def __init__(self, **kwargs):
self.fout = None
super(HFAnalysisInvMass, self).__init__(**kwargs)
self.tw = treewriter.RTreeWriter(tree_name='d0',
file_name=self.name + '.root')
self.twjc = treewriter.RTreeWriter(name='d0jc',
file_name=self.name + '_c.root')
def __init__(self, **kwargs):
self.fout = None
super(HFAnalysisInvMass, self).__init__(**kwargs)
self.fout = ROOT.TFile(self.name + '.root', 'recreate')
self.fout.cd()
self.tw = treewriter.RTreeWriter(tree_name='d0', fout=self.fout)
self.tw_gen = treewriter.RTreeWriter(tree_name='d0_gen',
fout=self.fout)
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
parser.add_argument('input', type=str, default='')
parser.add_argument('--nev', type=int, default=-1)
args = parser.parse_args()
finame = args.input
f = open(finame)
csv_f = csv.reader(f)
rfout = ROOT.TFile(finame.replace('.csv', '.root'), 'recreate')
rfout.cd()
tw = treewriter.RTreeWriter(tree_name='emissions', fout=rfout)
pinfo('working on a file', finame)
irow = 0
scols = []
nmax = args.nev
for row in tqdm.tqdm(csv_f):
if irow < 1:
scols = copy.deepcopy(row)
irow = irow + 1
continue
else:
tw.fill_branch('e', make_dict(scols, row))
tw.fill_tree()
if nmax > 0 and irow >= nmax:
break
irow = irow + 1
rfout.Write()
pinfo('file written:', rfout.GetName())
pinfo('done.')
def test_cxx_gzip_python_stream(args):
pinfo(args)
tw = treewriter.RTreeWriter(name='aleph', file_name=args.output)
nev = aleph_utils.get_n_events_gzip(args.input)
with gzip.open(args.input) as f:
_data = f.readlines()
data = aleph.StringVector()
__ = [
data.push_back("{}".format(s.decode("utf-8").strip('\n')))
for s in _data
]
pinfo(type(data))
pinfo('number of lines read', len(data))
reader = aleph.ReaderLines(data)
for i in tqdm(range(nev)):
if reader.read_next_event():
e = reader.get_event()
__ = [stream_particle(e, p, tw) for p in e.get_particles()]
else:
pinfo('no more events to read')
break
tw.write_and_close()
def get_event_id(self, df):
self.ev_id = df['ev_id'].values[0]
self.run_number = df['run_number'].values[0]
#pindent('processing', 'run_number=', self.run_number, 'ev_id=', self.ev_id, 'internal iev=', self.iev, 'cyclical iev=', self.iev_count)
if self.iev_count >= self.max_events or self.iev == 0:
#open a new file
if self.fout:
self.fout.Write()
self.fout.Close()
self.td = None
self.fout = None
outfname = self.file_output.replace('.root',
'_{}.root'.format(self.nfile))
pinfo('opening new file at', self.iev, 'file number', self.nfile,
'file name', outfname)
self.fout = ROOT.TFile(outfname, 'recreate')
self.nfile = self.nfile + 1
self.iev_count = 0
self.trees = {}
# these are branches
# for sdf in df:
for sdf in self.df:
foldername = os.path.dirname(sdf.split(';')[0])
tname = os.path.basename(sdf.split(';')[0])
self.fout.cd()
if self.td is None:
self.td = ROOT.TDirectoryFile(foldername, foldername)
self.td.cd()
# tw = ROOT.TNtuple(tname, tname, ':'.join(self.df[sdf].columns))
self.trees[tname] = treewriter.RTreeWriter(tree_name=tname,
fout=self.td)
# fill for loc of iev and run number for each tree
# for sdf in self.df:
# pinfo(self.df[sdf])
for sdf in self.df:
tname = os.path.basename(sdf.split(';')[0])
_df_sel = self.df[sdf].loc[
(self.df[sdf]['run_number'] == self.run_number)
& (self.df[sdf]['ev_id'] == self.ev_id)]
#if len(_df_sel) < 1:
# pwarning('no entries for', sdf, len(_df_sel), self.run_number, self.ev_id)
#else:
# pinfo('rows for', sdf, len(_df_sel))
for index, row in _df_sel.iterrows():
#print(row)
for c in _df_sel.columns:
# pindent(self.trees[tname])
# pindent(tname, index, c, '=', row[c])
val = row[c]
self.trees[tname].fill_branch(c, row[c])
# pindent(self.trees[tname].tree.GetName(), 'fill')
self.trees[tname].fill_tree()
self.iev = self.iev + 1
self.iev_count = self.iev_count + 1
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='event', fout=self.fout)
def __init__(self, **kwargs): self.configure_from_args(d0_tree_name='PWGHF_TreeCreator/tree_D0', track_tree_name='PWGHF_TreeCreator/tree_Particle', event_tree_name='PWGHF_TreeCreator/tree_event_char', enable_jet=True, enable_d0=True, offset_parts=0, output_prefix='./HFAIO', input_file = None) super(HFAIO, self).__init__(**kwargs) self.analyses = [] self.df_grouped = None self.df_events = None # temp output out_file_1 = unique_fname(self.output_prefix + '_djet_tout.root') self.tw = treewriter.RTreeWriter(name = 'd0j', file_name = out_file_1) out_file_2 = unique_fname(self.output_prefix + '_djet_correl_tout.root') self.twjc = treewriter.RTreeWriter(name = 'd0jc', file_name = out_file_2) if self.input_file: self.process_file(self.input_file)
def main(args):
if args.output == 'default.root':
args.output = args.input + '.root'
pinfo('args', args)
tw = treewriter.RTreeWriter(name='taleph', file_name=args.output)
with gzip.open(args.input) as f:
data = f.readlines()
pinfo('number of lines read', len(data))
for l in tqdm(data):
lstr = l.decode("utf-8")
if 'ALEPH_DATA RUN' in lstr:
run_number, event_number, ecm = get_event_info(lstr)
continue
if 'Primary vertex info' in lstr:
vflag, vx, vy, ex, ey = get_pvertex_info(lstr)
continue
if 'END_EVENT' in lstr:
continue
if 'px=' in lstr:
px, py, pz, m, q, pwflag, d0, z0, ntpc, nitc, nvdet = get_part(
lstr)
tw.fill_branches(run=run_number,
event=event_number,
ecm=ecm,
vflag=vflag,
vx=vx,
vy=vy,
ex=ex,
ey=ey,
px=px,
py=py,
pz=pz,
m=m,
q=q,
pwflag=pwflag,
d0=d0,
z0=z0,
ntpc=ntpc,
nitc=nitc,
nvdet=nvdet)
tw.fill_tree()
tw.write_and_close()
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 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=True,
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_zloss.root',
type=str)
parser.add_argument('--jetptmin',
help="jet pt minimum",
default=-1,
type=float)
parser.add_argument('--jetptmax',
help="jet pt maximum",
default=1e6,
type=float)
parser.add_argument('--eta', help="jet eta max", default=2.4, type=float)
parser.add_argument(
'--kt',
help="use kT algorithm instead of anti-kT for the subjets",
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(args.py_pthatmin) & fj.SelectorPtMax(
1000.0) & fj.SelectorAbsEtaMax(args.eta - jet_R0)
mycfg = []
if args.jetptmin > 0:
mycfg = ['PhaseSpace:pThatMin = {}'.format(args.jetptmin)]
jet_selector = fj.SelectorPtMin(args.jetptmin) & fj.SelectorPtMax(
args.jetptmax) & fj.SelectorAbsEtaMax(args.eta - jet_R0)
print(jet_def)
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
nbins = 20
# sjrs = [0.001 + x * 0.04 for x in range(0, nbins)]
sjrs = logbins(0.001, jet_R0, nbins)
print(sjrs)
print('log(1/r) :', [ROOT.TMath.Log(1 / r) for r in sjrs])
sjdefs = dict()
for sjr in sjrs:
if args.kt:
_jet_def = fj.JetDefinition(fj.kt_algorithm, sjr)
else:
_jet_def = fj.JetDefinition(fj.antikt_algorithm, sjr)
sjdefs[sjr] = _jet_def
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name='lsjvszloss', file_name=args.output)
tw.fout.cd()
h_zloss_r_q = dict()
h_zloss_r_g = dict()
for sjr in sjrs:
sname = 'h_zloss_glue_{}'.format(sjr)
_h_zloss_r_g = ROOT.TH1F(sname, sname, len(sjrs), 0., 1.)
