def getEff(self,emu,event_weight):
"""
Get efficiency and save to histogram
"""
custom_vars = addData.custom_variables(self.ttree,self.p_custom_vars,self.event_objects)
eff_conditions = self.p_settings.get(self.p_cfg_name,'eff_y').split(',') # things like ==1
logic_operator = []
good_condition = []
for ec,eff_c in enumerate(eff_conditions):
value = re.search(r'\d+',eff_c).group() # '==1' -> 1
cond = eff_c.split(value)[0] # '==1' -> '=='
if eff_c.endswith('.'):
good_condition.append( float(value) )
else:
good_condition.append( int(value) )
logic_operator.append( cond )
# self.p_eff_x_vars # loop variables that we are getting the efficiency as a function of!
# self.p_variables # loop variables that we are getting the efficiency of!
for eff_x_var in self.p_eff_x_vars:
for vv,variable in enumerate(self.p_variables):
eff_c = value = re.search(r'\d+',self.eff_conditions[vv]).group() # '==1' -> 1
if eff_x_var in custom_vars.keys():
eff_x_val = custom_vars[eff_x_var] # getattr( reco,quantity )()
else:
eff_x_val = None
# set it somewhere else; user needs to do this! (too modular...)
# something like the object pT (below) or another object's pT (define here)
_values = {'total':[],'good':[],'mis':[]}
reco_name,attr = variable.split('_')
quantity = self.key2attr[attr.lower()]
truth_name = self.truthObj[reco_name]['name']
dr_match = self.truthObj[reco_name]['dr'] # deltaR match value
reco_object = self.event_objects[reco_name]
truth_object = self.event_objects[truth_name]
# -- DeltaR match and fill hists
# loop over objects (if more than 1)
if type(reco_object)==list:
for reco in reco_object:
# fill total (matched)
if reco.DeltaR(truth_object) < dr_match:
result = self.getValue(reco,quantity)
self.data[variable+eff_c][emu]['total'].Fill(eff_x_val,event_weight)
# fill good (matched & tagged)
if self.ops[logic_operator[vv]](result,good_condition[vv]):
self.data[variable+eff_c][emu]['good'].Fill(eff_x_val,event_weight)
# fill mis (matched & mis-tagged)
else:
self.data[variable+eff_c][emu]['mis'].Fill(eff_x_val,event_weight)
else:
if reco_object.DeltaR(truth_object) < dr_match:
# fill total (matched)
result = self.getValue(reco,quantity)
self.data[variable+eff_c][emu]['total'].Fill(eff_x_val,event_weight)
# fill good (matched & tagged)
if self.ops[logic_operator[vv]](result,good_condition[vv]):
self.data[variable+eff_c][emu]['good'].Fill(eff_x_val,event_weight)
# fill mis (matched & mis-tagged)
else:
self.data[variable+eff_c][emu]['mis'].Fill(eff_x_val,event_weight)
return
def EventLoop(self):
"""Loop over events."""
total_entries = self.ttree.GetEntries()
if self.p_nevents < 0 or self.p_nevents > total_entries:
nEntries = total_entries
else:
nEntries = self.p_nevents
entry = 0
print
print
print " RUNNING OVER ",self.f.GetName(),": ",nEntries," entries"
print
while entry < nEntries:
self.ttree.GetEntry(entry)
if not entry%1000: print " -> Entry ",entry
## -- Get some event info
if self.ttree.ejets: lep = 'el'
else: lep = 'mu'
if self.p_lepton!='muel' and lep!=self.p_lepton:
entry+=1
continue
self.BuildObjects()
## -- Apply cuts (if necessary)
failed_event = False
if self.p_cuts:
failed_event = self.ApplyCuts()
if failed_event:
entry+=1
continue
scaleFactor = self.getSF()
if self.p_plot1d == 'efficiency':
self.getEff(lep,scaleFactor)
## -- Loop over variables that we want
else:
custom_vars = addData.custom_variables(self.ttree,self.p_custom_vars,self.event_objects)
for variable in self.p_variables:
try:
values = custom_vars[variable]
except KeyError:
if '.' in variable:
name,attr = variable.split('.')
quantity = self.key2attr[attr.lower()]
if type(self.event_objects[name])==list:
values = []
for py_object in self.event_objects[name]:
values.append( self.getValue(py_object,quantity) )
else:
values = [ self.getValue(py_object,quantity) ]
else:
values = self.event_objects[variable]
## -- If there are no values, no need to continue!
if not values:
continue
## -- Keeping everything as a list for uniform interfaces
if type(values)==float:
values = [values]
scaleFactor = [scaleFactor for _ in values]
## Scale by GeV for variables with that unit
if not any(x in variable for x in self.nonGeVvars):
values = [i/self.GeV for i in values]
## saving the data to plot later ##
self.save_event(variable,values,scaleFactor,lep)
entry+=1
# -- end this entry -- #
return
