def create_raincell(configs, **kwargs):
raincell_config = Config(configs)
raincell_io = RaincellNcfIO(raincell_config)
raincell_algo = RaincellAlgo(raincell_io, raincell_config)
schedule_date_str = kwargs['ds']
schedule_date = datetime.strptime(schedule_date_str, DATE_FORMAT)
base_dt = schedule_date
start_dt = base_dt - timedelta(days=2)
end_dt = base_dt + timedelta(days=3)
nc_f = nc_f_format.format(schedule_date.strftime(DATE_FORMAT))
nc_f_prev_1 = nc_f_format.format((schedule_date - timedelta(days=1)).strftime(DATE_FORMAT))
nc_f_prev_2 = nc_f_format.format((schedule_date - timedelta(days=2)).strftime(DATE_FORMAT))
print(nc_f)
print(nc_f_prev_1)
print(nc_f_prev_2)
raincell_algo.execute(
ncfs={
'nc_f': path.join(wrf_results_nfs, nc_f),
'nc_f_prev_days': [
path.join(wrf_results_nfs, nc_f_prev_1),
path.join(wrf_results_nfs, nc_f_prev_2)
]
},
start_dt=start_dt,
base_dt=base_dt,
end_dt=end_dt,
)
def create_outflow(configs, **kwargs):
outflow_config = Config(configs)
outflow_io = OutflowIO(outflow_config)
outflow_algo = OutflowAlgo(outflow_io, outflow_config)
schedule_date_str = kwargs['ds']
schedule_date = datetime.strptime(schedule_date_str, DATE_FORMAT)
base_dt = schedule_date
start_dt = base_dt - timedelta(days=2)
end_dt = base_dt + timedelta(days=3)
outflow_algo.execute(
start_dt=start_dt,
end_dt=end_dt
)
def create_dailyraincsv(configs, **kwargs):
dailyraincsv_config = Config(configs)
dailyraincsv_io = RainCsvIO(dailyraincsv_config)
dailyraincsv_algo = RainCsvAlgo(dailyraincsv_io, dailyraincsv_config)
schedule_date_str = kwargs['ds']
schedule_date = datetime.strptime(schedule_date_str, DATE_FORMAT)
base_dt = schedule_date
start_dt = base_dt - timedelta(days=2)
end_dt = base_dt + timedelta(days=3)
dailyraincsv_algo.execute(
start_dt=start_dt,
base_dt=base_dt,
end_dt=end_dt
)
hourly_forecast = OutflowAlgo.process_tidal_forecast(algo_input)
lines = []
with open(self.algo_config['init_tidal_config']) as init_tidal_conf_f:
init_tidal_levels = init_tidal_conf_f.readlines()
for init_tidal_level in init_tidal_levels:
if len(init_tidal_level.split()): # Check if not empty line
lines.append(init_tidal_level)
if init_tidal_level[0] == 'N':
lines.append('{0} {1:{w}} {2:{w}}\n'.format('S', 0, 0, w=self.algo_config['DAT_WIDTH']))
base_dt = dynamic_args['start_dt'].replace(minute=0, second=0, microsecond=0)
for dt_index, rows in hourly_forecast.iterrows():
hours_so_far = int((dt_index - base_dt).total_seconds()/3600)
tidal_value = float(rows['value'])
tidal_line = '{0} {1:{w}} {2:{w}{b}}\n'\
.format('S', hours_so_far, tidal_value, b='.2f', w=self.algo_config['DAT_WIDTH'])
lines.append(tidal_line)
return lines
@staticmethod
def process_tidal_forecast(tidal_forecast):
if not isinstance(tidal_forecast, pd.DataFrame):
raise TypeError('Given timeseries is not a pandas data-frame of time, value columns')
return tidal_forecast.resample('H').max().dropna()
if __name__ == '__main__':
outflow_config = Config('/home/nira/PycharmProjects/DI_Framework/flo2d_input_preparation/outflow/config.json')
outflow_io = OutflowIO(outflow_config)
outflow_algo = OutflowAlgo(outflow_io, outflow_config)
outflow_algo.execute(start_dt=datetime(2018,1,1,0,0,0), end_dt=datetime(2018,1,5,0,0,0))
@staticmethod
def prepare_line(res_min, batch_size, start_ts, end_ts, rainfall_df):
lines = []
header_line = "%d %d %s %s\n" % (res_min, batch_size, start_ts, end_ts)
lines.append(header_line)
cell_nos = np.sort(rainfall_df.columns)
for index, row in rainfall_df.iterrows():
for cell in cell_nos:
line = "%d %.1f\n" % (cell, row[cell])
lines.append(line)
return lines
if __name__ == '__main__':
raincell_config = Config(
'/home/nira/PycharmProjects/DI_Framework/flo2d_input_preparation/raincell/config.json'
)
raincell_io = RaincellNcfIO(raincell_config)
outflow_algo = RaincellAlgo(raincell_io, raincell_config)
outflow_algo.execute(
ncfs={
'nc_f':
"/home/nira/PycharmProjects/DI_Framework/resources/wrf_output/now/wrfout_d03_2018-01-03_18_00_00_rf",
'nc_f_prev_days': [
"/home/nira/PycharmProjects/DI_Framework/resources/wrf_output/prev_1/wrfout_d03_2018-01-02_18_00_00_rf",
"/home/nira/PycharmProjects/DI_Framework/resources/wrf_output/prev_2/wrfout_d03_2018-01-01_18_00_00_rf"
]
},
start_dt=datetime(2018, 1, 1, 0, 0, 0),
base_dt=datetime(2018, 1, 1, 0, 0, 0),
end_dt=datetime(2018, 1, 1, 0, 0, 0),
print('Input Integrity Error!', ex)
return None
def check_input_integrity(self, algo_input):
location_ids = self.algo_config['location-ids']
# Should be a pandas Dataframe with DatetimeIndex.
if not isinstance(algo_input, pd.DataFrame) or not isinstance(
algo_input.index, pd.DatetimeIndex):
raise AttributeError(
'algo_input should be a pandas DataFrame with DatetimeIndex.')
# Column names should be same as location_ids.
if set(location_ids) != set(algo_input.columns):
raise AttributeError(
'Columns values of input DataFrame should be same as location Ids.'
)
return True
if __name__ == '__main__':
raincsv_config = Config(
'/home/nira/PycharmProjects/DI_Framework/hec_hms_input_preparation/rain_csv/config.json'
)
raincsv_io = RainCsvIO(raincsv_config)
raincsv_algo = RainCsvAlgo(raincsv_io, raincsv_config)
raincsv_algo.execute(
start_dt=datetime(2018, 1, 1, 0, 0, 0),
base_dt=datetime(2018, 1, 3, 0, 0, 0),
end_dt=datetime(2018, 1, 6, 0, 0, 0),
)