for pf_type in pf_types:
name = pf_type['name']+'_'+eta_bin['name']
particles_eta_bin_pf_type = filter( pf_type['sel'], particles_eta_bin )
setattr( maker.event, name+'_sumPt', sum( [p.pt() for p in particles_eta_bin_pf_type], 0.))
setattr( maker.event, name+'_mult', len( particles_eta_bin_pf_type ) )
MEx = sum( [ -p.pt()*cos(p.phi()) for p in particles_eta_bin_pf_type], 0.)
MEy = sum( [ -p.pt()*sin(p.phi()) for p in particles_eta_bin_pf_type], 0.)
setattr( maker.event, name+'_met', sqrt(MEx**2 + MEy**2 ) )
setattr( maker.event, name+'_metPhi', atan2( MEy, MEx ) )
# minimumDR of good muons to high pt charged candidates
highPt_ch = filter( lambda p: p.pt()>5 and abs(p.pdgId()) == 211, pf )
maker.event.minDRGoodMuCh = sqrt( min( [ deltaR2( toDict(ch), toDict(mu) ) for mu in good_muons for ch in highPt_ch ] + [99**2]) )
maker.event.maxPtChCloseToMu = 0
for mu in good_muons:
chCloseToMu = filter( lambda p: deltaR2( toDict(p), toDict( mu ) )< 0.01**2, highPt_ch )
ptChCloseToMu = sum( [ p.pt() for p in chCloseToMu ], 0. )
print mu, ptChCloseToMu
if ptChCloseToMu>maker.event.maxPtChCloseToMu:
maker.event.maxPtChCloseToMu = ptChCloseToMu
#if maker.event.minDRGoodMuCh<0.01: assert False, ""
# get PF Mu's
pf_mu = filter( lambda p: p.pt()>15 and abs(p.pdgId()) == 13, pf )
# match with the good_muons from slimmed mu
good_to_pf_mu = {}
if len( pf_mu )>=2:
jets1_ = [j for j in r1.products['jets']] #if helpers.jetID( j )]
jets2_ = [j for j in r2.products['jets']] #if helpers.jetID( j )]
jets1 = [{
'pt': j.pt(),
'eta': j.eta(),
'phi': j.phi(),
'j': j
} for j in jets1_]
jets2 = [{
'pt': j.pt(),
'eta': j.eta(),
'phi': j.phi(),
'j': j
} for j in jets2_]
for c in zip(jets1, jets2):
if helpers.deltaR2(*c) < 0.2**2:
r = c[0]['pt'] / c[1]['pt']
ref_jet = c[0] if plan1RefJet else c[1]
if ref_jet['eta'] > 1.1 and ref_jet['eta'] < 3.2:
if ref_jet['phi'] > -1.07 and ref_jet['phi'] < -0.32:
NHEF_HEP17.Fill(c[0]['j'].neutralHadronEnergyFraction())
else:
NHEF_nonHEP17.Fill(c[0]['j'].neutralHadronEnergyFraction())
if ref_jet['eta'] > 1.1 and ref_jet['eta'] < 3.2 and ref_jet[
'phi'] > -1.07 and ref_jet['phi'] < -0.32:
resp_pt.Fill(ref_jet['pt'], r)
else:
# Without sorting, there is a jump between files with almost every event -> extremly slow
positions.sort()
logger.info("Have %i events in common.", len(intersec))
#Looping over common events
for i, p in enumerate(positions):
p1,p2 = p
r1.goToPosition(p1)
r2.goToPosition(p2)
if i%10000==0: logger.info("At %i/%i of common events.", i, len(positions))
jets1_ = [ j for j in r1.products['jets'] ] #if helpers.jetID( j )]
jets2_ = [ j for j in r2.products['jets'] ] #if helpers.jetID( j )]
jets1 = [{'pt':j.pt(), 'eta':j.eta(), 'phi':j.phi(), 'j':j } for j in jets1_]
jets2 = [{'pt':j.pt(), 'eta':j.eta(), 'phi':j.phi(), 'j':j } for j in jets2_]
for c in zip(jets1, jets2):
if helpers.deltaR2(*c)<0.2**2:
r = c[0]['pt']/c[1]['pt']
if c[0]['eta']>1.1 and c[0]['eta']<3.2 and c[0]['phi']>-1.07 and c[0]['phi']<-0.32:
resp_pt.Fill( c[0]['pt'], r )
else:
resp_pt_ref.Fill( c[0]['pt'], r )
