492 lines
13 KiB
Python
492 lines
13 KiB
Python
import pickle
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from sqlalchemy import text
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from orm import engine, connection
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import pandas as pd
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import networkx as nx
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import json
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import matplotlib.pyplot as plt
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# Prepare data
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Firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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BomNodes = pd.read_csv('input_data/input_product_data/BomNodes.csv', index_col=0)
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# SQL query
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with open('SQL_analysis_risk.sql', 'r') as f:
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str_sql = text(f.read())
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result = pd.read_sql(sql=str_sql, con=connection)
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result.to_csv('output_result/risk/count.csv', index=False, encoding='utf-8-sig')
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print(result)
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# G_bom
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plt.rcParams['font.sans-serif'] = 'SimHei'
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exp_id = 1
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G_bom_df = pd.read_sql(
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sql=text(f'select g_bom from iiabmdb.without_exp_experiment where id = {exp_id};'),
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con=connection
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)
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if G_bom_df.empty:
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raise ValueError(f"No g_bom found for exp_id = {exp_id}")
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G_bom_str = G_bom_df['g_bom'].tolist()[0]
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if G_bom_str is None:
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raise ValueError(f"g_bom data is None for exp_id = {exp_id}")
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G_bom = nx.adjacency_graph(json.loads(G_bom_str))
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pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
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node_labels = nx.get_node_attributes(G_bom, 'Name')
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node_labels = {
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7: 'Si Raw Mtl.',
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8: 'Photoresist & Reagents',
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9: 'Etch Solution',
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10: 'SiF4',
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11: 'Developer',
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12: 'PCE Superplasticizer',
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13: 'Metal Protectant',
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14: 'Deep Hole Cu Plating',
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15: 'Thinner',
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16: 'HP Boric Acid (Nuc.)',
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17: 'E-Grade Epoxy',
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18: 'Stripper',
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19: 'HP-MOC',
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20: 'CMP Slurry & Consumables',
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21: 'PR Remover',
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22: 'Poly-Si Cutting Fluid',
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23: 'Passivation',
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24: 'E-Grade Phenolic',
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25: 'Surfactant',
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26: 'Mag. Carrier',
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27: 'Wet Chems.',
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28: 'Plating Chems.',
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29: 'E-FR Materials',
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30: 'LC Alignment Agent',
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31: 'Func. Wet Chems.',
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32: 'InP',
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33: 'SiC',
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34: 'GaAs',
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35: 'GaN',
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36: 'AlN',
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37: 'Si3N4',
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38: 'SiC Substrate',
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39: 'GaN Substrate',
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40: 'Si Wafer',
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41: 'AlN Substrate',
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42: 'DUV LED Substrate',
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43: 'InP Substrate',
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44: 'Mono-Si Wafer',
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45: 'Poly-Si Wafer',
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46: 'InP Cryst./Wafer',
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47: 'SiC Cryst./Wafer',
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48: 'GaAs Wafer',
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49: 'GaN Cryst./Wafer',
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50: 'Si Epi Wafer',
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51: 'SiC Epi Wafer',
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52: 'AlN Epi',
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53: 'GaN Epi',
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54: 'InP Epi',
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55: 'LED Epi',
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56: 'EDA/IP',
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57: 'MPW Service',
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58: 'IC Design',
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59: 'Track System',
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60: 'Wafer Grinder',
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61: 'Etcher',
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62: 'Ox/Diff Furnace',
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63: 'Wafer Metrology',
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64: 'Crystal Grower',
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65: 'CMP Tool',
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66: 'Stepper',
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67: 'Wafer Dicer',
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68: 'Deposition System',
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69: 'Edge Profiler',
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70: 'Descum Tool',
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71: 'Clean System',
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72: 'SAF',
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73: 'Plating Eqpt.',
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74: 'Implanter',
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75: 'Trim/Form',
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76: 'Probe Card',
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77: 'ATE',
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78: 'PCM Eqpt.',
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79: 'Inspection Sys.',
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80: 'Prober',
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81: 'Dicing Saw',
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82: 'Handler',
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83: 'Backgrinder',
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84: 'Die Bonder',
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85: 'Reflow Oven',
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86: 'FT Tester',
