Tidy up river update function

app/update_river_model/file/update_river_data.py

@@ -14,62 +14,63 @@ no_miss_loops = 4
 
 # Load the river model
 with open('river_model.pk1','rb') as f:
-	river_dict = pk.load(f)
+    river_dict = pk.load(f)
 
 start_date = end_date
 for this_river in river_dict.values():
-	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 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:	
-
-	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)
+    print('Already up to date')
+
+else:    
+
+    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)
+                        if hasattr(this_river, 'river_obj'):
+                            this_river.catchment_precipitation = this_river.river_obj.catchment_precipitation
+            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)