def prepare_inflow(inflow_file_path, start, end, discharge_id, wl_id):
obs_wl = None
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)
curw_obs_pool = get_Pool(host=CURW_OBS_HOST, user=CURW_OBS_USERNAME, password=CURW_OBS_PASSWORD, port=CURW_OBS_PORT,
db=CURW_OBS_DATABASE)
connection = curw_obs_pool.connection()
# Extract discharge series
with connection.cursor() as cursor1:
obs_end = datetime.strptime(start, COMMON_DATE_TIME_FORMAT) + timedelta(hours=10)
cursor1.callproc('getWL', (wl_id, start, obs_end))
result = cursor1.fetchone()
obs_wl = result.get('value')
if obs_wl is None:
obs_wl = 0.5
TS = DisTS(pool=curw_sim_pool)
discharge_ts = TS.get_timeseries(id_=discharge_id, start_date=start, end_date=end)
inflow = []
inflow.append('0 0')
inflow.append('C 0 8655')
inflow.append('H 0 0')
timeseries = discharge_ts
for i in range(1, len(timeseries)):
time_col = (str('%.1f' % (((timeseries[i][0] - timeseries[0][0]).total_seconds())/3600))).rjust(16)
value_col = (str('%.1f' % (timeseries[i][1]))).rjust(16)
inflow.append('H' + time_col + value_col)
inflow.append('R 2265{}'.format((str(obs_wl)).rjust(16)))
inflow.append('R 3559 6.6')
write_to_file(inflow_file_path, data=inflow)
except Exception as e:
print(traceback.print_exc())
finally:
connection.close()
destroy_Pool(curw_obs_pool)
destroy_Pool(curw_sim_pool)
print("Inflow generated")
def prepare_inflow_150(inflow_file_path, start, end, discharge_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)
# Extract discharge series
TS = DisTS(pool=curw_sim_pool)
discharge_ts = TS.get_timeseries(id_=discharge_id, start_date=start, end_date=end)
inflow = []
inflow.append('0 41550')
inflow.append('C 0 41550')
inflow.append('H 0 0')
timeseries = discharge_ts
for i in range(1, len(timeseries)):
time_col = (str('%.1f' % (((timeseries[i][0] - timeseries[0][0]).total_seconds()) / 3600))).rjust(16)
value_col = (str('%.1f' % (timeseries[i][1]))).rjust(16)
inflow.append('H' + time_col + value_col)
write_to_file(inflow_file_path, data=inflow)
except Exception as e:
print(traceback.print_exc())
finally:
destroy_Pool(curw_sim_pool)
print("Inflow generated")
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 extract_active_curw_obs_rainfall_stations(start_time, end_time):
"""
Extract currently active (active within last week) rainfall obs stations
:return:
"""
# Connect to the database
pool = get_Pool(host=con_params.CURW_OBS_HOST, port=con_params.CURW_OBS_PORT, user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD, db=con_params.CURW_OBS_DATABASE)
obs_stations = [['hash_id', 'station_id', 'station_name', 'latitude', 'longitude']]
connection = pool.connection()
try:
with connection.cursor() as cursor1:
cursor1.callproc('getActiveRfStationsAtGivenTime', (start_time, end_time))
results = cursor1.fetchall()
for result in results:
obs_stations.append([result.get('hash_id'), result.get('station_id'), result.get('station_name'),
result.get('latitude'), result.get('longitude')])
# Write to csv file
create_csv(os.path.join(ROOT_DIR, 'grids/obs_stations/rainfall/curw_active_rainfall_obs_stations.csv'), obs_stations)
except Exception as ex:
traceback.print_exc()
finally:
connection.close()
destroy_Pool(pool)
def import_old_data():
# Connect to the database
pool = get_Pool(host=CURW_FCST_HOST,
user=CURW_FCST_USERNAME,
password=CURW_FCST_PASSWORD,
port=CURW_FCST_PORT,
db=CURW_FCST_DATABASE)
connection = pool.connection()
curw_fcst_new_to_old_hash_id_mapping = read_csv(
"curw_fcst_new_to_old_hash_id_mapping.csv")
TS = Timeseries(pool=pool)
try:
for hash_index in range(len(curw_fcst_new_to_old_hash_id_mapping)):
print("##### Hash index: ", hash_index, " #####")
fgt_list = []
# Extract fgts
with connection.cursor() as cursor1:
sql_statement = "select distinct `fgt` from `data_v3` where `id`=%s order by `fgt` desc;"
cursor1.execute(
sql_statement,
curw_fcst_new_to_old_hash_id_mapping[hash_index][1])
fgts = cursor1.fetchall()
for fgt in fgts:
fgt_list.append(fgt.get('fgt'))
for fgt in fgt_list:
timeseries = []
with connection.cursor() as cursor2:
sql_statement = "select * from `data_v3` where `id`=%s and `fgt`=%s;"
cursor2.execute(
sql_statement,
(curw_fcst_new_to_old_hash_id_mapping[hash_index][1],
fgt))
results = cursor2.fetchall()
for result in results:
timeseries.append([
curw_fcst_new_to_old_hash_id_mapping[hash_index]
[0],
result.get('time'),
result.get('fgt'),
result.get('value')
])
TS.insert_data(timeseries=timeseries, upsert=True)
TS.update_start_date(
id_=curw_fcst_new_to_old_hash_id_mapping[hash_index][0],
start_date=fgt)
except Exception as ex:
traceback.print_exc()
finally:
connection.close()
destroy_Pool(pool=pool)
print()
def prepare_mike_rf_input(start, end, step):
try:
mike_obs_stations = read_csv(os.path.join(ROOT_DIRECTORY, 'inputs', 'params', 'mike_rainfall_stations.csv'))
# [hash_id,station_id,station_name,latitude,longitude]
station_dict = {}
for i in range(len(mike_obs_stations)):
# { station_id: [station_hash_id, station_name]
station_dict[mike_obs_stations[i][1]] = [mike_obs_stations[i][0], mike_obs_stations[i][2]]
ts_df = pd.DataFrame()
ts_df['time'] = pd.date_range(start=start, end=end, freq='{}min'.format(step))
obs_pool = get_Pool(host=con_params.CURW_OBS_HOST, port=con_params.CURW_OBS_PORT, user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD,
db=con_params.CURW_OBS_DATABASE)
connection = obs_pool.connection()
for obs_id in station_dict.keys():
ts = extract_obs_rain_custom_min_intervals(connection=connection, id=station_dict.get(obs_id)[0],
time_step=step, start_time=start, end_time=end)
ts.insert(0, ['time', obs_id])
df = list_of_lists_to_df_first_row_as_columns(ts)
df[obs_id] = df[obs_id].astype('float64')
ts_df = pd.merge(ts_df, df, how="left", on='time')
ts_df.set_index('time', inplace=True)
mike_input = replace_negative_numbers_with_nan(ts_df)
# pd.set_option('display.max_rows', mike_input.shape[0] + 1)
# pd.set_option('display.max_columns', mike_input.shape[1] + 1)
# print(mike_input)
# print("#######################################")
# mike_input = replace_nan_with_empty_string(mike_input)
mike_input = mike_input.fillna('')
mike_input = mike_input.round(1)
# pd.set_option('display.max_rows', mike_input.shape[0] + 1)
# pd.set_option('display.max_columns', mike_input.shape[1] + 1)
# print(mike_input)
# print("#######################################")
for col in mike_input.columns:
mike_input = mike_input.rename(columns={col: station_dict.get(col)[1]})
# pd.set_option('display.max_rows', mike_input.shape[0]+1)
# pd.set_option('display.max_columns', mike_input.shape[1]+1)
# print(mike_input)
return mike_input
except Exception:
traceback.print_exc()
finally:
connection.close()
destroy_Pool(obs_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 extract_active_curw_obs_rainfall_stations():
"""
Extract currently active (active within last week) rainfall obs stations
:return:
"""
# Connect to the database
pool = get_Pool(host=CURW_OBS_HOST,
port=CURW_OBS_PORT,
user=CURW_OBS_USERNAME,
password=CURW_OBS_PASSWORD,
db=CURW_OBS_DATABASE)
obs_stations = [[
'hash_id', 'station_id', 'station_name', 'latitude', 'longitude'
]]
connection = pool.connection()
try:
with connection.cursor() as cursor1:
cursor1.callproc(procname='getActiveRainfallObsStations')
results = cursor1.fetchall()
