refactored that only runs for a single weater station and weather yr

energy_demand/geography/weather_region.py

@@ -740,8 +740,7 @@ def insert_peak_dh_shape(
 def get_weather_station_selection(
         all_weather_stations,
         counter,
-        weather_yr,
-        weather_by
+        weather_yr
     ):
     """Select weather stations based on
     position in list
@@ -753,23 +752,18 @@ def get_weather_station_selection(
     """
     all_stations_of_weather_yr = list(all_weather_stations[weather_yr].keys())
 
-    # Add base year
-    all_weather_stations_out = {}
-
     # Sort
     all_stations_of_weather_yr.sort()
 
     try:
         # Select station according ot position in list
         station_id = all_stations_of_weather_yr[counter]
 
-        all_weather_stations_out[weather_yr] = {station_id: all_weather_stations[weather_yr][station_id]}
-        continue_calculation = True
+        all_weather_stations_out = {station_id: all_weather_stations[weather_yr][station_id]}
     except:
         # Not enough stations to select position in list
-        continue_calculation = False
         station_id = False
-        all_weather_stations_out[weather_yr] = []
+        all_weather_stations_out = []
         print("... no weather station found")
 
-    return all_weather_stations_out, continue_calculation, station_id
+    return all_weather_stations_out, station_id

energy_demand/model.py

@@ -49,6 +49,8 @@ def __init__(
         # ----------------------------
         # Create Weather Regions
         # ----------------------------
+        print("KOK")
+        print(weather_stations)
         # current weather_yr
         weather_regions_weather_cy = {}
         for weather_region in weather_stations[weather_yr]:

energy_demand/plotting/fig_p2_weather_val.py

@@ -171,7 +171,7 @@ def run(
     bin_values = result_mapping.get_reasonable_bin_values_II(
         data_to_plot=list(uk_gdf[field_to_plot]),
         nr_of_intervals=nr_of_intervals)
-    print(float(uk_gdf[field_to_plot].max()))
+    print(float(uk_gdf[field_to_plot]))
     print("BINS " + str(bin_values))
 
     uk_gdf, cmap_rgb_colors, color_zero, min_value, max_value = user_defined_bin_classification(

energy_demand/plotting/fig_spatial_distribution_of_peak.py

@@ -29,13 +29,12 @@ def run(
     """
     """
     weather_yrs = []
+    calculated_yrs_paths = []
 
-    all_calculated_yrs_paths = []
     for scenario in scenarios:
 
         path_scenario = os.path.join(path_to_folder_with_scenarios, scenario)
         all_result_folders = os.listdir(path_scenario)
-        calculated_yrs_paths = []
 
         for result_folder in all_result_folders:
             try:
@@ -47,29 +46,12 @@ def run(
                 #    weather_station = int(split_path_name[1])
                 #except:
                 #    weather_station = "all_station"
-
                 tupyle_yr_path = (weather_yr, os.path.join(path_scenario))
                 calculated_yrs_paths.append(tupyle_yr_path)
 
             except ValueError:
                 pass
 
-        # delete 2015 if more than two becuase only base year for every calcuatio
-        only_weather_yr_paths = []
-        if len(calculated_yrs_paths) == 2:
-
-            for weather_yr, path in calculated_yrs_paths:
-                if weather_yr == 2015:
-                    pass
-                else:
-                    only_weather_yr_paths.append((weather_yr, path))
-        else:
-            only_weather_yr_paths = calculated_yrs_paths
-
-        # Add only weather yr not 2015
-        for entry in only_weather_yr_paths:
-            all_calculated_yrs_paths.append(entry)
-
     for simulation_yr in simulation_yrs:
         # -----------
         # Used across different plots
@@ -78,13 +60,13 @@ def run(
         #Fueltype to consider
         fueltype_str = 'electricity'
         fueltype_int = tech_related.get_fueltype_int(fueltype_str)
-        
+
         #results_container['ed_weatheryr_fueltype_regs_yh']
         container = {}
         container['abs_demand_in_peak_h'] = {}
         container['p_demand_in_peak_h'] = {}
 
-        for weather_yr, path_data_ed in all_calculated_yrs_paths:
+        for weather_yr, path_data_ed in calculated_yrs_paths:
             print("... prepare data {} {}".format(weather_yr, path_data_ed))
 
             path_to_weather_yr = os.path.join(path_data_ed, str(weather_yr))

energy_demand/read_write/data_loader.py

@@ -64,50 +64,53 @@ def create_weather_station_map(
         'longitude': [...],
         'latitude': [...]})
     """
-    # Convert dict to dataframe
-    df = pd.DataFrame.from_dict(stations_as_dict, orient='index')
+    if stations_as_dict == {}:
+        print("No stations available to plot")
+    else:
+        # Convert dict to dataframe
+        df = pd.DataFrame.from_dict(stations_as_dict, orient='index')
 
