"""レース内の勝率(New)・期待値(New)（単勝オッズ×勝率）の共通計算。"""
from __future__ import annotations

import math
from typing import Any


def win_prob_new_pcts(model_scores: list[float | None], *, temperature: float = 10.0) -> list[float]:
    """
    モデルスコア（predictions.win_probability に保存されている値）から、
    レース内合計100%の勝率(New)％を返す（softmax・温度付き）。
    """
    if not model_scores:
        return []
    max_s: float | None = None
    for s in model_scores:
        if s is not None:
            max_s = s if max_s is None else max(max_s, s)
    weights: list[float] = []
    sum_w = 0.0
    for s in model_scores:
        if s is None or max_s is None:
            w = 1.0
        else:
            z = (s - max_s) / temperature
            if z < -50.0:
                z = -50.0
            w = math.exp(z)
        weights.append(w)
        sum_w += w
    if sum_w <= 0.0:
        sum_w = float(len(weights))
        weights = [1.0] * len(weights)
    pcts = [round((w / sum_w) * 100.0, 1) for w in weights]
    delta = round(100.0 - sum(pcts), 1)
    if pcts and abs(delta) <= 5.0:
        pcts[-1] = round(pcts[-1] + delta, 1)
    return pcts


def expected_value_new(prob_pct: float | None, odds: float | None) -> float | None:
    """期待値(New) = (勝率%/100) × 単勝オッズ（倍率）。"""
    if prob_pct is None or odds is None:
        return None
    o = float(odds)
    if o <= 0:
        return None
    return round((float(prob_pct) / 100.0) * o, 4)


def attach_win_prob_and_ev(
    horses: list[dict[str, Any]],
    *,
    score_key: str = "model_score",
    odds_key: str = "odds",
    temperature: float = 10.0,
) -> list[dict[str, Any]]:
    """
    各要素に win_prob_new_pct / ev_new を付与して返す（破壊的更新）。
    horses: model_score, odds を持つ dict のリスト
    """
    scores = [h.get(score_key) for h in horses]
    norm_scores: list[float | None] = []
    for s in scores:
        if s is None or s == "":
            norm_scores.append(None)
        else:
            norm_scores.append(float(s))
    pcts = win_prob_new_pcts(norm_scores, temperature=temperature)
    for i, h in enumerate(horses):
        pct = pcts[i] if i < len(pcts) else None
        h["win_prob_new_pct"] = pct
        od = h.get(odds_key)
        odds_f = float(od) if od is not None and od != "" else None
        h["ev_new"] = expected_value_new(pct, odds_f)
    return horses


def marks_by_ev_new(horses: list[dict[str, Any]], *, top_n: int = 4) -> list[dict[str, Any]]:
    """期待値(New)の降順で ◎〜△ を付与したマーク行（最大 top_n 頭）。"""
    labels = ["◎本命", "〇対抗", "▲単穴", "△連下"]
    ranked = sorted(
        horses,
        key=lambda x: (
            float(x.get("ev_new") or 0.0),
            float(x.get("win_prob_new_pct") or 0.0),
        ),
        reverse=True,
    )
    out: list[dict[str, Any]] = []
    for i, row in enumerate(ranked[:top_n]):
        out.append(
            {
                "mark": labels[i] if i < len(labels) else f"候補{i + 1}",
                "horse_number": row.get("horse_number"),
                "horse_name": row.get("horse_name") or "",
                "win_prob_new_pct": row.get("win_prob_new_pct"),
                "ev_new": row.get("ev_new"),
                "odds": row.get("odds"),
                "model_score": row.get("model_score"),
            }
        )
    return out