diff --git a/src/main.py b/src/main.py
index 1c33b33c0db94cebf851616a13ffbce5e43cf535..015a105290c71848eb64863d519fb8de778bf79a 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,7 +1,7 @@
import pickle
import yaml
-from os.path import join
-from numpy import array
+from os.path import join, isfile
+from numpy import array, argsort
from pyomo.opt import SolverFactory
import time
@@ -12,8 +12,6 @@ from models import build_ip_model
if __name__ == '__main__':
- custom_log(' Starting data pre-processing')
-
model_parameters = read_inputs('../config_model.yml')
siting_parameters = model_parameters['siting_params']
tech_parameters = read_inputs('../config_techs.yml')
@@ -21,21 +19,37 @@ if __name__ == '__main__':
data_path = model_parameters['data_path']
spatial_resolution = model_parameters['spatial_resolution']
time_horizon = model_parameters['time_slice']
+
deployment_dict = get_deployment_vector(model_parameters['regions'],
model_parameters['technologies'],
model_parameters['deployments'])
- database = read_database(data_path, spatial_resolution)
- site_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
+ if isfile(join(data_path, 'input_data/criticality_matrix.p')):
+
+ custom_log(' WARNING! Instance data read from files. Make sure the files are the ones that you need.')
+ criticality_data = pickle.load(open(join(data_path, 'input_data/criticality_matrix.p', 'rb')))
+ site_coordinates = pickle.load(open(join(data_path, 'input_data/site_coordinates.p', 'rb')))
+ capacity_factors_data = pickle.load(open(join(data_path, 'input_data/capacity_factors_data.p', 'rb')))
+ site_positions = pickle.load(open(join(data_path, 'input_data/site_positions.p', 'rb')))
+
+ else:
+
+ custom_log('Files not available. Starting data pre-processing.')
- truncated_data = selected_data(database, site_coordinates, time_horizon)
- capacity_factors_data = return_output(truncated_data, data_path)
- time_windows_data = resource_quality_mapping(capacity_factors_data, siting_parameters)
+ database = read_database(data_path, spatial_resolution)
+ site_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)
+ time_windows_data = resource_quality_mapping(capacity_factors_data, siting_parameters)
+ criticality_data = xarray_to_ndarray(critical_window_mapping(time_windows_data, model_parameters))
+ site_positions = sites_position_mapping(time_windows_data)
- criticality_data = xarray_to_ndarray(critical_window_mapping(time_windows_data, model_parameters))
- site_positions = sites_position_mapping(time_windows_data)
+ pickle.dump(criticality_data, open(join(data_path, 'input_data/criticality_matrix.p', 'wb')), protocol=4)
+ pickle.dump(site_coordinates, open(join(data_path, 'input_data/site_coordinates.p', 'wb')), protocol=4)
+ pickle.dump(capacity_factors_data, open(join(data_path, 'input_data/capacity_factors_data.p', 'wb')), protocol=4)
+ pickle.dump(site_positions, open(join(data_path, 'input_data/site_positions.p', 'wb')), protocol=4)
- custom_log(' Data read. Building model.')
+ custom_log(' Data read. Building model.')
if siting_parameters['solution_method']['BB']['set']:
@@ -93,24 +107,25 @@ if __name__ == '__main__':
params['initial_temp'], params['no_runs'],
params['algorithm'])
- seed = 1 # for folder naming purposes only
- for i in range(jl_selected.shape[0]):
+ output_folder = init_folder(model_parameters, suffix='_c' + str(c) + '_MIRSA')
- output_folder = init_folder(model_parameters, c, suffix='_MIRSA_seed' + str(seed))
- seed += 1
+ 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)
- 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)
+ pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ pickle.dump(jl_traj, open(join(output_folder, 'trajectory_matrix.p'), 'wb'))
- jl_selected_seed = jl_selected[i, :]
- jl_objective_seed = jl_objective[i]
+ med_run = argsort(jl_objective)[c//2]
+ jl_selected_seed = jl_selected[med_run, :]
+ jl_objective_seed = jl_objective[med_run]
- jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- output_folder, benchmarks=True)
+ jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
+ retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
+ criticality_data, site_positions, c, jl_locations, jl_objective_seed,
+ output_folder, benchmarks=True)
elif siting_parameters['solution_method']['RAND']['set']:
@@ -140,6 +155,15 @@ if __name__ == '__main__':
pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ med_run = argsort(jl_objective)[c//2]
+ jl_selected_seed = jl_selected[med_run, :]
+ jl_objective_seed = jl_objective[med_run]
+
+ jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
+ retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
+ criticality_data, site_positions, c, jl_locations, jl_objective_seed,
+ output_folder, benchmarks=True)
+
elif siting_parameters['solution_method']['GRED']['set']:
custom_log(' GRED chosen to solve the IP. Opening a Julia instance. Resulting coordinates are not obtained!')
@@ -165,5 +189,14 @@ if __name__ == '__main__':
pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
+ med_run = argsort(jl_objective)[c//2]
+ jl_selected_seed = jl_selected[med_run, :]
+ jl_objective_seed = jl_objective[med_run]
+
+ jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
+ retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
+ criticality_data, site_positions, c, jl_locations, jl_objective_seed,
+ output_folder, benchmarks=True)
+
else:
raise ValueError(' This solution method is not available. ')