"""
麦麦智农 - PCSE 模拟引擎封装
基于 WOFOST 7.2 潜在生产（PP）与水分限制生产（WLP）模型
"""

import sqlite3
from collections import namedtuple
from typing import Literal

import pandas as pd

import pcse
from pcse.models import Wofost72_PP, Wofost72_WLP_CWB
from pcse.settings import settings
from pcse.base import ParameterProvider
from pcse.tests.db_input import (
    GridWeatherDataProvider,
    fetch_soildata,
    fetch_sitedata,
    fetch_cropdata,
    AgroManagementDataProvider,
)


def namedtuple_factory(cursor, row):
    fields = [column[0] for column in cursor.description]
    cls = namedtuple("Row", fields)
    return cls._make(row)


# Demo 数据库配置
DB_PATH = settings.PCSE_USER_HOME + "/pcse.db"

# 作物映射（crop_no -> 中文名/元数据）
CROP_META = {
    1: {"name": "冬小麦", "emoji": "🌾", "color": "#c69c5d", "en": "WINTER WHEAT"},
    2: {"name": "玉米", "emoji": "🌽", "color": "#e8a93f", "en": "GRAIN MAIZE"},
    3: {"name": "春大麦", "emoji": "🌿", "color": "#9cb85f", "en": "SPRING BARLEY"},
    7: {"name": "马铃薯", "emoji": "🥔", "color": "#b8a88a", "en": "POTATO"},
    10: {"name": "冬油菜", "emoji": "🌻", "color": "#d97836", "en": "WINTER RAPESEED"},
    11: {"name": "向日葵", "emoji": "🌻", "color": "#f4c430", "en": "SUNFLOWER"},
}

MODE_LABELS = {
    "pp": "潜在生产",
    "wlp": "水分限制生产",
}


def get_db_conn():
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = namedtuple_factory
    return conn


def list_available_years(grid_no: int = 31031) -> list[int]:
    """返回 Demo 数据库中指定 grid 有气象数据的年份列表。"""
    conn = get_db_conn()
    c = conn.cursor()
    rows = c.execute(
        "SELECT DISTINCT strftime('%Y', day) as year FROM grid_weather WHERE grid_no=? ORDER BY year",
        (grid_no,),
    ).fetchall()
    conn.close()
    return [int(r.year) for r in rows]


def list_available_crops(grid_no: int = 31031, year: int = 2000) -> list[dict]:
    """返回指定 grid 与年份有作物日历的作物列表。"""
    conn = get_db_conn()
    c = conn.cursor()
    rows = c.execute(
        "SELECT crop_no FROM crop_calendar WHERE grid_no=? AND year=? ORDER BY crop_no",
        (grid_no, year),
    ).fetchall()
    conn.close()
    result = []
    for r in rows:
        meta = CROP_META.get(r.crop_no)
        if meta:
            result.append({"crop_no": r.crop_no, **meta})
    return result


def run_wofost(
    grid_no: int = 31031,
    crop_no: int = 1,
    year: int = 2000,
    mode: Literal["pp", "wlp"] = "wlp",
) -> dict:
    """
    运行 WOFOST 模拟，返回包含输出时间序列与关键指标的字典。
    """
    conn = get_db_conn()
    try:
        agromanagement = AgroManagementDataProvider(conn, grid_no, crop_no, year)
        sited = fetch_sitedata(conn, grid_no, year)
        cropd = fetch_cropdata(conn, grid_no, year, crop_no)
        soild = fetch_soildata(conn, grid_no)
        parvalues = ParameterProvider(sitedata=sited, soildata=soild, cropdata=cropd)
        wdp = GridWeatherDataProvider(conn, grid_no=grid_no)

        if mode == "pp":
            wofsim = Wofost72_PP(parvalues, wdp, agromanagement)
        else:
            wofsim = Wofost72_WLP_CWB(parvalues, wdp, agromanagement)

        wofsim.run_till_terminate()
        output = wofsim.get_output()
    finally:
        conn.close()

    df = pd.DataFrame(output)
    if not df.empty and "day" in df.columns:
        df = df.set_index("day")

    # 关键指标
    if not df.empty:
        last = df.iloc[-1]
        max_lai = df["LAI"].max() if "LAI" in df.columns else None
        tagp = last.get("TAGP")
        twso = last.get("TWSO")
        sm_end = last.get("SM")
        dvs_end = last.get("DVS")
        duration = len(df)
    else:
        max_lai = tagp = twso = sm_end = dvs_end = duration = None

    # 生育期里程碑（基于 DVS）
    milestones = {}
    if not df.empty and "DVS" in df.columns:
        dvs_series = df["DVS"]
        # 出苗/开始生长 DVS ~0（实际在 calendar 开始日）
        # 开花 DVS ~1.0
        # 成熟 DVS ~2.0
        flowering = dvs_series[dvs_series >= 1.0]
        if not flowering.empty:
            milestones["flowering"] = flowering.index[0]
        maturity = dvs_series[dvs_series >= 2.0]
        if not maturity.empty:
            milestones["maturity"] = maturity.index[0]
        # 开始日
        milestones["start"] = df.index[0]
        milestones["end"] = df.index[-1]

    meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})

    return {
        "meta": {
            "grid_no": grid_no,
            "crop_no": crop_no,
            "year": year,
            "mode": mode,
            **meta,
            "mode_label": MODE_LABELS.get(mode, mode),
        },
        "df": df,
        "summary": {
            "tagp": tagp,  # 总地上生物量 kg/ha
            "twso": twso,  # 经济产量 kg/ha
            "max_lai": max_lai,
            "sm_end": sm_end,
            "dvs_end": dvs_end,
            "duration": duration,
            "milestones": milestones,
        },
    }


def run_multi_crop(
    grid_no: int = 31031,
    year: int = 2000,
    mode: Literal["pp", "wlp"] = "wlp",
) -> list[dict]:
    """
    对指定年份所有可用作物运行模拟，返回各作物汇总结果列表（按 TWSO 排序）。
    """
    crops = list_available_crops(grid_no, year)
    results = []
    for c in crops:
        try:
            res = run_wofost(grid_no=grid_no, crop_no=c["crop_no"], year=year, mode=mode)
            results.append({
                "crop_no": c["crop_no"],
                "name": c["name"],
                "emoji": c["emoji"],
                "color": c["color"],
                "tagp": res["summary"]["tagp"],
                "twso": res["summary"]["twso"],
                "max_lai": res["summary"]["max_lai"],
                "duration": res["summary"]["duration"],
            })
        except Exception:
            # 某些作物可能因数据缺失导致模拟失败，静默跳过
            continue
    results.sort(key=lambda x: (x["twso"] if x["twso"] is not None else -1), reverse=True)
    return results


def run_multi_year(
    grid_no: int = 31031,
    crop_no: int = 1,
    mode: Literal["pp", "wlp"] = "wlp",
) -> list[dict]:
    """
    对指定作物在所有可用年份运行模拟，返回各年份汇总结果列表。
    """
    years = list_available_years(grid_no)
    results = []
    meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
    for year in years:
        try:
            res = run_wofost(grid_no=grid_no, crop_no=crop_no, year=year, mode=mode)
            results.append({
                "year": year,
                "tagp": res["summary"]["tagp"],
                "twso": res["summary"]["twso"],
                "max_lai": res["summary"]["max_lai"],
                "duration": res["summary"]["duration"],
            })
        except Exception:
            continue
    return results