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serversdown a18712442f feat: preserve and encode raw 0C record in sidecar extensions for offline analysis 2026-05-08 21:50:01 +00:00
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# Python build artifacts
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Ground-up Python replacement for **Blastware**, Instantel's Windows-only software for
managing MiniMate Plus seismographs. Connects over direct RS-232 or cellular modem
(Sierra Wireless RV50 / RV55). Current version: **v0.31.0**.
(Sierra Wireless RV50 / RV55). Current version: **v0.14.3**.
Stack-level context — which repo owns what, and how the three project versions
pair — lives in `../terra-view/docs/tmi-stack.md`, which is also loaded as
`~/CLAUDE.md`.
---
## Where things stand (updated 2026-09-26)
Read this first when picking the project back up.
- **The Series-4 LIVE wire protocol is reverse-engineered end to end
(2026-09-25).** `docs/micromate_protocol_reference.md` is the Series-4
Rosetta Stone, sibling to `instantel_protocol_reference.md`. **A Micromate
answers Series III command frames** — three framing differences: responses
have **no leading `DLE`** (a bare `STX`), `payload[1]` is `0xC5` (Blastware
firmware) or `0x03` (Thor firmware) rather than `0x10`, and the data length
is a **uint16 BE at `payload[8:10]`** (as a byte it under-reads `SUB 0x1A`
by 47x). Read path, event chain, setups, scheduler, monitoring control and
per-event delete are all mapped; **the inbound call-home session is the only
protocol unknown left.**
⚠ **No command has ever been originated against a unit by this project.**
Every write was performed by THOR while we recorded. That line is worth
keeping.
⚠ `micromate/` still has **no live client** — it is codec-only. The
`minimateplus/` stack (transport/framing/protocol/client) has no Series-4
counterpart yet. `minimateplus.transport` is protocol-agnostic and reusable.
- **Bench tooling for device diagnosis (2026-09-25).** `bridges/mm_probe.py`
distinguishes the four faults THOR reports identically as "disconnected"
(refused / connect timeout / **connected but no reply** / replied) and names
what to try next. `bridges/mm_link.py` is a stand-in for a cellular modem
with a decoded log and fault injection. `scratch/mm_frame_parse.py` exists
because **`S3FrameParser` cannot see Micromate responses at all** — it scans
for `DLE+STX`, which never appears in Series-4 traffic.
- **A Micromate's USB-A host port drives FTDI and CDC-ACM only** — no Prolific,
in either firmware line. TMI buys both Sabrent (FTDI) and Benfei (PL2303)
cables and they are indistinguishable by eye. A PL2303 cable leaves a unit
with **no working modem port at all**; identify by `lsusb` VID, `0403` vs
`067b`. This accounted for a unit that could not be deployed.
- **Series-3 decode is verified per-sample at scale (v0.27.0).** The full DL2
archive decodes **14,338 / 14,338** paired files exactly against their
preserved Blastware ASCII exports — 1,249 waveform + 13,089 histogram, 45
units, files back to 2018. That is 11x the ground truth the prod store
carried, and it supersedes the old "per-sample on 11%, peak-only on 89%"
caveat. Harness: `scratch/verify_against_ascii.py` (note its saturation
carve-out — BW clamps clipped events, the decoder reports true counts).
Independent corroboration of the 32000-count scale: 19,244 healthy
channel-events sit at a pre-trigger floor of exactly 0.000 (62.7%), 94.5%
within ±1 quantisation unit, median +0.0000 — no zero-point bias.
- **Series-4 (Thor / Micromate) is now verified per-sample (2026-09-10).**
**1,057,536 / 1,057,536** geo samples across all 153 genuine Thor waveform
files reproduce Thor's own CSV export exactly; IDFH peaks are within 2% on
858/858 (median -0.004%). The ground truth was in the corpus all along —
Thor writes `CSV/<name>.IDFW.csv` beside each binary with a **per-sample**
four-column block. Harness: `scratch/verify_thor_against_csv.py`.
Four bugs, all fixed: geo LSB was `0.0003` (display rounding of the real
`0.000310308`, so every sample read **3.3% low**); the IDFH segment
validator required a zero counter high byte, **capping every histogram at
250 intervals**; record mode `00 00` (raw int16) was unhandled, silently
dropping each channel's first 512 samples; and the body-offset search
matched `00 02 00` *inside* record headers, decoding a rotation-shifted
body. IDFW is no longer pinned to `decode_waveform_legacy`.
Series-3 re-verified unchanged at 14,338/14,338 after the shared-codec
change.
- **Mic-disabled (3-channel) units are a distinct shape (2026-09-10).**
Verified on a second corpus (`~/thor-csv-req`, UM11402/UM12947/UM20147):
**139/139** waveforms per-sample exact, **877/877** histograms within 2%.
Two structural differences: the shorter header puts the waveform record
chain head at `0x0dba` (below the old `_BODY_SCAN_FLOOR` of `0x0E00`, so it
was invisible and Vert came up exactly 512 short), and the histogram
interval record is **56 bytes, not 72** — `16 × n_channels + 8`, derived per
segment from the cumulative interval counter, never assumed.
- **`40 NN` blocks are not capped at NN=8 (2026-09-11).** `data_block_len()`
rejected `NN > 0x08`, a guard with no evidence behind it — the corpora
available when it was written only used NN ∈ {1,2,3,4,8}. Loud UM12947
events use NN up to 196, and since the walker stops at the first
unrecognised tag rather than raising, this surfaced as silently short
channels. Verified on 167 UM12947 waveforms: length mismatches 22 → 0,
1,476,242/1,476,249 samples exact.
- **Production IDFW is now 575/575** — zero truncations, zero decode
failures, median PPV error −0.0007% across 8 units (was 41 truncated + 1
failing, −3.3%). Across all three ground-truth corpora: **459 files,
3,807,158/3,807,165 samples exact**; the 7 stragglers differ by one
4th-decimal tick and are Thor's own rounding — no single linear LSB can
reproduce every printed value (the constraints are infeasible by 7e-5
relative), so do NOT retune `_GEO_LSB_IPS`.
- **⚠ KNOWN BUG — the 5A walk breaks once a unit's buffer crosses 64 KB.**
`parse_strt_end_offset()` returns only `(end_key[2] << 8) | end_key[3]`,
discarding the key's page byte. An event starting at `0x0111F2A2` and ending
at `0x0112_1010` therefore reads `end_offset = 0x1010` — *behind* its own
start. The chunk loop then exits before fetching anything and TERM computes
a negative `offset_word`, which `struct.pack(">H", ...)` rejects: the
`/device/events` walk 500s. Reproduced on BE12599 (2026-09-19), which had
78 KB stored and had rolled into page `0x12`.
**Why it hid so long:** every 5A capture the walk was verified against came
from a freshly-erased BE11529 — all three confirmed TERM examples in
`framing.py` (`0x1ABE`, `0x21F2`, `0x417E`) sit inside page `0x11`. Prod is
unaffected: it ingests complete files via BW ACH, never this walk.
**Fixing it has two layers** — the arithmetic (`if end < start: end +=
0x10000`) stops the crash and bounds the loop correctly; carrying the page
byte through the chunk requests (`params[1]` 0x11 -> 0x12, counter rolling
over) needs a BW capture of a spanning event first. Do not ship layer one
alone without a loud truncation warning — a silently short event is the
failure mode this codec has been bitten by repeatedly.
- **Open, not blocking:** 14 sensitive-range files show an exact 8x
(= 10.0/1.25) units discrepancy; `scripts/backfill_sidecars.py --force` also
inserts DB rows for store files that have none (one-time per store) and the
dry-run does not report that count.
- **After any codec change, regenerate the store** — `backfill_sidecars.py`
then `backfill_event_shape.py`, DB backup first. Stored `.h5` files do not
update themselves. No `--force` needed as long as `TOOL_VERSION` was bumped
(it gates regeneration). ⚠ On the office NAS this takes **~2 hours**
(~1.5 files/sec vs 85/sec on the dev box — gzip-4 in `sfm/event_hdf5.py`
against a Synology CPU). Budget it up front.
**v0.27.0 does NOT owe prod a backfill** — verified: the partial-final-block
fix changes 0 of the 10,215 histograms in the prod store (the 4 recovered
files are archive-only and were never ingested).
✅ **The v0.30.0 Series-4 backfill HAS been run on prod (2026-09-25).** Every
stored Series-4 geophone value was ~3.3% low until then; that is corrected and
the job does not need repeating.
- **The "offset" hardware fault has its own journal** --
`docs/offset_investigation.md`. **5 of 45 units (11%)**, and the fault is
**persistent** — it stays until the geophone is serviced. Detect it with
`scratch/offset_scan3.py`: the resting floor in the **pre-trigger** window,
required to hold across pre/middle/end. Never score only the dominant-peak
axis and never use the mean — both produce false recoveries (see the
retraction banner in the journal). Instantel's autozero procedure and its
2027-2069 acceptance window are recorded there too. Best open lead is
`SUB 0x0E` (unimplemented), which may carry those very numbers.
When new information about a protocol is discovered, record it in the matching
reference **in addition to** this document:
| series | document |
|---|---|
| Series III (MiniMate Plus / BlastMate) | `docs/instantel_protocol_reference.md` |
| **Series IV (Micromate / THOR)** | **`docs/micromate_protocol_reference.md`** |
| Thor IDF file format | `docs/idf_protocol_reference.md` |
Both protocol references carry retractions in place rather than deleting what
turned out to be wrong — that convention has already saved re-deriving the same
mistakes twice, so keep it.
---
## Changelog & release convention
**Feature branches do NOT touch `CHANGELOG.md`. Write the entry on `dev`, as
part of finishing the merge, under `## Unreleased`. Cut the version on `dev` in a
dedicated release commit when you are ready to ship to `main`.**
- **The changelog is written on `dev`, never on a feature branch.** With
several branches in flight they all edit the same few lines at the top of
the file and conflict every time. Writing it once, after the merge, also
lets it describe what actually *landed* — including anything that changed
during conflict resolution.
- ⚠ **The merge is not finished until `## Unreleased` is updated.** Same sitting,
not "later" — that is the one failure mode of writing it after the fact.
Reconstruct from the branch's own commit messages:
`git log --oneline dev..<branch>` before you merge, or
`git log --oneline <merge-base>..<branch>` after.
- **No preamble under `## Unreleased`** — just the `### Added` / `### Changed` /
`### Fixed` lists. The themed opening paragraph gets written at release
time, when the whole release is visible and can be named honestly. A theme
written when the first item landed is stale by the third.
- ⚠ **State the operational consequence** on any entry touching the codec, the
waveform store, or the DB — **including when it is "none."** "requires
`backfill_sidecars.py` + `backfill_event_shape.py`, ~2 h on the NAS",
"`TOOL_VERSION` bumped", "no schema change, no migration". Silence is
ambiguous; "none" is information. This repo's changelog is how future-you
learns whether a deploy costs two hours.
- **Releases are cut on judgement, not on a schedule or a merge.** `Unreleased`
is the staging area for whatever is going into the next release; when enough
has accumulated to be worth shipping, it gets a number and a date. Nothing
about a merge to `dev` triggers a release.
- **Cutting a release** is its own `chore(release): vX.Y.Z — <theme>` commit on
`dev`, renaming `## Unreleased` → `## vX.Y.Z — YYYY-MM-DD` and touching:
`CHANGELOG.md`, `pyproject.toml`, the version line in `CLAUDE.md` and
`README.md`, and `minimateplus/event_file_io.py` (`TOOL_VERSION`) **when the
codec changed** — that constant gates `.h5` regeneration.
- **`main` carries only released versions.** No `## Unreleased` section there;
it lands via the `dev` → `main` PR. `main` lagging `dev` by a version is
normal.
---
## Architecture: three-tier conceptual model
seismo-relay is a **suite of cooperating components**, not a single app.
The three tiers below are the canonical mental model — the current
directory layout doesn't fully reflect them yet (some of what is
conceptually SDM lives under `sfm/` today), but new code should be
placed and named according to this model.
### 1. SFM — the device-side (active connection to physical units)
Replaces Blastware's *talk-to-the-meter* role. Lives where a connection
to a physical seismograph is open.
In scope:
- `minimateplus/{transport,framing,protocol,client}.py` — wire protocol
- `seismo_lab.py` — diagnostic GUI (a thick client for SFM)
- The `/device/*` HTTP endpoints in `sfm/server.py` —
`/device/info`, `/device/events`, `/device/monitor/*`, `/device/call_home`,
etc. Anything that opens a connection at the moment of the request.
- Future: a Thor / Micromate live client (mirror `minimateplus/`)
- Future: a control surface Terra-View can launch into — see the
README's Roadmap.
Does NOT own a database. Outputs `Event` objects. Has a "spun up when
needed" runtime profile rather than "always on".
### 2. SDM — the data-side (storage, ingest, and serving)
The new name for the receiving-and-storing role. Originally called SFM
because the FastAPI service started life as a thin device proxy, but
the actual role has migrated heavily toward data management. **For now
the directory remains `sfm/`** — renaming requires touching ~30-50
files in seismo-relay + ~10-15 in terra-view + a Docker volume
migration; deferred until the codebase is quiet enough to do it as a
clean refactor.
In scope:
- `sfm/database.py` (`SeismoDb`)
- `sfm/waveform_store.py`, `sfm/event_hdf5.py`
- The `/db/*` HTTP endpoints — `events`, `units`, `monitor_log`,
`sessions`, `false_trigger` mutations
- The `/db/import/*` ingest endpoints — `blastware_file` (series3),
`idf_file` (series4); anything that receives events FROM somewhere
- `scripts/backfill_sidecars.py`, `scripts/check_bw_report_preservation.py`,
and similar data-maintenance tools
- The `.sfm.json` sidecars and `.h5` files in the waveform store
- The shape that Terra-View consumes (Terra-View should never need to
reach into SFM/device-side endpoints to populate its UI)
Always-on, scaled for storage/serving, has the DB and waveform store.
### 3. Codec library — pure data interpretation (used by both sides)
Neither SFM nor SDM — a shared library both depend on.
In scope:
- `minimateplus/{waveform_codec,histogram_codec,event_file_io,bw_ascii_report,blastware_file}.py`
- `micromate/{idf_ascii_report,idf_file}.py`
These modules take bytes (off the wire on the SFM side, or from a
forwarded file on the SDM side) and return `Event` objects. They
should not import from `sfm/`, must not touch a DB, and have no I/O
beyond reading files passed as arguments. Keep them pure — both
tiers can then depend on them without circularity.
#### Thor IDF binary codec (updated 2026-09-10)
`micromate/idf_file.read_idf_file()` decodes both Thor IDFW
(waveform) and IDFH (histogram) binaries. **Verified per-sample
against Thor's own CSV exports** — see
`scratch/verify_thor_against_csv.py`.
- **IDFW** uses the series-3 record-chain `decode_waveform_v2()`. The
body offset is **not** fixed: it is `<chain-head record> + 7`, found
by `_find_waveform_body_offset()` anchoring on record headers. All
**153/153** genuine Thor waveform files decode per-sample exact
(1,057,536/1,057,536 samples).
- **IDFH** segment header is `[len_be][0a 00 00 00][counter_be][05 3f]`,
where `counter` is a **uint16 cumulative interval index** — it must
not be constrained to a zero high byte (that capped histograms at 250
intervals). Intervals whose `min > max` on all channels are unwritten
slots carrying a ±full-scale seed and are skipped. 858/858 files land
within 2% of Thor's PPV (median -0.004%).
- **Geo LSB is `0.000310308` in/s per count** (full scale 10.0 in/s =
32226.05 counts). Series-3's 32000-count scale does NOT apply.
- **Record modes** are `02 00` deltas (14 B header), `01 00` absolute,
`00 03` raw 12-bit, and `00 00` **raw int16** (all 10 B headers).
`01 00` and `00 00` are also valid as the implicit segment-0 preamble.
⚠ **Thor's histogram PPV has a 0.0050 in/s display floor.** 41.4% of
prod IDFH sidecars report a component PPV exceeding their own vector
sum — impossible. On quiet files our decode is *more* accurate than
the reference; do not "fix" the decoder to match it.
The two outlier `BE9439_*` files in the Thor example corpus are
actually Series III Blastware binaries that share the `.IDFW`/`.IDFH`
filename convention by accident. `read_idf_file()` detects them by
their BW STRT signature and raises NotImplementedError pointing
callers at `read_blastware_file()`. See
`docs/idf_protocol_reference.md` for full field layouts.
### Practical consequences
When deciding where new code goes, ask:
- *Does it need a connection to a device?* → SFM
- *Does it operate on stored events / sidecars / DB rows?* → SDM
- *Does it interpret bytes into structured data, with no I/O of its own?* → codec lib
Terra-View is downstream of SDM for data, and (per the roadmap) will
eventually invoke into SFM's device-control endpoints to provide a
"connect to unit" experience.
When new information about the protocol is discovered, please update the instantel_protocol_reference.md with the findings in addition to this document
---
@@ -313,20 +17,11 @@ minimateplus/ ← Python client library (primary focus)
protocol.py ← MiniMateProtocol — wire-level read/write methods
client.py ← MiniMateClient — high-level API (connect, get_events, …)
models.py ← DeviceInfo, EventRecord, ComplianceConfig, …
waveform_codec.py ← Body-codec block walker + decode_tran_initial (partial
per-sample decoder — see "Waveform body codec" section below)
sfm/server.py ← FastAPI REST server exposing device data over HTTP
seismo_lab.py ← Tkinter GUI (Bridge + Analyzer + Console tabs)
bridges/
mm_probe.py ← name the fault behind a dead unit (4 verdicts, read-only)
mm_link.py ← bench stand-in for a cellular modem, with fault injection
ach_mitm.py ← TCP relay for recording a Series-3 ACH session
docs/
instantel_protocol_reference.md ← Series III protocol spec ("the Rosetta Stone")
micromate_protocol_reference.md ← Series IV protocol spec + THOR's measured behaviour
idf_protocol_reference.md ← Thor IDF file format
instantel_protocol_reference.md ← reverse-engineered protocol spec ("the Rosetta Stone")
CHANGELOG.md ← version history
```
@@ -362,273 +57,6 @@ Full read pipeline + write pipeline + erase pipeline + monitor log + call home c
---
## Waveform body codec — FULLY DECODED (2026-05-11 late)
> ### ✅ The codec is fully cracked
>
> Every block type, every channel, every fixture event decodes byte-exact
> against BW's ASCII export. **47,364 ADC samples verified, zero errors.**
> The previous int16 LE interpretation was wrong — see the retraction
> trail in `docs/instantel_protocol_reference.md §7.6.1`.
>
> Authoritative implementation: `minimateplus/waveform_codec.py`
> (`decode_waveform_v2()`). Clean working notes:
> `docs/waveform_codec_re_status.md`.
>
> **NOTE:** `client.py:_decode_a5_waveform` still uses the broken
> legacy int16 LE decoder. Wiring `decode_waveform_v2` into the
> `.h5` sidecar path is the obvious next follow-up. Until that lands,
> `.h5` samples remain wrong — but the codec itself is fully solved.
The Blastware waveform-file body (between the 21-byte STRT record and
the 26-byte footer) is a tagged variable-length block stream with a
custom delta + RLE + variable-width codec.
### What's solved (2026-05-11)
- **Block framing** — 5 tag types (`10 NN`, `20 NN`, `00 NN`, `30 NN`,
`40 02`) with confirmed lengths. Implementation: `walk_body()` in
`minimateplus/waveform_codec.py`.
- **Per-channel codec** — preamble bytes [3:7] = `Tran[0]`, `Tran[1]`
as int16 BE in **16-count units** (LSB = 0.005 in/s). Then `10 NN`
(4-bit nibble deltas), `20 NN` (int8 deltas), and `00 NN` (RLE zero
deltas) carry per-channel deltas from sample 2 onward.
- **Channel rotation** — segments cycle **Tran → Vert → Long → MicL**
per `40 02` segment header. Each segment carries ~512 sample-sets of
ONE channel. The initial body (before the first `40 02`) is the
implicit Tran segment.
- **Segment header layout (20 bytes)** —
bytes [0:2] = previous-channel continuation delta #1 (int16 BE);
bytes [2:4] = previous-channel continuation delta #2;
bytes [6:8] = byte length to next header − 2;
bytes [8:12] = monotonic uint32 LE counter;
bytes [12:14] = constant `02 00`;
bytes [14:16] = THIS segment's channel sample 0 anchor (int16 BE);
bytes [16:18] = THIS segment's channel sample 1 anchor.
- **`decode_waveform_v2()`** returns full per-channel sample dicts.
Byte-exact against BW ASCII export for V70 (all 3 channels × 1 seg
each), JQ0 (T/V), and SP0 Long (all 3 segments = 1536 samples).
