def init(self):
self.hist = {}
self.hist["numGenTracks"] = ROOT.TH1F("numGenTracks", "numGenTracks",
100, -0.5, 99.5)
# EX3
self.hist["numVtx"] = ROOT.TH1F("numVtx", "numVtx", 5, -0.5, 4.5)
self.hist["jetPT"] = ROOT.TH1F("jetPT", "jetPT", 100, 0, 100)
# EX9
self.hist["jetBelowEta3PT"] = ROOT.TH1F("jetBelowEta3PT",
"jetBelowEta3PT", 100, 0, 100)
self.hist["jetBelowEta3Eta"] = ROOT.TH1F("jetBelowEta3Eta",
"jetBelowEta3Eta", 100, -5, 5)
for h in self.hist:
self.hist[h].Sumw2()
self.GetOutputList().Add(self.hist[h])
# EX 8.0
self.jetGetter = BetterJetGetter("PFAK5")
def init( self):
self.hist = {}
self.hist["numGenTracks"] = ROOT.TH1F("numGenTracks", "numGenTracks", 100, -0.5, 99.5)
# EX3
self.hist["numVtx"] = ROOT.TH1F("numVtx", "numVtx", 5, -0.5, 4.5)
self.hist["jetPT"] = ROOT.TH1F("jetPT", "jetPT", 100, 0, 100)
# EX9
self.hist["jetBelowEta3PT"] = ROOT.TH1F("jetBelowEta3PT", "jetBelowEta3PT", 100, 0, 100)
self.hist["jetBelowEta3Eta"] = ROOT.TH1F("jetBelowEta3Eta", "jetBelowEta3Eta" , 100, -5, 5)
for h in self.hist:
self.hist[h].Sumw2()
self.GetOutputList().Add(self.hist[h])
# EX 8.0
self.jetGetter = BetterJetGetter("PFAK5")
class SingleJet(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init( self):
self.hist = {}
self.hist["numGenTracks"] = ROOT.TH1F("numGenTracks", "numGenTracks", 100, -0.5, 99.5)
# EX3
self.hist["numVtx"] = ROOT.TH1F("numVtx", "numVtx", 5, -0.5, 4.5)
self.hist["jetPT"] = ROOT.TH1F("jetPT", "jetPT", 100, 0, 100)
# EX9
self.hist["jetBelowEta3PT"] = ROOT.TH1F("jetBelowEta3PT", "jetBelowEta3PT", 100, 0, 100)
self.hist["jetBelowEta3Eta"] = ROOT.TH1F("jetBelowEta3Eta", "jetBelowEta3Eta" , 100, -5, 5)
for h in self.hist:
self.hist[h].Sumw2()
self.GetOutputList().Add(self.hist[h])
# EX 8.0
self.jetGetter = BetterJetGetter("PFAK5")
def analyze(self):
# ex 4.5
if self.isData and self.fChain.jet15 < 0.5:
return
weight = 1.
# EX 6.
if not self.isData:
weight *= self.fChain.genWeight
num = 0
# genTracks
#num = self.fChain.genTracks.size()
#print num
#print self.maxEta # see slaveParams below
self.hist["numGenTracks"].Fill(num, weight)
# EX2.0
#print "vtx", self.fChain.ngoodVTX
# EX3.0
self.hist["numVtx"].Fill(self.fChain.ngoodVTX, weight)
# EX5.0
for i in xrange(self.fChain.PFAK5pt.size()):
pt = self.fChain.PFAK5pt.at(i)
if pt < 35: continue
self.hist["jetPT"].Fill(pt, weight)
# EX 8.0
self.jetGetter.newEvent(self.fChain)
#print "New event!"
for j in self.jetGetter.get("_central"):
if j.pt()<35.: continue
#print "A jet: ", j.pt(), j.eta()
# EX 9.0
for j in self.jetGetter.get("_central"):
if j.pt()<35.: continue
if abs(j.eta())>3.: continue
self.hist["jetBelowEta3Eta"].Fill(j.eta(), weight)
self.hist["jetBelowEta3PT"].Fill(j.pt(), weight)
return 1
def finalize(self):
print "Finalize:"
normFactor = self.getNormalizationFactor()
# exercise A.2
# this is the lumi for jet15 trigger in JetMETtau
if self.isData:
print "Expected norm factor is 1. Got", normFactor
print "Changeing norm factor to have properly normalized data histo"
normFactor = 1/0.013781
print " applying norm", normFactor
for h in self.hist:
self.hist[h].Scale(normFactor)
def __init__(self, triggerName, period=None, weight=False):
if period == None:
runMin = 0
runMax = 9999999
elif period == "A":
runMin = 0
runMax = 146219
elif period == "B":
runMin = 146240
runMax = 9999999
elif isinstance(period, (int, long)):
runMin = period
runMax = period
else:
print "HLTMCWeighter: Period not known:", period
sys.exit()
self.label = triggerName
shortName = triggerName.replace("HLT_", "")
shortName1 = shortName
shortName2 = shortName
if "_raw" in shortName:
shortName2 = shortName2.replace("_raw", "")
self.getter = BetterJetGetter("CaloRaw")
else:
self.getter = BetterJetGetter("Calo")
self.l1seeding = False
if "_L1Seeding" in shortName:
self.l1seeding = True
shortName2 = shortName2.replace("_L1Seeding", "")
self.etaRangeLowForMax = 3.0 # dj fb
self.etaRangeHighForMax = 5.1
self.fb = True
if shortName2 == "Jet15U":
self.fb = False
self.etaRangeLowForMax = -1.0
self.etaRangeHighForMax = 5.1
# hltEffHistos.root prescales_Jet15U.p
# CommonFSQFramework.Core/test/MNxsectionAna/trgEfficiency
fPrescales = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/prescales_"
+ shortName2 + ".p").fullPath()
prescales = pickle.load(open(fPrescales, "rb"))
fLumi = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/runLumi_"
+ shortName2 + ".p").fullPath()
runLumi = pickle.load(open(fLumi, "rb"))
# hltEffHistos_DoubleJet15U_ForwardBackward.root hltEffHistos_Jet15U.root
fName = "CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/hltEffHistos_" + shortName1 + ".root"
print self.label, "- using", fName
filePath = edm.FileInPath(fName).fullPath()
curPath = ROOT.gDirectory.GetPath()
rootFileTF = ROOT.TFile(filePath, "READ")
lst = rootFileTF.GetListOfKeys()
# nom_142304
h = {}
h["nom"] = None
h["denom"] = None
denom = None
self.runs = set()
binx = 38
biny = 4
sumNom = 0
for l in lst:
current = l.ReadObj()
name = current.GetName()
if not "nom_" in name: continue
spl = name.split("_")
hType = spl[0]
run = int(spl[1])
if run < runMin or run > runMax: continue
self.runs.add(run)
if weight:
if run not in prescales:
print "HLTMCWeighter: run not found", run
continue
if len(prescales[run]) == 0:
print "HLTMCWeighter: run found but empty", run
continue
prescale = iter(prescales[run]).next()
if run not in runLumi:
print "Lumi missing:", run
lumi = 0
else:
lumi = runLumi[run][0]
factor = lumi / prescale
else:
factor = 1
if hType == "nom":
a = 1
sumNom += current.GetBinContent(binx, biny)
if h[hType] == None:
#print "Clone"
h[hType] = current.Clone("clone_" + str(hType) + str(run))
h[hType].Scale(factor)
else:
h[hType].Add(current, factor)
#print "TTT", hType, h[hType].GetName(), h[hType].Integral()
binx = h["nom"].GetXaxis().FindBin(50)
biny = h["nom"].GetYaxis().FindBin(1)
#print binx, biny
nnom = h["nom"].GetBinContent(binx, biny)
ndenom = h["denom"].GetBinContent(binx, biny)
r = -1
if ndenom != 0:
r = float(nnom) / ndenom
if ndenom < nnom: print "asdfa", period
#print r, period,"|", nnom, ndenom
#print sumNom
nom = h["nom"].Clone()
#nom.Scale(1000)
denom = h["denom"].Clone()
h["nom"].Divide(h["denom"])
# the histogram is associated with a currently open rootfile (which soon will be closed). A clone is needed in root main dir
ROOT.gDirectory.cd(curPath)
self.efficiencyHisto = h["nom"].Clone()
