MO calculated with turb HF not from net

scripts/functions.py

@@ -8,6 +8,42 @@ import xarray as xr
 from datetime import timezone
 import copy
 
+def get_meteostation_data(infiles):
+    ds = xr.open_mfdataset(infiles, decode_times=False)
+    data = ds.to_dataframe().reset_index()
+    data = data.rename(columns = {"time":"timestamp","AirTC":"AirTC_Avg","Slrw":"SlrW_Avg", "WS":"WS_ms_S_WVT", "WindDir":"WindDir_D1_WVT", "BP":"BP_mbar_Avg"})
+        
+    data = data.sort_values(by=['timestamp'], axis=0)
+    data.reset_index(drop=True, inplace=True)
+
+    data = data.to_dict(orient='list')
+
+    for var in data.keys():
+        data[var] = np.array(data[var])
+
+    data["date_dt"] = np.array([datetime.utcfromtimestamp(value) for value in data["timestamp"]])
+
+    return data
+
+def get_tchain_data(infiles):
+    ds = xr.open_mfdataset(infiles, decode_times=False)
+    ds["temp"] = ds["temp"][0,:]
+    ds["temp_qual"] = ds["temp_qual"][0,:]
+    data = pd.DataFrame([np.array(ds["time"]), np.array(ds["temp"]), np.array(ds["temp_qual"])]).T
+    data.columns = ['timestamp', 'Temp', 'Temp_qual']
+    
+    data = data.sort_values(by=['timestamp'], axis=0)
+    data.reset_index(drop=True, inplace=True)
+
+    data = data.to_dict(orient='list')
+    
+    for var in data.keys():
+        data[var] = np.array(data[var])
+
+    data["date_dt"] = np.array([datetime.utcfromtimestamp(value) for value in data["timestamp"]])
+    
+    return data
+    
 def copy_variables(variables_dict):
     var_dict = dict()
     for var in variables_dict:
@@ -61,41 +97,7 @@ def chunk_from_date(files, date, chunk_period):
     return chunk_files
 
 
-def get_meteostation_data(infiles):
-    ds = xr.open_mfdataset(infiles, decode_times=False)
-    data = ds.to_dataframe().reset_index()
-    data = data.rename(columns = {"time":"timestamp","AirTC":"AirTC_Avg","Slrw":"SlrW_Avg", "WS":"WS_ms_S_WVT", "WindDir":"WindDir_D1_WVT", "BP":"BP_mbar_Avg"})
-        
-    data = data.sort_values(by=['timestamp'], axis=0)
-    data.reset_index(drop=True, inplace=True)
-
-    data = data.to_dict(orient='list')
-
-    for var in data.keys():
-        data[var] = np.array(data[var])
 
-    data["date_dt"] = np.array([datetime.utcfromtimestamp(value) for value in data["timestamp"]])
-
-    return data
-
-def get_tchain_data(infiles):
-    ds = xr.open_mfdataset(infiles, decode_times=False)
-    ds["temp"] = ds["temp"][0,:]
-    ds["temp_qual"] = ds["temp_qual"][0,:]
-    data = pd.DataFrame([np.array(ds["time"]), np.array(ds["temp"]), np.array(ds["temp_qual"])]).T
-    data.columns = ['timestamp', 'Temp', 'Temp_qual']
-    
-    data = data.sort_values(by=['timestamp'], axis=0)
-    data.reset_index(drop=True, inplace=True)
-
-    data = data.to_dict(orient='list')
-    
-    for var in data.keys():
-        data[var] = np.array(data[var])
-
-    data["date_dt"] = np.array([datetime.utcfromtimestamp(value) for value in data["timestamp"]])
-    
-    return data
 
 def get_thetis_data(infiles, threshold = 1.2):
     time = np.array([])   

scripts/heatflux.py

@@ -141,7 +141,7 @@ class Heatflux(object):
         self.data["sHFluxes_Qlat"] = np.copy(HF.Qlat)
         self.data["sHFluxes_Qsen"] = np.copy(HF.Qsen)
 
-        self.data["JB"] = sw.alpha(0.2,self.data["swT"],0)*9.81/sw.cp(0.2,self.data["swT"],0)*self.data["sHFnet"]/self.data["rho0"]
+        self.data["JB"] = sw.alpha(0.2,self.data["swT"],0)*9.81/sw.cp(0.2,self.data["swT"],0)*self.data["sHFturb0"]/self.data["rho0"]
         self.data["LMO"] = self.data["ustar"]**3/0.4/self.data["JB"]