major update commit before for OR paper.

src/helpers.py

@@ -1060,7 +1060,7 @@ def get_partition_index(input_dict, deployment_vector, capacity_split='per_tech'
         for region, tech in key_list:
             index_dict[region][tech] = [i + 1 for i in index_dict[region][tech]]
 
-    print(index_dict)
+    # print(index_dict)
 
     return index_dict
 

src/main.py

@@ -101,23 +101,22 @@ elif parameters['solution_method']['RAND']['set']:
             for i in range(it):
 
                 random_locations = []
-                random_locations_index = []
-                all_locations = []
-
                 for region in parameters['deployment_vector'].keys():
                     for tech in parameters['technologies']:
-
                         population = input_dict['coordinates_data'][region][tech]
                         k = parameters['deployment_vector'][region][tech]
-
-                        all_locations.extend(population)
                         random_locations.extend(sample(population, k))
 
+                all_locations = []
+                for region in parameters['deployment_vector'].keys():
+                    for tech in parameters['technologies']:
+                        all_locations.extend(input_dict['coordinates_data'][region][tech])
+                random_locations_index = []
                 for loc in random_locations:
                     idx = all_locations.index(loc)
-                    random_locations_index.append(idx)
+                    random_locations_index.append(idx + 1)
 
-                random_locations_index = sorted(random_locations_index)
+                random_locations_index = [i-1 for i in sorted(random_locations_index)]
 
                 xs = zeros(shape=input_dict['criticality_data'].shape[1])
                 xs[random_locations_index] = 1
@@ -128,6 +127,7 @@ elif parameters['solution_method']['RAND']['set']:
                 if objective > best_objective:
                     best_objective = objective
                     best_random_locations = random_locations
+
             random_locations_dict = {parameters['technologies'][0]: best_random_locations}
             retrieve_site_data(c, parameters, input_dict, output_folder, random_locations_dict, best_objective)
 

src/tools.py

@@ -870,22 +870,22 @@ def retrieve_index_dict(model_parameters, coordinate_dict):
     d = model_parameters['deployment_vector']
     if isinstance(d[list(d.keys())[0]], int):
         dict_deployment = d
-        print(dict_deployment)
+        # print(dict_deployment)
         n = sum(dict_deployment[item] for item in dict_deployment)
-        print(n)
+        # print(n)
         partitions = [item for item in d]
-        print(partitions)
+        # print(partitions)
         if model_parameters['deployment_constraint'] == 'country':
             indices = get_partition_index(coordinate_dict, d, capacity_split='per_country')
         elif model_parameters['deployment_constraint'] == 'tech':
             indices = get_partition_index(coordinate_dict, d, capacity_split='per_tech')
     else:
         dict_deployment = concatenate_dict_keys(d)
-        print(dict_deployment)
+        # print(dict_deployment)
         n = sum(dict_deployment[item] for item in dict_deployment)
-        print(n)
+        # print(n)
         partitions = [item for item in dict_deployment]
-        print(partitions)
+        # print(partitions)
         indices = concatenate_dict_keys(get_partition_index(coordinate_dict, d, capacity_split='per_country_and_tech'))
 
     return n, dict_deployment, partitions, indices