def analyzeEvents(self):
# Fill det track histograms
for fj_particles_det in self.df_fjparticles['fj_particles_det']:
self.fillTrackHistograms(fj_particles_det)
fj.ClusterSequence.print_banner()
print()
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector_det = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(self.eta_max - jetR)
jet_selector_truth_matched = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(self.eta_max - jetR)
print('jet definition is:', jet_def)
print('jet selector for det-level is:', jet_selector_det)
print('jet selector for truth-level matches is:', jet_selector_truth_matched,'\n')
# Iterate over the groupby and do jet-finding simultaneously for
# fj_1 and fj_2 per event, so that I can match jets -- and fill histograms
for fj_particles_det, fj_particles_truth in \
zip(self.df_fjparticles['fj_particles_det'],
self.df_fjparticles['fj_particles_truth']):
self.analyze_jets(fj_particles_det, fj_particles_truth, jet_def,
jet_selector_det, jet_selector_truth_matched)
def analyzeEvents(self):
# Fill det track histograms
[
self.fillTrackHistograms(fj_particles_det)
for fj_particles_det in self.df_fjparticles['fj_particles_det']
]
fj.ClusterSequence.print_banner()
print()
for jetR in self.jetR_list:
for beta in self.beta_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector_det = fj.SelectorPtMin(
5.0) & fj.SelectorAbsRapMax(0.9 - jetR)
jet_selector_truth_matched = fj.SelectorPtMin(5.0)
print('jet definition is:', jet_def)
print('jet selector for det-level is:', jet_selector_det, '\n')
print('jet selector for truth-level matches is:',
jet_selector_truth_matched, '\n')
# Then can use list comprehension to iterate over the groupby and do jet-finding
# simultaneously for fj_1 and fj_2 per event, so that I can match jets -- and fill histograms
result = [
self.analyzeJets(fj_particles_det, fj_particles_truth,
jet_def, jet_selector_det,
jet_selector_truth_matched, beta)
for fj_particles_det, fj_particles_truth in zip(
self.df_fjparticles['fj_particles_det'],
self.df_fjparticles['fj_particles_truth'])
]
def __init__(self, **kwargs):
self.configure_constants(
particle_eta_max=0.9,
particle_pt_max=
100, # reject jets with constituent with pT > max_particle_pt
jet_R=0.4,
jet_algorithm=fj.antikt_algorithm,
jet_pt_min=-200,
bge_rho_grid_size=-1, # no background subtraction for -1
sd_z_cuts=[0.1],
sd_betas=[0, 2],
show_progress=False,
name="MPJetAnalysis",
output_prefix='./')
self.jet_eta_max = self.particle_eta_max - self.jet_R * 1.05
self.jet_def = fj.JetDefinition(self.jet_algorithm, self.jet_R)
self.jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(
self.jet_eta_max)
self.particle_selector = fj.SelectorPtMin(0.15) & fj.SelectorAbsRapMax(
self.particle_eta_max)
self.jet_area_def = fj.AreaDefinition(
fj.active_area_explicit_ghosts,
fj.GhostedAreaSpec(self.particle_eta_max))
self.bg_estimator = None
self.constit_subtractor = None
self.jet_output = None
self.event_output = None
self.fout = None
super(MPJetAnalysis, self).__init__(**kwargs)
self.filename_output = '{}{}.root'.format(self.output_prefix,
self.name)
if self.fout is None:
self.fout = ROOT.TFile(self.filename_output, 'recreate')
if self.jet_output is None:
self.jet_output = treewriter.RTreeWriter(tree_name='tjet',
fout=self.fout)
if self.event_output is None:
self.event_output = treewriter.RTreeWriter(tree_name='tev',
fout=self.fout)
fj.ClusterSequence.print_banner()
print()
print(self)
print(' . particle selector', self.particle_selector)
print(' . jet selector', self.jet_selector)
print(' . jet definition:', self.jet_def)
# define softdrops
self.sds = []
for beta in self.sd_betas:
for zcut in self.sd_z_cuts:
_sd = fjcontrib.SoftDrop(beta, zcut, self.jet_R)
self.sds.append(_sd)
for _sd in self.sds:
print(' . sd:', _sd.description())
def analyze_event(self, event):
# Get list of hadrons from the event, and fill some histograms
hadrons = event.hadrons(min_track_pt=self.min_track_pt)
self.fill_hadron_histograms(hadrons)
# Get list of final-state partons from the event, and fill some histograms
partons = event.final_partons()
self.fill_parton_histograms(partons)
# Create list of fastjet::PseudoJets
fj_hadrons = []
fj_hadrons = self.fill_fastjet_constituents(hadrons)
# Loop through specified jet R
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(
self.min_jet_pt) & fj.SelectorAbsRapMax(5.)
