"""Pure versioned player rating materialization and aging policy.""" from __future__ import annotations from dataclasses import dataclass from datetime import date from decimal import Decimal, ROUND_HALF_UP import hashlib import json from players.constants import ALL_ATTRIBUTES, POSITION_WEIGHTS from .constants import TRAINABLE_PLAYER_ATTRIBUTES MATERIALIZATION_POLICY_KEY = "ftg.player.rating_materialization.v1" MATERIALIZATION_POLICY_VERSION = "1.0.0" AGING_POLICY_KEY = "ftg.player.aging_decline.v1" AGING_POLICY_VERSION = "1.0.0" _Q6 = Decimal("0.000001") def payload_sha256(payload: dict) -> str: return hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(",", ":"), default=str).encode()).hexdigest() def age_on(date_of_birth: date, on_date: date) -> int: return on_date.year - date_of_birth.year - ((on_date.month, on_date.day) < (date_of_birth.month, date_of_birth.day)) def calculate_overall(*, position: str, ratings: dict[str, int]) -> int: weights = POSITION_WEIGHTS.get(position, {}) total = Decimal("0") weight_total = Decimal("0") for attribute in ALL_ATTRIBUTES: weight = Decimal(str(weights.get(attribute, 1.0))) total += Decimal(int(ratings[attribute])) * weight weight_total += weight if not weight_total: return 1 return max(1, min(100, int((total / weight_total).quantize(Decimal("1"), rounding=ROUND_HALF_UP)))) @dataclass(frozen=True, slots=True) class AttributeChange: attribute_name: str rating_before: int rating_after: int credit_available_before: Decimal credit_consumed: Decimal credit_available_after: Decimal @property def delta(self) -> int: return self.rating_after - self.rating_before @dataclass(frozen=True, slots=True) class MaterializationResult: age_years: int overall_before: int overall_after: int potential: int changes: tuple[AttributeChange, ...] total_credit_consumed: Decimal ratings_after: dict[str, int] class PlayerRatingMaterializationPolicyV1: key = MATERIALIZATION_POLICY_KEY version = MATERIALIZATION_POLICY_VERSION @staticmethod def credit_cost(*, rating: int, age_years: int) -> Decimal: rating = int(rating) if rating < 60: base = Decimal("8") elif rating < 70: base = Decimal("10") elif rating < 80: base = Decimal("12") elif rating < 90: base = Decimal("16") elif rating < 95: base = Decimal("22") else: base = Decimal("30") if age_years <= 18: multiplier = Decimal("0.85") elif age_years <= 23: multiplier = Decimal("0.95") elif age_years <= 29: multiplier = Decimal("1.00") else: multiplier = Decimal("1.25") return (base * multiplier).quantize(_Q6) @classmethod def materialize(cls, *, position: str, ratings: dict[str, int], potential: int, date_of_birth: date, observed_on: date, available_credits: dict[str, Decimal], age_years: int | None = None) -> MaterializationResult: age = age_on(date_of_birth, observed_on) if age_years is None else age_years working = {name: int(ratings[name]) for name in ALL_ATTRIBUTES} overall_before = calculate_overall(position=position, ratings=working) if overall_before > int(potential): raise ValueError("overall cannot exceed potential before materialization") changes = [] total_consumed = Decimal("0") # After 34, accumulated development is retained but no longer materialized as positive growth. if age > 34: return MaterializationResult(age, overall_before, overall_before, int(potential), tuple(), Decimal("0.000000"), working) for attribute in TRAINABLE_PLAYER_ATTRIBUTES: available = Decimal(available_credits.get(attribute, Decimal("0"))).quantize(_Q6) before = working[attribute] current = before consumed = Decimal("0") while current < 100: cost = cls.credit_cost(rating=current, age_years=age) if available - consumed < cost: break candidate = dict(working) candidate[attribute] = current + 1 candidate_overall = calculate_overall(position=position, ratings=candidate) if candidate_overall > int(potential): break current += 1 working[attribute] = current consumed += cost if consumed > 0: changes.append(AttributeChange( attribute, before, current, available, consumed.quantize(_Q6), (available - consumed).quantize(_Q6) )) total_consumed += consumed overall_after = calculate_overall(position=position, ratings=working) return MaterializationResult( age, overall_before, overall_after, int(potential), tuple(changes), total_consumed.quantize(_Q6), working ) @dataclass(frozen=True, slots=True) class AgingResult: age_years: int overall_before: int overall_after: int base_decline_points: int applied_decline_points: int training_protection: Decimal retirement_eligible: bool attribute_deltas: dict[str, int] ratings_after: dict[str, int] class PlayerAgingPolicyV1: key = AGING_POLICY_KEY version = AGING_POLICY_VERSION _vulnerability = { "pace": Decimal("1.50"), "acceleration": Decimal("1.45"), "agility": Decimal("1.25"), "stamina": Decimal("1.15"), "jumping": Decimal("1.00"), "strength": Decimal("0.85"), "balance": Decimal("0.80"), "natural_fitness": Decimal("0.45"), "dribbling": Decimal("0.35"), "ball_control": Decimal("0.25"), "finishing": Decimal("0.22"), "shooting": Decimal("0.22"), "tackling": Decimal("0.20"), "marking": Decimal("0.18"), "crossing": Decimal("0.18"), "passing": Decimal("0.12"), "vision": Decimal("0.08"), "positioning": Decimal("0.05"), "decision_making": Decimal("0.04"), "composure": Decimal("0.04"), "anticipation": Decimal("0.05"), "off_ball": Decimal("0.08"), "concentration": Decimal("0.06"), "work_rate": Decimal("0.12"), "teamwork": Decimal("0.04"), "heading": Decimal("0.15"), "first_touch": Decimal("0.15"), "long_shots": Decimal("0.15"), "set_pieces": Decimal("0.05"), "aggression": Decimal("0.05"), "gk_diving": Decimal("0.75"), "gk_reflexes": Decimal("1.10"), "gk_one_on_one": Decimal("0.55"), "gk_handling": Decimal("0.25"), "gk_positioning": Decimal("0.08"), "gk_kicking": Decimal("0.18"), } @staticmethod def base_decline(age_years: int) -> int: if age_years <= 34: return 0 if age_years <= 36: return 2 if age_years <= 38: return 4 if age_years == 39: return 6 return 8 @classmethod def apply(cls, *, position: str, ratings: dict[str, int], date_of_birth: date, observed_on: date, season_training_by_attribute: dict[str, Decimal], player_seed: str, age_years: int | None = None) -> AgingResult: age = age_on(date_of_birth, observed_on) if age_years is None else age_years working = {name: int(ratings[name]) for name in ALL_ATTRIBUTES} overall_before = calculate_overall(position=position, ratings=working) base = cls.base_decline(age) total_training = sum((Decimal(v) for v in season_training_by_attribute.values()), Decimal("0")) training_protection = min(Decimal("0.70"), (total_training / Decimal("60")) * Decimal("0.70")) fitness_bonus = Decimal(working.get("natural_fitness", 50)) / Decimal("100") * Decimal("0.15") protection = min(Decimal("0.80"), training_protection + fitness_bonus).quantize(Decimal("0.0001")) applied = 0 if base == 0 else max(1, int((Decimal(base) * (Decimal("1") - protection)).quantize(Decimal("1"), rounding=ROUND_HALF_UP))) deltas = {name: 0 for name in TRAINABLE_PLAYER_ATTRIBUTES} def score(attr: str) -> tuple[Decimal, str]: trained = Decimal(season_training_by_attribute.get(attr, Decimal("0"))) targeted = min(Decimal("0.70"), trained / Decimal("12") * Decimal("0.70")) vulnerability = cls._vulnerability.get(attr, Decimal("0.10")) * (Decimal("1") - targeted) salt = int.from_bytes(hashlib.sha256(f"{player_seed}|{attr}".encode()).digest()[:2], "big") return (vulnerability * Decimal(working[attr]) + Decimal(salt % 100) / Decimal("10000"), attr) for _ in range(applied): eligible = [a for a in TRAINABLE_PLAYER_ATTRIBUTES if working[a] > 1] if not eligible: break attr = max(eligible, key=score) working[attr] -= 1 deltas[attr] -= 1 deltas = {k: v for k, v in deltas.items() if v} overall_after = calculate_overall(position=position, ratings=working) return AgingResult(age, overall_before, overall_after, base, sum(-v for v in deltas.values()), protection, age >= 40, deltas, working)