self.nom = nom.Clone("nomAA")
self.denom = denom.Clone("denomAA")
class HLTMCWeighter:
# https://wiki.physik.uzh.ch/lhcb/root:colorscheme
def set_palette(name="palette", ncontours=999):
if name == "gray" or name == "grayscale":
stops = [0.00, 0.34, 0.61, 0.84, 1.001]
#stops = [0.00, 340, 611, 840, 1000]
red = [1.00, 0.84, 0.61, 0.34, 0.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 0.84, 0.61, 0.34, 0.00]
# elif name == "whatever":
# (define more palettes)
else:
# default palette, looks cool
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [0.00, 0.00, 0.87, 1.00, 0.51]
green = [0.00, 0.81, 1.00, 0.20, 0.00]
blue = [0.51, 1.00, 0.12, 0.00, 0.00]
s = array('d', stops)
r = array('d', red)
g = array('d', green)
b = array('d', blue)
npoints = len(s)
TColor.CreateGradientColorTable(npoints, s, r, g, b, ncontours)
gStyle.SetNumberContours(ncontours)
def __init__(self, triggerName, period=None, weight=False):
if period == None:
runMin = 0
runMax = 9999999
elif period == "A":
runMin = 0
runMax = 146219
elif period == "B":
runMin = 146240
runMax = 9999999
elif isinstance(period, (int, long)):
runMin = period
runMax = period
else:
print "HLTMCWeighter: Period not known:", period
sys.exit()
self.label = triggerName
shortName = triggerName.replace("HLT_", "")
shortName1 = shortName
shortName2 = shortName
if "_raw" in shortName:
shortName2 = shortName2.replace("_raw", "")
self.getter = BetterJetGetter("CaloRaw")
else:
self.getter = BetterJetGetter("Calo")
self.l1seeding = False
if "_L1Seeding" in shortName:
self.l1seeding = True
shortName2 = shortName2.replace("_L1Seeding", "")
self.etaRangeLowForMax = 3.0 # dj fb
self.etaRangeHighForMax = 5.1
self.fb = True
if shortName2 == "Jet15U":
self.fb = False
self.etaRangeLowForMax = -1.0
self.etaRangeHighForMax = 5.1
# hltEffHistos.root prescales_Jet15U.p
# CommonFSQFramework.Core/test/MNxsectionAna/trgEfficiency
fPrescales = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/prescales_"
+ shortName2 + ".p").fullPath()
prescales = pickle.load(open(fPrescales, "rb"))
fLumi = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/runLumi_"
+ shortName2 + ".p").fullPath()
runLumi = pickle.load(open(fLumi, "rb"))
# hltEffHistos_DoubleJet15U_ForwardBackward.root hltEffHistos_Jet15U.root
fName = "CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/hltEffHistos_" + shortName1 + ".root"
print self.label, "- using", fName
filePath = edm.FileInPath(fName).fullPath()
curPath = ROOT.gDirectory.GetPath()
rootFileTF = ROOT.TFile(filePath, "READ")
lst = rootFileTF.GetListOfKeys()
# nom_142304
h = {}
h["nom"] = None
h["denom"] = None
denom = None
self.runs = set()
binx = 38
biny = 4
sumNom = 0
for l in lst:
current = l.ReadObj()
name = current.GetName()
if not "nom_" in name: continue
spl = name.split("_")
hType = spl[0]
run = int(spl[1])
if run < runMin or run > runMax: continue
self.runs.add(run)
if weight:
if run not in prescales:
print "HLTMCWeighter: run not found", run
continue
if len(prescales[run]) == 0:
print "HLTMCWeighter: run found but empty", run
continue
prescale = iter(prescales[run]).next()
if run not in runLumi:
print "Lumi missing:", run
lumi = 0
else:
lumi = runLumi[run][0]
factor = lumi / prescale
else:
factor = 1
if hType == "nom":
a = 1
sumNom += current.GetBinContent(binx, biny)
if h[hType] == None:
#print "Clone"
h[hType] = current.Clone("clone_" + str(hType) + str(run))
h[hType].Scale(factor)
else:
h[hType].Add(current, factor)
#print "TTT", hType, h[hType].GetName(), h[hType].Integral()
binx = h["nom"].GetXaxis().FindBin(50)
biny = h["nom"].GetYaxis().FindBin(1)
#print binx, biny
nnom = h["nom"].GetBinContent(binx, biny)
ndenom = h["denom"].GetBinContent(binx, biny)
r = -1
if ndenom != 0:
r = float(nnom) / ndenom
if ndenom < nnom: print "asdfa", period
#print r, period,"|", nnom, ndenom
#print sumNom
nom = h["nom"].Clone()
#nom.Scale(1000)
denom = h["denom"].Clone()
h["nom"].Divide(h["denom"])
# the histogram is associated with a currently open rootfile (which soon will be closed). A clone is needed in root main dir
ROOT.gDirectory.cd(curPath)
self.efficiencyHisto = h["nom"].Clone()
self.nom = nom.Clone("nomAA")
self.denom = denom.Clone("denomAA")
# TODO: check limits
def getEfficiency(self, eta, pt):
#if self.fb: # temporary, till workaround for HLT_FB logic is implemented
# eta = abs(eta)
binx = self.efficiencyHisto.GetXaxis().FindBin(pt)
biny = self.efficiencyHisto.GetYaxis().FindBin(eta)
return self.efficiencyHisto.GetBinContent(binx, biny)
def setGetter(self, getter):
self.getter = getter
self.cached = None
self.jets = None
def setJets(self, jets):
self.cached = None
self.jets = jets
def newEvent(self, ev):
self.cached = None
self.ev = ev
self.getter.newEvent(self.ev)
self.jets = None
# etaHalf = 0 - both
# etaHalf = + - positive
# etaHalf = - - negative
def getWeight(self, etaHalf=0):
# in case of etaHalf!=0 execute normally
# result will be cached in etaHalf==0 case
if etaHalf == 0 and self.cached != None:
return self.cached
if self.jets != None:
jets = self.jets
else:
jets = self.getter.get("_central")
plus = 0
minus = 0
'''
for j in jets:
if j.pt() < 35: continue
if abs(j.eta()) < 3.: continue
if j.eta() < 0.: minus += 1
else: plus += 1
if plus > 0 and minus > 0:
printD = True
else:
printD = False
'''
printD = False
#printD = True
if printD: print "################################", self.ev.event
if etaHalf == 0 and self.fb:
w = self.getWeight(-1) * self.getWeight(1)
if printD: print "FB weight", w
return w
trgFactor = 1.
#print "#"*10
for j in jets:
#eta = abs(j[0].eta())
eta = j.eta()
if printD: print j.eta(), j.pt(), etaHalf
if etaHalf != 0 and self.fb:
if eta * etaHalf < 0:
if printD: print " ---> etaHalf skip"
continue
if abs(eta) > 5.1: continue
aeta = abs(eta)
if aeta < self.etaRangeLowForMax or aeta > self.etaRangeHighForMax:
if printD: print " ---> print etaRange skip"
continue
pt = j.pt()
w1 = self.getEfficiency(eta, pt)
if printD: print " --->", w1
if w1 < 0.01 and pt > 50 and aeta > self.etaRangeLowForMax and aeta < self.etaRangeHighForMax:
w1 = 1
if printD: print " Changing w to 1"
trgFactor *= 1 - w1
if printD: print " --> factor:", trgFactor
#print 1.- trgFactor
ret = 1. - trgFactor
self.cached = ret
return ret
def dumpEfficiencyHisto(self, name=""):
'''
binx = self.efficiencyHisto.GetXaxis().FindBin(55)
for i in xrange(15):
eta = 4.5 + float(i)/10.