h_zloss_r_g[sjr] = _h_zloss_r_g
sname = 'h_zloss_quark_{}'.format(sjr)
_h_zloss_r_q = ROOT.TH1F(sname, sname, len(sjrs), 0., 1.)
h_zloss_r_q[sjr] = _h_zloss_r_q
lbins = logbins(ROOT.TMath.Log(1. / jet_R0), ROOT.TMath.Log(1. / sjrs[0]),
nbins)
print('lbins:', lbins)
sname = 'prof_zloss_vs_r_any'
prof_a = ROOT.TProfile(sname, sname, nbins, 0, jet_R0)
prof_a_log = ROOT.TProfile(sname + '_log', sname + '_log', nbins, lbins)
sname = 'prof_zloss_vs_r_glue'
prof_g = ROOT.TProfile(sname, sname, nbins, 0, jet_R0)
prof_g_log = ROOT.TProfile(sname + '_log', sname + '_log', nbins, lbins)
sname = 'prof_zloss_vs_r_quark'
prof_q = ROOT.TProfile(sname, sname, nbins, 0, jet_R0)
prof_q_log = ROOT.TProfile(sname + '_log', sname + '_log', nbins, lbins)
# prof_q_log = ROOT.TProfile(sname+'_log', sname+'_log', nbins, ROOT.TMath.Log(1./jet_R0), ROOT.TMath.Log(1./sjrs[0]))
sname = 'h2_zloss_vs_r_glue'
h2_zloss_r_g = ROOT.TH2F(sname, sname, nbins, 0., jet_R0, len(sjrs), 0.,
1.)
sname = 'h2_zloss_vs_r_quark'
h2_zloss_r_q = ROOT.TH2F(sname, sname, nbins, 0., jet_R0, len(sjrs), 0.,
1.)
# loop
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
tw.fill_branch("j", j)
for sjr in sjrs:
rc_jets = fj.sorted_by_pt(sjdefs[sjr](j.constituents()))
tw.fill_branch("sjr{}".format(sjr), rc_jets[0])
zloss = 1. - rc_jets[0].perp() / j.perp()
tw.fill_branch("sjr{}_zloss".format(sjr), zloss)
tw.fill_branch("ppid", j_type[0])
tw.fill_branch("pquark", j_type[1])
tw.fill_branch("pglue", j_type[2])
prof_a.Fill(sjr, zloss)
prof_a_log.Fill(ROOT.TMath.Log(1. / sjr), zloss)
if j_type[1]:
h_zloss_r_q[sjr].Fill(zloss)
h2_zloss_r_q.Fill(sjr, zloss)
prof_q.Fill(sjr, zloss)
prof_q_log.Fill(ROOT.TMath.Log(1. / sjr), zloss)
if j_type[2]:
h_zloss_r_g[sjr].Fill(zloss)
h2_zloss_r_g.Fill(sjr, zloss)
prof_g.Fill(sjr, zloss)
prof_g_log.Fill(ROOT.TMath.Log(1. / sjr), zloss)
tw.fill_tree()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly', prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--nw', help="no warn", default=False, action='store_true')
parser.add_argument('--ignore-mycfg', help="ignore some settings hardcoded here", default=False, action='store_true')
parser.add_argument('--enable-background', help="enable background calc", default=False, action='store_true')
parser.add_argument('--output', help="output file name", default='leadsj_vs_x_output.root', type=str)
# for background
parser.add_argument('--cent-bin', help="centraility bin 0 is the 0-5 percent most central bin", type=int, default=0)
parser.add_argument('--seed', help="pr gen seed", type=int, default=1111)
parser.add_argument('--harmonics', help="set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)",
type=int, default=5)
parser.add_argument('--eta', help="set eta range must be uniform (e.g. abs(eta) < 0.9, which is ALICE TPC fiducial acceptance)",
type=float, default=0.9)
parser.add_argument('--qa', help="PrintOutQAHistos", default=False, action='store_true')
parser.add_argument('--dRmax', default=0.25, type=float)
parser.add_argument('--alpha', default=0, type=float)
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(args.py_pthatmin) & fj.SelectorPtMax(1000.0) & fj.SelectorAbsEtaMax(args.eta - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
# mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 40']
mycfg = ['']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print (lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print (dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd01 = fjcontrib.SoftDrop(0, 0.1, 1.0)
print (sd01)
sd02 = fjcontrib.SoftDrop(0, 0.2, 1.0)
print (sd02)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print (jet_def_rc01)
print (jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = args.output)
tgbkg = None
be = None
if args.enable_background:
# ROOT.gSystem.Load("libpyjetty_TennGen.dylib")
# tgbkg = ROOT.TennGen() # //constructor
# tgbkg.SetCentralityBin(args.cent_bin) # //centraility bin 0 is the 0-5 % most central bin
# tgbkg.SetRandomSeed(args.seed) # //setting the seed
# tgbkg.SetHarmonics(args.harmonics) # // set harmonics flag (0 : v1 - v5) , (1 : v2 - v5) , (2: v3 - v5) , (3: v1 - v4) , (4: v1 - v3) , (5: uniform dN/dphi no harmonics) , (6 : v1 - v2 , v4 - v5) , (7 : v1 - v3 , v5) , (8 : v1 , v3 - v5) , (9 : v1 only) , (10 : v2 only) , (11 : v3 only) , (12 : v4 only) , (13 : v5 only)
# tgbkg.SetEtaRange(args.eta) # //set eta range must be uniform (e.g. |eta| < 0.9, which is ALICE TPC fiducial acceptance)
# tgbkg.PrintOutQAHistos(args.qa) #
# tgbkg.InitializeBackground() #
from pyjetty.mputils import BoltzmannEvent
be = BoltzmannEvent(mean_pt=0.7, multiplicity=2000 * args.eta * 2, max_eta=max_eta, max_pt=100)
print(be)
from pyjetty.mputils import CEventSubtractor, CSubtractorJetByJet
cs = CEventSubtractor(alpha=args.alpha, max_distance=args.dRmax, max_eta=args.eta, bge_rho_grid_size=0.25, max_pt_correct=100)
print(cs)
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton], 0, True)
parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
j_type = match_dR(j, partons, jet_R0 / 2.)
if j_type[0] is None:
if args.nw:
continue
pwarning('Jet with no parton label')
continue
j_sd02 = sd02.result(j)
sd02_info = fjcontrib.get_SD_jet_info(j_sd02)
j_sd01 = sd01.result(j)
sd01_info = fjcontrib.get_SD_jet_info(j_sd01)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches( j = j,
lund = [ls for ls in lund_gen.result(j)],
dyg1 = dy_groomer.result(j, 1),
sd01 = j_sd01,
sd01_z = sd01_info.z,
sd01_mu = sd01_info.mu,
sd01_Delta = sd01_info.dR,
sd02 = j_sd02,
sd02_z = sd02_info.z,
sd02_mu = sd02_info.mu,
sd02_Delta = sd02_info.dR,
# breaking compatibility
# sd = j_sd,
# sd_z = sd_info.z,
# sd_mu = sd_info.mu,
# sd_Delta = sd_info.dR,
lsjet01 = rc_sjets01[0],
nsjet01 = len(rc_sjets01),
sjet01 = rc_sjets01,
lsjet02 = rc_sjets02[0],
nsjet02 = len(rc_sjets02),
sjet02 = rc_sjets02,
ppid = j_type[0],
pquark = j_type[1],
pglue = j_type[2], # this is redundancy
pycode = pythia.info.code(),
pysigmagen = pythia.info.sigmaGen(),
pysigmaerr = pythia.info.sigmaErr(),
pyid1 = pythia.info.id1pdf(),
pyid2 = pythia.info.id1pdf(),
pyx1 = pythia.info.x1pdf(),
pyx2 = pythia.info.x2pdf(),
pypdf1 = pythia.info.pdf1(),
pyQfac = pythia.info.QFac(),
pyalphaS = pythia.info.alphaS(),
pypthat = pythia.info.pTHat(),
pymhat = pythia.info.mHat()
)
if be:
bg_parts = be.generate(offset=10000)
full_event = bg_parts
tmp = [full_event.push_back(psj) for psj in j.constituents()]
if cs:
cs_parts = cs.process_event(full_event)
rho = cs.bge_rho.rho()
bg_jets = fj.sorted_by_pt(jet_def(cs_parts))
for bj in bg_jets:
if fjtools.matched_pt(bj, j) > 0.5:
pass
