def main():
# Registering arguments will automatically add their description to the help menu
Script.registerArgument(["JobID: DIRAC Job ID"])
_, args = Script.parseCommandLine(ignoreErrors=True)
from DIRAC.Interfaces.API.Dirac import Dirac, parseArguments
dirac = Dirac()
exitCode = 0
errorList = []
for job in parseArguments(args):
result = dirac.getJobAttributes(job, printOutput=True)
if not result["OK"]:
errorList.append((job, result["Message"]))
exitCode = 2
for error in errorList:
print("ERROR %s: %s" % error)
DIRAC.exit(exitCode)
def main():
Script.parseCommandLine(ignoreErrors=True)
args = Script.getPositionalArgs()
if len(args) < 1:
Script.showHelp()
from DIRAC.Interfaces.API.Dirac import Dirac, parseArguments
dirac = Dirac()
exitCode = 0
errorList = []
for job in parseArguments(args):
result = dirac.getJobAttributes(job, printOutput=True)
if not result['OK']:
errorList.append((job, result['Message']))
exitCode = 2
for error in errorList:
print("ERROR %s: %s" % error)
DIRAC.exit(exitCode)
import DIRAC
from DIRAC.Core.Base import Script
Script.setUsageMessage('\n'.join([
__doc__.split('\n')[1], 'Usage:',
' %s [option|cfgfile] ... JobID ...' % Script.scriptName, 'Arguments:',
' JobID: DIRAC Job ID'
]))
Script.parseCommandLine(ignoreErrors=True)
args = Script.getPositionalArgs()
if len(args) < 1:
Script.showHelp()
from DIRAC.Interfaces.API.Dirac import Dirac, parseArguments
dirac = Dirac()
exitCode = 0
errorList = []
for job in parseArguments(args):
result = dirac.getJobAttributes(job, printOutput=True)
if not result['OK']:
errorList.append((job, result['Message']))
exitCode = 2
for error in errorList:
print "ERROR %s: %s" % error
DIRAC.exit(exitCode)
def main():
Script.registerSwitch("e", "extended", "Get extended printout",
setExtendedPrint)
_, args = Script.parseCommandLine(ignoreErrors=True)
from DIRAC import exit as DIRACExit
from DIRAC.Interfaces.API.Dirac import Dirac
from DIRAC.Interfaces.API.DiracAdmin import DiracAdmin
diracAdmin = DiracAdmin()
dirac = Dirac()
exitCode = 0
errorList = []
for gridID in args:
result = diracAdmin.getPilotInfo(gridID)
if not result["OK"]:
errorList.append((gridID, result["Message"]))
exitCode = 2
else:
res = result["Value"][gridID]
if extendedPrint:
tab = ""
for key in [
"PilotJobReference",
"Status",
"OwnerDN",
"OwnerGroup",
"SubmissionTime",
"DestinationSite",
"GridSite",
]:
if key in res:
diracAdmin.log.notice("%s%s: %s" %
(tab, key, res[key]))
if not tab:
tab = " "
diracAdmin.log.notice("")
for jobID in res["Jobs"]:
tab = " "
result = dirac.getJobAttributes(int(jobID))
if not result["OK"]:
errorList.append((gridID, result["Message"]))
exitCode = 2
else:
job = result["Value"]
diracAdmin.log.notice("%sJob ID: %s" % (tab, jobID))
tab += " "
for key in [
"OwnerDN",
"OwnerGroup",
"JobName",
"Status",
"StartExecTime",
"LastUpdateTime",
"EndExecTime",
]:
if key in job:
diracAdmin.log.notice("%s%s:" % (tab, key),
job[key])
diracAdmin.log.notice("")
else:
print(diracAdmin.pPrint.pformat({gridID: res}))
for error in errorList:
print("ERROR %s: %s" % error)
DIRACExit(exitCode)
'PilotJobReference',
'Status',
'OwnerDN',
'OwnerGroup',
'SubmissionTime',
'DestinationSite',
'GridSite',
]:
if key in res:
diracAdmin.log.notice('%s%s: %s' % (tab, key, res[key]))
if not tab:
tab = ' '
diracAdmin.log.notice('')
for jobID in res['Jobs']:
tab = ' '
result = dirac.getJobAttributes(int(jobID))
if not result['OK']:
errorList.append((gridID, result['Message']))
exitCode = 2
else:
job = result['Value']
diracAdmin.log.notice('%sJob ID: %s' % (tab, jobID))
tab += ' '
for key in [
