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)
),
]
#MVA scan
# for mva in numpy.linspace(0, 0.8, 9):
# cuts.append(
# ("%s_mva_scan_%s" % (cn, str(mva).replace(".","_")),
# baseline * cbline * Cuts.mva_wp(lep, mva)
# ),
# )
import cPickle as pickle
import gzip
class PickleSaver:
def __init__(self, fname):
self.of = gzip.GzipFile(fname, 'wb')
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))
print cutname
print hi.GetEntries(), hi.Integral()
except:
print "-1 -1"
for cutname, _cut in [
("HLT", Cuts.hlt(lep)),
("lep", Cuts.single_lepton(lep)),
("2J", Cuts.n_jets(2)),
("1T", Cuts.n_tags(1)),
("MVA", Cuts.mva_wp(lep))
]:
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))
print cutname
print hi.GetEntries(), hi.Integral()
except:
print "-1 -1"