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87: 'Wire Bonder',
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88: 'BGA Mounter',
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89: 'Molding Press',
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90: 'Power Devices',
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91: 'Diode',
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92: 'Transistor',
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93: 'Thyristor',
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94: 'Rectifier',
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95: 'IC Fab',
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96: 'IC PKG',
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97: 'DV',
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98: 'IPM',
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99: 'CP Test',
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100: 'FT Test',
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101: 'Bumping',
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102: 'DA Materials',
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103: 'Leadframe',
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104: 'Solder Ball',
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105: 'Substrate',
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106: 'EMC',
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107: 'Bond Wire',
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108: 'Underfill',
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109: 'Dicing Tape'
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}
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plt.figure(figsize=(12, 12), dpi=500)
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plt.axis('off') # 关闭坐标轴边框
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# 优化节点绘制参数
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nx.draw_networkx_nodes(
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G_bom, pos,
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node_size=100, # 优化节点尺寸
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linewidths=0.0 # 去除节点边框
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)
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# 优化边绘制参数
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nx.draw_networkx_edges(
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G_bom, pos,
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width=0.3, # 更细的边宽
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alpha=0.5 # 半透明边
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)
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# 优化标签参数
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nx.draw_networkx_labels(
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G_bom, pos,
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labels=node_labels,
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font_size=3, # 适当增大字号
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font_family='sans-serif', # 使用无衬线字体
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font_weight='bold', # 增强可读性
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)
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# 专业级保存参数设置
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plt.savefig(
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f"output_result/risk/g_bom_exp_id_{exp_id}.png",
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bbox_inches='tight', # 去除图像白边
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pad_inches=0.1, # 适当内边距
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facecolor='white' # 保证背景纯白
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)
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plt.close()
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# G_firm
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plt.rcParams['font.sans-serif'] = 'SimHei'
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sample_id = 1
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# G_firm_df = pd.read_sql(
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# sql=text(f'select g_firm from iiabmdb.without_exp_sample where id = {sample_id};'),
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# con=connection
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# )
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#
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# if G_firm_df.empty:
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# raise ValueError(f"No g_firm found for sample_id = {sample_id}")
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#
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# G_firm_str = G_firm_df['g_firm'].tolist()[0]
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# if G_firm_str is None:
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# raise ValueError(f"g_firm data is None for sample_id = {sample_id}")
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#
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# G_firm = nx.adjacency_graph(json.loads(G_firm_str))
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with open("firm_network.pkl", 'rb') as f:
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G_firm = pickle.load(f)
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print(f"Successfully loaded cached data from firm_network.pkl")
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# 1. 移除孤立节点
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isolated_nodes = list(nx.isolates(G_firm)) # 找出所有没有连接的孤立节点
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G_firm.remove_nodes_from(isolated_nodes) # 从图中移除这些节点
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# 2. 重新布局和绘图
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pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
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node_label = {key: key for key in nx.get_node_attributes(G_firm, 'Revenue_Log').keys()}
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node_label = {
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"7": "1",
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"9": "2",
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"829768": "4",
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"863079": "5",
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"1452048": "6",
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"2010673": "7",
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"2624175": "8",
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"2728939": "9",
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"5278074": "10",
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"5849940": "11",
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"7299120": "12",
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"9746245": "13",
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"11807506": "14",
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"15613202": "15",
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"24284343": "19",
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"24673506": "20",
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"25036634": "21",
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"25685135": "24",
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"25945288": "25",
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"26162741": "26",
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"26516263": "27",
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"27075840": "28",
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"27731896": "29",
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"29954548": "30",
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"43407343": "33",
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"70634828": "36",
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"71271700": "37",
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"80158773": "39",
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"118882692": "40",
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"145511905": "42",
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"151606446": "43",
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"152008168": "44",
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"159511306": "45",
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"191912252": "46",
