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relval_nano.py
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2 import math
3 
4 
5 class WFN:
6  def __init__(self, offset):
7  self.offset = offset
8  self.index=0
9  self.subindex=0
10  def __call__(self):
11  if self.subindex==100:
12  print("this is not going to work nicely")
13  self.subindex=0/0
14  r=float(f'{self.offset}.{self.index}{self.subindex:02d}')
15  self.subindex+=1
16  return r
17  def next(self):
18  self.index+=1
19  self.subindex=0
20  def subnext(self):
21  # go to the next tenth for the subindex 10 because of 02d formating
22  self.subindex = math.ceil(self.subindex/10.)*10
23 
24 workflows = Matrix()
25 
26 _runOnly20events={'-n':'20'}
27 _run10kevents={'-n':'10000'}
28 
29 _NANO_data = merge([{'-s':'NANO,DQM:@nanoAODDQM',
30  '--process':'NANO',
31  '--data':'',
32  '--eventcontent':'NANOAOD,DQM',
33  '--datatier':'NANOAOD,DQMIO',
34  '-n':'10000',
35  '--customise':'"Configuration/DataProcessing/Utils.addMonitoring"'
36  }])
37 _HARVEST_nano = merge([{'-s':'HARVESTING:@nanoAODDQM',
38  '--filetype':'DQM',
39  '--filein':'file:step2_inDQM.root',
40  '--conditions':'auto:run2_data'
41  }])
42 _HARVEST_data = merge([_HARVEST_nano, {'--data':''}])
43 
44 
45 run2_lumis={ 277168: [[1, 1708]],
46  277194: [[913, 913], [916, 916], [919, 919], [932, 932], [939, 939]],
47  283877: [[1, 1496]],
48  299649: [[155, 332]],
49  303885: [[60, 2052]],
50  305040: [[200, 700]],
51  305050: [[200, 700]],
52  305234: [[1, 200]],
53  305377: [[1, 500]],
54  315489: [[1, 100]],
55  320822: [[1, 200]],
56  }
57 run3_lumis={}
58 
59 _NANO_mc = merge([{'-s':'NANO,DQM:@nanoAODDQM',
60  '--process':'NANO',
61  '--mc':'',
62  '--eventcontent':'NANOAODSIM,DQM',
63  '--datatier':'NANOAODSIM,DQMIO',
64  '-n':'10000',
65  '--customise':'"Configuration/DataProcessing/Utils.addMonitoring"'
66  }])
67 _HARVEST_mc = merge([_HARVEST_nano, {'--mc':''}])
68 steps['HRV_NANO_mc'] = _HARVEST_mc
69 steps['HRV_NANO_data'] = _HARVEST_data
70 
71 
72 steps['TTbarMINIAOD10.6_UL16v2'] = {'INPUT':InputInfo(location='STD',
73  dataSet='/TTJets_SingleLeptFromT_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM')}
74 steps['NANO_mc10.6ul16v2']=merge([{'--era':'Run2_2016,run2_nanoAOD_106Xv2',
75  '--conditions':'auto:run2_mc'},
76  _NANO_mc])
77 
78 steps['TTbarMINIAOD10.6_UL17v2'] = {'INPUT':InputInfo(location='STD',
79  dataSet='/TTJets_SingleLeptFromT_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM')}
80 steps['NANO_mc10.6ul17v2']=merge([{'--era':'Run2_2017,run2_nanoAOD_106Xv2',
81  '--conditions':'auto:phase1_2017_realistic'},
82  _NANO_mc])
83 
84 steps['TTbarMINIAOD10.6_UL18v2'] = {'INPUT':InputInfo(location='STD',