if self.debug_level > 0:
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector, '\n')
# Do jet finding
jets = []
jets_selected = []
cs = fj.ClusterSequence(fj_hadrons, jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
# Fill some jet histograms
self.fill_jet_histograms(jets_selected, jetR)
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 = '../data/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)
def __init__(self, **kwargs):
self.configure_from_args(jet_R=0.4,
jet_algorithm=fj.antikt_algorithm,
particle_eta_max=0.9,
jet_pt_min=0.0,
particles=None,
explicit_ghosts=False)
super(JetAnalysis, self).__init__(**kwargs)
self.particle_selector = fj.SelectorAbsEtaMax(self.particle_eta_max)
self.jet_eta_max = self.particle_eta_max - self.jet_R * 1.05
# self.jet_eta_max = self.particle_eta_max - 0.05
self.jet_def = fj.JetDefinition(self.jet_algorithm, self.jet_R)
self.jet_selector = fj.SelectorPtMin(
self.jet_pt_min) & fj.SelectorAbsEtaMax(self.jet_eta_max)
if self.explicit_ghosts:
self.jet_area_def = fj.AreaDefinition(
fj.active_area_explicit_ghosts,
fj.GhostedAreaSpec(self.particle_eta_max))
else:
self.jet_area_def = fj.AreaDefinition(
fj.active_area, fj.GhostedAreaSpec(self.particle_eta_max))
if self.particles:
self.analyze_event(self.particles)
def analyzeEvents(self):
fj.ClusterSequence.print_banner()
print()
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(0.9 -
jetR)
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector, '\n')
# Define SoftDrop settings
sd = fjcontrib.SoftDrop(self.sd_beta, self.sd_zcut, jetR)
print('SoftDrop groomer is: {}'.format(sd.description()))
for beta in self.beta_list:
# Use list comprehension to do jet-finding and fill histograms
result = [
self.analyzeJets(fj_particles, jet_def, jet_selector, beta,
sd)
for fj_particles in self.df_fjparticles
]
def analyze_event(self, event):
# Create list of fastjet::PseudoJets (separately for jet shower particles and holes)
fj_hadrons_positive = self.fill_fastjet_constituents(event,
select_status='+')
fj_hadrons_negative = self.fill_fastjet_constituents(event,
select_status='-')
# Fill hadron histograms for jet shower particles
self.fill_hadron_histograms(fj_hadrons_positive, status='+')
self.fill_hadron_histograms(fj_hadrons_negative, status='-')
# Loop through specified jet R
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(
self.min_jet_pt) & fj.SelectorAbsRapMax(5.)
