from __future__ import annotations

import math

import numpy as np
import pandas as pd


def _sigmoid(x: float) -> float:
    return 1.0 / (1.0 + math.exp(-x))


def compute_trend_scores(df: pd.DataFrame) -> pd.DataFrame:
    """
    在“表结构不确定”的情况下，先基于可用字段做可解释评分：
    - 潜伏期识别（Potential Winners）：新上架/刚出现 + 指标增长快
    - 爆发力：若存在 tiktok/search 等字段则融合，否则使用销量/GMV加速度代理
    - 决策建议：跟卖指数 + 生命周期预警（缺失字段会自动降权）
    """
    out = df.copy()
    now = pd.Timestamp.now(tz="UTC")

    # 新品/潜伏期：first_seen 越近、同时 units/gmv 相对高 -> potential
    first_seen = pd.to_datetime(out.get("first_seen"), errors="coerce", utc=True)
    age_days = (now - first_seen).dt.total_seconds() / 86400.0
    age_days = age_days.fillna(999.0).clip(lower=0.0)

    units = pd.to_numeric(out.get("units"), errors="coerce").fillna(0.0)
    gmv = pd.to_numeric(out.get("gmv"), errors="coerce").fillna(0.0)

    # 规模归一：log1p 降噪
    units_s = np.log1p(units)
    gmv_s = np.log1p(gmv)

    freshness = 1.0 / (1.0 + (age_days / 7.0))  # 0~1
    scale = (units_s.rank(pct=True) * 0.6 + gmv_s.rank(pct=True) * 0.4).clip(0.0, 1.0)
    out["potential_score"] = (freshness * 0.55 + scale * 0.45).clip(0.0, 1.0)

    # 爆发力：优先融合可选字段
    tiktok_raw = out["tiktok_hot"] if "tiktok_hot" in out.columns else pd.Series(np.nan, index=out.index)
    search_raw = out["search_growth"] if "search_growth" in out.columns else pd.Series(np.nan, index=out.index)
    tiktok = pd.to_numeric(tiktok_raw, errors="coerce")
    search_g = pd.to_numeric(search_raw, errors="coerce")
    if tiktok.notna().any() or search_g.notna().any():
        tiktok_s = tiktok.fillna(tiktok.median() if tiktok.notna().any() else 0.0)
        search_s = search_g.fillna(search_g.median() if search_g.notna().any() else 0.0)
        burst = (
            pd.Series(tiktok_s).rank(pct=True) * 0.6
            + pd.Series(search_s).rank(pct=True) * 0.4
        ).clip(0.0, 1.0)
    else:
        # 无外部热度字段：用规模 + 新鲜度 作为代理
        burst = (scale * 0.65 + freshness * 0.35).clip(0.0, 1.0)
    out["burst_score"] = burst

    # 跟卖指数：竞争（records 越多）负向；利润空间/供应链难度若缺失则降级
    records = pd.to_numeric(out.get("records"), errors="coerce").fillna(0.0)
    competition = records.rank(pct=True).clip(0.0, 1.0)  # 越大越卷

    margin_raw = out["margin"] if "margin" in out.columns else pd.Series(np.nan, index=out.index)
    margin = pd.to_numeric(margin_raw, errors="coerce")
    if margin.notna().any():
        margin_s = margin.fillna(margin.median()).rank(pct=True).clip(0.0, 1.0)
        margin_w = 0.35
    else:
        margin_s = pd.Series(0.5, index=out.index)
        margin_w = 0.15

    supply_raw = out["supply_difficulty"] if "supply_difficulty" in out.columns else pd.Series(np.nan, index=out.index)
    supply = pd.to_numeric(supply_raw, errors="coerce")
    if supply.notna().any():
        supply_s = (1.0 - supply.fillna(supply.median()).rank(pct=True)).clip(0.0, 1.0)  # 越难越低分
        supply_w = 0.20
    else:
        supply_s = pd.Series(0.5, index=out.index)
        supply_w = 0.10

    # 趋势作为正向
    trend = (out["potential_score"] * 0.5 + out["burst_score"] * 0.5).clip(0.0, 1.0)
    trend_w = 0.45

    comp_w = 0.20
    follow = (
        trend * trend_w
        + margin_s * margin_w
        + supply_s * supply_w
        + (1.0 - competition) * comp_w
    )
    out["follow_score"] = follow.clip(0.0, 1.0)

    # 生命周期预警（简化）：过老 + 规模不增长 + 竞争高 => red-ocean / decline
    lifecycle = []
    for i in out.index:
        a = float(age_days.loc[i])
        comp = float(competition.loc[i])
        tr = float(trend.loc[i])
        if a > 120 and comp > 0.7 and tr < 0.4:
            lifecycle.append("decline_or_red_ocean")
        elif a > 60 and comp > 0.75:
            lifecycle.append("red_ocean")
        elif a < 21 and tr > 0.65:
            lifecycle.append("early_growth")
        else:
            lifecycle.append("normal")
    out["lifecycle"] = lifecycle

    return out