修改部分内容，包括变量名子，化为 小写，能运行的mesa的完整程序，不包括模型更改和结果输出。

controller_db.py

@@ -44,9 +44,9 @@ class ControllerDB:
         self.fill_sample_table()
 
     def fill_experiment_table(self):
-        Firm = pd.read_csv("input_data/Firm_amended.csv")
+        firm = pd.read_csv("input_data/Firm_amended.csv")
-        Firm['Code'] = Firm['Code'].astype('string')
+        firm['Code'] = firm['Code'].astype('string')
-        Firm.fillna(0, inplace=True)
+        firm.fillna(0, inplace=True)
 
         # fill dct_lst_init_disrupt_firm_prod
         # 存储 公司-在供应链结点的位置.. 0 ：‘1.1’
@@ -63,7 +63,8 @@ class ControllerDB:
         else:
             # 行索引 (index)：这一行在数据帧中的索引值。
             # 行数据 (row)：这一行的数据，是一个 pandas.Series 对象，包含该行的所有列和值。
-            for _, row in Firm.iterrows():
+
+            for _, row in firm.iterrows():
                 code = row['Code']
                 row = row['1':]
                 for product_code in row.index[row == 1].to_list():
@@ -72,18 +73,18 @@ class ControllerDB:
 
         # fill g_bom
         # 结点属性值 相当于 图上点的 原始 产品名称
-        BomNodes = pd.read_csv('input_data/BomNodes.csv', index_col=0)
+        bom_nodes = pd.read_csv('input_data/BomNodes.csv', index_col=0)
-        BomNodes.set_index('Code', inplace=True)
+        bom_nodes.set_index('Code', inplace=True)
 
-        BomCateNet = pd.read_csv('input_data/BomCateNet.csv', index_col=0)
+        bom_cate_net = pd.read_csv('input_data/BomCateNet.csv', index_col=0)
-        BomCateNet.fillna(0, inplace=True)
+        bom_cate_net.fillna(0, inplace=True)
         # 创建 可以多边的有向图 同时 转置操作 使得 上游指向下游结点 也就是 1.1.1 - 1.1 类似这种
-        g_bom = nx.from_pandas_adjacency(BomCateNet.T,
+        g_bom = nx.from_pandas_adjacency(bom_cate_net.T,
                                          create_using=nx.MultiDiGraph())
         # 填充每一个结点 的具体内容 通过 相同的 code 并且通过BomNodes.loc[code].to_dict()字典化 格式类似 格式 { code（0） :  {level: 0 ,name: 工业互联网   }}
         bom_labels_dict = {}
         for code in g_bom.nodes:
-            bom_labels_dict[code] = BomNodes.loc[code].to_dict()
+            bom_labels_dict[code] = bom_nodes.loc[code].to_dict()
         # 分配属性 给每一个结点 获得类似 格式：{1: {'label': 'A', 'value': 10},
         nx.set_node_attributes(g_bom, bom_labels_dict)
         # 改为json 格式

firm.py

@@ -229,7 +229,3 @@ class FirmAgent(Agent):
                 return False
         else:
             return False
-
-    def step(self):
-        # 在每个时间步进行的操作
-        pass

my_model.py

@@ -15,7 +15,7 @@ from product import ProductAgent
 class MyModel(Model):
     def __init__(self, params):
 
-        # self.num_agents = N
+        # 属性
         self.is_prf_size = params['prf_size']
         self.prf_conn = params['prf_conn']
         self.cap_limit_prob_type = params['cap_limit_prob_type']
@@ -35,10 +35,10 @@ class MyModel(Model):
         self.data_collector = DataCollector(
             agent_reporters={"Product": "name"}
         )
+
         # initialize graph bom
         self.G_bom = nx.adjacency_graph(json.loads(params['g_bom']))
 
-
         # Create the firm-product network graph
         self.G_FirmProd = nx.MultiDiGraph()
         # Create the firm network graph
@@ -54,13 +54,13 @@ class MyModel(Model):
         self.int_stop_ts = 0
         self.int_n_iter = int(params['n_iter'])
         self.dct_lst_init_disrupt_firm_prod = params['dct_lst_init_disrupt_firm_prod']
+
         # external variable
         self.int_n_max_trial = int(params['n_max_trial'])
         self.is_prf_size = bool(params['prf_size'])
-
         self.remove_t = int(params['remove_t'])
         self.int_netw_prf_n = int(params['netw_prf_n'])
-
+        # 方法执行
         self.initialize_product_network(params)
         self.initialize_firm_network()
         self.initialize_firm_product_network()
@@ -76,27 +76,25 @@ class MyModel(Model):
             print(f"Failed to initialize product network: {e}")
 
     def initialize_firm_network(self):
-        """ Initialize the firm network by reading firm data from a CSV file """
         # Read the firm data
-        Firm = pd.read_csv("input_data/Firm_amended.csv")
+        firm = pd.read_csv("input_data/Firm_amended.csv")
-        Firm['Code'] = Firm['Code'].astype('string')
+        firm['Code'] = firm['Code'].astype('string')
-        Firm.fillna(0, inplace=True)
+        firm.fillna(0, inplace=True)
-        Firm_attr = Firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
+        firm_attr = firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
 
         firm_product = []
-        for _, row in Firm.loc[:, '1':].iterrows():
+        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.loc[:, 'Product_Code'] = firm_product
-        Firm_attr.set_index('Code', inplace=True)
+        firm_attr.set_index('Code', inplace=True)
 
-
+        self.G_Firm.add_nodes_from(firm["Code"])
-        self.G_Firm.add_nodes_from(Firm["Code"])
 
         # Assign attributes to the firm nodes
-        firm_labels_dict = {code: Firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
+        firm_labels_dict = {code: firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
         nx.set_node_attributes(self.G_Firm, firm_labels_dict)
 
-        self.Firm = Firm
+        self.Firm = firm
 
     def initialize_firm_product_network(self):
         """ Initialize the firm-product network """
@@ -244,7 +242,6 @@ class MyModel(Model):
         # 更新 self.dct_lst_init_disrupt_firm_prod 字典，存储公司及其受干扰的产品
         self.dct_lst_init_disrupt_firm_prod = t_dct
 
-
         # 设置初始受干扰的公司产品状态
         for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
             for product in a_lst_product:

product.py

@@ -23,6 +23,3 @@ class ProductAgent(Agent):
         # 通过 unique_id 查找前驱节点对应的代理对象，直接从 self.product_agents 列表中获取
         return [agent for agent in self.model.product_agents if agent.unique_id in predecessors]
 
-    def step(self):
-        # 在每个时间步 进行的操作
-        pass