if self.debug_level > 0:
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector, '\n')
# Do jet finding
cs = fj.ClusterSequence(fj_hadrons_positive, jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
# Fill some jet histograms
self.fill_jet_histograms(jets_selected, fj_hadrons_negative, jetR)
def fj_example_02_area(event):
# cluster the event
jet_def = fj.JetDefinition(fj.antikt_algorithm, 0.4)
area_def = fj.AreaDefinition(fj.active_area, fj.GhostedAreaSpec(5.0))
cs = fj.ClusterSequenceArea(event, jet_def, area_def)
jets = fj.SelectorPtMin(5.0)(fj.sorted_by_pt(cs.inclusive_jets()))
print("jet def:", jet_def)
print("area def:", area_def)
print("#-------------------- initial jets --------------------")
print_jets(jets)
#----------------------------------------------------------------------
# estimate the background
maxrap = 4.0
grid_spacing = 0.55
gmbge = fj.GridMedianBackgroundEstimator(maxrap, grid_spacing)
gmbge.set_particles(event)
print("#-------------------- background properties --------------------")
print("rho = ", gmbge.rho())
print("sigma = ", gmbge.sigma())
print()
#----------------------------------------------------------------------
# subtract the jets
subtractor = fj.Subtractor(gmbge)
subtracted_jets = subtractor(jets)
print("#-------------------- subtracted jets --------------------")
print_jets(subtracted_jets)
def __init__(self,
jetRadius=0.4,
jetpTMin=1,
jetpTMax=None,
jetRapidityCut=None,
jetEtaCut=None,
**kwargs):
super().__init__(**kwargs)
self.clusterPower = -1
self.jetRadius = jetRadius
self.jetpTMin = jetpTMin
self.jetpTMax = jetpTMax
self.jetRapidityCut = jetRapidityCut
self.jetEtaCut = jetEtaCut
if self.clusterPower == -1:
self.jetDefinition = fj.JetDefinition(fj.antikt_algorithm,
self.jetRadius)
self.jetSelector = fj.SelectorPtMin(self.jetpTMin)
if self.jetpTMax != None:
self.jetSelector = self.jetSelector & fj.SelectorPtMax(
self.jetpTMax)
if self.jetRapidityCut != None:
self.jetSelector = self.jetSelector & fj.SelectorRapRange(
self.jetRapidityCut[0], self.jetRapidityCut[1])
if self.jetEtaCut != None:
self.jetSelector = self.jetSelector & fj.SelectorEtaRange(
self.jetEtaCut[0], self.jetEtaCut[1])
def main():
# set up our jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, 0.6)
selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(4.5)
filename = '../data/single-event.dat'
# get the event
event = read_event(filename)
# cluster it
jets = selector(jet_def(event))
# print out some information about the event and clustering
print("Event has {0} particles".format(len(event)))
print("jet definition is:", jet_def)
print("jet selector is:", selector, "\n")
# print the jets
print_jets(jets)
# get internal information about one of the jets
if (len(jets) > 0):
print("Number of constituents of jets[0] is {0}".format(
len(jets[0].constituents())))
def __init__(self, **kwargs):
self.configure_from_args(jet_R=0.4,
jet_algorithm=fj.antikt_algorithm,
particle_eta_max=0.9,
particles=None)
super(JetAnalysis, self).__init__(**kwargs)
self.bg_rho_range = fj.SelectorAbsEtaMax(self.particle_eta_max)
self.bg_jet_def = fj.JetDefinition(fj.kt_algorithm, self.jet_R)
self.bg_area_def = fj.AreaDefinition(
fj.active_area_explicit_ghosts,
fj.GhostedAreaSpec(self.particle_eta_max))
self.bg_estimator = fj.JetMedianBackgroundEstimator(
self.bg_rho_range, self.bg_jet_def, self.bg_area_def)
self.rho = 0
self.jet_eta_max = self.particle_eta_max - self.jet_R * 1.05
self.jet_def = fj.JetDefinition(self.jet_algorithm, self.jet_R)
self.jet_selector = fj.SelectorPtMin(0.0) & fj.SelectorAbsEtaMax(
self.jet_eta_max)
self.jet_area_def = fj.AreaDefinition(
fj.active_area_explicit_ghosts,
fj.GhostedAreaSpec(self.particle_eta_max))
if self.particles:
self.analyze_event(self.particles)
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 __init__(self, **kwargs):
self.fout = None
super(HFAnalysisInvMass, self).__init__(**kwargs)
self.fout = ROOT.TFile(self.name+'.root', 'recreate')
