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(80.0) & fj.SelectorPtMax(
100.0) & fj.SelectorAbsEtaMax(1)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 40']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print(lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd = fjcontrib.SoftDrop(0, 0.2, 1.0)
print(sd)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print(jet_def_rc01)
print(jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name='lsjvsx',
file_name='leadsj_vs_x_bias80.root')
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)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton],
0, True)
parts = pythiafjext.vectorize_select(
pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
j_type = match_dR(j, partons, jet_R0 / 2.)
j_sd = sd.result(j)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches(
j=j,
lund=[ls for ls in lund_gen.result(j)],
dyg1=dy_groomer.result(j, 1),
sd=j_sd,
sd_z=sd_info.z,
sd_mu=sd_info.mu,
sd_Delta=sd_info.dR,
lsjet01=rc_sjets01[0],
nsjet01=len(rc_sjets01),
sjet01=rc_sjets01,
lsjet02=rc_sjets02[0],
nsjet02=len(rc_sjets02),
sjet02=rc_sjets02,
ppid=j_type[0],
pquark=j_type[1],
pglue=j_type[2] # this is redundancy
)
tw.fill_tree()
pythia.stat()
tw.write_and_close()
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('--nw', help="no warn", default=False, action='store_true')
parser.add_argument('--ignore-mycfg', help="ignore some settings hardcoded here", default=False, action='store_true')
parser.add_argument('--enable-background', help="enable background calc", default=False, action='store_true')
parser.add_argument('--output', help="output file name", default='leadsj_vs_x_output.root', type=str)
# for background
parser.add_argument('--cent-bin', help="centraility bin 0 is the 0-5 percent most central bin", type=int, default=0)
parser.add_argument('--seed', help="pr gen seed", type=int, default=1111)
parser.add_argument('--harmonics', help="set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)",
type=int, default=5)
parser.add_argument('--eta', help="set eta range must be uniform (e.g. abs(eta) < 0.9, which is ALICE TPC fiducial acceptance)",
type=float, default=0.9)
parser.add_argument('--qa', help="PrintOutQAHistos", default=False, action='store_true')
parser.add_argument('--dRmax', default=0.25, type=float)
parser.add_argument('--alpha', default=0, type=float)
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(args.py_pthatmin) & fj.SelectorPtMax(1000.0) & fj.SelectorAbsEtaMax(args.eta - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 40']
mycfg = ['']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print (lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print (dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd01 = fjcontrib.SoftDrop(0, 0.1, 1.0)
print (sd01)
sd02 = fjcontrib.SoftDrop(0, 0.2, 1.0)
print (sd02)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print (jet_def_rc01)
print (jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = args.output)
tgbkg = None
be = None
if args.enable_background:
# ROOT.gSystem.Load("libpyjetty_TennGen.dylib")
# tgbkg = ROOT.TennGen() # //constructor
# tgbkg.SetCentralityBin(args.cent_bin) # //centraility bin 0 is the 0-5 % most central bin
# tgbkg.SetRandomSeed(args.seed) # //setting the seed
# tgbkg.SetHarmonics(args.harmonics) # // set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)
# tgbkg.SetEtaRange(args.eta) # //set eta range must be uniform (e.g. |eta| < 0.9, which is ALICE TPC fiducial acceptance)
# tgbkg.PrintOutQAHistos(args.qa) #
# tgbkg.InitializeBackground() #
from pyjetty.mputils import BoltzmannEvent
be = BoltzmannEvent(mean_pt=0.7, multiplicity=2000 * args.eta * 2, max_eta=max_eta, max_pt=100)
print(be)
from pyjetty.mputils import CEventSubtractor, CSubtractorJetByJet
cs = CEventSubtractor(alpha=args.alpha, max_distance=args.dRmax, max_eta=args.eta, bge_rho_grid_size=0.25, max_pt_correct=100)
print(cs)
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)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton], 0, True)
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
j_type = match_dR(j, partons, jet_R0 / 2.)
