forked from LPC-DM/CoffeaMaker
-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathnano_zpeak_uproot.py
160 lines (123 loc) · 7.71 KB
/
nano_zpeak_uproot.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
import uproot, uproot_methods
import numpy as np
import fnal_column_analysis_tools
from histbook import *
from fnal_column_analysis_tools.analysis_objects import JaggedCandidateArray
from awkward import JaggedArray, Table
#from vega import VegaLite as canvas # for ipyvega in Jupyter Notebook
import vegascope; canvas = vegascope.LocalCanvas()
extractor = fnal_column_analysis_tools.lookup_tools.extractor()
extractor.add_weight_sets(['* * eleTrig.root','* * muon_trig_Run2016BtoF.root'])
extractor.finalize()
evaluator = extractor.make_evaluator()
hist1 = Hist(bin("de_mass",50,0,200),weight='e_weight')
hist2 = Hist(bin("dm_mass",50,0,200),weight='m_weight')
f = "nano_5.root"
met_trigger_paths = ["HLT_PFMET170_NoiseCleaned",
"HLT_PFMET170_HBHECleaned",
"HLT_PFMET170_JetIdCleaned",
"HLT_PFMET170_NotCleaned",
#"HLT_PFMET170_HBHE_BeamHaloCleaned",
#"HLT_PFMETNoMu120_NoiseCleaned_PFMHTNoMu120_IDTight",
#"HLT_PFMETNoMu110_NoiseCleaned_PFMHTNoMu110_IDTight",
#"HLT_PFMETNoMu90_NoiseCleaned_PFMHTNoMu90_IDTight",
"HLT_PFMETNoMu90_PFMHTNoMu90_IDTight",
"HLT_PFMETNoMu100_PFMHTNoMu100_IDTight",
"HLT_PFMETNoMu110_PFMHTNoMu110_IDTight",
"HLT_PFMETNoMu120_PFMHTNoMu120_IDTight"]
met_trigger_columns = {path:path for path in met_trigger_paths}
singleele_trigger_paths = [#"HLT_Ele27_WP85_Gsf",
"HLT_Ele27_WPLoose_Gsf",
"HLT_Ele105_CaloIdVT_GsfTrkIdT",
"HLT_Ele27_WPTight_Gsf",
#"HLT_Ele30_WPTight_Gsf",
"HLT_Ele27_eta2p1_WPTight_Gsf",
"HLT_Ele32_eta2p1_WPTight_Gsf",
"HLT_Ele35_WPLoose_Gsf",
"HLT_ECALHT800"]
singleele_trigger_columns = {path:path for path in singleele_trigger_paths}
singlephoton_trigger_paths = ["HLT_Photon175",
#"HLT_Photon200",
"HLT_Photon165_HE10",
"HLT_Photon36_R9Id90_HE10_IsoM",
"HLT_Photon50_R9Id90_HE10_IsoM",
"HLT_Photon75_R9Id90_HE10_IsoM",
"HLT_Photon90_R9Id90_HE10_IsoM",
"HLT_Photon120_R9Id90_HE10_IsoM",
"HLT_Photon165_R9Id90_HE10_IsoM",
"HLT_Photon300_NoHE",
"HLT_ECALHT800",
"HLT_CaloJet500_NoJetID"]
singlephoton_trigger_columns = {path:path for path in singlephoton_trigger_paths}
electron_columns = {'pt':'Electron_pt','eta':'Electron_eta','phi':'Electron_phi','mass':'Electron_mass','iso':'Electron_pfRelIso03_all','dxy':'Electron_dxy','dz':'Electron_dz','id':'Electron_mvaSpring16GP_WP90'}
muon_columns = {'pt':'Muon_pt','eta':'Muon_eta','phi':'Muon_phi','mass':'Muon_mass','iso':'Muon_pfRelIso04_all','dxy':'Muon_dxy','dz':'Muon_dz'}
jet_columns = {'pt':'Jet_pt','eta':'Jet_eta','phi':'Jet_phi','mass':'Jet_mass','id':'Jet_jetId'}
tau_columns = {'pt':'Tau_pt','eta':'Tau_eta','phi':'Tau_phi','mass':'Tau_mass','decayMode':'Tau_idDecayMode','decayModeNew':'Tau_idDecayModeNewDMs','id':'Tau_idMVAnew'} # (idmVAnewDM does not exist in my file) idMVAnew
photon_columns = {'pt':'Photon_pt','eta':'Photon_eta','phi':'Photon_phi','mass':'Photon_mass',}
all_columns = [electron_columns,muon_columns,jet_columns,photon_columns,met_trigger_columns,singleele_trigger_columns,singlephoton_trigger_columns,tau_columns]
columns = []
for cols in all_columns: columns.extend(list(cols.values()))
for arrays in uproot.iterate('root_files/*.root','Events',columns,entrysteps=10000000000:
# initialize phyisics objects
triggers = {'MET':np.prod([arrays[val] for val in met_trigger_columns], axis=0),
'SingleEle':np.prod([arrays[val] for val in singleele_trigger_columns],axis=0),
'SinglePhoton':np.prod([arrays[val] for val in singlephoton_trigger_columns],axis=0)
}
electrons = JaggedCandidateArray.candidatesfromcounts(arrays[electron_columns['pt']].counts, **{key:arrays[val].content for key,val in electron_columns.items()})
