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#!/usr/bin/env python3
"""Combine N shards' persisted raw swing histograms (eval.c's
PersistMarginSafetyHistogramsToFile, "calibrate marginsafety <path>")
into one true aggregate: sum count/exceeded/sum/sumSq/histogram bins
bucket-by-bucket across all shard files, then recompute mean/stdev/
percentiles from the combined totals. Percentiles/means from
individual shards can't be validly averaged -- this reads raw counts
only and derives every statistic fresh from the combined totals.
Usage:
python3 aggregate_marginsafety_shards.py <shard_file> [<shard_file> ...]
"""
import sys
from collections import defaultdict
BIN_WIDTH = 10
def load(path):
"""Yield (tier, bucket, count, exceeded, sum_, sumsq, max_, bins)."""
with open(path) as f:
for line in f:
if line.startswith('#') or not line.strip():
continue
parts = line.split()
tier, bucket = parts[0], int(parts[1])
count, exceeded, sum_, sumsq, max_ = map(int, parts[2:7])
bins = list(map(int, parts[7:]))
yield tier, bucket, count, exceeded, sum_, sumsq, max_, bins
def percentile(bins, total, p):
target = p * total
running = 0
for i, c in enumerate(bins):
running += c
if running >= target:
return i * BIN_WIDTH + BIN_WIDTH // 2
return (len(bins) - 1) * BIN_WIDTH
def main():
if len(sys.argv) < 2:
print(__doc__)
sys.exit(1)
agg = defaultdict(lambda: {"count": 0, "exceeded": 0, "sum": 0,
"sumsq": 0, "max": 0})
agg_bins = {}
for path in sys.argv[1:]:
for tier, bucket, count, exceeded, sum_, sumsq, max_, bins in load(path):
key = (tier, bucket)
a = agg[key]
a["count"] += count
a["exceeded"] += exceeded
a["sum"] += sum_
a["sumsq"] += sumsq
a["max"] = max(a["max"], max_)
if key not in agg_bins:
agg_bins[key] = bins[:]
else:
for i, c in enumerate(bins):
agg_bins[key][i] += c
for tier in ("lazy", "superlazy"):
print(f"--- {tier} ---")
for bucket in range(8):
key = (tier, bucket)
if key not in agg:
continue
a = agg[key]
n = a["count"]
if n == 0:
print(f" bucket {bucket}: no exits taken")
continue
mean = a["sum"] / n
variance = max(0.0, a["sumsq"] / n - mean * mean)
stdev = variance ** 0.5
pct = 100.0 * a["exceeded"] / n
bins = agg_bins[key]
p10 = percentile(bins, n, 0.10)
p50 = percentile(bins, n, 0.50)
p90 = percentile(bins, n, 0.90)
p99 = percentile(bins, n, 0.99)
print(f" bucket {bucket}: n={n} exceeded={a['exceeded']} "
f"({pct:.3f}%) max={a['max']} avg={mean:.1f} "
f"stdev={stdev:.1f} p10={p10} p50={p50} p90={p90} p99={p99}")
if __name__ == "__main__":
main()
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