## "CMS_ttHl_thu_shape_ttH_x1Down", ## "CMS_ttHl_thu_shape_ttH_y1Up", ## "CMS_ttHl_thu_shape_ttH_y1Down", ## "CMS_ttHl_thu_shape_ttW_x1Up", ## "CMS_ttHl_thu_shape_ttW_x1Down", ## "CMS_ttHl_thu_shape_ttW_y1Up", ## "CMS_ttHl_thu_shape_ttW_y1Down", ## "CMS_ttHl_thu_shape_ttZ_x1Up", ## "CMS_ttHl_thu_shape_ttZ_x1Down", ## "CMS_ttHl_thu_shape_ttZ_y1Up", ## "CMS_ttHl_thu_shape_ttZ_y1Down" ], max_files_per_job = 20, era = ERA, use_lumi = True, lumi = LUMI, debug = False, running_method = "sbatch", num_parallel_jobs = 4, executable_addBackgrounds = "addBackgrounds", executable_addBackgroundJetToTauFakes = "addBackgroundLeptonFakes", # CV: use common executable for estimating jet->lepton and jet->tau_h fake background histograms_to_fit = [ "EventCounter", "numJets", "mvaOutput_1l_2tau_ttbar_TMVA", "mvaOutput_1l_2tau_ttbar_sklearn", "mTauTauVis" ], select_rle_output = True) analysis.create() run_analysis = query_yes_no("Start jobs ?") if run_analysis: analysis.run() else: sys.exit(0)
## "CMS_ttHl_thu_shape_ttZ_y1Up", ## "CMS_ttHl_thu_shape_ttZ_y1Down", ], max_files_per_job=max_files_per_job, era=ERA, use_lumi=True, lumi=LUMI, debug=False, running_method="sbatch", num_parallel_jobs=100, # Karl: speed up the hadd steps executable_addBackgrounds="addBackgrounds", executable_addBackgroundJetToTauFakes= "addBackgroundLeptonFakes", # CV: use common executable for estimating jet->lepton and jet->tau_h fake background histograms_to_fit=[ "EventCounter", "numJets", "mvaOutput_1l_1tau_ttbar", "mTauTauVis", "mTauTau", ], select_rle_output=True, ) analysis.create() run_analysis = query_yes_no("Start jobs ?") if run_analysis: analysis.run() else: sys.exit(0)
if __name__ == '__main__': logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s: %(message)s') ntupleProduction = prodNtupleConfig_3l_1tau( configDir=os.path.join("/home", getpass.getuser(), "ttHNtupleProduction", ERA, version), outputDir=os.path.join("/hdfs/local/ttH_2tau", getpass.getuser(), "ttHNtupleProduction", ERA, version), ##outputDir = os.path.join("/home", getpass.getuser(), "ttHNtupleProduction", ERA, version), executable_prodNtuple="produceNtuple_3l_1tau", cfgFile_prodNtuple="produceNtuple_3l_1tau_cfg.py", samples=samples, era=ERA, debug=False, running_method="sbatch", rle_directory='default', # [*] version=version, num_parallel_jobs=4) # [*] if rle_directory is set to 'default', then it looks files in /home/$USER/ttHAnalysis/era/version/rles/channel # set it to '', if no RLE selection is needed ntupleProduction.create() run_ntupleProduction = query_yes_no("Start jobs ?") if run_ntupleProduction: ntupleProduction.run() else: sys.exit(0)
check_output_files=check_output_files, running_method=running_method, max_files_per_job= 1, # so that we'd have 1-1 correspondence b/w input and output files mem_integrations_per_job=50 if mode != 'sync' else 10, max_mem_integrations= max_mem_integrations, # use -1 if you don't want to limit the nof MEM integrations num_parallel_jobs=num_parallel_jobs, leptonSelection=leptonSelection, hadTauSelection=hadTauSelectionAndWP, isDebug=debug, jet_cleaning_by_index=jet_cleaning_by_index, central_or_shift=central_or_shifts, dry_run=dry_run, use_nonnominal=use_nonnominal, use_home=use_home, submission_cmd=sys.argv, ) goodToGo = addMEMProduction.create() if goodToGo: if auto_exec: run_addMEMProduction = True elif no_exec: run_addMEMProduction = False else: run_addMEMProduction = query_yes_no("Start jobs ?") if run_addMEMProduction: addMEMProduction.run()
