def fill_emb_3(self, iev, jm):
# pdebug('@fill: jm[0]', jm[0], 'jm[1]', jm[1], 'jm[2]', jm[2])
self.twh.fill_branch('iev', iev)
self.twh.fill_branch('det', jm[0])
self.twh.fill_branch('part', jm[1])
self.twh.fill_branch('hybr', jm[2])
self.twh.fill_branch('dpt_pp', jm[0].pt() - jm[1].pt())
self.twh.fill_branch('dpt_emb', jm[2].pt() - jm[0].pt())
self.twh.fill_branch('dR_pp', jm[0].delta_R(jm[1]))
self.twh.fill_branch('dR_emb', jm[0].delta_R(jm[2]))
sd0 = self.sd.result(jm[0])
self.twh.fill_branch('sd_det', sd0)
sd0_pe1 = fj.PseudoJet()
sd0_pe2 = fj.PseudoJet()
sd0_has_parents = sd0.has_parents(sd0_pe1, sd0_pe2)
self.twh.fill_branch('sd_det_p1', sd0_pe1)
self.twh.fill_branch('sd_det_p2', sd0_pe2)
sdi0 = fjcontrib.get_SD_jet_info(sd0)
self.twh.fill_branch('sd_det_zg', sdi0.z)
self.twh.fill_branch('sd_det_Rg', sdi0.dR)
sd1 = self.sd.result(jm[1])
self.twh.fill_branch('sd_part', sd1)
sd1_pe1 = fj.PseudoJet()
sd1_pe2 = fj.PseudoJet()
sd1_has_parents = sd1.has_parents(sd1_pe1, sd1_pe2)
self.twh.fill_branch('sd_part_p1', sd1_pe1)
self.twh.fill_branch('sd_part_p2', sd1_pe2)
sdi1 = fjcontrib.get_SD_jet_info(sd1)
self.twh.fill_branch('sd_part_zg', sdi1.z)
self.twh.fill_branch('sd_part_Rg', sdi1.dR)
sd2 = self.sd.result(jm[2])
self.twh.fill_branch('sd_emb', sd2)
sd2_pe1 = fj.PseudoJet()
sd2_pe2 = fj.PseudoJet()
sd2_has_parents = sd2.has_parents(sd2_pe1, sd2_pe2)
self.twh.fill_branch('sd_emb_p1', sd2_pe1)
self.twh.fill_branch('sd_emb_p2', sd2_pe2)
sdi2 = fjcontrib.get_SD_jet_info(sd2)
self.twh.fill_branch('sd_emb_zg', sdi2.z)
self.twh.fill_branch('sd_emb_Rg', sdi2.dR)
m02_1 = -1
m02_2 = -1
if sd0_has_parents and sd2_has_parents:
m02_1 = fjtools.matched_pt(sd2_pe1, sd0_pe1)
m02_2 = fjtools.matched_pt(sd2_pe2, sd0_pe2)
self.twh.fill_branch('sd_det_emb_mpt1', m02_1)
self.twh.fill_branch('sd_det_emb_mpt2', m02_2)
self.twh.fill_branch('sd_det_split', sd0_has_parents)
self.twh.fill_branch('sd_part_split', sd1_has_parents)
self.twh.fill_branch('sd_emb_split', sd2_has_parents)
self.twh.fill_tree()
def fill_branches(tw, j, dy_groomer, alphas=[], sds=[]):
tw.fill_branch('j', j)
for a in alphas:
dy_groomed = dy_groomer.result(j, a)
# if dy_groomed.pair().pt() > 0:
# tw.fill_branch('dg_{:.1f}'.format(a), dy_groomed.harder())
# tw.fill_branch('dg_{:.1f}'.format(a), dy_groomed.softer())
tw.fill_branch('dg_{:.1f}'.format(a), dy_groomed)
max_pt_groomed = dy_groomer.max_pt_softer(j)
tw.fill_branch('max_ptsoft', max_pt_groomed)
max_z_groomed = dy_groomer.max_z(j)
tw.fill_branch('max_z', max_z_groomed)
max_kt_groomed = dy_groomer.max_kt(j)
tw.fill_branch('max_kt', max_kt_groomed)
max_kappa_groomed = dy_groomer.max_kappa(j)
tw.fill_branch('max_kappa', max_kappa_groomed)
max_tf_groomed = dy_groomer.max_tf(j)
tw.fill_branch('max_tf', max_tf_groomed)
min_tf_groomed = dy_groomer.min_tf(j)
tw.fill_branch('min_tf', min_tf_groomed)
for i, sd in enumerate(sds):
j_sd = sd.result(j)
tw.fill_branch('sd{}'.format(i), j_sd)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
tw.fill_branch('sd{}_z'.format(i), sd_info.z)
tw.fill_branch('sd{}_Delta'.format(i), sd_info.dR)
tw.fill_branch('sd{}_mu'.format(i), sd_info.mu)
tw.fill_branch('sd{}_kt'.format(i), sd_info.z * j_sd.pt() * sd_info.dR)
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))
def lund_coordinates_SD(self, jet_sd):
