156 lines
5.5 KiB
Python
156 lines
5.5 KiB
Python
"""
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高通量筛选与扩胞项目 - 主入口（支持并行）
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"""
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
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from analysis.database_analyzer import DatabaseAnalyzer
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from analysis.report_generator import ReportGenerator
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from core.scheduler import ParallelScheduler
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def print_banner():
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print("""
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╔═══════════════════════════════════════════════════════════════════╗
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║ 高通量筛选与扩胞项目 - 数据库分析工具 v2.0 ║
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║ 支持高性能并行计算 ║
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╚═══════════════════════════════════════════════════════════════════╝
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""")
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def detect_and_show_environment():
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"""检测并显示环境信息"""
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env = ParallelScheduler.detect_environment()
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print("【运行环境检测】")
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print(f" 主机名: {env['hostname']}")
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print(f" 本地CPU核数: {env['total_cores']}")
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print(f" SLURM集群: {'✅ 可用' if env['has_slurm'] else '❌ 不可用'}")
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if env['has_slurm'] and env['slurm_partitions']:
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print(f" 可用分区:")
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for p in env['slurm_partitions']:
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print(f" - {p['name']}: {p['nodes']}节点, {p['cores_per_node']}核/节点")
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return env
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def get_user_input(env: dict):
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"""获取用户输入"""
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# 数据库路径
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while True:
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db_path = input("\n📂 请输入数据库路径: ").strip()
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if os.path.exists(db_path):
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break
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print(f"❌ 路径不存在: {db_path}")
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# 目标阳离子
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cation = input("🎯 请输入目标阳离子 [默认: Li]: ").strip() or "Li"
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# 目标阴离子
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anion_input = input("🎯 请输入目标阴离子 (逗号分隔) [默认: O,S,Cl,Br]: ").strip()
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anions = set(a.strip() for a in anion_input.split(',')) if anion_input else {'O', 'S', 'Cl', 'Br'}
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# 阴离子模式
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print("\n阴离子模式:")
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print(" 1. 仅单一阴离子")
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print(" 2. 仅复合阴离子")
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print(" 3. 全部 (默认)")
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mode_choice = input("请选择 [1/2/3]: ").strip()
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anion_mode = {'1': 'single', '2': 'mixed', '3': 'all', '': 'all'}.get(mode_choice, 'all')
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# 并行配置
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print("\n" + "─" * 50)
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print("【并行计算配置】")
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default_cores = min(env['total_cores'], 32)
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cores_input = input(f"💻 最大可用核数 [默认: {default_cores}]: ").strip()
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max_cores = int(cores_input) if cores_input.isdigit() else default_cores
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print("\n任务复杂度 (影响每个Worker分配的核数):")
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print(" 1. 低 (1核/Worker) - 简单IO操作")
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print(" 2. 中 (2核/Worker) - 结构解析+检查 [默认]")
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print(" 3. 高 (4核/Worker) - 复杂计算")
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complexity_choice = input("请选择 [1/2/3]: ").strip()
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complexity = {'1': 'low', '2': 'medium', '3': 'high', '': 'medium'}.get(complexity_choice, 'medium')
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# 执行模式
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use_slurm = False
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if env['has_slurm']:
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slurm_choice = input("\n是否使用SLURM提交作业? [y/N]: ").strip().lower()
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use_slurm = slurm_choice == 'y'
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return {
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'database_path': db_path,
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'target_cation': cation,
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'target_anions': anions,
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'anion_mode': anion_mode,
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'max_cores': max_cores,
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'task_complexity': complexity,
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'use_slurm': use_slurm
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}
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def main():
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"""主函数"""
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print_banner()
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# 环境检测
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env = detect_and_show_environment()
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# 获取用户输入
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params = get_user_input(env)
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print("\n" + "═" * 60)
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print("开始数据库分析...")
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print("═" * 60)
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# 创建分析器
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analyzer = DatabaseAnalyzer(
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database_path=params['database_path'],
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target_cation=params['target_cation'],
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target_anions=params['target_anions'],
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anion_mode=params['anion_mode'],
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max_cores=params['max_cores'],
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task_complexity=params['task_complexity']
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)
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print(f"\n发现 {len(analyzer.cif_files)} 个CIF文件")
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if params['use_slurm']:
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# SLURM模式
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output_dir = input("输出目录 [默认: ./slurm_output]: ").strip() or "./slurm_output"
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job_id = analyzer.analyze_slurm(output_dir=output_dir)
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print(f"\n✅ SLURM作业已提交: {job_id}")
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print(f" 使用 'squeue -j {job_id}' 查看状态")
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print(f" 结果将保存到: {output_dir}")
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else:
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# 本地模式
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report = analyzer.analyze(show_progress=True)
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# 打印报告
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ReportGenerator.print_report(report, detailed=True)
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# 保存选项
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save_choice = input("\n是否保存报告? [y/N]: ").strip().lower()
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if save_choice == 'y':
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output_path = input("报告路径 [默认: analysis_report.json]: ").strip()
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output_path = output_path or "analysis_report.json"
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report.save(output_path)
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print(f"✅ 报告已保存到: {output_path}")
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# CSV导出
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csv_choice = input("是否导出详细CSV? [y/N]: ").strip().lower()
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if csv_choice == 'y':
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csv_path = input("CSV路径 [默认: analysis_details.csv]: ").strip()
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csv_path = csv_path or "analysis_details.csv"
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ReportGenerator.export_to_csv(report, csv_path)
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print("\n✅ 分析完成!")
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if __name__ == "__main__":
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main() |