- **`30 NN` block** — carries NN 12-bit signed deltas packed as NN/4
groups of 6 bytes each. Within each group, bytes [0:2] hold 4 ×
4-bit high nibbles (MSB first), bytes [2:6] hold 4 × int8 low bytes.
Each delta = `sign_extend_12((high_nibble << 8) | low_byte)`. Block
length = `NN × 1.5 + 2` bytes. ✅ confirmed against all 14 `30 NN`
blocks in the fixture bundle. 12-bit was chosen because ±2047 in
16-count units ≈ ±10 in/s = the geophone's full-scale range at
Normal sensitivity.
- **Wide-NN blocks (`1X NN`, `2X NN`)** — when a `10 NN` or `20 NN`
block's NN would exceed 0xFC, the codec uses a 12-bit NN encoding:
the low nibble of the type byte holds the high nibble of NN (so the
type byte appears as e.g. `0x11` instead of `0x10`). Effective
NN = `((type_byte & 0x0F) << 8) | nn_byte`. Block length follows
the same formula as the narrow form (`NN/2 + 2` for nibble blocks,
`NN + 2` for int8 blocks). Confirmed 2026-05-11 against SP0 cycle
3 V continuation (`11 90` = NN=400 nibble deltas in 202 bytes).
### ⚠ SUPERSEDED 2026-08-25 — the body is a RECORD CHAIN
Everything in this section below about `40 NN` segment headers, tagless
headers, variable header widths and channel rotation describes a model that
is **wrong**. The body is a chain of self-delimiting per-channel records:
off+2 len uint16 BE -> next_record = off + 2 + len
off+4 chan_id 0x46 Tran / 0x47 Vert / 0x48 Long / 0x49 MicL / 0x06 = END
off+8 mode 02 00 = deltas+anchors (14B hdr)
01 00 = ABSOLUTE values (10B hdr)
00 03 = raw 12-bit absolute, NO TAGS (10B hdr)
`40 NN` is an ordinary int16 BE data block (`2*NN + 2`), never a header. The
"variable prefix" of 0/2/4/6/8 bytes was walker drift, exactly
`4 - (old_stop - true_record_start)`.
All four channels now come out equal length in **1388/1388** files (was
156/1388); ASCII sample-count exact **75/75**, fully exact **73/75**; device
PPV on a live decode **1306/1306** waveform, **4458/4459** histogram.
The old model survives as `decode_waveform_legacy` because
`micromate/idf_file.py` pins it for Thor IDFW body-offset search.
### Framing cases added 2026-05-11 → 2026-08-25
Four more block-framing cases, each of which had been causing **silent
channel truncation** — `walk_body` ends its loop on an unrecognised tag
and `decode_waveform_v2` returns whatever channels it got, so an
unhandled tag surfaces as short channels with no error raised. Found by
diffing 75 production events against their preserved Blastware ASCII
exports (`<store>/<serial>/<file>_ASCII.TXT`).
- **Wide-NN RLE `0X NN`** — the 12-bit NN encoding documented above for
`1X`/`2X` **also applies to the `00 NN` RLE tag**. A narrow run maxes
out at NN=0xFC, so a quiet stretch longer than 252 samples must use
the wide form (e.g. `01 0c` = 268 repeats).
- **`30 NN` is not capped at NN=0x10** — data-section blocks reach at
least NN=0x18. The `NN × 1.5 + 2` length formula was already right;
only the guard was wrong.
- **`40 NN` segment headers are variable width** — NN is the *count of
int16 BE continuation deltas for the PREVIOUS channel*, so the header
is `2*NN + 16` bytes and every field after the deltas shifts by
`2*NN`. `40 01` (18 B) and `40 03` (22 B) both occur alongside the
common `40 02` (20 B).
- **Tagless segment headers** — a header can appear with **no `40 NN`
tag at all**: just the 14-byte tail
`[field2:2][len:2][channel_id:4][marker:2][anchors:4]`. This is the
NN=0 case (previous channel needed no continuation deltas).
**The header "counter" is really a channel id.** The 4-byte field long
documented as a "monotonic uint32 LE counter" is
`[channel_id][00][00][segment_index]`, with `0x46`=Tran `0x47`=Vert
`0x48`=Long `0x49`=MicL — verified on **1697/1697** segment headers
across the corpus, zero disagreements. `decode_waveform_v2` now takes
the channel from this field rather than from rotation position; a single
missed or extra header (exactly what tagless headers caused) desyncs
rotation and corrupts every channel after it.
Corpus result, end to end through the production path:
**exact 37 → 72, truncated 23 → 3, full-length value errors 15 → 0.**
### Histogram codec — multi-interval blocks (2026-08-26)
Sub-minute histogram intervals are packed several to a block, so every
block still covers exactly one minute:
| interval | intervals/block | stride |
|---|---|---|
| 1 min | 1 | 32 (the standard big-endian block) |
| 15 s | 4 | 92 |
| 2 s | 30 | 612 |
`stride = 12 + n * 20`. Block = `[00][segment][ctr uint16 LE][0a][00]`,
then n x 20-byte records of 8 x uint16 **LITTLE**-endian values
(`T_peak, T_halfp, V_peak, V_halfp, L_peak, L_halfp, M_peak, M_halfp`)
plus a 2-word tail whose first word is `0000` on every real interval,
then a 6-byte block trailer.
⚠ The standard 32-byte block is BIG-endian; this variant is LITTLE-endian.
Recovers **415 files** (216 on BE18193, 199 on BE9440) that decoded to
nothing. Ground truth `BE9440/K440L3AQ.T70H` matches its BW ASCII export
on every one of 17,130 geo peaks, 22,840 frequencies and 5,710 mic dB(L)
values; across all 455 affected files 1,354/1,365 channel peaks (99.2%)
match the device-reported PPV.
### Histogram codec — corrected 2026-08-25
The histogram block is **uniformly big-endian**, and the stream's final
block has its own tail signature. Two long-standing errors:
- **Peaks and half-periods are `uint16` big-endian**, not `uint8` +
an "annotation" byte. `T_peak` is `[5:7]`, `T_halfperiod` `[7:9]`,
`V_peak` `[9:11]`, and so on; only `block_ctr` at `[2:4]` is LE.
The old model silently **clipped any peak above 1.275 in/s** — the
final interval of `BE18193/T193LQ9K.OE0H` reads 8.270 in/s in BW's
export and decoded as 0.590. The "annotation" byte was the
half-period's high byte, which is why it was non-zero exactly on the
sub-Hz intervals BW renders as `<1.0`.
- **The marker is `block[4]` alone.** Testing `[4:6]` as a uint16 LE
marker forced `block[5] == 0`, which is what capped the peak at one
byte in the first place.
- **The last block of the stream carries tail `9c 06 00 42`** instead of
`1e 0a 00 00`, with arbitrary bytes at `[21:23]`. Rejecting it
dropped the final interval of nearly every histogram — frequently the
one holding the event peak, so the file's PPV read low.
Verified against 1211 production histograms paired with their BW ASCII
exports: **1211/1211 decode exactly** (interval count plus every
per-interval peak), and 842,442 per-interval frequency comparisons match
with zero mismatches. Before: 1 of 1196.
### What's NOT solved
- **MicL channel conversion to dB(L)** — the codec emits MicL as
raw ADC counts (same format as geo channels), but BW's ASCII export
shows mic in dB(L) with ~6 dB quantization steps. Need to map
ADC counts → dB(L) for direct comparison; likely
`dB = 20*log10(|counts|) + offset` or similar.
- **Variable-prefix segment descriptors** — 3 of the 75 ground-truth
production events still truncate. The walk reaches a segment header
whose channel-id field is preceded by a *variable-width* prefix (2, 4
or 6 bytes observed; the standard tagless form always has 4), carrying
an `01 00` marker instead of `02 00`. The marker is **not** simply an
anchor count — `01 00` records appear with both 2- and 4-byte anchor
fields in the same file. Examples: `BE12599/N599LPNB.JF0W` @1155,
`BE12599/N599LPWJ.980W` @849, `BE9558/K558LOF2.820W` @1485.
(The series-3 histogram codec was fixed 2026-08-25 — see below.)
- ~~**Micromate (UM-series) IDF decode is ~1000× low**~~ — FIXED 2026-09-10.
`UM11402_20260406130113.IDFW` now decodes Tran 1.1168 / Vert 4.3220 /
Long 0.9135, matching the device report exactly. Root cause was the
body-offset search landing inside a record header plus the unhandled
`00 00` record mode, not anything UM-specific.
- ~~**Thor IDF per-count LSB**~~ — RESOLVED 2026-09-10. The 0.983 ratio was
exactly `0.0003 / 0.000310308`. Thor's geo LSB is **0.000310308 in/s per
count** (full scale 10.0 in/s = 32226.05 counts), pinned to ±6e-11 by
intersecting 991,415 rounding constraints from Thor's own exports and
corroborated by the ±full-scale seed (`±32226`) left in unwritten IDFH
interval slots. Series-3's 32000-count scale does **not** carry over.
Note `10.0/32226` is very slightly wrong — see
`docs/idf_protocol_reference.md`.
### Decoded sample counts (across the fixture bundle)
| Event | Tran | Vert | Long | Total |
|---|---|---|---|---|
| event-a | 3328 | 3328 | 3328 | **9984** ← full event |
| event-b | 2304 | 2304 | 2304 | **6912** ← full event |
| event-c | 1280 | 1280 | 1280 | 3840 ← full event |
| event-d | 1280 | 1280 | 1280 | 3840 ← full event |
| JQ0 | 3328 | 3328 | 3328 | **9984** ← full event |
| V70 | 3328 | 3328 | 3328 | **9984** ← full event |
| SP0 | 3328 | 3328 | 3328 | **9984** ← full event |
| SS0 | 3078 | 3072 | 3072 | 9222 (1–7 tail samples missing) |
| SV0 | 3078 | 3072 | 3072 | 9222 (1–7 tail samples missing) |
**Total: 72,972 ADC samples verified byte-exact, zero errors.**
7 of 9 fixture events decode end-to-end across all three geo channels.
The remaining two (SS0 / SV0) decode all but the last 1–7 samples per
channel — a minor walker edge case.
### Production-code status (updated 2026-05-11 late)
`client.py:_decode_a5_waveform` now uses the verified codec via
`waveform_codec.decode_a5_frames()` — which calls
`blastware_file.extract_body_bytes()` to reconstruct the BW-binary
body from A5 frames, then `decode_waveform_v2()` to decode samples,
then `decoded_to_adc_counts()` to scale to int16 ADC counts (geos × 16;
mic pass-through). The `.h5` sidecars SFM produces now contain
correct samples for any event without walker edge cases.
**Geo full scale is 32000 ADC counts, NOT 32768** (fixed 2026-08-25).
One decoder unit = 16 ADC counts = exactly 0.005 in/s, so
`10.000 in/s / (0.005/16)` = 32000. Consumers must use
`sfm.event_hdf5._GEO_INT16_FS` / `event_file_io._GEO_INT16_FS` (both
32000). Dividing by 32768 reads every geophone sample 2.34% low —
that was a live bug in both modules until 2026-08-25. Mic is
unaffected (it back-solves its scale from the device-reported peak).
The original int16 LE decoder is preserved as
`_decode_a5_waveform_LEGACY` for reference but is not called.
MicL → dB(L) conversion utility:
`waveform_codec.mic_count_to_db(count)` — `count=±1 → ±81.94 dB`;
`count=813 → 140.14 dB` (matches BW display).
### Test fixtures
`tests/fixtures/decode-re-5-8-26/` and `tests/fixtures/5-11-26/` —
nine BW binary + ASCII pairs captured from a live BE11529. The
5-11-26 high-amplitude bundle (PPV 6–7 in/s) is what cracked the Tran
codec; the V70 (mic-heavy) + JQ0 (Vert-heavy) pair cracked the `00 NN`
RLE rule.
If the user uploads new events for codec RE, they go directly into a
dated subdirectory under `tests/fixtures/` (e.g. `tests/fixtures/5-18-26/`).
There used to be a separate `decode-re/` upload mirror but it was
removed once the fixtures directory became the canonical location.
---
## Protocol fundamentals
### DLE framing
@@ -1925,8 +1353,6 @@ body) because writing a dial string may require DLE escaping for embedded contro
## What's next
**See [README.md → Roadmap (Future)](README.md#roadmap-future) for the canonical deferred-work list.** This section is kept as a status log of in-progress / recently-shipped technical details (encoding schemes, byte layouts, etc.) that are too low-level for the README's roadmap.
- **Database** — SQLite store for events + monitor log entries; dedup by key; queryable
- **Histograms** — decode histogram-mode A5 data (noise floor tracking)
- **Blastware-compatible file output** — `write_blastware_file()` and `write_mlg()` implemented. `blastware_filename()` generates correct Blastware filenames (AB0 for direct, AB0W/AB0H for ACH). **Confirmed BYTE-PERFECT against BW reference (v0.14.3, 2026-05-05):** when fed the BW 5-1-26 3-sec capture's A5 frames, the SFM-built file matches BW's saved `M529LKIQ.G10` byte-for-byte (8708 bytes, 0 differences). Live SFM downloads of event 0 (3-sec) and event 1 (3-sec continuation) both open cleanly in Blastware with full Event Reports, frequency analysis, and waveform plots. Body assembly is just contiguous concatenation of frame contributions in stream order (probe → meta@0x1002 → meta@0x1004 → samples → TERM); no stripping, no overlay, no special handling. Histogram+Continuous mode deferred (5A stream for those events embeds histogram interval records that may need different handling — untested under v0.14.x). Extension mapping: extensions encode timestamp (AB0T for ACH, AB0 for direct), NOT recording mode. Filename format: `<prefix_letter><serial3><4-char-base36-stem><ext>`
@@ -1983,4 +1409,4 @@ body) because writing a dial string may require DLE escaping for embedded contro
To parse BW TX captures: use `bridges/captures/` scripts or adapt the `find_write_frames()` pattern
in `/tmp/analyze_write_payload.py` — it correctly handles `0x10 0x03` DLE-escaped ETX bytes
inside write frame data (the naive parser terminates early at the escaped `0x03`).
inside write frame data (the naive parser terminates early at the escaped `0x03`).
Dockerfile
-31
View File
@@ -1,31 +0,0 @@
FROM python:3.11-slim
WORKDIR /app
# tzdata is required for the TZ env var to take effect (python:slim
# omits the timezone database). Without it, datetime.now() / logging
# / matplotlib all stay in UTC regardless of TZ. Default zone gets
# set further down via ENV; users override per-deployment via the
# `TZ` env var in docker-compose.
RUN apt-get update && \
apt-get install -y --no-install-recommends curl tzdata && \
rm -rf /var/lib/apt/lists/*
# Default display timezone — applied to server logs, datetime.now(),
# matplotlib rendered timestamps, and any naïve-vs-aware datetime
# conversions in the PDF renderer. Override via TZ env var in
# docker-compose; storage in the DB is always UTC regardless.
ENV TZ=America/New_York
COPY pyproject.toml requirements.txt ./
COPY minimateplus ./minimateplus
COPY micromate ./micromate
COPY sfm ./sfm
COPY bridges ./bridges
COPY scripts ./scripts
RUN pip install --no-cache-dir -e .
EXPOSE 8200
CMD ["python", "-m", "uvicorn", "sfm.server:app", "--host", "0.0.0.0", "--port", "8200"]
README.md
+34 -282
View File
@@ -1,11 +1,7 @@
# seismo-relay `v0.31.0`
# seismo-relay `v0.15.0`
A ground-up replacement for **Blastware** — Instantel's aging Windows-only
software for managing seismographs. Supports both the **MiniMate Plus
(Series III)** and the **Micromate (Series IV / "Thor")** families:
Series III via the live RS-232 / TCP wire protocol *and* Blastware ACH file
ingest; Series IV currently via Thor TXT-paired IDF file ingest, with the
binary codec on the roadmap.
software for managing MiniMate Plus seismographs.
Built in Python. Runs on Windows, Linux, or macOS. Connects to instruments
over direct RS-232 or cellular modem (Sierra Wireless RV50 / RV55).
@@ -18,48 +14,11 @@ over direct RS-232 or cellular modem (Sierra Wireless RV50 / RV55).
> byte-perfect against Blastware captures across 2-sec, 3-sec, and 10-sec
> events.** Generated `.G10` / `.AB0` files open cleanly in Blastware with
> full Event Reports, frequency analysis, and waveform plots.
> **v0.16.0 (2026-05-11)** adds BW ASCII report ingestion to
> `/db/import/blastware_file` — paired with **series3-watcher v1.5.0**,
> every Blastware ACH event lands in SeismoDb with device-authoritative
> peaks, project metadata, sensor self-check, and ZC/Time-of-Peak data,
> without depending on the still-undecoded waveform body codec.
> **v0.18.0 (2026-05-19)** adds Thor / Micromate Series IV ingest at
> `/db/import/idf_file` — paired with **thor-watcher v0.3.0**, every
> `.IDFH` / `.IDFW` event file (plus its `.txt` sidecar) lands in
> SeismoDb the same way BW events do. See
> [`docs/idf_protocol_reference.md`](docs/idf_protocol_reference.md) for
> the IDF format reference and reverse-engineering plan.
> **v0.19.0 (2026-05-20)** separates Series III and Series IV at the
> code level: new `micromate/` package alongside `minimateplus/`, new
> `events.device_family` DB column ("series3" / "series4") so the UI
> and storage layer dispatch deterministically instead of sniffing
> filenames. Self-applying migration backfills existing rows from the
> binary filename extension.
> **v0.20.0 (2026-05-28)** closes out the Event-Report PDF iteration
> started in v0.17.x: histogram layouts render correctly against BW
> reference PDFs, the ASCII parser handles real-world edge cases
> (`OORANGE`, `>100 Hz`, histogram timestamps), and per-channel ZC
> Freq is surfaced in both modals (event browser + main webapp).
> Adds a server-wide `TZ` env var so operator-visible timestamps
> render in local time instead of UTC. New
> `scripts/backfill_sidecars.py --reparse-txt` lets parser fixes be
> applied retroactively to existing events without re-forwarding,
> using the `.TXT` files preserved at ingest time.
> **v0.21.0 (2026-05-29)** is the Thor / Series IV decoder release —
> `micromate/idf_file.read_idf_file()` now decodes both IDFW
> (waveform) and IDFH (histogram) binaries (87–99% sample fidelity
> on quiet IDFW events; all 859 IDFH corpus files decode cleanly).
> A new `micromate/idf_to_bw_report.py` adapter projects parsed
> Thor reports into the BW-shaped sidecar block, so Thor events
> flow through the existing Event Report PDF pipeline without a
> separate renderer. Terra-View v0.13.0 ships in parallel and
> closes Phase 1 of the SFM integration — see its CHANGELOG.
> **v0.22.0 (2026-07-03)** adds the SFM side of the full-snapshot
> bundle: `GET /db/snapshot` (WAL-safe DB copy), `GET /db/waveforms/
> recent.zip` (recent events' waveform files), and a gated
> `POST /db/restore` (`SFM_DB_RESTORE_ENABLED`, dev-only). Terra-View
> v0.17.0 drives them to pull a one-pass prod→dev refresh.
> See [CHANGELOG.md](CHANGELOG.md) for full version history.
> **v0.15.0 (2026-05-07)** adds layered per-event storage (BW binary +
> raw 5A pickle + HDF5 + `.sfm.json` sidecar), a plot-ready
> `sfm.plot.v1` JSON shape with server-side ADC-to-physical-units
> conversion, and a BW-file importer for ingesting externally-produced
> events. See [CHANGELOG.md](CHANGELOG.md) for full version history.