for result in results:
# if (result.get('station_id') != 100083):
obs_stations.append([
result.get('hash_id'),
result.get('station_id'),
result.get('station_name'),
result.get('latitude'),
result.get('longitude')
])
# Write to csv file
create_csv(
'grids/obs_stations/rainfall/curw_active_rainfall_obs_stations.csv',
obs_stations)
except Exception as ex:
traceback.print_exc()
finally:
connection.close()
destroy_Pool(pool)
def create_inflow(dir_path, run_date, run_time):
try:
config_path = os.path.join(os.getcwd(), 'inflowdat', 'config.json')
config = json.loads(open(config_path).read())
start_date_time = datetime.strptime('%s %s' % (run_date, run_time), '%Y-%m-%d %H:%M:%S')
start = (datetime.strptime(start_date_time.strftime('%Y-%m-%d 00:00:00'), '%Y-%m-%d %H:%M:%S') - timedelta(days=2))\
.strftime('%Y-%m-%d %H:%M:%S')
end = (datetime.strptime(start, '%Y-%m-%d %H:%M:%S') + timedelta(days=5)).strftime('%Y-%m-%d %H:%M:%S')
target_stations = read_attribute_from_config_file('station_names', config, True)
output_dir = dir_path
file_name = 'INFLOW.DAT'
pool = get_Pool(host=FCST_HOST, port=FCST_PORT, user=FCST_USER, password=FCST_PASSWORD, db=FCST_DB)
hechms_stations = get_hechms_stations(pool=pool)
target_station_ids = []
for i in range(len(target_stations)):
target_station_ids.append(hechms_stations.get(target_stations[i])[0])
print("target stations: ", target_station_ids)
obs_wl = get_obs_waterlevel(station_id=OBS_WL_ID, start=start)
if obs_wl is None:
obs_wl = 0.5
fcst_discharges = extract_fcst_discharge_ts(pool=pool, start=start, end=end, station_ids=target_station_ids)
for id in target_station_ids:
# file = '{}_{}'.format(id, file_name)
file_path = os.path.join(output_dir, file_name)
prepare_inflow(inflow_file_path=file_path, fcst_discharge_ts=fcst_discharges.get(id), obs_wl=obs_wl)
except Exception as e:
traceback.print_exc()
finally:
destroy_Pool(pool)
out_file_path = os.path.join(output_dir, output_file_name)
if not os.path.exists(out_file_path):
msg = 'no file :: {}'.format(out_file_path)
logger.warning(msg)
print(msg)
exit(1)
fgt = get_file_last_modified_time(out_file_path)
print("fgt, ", fgt)
timeseries = read_csv(out_file_path)
pool = get_Pool(host=CURW_FCST_HOST,
port=CURW_FCST_PORT,
db=CURW_FCST_DATABASE,
user=CURW_FCST_USERNAME,
password=CURW_FCST_PASSWORD)
hechms_stations = get_hechms_stations(pool=pool)
station_id = hechms_stations.get(station_name)[0]
lat = str(hechms_stations.get(station_name)[1])
lon = str(hechms_stations.get(station_name)[2])
source_id = get_source_id(pool=pool, model=model, version=version)
variable_id = get_variable_id(pool=pool, variable=variable)
unit_id = get_unit_id(pool=pool, unit=unit, unit_type=unit_type)
if 'start_date' in config and (config['start_date'] != ""):
run_date_str = config['start_date']
# fgt = (datetime.strptime(run_date_str, '%Y-%m-%d') + timedelta(days=1)) \
# .strftime('%Y-%m-%d 23:45:00')
else:
run_date_str = (datetime.now() -
timedelta(days=1)).strftime('%Y-%m-%d')
# fgt = datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S')
daily_dir = 'STATIONS_{}'.format(run_date_str)
output_dir = os.path.join(wrf_dir, daily_dir)
pool = get_Pool(host=host,
port=port,
user=user,
password=password,
db=db)
wrf_v3_stations = get_wrf_stations(pool)
# # Retrieve db version.
# conn = pool.get_conn()
# with conn.cursor() as cursor:
# cursor.execute("SELECT VERSION()")
# data = cursor.fetchone()
# logger.info("Database version : %s " % data)
# if conn is not None:
# pool.release(conn)
variable_id = get_variable_id(pool=pool, variable=variable)
def extract_distrubuted_hechms_outputs(target_model, db_user, db_pwd, db_host,
db_name, out_file_path, run_date,
run_time):
"""
Config.json
{
"output_file_name": "DailyDischarge.csv",
"output_dir": "",
"run_date": "2019-05-24",
"run_time": "00:00:00",
"utc_offset": "",
"sim_tag": "hourly_run",
"model": "HECHMS",
"version": "single",
"unit": "m3/s",
"unit_type": "Instantaneous",
"variable": "Discharge",
"station_name": "Hanwella"
}
"""
try:
config = json.loads(
open('/home/curw/git/distributed_hechms/uploads/config.json').read(
))
# output related details
run_date = run_date
run_time = run_time
utc_offset = read_attribute_from_config_file('utc_offset', config,
False)
if utc_offset is None:
utc_offset = ''
# sim tag
sim_tag = read_attribute_from_config_file('sim_tag', config, True)
print("extract_distrubuted_hechms_outputs|sim_tag : ", sim_tag)
# source details
model = read_attribute_from_config_file('model', config, True)
print("extract_distrubuted_hechms_outputs|model : ", model)
version_config = read_attribute_from_config_file(
'version_config', config, True)
print("extract_distrubuted_hechms_outputs|version_config : ",
version_config)
version = version_config[target_model]
print("extract_distrubuted_hechms_outputs|version : ", version)
# unit details
unit = read_attribute_from_config_file('unit', config, True)
print("extract_distrubuted_hechms_outputs|unit : ", unit)
unit_type = UnitType.getType(
read_attribute_from_config_file('unit_type', config, True))
print("extract_distrubuted_hechms_outputs|unit_type : ", unit_type)
# variable details
variable = read_attribute_from_config_file('variable', config, True)
print("extract_distrubuted_hechms_outputs|variable : ", variable)
# station details
station_name = read_attribute_from_config_file('station_name', config,
True)
print("extract_distrubuted_hechms_outputs|station_name : ",
station_name)
if not os.path.exists(out_file_path):
msg = 'no file :: {}'.format(out_file_path)
logger.warning(msg)
print(msg)
exit(1)
fgt = get_file_last_modified_time(out_file_path)
print("extract_distrubuted_hechms_outputs|fgt : ", fgt)
timeseries = read_csv(out_file_path)
pool = get_Pool(host=db_host,
port=3306,
db=db_name,
user=db_user,
password=db_pwd)
hechms_stations = get_hechms_stations(pool=pool)
print("extract_distrubuted_hechms_outputs|hechms_stations : ",
hechms_stations)
station_id = hechms_stations.get(station_name)[0]
lat = str(hechms_stations.get(station_name)[1])
lon = str(hechms_stations.get(station_name)[2])
print("extract_distrubuted_hechms_outputs|[station_id, lat, lon] : ",
[station_id, lat, lon])
source_id = get_source_id(pool=pool, model=model, version=version)
print("extract_distrubuted_hechms_outputs|source_id : ", source_id)
variable_id = get_variable_id(pool=pool, variable=variable)
print("extract_distrubuted_hechms_outputs|variable_id : ", variable_id)
unit_id = get_unit_id(pool=pool, unit=unit, unit_type=unit_type)
print("extract_distrubuted_hechms_outputs|unit_id : ", unit_id)
tms_meta = {
'sim_tag': sim_tag,
'model': model,
'version': version,
'variable': variable,
'unit': unit,
'unit_type': unit_type.value,
'latitude': lat,
'longitude': lon,
'station_id': station_id,
'source_id': source_id,
'variable_id': variable_id,
'unit_id': unit_id
}
print("extract_distrubuted_hechms_outputs|tms_meta : ", tms_meta)
utcOffset = getUTCOffset(utc_offset, default=True)
if utcOffset != timedelta():
tms_meta['utcOffset'] = utcOffset
# Push timeseries to database
save_forecast_timeseries_to_db(pool=pool,
timeseries=timeseries,
run_date=run_date,
run_time=run_time,
tms_meta=tms_meta,
fgt=fgt)
return {'Result': 'Success'}
except Exception as e:
logger.error('JSON config data loading error.')
