def main():
parser = get_arg_parser()
parser.add_argument('--jet-type',
choices=['ak8', 'ak15'],
required=True,
help='Jet type: ak8, ak15')
parser.add_argument('--channel',
choices=['photon', 'qcd', 'signal', 'inclusive'],
required=True,
help='Channel: photon, qcd, signal, inclusive')
parser.add_argument(
'--run-syst',
action='store_true',
default=False,
help='Run all the systematic trees. Default: %(default)s')
parser.add_argument('--run-data',
action='store_true',
default=False,
help='Run over data. Default: %(default)s')
parser.add_argument(
'--year',
type=str,
required=True,
help=
'Year: 2016, 2017, 2018, or comma separated list e.g., `2016,2017,2018`'
)
parser.add_argument(
'--sample-dir',
type=str,
default='samples',
help='Directory of the sample list files. Default: %(default)s')
args = parser.parse_args()
if not (args.post or args.add_weight or args.merge):
tar_cmssw(args.tarball_suffix)
if ',' in args.year:
years = [int(y) for y in args.year.split(',') if y]
for year in years:
for cat in ['data', 'mc']:
opts = copy.deepcopy(args)
if cat == 'data':
opts.run_data = True
opts.nfiles_per_job *= 2
opts.inputdir = os.path.join(
opts.inputdir.replace('_YEAR_', str(year)), cat)
opts.year = year
print(opts.inputdir, opts.year, opts.channel,
'data' if opts.run_data else 'mc',
'syst' if opts.run_syst else '')
_process(opts)
else:
_process(args)
def main():
parser = get_arg_parser()
parser.add_argument('--option',
type=str,
required=True,
help='Selection option')
parser.add_argument(
'--run-syst',
action='store_true',
default=False,
help='Run all the systematic trees. Default: %(default)s')
parser.add_argument('--run-data',
action='store_true',
default=False,
help='Run over data. Default: %(default)s')
parser.add_argument('--run-signal',
action='store_true',
default=False,
help='Run over signal. Default: %(default)s')
parser.add_argument(
'--year',
type=str,
required=True,
help=
'Year: 2016, 2017, 2018, or comma separated list e.g., `2016,2017,2018`'
)
parser.add_argument(
'--sample-dir',
type=str,
default='samples',
help='Directory of the sample list files. Default: %(default)s')
parser.add_argument('--run-mass-regression',
action='store_true',
default=True,
help='Run mass regression. Default: %(default)s')
args = parser.parse_args()
years = args.year.split(',')
categories = ['data' if args.run_data else 'mc']
for year in years:
for cat in categories:
opts = copy.deepcopy(args)
if cat == 'data':
opts.run_data = True
opts.nfiles_per_job *= 2
opts.year = year
logging.info('year=%s, cat=%s, syst=%s', opts.year,
'data' if opts.run_data else 'mc',
'syst' if opts.run_syst else 'none')
_process(opts)
def main():
parser = get_arg_parser()
parser.add_argument('--jet-type',
choices=['ak8', 'ak15'],
required=True,
help='Jet type: ak8, ak15')
parser.add_argument(
'--channel',
type=str,
required=True,
help=
'Channel: photon, qcd, muon, diboson, signal, inclusive, or comma separated list e.g., `qcd,photon`'
)
parser.add_argument(
'--sfbdt',
type=float,
default=0.5,
help=
'sfBDT cut, applies only to `qcd` and `photon` channels. Default: %(default)s'
)
parser.add_argument(
'--run-syst',
action='store_true',
default=False,
help='Run all the systematic trees. Default: %(default)s')
parser.add_argument('--run-data',
action='store_true',
default=False,
help='Run over data. Default: %(default)s')
parser.add_argument(
'--year',
type=str,
required=True,
help=
'Year: 2016, 2017, 2018, or comma separated list e.g., `2016,2017,2018`'
)
parser.add_argument(
'--sample-dir',
type=str,
default='samples',
help='Directory of the sample list files. Default: %(default)s')
parser.add_argument('--run-tagger',
action='store_true',
default=False,
help='Run tagger. Default: %(default)s')
parser.add_argument('--run-mass-regression',
action='store_true',
default=False,
help='Run mass regression. Default: %(default)s')
args = parser.parse_args()
if not (args.post or args.add_weight or args.merge):
tar_cmssw(args.tarball_suffix)
