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 main():
parser = argparse.ArgumentParser(description='jetscape in python', \
prog=os.path.basename(__file__))
parser.add_argument('-i', '--input', help='input file', \
default='low', type=str, required=True)
parser.add_argument('--nev', help='number of events', \
default=1000, type=int)
args = parser.parse_args()
plot_hadrons = False
plot_final_state_partons = True
plot_nth_partons = False
include_thermal_background = False
# Initialize histogram dictionary
hDict = initializeHistograms()
# Use pyhepmc_ng to parse the HepMC file
input_hepmc = pyhepmc_ng.ReaderAscii(args.input)
if input_hepmc.failed():
print("[error] unable to read from {}".format(args.input))
sys.exit(1)
# jet finder
fj.ClusterSequence.print_banner()
print()
jetR = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jetR)
jet_selector = fj.SelectorPtMin(50.0) & fj.SelectorAbsEtaMax(2)
# Jet re-clustering definition, for primary Lund plane
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, jetR)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
# Load thermal background: array of dataframes, one per event
if include_thermal_background:
thermal_background_array = get_thermal_background()
# Loop through events
all_jets_hadron = []
all_jets_parton = []
kt_shower_list = []
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():
nstop = pbar.n
pbar.close()
print('End of HepMC file at event {} '.format(nstop))
break
if plot_hadrons:
hadrons = get_hadrons(event_hepmc)
jets_hadron = find_jets(jet_def, jet_selector, hadrons)
all_jets_hadron.extend(jets_hadron)
if plot_final_state_partons:
parton_list = get_final_partons(event_hepmc, hDict)
elif plot_nth_partons:
# Get the first parton in the shower (it is apparently the child of the initiating parton)
first_parton = []
first_parton.append(
get_first_parton(event_hepmc, hDict).children[0])
#print('-----------------------------')
#print('First parton E: {}'.format(first_parton[0].momentum.e))
n = 3
parton_list = get_nth_partons(first_parton, n)
#print('Total number of partons: {}'.format(len(parton_list)))
fj_particles = []
for parton in parton_list:
fj_particles.append(
fj.PseudoJet(parton.momentum.px, parton.momentum.py,
parton.momentum.pz, parton.momentum.e))
# Append thermal background
if include_thermal_background:
thermal_background_df = thermal_background_array[0]
thermal_background_fjparticles = get_fjparticles(
thermal_background_df)
fj_particles.extend(thermal_background_fjparticles)
jets_parton = find_jets(jet_def, jet_selector, fj_particles)
all_jets_parton.extend(jets_parton)
# Analyze shower history
max_kt = get_max_kt_shower(event_hepmc, hDict)
kt_shower_list.append(max_kt)
pbar.update()
if pbar.n >= args.nev:
pbar.close()
print('{} event limit reached'.format(args.nev))
break
print('Constructing histograms...')
if plot_hadrons:
n_jets_hadron = len(all_jets_hadron)
print('n_jets_hadron: {}'.format(n_jets_hadron))
n_jets_parton = len(all_jets_parton)
print('n_jets_parton: {}'.format(n_jets_parton))
# Fill histogram
if plot_hadrons:
[fill_jet_histogram(hDict, jet) for jet in all_jets_hadron]
# Fill Lund diagram
# Note: l in lunds, l is the list of splittings in a given jet (following hardest splitting)
lunds_hadron = [lund_gen.result(jet) for jet in all_jets_hadron]
[
fill_lund_histogram(hDict, "hLundHadron", splitting_list)
for splitting_list in lunds_hadron
]
hDict['hLundHadron'].Scale(1. / n_jets_hadron, "width")
lunds_parton = [lund_gen.result(jet) for jet in all_jets_parton]
[
fill_lund_histogram(hDict, "hLundParton", splitting_list)
for splitting_list in lunds_parton
]
hDict['hLundParton'].Scale(1. / n_jets_parton, "width")
for splitting_list in lunds_parton:
max_kt_recluster = 0
for split in splitting_list:
kt = split.kt()
if kt > max_kt_recluster:
max_kt_recluster = kt
hDict['hMaxKT_2D'].Fill(np.log(max_kt_recluster), np.log(kt))
plot_histograms(hDict)
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 in python', prog=os.path.basename(__file__))
parser.add_argument('-i', '--input', help='input file', default='low', type=str, required=True)
