code clean-up

config_model.default.yml

-# Path to data folder
-data_path: 'path_to_data'
-
-# Spatial resolution (in degrees) of the potential sites.
-spatial_resolution: 0.25
-# Start time and end time of the analysis.
-time_slice: ['2018-01-01T00:00', '2018-01-31T23:00']
-# Technologies to deploy.
-regions: ['BE', 'LT', 'LV', 'EE', 'HR']
-technologies: ['wind_offshore']
-deployments: [[3], [4], [3], [2], [1]]
-
-siting_params:
-  smooth_measure: 'mean'            # median, percentiles
-  # Defines how \alpha is considered in space and time.
-  alpha: 'load_central'             # load_central, load_partition
-  # Normalization procedures (detailed in tools.py). (min, max)
-  norm_type: 'max'                  # max, min
-  # Time-window length used to compute the criticality indicator. Integer value.
-  delta: 1                          # \in \mathbb{N}
-  # Threshold
-  c: 1                              # < n, \in \mathbb{N}
-  # Solution method: BB or HEU or RAND or GRED.
-  solution_method:
-    SA:
-      set: False
-      neighborhood: 1
-      no_iterations: 100
-      no_epochs: 100
-      initial_temp: 200.
-      p: 0.05
-      no_runs: 3
-      no_runs_init: 5
-      algorithm: 'MIR' # 'MIR', 'SGH'
-    SGH:
-      set: True
-      p: 0.05
-      no_runs: 3
-      algorithm: 'SGH'
\ No newline at end of file

config_model.yml

@@ -6,10 +6,12 @@ data_path: '/data/dcradu/resite_ip/'
 spatial_resolution: 0.25
 # Start time and end time of the analysis.
 time_slice: ['2010-01-01T00:00', '2019-12-31T23:00']
+resampling_rate: 3
 # Technologies to deploy.
 regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE', 'HR']
 technologies: ['wind_offshore']
 deployments: [[80], [60], [57], [36], [35], [30], [28], [22], [20], [20], [15], [13], [10], [9], [6], [4], [3], [1], [1]]
+load_coverage: 0.3
 
 siting_params:
   # Defines how \alpha is considered in space and time.

config_model_partitioned.yml

-# Path to data folder
-#data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
-data_path: '/data/dcradu/resite_ip/'
-
-# Spatial resolution (in degrees) of the potential sites.
-spatial_resolution: 0.25
-# Start time and end time of the analysis.
-time_slice: ['2010-01-01T00:00', '2019-12-31T23:00']
-resampling_rate: 3
-# Technologies to deploy.
-regions: ['GB', 'NL', 'FR', 'DE', 'DK', 'NO', 'PL', 'IE', 'IT', 'SE', 'FI', 'ES', 'GR', 'PT', 'BE', 'LT', 'LV', 'EE', 'HR']
-technologies: ['wind_offshore']
-deployments: [[80], [60], [57], [36], [35], [30], [28], [22], [20], [20], [15], [13], [10], [9], [6], [4], [3], [1], [1]]
-load_coverage: 0.3
-
-siting_params:
-  # Defines how \alpha is considered in space and time.
-  alpha:
-    method: 'load' # 'potentials'
-    coverage: 'system' # 'partition'
-    smoothing: 'mean' # 'median'
-    norm: 'min' # 'max'
-  # Time-window length used to compute the criticality indicator. Integer value.
-  delta: 1                          # \in \mathbb{N}
-  # Threshold
-  c: 0.1                            # \in [0, 1]
-  # Solution method: BB or HEU or RAND or GRED.
-  solution_method:
-    neighborhood: 1
-    no_iterations: 5000
-    no_epochs: 500
-    initial_temp: 100.
-    no_runs: 30

config_model_unpartitioned.yml

-# Path to data folder
-#data_path: 'D:/ULg_PhD_work/datasets/resite_ip/'
-data_path: '/data/dcradu/resite_ip/'
-
-# Spatial resolution (in degrees) of the potential sites.
-spatial_resolution: 0.25
-# Start time and end time of the analysis.
-time_slice: ['2010-01-01T00:00', '2019-12-31T23:00']
-resampling_rate: 3
-# Technologies to deploy.
-regions: ['EU']
-technologies: ['wind_offshore']
-deployments: [[468]]
-load_coverage: 0.3
-
-siting_params:
-  # Defines how \alpha is considered in space and time.
-  alpha:
-    method: 'load' # 'potentials'
-    coverage: 'system' # 'partition'
-    smoothing: 'mean' # 'median'
-    norm: 'min' # 'max'
-  # Time-window length used to compute the criticality indicator. Integer value.
-  delta: 1                          # \in \mathbb{N}
-  # Threshold
-  c: 0.1                            # \in [0, 1]
-  # Solution method: BB or HEU or RAND or GRED.
-  solution_method:
-    neighborhood: 1
-    no_iterations: 5000
-    no_epochs: 500
-    initial_temp: 100.
-    no_runs: 30

