lofyerandfactory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
71db82393a
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
247 lines
10 KiB
Python
247 lines
10 KiB
Python
"""
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Era2 全局状态：全市266校 数据引擎 + PCA赋分引擎 + 统计引擎
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启动时一次性加载，后续API直接使用
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"""
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import logging
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import time
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import sys
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from pathlib import Path
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from typing import Dict, List, Optional
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from datetime import datetime
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import pandas as pd
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import numpy as np
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# era2 引擎路径
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ERA2_SCRIPTS = Path(__file__).parent.parent.parent.parent / "scripts" / "era2"
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sys.path.insert(0, str(ERA2_SCRIPTS))
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from data_engine_era2 import DataEngineEra2
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from engines.pca_scoring_engine_era2 import PcaScoringEngineEra2
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from engines.stats_engine_era2 import StatsEngineEra2
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from config_era2 import (
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SCHOOL_TYPE_MAP, DIMENSION_FRAMEWORK, LEVEL_DESCRIPTIONS,
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CLUSTER_CONFIG,
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)
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logger = logging.getLogger(__name__)
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# 输出目录
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OUTPUT_DIR = Path(__file__).parent.parent.parent.parent / "output" / "era2"
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class Era2State:
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"""Era2 全市数据的全局单例状态"""
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def __init__(self):
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self._initialized = False
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self.data_engine: Optional[DataEngineEra2] = None
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self.pca_engine: Optional[PcaScoringEngineEra2] = None
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self.stats_engine: Optional[StatsEngineEra2] = None
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self.sub_scores: Optional[pd.DataFrame] = None
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self.dim_scores: Optional[pd.DataFrame] = None
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self.schools: List[str] = []
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self.districts: List[str] = []
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self.district_schools: Dict[str, List[str]] = {}
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self.school_meta: Dict[str, dict] = {}
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def initialize(self):
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"""启动时加载全市数据并计算分数（耗时约40-60秒）"""
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if self._initialized:
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return
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start = time.time()
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logger.info("🚀 [Era2] 加载全市数据...")
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# 1. 加载全市数据
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self.data_engine = DataEngineEra2(use_city_data=True)
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self.data_engine.load_all()
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self.schools = self.data_engine.schools
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# 2. 构建区→学校映射
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self.school_meta = SCHOOL_TYPE_MAP
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self.district_schools = {}
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for school, info in SCHOOL_TYPE_MAP.items():
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district = info.get("district", "未知")
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if district not in self.district_schools:
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self.district_schools[district] = []
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if school in self.schools:
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self.district_schools[district].append(school)
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self.districts = sorted(self.district_schools.keys())
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# 3. PCA赋分（全市）
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logger.info(f"🔢 [Era2] PCA赋分 ({len(self.schools)}校)...")
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self.pca_engine = PcaScoringEngineEra2(self.data_engine)
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pca_sub_scores = self.pca_engine.compute_all()
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# 4. 统计引擎
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self.stats_engine = StatsEngineEra2()
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self.sub_scores = self.stats_engine.compute_dimension_scores_pca(pca_sub_scores)
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self.dim_scores = self.stats_engine.compute_dimension_aggregates(self.sub_scores)
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self._initialized = True
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elapsed = time.time() - start
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logger.info(f"✅ [Era2] 初始化完成: {len(self.schools)}校, {len(self.districts)}区, 耗时{elapsed:.1f}s")
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def get_districts_summary(self) -> List[dict]:
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"""返回所有区的摘要（中英两份报告分别计数）"""
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result = []
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for district in self.districts:
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schools_in_d = self.district_schools.get(district, [])
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# 区内平均分
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if schools_in_d and self.dim_scores is not None:
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valid = [s for s in schools_in_d if s in self.dim_scores.index]
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avg = float(self.dim_scores.loc[valid, "总体得分"].mean()) if valid else 50.0
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else:
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avg = 50.0
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# 已生成报告数（中文 / 英文）
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district_dir = OUTPUT_DIR / district
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if district_dir.exists():
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report_count_zh = len(list(district_dir.glob("*_报告.html")))
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report_count_en = len(list(district_dir.glob("*_report_en.html")))
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else:
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report_count_zh = 0
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report_count_en = 0
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# 任意一种语言已有视为 has_report，用于 UI 总数显示
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report_count = report_count_zh
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result.append({
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"district": district,
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"school_count": len(schools_in_d),
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"avg_score": round(avg, 2),
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"report_count": report_count,
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"report_count_zh": report_count_zh,
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"report_count_en": report_count_en,
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})
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return result
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def get_schools_in_district(self, district: str) -> List[dict]:
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"""返回某区所有学校的详细信息"""
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schools_in_d = self.district_schools.get(district, [])
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if not schools_in_d:
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return []
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# 排名（全市排名 + 区内排名）
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sorted_all = self.dim_scores["总体得分"].sort_values(ascending=False)
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dist_scores = self.dim_scores.loc[
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[s for s in schools_in_d if s in self.dim_scores.index], "总体得分"
