import uproot
from Tools.helpers import get_samples
from Tools.config_helpers import redirector_ucsd, redirector_fnal
from Tools.nano_mapping import make_fileset, nano_mapping
samples = get_samples(f_in='samples_QCD.yaml')
fileset = make_fileset(['QCD'], samples, redirector=redirector_ucsd, small=False)
print(fileset)
<<<<<<< HEAD
good = []
bad = []
#breakpoint()
for n in range(len(list(fileset.keys()))):
for f_in in fileset[list(fileset.keys())[n]]:
=======
fileset = make_fileset(['TTW'], samples, redirector=redirector_ucsd, small=False)
good = []
bad = []
for sample in list(fileset.keys()):
for f_in in fileset[sample]:
>>>>>>> 6987d93c61482b8369a70afe8a3071d806185974
print (f_in)
try:
tree = uproot.open(f_in)["Events"]
good.append(f_in)
import uproot
from Tools.helpers import get_samples
from Tools.config_helpers import redirector_ucsd, redirector_fnal
from Tools.nano_mapping import make_fileset, nano_mapping
samples = get_samples()
fileset = make_fileset(['QCD'],
samples,
redirector=redirector_ucsd,
small=False,
year='UL2018')
good = []
bad = []
for sample in list(fileset.keys()):
for f_in in fileset[sample]:
print(f_in)
try:
tree = uproot.open(f_in)["Events"]
good.append(f_in)
except OSError:
print("XRootD Error")
bad.append(f_in)
isData = True if DASname.count('Run20') else False
isFastSim = False if not DASname.count('Fast') else True
era = DASname[DASname.find("Run") + len('Run2000'):DASname.find("Run") +
len('Run2000A')]
if DASname.count('Autumn18') or DASname.count('Run2018'):
return 2018, era, isData, isFastSim
elif DASname.count('Fall17') or DASname.count('Run2017'):
return 2017, era, isData, isFastSim
elif DASname.count('Summer16') or DASname.count('Run2016'):
return 2016, era, isData, isFastSim
else:
### our private samples right now are all Autumn18 but have no identifier.
return 2018, 'X', False, False
samples = get_samples() # loads the nanoAOD samples
# load config
cfg = loadConfig()
print("Loaded version %s from config." % cfg['meta']['version'])
import argparse
argParser = argparse.ArgumentParser(description="Argument parser")
argParser.add_argument('--tag',
action='store',
default=None,
help="Tag on github for baby production")
argParser.add_argument('--user', action='store', help="Your github user name")
argParser.add_argument('--skim',
from Tools.nano_mapping import make_fileset, nano_mapping
from processor.meta_processor import get_sample_meta
overwrite = True
local = True
# load the config and the cache
cfg = loadConfig()
cacheName = 'charge_flip_check'
cache = dir_archive(os.path.join(os.path.expandvars(cfg['caches']['base']), cacheName), serialized=True)
histograms = sorted(list(desired_output.keys()))
year = 2018
samples = get_samples(2018)
#fileset = make_fileset(['TTW', 'TTZ'], samples, redirector=redirector_ucsd, small=True, n_max=5) # small, max 5 files per sample
#fileset = make_fileset(['DY'], samples, redirector=redirector_ucsd, small=True, n_max=10)
fileset = make_fileset(['top', 'DY',], redirector=redirector_ucsd, small=False)
add_processes_to_output(fileset, desired_output)
#meta = get_sample_meta(fileset, samples)
if local:
exe_args = {
'workers': 16,
'function_args': {'flatten': False},
'schema': NanoAODSchema,
nano_mapping = load_yaml(data_path + 'nano_mapping.yaml')
def make_fileset(datasets,
samples,
redirector=redirector_ucsd,
small=False,
n_max=1,
year=2018):
fileset = {}
#print (nano_mapping[year])
for dataset in datasets:
for nano_sample in nano_mapping[year][dataset]:
dbs_files = DBSSample(dataset=nano_sample).get_files()
files = [redirector + x.name for x in dbs_files]
if not small:
fileset.update({nano_sample: files})
else:
fileset.update({nano_sample: files[:n_max]})
return fileset
if __name__ == '__main__':
samples = get_samples()
samples.update(get_samples('samples_QCD.yaml'))
fileset = make_fileset(['TTW', 'TTZ', 'QCD'], samples, year=2018)