draw g_bom g_firm

LoadNet.py

@@ -0,0 +1,105 @@
+import pandas as pd
+import networkx as nx
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('data\\temp.csv')
+df['succ_equipment_code'] = df['equipment_code'].str.rsplit('.', n=1)
+df['succ_equipment_code'] = df['succ_equipment_code'].apply(lambda lst: lst[0] if len(lst)>1 else None)
+
+assert len(df['equipment_code']) == len((df['equipment_code'].unique())),\
+    "column equipment_code contains duplicate value"
+df.set_index('equipment_code', inplace=True)
+
+# graph bom
+g_bom = nx.DiGraph()
+g_bom.add_nodes_from(df.index)
+
+# edge
+for index, row in df.iterrows():
+    if row['succ_equipment_code']:
+        g_bom.add_edge(index, row['succ_equipment_code'])
+
+# attr
+df_attr = df.loc[:, ['equipment_name']]
+dct_attr = {}
+for code in df_attr.index:
+    dct_attr[code] = df_attr.loc[code].to_dict()
+nx.set_node_attributes(g_bom, dct_attr)
+
+# show
+plt.rcParams['font.sans-serif'] = 'SimHei'
+plt.figure(figsize=(12, 12), dpi=300)
+pos = nx.nx_agraph.graphviz_layout(g_bom,
+                                   prog='twopi',
+                                   args='')
+node_label = nx.get_node_attributes(g_bom, 'equipment_name')
+nx.draw(g_bom,
+        pos,
+        labels=node_label,
+        font_size=6)
+plt.savefig('g_bom_20230712.png')
+plt.close()
+
+# graph firm
+s_firm = pd.Series(df['firm_name'].unique())
+g_firm = nx.MultiDiGraph()
+g_firm.add_nodes_from(s_firm.index)
+
+# attr
+dct_attr = {}
+for firm_code in s_firm.index:
+    firm_name = s_firm[firm_code]
+    lst_prod_code = df.index[df['firm_name'] == firm_name].to_list()
+    dct_attr[firm_code] = {'firm_name': firm_name,
+                           'lst_prod_code': lst_prod_code}
+nx.set_node_attributes(g_firm, dct_attr)
+
+print(g_firm.nodes)
+print(g_firm.nodes[0])
+# edge
+for prod_code, row in df.iterrows():
+    firm_name = row['firm_name']
+    firm_code = s_firm.index[s_firm == firm_name].to_list()[0]
+    for succ_prod_code in g_bom.successors(prod_code):
+        # 目前df中一个prod code只对应一个firm
+        succ_firm_name = df.loc[df.index == succ_prod_code, 'firm_name'].to_list()[0]
+        succ_firm_code = s_firm.index[s_firm == succ_firm_name].to_list()[0]
+        g_firm.add_edges_from([(firm_code, succ_firm_code, {'product': prod_code})])
+        print((firm_name, succ_firm_name, prod_code))
+
+# show
+plt.rcParams['font.sans-serif'] = 'SimHei'
+plt.figure(figsize=(12, 12), dpi=300)
+pos = nx.nx_agraph.graphviz_layout(g_firm,
+                                   prog='twopi',
+                                   args='')
+node_label = nx.get_node_attributes(g_firm, 'firm_name')
+nx.draw(g_firm,
+        pos,
+        labels=node_label,
+        font_size=6)
+edge_label = nx.get_edge_attributes(g_firm, 'product')
+edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+nx.draw_networkx_edge_labels(g_firm,
+                             pos,
+                             edge_label,
+                             font_size=4)
+plt.savefig('g_firm_20230712.png')
+plt.close()
+
+# pos = nx.random_layout(g_firm)
+# nx.draw_networkx_nodes(g_firm, pos, node_color = 'r', node_size = 100, alpha = 1)
+# ax = plt.gca()
+# for e in g_firm.edges:
+#     ax.annotate("",
+#                 xy=pos[e[0]], xycoords='data',
+#                 xytext=pos[e[1]], textcoords='data',
+#                 arrowprops=dict(arrowstyle="->", color="0.5",
+#                                 shrinkA=5, shrinkB=5,
+#                                 patchA=None, patchB=None,
+#                                 connectionstyle="arc3,rad=rrr".replace('rrr',str(0.3*e[2])
+#                                 ),
+#                                 ),
+#                 )
+# plt.axis('off')
+# plt.show()

data/temp.csv

@@ -0,0 +1,6 @@
+equipment_code,equipment_model,equipment_name,is_set,set_level,set_type,set_time,document,product_level,product_type,product_category,firm_name,firm_code,is_supervise,supervise_name,supervise_expertise,supervise_expertise_other,software_set_version,software_lastest_version,supervise_contract_type,is_pred_firm,contact_name,contact_phone,comment,status,is_issued,task_source
+1,XXX1,XXX1,,,,,,,,,firm1,,,,,,,,,,,,,,,
+1.1,XXX1,XXX1-1,,,,,,,,,firm2,,,,,,,,,,,,,,,
+1.2,XXX1,XXX1-2,,,,,,,,,firm2,,,,,,,,,,,,,,,
+1.3,XXX1,XXX1-3,,,,,,,,,firm3,,,,,,,,,,,,,,,
+1.1.1,XXX1,XXX1-1-1,,,,,,,,,firm4,,,,,,,,,,,,,,,