config_techs.default.yml

-# Config file for various conversion technologies.
-wind_onshore:
-  where: 'onshore'
-  filters: ['resource_quality', 'population_density', 'orography', 'forestry', 'water_mask', 'latitude']
-  converter_IV: 'V110'
-  converter_III: 'E103'
-  converter_II: 'V90'
-  converter_I: 'E-126'
-  resource: 'wind'
-  deployment: 'onshore'
-  resource_threshold: 4. #m/s
-  population_density_threshold_low: 0.
-  population_density_threshold_high: 100.
-  protected_areas_selection: ['Ia', 'Ib', 'II']
-  protected_areas_distance_threshold: 10.
-  depth_threshold_low: 0.
-  depth_threshold_high: 0.
-  altitude_threshold: 1500.
-  terrain_slope_threshold: 0.03
-  forestry_ratio_threshold: 0.8
-  latitude_threshold: 65.
-
-wind_offshore:
-  where: 'offshore'
-  filters: ['resource_quality', 'bathymetry', 'latitude', 'distance', 'legacy']
-  converter_IV: 'V90'
-  converter_III: 'V90'
-  converter_II: 'V164'
-  converter_I: 'V164'
-  resource: 'wind'
-  deployment: 'offshore'
-  resource_threshold: 5. #m/s
-  population_density_threshold_low: 0.
-  population_density_threshold_high: 100.
-  protected_areas_selection: ['Ia', 'Ib', 'II', 'V']
-  protected_areas_distance_threshold: 5.
-  depth_threshold_low: 0.
-  depth_threshold_high: 199.
-  altitude_threshold: 0.
-  terrain_slope_threshold: 1.
-  forestry_ratio_threshold: 1.
-  latitude_threshold: 70.
-  distance_threshold_min: 22.2
-  distance_threshold_max: 222.0 # 111.
-  legacy_min: 0.1
-
-wind_floating:
-  where: 'offshore'
-  filters: ['resource_quality', 'bathymetry', 'latitude', 'distance']
-  converter_IV: 'V90'
-  converter_III: 'V90'
-  converter_II: 'V164'
-  converter_I: 'V164'
-  resource: 'wind'
-  deployment: 'floating'
-  resource_threshold: 9. #m/s
-  population_density_threshold_low: 0.
-  population_density_threshold_high: 100.
-  protected_areas_selection: ['Ia', 'Ib', 'II', 'V']
-  protected_areas_distance_threshold: 5.
-  depth_threshold_low: 200.
-  depth_threshold_high: 990.
-  altitude_threshold: 0.
-  terrain_slope_threshold: 1.
-  forestry_ratio_threshold: 1.
-  latitude_threshold: 65.
-  distance_threshold_min: 23.
-  distance_threshold_max: 180.
-
-pv_utility:
-  where: 'onshore'
-  filters: ['resource_quality', 'population_density', 'orography', 'forestry', 'water_mask', 'latitude']
-  converter: 'DEG15MC'
-  resource: 'solar'
-  deployment: 'utility'
-  resource_threshold: 130. #W/m2
-  population_density_threshold_low: 0.
-  population_density_threshold_high: 100.
-  protected_areas_selection: ['Ia', 'Ib', 'II', 'V']
-  protected_areas_distance_threshold: 5.
-  depth_threshold_low: 0.
-  depth_threshold_high: 0.
-  altitude_threshold: 1500.
-  terrain_slope_threshold: 0.03
-  forestry_ratio_threshold: 0.8
-  latitude_threshold: 65.
-
-#TODO: adjust pv_residential filters
-pv_residential:
-  where: 'onshore'
-  filters: ['population_density', 'water_mask']
-  converter: 'DD06M'
-  resource: 'solar'
-  deployment: 'residential'
-  resource_threshold: 100. #W/m2
-  population_density_threshold_low: 10.
-  population_density_threshold_high: 999999.
-  protected_areas_selection: ['Ia', 'Ib', 'II', 'V']
-  protected_areas_distance_threshold: 1.
-  depth_threshold_low: 0.
-  depth_threshold_high: 0.
-  altitude_threshold: 3000.
-  terrain_slope_threshold: 1.
-  forestry_ratio_threshold: 1.
-  latitude_threshold: 65.

