def get_timeseries_adapter(self):
db_config = self.input_config['db_config']
db_engine = get_engine(host=db_config['host'],
port=db_config['port'],
user=db_config['user'],
password=db_config['password'],
db=db_config['db'])
return Timeseries(get_sessionmaker(engine=db_engine))
run_date=run_date.strftime(DATE_FORMAT))
rainfall_arr = read_rainfall(run_dir,
rainfall_file_name)[0:TIME_INDEX.size]
rainfall_df_acc = pd.DataFrame({
'time': TIME_INDEX,
'value': rainfall_arr
}).set_index(keys='time')
rainfall_df_inst = rainfall_df_acc.diff(axis=0)
rainfall_df_inst.dropna(inplace=True)
db_engine = get_engine(host=DB_CONFIG['host'],
port=DB_CONFIG['port'],
user=DB_CONFIG['user'],
password=DB_CONFIG['password'],
db=DB_CONFIG['db'])
tms_adapter = Timeseries(get_sessionmaker(engine=db_engine))
TMS_META['station_name'] = station['name_in_db']
TMS_META['source'] = wrf_model['name_in_db']
tmss = separate_rainfall_for_event_types(today, rainfall_df_inst)
for tms in tmss:
TMS_META['event_type'] = tms['event_type']
tms_id = tms_adapter.get_timeseries_id(TMS_META)
if tms_id is None:
tms_id = tms_adapter.create_timeseries_id(
run_name=TMS_META['run_name'],
station={
'name': station['name_in_db'],
'id': station['id_in_db']
},