From 14dc232022110c8c9638c036dcd0ff2ce9914b29 Mon Sep 17 00:00:00 2001
From: dcradu <dcradu@uliege.be>
Date: Sun, 28 Feb 2021 11:55:50 +0100
Subject: [PATCH] renaming and removing ol results structure
---
src/helpers.py | 2 +-
src/main.py | 47 +++++++++++++++++++----------------------------
2 files changed, 20 insertions(+), 29 deletions(-)
diff --git a/src/helpers.py b/src/helpers.py
index 66e10d1..6980055 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -45,8 +45,8 @@ def xarray_to_ndarray(input_dict):
"""
key_list = return_dict_keys(input_dict)
- array_list = []
+ array_list = []
for region, tech in key_list:
array_list.append(input_dict[region][tech])
dataset = xr.concat(array_list, dim='locations')
diff --git a/src/main.py b/src/main.py
index 63211ea..f8bee2c 100644
--- a/src/main.py
+++ b/src/main.py
@@ -2,7 +2,6 @@ import pickle
import yaml
from os.path import join
from numpy import array
-from itertools import cycle
from pyomo.opt import SolverFactory
import time
@@ -30,11 +29,11 @@ if __name__ == '__main__':
site_coordinates = return_filtered_coordinates(database, model_parameters, tech_parameters)
truncated_data = selected_data(database, site_coordinates, time_horizon)
- output_data = return_output(truncated_data, data_path)
+ capacity_factors_data = return_output(truncated_data, data_path)
+ time_windows_data = resource_quality_mapping(capacity_factors_data, siting_parameters)
- smooth_data = resource_quality_mapping(output_data, siting_parameters)
- criticality_data = xarray_to_ndarray(critical_window_mapping(smooth_data, model_parameters))
- site_positions = sites_position_mapping(smooth_data)
+ criticality_data = xarray_to_ndarray(critical_window_mapping(time_windows_data, model_parameters))
+ site_positions = sites_position_mapping(time_windows_data)
custom_log(' Data read. Building model.')
@@ -67,7 +66,7 @@ if __name__ == '__main__':
objective = instance.objective()
x_values = array(list(instance.x.extract_values().values()))
comp_location_dict = retrieve_location_dict(x_values, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, output_data, criticality_data,
+ retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data, criticality_data,
site_positions, params['c'], comp_location_dict, objective, output_folder)
elif siting_parameters['solution_method']['MIRSA']['set']:
@@ -94,32 +93,24 @@ if __name__ == '__main__':
params['initial_temp'], params['no_runs'],
params['algorithm'])
- if params['purpose'] == 'planning':
- seed = params['seed']
- for i in range(jl_selected.shape[0]):
+ seed = params['seed']
+ for i in range(jl_selected.shape[0]):
- output_folder = init_folder(model_parameters, c, suffix='_MIRSA_seed' + str(seed))
- seed += 1
+ 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)
- jl_selected_seed = jl_selected[i, :]
- jl_objective_seed = jl_objective[i]
+ jl_selected_seed = jl_selected[i, :]
+ jl_objective_seed = jl_objective[i]
- jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- retrieve_site_data(model_parameters, deployment_dict, site_coordinates, output_data,
- criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- output_folder, benchmarks=True)
- else:
-
- output_folder = init_folder(model_parameters, suffix='_c' + str(c) + '_MIRSA')
-
- 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_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']:
--
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