def record_aviv_reco_jets(is_signal, input_list, events_to_read, event_data_dump):
tree_name = 'Nominal'
branches = [
'eventWeight',
('truth_particles',
['tpartpdgID', 'tpartstatus', 'tpartpT', 'tparteta', 'tpartphi', 'tpartm']
),
('reco_jets',
['tj0pT', 'j0_isPU', 'j0_QGTagger', 'j0_JVT', 'j0_fJVT_Tight', 'j0pT', 'j0eta', 'j0phi', 'j0m']
)
]
for event in event_iterator(input_list, tree_name, branches, events_to_read):
# Loop over reco jets and convert them into generic acorn_jet objects
#truth_particles = [ tp.copy() for tp in event['truth_particles'] ]
particle_list = []
for truth_particle in event['truth_particles']:
if truth_particle['tpartpdgID'] == autils.PDGID['higgs']: continue
v = TLorentzVector.from_ptetaphim(truth_particle['tpartpT'], truth_particle['tparteta'], truth_particle['tpartphi'], truth_particle['tpartm'])
particle_list.append( (v, truth_particle['tpartpdgID'], truth_particle['tpartstatus']) )
recorded_jets = []
for rj in event['reco_jets']:
v = TLorentzVector.from_ptetaphim(rj['j0pT'], rj['j0eta'], rj['j0phi'], rj['j0m'])
pdgid = match_jet(v, particle_list)
#is_pileup = rj['j0_isPU']
is_pileup = rj['tj0pT'] < 0
new_jet = acorn_jet(v, pdgid, is_pileup, rj['j0_JVT'], rj['j0_fJVT_Tight'], rj['j0_QGTagger'], None, [])
recorded_jets.append(new_jet)
# Categorize event, and then either discard the event or perform tagging on it
for category in event_data_dump.values():
category.add_event(recorded_jets, is_signal, event['eventWeight'])
def event_fails_photon_cut(truth_particles):
photon_pts = []
photon_4vector = TLorentzVector.from_ptetaphim(0,0,0,0)
for tp in truth_particles:
if tp['tpartpdgID'] != autils.PDG['photon']: continue
if tp['tpartstatus'] != autils.Status['photon_out']: continue
photon_pts.append(tp['tpartpT'])
v = TLorentzVector.from_ptetaphim(tp['tpartpT'], tp['tparteta'], tp['tpartphi'], tp['tpartm'])
photon_4vector += v
if len(photon_pts) != 2: return True
mgg = photon_4vector.mass
photon_pts.sort(reverse=True)
if photon_pts[0] < 0.35*mgg or photon_pts[1] < 0.25*mgg: return True
return False
def record_cmilkeV1_truth_jets(is_signal, input_list, events_to_read, event_data_dump):
tree_name = 'ntuple'
branches = [
'EventWeight',
('truth_jets',
['TruthJetPt', 'TruthJetEta', 'TruthJetPhi', 'TruthJetM', 'TruthJetID']
)
]
for event in event_iterator(input_list, tree_name, branches, events_to_read):
# Loop over truth jets and convert them into generic acorn_jet objects
recorded_jets = []
for truth_jet in event['truth_jets']:
v = TLorentzVector.from_ptetaphim(
truth_jet['TruthJetPt'],
truth_jet['TruthJetEta'],
truth_jet['TruthJetPhi'],
truth_jet['TruthJetM']
)
pdgid = truth_jet['TruthJetID']
# Create jet object storing the essential aspects of the ntuple truth jet,
# faking some of the data normally associated with reco jets
new_jet = acorn_jet(v, pdgid, False, True, True, -1)
recorded_jets.append(new_jet)
# Categorize event, and then either discard the event or perform tagging on it
for category in event_data_dump.values():
category.add_event(recorded_jets, is_signal, event['EventWeight'])
def __init__(self, event, selections):
jet_list = event.jets
total_4vector = TLorentzVector(0, 0, 0, 0)
for jet in jet_list:
total_4vector += jet.vector
mjjj = total_4vector.mass
self.discriminant = mjjj
def load_cmilke(event_generator, input_type, validation_data):
for event in event_generator:
validation_data['EventWeight'][input_type].append(event['EventWeight'])
init_jet_vectors = []
for reco_jet in event['reco_jets']:
if not reco_jet['JetJVT']: continue
if reco_jet['JetPt_calib'] < 20: continue
init_jet_vectors.append(
TLorentzVector.from_ptetaphim(reco_jet['JetPt_calib'],
reco_jet['JetEta_calib'],
reco_jet['JetPhi_calib'],
reco_jet['JetM_calib']))
#jet_vectors = [ v for v in init_jet_vectors if v.pt > 30 and abs(v.eta) < 4 ]
jet_vectors = init_jet_vectors
if len(jet_vectors) < 1: continue
if jet_vectors[0].pt < 60: continue
#if len(jet_vectors) > 1 and jet_vectors[1].pt < 60: continue
#if len(jet_vectors) == 3:
for v in jet_vectors:
validation_data['JetPt_calib'][input_type].append(v.pt)
validation_data['JetEta_calib'][input_type].append(v.eta)
validation_data['JetPhi_calib'][input_type].append(v.phi)
validation_data['JetM_calib'][input_type].append(v.mass)
def load_aviv(event_generator, input_type, validation_data):
for event in event_generator:
validation_data['eventWeight'][input_type].append(event['eventWeight'])
truth_particles = [tp.copy() for tp in event['truth_particles']]
init_jet_vectors = []
for truth_jet in event['truth_jets']:
v = TLorentzVector.from_ptetaphim(truth_jet['truthjpT'],
truth_jet['truthjeta'],
truth_jet['truthjphi'],
truth_jet['truthjm'])
pdgid = match_aviv_reco_jet3(v, truth_particles)
if pdgid == autils.PDGID['photon']: continue
init_jet_vectors.append(v)
jet_vectors = [
v for v in init_jet_vectors if v.pt > 20 and abs(v.eta) < 4
]
if len(jet_vectors) == 3:
for v in jet_vectors:
validation_data['truthjpT'][input_type].append(v.pt)
validation_data['truthjeta'][input_type].append(v.eta)
validation_data['truthjphi'][input_type].append(v.phi)
validation_data['truthjm'][input_type].append(v.mass)
load_extra(jet_vectors, input_type, validation_data)
def record_aviv_truth_jets(is_signal, input_list, events_to_read, event_data_dump):
tree_name = 'Nominal'
branches = [
'eventWeight',
('truth_particles',
['tpartpdgID', 'tpartstatus', 'tpartpT', 'tparteta', 'tpartphi', 'tpartm']
),
('truth_jets',
['truthjpT', 'truthjeta', 'truthjphi', 'truthjm']
)
]
for event in event_iterator(input_list, tree_name, branches, events_to_read):
# Loop over truth jets and convert them into generic acorn_jet objects
truth_particles = [ tp.copy() for tp in event['truth_particles'] ]
recorded_jets = []
for tj in event['truth_jets']:
v = TLorentzVector.from_ptetaphim(tj['truthjpT'], tj['truthjeta'], tj['truthjphi'], tj['truthjm'])
pdgid = match_aviv_reco_jet(v, truth_particles)
# Create jet object storing the essential aspects of the ntuple truth jet,
# faking some of the data normally associated with reco jets
new_jet = acorn_jet(v, pdgid, False, True, True, -1, None, [])
recorded_jets.append(new_jet)
# Categorize event, and then either discard the event or perform tagging on it
for category in event_data_dump.values():
category.add_event(recorded_jets, is_signal, event['eventWeight'])
def smart_total_invariant_mass(vectors):
num_candidates = len(vectors)
mjNs = []
for num_jets in range(2, num_candidates + 1):
for vec_collecton in itertools.combinations(vectors, num_jets):
total_vector = TLorentzVector(0, 0, 0, 0)
for vec in vec_collecton:
total_vector += vec
mjNs.append(total_vector.mass)
return max(mjNs)
def get_pdgID(vector_to_match, truth_particles):
for tp in truth_particles:
if tp['tpartpdgID'] == autils.PDG['photon']:
if tp['tpartstatus'] != autils.Status['photon_out']: continue
elif tp['tpartstatus'] != autils.Status['outgoing']: continue
truth_vec = TLorentzVector.from_ptetaphim(tp['tpartpT'], tp['tparteta'], tp['tpartphi'], tp['tpartm'])
