database store graph

controller_db.py

@@ -8,7 +8,8 @@ import random
 import pandas as pd
 import numpy as np
 import platform
-
+import networkx as nx
+import json
 
 class ControllerDB:
     dct_parameter = None
@@ -41,6 +42,8 @@ class ControllerDB:
         Firm = pd.read_csv("Firm_amended.csv")
         Firm['Code'] = Firm['Code'].astype('string')
         Firm.fillna(0, inplace=True)
+
+        # fill dct_lst_init_remove_firm_prod
         list_dct = []
         for _, row in Firm.iterrows():
             code = row['Code']
@@ -52,19 +55,35 @@ class ControllerDB:
         list_dct = [{'133': ['1.4.4.1']}]
         # list_dct = [{'2': ['1.1.3']}]
         # list_dct = [{'135': ['1.3.2.1']}]
+
+        # fill g_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.T,
+                                         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)
+        g_product_js = json.dumps(nx.adjacency_data(g_bom))
+
+        # insert exp
         for idx_exp, dct in enumerate(list_dct):
             self.add_experiment_1(idx_exp, self.dct_parameter['n_max_trial'],
-                                  dct)
+                                  dct, g_product_js) # same g_bom for all exp
             print(f'Inserted experiment for exp {idx_exp}!')
 
     def add_experiment_1(self, idx_exp, n_max_trial,
-                         dct_lst_init_remove_firm_prod):
+                         dct_lst_init_remove_firm_prod, g_bom):
         e = Experiment(
             idx_exp=idx_exp,
             n_sample=int(self.dct_parameter['n_sample']),
             n_iter=int(self.dct_parameter['n_iter']),
             n_max_trial=n_max_trial,
-            dct_lst_init_remove_firm_prod=dct_lst_init_remove_firm_prod)
+            dct_lst_init_remove_firm_prod=dct_lst_init_remove_firm_prod,
+            g_bom=g_bom)
         db_session.add(e)
         db_session.commit()
 

model.py

@@ -7,6 +7,7 @@ from firm import FirmAgent
 from product import ProductAgent
 from orm import db_session, Result
 import platform
+import json
 
 
 class Model(ap.Model):
@@ -20,18 +21,8 @@ class Model(ap.Model):
         self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod
 
         # 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.T,
-                                         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)
+        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")
@@ -90,13 +81,8 @@ class Model(ap.Model):
                     }) 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)
-        self.product_network = ap.Network(self, G_bom)
-
-        out_file = open("myfile.json", "w")
-        import json
-        json.dump(nx.adjacency_data(G_Firm), out_file)
-        out_file.close()
 
         # init product
         for ag_node, attr in self.product_network.graph.nodes(data=True):
@@ -151,6 +137,10 @@ class Model(ap.Model):
         # 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

orm.py

@@ -46,6 +46,7 @@ class Experiment(Base):
     # variables
     n_max_trial = Column(Integer, nullable=False)
     dct_lst_init_remove_firm_prod = Column(PickleType, nullable=False)
+    g_bom = Column(Text(4294000000), nullable=False)
 
     sample = relationship('Sample', back_populates='experiment', lazy='dynamic')
 
@@ -65,7 +66,7 @@ class Sample(Base):
     ts_done = Column(DateTime(timezone=True), onupdate=func.now())
     stop_t = Column(Integer, nullable=True)
 
-    firm_network = Column(Text(4294000000), nullable=True)
+    g_firm = Column(Text(4294000000), nullable=True)
     
     experiment = relationship('Experiment', back_populates='sample', uselist=False)
     result = relationship('Result', back_populates='sample', lazy='dynamic')

test.ipynb

@@ -65,13 +65,6 @@
     "list_succ_firms = [1, 1]\n",
     "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

test.py

@@ -0,0 +1,28 @@
+import networkx as nx
+import json
+
+in_file = open("myfile.json", "r")
+js_file = json.load(in_file)
+in_file.close()
+firm_network = nx.adjacency_graph(js_file)
+
+import matplotlib.pyplot as plt
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+pos = nx.nx_agraph.graphviz_layout(firm_network, prog="twopi", args="")
+node_label = nx.get_node_attributes(firm_network, 'Name')
+# print(node_label)
+node_degree = dict(firm_network.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(firm_network, 'Revenue_Log').values())
+node_size = list(map(lambda x: x**2, node_size))
+edge_label = nx.get_edge_attributes(firm_network, "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(firm_network, pos, node_size=node_size, labels=node_label, font_size=6)
+nx.draw_networkx_edge_labels(firm_network, pos, edge_label, font_size=4)
+plt.savefig("network1.png")