capacity init network

firm.py

@@ -29,12 +29,23 @@ class FirmAgent(ap.Agent):
                 # remove edge
                 # print(n1, n2, key, product_code)
                 self.firm_network.graph.remove_edge(n1, n2, key)
-                # remove customer up product
+                # remove customer up product if does not have alternative
                 customer = ap.AgentIter(self.model, n2).to_list()[0]
-                if remove_product not in customer.a_list_up_product_removed:
+                list_in_edges = list(
-                    customer.a_list_up_product_removed.append(remove_product)
+                    self.firm_network.graph.in_edges(n2,
-                    customer.dct_num_trial_up_product_removed[
+                                                     keys=True,
-                        remove_product] = 0
+                                                     data='Product'))
+                select_edges = [
+                    edge for edge in list_in_edges
+                    if edge[-1] == remove_product.code
+                ]
+                if len(select_edges) == 0:
+                    if remove_product not in \
+                            customer.a_list_up_product_removed:
+                        customer.a_list_up_product_removed.append(
+                            remove_product)
+                        customer.dct_num_trial_up_product_removed[
+                            remove_product] = 0
 
                 # # disrupt customer
                 # customer = ap.AgentIter(self.model, n2).to_list()[0]

model.py

@@ -71,20 +71,29 @@ class Model(ap.Model):
                         G_Firm.nodes[succ_firm]['Revenue_Log']
                         for succ_firm in list_succ_firms
                     ]
+                    # list_prob = [
+                    #     (v - min(list_revenue_log) + 1) /
+                    #     (max(list_revenue_log) - min(list_revenue_log) + 1)
+                    #     for v in list_revenue_log
+                    # ]
+                    # list_flag = [
+                    #     self.nprandom.choice([1, 0], p=[prob, 1 - prob])
+                    #     for prob in list_prob
+                    # ]
+                    # # print(list(zip(list_succ_firms,list_flag,list_prob)))
+                    # list_added_edges = [(node, succ_firm, {
+                    #     'Product': product_code
+                    # }) for succ_firm, flag in zip(list_succ_firms, list_flag)
+                    #                     if flag == 1]
                     list_prob = [
-                        (v - min(list_revenue_log) + 1) /
+                        size / sum(list_revenue_log)
-                        (max(list_revenue_log) - min(list_revenue_log) + 1)
+                        for size in list_revenue_log
-                        for v in list_revenue_log
                     ]
-                    list_flag = [
+                    succ_firm = self.nprandom.choice(list_succ_firms,
-                        self.nprandom.choice([1, 0], p=[prob, 1 - prob])
+                                                     p=list_prob)
-                        for prob in list_prob
-                    ]
-                    # print(list(zip(list_succ_firms,list_flag,list_prob)))
                     list_added_edges = [(node, succ_firm, {
                         'Product': product_code
-                    }) for succ_firm, flag in zip(list_succ_firms, list_flag)
+                    })]
-                                        if flag == 1]
                     G_Firm.add_edges_from(list_added_edges)
                     # print('-' * 20)
 
@@ -118,16 +127,19 @@ class Model(ap.Model):
                     code in attr['Product_Code']
                     for code in self.a_list_total_products.code
                 ]))
-            # init capacity as the degree of out edges of a specific product
+            # init capacity based on discrete uniform distribution
-            list_out_edges = list(
+            # list_out_edges = list(
-                self.firm_network.graph.out_edges(ag_node,
+            #     self.firm_network.graph.out_edges(ag_node,
-                                                  keys=True,
+            #                                       keys=True,
-                                                  data='Product'))
+            #                                       data='Product'))
+            # for product in firm_agent.a_list_product:
+            #     capacity = len([
+            #         edge for edge in list_out_edges if edge[-1] ==
+            #                 product.code])
+            #     firm_agent.dct_prod_capacity[product] = capacity
             for product in firm_agent.a_list_product:
-                capacity = len([
+                firm_agent.dct_prod_capacity[product] = self.nprandom.integers(
-                    edge for edge in list_out_edges if edge[-1] == product.code
+                    firm_agent.revenue_log / 5, firm_agent.revenue_log / 5 + 2)
-                ])
-                firm_agent.dct_prod_capacity[product] = capacity
             # print(firm_agent.name, firm_agent.dct_prod_capacity)
 
             self.firm_network.add_agents([firm_agent], [ag_node])
@@ -184,7 +196,7 @@ class Model(ap.Model):
                 firm.remove_edge_to_cus_and_cus_up_prod(product)
 
         for n_trial in range(self.int_n_max_trial):
-            print('='*20, n_trial, '='*20)
+            print('=' * 20, n_trial, '=' * 20)
             # seek_alt_supply
             for firm in self.a_list_total_firms:
                 if len(firm.a_list_up_product_removed) > 0:
@@ -241,6 +253,6 @@ class Model(ap.Model):
 
 model = Model(dct_sample_para)
 model.setup()
+model.draw_network()
 model.update()
 model.step()
-# model.draw_network()

test.ipynb

@@ -1,5 +1,27 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.randint(0.5, 3.5)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,