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Cricial
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firm.py
4
firm.py
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@ -2,7 +2,7 @@ from mesa import Agent
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class FirmAgent(Agent):
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def __init__(self, unique_id, model, type_region, revenue_log, n_equip_c, a_lst_product,
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def __init__(self, unique_id, model, type_region, revenue_log, a_lst_product,
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production_output, demand_quantity, R, P, C):
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# 调用超类的 __init__ 方法
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super().__init__(unique_id, model)
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@ -38,7 +38,7 @@ class FirmAgent(Agent):
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self.downer_i = [agent for u, v in self.firm_network.out_edges(self.unique_id)
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for agent in self.model.company_agents if agent.unique_id == u]
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# 设备c的数量 （总量） 使用这个来判断设备数量
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self.n_equip_c = n_equip_c
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#self.n_equip_c = n_equip_c
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# 设备c产量 根据设备量进行估算
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self.c_yield = production_output
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# 消耗材料量 根据设备量进行估算 { }
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40
my_model.py
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my_model.py
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@ -68,13 +68,14 @@ class MyModel(Model):
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# 方法执行
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self.initialize_product_network(params)
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self.resource_integration()
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self.j_comp_consumed_produced()
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self.initialize_agents()
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self.initialize_firm_network()
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self.initialize_firm_product_network()
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self.add_edges_to_firm_network()
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self.connect_unconnected_nodes()
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self.resource_integration()
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self.j_comp_consumed_produced()
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self.initialize_agents()
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self.initialize_disruptions()
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def initialize_product_network(self, params):
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@ -94,7 +95,7 @@ class MyModel(Model):
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def initialize_firm_network(self):
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# Read the firm data
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firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
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firm = pd.read_csv("input_data/input_firm_data/firm_amended.csv")
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firm['Code'] = firm['Code'].astype(str)
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@ -316,18 +317,16 @@ class MyModel(Model):
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for ag_node, attr in self.product_network.nodes(data=True):
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# 产业种类
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type2 = self.type2[self.type2["Index"] == ag_node]["产业种类"]
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# type2 = self.type2[self.type2["Index"] == ag_node]["产业种类"]
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# depreciation ratio 折旧比值
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# product_id = product_id.iloc[0]
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product = ProductAgent(ag_node, self, name=attr['Name'], type2=type2)
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product = ProductAgent(ag_node, self, name=attr['Name'], type2=0)
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self.add_agent(product)
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# self.grid.place_agent(product, ag_node)
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##print(f"Product agent created: {product.name}, ID: {product.unique_id}")
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for ag_node, attr in self.firm_network.nodes(data=True):
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a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']]
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firm_id = self.Firm['Code'] == ag_node
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n_equip_c = self.Firm.loc[firm_id, '设备数量'].values[0]
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demand_quantity = self.data_consumed[self.data_consumed['Firm_Code'] == ag_node]
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@ -335,17 +334,16 @@ class MyModel(Model):
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# c购买价格？ 数据预处理
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# c_price = self.Firm.loc[self.Firm['Code'] == ag_node, 'c_price'].values[0]
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# 资源 资源库存信息 利用 firm_resource
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R = self.firm_resource_R.loc[firm_id].to_list()[0]
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P = self.firm_resource_P.loc[firm_id].to_list()[0]
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C = self.firm_resource_C.loc[firm_id].to_list()[0]
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R = self.firm_resource_R.loc[int(ag_node)]
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P = self.firm_resource_P.loc[int(ag_node)]
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C = self.firm_resource_C.loc[int(ag_node)]
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firm_agent = FirmAgent(
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ag_node, self,
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type_region=attr['Type_Region'],
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revenue_log=attr['Revenue_Log'],
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n_equip_c=n_equip_c,
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a_lst_product=a_lst_product,
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demand_quantity=demand_quantity,
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production_output=production_output,
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@ -396,6 +394,7 @@ class MyModel(Model):
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self.product_agents.append(agent)
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def resource_integration(self):
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data_R = pd.read_csv("input_data/input_firm_data/firms_materials.csv")
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data_C = pd.read_csv("input_data/input_firm_data/firms_devices.csv")
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data_P = pd.read_csv("input_data/input_firm_data/firms_products.csv")
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@ -422,18 +421,21 @@ class MyModel(Model):
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data_consumed = pd.read_csv('input_data/input_firm_data/firms_materials.csv')
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data_produced = pd.read_csv('input_data/input_firm_data/firms_products.csv')
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data_not_consumed = data_consumed.groupby('Firm_Code')[['消耗材料id', '材料数量']] \
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.apply(lambda x: dict(zip(x['消耗材料id'], x['材料数量']))) \
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data_not_consumed = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
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.apply(lambda x: dict(zip(x['材料id'], x['材料数量']))) \
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.reset_index(name='Material_not_Consumed')
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# 这里简单设置为折半 考虑 企业的设备量
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# 可以引入 换算率 也就是 材料——计算产品比例 通过上游产业 现在假设为 2:1 的比例
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data_consumed = data_consumed.groupby('Firm_Code')[['消耗材料id', '材料数量']] \
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.apply(lambda x: dict(zip(x['消耗材料id'], x['材料数量'] / 2))) \
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data_consumed0 = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
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.apply(lambda x: dict(zip(x['材料id'], x['材料数量'] / 2))) \
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.reset_index(name='Material_not_Consumed')
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data_produced = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
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.apply(lambda x: dict(zip(x['材料id'], x['材料数量'] / 4))) \
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.reset_index(name='Material_not_Consumed')
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self.data_consumed = data_consumed
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self.data_produced = data_consumed / 2
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self.data_consumed = data_consumed0
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self.data_produced = data_produced
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def step(self):
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# 1. Remove edge to customer and disrupt customer up product
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