self.fout.cd()
# self.hinvmass = ROOT.TH1F('hinvmass', 'hinvmass', 400, 1.5, 2.5)
# self.hinvmass.Sumw2()
# self.hinvmasspt = ROOT.TH2F('hinvmasspt', 'hinvmasspt', 400, 1.5, 2.5, 50, 2, 12)
# self.hinvmasspt.Sumw2()
self.tw = treewriter.RTreeWriter(tree_name='d0', fout=self.fout)
# jet stuff
max_eta = 0.9
self.parts_selector = fj.SelectorPtMin(0.15) & fj.SelectorPtMax(100.0) & fj.SelectorAbsEtaMax(max_eta)
jet_R0 = 0.4
self.jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorPtMax(100.0) & fj.SelectorAbsEtaMax(max_eta - 1.05 * jet_R0)
self.jarho = JetAnalysisWithRho(jet_R=jet_R0, jet_algorithm=fj.antikt_algorithm, particle_eta_max=max_eta)
def analyze_event(self, event):
# Initialize empty list for each output observable
self.initialize_output_lists()
# Create list of fastjet::PseudoJets (separately for jet shower particles and holes)
fj_hadrons_positive = self.fill_fastjet_constituents(event, select_status='+')
fj_hadrons_negative = self.fill_fastjet_constituents(event, select_status='-')
# Create list of charged particles
fj_hadrons_positive_charged = self.fill_fastjet_constituents(event, select_status='+',
select_charged=True)
fj_hadrons_negative_charged = self.fill_fastjet_constituents(event, select_status='-',
select_charged=True)
# Fill hadron histograms for jet shower particles
self.fill_hadron_histograms(fj_hadrons_positive, status='+')
self.fill_hadron_histograms(fj_hadrons_negative, status='-')
# Loop through specified jet R
for jetR in self.jet_R:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(self.min_jet_pt) & fj.SelectorAbsRapMax(self.max_jet_y)
# Full jets
# -----------------
cs = fj.ClusterSequence(fj_hadrons_positive, jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
# Fill inclusive full jet histograms
[self.analyze_inclusive_jet(jet, fj_hadrons_positive, fj_hadrons_negative, jetR, full_jet=True) for jet in jets_selected]
# Charged jets
# -----------------
cs_charged = fj.ClusterSequence(fj_hadrons_positive_charged, jet_def)
jets_charged = fj.sorted_by_pt(cs_charged.inclusive_jets())
jets_selected_charged = jet_selector(jets_charged)
# Fill inclusive charged jet histograms
[self.analyze_inclusive_jet(jet, fj_hadrons_positive_charged, fj_hadrons_negative_charged, jetR, full_jet=False) for jet in jets_selected_charged]
# Fill semi-inclusive jet correlations
if self.semi_inclusive_chjet_observables:
if self.sqrts == 2760:
jetR_list = self.semi_inclusive_chjet_observables['IAA_alice']['jet_R']+self.semi_inclusive_chjet_observables['nsubjettiness_alice']['jet_R']
elif self.sqrts == 200:
jetR_list = self.semi_inclusive_chjet_observables['IAA_star']['jet_R']
if jetR in jetR_list:
self.fill_semi_inclusive_chjet_histograms(jets_selected_charged, fj_hadrons_positive_charged, fj_hadrons_negative_charged, jetR)
# Fill dijet histograms
if self.dijet_observables:
self.fill_dijet_histograms(jets_selected, fj_hadrons_negative, jetR)
def analyze_event(self, event):
# Create list of fastjet::PseudoJets (separately for jet shower particles and holes)
fj_hadrons_positive_all = self.fill_fastjet_constituents(event, select_status='+')
fj_hadrons_negative_all = self.fill_fastjet_constituents(event, select_status='-')
# Create list of charged particles
fj_hadrons_positive_charged_all = self.fill_fastjet_constituents(event, select_status='+',
select_charged=True)
fj_hadrons_negative_charged_all = self.fill_fastjet_constituents(event, select_status='-',
select_charged=True)
# Fill hadron histograms for jet shower particles
self.fill_hadron_histograms(fj_hadrons_positive_all, status='+')
self.fill_hadron_histograms(fj_hadrons_negative_all, status='-')
# Loop through several different constituent thresholds
for constituent_threshold in self.constituent_threshold:
# Set constituent threshold
fj_hadrons_positive = [hadron for hadron in fj_hadrons_positive_all if hadron.pt() > constituent_threshold]
