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Commit 8ba95cee authored by Mike Bedington's avatar Mike Bedington
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Improve river model updating function

parent cd79830d
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app/update_river_model/file/update_river_data.py
View file @ 8ba95cee
......@@ -10,6 +10,7 @@ import fvcom_river as fr
wrf_forecast_out_dir = sys.argv[1]
end_date = dt.datetime.strptime(sys.argv[2],'%Y-%m-%d')
no_miss_loops = 4
# Load the river model
with open('river_model.pk1','rb') as f:
......@@ -17,172 +18,56 @@ with open('river_model.pk1','rb') as f:
start_date = end_date
for this_river in river_dict.values():
this_river_update = np.max(this_river.catchment_precipitation[0])
if hasattr(this_river, 'catchment_precipitation'):
this_river_update = np.max(this_river.catchment_precipitation[0])
if this_river_update < start_date:
start_date = this_river_update
if start_date == end_date:
print('Already up to date')
else:
date_list = np.asarray([start_date + dt.timedelta(days=int(this_ind)) for this_ind in np.arange(0, (end_date - start_date).days + 1)])
missing_dates = []
for this_date in date_list:
print(this_date)
this_date_str = this_date.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
missing_dates.append(this_date)
missing_dates_1 = []
for this_date in missing_dates:
print('Trying to fill for {}'.format(this_date))
this_date_m1 = this_date - dt.timedelta(days=1)
this_date_str = this_date_m1.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
missing_dates_1.append(this_date)
missing_dates_2 = []
for this_date in missing_dates_1:
this_date_m1 = this_date - dt.timedelta(days=3)
this_date_str = this_date_m1.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
missing_dates_2.append(this_date)
missing_dates_3 = []
for this_date in missing_dates_2:
this_date_m1 = this_date - dt.timedelta(days=3)
this_date_str = this_date_m1.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
missing_dates_3.append(this_date)
for this_date in missing_dates_3:
this_date_m1 = this_date - dt.timedelta(days=4)
this_date_str = this_date_m1.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
print('Giving up on {}'.format(this_date))
missing_dates = np.asarray([start_date + dt.timedelta(days=int(this_ind)) for this_ind in np.arange(0, (end_date - start_date).days + 1)])
for this_missing_loop in np.arange(0, no_miss_loops):
new_missing_dates = []
for this_date in missing_dates:
if this_missing_loop > 0:
print('Trying again to fill for {}'.format(this_date))
else:
print(this_date)
this_date_m1 = this_date - dt.timedelta(days=int(this_missing_loop))
this_date_str = this_date_m1.strftime('%Y%m%d')
potential_files = gb.glob('{}/{}*_forecast/wrfout_d03*'.format(wrf_forecast_out_dir, this_date_str))
try:
this_wrf_nc = nc.Dataset(potential_files[-1], 'r')
wrf_date_str_raw = this_wrf_nc.variables['Times'][:]
wrf_date_str = np.asarray([b''.join(this_str) for this_str in wrf_date_str_raw])
wrf_dt = np.asarray([dt.datetime.strptime(this_str.decode('utf-8'),'%Y-%m-%d_%H:%M:%S') for this_str in wrf_date_str])
wrf_dt_date = np.asarray([this_dt.date() for this_dt in wrf_dt])
date_match = wrf_dt_date == this_date.date()
forecast_data = {'times': wrf_dt[date_match], 'RAINNC': this_wrf_nc.variables['RAINNC'][date_match,:,:],
'T2': this_wrf_nc.variables['T2'][date_match,:,:]}
this_wrf_nc.close()
for this_river_name, this_river in river_dict.items():
if hasattr(this_river, 'addToSeries'):
this_rain = np.sum(np.sum(forecast_data['RAINNC']*this_river.wrf_catchment_factors, axis=2), axis=1)
this_river.addToSeries('catchment_precipitation', this_rain, forecast_data['times'], override=True)
this_temp = np.zeros(len(forecast_data['times']))
for i in range(0, len(forecast_data['times'])):
this_temp[i] = np.average(forecast_data['T2'][i,:,:], weights=this_river.wrf_catchment_factors)
this_river.addToSeries('catchment_temp', this_temp, forecast_data['times'], override=True)
except:
new_missing_dates.append(this_date)
missing_dates = new_missing_dates[:]
with open('river_model.pk1','wb') as f:
pk.dump(river_dict, f, pk.HIGHEST_PROTOCOL)
......
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