deltaR = vector_to_match.delta_r(truth_vec)
if deltaR < 0.3: return tp['tpartpdgID']
return -1
def get_pdgID_raw(vector_to_match, truth_particles ):
for tp in truth_particles:
pt, eta, phi, m, pdgid, status = tp
if pdgid == autils.PDG['photon']:
if status != autils.Status['photon_out']: continue
elif status != autils.Status['outgoing']: continue
truth_vec = TLorentzVector.from_ptetaphim(pt, eta, phi, m)
deltaR = vector_to_match.delta_r(truth_vec)
if deltaR < 0.3: return pdgid
return -1
def make_data_tuple(event):
vector_list = []
for jet in event['jets']:
vec = LV.from_ptetaphie(
jet['vbf_candidates_pT'],
jet['vbf_candidates_eta'],
jet['vbf_candidates_phi'],
jet['vbf_candidates_E']
)
vector_list.append(vec)
return( prepare_tuple(vector_list) )
def count_tjets_with_truthj(event, truth_particles):
num_tjets = 0
num_tquarks = 0
for tj in autils.jet_iterator(event['truthj']):
v = TLorentzVector.from_ptetaphim(tj['truthjpT'], tj['truthjeta'], tj['truthjphi'], tj['truthjm'])
pdg = match_jet(v, truth_particles)
if pdg == autils.PDG['photon']: continue
if pdg < 0: continue
if tj['truthjpT'] < _pt_cut: continue
num_tjets += 1
if pdg in autils.PDG['quarks']: num_tquarks += 1
return num_tjets, num_tquarks
def _rec_build(nid, node):
branches = [
node.harder, node.softer
] if DGLGraphDatasetLund.fill_secondary else [node.harder]
for branch in branches:
if branch is None or branch.lundCoord is None:
# stop when reaching the leaf nodes
# we do not add the leaf nodes to the graph/tree as they do not have Lund coordinates
continue
cid = g.number_of_nodes()
if DGLGraphDatasetLund.node_coordinates == 'lund':
spatialCoord = branch.lundCoord.state()[:2]
else:
node_p4 = TLorentzVector(*branch.node)
spatialCoord = np.array([
delta_eta_reflect(node_p4, jet_p4),
node_p4.delta_phi(jet_p4)
],
dtype='float32')
g.add_node(cid,
coordinates=spatialCoord,
features=branch.lundCoord.state())
g.add_edge(cid, nid)
_rec_build(cid, branch)
def count_rjets(event, truth_particles):
num_rjets = 0
num_rquarks = 0
for rj in autils.jet_iterator(event['j0']):
v = TLorentzVector.from_ptetaphim(rj['j0pT'], rj['j0eta'], rj['j0phi'], rj['j0m'])
pdg = match_jet(v, truth_particles)
if pdg == autils.PDG['photon']: continue
#if rj['j0_isTightPhoton']: continue
if rj['j0pT'] < _pt_cut: continue
if not (rj['j0_JVT'] and rj['j0_fJVT_Tight']): continue
num_rjets += 1
if pdg in autils.PDG['quarks']: num_rquarks += 1
return num_rjets, num_rquarks
def _build_tree(self, root):
g = nx.Graph()
jet_p4 = TLorentzVector(*root.node)
def _rec_build(nid, node):
branches = [
node.harder, node.softer
] if DGLGraphDatasetLund.fill_secondary else [node.harder]
for branch in branches:
if branch is None or branch.lundCoord is None:
# stop when reaching the leaf nodes
# we do not add the leaf nodes to the graph/tree as they do not have Lund coordinates
continue
cid = g.number_of_nodes()
if DGLGraphDatasetLund.node_coordinates == 'lund':
spatialCoord = branch.lundCoord.state()[:2]
else:
node_p4 = TLorentzVector(*branch.node)
spatialCoord = np.array([
delta_eta_reflect(node_p4, jet_p4),
node_p4.delta_phi(jet_p4)
],
dtype='float32')
g.add_node(cid,
coordinates=spatialCoord,
features=branch.lundCoord.state())
g.add_edge(cid, nid)
_rec_build(cid, branch)
# add root
if root.lundCoord is not None:
if DGLGraphDatasetLund.node_coordinates == 'lund':
spatialCoord = root.lundCoord.state()[:2]
else:
spatialCoord = np.zeros(2, dtype='float32')
g.add_node(0,
coordinates=spatialCoord,
features=root.lundCoord.state())
_rec_build(0, root)
else:
# when a jet has only one particle (?)
g.add_node(0,
coordinates=np.zeros(2, dtype='float32'),
features=np.zeros(LundCoordinates.dimension,
dtype='float32'))
ret = dgl.from_networkx(g, node_attrs=['coordinates', 'features'])
# print(ret.number_of_nodes())
return ret
def prepare_events(cls, event_selections_tuple_list, label_list):
organized_data = {key: [] for key in cls.input_keys}
for event, selections in event_selections_tuple_list:
E_list = []
pt_list = []
eta_list = []
phi_list = []
Deta_list = []
mjj_list = []
total_vector = TLorentzVector(0, 0, 0, 0)
for jet_index, jet in enumerate(event.jets):
E_list.append(jet.vector.energy)
pt_list.append(jet.vector.pt)
eta_list.append(jet.vector.eta)
phi_list.append(jet.vector.phi)
for other_jet in event.jets[jet_index + 1:]:
Deta = abs(jet.vector.eta - other_jet.vector.eta)
mjj = (jet.vector + other_jet.vector).mass
Deta_list.append(Deta)
mjj_list.append(mjj)
total_vector += jet.vector
mjj_list.append(total_vector.mass)
organized_data['E'].append(E_list)
organized_data['pt'].append(pt_list)
organized_data['eta'].append(eta_list)
organized_data['phi'].append(phi_list)
organized_data['Deta'].append(Deta_list)
organized_data['mjj'].append(mjj_list)
if label_list != None: label_list.append(cls.get_label(event))
prepared_data = {
key: numpy.array(val)
for key, val in organized_data.items()
}
return prepared_data
def vector(self):
return TLorentzVector.from_ptetaphim(*self.floats[:4])
def draw(input_type):
correct_pt = 0
correct_mjj = 0
contains_leading_quark_pt = 0
contains_leading_quark_mjj = 0
tagged_pt = 0
tagged_mjj = 0
events_with_3_jets = 0
pt_based_jets = 0
mjj_based_jets = 0
ntuple_type = 'aviv' #sys.argv[1]
branch_list = _branch_options[ntuple_type]
tree_name = _tree_options[ntuple_type]
input_list = _input_type_options[ntuple_type][input_type]
for event in event_iterator(input_list, tree_name, branch_list, _Nevents):
particle_list = []
for truth_particle in event['truth_particles']:
if truth_particle['tpartpdgID'] == autils.PDGID['higgs']: continue
v = TLorentzVector.from_ptetaphim(truth_particle['tpartpT'],
truth_particle['tparteta'],
truth_particle['tpartphi'],
truth_particle['tpartm'])
particle_list.append((v, truth_particle['tpartpdgID'],
truth_particle['tpartstatus']))
#if truth_particle['tpartpdgID'] == 22 and truth_particle['tpartstatus'] not in (1,23): print(truth_particle['tpartstatus'])
jet_list = []
#for jet in event['truth_jets']:
num_dual_matched = 0
for jet in event['reco_jets']:
if not (jet['j0_JVT'] and jet['j0_fJVT_Tight']): continue
#v = TLorentzVector.from_ptetaphim(jet['truthjpT'], jet['truthjeta'], jet['truthjphi'], jet['truthjm'])
v = TLorentzVector.from_ptetaphim(jet['j0pT'], jet['j0eta'],
jet['j0phi'], jet['j0m'])
if v.pt < 30 or abs(v.eta) > 4: continue
pdgid, dual_matched = match_jet(v, particle_list)
if pdgid == autils.PDGID['photon']: continue
jet_list.append((v, pdgid in autils.PDGID['quarks']))
if not passes_cuts(input_type, jet_list): continue
