def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly', prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsEtaMax(2)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 100']
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
jets = jet_selector(jet_def(parts))
all_jets.extend(jets)
pythia.stat()
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print('making lund diagram for all jets...')
lunds = [lund_gen.result(j) for j in all_jets]
print('listing lund plane points... Delta, kt - for {} selected jets'.format(len(all_jets)))
for l in lunds:
print ('- jet pT={0:5.2f} eta={1:5.2f}'.format(l[0].pair().perp(), l[0].pair().eta()))
print (' Deltas={}'.format([s.Delta() for s in l]))
print (' kts={}'.format([s.Delta() for s in l]))
print ( )
print('[i] reclustering and using soft drop...')
jet_def_rc = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
print('Reclustering:', jet_def_rc)
rc = fjcontrib.Recluster(jet_def_rc, True)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
for i,j in enumerate(all_jets):
j_rc = rc.result(j)
print()
print('- [{0:3d}] orig pT={1:10.3f} reclustered pT={2:10.3f}'.format(i, j.perp(), j_rc.perp()))
j_sd = sd.result(j)
print(' |-> after soft drop pT={0:10.3f} delta={1:10.3f}'.format(j_sd.perp(), j_sd.perp() - j.perp()))
sd_info = fjcontrib.get_SD_jet_info(j_sd)
print(" |-> SD jet params z={0:10.3f} dR={1:10.3f} mu={2:10.3f}".format(sd_info.z, sd_info.dR, sd_info.mu))
pythia = create_and_init_pythia(sconfig_pythia)
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorPtMax(
200.0) & fj.SelectorAbsEtaMax(1)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
all_jets = []
for iEvent in tqdm(range(10), 'event'):
if not pythia.next(): continue
parts = pyfj.vectorize(pythia, True, -1, 1, False)
jets = jet_selector(jet_def(parts))
all_jets.extend(jets)
all_sd_jets = [sd.result(j) for j in all_jets]
pts = [j.pt() for j in all_jets]
sd_pts = [j.pt() for j in all_sd_jets]
sd_delta_pt = [delta for delta in deltas(all_jets, all_sd_jets)]
nangs0 = [pyfj.angularity(j, 0.) for j in all_jets]
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
lunds = [lund_gen.result(j) for j in all_jets]
print('listing lund plane points... Delta, kt - for {} selected jets'.format(
len(all_jets)))
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorPtMax(
200.0) & fj.SelectorAbsEtaMax(1)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
all_jets = []
all_jets_py = []
all_parts = []
for iEvent in tqdm(range(1000), 'event'):
if not pythia.next():
continue
parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
all_parts.append(fjpsj.pyfj_list(parts))
jets = fj.sorted_by_pt(jet_selector(jet_def(parts)))
ev_jets_py = []
for idx, j in enumerate(jets):
j.set_user_index(idx)
jpy = fjpsj.pyfj_from_psj(j)
ev_jets_py.append(jpy)
all_jets_py.append(ev_jets_py)
all_jets.extend(jets)
print('found jets:', len(all_jets), len(all_jets_py))
joblib.dump(all_jets_py, 'testing.joblib')
# print_jets(all_jets_py)
read_jets = joblib.load('testing.joblib')
# print_jets(read_jets)
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
mycfg = ['PhaseSpace:pThatMin = 100']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
print("[e] pythia initialization failed.")
return
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
jets = jet_selector(jet_def(parts))
all_jets.extend(jets)
pythia.stat()
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print('making lund diagram for all jets...', lund_gen.description())
lunds = [lund_gen.result(j) for j in all_jets]
print(
'listing lund plane points... Delta, kt - for {} selected jets'.format(
len(all_jets)))
for l in lunds:
print('- jet pT={0:5.2f} eta={1:5.2f}'.format(l[0].pair().perp(),
l[0].pair().eta()))
print(' Deltas={}'.format([s.Delta() for s in l]))
print(' kts={}'.format([s.kt() for s in l]))
print()
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
dy_groomed = [dy_groomer.result(j, 2) for j in all_jets]
print('listing results from dynamical grooming...')
for l in dy_groomed:
print(l, ' kt={}'.format(l.kt()))
for j in all_jets:
rdyng2 = dy_groomer.result(j, 2)
rdyng1 = dy_groomer.result(j, 1)
print('jet', j)
print('- all splits kts:',
sorted([s.kt() for s in dy_groomer.lund_splits()], reverse=True))
print('- dynG kt a=1:', rdyng1.kt(), 'a=2:', rdyng2.kt())
print()
return
print('[i] reclustering and using soft drop...')
