def prepare_mike_dis_input(start, end, tide_id):
try:
pool = get_Pool(host=con_params.CURW_SIM_HOST,
port=con_params.CURW_SIM_PORT,
user=con_params.CURW_SIM_USERNAME,
password=con_params.CURW_SIM_PASSWORD,
db=con_params.CURW_SIM_DATABASE)
TS = Timeseries(pool)
ts = TS.get_timeseries(id_=tide_id, start_date=start, end_date=end)
ts.insert(0, ['time', 'value'])
ts_df = list_of_lists_to_df_first_row_as_columns(ts)
ts_df['value'] = ts_df['value'].astype('float64')
tide_ts_df = pd.DataFrame()
tide_ts_df['time'] = pd.date_range(start=start, end=end, freq='15min')
tide_ts_df = pd.merge(tide_ts_df, ts_df, how="left", on='time')
tide_ts_df.set_index('time', inplace=True)
processed_tide_ts_df = replace_negative_99999_with_nan(tide_ts_df)
if np.isnan(processed_tide_ts_df.iloc[-1, 0]):
processed_tide_ts_df.iloc[-1, 0] = 0
processed_tide_ts_df = processed_tide_ts_df.dropna()
return processed_tide_ts_df
except Exception:
traceback.print_exc()
finally:
destroy_Pool(pool)
def prepare_outflow_250(outflow_file_path, start, end, tide_id):
try:
curw_sim_pool = get_Pool(host=CURW_SIM_HOST,
user=CURW_SIM_USERNAME,
password=CURW_SIM_PASSWORD,
port=CURW_SIM_PORT,
db=CURW_SIM_DATABASE)
TS = TideTS(pool=curw_sim_pool)
tide_ts = TS.get_timeseries(id_=tide_id,
start_date=start,
end_date=end)
tide_data = []
timeseries = tide_ts
for i in range(len(timeseries)):
time_col = (str(
'%.3f' %
(((timeseries[i][0] - timeseries[0][0]).total_seconds()) /
3600))).rjust(16)
value_col = (str('%.3f' % (timeseries[i][1]))).rjust(16)
tide_data.append('S' + time_col + value_col)
outflow = []
outflow.append('K 91')
outflow.append('K 171')
outflow.append('K 214')
outflow.append('K 491')
outflow.append('N 134 1')
outflow.extend(tide_data)
outflow.append('N 220 1')
outflow.extend(tide_data)
outflow.append('N 261 1')
outflow.extend(tide_data)
outflow.append('N 558 1')
outflow.extend(tide_data)
write_to_file(outflow_file_path, data=outflow)
tail_file_path = os.path.join(os.getcwd(), 'outflow', 'tail_150.txt')
tail_file = open(tail_file_path, "r")
tail = tail_file.read()
tail_file.close()
append_file_to_file(outflow_file_path, file_content=tail)
except Exception as e:
print(traceback.print_exc())
finally:
destroy_Pool(curw_sim_pool)
print("Outflow generated")
def update_tide_obs(curw_sim_pool, obs_connection, method, timestep, start_time, end_time, flo2d_model="flo2d"):
# [station_name,latitude,longitude,target]
extract_stations = read_csv('grids/tide_stations/extract_stations.csv')
extract_stations_dict = {} # keys: station_name , value: [latitude, longitude, target_model]
for obs_index in range(len(extract_stations)):
extract_stations_dict[extract_stations[obs_index][0]] = [extract_stations[obs_index][1],
extract_stations[obs_index][2],
extract_stations[obs_index][3]]
for station_name in extract_stations_dict.keys():
meta_data = {
'latitude': float('%.6f' % float(extract_stations_dict.get(station_name)[0])),
'longitude': float('%.6f' % float(extract_stations_dict.get(station_name)[1])),
'model': flo2d_model, 'method': method,
'grid_id': 'tide_{}'.format(station_name)
}
TS = Timeseries(pool=curw_sim_pool)
tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)
if tms_id is None:
tms_id = TS.generate_timeseries_id(meta_data=meta_data)
meta_data['id'] = tms_id
TS.insert_run(meta_data=meta_data)
processed_tide_ts = prepare_obs_tide_ts(connection=obs_connection, start_date=start_time, end_date=end_time,
extract_station=station_name)
for i in range(len(processed_tide_ts)):
if len(processed_tide_ts[i]) < 2:
processed_tide_ts.remove(processed_tide_ts[i])
final_tide_ts = fill_ts_missing_entries(start=start_time, end=end_time, timeseries=processed_tide_ts,
interpolation_method='linear', timestep=timestep)
if final_tide_ts is not None and len(final_tide_ts) > 0:
TS.insert_data(timeseries=final_tide_ts, tms_id=tms_id, upsert=True)
meta_data = {
'latitude':
float('%.6f' %
float(extract_stations_dict.get(station_name)[0])),
'longitude':
float('%.6f' %
float(extract_stations_dict.get(station_name)[1])),
'model':
extract_stations_dict.get(station_name)[2],
'method':
method,
'grid_id':
'tide_{}'.format(station_name)
}
TS = Timeseries(pool=curw_sim_pool)
tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)
start_date = (datetime.now() -
timedelta(days=10)).strftime('%Y-%m-%d %H:00:00')
end_date = (datetime.now() + timedelta(
hours=5, minutes=30)).strftime('%Y-%m-%d %H:00:00')
if tms_id is None:
tms_id = TS.generate_timeseries_id(meta_data=meta_data)
meta_data['id'] = tms_id
TS.insert_run(meta_data=meta_data)
else:
obs_end = TS.get_obs_end(tms_id)
if obs_end is not None:
meta_data = {
'latitude':
float('%.6f' %
float(extract_stations_dict.get(station_name)[0])),
'longitude':
float('%.6f' %
float(extract_stations_dict.get(station_name)[1])),
'model':
extract_stations_dict.get(station_name)[2],
'method':
method,
'grid_id':
'tide_{}'.format(station_name)
}
TS = Timeseries(pool=curw_sim_pool)
tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)
if tms_id is None:
tms_id = TS.generate_timeseries_id(meta_data=meta_data)
meta_data['id'] = tms_id
TS.insert_run(meta_data=meta_data)
obs_end = TS.get_obs_end(id_=tms_id)
ts_end = TS.get_timeseries_end(id_=tms_id)
processed_tide_ts = []
if method in ('TSF'):