def load_lists():
files = [
bucoffea_path(f"data/datasets/datasets_2017.txt"),
bucoffea_path(f"data/datasets/datasets_2018.txt")
]
lines = []
for fpath in files:
with open(fpath,"r") as f:
lines.extend(f.readlines())
lines = filter(lambda x: "NANOAOD" in x and not x.startswith("#"), lines)
return lines
def _configure(self, df):
dataset = df['dataset']
self._year = extract_year(dataset)
# Reload config based on year
cfg.DYNACONF_WORKS = "merge_configs"
cfg.MERGE_ENABLED_FOR_DYNACONF = True
cfg.SETTINGS_FILE_FOR_DYNACONF = bucoffea_path("config/monojet.yaml")
cfg.ENV_FOR_DYNACONF = f"era{self._year}"
cfg.reload()
def _configure(self, df=None):
cfg.DYNACONF_WORKS = "merge_configs"
cfg.MERGE_ENABLED_FOR_DYNACONF = True
cfg.SETTINGS_FILE_FOR_DYNACONF = bucoffea_path("config/vbfhinv.yaml")
# Reload config based on year
if df:
dataset = df['dataset']
self._year = extract_year(dataset)
cfg.ENV_FOR_DYNACONF = f"era{self._year}"
else:
cfg.ENV_FOR_DYNACONF = f"default"
cfg.reload()
def files_from_ac(regex):
"""Generate file list per dataset from T2_DE_RWTH
:param regex: Regular expression to match datasets
:type regex: string
:return: Mapping of dataset : [files]
:rtype: dict
"""
path = bucoffea_path('data/datasets/crabfiles.yml')
with open(path, 'r') as stream:
try:
fileset = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
for dataset, files in fileset.items():
if not re.match(regex, dataset):
continue
for ifile in reversed(files):
if not len(ifile):
files.remove(ifile)
fileset[dataset] = files
return fileset
def process(self, df):
self._configure(df)
output = self.accumulator.identity()
dataset = df['dataset']
# Lumi mask
year = extract_year(dataset)
if is_data(dataset):
if year == 2016:
json = bucoffea_path(
'data/json/Cert_271036-284044_13TeV_ReReco_07Aug2017_Collisions16_JSON.txt'
)
elif year == 2017:
json = bucoffea_path(
'data/json/Cert_294927-306462_13TeV_EOY2017ReReco_Collisions17_JSON_v1.txt'
)
elif year == 2018:
json = bucoffea_path(
'data/json/Cert_314472-325175_13TeV_17SeptEarlyReReco2018ABC_PromptEraD_Collisions18_JSON.txt'
)
lumi_mask = LumiMask(json)(df['run'], df['luminosityBlock'])
else:
lumi_mask = np.ones(df.size) == 1
# MET filters
if is_data(dataset):
filt_met = mask_and(df, cfg.FILTERS.DATA)
else:
filt_met = mask_and(df, cfg.FILTERS.MC)
if year == 2016:
trigger = 'HLT_Photon175'
else:
trigger = 'HLT_Photon200'
photons = setup_photons(df)
ak4 = setup_jets(df)
ak4 = ak4[
object_overlap(ak4, photons) \
& ak4.tightId \
& (ak4.pt > 100) \
& (ak4.abseta < 2.4)
]
event_mask = filt_met \
& lumi_mask \
& (ak4.counts > 0) \
& df[trigger] \
& (df['MET_pt'] < 60)
# Generator weight
weights = processor.Weights(size=df.size, storeIndividual=True)
if is_data(dataset):
weights.add('gen', np.ones(df.size))
else:
weights.add('gen', df['Generator_weight'])
photon_kinematics = (photons.pt > 200) & (photons.barrel)
# Medium
vals = photons[photon_kinematics & photons.mediumId].sieie[event_mask]
pt = photons[photon_kinematics & photons.mediumId].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# No Sieie
vals = photons[photon_kinematics
& medium_id_no_sieie(photons)].sieie[event_mask]
pt = photons[photon_kinematics
& medium_id_no_sieie(photons)].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium_nosieie',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# No Sieie, inverted isolation
vals = photons[photon_kinematics
& medium_id_no_sieie_inv_iso(photons)].sieie[event_mask]
pt = photons[photon_kinematics
& medium_id_no_sieie_inv_iso(photons)].pt[event_mask]
output['sieie'].fill(dataset=dataset,
cat='medium_nosieie_invertiso',
sieie=vals.flatten(),
pt=pt.flatten(),
weights=weight_shape(
vals,
weights.weight()[event_mask]))
# Keep track of weight sum
if not is_data(dataset):
output['sumw'][dataset] += df['genEventSumw']
output['sumw2'][dataset] += df['genEventSumw2']
return output
def do_submit(args):
"""Submit the analysis to HTCondor."""
