## "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)