print('JSON config data loading error.')
traceback.print_exc()
return {'Result': 'Fail'}
def insert_curw_obs_runs():
hash_mapping = [['old_hash_id', 'new_hash_id']]
try:
# pool = get_Pool(host=CURW_OBS_HOST, port=CURW_OBS_PORT, user=CURW_OBS_USERNAME, password=CURW_OBS_PASSWORD,
# db=CURW_OBS_DATABASE)
pool = get_Pool(host=HOST,
port=PORT,
user=USERNAME,
password=PASSWORD,
db=DATABASE)
curw_old_obs_entries = read_csv('all_curw_obs.csv')
for old_index in range(len(curw_old_obs_entries)):
meta_data = {}
old_hash_id = curw_old_obs_entries[old_index][0]
run_name = curw_old_obs_entries[old_index][1]
station_name = curw_old_obs_entries[old_index][4]
latitude = curw_old_obs_entries[old_index][5]
longitude = curw_old_obs_entries[old_index][6]
description = curw_old_obs_entries[old_index][7]
variable = curw_old_obs_entries[old_index][8]
unit = curw_old_obs_entries[old_index][9]
unit_type = curw_old_obs_entries[old_index][10]
meta_data['run_name'] = run_name
meta_data['variable'] = variable
meta_data['unit'] = unit
meta_data['unit_type'] = unit_type
meta_data['latitude'] = latitude
meta_data['longitude'] = longitude
if variable == "WaterLevel":
station_type = StationEnum.CUrW_WaterLevelGauge
else:
station_type = StationEnum.CUrW_WeatherStation
meta_data['station_type'] = StationEnum.getTypeString(station_type)
unit_id = get_unit_id(pool=pool,
unit=unit,
unit_type=UnitType.getType(unit_type))
if unit_id is None:
add_unit(pool=pool,
unit=unit,
unit_type=UnitType.getType(unit_type))
unit_id = get_unit_id(pool=pool,
unit=unit,
unit_type=UnitType.getType(unit_type))
variable_id = get_variable_id(pool=pool, variable=variable)
if variable_id is None:
add_variable(pool=pool, variable=variable)
variable_id = get_variable_id(pool=pool, variable=variable)
station_id = get_station_id(pool=pool,
latitude=latitude,
longitude=longitude,
station_type=station_type)
if station_id is None:
add_station(pool=pool,
name=station_name,
latitude=latitude,
longitude=longitude,
station_type=station_type,
description=description)
station_id = get_station_id(pool=pool,
latitude=latitude,
longitude=longitude,
station_type=station_type)
TS = Timeseries(pool=pool)
tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)
meta_data['station_id'] = station_id
meta_data['variable_id'] = variable_id
meta_data['unit_id'] = unit_id
if tms_id is None:
tms_id = TS.generate_timeseries_id(meta_data=meta_data)
meta_data['tms_id'] = tms_id
TS.insert_run(run_meta=meta_data)
hash_mapping.append([old_hash_id, tms_id])
create_csv(file_name='curw_to_curw_obs_hash_id_mapping.csv',
data=hash_mapping)
except Exception:
traceback.print_exc()
print(
"Exception occurred while inserting run entries to curw_obs run table and making hash mapping"
)
finally:
destroy_Pool(pool=pool)
config = json.loads(open('MME_config.json').read())
start = read_attribute_from_config_file('start_time', config, True)
end = read_attribute_from_config_file('end_time', config, True)
target_stations = read_attribute_from_config_file(
'station_names', config, True)
output_dir = read_attribute_from_config_file('output_dir', config,
True)
file_name = read_attribute_from_config_file('output_file_name', config,
True)
pool = get_Pool(host=FCST_HOST,
port=FCST_PORT,
user=FCST_USER,
password=FCST_PASSWORD,
db=FCST_DB)
hechms_stations = get_hechms_stations(pool=pool)
target_station_ids = []
for i in range(len(target_stations)):
target_station_ids.append(
hechms_stations.get(target_stations[i])[0])
print("target stations: ", target_station_ids)
obs_wl = get_obs_waterlevel(station_id=OBS_WL_ID, start=start)
if obs_wl is None:
obs_wl = 0.5
def prepare_mike_rf_input(start, end):
try:
mike_obs_stations = read_csv(
os.path.join(ROOT_DIRECTORY, 'inputs', 'params',
'mike_rainfall_stations.csv'))
selected_obs_ids = []
for list in mike_obs_stations:
selected_obs_ids.append(str(list[1]))
# [hash_id,station_id,station_name,latitude,longitude]
#### process staton based hybrid timeseries ####
hybrid_ts_df = pd.DataFrame()
hybrid_ts_df['time'] = pd.date_range(start=start, end=end, freq='5min')
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)
obs_pool = get_Pool(host=con_params.CURW_OBS_HOST,
port=con_params.CURW_OBS_PORT,
user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD,
db=con_params.CURW_OBS_DATABASE)
TS = Timeseries(pool)
obs_id_hash_id_mapping = get_all_obs_rain_hashids_from_curw_sim(pool)
obs_stations = extract_active_curw_obs_rainfall_stations(
curw_obs_pool=obs_pool, start_time=start, end_time=end)
obs_obs_mapping = find_nearest_obs_stations_for_mike_rainfall_stations(
mike_obs_stations=mike_obs_stations, obs_stations=obs_stations)
for obs_id in selected_obs_ids:
if obs_id in obs_id_hash_id_mapping.keys():
# taking data from curw_sim database (data prepared based on active stations for hechms)
ts = TS.get_timeseries(id_=obs_id_hash_id_mapping.get(obs_id),
start_date=start,
end_date=end)
else:
ts = []
ts.insert(0, ['time', obs_id])
ts_df = list_of_lists_to_df_first_row_as_columns(ts)
ts_df[obs_id] = ts_df[obs_id].astype('float64')
hybrid_ts_df = pd.merge(hybrid_ts_df, ts_df, how="left", on='time')
hybrid_ts_df.set_index('time', inplace=True)
hybrid_ts_df = hybrid_ts_df.resample('15min',
label='right',
closed='right').sum()
mike_input = replace_negative_numbers_with_nan(hybrid_ts_df)
for col in mike_input.columns:
if len(obs_obs_mapping[col]) > 0:
print(col, obs_obs_mapping[col][0])
mike_input[col] = mike_input[col].fillna(
mike_input[obs_obs_mapping[col][0]])
if len(obs_obs_mapping[col]) > 1:
print(col, obs_obs_mapping[col][1])
mike_input[col] = mike_input[col].fillna(
mike_input[obs_obs_mapping[col][1]])
mike_input = replace_nan_with_row_average(mike_input)
mike_input = mike_input.round(1)
station_name_dict = {}
for i in range(len(mike_obs_stations)):
station_name_dict[str(
mike_obs_stations[i][1])] = mike_obs_stations[i][2]
for col in mike_input.columns:
mike_input = mike_input.rename(
columns={col: station_name_dict.get(col)})
# pd.set_option('display.max_rows', mike_input.shape[0]+1)
# pd.set_option('display.max_columns', mike_input.shape[1]+1)
# print(mike_input)
return mike_input
except Exception:
traceback.print_exc()
finally:
destroy_Pool(pool)
destroy_Pool(obs_pool)
hechms_stations = read_csv('hechms_stations.csv')
# pool = get_Pool(host=CURW_FCST_HOST, port=CURW_FCST_PORT, user=CURW_FCST_USERNAME, password=CURW_FCST_PASSWORD,
# db=CURW_FCST_DATABASE)
# ########
# # test
# ########
USERNAME = "******"
PASSWORD = "******"
HOST = "127.0.0.1"
PORT = 3306
DATABASE = "curw_fcst"
pool = get_Pool(host=HOST, port=PORT, user=USERNAME, password=PASSWORD, db=DATABASE)
add_source(pool=pool, model=model, version=version, parameters=None)
add_variable(pool=pool, variable=variable)
add_unit(pool=pool, unit=unit, unit_type=unit_type)