quark_jets = [jet for jet in jet_list if jet[1]]
#print(jet_list)
events_with_3_jets += 1
# Leading Pt
pt_chosen_jets = jet_list[:2]
if input_type == 'sig':
correct_jets, has_leading_pt = check_if_signature_jets(
pt_chosen_jets, quark_jets[0])
correct_pt += int(correct_jets)
contains_leading_quark_pt += int(has_leading_pt)
if (pt_chosen_jets[0][0] + pt_chosen_jets[1][0]).mass > 500:
pt_based_jets += 1
#tagged_pt += int( (pt_chosen_jets[0][0] + pt_chosen_jets[1][0]).mass > 252 )
tagged_pt += int(
(pt_chosen_jets[0][0] + pt_chosen_jets[1][0]).mass > 700)
# Maximized Mjj
mass_pairs = sorted([((i[0] + j[0]).mass, [i, j])
for i, j in combinations(jet_list, 2)],
reverse=True,
key=lambda t: t[0])
mjj_chosen_jets = mass_pairs[0][1]
if input_type == 'sig':
correct_jets, has_leading_pt = check_if_signature_jets(
mjj_chosen_jets, quark_jets[0])
correct_mjj += int(correct_jets)
contains_leading_quark_mjj += int(has_leading_pt)
if (mjj_chosen_jets[0][0] + mjj_chosen_jets[1][0]).mass > 500:
mjj_based_jets += 1
#tagged_mjj += int( (mjj_chosen_jets[0][0] + mjj_chosen_jets[1][0]).mass > 310 )
tagged_mjj += int(
(mjj_chosen_jets[0][0] + mjj_chosen_jets[1][0]).mass > 700)
num_jets = events_with_3_jets
print()
print(num_jets)
if input_type == 'sig':
print('{}, {}, {:.02}, {:.02} | {:.02}, {:.02}'.format(
correct_pt, correct_mjj, correct_pt / num_jets,
correct_mjj / num_jets, contains_leading_quark_pt / num_jets,
contains_leading_quark_mjj / num_jets))
print('{}, {} | {}, {} | {:.02}, {:.02}'.format(
tagged_pt, pt_based_jets, tagged_mjj, mjj_based_jets,
tagged_pt / pt_based_jets, tagged_mjj / mjj_based_jets))
marker='.',
color='green')
#_plots['rocs'].add_marker('mjjmax', 1000, annotation='1000 GeV', marker='.', color='blue')
#_plots['rocs_2jet'].add_marker('mjjSL', 735, annotation='735 GeV', marker='*', color='red')
#_plots['rocs_3jet'].add_marker('mjjSL', 735, annotation='735 GeV', marker='*', color='red')
_output_branches = [
'run_number', 'event_number', 'mc_sf', 'ntag', 'njets', 'n_vbf_candidates',
('jets', [
'vbf_candidates_E', 'vbf_candidates_pT', 'vbf_candidates_eta',
'vbf_candidates_phi'
])
]
_output_branches += [f'FoxWolfram{i}' for i in _fw_moments]
make_reco_vector = lambda jet: LV.from_ptetaphie(jet['resolvedJets_pt'], jet[
'resolvedJets_eta'], jet['resolvedJets_phi'], jet['resolvedJets_E'])
make_nano_vector = lambda jet: LV.from_ptetaphie(jet['vbf_candidates_pT'], jet[
'vbf_candidates_eta'], jet['vbf_candidates_phi'], jet['vbf_candidates_E'])
def process_events(events, skip_num=0, bgd=False, cvv_value=-1):
basic_efficiency_count = [0, 0, 0]
num_jets = [0] * 20
num_shared = 0
num_not_shared = 0
num_pt_matched = 0
num_pt_not_matched = 0
num_negative_weighted = 0
for event_index, event in enumerate(events):
if event_index < skip_num: continue
weight = event['mc_sf'][0]
def total_invariant_mass(event):
total_vector = TLorentzVector(0,0,0,0)
for jet in event.jets: total_vector += jet.vector()
return total_vector.mass
'nresolvedJets',
(
'resolved_jets',
[
'resolvedJets_pt',