if j_type[0] is None:
if args.nw:
continue
pwarning('Jet with no parton label')
continue
j_sd02 = sd02.result(j)
sd02_info = fjcontrib.get_SD_jet_info(j_sd02)
j_sd01 = sd01.result(j)
sd01_info = fjcontrib.get_SD_jet_info(j_sd01)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches( j = j,
lund = [ls for ls in lund_gen.result(j)],
dyg1 = dy_groomer.result(j, 1),
sd01 = j_sd01,
sd01_z = sd01_info.z,
sd01_mu = sd01_info.mu,
sd01_Delta = sd01_info.dR,
sd02 = j_sd02,
sd02_z = sd02_info.z,
sd02_mu = sd02_info.mu,
sd02_Delta = sd02_info.dR,
# breaking compatibility
# sd = j_sd,
# sd_z = sd_info.z,
# sd_mu = sd_info.mu,
# sd_Delta = sd_info.dR,
lsjet01 = rc_sjets01[0],
nsjet01 = len(rc_sjets01),
sjet01 = rc_sjets01,
lsjet02 = rc_sjets02[0],
nsjet02 = len(rc_sjets02),
sjet02 = rc_sjets02,
ppid = j_type[0],
pquark = j_type[1],
pglue = j_type[2], # this is redundancy
pycode = pythia.info.code(),
pysigmagen = pythia.info.sigmaGen(),
pysigmaerr = pythia.info.sigmaErr(),
pyid1 = pythia.info.id1pdf(),
pyid2 = pythia.info.id1pdf(),
pyx1 = pythia.info.x1pdf(),
pyx2 = pythia.info.x2pdf(),
pypdf1 = pythia.info.pdf1(),
pyQfac = pythia.info.QFac(),
pyalphaS = pythia.info.alphaS(),
pypthat = pythia.info.pTHat(),
pymhat = pythia.info.mHat()
)
if be:
bg_parts = be.generate(offset=10000)
full_event = bg_parts
tmp = [full_event.push_back(psj) for psj in j.constituents()]
if cs:
cs_parts = cs.process_event(full_event)
rho = cs.bge_rho.rho()
bg_jets = fj.sorted_by_pt(jet_def(cs_parts))
for bj in bg_jets:
if fjtools.matched_pt(bj, j) > 0.5:
pass
tw.fill_tree()
pythia.stat()
tw.write_and_close()
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__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
parser.add_argument('--output',
help="output file name",
default="test_hjet_parton.root",
type=str)
parser.add_argument('--no-tt',
help="do not require TT to accept the event",
default=False,
action='store_true')
parser.add_argument('--charged',
help="analyze only the charged particles of the FS",
default=False,
action='store_true')
parser.add_argument('--max-jet-pt',
help="maximum jet pT to consider",
type=float,
default=100.)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
hadron_eta_max = 2.0
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(10.0) & fj.SelectorPtMax(
args.max_jet_pt) & fj.SelectorAbsEtaMax(hadron_eta_max - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(hadron_eta_max - jet_R0)
hTT6_selector = fj.SelectorPtMin(6) & fj.SelectorPtMax(
7) & fj.SelectorAbsEtaMax(hadron_eta_max)
hTT12_selector = fj.SelectorPtMin(12) & fj.SelectorPtMax(
50) & fj.SelectorAbsEtaMax(hadron_eta_max)
hTT20_selector = fj.SelectorPtMin(20) & fj.SelectorPtMax(
50) & fj.SelectorAbsEtaMax(hadron_eta_max)
pythia_fs_part_selection = [pythiafjext.kFinal]
if args.charged is True:
pwarning('running with charged particles in the final state')
pythia_fs_part_selection.append(pythiafjext.kCharged)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 6']
# mycfg = ['PhaseSpace:pThatMin = 12']
# mycfg = ['PhaseSpace:pThatMin = 40']
mycfg = []
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print(lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd = fjcontrib.SoftDrop(0, 0.2, 1.0)
print(sd)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print(jet_def_rc01)
print(jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name='lsjvsx', file_name=args.output)
zero_psj = fj.PseudoJet(0, 0, 10, 10)
if args.nev < 100:
args.nev = 100
t = tqdm.tqdm(total=args.nev)
while t.n < args.nev:
if not pythia.next():
continue
# information about the leading process
# print(pythia.info.code(), pythia.info.nameProc(pythia.info.code()))
# continue
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton],
0, True)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
parts = pythiafjext.vectorize_select(pythia, pythia_fs_part_selection,
0, False)
hTT6 = zero_psj
hTT6s = fj.sorted_by_pt(hTT6_selector(parts))
if len(hTT6s) > 0:
hTT6 = hTT6s[0]
hTT12 = zero_psj
hTT12s = fj.sorted_by_pt(hTT12_selector(parts))
if len(hTT12s) > 0:
hTT12 = hTT12s[0]
hTT20 = zero_psj
hTT20s = fj.sorted_by_pt(hTT20_selector(parts))
if len(hTT20s) > 0:
hTT20 = hTT20s[0]
if args.no_tt is False:
if hTT12.perp() < 1 and hTT6.perp() < 1 and hTT20.perp() < 1:
continue
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
t.update(1)
j_type = match_dR(j, partons, jet_R0 / 2.)