-    df['Coordinates'] = list(zip(df.longitude, df.latitude))
-    df['Coordinates'] = df['Coordinates'].apply(Point)
+        df['Coordinates'] = list(zip(df.longitude, df.latitude))
+        df['Coordinates'] = df['Coordinates'].apply(Point)
 
-    if path_shapefile is False:
-        world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
-        ax = world[world.name == "United Kingdom"].plot(
-            color='white', edgecolor='black')
-    else:
-        # Load uk shapefile
-        uk_shapefile = gpd.read_file(path_shapefile)
+        if path_shapefile is False:
+            world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
+            ax = world[world.name == "United Kingdom"].plot(
+                color='white', edgecolor='black')
+        else:
+            # Load uk shapefile
+            uk_shapefile = gpd.read_file(path_shapefile)
 
-        # Assign correct projection
-        crs = {'init': 'epsg:27700'} #27700 == OSGB_1936_British_National_Grid
-        uk_gdf = gpd.GeoDataFrame(uk_shapefile, crs=crs)
+            # Assign correct projection
+            crs = {'init': 'epsg:27700'} #27700 == OSGB_1936_British_National_Grid
+            uk_gdf = gpd.GeoDataFrame(uk_shapefile, crs=crs)
 
-        # Transform
-        uk_gdf = uk_gdf.to_crs({'init' :'epsg:4326'})
+            # Transform
+            uk_gdf = uk_gdf.to_crs({'init' :'epsg:4326'})
 
-        # Plot
-        ax = uk_gdf.plot(color='white', edgecolor='black')
+            # Plot
+            ax = uk_gdf.plot(color='white', edgecolor='black')
 
-    # print coordinates
-    crs = {'init': 'epsg:4326'}
-    gdf = gpd.GeoDataFrame(df, geometry='Coordinates') # crs=crs,
-    gdf.plot(ax=ax, color='red')
+        # print coordinates
+        crs = {'init': 'epsg:4326'}
+        gdf = gpd.GeoDataFrame(df, geometry='Coordinates') # crs=crs,
+        gdf.plot(ax=ax, color='red')
 
-    plt.savefig(fig_path)
+        plt.savefig(fig_path)
 
-     # ---------------------------------
-    # Save coordinates as file
-    # ---------------------------------
-    station_infos = ["name, latitue, longitude"]
-    fig_path = fig_path[:-4] + ".txt"
-    for station in stations_as_dict:
-        station_info = "{}, {}, {}".format(
-            station,
-            stations_as_dict[station]['latitude'],
-            stations_as_dict[station]['longitude'])
-        station_infos.append(station_info)
+        # ---------------------------------
+        # Save coordinates as file
+        # ---------------------------------
+        station_infos = ["name, latitue, longitude"]
+        fig_path = fig_path[:-4] + ".txt"
+        for station in stations_as_dict:
+            station_info = "{}, {}, {}".format(
+                station,
+                stations_as_dict[station]['latitude'],
+                stations_as_dict[station]['longitude'])
+            station_infos.append(station_info)
 
-    write_data.write_list_to_txt(fig_path, station_infos)
+        write_data.write_list_to_txt(fig_path, station_infos)
 
 def read_weather_stations_raw(path_to_csv):
     """Read in weather stations from csv file
@@ -870,7 +873,7 @@ def load_temp_data(local_paths, weather_yrs_scenario, save_fig=False):
     ----------
     local_paths : dict
         Local local_paths
-    weather_yrs_scenario : list
+    weather_yr_scenario : list
         Years to use temperatures
 
     Returns
@@ -883,13 +886,12 @@ def load_temp_data(local_paths, weather_yrs_scenario, save_fig=False):
     temp_data_short = defaultdict(dict)
     weather_stations_with_data = defaultdict(dict)
 
-    for year in weather_yrs_scenario:
-
-        weather_stations = read_weather_stations_raw(
-            local_paths['folder_path_weater_stations'])
+    weather_stations = read_weather_stations_raw(
+        local_paths['folder_path_weater_stations'])
 
+    for weather_yr_scenario in weather_yrs_scenario:
         temp_data = read_weather_data.read_weather_data_script_data(
-            local_paths['weather_data'], year)
+            local_paths['weather_data'], weather_yr_scenario)
 
         for station in weather_stations:
             try:
@@ -899,27 +901,19 @@ def load_temp_data(local_paths, weather_yrs_scenario, save_fig=False):
                 if weather_stations[station]['longitude'] > 2 or weather_stations[station]['longitude'] < -8.5:
                     pass
                 else:
-                    temp_data_short[year][station] = temp_data[station]
+                    temp_data_short[weather_yr_scenario][station] = temp_data[station]
             except:
                 logging.debug("no data for weather station " + str(station))
 