biny = self.efficiencyHisto.GetYaxis().FindBin(eta)
w = self.efficiencyHisto.GetBinContent(binx,biny)
print "a", eta, binx, biny, w
'''
#ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.10)
ROOT.gStyle.SetPadLeftMargin(0.10)
ROOT.gStyle.SetPadRightMargin(0.125)
#ROOT.gStyle.SetPalette(1)
self.set_palette()
ROOT.gStyle.SetOptStat(0)
c = ROOT.TCanvas("cc")
self.efficiencyHisto.SetXTitle("p_{T}^{raw}")
self.efficiencyHisto.SetYTitle("#eta_{jet}")
self.efficiencyHisto.Draw("COLZ")
c.Print("~/tmp/HLTMCWeighter_" + name + ".png")
rf = ROOT.TFile("~/tmp/HLTMCWeighter_" + name + ".root", "RECREATE")
todo = [self.efficiencyHisto, self.nom, self.denom]
for t in todo:
t.Write()
def __init__(self, triggerName, period=None, weight=False):
if period == None:
runMin = 0
runMax = 9999999
elif period == "A":
runMin = 0
runMax = 146219
elif period == "B":
runMin = 146240
runMax = 9999999
elif isinstance(period, (int, long)):
runMin = period
runMax = period
else:
print "HLTMCWeighter: Period not known:", period
sys.exit()
self.label = triggerName
shortName = triggerName.replace("HLT_", "")
shortName1 = shortName
shortName2 = shortName
if "_raw" in shortName:
shortName2 = shortName2.replace("_raw", "")
self.getter = BetterJetGetter("CaloRaw")
else:
self.getter = BetterJetGetter("Calo")
self.l1seeding = False
if "_L1Seeding" in shortName:
self.l1seeding = True
shortName2 = shortName2.replace("_L1Seeding", "")
self.etaRangeLowForMax = 3.0 # dj fb
self.etaRangeHighForMax = 5.1
self.fb = True
if shortName2 == "Jet15U":
self.fb = False
self.etaRangeLowForMax = -1.0
self.etaRangeHighForMax = 5.1
# hltEffHistos.root prescales_Jet15U.p
# CommonFSQFramework.Core/test/MNxsectionAna/trgEfficiency
fPrescales = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/prescales_" + shortName2 + ".p"
).fullPath()
prescales = pickle.load(open(fPrescales, "rb"))
fLumi = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/runLumi_" + shortName2 + ".p"
).fullPath()
runLumi = pickle.load(open(fLumi, "rb"))
# hltEffHistos_DoubleJet15U_ForwardBackward.root hltEffHistos_Jet15U.root
fName = "CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/hltEffHistos_" + shortName1 + ".root"
print self.label, "- using", fName
filePath = edm.FileInPath(fName).fullPath()
curPath = ROOT.gDirectory.GetPath()
rootFileTF = ROOT.TFile(filePath, "READ")
lst = rootFileTF.GetListOfKeys()
# nom_142304
h = {}
h["nom"] = None
h["denom"] = None
denom = None
self.runs = set()
binx = 38
biny = 4
sumNom = 0
for l in lst:
current = l.ReadObj()
name = current.GetName()
if not "nom_" in name:
continue
spl = name.split("_")
hType = spl[0]
run = int(spl[1])
if run < runMin or run > runMax:
continue
self.runs.add(run)
if weight:
if run not in prescales:
print "HLTMCWeighter: run not found", run
continue
if len(prescales[run]) == 0:
print "HLTMCWeighter: run found but empty", run
continue
prescale = iter(prescales[run]).next()
if run not in runLumi:
print "Lumi missing:", run
lumi = 0
else:
lumi = runLumi[run][0]
factor = lumi / prescale
else:
factor = 1
if hType == "nom":
a = 1
sumNom += current.GetBinContent(binx, biny)
if h[hType] == None:
# print "Clone"
h[hType] = current.Clone("clone_" + str(hType) + str(run))
h[hType].Scale(factor)
else:
h[hType].Add(current, factor)
# print "TTT", hType, h[hType].GetName(), h[hType].Integral()
binx = h["nom"].GetXaxis().FindBin(50)
biny = h["nom"].GetYaxis().FindBin(1)
# print binx, biny
nnom = h["nom"].GetBinContent(binx, biny)
ndenom = h["denom"].GetBinContent(binx, biny)
r = -1
if ndenom != 0:
r = float(nnom) / ndenom
if ndenom < nnom:
print "asdfa", period
# print r, period,"|", nnom, ndenom
# print sumNom
nom = h["nom"].Clone()
# nom.Scale(1000)
denom = h["denom"].Clone()
h["nom"].Divide(h["denom"])
# the histogram is associated with a currently open rootfile (which soon will be closed). A clone is needed in root main dir
ROOT.gDirectory.cd(curPath)
self.efficiencyHisto = h["nom"].Clone()
self.nom = nom.Clone("nomAA")
self.denom = denom.Clone("denomAA")
class HLTMCWeighter:
# https://wiki.physik.uzh.ch/lhcb/root:colorscheme
def set_palette(name="palette", ncontours=999):
if name == "gray" or name == "grayscale":
stops = [0.00, 0.34, 0.61, 0.84, 1.001]
# stops = [0.00, 340, 611, 840, 1000]
red = [1.00, 0.84, 0.61, 0.34, 0.00]
green = [1.00, 0.84, 0.61, 0.34, 0.00]
blue = [1.00, 0.84, 0.61, 0.34, 0.00]
# elif name == "whatever":
# (define more palettes)
else:
# default palette, looks cool
stops = [0.00, 0.34, 0.61, 0.84, 1.00]
red = [0.00, 0.00, 0.87, 1.00, 0.51]
green = [0.00, 0.81, 1.00, 0.20, 0.00]
blue = [0.51, 1.00, 0.12, 0.00, 0.00]
s = array("d", stops)
r = array("d", red)
g = array("d", green)
b = array("d", blue)
npoints = len(s)
TColor.CreateGradientColorTable(npoints, s, r, g, b, ncontours)
gStyle.SetNumberContours(ncontours)
def __init__(self, triggerName, period=None, weight=False):
if period == None:
runMin = 0
runMax = 9999999
elif period == "A":
runMin = 0
runMax = 146219
elif period == "B":
runMin = 146240
runMax = 9999999
elif isinstance(period, (int, long)):
runMin = period
runMax = period
else:
print "HLTMCWeighter: Period not known:", period
sys.exit()
self.label = triggerName
shortName = triggerName.replace("HLT_", "")
shortName1 = shortName
shortName2 = shortName
if "_raw" in shortName:
shortName2 = shortName2.replace("_raw", "")
self.getter = BetterJetGetter("CaloRaw")
else:
self.getter = BetterJetGetter("Calo")
self.l1seeding = False
if "_L1Seeding" in shortName:
self.l1seeding = True
shortName2 = shortName2.replace("_L1Seeding", "")
self.etaRangeLowForMax = 3.0 # dj fb
self.etaRangeHighForMax = 5.1
self.fb = True
if shortName2 == "Jet15U":
self.fb = False
self.etaRangeLowForMax = -1.0
self.etaRangeHighForMax = 5.1
# hltEffHistos.root prescales_Jet15U.p
# CommonFSQFramework.Core/test/MNxsectionAna/trgEfficiency
fPrescales = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/prescales_" + shortName2 + ".p"
).fullPath()
prescales = pickle.load(open(fPrescales, "rb"))
fLumi = edm.FileInPath(
"CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/runLumi_" + shortName2 + ".p"
).fullPath()
runLumi = pickle.load(open(fLumi, "rb"))
# hltEffHistos_DoubleJet15U_ForwardBackward.root hltEffHistos_Jet15U.root
fName = "CommonFSQFramework/Core/test/MNxsectionAna/trgEfficiency/hltEffHistos_" + shortName1 + ".root"
print self.label, "- using", fName
filePath = edm.FileInPath(fName).fullPath()
curPath = ROOT.gDirectory.GetPath()
rootFileTF = ROOT.TFile(filePath, "READ")
lst = rootFileTF.GetListOfKeys()
# nom_142304
h = {}
h["nom"] = None
h["denom"] = None
denom = None
self.runs = set()
binx = 38
biny = 4
sumNom = 0
for l in lst:
current = l.ReadObj()
name = current.GetName()
if not "nom_" in name:
continue
spl = name.split("_")
hType = spl[0]
run = int(spl[1])
if run < runMin or run > runMax:
continue
self.runs.add(run)
if weight:
if run not in prescales:
print "HLTMCWeighter: run not found", run
continue
if len(prescales[run]) == 0:
print "HLTMCWeighter: run found but empty", run
continue
prescale = iter(prescales[run]).next()
if run not in runLumi:
print "Lumi missing:", run
lumi = 0
else:
lumi = runLumi[run][0]
factor = lumi / prescale
else:
factor = 1
if hType == "nom":
a = 1
sumNom += current.GetBinContent(binx, biny)
if h[hType] == None:
# print "Clone"
h[hType] = current.Clone("clone_" + str(hType) + str(run))
h[hType].Scale(factor)
else:
h[hType].Add(current, factor)
# print "TTT", hType, h[hType].GetName(), h[hType].Integral()
binx = h["nom"].GetXaxis().FindBin(50)
biny = h["nom"].GetYaxis().FindBin(1)
# print binx, biny
nnom = h["nom"].GetBinContent(binx, biny)
ndenom = h["denom"].GetBinContent(binx, biny)
r = -1
if ndenom != 0:
r = float(nnom) / ndenom
if ndenom < nnom:
print "asdfa", period
# print r, period,"|", nnom, ndenom
# print sumNom
nom = h["nom"].Clone()
# nom.Scale(1000)
denom = h["denom"].Clone()
h["nom"].Divide(h["denom"])
# the histogram is associated with a currently open rootfile (which soon will be closed). A clone is needed in root main dir
ROOT.gDirectory.cd(curPath)
self.efficiencyHisto = h["nom"].Clone()
self.nom = nom.Clone("nomAA")
self.denom = denom.Clone("denomAA")
# TODO: check limits
def getEfficiency(self, eta, pt):
# if self.fb: # temporary, till workaround for HLT_FB logic is implemented
# eta = abs(eta)
binx = self.efficiencyHisto.GetXaxis().FindBin(pt)
biny = self.efficiencyHisto.GetYaxis().FindBin(eta)
return self.efficiencyHisto.GetBinContent(binx, biny)
def setGetter(self, getter):
self.getter = getter
self.cached = None
self.jets = None
def setJets(self, jets):
self.cached = None
self.jets = jets
def newEvent(self, ev):
self.cached = None
self.ev = ev
self.getter.newEvent(self.ev)
self.jets = None
# etaHalf = 0 - both
# etaHalf = + - positive
# etaHalf = - - negative
def getWeight(self, etaHalf=0):
# in case of etaHalf!=0 execute normally
# result will be cached in etaHalf==0 case
if etaHalf == 0 and self.cached != None:
return self.cached
if self.jets != None:
jets = self.jets
else:
jets = self.getter.get("_central")
plus = 0
minus = 0
"""
for j in jets:
if j.pt() < 35: continue
if abs(j.eta()) < 3.: continue
if j.eta() < 0.: minus += 1
else: plus += 1
if plus > 0 and minus > 0:
printD = True
else:
printD = False
"""
printD = False
# printD = True
if printD:
print "################################", self.ev.event
if etaHalf == 0 and self.fb:
w = self.getWeight(-1) * self.getWeight(1)
if printD:
print "FB weight", w
return w
trgFactor = 1.0
# print "#"*10
for j in jets:
# eta = abs(j[0].eta())
eta = j.eta()
if printD:
print j.eta(), j.pt(), etaHalf
if etaHalf != 0 and self.fb:
if eta * etaHalf < 0:
if printD:
print " ---> etaHalf skip"
continue
if abs(eta) > 5.1:
continue
aeta = abs(eta)
if aeta < self.etaRangeLowForMax or aeta > self.etaRangeHighForMax:
if printD:
print " ---> print etaRange skip"
continue
pt = j.pt()
w1 = self.getEfficiency(eta, pt)
if printD:
print " --->", w1
if w1 < 0.01 and pt > 50 and aeta > self.etaRangeLowForMax and aeta < self.etaRangeHighForMax:
w1 = 1
if printD:
print " Changing w to 1"
trgFactor *= 1 - w1
if printD:
print " --> factor:", trgFactor
# print 1.- trgFactor
ret = 1.0 - trgFactor
self.cached = ret
return ret
def dumpEfficiencyHisto(self, name=""):
"""
binx = self.efficiencyHisto.GetXaxis().FindBin(55)
for i in xrange(15):
eta = 4.5 + float(i)/10.