parser.add_argument('--nev', help='number of events', default=-1, type=int)
parser.add_argument('--jetptcut', help='jet pt cut', default=10., type=float)
args = parser.parse_args()
files = find_files(args.input, "*.txt")
print(files)
print('[i] number of files found {}'.format(len(files)))
# 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.SelectorAbsEtaMax(3)
if args.nev < 0:
args.nev = len(files)
if args.nev > len(files):
args.nev = len(files)
print("[i] running on {}".format(files[:args.nev]))
all_jets = []
for fin in tqdm.tqdm(files[:args.nev]):
input_hepmc = hepmc3ext.YuukaRead(fin)
if input_hepmc.failed():
print ("[error] unable to read from {}".format(fin))
sys.exit(1)
if input_hepmc.nextEvent():
jets = find_jets_hepmc(jet_def, jet_selector, input_hepmc, accept_status=[62])
all_jets.extend(jets)
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] 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)
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() > args.jetptcut]
jsel = [j for j in all_jets if j.perp() > args.jetptcut]
print('[i] {} jets above {} GeV/c'.format(len(jsel), args.jetptcut))
for l in lunds:
for s in l:
hLund.Fill(math.log(1./s.Delta()), math.log(s.kt()))
if len(jsel) > 0:
hLund.Scale(1./len(jsel))
fout.Write()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
_default_output_filename = os.path.basename(__file__).replace(
".py", "") + "_output.root"
parser.add_argument('--output', default=_default_output_filename, type=str)
parser.add_argument('--debug', default=0, type=int)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.6
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(2.0) & fj.SelectorAbsEtaMax(2)
print(jet_def)
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, jet_R0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print(jet_def_lund)
print(lund_gen)
outf = ROOT.TFile(args.output, 'recreate')
outf.cd()
t = ROOT.TTree('t', 't')
tw = RTreeWriter(tree=t)
# 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
if args.debug:
pwarning('-- event', i)
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0,
True)
if args.debug > 5:
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kHadron],
0, True)
if args.debug > 10:
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kAny], 0,
True)
if args.debug > 0:
for p in parts:
pypart = pythiafjext.getPythia8Particle(p)
if pypart.name()[:2] == 'D0':
pinfo(pypart.name(), pypart.id(), pypart.status(),
'final =?', pypart.isFinal())
jets = jet_selector(jet_def(parts))
for j in jets:
isD0_lead = False
lead_part = fj.sorted_by_E(j.constituents())[0]
pypart = pythiafjext.getPythia8Particle(lead_part)
if args.debug:
pinfo('leading id is', pypart.id(), pypart.name(), 'jet', j)
if abs(pypart.id()) == 421:
# pinfo('leading D0')
isD0_lead = True
l = lund_gen.result(j)
if len(l) > 0:
tw.fill_branch('Epair', [s.pair().e() for s in l])
tw.fill_branch('z', [s.z() for s in l])
tw.fill_branch('kt', [s.kt() for s in l])
tw.fill_branch('delta', [s.Delta() for s in l])
tw.fill_branch('D0lead', isD0_lead)
tw.fill_branch('lead_id', pypart.id())
tw.fill_tree()
else:
if args.debug:
pwarning("len of a lund is less than 1?", len(l), l)
pythia.stat()
outf.Write()
outf.Close()
print('[i] written', outf.GetName())
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorPtMax(
200.0) & fj.SelectorAbsEtaMax(1)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
all_jets = []
for iEvent in tqdm(range(10), 'event'):
if not pythia.next(): continue
parts = pyfj.vectorize(pythia, True, -1, 1, False)
jets = jet_selector(jet_def(parts))
all_jets.extend(jets)
all_sd_jets = [sd.result(j) for j in all_jets]
pts = [j.pt() for j in all_jets]
sd_pts = [j.pt() for j in all_sd_jets]
sd_delta_pt = [delta for delta in deltas(all_jets, all_sd_jets)]
nangs0 = [pyfj.angularity(j, 0.) for j in all_jets]
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
lunds = [lund_gen.result(j) for j in all_jets]
print('listing lund plane points... Delta, kt - for {} selected jets'.format(
len(all_jets)))
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]))
