def makeResponse(trueTree,measTree,bins,accCuts,massReq,plotName,var):
tTree=trueTree.CopyTree(accCuts+"&&"+massReq)
mTree=measTree.CopyTree(massReq)
hist_measured=TH1F("hist_measured","hist_measured",len(bins)-1,array('d',bins))
hist_truth=TH1F("hist_truth","hist_truth",len(bins)-1,array('d',bins))
print "about to make the RooUnfoldResponse"
print hist_truth
print hist_measured
response = RooUnfoldResponse(hist_measured, hist_truth);
recEvents={}
nr=0
for i in mTree:
recEvents[i.EVENT]={"zzMass":i.mass,
"zzPt":i.pt,
"gMass":i.gMass,
"evt":i.EVENT,
"gz1Mass":i.gz1Mass,
"gz2Mass":i.gz2Mass,
"dR_z1_gz1":i.dR_z1_gz1,
"dR_z2_gz2":i.dR_z2_gz2,
"dR_z1_gz2":i.dR_z1_gz2,
"dR_z2_gz1":i.dR_z2_gz1
}
if i.z1Pt > i.z2Pt:
recEvents[i.EVENT]["z1_eta_by_pt"]=i.z1Eta
recEvents[i.EVENT]["z2_eta_by_pt"]=i.z2Eta
recEvents[i.EVENT]["z1_pt_by_pt"]=i.z1Pt
recEvents[i.EVENT]["z2_pt_by_pt"]=i.z2Pt
else:
recEvents[i.EVENT]["z1_eta_by_pt"]=i.z2Eta
recEvents[i.EVENT]["z2_eta_by_pt"]=i.z1Eta
recEvents[i.EVENT]["z1_pt_by_pt"]=i.z2Pt
recEvents[i.EVENT]["z2_pt_by_pt"]=i.z1Pt
recEvents[i.EVENT]["leading_lep_pt"]=max(max(max(i.z1l1Pt,i.z1l2Pt),i.z2l1Pt),i.z2l2Pt)
recEvents[i.EVENT]["dR_Zs"]=sqrt( (i.z1Eta-i.z2Eta)**2 + (i.z1Phi-i.z2Phi)**2)
recEvents[i.EVENT]["dPhi_Zs"] = abs(dPhi(i.z1Phi, i.z2Phi))
recSet=set()
for i in recEvents:
recSet.add(i)
tTrueEvents={}
for i in tTree: #loop over truth events
tTrueEvents[i.EVENT]={'zzMass':i.zzMass,
'zzPt':i.zzPt,
'evt':i.EVENT}
if i.z1Pt > i.z2Pt:
tTrueEvents[i.EVENT]["z1_eta_by_pt"]=i.z1Eta
tTrueEvents[i.EVENT]["z2_eta_by_pt"]=i.z2Eta
tTrueEvents[i.EVENT]["z1_pt_by_pt"]=i.z1Pt
tTrueEvents[i.EVENT]["z2_pt_by_pt"]=i.z2Pt
else:
tTrueEvents[i.EVENT]["z1_eta_by_pt"]=i.z2Eta
tTrueEvents[i.EVENT]["z2_eta_by_pt"]=i.z1Eta
tTrueEvents[i.EVENT]["z1_pt_by_pt"]=i.z2Pt
tTrueEvents[i.EVENT]["z2_pt_by_pt"]=i.z1Pt
tTrueEvents[i.EVENT]["leading_lep_pt"]=max(max(max(i.z1l1Pt,i.z1l2Pt),i.z2l1Pt),i.z2l2Pt)
tTrueEvents[i.EVENT]["dR_Zs"]=sqrt( (i.z1Eta-i.z2Eta)**2 + (i.z1Phi-i.z2Phi)**2)
tTrueEvents[i.EVENT]["dPhi_Zs"] = abs(dPhi(i.z1Phi, i.z2Phi))
trueSet=set()
for i in tTrueEvents:
trueSet.add(i)
hit=0
miss=0
agree=0
# Training
measEvents={}
for event in trueSet:
if event in recSet:
agree=agree+1
#hack hack hack to make sure I can still close
if (recEvents[event]['dR_z1_gz1'] < 0.5 and recEvents[event]['dR_z2_gz2'] < 0.5) or (recEvents[event]['dR_z1_gz2']<0.5 and recEvents[event]['dR_z2_gz1']<0.5):
measEvents[event]=recEvents[event]
response.Fill(recEvents[event][var],tTrueEvents[event][var])
hit=hit+1
else:
response.Miss(tTrueEvents[event][var])
miss=miss+1
else:
response.Miss(tTrueEvents[event][var])
miss=miss+1
print len(trueSet),"true events used in training"
print len(recSet),"measured events used for training"
print len(recSet-trueSet),"measured not in true"
print "hit:",hit
print "miss:",miss
print "in both true and rec:",agree
c1=TCanvas("c1","c1",600,600)
c1.cd()
# Response matrix as a 2D-histogram: (x,y)=(measured,truth) from http://hepunx.rl.ac.uk/~adye/software/unfold/htmldoc/RooUnfoldResponse.html#RooUnfoldResponse:Hresponse%1
response.Hresponse().GetXaxis().SetTitle(plotName+" (Measured)")
response.Hresponse().GetYaxis().SetTitle(plotName+" (Truth)")
response.Hresponse().Draw("colz")
c1.SaveAs("diffDists/"+plotName+"_responseMat.png")
c1.SaveAs("diffDists/"+plotName+"_responseMat.C")
