"""球員道具盤（Props）蒙地卡羅模擬模組。"""

from __future__ import annotations

from dataclasses import dataclass

import numpy as np


@dataclass(frozen=True)
class PlayerPropsDistribution:
    shots: np.ndarray
    shots_on_target: np.ndarray
    passes: np.ndarray


def simulate_player_stats(
    player_metrics: dict,
    opponent_defense_metrics: dict,
    iterations: int = 10_000,
) -> PlayerPropsDistribution:
    """快速模擬球員事件次數分佈。"""

    if iterations <= 0:
        raise ValueError('iterations 必須大於 0')

    avg_touches = float(player_metrics.get('avg_touches', 45) or 0.0)
    base_shot_rate = float(player_metrics.get('shots_per_touch', 0.08) or 0.0)
    base_target_rate = float(player_metrics.get('shot_on_target_rate', 0.35) or 0.0)
    base_pass_rate = float(player_metrics.get('passes_per_touch', 0.65) or 0.0)

    opp_pressure = float(opponent_defense_metrics.get('pressing_index', 1.0) or 1.0)
    opp_tackling = float(opponent_defense_metrics.get('marking_index', 1.0) or 1.0)

    adj_touches = max(1.0, avg_touches * max(0.6, 1.0 / max(0.5, opp_pressure)))
    shot_lambda = adj_touches * base_shot_rate
    pass_lambda = adj_touches * base_pass_rate

    rng = np.random.default_rng()
    shots = rng.poisson(lam=shot_lambda, size=iterations)
    passes = rng.poisson(lam=pass_lambda, size=iterations)

    # 對方壓迫會降低射正率
    effective_target_rate = max(0.02, base_target_rate / max(opp_tackling, 0.3))
    shots_on_target = rng.binomial(shots, p=min(effective_target_rate, 0.99), size=iterations)

    return PlayerPropsDistribution(shots=shots.astype(int), shots_on_target=shots_on_target.astype(int), passes=passes.astype(int))


def evaluate_prop_bet(
    simulated_distribution: PlayerPropsDistribution,
    line: float,
    odds: float,
) -> dict[str, float | bool]:
    """從 10,000 次模擬結果計算超過盤口機率與 EV。"""

    if odds <= 1:
        raise ValueError('odds 必須大於 1')
    if line < 0:
        raise ValueError('line 必須大於等於 0')

    shots = simulated_distribution.shots
    if shots.size == 0:
        raise ValueError('distribution 為空')

    probability_over = float((shots > line).mean())
    from .ev_calculator import calculate_expected_value

    ev = calculate_expected_value(probability_over, odds)

    return {
        'metric': 'shots',
        'line': line,
        'over_probability': round(probability_over, 6),
        'under_probability': round(1.0 - probability_over, 6),
        'implied_ev': ev['ev_value'],
        'ev_percentage': ev['ev_percentage'],
        'is_value_bet': bool(ev['is_value_bet']),
    }