162 lines
5.2 KiB
Python
162 lines
5.2 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import re
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from datetime import datetime
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def create_backup(file_path):
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"""创建备份文件"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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backup_path = f"{file_path}.backup_{timestamp}"
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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with open(backup_path, 'w', encoding='utf-8') as f:
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f.write(content)
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print(f"已创建备份: {backup_path}")
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return backup_path
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def remove_modified_fields():
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"""删除没有真实修改版的岗位的modified字段，保持content对象完整"""
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# 有真实修改版的岗位列表（这些要保留modified）
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real_modified_positions = {
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"会展策划师",
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"会展执行助理",
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"会展讲解员",
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"活动策划师",
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"活动执行",
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"漫展策划师",
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"旅游规划师",
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"旅游计调专员",
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"景区运营专员",
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"文旅运营总监助理"
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}
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file_path = 'src/mocks/resumeInterviewMock.js'
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# 创建备份
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create_backup(file_path)
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# 读取文件内容
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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modified_count = 0
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kept_count = 0
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# 更精确的正则表达式：匹配content对象中的modified字段
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# 这个模式会匹配从position到content对象，找到modified字段并删除它
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def process_position(match):
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nonlocal modified_count, kept_count
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full_match = match.group(0)
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position_name = match.group(1)
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# 如果是有真实修改版的岗位，保持不变
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if position_name in real_modified_positions:
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kept_count += 1
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print(f"✓ 保留 {position_name} 的modified字段")
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return full_match
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# 删除modified字段，但保持content对象的结构
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# 查找并删除 ,modified: `...` 部分
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# 使用非贪婪匹配来找到modified字段
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pattern = r',\s*modified:\s*`[^`]*`'
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new_content = re.sub(pattern, '', full_match, flags=re.DOTALL)
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modified_count += 1
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print(f"✗ 删除 {position_name} 的modified字段")
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return new_content
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# 匹配从position到整个content对象的模式
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# 使用更精确的模式来确保不会破坏content对象的结构
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pattern = r'position:\s*"([^"]+)"[^}]*?content:\s*\{[^}]*?original:\s*`[^`]*?`(?:,\s*modified:\s*`[^`]*?`)?\s*\}'
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# 执行替换
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new_content = re.sub(pattern, process_position, content, flags=re.DOTALL)
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# 保存文件
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with open(file_path, 'w', encoding='utf-8') as f:
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f.write(new_content)
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print(f"\n处理完成！")
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print(f"- 删除了 {modified_count} 个岗位的modified字段")
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print(f"- 保留了 {kept_count} 个岗位的modified字段")
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def verify_results():
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"""验证处理结果"""
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file_path = 'src/mocks/resumeInterviewMock.js'
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# 首先检查文件语法
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import subprocess
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result = subprocess.run(['node', '-c', file_path], capture_output=True, text=True)
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if result.returncode != 0:
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print(f"⚠️ 文件语法错误: {result.stderr}")
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return False
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else:
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print("✓ 文件语法正确")
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 统计所有有modified字段的岗位
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pattern = r'position:\s*"([^"]+)"[^}]*?modified:\s*`'
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matches = re.findall(pattern, content, re.DOTALL)
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print("\n验证结果：")
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print("=" * 50)
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print(f"还有modified字段的岗位（共{len(set(matches))}个）：")
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unique_positions = set(matches)
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for position in sorted(unique_positions):
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print(f" - {position}")
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# 期望的岗位列表
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expected = {
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"会展策划师",
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"会展执行助理",
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"会展讲解员",
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"活动策划师",
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"活动执行",
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"漫展策划师",
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"旅游规划师",
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"旅游计调专员",
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"景区运营专员",
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"文旅运营总监助理"
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}
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# 检查是否匹配
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if unique_positions == expected:
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print("\n✓ 完美！所有岗位的modified字段都处理正确")
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# 额外检查：确认所有岗位的original字段还在
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original_pattern = r'position:\s*"([^"]+)"[^}]*?original:\s*`'
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original_matches = re.findall(original_pattern, content, re.DOTALL)
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print(f"\n共有 {len(set(original_matches))} 个岗位保留了original字段")
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return True
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else:
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missing = expected - unique_positions
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extra = unique_positions - expected
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if missing:
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print(f"\n⚠️ 缺少modified的岗位：{missing}")
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if extra:
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print(f"\n⚠️ 不应该有modified的岗位：{extra}")
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return False
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def main():
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print("开始修复：删除没有真实修改版的岗位的modified字段...")
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print("=" * 50)
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# 删除没有真实修改版的岗位的modified字段
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remove_modified_fields()
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# 验证结果
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if verify_results():
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print("\n✅ 修复成功！")
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else:
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print("\n❌ 修复可能有问题，请检查")
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if __name__ == "__main__":
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main()
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