sd_info = fjcontrib.get_SD_jet_info(jet_sd)
dR = sd_info.dR
z = sd_info.z
pt = jet_sd.pt()
kt = z * dR * pt
if dR > 1e-5:
return [np.log(1 / dR), np.log(kt)]
else:
return [sys.maxsize, -sys.maxsize]
def main():
# get the banner out of the way early on
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, 0.4)
selector = fj.SelectorPtMin(15.0) & fj.SelectorAbsRapMax(4.5)
print("jet definition is:", jet_def)
print("jet selector is:", selector, "\n")
filename = './Pythia-dijet-ptmin100-lhc-pileup-1ev.dat'
f = open(filename, 'r')
#filename = '/Users/gsalam/work/fastjet/data/Pythia-PtMin50-LHC-10kev.dat.gz'
#f = gzip.GzipFile(filename,'rb')
# get the event
iev = 0
while True:
event = read_event(f)
iev += 1
if (len(event) == 0): break
# cluster it
jets = selector(jet_def(event))
# print some info
npileup = 0
for p in event:
if (p.python_info().subevent_index > 0): npileup += 1
print("Event {0} has {1} particles (of which {2} from pileup)".format(
iev, len(event), npileup))
print_jets(jets)
jet_def_rc = fj.JetDefinition(fj.antikt_algorithm, 0.1)
rc = fjcontrib.Recluster(jet_def_rc, True)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
for i, j in enumerate(jets):
j_rc = rc.result(j)
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)
# j_sd.structure_of<fj.contrib.SoftDrop>().delta_R()
# print(j_sd.python_info())
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 fill_tree_data(jet, tw, sd, rho, iev=None, weight=None, sigma=None):
tw.clear()
if iev:
tw.fill_branch('ev_id', iev)
if weight:
tw.fill_branch('weight', weight)
if sigma:
tw.fill_branch('sigma', sigma)
good_jet = 0.0
if len(jet.constituents()) > 0:
if fj.sorted_by_pt(jet.constituents())[0].pt() < 100.:
good_jet = 1.0
tw.fill_branch('good', good_jet)
sd_jet = sd.result(jet)
sd_info_jet = fjcontrib.get_SD_jet_info(sd_jet)
tw.fill_branch('rho', rho)
tw.fill_branch('j', jet)
tw.fill_branch('j_ptc', jet.pt() - jet.area() * rho)
tw.fill_branch('sd_j', sd_jet)
tw.fill_branch('sd_j_cpt', sd_jet.pt() - sd_jet.area() * rho)
tw.fill_branch('sd_j_z', sd_info_jet.z)
tw.fill_branch('sd_j_dR', sd_info_jet.dR)
pe1 = fj.PseudoJet()
pe2 = fj.PseudoJet()
has_parents = sd_jet.has_parents(pe1, pe2)
tw.fill_branch('j_p1', pe1)
tw.fill_branch('j_p2', pe2)
if has_parents:
tw.fill_branch('j_p1_ptc', pe1.pt() - pe1.area() * rho)
tw.fill_branch('j_p2_ptc', pe2.pt() - pe2.area() * rho)
else:
tw.fill_branch('j_p1_ptc', -1000)
tw.fill_branch('j_p2_ptc', -1000)
tw.fill_tree()
return jet
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('--user-seed', help='PYTHIA starting seed', default=1111, type=int)
parser.add_argument('--output', default="output.root", type=str)
parser.add_argument('--sd_beta', help='SoftDrop beta', default=None, type=float)
parser.add_argument('--jetR', help='Jet radius/resolution parameter', default=0.4, type=float)
parser.add_argument('--no-match-level', help="Save simulation for only one level with " + \
"no matching. Options: 'p', 'h', 'ch'", default=None, type=str)
parser.add_argument('--p-ch-MPI', help="Match between parton level (no MPI) and charged " + \
"hadron level (with MPI) for ALICE response characterization",
action='store_true', default=False)
args = parser.parse_args()
level = args.no_match_level
if args.p_ch_MPI:
if level:
print("ERROR: --no-match-level and --p-ch-MPI cannot be set simultaneously.")
