def _estimate(self, region, channel, setup):
if setup.verbose:
printHeader("MC prediction for %s channel %s" %
(self.name, channel))
if channel == 'all':
return sum([
self.cachedEstimate(region, c, setup)
for c in ['MuMu', 'EE', 'EMu']
], u_float(0., 0.))
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([
region.cutString(setup.sys['selectionModifier']),
preSelection['cut']
])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s" % (cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel] / 1000. * u_float(
getYieldFromChain(self.sample[channel]['chain'],
cutString=cut,
weight=weight,
returnError=True))
def _estimate(self, region, channel, setup):
if setup.verbose: printHeader("MC prediction for %s channel %s" %(self.name, channel))
if channel=='all':
return sum( [ self.cachedEstimate(region, c, setup) for c in ['MuMu', 'EE', 'EMu'] ], u_float(0., 0.) )
else:
preSelection = setup.preselection('MC', channel=channel)
cut = "&&".join([region.cutString(setup.sys['selectionModifier']), preSelection['cut']])
weight = preSelection['weightStr']
if setup.verbose:
print "Using cut %s and weight %s"%(cut, weight)
if not self.sample[channel].has_key('chain'):
loadChain(self.sample[channel])
return setup.lumi[channel]/1000.*u_float(getYieldFromChain(self.sample[channel]['chain'], cutString = cut, weight=weight, returnError = True) )
def loadChains(self):
for s in sum([s.values() for s in self.sample.values()], []):
loadChain(
s) # if not type(s)==type([]) else [loadChain(t) for t in s]
def loadChains(self):
for s in sum([s.values() for s in self.sample.values()], []):
loadChain(s) # if not type(s)==type([]) else [loadChain(t) for t in s]
