def q2mumu(args, figname):
datatype = args[0]
label = args[1]
test = get_options(args, "test")
batch = get_options(args, "batch")
if batch:
cmd = create_batch_cmd()
bashname = "%s.sh" % figname
bashfile = create_bashfile_cmd(cmd, bashname, label, test=test)
logfile = set_logfile("fig", datatype, label, figname)
jobname = "effq2mm"
bsub_jobs(logfile, jobname, bashfile, test)
return
figfile = set_figfile(figname, label, ".pdf", test=test)
rootfile = atr.rootfile(datatype, label, test=test)
obj = atr.root_tree_obj(datatype, label)
chain = root_chain(rootfile, obj)
canvas = TCanvas("aCanvas", "Canvas", 600, 600)
# h_mm_gen = TH1F('mumumass_gen', '#mu^{+} #mu^{-} mass', 100, 0, 25)
# h_mm_reco = TH1F('mumumass_reco', '#mu^{+} #mu^{-} mass', 100, 0, 25)
lower = array("f", [0, 2, 4.3, 8.68, 10.09, 12.86, 14.18, 16, 19, 25])
h_mm_gen = TH1F("mumumass_gen", "#mu^{+} #mu^{-} mass", 9, lower)
h_mm_reco = TH1F("mumumass_reco", "#mu^{+} #mu^{-} mass", 9, lower)
if "B2KstarMuMu/RECO_100M_v1.1" in label:
Gen_muonPos_P4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("Gen_muonPos_P4", AddressOf(Gen_muonPos_P4_))
Gen_muonNeg_P4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("Gen_muonNeg_P4", AddressOf(Gen_muonNeg_P4_))
MuPP4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("MuPP4", AddressOf(MuPP4_))
MuMP4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("MuMP4", AddressOf(MuMP4_))
KstarP4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("KstarP4", AddressOf(KstarP4_))
elif (
"B2KstarMuMu/RECO_100M_v1.2" in label
or "B2KstarMuMu/RECO_100M_v1.4" in label
or "B2KstarMuMu/RECO_100M_v1.5" in label
):
Gen_muonPos_P4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("Gen_muonPos_P4", AddressOf(Gen_muonPos_P4_))
Gen_muonNeg_P4_ = TClonesArray("TLorentzVector")
chain.SetBranchAddress("Gen_muonNeg_P4", AddressOf(Gen_muonNeg_P4_))
reco_mup_p4_ = TLorentzVector()
chain.SetBranchAddress("reco_mup_p4", AddressOf(reco_mup_p4_))
reco_mum_p4_ = TLorentzVector()
chain.SetBranchAddress("reco_mum_p4", AddressOf(reco_mum_p4_))
else:
raise NameError(label)
ntot = chain.GetEntries()
if test:
ntot = 1000
if (
"B2KstarMuMu/RECO_100M_v1.2" in label
or "B2KstarMuMu/RECO_100M_v1.4" in label
or "B2KstarMuMu/RECO_100M_v1.5" in label
):
cuts_label = "5ifbv2.6.2"
cuts = select_b0s(cuts_label)
sys.stdout.write("Processing %s events ...\n" % ntot)
sys.stdout.flush()
nfill_gen = 0
nfill_reco = 0
for i in xrange(ntot):
chain.LoadTree(i)
chain.GetEntry(i)
if len(chain.Gen_muonPos_P4) > 0:
mup4_gen = chain.Gen_muonPos_P4[0]
mum4_gen = chain.Gen_muonNeg_P4[0]
try:
mumu_gen = mup4_gen + mum4_gen
except TypeError:
continue
h_mm_gen.Fill(mumu_gen.M2())
nfill_gen += 1
if "/HLT" in label and not cuts.pass_trigger(chain):
continue
if "OfflineHLT" in label and not chain.offline_hlt_passed:
continue
if "MCmatched" in label and not chain.mc_matched:
continue
if "B2KstarMuMu/RECO_100M_v1.1" in label and chain.nXcand > 0:
if label in ["B2KstarMuMu/RECO_100M_v1.1/Kstar"] and not cuts.pass_kstarmass(chain, 0):
continue