---
@@ -69,26 +28,17 @@ over direct RS-232 or cellular modem (Sierra Wireless RV50 / RV55).
seismo-relay/
├── seismo_lab.py ← Main GUI (Bridge + Analyzer + Download + Console tabs)
│
├── minimateplus/ ← Series III (MiniMate Plus) client library
├── minimateplus/ ← MiniMate Plus client library
│ ├── transport.py ← SerialTransport, TcpTransport, SocketTransport
│ ├── protocol.py ← DLE frame layer, SUB command dispatch
│ ├── client.py ← High-level client (connect, get_events, delete_all_events, push_config, get_call_home_config, …)
│ ├── framing.py ← Frame builders, DLE codec, S3FrameParser
│ ├── models.py ← DeviceInfo, Event, ComplianceConfig, MonitorLogEntry, CallHomeConfig, …
│ ├── bw_ascii_report.py ← Parse BW per-event ASCII reports (.TXT sidecars)
│ ├── event_file_io.py ← Read BW binaries, write .sfm.json sidecars
│ └── blastware_file.py ← Write events to Blastware-compatible .AB0 files
│
├── micromate/ ← Series IV (Micromate / Thor) client library (NEW v0.19)
│ ├── models.py ← IdfEvent, IdfReport, IdfPeaks, IdfProjectInfo, IdfSensorCheck (mic in native dB(L))
│ ├── idf_ascii_report.py ← Parse Thor .IDFW.txt / .IDFH.txt event sidecars
│ ├── idf_file.py ← Binary codec for .IDFW + .IDFH (v0.21.0+)
│ └── idf_to_bw_report.py ← Adapter projecting Thor IDF into the BW report shape (v0.21.0+)
│
├── sfm/ ← SFM REST API server (FastAPI, port 8200)
│ ├── server.py ← Live device endpoints + DB query + ingest endpoints + caching
│ ├── database.py ← SeismoDb — SQLite persistence (events, monitor_log, ach_sessions)
│ ├── waveform_store.py ← On-disk store for BW + IDF event binaries + .sfm.json sidecars
│ ├── server.py ← Live device endpoints + DB query endpoints + caching
│ ├── database.py ← SeismoDb — SQLite persistence (events, monitor_log, ach_sessions, sessions table)
│ └── sfm_webapp.html ← Embedded web UI with Call Home config tab
│
├── bridges/
@@ -105,8 +55,7 @@ seismo-relay/
│ └── frame_db.py ← SQLite frame database
│
└── docs/
├── instantel_protocol_reference.md ← Series III protocol spec (the Rosetta Stone)
└── idf_protocol_reference.md ← Series IV (Thor IDF) format reference + codec RE plan
└── instantel_protocol_reference.md ← Reverse-engineered protocol spec
```
---
@@ -198,35 +147,11 @@ Query the SQLite database written by `ach_server.py`. All read-only except
| Method | URL | Description |
|--------|-----|-------------|
| `GET` | `/db/units` | All known serials with summary stats |
| `GET` | `/db/events` | Triggered events (filter by serial, date range, false_trigger). Response rows include `device_family` ("series3" / "series4") so clients dispatch on unit type without sniffing filenames. |
| `GET` | `/db/events` | Triggered events (filter by serial, date range, false_trigger) |
| `GET` | `/db/monitor_log` | Monitoring intervals |
| `GET` | `/db/sessions` | ACH call-home session history |
| `PATCH` | `/db/events/{id}/false_trigger?value=true` | Flag / unflag false triggers |
### File ingest endpoints
Used by watcher daemons to push field-collected event files into the SFM DB
+ waveform store. Both accept multipart uploads of binary event files
optionally paired with their ASCII sidecar reports; both dedup by
`(serial, timestamp)` and UPSERT device-authoritative fields on re-import.
| Method | URL | Description |
|--------|-----|-------------|
| `POST` | `/db/import/blastware_file` | Series III: `.AB0*` / `.N00` binaries + paired `_ASCII.TXT`. Source: `series3-watcher`. |
| `POST` | `/db/import/idf_file` | Series IV: `.IDFH` / `.IDFW` binaries + paired `.IDFW.txt` / `.IDFH.txt`. Source: `thor-watcher`. |
### DB snapshot / restore endpoints
Back the "full snapshot bundle" prod→dev refresh Terra-View orchestrates
(Settings → Database). Snapshot/zip are read-only; restore is gated and
dev-only.
| Method | URL | Description |
|--------|-----|-------------|
| `GET` | `/db/snapshot` | WAL-safe point-in-time copy of `seismo_relay.db` via the SQLite online backup API. |
| `GET` | `/db/waveforms/recent.zip?n=…` | Zips the on-disk files (event file + `.h5` / sidecars) for the *n* most-recent events. |
| `POST` | `/db/restore` | Validate-first restore of an uploaded DB + waveforms; takes a WAL-safe pre-restore safety backup, path-traversal-guards the zip. Gated by `SFM_DB_RESTORE_ENABLED` (dormant → 404). |
---
## minimateplus library
@@ -288,77 +213,22 @@ not per individual event).
---
## micromate library
Series IV / Thor support, sibling to `minimateplus`. Currently scoped to
offline-file ingest from Thor's TXT exporter; live-device protocol is
deferred until the binary codec is cracked.
```python
from micromate import IdfEvent, parse_idf_report
# Parse a .IDFW.txt / .IDFH.txt sidecar (1014 example files round-trip cleanly)
text = open("UM11719_20231219162723.IDFW.txt").read()
report_dict = parse_idf_report(text) # permissive dict
# Wrap into a typed event using the device-native binary filename
event = IdfEvent.from_report(report_dict, "UM11719_20231219162723.IDFW")
event.serial # "UM11719"
event.kind # "Waveform" or "Histogram"
event.peaks.transverse_ips # 0.0251 (in/s, native unit)
event.peaks.mic_pspl_dbl # 99.4 (dB(L), Thor's native mic unit — NOT psi)
event.project_info.project # "UPMC Presby-Loc 3-Level1-1R Elevator Rm"
event.sensor_check.tran # True (passed self-check)
event.firmware_version # "Micromate ISEE 11.0AK"
event.calibration_text # "November 22, 2023 by Instantel"
# Bridge to the existing minimateplus.Event shape for the DB / sidecar paths
# (waveform_key is a 16-byte sha256 prefix when ingesting from a binary file)
bridged_event = event.to_minimateplus_event(waveform_key=b"\x00" * 16)
```
The binary codec (`.IDFW` / `.IDFH` event files themselves) is on the
roadmap — see [`docs/idf_protocol_reference.md`](docs/idf_protocol_reference.md)
for everything known so far, the two observed file signatures, and the
reverse-engineering plan. The `micromate/idf_file.py` stub is where
`read_idf_file()` will land.
---
## Database
`ach_server.py` and the file-ingest endpoints write to
`bridges/captures/seismo_relay.db` (SQLite, WAL mode) via the `SeismoDb`
persistence layer. Three tables, all unit-keyed by serial number:
`ach_server.py` writes to `bridges/captures/seismo_relay.db` (SQLite, WAL mode) using the
`SeismoDb` persistence layer. Four tables, all unit-keyed by serial number:
| Table | Key | Contents |
|-------|-----|----------|
| `ach_sessions` | UUID | Per-call-home audit record: serial, timestamp, peer IP, events_downloaded, monitor_entries, duration_seconds |
| `events` | UUID, UNIQUE(serial, timestamp) | Triggered events: timestamp, Tran/Vert/Long/VectorSum/Mic PPV, project/client/operator/sensor_location strings, sample_rate, record_type, false_trigger flag, **`device_family`** ("series3" / "series4"), `blastware_filename` (binary at-rest in `waveforms/`), sidecar references |
| `monitor_log` | UUID, UNIQUE(serial, start_time) | Monitoring intervals: serial, waveform_key, start_time, stop_time, duration_seconds, geo_threshold_ips |
| `events` | UUID, UNIQUE(serial, waveform_key) | Triggered events: timestamp, Tran/Vert/Long/VectorSum/Mic PPV, project/client/operator/sensor_location strings, sample_rate, record_type, false_trigger flag |
| `monitor_log` | UUID, UNIQUE(serial, waveform_key) | Monitoring intervals: serial, waveform_key, start_time, stop_time, duration_seconds, geo_threshold_ips |
| `events.false_trigger` | Boolean flag | PATCH endpoint to mark/unmark false triggers for review |
**Deduplication is by `(serial, timestamp)`** — the device clock is the
stable natural key. Repeat call-homes or re-runs UPSERT the row in place,
refreshing every device-authoritative field (peaks, project strings,
sample_rate, file references) so the latest writer wins. `false_trigger`
and `device_family` are preserved across UPSERTs. Earlier versions used
`(serial, waveform_key)` for dedup, but the device's event-key counter
resets to `0x01110000` after every erase, so timestamps are the correct
dedup field. Migration handles the transition transparently on first
startup.
**`device_family` (added v0.19.0)** discriminates Series III from Series
IV at the SQL level. Set by every import path; the UI dispatches on it
to render mic units correctly (Series III: psi → dBL conversion; Series
IV: native dBL passthrough). Existing rows are backfilled at first
startup of v0.19.0+ by sniffing the binary filename extension.
The on-disk waveform store lives at `bridges/captures/waveforms/<serial>/`
and holds the original event binaries (BW `.AB0*` / `.N00` for Series III,
`.IDFH` / `.IDFW` for Series IV) plus their `.sfm.json` review/metadata
sidecars. Series III events also produce `.a5.pkl` source-frame pickles
and `.h5` clean-waveform exports; Series IV doesn't yet (pending codec).
Deduplication is by `(serial, waveform_key)` — repeat call-homes or re-runs never
produce duplicate rows. Post-erase key reuse is handled automatically via the
high-water mark in `ach_state.json`. Key-based state tracking allows correct
handling of device erasures (external or post-download).
---
@@ -440,27 +310,18 @@ Use **com0com** or **VSPD** to create the virtual COM pair on Windows.
## Key Features
**Series III (MiniMate Plus) device support:**
- [x] Full read/write/erase pipelines over RS-232 or TCP/cellular
**Device support:**
- [x] Full read/write/erase pipelines
- [x] Compliance config (recording mode, sample rate, histogram interval, geo sensitivity, project strings)
- [x] Auto Call Home config (read/write ACH settings, dial string, time slots, retries)
- [x] Monitor control (start/stop, status polling, battery/memory)
- [x] Monitor log entries (continuous monitoring intervals without full waveform download)
- [x] Blastware file ingest at `/db/import/blastware_file` (paired with `series3-watcher`)
**Series IV (Micromate / Thor) device support:**
- [x] Thor IDF file ingest at `/db/import/idf_file` (paired with `thor-watcher`, v0.18.0+)
- [x] Native `IdfEvent` / `IdfReport` typed models — mic in dB(L), full title strings, sensor self-check, calibration, firmware version
- [x] Parser verified against 1,014 paired `.txt` sidecars in `thor-watcher/example-data/`
- [x] Binary `.IDFW` / `.IDFH` codec — ✅ v0.21.0. IDFW reuses `decode_waveform_v2()` on the body at offset `0x0f1f` (87–99% sample fidelity on quiet events); IDFH has a dedicated segment-based decoder (all 859 corpus files decode, 181,071 intervals total). See `micromate/idf_file.py` + `docs/idf_protocol_reference.md`.
- [ ] Live-device protocol — pending codec
**Data persistence:**
- [x] SQLite database (`seismo_relay.db`) with `events`, `monitor_log`, `ach_sessions` tables
- [x] Per-row `device_family` column ("series3" / "series4") for clean UI / unit-of-measurement dispatch (v0.19.0+)
- [x] Deduplication by `(serial, timestamp)` — natural key handles post-erase counter resets
- [x] UPSERT on re-import refreshes every device-authoritative field (peaks, project, sample_rate); preserves operator review state (`false_trigger`)
- [x] Post-erase key-reuse detection (tracks high-water mark in `ach_state.json`)
- [x] SQLite database (`seismo_relay.db`) with 4 tables: ach_sessions, events, monitor_log, plus false_trigger flag
- [x] Deduplication by waveform key (handles re-runs and repeat call-homes)
- [x] Post-erase key-reuse detection (tracks high-water mark)
- [x] Session state (`ach_state.json`) with downloaded keys and max key
**REST API:**
- [x] Live device endpoints with in-memory caching (`_LiveCache`)
@@ -468,7 +329,6 @@ Use **com0com** or **VSPD** to create the virtual COM pair on Windows.
- [x] DB query endpoints (units, events, monitor_log, sessions, false_trigger PATCH)
- [x] Call Home config read/write endpoints
- [x] Blastware file download endpoint (`/device/event/{index}/blastware_file`)
- [x] Import endpoints for both device families (`/db/import/blastware_file`, `/db/import/idf_file`)
**File output (v0.7+, byte-perfect as of v0.14.3):**
- [x] Blastware-compatible `.AB0` / `.G10` file generation (waveform + metadata)
@@ -496,118 +356,10 @@ Use **com0com** or **VSPD** to create the virtual COM pair on Windows.
## Roadmap (Future)
> **Where it stands *today*** — an honest per-capability maturity assessment,
> what to rely on, known issues, and the gap to a real tool:
> [`docs/sfm_tool_status.md`](docs/sfm_tool_status.md). This section covers
> where it is *going*.
### Strategic direction — where this is going
seismo-relay is being built as a **suite of cooperating components**
that together replace and improve on Blastware's role. Three logical
tiers:
1. **SFM** (device-side) — owns the active connection to a physical
unit. Today: `minimateplus/`, `/device/*` HTTP endpoints,
`seismo_lab.py`. Future: live Thor / Micromate support.
2. **SDM** (data-side) — owns the database, waveform store, ingest
pipelines, and the read-API that Terra-View consumes. Today this
code lives under `sfm/` for historical reasons; the role has
migrated and the eventual rename is on the long-tail cleanup list.
3. **Codec library** — pure data-interpretation: `minimateplus/*_codec.py`,
`bw_ascii_report.py`, `micromate/idf_*.py`. Used by both SFM and
SDM, depends on neither.
Terra-View is downstream of SDM for fleet listings, event detail, etc.
The long-term vision adds a **second link** from Terra-View → SFM for
direct device interaction (see below).
The codec work in this repo isn't trying to replace BW's network
layer — BW's ACH file forwarding and Thor's IDF call-home are
battle-tested. The value is in the receiving and processing side: turn
the stream of binary+ASCII pairs into something users can search,
filter, alert on, and report from.
### Terra-View ↔ SFM device control (the long-term vision)
Today Terra-View only reads from SDM (event listings, dashboards,
project reports). When a unit goes missing — operator notices in the
Terra-View dashboard — there's no way to *do* anything from the UI.
The path of least resistance is to RDP into a Windows box and open
Blastware, which defeats the purpose of having Terra-View.
Target experience:
- Operator notices a unit in Terra-View dashboard hasn't called in.
- Clicks unit detail → "Connect to Device" button.
- Terra-View opens an embedded view (modal or side-panel) that talks
to SFM's `/device/*` endpoints over the network.
- Live view: device clock, battery, memory, current monitor status.
- Actions: start/stop monitoring, push compliance config changes, pull
fresh events, run a sensor self-check, change call-home settings.
- Audit log: every connect / action recorded in SDM for the unit
history.
Implementation steps (concrete):
- [ ] **SFM authentication & authorization layer.** Today `/device/*`
endpoints are unauthenticated — anyone on the network can call
them. Need at minimum a token-based auth, ideally with a "who
can connect to which units" mapping. Hard prerequisite for
letting Terra-View users into the control surface.
- [ ] **Terra-View "Connect to Device" entry point** on the unit
detail page. Renders only when unit has connection info on file
and the user has permission.
- [ ] **Embedded live-monitor view** in Terra-View — equivalent to
`seismo_lab.py`'s Bridge tab, but in the browser. Polls SFM's
`/device/monitor/status` on an interval; sends start/stop via
`/device/monitor/{start,stop}`.
- [ ] **Action history** — every connect / push / action call records
a row in `unit_history`, viewable on the unit detail page.
- [ ] **Series IV live-device support in SFM** — currently `/device/*`
only supports MiniMate Plus. Blocks "Connect to Device" for
Thor units until done. Depends on Thor wire-protocol capture
and a `micromate/` parallel of the `minimateplus/` modules.
### High-impact (unblocks product features)
- [ ] **Series III waveform body codec reverse-engineering.** The 5A bulk-stream body is some kind of compressed/encoded format (not raw int16 LE as previously assumed — see §7.6.1 retraction in `docs/instantel_protocol_reference.md`). Structural framing is ~50% decoded on branch `claude/codec-re-cBGNe` (tagged-block walker, segment counters); per-byte sample mapping is still open. Until this lands, the in-app waveform viewer renders garbage and BW-import peak values fall back to `_peaks_from_samples()` saturation noise. Workaround: pair every BW-imported event with its `_ASCII.TXT` so the device-authoritative peaks land in the DB regardless of codec.
- [x] **Series IV (Thor IDF) binary codec reverse-engineering.** ✅ v0.21.0 — `micromate/idf_file.read_idf_file()` decodes both IDFW (waveform body at offset `0x0f1f`, reusing `decode_waveform_v2()`; 87–99% sample fidelity on quiet events) and IDFH (dedicated segment-based decoder: all 859 corpus files decode, 181,071 intervals, peaks within ~1.8% of sidecar values). `WaveformStore.save_imported_idf` now also projects parsed Thor data into a `bw_report` block via `micromate/idf_to_bw_report.py` so Thor events render in the existing Event Report PDF pipeline without a separate renderer.
- [ ] **In-app waveform viewer accuracy.** Depends on Series III codec decode. Plot.v1 JSON pipeline + viewer skeleton already exist; will start showing real waveforms automatically once `_decode_a5_waveform` produces correct samples. Series IV waveforms come online when the IDF codec lands.
- [ ] **Series IV live-device support.** Once the IDF binary is decoded, extend `micromate/` with `transport.py` / `framing.py` / `protocol.py` / `client.py` mirroring the `minimateplus/` package layout — depends on capturing Thor's wire protocol (TCP / RS-232 captures TBD).
- [ ] **Terra-view integration** — seismo-relay router, unit detail page, VISON-style event listing.
- [ ] **Vibration summary reports** — highest legit PPV per project → Word doc (false-trigger filtering first).
### BW ASCII report parser enhancements (built in v0.16.0)
- [x] **PPV field misses on certain TXT formats.** ✅ v0.20.0 — root cause was the `OORANGE` (Out Of Range) saturation marker that BW writes when a channel exceeds its full-scale; `_parse_number()` returned None for the non-numeric value. Parser now substitutes `geo_range_ips` as a lower bound + sets `ppv_saturated` flag. All 5 prod events (T190LD5Q.LK0W, T438L713.RY0W, K557L3YM.OE0W, + 2 others) now parse cleanly.
- [x] **Histogram-specific structural fields.** ✅ v0.20.0 — `Histogram Start/Stop Time+Date`, `Number of Intervals`, `Interval Size`, per-channel `Peak Time` + `Peak Date`, and `Peak Vector Sum Date` all parse now. Land in the sidecar's `bw_report.histogram` block.
- [ ] **Histogram interval bin-table parsing.** Trailing 792-row table (per-interval Peak/Freq per channel + MicL) in histogram TXTs is unparsed. Probably too big for the sidecar JSON; may want a separate `.histogram.h5` companion file.
- [x] **`>100 Hz` value parsing.** ✅ v0.20.0 — parser now mirrors the OORANGE pattern: stores 100.0 on `zc_freq_hz` + sets `zc_freq_above_range` flag. PDF + both modals render `>100 Hz` instead of `—`.
### Ingestion gaps
- [ ] **MLG forwarding.** `series3-watcher` forwards event binaries + their `_ASCII.TXT` reports, but skips `.MLG` per-unit monitor log files entirely. Adding an `POST /db/import/mlg_file` endpoint + watcher scan path would populate `monitor_log` for non-ACH-routed units (coverage queries, "was this unit monitoring on date X" lookups).
- [ ] **0C-record raw bytes persistence in the sidecar.** Currently on branch `claude/codec-re-cBGNe` as commit `a187124`; cherry-pick if useful as a standalone fix. Preserves the 210-byte 0C record under `extensions.raw_records.waveform_record_b64` so future field-offset analysis (Peak Acceleration / Time of Peak / etc. — the fields BW computes client-side from samples) can run offline.
### Operational
- [ ] **`series3-watcher` file archive manager** — 90-day-old events moved to `<watch_folder>_archive/<year>/<month>/` subfolders. Plan drafted in `claude/codec-re-cBGNe`'s plan-mode session; awaiting a 5-minute test on whether Blastware UI walks subfolders before any code lands (determines layout: in-place subfolders vs sibling archive).
- [ ] **Compliance config encoder** — build raw write payloads from a `ComplianceConfig` object.
- [ ] **Modem manager** — push RV50/RV55 configs via Sierra Wireless API.
- [ ] **Call Home dial_string write support** (requires DLE escaping for embedded control characters).
- [ ] **Histogram mode recording support** (5A stream analysis for mode 0x03 — separate from histogram ASCII parsing above).
### Test coverage
- [ ] Verify 30-sec event download — body may exceed `0xFFFF` and force the device into a different `end_key` encoding (none of the 2/3/10-sec test cases hit this boundary).
- [ ] Histogram mode (0x03) write via SFM — confirmed working for Single Shot / Continuous / Histogram+Continuous; Histogram (0x03) needs a live test from a non-Histogram starting state.
### Lower-priority cleanups
- [ ] Compliance write anchor-9 cleanup — when changing recording_mode via SFM, a spurious `0x10` may persist after Histogram→other mode transitions. Doesn't affect device operation but differs from BW's byte-perfect output.
- [ ] Locate "Sensor Check" byte in compliance config (need capture with Disabled vs Before-monitoring).
- [ ] Call Home — map time slots 3/4 offsets; confirm `modem_power_relay_enabled`.
- [ ] RV55 DCD/DTR — newer RV55 firmware doesn't assert DCD by default; units don't resume monitoring after call-home disconnect (`--restart-monitoring` flag deferred).
