"""
数据引擎：Excel解析 + 数据清洗 + 赋分计算
负责将原始Excel数据转换为每所学校的结构化得分数据
"""
import pandas as pd
import numpy as np
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import logging

from ..config import (
    EXCEL_BASIC_INFO, EXCEL_COURSE_IMPL, EXCEL_SUBJECT_IMPL,
    SCHOOL_NAME_MAP, SUBJECTS, SCHOOL_TYPE_MAP,
)

logger = logging.getLogger(__name__)


class DataEngine:
    """数据引擎：读取Excel、清洗、结构化"""

    def __init__(self, data_dir: Optional[Path] = None):
        self.data_dir = data_dir
        self._basic_info: Optional[pd.DataFrame] = None
        self._course_impl: Optional[pd.DataFrame] = None
        self._subject_impl: Optional[pd.DataFrame] = None

    def load_all(self) -> None:
        """加载所有Excel数据"""
        logger.info("开始加载Excel数据...")
        self._basic_info = self._load_basic_info()
        self._course_impl = self._load_course_impl()
        self._subject_impl = self._load_subject_impl()
        logger.info(
            f"数据加载完成: 基础信息={len(self._basic_info)}行, "
            f"课程实施={len(self._course_impl)}行, "
            f"学科课程={len(self._subject_impl)}行"
        )

    def _standardize_school_name(self, df: pd.DataFrame, col: str) -> pd.DataFrame:
        """标准化学校名称"""
        df[col] = df[col].map(lambda x: SCHOOL_NAME_MAP.get(x, x))
        return df

    def _load_basic_info(self) -> pd.DataFrame:
        """加载基础信息表"""
        df = pd.read_excel(EXCEL_BASIC_INFO)
        df = self._standardize_school_name(df, "学校名称")
        return df

    def _load_course_impl(self) -> pd.DataFrame:
        """加载课程实施情况表"""
        df = pd.read_excel(EXCEL_COURSE_IMPL)
        df = self._standardize_school_name(df, "学校简称")
        # 统一列名
        df = df.rename(columns={"学校简称": "学校名称"})
        return df

    def _load_subject_impl(self) -> pd.DataFrame:
        """加载学科课程实施情况表"""
        df = pd.read_excel(EXCEL_SUBJECT_IMPL)
        df = self._standardize_school_name(df, "学校简称")
        df = df.rename(columns={"学校简称": "学校名称"})
        return df

    @property
    def schools(self) -> List[str]:
        """获取所有学校名称列表"""
        if self._course_impl is None:
            self.load_all()
        return sorted(self._course_impl["学校名称"].unique().tolist())

    def get_school_basic_info(self, school: str) -> Dict:
        """获取学校基本信息"""
        if self._basic_info is None:
            self.load_all()
        df = self._basic_info[self._basic_info["学校名称"] == school]
        info = SCHOOL_TYPE_MAP.get(school, {})

        # 提取关键字段
        def _get_field(field_name):
            rows = df[df["字段名称"] == field_name]
            if len(rows) > 0:
                return rows.iloc[0]["字段值"]
            return None

        info.update({
            "school_name": school,
            "建校年份": _get_field("建校年份"),
            "教师总数": _get_field("学校教师总数"),
            "占地面积": _get_field("占地面积"),
            "建筑面积": _get_field("建筑面积"),
        })
        return info

    def get_school_course_data(self, school: str) -> pd.DataFrame:
        """获取某学校的课程实施情况数据"""
        if self._course_impl is None:
            self.load_all()
        return self._course_impl[self._course_impl["学校名称"] == school].copy()

    def get_school_subject_data(self, school: str, subject: Optional[str] = None) -> pd.DataFrame:
        """获取某学校的学科课程实施数据"""
        if self._subject_impl is None:
            self.load_all()
        df = self._subject_impl[self._subject_impl["学校名称"] == school].copy()
        if subject:
            df = df[df["学科"] == subject]
        return df

    # ========== 课程领导力相关数据提取 ==========

    def get_weekly_hours(self, school: str) -> pd.DataFrame:
        """获取学校各学科各年级各学期的周课时数据"""
        df = self.get_school_course_data(school)
        # 筛选周课时相关字段
        hours_fields = ["学科必修课周课时", "学科选择性必修课周课时", "学科类选修课周课时"]
        result = df[df["字段名称"].isin(hours_fields)].copy()
        result["字段取值"] = pd.to_numeric(result["字段取值"], errors="coerce")
        return result

    def get_course_norms(self, school: str) -> Dict:
        """获取学校课程规范落实相关数据（建设规范、档案等）"""
        df = self.get_school_course_data(school)
        norm_keywords = ["建设规范文本", "档案", "已经建成并使用", "尚未建成"]
        mask = df["字段名称"].apply(
            lambda x: any(k in str(x) for k in norm_keywords) if pd.notna(x) else False
        )
        return df[mask][["字段名称", "字段取值"]].to_dict("records")