exit(1)
if level not in [None, 'p', 'h', 'ch']:
print("ERROR: Unrecognized type %s. Please use 'p', 'h', or 'ch'" % args.type_only)
exit(1)
# Angularity beta values
betas = [1, 1.5, 2, 3]
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')
# Have at least 1 event
if args.nev < 1:
args.nev = 1
if args.p_ch_MPI:
d = vars(args)
d['py_noMPI'] = True
pythia_noMPI = pyconf.create_and_init_pythia_from_args(args, mycfg)
args_MPI = copy.deepcopy(args)
d = vars(args_MPI)
d['py_noMPI'] = False
pythia = pyconf.create_and_init_pythia_from_args(args_MPI, mycfg)
else:
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = args.jetR
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
print(jet_def)
# hadron level - ALICE
max_eta_hadron = 0.9
pwarning('max eta for particles after hadronization set to', max_eta_hadron)
parts_selector_h = fj.SelectorAbsEtaMax(max_eta_hadron)
jet_selector = fj.SelectorPtMin(5.0) #& fj.SelectorAbsEtaMax(max_eta_hadron - 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)
if not args.sd_beta is None:
args.output = args.output.replace('.root', '_sdbeta{}.root'.format(args.sd_beta))
outf = ROOT.TFile(args.output, 'recreate')
outf.cd()
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
count1 = 0
count2 = 0
# event loop
for iev in range(args.nev): #tqdm.tqdm(range(args.nev)):
if not pythia.next():
if args.p_ch_MPI:
pythia_noMPI.next()
continue
if args.p_ch_MPI and not pythia_noMPI.next():
continue
parts_pythia_p = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, True)
parts_pythia_p_selected = parts_selector_p(parts_pythia_p)
if args.p_ch_MPI:
parts_pythia_p = pythiafjext.vectorize_select(pythia_noMPI, [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], 0, True)
parts_pythia_h_selected = parts_selector_h(parts_pythia_h)
parts_pythia_hch = pythiafjext.vectorize_select(
pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, True)
parts_pythia_hch_selected = parts_selector_h(parts_pythia_hch)
# pinfo('debug partons...')
# for p in parts_pythia_p_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# pinfo('debug hadrons...')
# for p in parts_pythia_h_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# pinfo('debug ch. hadrons...')
# for p in parts_pythia_hch_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
jets_p = fj.sorted_by_pt(jet_selector(jet_def(parts_pythia_p)))
jets_h = fj.sorted_by_pt(jet_selector(jet_def(parts_pythia_h)))
jets_ch = fj.sorted_by_pt(jet_selector(jet_def(parts_pythia_hch)))
if level: # Only save info at one level w/o matching
jets = locals()["jets_%s" % level]
for jet in jets:
tw.fill_branch('iev', iev)
tw.fill_branch(level, jet)
kappa = 1
for beta in betas:
label = str(beta).replace('.', '')
tw.fill_branch('l_%s_%s' % (level, label),
lambda_beta_kappa(jet, jet_R0, beta, kappa))
continue
if not args.sd_beta is None:
sd = fjcontrib.SoftDrop(args.sd_beta, 0.1, jet_R0)
for i,jchh in enumerate(jets_ch):
# match hadron (full) jet
drhh_list = []
for j, jh in enumerate(jets_h):
drhh = jchh.delta_R(jh)
if drhh < jet_R0 / 2.:
drhh_list.append((j,jh))
if len(drhh_list) != 1:
count1 += 1
else: # Require unique match
j, jh = drhh_list[0]
# match parton level jet
dr_list = []
for k, jp in enumerate(jets_p):
dr = jh.delta_R(jp)
if dr < jet_R0 / 2.:
dr_list.append((k, jp))
if len(dr_list) != 1:
count2 += 1
else:
k, jp = dr_list[0]
# pwarning('event', iev)
# pinfo('matched jets: ch.h:', jchh.pt(), 'h:', jh.pt(),
# 'p:', jp.pt(), 'dr:', dr)
tw.fill_branch('iev', iev)
tw.fill_branch('ch', jchh)
tw.fill_branch('h', jh)
tw.fill_branch('p', jp)
kappa = 1
for beta in betas:
