def efficiency_plots(d_sample, var_name, hdict):
graphs = []
graphs_eta = []
for rel, rdict in d_sample.items():
tree = rdict['tree']
if 'leaves' not in rdict:
rdict['leaves'] = [leaf.GetName() for leaf in tree.GetListOfLeaves()]
used_vars = word_finder(hdict['var'])
if not set(used_vars).issubset(rdict['leaves']):
warnings.warn(
var_name + ' is missing in input file ' + rdict['file'].GetName())
return
num_sel = reco_cut
den_sel = '1'
discriminators = {"loose_id": den_sel}
if 'against' in var_name:
den_sel = reco_cut + ' && ' + loose_id
for mvaIDname, sel in discriminators.items():
graphs.append(makeEffPlotsVars(tree=tree,
varx='tau_genpt',
numeratorAddSelection=num_sel +
'&&' + hdict['var'],
baseSelection=sel +
'&& abs(tau_eta) < 2.3',
binning=ptPlotsBinning,
xtitle=options_dict[runtype].xlabel,
header=rel + mvaIDname, addon=rel + mvaIDname,
marker=rdict['marker'],
col=rdict['col']))
graphs_eta.append(makeEffPlotsVars(tree=tree,
varx='tau_geneta',
numeratorAddSelection=num_sel +
'&&' + hdict['var'],
baseSelection=sel + '&& tau_pt>20',
binning=etaPlotsBinning,
xtitle=options_dict[runtype].xlabel_eta,
header=rel + mvaIDname, addon=rel + mvaIDname,
marker=rdict['marker'],
col=rdict['col']))
overlay(graphs=graphs,
header=var_name,
addon=hdict['title'],
runtype=runtype,
tlabel=options_dict[runtype].tlabel)
overlay(graphs=graphs_eta,
header=var_name + '_eta',
addon=hdict['title'] + '_eta',
runtype=runtype,
tlabel=options_dict[runtype].tlabel)
def efficiency_plots(d_sample, var_name, hdict):
graphs = []
graphs_eta = []
for rel, rdict in sorted(d_sample.items(),
key=lambda item: item[1]["index"]):
tree = rdict['tree']
if 'leaves' not in rdict:
rdict['leaves'] = [
leaf.GetName() for leaf in tree.GetListOfLeaves()
]
used_vars = word_finder(hdict['var'])
if not set(used_vars).issubset(rdict['leaves']):
with open('missing_leaves.txt', 'a+') as f:
print >> f, var_name + ' is missing in input file ' + rdict[
'file'].GetName()
warnings.warn(var_name + ' is missing in input file ' +
rdict['file'].GetName())
return
num_sel = reco_cut
den_sel = '1'
discriminators = {"": den_sel}
if 'against' in var_name:
den_sel = gen_cut + ' && ' + loose_id
rel = "tauReco @ miniAOD" if rel == "slimmedTaus_slimmedTaus" else "tauReco @ AOD"
for mvaIDname, sel in discriminators.items():
graphs.append(
makeEffPlotsVars(tree=tree,
varx='tau_genpt',
numeratorAddSelection=num_sel + '&&' +
hdict['var'],
baseSelection=sel,
binning=ptPlotsBinning,
xtitle=options_dict[runtype].xlabel,
header=rel + mvaIDname,
addon=rel + mvaIDname,
marker=rdict['marker'],
col=rdict['col']))
graphs_eta.append(
makeEffPlotsVars(tree=tree,
varx='tau_geneta',
numeratorAddSelection=num_sel + '&&' +
hdict['var'],
baseSelection=sel,
binning=etaPlotsBinning,
xtitle=options_dict[runtype].xlabel_eta,
header=rel + mvaIDname,
addon=rel + mvaIDname,
marker=rdict['marker'],
col=rdict['col']))
overlay(graphs=graphs,
header=var_name,
addon=hdict['title'],
runtype=runtype,
tlabel=options_dict[runtype].tlabel)
overlay(graphs=graphs_eta,
header=var_name + '_eta',
addon=hdict['title'] + '_eta',
runtype=runtype,
tlabel=options_dict[runtype].tlabel)
def eff_plots_single(d_sample, vars_to_compare, var_dict):
'''Adapted from Olena's code - can possibly merge it with efficiency_plots
'''
if not vars_to_compare:
return
hists = []
histseta = []
for index, var_name in enumerate(vars_to_compare):
hdict = var_dict[var_name]
if varyLooseId and 'IsolationMVA' in var_name:
loose_id = 'tau_decayModeFindingOldDMs > 0.5 && ' + findLooseId(
var_name)
for _, rdict in sorted(d_sample.items(),
key=lambda item: item[1]["index"]):
tree = rdict['tree']
if 'leaves' not in rdict:
rdict['leaves'] = [
leaf.GetName() for leaf in tree.GetListOfLeaves()
]
used_vars = word_finder(hdict['var'])
if not set(used_vars).issubset(rdict['leaves']):
warnings.warn(var_name + ' is missing in input file ' +
rdict['file'].GetName())
return
num_sel = reco_cut
den_sel = '1'
discriminators = {"loose_id": den_sel}
if 'against' in var_name:
den_sel = gen_cut + ' && ' + loose_id
for mvaIDname, sel in discriminators.items():
dprint("\n\tmvaIDname:", mvaIDname, "hdict['var']:",
hdict['var'])
hists.append(
makeEffPlotsVars(tree=tree,
varx='tau_genpt',
numeratorAddSelection=num_sel + '&&' +
hdict['var'],
baseSelection=sel,
binning=ptPlotsBinning,
xtitle=options_dict[runtype].xlabel,
header=var_name + mvaIDname,
addon=var_name + mvaIDname,
marker=rdict['marker'],
col=int(colors[index])))
shiftAlongX(hists[-1], len(vars_to_compare), index)
histseta.append(
makeEffPlotsVars(tree=tree,
varx='tau_geneta',
numeratorAddSelection=num_sel + '&&' +
hdict['var'],
baseSelection=sel,
binning=etaPlotsBinning,
xtitle=options_dict[runtype].xlabel_eta,
header=var_name + mvaIDname,
addon=var_name + mvaIDname,
marker=rdict['marker'],
col=int(colors[index])))
shiftAlongX(histseta[-1], len(vars_to_compare), index)
overlay(graphs=hists,
header=vars_to_compare[0],
addon=hdict['title'],
runtype=runtype,
tlabel=options_dict[runtype].tlabel,
comparePerReleaseSuffix="_comparePerRelease")
overlay(graphs=histseta,
header=vars_to_compare[0] + '_eta',
addon=hdict['title'] + '_eta',
runtype=runtype,
tlabel=options_dict[runtype].tlabel,
comparePerReleaseSuffix="_comparePerRelease")