add SREBOT, SHARED, TSSBOT contents (fixup for #1223)
PR #1223 only staged the deletions of the old paths because the new top-level directories were still untracked when the commit was authored. This commit adds the actual restructured tree: SREBOT/ (existing bot), SHARED/ (vromfs, data_parser, ICONS/MAPS/FONTS, DAGOR_FILES, update_game_files), and TSSBOT/ (skeleton). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
FURRO404
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"""
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analytics.py
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SQB session analytics: map win rates, team composition analysis,
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player consistency, time-of-day performance, and opponent difficulty.
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"""
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# Standard Library Imports
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import json
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import logging
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import math
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import re
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from collections import Counter, defaultdict
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from datetime import datetime, timezone
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# Third-Party Library Imports
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import aiosqlite
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# Local Module Imports
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from .data_parser import get_unit_type_abbrev
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from .utils import SQ_BATTLES_DB_PATH, decompress_json
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async def get_map_stats(squadron_short: str, start_ts: int = 0) -> list[dict]:
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"""Win rate by map for a squadron.
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Returns list of dicts sorted by total games desc:
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[{map_name, wins, losses, total, win_rate}, ...]
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"""
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async with aiosqlite.connect(SQ_BATTLES_DB_PATH, timeout=30.0) as db:
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await db.execute("PRAGMA busy_timeout=30000;")
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rows = await db.execute_fetchall(
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"""SELECT map_name, winning_sq, losing_sq
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FROM match_summary
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WHERE (winning_sq = ? OR losing_sq = ?)
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AND endtime_unix >= ?""",
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(squadron_short, squadron_short, start_ts),
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)
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seen: dict[str, str] = {} # lowercase key -> first clean spelling
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def _normalize_map(raw: str) -> tuple[str, str]:
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clean = re.sub(r"^\s*\[[^\]]+\]\s*", "", raw).strip() or "Unknown"
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key = clean.lower()
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if key not in seen:
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seen[key] = clean
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return key, seen[key]
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stats: dict[str, dict] = {}
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for raw_name, winning_sq, losing_sq in rows:
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key, name = _normalize_map(raw_name or "Unknown")
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entry = stats.setdefault(key, {"map_name": name, "wins": 0, "losses": 0})
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if winning_sq == squadron_short:
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entry["wins"] += 1
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else:
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entry["losses"] += 1
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result = list(stats.values())
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for r in result:
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r["total"] = r["wins"] + r["losses"]
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r["win_rate"] = round(r["wins"] / r["total"] * 100, 1) if r["total"] else 0
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result.sort(key=lambda x: x["total"], reverse=True)
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return result
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async def get_comp_analysis(squadron_short: str, start_ts: int = 0) -> list[dict]:
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"""Vehicle type composition vs win rate.
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Parses winning_team_json/losing_team_json, classifies vehicles,
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builds comp signatures (e.g. "3T 1H 1F"), correlates with outcomes.
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Returns list sorted by usage:
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[{comp_signature, wins, losses, total, win_rate}, ...]
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"""
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async with aiosqlite.connect(SQ_BATTLES_DB_PATH, timeout=30.0) as db:
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await db.execute("PRAGMA busy_timeout=30000;")
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rows = await db.execute_fetchall(
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"""SELECT winning_sq, losing_sq, winning_team_json, losing_team_json
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FROM match_summary
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WHERE (winning_sq = ? OR losing_sq = ?)
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AND endtime_unix >= ?""",
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(squadron_short, squadron_short, start_ts),
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)
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stats: dict[str, dict] = {}
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for winning_sq, losing_sq, w_json, l_json in rows:
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is_win = winning_sq == squadron_short
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team_json = w_json if is_win else l_json
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if not team_json:
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continue
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try:
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team = decompress_json(team_json)
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except (json.JSONDecodeError, TypeError, OSError):
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continue
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# Classify vehicles
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type_counts: Counter = Counter()
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players = team.get("players", [])
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for p in players:
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veh_internal = p.get("vehicle", "")
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if not veh_internal or veh_internal == "DISCONNECTED":
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continue
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vtype = get_unit_type_abbrev(veh_internal)
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type_counts[vtype] += 1
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if not type_counts:
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continue
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# Build signature: sorted by count desc, then alpha
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sig_parts = sorted(type_counts.items(), key=lambda x: (-x[1], x[0]))
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signature = " ".join(f"{count}{abbr}" for abbr, count in sig_parts)
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entry = stats.setdefault(signature, {"comp_signature": signature, "wins": 0, "losses": 0})
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if is_win:
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entry["wins"] += 1
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else:
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entry["losses"] += 1
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result = list(stats.values())
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for r in result:
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r["total"] = r["wins"] + r["losses"]
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r["win_rate"] = round(r["wins"] / r["total"] * 100, 1) if r["total"] else 0
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result.sort(key=lambda x: x["total"], reverse=True)
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return result[:20]
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async def get_player_consistency(squadron_short: str, min_games: int = 50) -> list[dict]:
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"""Per-player performance variance.
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Returns list sorted by most recently played:
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[{uid, nick, avg_kills, std_kills, avg_deaths, std_deaths, games, consistency_score, last_played}, ...]