puHistogramProduction = puHistogramConfig( configDir = configDir, outputDir = outputDir, output_file = output_file, executable = "puHistogramProducer.sh", samples = samples, max_files_per_job = files_per_job, era = era, check_output_files = check_output_files, running_method = running_method, num_parallel_jobs = num_parallel_jobs, dry_run = dry_run, use_home = use_home, ) job_statistics = puHistogramProduction.create() for job_type, num_jobs in job_statistics.items(): logging.info(" #jobs of type '%s' = %i" % (job_type, num_jobs)) if auto_exec: run_puHistogramProduction = True elif no_exec: run_puHistogramProduction = False else: run_puHistogramProduction = query_yes_no("Start jobs ?") if run_puHistogramProduction: puHistogramProduction.run() else: sys.exit(0)
refGenWeightJobs = refGenWeightConfig( configDir=configDir, outputDir=outputDir, output_file=output_file, executable="getRefGenWeight.py", samples=samples, era=era, check_output_files=check_output_files, running_method=running_method, num_parallel_jobs=num_parallel_jobs, dry_run=dry_run, use_home=use_home, submission_cmd=sys.argv, ) job_statistics = refGenWeightJobs.create() for job_type, num_jobs in job_statistics.items(): logging.info(" #jobs of type '%s' = %i" % (job_type, num_jobs)) if auto_exec: run_refGenWeight = True elif no_exec: run_refGenWeight = False else: run_refGenWeight = query_yes_no("Start jobs ?") if run_refGenWeight: refGenWeightJobs.run() else: sys.exit(0)
if sample_name in [ "/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring16MiniAODv1-PUSpring16_80X_mcRun2_asymptotic_2016_v3_ext3-v1/MINIAODSIM", "/TT_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring16MiniAODv1-PUSpring16_80X_mcRun2_asymptotic_2016_v3_ext4-v1/MINIAODSIM", "/TTW/spring16DR80v6aMiniAODv1/FASTSIM" ]: sample_info["use_it"] = True #-------------------------------------------------------------------------------- if __name__ == '__main__': logging.basicConfig( stream = sys.stdout, level = logging.INFO, format = '%(asctime)s - %(levelname)s: %(message)s') ntupleProduction = prodNtupleConfig_3l_1tau( outputDir = os.path.join("/home", getpass.getuser(), "ttHNtupleProduction", ERA, version), executable_prodNtuple = "produceNtuple_3l_1tau", samples = samples, era = ERA, debug = False, running_method = "sbatch", num_parallel_jobs = 4) ntupleProduction.create() run_ntupleProduction = query_yes_no("Start jobs ?") if run_ntupleProduction: ntupleProduction.run() else: sys.exit(0)
#-------------------------------------------------------------------------------- version = "2016Dec12" ERA = "2016" if __name__ == '__main__': logging.basicConfig( stream = sys.stdout, level = logging.INFO, format = '%(asctime)s - %(levelname)s: %(message)s') addMEMProduction = addMEMConfig_2lss_1tau( treeName = 'tree', outputDir = os.path.join("/home", getpass.getuser(), "addMEM", ERA, version), executable_addMEM = "addMEM_2lss_1tau", samples = samples, era = ERA, debug = False, running_method = "sbatch", max_files_per_job = 1, mem_integrations_per_job = 50, max_mem_integrations = 20000, num_parallel_jobs = 4) goodToGo = addMEMProduction.create() if goodToGo: run_addMEMProduction = query_yes_no("Start jobs ?") if run_addMEMProduction: addMEMProduction.run()
outputDir=outputDir, output_file=output_file, executable="projectHistogram.sh", projection_module=projection_module, samples=samples, max_files_per_job=files_per_job, era=era, plot=plot, check_output_files=check_output_files, running_method=running_method, num_parallel_jobs=num_parallel_jobs, dry_run=dry_run, use_home=use_home, submission_cmd=sys.argv, ) job_statistics = projectionJobs.create() for job_type, num_jobs in job_statistics.items(): logging.info(" #jobs of type '%s' = %i" % (job_type, num_jobs)) if auto_exec: run_projection = True elif no_exec: run_projection = False else: run_projection = query_yes_no("Start jobs ?") if run_projection: projectionJobs.run() else: sys.exit(0)