190 lines
7.3 KiB
Python
190 lines
7.3 KiB
Python
"""
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分析路由
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"""
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import logging
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import json
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from datetime import datetime
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from typing import Optional, Dict, Any, List
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from fastapi import APIRouter, HTTPException, status, BackgroundTasks
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from pydantic import BaseModel
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import psutil
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import os
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import gc
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import shutil
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from app.core.config import settings
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from app.services.analysis import TimeSeriesAnalysisSystem
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logger = logging.getLogger(__name__)
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router = APIRouter()
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class AnalysisRequest(BaseModel):
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"""分析请求模型"""
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filename: str
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file_type: str = "csv"
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task_description: str = "时间序列数据分析"
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data_background: Dict[str, Any] = {}
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original_image: Optional[str] = None
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language: str = "zh"
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generate_plots: bool = False
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@router.get("/available_methods", summary="获取可用的分析方法")
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async def get_available_methods() -> dict:
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"""获取所有可用的分析方法"""
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return {
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"success": True,
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"methods": {
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'statistical_overview': {'name': '统计概览', 'description': '生成数据的基本统计信息和分布图表'},
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'time_series_analysis': {'name': '时间序列分析', 'description': '分析变量随时间变化的趋势和模式'},
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'acf_pacf_analysis': {'name': '自相关分析', 'description': '生成自相关和偏自相关函数图'},
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'stationarity_tests': {'name': '平稳性检验', 'description': '执行ADF、KPSS等平稳性检验'},
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'normality_tests': {'name': '正态性检验', 'description': '执行Shapiro-Wilk、Jarque-Bera正态性检验'},
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'seasonal_decomposition': {'name': '季节性分解', 'description': '分解时间序列的趋势、季节和残差成分'},
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'spectral_analysis': {'name': '频谱分析', 'description': '分析时间序列的频域特征'},
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'correlation_analysis': {'name': '相关性分析', 'description': '计算变量间的相关性并生成热力图'},
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'pca_scree_plot': {'name': 'PCA碎石图', 'description': '显示主成分分析的解释方差'},
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'pca_analysis': {'name': '主成分分析', 'description': '降维分析，识别数据的主要变化方向'},
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'feature_importance': {'name': '特征重要性', 'description': '分析各变量对目标预测的重要性'},
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'clustering_analysis': {'name': '聚类分析', 'description': '将数据点分组为具有相似特征的簇'},
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'factor_analysis': {'name': '因子分析', 'description': '识别潜在的因子结构'},
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'cointegration_test': {'name': '协整检验', 'description': '检验时间序列变量间的长期均衡关系'},
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'var_analysis': {'name': '向量自回归', 'description': '多变量时间序列建模和预测'}
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}
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}
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def check_memory():
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"""检查内存使用"""
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process = psutil.Process(os.getpid())
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memory_mb = process.memory_info().rss / 1024 / 1024
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logger.info(f"当前内存使用: {memory_mb:.2f} MB")
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if memory_mb > settings.MAX_MEMORY_MB:
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logger.warning(f"内存使用超过阈值 ({settings.MAX_MEMORY_MB} MB)，执行垃圾回收")
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gc.collect()
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@router.post("/analyze", summary="执行完整分析")
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async def analyze_data(request: AnalysisRequest, background_tasks: BackgroundTasks) -> dict:
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"""
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执行完整的时间序列分析
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流程:
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1. 加载并预处理数据
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2. 执行15种分析方法
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3. 调用AI API 进行深度分析
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4. 生成PDF/PPT/HTML报告
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"""
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try:
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logger.info("=" * 60)
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logger.info(f"开始分析: {request.filename}")
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logger.info(f"任务: {request.task_description}")
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logger.info(f"语言: {request.language}")
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logger.info("=" * 60)
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# 检查内存
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check_memory()
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# 检查文件存在
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file_path = settings.get_upload_path(request.filename)
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if not file_path.exists():
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"文件未找到: {request.filename}"
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)
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# 语言处理：支持 zh/en，其他值回退为 zh
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lang_key = request.language if request.language in {"zh", "en"} else "zh"
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# charts 模式下强制不生成图片，即使请求传了 generate_plots=true
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generate_plots = False
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if request.generate_plots:
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logger.info("generate_plots requested true, forcing false to skip image generation")
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# 创建分析器实例
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logger.info(f"初始化分析器 ({lang_key})...")
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analyzer = TimeSeriesAnalysisSystem(
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str(file_path),
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request.task_description,
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data_background=request.data_background,
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language=lang_key,
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generate_plots=generate_plots
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)
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# 运行分析
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logger.info("执行分析...")
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results_zh, log_zh = analyzer.run_analysis()
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if results_zh is None:
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logger.error("中文分析失败")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="分析失败"
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)
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logger.info("中文分析完成")
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# 准备返回数据
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response_data = {
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"success": True,
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"meta": {
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"filename": request.filename,
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"task_description": request.task_description,
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"language": lang_key,
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"generate_plots": generate_plots,
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"created_at": datetime.now().isoformat(),
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},
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"analysis": {
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lang_key: {
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"pdf_filename": None,
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"ppt_filename": None,
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"data_description": results_zh.get("data_description"),
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"preprocessing_steps": results_zh.get("preprocessing_steps", []),
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"api_analysis": results_zh.get("api_analysis", {}),
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"steps": results_zh.get("steps", []),
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"charts": results_zh.get("charts", {}),
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}
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},
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"images": {},
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"log": log_zh[-20:] if log_zh else [],
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"original_image": request.original_image if request.file_type == 'image' else None,
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}
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# 兼容旧前端：始终提供 analysis.zh
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if lang_key != "zh":
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response_data["analysis"]["zh"] = response_data["analysis"][lang_key]
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analysis_bucket = response_data["analysis"][lang_key]
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# 去除任何遗留的 image_path（兼容旧结构）
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steps = analysis_bucket.get("steps")
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if isinstance(steps, list):
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for step in steps:
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if isinstance(step, dict) and "image_path" in step:
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step.pop("image_path", None)
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# images 保持为空兼容旧前端
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response_data["images"] = {}
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logger.info("分析完成")
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return response_data
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"分析异常: {str(e)}", exc_info=True)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=str(e)
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)
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