108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
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import os
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import json
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from tags.objective_payload import compute_payload_objective_team_scores
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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CAREER_DB_PATH = os.path.join(BASE_DIR, "career_stats.json")
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def load_career_db():
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if not os.path.exists(CAREER_DB_PATH):
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return None
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with open(CAREER_DB_PATH, "r", encoding="utf-8") as f:
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return json.load(f)
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def top_players_by_tag(tag_name):
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db = load_career_db()
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if db is None:
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return []
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players = db["players"]
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league = db["league_averages"]
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results = []
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if tag_name == "Slayer":
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for pid, p in players.items():
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score = p.get("slayer_score_raw", 0.0)
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if score <= 0:
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continue
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results.append({
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"id": pid,
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"name": p["name"],
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"team": p["team_history"][-1] if p["team_history"] else "Unknown",
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"score_raw": score,
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"score_display": p.get("slayer_score_display", f"{score:.2f}"),
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})
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results = [r for r in results if r["score_raw"] > 0]
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results.sort(key=lambda x: x["score_raw"], reverse=True)
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return results
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if tag_name == "Payload Objective Specialist":
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# For rankings, we approximate team context by using league‑wide push components
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# and treat all players as if they were on one "virtual team".
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team_entries = [(pid, p) for pid, p in players.items()]
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obj_scores = compute_payload_objective_team_scores(team_entries)
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for pid, p in players.items():
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score = obj_scores.get(pid, 0.0)
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if score <= 0:
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continue
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results.append({
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"id": pid,
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"name": p["name"],
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"team": p["team_history"][-1] if p["team_history"] else "Unknown",
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"score_raw": score,
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"score_display": f"{score:.2f}",
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})
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results = [r for r in results if r["score_raw"] > 0]
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results.sort(key=lambda x: x["score_raw"], reverse=True)
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return results
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return []
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def rank_match_players_consistency(players):
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ranked = []
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for p in players:
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components = p.get("consistency_components", {})
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# Prefer normalized score if present, else raw consistency, else 0.0
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score = components.get("consistency_norm")
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if score is None:
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score = components.get("consistency", 0.0)
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ranked.append({
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"name": p.get("name", "Unknown"),
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"team": p.get("team", ""),
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"score_raw": score,
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"score_display": f"{score:.2f}",
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})
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ranked.sort(key=lambda x: x["score_raw"], reverse=True)
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for i, r in enumerate(ranked, start=1):
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r["rank"] = i
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return ranked
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def rank_match_players_clutch(players):
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ranked = []
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for p in players:
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score = p.get("clutch_components", {}).get("clutch_norm", 0.0)
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ranked.append({
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"name": p.get("name", "Unknown"),
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"team": p.get("team", ""),
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"score_raw": score,
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"score_display": f"{score:.2f}"
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})
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ranked.sort(key=lambda x: x["score_raw"], reverse=True)
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for i, r in enumerate(ranked, start=1):
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r["rank"] = i
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return ranked
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