)
args = parser.parse_args()
cuts_jet_tag = [
("%dj%dt"%(n,m), Cuts.n_jets(n)*Cuts.n_tags(m)) for n in [2,3] for m in [0,1,2]
]
# cuts_jet_tag = [
# ("2j1t", Cuts.n_jets(2)*Cuts.n_tags(1))
# ]
cuts = []
for cutname, cbline in cuts_jet_tag:
for lep in ["mu", "ele"]:
cn = "%s_%s" % (lep, cutname)
baseline = Cuts.lepton(lep) * Cuts.hlt(lep) * Cuts.metmt(lep) * Cuts.rms_lj
cuts += [
#Without the MET cut
("%s_nomet" % cn, Cuts.lepton(lep) * Cuts.hlt(lep) * Cuts.rms_lj * cbline),
#Baseline for fit
("%s_baseline" % cn, baseline * cbline),
#Cut-based check
("%s_cutbased_final" % cn,
baseline * cbline * Cuts.top_mass_sig * Cuts.eta_lj
),
#MVA-based selection
("%s_mva_loose" % cn,
baseline * cbline * Cuts.mva_wp(lep)
"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)
#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()
from plots.common.cuts import Cuts
import sys
if __name__=="__main__":
for fi in sys.argv[1:]:
print fi
samp = Sample.fromFile(fi)
print "Lumi scale factor: ", samp.lumiScaleFactor(20000)
for lep in ["mu", "ele"]:
if "/%s/"%lep not in fi:
continue
print lep
cut = None
for cutname, _cut in [
("hlt", Cuts.hlt(lep)),
("lep", Cuts.single_lepton(lep)),
("2J", Cuts.n_jets(2)),
("1T", Cuts.n_tags(1)),
("MET/MtW", Cuts.metmt(lep)),
("rms", Cuts.rms_lj),
("Mtop", Cuts.top_mass_sig),
("etalj", Cuts.eta_lj)
]:
if not cut:
cut = _cut
else:
cut *= _cut
try:
hi = samp.drawHistogram("eta_lj", str(cut), binning=[50, -5, 5])
hi.Scale(samp.lumiScaleFactor(20000))
#Create the sink to a file
hsaver = ObjectSaver(args.outfile)
#Load the samples
sample_nodes = [
SampleNode(hsaver, graph, inf, [], []) for inf in args.infiles
]
#Different lepton channels
channel = Node(graph, "channel", sample_nodes, [])
channels = dict()
# sample --> channels['mu'], channels['ele']
channels['mu'] = CutNode(
Cuts.hlt("mu")*Cuts.lepton("mu"),
graph, "mu", [channel], [], filter_funcs=[lambda x: is_samp(x, 'mu')]
)
channels['ele'] = CutNode(
Cuts.hlt("ele")*Cuts.lepton("ele"),
graph,
"ele", [channel], [], filter_funcs=[lambda x: is_samp(x, 'ele')]
)
# muons: channels['mu'] --> [iso, antiiso]
# electrons: channels['ele'] --> [iso, antiiso]
isos = dict()
isol = []
for lep, par in [
('mu', [channels['mu']]),
('ele', [channels['ele']])