def checkOptions(self, opts):
if not (hasattr(opts, 'fitfile')):
raise RuntimeError('Missing parameter')
self.trgEff = TriggerEff(getattr(opts, 'fitfile'))
self.trgEEeff = self.trgEff.getEEeff()
self.trgMMeff = self.trgEff.getMMeff()
self.trgEMeff = self.trgEff.getEMeff()
self.branch = opts.branch
def checkOptions(self,opts):
if not (hasattr(opts,'fitfile')):
raise RuntimeError('Missing parameter')
self.trgEff = TriggerEff(getattr(opts,'fitfile'))
self.trgEEeff = self.trgEff.getEEeff()
self.trgMMeff = self.trgEff.getMMeff()
self.trgEMeff = self.trgEff.getEMeff()
self.branch = opts.branch
class EffTrgFiller(TreeCloner):
def __init__(self):
pass
def help(self):
return '''Add a new trigger efficiency weight. The source files must be passed as an option'''
def addOptions(self, parser):
description = self.help()
group = optparse.OptionGroup(parser, self.label, description)
group.add_option(
'-f',
'--fitfile',
dest='fitfile',
help='path to the file containing the fit results',
)
group.add_option('-b',
'--branch',
dest='branch',
help='Name of the trigger efficiency weight branch',
default='triggW')
parser.add_option_group(group)
return group
def checkOptions(self, opts):
if not (hasattr(opts, 'fitfile')):
raise RuntimeError('Missing parameter')
self.trgEff = TriggerEff(getattr(opts, 'fitfile'))
self.trgEEeff = self.trgEff.getEEeff()
self.trgMMeff = self.trgEff.getMMeff()
self.trgEMeff = self.trgEff.getEMeff()
self.branch = opts.branch
def process(self, **kwargs):
tree = kwargs['tree']
input = kwargs['input']
output = kwargs['output']
self.connect(tree, input)
self.clone(output, [self.branch])
trgweight = numpy.ones(1, dtype=numpy.float32)
self.otree.Branch(self.branch, trgweight, self.branch + '/F')
nentries = self.itree.GetEntries()
print 'Total number of entries: ', nentries
# avoid dots to go faster
itree = self.itree
otree = self.otree
trgEff = self.trgEff
trgEEeff = self.trgEEeff
trgMMeff = self.trgMMeff
trgEMeff = self.trgEMeff
print '- Starting eventloop'
step = 5000
for i in xrange(nentries):
itree.GetEntry(i)
## print event count
if i > 0 and i % step == 0.:
print i, 'events processed.'
channel = itree.channel
if channel == 0:
e = trgEff.getTriggerEfficiency(trgMMeff, itree)
elif channel == 1:
e = trgEff.getTriggerEfficiency(trgEEeff, itree)
elif channel == 2 or channel == 3:
e = trgEff.getTriggerEfficiency(trgEMeff, itree)
else:
raise ValueError(
'channel=={0} What is that?!?!'.format(channel))
if e > 1:
print 'channel=={%d}, triggW: {%f} > 1 !!!' % (channel, e)
trgweight[0] = e
otree.Fill()
self.disconnect()
print '- Eventloop completed'
class EffTrgFiller(TreeCloner):
def __init__(self):
pass
def help(self):
return '''Add a new trigger efficiency weight. The source files must be passed as an option'''
def addOptions(self,parser):
description = self.help()
group = optparse.OptionGroup(parser,self.label, description)
group.add_option('-f', '--fitfile', dest='fitfile', help='path to the file containing the fit results',)
group.add_option('-b', '--branch', dest='branch', help='Name of the trigger efficiency weight branch', default='triggW')
parser.add_option_group(group)
return group
def checkOptions(self,opts):
if not (hasattr(opts,'fitfile')):
raise RuntimeError('Missing parameter')
self.trgEff = TriggerEff(getattr(opts,'fitfile'))
self.trgEEeff = self.trgEff.getEEeff()
self.trgMMeff = self.trgEff.getMMeff()
self.trgEMeff = self.trgEff.getEMeff()
self.branch = opts.branch
def process(self,**kwargs):
tree = kwargs['tree']
input = kwargs['input']
output = kwargs['output']
self.connect(tree,input)
self.clone(output,[self.branch])
trgweight = numpy.ones(1, dtype=numpy.float32)
self.otree.Branch(self.branch,trgweight,self.branch+'/F')
nentries = self.itree.GetEntries()
print 'Total number of entries: ',nentries
# avoid dots to go faster
itree = self.itree
otree = self.otree
trgEff = self.trgEff
trgEEeff = self.trgEEeff
trgMMeff = self.trgMMeff
trgEMeff = self.trgEMeff
print '- Starting eventloop'
step = 5000
for i in xrange(nentries):
itree.GetEntry(i)
## print event count
if i > 0 and i%step == 0.:
print i,'events processed.'
channel = itree.channel
if channel == 0:
e = trgEff.getTriggerEfficiency(trgMMeff, itree)
elif channel == 1:
e = trgEff.getTriggerEfficiency(trgEEeff, itree)
elif channel == 2 or channel == 3:
e = trgEff.getTriggerEfficiency(trgEMeff, itree)
else:
raise ValueError('channel=={0} What is that?!?!'.format(channel))
if e > 1:
print 'channel=={%d}, triggW: {%f} > 1 !!!' % (channel,e)
trgweight[0] = e
otree.Fill()
self.disconnect()
print '- Eventloop completed'