if not c[0]['pt']>pt_threshold: continue
#if not ( helpers.jetID(c[0]['j']) and helpers.jetID(c[1]['j']) ): continue
#if not c[1]['nHEF']>0.1: continue
#r = c[0]['nHEF']/c[1]['nHEF']
# 2D eta, phi
while r.run( ):
if max_events is not None and max_events>0 and count>=max_events:break
if sample.isData and ( not lumiList.contains(r.evt[0], r.evt[1])) : continue
electrons = [ e for e in r.products['electrons'] if e.energy()>50 ]
photons = [ p for p in r.products['photons'] if p.energy()>100 ]
for e in electrons:
if abs(e.eta())<1.3 : continue
if not e.electronID("cutBasedElectronID-Spring15-25ns-V1-standalone-tight"): continue
e_sc = {'eta':e.superCluster().eta(), 'phi':e.superCluster().phi()}
for p in photons:
p_sc = {'eta':p.superCluster().eta(), 'phi':p.superCluster().phi()}
if helpers.deltaR2(e_sc, p_sc) == 0.:
gOverE[sample.name].Fill( p.pt(), p.pt()/e.pt() )
gOverE_2D[sample.name].Fill( p.pt(), p.pt()/e.pt() )
count += 1
# Make plot
#profiles = [jetResponse_M2_0_100, jetResponse_M2_5_500, jetResponse_M2_0_500, jetResponse_M2_5_100, jetResponse_M0, jetResponse_M21p, jetResponse_M23p]
profiles = [ gOverE[sample.name] for sample in samples ]
prefix = preprefix+"max_events_%s_"%max_events if max_events is not None and max_events>0 else preprefix
histos = [ [h.ProjectionX()] for h in profiles ]
for i, h in enumerate(histos):
h[0].__dict__.update(profiles[i].__dict__)
# h[0].Divide(jetResponse_M2_0_100.ProjectionX())
jetResponsePlot = Plot.fromHisto(name = prefix+"gOverE", histos = histos, texX = "photon p_{T}", texY = "p_{T}(#gamma)/p_{T}(e)" )
# Fast intersect
intersec = set(position_r1.keys()).intersection(set(position_r2.keys()))
positions = [(position_r1[i], position_r2[i]) for i in intersec]
positions.sort()
logger.info("Have %i events in common.", len(intersec))
#Loop over pairs
for nEvent, pos in enumerate(positions):
if nEvent%10000==0:
logger.info("At event %i/%i", nEvent, len(intersec))
p1, p2 = pos
r1.goToPosition(p1)
r2.goToPosition(p2)
n = min([r1.data.nJet, r2.data.nJet])
jets1 = [{'rawPt':r1.data.Jet_rawPt[i], 'eta':r1.data.Jet_eta[i], 'phi':r1.data.Jet_phi[i]} for i in xrange(n) if abs(r1.data.Jet_eta[i])<1.3]
jets2 = [{'rawPt':r2.data.Jet_rawPt[i], 'eta':r2.data.Jet_eta[i], 'phi':r2.data.Jet_phi[i]} for i in xrange(n) if abs(r2.data.Jet_eta[i])<1.3]
if r1.data.evt == 642018L and r1.data.run==260431 and r1.data.lumi==1:
break
for c in zip(jets1, jets2):
if deltaR2(*c)<0.2**2:
jetResponse.Fill(c[1]['rawPt'],c[1]['rawPt']/c[0]['rawPt'])
## Make plot
#prefix="test3"
#jetResponsePlot = Plot.fromHisto(name = prefix+"jetResponse_eta13", histos = [[jetResponse_mc], [jetResponse_data]], texX = "raw Jet p_{T}", texY = "response ratio M2(0,100)/M2(5,500)" )
#plotting.draw(jetResponsePlot, plot_directory = "/afs/hephy.at/user/r/rschoefbeck/www/etc/", ratio = {'yRange':(0.985,1.025)}, logX = True, logY = False, sorting = False, yRange = (0.965, 1.045) )
def match(j1, j2):
return deltaR2(j1,j2)<0.2**2
MEx = sum(
[-p.pt() * cos(p.phi()) for p in particles_eta_bin_pf_type],
0.)
