set to list

controller_db.py

@@ -76,7 +76,8 @@ class ControllerDB:
         # list_dct = [{'2': ['1.1.3']}]
         # list_dct = [{'135': ['1.3.2.1']}]
         # list_dct = [{'79': ['2.1.3.4']}]
-        list_dct = [{'99': ['1.3.3']}]
+        # list_dct = [{'99': ['1.3.3']}]
+        list_dct = [{'41': ['1.4.5']}]
 
         # fill g_bom
         BomNodes = pd.read_csv('BomNodes.csv', index_col=0)

model.py

@@ -71,14 +71,13 @@ class Model(ap.Model):
             for product_code in G_Firm.nodes[node]['Product_Code']:
                 lst_pred_product_code += list(G_bom.predecessors(product_code))
             lst_pred_product_code = list(set(lst_pred_product_code))
+            # to generate consistant graph
             lst_pred_product_code = list(sorted(lst_pred_product_code))
-            # print(lst_pred_product_code)
             for pred_product_code in lst_pred_product_code:
                 # for each product predecessor (component) the firm need
                 # get a list of firm producing this component
                 lst_pred_firm = \
                     Firm['Code'][Firm[pred_product_code] == 1].to_list()
-                print(lst_pred_firm)
                 lst_pred_firm_size_damp = \
                     [G_Firm.nodes[pred_firm]['Revenue_Log'] **
                         self.flt_netw_sply_prf_size
@@ -100,21 +99,21 @@ class Model(ap.Model):
                 # graph firm prod
                 set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
                 set_pred_succ_code = set(G_bom.successors(pred_product_code))
-                set_use_pred_prod_code = \
-                    set_node_prod_code & set_pred_succ_code
+                lst_use_pred_prod_code = list(
+                    set_node_prod_code & set_pred_succ_code)
                 for pred_firm in lst_choose_firm:
                     pred_node = [n for n, v in G_FirmProd.nodes(data=True)
                                  if v['Firm_Code'] == pred_firm and
                                  v['Product_Code'] == pred_product_code][0]
-                    for use_pred_prod_code in set_use_pred_prod_code:
+                    for use_pred_prod_code in lst_use_pred_prod_code:
                         current_node = \
                             [n for n, v in G_FirmProd.nodes(data=True)
                              if v['Firm_Code'] == node and
                              v['Product_Code'] == use_pred_prod_code][0]
                         G_FirmProd.add_edge(pred_node, current_node)
 
-        nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
-        nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
+        # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
+        # nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
         # unconnected node
         # for node in nx.nodes(G_Firm):
         #     if node.
@@ -284,17 +283,17 @@ class Model(ap.Model):
                                     'Product': di_supp_prod.code
                                 })
                             ])
-                            print(f"proactive add {select_cand.code} to "
-                                  f"{pro_firm.code} "
-                                  f"for {di_supp_node['Firm_Code']} "
-                                  f"{di_supp_node['Product_Code']}")
+                            # print(f"proactive add {select_cand.code} to "
+                            #       f"{pro_firm.code} "
+                            #       f"for {di_supp_node['Firm_Code']} "
+                            #       f"{di_supp_node['Product_Code']}")
                             # change capacity
                             select_cand.dct_prod_capacity[di_supp_prod] -= 1
                             break
 
-        nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm_proactive.csv')
+        # nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm_proactive.csv')
         # draw network
-        # self.draw_network()
+        self.draw_network()
 
     def update(self):
         self.a_lst_total_firms.clean_before_time_step()