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly', prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--nw', help="no warn", default=False, action='store_true')
parser.add_argument('--ignore-mycfg', help="ignore some settings hardcoded here", default=False, action='store_true')
parser.add_argument('--enable-background', help="enable background calc", default=False, action='store_true')
parser.add_argument('--output', help="output file name", default='leadsj_vs_x_output.root', type=str)
# for background
parser.add_argument('--cent-bin', help="centraility bin 0 is the 0-5 percent most central bin", type=int, default=0)
parser.add_argument('--seed', help="pr gen seed", type=int, default=1111)
parser.add_argument('--harmonics', help="set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)",
type=int, default=5)
parser.add_argument('--eta', help="set eta range must be uniform (e.g. abs(eta) < 0.9, which is ALICE TPC fiducial acceptance)",
type=float, default=0.9)
parser.add_argument('--qa', help="PrintOutQAHistos", default=False, action='store_true')
parser.add_argument('--dRmax', default=0.25, type=float)
parser.add_argument('--alpha', default=0, type=float)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(args.py_pthatmin) & fj.SelectorPtMax(1000.0) & fj.SelectorAbsEtaMax(args.eta - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 40']
mycfg = ['']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print (lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print (dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd01 = fjcontrib.SoftDrop(0, 0.1, 1.0)
print (sd01)
sd02 = fjcontrib.SoftDrop(0, 0.2, 1.0)
print (sd02)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print (jet_def_rc01)
print (jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = args.output)
tgbkg = None
be = None
if args.enable_background:
# ROOT.gSystem.Load("libpyjetty_TennGen.dylib")
# tgbkg = ROOT.TennGen() # //constructor
# tgbkg.SetCentralityBin(args.cent_bin) # //centraility bin 0 is the 0-5 % most central bin
# tgbkg.SetRandomSeed(args.seed) # //setting the seed
# tgbkg.SetHarmonics(args.harmonics) # // set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)
# tgbkg.SetEtaRange(args.eta) # //set eta range must be uniform (e.g. |eta| < 0.9, which is ALICE TPC fiducial acceptance)
# tgbkg.PrintOutQAHistos(args.qa) #
# tgbkg.InitializeBackground() #
from pyjetty.mputils import BoltzmannEvent
be = BoltzmannEvent(mean_pt=0.7, multiplicity=2000 * args.eta * 2, max_eta=max_eta, max_pt=100)
print(be)
from pyjetty.mputils import CEventSubtractor, CSubtractorJetByJet
cs = CEventSubtractor(alpha=args.alpha, max_distance=args.dRmax, max_eta=args.eta, bge_rho_grid_size=0.25, max_pt_correct=100)
print(cs)
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton], 0, True)
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
j_type = match_dR(j, partons, jet_R0 / 2.)
if j_type[0] is None:
if args.nw:
continue
pwarning('Jet with no parton label')
continue
j_sd02 = sd02.result(j)
sd02_info = fjcontrib.get_SD_jet_info(j_sd02)
j_sd01 = sd01.result(j)
sd01_info = fjcontrib.get_SD_jet_info(j_sd01)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches( j = j,
lund = [ls for ls in lund_gen.result(j)],
dyg1 = dy_groomer.result(j, 1),
sd01 = j_sd01,
sd01_z = sd01_info.z,
sd01_mu = sd01_info.mu,
sd01_Delta = sd01_info.dR,
sd02 = j_sd02,
sd02_z = sd02_info.z,
sd02_mu = sd02_info.mu,
sd02_Delta = sd02_info.dR,
# breaking compatibility
# sd = j_sd,
# sd_z = sd_info.z,
# sd_mu = sd_info.mu,
# sd_Delta = sd_info.dR,
lsjet01 = rc_sjets01[0],
nsjet01 = len(rc_sjets01),
sjet01 = rc_sjets01,
lsjet02 = rc_sjets02[0],
nsjet02 = len(rc_sjets02),
sjet02 = rc_sjets02,
ppid = j_type[0],
pquark = j_type[1],
pglue = j_type[2], # this is redundancy
pycode = pythia.info.code(),
pysigmagen = pythia.info.sigmaGen(),
pysigmaerr = pythia.info.sigmaErr(),
pyid1 = pythia.info.id1pdf(),
pyid2 = pythia.info.id1pdf(),
pyx1 = pythia.info.x1pdf(),
pyx2 = pythia.info.x2pdf(),
pypdf1 = pythia.info.pdf1(),
pyQfac = pythia.info.QFac(),
pyalphaS = pythia.info.alphaS(),
pypthat = pythia.info.pTHat(),
