import agentpy as ap import pandas as pd import numpy as np import random import networkx as nx from firm import FirmAgent from product import ProductAgent from orm import db_session, Result import platform import json class Model(ap.Model): def setup(self): self.sample = self.p.sample self.int_stop_times, self.int_stop_t = 0, None self.random = random.Random(self.p.seed) self.nprandom = np.random.default_rng(self.p.seed) self.int_n_iter = int(self.p.n_iter) self.int_n_max_trial = int(self.p.n_max_trial) self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod # init graph bom G_bom = nx.adjacency_graph(json.loads(self.p.g_bom)) self.product_network = ap.Network(self, G_bom) # init graph firm Firm = pd.read_csv("Firm_amended.csv") Firm['Code'] = Firm['Code'].astype('string') 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', inplace=True) 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): for product_code in G_Firm.nodes[node]['Product_Code']: for succ_product_code in list(G_bom.successors(product_code)): # for each product of a certain firm # get each successor (finished product) of this product # get a list of firm producing this successor lst_succ_firm = Firm['Code'][Firm[succ_product_code] == 1].to_list() lst_succ_firm_size = [ G_Firm.nodes[succ_firm]['Revenue_Log'] for succ_firm in lst_succ_firm ] lst_prob = [ size / sum(lst_succ_firm_size) for size in lst_succ_firm_size ] # select multiple successors based on relative size of this firm lst_same_prod_firm = Firm['Code'][Firm[product_code] == 1].to_list() lst_same_prod_firm_size = [ G_Firm.nodes[f]['Revenue_Log'] for f in lst_same_prod_firm ] share = G_Firm.nodes[node]['Revenue_Log'] / sum( lst_same_prod_firm_size) n_succ_firm = round(share * len(lst_succ_firm)) if round( share * len(lst_succ_firm)) > 0 else 1 # at least one lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, p=lst_prob) lst_choose_firm = list( set(lst_choose_firm )) # nprandom.choice may have duplicates lst_add_edge = [(node, succ_firm, { 'Product': product_code }) for succ_firm in lst_choose_firm] G_Firm.add_edges_from(lst_add_edge) self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm)) self.firm_network = ap.Network(self, G_Firm) # init product for ag_node, attr in self.product_network.graph.nodes(data=True): product = ProductAgent(self, code=ag_node.label, name=attr['Name']) self.product_network.add_agents([product], [ag_node]) self.a_lst_total_products = ap.AgentList(self, self.product_network.agents) # init firm for ag_node, attr in self.firm_network.graph.nodes(data=True): firm_agent = FirmAgent( self, code=ag_node.label, name=attr['Name'], type_region=attr['Type_Region'], revenue_log=attr['Revenue_Log'], a_lst_product=self.a_lst_total_products.select([ code in attr['Product_Code'] for code in self.a_lst_total_products.code ])) # init extra capacity based on discrete uniform distribution for product in firm_agent.a_lst_product: firm_agent.dct_prod_capacity[product] = self.nprandom.integers( firm_agent.revenue_log / 5, firm_agent.revenue_log / 5 + 2) # print(firm_agent.name, firm_agent.dct_prod_capacity) self.firm_network.add_agents([firm_agent], [ag_node]) self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents) # init dct_list_remove_firm_prod (from string to agent) t_dct = {} for firm_code, lst_product in self.dct_lst_remove_firm_prod.items(): firm = self.a_lst_total_firms.select( self.a_lst_total_firms.code == firm_code)[0] t_dct[firm] = self.a_lst_total_products.select([ code in lst_product for code in self.a_lst_total_products.code ]) self.dct_lst_remove_firm_prod = t_dct self.dct_lst_disrupt_firm_prod = t_dct # init output self.lst_dct_lst_remove_firm_prod = [] self.lst_dct_lst_disrupt_firm_prod = [] # set the initial firm product that are removed for firm, a_lst_product in self.dct_lst_remove_firm_prod.items(): for product in a_lst_product: assert product in firm.a_lst_product, \ f"product {product.code} not in firm {firm.code}" firm.a_lst_product_removed.append(product) # draw network # self.draw_network() out_file = open("myfile.json", "w") json.dump(nx.adjacency_data(G_Firm), out_file) out_file.close() def update(self): self.a_lst_total_firms.clean_before_time_step() # output self.lst_dct_lst_remove_firm_prod.append( (self.t, self.dct_lst_remove_firm_prod)) self.lst_dct_lst_disrupt_firm_prod.append( (self.t, self.dct_lst_disrupt_firm_prod)) # stop simulation if reached terminal number of iteration if self.t == self.int_n_iter or len( self.dct_lst_remove_firm_prod) == 0: self.int_stop_times = self.t self.stop() def step(self): print('\n', '=' * 20, 'step', self.t, '=' * 20) print( 'dct_list_remove_firm_prod', { key.name: value.code for key, value in self.dct_lst_remove_firm_prod.items() }) # remove_edge_to_cus_and_cus_up_prod