updated .main.py file for cluster run

energy_demand/basic/demand_supply_interaction.py

@@ -9,12 +9,9 @@
 
 def constrained_results(
         results_constrained,
-        #results_unconstrained,
         results_unconstrained_no_heating,
         submodels_names,
         technologies,
-        reg_nrs,
-        fueltype_nrs
     ):
     """Prepare results for energy supply model for
     constrained model running mode (no heat is provided but
@@ -35,9 +32,9 @@ def constrained_results(
     results_constrained : dict
         Aggregated results in form
         {technology: np.array((sector, region, fueltype, timestep))}
-    results_unconstrained : array
-        Restuls of unconstrained mode
-        np.array((sector, regions, fueltype, timestep))
+    results_unconstrained_no_heating : dict
+        Aggregated results without heating
+        {technology: np.array((sector, region, fueltype, timestep))}
     submodels_names : list
         Names of sectors fur supply model
     technologies : dict
@@ -65,45 +62,16 @@ def constrained_results(
     # Add all constrained results (technology specific results)
     # Aggregate according to submodel, fueltype, technology, region, timestep
     # ----------------------------------------
-    #summed_heating_demand = np.zeros((len(submodels_names), reg_nrs, fueltype_nrs, 8760))
     for submodel_nr, submodel in enumerate(submodels_names):
         for tech, fuel_tech in results_constrained.items():
-            # ----
             # Technological simplifications because of different technology definition and because not all technologies are used in supply model
-            # ----
             tech_simplified = model_tech_simplification(tech)
             fueltype_str = technologies[tech_simplified].fueltype_str
             fueltype_int = technologies[tech_simplified].fueltype_int
             key_name = "{}_{}_{}".format(submodel, fueltype_str, tech_simplified)
 
-            # Out results
             supply_results[key_name] = fuel_tech[submodel_nr][:, fueltype_int, :]
 
-    #for key_name, values in supply_results.items():
-    #    if testing_functions.test_if_minus_value_in_array(values):
-    #        raise Exception("TTT {} {}".format(key_name, np.sum(values)))
-    #--------------------------------
-    # Get all non heating related enduse
-    # --------------------------------
-    #assert not testing_functions.test_if_minus_value_in_array(results_unconstrained)
-    #assert not testing_functions.test_if_minus_value_in_array(summed_heating_demand)
-    #print(fuel_tech[submodel_nr][:, fueltype_int, :].shape)
-    #print("ddddd")
-    #print(np.sum(summed_heating_demand))
-    #print(np.sum(results_unconstrained))
-    #print(np.sum(results_unconstrained_no_heating))
-    #print(results_unconstrained.shape)
-    #print(summed_heating_demand.shape)
-    #print(results_unconstrained_no_heating.shape)
-    #print("DDdf --")
-    #assert not testing_functions.test_if_minus_value_in_array(results_unconstrained_no_heating)
-    # Substract constrained fuel from nonconstrained (total) fuel
-    #non_heating_ed_OLD = results_unconstrained - summed_heating_demand #sum(results_constrained.values())
-
-    #print(np.sum(non_heating_ed_OLD))
-    #print(np.sum(non_heating_ed))
-    #print(non_heating_ed_OLD.shape)
-    #print(non_heating_ed.shape)
     assert not testing_functions.test_if_minus_value_in_array(results_unconstrained_no_heating)
 
     # ---------------------------------
@@ -125,7 +93,6 @@ def constrained_results(
     # --------------------------------------------
     for key_name, values in supply_results.items():
         if testing_functions.test_if_minus_value_in_array(values):
-            logging.info("info tt: {}  {}".format(values, np.sum(values)))
             raise Exception("Error d: Negative entry in results {} {}".format(key_name, np.sum(values)))
 
     return supply_results

energy_demand/main.py

@@ -7,15 +7,13 @@
     Examples
     python energy_demand/energy_demand/main.py C:/Users/cenv0553/ed/energy_demand/local_run_config_file.ini test_run
     python energy_demand/energy_demand/main.py C:/Users/cenv0553/ed/energy_demand/local_run_config_file.ini h_max NF1 h_max
-
 """
 import os
 import sys
 import time
 import logging
 from collections import defaultdict
 import pandas as pd
-import numpy as np
 
 from energy_demand.basic import basic_functions, date_prop, demand_supply_interaction, testing_functions, lookup_tables
 from energy_demand import model
@@ -24,8 +22,6 @@
 from energy_demand.validation import lad_validation
 from energy_demand.scripts import s_scenario_param, init_scripts, s_disaggregation
 from energy_demand.plotting import fig_enduse_yh
-from energy_demand import enduse_func
-from energy_demand.technologies import tech_related
 
