Author: Evan K. Friis, UC Davis
The PFTauEfficiencyAssociator produces ValueMap<pat::LookupTableRecord>s
associating an expected efficiency (or fake rate, from QCD) for a reco::PFTau
given its kinematics. The default configuration parameterizes the eff/fake rate
by pt, eta and jet width and stores the information in a TH3.
'''
import FWCore.ParameterSet.Config as cms
from RecoTauTag.TauAnalysisTools.tools.ntupleDefinitions import pftau_expressions, common_expressions
from RecoTauTag.TauAnalysisTools.fakeRate.histogramConfiguration import makeCuts
from RecoTauTag.TauAnalysisTools.fakeRate.associatorTools import *
# Get the histogram selection definitions. We don't need to worry about what
# the denominator is here, we just need the names
disc_configs = makeCuts(denominator="1")
protoEffciencyAssociator = cms.EDProducer("PFTauEfficiencyAssociatorFromTH3",
PFTauProducer = cms.InputTag("shrinkingConePFTauProducer"),
xAxisFunction = pftau_expressions.jetPt,
yAxisFunction = cms.string("abs(%s)" % pftau_expressions.jetEta.value()),
zAxisFunction = pftau_expressions.jetWidth,
efficiencySources = cms.PSet(
filename = cms.string("/afs/cern.ch/user/f/friis/public/TauPeformance_QCD_BCtoMu.root"),
# each efficiency source needs to be defined as a separate PSet
)
)
# Build the list of efficiency sources from the histogram production
# configuration disc_configs
MuEnrichedQCDEffSources = add_eff_sources(prefix="fr",
to_build = [
(source, source_info) for source, source_info in sources.iteritems()
if source in build_list ]
for source, source_info in to_build:
for location in list(source_info['sources'].keys()):
print "Parsing %s logs" % location
xSec, filterEff = source_info['sources'][location]
nEvents = getNEvents("%s/*.stderr" % location, "path")
weight = normalizeToOneInverseNanoBarn(
xSec, nEvents, filterEff)
# Update the info
source_info['sources'][location] = (
xSec, filterEff, nEvents, weight)
for source, source_info in to_build:
print '==========================='
print source
for loc, (xSec, filterEff, nEvents, weight) in source_info['sources'].iteritems():
print "Loc: %s, nEvents: %i, weight %f" % (loc, nEvents, weight)
for source, source_info in to_build:
# Build file list
files_and_weights = []
for loc, (xSec, filterEff, nEvents, weight) in source_info['sources'].iteritems():
for file in glob.glob("%s/*.root" % loc):
files_and_weights.append((file, weight))
make_plots(files_and_weights, selections=makeCuts(denominator), **source_info)
for source, source_info in to_build:
# Print out some info
print '==========================='
print source
for loc, (xSec, filterEff, nEvents,
weight) in source_info['sources'].iteritems():
print "Loc: %s, nEvents: %i, weight %f" % (loc, nEvents, weight)
# Build file list
if not options.ls:
files_and_weights = []
for loc, (xSec, filterEff, nEvents,
weight) in source_info['sources'].iteritems():
# Loop over the files to use for this run
for file in nthOfEveryMItems(
options.job, options.njobs,
glob.glob(os.path.join(working_dir, "%s/*.root" % loc))):
files_and_weights.append((file, weight))
# Change the output filename to reflect the batch job
source_info['output_file'] = source_info['output_file'].replace(
'.root', '_%i.root' % options.job)
source_info['output_file'] = os.path.join(working_dir,
source_info['output_file'])
# Temporarily switch back to jetpt/eta
make_plots(files_and_weights,
selections=makeCuts(denominator),
x_expr="$jetPt",
y_expr="abs($jetEta)",
**source_info)
Author: Evan K. Friis, UC Davis
The PFTauEfficiencyAssociator produces ValueMap<pat::LookupTableRecord>s
associating an expected efficiency (or fake rate, from QCD) for a reco::PFTau
given its kinematics. The default configuration parameterizes the eff/fake rate
by pt, eta and jet width and stores the information in a TH3.
'''
import FWCore.ParameterSet.Config as cms
from RecoTauTag.TauAnalysisTools.tools.ntupleDefinitions import pftau_expressions, common_expressions
from RecoTauTag.TauAnalysisTools.fakeRate.histogramConfiguration import makeCuts
from RecoTauTag.TauAnalysisTools.fakeRate.associatorTools import *
# Get the histogram selection definitions. We don't need to worry about what
# the denominator is here, we just need the names
disc_configs = makeCuts(denominator="1")
protoEffciencyAssociator = cms.EDProducer(
"PFTauEfficiencyAssociatorFromTH3",
PFTauProducer=cms.InputTag("shrinkingConePFTauProducer"),
xAxisFunction=pftau_expressions.jetPt,
yAxisFunction=cms.string("abs(%s)" % pftau_expressions.jetEta.value()),
zAxisFunction=pftau_expressions.jetWidth,
efficiencySources=cms.PSet(
filename=cms.string(
"/afs/cern.ch/user/f/friis/public/TauPeformance_QCD_BCtoMu.root"),
# each efficiency source needs to be defined as a separate PSet
))
# Build the list of efficiency sources from the histogram production
# configuration disc_configs
