184 lines
8.3 KiB
Python
184 lines
8.3 KiB
Python
"""复刻后端 OlympusDistributionService.calcRounds + reset()，重算某模式各箱的桶配比。
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为什么需要：`OlympusModeType.getOrnamentRange()` 改了（buy_fs 从 5~17 改成 7~17）之后，
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后台要「一键重算」才会按新范围重配 count。这个脚本把同一套算法跑在 py 侧，
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用来在没有后台操作的场合先把配比算出来（结果与后端一键重算一致）。
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算法移植自 Java（calcRounds）：基础权重 30×0.6^k → 二分 power 命中目标均值 →
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按权重分配固定池 → 单调约束 count[i] ≥ count[i+1] → 只在最大的 3 个桶间微调逼近目标。
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用法:
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python _recalc_boxes.py --mode buy_fs --dry # 只看结果
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python _recalc_boxes.py --mode buy_fs # 写库（count 覆盖、used 归零）
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"""
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import argparse
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import olympus_db as db
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from olympus.config import MODES, MODE_ORNAMENT_IDS, ORNAMENTS, payout_expect
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# 回退用的"桶期望"：桶 17 展示 1000x 但封顶发放，按实测值算（见 config.PAYOUT_EXPECT_OVERRIDE）
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_ORN_MUL = {o['id']: payout_expect(o['id']) for o in ORNAMENTS}
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# 该桶池子里一条回合都没有时才回退到 _ORN_MUL（名义/封顶修正值）。
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# 只要池子里有回合，就用它的实际均值：箱子发的是**自己这批固定回合**，它的均值就是这个箱子的真实期望。
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MIN_POOL_SAMPLE = 1
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def load_pool_expect(box_id, mode_code):
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"""该箱池子里每个桶的**实际**均值（AVG(actual_mul)）——配比按"这个箱子真正会赔出去多少"算，
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而不是按桶区间中点：桶内均值偏差（实测 -18%~+11%）和每箱池子的抽样差异会直接变成 RTP 偏差。"""
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with db.get_conn() as conn:
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cur = conn.cursor()
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cur.execute('''SELECT ornament_id, COUNT(*), AVG(actual_mul) FROM game_olympus_round
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WHERE box_id=%s GROUP BY ornament_id''', (box_id,))
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rows = cur.fetchall()
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exp = {i: _ORN_MUL[i] for i in MODE_ORNAMENT_IDS[mode_code]}
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for oid, n, avg in rows:
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if int(oid) in exp and int(n) >= MIN_POOL_SAMPLE and avg and float(avg) > 0:
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exp[int(oid)] = float(avg)
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return exp
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def derive_total_rounds(fixed_rounds, cr):
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cr = min(max(cr, 0.0), 0.9)
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return int(fixed_rounds / (1.0 - cr) + 0.5)
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def derive_fixed_rounds(total_rounds, cr):
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cr = min(max(cr, 0.0), 0.9)
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return int(total_rounds * (1.0 - cr) + 0.5)
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def calc_rounds(mode_code, total_rounds, target_rtp, expect=None):
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"""返回 {ornament_id: count}，合计 = total_rounds（对齐 Java calcRounds）
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expect：桶 → 该桶"实际赔出去多少"的期望；不传则用名义值（含桶 17 封顶修正）。"""
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mul = expect or _ORN_MUL
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cm = float(MODES[mode_code].cost_multiplier)
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target_avg = target_rtp * cm
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ids = sorted(MODE_ORNAMENT_IDS[mode_code])
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n = len(ids)
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if total_rounds <= n:
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return {i: 1 for i in ids}
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base_w = [30.0 * (0.6 ** i) for i in range(n)]
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remaining = total_rounds - n
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guaranteed = sum(mul[i] for i in ids)
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rem_target_avg = (target_avg * total_rounds - guaranteed) / remaining
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power, plo, phi = 1.0, 0.2, 10.0
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for _ in range(30):
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ws = [w ** power for w in base_w]
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sw = sum(ws)
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avg = sum(w * mul[i] for w, i in zip(ws, ids)) / sw if sw > 0 else 0.0
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if abs(avg - rem_target_avg) < max(rem_target_avg * 0.01, 0.001):
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break
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if avg < rem_target_avg:
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phi = power
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else:
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plo = power
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power = (plo + phi) / 2
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ws = [w ** power for w in base_w]
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tw = sum(ws)
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result = {i: 1 for i in ids}
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rem = remaining
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for i, w in zip(ids, ws):
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extra = int(w / tw * remaining + 0.5) if tw > 0 else 0
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put = min(extra, rem)
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if put > 0:
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result[i] += put
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rem -= put
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if rem > 0:
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result[ids[0]] += rem
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for k in range(n - 2, -1, -1): # 单调 count[i] >= count[i+1]
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if result[ids[k]] < result[ids[k + 1]]:
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result[ids[k]] = result[ids[k + 1]]
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diff = total_rounds - sum(result.values())
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if diff:
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result[ids[0]] += diff
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for _ in range(400): # 微调：任取两个桶搬 1 个（保持单调），只要更接近目标
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cur_mul = sum(result[i] * mul[i] for i in ids)
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delta = target_avg * total_rounds - cur_mul
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if abs(delta) < 0.01:
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break
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best, best_diff = None, abs(delta)
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for lo in ids:
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if result[lo] <= 1:
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continue
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for hi in ids:
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if lo == hi:
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continue
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change = mul[hi] - mul[lo]
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if abs(change) < 0.001:
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continue
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nd = abs(delta - change)
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if nd < best_diff:
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ok = True
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ca, idx_lo = result[lo] - 1, ids.index(lo)
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if idx_lo < n - 1 and ca < result[ids[idx_lo + 1]]:
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ok = False
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ca2, idx_hi = result[hi] + 1, ids.index(hi)
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if idx_hi > 0 and ca2 > result[ids[idx_hi - 1]]:
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ok = False
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if ids.index(lo) < ids.index(hi) and ca < ca2:
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ok = False
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if ok:
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best, best_diff = (lo, hi), nd
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if best is None:
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break
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result[best[0]] -= 1
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result[best[1]] += 1
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return result
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--mode', default='buy_fs')
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ap.add_argument('--dry', action='store_true')
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args = ap.parse_args()
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from olympus.config import AUDIENCES
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with db.get_conn() as conn:
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cur = conn.cursor()
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cur.execute('''SELECT box_id, rtp, rtp_converge_ratio, total_ornaments
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FROM game_olympus_box WHERE box_type=%s ORDER BY box_id''', (args.mode,))
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boxes = cur.fetchall()
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print('%s 共 %d 箱，桶范围 %s；池内实际均值按**每个箱子自己的池子**算（样本<%d的桶回退名义值）'
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% (args.mode, len(boxes), sorted(MODE_ORNAMENT_IDS[args.mode]), MIN_POOL_SAMPLE))
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for box_id, rtp, cr, total in boxes:
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rtp = float(rtp or 0.8)
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cr = float(cr if cr is not None else 0.15)
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fixed = derive_fixed_rounds(int(total or 1000), cr)
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pool_expect = load_pool_expect(box_id, args.mode)
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counts = calc_rounds(args.mode, fixed, rtp, pool_expect)
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s = sum(counts.values())
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cm = float(MODES[args.mode].cost_multiplier)
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pay_pool = sum(counts[i] * pool_expect[i] for i in counts)
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pay_nominal = sum(counts[i] * _ORN_MUL[i] for i in counts)
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print('box%-3d 固定池%d 配比 %s' % (
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box_id, s, {k: v for k, v in sorted(counts.items()) if v > 0}))
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print(' 目标RTP(本箱池内实际口径) %.2f%% 名义口径 %.2f%%'
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% (100.0 * pay_pool / s / cm, 100.0 * pay_nominal / s / cm))
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if args.dry:
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continue
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n_cycle = derive_total_rounds(s, cr)
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with db.get_conn() as conn:
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cur = conn.cursor()
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for aud in AUDIENCES:
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cur.execute('DELETE FROM game_olympus_box_ornament WHERE box_id=%s AND audience=%s',
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(box_id, aud))
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cur.executemany('''INSERT INTO game_olympus_box_ornament
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(box_id, ornament_id, audience, count, used) VALUES (%s,%s,%s,%s,0)''',
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[(box_id, i, aud, c) for i, c in sorted(counts.items()) if c > 0])
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cur.execute('UPDATE game_olympus_box SET total_ornaments=%s WHERE box_id=%s',
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(n_cycle, box_id))
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if not args.dry:
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print('已写库（count 覆盖、used 归零、total_ornaments=一轮抽数）。')
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print('提醒：本脚本只动库里的 used，**不会清 Redis 的本轮账本**'
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'（ruoyi:olympus:cycle:{boxId}:{audience}）；不清的话后台「实时RTP」会把'
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'重置前的旧抽数和新抽数混在一起显示。'
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'清账本：docker exec redis_cache redis-cli -a <pwd> DEL ruoyi:olympus:cycle:<boxId>:<audience>，'
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'或在后台对这几个箱点一次「一键重算分布」（它会同时清账本）。')
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print('记得再跑 python _make_templates.py 刷新模板。')
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if __name__ == '__main__':
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main()
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