85  dataSet='/TTJets_SingleLeptFromT_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM')}
86 steps['NANO_mc10.6ul18v2']=merge([{'--era':'Run2_2018,run2_nanoAOD_106Xv2',
87  '--conditions':'auto:phase1_2018_realistic'},
88  _NANO_mc])
89 
90 
91 steps['MuonEG2016MINIAOD10.6v2'] = {'INPUT':InputInfo(location='STD',ls=run2_lumis,
92  dataSet='/MuonEG/Run2016E-HIPM_UL2016_MiniAODv2-v2/MINIAOD')}
93 steps['NANO_data10.6ul16v2']=merge([{'--era':'Run2_2016,run2_nanoAOD_106Xv2,tracker_apv_vfp30_2016',
94  '--conditions':'auto:run2_data'},
95  _NANO_data])
96 
97 steps['MuonEG2017MINIAOD10.6v2'] = {'INPUT':InputInfo(location='STD',ls=run2_lumis,
98  dataSet='/MuonEG/Run2017F-UL2017_MiniAODv2-v1/MINIAOD')}
99 steps['NANO_data10.6ul17v2']=merge([{'--era':'Run2_2017,run2_nanoAOD_106Xv2',
100  '--conditions':'auto:run2_data'},
101  _NANO_data])
102 
103 
104 steps['MuonEG2018MINIAOD10.6v2'] = {'INPUT':InputInfo(location='STD',ls=run2_lumis,
105  dataSet='/MuonEG/Run2018D-UL2018_MiniAODv2-v1/MINIAOD')}
106 steps['NANO_data10.6ul18v2']=merge([{'--era':'Run2_2018,run2_nanoAOD_106Xv2',
107  '--conditions':'auto:run2_data'},
108  _NANO_data])
109 
110 steps['TTbarMINIAOD12.2'] = {'INPUT':InputInfo(location='STD',
111  dataSet='/TTToSemiLeptonic_TuneCP5_13p6TeV-powheg-pythia8/Run3Winter22MiniAOD-FlatPU0to70_122X_mcRun3_2021_realistic_v9-v2/MINIAODSIM')}
112 steps['NANO_mc12.2']=merge([{'--era':'Run3,run3_nanoAOD_122',
113  '--conditions':'auto:phase1_2022_realistic'},
114  _NANO_mc])
115 
116 
117 steps['TTbarMINIAOD12.4'] = {'INPUT':InputInfo(location='STD',
118  dataSet='/TT_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22MiniAODv3-124X_mcRun3_2022_realistic_v12-v3/MINIAODSIM')}
119 steps['NANO_mc12.4']=merge([{'--era':'Run3,run3_nanoAOD_124',
120  '--conditions':'124X_mcRun3_2022_realistic_v12'},
121  _NANO_mc])
122 
123 steps['MuonEG2022MINIAOD12.4'] = {'INPUT':InputInfo(location='STD',ls=run3_lumis,
124  dataSet='/MuonEG/Run2022D-PromptReco-v2/MINIAOD')}
125 steps['NANO_data12.4']=merge([{'--era':'Run3,run3_nanoAOD_124',
126  '--conditions':'auto:run3_data'},
127  _NANO_data])
128 steps['NANO_data12.4_prompt']=merge([{'--customise' : 'PhysicsTools/NanoAOD/nano_cff.nanoL1TrigObjCustomize', '-n' : '1000'},
129  steps['NANO_data12.4']])
130 
131 
132 steps['TTBarMINIAOD13.0'] = {'INPUT':InputInfo(location='STD',
133  dataSet='/RelValTTbar_14TeV/CMSSW_13_0_0-PU_130X_mcRun3_2022_realistic_v2_HS-v4/MINIAODSIM')}
134 
135 steps['NANO_mc13.0']=merge([{'--era':'Run3',
136  '--conditions':'130X_mcRun3_2022_realistic_v2'},
137  _NANO_mc])
138 
139 steps['MuonEG2023MINIAOD13.0'] = { 'INPUT':InputInfo(location='STD',ls=run3_lumis,
140  dataSet='/MuonEG/Run2023C-PromptReco-v4/MINIAOD')}
