feat(offset): detector v3 — pre-trigger floor with a constant-floor test

Brian's method, and better than v2's whole-record median: the pre-trigger
window is definitionally quiet (the buffer captured before the trigger fired),
whereas a median is merely robust to the event. Requiring the floor to hold
across pre-trigger / middle / end rejects transients that a median cannot.

    per channel:  pre/mid/end medians, spread = max - min
    offset when   |pre| >= floor AND spread <= 0.02 in/s
    real fault    >= 3 consecutive flagged events on that channel

The empirical noise floor justifies the threshold and validates the decoder:
across 19,244 non-flagged channel-events the pre-trigger floor is 62.7% exactly
0.000, 94.5% within +/-1 quantisation unit, median +0.0000, mean -0.0008. There
is no systematic zero-point bias — an independent confirmation of the
32000-count geo scale.

The result is threshold-insensitive across a 2x range (0.020 to 0.040 in/s),
which is what separates a real signal from a tuned one:

  FINAL: 5 of 45 units (11%) — BE9558, BE11529, BE12599, BE13117, BE18438

BE11007 and BE10895 drop out; the spread test identifies them as transients
rather than pedestals. v1's 11% headline was right by luck — it included
BE11007 and named the wrong channel on most units.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HgTe8CamXAHcAmaQ6QNcog

scratch/offset_scan3.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""Offset detector v3 — pre-trigger floor, with pre/mid/end consistency.
+
+Brian's method, and better than v2's whole-record median for one reason: the
+pre-trigger window is *definitionally* quiet (it is the buffer captured before
+the trigger fired), whereas a whole-record median is merely robust to the event.
+
+Per channel:
+    pre  = median of the first `pretrig_samples` samples (STRT record)
+    mid  = median of the middle third
+    end  = median of the final third
+    spread = max(pre,mid,end) - min(pre,mid,end)
+
+A DC offset is a *constant floor*: |pre| at or above the floor AND a small
+spread. A transient (settling, handling, a long-tailed event) moves one segment
+relative to the others and is rejected by the spread test.
+
+Floor default 0.025 in/s = 5 A/D counts (Instantel's own criterion; 1 count =
+0.005 in/s).  Quantisation is 0.005 in/s, so `spread` is measured in units of it.
+"""
+from __future__ import annotations
+import argparse, csv, re, statistics, sys
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from minimateplus.event_file_io import read_blastware_file
+
+GEO=("Tran","Vert","Long"); K=10.0/32000.0
+_WAVE=re.compile(r"\.[A-Za-z0-9]{2}0[Ww]$"); _STEM=re.compile(r"^([B-Z])(\d{3})")
+
+def serial_of(n):
+    m=_STEM.match(n)
+    return f"BE{(ord(m.group(1))-ord('B'))*1000+int(m.group(2))}" if m else "?"
+
+def scan(ps):
+    p=Path(ps)
+    try:
+        ev=read_blastware_file(p); s=ev.raw_samples or {}
+        if not all(s.get(c) for c in GEO): return None
+        pre_n=ev.pretrig_samples
+        ts=ev.timestamp
+        stamp=(f"{ts.year:04d}-{ts.month:02d}-{ts.day:02d}T"
+               f"{ts.hour:02d}:{ts.minute:02d}:{ts.second:02d}") if ts else ""
+        out=[]
+        for ch in GEO:
+            a=s[ch]; n=len(a); t=n//3
+            pre = a[:pre_n] if (pre_n and 0 < pre_n < n) else a[:t]
+            mid, end = a[t:2*t], a[2*t:]
+            if not pre or not mid or not end: continue
+            v=[statistics.median(x)*K for x in (pre,mid,end)]
+            out.append({"serial":serial_of(p.name),"timestamp":stamp,
+                        "filename":p.name,"channel":ch,
+                        "pretrig_n": pre_n or 0,
+                        "pre":round(v[0],4),"mid":round(v[1],4),"end":round(v[2],4),
+                        "spread":round(max(v)-min(v),4),
+                        "peak":round(max(abs(x) for x in a)*K,4)})
+        return out
+    except Exception:
+        return None
+
+def main():
+    ap=argparse.ArgumentParser()
+    ap.add_argument("--dir",required=True); ap.add_argument("--jobs",type=int,default=4)
+    ap.add_argument("--floor",type=float,default=0.025)
+    ap.add_argument("--max-spread",type=float,default=0.02)
+    ap.add_argument("--out",required=True)
+    a=ap.parse_args()
+    seen=set(); files=[]
+    for q in sorted(Path(a.dir).rglob("*")):
+        if q.is_file() and _WAVE.search(q.name) and q.name not in seen:
+            seen.add(q.name); files.append(str(q))
+    print(f"unique waveform binaries: {len(files)}",flush=True)
+    rows=[]
+    with ProcessPoolExecutor(max_workers=a.jobs) as ex:
+        for i,f in enumerate(as_completed([ex.submit(scan,p) for p in files]),1):
+            r=f.result()
+            if r: rows.extend(r)
+            if i%2000==0: print(f"  {i}/{len(files)}",flush=True)
+    for r in rows:
+        r["offset"]=int(abs(r["pre"])>=a.floor and r["spread"]<=a.max_spread)
+    cols=["serial","timestamp","filename","channel","pretrig_n","pre","mid","end","spread","peak","offset"]
+    with open(a.out,"w",newline="") as fh:
+        w=csv.DictWriter(fh,fieldnames=cols); w.writeheader(); w.writerows(rows)
+    from collections import defaultdict
+    per=defaultdict(set); tot=defaultdict(set)
+    for r in rows:
+        tot[r["serial"]].add(r["filename"])
+        if r["offset"]: per[r["serial"]].add(r["filename"])
+    print(f"\nfloor={a.floor} in/s ({a.floor/0.005:.0f} counts)  max spread={a.max_spread}")
+    print(f"units affected: {len(per)} of {len(tot)}")
+    for s in sorted(per,key=lambda s:-len(per[s])):
+        print(f"  {s:9} {len(per[s]):4} / {len(tot[s]):4} events")
+    print(f"\nwrote {a.out}")
+
+if __name__=="__main__": main()