def execute_item(self, item):
source_ts = Timeseries()
with open(item['source_file']) as f:
source_ts.read_file(f)
self.target_step.interval_type = item['interval_type']
target_ts, missing = source_ts.aggregate(
self.target_step,
missing_allowed=self.missing_allowed,
missing_flag=self.missing_flag)
with open(item['target_file'], 'w') as f:
target_ts.write_file(f, version=3)
def AggregateDbTimeseries(source_id, dest_id, db, read_tstep_func, transaction=None,
commit=True, missing_allowed=0.0,
missing_flag='MISSING', append_only=False,
last_incomplete=False, all_incomplete=False):
source = Timeseries(id=source_id, time_step=read_tstep_func(source_id))
dest_step = read_tstep_func(dest_id)
if append_only:
bounds = timeseries_bounding_dates_from_db(db = db, id = dest_id)
end_date = bounds[1] if bounds else None
source.read_from_db(db)
dest = source.aggregate(target_step=dest_step,
missing_allowed=missing_allowed,
missing_flag=missing_flag,
last_incomplete=last_incomplete,
all_incomplete=all_incomplete)[0]
dest.id = dest_id
if append_only:
d=dest.bounding_dates()
while (d is not None) and (end_date is not None) and d[0]<=end_date:
del dest[d[0]]
d=dest.bounding_dates()
dest.append_to_db(db=db, transaction=transaction, commit=commit)
else:
dest.write_to_db(db=db, transaction=transaction, commit=commit)
