248 lines
9.1 KiB
Python
248 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""
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生成 4 张课消图表（剔除U0序章）：
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1. L1 付费用户课消分布（堆叠柱状图）
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2. L2 付费用户课消分布（堆叠柱状图）
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3. L1 周人均课消趋势（折线图）
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4. L2 周人均课消趋势（折线图）
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"""
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import psycopg2
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from collections import defaultdict
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from datetime import datetime, timedelta, date
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import matplotlib.ticker as ticker
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import numpy as np
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# 中文字体
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import matplotlib.font_manager as fm
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font_path = '/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'
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fm.fontManager.addfont(font_path)
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prop = fm.FontProperties(fname=font_path)
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font_name = prop.get_name()
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plt.rcParams['font.family'] = font_name
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plt.rcParams['axes.unicode_minus'] = False
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print(f'使用字体: {font_name}')
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conn = psycopg2.connect(
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host="bj-postgres-16pob4sg.sql.tencentcdb.com",
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port=28591, user="ai_member",
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password="LdfjdjL83h3h3^$&**YGG*", dbname="vala_bi"
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)
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cur = conn.cursor()
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# ===== 配置 =====
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u0_chapters = {55, 56, 57, 58, 59, 343, 344, 345, 346, 348}
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overall_start = date(2025, 9, 1)
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overall_end = date(2026, 5, 11)
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weeks = []
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d = overall_start
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while d < overall_end:
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ws = d
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we = d + timedelta(days=6 - d.weekday())
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if we >= overall_end:
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we = overall_end - timedelta(days=1)
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weeks.append((ws, we))
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d = we + timedelta(days=1)
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# ===== 用户分类 =====
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print("分类付费用户...")
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cur.execute("""
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SELECT o.account_id, o.trade_no, o.order_status, o.pay_success_date,
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CASE WHEN o.goods_id IN (57, 60, 63) THEN 'L1'
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WHEN o.goods_id = 61 THEN 'L1+L2'
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WHEN o.goods_id IN (31, 32, 33, 54) THEN 'L2'
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ELSE '其他' END as level_type
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FROM bi_vala_order o
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INNER JOIN bi_vala_app_account a ON o.account_id = a.id
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WHERE a.status = 1 AND a.deleted_at IS NULL AND o.pay_success_date IS NOT NULL
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""")
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orders = cur.fetchall()
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cur.execute("SELECT trade_no FROM bi_refund_order WHERE status = 3")
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refund_trades = set(r[0] for r in cur.fetchall())
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user_data = defaultdict(lambda: {'levels': set(), 'orders': []})
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for aid, trade_no, order_status, pay_date, lt in orders:
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is_refunded = (order_status == 4 and trade_no in refund_trades)
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user_data[aid]['levels'].add(lt)
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user_data[aid]['orders'].append((pay_date.date(), is_refunded, lt))
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def classify(levels):
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h1, h2 = 'L1' in levels, 'L2' in levels
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return 'L1+L2' if ('L1+L2' in levels or (h1 and h2)) else ('仅L1' if h1 else ('仅L2' if h2 else '其他'))
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for aid in user_data:
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user_data[aid]['category'] = classify(user_data[aid]['levels'])
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def is_paid(aid, as_of):
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return sum(1 for pd, ref, lt in user_data[aid]['orders'] if pd <= as_of and not ref) > 0
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# ===== 课消 =====
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print("查询课消...")
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cons_map = {}
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for table_idx in range(8):
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tbl = f"bi_user_chapter_play_record_{table_idx}"
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cur.execute(f"""
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SELECT user_id, chapter_id, updated_at
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FROM {tbl}
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WHERE play_status = 1 AND updated_at >= '2025-09-01' AND updated_at < '2026-05-11'
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""")
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for uid, cid, ua in cur.fetchall():
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if cid in u0_chapters: continue
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key = (uid, cid)
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d = ua.date() if hasattr(ua, 'date') else datetime.strptime(str(ua)[:10], '%Y-%m-%d').date()
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if key not in cons_map or d < cons_map[key]:
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cons_map[key] = d
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# 角色映射
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print("角色映射...")
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all_uids = list(set(k[0] for k in cons_map))
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char2acct = {}
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bs = 500
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for i in range(0, len(all_uids), bs):
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batch = all_uids[i:i+bs]
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ph = ','.join(['%s'] * len(batch))
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cur.execute(f"SELECT id, account_id FROM bi_vala_app_character WHERE id IN ({ph})", batch)
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for cid, aid in cur.fetchall():
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char2acct[cid] = aid
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# ===== 按周汇总 =====
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print("按周汇总...")
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results = []
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for ws, we in weeks:
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paid_by_cat = defaultdict(set)
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for aid in user_data:
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if is_paid(aid, we):
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paid_by_cat[user_data[aid]['category']].add(aid)
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cons_by_cat = defaultdict(int)
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cons_users_by_cat = defaultdict(set)
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for (uid, ch_id), cons_date in cons_map.items():
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if ws <= cons_date <= we:
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aid = char2acct.get(uid)
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if aid:
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cat = user_data.get(aid, {}).get('category', '其他')
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if aid in paid_by_cat.get(cat, set()):
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cons_by_cat[cat] += 1
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cons_users_by_cat[cat].add(aid)
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row = {'ws': ws, 'we': we}
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for cat in ['仅L1', '仅L2', 'L1+L2']:
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n_paid = len(paid_by_cat.get(cat, set()))
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n_cons = cons_by_cat.get(cat, 0)
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n_cons_users = len(cons_users_by_cat.get(cat, set()))
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row[f'{cat}_paid'] = n_paid
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row[f'{cat}_cons'] = n_cons
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row[f'{cat}_cons_users'] = n_cons_users
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row[f'{cat}_no_cons'] = n_paid - n_cons_users
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row[f'{cat}_avg_all'] = round(n_cons / n_paid, 2) if n_paid > 0 else 0
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row[f'{cat}_avg_cons'] = round(n_cons / n_cons_users, 2) if n_cons_users > 0 else 0
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results.append(row)
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cur.close()
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conn.close()
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# ===== 图表生成 =====
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print("\n生成图表...")
