257 lines
12 KiB
Python
257 lines
12 KiB
Python
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from web.database import query_db
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class FeatureService:
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@staticmethod
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def get_player_features(steam_id):
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sql = "SELECT * FROM dm_player_features WHERE steam_id_64 = ?"
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return query_db('l3', sql, [steam_id], one=True)
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@staticmethod
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def get_players_list(page=1, per_page=20, sort_by='rating', search=None):
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offset = (page - 1) * per_page
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# Sort Mapping
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sort_map = {
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'rating': 'basic_avg_rating',
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'kd': 'basic_avg_kd',
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'kast': 'basic_avg_kast',
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'matches': 'matches_played'
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}
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order_col = sort_map.get(sort_by, 'basic_avg_rating')
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from web.services.stats_service import StatsService
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# Helper to attach match counts
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def attach_match_counts(player_list):
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if not player_list:
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return
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ids = [p['steam_id_64'] for p in player_list]
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# Batch query for counts from L2
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placeholders = ','.join('?' for _ in ids)
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sql = f"""
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SELECT steam_id_64, COUNT(*) as cnt
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FROM fact_match_players
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WHERE steam_id_64 IN ({placeholders})
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GROUP BY steam_id_64
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"""
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counts = query_db('l2', sql, ids)
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cnt_dict = {r['steam_id_64']: r['cnt'] for r in counts}
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for p in player_list:
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p['matches_played'] = cnt_dict.get(p['steam_id_64'], 0)
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if search:
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# ... existing search logic ...
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# Get all matching players
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l2_players, _ = StatsService.get_players(page=1, per_page=100, search=search)
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if not l2_players:
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return [], 0
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# ... (Merge logic) ...
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# I need to insert the match count logic inside the merge loop or after
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steam_ids = [p['steam_id_64'] for p in l2_players]
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placeholders = ','.join('?' for _ in steam_ids)
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sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({placeholders})"
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features = query_db('l3', sql, steam_ids)
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f_dict = {f['steam_id_64']: f for f in features}
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# Get counts for sorting
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count_sql = f"SELECT steam_id_64, COUNT(*) as cnt FROM fact_match_players WHERE steam_id_64 IN ({placeholders}) GROUP BY steam_id_64"
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counts = query_db('l2', count_sql, steam_ids)
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cnt_dict = {r['steam_id_64']: r['cnt'] for r in counts}
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merged = []
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for p in l2_players:
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f = f_dict.get(p['steam_id_64'])
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m = dict(p)
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if f:
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m.update(dict(f))
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else:
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# Fallback Calc
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stats = StatsService.get_player_basic_stats(p['steam_id_64'])
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if stats:
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m['basic_avg_rating'] = stats['rating']
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m['basic_avg_kd'] = stats['kd']
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m['basic_avg_kast'] = stats['kast']
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else:
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m['basic_avg_rating'] = 0
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m['basic_avg_kd'] = 0
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m['basic_avg_kast'] = 0 # Ensure kast exists
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m['matches_played'] = cnt_dict.get(p['steam_id_64'], 0)
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merged.append(m)
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merged.sort(key=lambda x: x.get(order_col, 0) or 0, reverse=True)
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total = len(merged)
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start = (page - 1) * per_page
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end = start + per_page
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return merged[start:end], total
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else:
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# Browse mode
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# Check L3
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l3_count = query_db('l3', "SELECT COUNT(*) as cnt FROM dm_player_features", one=True)['cnt']
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if l3_count == 0 or sort_by == 'matches':
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# If sorting by matches, we MUST use L2 counts because L3 might not have it or we want dynamic.
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# OR if L3 is empty.
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# Since L3 schema is unknown regarding 'matches_played', let's assume we fallback to L2 logic
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# but paginated in memory if dataset is small, or just fetch all L2 players?
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# Fetching all L2 players is bad if many.
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# But for 'matches' sort, we need to know counts for ALL to sort correctly.
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# Solution: Query L2 for top N players by match count.
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if sort_by == 'matches':
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# Query L2 for IDs ordered by count
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sql = """
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SELECT steam_id_64, COUNT(*) as cnt
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FROM fact_match_players
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GROUP BY steam_id_64
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ORDER BY cnt DESC
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LIMIT ? OFFSET ?
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"""
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top_ids = query_db('l2', sql, [per_page, offset])
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if not top_ids:
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return [], 0
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total = query_db('l2', "SELECT COUNT(DISTINCT steam_id_64) as cnt FROM fact_match_players", one=True)['cnt']
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ids = [r['steam_id_64'] for r in top_ids]
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# Fetch details for these IDs
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l2_players = StatsService.get_players_by_ids(ids)
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# Merge logic (reuse)
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merged = []
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# Fetch L3 features for these IDs to show stats
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p_ph = ','.join('?' for _ in ids)
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f_sql = f"SELECT * FROM dm_player_features WHERE steam_id_64 IN ({p_ph})"
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features = query_db('l3', f_sql, ids)
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f_dict = {f['steam_id_64']: f for f in features}
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cnt_dict = {r['steam_id_64']: r['cnt'] for r in top_ids}
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# Map L2 players to dict for easy access (though list order matters for sort?)
