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
for sample in [data_sample] + mc:
sample.normalization = 1.
# sample.reduceFiles( factor = 4 )
sample.reduceFiles(to=4)
#sample.scale /= sample.normalization
max_events = 30000
# Text on the plots
tex = ROOT.TLatex()
tex.SetNDC()
tex.SetTextSize(0.04)
tex.SetTextAlign(11) # align right
# fire up the cache
cache_dir_ = os.path.join(cache_dir, 'fake_cache')
dirDB = DirDB(cache_dir_)
pu_key = (triggerSelection, leptonSelection, jetSelection, args.era,
args.small, "pu")
if dirDB.contains(pu_key) and not args.overwrite:
reweight_histo = dirDB.get(pu_key)
logger.info("Found PU reweight in cache %s", cache_dir_)
else:
logger.info("Didn't find PU reweight histo %r. Obtaining it now.", pu_key)
data_nvtx_histo = data_sample.get1DHistoFromDraw(
"PV_npvsGood", [100 / 5, 0, 100],
selectionString=data_preselectionString,
weightString="weight")
data_nvtx_histo.Scale(1. / data_nvtx_histo.Integral())
for sample in [data_sample] + mc:
sample.normalization = 1.
#sample.reduceFiles( factor = 10 )
sample.reduceFiles( to=3 )
#sample.scale /= sample.normalization
max_events = 30000
# Text on the plots
tex = ROOT.TLatex()
tex.SetNDC()
tex.SetTextSize(0.04)
tex.SetTextAlign(11) # align right
# fire up the cache
cache_dir_ = os.path.join(cache_dir, 'fake_pu_cache')
dirDB = DirDB(cache_dir_)
pu_key = ( triggerSelection, leptonSelection, jetSelection, args.era, args.small)
if dirDB.contains( pu_key ) and not args.overwrite:
reweight_histo = dirDB.get( pu_key )
logger.info( "Found PU reweight in cache %s", cache_dir_ )
else:
logger.info( "Didn't find PU reweight histo %r. Obtaining it now.", pu_key)
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]
data_sample.read_variables = ["event/I","run/I"]
data_sample.style = styles.errorStyle(ROOT.kBlack)
data_sample.scale = 1.
lumi_scale = data_sample.lumi/1000
logger.info('Lumi scale is ' + str(lumi_scale))
for sample in mc:
# sample.scale = lumi_scale
sample.style = styles.fillStyle(sample.color, lineColor = sample.color)
sample.read_variables = ['Pileup_nTrueInt/F', 'GenMET_pt/F', 'GenMET_phi/F']
# append variables for systematics
if args.variation is not None:
sample.read_variables+=list(set(variations[args.variation]['read_variables']))
################################################################################
# Fire up the cache
dirDB = DirDB(os.path.join(plot_directory, 'systematicPlots', plot_subdirectory, args.selection, 'cache'))
################################################################################
# loop over modes
for mode in modes:
yields[mode] = {}
data_sample.texName = "data"
data_sample.setSelectionString([getLeptonSelection(mode)])
data_sample.name = "data"
data_sample.style = styles.errorStyle(ROOT.kBlack)
lumi_scale = data_sample.lumi/1000
for sample in mc: sample.style = styles.fillStyle(sample.color)
for sample in mc:
sample.setSelectionString([getLeptonSelection(mode)])
{ 'name':"(no fid.) reco MET filters", 'texName':'\\MET filters', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1", "weightString":weight_at_gen+"*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger"},
{ 'name':"(no fid.) reco HEM", 'texName':'HEM veto', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1", "weightString":weight_at_gen+"*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM"},
{ 'name':"(no fid.) reco L1Prefire", 'texName':'L1prefire weights', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1", "weightString":weight_at_gen+"*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire"},
{ 'name':"(no fid.) reco ==1 photon", 'texName':'$\\Ng=1$','selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire"},
{ 'name':"(no fid.) reco photon veto", 'texName':'extra photon veto', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire"},
{ 'name':"(no fid.) reco >=3 Jets", 'texName':'$\\nJet\\geq3$', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&nJetGood>=3", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire"},
{ 'name':"(no fid.) reco >=1 b-Jets", 'texName':'$\\nBTag\\geq1$', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&nJetGood>=3&&nBTagGood>=1", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire*reweightBTag_SF"},
{ 'name':"(no fid.) reco >=4 Jets", 'texName':'$\\nJet\\geq4$', 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&nJetGood>=4&&nBTagGood>=1", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire*reweightBTag_SF"},
# { 'name':"(no fid., no weights) reco >=1 b-Jets", 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&nJetGood>=3&&nBTagGood>=1", "weightString":"(1)"},
# { 'name':"(no fid.) reco >=4 Jets", 'selectionString':MET_filter_cut+"&&overlapRemoval==1&&nLeptonTight==1&&triggered&&nLeptonVetoIsoCorr==1&&nPhotonGood==1&&nPhotonNoChgIsoNoSieie==1&&nJetGood>=4&&nBTagGood>=1", "weightString":weight_at_gen+"*reweightPhotonSF*reweightPhotonElectronVetoSF*reweightLeptonTightSF*reweightLeptonTrackingTightSF*reweightPU*reweightTrigger*reweightHEM*reweightL1Prefire*reweightBTag_SF"},
]
from Analysis.Tools.DirDB import DirDB
# fire up the cache
cache_dir_ = os.path.join(cache_directory, 'phase_space_table')
dirDB = DirDB(cache_dir_)
def getCachedYieldFromDraw( sample, selectionString, weightString ):
key_ = ( sample.name, selectionString, weightString )
if dirDB.contains( key_ ):
return dirDB.get( key_ )
else:
result = sample.getYieldFromDraw( selectionString = selectionString, weightString = weightString )
dirDB.add( key_ , result )
return result
result = []
samples = [ttg, ttg0l, ttg1l, ttg2l ]#, tt0l, tt1l, tt2l ]
print " "*39 +"".join( ["{:9s}".format(s.name) for s in samples ] )
def initCache(self, cacheDir):
self.cache = DirDB( os.path.join( cacheDir, 'puProfilesDirDBCache' ))