#!/usr/bin/env python3 """ PHM-Vibench Metadata 快速验证和修复脚本 用于检查和修复 metadata_25_10_30.xlsx 中的关键问题 """ import pandas as pd import numpy as np import h5py import os from datetime import datetime import json import sys class MetadataChecker: def __init__(self): self.base_path = '/home/user/data/PHMbenchdata/PHM-Vibench' self.metadata_path = os.path.join(self.base_path, 'metadata_25_10_30.xlsx') self.h5_path = os.path.join(self.base_path, 'metadata_6_1.h5') self.backup_path = os.path.join(self.base_path, 'backups') self.log_path = os.path.join(self.base_path, 'log') # 确保目录存在 os.makedirs(self.backup_path, exist_ok=True) os.makedirs(self.log_path, exist_ok=True) def load_metadata(self): """加载metadata文件""" print(f"📂 加载 metadata 文件: {self.metadata_path}") self.df = pd.read_excel(self.metadata_path) print(f"✅ 成功加载 {len(self.df)} 条记录") def check_h5_data(self): """检查H5文件中的实际数据维度""" print("\n🔍 检查 H5 文件数据维度...") if not os.path.exists(self.h5_path): print(f"❌ H5文件不存在: {self.h5_path}") return with h5py.File(self.h5_path, 'r') as f: # Dataset_id 18 对应的Id范围: 47288-47566 print("\n=== Dataset_id 18 (Id: 47288-47566) ===") shapes_18 = [] for i in range(47288, min(47298, 47567)): # 检查前10个 key = str(i) if key in f: data = f[key][()] if isinstance(data, np.ndarray): shapes_18.append(data.shape) print(f" Id {i}: shape={data.shape}") # Dataset_id 20 对应的Id范围: 47567-50126 print("\n=== Dataset_id 20 (Id: 47567-47577) ===") shapes_20 = [] for i in range(47567, min(47577, 50127)): # 检查前10个 key = str(i) if key in f: data = f[key][()] if isinstance(data, np.ndarray): shapes_20.append(data.shape) print(f" Id {i}: shape={data.shape}") # 更多抽样检查 print("\n=== 抽样统计 ===") for i in range(47288, 47567, 20): # 每20个抽样 key = str(i) if key in f: data = f[key][()] if isinstance(data, np.ndarray): shapes_18.append(data.shape) for i in range(47567, 50127, 100): # 每100个抽样 key = str(i) if key in f: data = f[key][()] if isinstance(data, np.ndarray): shapes_20.append(data.shape) if shapes_18: unique_shapes_18 = set(shapes_18) print(f"\nDataset_id 18 数据维度分布:") for shape in unique_shapes_18: count = shapes_18.count(shape) print(f" {shape}: {count} 次") if shapes_20: unique_shapes_20 = set(shapes_20) print(f"\nDataset_id 20 数据维度分布:") for shape in unique_shapes_20: count = shapes_20.count(shape) print(f" {shape}: {count} 次") # 保存检查结果 self.h5_shapes_18 = shapes_18 if shapes_18 else [] self.h5_shapes_20 = shapes_20 if shapes_20 else [] def analyze_metadata_issues(self): """分析metadata中的问题""" print("\n📊 分析 metadata 问题...") # Dataset_id 18 df_18 = self.df[self.df['Dataset_id'] == 18] print(f"\nDataset_id 18:") print(f" 记录数: {len(df_18)}") if len(df_18) > 0: print(f" Channel: {df_18['Channel'].unique()}") print(f" Sample_lenth: {df_18['Sample_lenth'].unique()}") print(f" Name: {df_18['Name'].unique()}") self.meta_channel_18 = df_18['Channel'].iloc[0] self.meta_length_18 = df_18['Sample_lenth'].iloc[0] # Dataset_id 20 df_20 = self.df[self.df['Dataset_id'] == 20] print(f"\nDataset_id 20:") print(f" 记录数: {len(df_20)}") if len(df_20) > 0: print(f" Channel: {df_20['Channel'].unique()}") print(f" Sample_lenth: {df_20['Sample_lenth'].unique()}") print(f" Name: {df_20['Name'].unique()}") self.meta_channel_20 = df_20['Channel'].iloc[0] self.meta_length_20 = df_20['Sample_lenth'].iloc[0] def identify_issues(self): """识别需要修复的问题""" print("\n⚠️ 识别问题...") self.issues = [] # 检查Dataset_id 18 if hasattr(self, 'h5_shapes_18') and self.h5_shapes_18: # 统计各种形状的出现次数 shape_counts_18 = {} for shape in self.h5_shapes_18: shape_counts_18[shape] = shape_counts_18.get(shape, 