Load Firm - choose customer

firm.py

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+import agentpy as ap
+
+
+class Firm(ap.Agent):
+    def setup(self):
+        self.firm_network = self.model.firm_network

model.py

@@ -0,0 +1,105 @@
+import agentpy as ap
+import pandas as pd
+import numpy as np
+import networkx as nx
+
+sample = 0
+sample = 0
+dct_sample_para = {'sample': sample, 'seed': sample}
+
+
+class Model(ap.Model):
+    def setup(self):
+        self.sample = self.p.sample
+        self.nprandom = np.random.default_rng(self.p.seed)
+
+        # init graph bom
+        BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
+        BomNodes.set_index('Code', inplace=True)
+        BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0)
+        BomCateNet.fillna(0, inplace=True)
+
+        G_bom = nx.from_pandas_adjacency(BomCateNet,
+                                         create_using=nx.MultiDiGraph())
+
+        bom_labels_dict = {}
+        for code in G_bom.nodes:
+            bom_labels_dict[code] = BomNodes.loc[code].to_dict()
+        nx.set_node_attributes(G_bom, bom_labels_dict)
+
+        # init graph firm
+        Firm = pd.read_csv("Firm_amended.csv")
+        Firm.fillna(0, inplace=True)
+        Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
+        firm_product = []
+        for _, row in Firm.loc[:, '1':].iterrows():
+            firm_product.append(row[row == 1].index.to_list())
+        Firm_attr.loc[:, 'Product_Code'] = firm_product
+        Firm_attr.set_index('Code')
+        G_Firm = nx.MultiDiGraph()
+        G_Firm.add_nodes_from(Firm["Code"])
+
+        firm_labels_dict = {}
+        for code in G_Firm.nodes:
+            firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
+        nx.set_node_attributes(G_Firm, firm_labels_dict)
+
+        # add edge to G_firm according to G_bom
+        for node in nx.nodes(G_Firm):
+            # print(node, '-'*20)
+            list_pred_product_code = []
+            for product_code in G_Firm.nodes[node]['Product_Code']:
+                list_pred_product_code += list(
+                    G_bom.predecessors(product_code))
+            list_pred_product_code = list(set(list_pred_product_code))
+            for pred_product_code in list_pred_product_code:
+                # print(pred_product_code)
+                list_pred_firms = Firm.index[Firm[pred_product_code] ==
+                                             1].to_list()
+                list_revenue_log = [
+                    G_Firm.nodes[pred_firm]['Revenue_Log']
+                    for pred_firm in list_pred_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_pred_firms,list_flag, list_prob)))
+                list_added_edges = [
+                    (node, pred_firm)
+                    for pred_firm, flag in zip(list_pred_firms, list_flag)
+                    if flag == 1
+                ]
+                G_Firm.add_edges_from(list_added_edges)
+                # print('-'*20)
+
+        self.firm_network = ap.Network(self, G_Firm)
+
+    def draw_network(self):
+        import matplotlib.pyplot as plt
+        plt.rcParams['font.sans-serif'] = 'SimHei'
+        pos = nx.nx_agraph.graphviz_layout(self.firm_network.graph,
+                                           prog="twopi",
+                                           args="")
+        node_label = nx.get_node_attributes(self.firm_network.graph, 'Name')
+        node_size = list(
+            nx.get_node_attributes(self.firm_network.graph,
+                                   'Revenue_Log').values())
+        node_size = list(map(lambda x: x**2, node_size))
+        plt.figure(figsize=(12, 12), dpi=300)
+        nx.draw(self.firm_network.graph,
+                pos,
+                node_size=node_size,
+                labels=node_label,
+                font_size=6)
+        plt.savefig("network.png")
+
+
+model = Model(dct_sample_para)
+model.setup()
+model.draw_network()