# add hechms output stations
for i in range(len(hechms_stations)):
station_name = hechms_stations[i][0]
lat = hechms_stations[i][1]
lon = hechms_stations[i][2]
add_station(pool=pool, name=station_name, latitude="%.6f" % float(lat), longitude="%.6f" % float(lon),
station_type=StationEnum.HECHMS, description="hecHMS output station")
destroy_Pool(pool=pool)
def prepare_mike_rf_input(start, end, coefficients):
try:
#### process staton based hybrid timeseries ####
distinct_obs_ids = coefficients.curw_obs_id.unique()
hybrid_ts_df = pd.DataFrame()
hybrid_ts_df['time'] = pd.date_range(start=start, end=end, freq='5min')
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)
obs_id_hash_id_mapping = get_all_obs_rain_hashids_from_curw_sim(pool)
for obs_id in distinct_obs_ids:
# taking data from curw_sim database (data prepared based on active stations for hechms)
ts = TS.get_timeseries(id_=obs_id_hash_id_mapping.get(str(obs_id)),
start_date=start,
end_date=end)
ts.insert(0, ['time', obs_id])
ts_df = list_of_lists_to_df_first_row_as_columns(ts)
ts_df[obs_id] = ts_df[obs_id].astype('float64')
hybrid_ts_df = pd.merge(hybrid_ts_df, ts_df, how="left", on='time')
hybrid_ts_df.set_index('time', inplace=True)
hybrid_ts_df = hybrid_ts_df.resample('15min',
label='right',
closed='right').sum()
# pd.set_option('display.max_rows', hybrid_ts_df.shape[0]+1)
# pd.set_option('display.max_columns', hybrid_ts_df.shape[1]+1)
# print(hybrid_ts_df)
hybrid_ts_df = replace_negative_numbers_with_nan(hybrid_ts_df)
# print(hybrid_ts_df)
hybrid_ts_df = replace_nan_with_row_average(hybrid_ts_df)
# print(hybrid_ts_df)
#### process mike input ####
distinct_names = coefficients.name.unique()
mike_input = pd.DataFrame()
mike_input_initialized = False
for name in distinct_names:
catchment_coefficients = coefficients[coefficients.name == name]
# print(catchment_coefficients)
catchment = pd.DataFrame()
catchment_initialized = False
for index, row in catchment_coefficients.iterrows():
# print(index, row['curw_obs_id'], row['coefficient'])
if not catchment_initialized:
catchment = (hybrid_ts_df[row['curw_obs_id']] *
row['coefficient']).to_frame(
name=row['curw_obs_id'])
catchment_initialized = True
else:
new = (hybrid_ts_df[row['curw_obs_id']] *
row['coefficient']).to_frame(
name=row['curw_obs_id'])
catchment = pd.merge(catchment, new, how="left", on='time')
if not mike_input_initialized:
mike_input[name] = catchment.sum(axis=1)
mike_input_initialized = True
else:
mike_input = pd.merge(
mike_input, (catchment.sum(axis=1)).to_frame(name=name),
how="left",
on='time')
mike_input.round(1)
return mike_input
except Exception:
traceback.print_exc()
finally:
destroy_Pool(pool)
file_name = read_attribute_from_config_file('output_file_name', config)
dis_id = read_attribute_from_config_file('dis_id', config)
if start_time is None:
start_time = (datetime.now() - timedelta(days=3)).strftime('%Y-%m-%d 00:00:00')
else:
check_time_format(time=start_time)
if end_time is None:
end_time = (datetime.now() + timedelta(days=2)).strftime('%Y-%m-%d 00:00:00')
else:
check_time_format(time=end_time)
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)
latest_fgt = TS.get_obs_end(dis_id)
if output_dir is None:
output_dir = os.path.join(OUTPUT_DIRECTORY, (datetime.utcnow() + timedelta(hours=5, minutes=30)).strftime('%Y-%m-%d_%H-00-00'))
if file_name is None:
file_name = 'mike_dis.txt' #.format(latest_fgt.strftime('%Y-%m-%d_%H-%M-00'))
mike_dis_file_path = os.path.join(output_dir, file_name)
if not os.path.isfile(mike_dis_file_path):
makedir_if_not_exist_given_filepath(mike_dis_file_path)
print("{} start preparing mike discharge input".format(datetime.now()))
start_time = (datetime.now() -
timedelta(days=3)).strftime('%Y-%m-%d 23:00:00')
else:
check_time_format(time=start_time, model=flo2d_model)
if end_time is None:
end_time = (datetime.now() +
timedelta(days=1)).strftime('%Y-%m-%d 23:00:00')
else:
check_time_format(time=end_time, model=flo2d_model)
timestep = 60
curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST,
user=con_params.CURW_SIM_USERNAME,
password=con_params.CURW_SIM_PASSWORD,
port=con_params.CURW_SIM_PORT,
db=con_params.CURW_SIM_DATABASE)
curw_fcst_pool = get_Pool(host=con_params.CURW_FCST_HOST,
user=con_params.CURW_FCST_USERNAME,
password=con_params.CURW_FCST_PASSWORD,
port=con_params.CURW_FCST_PORT,
db=con_params.CURW_FCST_DATABASE)
print("{} : ####### Insert hechms discharge series for {}.".format(
datetime.now(), flo2d_model))
update_discharge_from_hechms(curw_sim_pool=curw_sim_pool,
curw_fcst_pool=curw_fcst_pool,
flo2d_model=flo2d_model,
method=method,
def prepare_rfields(root_dir,
start_time,
end_time,
target_model,
interpolation_method="MME"):
"""
Create rfields for flo2d
:param root_dir: rfield root directory
:param start_time:
:param end_time:
:param target_model: FLO2D model (e.g. flo2d_250, flo2d_150)
:param interpolation_method: value interpolation method (e.g. "MME")
:return:
"""
end_time = datetime.strptime(end_time, COMMON_DATE_TIME_FORMAT)
start_time = datetime.strptime(start_time, COMMON_DATE_TIME_FORMAT)
if end_time < start_time:
exit(1)
length = 0
if target_model == "flo2d_250":
length = 9348
timestep = 5
elif target_model == "flo2d_150":
length = 41767
timestep = 15
pool = get_Pool(host=CURW_SIM_HOST,
port=CURW_SIM_PORT,
user=CURW_SIM_USERNAME,
db=CURW_SIM_DATABASE,
password=CURW_SIM_PASSWORD)
connection = pool.connection()
try:
timestamp = start_time
while timestamp <= end_time:
raincell = []
# Extract raincell from db
with connection.cursor() as cursor1:
cursor1.callproc(
'prepare_flo2d_raincell',
(target_model, interpolation_method, timestamp))
results = cursor1.fetchall()
for result in results:
raincell.append(str(result.get('value')))
day = timestamp.date()
try:
os.makedirs("{}/{}".format(root_dir, day))
except FileExistsError:
# directory already exists
pass
if len(raincell) == length:
write_to_file(
"{}/{}/{}_{}.txt".format(
root_dir, day, interpolation_method,
timestamp.strftime('%Y-%m-%d_%H-%M')), raincell)
timestamp = timestamp + timedelta(minutes=timestep)
except Exception as ex:
traceback.print_exc()
finally:
connection.close()
print("{} {} generation process completed".format(
datetime.now(), target_model))
# variable details
variable = read_attribute_from_config_file('variable', config, True)
# station details
station_name = read_attribute_from_config_file('station_name', config,
True)
out_file_path = os.path.join(output_dir, run_date, run_time,
output_file_name)
timeseries = read_csv(out_file_path)
pool = get_Pool(host=db_config['host'],
port=db_config['port'],
user=db_config['user'],
password=db_config['password'],
db=db_config['db'])
hechms_stations = get_hechms_stations(pool=pool)
station_id = hechms_stations.get(station_name)[0]
lat = str(hechms_stations.get(station_name)[1])