if j_type[0] is None:
continue
j_sd = sd.result(j)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches(
j=j,
mult=len(parts),
lund=[ls for ls in lund_gen.result(j)],
dyg1=dy_groomer.result(j, 1),
sd=j_sd,
sd_z=sd_info.z,
sd_mu=sd_info.mu,
sd_Delta=sd_info.dR,
lsjet01=rc_sjets01[0],
nsjet01=len(rc_sjets01),
sjet01=rc_sjets01,
lsjet02=rc_sjets02[0],
nsjet02=len(rc_sjets02),
sjet02=rc_sjets02,
hTT6=hTT6,
hTT12=hTT12,
hTT20=hTT20,
dphi6=j.delta_phi_to(hTT6),
dphi12=j.delta_phi_to(hTT12),
dphi20=j.delta_phi_to(hTT20),
ppid=j_type[0],
pquark=j_type[1],
pglue=j_type[2], # this is redundancy
pycode=pythia.info.code(),
pysigmagen=pythia.info.sigmaGen(),
pysigmaerr=pythia.info.sigmaErr(),
pyid1=pythia.info.id1pdf(),
pyid2=pythia.info.id1pdf(),
pyx1=pythia.info.x1pdf(),
pyx2=pythia.info.x2pdf(),
pypdf1=pythia.info.pdf1(),
pyQfac=pythia.info.QFac(),
pyalphaS=pythia.info.alphaS(),
pypthat=pythia.info.pTHat(),
pymhat=pythia.info.mHat())
tw.fill_tree()
t.close()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
# could use --py-seed
parser.add_argument('--fj-R', help='jet finder R', default=0.8, type=float)
parser.add_argument('--user-seed',
help='pythia seed',
default=1111,
type=int)
parser.add_argument('--output',
default="pythia_dyngroom_test_output.root",
type=str)
parser.add_argument('--min-jet-pt',
help='jet pt selection',
default=450.,
type=float)
parser.add_argument('--max-jet-pt',
help='jet pt selection',
default=1000.,
type=float)
args = parser.parse_args()
if args.user_seed < 0:
args.user_seed = 1111
pinfo('user seed for pythia', args.user_seed)
# mycfg = ['PhaseSpace:pThatMin = 100']
mycfg = ['Random:setSeed=on', 'Random:seed={}'.format(args.user_seed)]
mycfg.append('HadronLevel:all=off')
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if args.nev < 100:
args.nev = 100
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = args.fj_R
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
print(jet_def)
# hadron level - ALICE
max_eta_hadron = 3.
