finished weather scripts before pulling them out

energy_demand/main.py

@@ -158,14 +158,15 @@ def energy_demand_model(
     # ----------------------------------------------------------------------
     # Load data
     # ----------------------------------------------------------------------
+    region_name_attribute ='lad_uk_2016'
     data['scenario_data'] = defaultdict(dict)
     data['enduses'], data['sectors'], data['fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(data['paths'])
     data['regions'] = read_data.get_region_names(name_region_set)
     data['reg_coord'] = basic_functions.get_long_lat_decimal_degrees(read_data.get_region_centroids(name_region_set))
-    data['scenario_data']['population'] = data_loader.read_scenario_data(name_population_dataset, region_name='lad_uk_2016', value_name='population')
+    data['scenario_data']['population'] = data_loader.read_scenario_data(name_population_dataset, region_name=region_name_attribute, value_name='population')
 
-    data['scenario_data']['gva_industry'] = data_loader.read_scenario_data_gva(name_gva_dataset, region_name='lad_uk_2016', value_name='gva_per_head', all_dummy_data=False)
-    data['scenario_data']['gva_per_head'] = data_loader.read_scenario_data(name_gva_dataset_per_head, region_name='lad_uk_2016', value_name='gva_per_head')
+    data['scenario_data']['gva_industry'] = data_loader.read_scenario_data_gva(name_gva_dataset, region_name=region_name_attribute, value_name='gva_per_head', all_dummy_data=False)
+    data['scenario_data']['gva_per_head'] = data_loader.read_scenario_data(name_gva_dataset_per_head, region_name=region_name_attribute, value_name='gva_per_head')
 
     # -----------------------------
     # Assumptions
@@ -272,6 +273,7 @@ def energy_demand_model(
         regions=data['regions'],
         weather_realisation=weather_realisation,
         path_weather_data=path_weather_data,
+        region_name_attribute=region_name_attribute,
         same_base_year_weather=False)
 
     # ------------------------------------------------------------
@@ -401,7 +403,6 @@ def energy_demand_model(
     # Main model run function
     # -----------------------
     for sim_yr in data['assumptions'].sim_yrs:
-
         print("Local simulation for year:  " + str(sim_yr))
         setattr(data['assumptions'], 'curr_yr', sim_yr) # Set current year
 
@@ -459,7 +460,7 @@ def energy_demand_model(
                 config['CRITERIA']['plot_crit'])
 
         # -------------------------------------
-        # # Generate YAML file with keynames for `sector_model`
+        # Generate ouputs
         # -------------------------------------
         if config['CRITERIA']['mode_constrained']:
             supply_results = demand_supply_interaction.constrained_results(
@@ -557,6 +558,9 @@ def energy_demand_model(
 
             print("... Finished writing results to file")
 
+    for key_name in sim_obj.supply_results.keys():
+        print("KEYNAME " + str(key_name))
+
     print("-------------------------")
     print("... Finished running HIRE")
     print("-------------------------")

energy_demand/read_write/data_loader.py

@@ -886,6 +886,7 @@ def load_temp_data(
         regions,
         weather_realisation,
         path_weather_data,
+        region_name_attribute,
         same_base_year_weather=False,
     ):
     """Read in cleaned temperature and weather station data
@@ -932,11 +933,11 @@ def load_temp_data(
         df_timestep_t_max = df_t_max.loc[df_t_max['timestep'] == weather_data_yr]
 
         for region in regions:
-            df_timestep_station_t_min = df_timestep_t_min.loc[df_timestep_t_min['region'] == region]
-            df_timestep_station_t_max = df_timestep_t_max.loc[df_timestep_t_max['region'] == region]
+            df_timestep_station_t_min = df_timestep_t_min.loc[df_timestep_t_min[region_name_attribute] == region]
+            df_timestep_station_t_max = df_timestep_t_max.loc[df_timestep_t_max[region_name_attribute] == region]
 