141 
142 steps['NANO_data13.0']=merge([{'--era':'Run3',
143  '--conditions':'auto:run3_data'},
144  _NANO_data])
145 
146 steps['NANO_data13.0_prompt']=merge([{'--customise' : 'PhysicsTools/NanoAOD/nano_cff.nanoL1TrigObjCustomize', '-n' : '1000'},
147  steps['NANO_data13.0']])
148 
149 
150 steps['TTBarMINIAOD13.2'] = {'INPUT':InputInfo(location='STD',
151 
152  dataSet='/RelValTTbar_14TeV/CMSSW_13_0_0-PU_130X_mcRun3_2022_realistic_v2_HS-v4/MINIAODSIM')}
153 
154 steps['NANO_mc13.2']=merge([{'--era':'Run3',
155  '--conditions':'auto:phase1_2022_realistic'},
156  _NANO_mc])
157 
158 _wfn=WFN(2500)
159 
161 workflows[_wfn()] = ['NANOmc106Xul16v2', ['TTbarMINIAOD10.6_UL16v2','NANO_mc10.6ul16v2', 'HRV_NANO_mc']]
162 workflows[_wfn()] = ['NANOmc106Xul17v2', ['TTbarMINIAOD10.6_UL17v2','NANO_mc10.6ul17v2', 'HRV_NANO_mc']]
163 workflows[_wfn()] = ['NANOmc106Xul18v2', ['TTbarMINIAOD10.6_UL18v2','NANO_mc10.6ul18v2', 'HRV_NANO_mc']]
164 _wfn.subnext()
165 workflows[_wfn()] = ['NANOdata106Xul16v2', ['MuonEG2016MINIAOD10.6v2', 'NANO_data10.6ul16v2', 'HRV_NANO_data']]
166 workflows[_wfn()] = ['NANOdata106Xul17v2', ['MuonEG2017MINIAOD10.6v2', 'NANO_data10.6ul17v2', 'HRV_NANO_data']]
167 workflows[_wfn()] = ['NANOdata106Xul18v2', ['MuonEG2018MINIAOD10.6v2', 'NANO_data10.6ul18v2', 'HRV_NANO_data']]
168 
169 _wfn.next()
170 
172 workflows[_wfn()] = ['NANOmc122Xrun3', ['TTbarMINIAOD12.2','NANO_mc12.2', 'HRV_NANO_mc']]
173 
174 _wfn.next()
175 
177 workflows[_wfn()] = ['NANOmc124Xrun3', ['TTbarMINIAOD12.4','NANO_mc12.4', 'HRV_NANO_mc']]
178 _wfn.subnext()
179 workflows[_wfn()] = ['NANOdata124Xrun3', ['MuonEG2022MINIAOD12.4','NANO_data12.4', 'HRV_NANO_data']]
180 workflows[_wfn()] = ['NANOdata124Xrun3', ['MuonEG2022MINIAOD12.4','NANO_data12.4_prompt', 'HRV_NANO_data']]
181 
182 _wfn.next()
183 
185 workflows[_wfn()] = ['NANOmc130X', ['TTBarMINIAOD13.0', 'NANO_mc13.0', 'HRV_NANO_mc']]
186 _wfn.subnext()
187 workflows[_wfn()] = ['NANOdata130Xrun3', ['MuonEG2023MINIAOD13.0', 'NANO_data13.0', 'HRV_NANO_data']]
188 workflows[_wfn()] = ['NANOdata130Xrun3', ['MuonEG2023MINIAOD13.0', 'NANO_data13.0_prompt', 'HRV_NANO_data']]
189 
190 _wfn.next()
191 
193 workflows[_wfn()] = ['NANOmc132X', ['TTBarMINIAOD13.2', 'NANO_mc13.2', 'HRV_NANO_mc']]
Definition: merge.py:1
def next(self)
Definition: relval_nano.py:17
void print(TMatrixD &m, const char *label=nullptr, bool mathematicaFormat=false)
Definition: Utilities.cc:47
as simple class to number workflows dynamically
Definition: relval_nano.py:5
def __init__(self, offset)
Definition: relval_nano.py:6
portabletest::Matrix Matrix
def subnext(self)
Definition: relval_nano.py:20
def __call__(self)
Definition: relval_nano.py:10