10.09添加git

risk_analysis_prod_network.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import networkx as nx
+
+plt.rcParams['font.sans-serif'] = 'SimHei'
+
+count_prod = pd.read_csv("output_result/risk/count_prod.csv")
+print(count_prod)
+
+# category
+print(count_prod.describe())
+
+# prod_networkx
+# BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
+# BomNodes.set_index('Code', inplace=True)
+# BomCateNet = pd.read_csv('input_data/input_product_data/BomCateNet.csv', index_col=0)
+# BomCateNet.fillna(0, inplace=True)
+
+bom_nodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+bom_nodes['Code'] = bom_nodes['Code'].astype(str)
+bom_nodes.set_index('Index', inplace=True)
+
+bom_cate_net = pd.read_csv('input_data/input_product_data/合成结点.csv')
+g_bom = nx.from_pandas_edgelist(bom_cate_net, source='UPID', target='ID', create_using=nx.MultiDiGraph())
+
+labels_dict = {}
+for code in g_bom.nodes:
+    node_attr = bom_nodes.loc[code].to_dict()
+    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))/10
+    node_attr['node_color'] = count_prod['count'].get(index, 0)
+    labels_dict[code] = node_attr
+nx.set_node_attributes(g_bom, labels_dict)
+# print(labels_dict)
+
+pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
+dict_node_name = nx.get_node_attributes(g_bom, 'Name')
+node_labels = {}
+for node in nx.nodes(g_bom):
+    node_labels[node] = f"{node} {str(dict_node_name[node])}"
+    # node_labels[node] = f"{str(dict_node_name[node])}"
+colors = list(nx.get_node_attributes(g_bom, 'node_color').values())
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+# 创建绘图对象
+fig = plt.figure(figsize=(10, 10), dpi=300)
+ax = fig.add_subplot(111)
+
+# 绘制网络图（优化样式参数）
+nx.draw(g_bom, pos,
+        node_size=list(nx.get_node_attributes(g_bom, 'node_size').values()),
+        labels=node_labels,
+        font_size=3,
+        node_color=colors,
+        cmap=cmap,
+        vmin=vmin,
+        vmax=vmax,
+        edge_color='#808080',  # 中性灰
+        width=0.3,
+        edgecolors='#404040',
+        linewidths=0.2)
+
+# 创建颜色条（修正实现方式）
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
+sm.set_array([])
+
+# 设置颜色条位置和样式
+cax = fig.add_axes([0.88, 0.3, 0.015, 0.4])  # 右侧垂直对齐
+cb = plt.colorbar(sm, cax=cax)
+cb.ax.tick_params(labelsize=4, width=0.5, colors='#333333')
+cb.outline.set_linewidth(0.5)
+cb.set_label('Risk Level', fontsize=5, labelpad=2)
+
+# 添加图元信息
+ax.set_title("Production Risk Network", fontsize=6, pad=8, color='#2F2F2F')
+plt.text(0.5, 0.02, 'Data: USTB Production System | Viz: DeepSeek-R1',
+         ha='center', fontsize=3, color='#666666',
+         transform=fig.transFigure)
+
+# 调整边界和保存
+plt.subplots_adjust(left=0.05, right=0.85, top=0.95, bottom=0.1)  # 适应颜色条
+plt.savefig(r"output_result/risk/count_prod_network.png",  # 规范路径格式
+           dpi=600,
+           bbox_inches='tight',
+           pad_inches=0.05,
+           transparent=False)
+plt.close()
+
+# dcp_prod
+count_dcp = pd.read_csv("output_result/risk/count_dcp.csv",
+                        dtype={
+                            'up_id_firm': str,
+                            'down_id_firm': str
+                        })
+count_dcp_prod = count_dcp.groupby(
+    ['up_id_product',
+     'down_id_product'])['count'].sum()