'resolvedJets_phi',
'resolvedJets_eta',
'resolvedJets_E', #resolved pt in GeV
'resolvedJets_HadronConeExclTruthLabelID',
'resolvedJets_is_DL1r_FixedCutBEff_77'
])
]
_output_branches = ['event_number']
make_reco_vector = lambda jet: LV.from_ptetaphie(jet['resolvedJets_pt'], jet[
'resolvedJets_eta'], jet['resolvedJets_phi'], jet['resolvedJets_E'])
def get_vbf_jet_info(non_b_tagged_jets):
vbf_jet_info = {'x': [], 'y': [], 'c': []}
if len(non_b_tagged_jets) > 1:
mjj_pair = max([
((pair[0] + pair[1]).mass, pair)
for pair in itertools.combinations(non_b_tagged_jets, 2)
])[1]
color = 'white' if abs(mjj_pair[0].eta -
mjj_pair[1].eta) > 3 else 'black'
vbf_jet_info['x'] = [mjj_pair[0].eta, mjj_pair[1].eta]
vbf_jet_info['y'] = [mjj_pair[0].phi, mjj_pair[1].phi]
vbf_jet_info['c'] = [color, color]
return vbf_jet_info
def total_invariant_mass(vectors):
total_vector = TLorentzVector(0, 0, 0, 0)
for vec in vectors:
total_vector += vec
return total_vector.mass
def draw(input_type):
correct_pt = 0
correct_mjj = 0
tagged_pt = 0
tagged_mjj = 0
events_with_3_jets = 0
ntuple_type = 'aviv' #sys.argv[1]
branch_list = _branch_options[ntuple_type]
tree_name = _tree_options[ntuple_type]
input_list = _input_type_options[ntuple_type][input_type]
total_events_read_in = 0
for basket in basket_generator(input_list, tree_name, branch_list):
for event_index in range(len(basket[b'eventWeight'])):
total_events_read_in += 1
if total_events_read_in > _Nevents: break
particle_list = []
for particle_index in range(len(
basket[b'tpartpdgID'][event_index])):
if basket[b'tpartpdgID'][event_index][
particle_index] == autils.PDGID['higgs']:
continue
v = TLorentzVector.from_ptetaphim(
basket[b'tpartpT'][event_index][particle_index],
basket[b'tparteta'][event_index][particle_index],
basket[b'tpartphi'][event_index][particle_index],
basket[b'tpartm'][event_index][particle_index])
particle_list.append(
(v, basket[b'tpartpdgID'][event_index][particle_index],
basket[b'tpartstatus'][event_index][particle_index]))
jet_list = []
#for jet in event['truth_jets']:
num_dual_matched = 0
for jet_index in range(len(basket[b'j0truthid'][event_index])):
if not (basket[b'j0_JVT'][event_index][jet_index]
and basket[b'j0_fJVT_Tight'][event_index][jet_index]):
continue
#v = TLorentzVector.from_ptetaphim(basket[b'truthjpT'][event_index][jet_index], basket[b'truthjeta'][event_index][jet_index], basket[b'truthjphi'][event_index][jet_index], basket[b'truthjm'][event_index][jet_index])
v = TLorentzVector.from_ptetaphim(
basket[b'j0pT'][event_index][jet_index],
basket[b'j0eta'][event_index][jet_index],
basket[b'j0phi'][event_index][jet_index],
basket[b'j0m'][event_index][jet_index])
if v.pt < 30 or abs(v.eta) > 4: continue
pdgid = match_jet(v, particle_list)
if pdgid == autils.PDGID['photon']: continue
jet_list.append((v, pdgid in autils.PDGID['quarks']))
#pt_ordered_jets = sorted(jet_list, key=lambda j: j[0].pt, reverse=True)
if not passes_cuts(input_type, jet_list): continue
events_with_3_jets += 1
# Leading Pt
#pt_chosen_jets = pt_ordered_jets[:2]