-            t_min = list(df_timestep_station_t_min['value'].values)
-            t_max = list(df_timestep_station_t_max['value'].values)
+            t_min = list(df_timestep_station_t_min['t_min'].values)
+            t_max = list(df_timestep_station_t_max['t_max'].values)
 
             temp_data_short[sim_yr][region] = {
                 't_min': np.array(t_min),

energy_demand/scripts/weather_at_home_data_processing/create_realisation.py

@@ -4,7 +4,23 @@
 import pandas as pd
 import numpy as np
 
-from energy_demand.basic import basic_functions
+def create_folder(path_folder, name_subfolder=None):
+    """Creates folder or subfolder
+
+    Arguments
+    ----------
+    path : str
+        Path to folder
+    folder_name : str, default=None
+        Name of subfolder to create
+    """
+    if not name_subfolder:
+        if not os.path.exists(path_folder):
+            os.makedirs(path_folder)
+    else:
+        path_result_subolder = os.path.join(path_folder, name_subfolder)
+        if not os.path.exists(path_result_subolder):
+            os.makedirs(path_result_subolder)
 
 def remap_year(year):
     """Remap year"""
@@ -23,6 +39,7 @@ def generate_weather_at_home_realisation(
         path_stiching_table,
         base_yr_remapped_weather_path,
         scenarios=range(100),
+        attributes=['t_min', 't_max', 'rsds', 'wss'],
         years=range(2015, 2051)
     ):
     """
@@ -31,7 +48,7 @@ def generate_weather_at_home_realisation(
     # Create result path
     result_path_realizations = os.path.join(path_results, "_realizations")
 
-    basic_functions.create_folder(result_path_realizations)
+    create_folder(result_path_realizations)
     result_path_realizations = "C:/AAA"
     # Read in stiching table
     df_path_stiching_table = pd.read_table(path_stiching_table, sep=" ")
@@ -44,8 +61,6 @@ def generate_weather_at_home_realisation(
 
     for scenario_nr in scenarios:
         realisation = realisations[scenario_nr]
-
-        attributes = ['t_min', 't_max']#, 'rsds', 'wss']
         for attribute in attributes:
             columns = ['timestep', 'station_id', 'longitude', 'latitude', 'yearday', attribute]
 

energy_demand/scripts/weather_at_home_data_processing/map_weather_data.py

@@ -5,11 +5,27 @@
 import pandas as pd
 from haversine import haversine
 
-from energy_demand.basic import basic_functions
+def create_folder(path_folder, name_subfolder=None):
+    """Creates folder or subfolder
+
+    Arguments
+    ----------
+    path : str
+        Path to folder
+    folder_name : str, default=None
+        Name of subfolder to create
+    """
+    if not name_subfolder:
+        if not os.path.exists(path_folder):
+            os.makedirs(path_folder)
+    else:
+        path_result_subolder = os.path.join(path_folder, name_subfolder)
+        if not os.path.exists(path_result_subolder):
+            os.makedirs(path_result_subolder)
 
 def spatially_map_data(
         path_results,
-        result_folder,
+        result_out_path,
         path_weather_at_home_stations,
         path_input_coordinates,
         attributes,
@@ -19,9 +35,7 @@ def spatially_map_data(
 
     # Path to scenarios
     path_to_scenario_data = os.path.join(path_results, '_realizations')
-    result_out_path = os.path.join(path_results, result_folder)
-    basic_functions.create_folder(result_out_path)
-    result_out_path = "C:/AAA/energy_demand"
+    create_folder(result_out_path)
 
     # Read in input stations and coordinages to map
     stations_to_map_to = pd.read_csv(path_input_coordinates)
@@ -40,8 +54,7 @@ def spatially_map_data(
         for index in stations_grid_cells.index:
             stations_grid_cells_dict[index] = {
                 'longitude': stations_grid_cells.loc[index, 'longitude'],
-                'latitude': stations_grid_cells.loc[index, 'latitude']
-            }
+                'latitude': stations_grid_cells.loc[index, 'latitude']}
 
         weather_stations_per_attribute[attribute] = stations_grid_cells_dict
 
@@ -64,7 +77,7 @@ def spatially_map_data(
             closest_weather_ids[index][attribute] = closest_marius_station
 