biny = self.efficiencyHisto.GetYaxis().FindBin(eta)
w = self.efficiencyHisto.GetBinContent(binx,biny)
print "a", eta, binx, biny, w
"""
# ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.10)
ROOT.gStyle.SetPadLeftMargin(0.10)
ROOT.gStyle.SetPadRightMargin(0.125)
# ROOT.gStyle.SetPalette(1)
self.set_palette()
ROOT.gStyle.SetOptStat(0)
c = ROOT.TCanvas("cc")
self.efficiencyHisto.SetXTitle("p_{T}^{raw}")
self.efficiencyHisto.SetYTitle("#eta_{jet}")
self.efficiencyHisto.Draw("COLZ")
c.Print("~/tmp/HLTMCWeighter_" + name + ".png")
rf = ROOT.TFile("~/tmp/HLTMCWeighter_" + name + ".root", "RECREATE")
todo = [self.efficiencyHisto, self.nom, self.denom]
for t in todo:
t.Write()
def init( self):
self.variantFilter = BaseDijetAna.variant(self.variant)
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzerClean", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
# since shifts for PU are in fact changes in weight
# there is no sense to repeat whole analaysis
# we will use the central value
self.shiftsPU = ["_central"]
if not self.isData and self.doShiftsPU:
self.shiftsPU.append("_puUp")
self.shiftsPU.append("_puDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binsNew = self.variantFilter.bins()
# note: set gives as unique items, since _central is repeated
self.hist["ptHat"] = ROOT.TH1F("ptHat", "ptHat", 100, 0, 50)
for shift in set(self.todoShifts+self.shiftsPU):
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, len(binsNew)-1, binsNew)
self.hist["miss"+t] = self.hist["xsVsDeltaEta"+t].Clone("miss"+t)
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
dummy = ROOT.TH2F("dummy"+t, "dummy"+t, len(binsNew)-1, binsNew, len(binsNew)-1, binsNew)
self.hist["response"+t]= ROOT.RooUnfoldResponse(self.hist["xsVsDeltaEta"+t],
self.hist["xsVsDeltaEta"+t],
dummy,
"response"+t,"response"+t)
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15",
len(binsNew)-1, binsNew)
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb",
len(binsNew)-1, binsNew)
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework/Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
self.jetGetter = BetterJetGetter("PFAK5")
self.getterForTriggerModelling = BetterJetGetter("CaloRaw")
class SingleJet(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init(self):
self.hist = {}
self.hist["numGenTracks"] = ROOT.TH1F("numGenTracks", "numGenTracks",
100, -0.5, 99.5)
# EX3
self.hist["numVtx"] = ROOT.TH1F("numVtx", "numVtx", 5, -0.5, 4.5)
self.hist["jetPT"] = ROOT.TH1F("jetPT", "jetPT", 100, 0, 100)
# EX9
self.hist["jetBelowEta3PT"] = ROOT.TH1F("jetBelowEta3PT",
"jetBelowEta3PT", 100, 0, 100)
self.hist["jetBelowEta3Eta"] = ROOT.TH1F("jetBelowEta3Eta",
"jetBelowEta3Eta", 100, -5, 5)
for h in self.hist:
self.hist[h].Sumw2()
self.GetOutputList().Add(self.hist[h])
# EX 8.0
self.jetGetter = BetterJetGetter("PFAK5")
def analyze(self):
# ex 4.5
if self.isData and self.fChain.jet15 < 0.5:
return
weight = 1.
# EX 6.
if not self.isData:
weight *= self.fChain.genWeight
num = 0
# genTracks
#num = self.fChain.genTracks.size()
#print num
#print self.maxEta # see slaveParams below
self.hist["numGenTracks"].Fill(num, weight)
# EX2.0
#print "vtx", self.fChain.ngoodVTX
# EX3.0
self.hist["numVtx"].Fill(self.fChain.ngoodVTX, weight)
# EX5.0
for i in xrange(self.fChain.PFAK5pt.size()):
pt = self.fChain.PFAK5pt.at(i)
if pt < 35: continue
self.hist["jetPT"].Fill(pt, weight)
# EX 8.0
self.jetGetter.newEvent(self.fChain)
#print "New event!"
for j in self.jetGetter.get("_central"):
if j.pt() < 35.: continue
#print "A jet: ", j.pt(), j.eta()
# EX 9.0
for j in self.jetGetter.get("_central"):
if j.pt() < 35.: continue
if abs(j.eta()) > 3.: continue
self.hist["jetBelowEta3Eta"].Fill(j.eta(), weight)
self.hist["jetBelowEta3PT"].Fill(j.pt(), weight)
return 1
def finalize(self):
print "Finalize:"
normFactor = self.getNormalizationFactor()
# exercise A.2
# this is the lumi for jet15 trigger in JetMETtau
if self.isData:
print "Expected norm factor is 1. Got", normFactor
print "Changeing norm factor to have properly normalized data histo"
normFactor = 1 / 0.013781
print " applying norm", normFactor
for h in self.hist:
self.hist[h].Scale(normFactor)
class MNxsAnalyzerClean(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init( self):
self.variantFilter = BaseDijetAna.variant(self.variant)
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzerClean", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
# since shifts for PU are in fact changes in weight
# there is no sense to repeat whole analaysis
# we will use the central value
self.shiftsPU = ["_central"]
if not self.isData and self.doShiftsPU:
self.shiftsPU.append("_puUp")
self.shiftsPU.append("_puDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binsNew = self.variantFilter.bins()
# note: set gives as unique items, since _central is repeated
self.hist["ptHat"] = ROOT.TH1F("ptHat", "ptHat", 100, 0, 50)
for shift in set(self.todoShifts+self.shiftsPU):
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, len(binsNew)-1, binsNew)
self.hist["miss"+t] = self.hist["xsVsDeltaEta"+t].Clone("miss"+t)
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
dummy = ROOT.TH2F("dummy"+t, "dummy"+t, len(binsNew)-1, binsNew, len(binsNew)-1, binsNew)
self.hist["response"+t]= ROOT.RooUnfoldResponse(self.hist["xsVsDeltaEta"+t],
self.hist["xsVsDeltaEta"+t],
dummy,
"response"+t,"response"+t)
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15",
len(binsNew)-1, binsNew)
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb",
len(binsNew)-1, binsNew)
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework/Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
self.jetGetter = BetterJetGetter("PFAK5")
self.getterForTriggerModelling = BetterJetGetter("CaloRaw")
# note: for the ptHat reweighing we will do only the central variation.
# otherwise the changes from the JEC/JER variations would be fixed
# by the ptHat reweighing procedure
#
# leadPt:qScale gives a nice linear response
#
# todo: trigger correction for data
def doPtHatReweighing(self, weightBase):
if self.isData:
if self.fChain.jet15 < 0.5:
return
weight = weightBase
else:
if not self.triggerFired("_jet15"):
return
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters["_jet15_central"].weight(truePU)
weight = weightBase*puWeight
sortKey = lambda j: j.pt()*(j.pt()>self.threshold)\
*(j.jetid()>0.5)*(abs(j.eta())<4.7)
# if empty sequence...
try:
bestJet = max(self.jetGetter.get("_central"), key=sortKey)
except ValueError:
return
if not sortKey(bestJet): return # do we really have a jet passing the criteria?
pt, eta = bestJet.pt(), bestJet.eta()
self.var["leadPt"][0] = pt
self.var["leadEta"][0] = eta
self.var["weight"][0] = weight
if not self.isData:
#self.var["alphaQCD"][0] = self.fChain.alphaQCD
self.var["qScale"][0] = self.fChain.qScale
self.tree.Fill()
def triggerFired(self, case):
if case == "_jet15" and self.MC_jet15_triggerFired_cached != None:
return self.MC_jet15_triggerFired_cached
if case == "_dj15fb" and self.MC_dj15fb_triggerFired_cached != None:
return self.MC_dj15fb_triggerFired_cached
ev = self.fChain.event
rnd4eff = ev%10000/9999.