pt_chosen_jets = jet_list[:2]
if input_type == 'sig':
correct_jets = check_if_signature_jets(pt_chosen_jets)
correct_pt += int(correct_jets)
tagged_pt += int(
(pt_chosen_jets[0][0] + pt_chosen_jets[1][0]).mass > 252)
# Maximized Mjj
mass_pairs = sorted([((i[0] + j[0]).mass, [i, j])
for i, j in combinations(jet_list, 2)],
reverse=True,
key=lambda t: t[0])
mjj_chosen_jets = mass_pairs[0][1]
if input_type == 'sig':
correct_jets = check_if_signature_jets(mjj_chosen_jets)
correct_mjj += int(correct_jets)
tagged_mjj += int(
(mjj_chosen_jets[0][0] + mjj_chosen_jets[1][0]).mass > 310)
if total_events_read_in > _Nevents: break
num_jets = events_with_3_jets
print()
print(num_jets)
if input_type == 'sig':
print('{}, {}, {:.02}, {:.02}'.format(correct_pt, correct_mjj,
correct_pt / num_jets,
correct_mjj / num_jets))
print('{}, {}, {:.02}, {:.02}'.format(tagged_pt, tagged_mjj,
tagged_pt / num_jets,
tagged_mjj / num_jets))
def test(input_type):
input_list = _input_type_options[input_type]
final = []
for ntuple_file in input_list:
print('\nnutple file: ' + ntuple_file)
tree = uproot.rootio.open(ntuple_file)['Nominal']
tree_iterator = tree.iterate(branches=branch_list, entrysteps=10000)
for basket_number, basket in enumerate(tree_iterator):
for i in range(Nevents):
tps = []
for j in range( len(basket[b'tpartpT'][i]) ):
Tpt = basket[b'tpartpT'][i][j]
Teta = basket[b'tparteta'][i][j]
Tphi = basket[b'tpartphi'][i][j]
Tm = basket[b'tpartm'][i][j]
TpdgID = basket[b'tpartpdgID'][i][j]
Tstatus = basket[b'tpartstatus'][i][j]
tps.append( (Tpt, Teta, Tphi, Tm, TpdgID, Tstatus) )
vs = []
tmp = []
for j in range( len(basket[b'j0pT'][i]) ):
if not ( basket[b'j0pT'][i][j] > 30 and abs(basket[b'j0eta'][i][j]) < 4 ): continue
if not ( basket[b'j0_JVT'][i][j] and basket[b'j0_fJVT_Tight'][i][j]): continue
v = TLorentzVector.from_ptetaphim(basket[b'j0pT'][i][j], basket[b'j0eta'][i][j], basket[b'j0phi'][i][j], basket[b'j0m'][i][j])
ID = get_pdgID_raw(v,tps)
tmp.append(ID)
#ID = rj['j0truthid']
if ID != 22:
#if not basket[b'j0_isTightPhoton'][i][j]:
vs.append( ID in range(1,7) )
if len(vs) == 3 and vs.count(1) == 2: final.append( vs[0] and vs[1] )
break
break
print()
print( final.count(1) / len(final) )
final = []
for event in event_iterator(input_list, 'Nominal', _branch_list, Nevents):
#tp = list(event['truth_particles'])
tps = [ tp.copy() for tp in event['truth_particles'] ]
#for tp in event['truth_particles']: pass
vs = []
tmp = []
for rj in event['reco_jets']:
if not (rj['j0pT'] > 30 and abs(rj['j0eta']) < 4 ): continue
if not (rj['j0_JVT'] and rj['j0_fJVT_Tight']): continue
v = TLorentzVector.from_ptetaphim(rj['j0pT'], rj['j0eta'], rj['j0phi'], rj['j0m'])
ID = get_pdgID(v,tps)
#print()
tmp.append(ID)
if ID != 22:
#if not rj['j0_isTightPhoton']:
vs.append( ID in range(1,7) )
if len(vs) == 3 and vs.count(1) == 2: final.append( vs[0] and vs[1] )
print( final.count(1) / len(final) )
def lifetime_weight(pf, fake = True):
print("Adding lifetime weight branch...")