muons = JaggedCandidateArray.candidatesfromcounts(arrays[muon_columns['pt']].counts, **{key:arrays[val].content for key,val in muon_columns.items()})
taus = JaggedCandidateArray.candidatesfromcounts(arrays[tau_columns['pt']].counts, **{key:arrays[val].content for key,val in tau_columns.items()})
photons = JaggedCandidateArray.candidatesfromcounts(arrays[photon_columns['pt']].counts, **{key:arrays[val].content for key,val in photon_columns.items()})
jets = JaggedCandidateArray.candidatesfromcounts(arrays[jet_columns['pt']].counts, **{key:arrays[val].content for key,val in jet_columns.items()})
# end initialize
# physical objects selection
loose_electron_selection = (electrons.pt>7)&(abs(electrons.eta)<2.4)&(abs(electrons.dxy)<0.05)&(abs(electrons.dz)<0.2)&(electrons.iso<0.4)&(electrons.id)
loose_muon_selection = (muons.pt>5)&(abs(muons.eta)<2.4)&(abs(muons.dxy)<0.5)&(abs(muons.dz)<1.0)&(muons.iso<0.4)
loose_photon_selection = (photons.pt>15)*(abs(photons.eta)<2.5)
tau_selection = (taus.pt>18)&(abs(taus.eta)<2.3)&(taus.decayMode)&((taus.id&2)!=0)
jet_selection = (jets.pt>25)&(abs(jets.eta)<4.5)&((jets.id&2)!=0)
loose_electrons = electrons[loose_electron_selection]
loose_muons = muons[loose_muon_selection]
loose_photons = photons[loose_photon_selection]
selected_taus = taus[tau_selection]
selected_jets = jets[jet_selection]
# end seletion
# clean leptons
e_combinations = loose_electrons.p4.cross(selected_jets.p4, nested=True)
mask = (e_combinations.i0.delta_r(e_combinations.i1) < 0.3 ).any()
clean_electrons = loose_electrons[~mask]
m_combinations = loose_muons.p4.cross(selected_jets.p4, nested=True)
mask = (m_combinations.i0.delta_r(m_combinations.i1) < 0.3).any()
clean_muons = loose_muons[mask]
clean_leptons = JaggedArray.fromiter([clean_electrons, clean_muons])
# once merge is done
# mask = loose_electrons.p4.match(selected_jets.p4, 0.3)
# clean electrons = loose_electrons[~mask]
# end cleaning
# weights evaluation
e_counts = clean_electrons.counts
e_sfTrigg = np.ones(clean_electrons.size)
e_sfTrigg[e_counts>0] = 1 - evaluator["hEffEtaPt"](clean_electrons.eta[e_counts>0,0], clean_electrons.pt[e_counts > 0,0])
e_sfTrigg[e_counts > 1] = 1- (1- evaluator["hEffEtaPt"](clean_electrons.eta[e_counts>1,0], clean_electrons.pt[e_counts > 1,0]))*(1- evaluator["hEffEtaPt"](clean_electrons.eta[e_counts>1,1], clean_electrons.pt[e_counts > 1,1]))
m_counts = clean_muons.counts
m_sfTrigg = np.ones(clean_muons.size)
m_sfTrigg[m_counts>0] = evaluator["IsoMu24_OR_IsoTkMu24_PtEtaBins/efficienciesDATA/pt_abseta_DATA"](clean_muons.eta[m_counts>0,0], clean_muons.pt[m_counts > 0,0])
m_sfTrigg[m_counts > 1] = 1- (1- evaluator["IsoMu24_OR_IsoTkMu24_PtEtaBins/efficienciesDATA/pt_abseta_DATA"](clean_muons.eta[m_counts>1,0], clean_muons.pt[m_counts > 1,0]))*(1- evaluator["IsoMu24_OR_IsoTkMu24_PtEtaBins/efficienciesDATA/pt_abseta_DATA"](clean_muons.eta[m_counts>1,1], clean_muons.pt[m_counts > 1,1]))
# end weights
# find dileptons
dielectrons = clean_electrons.distincts()
dielectron_mass = dielectrons.mass.content
e_sfTrigg = e_sfTrigg[clean_electrons.counts>1]
dimuons = clean_muons.distincts() # the function distincts returns a jagged array with the sum of the four momentum of all distinct pairs in the original jagged array
dimuon_mass = dimuons.mass.content
m_sfTrigg = m_sfTrigg[clean_muons.counts>1]
# end dileptons
# plots
e_weight=e_sfTrigg
m_weight=m_sfTrigg
hist1.fill(de_mass=dielectron_mass,e_weight=e_weight)
hist2.fill(dm_mass=dimuon_mass, m_weight=m_weight)
# end plots
beside(
hist1.step("de_mass"),
hist2.step("dm_mass")).to(canvas)
#print evaluator["hEffEtaPt"](loose_electrons.eta[e_counts>0,0], loose_electrons.pt[e_counts > 0,0])