def analysis(self, df):
djmm = fjtools.DJetMatchMaker()
djmm.set_ch_pt_eta_phi(self.df_tracks['ParticlePt'].values,
self.df_tracks['ParticleEta'].values,
self.df_tracks['ParticlePhi'].values)
djmm.set_Ds_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values,
df['phi_cand'].values, df['inv_mass'].values)
djmm.set_daughters0_pt_eta_phi(df['pt_prong0'].values,
df['eta_prong0'].values,
df['phi_prong0'].values)
djmm.set_daughters1_pt_eta_phi(df['pt_prong1'].values,
df['eta_prong1'].values,
df['phi_prong1'].values)
self.tw.fill_branches(dpsj=djmm.Ds)
self.tw.fill_tree()
for id0, d0 in enumerate(djmm.Ds):
_parts_and_ds = djmm.match(0.005, id0)
_parts_and_ds.push_back(d0)
ja = jet_analysis.JetAnalysis(jet_R=0.4,
particle_eta_max=0.9,
jet_pt_min=2.0)
ja.analyze_event(_parts_and_ds)
if len(ja.jets) < 1:
continue
jets = ja.jets_as_psj_vector()
djets = djmm.filter_D0_jets(jets)
if len(djets) > 0:
j = djets[0]
dcand = djmm.get_Dcand_in_jet(j)
sja = jet_analysis.JetAnalysis(jet_R=0.1,
particle_eta_max=0.9,
jet_pt_min=2.0)
sja.analyze_event(j.constituents())
lsj = fj.sorted_by_pt(sja.jets_as_psj_vector())
if len(lsj) < 1:
continue
sj_dcand = djmm.get_Dcand_in_jet(lsj[0])
is_Dsj = 0
if len(sj_dcand) > 0:
# if sj_dcand[0].m() == dcand[0].m() and sj_dcand[0].perp() == dcand[0].perp():
if sj_dcand[0].delta_R(dcand[0]) == 0.0:
is_Dsj = 1
self.twjc.fill_branches(jet=j,
dR=j.delta_R(dcand[0]),
D=dcand[0],
lsj=lsj[0],
Dsj=is_Dsj,
a10=fjext.angularity(j, 1.0, 0.4),
a15=fjext.angularity(j, 0.5, 0.4),
a20=fjext.angularity(j, 0.0, 0.4),
a30=fjext.angularity(j, -1.0, 0.4))
self.twjc.fill_tree()
if len(djets) > 1:
perror("more than one jet per D candidate?")
return True
def process_event(self, df):
self.pbar.update(1)
if 'ev_id_ext' in list(self.event_df):
_ev_query = "run_number == {} & ev_id == {} & ev_id_ext == {}".format(df['run_number'], df['ev_id'], df['ev_id_ext'])
else:
_ev_query = "run_number == {} & ev_id == {}".format(df['run_number'], df['ev_id'])
_df_d0 = self.d0_df.query(_ev_query)
_df_d0.reset_index(drop=True)
_n_d0s = len(_df_d0.index)
if _n_d0s < 1:
return
_ev_query = ""
_df_tracks = self.track_df.query(_ev_query)
_df_tracks.reset_index(drop=True)
_parts = fjext.vectorize_pt_eta_phi(_df_tracks['ParticlePt'].values, _df_tracks['ParticleEta'].values, _df_tracks['ParticlePhi'].values)
self._user_index_offset = 10000
# _d0s = fjext.vectorize_pt_eta_phi(_df_d0['pt_cand'].values, _df_d0['eta_cand'].values, _df_d0['phi_cand'].values, self._user_index_offset)
_d0s = fjext.vectorize_pt_eta_phi_m(_df_d0['pt_cand'].values, _df_d0['eta_cand'].values, _df_d0['phi_cand'].values, _df_d0['inv_mass'].values, self._user_index_offset)
_d0s_gh = [p * 1.e-6 for p in _d0s]
_parts_and_ds = _parts
_tmp = [_parts_and_ds.push_back(p) for p in _d0s_gh]
# pinfo('n parts = ', len(_parts_and_ds))
ja = jet_analysis.JetAnalysis(jet_R = 0.6, particle_eta_max=0.9, jet_pt_min=5.0)
ja.analyze_event(_parts_and_ds)
_d0_imass_list = _df_d0['inv_mass'].values.tolist()
self.tw.fill_branches(dpsj = _d0s)
self.tw.fill_branches(dpsjgh = _d0s_gh)