     # ----------------------------------------
-    # Temperature data
+    # Get data
     # ----------------------------------------
     for scenario_nr in scenarios:
         scenario_name = scenario_names[scenario_nr]
@@ -87,7 +100,7 @@ def spatially_map_data(
                 data_yr = data.loc[data['timestep'] == year]
 
                 for index in stations_to_map_to.index:
-                    region_name = stations_to_map_to.loc[index, 'region_name']
+                    region_name = stations_to_map_to.loc[index, 'region_id']
                     closest_weather_station_id = closest_weather_ids[index][attribute]
                     closest_data = data_yr.loc[closest_weather_station_id]
                     closest_data_list = list(closest_data.values)
@@ -100,20 +113,6 @@ def spatially_map_data(
                             year,
                             int(row[position_yearday])])
 
-                    '''for yearday in range(0, 365):
-                        values_row = closest_data.loc[closest_data['yearday'] == yearday]
-                        value = values_row[attribute].values[0]
-                        
-                        #print("        ... value: {} attribute: {} {} {}".format(value, attribute, index, closest_weather_station_id), flush=True)
-                        list_entry = (
-                            index,
-                            scenario_nr,
-                            attribute,
-                            value,
-                            year,
-                            yearday)
-                        stations_to_map_to_list.append(list_entry)'''
-
             # ----------------------------------------------------------
             # Write out data
             # ----------------------------------------------------------

energy_demand/scripts/weather_at_home_data_processing/workflow.py

@@ -39,14 +39,13 @@
 path_results = "X:/nismod/data/energy_supply/weather_files" # Path to store results
 base_yr_remapped_weather_path = "X:/nismod/data/energy_demand/J-MARIUS_data/_weather_data_cleaned/2015_remapped"
 
-#
-result_folder ='_spatially_mapped_supply_data'
+# Energy supply data
+result_folder = "C:/AAA/energy_supply"
 path_input_coordinates = os.path.abspath("X:/nismod/data/energy_supply/regions_input_supply_model.csv") # Path to file with coordinates to map on to
 
-#
-result_folder ='_spatially_mapped_demand_data'
-path_input_coordinates = os.path.abspath("X:/nismod/data/energy_supply/regions_energy_demand_model.csv") # Path to file with coordinates to map on to
-
+# Energy demand data
+#result_folder ='_spatially_mapped_demand_data'
+#path_input_coordinates = os.path.abspath("X:/nismod/data/energy_supply/regions_energy_demand_model.csv") # Path to file with coordinates to map on to
 
 extract_data = False
 stich_together = False
@@ -71,8 +70,8 @@
         path_results=path_results,
         path_stiching_table=path_stiching_table,
         base_yr_remapped_weather_path=base_yr_remapped_weather_path,
-        scenarios=[27])#range(47, 61))
-        #scenarios=range(28, 62))
+        attributes=['t_min', 't_max', 'rsds', 'wss'],
+        scenarios=range(0, 100))
     print("... finished creating realisations")
 
 if append_closest_weather_data:
@@ -81,9 +80,9 @@
     # =================================
     map_weather_data.spatially_map_data(
         path_results=path_results,
-        result_folder=result_folder,
+        result_out_path=result_folder,
         path_weather_at_home_stations=os.path.join(path_results, "_cleaned_csv"),
         path_input_coordinates=path_input_coordinates,
-        attributes=['t_min', 't_max'],# 'wss','rsds'], #['wss','rsds'], #
-        scenarios=range(10))
+        attributes=['t_min', 't_max', 'wss','rsds'],
+        scenarios=range(0, 100))
     print("... append closest weather information")

local_run_config_file.ini

@@ -12,7 +12,7 @@ user_defined_weather_by = 2015
 
 [CRITERIA]
 cluster_calc = False
-mode_constrained = True
+mode_constrained = False
 virtual_building_stock_criteria = True
 write_out_national = False
 reg_selection = False