- [ ] **NULL-timestamp duplicate-row dedup.** A small handful of events (2 known on prod as of 2026-05-22) have `events.timestamp IS NULL` because the codec couldn't extract a timestamp from the binary footer. The `UNIQUE(serial, timestamp)` constraint doesn't fire on `NULL` (SQL semantics: `NULL ≠ NULL`), so every `--force` backfill INSERTs a new row instead of UPSERTing the existing one. Cleanup: a one-shot SQL query that keeps only the newest row per `(serial, blastware_filename)` and deletes the rest. Longer-term: extend the unique key to `(serial, COALESCE(timestamp, blastware_filename))` or reject inserts with NULL timestamp.
- [ ] **Histogram body sub-format with `byte[5] != 0`.** ~3 events on prod (`T190LD5Q.LD0H`, `O121L4L1.GU0H`) use a histogram body my walker doesn't recognize — the first block has `byte[5] = 0x01` or `0x07` instead of `0x00`, and the entire body lacks the `1e 0a 00 00` tail signature. Codec returns 0 valid blocks; their DB PVS comes from the bw_report ASCII overlay (which BW computed from the same binary, so the DB columns are correct). Only the `.h5` waveform plot is empty. Cracking the sub-format would unlock the plot. Needs binary+ASCII pairs from a few `byte[5]!=0` events; same RE approach as the K558 case.
- [ ] **Histogram body sub-format with `byte[5] == 0x00` but undecodable.** Observed 2026-05-28 on BE17353 (S353) events: `S353L4H2.FZ0H`, `S353L4H2.P00H`, `S353L4H3.7O0H`, `S353L4H3.E10H`. Body starts `00 00 00 01 0a 00 XX 00 ...` which LOOKS like a valid histogram block header (marker 0x000a at byte[4:6] ✓, byte[5]=0x00 normal-format ✓), but the walker finds zero data blocks across the whole body. Likely an extra header before the block stream OR a different tail signature than `1e 0a 00 00`. Smaller body lengths (1900-2100 bytes) suggest these may be short-recording histogram variants. Same operational impact as the byte[5]!=0 case: event ingests cleanly, DB peaks correct via bw_report overlay, only the chart is empty. Worth dumping a hex view of one body to diagnose.
- [ ] **Sensor-check waveform extraction from the BW binary.** BW's Event Report PDFs include a narrow panel on the right side of the waveform plot showing each channel's response to the sensor self-check signal (a damped sinusoid for geo, sawtooth-at-test-freq for mic). Our parser captures the test RESULTS (`test_freq_hz`, `test_ratio`, `test_amplitude_mv`, `test_results` pass/fail) and the PDF + modal display them as text — but BW's per-sample sensor-check waveform isn't accessible to us today. Two paths to add it: (a) RE the binary to find where the sensor-check samples are stored — could be a section before STRT, after the footer, or in a separate sub-record; protocol reference doesn't currently mention it. (b) If samples aren't in the binary, synthesize a representative waveform from the test parameters (damped sinusoid at `test_freq_hz` with damping from `test_ratio`). Path (a) is the honest answer; path (b) is decorative. Until either lands, the text-only sensor-check display in the report is fine.
- [ ] Verify 30-sec event download — body may exceed `0xFFFF` and force the device into a different `end_key` encoding (none of 2/3/10-sec test cases hit this boundary)
- [ ] Terra-view integration — seismo-relay router, unit detail page, VISON-style event listing
- [ ] Vibration summary reports — highest legit PPV per project → Word doc (false trigger filtering first)
- [ ] Compliance config encoder — build raw write payloads from a `ComplianceConfig` object
- [ ] Modem manager — push RV50/RV55 configs via Sierra Wireless API
- [ ] Histogram mode recording support (5A stream analysis for mode 0x03)
- [ ] Call Home dial_string write support (requires DLE escaping for embedded control characters)
analysis/README.md
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# analysis/ — exploratory scripts for waveform-body RE
**These are scratch.** Run them, read them, copy them, but don't trust
them as documentation. When a finding is verified it gets promoted
to `minimateplus/waveform_codec.py` and `tests/test_waveform_codec.py`;
when it's wrong it stays here as a fossil.
Authoritative status lives in:
- `docs/waveform_codec_re_status.md` (current truth, working note)
- `minimateplus/waveform_codec.py` (verified implementation + docstring)
- `tests/test_waveform_codec.py` (regression locks against fixtures)
---
## Still useful
| File | What it does |
|---|---|
| `load_bundle.py` | Fixture loader. Parses BW binary + ASCII TXT into a `Bundle` dataclass with samples, metadata, body bytes. Used by most other scripts here. |
| `verify_tran.py` | Verifies `decode_tran_initial` against fixture ground truth across all events. Useful when you change the decoder and want a quick sanity check. |
| `inspect_5_11.py` | Inspects the 5-11-26 high-amplitude bundle's body structure, prints metadata, peaks, and block counts. |
| `walk_5_11.py` | Walks blocks for the 5-11-26 bundle and prints offset/tag/length/data. |
| `seg1_blocks.py` | Dumps all blocks in segment 1 of each event. The starting point for cracking multi-segment Tran continuation. |
| `full_tran.py` | Multi-segment Tran decoder attempt (broken — diverges at sample ~512). Useful as a starting scaffold for the next experiment. |
| `multi_segment.py` | Earlier multi-segment attempt with different segment-header consumption strategies. Records what didn't work. |
| `test_rle.py` | Tests `00 NN` interpretation as zero-RLE with different divisor values. Documents how the RLE rule was confirmed. |
## Superseded — keep for archaeology
| File | Superseded by |
|---|---|
| `walk_v2.py` … `walk_v5.py` | `walk_v6.py` and ultimately `minimateplus/waveform_codec.walk_body`. Each version represents one round of refinement. Don't read in isolation — read the diff between them to see what was learned. |
| `walk_chunks.py` | `walk_v6.py` / production walker |
| `decode_v1.py` | First naive decoder attempt. Wrong but readable. |
## Pure exploration — read if curious
| File | What it explored |
|---|---|
| `inspect_body.py` | Byte-frequency stats per event. Established that bytes 0x00 / 0x10 dominate. |
| `find_blocks.py` | Searched for repeating 2-byte tag patterns. |
| `find_signal_runs.py` | Searched for stretches of bytes that "look like a smooth signal" (small inter-byte deltas). Found the `20 NN` literal blocks. |
| `dump_head.py`, `dump_trailer.py`, `dump_around.py` | Hex dumpers at various body positions. |
| `compare_cd.py` | Byte-diff between event-c and event-d (same length, similar signal). Used to identify structural vs data bytes. |
| `brute_force.py` | Tested 96 combinations of channel-permutation × nibble-order × sign-convention × init-from-header on the quiet bundle. All failed because the quiet bundle had T[0]=T[1]=0, making the preamble undetectable. |
| `try_nibbles.py`, `try_layouts.py` | Earlier channel-interleaving hypotheses. All wrong. |
| `test_tran_continue.py` | Test of "Tran continues uninterrupted across `30 04` blocks" hypothesis. Disproven. |
---
## Adding new scripts
If you're picking up the codec work, feel free to add new scripts here.
Suggested conventions:
- Start the filename with what you're testing: `test_<hypothesis>.py`,
`verify_<piece>.py`, `inspect_<region>.py`.
- Print enough output that the reader can see exactly which events
match / diverge and where.
- When a finding is solid, move the verified logic to
`minimateplus/waveform_codec.py` and add a regression test in
`tests/test_waveform_codec.py` — don't leave the truth only in
this directory.
- If a script is fully superseded, leave it in place (don't delete) —
the fossil record is useful when re-evaluating hypotheses later.
analysis/brute_force.py
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@@ -1,93 +0,0 @@
"""Brute-force test channel permutations / nibble orders on event-d (simplest signal)."""
import sys
import itertools
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
from minimateplus.waveform_codec import walk_body
def s4(n):
return n if n < 8 else n - 16
def decode(body, channel_perm, nibble_order, sign_mode, init_from_header):
"""Try one decoder configuration on event-d. Returns first 8 cumulative samples per channel."""
blocks = walk_body(body)
# Initial values from bytes [4:7] if init_from_header else 0
if init_from_header:
init = [body[4] if body[4] < 128 else body[4] - 256,
body[5] if body[5] < 128 else body[5] - 256,
body[6] if body[6] < 128 else body[6] - 256,
0]
else:
init = [0, 0, 0, 0]
cur = list(init)
out = [[init[0]], [init[1]], [init[2]], [init[3]]] # sample 0 = init
nibble_idx = 0 # within delta stream; channel = channel_perm[nibble_idx % 4]
# Walk only the 10 NN data blocks
for blk in blocks:
if blk.tag_hi != 0x10:
continue
for byte in blk.data:
if nibble_order == 'high_first':
nib1, nib2 = (byte >> 4) & 0xF, byte & 0xF
else:
nib1, nib2 = byte & 0xF, (byte >> 4) & 0xF
for nib in (nib1, nib2):
if sign_mode == 'signed':
delta = s4(nib)
else:
delta = nib
ch = channel_perm[nibble_idx % 4]
cur[ch] += delta
if (nibble_idx + 1) % 4 == 0:
out[0].append(cur[0])
out[1].append(cur[1])
out[2].append(cur[2])
out[3].append(cur[3])
nibble_idx += 1
if len(out[0]) >= 16:
return out
return out
def best_match(pred, truth, n=10):
"""Sum of squared differences in first n samples."""
n = min(n, len(pred), len(truth))
return sum((pred[i] - truth[i])**2 for i in range(n))
def main():
b = load_bundle("event-d")
# truth in 16-count units
tr = {ch: [round(v * 200) for v in b.samples[ch]] for ch in ("Tran", "Vert", "Long")}
print("Truth event-d first 10 samples:")
for ch in ("Tran", "Vert", "Long"):
print(f" {ch}: {tr[ch][:10]}")
# Test 96 combinations
best = []
for perm in itertools.permutations([0, 1, 2, 3]):
for nibble_order in ('high_first', 'low_first'):
for sign in ('signed', 'unsigned'):
for init_h in (False, True):
decoded = decode(b.body, perm, nibble_order, sign, init_h)
# Score as TVL channel-sum
score = sum(
best_match(decoded[i], tr[ch], n=10)
for i, ch in enumerate(("Tran", "Vert", "Long"))
if i < 3
)
label = f"perm={perm} nib={nibble_order[:1]} sign={sign[:3]} init={init_h}"
best.append((score, label, decoded))
best.sort(key=lambda x: x[0])
print(f"\nTop 10 configurations:")
for s, lbl, dec in best[:10]:
print(f" score={s:>5} {lbl} T={dec[0][:8]} V={dec[1][:8]} L={dec[2][:8]}")
if __name__ == "__main__":
main()
analysis/compare_cd.py
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"""Compare event-c and event-d (same N_samples) to find header vs data bytes."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def main():
bc = load_bundle("event-c")
bd = load_bundle("event-d")
# Compare prefixes
nc, nd = len(bc.body), len(bd.body)
n = min(nc, nd)
diffs = []
for i in range(n):
if bc.body[i] != bd.body[i]:
diffs.append(i)
print(f"event-c body={nc}, event-d body={nd}")
print(f"Total diffs (first {n}): {len(diffs)}")
# Show common prefix
same_prefix = 0
for i in range(n):
if bc.body[i] == bd.body[i]:
same_prefix += 1
else:
break
print(f"Common prefix length: {same_prefix}")
print(f"event-c prefix: {bc.body[:same_prefix].hex(' ')}")
# Look for runs of common bytes
print(f"\nFirst 32 diff positions: {diffs[:32]}")
# Show the "diff fingerprint" of the first 100 bytes
print(f"\n pos c d")
for i in range(0, 100):
marker = " " if bc.body[i] == bd.body[i] else "*"
bd_b = bd.body[i] if i < nd else None
print(f" {i:>3} {bc.body[i]:02x}{marker} {bd_b:02x}" if bd_b is not None else f" {i:>3} {bc.body[i]:02x}{marker}")
if __name__ == "__main__":
main()
analysis/decode_v1.py
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@@ -1,99 +0,0 @@
"""
Decoder v1: nibble-pair signed deltas in 10 NN blocks, 4-channel round-robin.
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def s4(n):
return n if n < 8 else n - 16
def walk_blocks(body, start):
i = start
blocks = []
while i + 1 < len(body):
t0, t1 = body[i], body[i + 1]
if t0 == 0x10 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 // 2 + 2
data = bytes(body[i + 2 : i + length])
blocks.append(("10", t1, data))
i += length
elif t0 == 0x20 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 + 2
data = bytes(body[i + 2 : i + length])
blocks.append(("20", t1, data))
i += length
elif t0 == 0x00 and t1 % 4 == 0:
blocks.append(("00", t1, b""))
i += 2
elif t0 == 0x30 and t1 % 4 == 0 and 0 < t1 <= 0x10:
length = t1 * 4
data = bytes(body[i + 2 : i + length])
blocks.append(("30", t1, data))
i += length
elif t0 == 0x40 and t1 == 0x02:
length = 20
data = bytes(body[i + 2 : i + length])
blocks.append(("40", t1, data))
i += length
else:
blocks.append(("??", t0, bytes(body[i:i+8])))
break
return blocks
def decode_v1(body, start, n_samples):
"""Decode by accumulating nibble-pair deltas from all 10 NN blocks."""
blocks = walk_blocks(body, start)
# 4 channels: T, V, L, M
cur = [0, 0, 0, 0]
out = [[], [], [], []]
sample_index = 0 # how many sample-sets emitted
for typ, NN, data in blocks:
if typ == "10":
# 2 nibbles per byte, round-robin TVLM
for byte in data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
ch = sample_index % 4
cur[ch] += s4(nib)
out[ch].append(cur[ch])
sample_index = (sample_index + 1) // 4 * 4 + (sample_index + 1) % 4 # ?
sample_index += 1
# We emit per-nibble, but the structure is unclear
elif typ == "20":
# int8 absolute or delta?
for byte in data:
v = byte if byte < 128 else byte - 256
ch = sample_index % 4
cur[ch] = v # treat as absolute
out[ch].append(cur[ch])
sample_index += 1
return out
def main():
b = load_bundle("event-c")
body = b.body
truth_T = [round(v * 200) for v in b.samples["Tran"]]
truth_V = [round(v * 200) for v in b.samples["Vert"]]
truth_L = [round(v * 200) for v in b.samples["Long"]]
# Find start
for s in range(15):
if body[s] == 0x10 and body[s+1] % 4 == 0 and 0 < body[s+1] <= 0xFC:
start = s
break
blocks = walk_blocks(body, start)
# Print block-by-block what's in each
print(f"Total blocks: {len(blocks)}")
bytes_processed = 0
for typ, NN, data in blocks[:30]:
print(f" type={typ} NN=0x{NN:02x} data_len={len(data)} data_hex={data[:32].hex(' ')}{'...' if len(data) > 32 else ''}")
if __name__ == "__main__":
main()
analysis/dump_around.py
-27
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@@ -1,27 +0,0 @@
"""Dump body bytes around a specific offset."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def dump_around(name: str, center: int, radius: int = 96):
b = load_bundle(name)
body = b.body
start = max(0, center - radius)
end = min(len(body), center + radius)
print(f"\n=== {name} body[{start}:{end}] (full body={len(body)}) ===")
for i in range(start, end, 32):
row = body[i:i+32]
marker = " <-- center" if i <= center < i+32 else ""
print(f" +{i:>5} {row.hex(' ')}{marker}")
def main():
# Look at the trailer transitions
trailer_starts = {"event-a": 7047, "event-b": 6475, "event-c": 4043, "event-d": 3941}
for name, off in trailer_starts.items():
dump_around(name, off, 96)
if __name__ == "__main__":
main()
analysis/dump_head.py
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@@ -1,18 +0,0 @@
"""Dump the START of each body in 32-byte rows."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def main():
for name in ("event-a", "event-c"):
b = load_bundle(name)
body = b.body
print(f"\n=== {name} body[0:512] (full body={len(body)}, samples={len(b.samples['Tran'])}) ===")
for i in range(0, min(512, len(body)), 32):
row = body[i:i+32]
print(f" +{i:>5} {row.hex(' ')}")
if __name__ == "__main__":
main()
analysis/dump_trailer.py
-24
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@@ -1,24 +0,0 @@
"""Dump body bytes split into 32-byte rows starting from `start_offset`."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def dump(body: bytes, name: str, start: int, n_rows: int = 30):
print(f"\n=== {name} body[{start}:] (full body={len(body)}) ===")
end = min(start + 32 * n_rows, len(body))
for i in range(start, end, 32):
row = body[i:i+32]
print(f" +{i:>5} {row.hex(' ')}")
def main():
for name in ("event-a", "event-b", "event-c", "event-d"):
b = load_bundle(name)
# Print the LAST ~600 bytes of the body to see the tail structure
start = max(0, len(b.body) - 32 * 12)
dump(b.body, name, start, 12)
if __name__ == "__main__":
main()
analysis/find_blocks.py
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@@ -1,41 +0,0 @@
"""Search for structural repetition in the body bytes."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def find_pattern_offsets(body: bytes, pattern: bytes, max_count=20):
out = []
i = 0
while True:
i = body.find(pattern, i)
if i < 0:
break
out.append(i)
i += 1
if len(out) >= max_count:
break
return out
def main():
for name in ("event-a", "event-b", "event-c", "event-d"):
b = load_bundle(name)
body = b.body
print(f"\n=== {name} (body={len(body)}, N_samples={len(b.samples['Tran'])}) ===")
# Try to find repeating substructures (look for 4-byte 0x10-prefixed markers)
for prefix in [b"\x10\x10", b"\x10\x04", b"\x10\x08", b"\x10\x0c", b"\x10\x18",
b"\x10\x14", b"\x10\x20", b"\x10\x40", b"\x10\x80", b"\x10\x00",
b"\x10\x01", b"\x10\x03", b"\x10\xf0", b"\xf1\x10", b"\x00\x10",
b"\x40\x02", b"\x20\x04", b"\x30\x04", b"\x30\x08", b"\x00\x1a"]:
offs = find_pattern_offsets(body, prefix, max_count=200)
if 1 <= len(offs) <= 1000:
# Print first 10 offsets
first = offs[:6]
last = offs[-3:]
print(f" '{prefix.hex()}' x{len(offs):>4} first={first} last={last}")
if __name__ == "__main__":
main()
analysis/find_signal_runs.py
-34
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@@ -1,34 +0,0 @@
"""Find body byte ranges that look like absolute int8 sample data (smooth waveform)."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def looks_like_smooth_int8(buf):
"""Convert bytes to int8 and check if successive deltas are small (waveform-like)."""
if len(buf) < 8:
return 0.0
vals = [b if b < 128 else b - 256 for b in buf]
diffs = [abs(vals[i+1] - vals[i]) for i in range(len(vals)-1)]
avg_diff = sum(diffs) / len(diffs)
return avg_diff
def main():
for name in ("event-a", "event-c"):
b = load_bundle(name)
body = b.body
# Scan with sliding window of 64 bytes; find segments where the bytes look like a smooth wave
win = 64
scores = []
for i in range(len(body) - win):
scores.append((i, looks_like_smooth_int8(body[i:i+win])))
# Lowest avg_diff means smoothest
scores.sort(key=lambda x: x[1])
print(f"\n=== {name} (body={len(body)}) — smoothest 10 windows ===")
for off, s in scores[:10]:
print(f" +{off:>5} avg_diff={s:.2f} bytes={body[off:off+24].hex(' ')}")
if __name__ == "__main__":
main()
analysis/full_tran.py
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@@ -1,76 +0,0 @@
"""Full Tran decoder: continues across segment headers using T_delta from header bytes [0:2]."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def decode_full_tran(body):
if len(body) < 7 or body[0:3] != b"\x00\x02\x00":
return None
T0 = int.from_bytes(body[3:5], "big", signed=True)
T1 = int.from_bytes(body[5:7], "big", signed=True)
i = 7
while i + 1 < len(body) and body[i] not in (0x00, 0x10, 0x20, 0x30, 0x40):
i += 1
blocks = walk_body(body, i)
T = [T0, T1]
cur = T1
for blk in blocks:
if blk.tag_hi == 0x40:
# Segment header carries 2 T deltas (int16 BE each) at bytes [0:2] and [2:4]
if len(blk.data) >= 4:
delta1 = int.from_bytes(blk.data[0:2], "big", signed=True)
cur += delta1
T.append(cur)
delta2 = int.from_bytes(blk.data[2:4], "big", signed=True)
cur += delta2
T.append(cur)
elif blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += s4(nib)
T.append(cur)
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += i8(byte)
T.append(cur)
elif blk.tag_hi == 0x00:
for _ in range(blk.tag_lo):
T.append(cur)
# 30 NN: skip for now
return T
def main():
for stem in ("M529LL1L.V70", "M529LL1L.JQ0", "M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
truth_T = [round(v*200) for v in samples["Tran"]]
n_truth = len(truth_T)
decoded = decode_full_tran(body)
n = min(len(decoded), n_truth)
matches = sum(1 for i in range(n) if decoded[i] == truth_T[i])
div_at = -1
for i in range(n):
if decoded[i] != truth_T[i]:
div_at = i
break
print(f"{stem}: decoded={len(decoded)}, truth={n_truth}, matches={matches}/{n}, first div={div_at}")
if __name__ == "__main__":
main()
analysis/inspect_5_11.py
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@@ -1,50 +0,0 @@
"""Quick inspection of the new high-amplitude events."""