years = args.year.split(',')
channels = args.channel.split(',')
categories = ['data' if args.run_data else 'mc']
for year in years:
for chn in channels:
for cat in categories:
opts = copy.deepcopy(args)
if cat == 'data':
opts.run_data = True
opts.nfiles_per_job *= 2
opts.inputdir = opts.inputdir.rstrip('/').replace(
'_YEAR_', year)
assert (year in opts.inputdir)
if opts.inputdir.rsplit('/', 1)[1] not in ['data', 'mc']:
opts.inputdir = os.path.join(opts.inputdir, cat)
assert (opts.inputdir.endswith(cat))
opts.year = year
opts.channel = chn
logging.info(
'inputdir=%s, year=%s, channel=%s, cat=%s, syst=%s',
opts.inputdir, opts.year, opts.channel,
'data' if opts.run_data else 'mc',
'syst' if opts.run_syst else 'none')
_process(opts)
def main():
parser = get_arg_parser()
parser.add_argument('--channel',
choices=['muon', 'photon', 'qcd'],
required=True,
help='Channel: muon, photon, qcd'
)
parser.add_argument('--run-syst',
action='store_true', default=False,
help='Run all the systematic trees. Default: %(default)s'
)
parser.add_argument('--run-data',
action='store_true', default=False,
help='Run over data. Default: %(default)s'
)
args = parser.parse_args()
channel = args.channel
default_config['channel'] = channel
if not (args.post or args.add_weight or args.merge):
tar_cmssw()
# args.batch = True
basename = os.path.basename(args.outputdir) + '_' + channel
args.outputdir = os.path.join(os.path.dirname(args.outputdir), basename, 'data' if args.run_data else 'mc')
args.jobdir = os.path.join('jobs_%s' % basename, 'data' if args.run_data else 'mc')
args.datasets = 'samples/%s.conf' % channel
args.cut = cut_dict[channel]
args.imports = [('PhysicsTools.NanoHRTTools.producers.hrtSFTreeProducer', 'hrtSFTreeFromConfig')]
if not args.run_data:
args.imports.extend([
('PhysicsTools.NanoAODTools.postprocessing.modules.common.puWeightProducer', 'puWeight'),
])
if args.channel == 'muon':
args.imports.append(('PhysicsTools.NanoHRTTools.producers.topPtWeightProducer', 'topPtWeight'))
# data, or just nominal MC
if args.run_data or not args.run_syst:
cfg = copy.deepcopy(default_config)
if args.run_data:
cfg['data'] = True
run(args, configs={hrt_cfgname: cfg})
return
# MC for syst.
if args.run_syst:
# nominal w/ PDF/Scale wegihts
logging.info('Start making nominal trees with PDF/scale weights...')
syst_name = 'LHEWeight'
opts = copy.deepcopy(args)
cfg = copy.deepcopy(default_config)
opts.outputdir = os.path.join(os.path.dirname(opts.outputdir), syst_name)
opts.jobdir = os.path.join(os.path.dirname(opts.jobdir), syst_name)
opts.branchsel_out = 'keep_and_drop_output_LHEweights.txt'
# opts.datasets = 'samples/%s_syst.conf' % channel
# if not os.path.exists(opts.datasets):
# opts.datasets = args.datasets
run(opts, configs={hrt_cfgname: cfg})
# JES up/down
for variation in ['up', 'down']:
syst_name = 'jes_%s' % variation
logging.info('Start making %s trees...' % syst_name)
opts = copy.deepcopy(args)
cfg = copy.deepcopy(default_config)
cfg['jes'] = variation
opts.outputdir = os.path.join(os.path.dirname(opts.outputdir), syst_name)
opts.jobdir = os.path.join(os.path.dirname(opts.jobdir), syst_name)
run(opts, configs={hrt_cfgname: cfg})
# JER up/down
for variation in ['up', 'down']:
syst_name = 'jer_%s' % variation
logging.info('Start making %s trees...' % syst_name)
opts = copy.deepcopy(args)
cfg = copy.deepcopy(default_config)
cfg['jer'] = variation
opts.outputdir = os.path.join(os.path.dirname(opts.outputdir), syst_name)
opts.jobdir = os.path.join(os.path.dirname(opts.jobdir), syst_name)
run(opts, configs={hrt_cfgname: cfg})
# MET unclustEn up/down
for variation in ['up', 'down']:
syst_name = 'met_%s' % variation
logging.info('Start making %s trees...' % syst_name)
opts = copy.deepcopy(args)
cfg = copy.deepcopy(default_config)
cfg['met_unclustered'] = variation
opts.outputdir = os.path.join(os.path.dirname(opts.outputdir), syst_name)
opts.jobdir = os.path.join(os.path.dirname(opts.jobdir), syst_name)
run(opts, configs={hrt_cfgname: cfg})