pymhat = pythia.info.mHat()
)
if be:
bg_parts = be.generate(offset=10000)
full_event = bg_parts
tmp = [full_event.push_back(psj) for psj in j.constituents()]
if cs:
cs_parts = cs.process_event(full_event)
rho = cs.bge_rho.rho()
bg_jets = fj.sorted_by_pt(jet_def(cs_parts))
for bj in bg_jets:
if fjtools.matched_pt(bj, j) > 0.5:
pass
tw.fill_tree()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(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='read jewel to lund jets object and pickle with joblib',
prog=os.path.basename(__file__))
parser.add_argument('-d',
'--dir',
help='input directory',
default='',
type=str,
required=True)
parser.add_argument('-n',
'--nfiles',
help='number of files',
default=-1,
type=int,
required=False)
parser.add_argument('-p',
'--pattern',
help='input directory',
default='',
type=str,
required=True)
parser.add_argument('--nev', help='number of events', default=-1, type=int)
parser.add_argument('--clean',
help='remove output before writing',
action='store_true',
default=False)
args = parser.parse_args()
files = find_files(args.dir, args.pattern)
# 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(40.0) & fj.SelectorPtMax(
500.0) & fj.SelectorAbsEtaMax(2)
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)
if args.nfiles > 0:
rfiles = files[:args.nfiles]
else:
rfiles = files
print("[i] found {} files - running for {} files".format(
len(files), len(rfiles)))
for ifile, fn in enumerate(tqdm.tqdm(rfiles)):
# now lets read the HEPMC file and do some jet finding
input_hepmc = hepmc2wrap.ReadHepMCFile(fn)
if input_hepmc.failed():
print("[error] unable to read from {}".format(fn))
sys.exit(1)
all_jets = []
for iev in tqdm.tqdm(range(args.nev)):
if input_hepmc.failed():
break
if input_hepmc.NextEvent():
jets_hepmc = find_jets_hepmc(jet_def,
jet_selector,
input_hepmc,
final=True)
all_jets.extend(jets_hepmc)
lunds = [lund_gen.result(j) for j in all_jets]
lunds_pickle = [
lundjet.LundJet(j, lund_gen.result(j)) for j in all_jets
]
all_jets_sd = [sd.result(j) for j in all_jets]
lunds_sd = [lund_gen.result(j) for j in all_jets_sd]
lunds_sd_pickle = [
lundjet.LundJet(j, lund_gen.result(j)) for j in all_jets_sd
]
joblib_file = 'lund_{}.joblib'.format(os.path.basename(fn))
it = 0
while os.path.exists(joblib_file):
if args.clean:
os.unlink(joblib_file)
break
joblib_file = 'lund_{}_{}.joblib'.format(os.path.basename(fn), it)
it += 1
joblib.dump(lunds_pickle, joblib_file)
joblib_file_sd = 'lund_sd_{}.joblib'.format(os.path.basename(fn))
it = 0
while os.path.exists(joblib_file_sd):
if args.clean:
os.unlink(joblib_file_sd)
break
joblib_file_sd = 'lund_sd_{}_{}.joblib'.format(
os.path.basename(fn), it)
it += 1
joblib.dump(lunds_sd_pickle, joblib_file_sd)
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(100.0) & fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
mycfg = ['PhaseSpace:pThatMin = 100']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
print("[e] pythia initialization failed.")
return
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
jets = jet_selector(jet_def(parts))
all_jets.extend(jets)
pythia.stat()
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print('making lund diagram for all jets...', lund_gen.description())
lunds = [lund_gen.result(j) for j in all_jets]
print(
'listing lund plane points... Delta, kt - for {} selected jets'.format(
len(all_jets)))
for l in lunds:
print('- jet pT={0:5.2f} eta={1:5.2f}'.format(l[0].pair().perp(),
l[0].pair().eta()))
print(' Deltas={}'.format([s.Delta() for s in l]))
print(' kts={}'.format([s.kt() for s in l]))
print()
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
dy_groomed = [dy_groomer.result(j, 2) for j in all_jets]
print('listing results from dynamical grooming...')
for l in dy_groomed:
print(l, ' kt={}'.format(l.kt()))
for j in all_jets:
rdyng2 = dy_groomer.result(j, 2)
rdyng1 = dy_groomer.result(j, 1)
print('jet', j)
print('- all splits kts:',
sorted([s.kt() for s in dy_groomer.lund_splits()], reverse=True))
print('- dynG kt a=1:', rdyng1.kt(), 'a=2:', rdyng2.kt())
print()
return
print('[i] reclustering and using soft drop...')
