优化图形

risk_analysis_firm_network.py

@@ -58,7 +58,7 @@ for _, row in count_dcp.iterrows():
     G_firm.add_edges_from(lst_add_edge)
 
 # dcp_networkx
-pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="")
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
 node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
 # desensitize
 node_label = {
@@ -66,7 +66,7 @@ node_label = {
     for key in node_label.keys()
 }
 node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
-node_size = list(map(lambda x: x**2, node_size))
+node_size = list(map(lambda x: x**3, node_size))
 edge_label = nx.get_edge_attributes(G_firm, "edge_label")
 edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
 edge_width = nx.get_edge_attributes(G_firm, "edge_width")
@@ -76,13 +76,13 @@ colors = [w for (n1, n2, _), w in colors.items()]
 vmin = min(colors)
 vmax = max(colors)
 cmap = plt.cm.Blues
-fig = plt.figure(figsize=(10, 8), dpi=300)
+fig = plt.figure(figsize=(10, 8), dpi=500)
 nx.draw(G_firm,
         pos,
         node_size=node_size,
         labels=node_label,
         font_size=6,
-        width=2,
+        width=1,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
@@ -94,5 +94,5 @@ position = fig.add_axes([0.95, 0.05, 0.01, 0.3])
 cb = plt.colorbar(sm, fraction=0.01, cax=position)
 cb.ax.tick_params(labelsize=4)
 cb.outline.set_visible(False)
-plt.savefig("output_result\\risk\\count_dcp_network")
+plt.savefig("output_result\\risk\\count_dcp_network_twopi")
 plt.close()

risk_analysis_prod_network.py

@@ -29,7 +29,7 @@ for code in g_bom.nodes:
     index_list = count_prod[count_prod['id_product'] == code].index.tolist()
     index = index_list[0] if len(index_list) == 1 else -1
     node_attr['count'] = count_prod['count'].get(index, 0)
-    node_attr['node_size'] = count_prod['count'].get(index, 0)
+    node_attr['node_size'] = (count_prod['count'].get(index, 0) + 10) / 10
     node_attr['node_color'] = count_prod['count'].get(index, 0)
     labels_dict[code] = node_attr
 nx.set_node_attributes(g_bom, labels_dict)
@@ -50,7 +50,7 @@ nx.draw(g_bom,
         pos,
         node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
         labels=node_labels,
-        font_size=6,
+        font_size=3,
         node_color=colors,
         cmap=cmap,
         vmin=vmin,
@@ -79,7 +79,7 @@ count_dcp_prod.sort_values('count', inplace=True, ascending=False)
 count_dcp_prod.to_csv('output_result\\risk\\count_dcp_prod.csv',
                       index=False,
                       encoding='utf-8-sig')
-count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 50]
+count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 1000]
 # print(count_dcp_prod)
 
 list_prod = count_dcp_prod['up_id_product'].tolist(
@@ -102,7 +102,6 @@ for code in list_prod:
     bom_labels_dict[code] = dct_attr
 nx.set_node_attributes(g_bom, bom_labels_dict)
 
-
 count_max = count_dcp_prod['count'].max()
 count_min = count_dcp_prod['count'].min()
 k = 5 / (count_max - count_min)
@@ -135,26 +134,27 @@ pos_new = {}
 for node, p in pos.items():
     pos_new[node] = (p[1], p[0])
 
-fig = plt.figure(figsize=(6, 10), dpi=300)
+fig = plt.figure(figsize=(6, 6), dpi=500)
 # plt.subplots_adjust(right=0.7)
 nx.draw(g_bom,
         pos_new,
         node_size=25,
         labels=node_labels,
         font_size=5,
-        width=1.5,
+        width=0.5,
         edge_color=colors,
         edge_cmap=cmap,
         edge_vmin=vmin,
-        edge_vmax=vmax)
+        edge_vmax=vmax,
+        alpha=1)
 plt.axis('off')
 axis = plt.gca()
-axis.set_xlim([1.2*x for x in axis.get_xlim()])
-axis.set_ylim([1.2*y for y in axis.get_ylim()])
+axis.set_xlim([1 * x for x in axis.get_xlim()])
+axis.set_ylim([1 * y for y in axis.get_ylim()])
 
 sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
 sm._A = []
-position = fig.add_axes([0.75, 0.1, 0.01, 0.2])
+position = fig.add_axes([0.85, 0.75, 0.01, 0.2])
 cb = plt.colorbar(sm, fraction=0.01, cax=position)
 cb.ax.tick_params(labelsize=3)
 cb.outline.set_visible(False)

risk_analysis_sum_result.py

@@ -70,19 +70,24 @@ with open("firm_network.pkl", 'rb') as f:
     G_firm = pickle.load(f)
 print(f"Successfully loaded cached data from firm_network.pkl")
 
-pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
-node_label = nx.get_node_attributes(G_firm, 'Revenue_Log')
-node_label = {key: key for key in node_label.keys()}
-node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
-edge_label = nx.get_edge_attributes(G_firm, "Product")
-edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
+# 1. 移除孤立节点
+isolated_nodes = list(nx.isolates(G_firm))  # 找出所有没有连接的孤立节点
+G_firm.remove_nodes_from(isolated_nodes)   # 从图中移除这些节点
 
-plt.figure(figsize=(12, 12), dpi=300)
-nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=5)
-nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
-plt.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png")
+# 2. 重新布局和绘图
+pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
+node_label = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
+node_size = [value * 10 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]  # 节点大小扩大10倍
+edge_label = {(n1, n2): label for (n1, n2, _), label in nx.get_edge_attributes(G_firm, "Product").items()}
+
+plt.figure(figsize=(12, 12), dpi=500)
+nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=5, width=0.5)
+nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=2)
+plt.axis('equal')  # 锁定坐标轴比例，确保图形内容是正方形
+plt.savefig(f"output_result/risk/g_firm_sample_id_{sample_id}_de.png", bbox_inches='tight', pad_inches=0.1)
 plt.close()
 
+
 # Count firm product
 count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
 count_firm_prod.name = 'count'
@@ -140,4 +145,3 @@ count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-
 
 # 输出结果
 print(count_dcp)
-