self.tw.fill_branches(minv = _d0_imass_list)
self.tw.fill_branches(jets = ja.jets_as_psj_vector())
self.tw.fill_tree()
self.d0_jet_correl(ja.jets, _d0s, _d0_imass_list)
def process_d0s_1(self, df):
self.pbar.update(1)
_n_d0s = len(df)
if _n_d0s < 1:
return
# pinfo(df)
if 'ev_id_ext' in list(self.event_df):
_ev_query = "run_number == {} & ev_id == {} & ev_id_ext == {}".format(df['run_number'].values[0], df['ev_id'].values[0], df['ev_id_ext'].values[0])
else:
_ev_query = "run_number == {} & ev_id == {}".format(df['run_number'].values[0], df['ev_id'].values[0])
_df_tracks = self.track_df.query(_ev_query)
_df_tracks.reset_index(drop=True)
djmm = fjtools.DJetMatchMaker()
djmm.set_ch_pt_eta_phi(_df_tracks['ParticlePt'].values, _df_tracks['ParticleEta'].values, _df_tracks['ParticlePhi'].values)
djmm.set_Ds_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values, df['phi_cand'].values, df['inv_mass'].values)
if _n_d0s > 1:
print('-- event break - 2D0 event:')
print('pt_cand: ', df['pt_cand'].values)
print('eta_cand: ', df['eta_cand'].values)
print('phi_cand: ', df['phi_cand'].values)
print('inv_mass: ', df['inv_mass'].values)
print('pt_prong0: ', df['pt_prong0'].values)
print('eta_prong0: ', df['eta_prong0'].values)
print('phi_prong0: ', df['phi_prong0'].values)
print('pt_prong1: ', df['pt_prong1'].values)
print('eta_prong1: ', df['eta_prong1'].values)
print('phi_prong1: ', df['phi_prong1'].values)
djmm.set_daughters0_pt_eta_phi(df['pt_prong0'].values, df['eta_prong0'].values, df['phi_prong0'].values)
djmm.set_daughters1_pt_eta_phi(df['pt_prong1'].values, df['eta_prong1'].values, df['phi_prong1'].values)
self.tw.fill_branches(dpsj = djmm.Ds)
self.tw.fill_tree()
_parts_and_ds = djmm.match(0.005)
[_parts_and_ds.push_back(p) for p in djmm.Ds]
ja = jet_analysis.JetAnalysis(jet_R = 0.2, particle_eta_max=0.9, jet_pt_min=2.0)
ja.analyze_event(_parts_and_ds)
if len(ja.jets) < 1:
return True
# self.d0_jet_correl(ja.jets, djmm.Ds)
jets = ja.jets_as_psj_vector()
djets = djmm.filter_D0_jets(jets)
_tmp = [self.twjc.fill_branches(jet = j, dR = j.delta_R(dcand), D = dcand) for j in djets for dcand in djmm.get_Dcand_in_jet(j)]
if len(_tmp) > 0:
self.twjc.fill_tree()
djpairs = [[j,dcand] for j in djets for dcand in djmm.get_Dcand_in_jet(j)]
for p in djpairs:
j = p[0]
dcand = p[1]
if j.delta_R(dcand) < 0.01 and dcand.perp() / j.perp() < 0.55:
self.print_jet(j)
return True
def process_d0s_0(self, df):
self.pbar.update(1)
_n_d0s = len(df)
if _n_d0s < 1:
return
# pinfo(df)
if 'ev_id_ext' in list(self.event_df):
# _ev_query = "run_number == {} & ev_id == {} & ev_id_ext == {}".format(df['run_number'], df['ev_id'], df['ev_id_ext'])
_ev_query = "run_number == {} & ev_id == {} & ev_id_ext == {}".format(df['run_number'].values[0], df['ev_id'].values[0], df['ev_id_ext'].values[0])
else:
_ev_query = "run_number == {} & ev_id == {}".format(df['run_number'].values[0], df['ev_id'].values[0])
_df_tracks = self.track_df.query(_ev_query)
_df_tracks.reset_index(drop=True)
djmm = fjtools.DJetMatchMaker()
