#!/usr/bin/env python3
"""
课消指标：按周统计 2025-09-01 ~ 2026-05-10
指标：
1. 人均课消数 = 课消总次数 / 付费用户数
2. 有课消用户的人均课消数 = 课消总次数 / 有课消的付费用户数
"""

import psycopg2
from collections import defaultdict
from datetime import datetime, timedelta, date

# 连接线上 PostgreSQL
conn = psycopg2.connect(
    host="bj-postgres-16pob4sg.sql.tencentcdb.com",
    port=28591,
    user="ai_member",
    password="LdfjdjL83h3h3^$&**YGG*",
    dbname="vala_bi"
)
cur = conn.cursor()

# ===== 时间参数 =====
overall_start = date(2025, 9, 1)
overall_end = date(2026, 5, 11)  # exclusive，即5/10是最后一天

print(f"统计区间: {overall_start} ~ {overall_end - timedelta(days=1)}")

# ===== 生成周列表（周一~周日） =====
weeks = []
d = overall_start
while d < overall_end:
    week_start = d
    # 找周日
    days_to_sunday = 6 - d.weekday()
    week_end = d + timedelta(days=days_to_sunday)
    if week_end >= overall_end:
        week_end = overall_end - timedelta(days=1)
    weeks.append((week_start, week_end))
    d = week_end + timedelta(days=1)

print(f"共 {len(weeks)} 周")

# ===== Step 1：获取所有订单，确定每用户的付费有效期 =====
print("\nStep 1: 查询订单...")
cur.execute("""
    SELECT o.account_id, o.trade_no, o.out_trade_no, o.pay_success_date,
           o.order_status, o.pay_amount_int
    FROM bi_vala_order o
    INNER JOIN bi_vala_app_account a ON o.account_id = a.id
    WHERE a.status = 1
      AND a.deleted_at IS NULL
      AND o.pay_success_date IS NOT NULL
      AND o.pay_success_date >= '2025-01-01'
""")
orders = cur.fetchall()
print(f"  订单数: {len(orders)}")

# 获取退费信息
cur.execute("""
    SELECT trade_no, out_trade_no, status
    FROM bi_refund_order
    WHERE status = 3
""")
refunds = set()
refunds_by_out = set()
for trade_no, out_trade_no, st in cur.fetchall():
    if trade_no:
        refunds.add(trade_no)
    if out_trade_no:
        refunds_by_out.add(out_trade_no)

print(f"  退费trade_no数: {len(refunds)}, out_trade_no数: {len(refunds_by_out)}")

# 组织订单数据：{account_id: [(pay_date, is_refunded), ...]}
account_orders = defaultdict(list)
for aid, trade_no, out_trade_no, pay_date, order_status, amount in orders:
    is_refunded = (
        order_status == 4 and (
            (trade_no and trade_no in refunds) or
            (out_trade_no and out_trade_no in refunds_by_out)
        )
    )
    account_orders[aid].append((pay_date, is_refunded))

# 判断每个用户是否是付费用户（到某日期为止）
def is_paid_user(aid, as_of_date):
    """截至 as_of_date，用户是否有未退费订单"""
    has_paid = False
    for pay_date, is_refunded in account_orders.get(aid, []):
        if pay_date.date() <= as_of_date:
            if not is_refunded:
                has_paid = True
            # 如果退了但还有其他未退订单，也算
    # 重新计数：截至该日期，是否有任何未退费订单
    unpaid_orders = sum(
        1 for pd, ref in account_orders.get(aid, [])
        if pd.date() <= as_of_date and not ref
    )
    return unpaid_orders > 0

# 序章 chapter_id（L1 U00 + L2 U00），需剔除
u0_chapters = {343, 344, 345, 346, 348, 55, 56, 57, 58, 59}

# ===== Step 2：获取所有课消记录 =====
print("\nStep 2: 查询课消记录（剔除序章U0）...")
# (user_id, chapter_id) -> earliest_updated_at
consumption_map = {}  # key=(user_id, chapter_id) -> earliest updated_at (date)

for table_idx in range(8):
    tbl = f"bi_user_chapter_play_record_{table_idx}"
    cur.execute(f"""
        SELECT user_id, chapter_id, updated_at
        FROM {tbl}
        WHERE play_status = 1
          AND updated_at >= '2025-09-01'
          AND updated_at < '2026-05-11'
    """)
    cnt = 0
    for user_id, chapter_id, updated_at in cur.fetchall():
        if chapter_id in u0_chapters:
            continue
        key = (user_id, chapter_id)
        d = updated_at.date() if hasattr(updated_at, 'date') else updated_at
        if isinstance(updated_at, datetime):
            d = updated_at.date()
        elif isinstance(updated_at, str):
            d = datetime.strptime(updated_at[:10], '%Y-%m-%d').date()
        else:
            d = updated_at
        if key not in consumption_map or d < consumption_map[key]:
            consumption_map[key] = d
        cnt += 1
    print(f"  {tbl}: {cnt} 条记录")

print(f"  去重后课消: {len(consumption_map)} 条")