MEy = sum(
[-p.pt() * sin(p.phi()) for p in particles_eta_bin_pf_type],
0.)
setattr(maker.event, name + '_met', sqrt(MEx**2 + MEy**2))
setattr(maker.event, name + '_metPhi', atan2(MEy, MEx))
# minimumDR of good muons to high pt charged candidates
highPt_ch = filter(lambda p: p.pt() > 5 and abs(p.pdgId()) == 211, pf)
maker.event.minDRGoodMuCh = sqrt(
min([
deltaR2(toDict(ch), toDict(mu)) for mu in good_muons
for ch in highPt_ch
] + [99**2]))
maker.event.maxPtChCloseToMu = 0
for mu in good_muons:
chCloseToMu = filter(
lambda p: deltaR2(toDict(p), toDict(mu)) < 0.01**2, highPt_ch)
ptChCloseToMu = sum([p.pt() for p in chCloseToMu], 0.)
print mu, ptChCloseToMu
if ptChCloseToMu > maker.event.maxPtChCloseToMu:
maker.event.maxPtChCloseToMu = ptChCloseToMu
#if maker.event.minDRGoodMuCh<0.01: assert False, ""
# get PF Mu's
pf_mu = filter(lambda p: p.pt() > 15 and abs(p.pdgId()) == 13, pf)
while r.run( ):
if max_events is not None and max_events>0 and count>=max_events:break
if sample.isData and ( not lumiList.contains(r.evt[0], r.evt[1])) : continue
electrons = [ e for e in r.products['electrons'] if e.energy()>50 ]
photons = [ p for p in r.products['photons'] if p.energy()>100 ]
for e in electrons:
if abs(e.eta())<1.3 : continue
if not e.electronID("cutBasedElectronID-Spring15-25ns-V1-standalone-tight"): continue
e_sc = {'eta':e.superCluster().eta(), 'phi':e.superCluster().phi()}
for p in photons:
p_sc = {'eta':p.superCluster().eta(), 'phi':p.superCluster().phi()}
if helpers.deltaR2(e_sc, p_sc) == 0.:
gOverE[sample.name].Fill( p.pt(), p.pt()/e.pt() )
gOverE_2D[sample.name].Fill( p.pt(), p.pt()/e.pt() )
count += 1
# Make plot
#profiles = [jetResponse_M2_0_100, jetResponse_M2_5_500, jetResponse_M2_0_500, jetResponse_M2_5_100, jetResponse_M0, jetResponse_M21p, jetResponse_M23p]
profiles = [ gOverE[sample.name] for sample in samples ]
prefix = preprefix+"max_events_%s_"%max_events if max_events is not None and max_events>0 else preprefix
histos = [ [h.ProjectionX()] for h in profiles ]
for i, h in enumerate(histos):
h[0].__dict__.update(profiles[i].__dict__)
# h[0].Divide(jetResponse_M2_0_100.ProjectionX())
jetResponsePlot = Plot.fromHisto(name = prefix+"gOverE", histos = histos, texX = "photon p_{T}", texY = "p_{T}(#gamma)/p_{T}(e)" )
def match(j1, j2):
return deltaR2(j1, j2) < 0.2**2
'genInfo': {'type':' GenEventInfoProduct', 'label': "generator"},
'rho': {'type':'double', 'label':"fixedGridRhoFastjetAll"},
}
r1 = sample.fwliteReader( products = products )
r1.start()
i=0
while r1.run():
#pt_hat = r1.products['genInfo'].binningValues()[0]
#if i%10000==0: logger.info("At %i", i)
#if not (pt_hat > pt_hat_min and pt_hat <pt_hat_max): continue
id_jets = [ j.correctedJet("Uncorrected") for j in r1.products['jets'] if helpers.jetID( j )]
for j in id_jets:
gj = j.genJet()
if gj and helpers.deltaR2({'eta':gj.eta(), 'phi':gj.phi()}, {'eta':j.eta(), 'phi':j.phi()}) < 0.2**2:
jet_corr_factor = jetCorrector_mc.correction( j.pt(), j.eta(), j.jetArea(), r1.products['rho'][0], 1 )
for eta_th in reversed(eta_thresholds):
if abs(gj.eta())>eta_th:
resp[eta_th].Fill( gj.pt(), j.pt()*jet_corr_factor / gj.pt() )
#print eta_th, gj.eta(), j.pt(), gj.pt()
break
i+=1
if i>max_events: break
# Make plot
profiles = [resp[t] for t in eta_thresholds]