tw.fill_tree()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--ignore-mycfg',
help="ignore some settings hardcoded here",
default=False,
action='store_true')
args = parser.parse_args()
# print the banner first
fj.ClusterSequence.print_banner()
print()
# set up our jet definition and a jet selector
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector = fj.SelectorPtMin(80.0) & fj.SelectorPtMax(
100.0) & fj.SelectorAbsEtaMax(1)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
print(jet_def)
all_jets = []
mycfg = ['PhaseSpace:pThatMin = 80']
# mycfg = ['PhaseSpace:pThatMin = 40']
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
jet_def_lund = fj.JetDefinition(fj.cambridge_algorithm, 1.0)
lund_gen = fjcontrib.LundGenerator(jet_def_lund)
print(lund_gen.description())
dy_groomer = fjcontrib.DynamicalGroomer(jet_def_lund)
print(dy_groomer.description())
# sd = fjcontrib.SoftDrop(0, 0.1, 1.0)
sd = fjcontrib.SoftDrop(0, 0.2, 1.0)
print(sd)
# jet_def_rc01 = fj.JetDefinition(fj.cambridge_algorithm, 0.1)
# jet_def_rc02 = fj.JetDefinition(fj.cambridge_algorithm, 0.2)
# print (jet_def_rc01)
# print (jet_def_rc02)
# rc = fjcontrib.Recluster(jet_def_rc, True)
jet_def_rc01 = fj.JetDefinition(fj.antikt_algorithm, 0.1)
jet_def_rc02 = fj.JetDefinition(fj.antikt_algorithm, 0.2)
print(jet_def_rc01)
print(jet_def_rc02)
#rc = fjcontrib.Recluster(jet_def_rc, True)
# tw = treewriter.RTreeWriter(name = 'lsjvsx', file_name = 'leadsj_vs_x.root')
tw = treewriter.RTreeWriter(name='lsjvsx',
file_name='leadsj_vs_x_bias80.root')
if args.nev < 100:
args.nev = 100
for i in tqdm.tqdm(range(args.nev)):
if not pythia.next():
continue
# parts = pythiafjext.vectorize(pythia, True, -1, 1, False)
partons = pythiafjext.vectorize_select(pythia, [pythiafjext.kParton],
0, True)
parts = pythiafjext.vectorize_select(
pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False)
jets = jet_selector(jet_def(parts))
# for j in tqdm.tqdm(jets):
for j in jets:
j_type = match_dR(j, partons, jet_R0 / 2.)
j_sd = sd.result(j)
sd_info = fjcontrib.get_SD_jet_info(j_sd)
rc_sjets01 = fj.sorted_by_pt(jet_def_rc01(j.constituents()))
rc_sjets02 = fj.sorted_by_pt(jet_def_rc02(j.constituents()))
tw.fill_branches(
j=j,
lund=[ls for ls in lund_gen.result(j)],
dyg1=dy_groomer.result(j, 1),
sd=j_sd,
sd_z=sd_info.z,
sd_mu=sd_info.mu,
sd_Delta=sd_info.dR,
lsjet01=rc_sjets01[0],
nsjet01=len(rc_sjets01),
sjet01=rc_sjets01,
lsjet02=rc_sjets02[0],
nsjet02=len(rc_sjets02),
sjet02=rc_sjets02,
ppid=j_type[0],
pquark=j_type[1],
pglue=j_type[2] # this is redundancy
)
tw.fill_tree()
pythia.stat()
tw.write_and_close()
def main():
parser = argparse.ArgumentParser(description='pythia8 fastjet on the fly',
prog=os.path.basename(__file__))
pyconf.add_standard_pythia_args(parser)
parser.add_argument('--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='test the TennGen', 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('--cent-bin', help="centraility bin 0 is the 0-5 % 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. |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')
args = parser.parse_args()
args.py_pthatmin = 100
mycfg = ['PhaseSpace:pThatMin = {}'.format(args.py_pthatmin)]
if args.ignore_mycfg:
mycfg = []
pythia = pyconf.create_and_init_pythia_from_args(args, mycfg)
if not pythia:
perror("pythia initialization failed.")
