70 lines
5.3 KiB
Python
70 lines
5.3 KiB
Python
"""Read-only checks and a compact report for the generated sample histories."""
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import math
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from database import connect
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from health_reports import window_metrics
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from datetime import timedelta
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def report():
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with connect() as conn:
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for game in conn.execute("SELECT id,name,sample_key,properties FROM games WHERE properties->>'sampleVersion'='3' ORDER BY id").fetchall():
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gid=game['id']
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summary=conn.execute('''WITH counts AS(SELECT player_id,count(*) n FROM sessions WHERE game_id=%s GROUP BY player_id)
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SELECT count(*) players,100.0*count(*) FILTER(WHERE n=1)/count(*) one_session FROM counts''',(gid,)).fetchone()
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minutes=conn.execute('SELECT avg(extract(epoch FROM ended_at-started_at))/60 minutes FROM sessions WHERE game_id=%s',(gid,)).fetchone()['minutes']
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returns=[]
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for day in [1,7,30]:
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row=conn.execute('''SELECT 100.0*count(*) FILTER(WHERE EXISTS(SELECT 1 FROM sessions s
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WHERE s.game_id=p.game_id AND s.player_id=p.id AND (s.started_at AT TIME ZONE 'UTC')::date=(p.first_seen AT TIME ZONE 'UTC')::date+%s)) / nullif(count(*),0) rate
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FROM players p WHERE p.game_id=%s AND (p.first_seen AT TIME ZONE 'UTC')::date+%s<current_date''',(day,gid,day)).fetchone()
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returns.append(float(row['rate']))
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spending=conn.execute('''SELECT player_id,sum(unit_price_robux*quantity) total FROM product_sales
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WHERE game_id=%s AND unit_price_robux>0 GROUP BY player_id ORDER BY total DESC''',(gid,)).fetchall()
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total=sum(r['total'] for r in spending)
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top_share=100*sum(r['total'] for r in spending[:max(1,math.ceil(len(spending)*.01))])/total
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bad_sales=conn.execute('''SELECT count(*) n FROM product_sales p JOIN sessions s ON s.game_id=p.game_id AND s.id=p.session_id
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WHERE p.game_id=%s AND (p.purchased_at<s.started_at OR p.purchased_at>s.ended_at)''',(gid,)).fetchone()['n']
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overlaps=conn.execute('''WITH ordered AS(SELECT started_at,lag(ended_at) OVER(PARTITION BY player_id ORDER BY started_at) previous_end
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FROM sessions WHERE game_id=%s) SELECT count(*) n FROM ordered WHERE started_at<previous_end''',(gid,)).fetchone()['n']
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bad_balances=conn.execute('''SELECT count(*) n FROM player_snapshots WHERE game_id=%s AND data ? 'cash'
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AND (data->>'cash')::numeric<>(data->>'cashEarned')::numeric-(data->>'cashSpent')::numeric''',(gid,)).fetchone()['n']
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bad_snapshots=conn.execute('''SELECT count(*) n FROM player_statistics WHERE game_id=%s AND latest_data IS NOT NULL
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AND ((latest_data->>'robux')::numeric<>robux_recorded OR (latest_data->>'purchases')::numeric<>products_bought)''',(gid,)).fetchone()['n']
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assert returns[0]>returns[1]>returns[2], (game,returns)
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assert 3<float(minutes)<30, (game,minutes)
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assert summary['one_session']>40, (game,summary)
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assert len(spending)<summary['players']*.1, (game,len(spending))
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assert top_share>5, (game,top_share)
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assert bad_sales==0, (game,bad_sales)
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assert overlaps==0, (game,overlaps)
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assert bad_balances==0, (game,bad_balances)
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assert bad_snapshots==0, (game,bad_snapshots)
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today=conn.execute('SELECT current_date today').fetchone()['today']
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current=window_metrics(conn,gid,today-timedelta(days=7),today)
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previous=window_metrics(conn,gid,today-timedelta(days=14),today-timedelta(days=7))
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audience_ratio=current['average_dau']/previous['average_dau']
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ccu_ratio=current['average_ccu']/previous['average_ccu']
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scenario=game['properties']['sampleScenario']
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if scenario=='stable':
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assert .80<audience_ratio<1.20,(game['name'],audience_ratio)
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assert .70<ccu_ratio<1.30,(game['name'],ccu_ratio)
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elif scenario=='declining':
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assert audience_ratio<.80,(game['name'],audience_ratio)
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assert ccu_ratio<.80,(game['name'],ccu_ratio)
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else:
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assert audience_ratio>2,(game['name'],audience_ratio)
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assert ccu_ratio>2,(game['name'],ccu_ratio)
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groups=conn.execute("SELECT metadata->>'simulationGroup' persona,count(*) n FROM players WHERE game_id=%s GROUP BY persona",(gid,)).fetchall()
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assert len(groups)==8,(game['name'],groups)
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grinder_spend=conn.execute("SELECT coalesce(sum(s.unit_price_robux*s.quantity),0) robux FROM product_sales s JOIN players p ON p.game_id=s.game_id AND p.id=s.player_id WHERE s.game_id=%s AND p.metadata->>'simulationGroup'='grinder'",(gid,)).fetchone()['robux']
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assert grinder_spend==0,(game['name'],grinder_spend)
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print(f" Scenario {scenario}: DAU {100*(audience_ratio-1):+.1f}%, CCU {100*(ccu_ratio-1):+.1f}% week-on-week; eight personalities present.")
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print(f"{game['name']}: {summary['players']:,} players | one session {summary['one_session']:.1f}% | average session {minutes:.1f} min")
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print(f" D1/D7/D30: {' / '.join(f'{r:.2f}%' for r in returns)} | payers {len(spending):,} ({100*len(spending)/summary['players']:.1f}%) | top 1% payer revenue share {top_share:.1f}%")
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print('Sample distribution and timeline checks passed.')
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if __name__=='__main__':
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report()
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