'OwnerDN', 'OwnerGroup', 'JobName', 'Status',
'StartExecTime', 'LastUpdateTime', 'EndExecTime'
]:
if key in job:
diracAdmin.log.notice('%s%s:' % (tab, key),
job[key])
diracAdmin.log.notice('')
'PilotJobReference',
'Status',
'OwnerDN',
'OwnerGroup',
'SubmissionTime',
'DestinationSite',
'GridSite',
]:
if key in res:
diracAdmin.log.notice('%s%s: %s' % (tab, key, res[key]))
if not tab:
tab = ' '
diracAdmin.log.notice('')
for jobID in res['Jobs']:
tab = ' '
result = dirac.getJobAttributes(int(jobID))
if not result['OK']:
errorList.append((gridID, result['Message']))
exitCode = 2
else:
job = result['Value']
diracAdmin.log.notice('%sJob ID: %s' % (tab, jobID))
tab += ' '
for key in ['OwnerDN', 'OwnerGroup', 'JobName', 'Status', 'StartExecTime', 'LastUpdateTime', 'EndExecTime']:
if key in job:
diracAdmin.log.notice('%s%s:' % (tab, key), job[key])
diracAdmin.log.notice('')
else:
print diracAdmin.pPrint.pformat({gridID: res})
import DIRAC
from DIRAC.Core.Base import Script
Script.setUsageMessage( '\n'.join( [ __doc__.split( '\n' )[1],
'Usage:',
' %s [option|cfgfile] ... JobID ...' % Script.scriptName,
'Arguments:',
' JobID: DIRAC Job ID' ] ) )
Script.parseCommandLine( ignoreErrors = True )
args = Script.getPositionalArgs()
if len( args ) < 1:
Script.showHelp()
from DIRAC.Interfaces.API.Dirac import Dirac, parseArguments
dirac = Dirac()
exitCode = 0
errorList = []
for job in parseArguments( args ):
result = dirac.getJobAttributes( job, printOutput = True )
if not result['OK']:
errorList.append( ( job, result['Message'] ) )
exitCode = 2
for error in errorList:
print "ERROR %s: %s" % error
DIRAC.exit( exitCode )
def main():
"""
Launch job on the GRID
"""
# this thing pilots everything related to the GRID
dirac = Dirac()
if switches["output_type"] in "TRAINING":
print("Preparing submission for TRAINING data")
elif switches["output_type"] in "DL2":
print("Preparing submission for DL2 data")
else:
print("You have to choose either TRAINING or DL2 as output type!")
sys.exit()
# Read configuration file
cfg = load_config(switches["config_file"])
# Analysis
config_path = cfg["General"]["config_path"]
config_file = cfg["General"]["config_file"]
mode = cfg["General"]["mode"] # One mode naw
particle = cfg["General"]["particle"]
estimate_energy = cfg["General"]["estimate_energy"]
force_tailcut_for_extended_cleaning = cfg["General"][
"force_tailcut_for_extended_cleaning"]
# Take parameters from the analysis configuration file
ana_cfg = load_config(os.path.join(config_path, config_file))
config_name = ana_cfg["General"]["config_name"]
cam_id_list = ana_cfg["General"]["cam_id_list"]
# Regressor and classifier methods
regressor_method = ana_cfg["EnergyRegressor"]["method_name"]
classifier_method = ana_cfg["GammaHadronClassifier"]["method_name"]
# Someone might want to create DL2 without score or energy estimation
if regressor_method in ["None", "none", None]:
use_regressor = False
else:
use_regressor = True
if classifier_method in ["None", "none", None]:
use_classifier = False
else:
use_classifier = True
# GRID
outdir = os.path.join(cfg["GRID"]["outdir"], config_name)
n_file_per_job = cfg["GRID"]["n_file_per_job"]
n_jobs_max = cfg["GRID"]["n_jobs_max"]
model_dir = cfg["GRID"]["model_dir"]
training_dir_energy = cfg["GRID"]["training_dir_energy"]
training_dir_classification = cfg["GRID"]["training_dir_classification"]
dl2_dir = cfg["GRID"]["dl2_dir"]
home_grid = cfg["GRID"]["home_grid"]
user_name = cfg["GRID"]["user_name"]
banned_sites = cfg["GRID"]["banned_sites"]
# HACK
if force_tailcut_for_extended_cleaning is True:
print("Force tail cuts for extended cleaning!!!")
