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)