jet_def_rc = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
print('Reclustering:', jet_def_rc)
rc = fjcontrib.Recluster(jet_def_rc, True)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
for i, j in enumerate(all_jets):
j_rc = rc.result(j)
print()
print('- [{0:3d}] orig pT={1:10.3f} reclustered pT={2:10.3f}'.format(
i, j.perp(), j_rc.perp()))
j_sd = sd.result(j)
print(' |-> after soft drop pT={0:10.3f} delta={1:10.3f}'.format(
j_sd.perp(),
j_sd.perp() - j.perp()))
sd_info = fjcontrib.get_SD_jet_info(j_sd)
print(
" |-> SD jet params z={0:10.3f} dR={1:10.3f} mu={2:10.3f}".format(
sd_info.z, sd_info.dR, sd_info.mu))
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
parser.add_argument('-o',
'--output',
help='output file name',
default='pythia_lund_sd.root',
type=str)
pyconf.add_standard_pythia_args(parser)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
jet_selector = fj.SelectorPtMin(10) & fj.SelectorAbsEtaMax(1)
print(jet_def)
fout = r.TFile(args.output, 'RECREATE')
fout.cd()
tnjets = r.TNtuple('jets', 'jets',
'iev:nj:pt:eta:phi:f5:f6:f5dR:f6dR:proc:xsec')
tnjets_sd = r.TNtuple('jets_sd', 'jets_sd',
'iev:nj:pt:f5:f6:f5dR:f6dR:proc:xsec:pt_sd:z:dR:mu')
tnlund = r.TNtuple('lund', 'lund',
"iev:nj:pt:f5:f6:f5dR:f6dR:ns:pts:delta:kt")
nbins = 20
lbins = logbins(10, 500, nbins)
hjpt = r.TH1F('hjpt', 'hjpt', nbins, lbins)
hjptq = r.TH1F('hjptq', 'hjptq', nbins, lbins)
hjptg = r.TH1F('hjptg', 'hjptg', nbins, lbins)
hjptsd = r.TH1F('hjptsd', 'hjptsd', nbins, lbins)
hjptsdq = r.TH1F('hjptsdq', 'hjptsdq', nbins, lbins)
hjptsdg = r.TH1F('hjptsdg', 'hjptsdg', nbins, lbins)
hjptsdkt10 = r.TH1F('hjptsdkt10', 'hjptsdkt10', nbins, lbins)
hjptsdqkt10 = r.TH1F('hjptsdqkt10', 'hjptsdqkt10', nbins, lbins)
hjptsdgkt10 = r.TH1F('hjptsdgkt10', 'hjptsdgkt10', nbins, lbins)
hjptsdkt30 = r.TH1F('hjptsdkt30', 'hjptsdkt30', nbins, lbins)
hjptsdqkt30 = r.TH1F('hjptsdqkt30', 'hjptsdqkt30', nbins, lbins)
hjptsdgkt30 = r.TH1F('hjptsdgkt30', 'hjptsdgkt30', nbins, lbins)
hjptktsd = r.TH2F('hjptktsd', 'hjptktsd', 50, 0, 500, 50, 0, 500)
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
parts = pythiafjext.vectorize(pythia, True, -2, 2, False)
jets = fj.sorted_by_pt(jet_selector(jet_def(parts)))
f5 = pythia.event[5]
f6 = pythia.event[6]
f5psj = fj.PseudoJet(f5.px(), f5.py(), f5.pz(), f5.e())
f6psj = fj.PseudoJet(f6.px(), f6.py(), f6.pz(), f6.e())
iproc = pythia.info.code()
xsec = pythia.info.sigmaGen()
for nj, j in enumerate(jets):
f5dR = f5psj.delta_R(j)
f6dR = f6psj.delta_R(j)
tnjets.Fill(i, nj, j.pt(), j.eta(), j.phi(), f5.id(), f6.id(),
f5dR, f6dR, iproc, xsec)
lund = lund_gen.result(j)
for ns, s in enumerate(lund):
tnlund.Fill(i, nj, j.pt(), f5.id(), f6.id(), f5dR, f6dR, ns,
s.pair().perp(), s.Delta(), s.kt())
j_sd = sd.result(j)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
tnjets_sd.Fill(i, nj, j.pt(), f5.id(), f6.id(), f5dR, f6dR, iproc,
xsec, j_sd.pt(), sd_info.z, sd_info.dR, sd_info.mu)
# fill the histograms
f = None
if f5dR < 0.4: f = f5
if f6dR < 0.4: f = f6
if f is not None:
kt = sd_info.z * j_sd.perp()
hjpt.Fill(j.pt())
if sd_info.z >= 0:
hjptsd.Fill(j.pt())
if kt > 10: hjptsdkt10.Fill(j.pt())
if kt > 30: hjptsdkt30.Fill(j.pt())
if f.id() != 21:
hjptq.Fill(j.pt())
if sd_info.z >= 0:
hjptsdq.Fill(j.pt())
if kt > 10: hjptsdqkt10.Fill(j.pt())
if kt > 30: hjptsdqkt30.Fill(j.pt())
else:
hjptg.Fill(j.pt())
if sd_info.z >= 0:
hjptsdg.Fill(j.pt())
if kt > 10: hjptsdgkt10.Fill(j.pt())
if kt > 30: hjptsdgkt30.Fill(j.pt())
pythia.stat()
fout.cd()
hjptq_r = hjptq.Clone('hjptq_r')
hjptq_r.Sumw2()
hjptq_r.Divide(hjpt)
hjptg_r = hjptg.Clone('hjptg_r')
hjptg_r.Sumw2()
hjptg_r.Divide(hjpt)
hjptsdq_r = hjptsdq.Clone('hjptsdq_r')
hjptsdq_r.Sumw2()
hjptsdq_r.Divide(hjptsd)
hjptsdg_r = hjptsdg.Clone('hjptsdg_r')
hjptsdg_r.Sumw2()
hjptsdg_r.Divide(hjptsd)
hjptsdqkt10_r = hjptsdqkt10.Clone('hjptsdqkt10_r')
hjptsdqkt10_r.Sumw2()
hjptsdqkt10_r.Divide(hjptsdkt10)
hjptsdgkt10_r = hjptsdgkt10.Clone('hjptsdgkt10_r')
hjptsdgkt10_r.Sumw2()
hjptsdgkt10_r.Divide(hjptsdkt10)
hjptsdqkt30_r = hjptsdqkt30.Clone('hjptsdqkt30_r')
hjptsdqkt30_r.Sumw2()
hjptsdqkt30_r.Divide(hjptsdkt30)
hjptsdgkt30_r = hjptsdgkt30.Clone('hjptsdgkt30_r')
hjptsdgkt30_r.Sumw2()
hjptsdgkt30_r.Divide(hjptsdkt30)
fout.Write()
fout.Close()