    # ========== 教学变革力相关数据提取 ==========

    def get_teaching_reform_data(self, school: str) -> Dict:
        """获取教学方式变革相关数据（认识程度、落实程度、实施方式）"""
        df = self.get_school_subject_data(school)
        reform_keywords = [
            "认识程度", "落实程度", "认识", "落实",
            "理解式学习", "自主性学习", "实践性学习", "跨学科学习",
            "信息技术与教学融合", "信息融入教学",
        ]
        mask = df["字段名称"].apply(
            lambda x: any(k in str(x) for k in reform_keywords) if pd.notna(x) else False
        )
        return df[mask]

    def get_homework_data(self, school: str) -> Dict:
        """获取作业设计与管理数据"""
        df = self.get_school_subject_data(school)
        hw_keywords = [
            "作业", "实践类", "表现类", "跨学科", "团队合作",
            "批改", "评价", "属性标注", "时长控制",
        ]
        mask = df["字段名称"].apply(
            lambda x: any(k in str(x) for k in hw_keywords) if pd.notna(x) else False
        )
        return df[mask]

    # ========== 通用数据提取方法 ==========

    def get_field_value(self, school: str, source: str, field_name: str,
                        subject: Optional[str] = None) -> Optional[str]:
        """通用字段值获取"""
        if source == "basic":
            df = self._basic_info[self._basic_info["学校名称"] == school]
            col = "字段名称"
            val_col = "字段值"
        elif source == "course":
            df = self.get_school_course_data(school)
            col = "字段名称"
            val_col = "字段取值"
        elif source == "subject":
            df = self.get_school_subject_data(school, subject)
            col = "字段名称"
            val_col = "字段取值"
        else:
            return None

        rows = df[df[col] == field_name]
        if len(rows) > 0:
            return rows.iloc[0][val_col]
        return None

    def get_field_by_id(self, school: str, source: str, field_id: str,
                        subject: Optional[str] = None) -> List[Dict]:
        """通过字段ID获取数据"""
        if source == "basic":
            df = self._basic_info[self._basic_info["学校名称"] == school]
        elif source == "course":
            df = self.get_school_course_data(school)
        elif source == "subject":
            df = self.get_school_subject_data(school, subject)
        else:
            return []

        rows = df[df["字段ID"] == field_id]
        return rows.to_dict("records")

    def build_score_matrix(self) -> pd.DataFrame:
        """
        构建所有学校×所有字段的得分矩阵
        这是赋分引擎的输入
        """
        if self._course_impl is None:
            self.load_all()

        records = []

        for school in self.schools:
            # 课程实施表数据
            course_df = self.get_school_course_data(school)
            for _, row in course_df.iterrows():
                records.append({
                    "学校": school,
                    "来源": "course",
                    "题号": row.get("题号"),
                    "字段ID": row.get("字段ID"),
                    "字段名称": row.get("字段名称"),
                    "字段取值": row.get("字段取值"),
                    "学科": row.get("学科", "不分学科"),
                    "年级": row.get("年级", "不分年级"),
                    "学期": row.get("学期", ""),
                })

            # 学科课程表数据
            subject_df = self.get_school_subject_data(school)
            for _, row in subject_df.iterrows():
                records.append({
                    "学校": school,
                    "来源": "subject",
                    "题号": row.get("题号"),
                    "字段ID": row.get("字段ID"),
                    "字段名称": row.get("字段名称"),
                    "字段取值": row.get("字段取值"),
                    "学科": row.get("学科", "不分学科"),
                    "年级": "",
                    "学期": "",
                })

        matrix = pd.DataFrame(records)
        logger.info(f"得分矩阵构建完成: {len(matrix)}行, {len(self.schools)}所学校")
        return matrix

    def summary(self) -> Dict:
        """数据摘要"""
        if self._basic_info is None:
            self.load_all()
        return {
            "schools": self.schools,
            "school_count": len(self.schools),
            "basic_info_rows": len(self._basic_info),
            "course_impl_rows": len(self._course_impl),
            "subject_impl_rows": len(self._subject_impl),
            "subjects": SUBJECTS,
            "school_types": {s: SCHOOL_TYPE_MAP[s]["type"] for s in self.schools},
        }