if label in ["B2KstarMuMu/RECO_100M_v1.1/lxysig"] and not cuts.pass_lxysig(chain, 0):
continue
mup4 = chain.MuPP4[0]
mum4 = chain.MuMP4[0]
mumu = mup4 + mum4
h_mm_reco.Fill(mumu.M2())
nfill_reco += 1
if "B2KstarMuMu/RECO_100M_v1.2" in label:
mup4 = chain.reco_mup_p4
mum4 = chain.reco_mum_p4
mumu = mup4 + mum4
h_mm_reco.Fill(mumu.M2())
nfill_reco += 1
if "B2KstarMuMu/RECO_100M_v1.4" in label or "B2KstarMuMu/RECO_100M_v1.5" in label:
h_mm_reco.Fill(mumu_gen.M2())
nfill_reco += 1
sys.stdout.write("Filled events: GEN: %s, RECO: %s. \n" % (nfill_gen, nfill_reco))
hist = h_mm_reco
hist.Divide(h_mm_gen)
hist.SetTitle("RECO Efficiency")
hist.GetXaxis().SetTitle("q^{2} (GeV^{2}/c^{2})")
hist.Draw()
canvas.SaveAs(figfile)
hist.Delete()
def q2mumu(args, figname):
datatype = args[0]
label = args[1]
test = get_options(args, 'test')
batch = get_options(args, 'batch')
if batch:
cmd = create_batch_cmd()
bashname = '%s.sh' % figname
bashfile = create_bashfile_cmd(cmd, bashname, label, test=test)
logfile = set_logfile('fig', datatype, label, figname)
jobname = 'effq2mm'
bsub_jobs(logfile, jobname, bashfile, test)
return
figfile = set_figfile(figname, label, '.pdf', test=test)
rootfile = atr.rootfile(datatype, label, test=test)
obj = atr.root_tree_obj(datatype, label)
chain = root_chain(rootfile, obj)
canvas = TCanvas("aCanvas", "Canvas", 600, 600)
#h_mm_gen = TH1F('mumumass_gen', '#mu^{+} #mu^{-} mass', 100, 0, 25)
#h_mm_reco = TH1F('mumumass_reco', '#mu^{+} #mu^{-} mass', 100, 0, 25)
lower = array('f', [0, 2, 4.3, 8.68, 10.09, 12.86, 14.18, 16, 19, 25])
h_mm_gen = TH1F('mumumass_gen', '#mu^{+} #mu^{-} mass', 9, lower)
h_mm_reco = TH1F('mumumass_reco', '#mu^{+} #mu^{-} mass', 9, lower)
if 'B2KstarMuMu/RECO_100M_v1.1' in label:
Gen_muonPos_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonPos_P4', AddressOf(Gen_muonPos_P4_))
Gen_muonNeg_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonNeg_P4', AddressOf(Gen_muonNeg_P4_))
MuPP4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('MuPP4', AddressOf(MuPP4_))
MuMP4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('MuMP4', AddressOf(MuMP4_))
KstarP4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('KstarP4', AddressOf(KstarP4_))
elif 'B2KstarMuMu/RECO_100M_v1.2' in label or \
'B2KstarMuMu/RECO_100M_v1.4' in label or \
'B2KstarMuMu/RECO_100M_v1.5' in label:
Gen_muonPos_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonPos_P4', AddressOf(Gen_muonPos_P4_))
Gen_muonNeg_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonNeg_P4', AddressOf(Gen_muonNeg_P4_))
reco_mup_p4_ = TLorentzVector()
chain.SetBranchAddress('reco_mup_p4', AddressOf(reco_mup_p4_))
reco_mum_p4_ = TLorentzVector()
chain.SetBranchAddress('reco_mum_p4', AddressOf(reco_mum_p4_))
else:
raise NameError(label)
ntot = chain.GetEntries()
if test:
ntot = 1000
if 'B2KstarMuMu/RECO_100M_v1.2' in label or \
'B2KstarMuMu/RECO_100M_v1.4' in label or \
'B2KstarMuMu/RECO_100M_v1.5' in label:
cuts_label = '5ifbv2.6.2'
cuts = select_b0s(cuts_label)
sys.stdout.write('Processing %s events ...\n' % ntot)
sys.stdout.flush()