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].sort_values(ascending=False)
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result = []
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for dist_rank, (school, score) in enumerate(dist_scores.items(), 1):
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info = self.school_meta.get(school, {})
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city_rank = int((sorted_all >= score).sum())
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# 报告状态（中文 + 英文）
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report_path_zh = OUTPUT_DIR / district / f"{school}_报告.html"
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report_path_en = OUTPUT_DIR / district / f"{school}_report_en.html"
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has_report_zh = report_path_zh.exists()
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has_report_en = report_path_en.exists()
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# 兼容老字段
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has_report = has_report_zh or has_report_en
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report_generated_at_zh = ""
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report_size_zh = 0
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report_generated_at_en = ""
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report_size_en = 0
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if has_report_zh:
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stat = report_path_zh.stat()
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report_size_zh = stat.st_size
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report_generated_at_zh = datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M")
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if has_report_en:
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stat = report_path_en.stat()
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report_size_en = stat.st_size
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report_generated_at_en = datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M")
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# 兼容老字段：优先用中文版，没有则用英文版
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report_size = report_size_zh or report_size_en
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report_generated_at = report_generated_at_zh or report_generated_at_en
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# 聚类
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dim_cols = [c for c in self.dim_scores.columns if c in DIMENSION_FRAMEWORK]
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valid_schools = [s for s in schools_in_d if s in self.dim_scores.index]
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cluster_result = self.stats_engine.cluster_analysis(
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self.dim_scores.loc[valid_schools, dim_cols], dimension_name="总体"
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)
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cluster = cluster_result["school_clusters"].get(school, "")
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result.append({
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"name": school,
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"district": district,
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"type": info.get("type", ""),
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"nature": info.get("nature", ""),
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"area": info.get("area", ""),
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"score": round(float(score), 2),
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"district_rank": dist_rank,
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"city_rank": city_rank,
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"total_in_district": len(dist_scores),
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"total_in_city": len(sorted_all),
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"cluster": cluster,
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# 兼容老字段
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"has_report": has_report,
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"report_size": report_size,
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"report_generated_at": report_generated_at,
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# 中英分开
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"has_report_zh": has_report_zh,
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"has_report_en": has_report_en,
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"report_size_zh": report_size_zh,
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"report_size_en": report_size_en,
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"report_generated_at_zh": report_generated_at_zh,
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"report_generated_at_en": report_generated_at_en,
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})
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return result
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def get_report_data(self, school: str, district: str) -> dict:
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"""生成单校报告数据包"""
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district_schools = self.district_schools.get(district, [])
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report_data = self.stats_engine.compute_school_report_data(
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school, self.sub_scores, self.dim_scores,
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district_schools=district_schools,
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)
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report_data["district"] = district
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report_data["total_schools_in_district"] = len(district_schools)
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return report_data
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def get_report_history(self) -> List[dict]:
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"""扫描所有已生成的报告，返回历史列表（合并中英两份，每校最多两条）"""
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history = []
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if not OUTPUT_DIR.exists():
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return history
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for district_dir in sorted(OUTPUT_DIR.iterdir()):
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if not district_dir.is_dir():
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continue
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district = district_dir.name
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# 同时收集中文 (_报告.html) 和英文 (_report_en.html) 报告
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collected = []
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for html_file in sorted(district_dir.glob("*_报告.html")):
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school = html_file.stem.replace("_报告", "")
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collected.append((school, html_file, "zh"))
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for html_file in sorted(district_dir.glob("*_report_en.html")):
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school = html_file.stem.replace("_report_en", "")
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collected.append((school, html_file, "en"))
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for school, html_file, lang in collected:
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stat = html_file.stat()
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json_path = district_dir / f"{school}_report_data.json"
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score = None
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if json_path.exists():
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try:
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import json
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data = json.loads(json_path.read_text("utf-8"))
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score = data.get("overall", {}).get("score")
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except Exception:
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pass
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history.append({
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"school": school,
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"district": district,
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"type": self.school_meta.get(school, {}).get("type", ""),
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"score": score,
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"file_size": stat.st_size,
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"generated_at": datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M:%S"),
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"file_name": html_file.name,
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"lang": lang,
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})
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return history
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# 全局单例
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era2_state = Era2State()
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