import htcondor
if args.datasrc == 'das':
dataset_files = files_from_das(regex=args.dataset)
elif args.datasrc == 'ac':
dataset_files = files_from_ac(regex=args.dataset)
else:
dataset_files = files_from_eos(regex=args.dataset)
# Test mode: One file per data set
if args.test:
tmp = {}
for k, v in dataset_files.items():
tmp[k] = v[:1]
dataset_files = tmp
# Time tagged submission directory
timetag = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
foldername = timetag + (f"_{args.name}" if args.name else "")
subdir = os.path.abspath(pjoin("./submission/", foldername))
if not os.path.exists(subdir):
os.makedirs(subdir)
# Sub-directory to store submission files
filedir = 'files'
if not os.path.exists(pjoin(subdir, filedir)):
os.makedirs(pjoin(subdir, filedir))
# Get proxy and copy to a safe location on AFS
proxy = vo_proxy_path()
proxydir = os.path.expanduser("~/.voms/")
if not os.path.exists(proxydir):
os.makedirs(proxydir)
shutil.copy2(proxy, proxydir)
for dataset, files in dataset_files.items():
print(f"Submitting dataset: {dataset}.")
chunks = chunkify(files, int(len(files) / args.filesperjob + 1))
for ichunk, chunk in enumerate(chunks):
# Save input files to a txt file and send to job
tmpfile = pjoin(
subdir, filedir,
f"input_{dataset}_{ichunk:03d}of{len(chunks):03d}.txt")
with open(tmpfile, "w") as f:
for file in chunk:
f.write(f"{file}\n")
arguments = [
# pjoin(proxydir, os.path.basename(proxy)),
"$(Proxy_path)",
str(Path(__file__).absolute()),
args.processor,
f'--outpath {pjoin(subdir, "output")}',
f'--jobs {args.jobs}',
'worker',
f'--dataset {dataset}',
f'--filelist {os.path.basename(tmpfile)}',
f'--chunk {ichunk}'
]
input_files = [
os.path.abspath(tmpfile),
]
environment = {"NOPREFETCH": str(args.no_prefetch).lower()}
sub = htcondor.Submit({
"Proxy_path":
pjoin(proxydir, os.path.basename(proxy)),
"Initialdir":
subdir,
"executable":
bucoffea_path("execute/htcondor_wrap.sh"),
"should_transfer_files":
"YES",
"when_to_transfer_output":
"ON_EXIT",
"transfer_input_files":
", ".join(input_files),
"getenv":
"true",
"environment":
'"' + ' '.join([f"{k}={v}"
for k, v in environment.items()]) + '"',
"arguments":
" ".join(arguments),
"Output":
f"{filedir}/out_{dataset}_{ichunk:03d}of{len(chunks):03d}.txt",
"Error":
f"{filedir}/err_{dataset}_{ichunk:03d}of{len(chunks):03d}.txt",
"log":
f"{filedir}/log_{dataset}_{ichunk:03d}of{len(chunks):03d}.txt",
# "log" :f"/dev/null",
"request_cpus":
str(args.jobs),
"+MaxRuntime":
f"{60*60*8}"
})
jdl = pjoin(subdir, filedir, f'job_{dataset}_{ichunk}.jdl')
with open(jdl, "w") as f:
f.write(str(sub))
f.write("\nqueue 1\n")
if args.dry:
jobid = -1
else:
jobid = condor_submit(jdl)
print(f"Submitted job {jobid}")
with open("submission_history.txt", "a") as f:
f.write(f"{datetime.now()} {jobid}\n")