label = str(beta).replace('.', '')
tw.fill_branch("l_ch_%s" % label,
lambda_beta_kappa(jchh, jet_R0, beta, kappa))
tw.fill_branch("l_h_%s" % label,
lambda_beta_kappa(jh, jet_R0, beta, kappa))
tw.fill_branch("l_p_%s" % label,
lambda_beta_kappa(jp, jet_R0, beta, kappa))
if not args.sd_beta is None:
jchh_sd = sd.result(jchh)
jchh_sd_info = fjcontrib.get_SD_jet_info(jchh_sd)
jh_sd = sd.result(jh)
jh_sd_info = fjcontrib.get_SD_jet_info(jh_sd)
jp_sd = sd.result(jp)
jp_sd_info = fjcontrib.get_SD_jet_info(jp_sd)
tw.fill_branch('p_zg', jp_sd_info.z)
tw.fill_branch('p_Rg', jp_sd_info.dR)
tw.fill_branch('p_thg', jp_sd_info.dR/jet_R0)
tw.fill_branch('p_mug', jp_sd_info.mu)
tw.fill_branch('h_zg', jh_sd_info.z)
tw.fill_branch('h_Rg', jh_sd_info.dR)
tw.fill_branch('h_thg', jh_sd_info.dR/jet_R0)
tw.fill_branch('h_mug', jh_sd_info.mu)
tw.fill_branch('ch_zg', jchh_sd_info.z)
tw.fill_branch('ch_Rg', jchh_sd_info.dR)
tw.fill_branch('ch_thg', jchh_sd_info.dR/jet_R0)
tw.fill_branch('ch_mug', jchh_sd_info.mu)
#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))
tw.fill_tree()
if args.p_ch_MPI:
pythia_noMPI.stat()
pythia.stat()
print("%i jets cut at first match criteria; %i jets cut at second match criteria." %
(count1, count2))
outf.Write()
outf.Close()
def fill_tree_matched(signal_jet,
emb_jet,
tw,
sd,
rho,
iev=None,
weight=None,
sigma=None):
tw.clear()
mpt = fjtools.matched_pt(emb_jet, signal_jet)
if mpt <= 0.5:
return None
tw.fill_branch('j_mpt', mpt)
if iev:
tw.fill_branch('ev_id', iev)
if weight:
tw.fill_branch('weight', weight)
if sigma:
tw.fill_branch('sigma', sigma)
sd_signal_jet = sd.result(signal_jet)
sd_info_signal_jet = fjcontrib.get_SD_jet_info(sd_signal_jet)
sd_emb_jet = sd.result(emb_jet)
sd_info_emb_jet = fjcontrib.get_SD_jet_info(sd_emb_jet)
tw.fill_branch('rho', rho)
tw.fill_branch('j', signal_jet)
tw.fill_branch('sd_j', sd_signal_jet)
tw.fill_branch('sd_j_z', sd_info_signal_jet.z)
tw.fill_branch('sd_j_dR', sd_info_signal_jet.dR)
tw.fill_branch('j_nc', len(signal_jet.constituents()))
tw.fill_branch('ej', emb_jet)
tw.fill_branch('ej_ptc', emb_jet.pt() - emb_jet.area() * rho)
tw.fill_branch('sd_ej', sd_emb_jet)
tw.fill_branch('sd_ej_cpt', sd_emb_jet.pt() - sd_emb_jet.area() * rho)
tw.fill_branch('sd_ej_z', sd_info_emb_jet.z)
tw.fill_branch('sd_ej_dR', sd_info_emb_jet.dR)
p1 = fj.PseudoJet()
p2 = fj.PseudoJet()
has_parents_signal = sd_signal_jet.has_parents(p1, p2)
# print('signal_jet:', has_parents, len(p1.constituents()), len(p2.constituents()))
tw.fill_branch('j_p1', p1)
tw.fill_branch('j_p2', p2)
pe1 = fj.PseudoJet()
pe2 = fj.PseudoJet()
has_parents_emb = sd_emb_jet.has_parents(pe1, pe2)
tw.fill_branch('ej_p1', pe1)
tw.fill_branch('ej_p2', pe2)
if has_parents_emb:
tw.fill_branch('ej_p1_ptc', pe1.pt() - pe1.area() * rho)
tw.fill_branch('ej_p2_ptc', pe2.pt() - pe2.area() * rho)
else:
tw.fill_branch('ej_p1_ptc', -1000)
tw.fill_branch('ej_p2_ptc', -1000)
mpt1 = -1.0 # not passed SD
mpt2 = -1.0 # not passed SD
if has_parents_signal and has_parents_emb:
mpt1 = fjtools.matched_pt(pe1, p1)
mpt2 = fjtools.matched_pt(pe2, p2)
tw.fill_branch('mpt1', mpt1)
tw.fill_branch('mpt2', mpt2)
# print('signal_jet:', has_parents, len(pe1.constituents()), len(pe2.constituents()))
# print('emb_jets', has_parents, len(pe1.constituents()), len(pe2.constituents()))
# for c in pe2.constituents():
# cp1 = fj.PseudoJet()
# cp2 = fj.PseudoJet()
# print(' - ', c.has_parents(cp1, cp2))
#tw.fill_branch('jsd', sd_j)
#tw.fill_branch('jm', ej)
tw.fill_tree()
return emb_jet
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 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)