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# Actually top_ids is sorted.
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p_dict = {p['steam_id_64']: p for p in l2_players}
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for r in top_ids: # Preserve order
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sid = r['steam_id_64']
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p = p_dict.get(sid)
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if not p: continue
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m = dict(p)
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f = f_dict.get(sid)
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if f:
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m.update(dict(f))
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else:
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stats = StatsService.get_player_basic_stats(sid)
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if stats:
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m['basic_avg_rating'] = stats['rating']
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m['basic_avg_kd'] = stats['kd']
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m['basic_avg_kast'] = stats['kast']
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else:
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m['basic_avg_rating'] = 0
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m['basic_avg_kd'] = 0
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m['basic_avg_kast'] = 0
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m['matches_played'] = r['cnt']
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merged.append(m)
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return merged, total
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# L3 empty fallback (existing logic)
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l2_players, total = StatsService.get_players(page, per_page, sort_by=None)
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merged = []
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attach_match_counts(l2_players) # Helper
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for p in l2_players:
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m = dict(p)
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stats = StatsService.get_player_basic_stats(p['steam_id_64'])
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if stats:
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m['basic_avg_rating'] = stats['rating']
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m['basic_avg_kd'] = stats['kd']
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m['basic_avg_kast'] = stats['kast']
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else:
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m['basic_avg_rating'] = 0
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m['basic_avg_kd'] = 0
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m['basic_avg_kast'] = 0
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m['matches_played'] = p.get('matches_played', 0)
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merged.append(m)
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if sort_by != 'rating':
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merged.sort(key=lambda x: x.get(order_col, 0) or 0, reverse=True)
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return merged, total
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# Normal L3 browse (sort by rating/kd/kast)
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sql = f"SELECT * FROM dm_player_features ORDER BY {order_col} DESC LIMIT ? OFFSET ?"
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features = query_db('l3', sql, [per_page, offset])
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total = query_db('l3', "SELECT COUNT(*) as cnt FROM dm_player_features", one=True)['cnt']
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if not features:
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return [], total
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steam_ids = [f['steam_id_64'] for f in features]
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l2_players = StatsService.get_players_by_ids(steam_ids)
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p_dict = {p['steam_id_64']: p for p in l2_players}
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merged = []
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for f in features:
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m = dict(f)
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p = p_dict.get(f['steam_id_64'])
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if p:
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m.update(dict(p))
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else:
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m['username'] = f['steam_id_64'] # Fallback
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m['avatar_url'] = None
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merged.append(m)
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return merged, total
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@staticmethod
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def get_top_players(limit=20, sort_by='basic_avg_rating'):
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# Safety check for sort_by to prevent injection
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allowed_sorts = ['basic_avg_rating', 'basic_avg_kd', 'basic_avg_kast', 'basic_avg_rws']
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if sort_by not in allowed_sorts:
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sort_by = 'basic_avg_rating'
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sql = f"""
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SELECT f.*, p.username, p.avatar_url
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FROM dm_player_features f
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LEFT JOIN l2.dim_players p ON f.steam_id_64 = p.steam_id_64
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ORDER BY {sort_by} DESC
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LIMIT ?
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"""
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# Note: Cross-database join (l2.dim_players) works in SQLite if attached.
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# But `query_db` connects to one DB.
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# Strategy: Fetch features, then fetch player infos from L2. Or attach DB.
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# Simple strategy: Fetch features, then extract steam_ids and batch fetch from L2 in StatsService.
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# Or simpler: Just return features and let the controller/template handle the name/avatar via another call or pre-fetching.
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# Actually, for "Player List" view, we really want L3 data joined with L2 names.
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# I will change this to just return features for now, and handle joining in the route handler or via a helper that attaches databases.
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# Attaching is better.
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return query_db('l3', f"SELECT * FROM dm_player_features ORDER BY {sort_by} DESC LIMIT ?", [limit])
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@staticmethod
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def get_player_trend(steam_id, limit=30):
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# This requires `fact_match_features` or querying L2 matches for historical data.
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# WebRDD says: "Trend graph: Recent 10/20 matches Rating trend (Chart.js)."
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# We can get this from L2 fact_match_players.
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sql = """
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SELECT m.start_time, mp.rating, mp.kd_ratio, mp.adr, m.match_id
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FROM fact_match_players mp
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JOIN fact_matches m ON mp.match_id = m.match_id
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WHERE mp.steam_id_64 = ?
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ORDER BY m.start_time DESC
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LIMIT ?
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"""
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# This query needs to run against L2.
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# So this method should actually be in StatsService or FeatureService connecting to L2.
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# I will put it here but note it uses L2. Actually, better to put in StatsService if it uses L2 tables.
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# But FeatureService conceptualizes "Trends". I'll move it to StatsService for implementation correctness (DB context).
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pass
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