src/helpers.py

@@ -539,37 +539,6 @@ def get_partition_index(input_dict):
     return index_dict
 
 
-def init_folder(parameters, no_locs, c, suffix=None):
-    """Initilize an output folder.
-
-    Parameters:
-    ------------
-    parameters : dict
-        Parameters dictionary.
-
-    Returns:
-    ------------
-    path : str
-        Relative path of the folder.
-
-    """
-    output_data_path = join(parameters['data_path'], 'output')
-
-    no_locs = str(no_locs)
-    no_part = str(len(parameters['regions']))
-    no_yrs = str(int(round((to_datetime(parameters['time_slice'][1]) -
-                            to_datetime(parameters['time_slice'][0])) / timedelta64(1, 'Y'), 0)))
-    c = str(c)
-
-    if not isdir(output_data_path):
-        makedirs(abspath(output_data_path))
-
-    path = abspath(output_data_path + '/' + no_yrs + 'y_n' + no_locs + '_k' + no_part + '_c' + c + suffix)
-    makedirs(path)
-
-    return path
-
-
 def generate_jl_input(deployment_dict, filtered_coordinates, site_positions, legacy_sites):
 
     concat_deployment_dict = concatenate_dict_keys(deployment_dict)

src/main.py

 import yaml
 import julia
-from os.path import join, isfile
+from julia import Main
+from os.path import join, isdir
+from os import makedirs
 from numpy import argmax, ceil, float64
-import argparse
 import pickle
+from time import strftime
 
 from copy import deepcopy
-from helpers import read_inputs, init_folder, xarray_to_ndarray, generate_jl_input, \
-    get_potential_per_site, capacity_to_cardinality
+from helpers import read_inputs, xarray_to_ndarray, generate_jl_input, get_potential_per_site, capacity_to_cardinality
 from tools import read_database, return_filtered_coordinates, selected_data, return_output, resource_quality_mapping, \
     critical_window_mapping, sites_position_mapping, retrieve_location_dict, retrieve_site_data
 
@@ -16,82 +17,29 @@ logging.basicConfig(level=logging.INFO, format=f"%(levelname)s %(asctime)s - %(m
 logging.disable(logging.CRITICAL)
 logger = logging.getLogger(__name__)
 
-
-def parse_args():
-
-    parser = argparse.ArgumentParser(description='Command line arguments.')
-
-    parser.add_argument('--k', type=str, default=None)
-    parser.add_argument('--c', type=float)
-    parser.add_argument('--alpha_method', type=str, default=None)
-    parser.add_argument('--alpha_coverage', type=str, default=None)
-    parser.add_argument('--delta', type=int, default=None)
-    parser.add_argument('--resampling_rate', type=str)
-    parser.add_argument('--maxdepth', type=str)
-
-    parsed_args = vars(parser.parse_args())
-
-    return parsed_args
-
-
 if __name__ == '__main__':
 
-    args = parse_args()
-
     logger.info('Starting data pre-processing.')
 
-    model_parameters = read_inputs(f"../config_model_{args['k']}.yml")
-    model_parameters['resampling_rate'] = args['resampling_rate']
+    model_parameters = read_inputs(f"../config_model.yml")
     siting_parameters = model_parameters['siting_params']
     tech_parameters = read_inputs('../config_techs.yml')
 
-    siting_parameters['alpha']['method'] = args['alpha_method']
-    siting_parameters['alpha']['coverage'] = args['alpha_coverage']
-    siting_parameters['delta'] = int(args['delta'])
-    siting_parameters['c'] = args['c']
-
     data_path = model_parameters['data_path']
     spatial_resolution = model_parameters['spatial_resolution']
     time_horizon = model_parameters['time_slice']
 
     database = read_database(data_path, spatial_resolution)
+    site_coordinates, legacy_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
+    truncated_data = selected_data(database, site_coordinates, time_horizon)
+    capacity_factors_data = return_output(truncated_data, data_path)
 
