def main(earliest_night, latest_night, data_dir, source, max_delta_t, parts, password, conditions):
''' This script connects to the rundb and fetches all runs belonging to the specified source.
Provide time range by specifying ealriest and lates night to fetch. As in 20131001, 20141001.
This script will produce a json file containing paths to the data files and their drs files. The
path prefix is specified by the DATA_DIR argument. The files in this folder will not actually be read
by this script. It simply needs the path to construct the json file containing the full paths to the raw data files
'''
logging.basicConfig(level=logging.INFO)
factdb = create_engine("mysql+pymysql://factread:{}@129.194.168.95/factdata".format(password))
data_conditions=dcc.conditions[conditions]
mapping = erna.load(earliest_night, latest_night, data_dir, source_name=source, timedelta_in_minutes=max_delta_t, factdb=factdb, data_conditions=data_conditions)
if mapping.empty:
logger.error('No entries matching the conditions could be found in the RunDB')
return
if parts > 1:
split_indices = np.array_split(np.arange(len(mapping)), parts)
for num, indices in enumerate(split_indices):
df = mapping[indices.min(): indices.max()]
filename = "{}_{}_{}_part_{}.json".format(earliest_night, latest_night, source.replace(' ', '_'), num)
logger.info("Writing {} entries to json file {}".format(len(df), filename))
df.to_json(filename, orient='records', date_format='epoch' )
else:
filename = earliest_night + "_" + latest_night + "_" + source + ".json"
logger.info("Writing list to json file {}".format(filename))
mapping.to_json(filename, orient='records', date_format='epoch' )
def main(earliest_night, latest_night, data_dir, jar, xml, db, out, queue, walltime, engine, num_runs, vmem, log_level, port, source, conditions, max_delta_t, local, password):
level=logging.INFO
if log_level is 'DEBUG':
level = logging.DEBUG
elif log_level is 'WARN':
level = logging.WARN
elif log_level is 'INFO':
level = logging.INFO
logging.captureWarnings(True)
logging.basicConfig(format=('%(asctime)s - %(name)s - %(levelname)s - ' + '%(message)s'), level=level)
jarpath = os.path.abspath(jar)
xmlpath =os. path.abspath(xml)
outpath = os.path.abspath(out)
erna.ensure_output(out)
db_path = os.path.abspath(db)
output_directory = os.path.dirname(outpath)
#create dir if it doesnt exist
os.makedirs(output_directory, exist_ok=True)
logger.info("Writing output data to {}".format(out))
factdb = sqlalchemy.create_engine("mysql+pymysql://factread:{}@129.194.168.95/factdata".format(password))
data_conditions=dcc.conditions[conditions]
df_runs = erna.load(earliest_night, latest_night, data_dir, source_name=source, timedelta_in_minutes=max_delta_t, factdb=factdb, data_conditions=data_conditions)
logger.info("Would process {} jobs with {} runs per job".format(len(df_runs)//num_runs, num_runs))
click.confirm('Do you want to continue processing and start jobs?', abort=True)
job_list = make_jobs(jarpath, xmlpath, db_path, output_directory, df_runs, engine, queue, vmem, num_runs, walltime)
job_outputs = gridmap.process_jobs(job_list, max_processes=len(job_list), local=local)
erna.collect_output(job_outputs, out, df_runs)
def main(
earliest_night,
latest_night,
data_dir,
jar,
xml,
db,
out,
queue,
mail,
walltime,
engine,
num_runs,
qjobs,
vmem,
log_level,
port,
source,
conditions,
max_delta_t,
local,
password,
):
level = logging.INFO
if log_level is "DEBUG":
level = logging.DEBUG
elif log_level is "WARN":
level = logging.WARN
elif log_level is "INFO":
level = logging.INFO
logging.captureWarnings(True)
logging.basicConfig(format=("%(asctime)s - %(levelname)s - " + "%(message)s"), level=level)
jarpath = os.path.abspath(jar)
xmlpath = os.path.abspath(xml)
outpath = os.path.abspath(out)
erna.ensure_output(out)
logger.info("Output data will be written to {}".format(out))
db_path = os.path.abspath(db)
output_directory = os.path.dirname(outpath)
# create dir if it doesnt exist
os.makedirs(output_directory, exist_ok=True)
factdb = sqlalchemy.create_engine("mysql+pymysql://factread:{}@129.194.168.95/factdata".format(password))
data_conditions = dcc.conditions[conditions]
df_loaded = erna.load(
earliest_night,
latest_night,
data_dir,
source_name=source,
timedelta_in_minutes=max_delta_t,
factdb=factdb,
data_conditions=data_conditions,
)
df_loaded.to_hdf(out + ".tmp", "loaded", mode="a")
logger.info("Processing {} jobs with {} runs per job.".format(int(len(df_loaded) / num_runs), num_runs))
click.confirm("Do you want to continue processing and start jobs?", abort=True)
# ensure that the max number of queuable jobs is smaller than the total number of jobs
if qjobs > len(df_loaded):
qjobs = len(df_loaded)
nfinished = 0
nsubmited = 1
running_jobs = []
pending_jobs = []
last_finished = []
jobids = []
job_output_paths = []
df_submitted = pd.DataFrame()
# copy then dataframe with loaded jobs to submit
df_runs = df_loaded.copy()
# operate submission loop, as long as jobs need to be submitted
while nfinished < nsubmited:
n_toqueue = qjobs - (len(pending_jobs) + len(running_jobs))
logger.info("{} jobs to be queued".format(n_toqueue))
if (n_toqueue > 0) and (len(df_runs) > 0):
df_to_submit = df_runs.head(n_toqueue * num_runs).copy()
processing_identifier = "{}_{}".format(source, time.strftime("%Y%m%d%H%M"))
df_submitted_last = submit_qsub_jobs(
processing_identifier,
jarpath,
xmlpath,
db_path,
df_to_submit,
engine,
queue,
vmem,
num_runs,
walltime,
db,
mail,
)
df_submitted = df_submitted.append(df_submitted_last)
# append submitted jobids
jobids = df_submitted["JOBID"].unique()
df_runs = df_runs.drop(df_to_submit.index)
nsubmited = len(jobids)
logger.info("Submitted {} jobs in last bunch".format(len(df_submitted_last)))
logger.info("Submitted {} jobs in total".format(nsubmited))
finished_jobs = q.get_finished_jobs(jobids)
running_jobs = q.get_running_jobs(jobids)
pending_jobs = q.get_pending_jobs(jobids)
nfinished = len(finished_jobs)
logger.info(
"Processing Status: running: {}, pending: {}, queued: {}, finished: {}/{}".format(
len(running_jobs), len(pending_jobs), nsubmited - nfinished, nfinished, nsubmited
)
)
last_finished = np.setdiff1d(finished_jobs, last_finished)
if len(last_finished) > 0:
last_paths = last_finished_out_paths(df_submitted, last_finished)
job_output_paths = np.append(job_output_paths, last_paths)
last_finished = finished_jobs
if nfinished < nsubmited:
logger.info("Checking qstat in 5 min again")
time.sleep(5 * 60)
logger.info("All jobs have been finished, processing done")
job_outputs = read_outputs_to_list(job_output_paths)
erna.collect_output(job_outputs, out, df_started_runs=df_loaded)
# erna.collect_output(job_output_paths, out)
df_loaded.to_hdf(out, "loaded", mode="a")
df_submitted.to_hdf(out, "jobinfo", mode="a")
os.remove(out + ".tmp")