fj_hadrons_negative = [hadron for hadron in fj_hadrons_negative_all if hadron.pt() > constituent_threshold]
fj_hadrons_positive_charged = [hadron for hadron in fj_hadrons_positive_charged_all if hadron.pt() > constituent_threshold]
fj_hadrons_negative_charged = [hadron for hadron in fj_hadrons_negative_charged_all if hadron.pt() > constituent_threshold]
# Loop through specified jet R
for jetR in self.jet_R:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(self.min_jet_pt) & fj.SelectorAbsRapMax(self.max_jet_y)
if self.debug_level > 0:
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector, '\n')
# Full jets
# -----------------
cs = fj.ClusterSequence(fj_hadrons_positive, jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
# Fill inclusive full jet histograms
[self.analyze_inclusive_jet(jet, fj_hadrons_positive, fj_hadrons_negative, jetR, constituent_threshold, charged=False) for jet in jets_selected]
# Charged jets
# -----------------
cs_charged = fj.ClusterSequence(fj_hadrons_positive_charged, jet_def)
jets_charged = fj.sorted_by_pt(cs_charged.inclusive_jets())
jets_selected_charged = jet_selector(jets_charged)
# Fill inclusive charged jet histograms
[self.analyze_inclusive_jet(jet, fj_hadrons_positive_charged, fj_hadrons_negative_charged, jetR, constituent_threshold, charged=True) for jet in jets_selected_charged]
# Fill jet correlations
self.fill_semi_inclusive_chjet_histograms(jets_selected_charged, fj_hadrons_positive_charged, fj_hadrons_negative_charged, jetR, constituent_threshold)
def analyze_event(self, fj_particles):
# Perform constituent subtraction for each R_max (do this once, for all jetR)
if not self.is_pp:
fj_particles_subtracted = [
self.constituent_subtractor[i].process_event(fj_particles)
for i, R_max in enumerate(self.max_distance)
]
# Loop through jetR, and process event for each R
for jetR in self.jetR_list:
# Keep track of whether to fill R-independent histograms
self.fill_R_indep_hists = (jetR == self.jetR_list[0])
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(0.9 -
jetR)
if self.debug_level > 2:
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector, '\n')
# Analyze
if self.is_pp:
# Do jet finding
cs = fj.ClusterSequence(fj_particles, jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
self.analyze_jets(jets_selected, jetR)
else:
for i, R_max in enumerate(self.max_distance):
if self.debug_level > 1:
print('R_max: {}'.format(R_max))
# Keep track of whether to fill R_max-independent histograms
self.fill_Rmax_indep_hists = (i == 0)
# Perform constituent subtraction
rho = self.constituent_subtractor[i].bge_rho.rho()
if self.fill_R_indep_hists and self.fill_Rmax_indep_hists:
getattr(self, 'hRho').Fill(rho)
# Do jet finding (re-do each time, to make sure matching info gets reset)
cs = fj.ClusterSequence(fj_particles_subtracted[i],
jet_def)
jets = fj.sorted_by_pt(cs.inclusive_jets())
jets_selected = jet_selector(jets)
self.analyze_jets(jets_selected, jetR, R_max=R_max)
def main(args):
nevents = args.nevents
sconfig_pythia = get_pythia_config(args)
if args.generate:
pythia = create_and_init_pythia(sconfig_pythia)
if not pythia:
return
all_jets = []
for iEvent in tqdm(range(nevents), 'event'):
if not pythia.next(): continue
print("[i] done generating")
if args.write:
pythia = create_and_init_pythia(sconfig_pythia)
if not pythia:
return
pyhepmcwriter = mp.Pythia8HepMCWrapper(args.write)
all_jets = []
for iEvent in tqdm(range(nevents), 'event'):
if not pythia.next(): continue
pyhepmcwriter.fillEvent(pythia)
print("[i] done writing to {}".format(args.write))
if args.read:
import pyhepmc_ng
input = pyhepmc_ng.ReaderAsciiHepMC2(args.read)
if input.failed():
print("[error] unable to read from {}".format(args.read))
return
# 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(100.0) & fj.SelectorPtMax(
200.0) & fj.SelectorAbsEtaMax(1)
event = pyhepmc_ng.GenEvent()
pbar = tqdm(range(nevents))
while not input.failed():
e = input.read_event(event)
if input.failed():