lon = str(hechms_stations.get(station_name)[2])
source_id = get_source_id(pool=pool, model=model, version=version)
variable_id = get_variable_id(pool=pool, variable=variable)
unit_id = get_unit_id(pool=pool, unit=unit, unit_type=unit_type)
def update_rainfall_obs(flo2d_model, method, grid_interpolation, timestep,
start_time, end_time):
"""
Update rainfall observations for flo2d models
:param flo2d_model: flo2d model
:param method: value interpolation method
:param grid_interpolation: grid interpolation method
:param timestep: output timeseries timestep
:return:
"""
obs_start = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')
try:
# Connect to the database
curw_obs_pool = get_Pool(host=con_params.CURW_OBS_HOST,
user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD,
port=con_params.CURW_OBS_PORT,
db=con_params.CURW_OBS_DATABASE)
curw_obs_connection = curw_obs_pool.connection()
curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST,
user=con_params.CURW_SIM_USERNAME,
password=con_params.CURW_SIM_PASSWORD,
port=con_params.CURW_SIM_PORT,
db=con_params.CURW_SIM_DATABASE)
TS = Sim_Timeseries(pool=curw_sim_pool)
# [hash_id, station_id, station_name, latitude, longitude]
# active_obs_stations = read_csv(os.path.join(ROOT_DIR,'grids/obs_stations/rainfall/curw_active_rainfall_obs_stations.csv'))
active_obs_stations = extract_active_curw_obs_rainfall_stations(
start_time=start_time, end_time=end_time)[1:]
flo2d_grids = read_csv(
os.path.join(ROOT_DIR, 'grids/flo2d/{}m.csv'.format(
flo2d_model))) # [Grid_ ID, X(longitude), Y(latitude)]
stations_dict_for_obs = {} # keys: obs station id , value: hash id
for obs_index in range(len(active_obs_stations)):
stations_dict_for_obs[active_obs_stations[obs_index]
[1]] = active_obs_stations[obs_index][0]
# flo2d_obs_mapping = get_flo2d_cells_to_obs_grid_mappings(pool=curw_sim_pool, grid_interpolation=grid_interpolation, flo2d_model=flo2d_model)
flo2d_obs_mapping = find_nearest_obs_stations_for_flo2d_stations(
flo2d_stations_csv=os.path.join(
ROOT_DIR, 'grids/flo2d/{}m.csv'.format(flo2d_model)),
obs_stations=active_obs_stations,
flo2d_model=flo2d_model)
for flo2d_index in range(len(flo2d_grids)):
lat = flo2d_grids[flo2d_index][2]
lon = flo2d_grids[flo2d_index][1]
cell_id = flo2d_grids[flo2d_index][0]
meta_data = {
'latitude':
float('%.6f' % float(lat)),
'longitude':
float('%.6f' % float(lon)),
'model':
flo2d_model,
'method':
method,
'grid_id':
'{}_{}_{}'.format(flo2d_model, grid_interpolation,
(str(cell_id)).zfill(10))
}
tms_id = TS.get_timeseries_id(grid_id=meta_data.get('grid_id'),
method=meta_data.get('method'))
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)
print("grid_id:", cell_id)
print("grid map:", flo2d_obs_mapping.get(cell_id))
obs1_station_id = flo2d_obs_mapping.get(cell_id)[0]
obs2_station_id = flo2d_obs_mapping.get(cell_id)[1]
obs3_station_id = flo2d_obs_mapping.get(cell_id)[2]
obs_timeseries = []
if timestep == 5:
if obs1_station_id != str(-1):
obs1_hash_id = stations_dict_for_obs.get(obs1_station_id)
ts = extract_obs_rain_5_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs1_hash_id,
end_time=end_time)
if ts is not None and len(ts) > 1:
obs_timeseries.extend(
process_5_min_ts(newly_extracted_timeseries=ts,
expected_start=obs_start)[1:])
# obs_start = ts[-1][0]
if obs2_station_id != str(-1):
obs2_hash_id = stations_dict_for_obs.get(
obs2_station_id)
ts2 = extract_obs_rain_5_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs2_hash_id,
end_time=end_time)
if ts2 is not None and len(ts2) > 1:
obs_timeseries = fill_missing_values(
newly_extracted_timeseries=ts2,
OBS_TS=obs_timeseries)
if obs_timeseries is not None and len(
obs_timeseries) > 0:
expected_start = obs_timeseries[-1][0]
else:
expected_start = obs_start
obs_timeseries.extend(
process_5_min_ts(
newly_extracted_timeseries=ts2,
expected_start=expected_start)[1:])
# obs_start = ts2[-1][0]
if obs3_station_id != str(-1):
obs3_hash_id = stations_dict_for_obs.get(
obs3_station_id)
ts3 = extract_obs_rain_5_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs3_hash_id,
end_time=end_time)
if ts3 is not None and len(ts3) > 1 and len(
obs_timeseries) > 0:
obs_timeseries = fill_missing_values(
newly_extracted_timeseries=ts3,
OBS_TS=obs_timeseries)
if obs_timeseries is not None:
expected_start = obs_timeseries[-1][0]
else:
expected_start = obs_start
obs_timeseries.extend(
process_5_min_ts(
newly_extracted_timeseries=ts3,
expected_start=expected_start)[1:])
elif timestep == 15:
if obs1_station_id != str(-1):
obs1_hash_id = stations_dict_for_obs.get(obs1_station_id)
ts = extract_obs_rain_15_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs1_hash_id,
end_time=end_time)
if ts is not None and len(ts) > 1:
obs_timeseries.extend(
process_15_min_ts(newly_extracted_timeseries=ts,
expected_start=obs_start)[1:])
# obs_start = ts[-1][0]
if obs2_station_id != str(-1):
obs2_hash_id = stations_dict_for_obs.get(
obs2_station_id)
ts2 = extract_obs_rain_15_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs2_hash_id,
end_time=end_time)
if ts2 is not None and len(ts2) > 1:
obs_timeseries = fill_missing_values(
newly_extracted_timeseries=ts2,
OBS_TS=obs_timeseries)
if obs_timeseries is not None and len(
obs_timeseries) > 0:
expected_start = obs_timeseries[-1][0]
else:
expected_start = obs_start
obs_timeseries.extend(
process_15_min_ts(
newly_extracted_timeseries=ts2,
expected_start=expected_start)[1:])
# obs_start = ts2[-1][0]
if obs3_station_id != str(-1):
obs3_hash_id = stations_dict_for_obs.get(
obs3_station_id)
ts3 = extract_obs_rain_15_min_ts(
connection=curw_obs_connection,
start_time=obs_start,
id=obs3_hash_id,
end_time=end_time)
if ts3 is not None and len(ts3) > 1 and len(
obs_timeseries) > 0:
obs_timeseries = fill_missing_values(
newly_extracted_timeseries=ts3,
OBS_TS=obs_timeseries)
if obs_timeseries is not None:
expected_start = obs_timeseries[-1][0]
else:
expected_start = obs_start
obs_timeseries.extend(
process_15_min_ts(
newly_extracted_timeseries=ts3,
expected_start=expected_start)[1:])
for i in range(len(obs_timeseries)):
if obs_timeseries[i][1] == -99999:
obs_timeseries[i][1] = 0
print("### obs timeseries length ###", len(obs_timeseries))
if obs_timeseries is not None and len(
obs_timeseries) > 0 and obs_timeseries[-1][0] != end_time:
obs_timeseries.append(
[datetime.strptime(end_time, DATE_TIME_FORMAT), 0])
final_ts = process_continuous_ts(original_ts=obs_timeseries,
expected_start=datetime.strptime(
start_time, DATE_TIME_FORMAT),
filling_value=0,
timestep=timestep)
if final_ts is not None and len(final_ts) > 0:
TS.insert_data(timeseries=final_ts, tms_id=tms_id, upsert=True)
TS.update_latest_obs(id_=tms_id, obs_end=(final_ts[-1][1]))
except Exception as e:
traceback.print_exc()
logger.error(
"Exception occurred while updating obs rainfalls in curw_sim.")