if case == "_jet15":
if self.MC_dj15fb_triggerFired_cached == None and self.MC_jet15_triggerFired_cached == None:
self.getterForTriggerModelling.newEvent(self.fChain)
self.jetsForTRG = list(self.getterForTriggerModelling.get("_central"))
# calls needed to reset cached values
self.HLTMCWeighterJ15L1Raw.setJets(self.jetsForTRG)
self.HLTMCWeighterJ15Raw.setJets(self.jetsForTRG)
#self.HLTMCWeighterJ15L1Raw.setGetter(self.getterForTriggerModelling)
#self.HLTMCWeighterJ15Raw.setGetter(self.getterForTriggerModelling)
#self.HLTMCWeighterJ15L1Raw.newEvent(self.fChain)
#self.HLTMCWeighterJ15Raw.newEvent(self.fChain)
w1 = self.HLTMCWeighterJ15L1Raw.getWeight()
w2 = self.HLTMCWeighterJ15Raw.getWeight()
self.MC_jet15_triggerFired_cached = w1*w2 > rnd4eff
return self.MC_jet15_triggerFired_cached
elif case == "_dj15fb":
if self.MC_dj15fb_triggerFired_cached == None and self.MC_jet15_triggerFired_cached == None:
self.getterForTriggerModelling.newEvent(self.fChain)
self.jetsForTRG = list(self.getterForTriggerModelling.get("_central"))
# calls needed to reset cached values
self.HLTMCWeighterJ15FBL1Raw.setJets(self.jetsForTRG)
self.HLTMCWeighterJ15FBRaw.setJets(self.jetsForTRG)
#self.HLTMCWeighterJ15FBL1Raw.setGetter(self.getterForTriggerModelling)
#self.HLTMCWeighterJ15FBRaw.setGetter(self.getterForTriggerModelling)
#self.HLTMCWeighterJ15FBL1Raw.newEvent(self.fChain)
#self.HLTMCWeighterJ15FBRaw.newEvent(self.fChain)
w1 = self.HLTMCWeighterJ15FBL1Raw.getWeight()
w2 = self.HLTMCWeighterJ15FBRaw.getWeight()
self.MC_dj15fb_triggerFired_cached = w1*w2 > rnd4eff
return self.MC_dj15fb_triggerFired_cached
else:
raise Excecption("triggerFired: case not known: "+str(case))
def topology(self, j1, j2):
eta1=j1.eta()
eta2=j2.eta()
if min(abs(eta1), abs(eta2)) > 3. and eta1*eta2<0: return "_dj15fb"
return "_jet15"
def dr(self, a,b):
de = a.eta()-b.eta()
dp = a.phi()-b.phi()
pi = 3.1415
if dp > pi: dp -= 2*pi
if dp < -pi: dp += 2*pi
return math.sqrt(de*de+dp*dp)
'''
def analyze(self):
self.pr.enable()
self.analyzeTT()
self.pr.disable()
#'''
#def analyzeTT(self):
def analyze(self):
self.MC_jet15_triggerFired_cached = None
self.MC_dj15fb_triggerFired_cached = None
if self.fChain.ngoodVTX == 0: return
self.jetGetter.newEvent(self.fChain)
weightBase = 1.
weightBaseNoMCNorm = 1.
puWeights = {}
puWeights["_jet15"] = {}
puWeights["_dj15fb"] = {}
if not self.isData:
weightBase *= self.fChain.genWeight*self.normFactor
weightBaseNoMCNorm *= self.fChain.genWeight
truePU = self.fChain.puTrueNumInteractions
for lw in self.lumiWeighters:
spl = lw.split("_")
trgName = "_"+spl[1]
variationName = "_"+spl[2]
puWeights[trgName][variationName] = self.lumiWeighters[lw].weight(truePU)
# note: this is intentionally before ptHat reweighing
if not self.isData:
self.hist["ptHat"].Fill(self.fChain.qScale, weightBase)
if not self.isData and self.applyPtHatReweighing:
ptHat = self.fChain.qScale
w = 1.
for weighter in self.ptHatW:
w*=max(weighter.Eval(ptHat), 0.)
#print "W:", ptHat, weighter.Eval(ptHat)
weightBase *= w
weightBaseNoMCNorm*= w
if self.onlyPtHatReweighing:
self.doPtHatReweighing(weightBase)
return
if not self.isData:
goodGenJets = self.variantFilter.filterCol([j for j in self.fChain.genJets \
if j.pt()>self.threshold and abs(j.eta()) < 4.7 ])
for shift in self.todoShifts:
matchedPairs = set()
# todo: j.jetID() > 0.5
goodRecoJets = self.variantFilter.filterCol([j for j in self.jetGetter.get(shift) \
if j.pt()>self.threshold and abs(j.eta()) < 4.7 and j.jetid()])
for i1 in xrange(len(goodRecoJets)):
for i2 in xrange(i1+1, len(goodRecoJets)):
j1 = goodRecoJets[i1]
j2 = goodRecoJets[i2]
if not self.variantFilter.filterPair(j1, j2): continue
topology = self.topology(j1, j2)
weight = {}
if self.isData:
if topology == "_jet15":
hasTrigger = self.fChain.jet15 > 0.5
weight["_central"] = 1
else:
hasTrigger = self.fChain.doubleJ15FB > 0.5
weight["_central"] = 1
else:
hasTrigger = self.triggerFired(topology)
weightNoNorm = {}
if shift == "_central":
for s in self.shiftsPU: # note: shifts pu contains central, puUp and puDown
weightPU = puWeights[topology][s]
weight[s] = weightPU*weightBase
weightNoNorm[s] = weightPU*weightBaseNoMCNorm
else:
weightPU = puWeights[topology]["_central"]
weight[shift] = weightPU*weightBase
weightNoNorm[shift] = weightPU*weightBaseNoMCNorm
if not hasTrigger: continue
detaDet = self.variantFilter.xsVariable(j1, j2) # note: we should rename this...
ptSorted = sorted( [j1, j2], key = lambda j: -j.pt())
for w in weight:
histoName = w + topology
self.hist["ptLead"+histoName].Fill(ptSorted[0].pt(), weight[w])
self.hist["etaLead"+histoName].Fill(ptSorted[0].eta(), weight[w])
self.hist["ptSublead"+histoName].Fill(ptSorted[1].pt(), weight[w])
self.hist["etaSublead"+histoName].Fill(ptSorted[1].eta(), weight[w])
self.hist["xsVsDeltaEta"+histoName].Fill(detaDet, weight[w])
self.hist["vtx"+histoName].Fill(self.fChain.ngoodVTX, weight[w])
# todo: fill detLevel Histograms
# for MC - check if there is a matching pair, save result inside matchedPairs set
if not self.isData: # and len(goodGenJets) > 1:
matched=() # empty tuple
if len(goodGenJets) > 1:
closestGenJetI1 = min(xrange(len(goodGenJets)), key = lambda i: self.dr(j1, goodGenJets[i]) )
closestGenJetI2 = min(xrange(len(goodGenJets)), key = lambda i: self.dr(j2, goodGenJets[i]) )
if self.variantFilter.filterPair(goodGenJets[closestGenJetI1], goodGenJets[closestGenJetI2]):
dr1 = self.dr(j1, goodGenJets[closestGenJetI1])
dr2 = self.dr(j2, goodGenJets[closestGenJetI2])
if max(dr1, dr2) < 0.3:
if closestGenJetI1 != closestGenJetI2:
# check the topology is correct
genTopology = self.topology(goodGenJets[closestGenJetI1], goodGenJets[closestGenJetI2])
if genTopology == topology:
matchCand=(min(closestGenJetI1,closestGenJetI2), max(closestGenJetI1,closestGenJetI2))
if not matchCand in matchedPairs:
matched=matchCand
matchedPairs.add(matchCand)
else:
pass
#print "XXX Warning, matched to same genJet"
#print j1.pt(), j1.eta(), j1.phi()
#print j2.pt(), j2.eta(), j2.phi()
#def pr(x): print x
#map(lambda x: pr("Gen: {0} {1} {2}".format(x.pt(), x.eta() , x.phi())), goodGenJets )
# add this point we know, if this is
# a fake: no matched genJetPair (or genJet pair from different category)
# a fill: matched genJetPai
#fill the response matrix
if len(matched)>0:
if self.unfoldEnabled:
detaGen = self.variantFilter.xsVariable(goodGenJets[matched[0]],goodGenJets[matched[1]])
for w in weightNoNorm:
histoName = w + topology
self.hist["response"+histoName].Fill(detaDet, detaGen, weightNoNorm[w])
else:
if self.unfoldEnabled:
for w in weightNoNorm:
histoName = w + topology
self.hist["response"+histoName].Fake(detaDet, weightNoNorm[w])
# Now: fill miss cateogory
# note: this is still happening in "shift" loop
if self.unfoldEnabled and not self.isData:
for i1 in xrange(len(goodGenJets)):
for i2 in xrange(i1+1, len(goodGenJets)):
if not self.variantFilter.filterPair(goodGenJets[i1], goodGenJets[i2]): continue
genTopology = None
detaGen = None
if shift == "_central":
genTopology = self.topology(goodGenJets[i1], goodGenJets[i2])
detaGen = self.variantFilter.xsVariable(goodGenJets[i1], goodGenJets[i2])
self.hist["detaGen"+genTopology].Fill(detaGen, weightBase)
if (i1, i2) in matchedPairs: continue
if genTopology == None:
genTopology = self.topology(goodGenJets[i1], goodGenJets[i2])
# note: code repeated here, see above defintion of weightNoNorm
weightNoNorm = {}
if shift == "_central":
for s in self.shiftsPU: # note: shifts pu contains central, puUp and puDown
weightPU = puWeights[genTopology][s]
weightNoNorm[s] = weightPU*weightBaseNoMCNorm
else:
weightPU = puWeights[genTopology]["_central"]
weightNoNorm[shift] = weightPU*weightBaseNoMCNorm
if detaGen == None:
detaGen = self.variantFilter.xsVariable(goodGenJets[i1], goodGenJets[i2])
for w in weightNoNorm:
histoName = w + genTopology
self.hist["response"+histoName].Miss(detaGen, weightNoNorm[w])
self.hist["miss"+histoName].Fill(detaGen, weightNoNorm[w])
def finalize(self):
print "Finalize:"
if hasattr(self, "pr"):
dname = "/nfs/dust/cms/user/fruboest/2015.01.MN/slc6/CMSSW_7_0_5/src/CommonFSQFramework/Core/test/MNxsectionAna/bak/"
profName = dname + "stats"
self.pr.dump_stats(profName)
for h in self.hist:
if not h.startswith("response"): continue
def cloneAndAdd(h, name):
hh = h.Clone(name)
hh.SetName(name)
self.addToOutput(hh)
cloneAndAdd(self.hist[h].Hfakes(), "fake"+h)
cloneAndAdd(self.hist[h].Htruth(), "truth"+h)
cloneAndAdd(self.hist[h].Hmeasured(), "measured"+h)
cloneAndAdd(self.hist[h].Hresponse(), "resp"+h)
class MNxsAnalyzerClean(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init( self):
self.variantFilter = BaseDijetAna.variant(self.variant)
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzerClean", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
# since shifts for PU are in fact changes in weight
# there is no sense to repeat whole analaysis
# we will use the central value
self.shiftsPU = ["_central"]
if not self.isData and self.doShiftsPU:
self.shiftsPU.append("_puUp")
self.shiftsPU.append("_puDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binsNew = self.variantFilter.bins()
# note: set gives as unique items, since _central is repeated
self.hist["ptHat"] = ROOT.TH1F("ptHat", "ptHat", 100, 0, 50)
for shift in set(self.todoShifts+self.shiftsPU):
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, len(binsNew)-1, binsNew)
self.hist["miss"+t] = self.hist["xsVsDeltaEta"+t].Clone("miss"+t)
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
dummy = ROOT.TH2F("dummy"+t, "dummy"+t, len(binsNew)-1, binsNew, len(binsNew)-1, binsNew)
self.hist["response"+t]= ROOT.RooUnfoldResponse(self.hist["xsVsDeltaEta"+t],
self.hist["xsVsDeltaEta"+t],
dummy,
"response"+t,"response"+t)
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15",
len(binsNew)-1, binsNew)
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb",
len(binsNew)-1, binsNew)
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework/Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
self.jetGetter = BetterJetGetter("PFAK5")
self.getterForTriggerModelling = BetterJetGetter("CaloRaw")