break
fjparts = []
for i, p in enumerate(event.particles):
if p.status == 1:
psj = fj.PseudoJet(p.momentum.px, p.momentum.py,
p.momentum.pz, p.momentum.e)
psj.set_user_index(i)
fjparts.append(psj)
jets = jet_selector(jet_def(fjparts))
pbar.update()
def fjPseudoJet(self):
jet_def = fj.JetDefinition(fj.antikt_algorithm, 1.0)
jet_selector = fj.SelectorPtMin(0.0)
self.constituents_psj = []
for c in self.constituents:
self.constituents_psj.append(c)
pyfj_from_psj(constituents)
jets = jet_selector(jet_def(self.constituents_psj))
if len(jets) < 1 or len(jets) > 1:
print('[error] reclustering resulted in more that 1 jets')
psj = jets[0]
return psj
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 = rt.Recluster(jet_def_rc, True)
sd = rt.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 = rt.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 __init__(self, **kwargs):
self.configure_from_args(tree_name='tree_Particle',
tree_name_gen='tree_Particle_gen',
args=None)
super(Embedding, self).__init__(**kwargs)
self.copy_attributes(self.args)
self.jet_def = fj.JetDefinition(fj.antikt_algorithm, self.jetR)
if self.benchmark:
self.jet_selector = fj.SelectorPtMin(80.0) & fj.SelectorPtMax(
100.0) & fj.SelectorAbsEtaMax(self.max_eta - 1.05 * self.jetR)
# jet_selector_cs = fj.SelectorPtMin(50.0) & fj.SelectorAbsEtaMax(max_eta - 1.05 * self.jetR)
else:
self.jet_selector = fj.SelectorAbsEtaMax(self.max_eta -
1.05 * self.jetR)
self.parts_selector = fj.SelectorAbsEtaMax(self.max_eta)
self.output = EmbeddingOutput(args=self.args)
# self.output.copy_attributes(self)
self.sd = fjcontrib.SoftDrop(0, self.sd_zcut, self.jetR)
self.ja_part = JetAnalysis(jet_R=self.jetR,
jet_algorithm=fj.antikt_algorithm,
jet_pt_min=5.,
particle_eta_max=self.max_eta)
self.ja_det = JetAnalysis(jet_R=self.jetR,
jet_algorithm=fj.antikt_algorithm,
jet_pt_min=self.jetptcut,
particle_eta_max=self.max_eta)
self.ja_hybrid = JetAnalysis(jet_R=self.jetR,
jet_algorithm=fj.antikt_algorithm,
jet_pt_min=5.,
particle_eta_max=self.max_eta)
self.dataPbPb = DataBackgroundIO(name='Data PbPb',
file_list=self.datalistAA)
self.det_sim = DataIO(name='Sim Pythia Detector level',
file_list=self.simulationpp,
random_file_order=False)
self.part_sim = DataIO(name='Sim Pythia Particle level',
file_list=self.simulationpp,
random_file_order=False,
tree_name='tree_Particle_gen')
self.cs = None
if self.dRmax > 0:
self.cs = CEventSubtractor(alpha=self.alpha,
max_distance=self.dRmax,
max_eta=self.max_eta,
bge_rho_grid_size=0.25,
max_pt_correct=100)
def analyzeEvents(self):
fj.ClusterSequence.print_banner()
print()
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(5.0) & fj.SelectorAbsRapMax(self.eta_max - jetR)
print('jet definition is:', jet_def)
print('jet selector is:', jet_selector,'\n')
for alpha in self.alpha_list:
for fj_particles in self.df_fjparticles:
# do jet-finding and fill histograms
self.analyzeJets(fj_particles, jet_def, jet_selector, alpha)
def analyze_event(self, fj_particles_hard, fj_particles_combined_beforeCS):
# Check that the entries exist appropriately
if type(fj_particles_hard) != fj.vectorPJ or type(
fj_particles_combined_beforeCS) != fj.vectorPJ:
print('fj_particles type mismatch -- skipping event')
return
# Perform constituent subtraction for each R_max
fj_particles_combined = []
for i, R_max in enumerate(self.max_distance):
if R_max == 0:
fj_particles_combined.append(fj_particles_combined_beforeCS)
else:
fj_particles_combined.append(
self.constituent_subtractor[i].process_event(
fj_particles_combined_beforeCS))
# Loop through jetR, and process event for each R
for jetR in self.jetR_list:
# Set jet definition and a jet selector
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(
self.min_jet_pt) & fj.SelectorAbsRapMax(self.eta_max - jetR)
for i, R_max in enumerate(self.max_distance):
#print()
#print(R_max)
#print('Total number of combined particles: {}'.format(len([p.pt() for p in fj_particles_combined_beforeCS])))