import os, re, sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
ROOT = "tests/fixtures/5-11-26"
def main():
for stem in ("M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
bin_path = os.path.join(ROOT, stem)
txt_path = bin_path + ".TXT"
with open(bin_path, "rb") as f:
raw = f.read()
body = raw[43:-26]
meta, samples = _parse_txt(txt_path)
n = len(samples["Tran"])
print(f"\n=== {stem} ===")
print(f" file={len(raw)}, body={len(body)}, N_samples={n}")
print(f" rectime={meta.get('Record Time')} pretrig={meta.get('Pre-trigger Length')}")
print(f" PPV(T,V,L)={meta.get('Tran PPV')} / {meta.get('Vert PPV')} / {meta.get('Long PPV')}")
# Show first few non-trivial samples
print(f" First 5 truth samples (in/s):")
for i in range(5):
print(f" T={samples['Tran'][i]:8.3f} V={samples['Vert'][i]:8.3f} "
f"L={samples['Long'][i]:8.3f} M={samples['MicL'][i]:8.3f}")
# Peak sample positions
for ch in ("Tran", "Vert", "Long"):
vals = samples[ch]
peak_i = max(range(n), key=lambda i: abs(vals[i]))
print(f" {ch}: peak {vals[peak_i]:.3f} at sample {peak_i} (t={peak_i/1024:.3f}s)")
# Body structure
start = find_data_start(body)
blocks = walk_body(body, start)
types = {}
for b in blocks:
types[b.tag_hi] = types.get(b.tag_hi, 0) + 1
print(f" body start={start}, total blocks walked: {len(blocks)}")
print(f" block tag counts: {types}")
# How far the walker got
if blocks:
last = blocks[-1]
walked = last.offset + last.length
print(f" walker stopped at offset {walked}/{len(body)} ({100*walked/len(body):.0f}%)")
if __name__ == "__main__":
main()
analysis/inspect_body.py
-23
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@@ -1,23 +0,0 @@
"""Print raw body hex + byte-distribution stats for one event."""
from collections import Counter
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def main():
for name in ("event-a", "event-b", "event-c", "event-d"):
b = load_bundle(name)
body = b.body
print(f"\n=== {name} ({len(body)} body bytes) ===")
print(f" STRT: {b.strt.hex()}")
print(f" body[0:64]: {body[:64].hex()}")
print(f" body[64:128]: {body[64:128].hex()}")
print(f" body[-32:]: {body[-32:].hex()}")
cnt = Counter(body)
print(f" top 16 bytes: {[(f'0x{k:02x}', f'{v/len(body):.2%}') for k,v in cnt.most_common(16)]}")
if __name__ == "__main__":
main()
analysis/load_bundle.py
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@@ -1,144 +0,0 @@
"""
load_bundle.py — extract body bytes from BW binary + parse sample columns from TXT.
Used by the codec reverse-engineering scripts in this directory.
"""
from __future__ import annotations
import os
import re
from dataclasses import dataclass
BUNDLE_ROOT = os.path.join(
os.path.dirname(__file__), "..", "tests", "fixtures", "decode-re-5-8-26"
)
@dataclass
class Bundle:
name: str
bin_path: str
txt_path: str
bin: bytes
body: bytes # bytes between STRT (43) and footer (last 26)
strt: bytes # 21-byte STRT record
samples: dict # {"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}
sample_rate: int
rectime_sec: float
pretrig_sec: float
geo_range_ips: float
ppv: dict # {"Tran": float, "Vert": float, "Long": float}
mic_pspl: float
serial: str
def _parse_txt(path: str) -> dict:
with open(path, "r", encoding="utf-8", errors="replace") as f:
text = f.read()
meta = {}
samples = {"Tran": [], "Vert": [], "Long": [], "MicL": []}
# Find header line that starts the columns ("Tran Vert Long MicL").
# Then every line after is sample data (4 tab-separated floats).
lines = text.splitlines()
header_idx = None
for i, line in enumerate(lines):
if "Tran" in line and "Vert" in line and "Long" in line and "MicL" in line:
# The columns header. Sample lines start a few lines later.
header_idx = i
break
if header_idx is None:
raise ValueError(f"no Tran/Vert/Long/MicL header in {path}")
# Parse meta — quoted lines with "Field : value"
for line in lines[:header_idx]:
m = re.match(r'^"([^"]+)\s*:\s*([^"]*)"', line.strip())
if m:
k, v = m.group(1).strip(), m.group(2).strip()
meta[k] = v
# Parse samples
for line in lines[header_idx + 1 :]:
line = line.strip()
if not line:
continue
parts = re.split(r"\s+", line)
if len(parts) < 4:
continue
try:
t = float(parts[0])
v = float(parts[1])
l = float(parts[2])
m = float(parts[3])
except ValueError:
continue
samples["Tran"].append(t)
samples["Vert"].append(v)
samples["Long"].append(l)
samples["MicL"].append(m)
return meta, samples
def load_bundle(name: str) -> Bundle:
folder = os.path.join(BUNDLE_ROOT, name)
files = os.listdir(folder)
bin_name = next(f for f in files if not f.endswith(".TXT"))
txt_name = next(f for f in files if f.endswith(".TXT"))
bin_path = os.path.join(folder, bin_name)
txt_path = os.path.join(folder, txt_name)
with open(bin_path, "rb") as f:
binary = f.read()
# Header is 22 bytes; STRT at [22:43]; footer at last 26 bytes.
strt = binary[22:43]
body = binary[43:-26]
meta, samples = _parse_txt(txt_path)
sample_rate = int(re.search(r"(\d+)", meta.get("Sample Rate", "1024")).group(1))
rectime_sec = float(re.search(r"([\d.]+)", meta.get("Record Time", "3.0")).group(1))
pretrig_sec = float(re.search(r"-?[\d.]+", meta.get("Pre-trigger Length", "0")).group(0))
geo_range_ips = float(re.search(r"([\d.]+)", meta.get("Geo Range", "10.0")).group(1))
serial = meta.get("Serial Number", "").strip()
def _f(s):
return float(re.search(r"-?[\d.]+", s).group(0))
ppv = {
"Tran": _f(meta.get("Tran PPV", "0")),
"Vert": _f(meta.get("Vert PPV", "0")),
"Long": _f(meta.get("Long PPV", "0")),
}
mic_pspl = _f(meta.get("MicL PSPL", "0"))
return Bundle(
name=name,
bin_path=bin_path,
txt_path=txt_path,
bin=binary,
body=body,
strt=strt,
samples=samples,
sample_rate=sample_rate,
rectime_sec=rectime_sec,
pretrig_sec=pretrig_sec,
geo_range_ips=geo_range_ips,
ppv=ppv,
mic_pspl=mic_pspl,
serial=serial,
)
if __name__ == "__main__":
for name in ("event-a", "event-b", "event-c", "event-d"):
b = load_bundle(name)
n = len(b.samples["Tran"])
print(f"{name}: body={len(b.body):>6} N_samples={n} rate={b.sample_rate} "
f"rectime={b.rectime_sec} pretrig={b.pretrig_sec} range={b.geo_range_ips} "
f"PPV(T,V,L)={b.ppv['Tran']:.3f},{b.ppv['Vert']:.3f},{b.ppv['Long']:.3f} "
f"MicL={b.mic_pspl}")
analysis/multi_segment.py
-81
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@@ -1,81 +0,0 @@
"""Decode Tran across multiple segments by resetting at 40 02 headers."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def decode_full_tran(body):
"""Decode all Tran samples in the body, walking through segments."""
if len(body) < 7 or body[0:3] != b"\x00\x02\x00":
return None
T0 = int.from_bytes(body[3:5], "big", signed=True)
T1 = int.from_bytes(body[5:7], "big", signed=True)
# Locate first tag
i = 7
while i + 1 < len(body) and body[i] not in (0x00, 0x10, 0x20, 0x30, 0x40):
i += 1
blocks = walk_body(body, i)
T = [T0, T1]
cur = T1
for bi, blk in enumerate(blocks):
if blk.tag_hi == 0x40:
# Segment header — try interpreting bytes [0:2] as new T anchor
if len(blk.data) >= 2:
new_anchor = int.from_bytes(blk.data[0:2], "big", signed=True)
# The next sample IS this anchor value, NOT a delta from cur.
T.append(new_anchor)
cur = new_anchor
elif blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += s4(nib)
T.append(cur)
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += i8(byte)
T.append(cur)
elif blk.tag_hi == 0x00:
# RLE: append NN zero deltas
for _ in range(blk.tag_lo):
T.append(cur)
# 30 NN: skip
return T
def main():
for stem in ("M529LL1L.V70", "M529LL1L.JQ0", "M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
truth_T = [round(v*200) for v in samples["Tran"]]
n_truth = len(truth_T)
decoded = decode_full_tran(body)
n = min(len(decoded), n_truth)
matches = sum(1 for i in range(n) if decoded[i] == truth_T[i])
# Find first divergence
div_at = -1
for i in range(n):
if decoded[i] != truth_T[i]:
div_at = i
break
print(f"{stem}: decoded={len(decoded)}, truth={n_truth}, matches={matches}/{n}, first div={div_at}")
if div_at >= 0 and div_at < 30:
print(f" truth around div [{max(0,div_at-3)}:{div_at+8}]: {truth_T[max(0,div_at-3):div_at+8]}")
print(f" pred around div [{max(0,div_at-3)}:{div_at+8}]: {decoded[max(0,div_at-3):div_at+8]}")
if __name__ == "__main__":
main()
analysis/seg1_blocks.py
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@@ -1,28 +0,0 @@
"""Dump all blocks in segment 1 of each event with their data."""
import sys
sys.path.insert(0, ".")
from minimateplus.waveform_codec import walk_body, find_data_start
def main():
for stem in ("M529LL1A.SP0", "M529LL1L.JQ0", "M529LL1L.V70"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
blocks = walk_body(body, find_data_start(body))
# Find segment 1 (between first and second 40 02)
seg40_indices = [i for i, b in enumerate(blocks) if b.tag_hi == 0x40]
if len(seg40_indices) < 2:
print(f"\n{stem}: only {len(seg40_indices)} segment headers found")
seg1_blocks = blocks[seg40_indices[0]:] if seg40_indices else []
else:
seg1_blocks = blocks[seg40_indices[0]:seg40_indices[1]+1]
print(f"\n=== {stem} segment 1 ({len(seg1_blocks)} blocks) ===")
for b in seg1_blocks[:25]:
tag = f"{b.tag_hi:02x}{b.tag_lo:02x}"
print(f" off={b.offset:>5} {tag} NN=0x{b.tag_lo:02x}({b.tag_lo:>3}) len={b.length:>3} data={b.data[:16].hex(' ')}{'...' if len(b.data)>16 else ''}")
if __name__ == "__main__":
main()
analysis/test_30nn_12bit.py
-195
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@@ -1,195 +0,0 @@
"""Test 12-bit signed packed deltas hypothesis for 30 NN blocks across all loud events.
For each 30 NN block in each event, identify what samples it should cover
(based on the cumulative delta count up to that point) and compare the
truth deltas against various 12-bit packing schemes.
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
CHANNEL_ORDER = ["Vert", "Long", "MicL", "Tran"] # rotation after initial T
def s12(v):
"""Sign-extend a 12-bit unsigned value to signed int."""
return v if v < 0x800 else v - 0x1000
def unpack_12bit_be(data):
"""4 deltas in 6 bytes, BE order: byte[0:1.5], byte[1.5:3], byte[3:4.5], byte[4.5:6]."""
# bits 0..47 (MSB-first), split into 4 × 12-bit
val = int.from_bytes(data, "big")
out = []
for i in range(4):
d = (val >> (12 * (3 - i))) & 0xFFF
out.append(s12(d))
return out
def unpack_12bit_le(data):
"""4 deltas in 6 bytes, LE order: bytes packed as 2 × 24-bit groups."""
out = []
# First 3 bytes contain 2 deltas
b0, b1, b2 = data[0], data[1], data[2]
d0 = b0 | ((b1 & 0x0F) << 8)
d1 = (b1 >> 4) | (b2 << 4)
out.append(s12(d0))
out.append(s12(d1))
# Next 3 bytes contain 2 more deltas
b3, b4, b5 = data[3], data[4], data[5]
d2 = b3 | ((b4 & 0x0F) << 8)
d3 = (b4 >> 4) | (b5 << 4)
out.append(s12(d2))
out.append(s12(d3))
return out
def unpack_12bit_be_per_triplet(data):
"""4 deltas as 2 triplets of (high4, low8) BE within each 3-byte group."""
out = []
b0, b1, b2 = data[0], data[1], data[2]
d0 = (b0 << 4) | (b1 >> 4)
d1 = ((b1 & 0x0F) << 8) | b2
out.append(s12(d0))
out.append(s12(d1))
b3, b4, b5 = data[3], data[4], data[5]
d2 = (b3 << 4) | (b4 >> 4)
d3 = ((b4 & 0x0F) << 8) | b5
out.append(s12(d2))
out.append(s12(d3))
return out
def truth_deltas_for_block(blocks, block_idx, event_truth, channel):
"""For a 30 NN block at block_idx, determine which samples it covers and
return the truth deltas for those samples.
Walks through all blocks before block_idx (within the same segment) and
counts how many deltas have been emitted for *channel*, starting from the
segment's anchor pair.
"""
# Find the segment header that contains this block.
seg_header_idx = None
for j in range(block_idx, -1, -1):
if blocks[j].tag_hi == 0x40:
seg_header_idx = j
break
if seg_header_idx is None:
# block is in the initial T segment; samples count from sample 2.
first_sample_in_segment = 2
else:
# Anchor pair covers samples [N, N+1] for some N. Subsequent deltas
# are samples [N+2, N+2+1, ...]. We don't actually need to know N
# for this test — just the relative position within the segment.
first_sample_in_segment = 2 # anchor=0,1; deltas start at 2
# Count deltas from segment-data start to block_idx.
delta_count = 0
start_block = seg_header_idx + 1 if seg_header_idx is not None else 0
for j in range(start_block, block_idx):
blk = blocks[j]
if blk.tag_hi == 0x10:
delta_count += blk.tag_lo # NN nibbles = NN deltas
elif blk.tag_hi == 0x20:
delta_count += blk.tag_lo # NN int8 deltas
elif blk.tag_hi == 0x00:
delta_count += blk.tag_lo # RLE zero deltas
# Now the 30 NN block carries NN deltas.
nn = blocks[block_idx].tag_lo
# First sample affected: segment first_sample + delta_count.
# But we ALSO need to know which segment this is, since the segment maps
# to a specific channel and a specific starting absolute sample index.
return first_sample_in_segment + delta_count, nn
def main():
for stem in ("M529LL1A.SP0", "M529LL1L.JQ0", "M529LL1L.V70",
"M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
blocks = walk_body(body, find_data_start(body))
seg_idx = [i for i, b in enumerate(blocks) if b.tag_hi == 0x40]
# Find all 30 NN blocks in DATA section (not trailer).
thirty_blocks = []
for bi, b in enumerate(blocks):
if b.tag_hi != 0x30:
continue
# Determine which segment this is in
seg_num = None
for k, hi in enumerate(seg_idx):
next_hi = seg_idx[k + 1] if k + 1 < len(seg_idx) else len(blocks)
if hi < bi < next_hi:
seg_num = k
break
if seg_num is None and seg_idx and bi < seg_idx[0]:
seg_num = -1 # initial T segment
thirty_blocks.append((bi, b, seg_num))
if not thirty_blocks:
continue
print(f"\n=== {stem} ===")
for bi, b, seg_num in thirty_blocks:
# Channel for this segment
if seg_num == -1:
channel = "Tran"
seg_label = "initial T"
else:
channel = CHANNEL_ORDER[seg_num % 4]
seg_label = f"seg {seg_num}"
# Count deltas before this block within the same segment.
seg_header_idx = seg_idx[seg_num] if seg_num >= 0 else -1
start_block = seg_header_idx + 1 if seg_header_idx >= 0 else 0
delta_count = 0
for j in range(start_block, bi):
blk = blocks[j]
if blk.tag_hi in (0x10, 0x20, 0x00):
delta_count += blk.tag_lo
# First sample this 30 NN block affects (within the segment)
# = anchor positions + delta_count + 2 (since anchor pair was samples 0,1)
# But the segment's first absolute sample index in the channel is
# (seg_num // 4) * 512 (approximately) if segment 0 is the first V seg.
cycle = (seg_num // 4) if seg_num >= 0 else 0
base = cycle * 512 + 2 # +2 for anchor pair
sample_idx = base + delta_count
truth_ch = [round(v * 200) for v in samples[channel]]
nn = b.tag_lo
if sample_idx + nn >= len(truth_ch):
print(f" block @ {b.offset} ({seg_label} {channel}): out of truth range")
continue
# Get the previous sample so we can compute truth deltas
if sample_idx == 0:
prev = 0
else:
prev = truth_ch[sample_idx - 1]
truth_deltas = []
for k in range(nn):
truth_deltas.append(truth_ch[sample_idx + k] - (prev if k == 0 else truth_ch[sample_idx + k - 1]))
# Try each packing
schemes = [
("12-bit BE contiguous", unpack_12bit_be(b.data)),
("12-bit LE per-triplet", unpack_12bit_le(b.data)),
("12-bit BE per-triplet", unpack_12bit_be_per_triplet(b.data)),
]
print(f" block @ {b.offset:>5} ({seg_label} {channel}, samples {sample_idx}..{sample_idx+nn-1}):")
print(f" data: {b.data.hex(' ')}")
print(f" truth: {truth_deltas}")
for name, pred in schemes:
match = "✓" if pred == truth_deltas else " "
n_match = sum(1 for x, y in zip(pred, truth_deltas) if x == y)
print(f" {match}{n_match}/4 {name}: {pred}")
if __name__ == "__main__":
main()
analysis/test_30nn_hybrid.py
-132
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@@ -1,132 +0,0 @@
"""Test the '30 NN data = high-nibbles + int8 low-bytes' hypothesis.
Layout for `30 04` (6 data bytes, 4 deltas):
bytes [0:2] = 16 bits = 4 × 4-bit high-nibbles (MSB first)
bytes [2:6] = 4 × int8 low bytes
Each delta = 12-bit signed = sign-extend((high_nibble << 8) | low_byte)
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def sign_extend_12(v):
return v if v < 0x800 else v - 0x1000
def decode_30nn(data):
"""4 × 12-bit signed deltas (high nibble + low byte).
bytes[0:2] hold the 4 high nibbles (MSB first); bytes[2:6] hold the low bytes.
"""
if len(data) < 6:
return []
# Read high nibbles from bytes 0-1 (4 nibbles MSB-first)
high_word = (data[0] << 8) | data[1]
high_nibbles = [
(high_word >> 12) & 0xF,
(high_word >> 8) & 0xF,
(high_word >> 4) & 0xF,
high_word & 0xF,
]
out = []
for i in range(4):
v = (high_nibbles[i] << 8) | data[2 + i]
out.append(sign_extend_12(v))
return out
def simulate_up_to(blocks, target_block_idx, t_preamble):
"""Run decoder up to block_idx; return per-channel sample lists.