# note: for the ptHat reweighing we will do only the central variation.
# otherwise the changes from the JEC/JER variations would be fixed
# by the ptHat reweighing procedure
#
# leadPt:qScale gives a nice linear response
#
# todo: trigger correction for data
def doPtHatReweighing(self, weightBase):
if self.isData:
if self.fChain.jet15 < 0.5:
return
weight = weightBase
else:
if not self.triggerFired("_jet15"):
return
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters["_jet15_central"].weight(truePU)
weight = weightBase*puWeight
sortKey = lambda j: j.pt()*(j.pt()>self.threshold)\
*(j.jetid()>0.5)*(abs(j.eta())<4.7)
# if empty sequence...
try:
bestJet = max(self.jetGetter.get("_central"), key=sortKey)
except ValueError:
return
if not sortKey(bestJet): return # do we really have a jet passing the criteria?
pt, eta = bestJet.pt(), bestJet.eta()
self.var["leadPt"][0] = pt
self.var["leadEta"][0] = eta
self.var["weight"][0] = weight
if not self.isData:
#self.var["alphaQCD"][0] = self.fChain.alphaQCD
self.var["qScale"][0] = self.fChain.qScale
self.tree.Fill()
def triggerFired(self, case):
if case == "_jet15" and self.MC_jet15_triggerFired_cached != None:
return self.MC_jet15_triggerFired_cached
if case == "_dj15fb" and self.MC_dj15fb_triggerFired_cached != None:
return self.MC_dj15fb_triggerFired_cached
def init( self):
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzer", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binningDeta = (19, 0, 9.5)
for shift in self.todoShifts:
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaGen"+t] = ROOT.TH1F("xsGen"+t, "xsGen"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaFake"+t] = ROOT.TH1F("xsFake"+t, "xsFake"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaMiss"+t] = ROOT.TH1F("xsMiss"+t, "xsMiss"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
self.hist["response"+t]= ROOT.RooUnfoldResponse(binningDeta[0], binningDeta[1], binningDeta[2], "response"+t,"response"+t)
self.hist["evcnt"] = ROOT.TH1F("evcnt_central_jet15", "evcnt_central_jet15", 1, -0.5, 0.5)
#self.hist["detaGen"] = ROOT.TH1F("detaGen_central_sum", "detaGen_central_sum", binningDeta[0], binningDeta[1], binningDeta[2])
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGenVsRec"] = ROOT.TH2F("detaGenVsRec_central_jet15", "detaGenVsRec_central_jet15",\
binningDeta[0]*20, binningDeta[1], binningDeta[2],\
binningDeta[0]*20, binningDeta[1], binningDeta[2])
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework.Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
#todo = ["ptHatWeighters_invx_pass1.root_invX",
# "ptHatWeighters_invx_pass2.root_invX",
# "ptHatWeighters_invx_pass3.root_invX",]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework.Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
#print "PTHat weighter set to", self.datasetName+"/ptHatW"
#print "PTHat test@30:", self.ptHatW.Eval(30)
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
#self.jetGetter = JetGetter("PF")
#if hasattr(self, "jetUncFile"):
# self.jetGetter.setJecUncertainty(self.jetUncFile)
self.jetGetter = BetterJetGetter("PFAK5")
class MNxsAnalyzer(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init( self):
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzer", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binningDeta = (19, 0, 9.5)
for shift in self.todoShifts:
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaGen"+t] = ROOT.TH1F("xsGen"+t, "xsGen"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaFake"+t] = ROOT.TH1F("xsFake"+t, "xsFake"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaMiss"+t] = ROOT.TH1F("xsMiss"+t, "xsMiss"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
self.hist["response"+t]= ROOT.RooUnfoldResponse(binningDeta[0], binningDeta[1], binningDeta[2], "response"+t,"response"+t)
self.hist["evcnt"] = ROOT.TH1F("evcnt_central_jet15", "evcnt_central_jet15", 1, -0.5, 0.5)
#self.hist["detaGen"] = ROOT.TH1F("detaGen_central_sum", "detaGen_central_sum", binningDeta[0], binningDeta[1], binningDeta[2])
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGenVsRec"] = ROOT.TH2F("detaGenVsRec_central_jet15", "detaGenVsRec_central_jet15",\
binningDeta[0]*20, binningDeta[1], binningDeta[2],\
binningDeta[0]*20, binningDeta[1], binningDeta[2])
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework.Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
#todo = ["ptHatWeighters_invx_pass1.root_invX",
# "ptHatWeighters_invx_pass2.root_invX",
# "ptHatWeighters_invx_pass3.root_invX",]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework.Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
#print "PTHat weighter set to", self.datasetName+"/ptHatW"
#print "PTHat test@30:", self.ptHatW.Eval(30)
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
#self.jetGetter = JetGetter("PF")
#if hasattr(self, "jetUncFile"):
# self.jetGetter.setJecUncertainty(self.jetUncFile)
self.jetGetter = BetterJetGetter("PFAK5")
def dr(self, a,b):
de = a.eta()-b.eta()
dp = a.phi()-b.phi()
pi = 3.1415
if dp > pi: dp -= 2*pi
if dp < -pi: dp += 2*pi
#print a.phi(), b.phi(), dp
return math.sqrt(de*de+dp*dp)
def doGri(self, weight, variation):
fil = lambda x: x.pt() > self.threshold and abs(x.eta())<4.7
genJ = filter(fil, self.fChain.genJets) # 1
detJ =filter(fil, self.jetGetter.get(variation)) #1
##print "AAA", len(genJ), len(detJ)
postfix = variation+"_jet15"
if len(genJ)<2:
if len(detJ) < 2:
return
detJB = min(detJ, key= lambda j: j.eta() )
detJF = max(detJ, key= lambda j: j.eta() )
deta = abs(detJB.eta()-detJF.eta())
self.hist["xsVsDeltaEtaFake"+postfix].Fill(deta, weight)
return # 2
gjB = min(genJ, key= lambda j: j.eta() )
gjF = max(genJ, key= lambda j: j.eta() )
detaGen = gjF.eta() - gjB.eta()
self.hist["xsVsDeltaEtaGen"+postfix].Fill(detaGen, weight) # 3
if len(detJ)<2:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 4
self.hist["response"+postfix].Miss(detaGen, weight)
return
closestDetJet1 = min(detJ, key = lambda j: self.dr(j, gjB) ) # 5, eta restriction imposed in step 1
dr1 = self.dr(closestDetJet1, gjB)
if dr1 > 0.3:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 6
self.hist["response"+postfix].Miss(detaGen, weight)
return
else:
closestDetJet2 = min(detJ, key = lambda j: self.dr(j, gjF) ) # 5,
dr2 = self.dr(closestDetJet2, gjF)
if dr2 > 0.3:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 6
self.hist["response"+postfix].Miss(detaGen, weight)
return
detaDet = abs(closestDetJet1.eta()- closestDetJet2.eta())
self.hist["response"+postfix].Fill(detaDet, detaGen, weight) # 7
# note: for the ptHat reweighing we will do only the central variation.
# otherwise the changes from the JEC/JER variations would be fixed
# by the ptHat reweighing procedure
#
# leadPt:qScale gives a nice linear response
#
# todo: trigger correction for data
def doPtHatReweighing(self, weightBase):
if self.isData:
if self.fChain.jet15 < 0.5:
return
weight = weightBase
else:
if not self.triggerFired("_jet15"):
return
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters["_jet15_central"].weight(truePU)
weight = weightBase*puWeight
sortKey = lambda j: j.pt()*(j.pt()>self.threshold)\
*(j.jetid()>0.5)*(abs(j.eta())<4.7)
# if empty sequence...
try:
bestJet = max(self.jetGetter.get("_central"), key=sortKey)
except ValueError:
return
if not sortKey(bestJet): return # do we really have a jet passing the criteria?