def main():
parser = argparse.ArgumentParser(description='pythia8 in python',
prog=os.path.basename(__file__))
parser.add_argument('-i',
'--input',
help='input file',
default='low',
type=str,
required=True)
parser.add_argument('--hepmc',
help='what format 2 or 3',
default=2,
type=int)
parser.add_argument('--nev', help='number of events', default=10, type=int)
args = parser.parse_args()
###
# now lets read the HEPMC file and do some jet finding
if args.hepmc == 3:
input_hepmc = pyhepmc_ng.ReaderAscii(args.input)
if args.hepmc == 2:
input_hepmc = pyhepmc_ng.ReaderAsciiHepMC2(args.input)
if input_hepmc.failed():
print("[error] unable to read from {}".format(args.input))
sys.exit(1)
# jet finder
# print the banner first
fj.ClusterSequence.print_banner()
print()
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(10.0) & fj.SelectorPtMax(
500.0) & fj.SelectorAbsEtaMax(3)
all_jets = []
event_hepmc = pyhepmc_ng.GenEvent()
pbar = tqdm.tqdm(range(args.nev))
while not input_hepmc.failed():
ev = input_hepmc.read_event(event_hepmc)
if input_hepmc.failed():
break
jets_hepmc = find_jets_hepmc(jet_def, jet_selector, event_hepmc)
all_jets.extend(jets_hepmc)
pbar.update()
if pbar.n >= args.nev:
break
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print('[i] making lund diagram for all jets...')
lunds = [lund_gen.result(j) for j in all_jets]
print('[i] listing lund plane points... Delta, kt - for {} selected jets'.
format(len(all_jets)))
for l in lunds:
if len(l) < 1:
continue
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()
print('[i] reclustering and using soft drop...')
jet_def_rc = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
print('[i] Reclustering:', jet_def_rc)
all_jets_sd = []
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()))
all_jets_sd.append(j_sd)
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))
fout = ROOT.TFile('hepmc_jetreco.root', 'recreate')
lbins = logbins(1., 500, 50)
hJetPt04 = ROOT.TH1D("hJetPt04", "hJetPt04", 50, lbins)
hJetPt04sd = ROOT.TH1D("hJetPt04sd", "hJetPt04sd", 50, lbins)
[hJetPt04.Fill(j.perp()) for j in all_jets]
[hJetPt04sd.Fill(j.perp()) for j in all_jets_sd]
hLund = ROOT.TH2D("hLund", "hLund", 60, 0, 6, 100, -4, 5)
lunds = [lund_gen.result(j) for j in all_jets if j.perp() > 100]
j100 = [j for j in all_jets if j.perp() > 100]
print('{} jets above 100 GeV/c'.format(len(j100)))
for l in lunds:
for s in l:
hLund.Fill(math.log(1. / s.Delta()), math.log(s.kt()))
fout.Write()
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()
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, 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()
def process_event(self, event):
# assume this is done already (when creating the ntuples):
# self.fj_particles_selected = self.particle_selector(fj_particles)
if self.constit_subtractor:
self.fj_particles_selected = self.constit_subtractor.process_event(
event.particles)
else:
self.fj_particles_selected = event.particles
self.cs = fj.ClusterSequenceArea(self.fj_particles_selected,
self.jet_def, self.jet_area_def)
# self.cs = fj.ClusterSequence(self.fj_particles_selected, self.jet_def)
self.jets = fj.sorted_by_pt(self.cs.inclusive_jets())
self.rho = 0
if self.bg_estimator:
self.bg_estimator.set_particles(self.fj_particles_selected)
self.rho = self.bg_estimator.rho()
if self.jets[0].pt(
) - self.rho * self.jets[0].area() < self.jet_pt_min:
return False
self.jets_selected = self.jet_selector(self.jets)
# remove jets containing bad (high pT) particles
self.jets_detok = list(
filter(
lambda j: max([t.pt() for t in j.constituents()]) < self.