nfill_gen = 0
nfill_reco = 0
for i in xrange(ntot):
chain.LoadTree(i)
chain.GetEntry(i)
if len(chain.Gen_muonPos_P4) > 0:
mup4_gen = chain.Gen_muonPos_P4[0]
mum4_gen = chain.Gen_muonNeg_P4[0]
try:
mumu_gen = mup4_gen + mum4_gen
except TypeError:
continue
h_mm_gen.Fill(mumu_gen.M2())
nfill_gen += 1
if '/HLT' in label and not cuts.pass_trigger(chain):
continue
if 'OfflineHLT' in label and not chain.offline_hlt_passed:
continue
if 'MCmatched' in label and not chain.mc_matched:
continue
if 'B2KstarMuMu/RECO_100M_v1.1' in label and chain.nXcand > 0:
if label in ['B2KstarMuMu/RECO_100M_v1.1/Kstar'] and \
not cuts.pass_kstarmass(chain, 0):
continue
if label in ['B2KstarMuMu/RECO_100M_v1.1/lxysig'] and \
not cuts.pass_lxysig(chain, 0):
continue
mup4 = chain.MuPP4[0]
mum4 = chain.MuMP4[0]
mumu = mup4 + mum4
h_mm_reco.Fill(mumu.M2())
nfill_reco += 1
if 'B2KstarMuMu/RECO_100M_v1.2' in label:
mup4 = chain.reco_mup_p4
mum4 = chain.reco_mum_p4
mumu = mup4 + mum4
h_mm_reco.Fill(mumu.M2())
nfill_reco += 1
if 'B2KstarMuMu/RECO_100M_v1.4' in label or \
'B2KstarMuMu/RECO_100M_v1.5' in label:
h_mm_reco.Fill(mumu_gen.M2())
nfill_reco += 1
sys.stdout.write('Filled events: GEN: %s, RECO: %s. \n' %
(nfill_gen, nfill_reco))
hist = h_mm_reco
hist.Divide(h_mm_gen)
hist.SetTitle('RECO Efficiency')
hist.GetXaxis().SetTitle('q^{2} (GeV^{2}/c^{2})')
hist.Draw()
canvas.SaveAs(figfile)
hist.Delete()
def output_evt_mass_b0(chain, datatype, label, job, test=False, pbar=False):
selfile = set_selfile(datatype, label, 'tree', job=job, test=test)
f = TFile.Open(selfile, 'RECREATE')
t = TTree('sel', 'sel')
s = SelTree(t)
cuts = select_b0s(label)
pass_trigger = cuts.pass_trigger
pass_b0s = cuts.pass_b0s
pass_jpsimass = cuts.pass_jpsimass
pass_psi2smass = cuts.pass_psi2smass
nsel = 0
ntot = 0
xp4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('xP4', AddressOf(xp4_))
kstarp4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('KstarP4', AddressOf(kstarp4_))
entries = chain.GetEntries()
if test:
entries = 3000
if pbar:
pb = get_progressbar(maxval=entries)
for i in xrange(entries):
ntot += 1
if pbar:
pb.update(i+1)
ientry = chain.LoadTree(i)
if ientry < 0:
break
nb = chain.GetEntry(i)
if nb <= 0:
continue
if not pass_trigger(chain):
continue
chain.LoadTree(i)
chain.GetEntry(i)
b0s = pass_b0s(chain)
if b0s == []:
continue
s.run[0] = chain.runNb
s.event[0] = chain.eventNb
#nb0 = 0
#nj = 0
#np = 0
#nout = 0
for i in b0s:
#nb0 += 1
b0p4 = chain.xP4[i]
kstarp4 = chain.KstarP4[i]
oniap4 = b0p4 - kstarp4
s.b0mass[0] = b0p4.M()
s.oniamass[0] = oniap4.M()
s.b0massinjpsi[0] = 0
s.b0massinpsi2s[0] = 0
s.b0massout[0] = 0
if pass_jpsimass(oniap4.M()):
s.b0massinjpsi[0] = b0p4.M()
#nj += 1
elif pass_psi2smass(oniap4.M()):
s.b0massinpsi2s[0] = b0p4.M()
#np += 1
else:
s.b0massout[0] = b0p4.M()
#nout += 1
#print '%s + %s + %s = %s' %(nj, np, nout, nb0)
t.Fill()
nsel += 1
if nsel > 100 and test:
break
f.Write()
f.Close()
if pbar:
pb.finish()
return ntot, nsel, selfile
def mup(args, figname):
datatype = args[0]
label = args[1]
test = get_options(args, 'test')