#print('After constituent subtraction {}: {}'.format(i, len([p.pt() for p in fj_particles_combined[i]])))
# Do jet finding (re-do each time, to make sure matching info gets reset)
cs_combined = fj.ClusterSequence(fj_particles_combined[i],
jet_def)
jets_combined = fj.sorted_by_pt(cs_combined.inclusive_jets())
jets_combined_selected = jet_selector(jets_combined)
cs_hard = fj.ClusterSequence(fj_particles_hard, jet_def)
jets_hard = fj.sorted_by_pt(cs_hard.inclusive_jets())
jets_hard_selected = jet_selector(jets_hard)
self.analyze_jets(jets_combined_selected,
jets_hard_selected,
jetR,
R_max=R_max)
def main():
input_file = "$HOME/data/jetscape/test_out.hepmc"
if len(sys.argv) > 1:
input_file = sys.argv[1]
input_file = os.path.expandvars(input_file)
print('[i] reading from:', input_file)
# input = pyhepmc_ng.ReaderAsciiHepMC2(input_file)
input = pyhepmc_ng.ReaderAscii(input_file)
if input.failed():
print("[error] unable to read from {}".format(input_file))
return
nevents = 1000
# 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(0.0) & fj.SelectorPtMax(
200.0) & fj.SelectorAbsEtaMax(3)
all_jets = []
event = pyhepmc_ng.GenEvent()
pbar = tqdm.tqdm(range(nevents))
while not input.failed():
e = input.read_event(event)
if input.failed():
break
fjparts = []
for i, p in enumerate(event.particles):
if p.status == 1 and not p.end_vertex:
psj = fj.PseudoJet(p.momentum.px, p.momentum.py, p.momentum.pz,
p.momentum.e)
psj.set_user_index(i)
fjparts.append(psj)
jets = jet_selector(jet_def(fjparts))
all_jets.append([[j.pt(), j.eta()] for j in jets])
pbar.update()
for j in jets:
hjetpt.Fill(j.perp())
if pbar.n >= nevents:
break
pbar.close()
def main_jets(args):
outfname = '{}_output.root'.format(args.read).replace('.dat', '')
print("output file: {}".format(outfname))
foutput = r.TFile(outfname, "recreate")
foutput.cd()
lbins = logbins(1., 100, 10)
hjetpt = r.TH1F('hjetpt', 'hjetpt', 10, lbins)
input = pyhepmc_ng.ReaderAsciiHepMC2(args.read)
if input.failed():
print ("[error] unable to read from {}".format(args.read))
return
# 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(0.0) & fj.SelectorPtMax(200.0) & fj.SelectorAbsEtaMax(3)
all_jets = []
event = pyhepmc_ng.GenEvent()
pbar = tqdm(range(args.nevents))
while not input.failed():
e = input.read_event(event)
if input.failed():
break
fjparts = []
for i,p in enumerate(event.particles):
if p.status == 1 and not p.end_vertex:
psj = fj.PseudoJet(p.momentum.px, p.momentum.py, p.momentum.pz, p.momentum.e)
psj.set_user_index(i)
fjparts.append(psj)
jets = jet_selector(jet_def(fjparts))
all_jets.append([ [j.pt(), j.eta()] for j in jets])
pbar.update()
for j in jets:
hjetpt.Fill(j.perp())
if pbar.n >= args.nevents:
break
foutput.Write()
foutput.Close()
joblib.dump(all_jets, outfname.replace(".root", ".joblib"))
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")
# create a user-defined recombiner which checks for photons and
# sums user-indices (see below for details)
recombiner = UserRecombiner()
jet_def_user_recomb = fj.JetDefinition(fj.antikt_algorithm, 0.4)
jet_def_user_recomb.set_python_recombiner(recombiner)
print("jet definition with user recombiner is:", jet_def_user_recomb)
filename = '../data/Pythia-dijet-ptmin100-lhc-pileup-1ev.dat'
f = open(filename, 'r')
# get the event
iev = 0
while True:
event = read_event(f)
iev += 1
if (len(event) == 0): break
# cluster it with the default recombiner and print some info
jets = selector(jet_def(event))
print("Event {0} has {1} particles".format(iev, len(event)))
print_jets(jets)
print("")
# now re-cluster with our user-defined recombiner
jets = selector(jet_def_user_recomb(event))
print("")
print("CHECK: below, the user index (built through the recombiner)")
print(" should correspond to the number of photons")
print("")
print_jets(jets)
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 = '../data/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))