particle_pt_max, self.jets_selected))
# print(len(self.jets_selected), len(self.jets_detok))
self.sd_jets = []
self.sd_jets_info = []
for isd, sd in enumerate(self.sds):
self.sd_jets.append([])
self.sd_jets_info.append([])
for j in self.jets_detok:
for isd, sd in enumerate(self.sds):
jet_sd = sd.result(j)
self.sd_jets[isd].append(jet_sd)
self.sd_jets_info[isd].append(
fjcontrib.get_SD_jet_info(jet_sd))
if self.event_output:
self.event_output.fill_branch('ev_id', event.ev_id)
self.event_output.fill_branch('run_number', event.run_number)
self.event_output.fill_branch('rho', self.rho)
self.event_output.fill_tree()
if self.jet_output:
self.jet_output.fill_branch('ev_id', event.ev_id)
self.jet_output.fill_branch('run_number', event.run_number)
self.jet_output.fill_branch('rho', self.rho)
self.jet_output.fill_branch('jet', [j for j in self.jets_detok])
self.jet_output.fill_branch(
'jet_ptsub',
[j.pt() - (self.rho * j.area()) for j in self.jets_detok])
for isd, sd in enumerate(self.sds):
bname = 'jet_sd{}pt'.format(self.sd_betas[isd])
self.jet_output.fill_branch(
bname, [j.pt() for j in self.sd_jets[isd]])
bname = 'jet_sd{}zg'.format(self.sd_betas[isd])
self.jet_output.fill_branch(
bname, [j.z for j in self.sd_jets_info[isd]])
bname = 'jet_sd{}Rg'.format(self.sd_betas[isd])
self.jet_output.fill_branch(
bname, [j.dR for j in self.sd_jets_info[isd]])
bname = 'jet_sd{}thetag'.format(self.sd_betas[isd])
self.jet_output.fill_branch(
bname, [j.dR / self.jet_R for j in self.sd_jets_info[isd]])
self.jet_output.fill_tree()
return True
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 < 10:
args.nev = 10
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
attach_pythia_particle_info = True
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal],
attach_pythia_particle_info)
jets = jet_selector(jet_def(parts))
for j in jets:
gshop = fjcontrib.GroomerShop(j)
# note these LundDeclustering objects can be streamed to a tree using RTreeStreamer
dg01 = gshop.dynamical(0.1)
idg01 = gshop.index(dg01)
dg20 = gshop.dynamical(2.0)
idg20 = gshop.index(dg20)
max_kt = gshop.max_kt()
imax_kt = gshop.index(max_kt)
max_pt_softer = gshop.max_pt_softer()
# check if the same split selected:
# if dg01.pair() == dg20.pair():
# or use indices
if idg01 == idg20 and idg01 > 0:
print('- interesting jet?:')
print(' dg01 :', dg01.as_string(), 'index dg01:',
idg01)
print(' dg20 :', dg20.as_string(), 'index dg20:',
idg20)
print(' max_kt :', max_kt.as_string(), 'index max_kt:',
imax_kt)
print(' max_pt_softer:', max_pt_softer.as_string())
print(' max_kappa :', gshop.max_kappa().as_string())
print(' max_tf :', gshop.max_tf().as_string())
print(' min_tf :', gshop.min_tf().as_string())
print(' max_z :', gshop.max_z().as_string())
print('softdrop check for jet:', j)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
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={} dR={} mu={}".format(
sd_info.z, sd_info.dR, sd_info.mu))
print(' |-> GroomerShop::soft_drop',
gshop.soft_drop(0, 0.1, 1.0).as_string())
# or call with no radius param - will use max allowed
# print(' |-> GroomerShop::soft_drop', gshop.soft_drop(0, 0.1).as_string())
pythia.stat()
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)
# could use --py-seed
parser.add_argument('--user-seed',
help='pythia seed',
default=1111,
type=int)
parser.add_argument('--output', default="output.root", type=str)
parser.add_argument('--beta', help='sd beta', default=0, type=float)
parser.add_argument('--jetR', help='jet radius', default=0.4, 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.jetR
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
print(jet_def)
# hadron level - ALICE
max_eta_hadron = 0.9
pwarning('max eta for particles after hadronization set to',
max_eta_hadron)
parts_selector_h = fj.SelectorAbsEtaMax(max_eta_hadron)
jet_selector = fj.SelectorPtMin(20.0) & fj.SelectorPtMax(
100.0) & 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.replace('.root', '_beta{}.root'.format(args.beta)),
'recreate')
outf.cd()
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
# event loop
for iev in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
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], 0,
True)
parts_pythia_h_selected = parts_selector_h(parts_pythia_h)
parts_pythia_hch = pythiafjext.vectorize_select(
pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, True)
parts_pythia_hch_selected = parts_selector_h(parts_pythia_hch)