0) + 1 # 找出最常见的形状 most_common_shape = max(shape_counts_18.items(), key=lambda x: x[1]) actual_length = int(most_common_shape[0][0]) actual_channels = int(most_common_shape[0][1]) print(f"\nDataset_id 18 维度统计:") for shape, count in sorted(shape_counts_18.items(), key=lambda x: x[1], reverse=True)[:5]: print(f" {shape}: {count}次") if self.meta_length_18 != actual_length: self.issues.append({ 'dataset_id': 18, 'type': 'sample_length_mismatch', 'metadata_value': self.meta_length_18, 'h5_value': actual_length, 'description': f"Sample_lenth不匹配: metadata={self.meta_length_18}, H5实际={actual_length}" }) if self.meta_channel_18 != actual_channels: self.issues.append({ 'dataset_id': 18, 'type': 'channel_mismatch', 'metadata_value': self.meta_channel_18, 'h5_value': actual_channels, 'description': f"Channel不匹配: metadata={self.meta_channel_18}, H5实际={actual_channels}" }) # 检查Dataset_id 20 if hasattr(self, 'h5_shapes_20') and self.h5_shapes_20: # 统计各种形状的出现次数 shape_counts_20 = {} for shape in self.h5_shapes_20: shape_counts_20[shape] = shape_counts_20.get(shape, 0) + 1 # 找出最常见的形状 most_common_shape = max(shape_counts_20.items(), key=lambda x: x[1]) actual_length = int(most_common_shape[0][0]) actual_channels = int(most_common_shape[0][1]) print(f"\nDataset_id 20 维度统计:") for shape, count in sorted(shape_counts_20.items(), key=lambda x: x[1], reverse=True)[:5]: print(f" {shape}: {count}次") if self.meta_length_20 != actual_length: self.issues.append({ 'dataset_id': 20, 'type': 'sample_length_mismatch', 'metadata_value': self.meta_length_20, 'h5_value': actual_length, 'description': f"Sample_lenth不匹配: metadata={self.meta_length_20}, H5实际={actual_length}" }) if self.meta_channel_20 != actual_channels: self.issues.append({ 'dataset_id': 20, 'type': 'channel_mismatch', 'metadata_value': self.meta_channel_20, 'h5_value': actual_channels, 'description': f"Channel不匹配: metadata={self.meta_channel_20}, H5实际={actual_channels}" }) # 输出问题汇总 if self.issues: print(f"\n发现 {len(self.issues)} 个问题:") for i, issue in enumerate(self.issues, 1): print(f"\n问题 {i}: Dataset_id {issue['dataset_id']}") print(f" 类型: {issue['type']}") print(f" 描述: {issue['description']}") else: print("\n✅ 未发现明显问题") def backup_metadata(self): """备份原始metadata文件""" timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") backup_file = os.path.join(self.backup_path, f"metadata_25_10_30_backup_{timestamp}.xlsx") print(f"\n💾 备份原始文件到: {backup_file}") self.df.to_excel(backup_file, index=False) print("✅ 备份完成") return backup_file def fix_metadata(self): """修复metadata中的问题""" if not self.issues: print("\n✅ 没有需要修复的问题") return print("\n🔧 开始修复 metadata...") # 创建修复后的DataFrame副本 df_fixed = self.df.copy() fix_log = [] for issue in self.issues: dataset_id = issue['dataset_id'] if issue['type'] == 'sample_length_mismatch': mask = df_fixed['Dataset_id'] == dataset_id old_value = df_fixed.loc[mask, 'Sample_lenth'].iloc[0] new_value = issue['h5_value'] df_fixed.loc[mask, 'Sample_lenth'] = new_value fix_log.append({ 'dataset_id': dataset_id, 'field': 'Sample_lenth', 'old_value': old_value, 'new_value': new_value, 'records_affected': mask.sum() }) print(f" ✓ Dataset_id {dataset_id}: Sample_lenth {old_value} → {new_value}") elif issue['type'] == 'channel_mismatch': mask = df_fixed['Dataset_id'] == dataset_id old_value = df_fixed.loc[mask, 'Channel'].iloc[0] new_value = issue['h5_value'] df_fixed.loc[mask, 'Channel'] = new_value fix_log.append({ 'dataset_id': dataset_id, 'field': 'Channel', 'old_value': old_value, 'new_value': new_value, 