jet_def_rc = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
print('Reclustering:', jet_def_rc)
rc = fjcontrib.Recluster(jet_def_rc, True)
sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
for i, j in enumerate(all_jets):
j_rc = rc.result(j)
print()
print('- [{0:3d}] orig pT={1:10.3f} reclustered pT={2:10.3f}'.format(
i, j.perp(), j_rc.perp()))
j_sd = sd.result(j)
print(' |-> after soft drop pT={0:10.3f} delta={1:10.3f}'.format(
j_sd.perp(),
j_sd.perp() - j.perp()))
sd_info = fjcontrib.get_SD_jet_info(j_sd)
print(
" |-> SD jet params z={0:10.3f} dR={1:10.3f} mu={2:10.3f}".format(
sd_info.z, sd_info.dR, sd_info.mu))
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
parser.add_argument('--output',
help="output file name",
default="test_hjet_parton.root",
type=str)
parser.add_argument('--no-tt',
help="do not require TT to accept the event",
default=False,
action='store_true')
parser.add_argument('--charged',
help="analyze only the charged particles of the FS",
default=False,
action='store_true')
parser.add_argument('--max-jet-pt',
help="maximum jet pT to consider",
type=float,
default=100.)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
hadron_eta_max = 2.0
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(10.0) & fj.SelectorPtMax(
args.max_jet_pt) & fj.SelectorAbsEtaMax(hadron_eta_max - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(hadron_eta_max - jet_R0)
hTT6_selector = fj.SelectorPtMin(6) & fj.SelectorPtMax(
7) & fj.SelectorAbsEtaMax(hadron_eta_max)
hTT12_selector = fj.SelectorPtMin(12) & fj.SelectorPtMax(
50) & fj.SelectorAbsEtaMax(hadron_eta_max)
hTT20_selector = fj.SelectorPtMin(20) & fj.SelectorPtMax(
50) & fj.SelectorAbsEtaMax(hadron_eta_max)
pythia_fs_part_selection = [pythiafjext.kFinal]
if args.charged is True:
pwarning('running with charged particles in the final state')
pythia_fs_part_selection.append(pythiafjext.kCharged)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 6']
# mycfg = ['PhaseSpace:pThatMin = 12']
# mycfg = ['PhaseSpace:pThatMin = 40']
mycfg = []
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print(lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd = fjcontrib.SoftDrop(0, 0.2, 1.0)
print(sd)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print(jet_def_rc01)
print(jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name='lsjvsx', file_name=args.output)
zero_psj = fj.PseudoJet(0, 0, 10, 10)
if args.nev < 100:
args.nev = 100
t = tqdm.tqdm(total=args.nev)
while t.n < args.nev:
if not pythia.next():
continue
# information about the leading process
# print(pythia.info.code(), pythia.info.nameProc(pythia.info.code()))
# continue
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton],
0, True)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
parts = pythiafjext.vectorize_select(pythia, pythia_fs_part_selection,
0, False)
hTT6 = zero_psj
hTT6s = fj.sorted_by_pt(hTT6_selector(parts))
if len(hTT6s) > 0:
hTT6 = hTT6s[0]
hTT12 = zero_psj
hTT12s = fj.sorted_by_pt(hTT12_selector(parts))
if len(hTT12s) > 0:
hTT12 = hTT12s[0]
hTT20 = zero_psj
hTT20s = fj.sorted_by_pt(hTT20_selector(parts))
if len(hTT20s) > 0:
hTT20 = hTT20s[0]
if args.no_tt is False:
if hTT12.perp() < 1 and hTT6.perp() < 1 and hTT20.perp() < 1:
continue
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
t.update(1)
j_type = match_dR(j, partons, jet_R0 / 2.)
if j_type[0] is None:
continue
j_sd = sd.result(j)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches(
j=j,
mult=len(parts),
lund=[ls for ls in lund_gen.result(j)],
dyg1=dy_groomer.result(j, 1),
sd=j_sd,
sd_z=sd_info.z,
sd_mu=sd_info.mu,
sd_Delta=sd_info.dR,
lsjet01=rc_sjets01[0],
nsjet01=len(rc_sjets01),
sjet01=rc_sjets01,
lsjet02=rc_sjets02[0],
nsjet02=len(rc_sjets02),
sjet02=rc_sjets02,
hTT6=hTT6,
hTT12=hTT12,
hTT20=hTT20,
dphi6=j.delta_phi_to(hTT6),
dphi12=j.delta_phi_to(hTT12),
dphi20=j.delta_phi_to(hTT20),
ppid=j_type[0],
pquark=j_type[1],
pglue=j_type[2], # this is redundancy
pycode=pythia.info.code(),
pysigmagen=pythia.info.sigmaGen(),
pysigmaerr=pythia.info.sigmaErr(),
pyid1=pythia.info.id1pdf(),
pyid2=pythia.info.id1pdf(),
pyx1=pythia.info.x1pdf(),
pyx2=pythia.info.x2pdf(),
pypdf1=pythia.info.pdf1(),
pyQfac=pythia.info.QFac(),
pyalphaS=pythia.info.alphaS(),
pypthat=pythia.info.pTHat(),
pymhat=pythia.info.mHat())
tw.fill_tree()
t.close()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
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()