# Prepare command to launch script
source_ctapipe = "source /cvmfs/cta.in2p3.fr/software/conda/dev/setupConda.sh"
source_ctapipe += " && conda activate ctapipe_v0.11.0"
if switches["output_type"] in "TRAINING":
execute = "data_training.py"
script_args = [
"--config_file={}".format(config_file),
"--estimate_energy={}".format(str(estimate_energy)),
"--regressor_config={}.yaml".format(regressor_method),
"--regressor_dir=./",
"--outfile {outfile}",
"--indir ./ --infile_list={infile_name}",
"--max_events={}".format(switches["max_events"]),
"--{mode}",
"--cam_ids {}".format(cam_id_list),
]
output_filename_template = "TRAINING"
elif switches["output_type"] in "DL2":
execute = "write_dl2.py"
script_args = [
"--config_file={}".format(config_file),
"--regressor_config={}.yaml".format(regressor_method),
"--regressor_dir=./",
"--classifier_config={}.yaml".format(classifier_method),
"--classifier_dir=./",
"--outfile {outfile}",
"--indir ./ --infile_list={infile_name}",
"--max_events={}".format(switches["max_events"]),
"--{mode}",
"--force_tailcut_for_extended_cleaning={}".format(
force_tailcut_for_extended_cleaning),
"--cam_ids {}".format(cam_id_list),
]
output_filename_template = "DL2"
# Make the script save also the full calibrated images if required
if switches["save_images"] is True:
script_args.append("--save_images")
# Make the script print debug information if required
if switches["debug_script"] is True:
script_args.append("--debug")
cmd = [source_ctapipe, "&&", "./" + execute]
cmd += script_args
pilot_args_write = " ".join(cmd)
# For table merging if multiple runs
pilot_args_merge = " ".join([
source_ctapipe,
"&&",
"./merge_tables.py",
"--template_file_name",
"{in_name}",
"--outfile",
"{out_name}",
])
prod3b_filelist = dict()
if estimate_energy is False and switches["output_type"] in "TRAINING":
prod3b_filelist["gamma"] = cfg["EnergyRegressor"]["gamma_list"]
elif estimate_energy is True and switches["output_type"] in "TRAINING":
prod3b_filelist["gamma"] = cfg["GammaHadronClassifier"]["gamma_list"]
prod3b_filelist["proton"] = cfg["GammaHadronClassifier"]["proton_list"]
elif switches["output_type"] in "DL2":
prod3b_filelist["gamma"] = cfg["Performance"]["gamma_list"]
prod3b_filelist["proton"] = cfg["Performance"]["proton_list"]
prod3b_filelist["electron"] = cfg["Performance"]["electron_list"]
# from IPython import embed
# embed()
# Split list of files according to stoprage elements
with open(prod3b_filelist[particle]) as f:
filelist = f.readlines()
filelist = ["{}".format(_.replace("\n", "")) for _ in filelist]
res = dirac.splitInputData(filelist, n_file_per_job)
list_run_to_loop_on = res["Value"]
# define a template name for the file that's going to be written out.
# the placeholder braces are going to get set during the file-loop
output_filename = output_filename_template
output_path = outdir
if estimate_energy is False and switches["output_type"] in "TRAINING":
output_path += "/{}/".format(training_dir_energy)
step = "energy"
if estimate_energy is True and switches["output_type"] in "TRAINING":
output_path += "/{}/".format(training_dir_classification)
step = "classification"
if switches["output_type"] in "DL2":
if force_tailcut_for_extended_cleaning is False:
output_path += "/{}/".format(dl2_dir)
else:
output_path += "/{}_force_tc_extended_cleaning/".format(dl2_dir)
step = ""
output_filename += "_{}.h5"