djmm.set_ch_pt_eta_phi(_df_tracks['ParticlePt'].values, _df_tracks['ParticleEta'].values, _df_tracks['ParticlePhi'].values)
# _parts = fjext.vectorize_pt_eta_phi(_df_tracks['ParticlePt'].values, _df_tracks['ParticleEta'].values, _df_tracks['ParticlePhi'].values)
self._user_index_offset = 10000
self.D0_index_offset = 10000
# _d0s = fjext.vectorize_pt_eta_phi([df['pt_cand']], [df['eta_cand']], [df['phi_cand']], self._user_index_offset)
# _d0s = fjext.vectorize_pt_eta_phi(df['pt_cand'].values, df['eta_cand'].values, df['phi_cand'].values, self._user_index_offset)
# _d0s = fjext.vectorize_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values, df['phi_cand'].values, df['inv_mass'].values, self._user_index_offset)
djmm.set_Ds_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values, df['phi_cand'].values, df['inv_mass'].values, self.D0_index_offset)
_d0s = djmm.Ds;
_d0s_gh = [p * 1.e-6 for p in _d0s]
# for di in range(_d0s.size()):
# print(djmm.Ds[di].m(), df['inv_mass'].values[di])
_d0_imass_list = df['inv_mass'].values.tolist()
# _d0_imass_list = [df['inv_mass']]
self.tw.fill_branches(dpsj = _d0s, dpsjgh = _d0s_gh, minv = _d0_imass_list)
self.tw.fill_tree()
self.daughter_user_index_offset = self.D0_index_offset * 2
djmm.set_daughters0_pt_eta_phi(df['pt_prong0'].values, df['eta_prong0'].values, df['phi_prong0'].values, self.daughter_user_index_offset)
djmm.set_daughters1_pt_eta_phi(df['pt_prong1'].values, df['eta_prong1'].values, df['phi_prong1'].values, self.daughter_user_index_offset * 2)
pinfo('n D0s', len(_d0s))
# djmm.match(0.1)
# djmm.match(0.01)
# djmm.match(0.005)
_parts_and_ds = djmm.match(0.005)
# _parts_and_ds = _parts
_tmp = [_parts_and_ds.push_back(p) for p in djmm.Ds]
# # pinfo('n parts = ', len(_parts_and_ds))
ja = jet_analysis.JetAnalysis(jet_R = 0.2, particle_eta_max=0.9, jet_pt_min=2.0)
ja.analyze_event(_parts_and_ds)
if len(ja.jets) < 1:
return True
# self.d0_jet_correl_ghosts(ja.jets, _d0s, _d0_imass_list)
self.d0_jet_correl(ja.jets, djmm.Ds)
return True
def analysis_gen(self, df):
m_gen_array = np.full((self.df_gen_tracks['ParticlePt'].values.size),
0.1396)
djmm_gen = fjtools.DJetMatchMaker()
djmm_gen.set_ch_pt_eta_phi_m(self.df_gen_tracks['ParticlePt'].values,
self.df_gen_tracks['ParticleEta'].values,
self.df_gen_tracks['ParticlePhi'].values,
m_gen_array)
djmm_gen.set_Ds_pt_eta_phi(df['pt_cand'].values, df['eta_cand'].values,
df['phi_cand'].values)
for id0, d0 in enumerate(djmm_gen.Ds):
_parts_and_ds = djmm_gen.ch
_parts_and_ds.push_back(d0)
ja_gen = jet_analysis.JetAnalysis(
jet_R=0.4,
jet_RecombinationScheme=fj.E_scheme,
particle_eta_max=0.9,
jet_pt_min=5.0)
ja_gen.analyze_event(_parts_and_ds)
if len(ja_gen.jets) < 1:
continue
jets = ja_gen.jets_as_psj_vector()
djets = djmm_gen.filter_D0_jets(jets)
if len(djets) > 0:
j = djets[0]
dcand = djmm_gen.get_Dcand_in_jet(j)
D_cand_type = df['cand_type'].values
self.twjc_gen.fill_branches(jet=j,
dR=j.delta_R(dcand[0]),
D=dcand[0],
Dmeson_cand_type=float(
D_cand_type[id0]))
self.twjc_gen.fill_tree()
if len(djets) > 1:
perror("more than one jet per D candidate?")
