firm.py

@@ -1,6 +0,0 @@
-import agentpy as ap
-
-
-class Firm(ap.Agent):
-    def setup(self):
-        self.firm_network = self.model.firm_network

model.py

@@ -1,105 +0,0 @@
-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()