c1.Delete()
return response
def main(args):
fin = TFile(args.filein,"update")
print args.trees
stuff=[]
if "all" in args.trees:
for t in fin.GetListOfKeys():
if t.ReadObj().Class().InheritsFrom(TTree.Class()) is True:
stuff.append(t.GetName())
else:
continue
else:
stuff=[i for i in args.trees]
for i in stuff:
if "genEventTree" not in i and "mmee" not in i and "eemm" not in i and "eeee" not in i and "mmmm" not in i and "llll" not in i:
stuff.remove(i)
# vars needed as inputs: z1Eta, z2Eta, z1Pt, z2Pt, z1Phi, z2Phi, mass, pt, zilj[Eta, Phi, Pt], z1Mass, z2Mass
ins=["zzMass", "zzPt", "zzEta", "z1Pt", "z2Pt", "z1Eta", "z2Eta", "z1Phi", "z2Phi"]
for i in ["z1l1Pt", "z1l1Eta", "z1l1Phi", "z1Mass", "z1l2Pt", "z1l2Eta", "z1l2Phi", "z2Mass"]:
ins.append(i)
for i in ["z2l1Pt", "z2l1Eta", "z2l1Phi", "z2l2Pt", "z2l2Eta", "z2l2Phi"]:
ins.append(i)
inputVars={}
fin = TFile(args.filein,"update")
for t in stuff:
fin = TFile(args.filein,"update")
tree = fin.Get(t)
newtree = TTree(tree.GetName()+"_moreVars",tree.GetName()+"_moreVars")
n={}
leplegs=["z1l1","z1l2","z2l1","z2l2"]
for i in ins:
inputVars[i] = -137.0
# ouput vars : z_Eta (leading by Pt), z_Pt (leading by Pt), dR between Z, dPhi between Z, leading lepton Pt
outs=["z1_eta_by_pt", "z2_eta_by_pt", "z1_pt_by_pt","z2_pt_by_pt", "dR_Zs", "dPhi_Zs", "leading_lep_pt"]
for outvar in outs:
# TODO do I need these genlevel for reco branch??
n[outvar] = N.zeros(1,dtype=float)
newtree.Branch(outvar, n[outvar],outvar+"/d")
for i in tree:
# get ins
# these will have to be different if it's the truth/measured tree...
for var in ["Mass" , "Pt", "Eta"]:
if "gen" in tree.GetName():
inputVars["zz"+var] = i.GetLeaf("zz"+var).GetValue()
else:
inputVars["zzMass"] = i.GetLeaf("mass").GetValue()
# inputVars["zzEta"] = i.GetLeaf("eta").GetValue()
inputVars["zzPt"] = i.GetLeaf("pt").GetValue()
for var in ["Mass", "Pt", "Eta", "Phi"]:
inputVars["z1"+var] = i.GetLeaf("z1"+var).GetValue()
for var in ["Mass", "Pt", "Eta", "Phi"]:
inputVars["z2"+var] = i.GetLeaf("z2"+var).GetValue()
for l in range(len(leplegs)): # for each lepton, figure out type and get its weight.
try:
inputVars[leplegs[l]+"Pt"] = i.GetLeaf(leplegs[l]+"Pt").GetValue()
inputVars[leplegs[l]+"Eta"] = i.GetLeaf(leplegs[l]+"Eta").GetValue()
inputVars[leplegs[l]+"Phi"] = i.GetLeaf(leplegs[l]+"Phi").GetValue()
except ReferenceError: # this tree sucks and doesn't have the right vars
print "Trying to get",leplegs[l],"from tree",tree.GetName()
continue
#set outs
if inputVars["z1Pt"]>inputVars["z2Pt"]:
n["z1_eta_by_pt"][0]=inputVars["z1Eta"]
n["z1_pt_by_pt"][0]=inputVars["z1Pt"]
n["z2_eta_by_pt"][0]=inputVars["z2Eta"]
n["z2_pt_by_pt"][0]=inputVars["z2Pt"]
else:
n["z1_eta_by_pt"][0]=inputVars["z2Eta"]
n["z1_pt_by_pt"][0]=inputVars["z2Pt"]
n["z2_eta_by_pt"][0]=inputVars["z1Eta"]
n["z2_pt_by_pt"][0]=inputVars["z1Pt"]
n["leading_lep_pt"][0]=max(max(max(inputVars["z1l1Pt"],inputVars["z1l2Pt"]),inputVars["z2l1Pt"]),inputVars["z2l2Pt"])
n["dR_Zs"][0]=sqrt( (inputVars["z1Eta"]-inputVars["z2Eta"])**2 + (inputVars["z1Phi"]-inputVars["z2Phi"])**2)
n["dPhi_Zs"][0] = dPhi(inputVars["z1Phi"], inputVars["z2Phi"])
newtree.Fill()
print tree.GetName(),"has",tree.GetEntries(),"entries"
print newtree.GetName(),"has",tree.GetEntries(),"entries"
newtree.Write()
tree.AddFriend(newtree.GetName())
tree.Write("",TObject.kOverwrite)
fin.Close()