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"""
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async with aiosqlite.connect(SQ_BATTLES_DB_PATH, timeout=30.0) as db:
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await db.execute("PRAGMA busy_timeout=30000;")
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# squadron_name in player_games_hist stores the short name (e.g. "DSPL")
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rows = await db.execute_fetchall(
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"""SELECT UID, nick,
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SUM(ground_kills + air_kills) as total_kills,
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SUM(deaths) as total_deaths,
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session_id,
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MAX(endtime_unix) as last_played
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FROM player_games_hist
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WHERE squadron_name = ?
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GROUP BY UID, session_id""",
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(squadron_short,),
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)
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# Aggregate per-player
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player_data: dict[str, dict] = {}
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for uid, nick, kills, deaths, session_id, last_played in rows:
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entry = player_data.setdefault(uid, {"uid": uid, "nick": nick, "kills": [], "deaths": [], "last_played": 0})
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entry["nick"] = nick # keep latest nick
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entry["kills"].append(kills or 0)
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entry["deaths"].append(deaths or 0)
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entry["last_played"] = max(entry["last_played"], last_played or 0)
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result = []
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for uid, data in player_data.items():
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games = len(data["kills"])
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if games < min_games:
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continue
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avg_kills = sum(data["kills"]) / games
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avg_deaths = sum(data["deaths"]) / games
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std_kills = math.sqrt(sum((k - avg_kills) ** 2 for k in data["kills"]) / games)
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std_deaths = math.sqrt(sum((d - avg_deaths) ** 2 for d in data["deaths"]) / games)
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# Consistency score: lower is more consistent (normalized std dev)
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consistency = (std_kills + std_deaths) / max(avg_kills + avg_deaths, 1)
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result.append({
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"uid": uid,
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"nick": data["nick"],
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"avg_kills": round(avg_kills, 2),
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"std_kills": round(std_kills, 2),
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"avg_deaths": round(avg_deaths, 2),
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"std_deaths": round(std_deaths, 2),
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"games": games,
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"consistency_score": round(consistency, 3),
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"last_played": data["last_played"],
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})
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result.sort(key=lambda x: x["avg_kills"] / max(x["avg_deaths"], 0.01), reverse=True)
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return result[:128]
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async def get_matchup_history(squadron_short: str, start_ts: int = 0) -> dict:
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"""Top opponents this squadron has won against and lost to.
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Returns:
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{
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"won_against": [{opponent, wins, losses, total, win_rate}, ...up to 10],
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"lost_against": [{opponent, wins, losses, total, win_rate}, ...up to 10],
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"total_opponents": int,
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}
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"""
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async with aiosqlite.connect(SQ_BATTLES_DB_PATH, timeout=30.0) as db:
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await db.execute("PRAGMA busy_timeout=30000;")
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rows = await db.execute_fetchall(
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"""SELECT winning_sq, losing_sq
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FROM match_summary
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WHERE (winning_sq = ? OR losing_sq = ?)
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AND endtime_unix >= ?""",
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(squadron_short, squadron_short, start_ts),
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)
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stats: dict[str, dict] = {}
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for winning_sq, losing_sq in rows:
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if not winning_sq or not losing_sq or winning_sq == losing_sq:
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continue
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opponent = losing_sq if winning_sq == squadron_short else winning_sq
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if not opponent:
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continue
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entry = stats.setdefault(opponent, {"opponent": opponent, "wins": 0, "losses": 0})
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if winning_sq == squadron_short:
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entry["wins"] += 1
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else:
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entry["losses"] += 1
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enriched = []
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for r in stats.values():
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r["total"] = r["wins"] + r["losses"]
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r["win_rate"] = round(r["wins"] / r["total"] * 100, 1) if r["total"] else 0
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enriched.append(r)
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won_against = sorted(enriched, key=lambda x: (x["wins"], x["total"]), reverse=True)[:10]
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lost_against = sorted(enriched, key=lambda x: (x["losses"], x["total"]), reverse=True)[:10]
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return {
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"won_against": won_against,
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"lost_against": lost_against,
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"total_opponents": len(enriched),
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}
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async def get_time_performance(squadron_short: str) -> dict:
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"""Win rate bucketed by hour of day (UTC).
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Returns dict keyed by hour (0-23):
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{hour: {wins, losses, total, win_rate}, ...}
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"""
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async with aiosqlite.connect(SQ_BATTLES_DB_PATH, timeout=30.0) as db:
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await db.execute("PRAGMA busy_timeout=30000;")
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rows = await db.execute_fetchall(
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"""SELECT endtime_unix, winning_sq, losing_sq
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FROM match_summary
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WHERE (winning_sq = ? OR losing_sq = ?)
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AND endtime_unix > 0""",
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(squadron_short, squadron_short),
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)
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stats: dict[int, dict] = {}
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for endtime_unix, winning_sq, losing_sq in rows:
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hour = datetime.fromtimestamp(endtime_unix, tz=timezone.utc).hour
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entry = stats.setdefault(hour, {"wins": 0, "losses": 0})
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if winning_sq == squadron_short:
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entry["wins"] += 1
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else:
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entry["losses"] += 1
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result = {}
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for hour in sorted(stats.keys()):
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e = stats[hour]
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total = e["wins"] + e["losses"]
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result[hour] = {
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"wins": e["wins"],
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"losses": e["losses"],
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"total": total,
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"win_rate": round(e["wins"] / total * 100, 1) if total else 0,
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}
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return result
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