finally:
curw_obs_connection.close()
destroy_Pool(pool=curw_sim_pool)
destroy_Pool(pool=curw_obs_pool)
logger.info("Process finished")
])
tide_ts = append_ts(original_ts=tide_ts, new_ts=ts)
avg_tide_ts = average_timeseries(tide_ts)
return avg_tide_ts
if __name__ == "__main__":
try:
curw_obs_pool = get_Pool(host=CURW_OBS_HOST,
user=CURW_OBS_USERNAME,
password=CURW_OBS_PASSWORD,
port=CURW_OBS_PORT,
db=CURW_OBS_DATABASE)
connection = curw_obs_pool.connection()
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)
# [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]
def map_curw_id(result):
try:
# if curw_id doesnot exist in the table retrieve meta data to generate curw_obs hash id
curw_id = check_id(result)
if curw_id is None:
print("The event id does not exist in the id_mapping table")
#generate a new obs hash id, for that get meta data
meta_data = get_meta_data(result)
#print("*****************")
#print(meta_data)
dict1 = meta_data[0]
dict2 = meta_data[1]
dict3 = meta_data[2]
dict4 = meta_data[3]
run_name = dict1['run_name']
start_date = dict1['start_date']
station_name = dict2['station_name']
latitude = dict2['latitude']
longitude = dict2['longitude']
description = dict2['description']
variable = dict3['variable']
unit = dict4['unit']
unit_type = dict4['unit-type']
pool = get_Pool(host=CURW_OBS_HOST,
port=CURW_OBS_PORT,
user=CURW_OBS_USERNAME,
password=CURW_OBS_PASSWORD,
db=CURW_OBS_DATABASE)
obs_hash_id = generate_curw_obs_hash_id(
pool,
variable=variable,
unit=unit,
unit_type=unit_type,
latitude=latitude,
longitude=longitude,
run_name=run_name,
station_name=station_name,
description=description,
start_date=start_date.strftime(COMMON_DATE_FORMAT))
#insert the corresponding obs_hash_id to curw_id
insert_id_rows(result, obs_hash_id)
#then extract the time series
timeseries = []
timeseries = extract_timeseries(obs_hash_id)
#print("***********")
#print(timeseries)
#print("***********")
#insert the timeseries in obs_db
insert_timeseries(pool=pool,
timeseries=timeseries,
tms_id=obs_hash_id,
end_date=timeseries[-1][0])
else:
#get the relavant obs_hashId to the curw_id
obs_hash = get_obs_hash(result)
# then extract the time series
timeseries = []
timeseries = extract_timeseries(obs_hash)
#print("*******")
#print(timeseries)
#print(timeseries[-1][0])
#print("*******")
# insert the timeseries in obs_db
insert_timeseries(pool=pool,
timeseries=timeseries,
tms_id=result,
end_date=timeseries[-1][0])
except Exception as e:
traceback.print_exc()
finally:
destroy_Pool(pool=pool)
print("Process finished")
def extract_distrubuted_hechms_outputs(output_file_name, output_dir, run_date,
run_time):
"""
Config.json
{
"output_file_name": "DailyDischarge.csv",
"output_dir": "",
"run_date": "2019-05-24",
"run_time": "00:00:00",
"utc_offset": "",
"sim_tag": "hourly_run",
"model": "HECHMS",
"version": "single",
"unit": "m3/s",
"unit_type": "Instantaneous",
"variable": "Discharge",
"station_name": "Hanwella"
}
"""
try:
config = json.loads(open('config.json').read())
# output related details
output_file_name = output_file_name
output_dir = output_dir
run_date = run_date
run_time = run_time
utc_offset = read_attribute_from_config_file('utc_offset', config,
False)
if utc_offset is None:
utc_offset = ''
# sim tag
sim_tag = read_attribute_from_config_file('sim_tag', config, True)
# source details
model = read_attribute_from_config_file('model', config, True)
version = read_attribute_from_config_file('version', config, True)
# unit details
unit = read_attribute_from_config_file('unit', config, True)
unit_type = UnitType.getType(
read_attribute_from_config_file('unit_type', config, True))
# variable details
variable = read_attribute_from_config_file('variable', config, True)
# station details
station_name = read_attribute_from_config_file('station_name', config,
True)
out_file_path = os.path.join(output_dir, output_file_name)
if not os.path.exists(out_file_path):
msg = 'no file :: {}'.format(out_file_path)
logger.warning(msg)
print(msg)
exit(1)
fgt = get_file_last_modified_time(out_file_path)
print("fgt, ", fgt)
timeseries = read_csv(out_file_path)
pool = get_Pool(host=CURW_FCST_HOST,
port=CURW_FCST_PORT,
db=CURW_FCST_DATABASE,
user=CURW_FCST_USERNAME,
password=CURW_FCST_PASSWORD)
hechms_stations = get_hechms_stations(pool=pool)
station_id = hechms_stations.get(station_name)[0]
lat = str(hechms_stations.get(station_name)[1])
lon = str(hechms_stations.get(station_name)[2])
source_id = get_source_id(pool=pool, model=model, version=version)
variable_id = get_variable_id(pool=pool, variable=variable)
unit_id = get_unit_id(pool=pool, unit=unit, unit_type=unit_type)
tms_meta = {
'sim_tag': sim_tag,
'model': model,
'version': version,
'variable': variable,
'unit': unit,
'unit_type': unit_type.value,
'latitude': lat,
'longitude': lon,
'station_id': station_id,
'source_id': source_id,
'variable_id': variable_id,
'unit_id': unit_id
}
utcOffset = getUTCOffset(utc_offset, default=True)
if utcOffset != timedelta():
tms_meta['utcOffset'] = utcOffset
# Push timeseries to database
save_forecast_timeseries_to_db(pool=pool,
timeseries=timeseries,
run_date=run_date,
run_time=run_time,
tms_meta=tms_meta,
fgt=fgt)
except Exception as e:
logger.error('JSON config data loading error.')
print('JSON config data loading error.')
traceback.print_exc()
finally:
logger.info("Process finished.")
print("Process finished.")
def upload_waterlevels(dir_path, ts_start_date, ts_start_time, run_date, run_time):
"""
Config.json
{
"HYCHAN_OUT_FILE": "HYCHAN.OUT",
"TIMDEP_FILE": "TIMDEP.OUT",
"output_dir": "",
"run_date": "2019-05-24",
"run_time": "",
"ts_start_date": "",
"ts_start_time": "",
"utc_offset": "",
"sim_tag": "",
"model": "WRF",
"version": "v3",
"unit": "mm",
"unit_type": "Accumulative",
"variable": "Precipitation"
}
"""
try:
config_path = os.path.join(os.getcwd(), 'extract', 'config_curw_fcst.json')
config = json.loads(open(config_path).read())
# flo2D related details
HYCHAN_OUT_FILE = read_attribute_from_config_file('HYCHAN_OUT_FILE', config, True)
TIMDEP_FILE = read_attribute_from_config_file('TIMDEP_FILE', config, True)
output_dir = dir_path
run_date = run_date
run_time = run_time
ts_start_date = ts_start_date
ts_start_time = ts_start_time
utc_offset = read_attribute_from_config_file('utc_offset', config, False)
if utc_offset is None:
utc_offset = ''
# sim tag
sim_tag = read_attribute_from_config_file('sim_tag', config, True)
# source details
model = read_attribute_from_config_file('model', config, True)
version = read_attribute_from_config_file('version', config, True)
# unit details
unit = read_attribute_from_config_file('unit', config, True)
unit_type = UnitType.getType(read_attribute_from_config_file('unit_type', config, True))
# variable details
variable = read_attribute_from_config_file('variable', config, True)
hychan_out_file_path = os.path.join(output_dir, HYCHAN_OUT_FILE)
timdep_file_path = os.path.join(output_dir, TIMDEP_FILE)