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output_dir = '/root/.openclaw/workspace/output'
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configs = {
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'L1': {'cat': '仅L1', 'color': '#4A90D9', 'light': '#A8CFF1', 'label': 'L1'},
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'L2': {'cat': '仅L2', 'color': '#E85D47', 'light': '#F4A9A0', 'label': 'L2'},
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}
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for key, cfg in configs.items():
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cat = cfg['cat']
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color = cfg['color']
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light = cfg['light']
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label = cfg['label']
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# 过滤无数据周
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first = next(i for i, r in enumerate(results) if r[f'{cat}_paid'] > 0)
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data = results[first:]
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xs = [r['ws'] + timedelta(days=3) for r in data]
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labels = [r['ws'].strftime('%m/%d') for r in data]
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paid = [r[f'{cat}_paid'] for r in data]
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cons_users = [r[f'{cat}_cons_users'] for r in data]
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no_cons = [r[f'{cat}_no_cons'] for r in data]
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avg_all = [r[f'{cat}_avg_all'] for r in data]
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avg_cons = [r[f'{cat}_avg_cons'] for r in data]
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# --- 图1: 堆叠柱状图 ---
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fig, ax = plt.subplots(figsize=(18, 8))
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x_idx = np.arange(len(xs))
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bar_w = 0.65
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p1 = ax.bar(x_idx, cons_users, bar_w, color=light, label='有课消用户', zorder=3)
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p2 = ax.bar(x_idx, no_cons, bar_w, bottom=cons_users, color='#D0D0D0', label='无课消用户', zorder=3)
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# 标注付费总数
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for i, (p, c, n) in enumerate(zip(paid, cons_users, no_cons)):
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if i % max(1, len(data)//12) == 0:
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ax.annotate(str(p), (i, p), textcoords='offset points', xytext=(0, 6),
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fontsize=8, ha='center', color='#333333', fontweight='bold')
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ax.set_xticks(x_idx[::max(1, len(data)//12)])
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ax.set_xticklabels([labels[i] for i in range(0, len(data), max(1, len(data)//12))], fontsize=9, rotation=45)
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ax.set_ylabel('用户数', fontsize=13)
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ax.set_title(f'{label} 付费用户周课消分布（剔除U0序章）', fontsize=16, fontweight='bold')
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ax.legend(fontsize=12, loc='upper left')
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ax.grid(axis='y', alpha=0.3, zorder=0)
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ax.set_xlim(-0.5, len(x_idx) - 0.5)
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# 无消率标注
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no_rate = no_cons[-1] / paid[-1] * 100 if paid[-1] else 0
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ax.text(0.97, 0.95, f'无课消率: {no_rate:.0f}%', transform=ax.transAxes,
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fontsize=11, ha='right', va='top', color='#999999', fontstyle='italic')
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plt.tight_layout()
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path1 = f'{output_dir}/{key}_users_stack.png'
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plt.savefig(path1, dpi=150, bbox_inches='tight', facecolor='white')
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plt.close()
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print(f' ✅ {path1}')
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# --- 图2: 折线图 ---
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fig, ax = plt.subplots(figsize=(18, 8))
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ax.plot(xs, avg_all, 'o-', color='#999999', linewidth=2.2, markersize=5,
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label='周人均课消（全部付费用户）', linestyle='--', markerfacecolor='white')
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ax.plot(xs, avg_cons, 's-', color=color, linewidth=2.8, markersize=5,
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label='周有消人均课消', markerfacecolor='white')
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# 填色区域
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ax.fill_between(xs, avg_all, avg_cons, alpha=0.08, color=color)
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# 标注关键数据点
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for i in range(len(xs)):
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if i % max(1, len(data)//8) == 0:
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ax.annotate(f'{avg_all[i]:.1f}', (xs[i], avg_all[i]), textcoords='offset points',
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xytext=(0, -16), fontsize=7.5, color='#999999', ha='center')
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ax.annotate(f'{avg_cons[i]:.1f}', (xs[i], avg_cons[i]), textcoords='offset points',
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xytext=(0, 8), fontsize=7.5, color=color, ha='center', fontweight='bold')
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
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ax.xaxis.set_major_locator(mdates.MonthLocator())
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45, fontsize=9)
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ax.set_ylabel('课消数（节/周）', fontsize=13)
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ax.set_title(f'{label} 周人均课消趋势（剔除U0序章）', fontsize=16, fontweight='bold')
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ax.legend(fontsize=12, loc='upper left')
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ax.grid(True, alpha=0.3)
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ax.set_xlim(date(2025, 8, 30), date(2026, 5, 12))
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plt.tight_layout()
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path2 = f'{output_dir}/{key}_avg_trend.png'
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plt.savefig(path2, dpi=150, bbox_inches='tight', facecolor='white')
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plt.close()
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print(f' ✅ {path2}')
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print('\n全部 4 张图表已生成！')
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