fixed reording of multiple val

energy_demand/basic/date_prop.py

@@ -2,9 +2,10 @@
 Contains funtions related to handeling date and
 time related functionality
 """
-import numpy as np
 from datetime import date
 from datetime import timedelta
+import numpy as np
+
 from energy_demand.basic import basic_functions
 
 def convert_h_to_day_year_and_h(hour):
@@ -46,7 +47,6 @@ def get_seasonal_weeks():
     autumn_week = list(range(
         date_prop.date_to_yearday(year_to_model, 10, 12),
         date_prop.date_to_yearday(year_to_model, 10, 26))) #Oct'''
-
     winter_week = list(range(
         date_to_yearday(2015, 1, 12), date_to_yearday(2015, 1, 19))) #Jan
     spring_week = list(range(

energy_demand/main.py

@@ -467,7 +467,7 @@ def energy_demand_model(
     # Main model run function
     # -----------------------
     for sim_yr in data['assumptions'].simulated_yrs:
-        print("Loal simulation for year:  " + str(sim_yr), flush=True)
+        print("Local simulation for year:  " + str(sim_yr), flush=True)
 
         # Set current year
         setattr(data['assumptions'], 'curr_yr', sim_yr)

energy_demand/plotting/fig_spatial_distribution_of_peak.py

@@ -10,11 +10,8 @@
 from energy_demand.basic import date_prop
 from energy_demand.basic import basic_functions
 from energy_demand.basic import lookup_tables
-from energy_demand.basic import basic_functions
-from energy_demand.basic import conversions
 from energy_demand.technologies import tech_related
 from energy_demand.plotting import basic_plot_functions
-from energy_demand.read_write import write_data
 from energy_demand.plotting import result_mapping
 from energy_demand.plotting import fig_p2_weather_val
 
@@ -86,11 +83,8 @@ def run(
             # Calculate hour with national peak demand
             # This may be different depending on the weather yr
             # ---------------------------------------------------
-            print("sstr")
-            print(results_container['ed_fueltype_regs_yh'][simulation_yr][fueltype_int].shape)
             sum_all_regs_fueltype_8760 = np.sum(results_container['ed_fueltype_regs_yh'][simulation_yr][fueltype_int], axis=0) # Sum for every hour
-            print("sum_all_regs_fueltype_8760")
-            print(sum_all_regs_fueltype_8760.shape)
+
             max_day = int(basic_functions.round_down((np.argmax(sum_all_regs_fueltype_8760) / 24), 1))
             max_h = np.argmax(sum_all_regs_fueltype_8760)
 

energy_demand/plotting/figs_p2.py

@@ -161,7 +161,6 @@ def plot_fig_spatio_temporal_validation(
     weather_yr_station_tot_fueltype_yh = defaultdict(dict)
     residential_results = defaultdict(dict)
     for scenario_folder in all_result_folders:
-        print("Scenario folder: " + str(scenario_folder))
         result_folders = os.listdir(os.path.join(path_regional_calculations, scenario_folder))
         for result_folder in result_folders:
             try:
@@ -236,7 +235,10 @@ def plot_fig_spatio_temporal_validation(
     # Compare regional and non regional and actual demand over time
     # *****************************************************************
     simulation_yr_to_plot = 2015
-    period_to_plot = list(range(0, 8760))
+    period_to_plot = list(range(0, 8760)) #TODO PLANE
+    winter_week, _, _, _ = date_prop.get_seasonal_weeks()
+    period_to_plot = winter_week
+
     fig_p2_temporal_validation.run(
         data_input=data_container['tot_fueltype_yh'],
         weather_yr=2015,

energy_demand/scripts/batch_commands_main.py

@@ -23,6 +23,7 @@ def my_function(simulation_number):
     weather_yrs = list(weather_yrs_stations.keys())
     print("all weather yrs:         " + str(weather_yrs))
     print("Total nr of stations:    " + str(len(weather_yrs)))
+
     if same_weather_yr:
         weather_yr = defined_weather_yr
     else:

energy_demand/validation/lad_validation.py

@@ -789,7 +789,15 @@ def spatial_validation_multiple(
         y_real_demand = []
         y_modelled_demand = []
 
-        for reg_geocode in regions:
+        # -----------------
+        # Sort results according to size
+        # -----------------
+        sorted_dict_real = sorted(
+            result_dict['real_demand'].items(),
+            key=operator.itemgetter(1))
+
+        #for reg_geocode in regions:
+        for reg_geocode, _ in sorted_dict_real:
             # Test if real and modelled data are both available
             try:
                 real = result_dict['real_demand'][reg_geocode]
@@ -811,12 +819,7 @@ def spatial_validation_multiple(
         std_dev_p = np.std(diff_real_modelled_p)        # Given as percent
         std_dev_abs = np.std(diff_real_modelled_abs)    # Given as energy unit
 
-        # -----------------
-        # Sort results according to size
-        # -----------------
-        sorted_dict_real = sorted(
-            result_dict['real_demand'].items(),
-            key=operator.itemgetter(1))
+
 
         x_values = np.arange(0, len(y_real_demand), 1)