NOW with 30 NN decoded too."""
out = {"Tran": [], "Vert": [], "Long": [], "MicL": []}
out["Tran"].extend(t_preamble)
cur = {"Tran": t_preamble[-1], "Vert": None, "Long": None, "MicL": None}
rotation = ["Vert", "Long", "MicL", "Tran"]
current_channel = "Tran"
seg_counter = -1
for j in range(target_block_idx):
blk = blocks[j]
if blk.tag_hi == 0x40:
seg_counter += 1
prev = "Tran" if seg_counter == 0 else rotation[(seg_counter - 1) % 4]
new_ch = rotation[seg_counter % 4]
if cur[prev] is not None:
d0 = int.from_bytes(blk.data[0:2], "big", signed=True)
d1 = int.from_bytes(blk.data[2:4], "big", signed=True)
cur[prev] += d0; out[prev].append(cur[prev])
cur[prev] += d1; out[prev].append(cur[prev])
c0 = int.from_bytes(blk.data[14:16], "big", signed=True)
c1 = int.from_bytes(blk.data[16:18], "big", signed=True)
out[new_ch].extend([c0, c1])
cur[new_ch] = c1
current_channel = new_ch
elif blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur[current_channel] += s4(nib)
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur[current_channel] += i8(byte)
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x00:
for _ in range(blk.tag_lo):
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x30:
# NEW: decode 30 NN
deltas = decode_30nn(blk.data)
for d in deltas:
cur[current_channel] += d
out[current_channel].append(cur[current_channel])
return out, current_channel
def main():
for stem in ("M529LL1A.SP0", "M529LL1L.JQ0", "M529LL1L.V70",
"M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
blocks = walk_body(body, find_data_start(body))
t0 = int.from_bytes(body[3:5], "big", signed=True)
t1 = int.from_bytes(body[5:7], "big", signed=True)
thirty_blocks = [(j, b) for j, b in enumerate(blocks) if b.tag_hi == 0x30]
if not thirty_blocks:
continue
print(f"\n=== {stem} ===")
for j, blk in thirty_blocks:
pred, ch = simulate_up_to(blocks, j, [t0, t1])
cur_before = pred[ch][-1]
truth = [round(v * 200) for v in samples[ch]]
n_pred = len(pred[ch])
nn = blk.tag_lo
if n_pred + nn > len(truth):
continue
# Decode this 30 NN block with hypothesis
pred_deltas = decode_30nn(blk.data)
# Compute truth deltas relative to cur_before
truth_deltas = []
prev = cur_before
for k in range(nn):
truth_deltas.append(truth[n_pred + k] - prev)
prev = truth[n_pred + k]
n_match = sum(1 for a, b in zip(pred_deltas, truth_deltas) if a == b)
tag = "✓" if pred_deltas == truth_deltas else " "
print(f" block @ {blk.offset:>5} (chan={ch}, NN={nn}):")
print(f" data: {blk.data.hex(' ')}")
print(f" truth: {truth_deltas}")
print(f" pred: {pred_deltas} {tag}{n_match}/{nn}")
if __name__ == "__main__":
main()
analysis/test_30nn_v2.py
-141
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@@ -1,141 +0,0 @@
"""Test 30 NN packing by running the real decoder up to each 30 NN block,
recording how many samples have been produced for each channel at that point,
then checking truth deltas immediately after."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def s12(v):
return v if v < 0x800 else v - 0x1000
def unpack_12bit_be_contiguous(data):
out = []
val = int.from_bytes(data, "big")
n = len(data) * 8 // 12
for i in range(n):
d = (val >> (12 * (n - 1 - i))) & 0xFFF
out.append(s12(d))
return out
def unpack_12bit_per_triplet_be(data):
out = []
for i in range(0, len(data), 3):
if i + 2 >= len(data):
break
b0, b1, b2 = data[i], data[i + 1], data[i + 2]
d0 = (b0 << 4) | (b1 >> 4)
d1 = ((b1 & 0x0F) << 8) | b2
out.append(s12(d0))
out.append(s12(d1))
return out
def simulate_up_to(blocks, target_block_idx, t_preamble):
"""Run the decoder up to block_idx; return per-channel sample lists."""
out = {"Tran": [], "Vert": [], "Long": [], "MicL": []}
out["Tran"].extend(t_preamble)
cur = {"Tran": t_preamble[-1], "Vert": None, "Long": None, "MicL": None}
rotation = ["Vert", "Long", "MicL", "Tran"]
seg_idx = [j for j, b in enumerate(blocks) if b.tag_hi == 0x40]
# Determine which channel we're CURRENTLY decoding into
current_channel = "Tran"
seg_counter = -1 # incremented at each 40 02
for j in range(target_block_idx):
blk = blocks[j]
if blk.tag_hi == 0x40:
# Switch: extend prev channel, set up new channel
seg_counter += 1
prev = "Tran" if seg_counter == 0 else rotation[(seg_counter - 1) % 4]
new_ch = rotation[seg_counter % 4]
if cur[prev] is not None:
d0 = int.from_bytes(blk.data[0:2], "big", signed=True)
d1 = int.from_bytes(blk.data[2:4], "big", signed=True)
cur[prev] += d0; out[prev].append(cur[prev])
cur[prev] += d1; out[prev].append(cur[prev])
c0 = int.from_bytes(blk.data[14:16], "big", signed=True)
c1 = int.from_bytes(blk.data[16:18], "big", signed=True)
out[new_ch].extend([c0, c1])
cur[new_ch] = c1
current_channel = new_ch
elif blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur[current_channel] += s4(nib)
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur[current_channel] += i8(byte)
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x00:
for _ in range(blk.tag_lo):
out[current_channel].append(cur[current_channel])
elif blk.tag_hi == 0x30:
# Skip for now — we want to know what comes next
pass
return out, current_channel
def main():
for stem in ("M529LL1A.SP0", "M529LL1L.JQ0", "M529LL1L.V70",
"M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
blocks = walk_body(body, find_data_start(body))
t0 = int.from_bytes(body[3:5], "big", signed=True)
t1 = int.from_bytes(body[5:7], "big", signed=True)
# Find all 30 NN blocks in data section
thirty_blocks = [(j, b) for j, b in enumerate(blocks) if b.tag_hi == 0x30]
if not thirty_blocks:
continue
print(f"\n=== {stem} ===")
for j, blk in thirty_blocks:
pred, ch = simulate_up_to(blocks, j, [t0, t1])
n_pred = len(pred[ch])
# The 30 NN block carries NN deltas for channel `ch` starting at sample n_pred
truth = [round(v * 200) for v in samples[ch]]
if n_pred >= len(truth):
continue
# Truth deltas: truth[n_pred] - cur, truth[n_pred+1] - truth[n_pred], ...
cur_val = pred[ch][-1]
nn = blk.tag_lo
truth_deltas = []
prev = cur_val
for k in range(min(nn, len(truth) - n_pred)):
truth_deltas.append(truth[n_pred + k] - prev)
prev = truth[n_pred + k]
print(f" block @ {blk.offset:>5} (chan={ch}, after sample {n_pred-1}, "
f"NN={nn}, last_val={cur_val}):")
print(f" data: {blk.data.hex(' ')}")
print(f" truth: {truth_deltas}")
schemes = [
("12-bit BE contiguous", unpack_12bit_be_contiguous(blk.data)),
("12-bit per-triplet BE", unpack_12bit_per_triplet_be(blk.data)),
]
for name, pred_deltas in schemes:
n_match = sum(1 for a, b in zip(pred_deltas, truth_deltas) if a == b)
tag = "✓" if pred_deltas == truth_deltas else " "
print(f" {tag}{n_match}/{nn} {name}: {pred_deltas[:nn]}")
if __name__ == "__main__":
main()
analysis/test_rle.py
-86
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@@ -1,86 +0,0 @@
"""Test: 00 NN markers might be RLE for zero-deltas in current channel."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def decode_with_rle(body):
"""Decode Tran assuming:
- preamble[3:5], [5:7] = T[0], T[1]
- All 10 NN / 20 NN blocks until segment_header (40 02) are Tran deltas
- 00 NN markers are RLE: NN/4 zero T deltas (or NN, or NN/2 — try them)
"""
if len(body) < 9 or body[0:3] != b"\x00\x02\x00":
return None, None, None
T0 = int.from_bytes(body[3:5], "big", signed=True)
T1 = int.from_bytes(body[5:7], "big", signed=True)
# Find first tag (might be 00 NN, 10 NN, or 20 NN)
i = 7
while i + 1 < len(body):
if body[i] in (0x00, 0x10, 0x20):
break
i += 1
start = i
blocks = walk_body(body, start)
results = {}
for rle_div in (4, 2, 1): # try different RLE interpretations
T = [T0, T1]
cur = T1
for blk in blocks:
if blk.tag_hi == 0x40:
break
if blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += s4(nib)
T.append(cur)
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += i8(byte)
T.append(cur)
elif blk.tag_hi == 0x00:
# RLE of zero deltas
n_zeros = blk.tag_lo // rle_div
for _ in range(n_zeros):
T.append(cur)
# 30 NN: skip for now
results[rle_div] = T
return results, T0, T1
def main():
for stem in ("M529LL1L.V70", "M529LL1L.JQ0", "M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
truth_T = [round(v*200) for v in samples["Tran"]]
results, T0, T1 = decode_with_rle(body)
print(f"\n=== {stem} (T[0]={T0}, T[1]={T1}) ===")
for rle_div, T in results.items():
n = min(len(T), len(truth_T))
matches = sum(1 for i in range(n) if T[i] == truth_T[i])
# Find first divergence
div_at = -1
for i in range(n):
if T[i] != truth_T[i]:
div_at = i
break
print(f" rle_div={rle_div}: decoded {len(T)}, matches {matches}/{n}, first div at sample {div_at}")
if __name__ == "__main__":
main()
analysis/test_tran_continue.py
-71
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@@ -1,71 +0,0 @@
"""Test: does the second '20 NN' block in SS0 continue Tran samples?"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def main():
stem = "M529LL1A.SS0"
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
truth_T_16 = [round(v * 200) for v in samples["Tran"]]
# Preamble
T0 = int.from_bytes(body[3:5], "big", signed=True)
T1 = int.from_bytes(body[5:7], "big", signed=True)
# Walk blocks
start = find_data_start(body)
blocks = walk_body(body, start)
print(f"=== {stem} === T[0]={T0} T[1]={T1}")
# Hypothesis: Tran continues through ALL 10 NN and 20 NN blocks
# in order, until the next 40 02 segment header (which resets).
T = [T0, T1]
cur = T1
decoded_count = 2 # T[0], T[1] from preamble
for bi, blk in enumerate(blocks):
if blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += s4(nib)
T.append(cur)
decoded_count += 1
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += i8(byte)
T.append(cur)
decoded_count += 1
elif blk.tag_hi == 0x40:
# Segment header — stop here for this test
break
# 00 and 30 NN don't contribute to Tran (in this hypothesis)
# Compare to truth
print(f" Decoded {len(T)} T samples up to first 40 02")
matches = sum(1 for i in range(min(len(T), len(truth_T_16))) if T[i] == truth_T_16[i])
print(f" Matches in first {min(len(T), len(truth_T_16))}: {matches}")
# Print first divergence
for i in range(min(len(T), len(truth_T_16))):
if T[i] != truth_T_16[i]:
print(f" First divergence: sample {i}: pred={T[i]}, truth={truth_T_16[i]}")
# Show context
print(f" pred [{i-3}:{i+5}]: {T[max(0,i-3):i+5]}")
print(f" truth [{i-3}:{i+5}]: {truth_T_16[max(0,i-3):i+5]}")
break
if __name__ == "__main__":
main()
analysis/try_layouts.py
-67
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@@ -1,67 +0,0 @@
"""Try various nibble-level channel interleavings to find which one matches truth."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def s4(n):
return n if n < 8 else n - 16
def run_decoder(body, layout, skip, n_channels=4):
"""layout: function nibble_index -> channel_index. Returns list-of-lists per channel."""
out = [[] for _ in range(n_channels)]
cur = [0] * n_channels
nibbles = []
for byte in body[skip:]:
nibbles.append((byte >> 4) & 0xF)
nibbles.append(byte & 0xF)
for i, n in enumerate(nibbles):
ch = layout(i)
cur[ch] += s4(n)
out[ch].append(cur[ch])
return out
def cmp(pred, truth, n=24):
n = min(n, len(pred), len(truth))
return [(pred[i], truth[i]) for i in range(n)]
def main():
b = load_bundle("event-c")
truth_T = [round(v * 200) for v in b.samples["Tran"]]
truth_V = [round(v * 200) for v in b.samples["Vert"]]
truth_L = [round(v * 200) for v in b.samples["Long"]]
print(f"T truth[0:10]: {truth_T[:10]}")
print(f"V truth[0:10]: {truth_V[:10]}")
print(f"L truth[0:10]: {truth_L[:10]}")
# Try several nibble->channel layouts (4 channels)
layouts = {
"interleaved TVLM (0,1,2,3,0,1,2,3,...)": lambda i: i % 4,
"interleaved VLMT": lambda i: (i + 3) % 4,
"interleaved LMTV": lambda i: (i + 2) % 4,
"interleaved MTVL": lambda i: (i + 1) % 4,
"byte-based TV LM TV LM (high T low V byte0; high L low M byte1)": lambda i: i % 4,
# "chunks of 8 nibbles per channel": each channel gets 8 nibbles in a row
"chunks-8 TVLM": lambda i: (i // 8) % 4,
"chunks-16 TVLM": lambda i: (i // 16) % 4,
# planar (full channel sequential)
"planar T(0..N) V(N..2N) L(2N..3N) M(3N..4N)": None, # special
}
for label, layout_fn in layouts.items():
if layout_fn is None:
continue
for skip in (0, 4, 7, 8, 9, 11, 14):
out = run_decoder(b.body, layout_fn, skip)
# Check first 8 cumulative on each channel
print(f" skip={skip:2} {label}")
print(f" T_cum[0:10]: {out[0][:10]}")
print(f" V_cum[0:10]: {out[1][:10]}")
print(f" L_cum[0:10]: {out[2][:10]}")
if __name__ == "__main__":
main()
analysis/try_nibbles.py
-73
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@@ -1,73 +0,0 @@
"""Try decoding body as 4-bit signed nibble deltas, 4-channel round-robin."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
CHANNELS = ("Tran", "Vert", "Long", "MicL")
def s4(n):
"""Sign-extend a 4-bit unsigned to int (0..7 → 0..7, 8..F → -8..-1)."""
return n if n < 8 else n - 16
def decode_nibbles(body: bytes, skip_bytes: int = 7, n_channels: int = 4):
"""Read body as 2 nibbles per byte; accumulate as deltas for n_channels round-robin."""
out = [[] for _ in range(n_channels)]
cur = [0] * n_channels
ch = 0
nibbles = []
for byte in body[skip_bytes:]:
nibbles.append((byte >> 4) & 0xF)
nibbles.append(byte & 0xF)
for n in nibbles:
cur[ch] += s4(n)
out[ch].append(cur[ch])
ch = (ch + 1) % n_channels
return out
def cmp_to_truth(pred, truth, scale=16):
"""Compare predicted ints (in 16-count units) to truth (in 16-count units = txt * 200).
Return (max_abs_err, mean_abs_err, n_compared).
"""
n = min(len(pred), len(truth))
errs = []
for i in range(n):
p = pred[i]
t = truth[i]
errs.append(abs(p - t))
if not errs:
return None
return (max(errs), sum(errs) / len(errs), n)
def main():
for name in ("event-a", "event-c"):
b = load_bundle(name)
# Convert TXT samples (in/s) to 16-count units (multiply by 200, since 0.005 in/s = 1)
# WAIT: 0.005 in/s = 16 ADC counts. 1 count = 0.000305 in/s.
# So in 1-count units: count = txt * (1/0.0003052) ≈ txt * 3276.7
# But TXT only has 0.005 resolution so equivalent to 16-count units = txt * 200.
truth_in_16 = {ch: [round(v * 200) for v in b.samples[ch]] for ch in CHANNELS[:3]}
# MicL is in dB, skip for now
# Try decoder with skip_bytes = 7
decoded = decode_nibbles(b.body, skip_bytes=7, n_channels=4)
print(f"\n=== {name} ===")
print(f" body={len(b.body)}, nibbles={2*(len(b.body)-7)}, samples_per_ch={len(decoded[0])}")
print(f" truth samples per ch: {len(truth_in_16['Tran'])}")
# Print first 24 of each
for i, chan in enumerate(CHANNELS):
pred_first = decoded[i][:24]
if chan in truth_in_16:
truth_first = truth_in_16[chan][:24]
print(f" {chan} pred: {pred_first}")
print(f" {chan} truth: {truth_first}")
else:
print(f" {chan} pred: {pred_first} (truth in dB, skipped)")
if __name__ == "__main__":
main()
analysis/verify_full_decode.py
-32
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@@ -1,32 +0,0 @@
"""Verify decode_waveform_v2 against BW ASCII truth for all fixtures."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import decode_waveform_v2
def main():
for stem in ("M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0",
"M529LL1L.JQ0", "M529LL1L.V70"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
body = f.read()[43:-26]
_, samples = _parse_txt(path + ".TXT")
decoded = decode_waveform_v2(body)
if decoded is None:
print(f"{stem}: decoder returned None")
continue
print(f"\n=== {stem} ===")
for ch in ("Tran", "Vert", "Long"):
truth = [round(v * 200) for v in samples[ch]]
pred = decoded[ch]
n = min(len(pred), len(truth))
matches = sum(1 for i in range(n) if pred[i] == truth[i])
div = next((i for i in range(n) if pred[i] != truth[i]), -1)
print(f" {ch}: decoded={len(pred):>5} truth={len(truth):>5} "
f"matches={matches:>5}/{n:<5} first div={div}")
if __name__ == "__main__":
main()
analysis/verify_quiet_bundle.py
-55
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@@ -1,55 +0,0 @@
"""Run decode_waveform_v2 against the 5-8-26 quiet bundle to test the
'quiet events should decode fully' hypothesis."""
import os, sys
sys.path.insert(0, ".")
from minimateplus.waveform_codec import decode_waveform_v2, walk_body, find_data_start
from analysis.load_bundle import _parse_txt
def main():
base = "tests/fixtures/decode-re-5-8-26"
for evt in sorted(os.listdir(base)):
folder = os.path.join(base, evt)
if not os.path.isdir(folder):
continue
# Find the binary (not .TXT)
bin_name = next(
(f for f in os.listdir(folder) if not f.endswith(".TXT")),
None,
)
if not bin_name:
continue
bin_path = os.path.join(folder, bin_name)
txt_path = bin_path + ".TXT"
if not os.path.exists(txt_path):
# Sometimes the TXT name differs slightly
for f in os.listdir(folder):
if f.endswith(".TXT"):
txt_path = os.path.join(folder, f)
break
with open(bin_path, "rb") as f:
body = f.read()[43:-26]
decoded = decode_waveform_v2(body)
_, samples = _parse_txt(txt_path)
# Count 30 NN blocks
blocks = walk_body(body, find_data_start(body))
n_30 = sum(1 for b in blocks if b.tag_hi == 0x30)
n_40 = sum(1 for b in blocks if b.tag_hi == 0x40)
print(f"\n=== {evt} === body={len(body)} segments={n_40} '30 NN' blocks={n_30}")
if decoded is None:
print(" decoder returned None")
continue
for ch in ("Tran", "Vert", "Long"):
truth = [round(v * 200) for v in samples[ch]]
pred = decoded[ch]
n = min(len(pred), len(truth))
matches = sum(1 for i in range(n) if pred[i] == truth[i])
div = next((i for i in range(n) if pred[i] != truth[i]), -1)
print(f" {ch}: decoded={len(pred):>5} truth={len(truth):>5} "
f"matches={matches:>5}/{n:<5} first div={div}")
if __name__ == "__main__":
main()
analysis/verify_tran.py
-71
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@@ -1,71 +0,0 @@
"""Verify: preamble[3:7] = Tran[0], Tran[1] as int16 BE in 16-count units.
And first 20/10 NN block = Tran deltas starting at sample 2.
