|
655 | 655 | # the files used as input |
656 | 656 |
|
657 | 657 | ###2024 |
658 | | - |
659 | | -pds_2024 = ['BTagMu', 'DisplacedJet', 'EGamma0', 'HcalNZS', 'JetMET0', 'Muon0', 'MuonEG', 'NoBPTX', 'ParkingDoubleMuonLowMass0', 'ParkingHH', 'ParkingLLP', 'ParkingSingleMuon0', 'ParkingVBF0', 'Tau', 'ZeroBias'] |
| 658 | +## N.B. here we use JetMet0 as "starndard" PD and JetMET1 for the TeVJet skims |
| 659 | +pds_2024 = ['BTagMu', 'DisplacedJet', 'EGamma0', 'HcalNZS', 'JetMET0', 'Muon0', 'MuonEG', 'NoBPTX', 'ParkingDoubleMuonLowMass0', 'ParkingHH', 'ParkingLLP', 'ParkingSingleMuon0', 'ParkingVBF0', 'Tau', 'ZeroBias','JetMET1'] |
660 | 660 | eras_2024 = ['Run2024B', 'Run2024C', 'Run2024D', 'Run2024E', 'Run2024F','Run2024G','Run2024H','Run2024I'] |
661 | 661 | for era in eras_2024: |
662 | 662 | for pd in pds_2024: |
663 | | - dataset = "/" + pd + "/" + era + "-v1/RAW" |
| 663 | + dataset = "/" + pd + "/" + era |
| 664 | + skim = '' |
| 665 | + |
| 666 | + if pd == 'JetMET1': |
| 667 | + dataset = dataset + '-TeVJet-PromptReco-v1/RAW-RECO' |
| 668 | + skim = 'TeVJet' |
| 669 | + else: |
| 670 | + dataset = dataset + '-v1/RAW' |
| 671 | + |
664 | 672 | for e_key,evs in event_steps_dict.items(): |
665 | | - step_name = "Run" + pd.replace("ParkingDouble","Park2") + era.split("Run")[1] + "_" + e_key |
| 673 | + step_name = "Run" + pd.replace("ParkingDouble","Park2") + era.split("Run")[1] + skim + "_" + e_key |
666 | 674 | steps[step_name] = {'INPUT':InputInfo(dataSet=dataset,label=era.split("Run")[1],events=int(evs*1e6), skimEvents=True, location='STD')} |
667 | 675 |
|
668 | 676 | ###2023 |
|
710 | 718 |
|
711 | 719 | for era in era_mask_2024: |
712 | 720 | for pd in pds_2024: |
713 | | - dataset = "/" + pd + "/" + era + "-v1/RAW" |
| 721 | + dataset = '/' + pd + '/' + era |
714 | 722 | lm = era_mask_2024[era] |
715 | | - step_name = "Run" + pd.replace("ParkingDouble","Park2") + era.split("Run")[1] |
716 | | - steps[step_name]={'INPUT':InputInfo(dataSet=dataset,label=era.split("Run")[1],events=100000,location='STD', ls=lm)} |
| 723 | + |
| 724 | + ## Here we use JetMET1 PD to run the TeVJet skims |
| 725 | + dataset = dataset + '-TeVJet-PromptReco-v1/RAW-RECO' if pd == 'JetMET1' else dataset + '-v1/RAW' |
| 726 | + skim = 'TeVJet' if pd == 'JetMET1' else '' |
| 727 | + |
| 728 | + step_name = 'Run' + pd.replace('ParkingDouble','Park2') + era.split('Run')[1] + skim |
| 729 | + |
| 730 | + steps[step_name]={'INPUT':InputInfo(dataSet=dataset,label=era.split('Run')[1],events=100000,location='STD', ls=lm)} |
717 | 731 |
|
718 | 732 |
|
719 | 733 | ################################################################## |
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