for firm, a_lst_product in self.dct_lst_remove_firm_prod.items(): for product in a_lst_product: firm.remove_edge_to_cus_remove_cus_up_prod(product) for n_trial in range(self.int_n_max_trial): print('=' * 10, 'trial', n_trial, '=' * 10) # seek_alt_supply # shuffle self.a_lst_total_firms self.a_lst_total_firms = self.a_lst_total_firms.shuffle() for firm in self.a_lst_total_firms: if len(firm.a_lst_up_product_removed) > 0: firm.seek_alt_supply() # handle_request # shuffle self.a_lst_total_firms self.a_lst_total_firms = self.a_lst_total_firms.shuffle() for firm in self.a_lst_total_firms: if len(firm.dct_request_prod_from_firm) > 0: firm.handle_request() # reset dct_request_prod_from_firm self.a_lst_total_firms.clean_before_trial() # do not use: # self.a_lst_total_firms.dct_request_prod_from_firm = {} why? # based on a_lst_up_product_removed # update a_lst_product_disrupted / a_lst_product_removed # update dct_lst_disrupt_firm_prod / dct_lst_remove_firm_prod self.dct_lst_remove_firm_prod = {} self.dct_lst_disrupt_firm_prod = {} for firm in self.a_lst_total_firms: if len(firm.a_lst_up_product_removed) > 0: print(firm.name, 'a_lst_up_product_removed', [ product.code for product in firm.a_lst_up_product_removed ]) for product in firm.a_lst_product: n_up_product_removed = 0 for up_product_removed in firm.a_lst_up_product_removed: if product in up_product_removed.a_successors(): n_up_product_removed += 1 if n_up_product_removed == 0: continue else: # update a_lst_product_disrupted / dct_lst_disrupt_firm_prod if product not in firm.a_lst_product_disrupted: firm.a_lst_product_disrupted.append(product) if firm in self.dct_lst_disrupt_firm_prod.keys(): self.dct_lst_disrupt_firm_prod[firm].append( product) else: self.dct_lst_disrupt_firm_prod[ firm] = ap.AgentList( self.model, [product]) # update a_lst_product_removed / dct_list_remove_firm_prod # mark disrupted firm as removed based conditionally lost_percent = n_up_product_removed / len( product.a_predecessors()) lst_size = self.a_lst_total_firms.revenue_log std_size = (firm.revenue_log - min(lst_size) + 1) / (max(lst_size) - min(lst_size) + 1) prod_remove = 1 - std_size * (1 - lost_percent) if self.nprandom.choice( [True, False], p=[prod_remove, 1 - prod_remove]): firm.a_lst_product_removed.append(product) if firm in self.dct_lst_remove_firm_prod.keys(): self.dct_lst_remove_firm_prod[firm].append( product) else: self.dct_lst_remove_firm_prod[ firm] = ap.AgentList( self.model, [product]) print( 'dct_list_remove_firm_prod', { key.name: value.code for key, value in self.dct_lst_remove_firm_prod.items() }) def end(self): print('/' * 20, 'output', '/' * 20) print('dct_list_remove_firm_prod') for t, dct in self.lst_dct_lst_remove_firm_prod: for firm, a_lst_product in dct.items(): for product in a_lst_product: print(t, firm.name, product.code) print('dct_lst_disrupt_firm_prod') for t, dct in self.lst_dct_lst_disrupt_firm_prod: for firm, a_lst_product in dct.items(): for product in a_lst_product: print(t, firm.name, product.code) qry_result = db_session.query(Result).filter_by(s_id=self.sample.id) if qry_result.count() == 0: lst_result_info = [] for t, dct in self.lst_dct_lst_disrupt_firm_prod: for firm, a_lst_product in dct.items(): for product in a_lst_product: db_r = Result(s_id=self.sample.id, id_firm=firm.code, id_product=product.code, ts=t, is_disrupted=True) lst_result_info.append(db_r) db_session.bulk_save_objects(lst_result_info) db_session.commit() for t, dct in self.lst_dct_lst_remove_firm_prod: for firm, a_lst_product in dct.items(): for product in a_lst_product: # only firm disrupted can be removed theoretically qry_f_p = db_session.query(Result).filter( Result.s_id == self.sample.id, Result.id_firm == firm.code, Result.id_product == product.code) if qry_f_p.count() == 1: qry_f_p.update({"is_removed": True}) db_session.commit() self.sample.is_done_flag = 1 self.sample.computer_name = platform.node() self.sample.stop_t = self.int_stop_times db_session.commit() 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') # print(node_label) node_degree = dict(self.firm_network.graph.out_degree()) node_label = { key: f"{node_label[key]} {node_degree[key]}" for key in node_label.keys() } node_size = list( nx.get_node_attributes(self.firm_network.graph, 'Revenue_Log').values()) node_size = list(map(lambda x: x**2, node_size)) edge_label = nx.get_edge_attributes(self.firm_network.graph, "Product") # multi(di)graphs, the keys are 3-tuples edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()} plt.figure(figsize=(12, 12), dpi=300) nx.draw(self.firm_network.graph, pos, node_size=node_size, labels=node_label, font_size=6) nx.draw_networkx_edge_labels(self.firm_network.graph, pos, edge_label, font_size=4) plt.savefig("network.png")