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) )
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]
'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
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