 def energy_demand_model(
         regions,
@@ -475,7 +471,7 @@ def energy_demand_model(
         if config['CRITERIA']['mode_constrained']:
             supply_results = demand_supply_interaction.constrained_results(
                 sim_obj.results_constrained,
-                sim_obj.results_unconstrained,
+                sim_obj.results_unconstrained_no_heating,
                 data['assumptions'].submodels_names,
                 data['assumptions'].technologies)
         else:
@@ -541,18 +537,7 @@ def energy_demand_model(
                     data['weather_yr_result_paths']['data_results_model_run_results_txt'],
                     sim_obj.tot_fuel_y_enduse_specific_yh,
                     "out_enduse_specific")
-
-                # Check peak
-                fueltype_int = tech_related.get_fueltype_int('electricity')
-                national_hourly_demand = np.sum(sim_obj.ed_fueltype_regs_yh[fueltype_int], axis=0)
-                peak_day_electricity, _ = enduse_func.get_peak_day_single_fueltype(national_hourly_demand)
-                selected_hours = date_prop.convert_yearday_to_8760h_selection(peak_day_electricity)
             else:
-                #write_data.write_residential_tot_demands(
-                #    sim_yr,
-                #    data['weather_yr_result_paths']['data_results_model_run_results_txt'],
-                #    sim_obj.ed_residential_tot_reg_y,
-                #    "ed_residential_tot_reg_y")
                 write_data.write_supply_results(
                     sim_yr,
                     "ed_fueltype_regs_yh",

energy_demand/model.py

@@ -73,7 +73,6 @@ def __init__(
         """Constructor
         """
         self.curr_yr = assumptions.curr_yr
-
         # ------------------------
         # Create Dwelling Stock
         # ------------------------
@@ -241,29 +240,15 @@ def aggregate_across_all_regs(
         fueltypes_nr,
         reg_nrs)
 
-    # ----------------------------------------------------
-    # Unconstrained results
-    # np.array of all fueltypes (submodel, region, fueltype, hours)
-    # ----------------------------------------------------
-    '''aggr_results['results_unconstrained'] = aggregate_result_unconstrained(
-        assumptions.nr_of_submodels,
-        assumptions.lookup_sector_enduses,
-        aggr_results['ed_enduse_fueltype_regs_yh'],
-        fueltypes_nr,
-        reg_nrs)'''
-
     # ----------------------------------------------------
     # Generate dict for supply model
     # ----------------------------------------------------
     if criterias['mode_constrained']:
         aggr_results['supply_results'] = demand_supply_interaction.constrained_results(
             aggr_results['results_constrained'],
-            #aggr_results['results_unconstrained'],
             aggr_results['results_unconstrained_no_heating'],
             assumptions.submodels_names,
-            technologies,
-            reg_nrs,
-            assumptions.fueltypes_nr)
+            technologies)
     else:
         aggr_results['supply_results'] = demand_supply_interaction.unconstrained_results(
             aggr_results['results_unconstrained'],
@@ -671,44 +656,6 @@ def create_dwelling_stock(regions, curr_yr, data):
     #data['ss_dw_stock'][region][curr_yr] = dw_stock.createNEWCASTLE_dwelling_stock(self.curr_yr)
     return data
 
-def aggregate_result_unconstrained(
-        nr_of_submodels,
-        lookup_sector_enduses,
-        ed_enduse_fueltype_regs_yh,
-        fueltypes_nr,
-        reg_nrs
-    ):
-    """Aggregated unconstrained results. Summarise energy demand
-    of unconstrained mode (heat is provided)
-
-    Arguments
-    ---------
-    nr_of_submodels : int
-        Number of submodels
-    submodels_enduses : dict
-        Submodels and enduses
-    ed_enduse_fueltype_regs_yh : array
-        Fuel array
-    fueltypes_nr : int
-        Number of fueltypes
-    reg_nrs : int
-        Number of regions
-
-    Returns
-    --------
-    constrained_array : array
-        assumptions.nr_of_submodels, reg_nrs, fueltypes_nr, 8760
-    """
-    constrained_array = np.zeros((nr_of_submodels, reg_nrs, fueltypes_nr, 8760), dtype="float")
-
-    for submodel_nr, enduse_array_nrs in lookup_sector_enduses.items():
-        for enduse_array_nr in enduse_array_nrs:
-            for fueltype_nr in range(fueltypes_nr):
-                for region_nr in range(reg_nrs):
-                    constrained_array[submodel_nr][region_nr][fueltype_nr] += ed_enduse_fueltype_regs_yh[enduse_array_nr][fueltype_nr][region_nr]
-
-    return constrained_array
-
 def aggregate_single_region(
         reg_nrs,
         lookup_enduses,
@@ -792,7 +739,6 @@ def aggregate_single_region(
                             if tech not in aggr_results['results_constrained']:
                                 aggr_results['results_constrained'][tech] = np.zeros((len(submodel_to_idx), reg_nrs, fueltypes_nr, 8760), dtype="float")
                             aggr_results['results_constrained'][tech][submodel_nr][reg_array_nr][tech_fueltype] += reshaped_fuel
-
                     else:
                         aggr_results['results_unconstrained_no_heating'][submodel_nr][reg_array_nr][tech_fueltype] += reshaped_fuel
                 else:
@@ -805,7 +751,7 @@ def aggregate_single_region(
 
             for fueltype_nr, fuels_8760 in enumerate(fueltype_yh_8760):
                 aggr_results['ed_enduse_fueltype_regs_yh'][enduse_array_nr][fueltype_nr][reg_array_nr] += fuels_8760
-                
+
                 aggr_results['results_unconstrained_no_heating'][submodel_nr][reg_array_nr][fueltype_nr] += fuels_8760
 
     return aggr_results

energy_demand/read_write/data_loader.py

@@ -1263,7 +1263,7 @@ def ss_read_shapes_enduse_techs(ss_shapes_dh, ss_shapes_yd):
     return ss_all_tech_shapes_dh, ss_all_tech_shapes_yd
 
 def read_scenario_data(path_to_csv, value_name='value', region_name='region'):
-    """
+    """Function to read in scenario data
     """
     data = {}
 
