Fix in input profile processor

input_profile_processor/input_profile_processor.py

@@ -68,11 +68,20 @@ def resample_profile(name, profile, t_start, t_horizon, t_step, t_last):
         print(f'For input profile {name} {len(missing_indices)} {"values are" if len(missing_indices) > 1 else "value is"} extrapolated to the beginning of the profile!')
         profile = pd.concat([pd.DataFrame([profile.iloc[0]], index = missing_indices), profile])
     if profile.index[-1] < t_last:
-        missing_indices = pd.date_range(profile.index[-1] ++ timedelta(hours = t_step), t_last, freq = str(t_step) + 'H')
+        missing_indices = pd.date_range(profile.index[-1] + timedelta(hours = t_step), t_last, freq = str(t_step) + 'H')
         print(f'For input profile {name} {len(missing_indices)} {"values are" if len(missing_indices) > 1 else "value is"} extrapolated to the end of the profile!')
         profile = pd.concat([profile, pd.DataFrame([profile.iloc[-1]], index = missing_indices)])
     return profile
 
+def expand_profile_to_year(profile, year, t_step):
+    if datetime(year, 1, 1, 0) < profile.index[0]:
+        missing_indices = pd.date_range(datetime(year, 1, 1, 0), profile.index[0] - timedelta(hours = t_step), freq = str(t_step) + 'H')
+        profile = pd.concat([pd.DataFrame([profile.iloc[0]], index  =missing_indices), profile])
+    if profile.index[-1] < datetime(year + 1, 1, 1, 0) - timedelta(hours = t_step):
+        missing_indices = pd.date_range(profile.index[-1] + timedelta(hours = t_step), datetime(year + 1, 1, 1, 0) - timedelta(hours = t_step), freq = str(t_step) + 'H')
+        profile = pd.concat([profile, pd.DataFrame([profile.iloc[-1]], index = missing_indices)])
+    return profile
+
 def load_profile(name, file, t_start, t_horizon, t_step):
     t_last = t_start + pd.Timedelta(hours = t_horizon * t_step - t_step)
     profile = pd.read_csv(file, index_col = 0)
@@ -149,6 +158,11 @@ def generate_profile(name, profile_type, parameters, input_profiles, t_start, t_
             # Fixme: non-residential building only have building_class = 0
             # residential building could varies from 1 to 11
             # same problem for hot water demand.
+            # temporary fix
+            if len(input_profiles[parameters['temperature']][1]) < len(demand_df.index):
+                temp = list(input_profiles[parameters['temperature']])
+                temp[1] = expand_profile_to_year(input_profiles[parameters['temperature']][1], year, t_step)
+                input_profiles[parameters['temperature']] = tuple(temp)
             year_profile = ThermalDemand(demand_df.index,
                                          shlp_type='EFH',
                                          temperature=input_profiles[parameters['temperature']][1],
@@ -166,6 +180,11 @@ def generate_profile(name, profile_type, parameters, input_profiles, t_start, t_
             demand_df = pd.DataFrame(index=pd.date_range(datetime(year, 1, 1, 0),
                                                          periods=8760 / t_step,
                                                          freq=str(t_step) + 'H'))
+            # temporary fix
+            if len(input_profiles[parameters['temperature']][1]) < len(demand_df.index):
+                temp = list(input_profiles[parameters['temperature']])
+                temp[1] = expand_profile_to_year(input_profiles[parameters['temperature']][1], year, t_step)
+                input_profiles[parameters['temperature']] = tuple(temp)
             year_profile = ThermalDemand(demand_df.index,
                                          shlp_type='EFH',
                                          temperature=input_profiles[parameters['temperature']][1],