batch = get_options(args, 'batch')
if batch:
cmd = create_batch_cmd()
bashname = '%s.sh' %figname
bashfile = create_bashfile_cmd(cmd, bashname, label, test=test)
logfile = set_logfile('fig', datatype, label, figname)
jobname = 'figptmu'
bsub_jobs(logfile, jobname, bashfile, test)
return
figfile = set_figfile(figname, label, '.pdf', test=test)
rootfile = atr.rootfile(datatype, label, test=test)
obj = atr.root_tree_obj(datatype, label)
chain = root_chain(rootfile, obj)
canvas = TCanvas("aCanvas", "Canvas", 600, 600)
hist = TH1F('mupt', '#mu^{+} p_{T}', 100, 0, 20)
if 'B2KstarMuMu/RECO_100M_v1.1' in label or \
'B2KstarMuMu/RECO_100M_v1.2' in label:
Gen_muonPos_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonPos_P4', AddressOf(Gen_muonPos_P4_))
MuPP4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('MuPP4', AddressOf(MuPP4_))
if 'B2KstarMuMu/RECO_100M_v1.3' in label or \
'B2KstarMuMu/RECO_100M_v1.4' in label or \
'B2KstarMuMu/RECO_100M_v1.5' in label:
Gen_muonPos_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonPos_P4', AddressOf(Gen_muonPos_P4_))
Gen_muonNeg_P4_ = TClonesArray('TLorentzVector')
chain.SetBranchAddress('Gen_muonNeg_P4', AddressOf(Gen_muonNeg_P4_))
reco_mup_p4_ = TLorentzVector()
chain.SetBranchAddress('reco_mup_p4', AddressOf(reco_mup_p4_))
reco_mum_p4_ = TLorentzVector()
chain.SetBranchAddress('reco_mum_p4', AddressOf(reco_mum_p4_))
ntot = chain.GetEntries()
if test:
ntot = 1000
if label in ['B2KstarMuMu/RECO_100M_v1.1/HLT',
'B2KstarMuMu/RECO_100M_v1.2/HLT',
'B2KstarMuMu/RECO_100M_v1.3/HLT',
'B2KstarMuMu/RECO_100M_v1.3/MCmatched/HLT',
'B2KstarMuMu/RECO_100M_v1.4/MCmatched/HLT',
'B2KstarMuMu/RECO_100M_v1.5/MCmatched/OfflineHLT',
]:
cuts_label = '5ifbv2.6.2'
cuts = select_b0s(cuts_label)
sys.stdout.write('Processing %s events ...\n' %ntot)
sys.stdout.flush()
nfill = 0
for i in xrange(ntot):
chain.LoadTree(i)
chain.GetEntry(i)
if label in ['B2KstarMuMu/RECO_100M_v1.1/HLT',
'B2KstarMuMu/RECO_100M_v1.2/HLT',
'B2KstarMuMu/RECO_100M_v1.3/HLT',
'B2KstarMuMu/RECO_100M_v1.3/MCmatched/HLT',
'B2KstarMuMu/RECO_100M_v1.4/MCmatched/HLT',
] and \
not cuts.pass_trigger(chain):
continue
if label in ['B2KstarMuMu/RECO_100M_v1.5/MCmatched/OfflineHLT',
] and \
not chain.offline_hlt_passed:
continue
if 'GEN' in label:
if 'B2KstarMuMu/RECO_100M_v1.5' in label:
# need others if necessary for backward compatibility.
mup4 = chain.Gen_muonPos_P4[0]
elif 'B2KstarMuMu/RECO_100M_v1.6' in label:
mup4 = TVector3(chain.genMupPx, chain.genMupPy, chain.genMupPz)
else:
raise NameError(label)
if 'MCmatched' in label and not chain.mc_matched:
continue
if 'B2KstarMuMu/RECO_100M_v1.1' in label:
if chain.nXcand <= 0:
continue
mup4 = chain.MuPP4[0]
if 'B2KstarMuMu/RECO_100M_v1.2' in label or \
'B2KstarMuMu/RECO_100M_v1.3' in label or \
'B2KstarMuMu/RECO_100M_v1.4' in label or \
'B2KstarMuMu/RECO_100M_v1.5' in label:
mup4 = chain.reco_mup_p4
hist.Fill(mup4.Pt())
nfill += 1
sys.stdout.write('Filled %s events. \n' %nfill)
hist.GetXaxis().SetTitle('p_{T} (GeV/c)')
hist.Draw()
canvas.SaveAs(figfile)
hist.Delete()