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"194210021": "47",
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"203314437": "48",
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"213386023": "49",
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"218633337": "50",
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"251189644": "53",
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"271860868": "55",
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"278221281": "56",
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"301209792": "57",
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"343012684": "59",
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"354897041": "60",
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"400488703": "62",
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"400692942": "63",
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"413274977": "64",
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"420984285": "65",
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"448033045": "66",
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"453289520": "67",
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"474279224": "68",
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"483081978": "69",
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"495782506": "70",
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"503176785": "73",
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"549184982": "75",
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"560866402": "76",
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"561545339": "77",
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"571058167": "78",
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"581407487": "79",
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"591452402": "80",
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"593312758": "81",
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"594378026": "82",
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"607512171": "83",
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"615763365": "84",
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"620220747": "85",
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"631449822": "86",
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"644292599": "87",
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"653528340": "88",
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"654825436": "89",
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"688155470": "92",
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"695995052": "93",
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"750610681": "95",
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"762985858": "96",
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"771821595": "97",
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"857978527": "100",
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"868012326": "101",
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"887840774": "102",
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"888356483": "103",
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"888395016": "104",
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"888478182": "105",
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"930767828": "107",
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"996174506": "108",
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"1033972427": "110",
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"1128343125": "111",
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"1217957486": "113",
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"1307012237": "115",
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"1375606900": "116",
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"1549474227": "118",
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"1606833003": "120",
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"1679596339": "121",
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"2310825263": "122",
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"2311838590": "124",
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"2312490120": "125",
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"2316990095": "128",
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"2317245827": "129",
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"2317841563": "131",
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"2320102626": "132",
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"2320475044": "133",
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"2321109759": "134",
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"2324787028": "137",
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"2324844174": "138",
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"2326478786": "139",
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"2327031723": "140",
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"2327979389": "141",
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"2329375731": "142",
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"2333843479": "143",
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"2337952436": "146",
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"2339188563": "147",
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"2339684065": "148",
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"2341555098": "149",
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"2343704209": "150",
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"2348941764": "151",
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"2352036411": "155",
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"2354145351": "157",
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"2424229017": "159",
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"2545430247": "161",
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"2820140348": "163",
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"2944892892": "165",
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"3025036704": "168",
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"3026382513": "169",
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"3045721313": "171",
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"3047163873": "172",
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"3048263744": "173",
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"3069206426": "174",
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"3070859372": "175",
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"3072715478": "176",
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"3103797386": "177",
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"3111033905": "178",
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"3113895788": "179",
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"3120341363": "180",
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"3122923980": "181",
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"3127420424": "182",
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"3133307899": "183",
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"3147511625": "184",
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"3177507356": "185",
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"3188903709": "186",
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"3195502499": "187",
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"3203777710": "188",
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"3211956484": "189",
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"3215814536": "190",
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"3221190269": "191",
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"3226664625": "192",
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"3267688490": "193",
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"3269039233": "194",
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"3269940677": "195",
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"3271705843": "196",
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"3299144127": "197",
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"3312358902": "198",
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"3344297292": "200",
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"3372913783": "201",
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"3373311444": "202",