# pinfo('debug partons...')
# for p in parts_pythia_p_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# pinfo('debug hadrons...')
# for p in parts_pythia_h_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# pinfo('debug ch. hadrons...')
# for p in parts_pythia_hch_selected:
# pyp = pythiafjext.getPythia8Particle(p)
# print(pyp.name())
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
jets_p = fj.sorted_by_pt(jet_def(parts_pythia_p))
jets_h = fj.sorted_by_pt(jet_def(parts_pythia_h))
jets_ch_h = fj.sorted_by_pt(jet_selector(jet_def(parts_pythia_hch)))
sd = fjcontrib.SoftDrop(args.beta, 0.1, jet_R0)
for i, jchh in enumerate(jets_ch_h):
# match hadron (full) jet
for j, jh in enumerate(jets_h):
drhh = jchh.delta_R(jh)
if drhh < jet_R0 / 2.:
# match parton level jet
for k, jp in enumerate(jets_p):
dr = jh.delta_R(jp)
if dr < jet_R0 / 2.:
jchh_sd = sd.result(jchh)
jchh_sd_info = fjcontrib.get_SD_jet_info(jchh_sd)
jh_sd = sd.result(jh)
jh_sd_info = fjcontrib.get_SD_jet_info(jh_sd)
jp_sd = sd.result(jp)
jp_sd_info = fjcontrib.get_SD_jet_info(jp_sd)
# pwarning('event', iev)
# pinfo('matched jets: ch.h:', jchh.pt(), 'h:', jh.pt(), 'p:', jp.pt(), 'dr:', dr)
tw.fill_branch('iev', iev)
tw.fill_branch('ch', jchh)
tw.fill_branch('h', jh)
tw.fill_branch('p', jp)
tw.fill_branch('p_zg', jp_sd_info.z)
tw.fill_branch('p_Rg', jp_sd_info.dR)
tw.fill_branch('p_thg', jp_sd_info.dR / jet_R0)
tw.fill_branch('p_mug', jp_sd_info.mu)
tw.fill_branch('h_zg', jh_sd_info.z)
tw.fill_branch('h_Rg', jh_sd_info.dR)
tw.fill_branch('h_thg', jh_sd_info.dR / jet_R0)
tw.fill_branch('h_mug', jh_sd_info.mu)
tw.fill_branch('ch_zg', jchh_sd_info.z)
tw.fill_branch('ch_Rg', jchh_sd_info.dR)
tw.fill_branch('ch_thg', jchh_sd_info.dR / jet_R0)
tw.fill_branch('ch_mug', jchh_sd_info.mu)
tw.fill_tree()
#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.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)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
parser.add_argument('--output', default="output.root", type=str)
parser.add_argument('--alpha', default=0, type=float)
parser.add_argument('--dRmax', default=0.25, type=float)
parts.add_argument('--zcut', default=0.1, type=float)
parser.add_argument('--overwrite',
help="overwrite output",
default=False,
action='store_true')
args = parser.parse_args()
if args.output == 'output.root':
args.output = 'output_alpha_{}_dRmax_{}_SDzcut_{}.root'.format(
args.alpha, args.dRmax, args.zcut)
if os.path.isfile(args.output):
if not args.overwrite:
print('[i] output', args.output,
'exists - use --overwrite to do just that...')
return
# 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 - 1.05 * jet_R0)
jet_selector_cs = fj.SelectorPtMin(50.0) & fj.SelectorAbsEtaMax(1 - 1.05 *
jet_R0)
# jet_selector = fj.SelectorPtMin(10.0) & fj.SelectorPtMax(20.0) & fj.SelectorAbsEtaMax(1 - 1.05 * jet_R0)
# jet_selector_cs = fj.SelectorPtMin(0.0) & fj.SelectorAbsEtaMax(1 - 1.05 * jet_R0)
print(jet_def)
mycfg = ['PhaseSpace:pThatMin = 80', 'PhaseSpace:pThatMax = -1']
# mycfg = []
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
print("[e] pythia initialization failed.")