if fake:
ctau_weight_central = np.ones(len(pf))
ctau_weight_up = np.ones(len(pf))
ctau_weight_down = np.ones(len(pf))
pf['ctau_weight_central'] = ctau_weight_central
pf['ctau_weight_up'] = ctau_weight_up
pf['ctau_weight_down'] = ctau_weight_down
return pf
else:
Bc_mass = 6.274
ctau_pdg = 0.510e-12 * speed_of_light * 1000. # in mm
ctau_actual = 0.1358
ctau_up = (0.510+0.009)*1e-12 * speed_of_light * 1000. # in mm
ctau_down = (0.510-0.009)*1e-12 * speed_of_light * 1000. # in mm
ctau_weight_central = []
ctau_weight_up = []
ctau_weight_down = []
for i in range(len(pf)):
flag = 0
#jpsi vertex
if( abs(pf.mu1_mother_pdgId[i]) == 443 ):
jpsi_vertex = TVector3(pf.mu1_mother_vx[i],pf.mu1_mother_vy[i],pf.mu1_mother_vz[i])
elif( abs(pf.mu2_mother_pdgId[i]) == 443 ):
jpsi_vertex = TVector3(pf.mu2_mother_vx[i],pf.mu2_mother_vy[i],pf.mu2_mother_vz[i])
else:
flag = 1
#Bc vertex
if(abs(pf.mu1_grandmother_pdgId[i]) == 541):
Bc_vertex = TVector3(pf.mu1_grandmother_vx[i],pf.mu1_grandmother_vy[i],pf.mu1_grandmother_vz[i])
Bc_p4 = TLorentzVector.from_ptetaphim(pf.mu1_grandmother_pt[i],pf.mu1_grandmother_eta[i],pf.mu1_grandmother_phi[i],Bc_mass)
elif(abs(pf.mu2_grandmother_pdgId[i]) == 541):
Bc_vertex = TVector3(pf.mu2_grandmother_vx[i],pf.mu2_grandmother_vy[i],pf.mu2_grandmother_vz[i])
Bc_p4 = TLorentzVector.from_ptetaphim(pf.mu2_grandmother_pt[i],pf.mu2_grandmother_eta[i],pf.mu2_grandmother_phi[i],Bc_mass)
else:
flag = 1
if(flag == 1):
ctau_weight_central.append(1)
ctau_weight_up.append (1)
ctau_weight_down.append(1)
else:
# distance
lxyz = (jpsi_vertex - Bc_vertex).mag
beta = Bc_p4.beta
gamma = Bc_p4.gamma
ct = lxyz/(beta * gamma)
#print(lxyz,beta,gamma,ct)
ctau_weight_central.append( weight_to_new_ctau(ctau_actual, ctau_pdg , ct*10.))
ctau_weight_up.append (weight_to_new_ctau(ctau_actual, ctau_up , ct*10.))
ctau_weight_down.append(weight_to_new_ctau(ctau_actual, ctau_down, ct*10.))
pf['ctau_weight_central'] = ctau_weight_central
pf['ctau_weight_up'] = ctau_weight_up
pf['ctau_weight_down'] = ctau_weight_down
return pf
import itertools
from uproot_methods import TLorentzVector as LV
_fourvec_names = [ f'vbf_candidates_{v}' for v in ['pT', 'eta', 'phi', 'E'] ]
make_vector_list = lambda datarow: [ LV.from_ptetaphie(*vec) for vec in zip(*datarow[_fourvec_names]) ]
def valid_vbf(datarow):
vector_list = make_vector_list(datarow)
Deta = max([ ( (i+j).mass, abs(i.eta-j.eta) ) for i,j in itertools.combinations(vector_list, 2) ])[1]
return Deta > 3
def get_features_mjj_deta(datarow):
vector_list = make_vector_list(datarow)
pair_list = [ (i,j) for i,j in itertools.combinations(vector_list, 2) ]
if len(pair_list) > 0:
mjj_deta_pair_list = [ ( (i+j).mass, abs(i.eta-j.eta) ) for i,j in pair_list]
mjj_deta_pair_list.sort(reverse=True)
prepared_features = [
mjj_deta_pair_list[0][0],
mjj_deta_pair_list[0][1]
]
else:
prepared_features = [-1,-1]
return prepared_features