# sets all the local files that are going to be uploaded with the job
# plus the pickled classifier
# if file name starts with `LFN:`, it will be copied from the GRID
input_sandbox = [
# Utility to assign one job to one command...
os.path.expandvars("$GRID/pilot.sh"),
os.path.expandvars("$PROTOPIPE/protopipe/"),
os.path.expandvars("$GRID/merge_tables.py"),
# python wrapper for the mr_filter wavelet cleaning
# os.path.expandvars("$PYWI/pywi/"),
# os.path.expandvars("$PYWICTA/pywicta/"),
# script that is being run
os.path.expandvars("$PROTOPIPE/protopipe/scripts/" + execute),
# Configuration file
os.path.expandvars(os.path.join(config_path, config_file)),
]
models_to_upload = []
configs_to_upload = []
if estimate_energy is True and switches["output_type"] in "TRAINING":
config_path_template = "LFN:" + os.path.join(home_grid, outdir,
model_dir, "{}.yaml")
config_to_upload = config_path_template.format(regressor_method)
model_path_template = "LFN:" + os.path.join(
home_grid, outdir, model_dir, "regressor_{}_{}.pkl.gz")
for cam_id in cam_id_list:
model_to_upload = model_path_template.format(
cam_id, regressor_method) # TBC
print("The following model(s) will be uploaded to the GRID:")
print(model_to_upload)
models_to_upload.append(model_to_upload)
print(
"The following configs(s) for such models will be uploaded to the GRID:"
)
print(config_to_upload)
configs_to_upload.append(config_to_upload)
# input_sandbox.append(model_to_upload)
elif estimate_energy is False and switches["output_type"] in "TRAINING":
pass
else: # Charge also classifer for DL2
model_type_list = ["regressor", "classifier"]
model_method_list = [regressor_method, classifier_method]
config_path_template = "LFN:" + os.path.join(home_grid, outdir,
model_dir, "{}.yaml")
model_path_template = "LFN:" + os.path.join(
home_grid, outdir, model_dir, "{}_{}_{}.pkl.gz")
if force_tailcut_for_extended_cleaning is True:
force_mode = mode.replace("wave", "tail")
print("################")
print(force_mode)
else:
force_mode = mode
for idx, model_type in enumerate(model_type_list):
print(
"The following configuration file will be uploaded to the GRID:"
)
config_to_upload = config_path_template.format(
model_method_list[idx])
print(config_to_upload)
configs_to_upload.append(config_to_upload) # upload only 1 copy
print(
"The following model(s) related to such configuration file will be uploaded to the GRID:"
)
for cam_id in cam_id_list:
if model_type in "regressor" and use_regressor is False:
print("Do not upload regressor model on GRID!!!")
continue
if model_type in "classifier" and use_classifier is False:
print("Do not upload classifier model on GRID!!!")
continue
model_to_upload = model_path_template.format(
model_type_list[idx], cam_id, model_method_list[idx])
print(model_to_upload)
models_to_upload.append(model_to_upload)
# input_sandbox.append(model_to_upload)
# summary before submitting
print("\nDEBUG> running as:")
print(pilot_args_write)
print("\nDEBUG> with input_sandbox:")
print(input_sandbox)
print("\nDEBUG> with output file:")
print(output_filename.format("{job_name}"))
print("\nDEBUG> Particles:")
print(particle)
print("\nDEBUG> Energy estimation:")
print(estimate_energy)
# ######## ## ## ## ## ## ## #### ## ## ######
# ## ## ## ## ### ## ### ## ## ### ## ## ##
# ## ## ## ## #### ## #### ## ## #### ## ##
# ######## ## ## ## ## ## ## ## ## ## ## ## ## ## ####
# ## ## ## ## ## #### ## #### ## ## #### ## ##
# ## ## ## ## ## ### ## ### ## ## ### ## ##
# ## ## ####### ## ## ## ## #### ## ## ######
# list of files on the GRID SE space
# not submitting jobs where we already have the output
batcmd = "dirac-dms-user-lfns --BaseDir {}".format(
os.path.join(home_grid, output_path))
result = subprocess.check_output(batcmd, shell=True)
try:
grid_filelist = open(result.split()[-1]).read()
except IOError:
raise IOError("ERROR> cannot read GRID filelist...")