pwarning('max eta for particles after hadronization set to',
max_eta_hadron)
parts_selector_h = fj.SelectorAbsEtaMax(max_eta_hadron)
jet_selector = fj.SelectorPtMin(args.min_jet_pt) & fj.SelectorPtMax(
args.max_jet_pt) & fj.SelectorAbsEtaMax(max_eta_hadron - 1.05 * jet_R0)
max_eta_parton = max_eta_hadron + 2. * jet_R0
pwarning('max eta for partons set to', max_eta_parton)
parts_selector_p = fj.SelectorAbsEtaMax(max_eta_parton)
outf = ROOT.TFile(args.output, 'recreate')
outf.cd()
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
# event loop
for iev in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
# parts_pythia_p = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], add_particle_info = True)
parts_pythia_p = pythiafjext.vectorize_select(pythia,
[pythiafjext.kFinal], 0,
True)
parts_pythia_p_selected = parts_selector_p(parts_pythia_p)
hstatus = pythia.forceHadronLevel()
if not hstatus:
pwarning('forceHadronLevel false event', iev)
continue
# parts_pythia_h = pythiafjext.vectorize_select(pythia, [pythiafjext.kHadron, pythiafjext.kCharged])
# parts_pythia_h = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], add_particle_info = True)
parts_pythia_h = pythiafjext.vectorize_select(pythia,
[pythiafjext.kFinal], 0,
True)
parts_pythia_h_selected = parts_selector_h(parts_pythia_h)
jets_p = jet_selector(fj.sorted_by_pt(jet_def(parts_pythia_p)))
jets_h = jet_selector(fj.sorted_by_pt(jet_def(parts_pythia_h)))
for j in jets_p:
tw.fill_branch('iev', iev)
tw.fill_branch('parton_j', j)
for a in [0.1, 1.0, 2.0]:
dy_groomed = dy_groomer.result(j, a)
if dy_groomed.pair().pt() > 0:
tw.fill_branch('parton_j_logkt_dg{:.1f}'.format(a),
ROOT.TMath.Log(dy_groomed.kt()))
tw.fill_branch('parton_j_log1odR_dg{:.1f}'.format(a),
ROOT.TMath.Log(1 / dy_groomed.Delta()))
max_pt_groomed = dy_groomer.max_pt_softer(j)
if max_pt_groomed.pair().pt() > 0:
tw.fill_branch('parton_j_logkt_max_pt_softer',
ROOT.TMath.Log(max_pt_groomed.kt()))
tw.fill_branch('parton_j_log1odR_max_pt_softer',
ROOT.TMath.Log(1 / max_pt_groomed.Delta()))
max_z_groomed = dy_groomer.max_z(j)
if max_z_groomed.pair().pt() > 0:
tw.fill_branch('parton_j_logkt_max_z',
ROOT.TMath.Log(max_z_groomed.kt()))
tw.fill_branch('parton_j_log1odR_max_z',
ROOT.TMath.Log(1 / max_z_groomed.Delta()))
max_kt_groomed = dy_groomer.max_kt(j)
if max_kt_groomed.pair().pt() > 0:
tw.fill_branch('parton_j_logkt_max_kt',
ROOT.TMath.Log(max_kt_groomed.kt()))
tw.fill_branch('parton_j_log1odR_max_kt',
ROOT.TMath.Log(1 / max_kt_groomed.Delta()))
max_kappa_groomed = dy_groomer.max_kappa(j)
if max_kappa_groomed.pair().pt() > 0:
tw.fill_branch('parton_j_logkt_max_kappa',
ROOT.TMath.Log(max_kappa_groomed.kt()))
tw.fill_branch('parton_j_log1odR_max_kappa',
ROOT.TMath.Log(1 / max_kappa_groomed.Delta()))
for j in jets_h:
tw.fill_branch('hadron_j', j)
for a in [0.1, 1.0, 2.0]:
dy_groomed = dy_groomer.result(j, a)
if dy_groomed.pair().pt() > 0:
tw.fill_branch('hadron_j_logkt_dg{:.1f}'.format(a),
ROOT.TMath.Log(dy_groomed.kt()))
tw.fill_branch('hadron_j_log1odR_dg{:.1f}'.format(a),
ROOT.TMath.Log(1 / dy_groomed.Delta()))
max_pt_groomed = dy_groomer.max_pt_softer(j)
if max_pt_groomed.pair().pt() > 0:
tw.fill_branch('hadron_j_logkt_max_pt_softer',
ROOT.TMath.Log(max_pt_groomed.kt()))
tw.fill_branch('hadron_j_log1odR_max_pt_softer',
ROOT.TMath.Log(1 / max_pt_groomed.Delta()))
max_z_groomed = dy_groomer.max_z(j)
if max_z_groomed.pair().pt() > 0:
tw.fill_branch('hadron_j_logkt_max_z',
ROOT.TMath.Log(max_z_groomed.kt()))
tw.fill_branch('hadron_j_log1odR_max_z',
ROOT.TMath.Log(1 / max_z_groomed.Delta()))
max_kt_groomed = dy_groomer.max_kt(j)
if max_kt_groomed.pair().pt() > 0:
tw.fill_branch('hadron_j_logkt_max_kt',
ROOT.TMath.Log(max_kt_groomed.kt()))
tw.fill_branch('hadron_j_log1odR_max_kt',
ROOT.TMath.Log(1 / max_kt_groomed.Delta()))
max_kappa_groomed = dy_groomer.max_kappa(j)
if max_kappa_groomed.pair().pt() > 0:
tw.fill_branch('hadron_j_logkt_max_kappa',
ROOT.TMath.Log(max_kappa_groomed.kt()))
tw.fill_branch('hadron_j_log1odR_max_kappa',
ROOT.TMath.Log(1 / max_kappa_groomed.Delta()))
if len(jets_p) > 0 or len(jets_h) > 0:
tw.fill_tree()
pythia.stat()
outf.Write()
outf.Close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
# could use --py-seed
parser.add_argument('--fj-R', help='jet finder R', default=0.8, type=float)
parser.add_argument('--user-seed',
help='pythia seed',
default=-1,
type=int)
parser.add_argument('--output',
default='{}.root'.format(os.path.basename(__file__)),
type=str)
parser.add_argument('--min-jet-pt',
help='jet pt selection',
default=50.,
type=float)
parser.add_argument('--max-jet-pt',
help='jet pt selection',
default=1000.,
type=float)
parser.add_argument('--npart-min',
help='minimum npart in Argantyr',
default=2,
type=int)
args = parser.parse_args()
if args.user_seed < 0:
args.user_seed = -1
mycfg = []
else:
pinfo('user seed for pythia', args.user_seed)
# mycfg = ['PhaseSpace:pThatMin = 100']
mycfg = ['Random:setSeed=on', 'Random:seed={}'.format(args.user_seed)]
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if args.nev < 100:
args.nev = 100
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = args.fj_R
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
print(jet_def)
# hadron level - ALICE
max_eta_hadron = 3.
pwarning('max eta for particles after hadronization set to',
max_eta_hadron)
parts_selector_h = fj.SelectorAbsEtaMax(max_eta_hadron)
jet_selector = fj.SelectorPtMin(args.min_jet_pt) & fj.SelectorPtMax(
args.max_jet_pt) & fj.SelectorAbsEtaMax(max_eta_hadron - 1.05 * jet_R0)
parts_selector_cent = fj.SelectorAbsEtaMax(5.) & fj.SelectorAbsEtaMin(3.)
hepmc2output = '{}.hepmc2.dat'.format(args.output.replace('.root', ''))
pyhepmc2writer = pythiaext.Pythia8HepMC2Wrapper(hepmc2output)
outf = ROOT.TFile(args.output, 'recreate')
outf.cd()
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
tch = ROOT.TTree('tch', 'tch')
twch = RTreeWriter(tree=tch)
te = ROOT.TTree('te', 'te')
twe = RTreeWriter(tree=te)
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
sds = []
sd01 = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd02 = fjcontrib.SoftDrop(0, 0.2, 1.0)
sds.append(sd01)
sds.append(sd02)
# event loop
for iev in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
twe.clear()
tw.clear()
twch.clear()
weight = pythia.info.weight()
if args.py_PbPb:
