class puProfile:
def __init__( self, source_sample, cacheDir=os.path.join(cache_directory,"puProfiles") ):
if not os.path.isdir( cacheDir ): os.makedirs( cacheDir )
self.source_sample = source_sample
self.cacheDir = cacheDir
self.initCache( cacheDir )
self.binning = [ 100, 0, 100 ]
self.draw_string = "Pileup_nTrueInt"
def initCache(self, cacheDir):
self.cache = DirDB( os.path.join( cacheDir, 'puProfilesDirDBCache' ))
def uniqueKey( self, *arg ):
'''No dressing required'''
return arg
def cachedTemplate( self, selection, weight = '(1)', save = True, overwrite = False):
key = (selection, weight, self.source_sample.name)
if (self.cache and self.cache.contains(key)) and not overwrite:
result = self.cache.get(key)
logger.info( "Loaded MC PU profile from %s"%(self.cacheDir) )
logger.debug( "Key used: %s result: %r"%(key, result) )
elif self.cache:
logger.info( "Obtain PU profile for %s"%( key, ) )
result = self.makeTemplate( selection = selection, weight = weight)
if result:
result = self.cache.add( key, result, overwrite=save )
logger.info( "Adding PU profile to cache for %s : %r" %( key, result) )
else:
logger.warning( "Couldn't create PU profile to cache for %s : %r" %( key, result) )
else:
result = self.makeTemplate( selection = selection, weight = weight)
return result
def makeTemplate( self, selection, weight='(1)' ):
logger.info( "Make PU profile for sample %s and selection %s and weight %s", self.source_sample.name, selection, weight )
h_source = self.source_sample.get1DHistoFromDraw(self.draw_string, self.binning, selectionString = selection, weightString = weight )
logger.info( "PU histogram contains %s weighted events", h_source.Integral() )
h_source.Scale( 1./h_source.Integral() )
return h_source
mc_histos = [
s.get1DHistoFromDraw("PV_npvsGood", [100 / 5, 0, 100],
selectionString=mc_preselectionString,
weightString="weight*reweightBTag_SF") for s in mc
]
mc_histo = mc_histos[0]
for h in mc_histos[1:]:
mc_histo.Add(h)
mc_histo.Scale(1. / mc_histo.Integral())
reweight_histo = data_nvtx_histo.Clone()
reweight_histo.Divide(mc_histo)
dirDB.add(pu_key, reweight_histo)
logger.info("Added PU reweight to cache %s", cache_dir_)
# define reweight
def nvtx_puRW(event, sample):
return reweight_histo.GetBinContent(
reweight_histo.FindBin(event.PV_npvsGood))
templateFit_QCD_key = (triggerSelection, leptonSelection, jetSelection,
args.era, args.small, "template_QCD")
templateFit_EWK_key = (triggerSelection, leptonSelection, jetSelection,
args.era, args.small, "template_EWK")
if dirDB.contains(templateFit_EWK_key) and not args.overwrite:
logger.info("Found template fit in cache %s", cache_dir_)
data_selectionString = "&&".join([getFilterCut(isData=True, year=year), triggerSelection, leptonSelection, jetSelection])
data_nvtx_histo = data_sample.get1DHistoFromDraw( "PV_npvsGood", [100, 0, 100], selectionString=data_selectionString, weightString = "weight" )
data_nvtx_histo.Scale(1./data_nvtx_histo.Integral())
mc_selectionString = "&&".join([getFilterCut(isData=False, year=year), triggerSelection, leptonSelection, jetSelection])
mc_histos = [ s.get1DHistoFromDraw( "PV_npvsGood", [100, 0, 100], selectionString=mc_selectionString, weightString = "weight*reweightBTag_SF") for s in mc]
mc_histo = mc_histos[0]
for h in mc_histos[1:]:
mc_histo.Add( h )
mc_histo.Scale(1./mc_histo.Integral())
reweight_histo = data_nvtx_histo.Clone()
reweight_histo.Divide(mc_histo)
dirDB.add( pu_key, reweight_histo )
logger.info( "Added PU reweight to cache %s", cache_dir_ )
# define reweight
def nvtx_puRW( event, sample ):
return reweight_histo.GetBinContent(reweight_histo.FindBin( event.PV_npvsGood ))
#lumi_scale = data_sample.lumi/1000
data_sample.scale = 1.
for sample in mc:
sample.weight = nvtx_puRW
def drawObjects():
lines = [
(0.15, 0.95, 'CMS Preliminary'),
if args.variation == 'central':
normalisation_data = data_sample.scale*data_sample.getYieldFromDraw( selectionString = normalization_selection_string, weightString = data_weight_string)['val']
else:
normalisation_data = -1
logger.info( "Making plots.")
plotting.fill(plots, read_variables = read_variables, sequence = sequence)
# Delete lambda because we can't serialize it
for plot in plots:
del plot.weight
# save
dirDB.add( key, (normalisation_mc, normalisation_data, [plot.histos for plot in plots]), overwrite = args.overwrite)
logger.info( "Done with %s in channel %s.", args.variation, mode)
if args.variation is not None:
logger.info( "Done with modes %s and variation %s of selection %s. Quit now.", ",".join( modes ), args.variation, args.selection )
sys.exit(0)
################################################################################
# Systematic pairs:( 'name', 'up', 'down' )
systematics = [\
{'name':'JEC', 'pair':('jesTotalUp', 'jesTotalDown')},
# {'name':'Unclustered', 'pair':('unclustEnUp', 'unclustEnDown') },
{'name':'PU', 'pair':('PUUp', 'PUDown')},
{'name':'BTag_b', 'pair':('BTag_SF_b_Down', 'BTag_SF_b_Up' )},
{'name':'BTag_l', 'pair':('BTag_SF_l_Down', 'BTag_SF_l_Up')},