return
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() #
jet_R0 = 0.4
jet_def = fj.JetDefinition(fj.antikt_algorithm, jet_R0)
jet_selector_pythia = fj.SelectorPtMin(args.py_pthatmin) & fj.SelectorPtMax(1000.0) &fj.SelectorAbsEtaMax(args.eta - jet_R0)
jet_selector_hybrid = fj.SelectorPtMin(10) & fj.SelectorPtMax(1000.0) &fj.SelectorAbsEtaMax(args.eta - jet_R0)
# jet_selector = fj.SelectorPtMin(40.0) & fj.SelectorPtMax(200.0) &fj.SelectorAbsEtaMax(1)
parts_selector = fj.SelectorAbsEtaMax(args.eta)
print(jet_def)
tw = treewriter.RTreeWriter(name = 'tparts', file_name = 'test_TennGen.root')
if args.nev < 100:
args.nev = 100
pbar = tqdm.tqdm(total = args.nev)
while pbar.n < args.nev:
if not pythia.next():
continue
# get pythia particles
# parts = pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal, pythiafjext.kCharged], 0, False)
_py_fj_parts = parts_selector(pythiafjext.vectorize_select(pythia, [pythiafjext.kFinal], 0, False))
# get jets w/o area determination
# pythia_jets = jet_selector_pythia(jet_def(_py_fj_parts))
# with area determination
jet_area_def = fj.AreaDefinition(fj.active_area, fj.GhostedAreaSpec(args.eta))
cs = fj.ClusterSequenceArea(_py_fj_parts, jet_def, jet_area_def)
pythia_jets = jet_selector_pythia(cs.inclusive_jets())
if len(pythia_jets) < 1:
continue
pbar.update(1)
tw.fill_branches(pyj = pythia_jets)
# now generate bg
bg_tclones = tgbkg.GetBackground()
# tgbkg.GetRandomSeed()
nParticles = bg_tclones.GetEntries();
# pinfo('event', pbar.n, 'number of parts', nParticles)
# _parts = { 'pt' : [], 'eta' : [], 'phi' : [], 'kf' : []}
_parts = [[], [], [], []]
_ = [[_parts[0].append(p[0].Pt()), _parts[1].append(p[0].Eta()), _parts[2].append(p[0].Phi()), _parts[3].append(p[1])] for p in [[tlv_from_tmcparticle(_p), _p.GetKF()] for _p in bg_tclones if _p.GetEnergy()>0]]
_bg_fj_parts = fjext.vectorize_pt_eta_phi(_parts[0], _parts[1], _parts[2], 1000) #bg particles with index > 1000
# add background and pythia
_fj_parts = []
_ = [_fj_parts.append(_p) for _p in _py_fj_parts]
_ = [_fj_parts.append(_p) for _p in _bg_fj_parts]
# stream all particles
_ = [tw.fill_branches(part_pt = _pfj.perp(), part_eta = _pfj.eta(), part_phi = _pfj.phi(), part_idx=_pfj.user_index()) for _pfj in _fj_parts]
# find jets in the hybrid event
# w/o area
# jets = jet_selector_hybrid(jet_def(_fj_parts))
# w/area
cs_hybrid = fj.ClusterSequenceArea(_fj_parts, jet_def, jet_area_def)
jets = jet_selector_hybrid(cs_hybrid.inclusive_jets())
# stream jets from the hybrid event
tw.fill_branches(j = jets)
# estimate the background
bg_rho_range = fj.SelectorAbsEtaMax(args.eta * 1.1)
bg_jet_def = fj.JetDefinition(fj.kt_algorithm, jet_R0)
bg_area_def = fj.AreaDefinition(fj.active_area_explicit_ghosts, fj.GhostedAreaSpec(args.eta))
# bg_area_def = fj.AreaDefinition(fj.active_area, fj.GhostedAreaSpec(args.eta)) #active area defunct for bg estim
bg_estimator = fj.JetMedianBackgroundEstimator(bg_rho_range, bg_jet_def, bg_area_def)
bg_estimator.set_particles(_fj_parts)
if len(_fj_parts) < 0:
perror('no particles in the hybrid event?')
continue
rho = bg_estimator.rho()
sigma = bg_estimator.sigma()
corr_jet_pt = [j.pt() - j.area() * rho for j in jets]
# matches = [j.perp(), matched_jet(j, pythia_jets) for j in jets]
delta_pt = [delta_pt_matched(j, pythia_jets, rho) for j in jets]
tw.fill_branches(j_corr_pt = corr_jet_pt, dpt = delta_pt)
tw.fill_branches(rho = rho, rho_sigma = sigma)
tw.fill_tree()
bg_tclones.Clear()
pbar.close()
tgbkg.CloserFunction()
tw.write_and_close()