# from main113.cc
# Also fill the number of (absorptively and diffractively)
# wounded nucleaons.
nw = pythia.info.hiinfo.nAbsTarg() + pythia.info.hiinfo.nDiffTarg(
) + pythia.info.hiinfo.nAbsProj() + pythia.info.hiinfo.nDiffProj()
fill_ncoll_branches(pythia, twe)
else:
nw = 2
twe.fill_branch('nw', nw)
twe.fill_branch('w', weight)
parts_pythia_h = pythiafjext.vectorize_select(pythia,
[pythiafjext.kFinal], 0,
False)
parts_pythia_h_selected = parts_selector_h(parts_pythia_h)
parts_pythia_ch = pythiafjext.vectorize_select(
pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
parts_pythia_ch_selected = parts_selector_h(parts_pythia_ch)
nch_total = len(parts_pythia_ch)
twe.fill_branch('nch', nch_total)
ncharged_fwd = len(parts_selector_cent(parts_pythia_ch))
twe.fill_branch('nchfwd', ncharged_fwd)
twe.fill_branch('iev', iev)
if args.py_PbPb and args.npart_min > nw:
twe.fill_tree()
continue
if args.py_PbPb:
pyhepmc2writer.fillEvent(pythia)
# do the rest only if centrality right
tw.fill_branch('iev', iev)
tw.fill_branch('w', weight)
twch.fill_branch('iev', iev)
twch.fill_branch('w', weight)
jets_h = jet_selector(fj.sorted_by_pt(jet_def(parts_pythia_h)))
jets_h_ch = jet_selector(fj.sorted_by_pt(jet_def(parts_pythia_ch)))
[
fill_branches(tw, j, dy_groomer, alphas=[0.1, 1.0, 2.0], sds=sds)
for j in jets_h
]
if len(jets_h) > 0:
tw.fill_tree()
if args.py_PbPb is False:
pyhepmc2writer.fillEvent(pythia)
[
fill_branches(twch, j, dy_groomer, alphas=[0.1, 1.0, 2.0], sds=sds)
for j in jets_h_ch
]
if len(jets_h_ch) > 0:
twch.fill_tree()
twe.fill_tree()
pythia.stat()
outf.Write()
outf.Close()
def analyze_groomed_jet(self, grooming_setting, jet, jetR, R_max):
# Check additional acceptance criteria
if not self.utils.is_det_jet_accepted(jet):
return
jet_pt_ungroomed = jet.pt()
grooming_label = self.utils.grooming_label(grooming_setting)
# Construct SD groomer, and groom jet
if 'sd' in grooming_setting:
zcut = grooming_setting['sd'][0]
beta = grooming_setting['sd'][1]
sd = fjcontrib.SoftDrop(beta, zcut, jetR)
jet_def_recluster = fj.JetDefinition(fj.cambridge_algorithm, jetR)
reclusterer = fjcontrib.Recluster(jet_def_recluster)
sd.set_reclustering(True, reclusterer)
if self.debug_level > 2:
print('SoftDrop groomer is: {}'.format(sd.description()))
jet_sd = sd.result(jet)
# Construct Dynamical groomer, and groom jet
if 'dg' in grooming_setting:
a = grooming_setting['dg'][0]
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 2 * jetR)
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
if self.debug_level > 2:
print('Dynamical groomer is: {}'.format(
dy_groomer.description()))
jet_dg_lund = dy_groomer.result(jet, a)
jet_dg = jet_dg_lund.pair()
# Compute groomed observables
if 'sd' in grooming_setting:
# If both SD and DG are specified, first apply DG, then SD
if 'dg' in grooming_setting:
if jet_dg.has_constituents():
jet_groomed = sd.result(jet_dg)
else:
return
else:
jet_groomed = jet_sd
sd_info = fjcontrib.get_SD_jet_info(jet_groomed)
theta_g = sd_info.dR / jetR
zg = sd_info.z
elif 'dg' in grooming_setting:
jet_groomed = jet_dg
# (https://phab.hepforge.org/source/fastjetsvn/browse/contrib/contribs/LundPlane/tags/1.0.3/LundGenerator.hh)
dR = jet_dg_lund.Delta()
theta_g = dR / jetR
zg = jet_dg_lund.z()
# Fill histograms
if R_max:
suffix = '_Rmax{}'.format(R_max)
else:
suffix = ''
if grooming_setting in self.obs_grooming_settings['theta_g']:
getattr(
self, 'h_theta_g_JetPt_R{}_{}{}'.format(
jetR, grooming_label,
suffix)).Fill(jet_pt_ungroomed, theta_g)
if grooming_setting in self.obs_grooming_settings['zg']:
getattr(self, 'h_zg_JetPt_R{}_{}{}'.format(
jetR, grooming_label, suffix)).Fill(jet_pt_ungroomed, zg)
# Fill jet axis difference
if 'jet_axis' in self.observable_list:
# Recluster with WTA (with larger jet R)
jet_def_wta = fj.JetDefinition(fj.cambridge_algorithm, 2 * jetR)
jet_def_wta.set_recombination_scheme(fj.WTA_pt_scheme)
if self.debug_level > 2:
print('WTA jet definition is:', jet_def_wta)
reclusterer_wta = fjcontrib.Recluster(jet_def_wta)
jet_wta = reclusterer_wta.result(jet)
self.fill_jet_axis_histograms(jet, jet_groomed, jet_wta, jetR,
grooming_setting, grooming_label)