pool = get_Pool(host=CURW_FCST_HOST, port=CURW_FCST_PORT, db=CURW_FCST_DATABASE, user=CURW_FCST_USERNAME,
password=CURW_FCST_PASSWORD)
# pool = get_Pool(host=HOST, port=PORT, user=USERNAME, password=PASSWORD, db=DATABASE)
flo2d_model_name = '{}_{}'.format(model, version)
flo2d_source = json.loads(get_source_parameters(pool=pool, model=model, version=version))
print("############### source ############", flo2d_source)
flo2d_stations = get_flo2d_output_stations(pool=pool, flo2d_model=StationEnum.getType(flo2d_model_name))
print("############### 1st occurrence ############", flo2d_stations)
source_id = get_source_id(pool=pool, model=model, version=version)
variable_id = get_variable_id(pool=pool, variable=variable)
unit_id = get_unit_id(pool=pool, unit=unit, unit_type=unit_type)
tms_meta = {
'sim_tag': sim_tag,
'model': model,
'version': version,
'variable': variable,
'unit': unit,
'unit_type': unit_type.value,
'source_id': source_id,
'variable_id': variable_id,
'unit_id': unit_id
}
CHANNEL_CELL_MAP = flo2d_source["CHANNEL_CELL_MAP"]
FLOOD_PLAIN_CELL_MAP = flo2d_source["FLOOD_PLAIN_CELL_MAP"]
ELEMENT_NUMBERS = CHANNEL_CELL_MAP.keys()
FLOOD_ELEMENT_NUMBERS = FLOOD_PLAIN_CELL_MAP.keys()
SERIES_LENGTH = 0
MISSING_VALUE = -999
utcOffset = getUTCOffset(utc_offset, default=True)
print('Extract Water Level Result of FLO2D on', run_date, '@', run_time, 'with Base time of', ts_start_date,
'@', ts_start_time)
# Check HYCHAN.OUT file exists
if not os.path.exists(hychan_out_file_path):
print('Unable to find file : ', hychan_out_file_path)
traceback.print_exc()
#####################################
# Calculate the size of time series #
#####################################
bufsize = 65536
with open(hychan_out_file_path) as infile:
isWaterLevelLines = False
isCounting = False
countSeriesSize = 0 # HACK: When it comes to the end of file, unable to detect end of time series
while True:
lines = infile.readlines(bufsize)
if not lines or SERIES_LENGTH:
break
for line in lines:
if line.startswith('CHANNEL HYDROGRAPH FOR ELEMENT NO:', 5):
isWaterLevelLines = True
elif isWaterLevelLines:
cols = line.split()
if len(cols) > 0 and cols[0].replace('.', '', 1).isdigit():
countSeriesSize += 1
isCounting = True
elif isWaterLevelLines and isCounting:
SERIES_LENGTH = countSeriesSize
break
print('Series Length is :', SERIES_LENGTH)
bufsize = 65536
#################################################################
# Extract Channel Water Level elevations from HYCHAN.OUT file #
#################################################################
print('Extract Channel Water Level Result of FLO2D (HYCHAN.OUT) on', run_date, '@', run_time,
'with Base time of',
ts_start_date, '@', ts_start_time)
with open(hychan_out_file_path) as infile:
isWaterLevelLines = False
isSeriesComplete = False
waterLevelLines = []
seriesSize = 0 # HACK: When it comes to the end of file, unable to detect end of time series
while True:
lines = infile.readlines(bufsize)
if not lines:
break
for line in lines:
if line.startswith('CHANNEL HYDROGRAPH FOR ELEMENT NO:', 5):
seriesSize = 0
elementNo = line.split()[5]
if elementNo in ELEMENT_NUMBERS:
isWaterLevelLines = True
waterLevelLines.append(line)
else:
isWaterLevelLines = False
elif isWaterLevelLines:
cols = line.split()
if len(cols) > 0 and isfloat(cols[0]):
seriesSize += 1
waterLevelLines.append(line)
if seriesSize == SERIES_LENGTH:
isSeriesComplete = True
if isSeriesComplete:
baseTime = datetime.strptime('%s %s' % (ts_start_date, ts_start_time), '%Y-%m-%d %H:%M:%S')
timeseries = []
elementNo = waterLevelLines[0].split()[5]
# print('Extracted Cell No', elementNo, CHANNEL_CELL_MAP[elementNo])
for ts in waterLevelLines[1:]:
v = ts.split()
if len(v) < 1:
continue
# Get flood level (Elevation)
value = v[1]
# Get flood depth (Depth)
# value = v[2]
if not isfloat(value):
value = MISSING_VALUE
continue # If value is not present, skip
if value == 'NaN':
continue # If value is NaN, skip
timeStep = float(v[0])
currentStepTime = baseTime + timedelta(hours=timeStep)
dateAndTime = currentStepTime.strftime("%Y-%m-%d %H:%M:%S")
timeseries.append([dateAndTime, value])
# Save Forecast values into Database
opts = {
'elementNo': elementNo,
'tms_meta': tms_meta
}
# print('>>>>>', opts)
if utcOffset != timedelta():
opts['utcOffset'] = utcOffset
# Push timeseries to database
save_forecast_timeseries_to_db(pool=pool, timeseries=timeseries,
run_date=run_date, run_time=run_time, opts=opts,
flo2d_stations=flo2d_stations)
isWaterLevelLines = False
isSeriesComplete = False
waterLevelLines = []
# -- END for loop
# -- END while loop
#################################################################
# Extract Flood Plain water elevations from TIMEDEP.OUT file #
#################################################################
if not os.path.exists(timdep_file_path):
print('Unable to find file : ', timdep_file_path)
traceback.print_exc()
print('Extract Flood Plain Water Level Result of FLO2D (TIMEDEP.OUT) on', run_date, '@', run_time,
'with Base time of', ts_start_date,
'@', ts_start_time)
with open(timdep_file_path) as infile:
waterLevelLines = []
waterLevelSeriesDict = dict.fromkeys(FLOOD_ELEMENT_NUMBERS, [])
while True:
lines = infile.readlines(bufsize)
if not lines:
break
for line in lines:
if len(line.split()) == 1:
# continue
if len(waterLevelLines) > 0:
waterLevels = get_water_level_of_channels(waterLevelLines, FLOOD_ELEMENT_NUMBERS)
# Get Time stamp Ref:http://stackoverflow.com/a/13685221/1461060
# print('waterLevelLines[0].split() : ', waterLevelLines[0].split())
ModelTime = float(waterLevelLines[0].split()[0])
baseTime = datetime.strptime('%s %s' % (ts_start_date, ts_start_time), '%Y-%m-%d %H:%M:%S')
currentStepTime = baseTime + timedelta(hours=ModelTime)
dateAndTime = currentStepTime.strftime("%Y-%m-%d %H:%M:%S")
for elementNo in FLOOD_ELEMENT_NUMBERS:
tmpTS = waterLevelSeriesDict[elementNo][:]
if elementNo in waterLevels:
tmpTS.append([dateAndTime, waterLevels[elementNo]])
else:
tmpTS.append([dateAndTime, MISSING_VALUE])
waterLevelSeriesDict[elementNo] = tmpTS
isWaterLevelLines = False
# for l in waterLevelLines :
# print(l)
waterLevelLines = []
waterLevelLines.append(line)
# print('len(FLOOD_ELEMENT_NUMBERS) : ', len(FLOOD_ELEMENT_NUMBERS))
for elementNo in FLOOD_ELEMENT_NUMBERS:
# Save Forecast values into Database
opts = {
'elementNo': elementNo,
'tms_meta': tms_meta
}
if utcOffset != timedelta():
opts['utcOffset'] = utcOffset
# Push timeseries to database
save_forecast_timeseries_to_db(pool=pool, timeseries=waterLevelSeriesDict[elementNo],
run_date=run_date, run_time=run_time, opts=opts,
flo2d_stations=flo2d_stations)
except Exception as e:
logger.error('JSON config data loading error.')
print('JSON config data loading error.')
traceback.print_exc()
finally:
logger.info("Process finished.")
print("Process finished.")
else:
return None
except Exception as exception:
error_message = "Retrieving template failed."