# get jobs from today and yesterday...
days = []
for i in range(2): # how many days do you want to look back?
days.append(
(datetime.date.today() - datetime.timedelta(days=i)).isoformat())
# get list of run_tokens that are currently running / waiting
running_ids = set()
running_names = []
for status in ["Waiting", "Running", "Checking"]:
for day in days:
try:
[
running_ids.add(id) for id in dirac.selectJobs(
status=status, date=day, owner=user_name)["Value"]
]
except KeyError:
pass
n_jobs = len(running_ids)
if n_jobs > 0:
print("Scanning {} running/waiting jobs... please wait...".format(
n_jobs))
for i, id in enumerate(running_ids):
if ((100 * i) / n_jobs) % 5 == 0:
print("\r{} %".format(((20 * i) / n_jobs) * 5)),
jobname = dirac.getJobAttributes(id)["Value"]["JobName"]
running_names.append(jobname)
else:
print("\n... done")
for bunch in list_run_to_loop_on:
# for bunch in bunches_of_run:
# from IPython import embed
# embed()
# this selects the `runxxx` part of the first and last file in the run
# list and joins them with a dash so that we get a nice identifier in
# the output file name.
# if there is only one file in the list, use only that one
# run_token = re.split('_', bunch[+0])[3] # JLK JLK
run_token = re.split("_", bunch[0])[3]
if len(bunch) > 1:
run_token = "-".join([run_token, re.split("_", bunch[-1])[3]])
print("-" * 50)
print("-" * 50)
# setting output name
output_filenames = dict()
if switches["output_type"] in "DL2":
job_name = "protopipe_{}_{}_{}_{}_{}".format(
config_name, switches["output_type"], particle, run_token,
mode)
output_filenames[mode] = output_filename.format("_".join(
[particle, mode, run_token]))
else:
job_name = "protopipe_{}_{}_{}_{}_{}_{}".format(
config_name, switches["output_type"], step, particle,
run_token, mode)
output_filenames[mode] = output_filename.format("_".join(
[step, particle, mode, run_token]))
# if job already running / waiting, skip
if job_name in running_names:
print("\n WARNING> {} still running\n".format(job_name))
continue
print("Output file name: {}".format(output_filenames[mode]))
# if file already in GRID storage, skip
# (you cannot overwrite it there, delete it and resubmit)
# (assumes tail and wave will always be written out together)
already_exist = False
file_on_grid = os.path.join(output_path, output_filenames[mode])
print("DEBUG> check for existing file on GRID...")
if file_on_grid in grid_filelist:
print("\n WARNING> {} already on GRID SE\n".format(job_name))
continue
if n_jobs_max == 0:
print("WARNING> maximum number of jobs to submit reached")
print("WARNING> breaking loop now")
break
else:
n_jobs_max -= 1
j = Job()
# runtime in seconds times 8 (CPU normalisation factor)
j.setCPUTime(6 * 3600 * 8)
j.setName(job_name)
j.setInputSandbox(input_sandbox)
if banned_sites:
j.setBannedSites(banned_sites)
# Add simtel files as input data
j.setInputData(bunch)
for run_file in bunch:
file_token = re.split("_", run_file)[3]
# wait for a random number of seconds (up to five minutes) before
# starting to add a bit more entropy in the starting times of the
# dirac queries.
# if too many jobs try in parallel to access the SEs,
# the interface crashes
# #sleep = random.randint(0, 20) # seconds
# #j.setExecutable('sleep', str(sleep))
# JLK: Try to stop doing that
# consecutively downloads the data files, processes them,
# deletes the input
# and goes on to the next input file;
# afterwards, the output files are merged
# j.setExecutable('dirac-dms-get-file', "LFN:" + run_file)
# source the miniconda ctapipe environment and
# run the python script with all its arguments
if switches["output_type"] in "DL2":
output_filename_temp = output_filename.format("_".join(
[particle, mode, file_token]))
if switches["output_type"] in "TRAINING":
output_filename_temp = output_filename.format("_".join(
[step, particle, mode, file_token]))
j.setExecutable(
"./pilot.sh",
pilot_args_write.format(
outfile=output_filename_temp,
infile_name=os.path.basename(run_file),
mode=mode,
),
)
# remove the current file to clear space
j.setExecutable("rm", os.path.basename(run_file))
# simple `ls` for good measure
j.setExecutable("ls", "-lh")
# if there is more than one file per job, merge the output tables
if len(bunch) > 1:
names = []
names.append(("*_{}_".format(particle), output_filenames[mode]))
for in_name, out_name in names:
print("in_name: {}, out_name: {}".format(in_name, out_name))
j.setExecutable(
"./pilot.sh",
pilot_args_merge.format(in_name=in_name,
out_name=out_name),
)
print("DEBUG> args append: {}".format(
pilot_args_merge.format(in_name=in_name,
out_name=out_name)))
bunch.extend(models_to_upload)
bunch.extend(configs_to_upload)
j.setInputData(bunch)
print("Input data set to job = {}".format(bunch))
outputs = []
outputs.append(output_filenames[mode])
print("Output file path: {}{}".format(output_path,
output_filenames[mode]))
j.setOutputData(outputs, outputSE=None, outputPath=output_path)
# check if we should somehow stop doing what we are doing
if switches["dry"] is True:
print("\nThis is a DRY RUN! -- NO job has been submitted!")