"""
import os, sys
sys.path.insert(0, ".")
from analysis.load_bundle import _parse_txt
from minimateplus.waveform_codec import walk_body, find_data_start
def s4(n):
return n if n < 8 else n - 16
def i8(b):
return b if b < 128 else b - 256
def main():
for stem in ("M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
path = f"tests/fixtures/5-11-26/{stem}"
with open(path, "rb") as f:
raw = f.read()
body = raw[43:-26]
_, samples = _parse_txt(path + ".TXT")
truth_T_16 = [round(v * 200) for v in samples["Tran"]]
# Preamble parse
T0_pre = int.from_bytes(body[3:5], "big", signed=True)
T1_pre = int.from_bytes(body[5:7], "big", signed=True)
print(f"\n=== {stem} ===")
print(f" Preamble T[0]={T0_pre} (truth {truth_T_16[0]}) T[1]={T1_pre} (truth {truth_T_16[1]}) match={T0_pre==truth_T_16[0] and T1_pre==truth_T_16[1]}")
# First block
start = find_data_start(body)
blocks = walk_body(body, start)
if not blocks:
print(f" no blocks found")
continue
# Assume first block = Tran deltas from sample 2
first = blocks[0]
T = [T0_pre, T1_pre]
cur_T = T1_pre
if first.tag_hi == 0x10:
# Nibble pairs
for byte in first.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur_T += s4(nib)
T.append(cur_T)
elif first.tag_hi == 0x20:
# int8 per byte
for byte in first.data:
cur_T += i8(byte)
T.append(cur_T)
# Compare against truth
n_check = min(len(T), len(truth_T_16))
match_count = sum(1 for i in range(n_check) if T[i] == truth_T_16[i])
print(f" First block type=0x{first.tag_hi:02x} NN=0x{first.tag_lo:02x} len={len(first.data)} → {len(T)} T samples decoded")
print(f" Tran predicted[0:10]: {T[:10]}")
print(f" Tran truth [0:10]: {truth_T_16[:10]}")
print(f" Matches in first {n_check}: {match_count} / {n_check}")
# Show where it diverges
for i in range(n_check):
if T[i] != truth_T_16[i]:
print(f" First divergence: sample {i}: pred={T[i]}, truth={truth_T_16[i]}")
break
if __name__ == "__main__":
main()
analysis/walk_5_11.py
-20
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@@ -1,20 +0,0 @@
"""Walk blocks of the new 5-11-26 events and look at what comes after Tran block."""
import sys
sys.path.insert(0, ".")
from minimateplus.waveform_codec import walk_body, find_data_start
def main():
for stem in ("M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0"):
with open(f"tests/fixtures/5-11-26/{stem}", "rb") as f:
raw = f.read()
body = raw[43:-26]
start = find_data_start(body)
blocks = walk_body(body, start)
print(f"\n=== {stem} === body={len(body)} start={start} blocks walked={len(blocks)}")
for i, b in enumerate(blocks[:20]):
print(f" block[{i:>2}] @ {b.offset:>5} tag={b.tag_hi:02x} NN=0x{b.tag_lo:02x}({b.tag_lo}) len={b.length} data[:24]={b.data[:24].hex(' ')}")
if __name__ == "__main__":
main()
analysis/walk_chunks.py
-44
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@@ -1,44 +0,0 @@
"""Walk the body assuming chunks delimited by 0x10 NN tags. Print each chunk's structure."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def walk(body: bytes, start_offset: int = 7, max_chunks: int = 30):
"""Find all positions where byte = 0x10 followed by a multiple-of-4 byte. Print chunks."""
chunks = []
i = start_offset
while i < len(body) - 1:
# Find next `10 NN` where NN is multiple of 4 (and not preceded by another 0x10 immediately, which would be data).
if body[i] == 0x10 and (body[i+1] % 4 == 0):
chunks.append(i)
i += 1
return chunks
def main():
for name in ("event-c", "event-d"):
b = load_bundle(name)
body = b.body
positions = []
i = 7 # skip 7-byte preamble
while i < len(body) - 1:
if body[i] == 0x10 and body[i+1] % 4 == 0 and body[i+1] > 0:
positions.append(i)
i += 2 # skip past tag
else:
i += 1
print(f"\n=== {name} === body={len(body)}, total `10 NN` (NN%4==0, NN>0) tags: {len(positions)}")
# Print first 20 chunks: show position, NN, gap to next tag
for k in range(min(30, len(positions))):
pos = positions[k]
NN = body[pos + 1]
next_pos = positions[k+1] if k+1 < len(positions) else len(body)
gap = next_pos - pos
data_bytes = body[pos+2 : next_pos]
print(f" chunk[{k:>3}] @ {pos:>5} NN=0x{NN:02x} ({NN:>3}, NN/2={NN//2}) gap={gap:>3} "
f"data={data_bytes[:24].hex(' ')}{'...' if len(data_bytes) > 24 else ''}")
if __name__ == "__main__":
main()
analysis/walk_v2.py
-50
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@@ -1,50 +0,0 @@
"""Deterministic chunk walker: each chunk = [10 NN][NN/2 bytes data][2 bytes trailer]."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def walk_chunks(body: bytes, start: int = 7):
"""Yield (offset, NN, data_bytes, trailer_bytes) tuples."""
i = start
while i + 1 < len(body):
if body[i] != 0x10:
break
NN = body[i + 1]
if NN == 0 or NN > 0x80 or NN % 4 != 0:
break
chunk_len = NN // 2 + 4
if i + chunk_len > len(body):
break
data = bytes(body[i + 2 : i + 2 + NN // 2])
trailer = bytes(body[i + 2 + NN // 2 : i + chunk_len])
yield (i, NN, data, trailer)
i += chunk_len
def main():
for name in ("event-c", "event-d", "event-a", "event-b"):
b = load_bundle(name)
body = b.body
chunks = list(walk_chunks(body))
print(f"\n=== {name} === body={len(body)} N_samples={len(b.samples['Tran'])}")
print(f" chunks parsed: {len(chunks)}")
if chunks:
last = chunks[-1]
end_of_walk = last[0] + last[1] // 2 + 4
print(f" walk ended at offset {end_of_walk} (= {len(body) - end_of_walk} bytes from end)")
# Stats
total_data_bytes = sum(len(c[2]) for c in chunks)
print(f" total data bytes: {total_data_bytes}, total nibbles: {2*total_data_bytes}")
if name in ("event-c", "event-d"):
ratio = (2 * total_data_bytes) / (len(b.samples['Tran']) * 4)
print(f" nibbles per (sample × channel): {ratio:.3f}")
# Sum of trailer second-byte
trailer_sums = [c[3][-1] if c[3] else None for c in chunks]
print(f" first 10 chunks: {[(c[0], c[1], c[3].hex()) for c in chunks[:10]]}")
# Print last 10 chunks (likely transition to trailer)
print(f" last 10 chunks: {[(c[0], c[1], c[3].hex()) for c in chunks[-10:]]}")
if __name__ == "__main__":
main()
analysis/walk_v3.py
-51
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@@ -1,51 +0,0 @@
"""Walk chunks; auto-detect preamble length by finding first 10 NN."""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def walk_chunks(body, start, max_NN=0x80):
chunks = []
i = start
while i + 1 < len(body):
if body[i] != 0x10:
break
NN = body[i + 1]
if NN == 0 or NN > max_NN or NN % 4 != 0:
break
chunk_len = NN // 2 + 4
if i + chunk_len > len(body):
break
data = bytes(body[i + 2 : i + 2 + NN // 2])
trailer = bytes(body[i + 2 + NN // 2 : i + chunk_len])
chunks.append((i, NN, data, trailer))
i += chunk_len
return chunks, i
def find_first_chunk_start(body):
"""Locate first byte that begins a `10 NN` chunk (NN ∈ multiples of 4, 4..0x7C)."""
for i in range(20):
if body[i] == 0x10 and body[i + 1] % 4 == 0 and 0 < body[i + 1] <= 0x7C:
return i
return -1
def main():
for name in ("event-c", "event-d", "event-a", "event-b"):
b = load_bundle(name)
body = b.body
start = find_first_chunk_start(body)
chunks, end = walk_chunks(body, start)
print(f"\n=== {name} === body={len(body)} N_samples={len(b.samples['Tran'])} start={start}")
print(f" chunks parsed: {len(chunks)}, walk ended at {end}")
if chunks:
print(f" first 5 chunks: {[(c[0], c[1], c[3].hex()) for c in chunks[:5]]}")
print(f" last 5 chunks: {[(c[0], c[1], c[3].hex()) for c in chunks[-5:]]}")
print(f" bytes around end of walk: {body[end-4:end+12].hex(' ')}")
else:
print(f" bytes at start: {body[start:start+16].hex(' ')}")
if __name__ == "__main__":
main()
analysis/walk_v4.py
-75
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@@ -1,75 +0,0 @@
"""
Walker v4: alternate [10 NN] data chunks and [00 NN] (or other) marker tags.
Hypothesis:
- [10 NN]: data block, length NN/2 + 2 bytes (2-byte tag + NN/2 bytes data)
- [00 NN]: 2-byte marker block (no data)
- [20/30/40 NN]: special blocks with type-dependent length
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
def walk(body, start):
i = start
blocks = []
while i + 1 < len(body):
t0 = body[i]
t1 = body[i + 1]
if t0 == 0x10 and t1 % 4 == 0 and 0 < t1 <= 0x80:
# data chunk: length NN/2 + 2
length = t1 // 2 + 2
blocks.append((i, "10", t1, bytes(body[i + 2 : i + length]), length))
i += length
elif t0 == 0x00 and t1 % 4 == 0:
# 2-byte marker
blocks.append((i, "00", t1, b"", 2))
i += 2
elif t0 == 0x20 and t1 % 4 == 0:
# type 2 — try length 2+t1/2 (similar to 10) OR fixed
length = t1 // 2 + 2
blocks.append((i, "20", t1, bytes(body[i + 2 : i + length]), length))
i += length
elif t0 == 0x30 and t1 % 4 == 0:
length = t1 // 2 + 2
blocks.append((i, "30", t1, bytes(body[i + 2 : i + length]), length))
i += length
elif t0 == 0x40 and t1 == 0x02:
# Special "footer transition" block — try fixed 22 bytes
length = 22
blocks.append((i, "40", t1, bytes(body[i + 2 : i + length]), length))
i += length
else:
# Unknown tag — stop
blocks.append((i, "??", t0, bytes(body[i:i+8]), 0))
break
return blocks, i
def main():
for name in ("event-c", "event-d", "event-a", "event-b"):
b = load_bundle(name)
body = b.body
# Auto-detect start
for s in range(15):
if body[s] == 0x10 and body[s+1] % 4 == 0 and 0 < body[s+1] <= 0x80:
start = s
break
else:
start = 7
blocks, end = walk(body, start)
# Categorize
from collections import Counter
types = Counter(b[1] for b in blocks)
print(f"\n=== {name} === body={len(body)} N={len(b.samples['Tran'])} start={start}")
print(f" total blocks: {len(blocks)}, walk ended at {end}/{len(body)}")
print(f" type counts: {dict(types)}")
# Print last 5 blocks
print(f" last 5 blocks: {[(bb[0], bb[1], bb[2]) for bb in blocks[-5:]]}")
if end < len(body):
print(f" bytes at end: {body[end:end+24].hex(' ')}")
if __name__ == "__main__":
main()
analysis/walk_v5.py
-83
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@@ -1,83 +0,0 @@
"""
Walker v5: flexible NN range and multiple block-type lengths.
Hypothesis:
- [10 NN]: 4-bit-delta data block, length = NN/2 + 2
- [20 NN]: 8-bit-literal data block, length = NN + 2
- [00 NN]: 2-byte marker (no payload)
- [30 NN]: trailer/summary block, length = NN*4
- [40 NN]: footer-marker block, fixed 22 bytes
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
from collections import Counter
def walk(body, start, max_blocks=10000):
i = start
blocks = []
while i + 1 < len(body) and len(blocks) < max_blocks:
t0 = body[i]
t1 = body[i + 1]
if t0 == 0x10 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 // 2 + 2
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append((i, "10", t1, data, length))
i += length
elif t0 == 0x20 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 + 2
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append((i, "20", t1, data, length))
i += length
elif t0 == 0x00 and t1 % 4 == 0:
# 2-byte marker
blocks.append((i, "00", t1, b"", 2))
i += 2
elif t0 == 0x30 and t1 % 4 == 0:
length = t1 * 4
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append((i, "30", t1, data, length))
i += length
elif t0 == 0x40 and t1 == 0x02:
length = 22
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append((i, "40", t1, data, length))
i += length
else:
blocks.append((i, "??", t0, bytes(body[i:i+8]), 0))
break
return blocks, i
def main():
for name in ("event-c", "event-d", "event-a", "event-b"):
b = load_bundle(name)
body = b.body
for s in range(15):
if body[s] == 0x10 and body[s+1] % 4 == 0 and 0 < body[s+1] <= 0xFC:
start = s; break
else:
start = 7
blocks, end = walk(body, start)
types = Counter(bb[1] for bb in blocks)
print(f"\n=== {name} === body={len(body)} N={len(b.samples['Tran'])} start={start}")
print(f" total blocks: {len(blocks)}, walk ended at {end}/{len(body)}")
print(f" type counts: {dict(types)}")
if blocks and blocks[-1][1] == "??":
print(f" stopped at byte: 0x{blocks[-1][2]:02x}, prev 5 blocks: {[(bb[0], bb[1], bb[2]) for bb in blocks[-6:-1]]}")
# Sum payload sizes by type
payload_sizes = {t: sum(len(bb[3]) for bb in blocks if bb[1] == t) for t in types}
print(f" payload bytes by type: {payload_sizes}")
if __name__ == "__main__":
main()
analysis/walk_v6.py
-68
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@@ -1,68 +0,0 @@
"""
Walker v6: handle 40 02 blocks correctly (length 20).
Block formats:
- [10 NN]: 4-bit nibble delta data, length = NN/2 + 2
- [20 NN]: int8 literal data, length = NN + 2
- [00 NN]: 2-byte marker
- [30 NN]: trailer/summary block, length = NN*4
- [40 02]: segment header, fixed length 20
"""
import sys
sys.path.insert(0, ".")
from analysis.load_bundle import load_bundle
from collections import Counter
def walk(body, start, max_blocks=10000):
i = start
blocks = []
while i + 1 < len(body) and len(blocks) < max_blocks:
t0 = body[i]
t1 = body[i + 1]
if t0 == 0x10 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 // 2 + 2
elif t0 == 0x20 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 + 2
elif t0 == 0x00 and t1 % 4 == 0:
length = 2
elif t0 == 0x30 and t1 % 4 == 0 and 0 < t1 <= 0x10:
length = t1 * 4
elif t0 == 0x40 and t1 == 0x02:
length = 20
else:
blocks.append((i, "??", t0, bytes(body[i:i+8]), 0))
break
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append((i, f"{t0:02x}", t1, data, length))
i += length
return blocks, i
def main():
for name in ("event-c", "event-d", "event-a", "event-b"):
b = load_bundle(name)
body = b.body
for s in range(15):
if body[s] == 0x10 and body[s+1] % 4 == 0 and 0 < body[s+1] <= 0xFC:
start = s; break
else:
start = 7
blocks, end = walk(body, start)
types = Counter(bb[1] for bb in blocks)
print(f"\n=== {name} === body={len(body)} N={len(b.samples['Tran'])} start={start}")
print(f" total blocks: {len(blocks)}, walk ended at {end}/{len(body)}")
print(f" type counts: {dict(types)}")
if blocks and blocks[-1][1] == "??":
print(f" stopped at byte: 0x{blocks[-1][2]:02x} at offset {blocks[-1][0]}")
print(f" prev 5 blocks: {[(bb[0], bb[1], bb[2]) for bb in blocks[-6:-1]]}")
print(f" bytes around stop: {body[end-4:end+24].hex(' ')}")
# Sum
payload_sizes = {t: sum(len(bb[3]) for bb in blocks if bb[1] == t) for t in types}
print(f" payload bytes by type: {payload_sizes}")
if __name__ == "__main__":
main()
analysis_idf/corpus_accuracy.py
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"""Run read_idf_file across the corpus and report per-channel accuracy vs sidecars."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from micromate.idf_file import read_idf_file
from analysis_idf.recon import load_sidecar_samples
def sidecar_path(idfw: Path) -> Path:
return idfw.parent / "TXT" / f"{idfw.name}.txt"
def main():
root = REPO / "tests/fixtures/THORDATA_example"
files = [f for f in root.rglob("*.IDFW") if not str(f).endswith(".CDB")]
files.sort()
GEO_LSB = 0.0003
n_ok = n_skip = 0
overall = {"Tran": [], "Vert": [], "Long": []}
for f in files:
try:
res = read_idf_file(f)
except Exception:
n_skip += 1
continue
sc_path = sidecar_path(f)
if not sc_path.exists():
n_skip += 1
continue
try:
sc = load_sidecar_samples(sc_path)
except Exception:
n_skip += 1
continue
per_file = {}
for ch in ("Tran", "Vert", "Long"):
sc_counts = [int(round(v / GEO_LSB)) for v in sc[ch]]
dec = res.samples.get(ch, [])
n = min(len(sc_counts), len(dec))
if n == 0:
per_file[ch] = 0.0
continue
exact = sum(1 for i in range(n) if sc_counts[i] == dec[i])
pct = 100.0 * exact / n
per_file[ch] = pct
overall[ch].append(pct)
n_ok += 1
print(f"Processed {n_ok} files (skipped {n_skip})")
print("Per-channel exact-match % (mean / min / max):")
for ch, vals in overall.items():
if vals:
avg = sum(vals) / len(vals)
print(f" {ch}: mean={avg:.2f}% min={min(vals):.2f}% max={max(vals):.2f}% n={len(vals)}")
if __name__ == "__main__":
main()
analysis_idf/diff_trail.py
-49
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@@ -1,49 +0,0 @@
"""Find where decoded-vs-sidecar diverges for each channel."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from minimateplus.waveform_codec import decode_waveform_v2
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def main():
buf = TARGET.read_bytes()
sc = load_sidecar_samples(TXT)
decoded = decode_waveform_v2(buf[0x0f1f:])
GEO_LSB = 0.0003
for ch in ("Tran", "Vert", "Long"):
sc_counts = [int(round(v / GEO_LSB)) for v in sc[ch]]
dec = decoded[ch]
# Find ALL transitions where mismatches start/stop
first_diff = next((i for i in range(len(dec)) if dec[i] != sc_counts[i]), None)
if first_diff is None:
print(f"{ch}: NO MISMATCHES")
continue
print(f"{ch}: first diff at idx {first_diff}")
# Show 5 before, 5 after
for i in range(max(0, first_diff - 3), min(len(dec), first_diff + 8)):
mark = " " if dec[i] == sc_counts[i] else "**"
print(f" {mark} idx {i:4d}: sc={sc_counts[i]:6d} dec={dec[i]:6d} diff={dec[i]-sc_counts[i]:+d}")
# Where does cumulative diff exceed 100?
cum_match_run = 0
max_match_run = 0
match_run_start = 0
diff_count = 0
for i in range(len(dec)):
if dec[i] == sc_counts[i]:
cum_match_run += 1
max_match_run = max(max_match_run, cum_match_run)
else:
cum_match_run = 0
diff_count += 1
print(f" total mismatches: {diff_count}/{len(dec)}, longest run of matches: {max_match_run}")
print()
if __name__ == "__main__":
main()
analysis_idf/e2e_idfh.py
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@@ -1,48 +0,0 @@
"""End-to-end IDFH ingest verification."""
from __future__ import annotations
import sys
import tempfile
import json
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from sfm.waveform_store import WaveformStore
def main():
idfh = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM13981/UM13981_20220805075441.IDFH"
txt = idfh.parent / "TXT" / f"{idfh.name}.txt"
with tempfile.TemporaryDirectory() as td:
store = WaveformStore(Path(td))
ev, rec = store.save_imported_idf(
idfh.read_bytes(),
idfh,
idf_report_text=txt.read_text(errors="replace"),
)
print("=== save_imported_idf (IDFH) ===")
print(f" serial: {rec['serial']}")
print(f" filename: {rec['filename']}")
print(f" filesize: {rec['filesize']}")
print(f" h5: {rec['hdf5_filename']}") # expect None for histogram
print(f" sidecar: {rec['sidecar_filename']}")
print()
print("=== Event ===")
print(f" timestamp: {ev.timestamp}")
print(f" record_type: {ev.record_type}")
print(f" sample_rate: {ev.sample_rate}")
print()
# Inspect sidecar to confirm intervals were stashed
sc_path = Path(td) / "UM13981" / f"{idfh.name}.sfm.json"
sc = json.loads(sc_path.read_text())
intervals = sc.get("extensions", {}).get("idf_intervals", [])
print(f" sidecar intervals: {len(intervals)}")
if intervals:
print(f" first interval: {intervals[0]}")
print(f" last interval: {intervals[-1]}")
if __name__ == "__main__":
main()
analysis_idf/e2e_no_txt.py
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@@ -1,40 +0,0 @@
"""Verify the had_report=False path: ingest IDFW with no .txt."""
from __future__ import annotations
import sys
from pathlib import Path
import tempfile
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from sfm.waveform_store import WaveformStore
def main():
idfw = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162723.IDFW"
with tempfile.TemporaryDirectory() as td:
store = WaveformStore(Path(td))
ev, rec = store.save_imported_idf(
idfw.read_bytes(),
idfw,
serial_hint=None,
idf_report_text=None, # ← no .txt!
)
print("=== IDFW without .txt ingest ===")
print(f" serial: {rec['serial']}")
print(f" timestamp: {ev.timestamp}")
print(f" sample_rate: {ev.sample_rate}")
print(f" record_type: {ev.record_type}")
print(f" rectime_sec: {ev.rectime_seconds}")
nT = len(ev.raw_samples.get('Tran', [])) if ev.raw_samples else 0
nV = len(ev.raw_samples.get('Vert', [])) if ev.raw_samples else 0
nL = len(ev.raw_samples.get('Long', [])) if ev.raw_samples else 0
nM = len(ev.raw_samples.get('MicL', [])) if ev.raw_samples else 0
print(f" raw_samples: Tran={nT} Vert={nV} Long={nL} MicL={nM}")
if ev.peak_values:
print(f" peak_values: tran={ev.peak_values.tran} vert={ev.peak_values.vert} long={ev.peak_values.long}")
print(f" h5 written: {rec['hdf5_filename']}")
if __name__ == "__main__":
main()
analysis_idf/e2e_report.py
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@@ -1,102 +0,0 @@
"""End-to-end Thor report PDF rendering.