pt, eta = bestJet.pt(), bestJet.eta()
self.var["leadPt"][0] = pt
self.var["leadEta"][0] = eta
self.var["weight"][0] = weight
if not self.isData:
#self.var["alphaQCD"][0] = self.fChain.alphaQCD
self.var["qScale"][0] = self.fChain.qScale
self.tree.Fill()
def triggerFired(self, case):
if case == "_jet15" and self.MC_jet15_triggerFired_cached != None:
return self.MC_jet15_triggerFired_cached
if case == "_dj15fb" and self.MC_dj15fb_triggerFired_cached != None:
return self.MC_dj15fb_triggerFired_cached
ev = self.fChain.event
rnd4eff = ev%10000/9999.
if case == "_jet15":
self.HLTMCWeighterJ15L1Raw.newEvent(self.fChain)
self.HLTMCWeighterJ15Raw.newEvent(self.fChain)
w1 = self.HLTMCWeighterJ15L1Raw.getWeight()
w2 = self.HLTMCWeighterJ15Raw.getWeight()
self.MC_jet15_triggerFired_cached = w1*w2 > rnd4eff
return self.MC_jet15_triggerFired_cached
elif case == "_dj15fb":
self.HLTMCWeighterJ15FBL1Raw.newEvent(self.fChain)
self.HLTMCWeighterJ15FBRaw.newEvent(self.fChain)
w1 = self.HLTMCWeighterJ15FBL1Raw.getWeight()
w2 = self.HLTMCWeighterJ15FBRaw.getWeight()
self.MC_dj15fb_triggerFired_cached = w1*w2 > rnd4eff
return self.MC_dj15fb_triggerFired_cached
else:
raise Excecption("triggerFired: case not known: "+str(case))
def analyze(self):
self.MC_jet15_triggerFired_cached = None
self.MC_dj15fb_triggerFired_cached = None
self.hist["evcnt"].Fill(0)
if self.fChain.ngoodVTX == 0: return
self.jetGetter.newEvent(self.fChain)
weightBase = 1.
if not self.isData:
weightBase *= self.fChain.genWeight*self.normFactor
#print ev, w1*w2, rnd4eff, triggerFired
if not self.isData and self.applyPtHatReweighing:
ptHat = self.fChain.qScale
w = 1.
for weighter in self.ptHatW:
w*=max(weighter.Eval(ptHat), 0.)
#print "W:", ptHat, weighter.Eval(ptHat)
weightBase *= w
if self.onlyPtHatReweighing:
self.doPtHatReweighing(weightBase)
return
# fill the roounfoldresponse
if not self.isData:
genDEta = None
genTopology = None
etas = []
for j in self.fChain.genJets:
if j.pt() < self.threshold: continue
eta = j.eta()
if abs(j.eta())>4.7: continue
etas.append(eta)
if len(etas)>1:
fwd = max(etas)
bkw = min(etas)
genTopology = "CF"
if fwd > 3 and bkw < -3:
genTopology = "FB"
genDEta = fwd - bkw
#self.hist["detaGen"].Fill(genDEta, weightBase) # basic gen level distribution shouldnt be PU dependent
if genTopology == "FB":
self.hist["detaGen_dj15fb"].Fill(genDEta, weightBase) # basic gen level distribution shouldnt be PU dependent
else:
self.hist["detaGen_jet15"].Fill(genDEta, weightBase) # basic gen level distribution shouldnt be PU dependent
for shift in self.todoShifts:
# Q&D warning: to test this you need to comment out all filling of the response object below
#truePU = self.fChain.puTrueNumInteractions
#puWeight = self.lumiWeighters["_jet15_central"].weight(truePU)
#self.doGri(weightBase*puWeight, shift)
# find best dijet pair
mostFwdJet = None
mostBkgJet = None
mostFwdJetEta = None
mostBkgJetEta = None
mostFwdJetPt = None
mostBkgJetPt = None
for jet in self.jetGetter.get(shift):
#if jetID.at(i) < 0.5: continue
if not jet.jetid(): continue
eta = jet.eta()
if abs(eta) > 4.7: continue
ptShifted = jet.pt()
if ptShifted < self.threshold: continue
if mostFwdJet == None or mostFwdJetEta < eta:
mostFwdJet = jet
mostFwdJetEta = eta
mostFwdJetPt = ptShifted
if mostBkgJet == None or mostBkgJetEta > eta:
mostBkgJet = jet
mostBkgJetEta = eta
mostBkgJetPt = ptShifted
pairFound = False
if mostFwdJet != None and mostBkgJet != mostFwdJet:
pairFound = True
isMiss = True # mark if dijet pair was not found (needed to correctly fill response)
# A dijet pair was found. Check trigger for data
# fill histograms
if pairFound:
deta = abs(mostFwdJetEta - mostBkgJetEta)
if not self.isData and genDEta:
if shift == "_central":
self.hist["detaGenVsRec"].Fill(genDEta, deta, weightBase)
# detaGenVsRec
triggerToUse = "_jet15"
if abs(mostFwdJetEta) > 3 and abs(mostBkgJetEta) > 3 and mostFwdJetEta*mostBkgJetEta<0:
triggerToUse = "_dj15fb"
# TODO: we need to separate gen level categories!!
gotTrigger = True
if self.isData: # check trigger
if triggerToUse == "_jet15":
gotTrigger = self.fChain.jet15 > 0.5
elif triggerToUse == "_dj15fb":
gotTrigger = self.fChain.doubleJ15FB > 0.5
else:
raise Exception("Trigger not known??? "+triggerToUse)
else:
gotTrigger = self.triggerFired(triggerToUse)
if gotTrigger:
# calculate weight for MC
weight = weightBase
if not self.isData:
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters[triggerToUse+"_central"].weight(truePU)
weight *= puWeight
histoName = shift +triggerToUse
self.hist["xsVsDeltaEta"+histoName].Fill(deta, weight)
if not self.isData:
isMiss = False
isCorrectTopo = (triggerToUse == "_jet15") and genTopology == "CF" or (triggerToUse == "_dj15fb") and genTopology == "FB"
#'''
if genDEta == None or not isCorrectTopo: # fake pair, e.g. from bkg or we landed in a wrong category
if self.unfoldEnabled:
self.hist["response"+histoName].Fake(deta, weight)
else:
if self.unfoldEnabled:
self.hist["response"+histoName].Fill(deta, genDEta, weight)
#'''
# fill also some control plots
leadJet = mostBkgJet
subleadJet = mostFwdJet
ptLead = mostBkgJetPt
ptSublead = mostFwdJetPt
etaLead = mostBkgJetEta
etaSublead = mostFwdJetEta
if ptSublead > ptLead:
leadJet, subleadJet = subleadJet, leadJet
ptLead, ptSublead = ptSublead, ptLead
etaLead, etaSublead = etaSublead, etaLead
self.hist["vtx"+histoName].Fill(self.fChain.ngoodVTX, weight)
self.hist["ptLead"+histoName].Fill(ptLead, weight)
self.hist["ptSublead"+histoName].Fill(ptSublead, weight)
self.hist["etaLead"+histoName].Fill(etaLead, weight)
self.hist["etaSublead"+histoName].Fill(etaSublead, weight)
if not self.isData and genDEta and isMiss:
if genTopology == "CF":
triggerToUse = "_jet15"
else:
triggerToUse = "_dj15fb"
histoName = shift +triggerToUse
weight = weightBase
if not self.isData:
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters[triggerToUse+"_central"].weight(truePU)
#print "Miss", triggerToUse, genDEta, shift
#'''
if self.unfoldEnabled:
self.hist["response"+histoName].Miss(genDEta, weight)
#'''
def finalize(self):
print "Finalize:"
class MNxsAnalyzer(CommonFSQFramework.Core.ExampleProofReader.ExampleProofReader):
def init( self):
if not self.isData:
#self.hltMCWeighter = HLTMCWeighter("HLT_Jet15U")
self.HLTMCWeighterJ15Raw = HLTMCWeighter("HLT_Jet15U_raw")
self.HLTMCWeighterJ15L1Raw = HLTMCWeighter("HLT_Jet15U_L1Seeding_raw")
self.HLTMCWeighterJ15FBL1Raw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_L1Seeding_raw")
self.HLTMCWeighterJ15FBRaw = HLTMCWeighter("HLT_DoubleJet15U_ForwardBackward_raw")
self.normFactor = self.getNormalizationFactor()
#sys.stdout = sys.stderr
#self.pr = cProfile.Profile()
print "XXX init - MNxsAnalyzer", self.datasetName, self.isData
self.todoShifts = ["_central"]
if not self.isData and self.doPtShiftsJEC:
self.todoShifts.append("_jecUp")
self.todoShifts.append("_jecDown")
if not self.isData and self.doPtShiftsJER:
self.todoShifts.append("_jerUp")
self.todoShifts.append("_jerDown")
self.hist = {}
todoTrg = ["_jet15", "_dj15fb"]
binningDeta = (19, 0, 9.5)
for shift in self.todoShifts:
for trg in todoTrg:
t = shift+trg
self.hist["ptLead"+t] = ROOT.TH1F("ptLead"+t, "ptLead"+t, 100, 0, 100)
self.hist["ptSublead"+t] = ROOT.TH1F("ptSublead"+t, "ptSublead"+t, 100, 0, 100)