return
sd_zcut = args.zcut
sd = fjcontrib.SoftDrop(0, sd_zcut, jet_R0)
max_eta = 1
cs = CEventSubtractor(alpha=args.alpha,
max_distance=args.dRmax,
max_eta=max_eta,
bge_rho_grid_size=0.25,
max_pt_correct=100)
be = BoltzmannEvent(mean_pt=0.6,
multiplicity=1000 * max_eta * 2,
max_eta=max_eta,
max_pt=100)
parts_selector = fj.SelectorAbsEtaMax(max_eta)
if args.nev < 100:
args.nev = 100
outf = ROOT.TFile(args.output, 'recreate')
outf.cd()
tn = ROOT.TNtuple(
'tn', 'tn',
'n:pt:phi:eta:cspt:csphi:cseta:dR:dpt:rg:csrg:z:csz:dRg:dzg')
hpt = ROOT.TH1F('hpt', 'hpt', 40, 00, 160)
hptcs = ROOT.TH1F('hptcs', 'hptcs', 40, 00, 160)
hdpt = ROOT.TH1F('hdpt', 'hdpt', 40, -50, 50)
hrg = ROOT.TH1F('hrg', 'hrg', 40, -1.1, 0.9)
hrgcs = ROOT.TH1F('hrgcs', 'hrgcs', 40, -1.1, 0.9)
hdrg = ROOT.TH1F('hdrg', 'hdrg', 40, -1.1, 0.9)
hdz = ROOT.TH1F('hdz', 'hdz', 40, -1.1, 0.9)
hdzz = ROOT.TH2F('hdzz', 'hdzz', 40, -1.1, 0.9, 40, -1.1, 0.9)
hdphi = ROOT.TH1F('hdphi', 'hdphi', 90, -ROOT.TMath.Pi(), ROOT.TMath.Pi())
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
parts_pythia = pythiafjext.vectorize_select(pythia,
[pythiafjext.kFinal])
parts = parts_selector(parts_pythia)
signal_jets = fj.sorted_by_pt(jet_selector(jet_def(parts)))
if len(signal_jets) < 1:
continue
bg_parts = be.generate(offset=10000)
full_event = bg_parts
sjet = signal_jets[0]
lc = [full_event.push_back(psj) for psj in sjet.constituents()]
# idxs = [psj.user_index() for psj in sjet.constituents()]
# print('pythia jet:', idxs)
cs_parts = cs.process_event(full_event)
cs_signal_jets = fj.sorted_by_pt(jet_selector_cs(jet_def(cs_parts)))
emb_jets = fj.sorted_by_pt(jet_selector_cs(jet_def(full_event)))
# max_eta_part = max([j.eta() for j in full_event])
# print ('number of particles', len(full_event), max_eta_part)
# mean_pt = sum([j.pt() for j in bg_parts]) / len(bg_parts)
# print ('mean pt in bg', mean_pt)
# print ('number of CS particles', len(cs_parts))
sd_signal_jet = sd.result(sjet)
sd_info_signal_jet = fjcontrib.get_SD_jet_info(sd_signal_jet)
# for j in cs_signal_jets:
for j in emb_jets:
if matched_pt(sjet, j) <= 0.5:
continue
sd_j = sd.result(j)
sd_info_j = fjcontrib.get_SD_jet_info(sd_j)
rho = cs.bge_rho.rho()
tn.Fill(i, sjet.pt(), sjet.phi(), sjet.eta(), j.pt(), j.phi(),
j.eta(), j.delta_R(sjet),
j.pt() - sjet.pt(), sd_info_signal_jet.dR, sd_info_j.dR,
sd_info_j.dR - sd_info_signal_jet.dR, sd_info_signal_jet.z,
sd_info_j.z, sd_info_j.z - sd_info_signal_jet.z)
hpt.Fill(sjet.pt())
hptcs.Fill(j.pt())
hdpt.Fill(j.pt() - sjet.pt())
hrg.Fill(sd_info_signal_jet.dR)
hrgcs.Fill(sd_info_j.dR)
hdphi.Fill(sjet.delta_phi_to(j))
if sd_info_j.dR > 0 and sd_info_signal_jet.dR > 0:
hdrg.Fill(sd_info_j.dR - sd_info_signal_jet.dR)
if sd_info_j.z > 0 and sd_info_signal_jet.z > 0:
hdz.Fill(sd_info_j.z - sd_info_signal_jet.z)
hdzz.Fill(sd_info_j.z, sd_info_j.z - sd_info_signal_jet.z)
# for i,j in enumerate(signal_jets):
# j_sd = sd.result(j)
# 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.stat()
outf.Write()
outf.Close()