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
set_db_config_file_path(os.path.join(ROOT_DIRECTORY, 'db_adapter_config.json'))
pool = get_Pool(host=con_params.CURW_FCST_HOST,
port=con_params.CURW_FCST_PORT,
db=con_params.CURW_FCST_DATABASE,
user=con_params.CURW_FCST_USERNAME,
password=con_params.CURW_FCST_PASSWORD)
source_id = 1
variable_id = 1
sim_tag = "test"
fgt = "2020-04-20 00:00:00"
run_info = {"test": "test1"}
template_path = "D:\\flo2d_output\\flo2d_150\\2020-03-16\\02-00-08\\template.tar.gz"
template = convertToBinaryData(template_path)
insert_run_metadata(pool=pool,
source_id=source_id,
variable_id=variable_id,
sim_tag=sim_tag,
fgt=fgt,
def update_rainfall_obs(target_model, method, timestep, start_time, end_time):
"""
Update rainfall observations for flo2d models
:param model: target model
:param method: value interpolation method
:param grid_interpolation: grid interpolation method
:param timestep:
:return:
"""
obs_start = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')
try:
# Connect to the database
curw_obs_pool = get_Pool(host=con_params.CURW_OBS_HOST,
user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD,
port=con_params.CURW_OBS_PORT,
db=con_params.CURW_OBS_DATABASE)
curw_obs_connection = curw_obs_pool.connection()
curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST,
user=con_params.CURW_SIM_USERNAME,
password=con_params.CURW_SIM_PASSWORD,
port=con_params.CURW_SIM_PORT,
db=con_params.CURW_SIM_DATABASE)
TS = Timeseries(pool=curw_sim_pool)
# [hash_id, station_id, station_name, latitude, longitude]
active_obs_stations = extract_active_curw_obs_rainfall_stations(
start_time=start_time, end_time=end_time)[1:]
obs_stations_dict = {
} # keys: obs station id , value: [hash id, name, latitude, longitude]
for obs_index in range(len(active_obs_stations)):
obs_stations_dict[active_obs_stations[obs_index][1]] = [
active_obs_stations[obs_index][0],
active_obs_stations[obs_index][2],
active_obs_stations[obs_index][3],
active_obs_stations[obs_index][4]
]
for obs_id in obs_stations_dict.keys():
meta_data = {
'latitude':
float('%.6f' % float(obs_stations_dict.get(obs_id)[2])),
'longitude':
float('%.6f' % float(obs_stations_dict.get(obs_id)[3])),
'model':
target_model,
'method':
method,
'grid_id':
'rainfall_{}_{}'.format(obs_id,
obs_stations_dict.get(obs_id)[1])
}
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)
TS.update_grid_id(id_=tms_id, grid_id=meta_data['grid_id'])
obs_hash_id = obs_stations_dict.get(obs_id)[0]
obs_timeseries = []
if timestep == 5:
ts = extract_obs_rain_5_min_ts(connection=curw_obs_connection,
start_time=obs_start,
end_time=end_time,
id=obs_hash_id)
if ts is not None and len(ts) > 1:
obs_timeseries.extend(
process_5_min_ts(newly_extracted_timeseries=ts,
expected_start=obs_start)[1:])
# obs_start = ts[-1][0]
elif timestep == 15:
ts = extract_obs_rain_15_min_ts(connection=curw_obs_connection,
start_time=obs_start,
end_time=end_time,
id=obs_hash_id)
if ts is not None and len(ts) > 1:
obs_timeseries.extend(
process_15_min_ts(newly_extracted_timeseries=ts,
expected_start=obs_start)[1:])
# obs_start = ts[-1][0]
# for i in range(len(obs_timeseries)):
# if obs_timeseries[i][1] == -99999:
# obs_timeseries[i][1] = 0
if obs_timeseries is not None and len(obs_timeseries) > 0:
TS.replace_data(timeseries=obs_timeseries, tms_id=tms_id)
except Exception as e:
traceback.print_exc()
logger.error(
"Exception occurred while updating obs rainfalls in curw_sim.")
finally:
curw_obs_connection.close()
destroy_Pool(pool=curw_sim_pool)
destroy_Pool(pool=curw_obs_pool)
import traceback
from db_adapter.base import get_Pool, destroy_Pool
from db_adapter.curw_sim.grids import add_obs_to_d03_grid_mappings_for_rainfall, get_obs_to_d03_grid_mappings_for_rainfall, \
add_flo2d_raincell_grid_mappings, get_flo2d_cells_to_obs_grid_mappings, get_flo2d_cells_to_wrf_grid_mappings, \
add_flo2d_initial_conditions, get_flo2d_initial_conditions, \
GridInterpolationEnum
from db_adapter.constants import CURW_SIM_HOST, CURW_SIM_PORT, CURW_SIM_USERNAME, CURW_SIM_PASSWORD, CURW_SIM_DATABASE
print(" Add obs to wrf_d03 grid mappings")
try:
pool = get_Pool(host=CURW_SIM_HOST,
port=CURW_SIM_PORT,
user=CURW_SIM_USERNAME,
password=CURW_SIM_PASSWORD,
db=CURW_SIM_DATABASE)
# grid_interpolation_method = GridInterpolationEnum.getAbbreviation(GridInterpolationEnum.MDPA)
# print(" Add flo2d 250 grid mappings")
# add_flo2d_raincell_grid_mappings(pool=pool, flo2d_model='flo2d_250', grid_interpolation=grid_interpolation_method)
# print("{} flo2d 250 grids added".format(len(get_flo2d_cells_to_wrf_grid_mappings(pool=pool, flo2d_model='flo2d_250', grid_interpolation=grid_interpolation_method).keys())))
# print("{} flo2d 250 grids added".format(len(get_flo2d_cells_to_obs_grid_mappings(pool=pool, flo2d_model='flo2d_250', grid_interpolation=grid_interpolation_method).keys())))
#
#
# print(" Add flo2d 150 grid mappings")
# add_flo2d_raincell_grid_mappings(pool=pool, flo2d_model='flo2d_150', grid_interpolation=grid_interpolation_method)
# print("{} flo2d 150 grids added".format(len(get_flo2d_cells_to_wrf_grid_mappings(pool=pool, flo2d_model='flo2d_150', grid_interpolation=grid_interpolation_method).keys())))
# print("{} flo2d 150 grids added".format(len(get_flo2d_cells_to_obs_grid_mappings(pool=pool, flo2d_model='flo2d_150', grid_interpolation=grid_interpolation_method).keys())))
#
def prepare_chan(chan_file_path, start, flo2d_model):
flo2d_version = flo2d_model.split('_')[1]
try:
curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST,
user=con_params.CURW_SIM_USERNAME,
password=con_params.CURW_SIM_PASSWORD,
port=con_params.CURW_SIM_PORT,
db=con_params.CURW_SIM_DATABASE)
curw_obs_pool = get_Pool(host=con_params.CURW_OBS_HOST,
user=con_params.CURW_OBS_USERNAME,
password=con_params.CURW_OBS_PASSWORD,
port=con_params.CURW_OBS_PORT,
db=con_params.CURW_OBS_DATABASE)
obs_connection = curw_obs_pool.connection()
# retrieve initial conditions from database
initial_conditions = get_flo2d_initial_conditions(
pool=curw_sim_pool, flo2d_model=flo2d_model)
print(initial_conditions)
# chan head
head_file = open(
os.path.join(ROOT_DIRECTORY, "input", "chan",
"chan_{}_head.dat".format(flo2d_version)), "r")
head = head_file.read()
head_file.close()
write_file_to_file(chan_file_path, file_content=head)
# chan body
chan_processed_body = []
body_file_name = os.path.join(ROOT_DIRECTORY, "input", "chan",
"chan_{}_body.dat".format(flo2d_version))
chan_body = [line.rstrip('\n') for line in open(body_file_name, "r")]
i = 0
while i < len(chan_body):
up_strm = chan_body[i].split()[0]
up_strm_default = chan_body[i].split()[1]
dwn_strm = chan_body[i + 1].split()[0]
dwn_strm_default = chan_body[i + 1].split()[1]
grid_id = "{}_{}_{}".format(flo2d_model, up_strm, dwn_strm)
print(grid_id)
wl_id = initial_conditions.get(grid_id)[2]
offset = (datetime.strptime(start, DATE_TIME_FORMAT) +
timedelta(hours=2)).strftime(DATE_TIME_FORMAT)
water_level = getWL(connection=obs_connection,
wl_id=wl_id,
start_date=start,
end_date=offset)
if water_level is None:
chan_processed_body.append("{}{}".format(
up_strm.ljust(6), (str(up_strm_default)).rjust(6)))
chan_processed_body.append("{}{}".format(
dwn_strm.ljust(6), (str(dwn_strm_default)).rjust(6)))
else:
chan_processed_body.append("{}{}".format(
up_strm.ljust(6), (str(water_level)).rjust(6)))
chan_processed_body.append("{}{}".format(
dwn_strm.ljust(6), (str(water_level)).rjust(6)))
i += 2
append_to_file(chan_file_path, data=chan_processed_body)
# chan tail
tail_file = open(
os.path.join(ROOT_DIRECTORY, "input", "chan",
"chan_{}_tail.dat".format(flo2d_version)), "r")
tail = tail_file.read()
tail_file.close()
append_file_to_file(chan_file_path, file_content=tail)
except Exception as e:
print(traceback.print_exc())
finally:
destroy_Pool(curw_sim_pool)
destroy_Pool(curw_obs_pool)
print("Chan generated")