@@ -1273,8 +1273,8 @@ def read_scenario_data(path_to_csv, value_name='value', region_name='region'):
     with open(path_to_csv, 'r') as csvfile:
         rows = csv.reader(csvfile, delimiter=',')
         headings = next(rows)  # Skip first row
-        for row in rows:
 
+        for row in rows:
             region = str(row[read_data.get_position(headings, region_name)])
             year = int(float(row[read_data.get_position(headings, 'timestep')]))
             value = float(row[read_data.get_position(headings, value_name)])
@@ -1310,13 +1310,11 @@ def read_scenario_data_gva(path_to_csv, region_name='region', value_name='value'
                 for year_dummy in range(2015, 2051):
                     for sector_dummy in range(1, 47):
                         dummy_sector_value = 1
-
                         try:
                             out_dict[year_dummy][region][sector_dummy] = dummy_sector_value
                         except KeyError:
                             out_dict[year_dummy] = defaultdict(dict)
                             out_dict[year_dummy][region][sector_dummy] = dummy_sector_value
-
             else:
                 if row[read_data.get_position(headings, 'timestep')] == '': #No data provided
                     region = str(row[read_data.get_position(headings, region_name)])

energy_demand/result_processing/p3_weather_plots.py

@@ -235,7 +235,7 @@ def main(
                         [results_container['regional_share_national_peak'][simulation_yr_to_plot]],
                         columns=data['regions'])
                     regional_share_national_peak = regional_share_national_peak.append(realisation_data)
-                
+
                     # --Regional percentage of national peak demand per person
                     realisation_data = pd.DataFrame(
                         [results_container['regional_share_national_peak_pp'][simulation_yr_to_plot]],

energy_demand/scripts/s_post_installation.py

@@ -71,7 +71,7 @@ def post_install_setup(args):
         basic_functions.create_folder(folder)
 
     print("... Read in residential submodel load profiles", flush=True)
-    '''s_rs_raw_shapes.run(
+    s_rs_raw_shapes.run(
         data['paths'],
         data['local_paths'],
         base_yr)
@@ -83,7 +83,7 @@ def post_install_setup(args):
         data['lookups'])
 
     # Input data preparation
-    print("Generate additional data", flush=True)'''
+    print("Generate additional data", flush=True)
 
     # Extract NISMOD population data
     path_to_zip_file = os.path.join(local_data_path,"population-economic-smif-csv-from-nismod-db.zip")

energy_demand/scripts/weather_scripts/weather_scenario.py

@@ -217,8 +217,8 @@ def write_weather_data(data_list):
             stations_data[station_id_cnt] = station_data
 
             # Weather station metadata
-            station_lon = row[1]
             station_lat = row[0]
+            station_lon = row[1]
 
             station_id = "station_id_{}".format(station_id_cnt)
 
@@ -259,11 +259,11 @@ def weather_dat_prepare(data_path, result_path, years_to_clean=range(2020, 2049)
             # Create folder
             path_realization = os.path.join(path_year, realization_name)
             basic_functions.create_folder(path_realization)
-            
+
             # Data to extract
             path_tasmin = os.path.join(path_realizations, realization_name, 'daily', 'WAH_{}_tasmin_daily_g2_{}.nc'.format(realization_name, year))
             path_tasmax = os.path.join(path_realizations, realization_name, 'daily', 'WAH_{}_tasmax_daily_g2_{}.nc'.format(realization_name, year))
-            
+
             # Load data
             print("     ..load data", flush=True)
             df_min = get_temp_data_from_nc(path_tasmin, 'tasmin')
@@ -295,7 +295,7 @@ def weather_dat_prepare(data_path, result_path, years_to_clean=range(2020, 2049)
     print("... finished cleaning weather data")
 
 clean_original_files = False
-stich_weather_scenario = True
+stich_weather_scenario = False
 
 if clean_original_files:
     # ------------------------

energy_demand/wrapper_model.py

@@ -137,10 +137,11 @@ def load_data_before_simulation(
         name_population_dataset = config['DATA_PATHS']['path_population_data_for_disaggregation_msoa']
     else:
         name_population_dataset = config['DATA_PATHS']['path_population_data_for_disaggregation_lad']
+
     data['pop_for_disag'] = data_loader.read_scenario_data(
         name_population_dataset,
-        region_name='lad_uk_2016', 
-        value_name='population')
+        region_name='region',
+        value_name='value')
 
     # ------------------------------------------------
     # Load building related data