return True
def process_d0s(self, df):
self.pbar.update(1)
_n_d0s = len(df)
if _n_d0s < 1:
return
# pinfo(df)
if 'ev_id_ext' in list(self.event_df):
_ev_query = "run_number == {} & ev_id == {} & ev_id_ext == {}".format(df['run_number'].values[0], df['ev_id'].values[0], df['ev_id_ext'].values[0])
else:
_ev_query = "run_number == {} & ev_id == {}".format(df['run_number'].values[0], df['ev_id'].values[0])
_df_tracks = self.track_df.query(_ev_query)
_df_tracks.reset_index(drop=True)
djmm = fjtools.DJetMatchMaker()
djmm.set_ch_pt_eta_phi(_df_tracks['ParticlePt'].values, _df_tracks['ParticleEta'].values, _df_tracks['ParticlePhi'].values)
djmm.set_Ds_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values, df['phi_cand'].values, df['inv_mass'].values)
djmm.set_daughters0_pt_eta_phi(df['pt_prong0'].values, df['eta_prong0'].values, df['phi_prong0'].values)
djmm.set_daughters1_pt_eta_phi(df['pt_prong1'].values, df['eta_prong1'].values, df['phi_prong1'].values)
# if _n_d0s > 1:
# print('-- event break - 2D0 event:')
# print('pt_cand: ', df['pt_cand'].values)
# print('eta_cand: ', df['eta_cand'].values)
# print('phi_cand: ', df['phi_cand'].values)
# print('inv_mass: ', df['inv_mass'].values)
# print('pt_prong0: ', df['pt_prong0'].values)
# print('eta_prong0: ', df['eta_prong0'].values)
# print('phi_prong0: ', df['phi_prong0'].values)
# print('pt_prong1: ', df['pt_prong1'].values)
# print('eta_prong1: ', df['eta_prong1'].values)
# print('phi_prong1: ', df['phi_prong1'].values)
self.tw.fill_branches(dpsj = djmm.Ds)
self.tw.fill_tree()
for id0, d0 in enumerate(djmm.Ds):
_parts_and_ds = djmm.match(0.005, id0)
_parts_and_ds.push_back(d0)
ja = jet_analysis.JetAnalysis(jet_R = 0.4, particle_eta_max=0.9, jet_pt_min=2.0)
ja.analyze_event(_parts_and_ds)
if len(ja.jets) < 1:
continue
jets = ja.jets_as_psj_vector()
djets = djmm.filter_D0_jets(jets)
if len(djets) > 0:
j = djets[0]
dcand = djmm.get_Dcand_in_jet(j)
sja = jet_analysis.JetAnalysis(jet_R = 0.1, particle_eta_max=0.9, jet_pt_min=2.0)
sja.analyze_event(j.constituents())
lsj = fj.sorted_by_pt(sja.jets_as_psj_vector())
sj_dcand = djmm.get_Dcand_in_jet(lsj[0])
is_Dsj = 0
if len(sj_dcand) > 0:
# if sj_dcand[0].m() == dcand[0].m() and sj_dcand[0].perp() == dcand[0].perp():
if sj_dcand[0].delta_R(dcand[0]) == 0.0:
is_Dsj = 1
self.twjc.fill_branches(jet = j, dR = j.delta_R(dcand[0]), D = dcand[0], lsj = lsj[0], Dsj = is_Dsj
, a10 = fjext.angularity(j, 1.0, 0.4)
, a15 = fjext.angularity(j, 0.5, 0.4)
, a20 = fjext.angularity(j, 0.0, 0.4)
, a30 = fjext.angularity(j, -1.0, 0.4))
self.twjc.fill_tree()
if len(djets) > 1:
perror("more than one jet per D candidate?")