# Create a FastJet selector based on a Python function (which
# takes a PseudoJet and returns True if the PseudoJet passes the
# selection condition). The resulting selector can be used in the
# same way as any normal FastJet selector.
#
# See print_jets below for other examples of python-defined
# Selectors (built either from a class or from a function)
sel_pileup = fj.SelectorPython(is_pileup)
n_pileup_particles = sel_pileup.count(event)
# print some info
print("Event {0} has {1} particles (of which {2} from pileup)".format(
iev, len(event), n_pileup_particles))
print_jets(jets)
def get_event(self):
if not self.pythia:
return None
parts_selector = fj.SelectorAbsEtaMax(self.eta_max)
jet_selector = fj.SelectorPtMin(
self.jet_pt_min) & fj.SelectorAbsEtaMax(self.eta_max -
1.05 * self.jet_R0)
jet_def = fj.JetDefinition(fj.antikt_algorithm, self.jet_R0)
while True:
if not self.pythia.next():
continue
self.parts_pythia = pythiafjext.vectorize_select(
self.pythia, [pythiafjext.kFinal])
parts_gen = parts_selector(self.parts_pythia)
signal_jets = fj.sorted_by_pt(
jet_selector(jet_def(self.parts_pythia)))
if len(signal_jets) < 1:
continue
else:
break
return self.parts_pythia
def main():
parser = argparse.ArgumentParser(description='pythia8 in python',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
args = parser.parse_args()
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
max_eta_hadron = 3
jet_R0 = 0.4
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorPtMax(
125.0) & fj.SelectorAbsEtaMax(max_eta_hadron - 1.05 * jet_R0)
parts_selector_h = fj.SelectorAbsEtaMax(max_eta_hadron)
fj.ClusterSequence.print_banner()
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
pbar = tqdm.tqdm(range(args.nev))
for i in pbar:
if not pythia.next():
pbar.update(-1)
continue
parts_pythia_h = pythiafjext.vectorize_select(pythia,
[pythiafjext.kFinal], 0,
False)
parts_pythia_h_selected = parts_selector_h(parts_pythia_h)
jets_h = fj.sorted_by_pt(jet_selector(
jet_def(parts_pythia_h_selected)))
if len(jets_h) < 1:
continue
# do your things with jets here...
pythia.stat()
pythia.settings.writeFile(args.py_cmnd_out)
def do_cs_jet_by_jet(full_event, ptmin=100.):
# clustering with ghosts and get the jets
jet_def = fj.JetDefinition(fj.antikt_algorithm, 0.4)
ghost_RapMax = 3.
ghost_spec = fj.GhostedAreaSpec(ghost_RapMax, 1, ghost_area)
area_def = fj.AreaDefinition(fj.active_area_explicit_ghosts, ghost_spec)
clust_seq_full = fj.ClusterSequenceArea(full_event, jet_def, area_def)
full_jets = clust_seq_full.inclusive_jets(ptmin)
# background estimation
jet_def_bge = fj.JetDefinition(fj.kt_algorithm, 0.4)
area_def_bge = fj.AreaDefinition(fj.active_area_explicit_ghosts,
ghost_spec)
bge_range = fj.SelectorAbsRapMax(3.0)
bge = fj.JetMedianBackgroundEstimator(bge_range, jet_def_bge, area_def_bge)
bge.set_particles(full_event)
# subtractor
subtractor = cs.ConstituentSubtractor()
subtractor.set_distance_type(0)
subtractor.set_max_distance(max_distance)
subtractor.set_alpha(alpha)
subtractor.set_max_eta(3.0)
subtractor.set_background_estimator(bge)
subtractor.set_common_bge_for_rho_and_rhom()
#sel_max_pt = fj.SelectorPtMax(10)
#subtractor.set_particle_selector(sel_max_pt)
# do subtraction
corrected_jets = cs.PseudoJetVec()
for jet in full_jets:
subtracted_jet = subtractor.result(jet)
corrected_jets.push_back(subtracted_jet)
# pt cut
selector = fj.SelectorPtMin(ptmin)
return selector(corrected_jets), clust_seq_full