self.hist["etaLead"+t] = ROOT.TH1F("etaLead"+t, "etaLead"+t, 100, -5, 5)
self.hist["etaSublead"+t] = ROOT.TH1F("etaSublead"+t, "etaSublead"+t, 100, -5, 5)
self.hist["xsVsDeltaEta"+t] = ROOT.TH1F("xs"+t, "xs"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaGen"+t] = ROOT.TH1F("xsGen"+t, "xsGen"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaFake"+t] = ROOT.TH1F("xsFake"+t, "xsFake"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["xsVsDeltaEtaMiss"+t] = ROOT.TH1F("xsMiss"+t, "xsMiss"+t, binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["vtx"+t] = ROOT.TH1F("vtx"+t, "vtx"+t, 10, -0.5, 9.5)
if self.unfoldEnabled:
self.hist["response"+t]= ROOT.RooUnfoldResponse(binningDeta[0], binningDeta[1], binningDeta[2], "response"+t,"response"+t)
self.hist["evcnt"] = ROOT.TH1F("evcnt_central_jet15", "evcnt_central_jet15", 1, -0.5, 0.5)
#self.hist["detaGen"] = ROOT.TH1F("detaGen_central_sum", "detaGen_central_sum", binningDeta[0], binningDeta[1], binningDeta[2])
# in principle trigger does not applies to gen plots. We keep consistent naming though, so the unfolded result to gen level plots is possible
# in each category
self.hist["detaGen_jet15"] = ROOT.TH1F("detaGen_central_jet15", "detaGen_central_jet15", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGen_dj15fb"] = ROOT.TH1F("detaGen_central_dj15fb", "detaGen_central_dj15fb", binningDeta[0], binningDeta[1], binningDeta[2])
self.hist["detaGenVsRec"] = ROOT.TH2F("detaGenVsRec_central_jet15", "detaGenVsRec_central_jet15",\
binningDeta[0]*20, binningDeta[1], binningDeta[2],\
binningDeta[0]*20, binningDeta[1], binningDeta[2])
if self.onlyPtHatReweighing:
self.var = {}
self.var["leadPt"] = array('d', [0])
self.var["leadEta"] = array('d', [0])
self.var["weight"] = array('d', [0]) # only jet15 trigger??
#self.var["alphaQCD"] = array('d', [0])
self.var["qScale"] = array('d', [0])
self.tree = ROOT.TTree("data", "data")
for v in self.var:
self.tree.Branch(v, self.var[v], v+"/D")
self.addToOutput(self.tree)
else:
for h in self.hist:
if not h.startswith("response"):
self.hist[h].Sumw2()
#self.GetOutputList().Add(self.hist[h])
self.addToOutput(self.hist[h])
if self.applyPtHatReweighing and not self.isData:
fp = "CommonFSQFramework.Core/test/MNxsectionAna/"
todo = ["ptHatWeighters.root"]
#todo = ["ptHatWeighters_invx_pass1.root_invX",
# "ptHatWeighters_invx_pass2.root_invX",
# "ptHatWeighters_invx_pass3.root_invX",]
self.ptHatW = []
for t in todo:
ptHatFileName = edm.FileInPath("CommonFSQFramework.Core/test/MNxsectionAna/"+t).fullPath()
ptHatFile = ROOT.TFile(ptHatFileName)
self.ptHatW.append(ptHatFile.Get(self.datasetName+"/ptHatW"))
#print "PTHat weighter set to", self.datasetName+"/ptHatW"
#print "PTHat test@30:", self.ptHatW.Eval(30)
puFiles = {}
# CommonFSQFramework.Core/test/MNxsectionAna/
jet15FileV2 = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/PUJet15V2.root").fullPath() # MC gen distribution
puFiles["dj15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_0.root").fullPath()
puFiles["dj15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_1_05.root").fullPath()
puFiles["dj15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_dj15_0_95.root").fullPath()
puFiles["j15_1"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_0.root").fullPath()
puFiles["j15_1_05"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_1_05.root").fullPath()
puFiles["j15_0_95"] = edm.FileInPath("CommonFSQFramework/Core/test/MNxsectionAna/data/pu_j15_0_95.root").fullPath()
self.lumiWeighters = {}
self.lumiWeighters["_jet15_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1"], "MC", "pileup")
self.lumiWeighters["_jet15_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_1_05"], "MC", "pileup")
self.lumiWeighters["_jet15_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["j15_0_95"], "MC", "pileup")
self.lumiWeighters["_dj15fb_central"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puUp"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_1_05"], "MC", "pileup")
self.lumiWeighters["_dj15fb_puDown"] = edm.LumiReWeighting(jet15FileV2, puFiles["dj15_0_95"], "MC", "pileup")
#self.jetGetter = JetGetter("PF")
#if hasattr(self, "jetUncFile"):
# self.jetGetter.setJecUncertainty(self.jetUncFile)
self.jetGetter = BetterJetGetter("PFAK5")
def dr(self, a,b):
de = a.eta()-b.eta()
dp = a.phi()-b.phi()
pi = 3.1415
if dp > pi: dp -= 2*pi
if dp < -pi: dp += 2*pi
#print a.phi(), b.phi(), dp
return math.sqrt(de*de+dp*dp)
def doGri(self, weight, variation):
fil = lambda x: x.pt() > self.threshold and abs(x.eta())<4.7
genJ = filter(fil, self.fChain.genJets) # 1
detJ =filter(fil, self.jetGetter.get(variation)) #1
##print "AAA", len(genJ), len(detJ)
postfix = variation+"_jet15"
if len(genJ)<2:
if len(detJ) < 2:
return
detJB = min(detJ, key= lambda j: j.eta() )
detJF = max(detJ, key= lambda j: j.eta() )
deta = abs(detJB.eta()-detJF.eta())
self.hist["xsVsDeltaEtaFake"+postfix].Fill(deta, weight)
return # 2
gjB = min(genJ, key= lambda j: j.eta() )
gjF = max(genJ, key= lambda j: j.eta() )
detaGen = gjF.eta() - gjB.eta()
self.hist["xsVsDeltaEtaGen"+postfix].Fill(detaGen, weight) # 3
if len(detJ)<2:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 4
self.hist["response"+postfix].Miss(detaGen, weight)
return
closestDetJet1 = min(detJ, key = lambda j: self.dr(j, gjB) ) # 5, eta restriction imposed in step 1
dr1 = self.dr(closestDetJet1, gjB)
if dr1 > 0.3:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 6
self.hist["response"+postfix].Miss(detaGen, weight)
return
else:
closestDetJet2 = min(detJ, key = lambda j: self.dr(j, gjF) ) # 5,
dr2 = self.dr(closestDetJet2, gjF)
if dr2 > 0.3:
self.hist["xsVsDeltaEtaMiss"+postfix].Fill(detaGen, weight) # 6
self.hist["response"+postfix].Miss(detaGen, weight)
return
detaDet = abs(closestDetJet1.eta()- closestDetJet2.eta())
self.hist["response"+postfix].Fill(detaDet, detaGen, weight) # 7
# note: for the ptHat reweighing we will do only the central variation.
# otherwise the changes from the JEC/JER variations would be fixed
# by the ptHat reweighing procedure
#
# leadPt:qScale gives a nice linear response
#
# todo: trigger correction for data
def doPtHatReweighing(self, weightBase):
if self.isData:
if self.fChain.jet15 < 0.5:
return
weight = weightBase
else:
if not self.triggerFired("_jet15"):
return
truePU = self.fChain.puTrueNumInteractions
puWeight = self.lumiWeighters["_jet15_central"].weight(truePU)
weight = weightBase*puWeight
sortKey = lambda j: j.pt()*(j.pt()>self.threshold)\
*(j.jetid()>0.5)*(abs(j.eta())<4.7)
# if empty sequence...
try:
bestJet = max(self.jetGetter.get("_central"), key=sortKey)
except ValueError:
return
if not sortKey(bestJet): return # do we really have a jet passing the criteria?
pt, eta = bestJet.pt(), bestJet.eta()
self.var["leadPt"][0] = pt
self.var["leadEta"][0] = eta
self.var["weight"][0] = weight
if not self.isData:
#self.var["alphaQCD"][0] = self.fChain.alphaQCD
self.var["qScale"][0] = self.fChain.qScale
self.tree.Fill()
def triggerFired(self, case):
if case == "_jet15" and self.MC_jet15_triggerFired_cached != None:
return self.MC_jet15_triggerFired_cached
if case == "_dj15fb" and self.MC_dj15fb_triggerFired_cached != None:
return self.MC_dj15fb_triggerFired_cached