return True
def analysis(self, df):
#print(df)
m_array = np.full((self.df_tracks['ParticlePt'].values.size), 0.1396)
djmm = fjtools.DJetMatchMaker()
djmm.set_ch_pt_eta_phi_m(self.df_tracks['ParticlePt'].values,
self.df_tracks['ParticleEta'].values,
self.df_tracks['ParticlePhi'].values, m_array)
djmm.set_Ds_pt_eta_phi_m(df['pt_cand'].values, df['eta_cand'].values,
df['phi_cand'].values, df['inv_mass'].values)
djmm.set_daughters0_pt_eta_phi(df['pt_prong0'].values,
df['eta_prong0'].values,
df['phi_prong0'].values)
djmm.set_daughters1_pt_eta_phi(df['pt_prong1'].values,
df['eta_prong1'].values,
df['phi_prong1'].values)
#run for each D candidate to build jet
for id0, d0 in enumerate(djmm.Ds):
#daughter tracks matching
_parts_and_ds = djmm.match(0.005, id0)
#replacing daughter tracks with matched D0 candidate
#including D0
_parts_and_ds.push_back(d0)
#jet reconstruction with D0 and charged particle
jetR = 0.4
ja = jet_analysis.JetAnalysis(jet_R=jetR,
jet_RecombinationScheme=fj.E_scheme,
particle_eta_max=0.9,
jet_pt_min=5.0)
ja.analyze_event(_parts_and_ds)
if len(ja.jets) < 1:
continue
jets = ja.jets_as_psj_vector()
#filtering D0 jets
djets = djmm.filter_D0_jets(jets)
if len(djets) > 0:
j = djets[0]
dcand = djmm.get_Dcand_in_jet(j)
#number of constitutents > 1
#if len(j.constituents())<=1:
# continue
#jets with the winner take all axis################################
jet_def_wta = fj.JetDefinition(fj.cambridge_algorithm,
2 * jetR)
jet_def_wta.set_recombination_scheme(fj.WTA_pt_scheme)
#print('WTA jet definition is:', jet_def_wta)
reclusterer_wta = fjcontrib.Recluster(jet_def_wta)
jet_wta = reclusterer_wta.result(j)
################################
D_cosp = df['cos_p'].values
D_cosTStar = df['cos_t_star'].values
D_NormalisedDecayLength = df['norm_dl_xy'].values
D_ImpactParameterProduct = df['imp_par_prod'].values
D_cand_type = df['cand_type'].values
self.twjc.fill_branches(
jet=j,
jetWta=jet_wta,
dR=j.delta_R(dcand[0]),
dRWTA=jet_wta.delta_R(dcand[0]),
D=dcand[0],
cos_p=float(D_cosp[id0]),
D_cos_t_star=float(D_cosTStar[id0]),
D_norm_dlxy=float(D_NormalisedDecayLength[id0]),
D_imp_par_prod=float(D_ImpactParameterProduct[id0]),
Dmeson_cand_type=float(D_cand_type[id0]))
self.twjc.fill_tree()
if len(djets) > 1:
perror("more than one jet per D candidate?")
return True