-    if isfile(join(data_path, f"input/capacity_factors_data_{args['k']}"
-                              f"_{args['resampling_rate']}h_{args['maxdepth']}m.p")):
-
-        capacity_factors_data = \
-            pickle.load(open(join(data_path,
-                                  f"input/capacity_factors_data_{args['k']}_{args['resampling_rate']}h_"
-                                  f"{args['maxdepth']}m.p"), 'rb'))
-        site_coordinates = \
-            pickle.load(open(join(data_path, f"input/site_coordinates_{args['k']}_{args['maxdepth']}m.p"), 'rb'))
-        legacy_coordinates = \
-            pickle.load(open(join(data_path, f"input/legacy_coordinates_{args['k']}_{args['maxdepth']}m.p"), 'rb'))
-        logger.info('Input files read from disk.')
-
-    else:
-
-        site_coordinates, legacy_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
-        truncated_data = selected_data(database, site_coordinates, time_horizon)
-        capacity_factors_data = return_output(truncated_data, data_path)
-
-        resampled_data = deepcopy(capacity_factors_data)
-        rate = model_parameters['resampling_rate']
-        for region in capacity_factors_data.keys():
-            for tech in capacity_factors_data[region].keys():
-                resampled_data[region][tech] = \
-                    capacity_factors_data[region][tech].resample(time=f"{rate}H").mean(dim='time')
-
-        pickle.dump(resampled_data,
-                    open(join(data_path, f"input/capacity_factors_data_{args['k']}_{args['resampling_rate']}h_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        pickle.dump(site_coordinates,
-                    open(join(data_path, f"input/site_coordinates_{args['k']}_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        pickle.dump(legacy_coordinates,
-                    open(join(data_path, f"input/legacy_coordinates_{args['k']}_"
-                                         f"{args['maxdepth']}m.p"), 'wb'), protocol=4)
-        logger.info('Input files written to disk.')
+    resampled_data = deepcopy(capacity_factors_data)
+    rate = model_parameters['resampling_rate']
+    for region in capacity_factors_data.keys():
+        for tech in capacity_factors_data[region].keys():
+            resampled_data[region][tech] = \
+                capacity_factors_data[region][tech].resample(time=f"{rate}H").mean(dim='time')
 
     time_windows_data = resource_quality_mapping(capacity_factors_data, siting_parameters)
     site_positions = sites_position_mapping(time_windows_data)
@@ -105,29 +53,27 @@ if __name__ == '__main__':
     total_no_locs = sum(deployment_dict[r][t] for r in deployment_dict.keys() for t in deployment_dict[r].keys())
     c = int(ceil(siting_parameters['c'] * total_no_locs))
 
-    output_folder = init_folder(model_parameters, total_no_locs, c,
-                                suffix=f"_{args['alpha_method']}_{args['alpha_coverage']}_d{args['delta']}")
+    output_dir = join(data_path, f"output/{strftime('%Y%m%d_%H%M%S')}/")
+    if not isdir(output_dir):
+        makedirs(output_dir)
+
+    with open(join(output_dir, 'config_model.yaml'), 'w') as outfile:
+        yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
+    with open(join(output_dir, 'config_techs.yaml'), 'w') as outfile:
+        yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
 
     logger.info('Data pre-processing finished. Opening Julia instance.')
 
     j = julia.Julia(compiled_modules=False)
-    from julia import Main
     Main.include("jl/SitingHeuristics.jl")
 
     params = siting_parameters['solution_method']
-
-    jl_sel, jl_obj, jl_tra = Main.main_SA(jl_dict['index_dict'],
-                                          jl_dict['deployment_dict'],
-                                          jl_dict['legacy_site_list'],
-                                          criticality_data.astype('float64'), float64(c),
-                                          params['neighborhood'], params['initial_temp'],
-                                          params['no_iterations'], params['no_epochs'], params['no_runs'])
-
-    with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
-        yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
-    with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
-        yaml.dump(tech_parameters, outfile, default_flow_style=False, sort_keys=False)
-
+    jl_sel, jl_obj, _ = Main.main_SA(jl_dict['index_dict'],
+                                     jl_dict['deployment_dict'],
+                                     jl_dict['legacy_site_list'],
+                                     criticality_data.astype('float64'), float64(c),
+                                     params['neighborhood'], params['initial_temp'],
+                                     params['no_iterations'], params['no_epochs'], params['no_runs'])
     logger.info('Siting heuristics done. Writing results to disk.')
 
     jl_objective_pick = argmax(jl_obj)
@@ -135,10 +81,9 @@ if __name__ == '__main__':
 
     locations_dict = retrieve_location_dict(jl_locations_vector, model_parameters, site_positions)
     retrieve_site_data(model_parameters, capacity_factors_data, criticality_data, deployment_dict,
-                       site_positions, locations_dict, legacy_coordinates, output_folder, benchmark="PROD")
+                       site_positions, locations_dict, legacy_coordinates, output_dir, benchmark="PROD")
 
-    pickle.dump(jl_sel, open(join(output_folder, 'solution_matrix.p'), 'wb'))
-    pickle.dump(jl_obj, open(join(output_folder, 'objective_vector.p'), 'wb'))
-    pickle.dump(jl_tra, open(join(output_folder, 'trajectory_matrix.p'), 'wb'))
+    pickle.dump(jl_sel, open(join(output_dir, 'solution_matrix.p'), 'wb'))
+    pickle.dump(jl_obj, open(join(output_dir, 'objective_vector.p'), 'wb'))
 
-    logger.info(f"Results written to {output_folder}")
+    logger.info(f"Results written to {output_dir}")