#!/usr/bin/env python3
"""Summarise Suramadu's per-frame stats from a running match's container log.

    scripts/perf/stats.py [HH:MM:SS] [--session NAME]

Suramadu logs one `stats,<session>,<key>,<value>` line per metric per frame when
allowStatisticsLogging is on, which suramadu.config.template sets. Those lines are
the only per-frame server-side numbers there are, and reading them by eye does not
scale past a few frames.

The percentiles matter more than the medians here. A board repaint is rare and
huge; a cursor move is constant and small. Anything that reports only a median
will say a 16-unit match performs like a 4-unit one, which is false - see
PERFORMANCE.md, "How a match scales with units on the board".
"""

import argparse
import collections
import re
import statistics
import subprocess
import sys


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "since",
        nargs="?",
        default="00:00:00",
        help="only lines at or after this UTC HH:MM:SS",
    )
    ap.add_argument(
        "--session",
        default="perf",
        help="ARENA_SESSION of the container to read (default: perf)",
    )
    args = ap.parse_args()

    ids = subprocess.run(
        ["docker", "ps", "-q", "-f", f"name=arena-{args.session}"],
        capture_output=True,
        text=True,
    ).stdout.split()
    if not ids:
        sys.exit(f"no running container named arena-{args.session}")
    out = subprocess.run(["docker", "logs", ids[0]], capture_output=True)
    log = out.stdout.decode("utf8", "replace") + out.stderr.decode("utf8", "replace")

    vals = collections.defaultdict(list)
    for line in log.splitlines():
        m = re.search(r"(\d\d:\d\d:\d\d)\.\d+ stats,[^,]*,([A-Za-z]+),(-?\d+)", line)
        if m and m.group(1) >= args.since:
            vals[m.group(2)].append(int(m.group(3)))

    if not vals:
        sys.exit(
            "no stats lines found - is allowStatisticsLogging on, and has anyone connected?"
        )
    print(f"{'metric':26s} {'n':>5s} {'median':>10s} {'p90':>10s} {'max':>10s}")
    for k in sorted(vals):
        v = sorted(vals[k])
        p90 = v[min(len(v) - 1, int(len(v) * 0.9))]
        print(f"{k:26s} {len(v):5d} {statistics.median(v):10.0f} {p90:10d} {v[-1]:10d}")


if __name__ == "__main__":
    main()