print("Name of the job: {}".format(job_name))
print("Name of the output file: {}".format(outputs))
print("Output path from GRID home: {}".format(output_path))
break
# this sends the job to the GRID and uploads all the
# files into the input sandbox in the process
print("\nSUBMITTING job with the following INPUT SANDBOX:")
print(input_sandbox)
print("Submission RESULT: {}\n".format(dirac.submitJob(j)["Value"]))
# break if this is only a test submission
if switches["test"] is True:
print("This is a TEST RUN! -- Only ONE job will be submitted!")
print("Name of the job: {}".format(job_name))
print("Name of the output file: {}".format(outputs))
print("Output path from GRID home: {}".format(output_path))
break
# since there are two nested loops, need to break again
if switches["test"] is True:
break
try:
os.remove("datapipe.tar.gz")
os.remove("modules.tar.gz")
except:
pass
# Upload analysis configuration file for provenance
SE_LIST = ['CC-IN2P3-USER', 'DESY-ZN-USER', 'CNAF-USER', 'CEA-USER']
analysis_config_local = os.path.join(config_path, config_file)
# the configuration file is uploaded to the data directory because
# the training samples (as well as their cleaning settings) are independent
analysis_config_dirac = os.path.join(home_grid, output_path, config_file)
print("Uploading {} to {}...".format(analysis_config_local,
analysis_config_dirac))
if switches["dry"] is False:
# Upload this file to all Dirac Storage Elements in SE_LIST
for se in SE_LIST:
# the uploaded config file overwrites any old copy
ana_cfg_upload_cmd = "dirac-dms-add-file -f {} {} {}".format(
analysis_config_dirac, analysis_config_local, se)
ana_cfg_upload_result = subprocess.check_output(ana_cfg_upload_cmd,
shell=True)
print(ana_cfg_upload_result)
else:
print("This is a DRY RUN! -- analysis.yaml has NOT been uploaded.")
print("\nall done -- exiting now")
exit()
def main():
global extendedPrint
Script.registerSwitch('e', 'extended', 'Get extended printout',
setExtendedPrint)
Script.parseCommandLine(ignoreErrors=True)
from DIRAC import exit as DIRACExit
from DIRAC.Interfaces.API.Dirac import Dirac
from DIRAC.Interfaces.API.DiracAdmin import DiracAdmin
args = Script.getPositionalArgs()
if len(args) < 1:
Script.showHelp()
diracAdmin = DiracAdmin()
dirac = Dirac()
exitCode = 0
errorList = []
for gridID in args:
result = diracAdmin.getPilotInfo(gridID)
if not result['OK']:
errorList.append((gridID, result['Message']))
exitCode = 2
else:
res = result['Value'][gridID]
if extendedPrint:
tab = ''
for key in [
'PilotJobReference',
'Status',
'OwnerDN',
'OwnerGroup',
'SubmissionTime',
'DestinationSite',
'GridSite',
]:
if key in res:
diracAdmin.log.notice('%s%s: %s' %
(tab, key, res[key]))
if not tab:
tab = ' '
diracAdmin.log.notice('')
for jobID in res['Jobs']:
tab = ' '
result = dirac.getJobAttributes(int(jobID))
if not result['OK']:
errorList.append((gridID, result['Message']))
exitCode = 2
else:
job = result['Value']
diracAdmin.log.notice('%sJob ID: %s' % (tab, jobID))
tab += ' '
for key in [
'OwnerDN', 'OwnerGroup', 'JobName', 'Status',
'StartExecTime', 'LastUpdateTime',
'EndExecTime'
]:
if key in job:
diracAdmin.log.notice('%s%s:' % (tab, key),
job[key])
diracAdmin.log.notice('')
else:
print(diracAdmin.pPrint.pformat({gridID: res}))
for error in errorList:
print("ERROR %s: %s" % error)
DIRACExit(exitCode)