+count_dcp_prod = count_dcp_prod.reset_index()
+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'] > 1000]
+# print(count_dcp_prod)
+
+list_prod = count_dcp_prod['up_id_product'].tolist(
+) + count_dcp['down_id_product'].tolist()
+list_prod = list(set(list_prod))
+
+# init graph bom
+
+BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv')
+BomNodes.set_index('Index', inplace=True)
+
+g_bom = nx.MultiDiGraph()
+g_bom.add_nodes_from(list_prod)
+
+bom_labels_dict = {}
+for code in list_prod:
+    dct_attr = BomNodes.loc[code].to_dict()
+    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)
+for _, row in count_dcp_prod.iterrows():
+    # print(row)
+    lst_add_edge = [(
+        row['up_id_product'],
+        row['down_id_product'],
+        {
+            'count': row['count']
+        })]
+    g_bom.add_edges_from(lst_add_edge)
+
+# dcp_networkx
+pos = nx.nx_agraph.graphviz_layout(g_bom, prog="twopi", args="")
+node_labels = nx.get_node_attributes(g_bom, 'Name')
+
+temp = {}
+for key, value in node_labels.items():
+    temp[key] = str(key) + " " + value
+node_labels = temp
+node_labels ={
+    38: 'SiC Substrate',
+    39: 'GaN Substrate',
+    40: 'Si Substrate',
+    41: 'AlN Substrate',
+    42: 'DUV LED Substrate',
+    43: 'InP Substrate',
+    44: 'Mono-Si Wafer',
+    45: 'Poly-Si Wafer',
+    46: 'InP Cryst./Wafer',
+    47: 'SiC Cryst./Wafer',
+    48: 'GaAs Wafer',
+    49: 'GaN Cryst./Wafer',
+    50: 'Si Epi Wafer',
+    51: 'SiC Epi Wafer',
+    52: 'AlN Epi',
+    53: 'GaN Epi',
+    54: 'InP Epi',
+    55: 'LED Epi Wafer',
+    90: 'Power Devices',
+    91: 'Diode',
+    92: 'Transistor',
+    93: 'Thyristor',
+    94: 'Rectifier',
+    95: 'IC Fab',
+    99: 'Wafer Test'
+}
+colors = nx.get_edge_attributes(g_bom, "count")
+colors = [w for (n1, n2, _), w in colors.items()]
+vmin = min(colors)
+vmax = max(colors)
+cmap = plt.cm.Blues
+
+pos_new = {node: (p[1], p[0]) for node, p in pos.items()}  # 字典推导式优化
+
+fig = plt.figure(figsize=(8, 8), dpi=300)
+plt.subplots_adjust(right=0.85)  # 关键调整：右侧保留15%空白
+
+# 使用Axes对象精准控制
+main_ax = fig.add_axes([0.1, 0.1, 0.75, 0.8])  # 主图占左75%宽，上下各留10%边距
+nx.draw(g_bom, pos_new,
+        ax=main_ax,
+        node_size=50,
+        labels=node_labels,
+        font_size=5,
+        width=1.5,
+        edge_color=colors,
+        edge_cmap=cmap,
+        edge_vmin=vmin,
+        edge_vmax=vmax,
+        )
+main_ax.axis('off')
+
+# 颜色条定位系统
+cbar_ax = fig.add_axes([0.86, 0.15, 0.015, 0.3])  # 右边缘86%位置，底部15%起，占30%高度
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
+sm._A = []  # 必需的空数组
+
+# 微调颜色条样式
+cbar = fig.colorbar(sm, cax=cbar_ax, orientation='vertical')
+cbar.ax.tick_params(labelsize=4,
+                    width=0.3,    # 刻度线粗细
+                    length=1.5,   # 刻度线长度
+                    pad=0.8)      # 标签与条间距
+cbar.outline.set_linewidth(0.5)   # 边框线宽
+
+# 输出前验证边界
+print(f"Colorbar position: {cbar_ax.get_position().bounds}")  # 应输出(0.86,0.15,0.015,0.3)
+
+# 专业级保存参数
+plt.savefig("output_result/risk/count_dcp_prod_network.png",
+            dpi=900,
+            bbox_inches='tight',  # 自动裁剪白边
+            pad_inches=0.05,      # 保留0.05英寸边距
+            metadata={'CreationDate': None})  # 避免时间戳污染元数据
+plt.close()