"binning": [20, -1, 1]
}
variables["bdt"] = {
"name": "bdt",
"var": Cuts.mva_vars['mu'],
"binning": [60, -1, 1]
}
weights = [
("weight__nominal", Weights.total_weight("mu")),
]
c1 = (
"2j0t",
Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(0)*Cuts.lepton("mu")*Cuts.hlt("mu"),
)
c2 = (
"2j1t",
Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.lepton("mu")*Cuts.hlt("mu"),
)
c3 = (
"final_cb_2j1t",
Cuts.final(2,1),
)
cuts = [c1, c2, c3]
varnodes = {}
for k, v in variables.items():
for c in cuts:
vn = tree.hist_node(graph, v, c, weights)
cutlist['3j0t']=Cuts.n_jets(3)*Cuts.n_tags(0)
cutlist['3j1t']=Cuts.n_jets(3)*Cuts.n_tags(1)
cutlist['3j2t']=Cuts.n_jets(3)*Cuts.n_tags(2)
#Needed for RMS cut validation
cutlist['presel_ele_no_rms']=Cuts.hlt_isoele*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_electron
cutlist['presel_mu_no_rms']=Cuts.hlt_isomu*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_muon
cutlist['presel_ele'] = cutlist['presel_ele_no_rms']*Cuts.rms_lj
cutlist['presel_mu'] = cutlist['presel_mu_no_rms']*Cuts.rms_lj
cutlist['nomet_ele']=cutlist['presel_ele']*Cuts.top_mass_sig*Cuts.eta_lj
cutlist['nomt_mu']=cutlist['presel_mu']*Cuts.top_mass_sig*Cuts.eta_lj
cutlist['noeta_ele']=cutlist['presel_ele']*Cuts.top_mass_sig*Cuts.met()
cutlist['noeta_mu']=cutlist['presel_mu']*Cuts.top_mass_sig*Cuts.mt_mu()
cutlist['final_ele']=cutlist['nomet_ele']*Cuts.met()
cutlist['final_mu']=cutlist['nomt_mu']*Cuts.mt_mu()
mva_var = Cuts.mva_vars
mva_var_qcd = Cuts.mva_vars_qcd
bdt = Cuts.mva_wps['bdt']
cutlist['bdt_mu_loose'] = Cuts.mt_mu()*Cut('%s>%f' % (mva_var['mu'],bdt['mu']['loose']))
cutlist['bdt_ele_loose'] = Cuts.met()*Cut('%s>%f' % (mva_var['ele'],bdt['ele']['loose']))
cutlist['bdt_mu_tight'] = Cuts.mt_mu()*Cut('%s>%f' % (mva_var['mu'],bdt['mu']['tight']))
cutlist['bdt_ele_tight'] = Cuts.met()*Cut('%s>%f' % (mva_var['ele'],bdt['ele']['tight']))
# Using MVA based QCD removal
cutlist['qcdmva_ele']=Cut('%s>%f' % (mva_var_qcd['ele'],bdt['ele_qcd']))
#ROOT functions can be used
("abs_eta_lj", "abs(eta_lj)", [20, 0, 5]),
("bdt", Cuts.mva_vars['mu'], [60, -1, 1]),
]
#Define all the weight strategies that we want to apply
weights = [
("weight__nominal", Weights.total_weight("mu")),
#Demonstrate reweighting
("weight__puw", Weights.pu("nominal")),
("weight__puw_up", Weights.pu("up")),
]
#All the cut regions that we are interested in
cuts = [
("2j0t", Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(0)*Cuts.lepton("mu")*Cuts.hlt("mu")),
("2j1t", Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.lepton("mu")*Cuts.hlt("mu")),
("final_cb_2j1t", Cuts.final(2,1))
]
#Construct the analysis chain
snodes, out = analysis_tree(cuts, weights, variables, args.infiles, args.outfile)
print "Recursing down"
for sn in snodes:
sn.recurseDown()
out.close()
mets = dict()
#No MET cut requirement
mets['off'] = CutNode(Cuts.no_cut, graph, "met__off", [met], [],
)
for met_syst in ["nominal", "up", "down"]:
mets['met_' + met_syst] = CutNode(
Cuts.met(met_syst),
graph, "met__met_" + met_syst,
[met], [],
filter_funcs=[lambda x: is_chan(x, 'ele')]
)
mets['mtw_' + met_syst] = CutNode(
Cuts.mt_mu(met_syst),
graph, "met__mtw_" + met_syst, [met], [],
filter_funcs=[lambda x: is_chan(x, 'mu')]
)
# purifications ---> cutbased, MVA
purification = Node(graph, "signalenr", met.children(), [])
purifications = dict()
purifications['cutbased'] = Node(graph, "cutbased", [purification], [])
purifications['mva'] = Node(graph, "mva", [purification], [])
# cutbased ---> Mtop ---> SR
mtop = Node(graph, "mtop", [purifications['cutbased']], [])
mtops = dict()
mtops['SR'] = CutNode(Cuts.top_mass_sig, graph, "mtop__SR", [mtop], [])
proc = 'ele'
if '/mu/' in dir:
prt = 'mu'
proc = 'mu'
physics_processes = PhysicsProcess.get_proc_dict(lepton_channel=proc)
merge_cmds = PhysicsProcess.get_merge_dict(physics_processes)
if len(sys.argv) == 3:
flist=[dir+'/'+sys.argv[2]]
else:
flist=get_file_list(merge_cmds,sys.argv[1])
cut = str(cutlist['2j1t']*cutlist['presel_ele']*Cuts.met)
if '/mu/' in dir:
cut = str(cutlist['2j1t']*cutlist['presel_mu']*Cuts.mt_mu())
for f in flist:
print "Starting:",f
tf=TFile(f,'UPDATE')
t=tf.Get("trees/Events")
t.AddFriend('trees/WJets_weights')
print cut
ct=t.CopyTree(cut)
print t, ct
print t.GetEntries(), ct.GetEntries()
ct.SetName("Events_MVAwQCD")
tf.cd("trees")
ct.Write("", TObject.kOverwrite)
tf.Close()
mvaFileList['sig']['eval']= [
'T_t_ToLeptons',
'Tbar_t_ToLeptons'
]
mvaFileList['bg']['train']= [
'TTJets_MassiveBinDECAY',
'WJets_inclusive'
]
mvaFileList['bg']['eval']= [
'TTJets_FullLept',
'TTJets_SemiLept',
'W1Jets_exclusive',
'W2Jets_exclusive',
'W3Jets_exclusive',
'W4Jets_exclusive'
]
varList={}
varList['ele'] = [ 'top_mass','eta_lj','C','met','mt_el','mass_bj','mass_lj','el_pt','pt_bj' ]
varList['mu'] = [ 'top_mass','eta_lj','C','met','mt_mu','mass_bj','mass_lj','mu_pt','pt_bj' ]
varRank={}
varRank['ele'] = ['top_mass', 'C', 'eta_lj', 'el_pt', 'mt_el', 'pt_bj', 'mass_bj', 'met', 'mass_lj']
varRank['mu'] = ['top_mass', 'eta_lj', 'C', 'mu_pt', 'mt_mu', 'met', 'mass_bj', 'pt_bj', 'mass_lj']
from plots.common.cuts import Cut,Cuts
cuts = {}
cuts['ele'] = Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.hlt_isoele*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_electron*Cuts.rms_lj*Cuts.met()
cuts['mu'] = Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.hlt_isomu*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_muon*Cuts.rms_lj*Cuts.mt_mu()
