
# We must not split Generation[iref] + Fit[iref, *]
# this is exactly what we do - splitting on
# iref!
cmd = defs.Command(category, cmdStrings)
cmds.append(cmd)
# remove that pdfindex..... from the dictionary!
del physicsModelParametersToSet["pdfindex_{category}".format(
category=names2RepsToUse[category])]
# cmd = defs.Command(category, cmdStrings)
# cmds.append(cmd)
# remove the current pdfindex from the list of nuisances to be frozen!
ourNuisances.remove(
"pdfindex_{category}".format(category=names2RepsToUse[category]))
label = "biasScan_{signalModel}".format(signalModel=args.signalModel)
createLaunchers(cmds, submitDir, label)
}
physicsModelParametersToSet = {}
nuisancesToFreeze = ["MH"]
def map2string(m, sep=","):
return ",".join(["{key}={value}".format(key=x, value=m[x]) for x in m])
#######################
### Directory Setup ###
#######################
projectDirLocal = "/Users/vk/software/Analysis/files/analysis_results/"
projectDirLxplus = "/afs/cern.ch/work/v/vkhriste/Projects/HiggsAnalysis/analysis_results"
cmsswDir = "/afs/cern.ch/work/v/vkhriste/Projects/HiggsAnalysis/CMSSW_7_4_9/src"
projectDirToUse = projectDirLxplus
histDir = os.path.join(projectDirToUse, "results", jobLabel)
distributionsDir = os.path.join(projectDirToUse, "distributions", jobLabel);
aux.mkdir(distributionsDir)
backgroundfitsDir = os.path.join(projectDirToUse, "backgroundfits", jobLabel)
aux.mkdir(backgroundfitsDir)
signalfitsDir = os.path.join(projectDirToUse, "signalfits", jobLabel)
aux.mkdir(signalfitsDir)
singalfitinterpolationsDir = os.path.join(projectDirToUse, "signalfitinterpolations", jobLabel)
aux.mkdir(singalfitinterpolationsDir)
signalfitinterpolationswithsplineDir = os.path.join(projectDirToUse, "signalfitinterpolationswithspline", jobLabel)
aux.mkdir(signalfitinterpolationswithsplineDir)
backgroundfitswithroomultipdfDir = os.path.join(projectDirToUse, "backgroundfitswithroomultipdf", jobLabel)
aux.mkdir(backgroundfitswithroomultipdfDir)
datacardsworkspacesDir = os.path.join(projectDirToUse, "datacardsworkspaces", jobLabel)
aux.mkdir(datacardsworkspacesDir)
combineoutputDir = os.path.join(projectDirToUse, "combineoutput", jobLabel)
aux.mkdir(combineoutputDir)
combinesubmissionsDir = os.path.join(projectDirToUse, "combinesubmissions", jobLabel)