#!/usr/bin/env python3
"""V4 results analysis: errors, execution modes, latency by mode."""
import json
import numpy as np
from pathlib import Path
from collections import Counter

BASE = Path(__file__).parent

def load_rows(jsonl_path):
    rows = []
    with open(jsonl_path) as f:
        for line in f:
            rows.append(json.loads(line))
    return rows

for name, path in [
    ("Exp1 1P7D cap=16", BASE / "exp1_1p7d_kvc_cap16_metrics.jsonl"),
    ("Exp2 2P6D cap=16", BASE / "exp2_2p6d_kvc_cap16_metrics.jsonl"),
]:
    rows = load_rows(path)
    print(f"\n========== {name} ==========")
    ok = [r for r in rows if r.get("error") is None]
    err = [r for r in rows if r.get("error") is not None]
    print(f"Total: {len(rows)}, OK: {len(ok)}, Errors: {len(err)}")

    # Errors finish_reason
    if err:
        finish_reasons = Counter()
        for r in err:
            fr = str(r.get("finish_reason") or r.get("error") or "?")
            # Truncate long messages
            short = fr[:120]
            finish_reasons[short] += 1
        print(f"\nError finish_reasons (top 5):")
        for fr, cnt in finish_reasons.most_common(5):
            print(f"  {cnt}x: {fr}")

    # Execution mode latency breakdown
    modes = Counter(r["execution_mode"] for r in ok)
    print(f"\nTop execution modes by latency:")
    print(f"{'mode':<55}{'n':<8}{'%':<8}{'P50 lat':<10}{'P90 lat':<10}{'TTFT P50':<10}")
    for mode, count in modes.most_common(8):
        mode_rows = [r for r in ok if r["execution_mode"] == mode]
        lats = [r["latency_s"] for r in mode_rows]
        ttfts = [r["ttft_s"] for r in mode_rows]
        print(f"  {mode:<53}{count:<8}{count/len(ok)*100:>5.1f}%  {np.percentile(lats,50):>7.3f}s  {np.percentile(lats,90):>7.3f}s  {np.percentile(ttfts,50):>7.3f}s")

    # Per-D load
    per_d = Counter(r.get("assigned_decode_node", "?") for r in ok)
    print(f"\nPer-D load: max/min ratio = {max(per_d.values())/max(min(per_d.values()),1):.2f}x")
    print(f"  {dict(per_d.most_common())}")