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"3384021594": "203",
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"3395900897": "205",
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"3398677646": "206",
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"3407754893": "207",
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"3433628561": "209",
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"3445244192": "212",
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"3445928818": "213",
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"4208851809": "216",
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"5007015990": "218",
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"11164476478": "219",
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"517717050": "223",
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"737770776": "224",
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"872394725": "225",
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"2311581270": "226",
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"2313209417": "227",
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"2347013470": "228",
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"2350418059": "229",
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"3031009366": "234",
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"3089095447": "235",
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"3100891962": "236",
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"3188352290": "238",
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"3288105727": "239",
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"3462551351": "240"
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}
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node_size = [value * 5 for value in nx.get_node_attributes(G_firm, 'Revenue_Log').values()]
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edge_label = {(n1, n2): label for (n1, n2, _), label in nx.get_edge_attributes(G_firm, "Product").items()}
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plt.figure(figsize=(15, 15), dpi=500)
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plt.axis('off') # 完全关闭坐标轴系统
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# 分层绘制网络组件
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nodes = nx.draw_networkx_nodes(
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G_firm, pos,
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node_size=node_size, # 保持原始尺寸设置
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)
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edges = nx.draw_networkx_edges(
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G_firm, pos,
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width=0.3, # 保持原始线宽设置
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)
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# 优化节点标签
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labels = nx.draw_networkx_labels(
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G_firm, pos,
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labels=node_label,
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font_size=6, # 保持原始字号
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)
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# 增强边标签可读性
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edge_labels = nx.draw_networkx_edge_labels(
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G_firm, pos,
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edge_labels=edge_label,
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font_size=2,
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label_pos=0.5, # 标签沿边偏移量
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rotate=False, # 禁止自动旋转
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)
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# 专业级输出配置
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plt.savefig(
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f"output_result/risk/g_firm_sample_id_{sample_id}_de.png",
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bbox_inches='tight',
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pad_inches=0.05, # 更紧凑的边距
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facecolor='white', # 强制白色背景
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metadata={
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'Title': f"Supply Chain Risk Map - Sample {sample_id}",
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'Author': 'USTB Risk Analytics',
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'Copyright': 'Confidential'
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}
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)
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plt.close()
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# Count firm product
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count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
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count_firm_prod.name = 'count'
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count_firm_prod = count_firm_prod.to_frame().reset_index()
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count_firm_prod.to_csv('output_result/risk/count_firm_prod.csv', index=False, encoding='utf-8-sig')
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print(count_firm_prod)
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# Count firm
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count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
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count_firm = count_firm.to_frame().reset_index()
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count_firm.sort_values('count', inplace=True, ascending=False)
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count_firm.to_csv('output_result/risk/count_firm.csv', index=False, encoding='utf-8-sig')
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print(count_firm)
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# Count product
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count_prod = count_firm_prod.groupby('id_product')['count'].sum()
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count_prod = count_prod.to_frame().reset_index()
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count_prod.sort_values('count', inplace=True, ascending=False)
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count_prod.to_csv('output_result/risk/count_prod.csv', index=False, encoding='utf-8-sig')
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print(count_prod)
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# DCP disruption causing probability
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result_disrupt_ts_above_0 = result[result['ts'] > 0]
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print(result_disrupt_ts_above_0)
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result_dcp = pd.DataFrame(columns=[
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's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
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])
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result_dcp_list = [] # 用列表收集数据，避免DataFrame逐行增长的问题
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for sid, group in result.groupby('s_id'):
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ts_start = max(group['ts'])
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while ts_start >= 1:
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ts_end = ts_start - 1
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while ts_end >= 0:
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up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
|
||
down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
|
||
for _, up_row in up.iterrows():
|
||
for _, down_row in down.iterrows():
|
||
result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
|
||
ts_end -= 1
|
||
ts_start -= 1
|
||
|
||
# 转换为DataFrame
|
||
result_dcp = pd.DataFrame(result_dcp_list, columns=[
|
||
's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
|
||
])
|
||
|
||
# 统计
|
||
count_dcp = result_dcp.value_counts(
|
||
subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
|
||
).reset_index(name='count')
|
||
|
||
# 保存文件
|
||
count_dcp.to_csv('output_result/risk/count_dcp.csv', index=False, encoding='utf-8-sig')
|
||
|
||
# 输出结果
|
||
print(count_dcp)
|