'records_affected': mask.sum() }) print(f" ✓ Dataset_id {dataset_id}: Channel {old_value} → {new_value}") # 保存修复后的文件 fixed_file = os.path.join(self.base_path, 'metadata_25_10_30_fixed.xlsx') df_fixed.to_excel(fixed_file, index=False) print(f"\n✅ 修复后的文件保存到: {fixed_file}") # 保存修复日志 log_file = os.path.join(self.log_path, 'fix_log.json') # 转换numpy类型为python原生类型 issues_serializable = [] for issue in self.issues: issue_copy = issue.copy() if 'metadata_value' in issue_copy: issue_copy['metadata_value'] = int(issue_copy['metadata_value']) if hasattr(issue_copy['metadata_value'], 'dtype') else issue_copy['metadata_value'] if 'h5_value' in issue_copy: issue_copy['h5_value'] = int(issue_copy['h5_value']) if hasattr(issue_copy['h5_value'], 'dtype') else issue_copy['h5_value'] issues_serializable.append(issue_copy) fixes_serializable = [] for fix in fix_log: fix_copy = fix.copy() for key in ['old_value', 'new_value']: if key in fix_copy and hasattr(fix_copy[key], 'dtype'): fix_copy[key] = int(fix_copy[key]) if 'records_affected' in fix_copy and hasattr(fix_copy['records_affected'], 'dtype'): fix_copy['records_affected'] = int(fix_copy['records_affected']) fixes_serializable.append(fix_copy) with open(log_file, 'w', encoding='utf-8') as f: json.dump({ 'timestamp': datetime.now().isoformat(), 'issues_found': issues_serializable, 'fixes_applied': fixes_serializable }, f, indent=2, ensure_ascii=False) print(f"📝 修复日志保存到: {log_file}") self.df_fixed = df_fixed self.fix_log = fix_log def generate_report(self): """生成验证报告""" report = { 'timestamp': datetime.now().isoformat(), 'metadata_file': self.metadata_path, 'h5_file': self.h5_path, 'total_records': len(self.df), 'issues_found': len(self.issues) if hasattr(self, 'issues') else 0, 'dataset_analysis': { 'dataset_18': { 'id_range': '47288-47566', 'records': len(self.df[self.df['Dataset_id'] == 18]), 'metadata_channel': self.meta_channel_18 if hasattr(self, 'meta_channel_18') else None, 'metadata_length': self.meta_length_18 if hasattr(self, 'meta_length_18') else None }, 'dataset_20': { 'id_range': '47567-50126', 'records': len(self.df[self.df['Dataset_id'] == 20]), 'metadata_channel': self.meta_channel_20 if hasattr(self, 'meta_channel_20') else None, 'metadata_length': self.meta_length_20 if hasattr(self, 'meta_length_20') else None } } } report_file = os.path.join(self.log_path, 'validation_report.json') with open(report_file, 'w', encoding='utf-8') as f: json.dump(report, f, indent=2, ensure_ascii=False) print(f"\n📊 验证报告保存到: {report_file}") def run(self, auto_fix=False): """运行完整的验证和修复流程""" print("=" * 60) print("🚀 PHM-Vibench Metadata 验证和修复工具") print("=" * 60) # 1. 加载数据 self.load_metadata() # 2. 检查H5数据 self.check_h5_data() # 3. 分析metadata self.analyze_metadata_issues() # 4. 识别问题 self.identify_issues() # 5. 备份 backup_file = self.backup_metadata() # 6. 修复（如果需要） if auto_fix and self.issues: self.fix_metadata() # 7. 生成报告 self.generate_report() print("\n" + "=" * 60) print("✅ 验证完成!") print("=" * 60) if hasattr(self, 'issues') and self.issues: print(f"\n发现 {len(self.issues)} 个问题需要修复") print("使用 --fix 参数执行自动修复") else: print("\n✅ 未发现问题") def main(): import argparse parser = argparse.ArgumentParser(description='PHM-Vibench Metadata 验证和修复工具') parser.add_argument('--fix', action='store_true', help='自动修复发现的问题') parser.add_argument('--check-only', action='store_true', help='仅检查不修复') args = parser.parse_args() checker = MetadataChecker() if args.check_only: checker.run(auto_fix=False) else: checker.run(auto_fix=args.fix) if __name__ == "__main__": main()