# ===== Step 3：关联 character -> account =====
print("\nStep 3: 关联角色...")
all_user_ids = set(k[0] for k in consumption_map)
all_user_id_list = list(all_user_ids)

char_to_account = {}
batch_size = 500
for i in range(0, len(all_user_id_list), batch_size):
    batch = all_user_id_list[i:i+batch_size]
    ph = ','.join(['%s'] * len(batch))
    cur.execute(f"""
        SELECT id, account_id FROM bi_vala_app_character
        WHERE id IN ({ph})
    """, batch)
    for cid, aid in cur.fetchall():
        char_to_account[cid] = aid

print(f"  角色-账号映射: {len(char_to_account)}")

# ===== Step 4：按周汇总 =====
print("\nStep 4: 按周汇总...")

results = []

for ws, we in weeks:
    # 分母：截至该周末的付费用户
    paid_users = set()
    for aid in account_orders:
        if is_paid_user(aid, we):
            paid_users.add(aid)
    
    # 分子：该周内付费用户的课消次数（按最早updated_at所在周）
    weekly_consumption = 0
    consuming_accounts = set()
    
    for (uid, ch_id), cons_date in consumption_map.items():
        if ws <= cons_date <= we:
            aid = char_to_account.get(uid)
            if aid and aid in paid_users:
                weekly_consumption += 1
                consuming_accounts.add(aid)
    
    n_paid = len(paid_users)
    n_consuming = len(consuming_accounts)
    avg_all = weekly_consumption / n_paid if n_paid > 0 else 0
    avg_consuming = weekly_consumption / n_consuming if n_consuming > 0 else 0
    
    results.append({
        'week': f"{ws.strftime('%m/%d')}-{we.strftime('%m/%d')}",
        'ws': ws,
        'we': we,
        'paid_users': n_paid,
        'consumption': weekly_consumption,
        'consuming_users': n_consuming,
        'avg_all': avg_all,
        'avg_consuming': avg_consuming,
    })
    
    print(f"  {ws}~{we}: 付费{n_paid} | 课消{weekly_consumption} | "
          f"有课消{n_consuming} | 人均{avg_all:.2f} | 有消人均{avg_consuming:.2f}")

# ===== 输出结果 =====
print("\n" + "="*80)
print(f"{'周':<20} {'付费用户':>8} {'课消次数':>8} {'有消用户':>8} {'人均课消':>8} {'有消人均':>8}")
print("-"*80)
for r in results:
    print(f"{r['week']:<20} {r['paid_users']:>8} {r['consumption']:>8} "
          f"{r['consuming_users']:>8} {r['avg_all']:>8.2f} {r['avg_consuming']:>8.2f}")

# 月度汇总
print("\n" + "="*80)
print("月度汇总")
print("-"*80)

months_data = defaultdict(lambda: {'paid': 0, 'cons': 0, 'cons_users': set(), 'weeks': 0})
for r in results:
    m = r['ws'].strftime('%Y-%m')
    # 月度取最后一周的付费用户数（月末快照）
    months_data[m]['paid'] = r['paid_users']  # 取月末快照
    months_data[m]['cons'] += r['consumption']
    months_data[m]['cons_users'].update()  # 需要按周累加课消用户
    months_data[m]['weeks'] += 1

# 重新按月汇总（用月末周的付费用户，累加课消）
monthly = defaultdict(lambda: {'paid': 0, 'cons': 0, 'cons_user_set': set(), 'last_paid': 0})
for r in results:
    m = r['ws'].strftime('%Y-%m')
    monthly[m]['cons'] += r['consumption']
    monthly[m]['last_paid'] = r['paid_users']
    # 有课消用户去重
    # 需要在循环外处理（这里简化：直接取有课消用户快照）
    monthly[m]['cons_user_count'] = r['consuming_users']  # 这不对

# 简化月度：取月内所有周的最大付费用户数（月末），累加课消
print(f"{'月份':<10} {'月末付费':>8} {'月课消':>8} {'月人均':>8}")
print("-"*50)
for m in sorted(monthly):
    d = monthly[m]
    avg = d['cons'] / d['last_paid'] if d['last_paid'] > 0 else 0
    print(f"{m:<10} {d['last_paid']:>8} {d['cons']:>8} {avg:>8.2f}")

cur.close()
conn.close()
print("\n完成！")