-        for station_id in temp_data_short[year].keys():
+        for station_id in temp_data_short[weather_yr_scenario].keys():
             try:
-                weather_stations_with_data[year][station_id] = weather_stations[station_id]
+                weather_stations_with_data[weather_yr_scenario][station_id] = weather_stations[station_id]
             except:
-                del temp_data_short[year][station_id]
+                del temp_data_short[weather_yr_scenario][station_id]
 
         logging.info(
-            "Info: Number of weather stations: {} year: Number of temp data: {}, year: {}".format(
-                len(weather_stations_with_data), len(temp_data_short[year]), weather_yrs_scenario))
-
-        if not save_fig:
-            pass
-        else:
-            create_weather_station_map(
-                weather_stations_with_data[year],
-                os.path.join(save_fig, 'weather_station_distribution_{}.pdf'.format(year)),
-                path_shapefile=local_paths['lad_shapefile'])
+            "Info: Number of weather stations: {} weather_yr_scenario: Number of temp data: {}, weather_yr_scenario: {}".format(
+                len(weather_stations_with_data), len(temp_data_short[weather_yr_scenario]), weather_yr_scenario))
 
     return dict(weather_stations_with_data), dict(temp_data_short)
 

energy_demand/read_write/read_weather_data.py

@@ -4,6 +4,20 @@
 import numpy as np
 from collections import defaultdict
 
+def get_all_weather_yrs(path_to_csvs):
+    """Get all weather yrs
+    TODO
+    """
+    weather_yrs = set([])
+
+    all_txt_files_in_folder = os.listdir(path_to_csvs)
+
+    for file_path in all_txt_files_in_folder:
+        year = int(file_path.split("__")[0])
+        weather_yrs.add(year)
+
+    return list(weather_yrs)
+
 def read_weather_data_script_data(path_to_csv, weather_yrs_scenario):
     """Read in weather data from script data
 

energy_demand/result_processing/paper_II.py

@@ -63,11 +63,21 @@ def paper_II_plots(
     basic_functions.del_previous_setup(path_out_plots)
     basic_functions.create_folder(path_out_plots)
 
+    ####################################################################
+    # Plot weather station availability map
+    ####################################################################
+
+
     ####################################################################
     # Plot spatial distribution of variability depending on weather year
+    # Plot the variability of the contribution of regional peak demand
+    # to national peak demand
     ####################################################################
     if plot_crit_dict['plot_spatial_distribution_of_peak']:
 
+        # Select simulation years
+        simulation_yrs = [2015, 2050]
+
         # Select field to plot
         field_to_plot = "std_deviation_p_demand_peak_h"
         #field_to_plot = "std_deviation_abs_demand_peak_h"
@@ -76,11 +86,39 @@ def paper_II_plots(
             only_scenarios,
             path_to_folder_with_scenarios,
             path_shapefile_input,
-            simulation_yrs=[2015, 2050],
+            simulation_yrs=simulation_yrs,
             field_to_plot=field_to_plot,
             fig_path=path_out_plots)
 
+    ####################################################################
+    # Plot regional vs national spatio-temporal validation
+    ####################################################################
+    '''path_regional_calculations = "C:/Users/cenv0553/ed/results/_for_FIG2a/_regional_calculations"
+    path_rolling_elec_demand = os.path.join(
+        "C:/Users/cenv0553/ed/energy_demand/energy_demand/config_data",
+        '01-validation_datasets', '01_national_elec_2015', 'elec_demand_2015.csv')
+    path_temporal_elec_validation = os.path.join(
+        "C:/Users/cenv0553/ed/energy_demand/energy_demand/config_data",
+        '01-validation_datasets', '02_subnational_elec', 'data_2015_elec_domestic.csv')
+    path_temporal_gas_validation = os.path.join(
+        "C:/Users/cenv0553/ed/energy_demand/energy_demand/config_data",
+        '01-validation_datasets', '03_subnational_gas', 'data_2015_gas_domestic.csv')
+    path_non_regional_elec_2015 = os.path.abspath(
+        os.path.join(path_regional_calculations, '..', "_non_regional_calculations_2015"))'''
+
+    # Plot figure national an regional validation comparison
+    '''figs_p2.plot_fig_spatio_temporal_validation(
+        path_regional_calculations=path_regional_calculations,
+        path_rolling_elec_demand=path_rolling_elec_demand,
+        path_temporal_elec_validation=path_temporal_elec_validation,
+        path_temporal_gas_validation=path_temporal_gas_validation,
+            path_non_regional_elec_2015=path_non_regional_elec_2015)'''
+
+    ####################################################################
+    # 
+    ####################################################################
     for scenario in only_scenarios:
+
         # -----------
         # plot where spatio-temporal comparison between national and regional
         # -----------