#!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
"""Per-class table from meter logs, by the pre-registered statistic (mean of per-pass differences
of medians, SE across passes), plus a sensitivity column that was NOT pre-registered: the same
with a 10%-trimmed mean inside each point, which keeps sub-quantum information a median of
10 mW-quantised samples throws away. Passes flagged in the log are dropped.
    python3 -I power_tables.py a.dat b.dat ...
"""
import statistics as st, sys, re

def trimmed(xs, f=0.1):
    s = sorted(xs); k = int(len(s) * f)
    s = s[k:len(s) - k] if k else s
    return sum(s) / len(s)

for path in sys.argv[1:]:
    pts, flagged = {}, set()
    for line in open(path):
        m = re.search(r'pass=(\d+)', line)
        if line.startswith('#'):
            if m and ('FLAGGED' in line or 'FOREIGN-LOAD' in line or 'NOT-OURS' in line):
                flagged.add(int(m.group(1)))
            continue
        f = line.split()
        if len(f) >= 4 and f[0].isdigit() and f[3].isdigit():
            pts.setdefault((int(f[0]), f[1]), []).append(int(f[3]) / 1000.0)
    passes = sorted({p for p, _ in pts} - flagged)
    dm = [st.median(pts[(p, 'run')]) - st.median(pts[(p, 'halt')]) for p in passes]
    dt = [trimmed(pts[(p, 'run')]) - trimmed(pts[(p, 'halt')]) for p in passes]
    n = len(passes)
    fmt = lambda d: f"{st.mean(d):7.1f} +/- {st.stdev(d) / n ** 0.5:4.1f}"
    pr = st.mean(st.median(pts[(p, 'run')]) for p in passes)
    print(f"{path.split('/')[-1]:24s} passes {n:2d}  medians {fmt(dm)} mW   trimmed {fmt(dt)} mW   P_run {pr:6.0f} mW  flagged {sorted(flagged)}")