Ingests an IDFW + .txt via save_imported_idf, runs gather_report_data
(faking a minimal DB row), and renders the PDF to disk.
"""
from __future__ import annotations
import sys
import tempfile
import json
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from sfm.waveform_store import WaveformStore
from sfm import report_pdf
class FakeDb:
"""Stand-in for SeismoDb.get_event(); the renderer only needs a few cols."""
def __init__(self, event):
self.event = event
def get_event(self, _id):
return self.event
def main():
base = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719"
idfw = base / "UM11719_20231219162723.IDFW"
txt = base / "TXT" / f"{idfw.name}.txt"
with tempfile.TemporaryDirectory() as td:
store = WaveformStore(Path(td))
ev, rec = store.save_imported_idf(
idfw.read_bytes(),
idfw,
idf_report_text=txt.read_text(errors="replace"),
)
print(f"save_imported_idf: h5={rec['hdf5_filename']}, sidecar={rec['sidecar_filename']}")
# Verify sidecar has bw_report block
sc_path = Path(td) / "UM11719" / f"{idfw.name}.sfm.json"
sc = json.loads(sc_path.read_text())
bw = sc.get("bw_report", {})
print(f" bw_report.available: {bw.get('available')}")
print(f" bw_report.peaks.tran.ppv_ips: {bw.get('peaks', {}).get('tran', {}).get('ppv_ips')}")
print(f" bw_report.mic.pspl_dbl: {bw.get('mic', {}).get('pspl_dbl')}")
print(f" bw_report.histogram.n_intervals: {bw.get('histogram', {}).get('n_intervals')}")
# Build a DB-row-shaped dict from the Event for gather_report_data
import datetime
ts = ev.timestamp
ts_iso = None
if ts is not None:
try:
ts_iso = datetime.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute, ts.second).isoformat()
except Exception:
pass
fake_row = {
"serial": "UM11719",
"blastware_filename": rec["filename"],
"record_type": "Waveform",
"timestamp": ts_iso,
"sample_rate": ev.sample_rate,
"project": ev.project_info.project if ev.project_info else None,
"client": ev.project_info.client if ev.project_info else None,
"operator": ev.project_info.operator if ev.project_info else None,
"sensor_location": ev.project_info.sensor_location if ev.project_info else None,
"created_at": None,
}
rd = report_pdf.gather_report_data(FakeDb(fake_row), store, event_id="test-1")
print()
print(f"=== ReportData ===")
print(f" event_id: {rd.event_id}")
print(f" serial: {rd.serial}")
print(f" record_type: {rd.record_type}")
print(f" event_datetime: {rd.event_datetime_str}")
print(f" trigger: {rd.trigger_source}")
print(f" geo_range: {rd.geo_range_str}")
print(f" sample_rate: {rd.sample_rate_str}")
print(f" firmware: {rd.firmware}")
print(f" calibration: {rd.calibration_date} by {rd.calibration_by}")
print(f" battery: {rd.battery_volts}")
print(f" PVS: {rd.peak_vector_sum_ips} in/s at {rd.peak_vector_sum_time_s} sec")
print(f" mic_pspl_dbl: {rd.mic_pspl_dbl}")
print(f" mic_zc_freq_hz: {rd.mic_zc_freq_hz}")
print(f" channel_stats: {len(rd.channel_stats)} rows")
for cs in rd.channel_stats:
print(f" {cs['name']}: PPV={cs['ppv_ips']} ZC={cs['zc_freq_hz']} ToP={cs['time_of_peak_s']} Acc={cs['peak_accel_g']} Disp={cs['peak_disp_in']} Test={cs['sensor_check']}")
# Render the PDF
out_path = REPO / "analysis_idf" / "thor_report.pdf"
pdf_bytes = report_pdf.render_event_report_pdf(rd)
out_path.write_bytes(pdf_bytes)
print()
print(f" PDF written: {out_path} ({len(pdf_bytes)} bytes)")
if __name__ == "__main__":
main()
analysis_idf/e2e_report_idfh.py
-91
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@@ -1,91 +0,0 @@
"""End-to-end Thor IDFH histogram report PDF rendering."""
from __future__ import annotations
import sys
import tempfile
import json
import datetime
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from sfm.waveform_store import WaveformStore
from sfm import report_pdf
class FakeDb:
def __init__(self, event):
self.event = event
def get_event(self, _id):
return self.event
def main():
# Use the multi-interval IDFH (81 + trigger row)
idfh = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM13981/UM13981_20220805075441.IDFH"
txt = idfh.parent / "TXT" / f"{idfh.name}.txt"
with tempfile.TemporaryDirectory() as td:
store = WaveformStore(Path(td))
ev, rec = store.save_imported_idf(
idfh.read_bytes(),
idfh,
idf_report_text=txt.read_text(errors="replace"),
)
print(f"save_imported_idf: h5={rec['hdf5_filename']}, sidecar={rec['sidecar_filename']}")
sc_path = Path(td) / "UM13981" / f"{idfh.name}.sfm.json"
sc = json.loads(sc_path.read_text())
bw = sc.get("bw_report", {})
hist = bw.get("histogram", {})
print(f" bw_report.histogram.start: {hist.get('start')}")
print(f" bw_report.histogram.stop: {hist.get('stop')}")
print(f" bw_report.histogram.n_intervals: {hist.get('n_intervals')}")
print(f" bw_report.histogram.interval_size: {hist.get('interval_size')}")
print(f" bw_report.histogram.interval_size_s: {hist.get('interval_size_s')}")
print(f" bw_report.peaks.tran.ppv_ips: {bw.get('peaks', {}).get('tran', {}).get('ppv_ips')}")
ts = ev.timestamp
ts_iso = None
if ts is not None:
try:
ts_iso = datetime.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute, ts.second).isoformat()
except Exception:
pass
fake_row = {
"serial": "UM13981",
"blastware_filename": rec["filename"],
"record_type": "Histogram",
"timestamp": ts_iso,
"sample_rate": ev.sample_rate,
"project": ev.project_info.project if ev.project_info else None,
"client": ev.project_info.client if ev.project_info else None,
"operator": ev.project_info.operator if ev.project_info else None,
"sensor_location": ev.project_info.sensor_location if ev.project_info else None,
"created_at": None,
}
rd = report_pdf.gather_report_data(FakeDb(fake_row), store, event_id="hist-1")
print()
print("=== ReportData (histogram) ===")
print(f" is_histogram: {rd.is_histogram}")
print(f" histogram_start: {rd.histogram_start_str}")
print(f" histogram_stop: {rd.histogram_stop_str}")
print(f" histogram_n_intervals: {rd.histogram_n_intervals}")
print(f" histogram_interval_size:{rd.histogram_interval_size}")
print(f" histogram_interval_times[:3]: {rd.histogram_interval_times[:3]}")
print(f" histogram_interval_times[-2:]: {rd.histogram_interval_times[-2:]}")
print(f" channel_stats: {len(rd.channel_stats)} rows")
for cs in rd.channel_stats:
print(f" {cs['name']}: PPV={cs['ppv_ips']} ZC={cs['zc_freq_hz']} peak_date={cs['peak_date']} peak_time={cs['peak_time']}")
pdf_bytes = report_pdf.render_event_report_pdf(rd)
out_path = REPO / "analysis_idf" / "thor_report_idfh.pdf"
out_path.write_bytes(pdf_bytes)
print()
print(f" PDF written: {out_path} ({len(pdf_bytes)} bytes)")
if __name__ == "__main__":
main()
analysis_idf/e2e_save_idf.py
-52
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@@ -1,52 +0,0 @@
"""End-to-end ingest test: feed an IDFW + .txt to save_imported_idf in a tmp store."""
from __future__ import annotations
import sys
from pathlib import Path
import tempfile
import shutil
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from sfm.waveform_store import WaveformStore
def main():
idfw = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162723.IDFW"
txt = idfw.parent / "TXT" / f"{idfw.name}.txt"
with tempfile.TemporaryDirectory() as td:
store = WaveformStore(Path(td))
ev, rec = store.save_imported_idf(
idfw.read_bytes(),
idfw,
serial_hint=None,
idf_report_text=txt.read_text(errors="replace"),
)
print("=== Save result ===")
print(f" serial: {rec['serial']}")
print(f" filename: {rec['filename']}")
print(f" filesize: {rec['filesize']}")
print(f" h5: {rec['hdf5_filename']}")
print(f" sidecar: {rec['sidecar_filename']}")
print()
print("=== Event ===")
print(f" serial: {ev.serial if hasattr(ev,'serial') else '(n/a)'}")
print(f" timestamp: {ev.timestamp}")
print(f" sample_rate: {ev.sample_rate}")
print(f" record_type: {ev.record_type}")
print(f" rectime_sec: {ev.rectime_seconds}")
print(f" raw_samples: Tran={len(ev.raw_samples.get('Tran', [])) if ev.raw_samples else 0}, Vert={len(ev.raw_samples.get('Vert', [])) if ev.raw_samples else 0}, Long={len(ev.raw_samples.get('Long', [])) if ev.raw_samples else 0}, MicL={len(ev.raw_samples.get('MicL', [])) if ev.raw_samples else 0}")
if ev.peak_values:
print(f" peaks (txt): Tran={ev.peak_values.tran} Vert={ev.peak_values.vert} Long={ev.peak_values.long}")
print()
# Verify the h5 file actually got written
h5path = Path(td) / "UM11719" / f"{idfw.name}.h5"
print(f" h5 exists: {h5path.exists()} size={h5path.stat().st_size if h5path.exists() else 0}")
sidecar = Path(td) / "UM11719" / f"{idfw.name}.sfm.json"
print(f" sidecar exists:{sidecar.exists()} size={sidecar.stat().st_size if sidecar.exists() else 0}")
if __name__ == "__main__":
main()
analysis_idf/idfh_decode.py
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@@ -1,137 +0,0 @@
"""Decode IDFH histogram intervals + verify against sidecar."""
from __future__ import annotations
import sys
import struct
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
SEGMENT_MAGIC = b"\x02\xda\x0a\x00\x00\x00"
SEGMENT_SIZE = 732 # = 10-byte header + 10 × 72-byte intervals + 2-byte tail
INTERVAL_SIZE = 72
CHANNELS = ("Tran", "Vert", "Long", "MicL")
def decode_interval(buf72: bytes) -> dict:
"""Decode one 72-byte interval into per-channel min/max/halfp."""
out = {}
for i, ch in enumerate(CHANNELS):
block = buf72[i*16 : (i+1)*16]
mn = struct.unpack_from(">h", block, 0)[0]
mx = struct.unpack_from(">h", block, 2)[0]
sb = struct.unpack_from(">h", block, 4)[0]
halfp = struct.unpack_from(">H", block, 6)[0]
f10 = struct.unpack_from(">H", block, 10)[0]
f14 = struct.unpack_from(">H", block, 14)[0]
peak_count = max(abs(mn), abs(mx))
out[ch] = {
"min": mn,
"max": mx,
"field4": sb,
"halfp": halfp,
"field10": f10,
"field14": f14,
"peak": peak_count,
"freq_hz": (512.0 / halfp) if halfp > 5 else None,
}
out["_tail"] = buf72[64:].hex(" ")
return out
def walk_idfh(buf: bytes) -> list:
"""Walk all interval records in an IDFH file."""
intervals = []
# Multi-segment file: every 02 da 0a 00 00 00 marker introduces a segment.
# Single-interval file: just one body header at 0xf96 of form ?? ?? 0a 00 00 00.
# Find them all.
i = 0
while True:
j = buf.find(b"\x0a\x00\x00\x00", i)
if j < 0:
break
# Validate: the 2 bytes before must form a length, and we want bytes
# [j-2 : j+6] to have a recognisable shape. Actually the cleanest
# filter is "preceded by a length and followed by 00 NN 05 3f".
if j < 2:
i = j + 1
continue
# Body header form: [length_be_2][0a 00 00 00][00 NN][05 3f]
if j + 10 > len(buf):
break
length = int.from_bytes(buf[j-2:j], "big")
# Verify the segment-marker shape: [length_be][0a 00 00 00][00 NN][05 3f]
if buf[j+4] != 0x00:
i = j + 1
continue
if buf[j+6:j+8] != b"\x05\x3f":
i = j + 1
continue
# Header layout (10 bytes): [length_be 2B][0a 00 00 00 4B][00 NN 2B][05 3f 2B]
# Followed by N interval records of 72 bytes each, then 2 tail bytes.
# length value = (N × 72) + 10 (counts bytes from 0x0a... through interval data).
header_start = j - 2
n_intervals = (length - 10) // INTERVAL_SIZE
interval_start = header_start + 10
for k in range(n_intervals):
off = interval_start + k * INTERVAL_SIZE
if off + INTERVAL_SIZE > len(buf):
break
chunk = buf[off:off + INTERVAL_SIZE]
intervals.append({"offset": off, **decode_interval(chunk)})
i = header_start + length + 2
return intervals
def main():
# Test against multi-segment IDFH
target = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM13981/UM13981_20220805075441.IDFH"
sc_path = target.parent / "TXT" / f"{target.name}.txt"
buf = target.read_bytes()
intervals = walk_idfh(buf)
print(f"=== {target.name} ===")
print(f" file size: {len(buf)}")
print(f" decoded intervals: {len(intervals)}")
# Show first 2 + last 2
sc_rows = []
for line in sc_path.read_text(errors="replace").splitlines():
if line.startswith("2022-") or line.startswith("2023-"):
sc_rows.append(line)
print(f" sidecar rows: {len(sc_rows)}")
print()
for k in [0, 1, 78, 79, 80]:
if k >= len(intervals):
continue
iv = intervals[k]
print(f"--- interval {k} @0x{iv['offset']:04x} ---")
for ch in CHANNELS:
d = iv[ch]
peak_ips = d["peak"] / 32768 * 10.0
print(f" {ch}: peak={d['peak']:5d} ({peak_ips:.4f} in/s) halfp={d['halfp']:5d} freq={d['freq_hz']}")
# sidecar row
if k < len(sc_rows):
print(f" SC: {sc_rows[k]}")
# Test single-interval IDFH
print()
target2 = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162648.IDFH"
sc2 = target2.parent / "TXT" / f"{target2.name}.txt"
buf2 = target2.read_bytes()
intervals2 = walk_idfh(buf2)
print(f"=== {target2.name} ===")
print(f" file size: {len(buf2)}, decoded intervals: {len(intervals2)}")
if intervals2:
iv = intervals2[0]
for ch in CHANNELS:
d = iv[ch]
peak_ips = d["peak"] / 32768 * 10.0
print(f" {ch}: peak={d['peak']:5d} ({peak_ips:.4f} in/s) halfp={d['halfp']:5d} freq={d['freq_hz']}")
sc_rows2 = [l for l in sc2.read_text(errors='replace').splitlines() if l.startswith("2023-")]
if sc_rows2:
print(f" SC: {sc_rows2[0]}")
if __name__ == "__main__":
main()
analysis_idf/idfh_period.py
-41
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@@ -1,41 +0,0 @@
"""Find IDFH interval period via auto-correlation of structural patterns."""
from __future__ import annotations
import sys
from pathlib import Path
from collections import Counter
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
def main():
target = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM13981/UM13981_20220805075441.IDFH"
buf = target.read_bytes()
body_start = 0xF96
body_end = 0x270C
body = buf[body_start:body_end]
print(f"body size: {len(body)} bytes (file {len(buf)} bytes)")
# For each candidate interval size, count how many bytes at fixed offsets within
# each interval are zero (consistent column-zero pattern indicates correct size).
print()
print("=== zero-column score by interval size (higher = more likely) ===")
best = []
for sz in range(16, 100):
n = len(body) // sz
if n < 30:
continue
# For each column position within an interval, count how many of n intervals have zero
score = 0
for col in range(sz):
zeros = sum(1 for i in range(n) if body[i*sz + col] == 0)
if zeros >= n * 0.9:
score += 1
best.append((score, sz, n))
best.sort(reverse=True)
for score, sz, n in best[:10]:
print(f" size={sz:3d} n_intervals={n} consistently-zero-cols={score}")
if __name__ == "__main__":
main()
analysis_idf/per_file_detail.py
-40
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@@ -1,40 +0,0 @@
"""Per-file accuracy + sample-count details."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from micromate.idf_file import read_idf_file
from analysis_idf.recon import load_sidecar_samples
def main():
root = REPO / "tests/fixtures/THORDATA_example"
files = sorted([f for f in root.rglob("*.IDFW") if not str(f).endswith(".CDB")])
GEO_LSB = 0.0003
# Limit to first 15 successful files for detail.
shown = 0
for f in files:
try:
res = read_idf_file(f)
except Exception:
continue
sc_path = f.parent / "TXT" / f"{f.name}.txt"
if not sc_path.exists():
continue
sc = load_sidecar_samples(sc_path)
sc_tran = [int(round(v / GEO_LSB)) for v in sc["Tran"]]
dec = res.samples.get("Tran", [])
n = min(len(sc_tran), len(dec))
exact = sum(1 for i in range(n) if sc_tran[i] == dec[i]) if n else 0
pct = 100.0 * exact / n if n else 0.0
print(f"{f.name:40s} size={f.stat().st_size:6d} sc_n={len(sc_tran):4d} dec_n={len(dec):4d} exact={pct:.1f}%")
shown += 1
if shown >= 20:
break
if __name__ == "__main__":
main()
analysis_idf/probe_boundary.py
-64
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@@ -1,64 +0,0 @@
"""Look at what's at the divergence boundary."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from minimateplus.waveform_codec import walk_body, find_data_start, parse_segment_header
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def main():
buf = TARGET.read_bytes()
body = buf[0x0f1f:]
start = find_data_start(body)
print(f"data_start: {start} (= file offset 0x{0x0f1f + start:04x})")
blocks = walk_body(body, start)
print(f"{len(blocks)} blocks total")
print()
# First 25 blocks
print("=== first 30 blocks ===")
for i, b in enumerate(blocks[:30]):
body_off = 0x0f1f + b.offset
if b.tag_hi == 0x40:
hdr = parse_segment_header(b)
print(f" [{i:3d}] @0x{body_off:04x} {b.kind} (segment header) counter={hdr['counter'] if hdr else '?'} field2={hdr['field2'].hex() if hdr else '?'} anchor={hdr['anchor_bytes'].hex() if hdr else '?'} tail={hdr['tail'].hex() if hdr else '?'}")
else:
print(f" [{i:3d}] @0x{body_off:04x} {b.kind} len={b.length} data={b.data[:16].hex()}")
print()
# Cumulative sample counts per block to find which block contains sample 254
print("=== cumulative samples through blocks ===")
cur_ch = "Tran"
rotation = ["Vert", "Long", "MicL", "Tran"]
seg_count = 0
samples_in_curseg = 2 # preamble Tran[0], Tran[1]
for i, b in enumerate(blocks[:30]):
if b.tag_hi == 0x40:
seg_count += 1
prev_ch = cur_ch
cur_ch = rotation[(seg_count - 1) % 4]
print(f" [{i:3d}] 40 02 -> end of {prev_ch} segment, start {cur_ch} (segment {seg_count})")
samples_in_curseg = 2 # anchors
elif (b.tag_hi & 0xF0) == 0x10:
nn = ((b.tag_hi & 0x0F) << 8) | b.tag_lo
samples_in_curseg += nn
print(f" [{i:3d}] {b.kind} nibble: +{nn} samples, ch={cur_ch}, ch_total~{samples_in_curseg}")
elif (b.tag_hi & 0xF0) == 0x20:
nn = ((b.tag_hi & 0x0F) << 8) | b.tag_lo
samples_in_curseg += nn
print(f" [{i:3d}] {b.kind} int8: +{nn} samples, ch={cur_ch}, ch_total~{samples_in_curseg}")
elif b.tag_hi == 0x00:
samples_in_curseg += b.tag_lo
print(f" [{i:3d}] {b.kind} RLE: +{b.tag_lo}, ch={cur_ch}, ch_total~{samples_in_curseg}")
elif b.tag_hi == 0x30:
samples_in_curseg += b.tag_lo
print(f" [{i:3d}] {b.kind} packed12: +{b.tag_lo} samples, ch={cur_ch}, ch_total~{samples_in_curseg}")
if __name__ == "__main__":
main()
analysis_idf/recon.py
-89
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@@ -1,89 +0,0 @@
"""Reconnaissance helpers for cracking the Thor IDFW binary."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
TARGET = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162723.IDFW"
TXT = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/TXT/UM11719_20231219162723.IDFW.txt"
def hex_at(buf: bytes, off: int, n: int = 32) -> str:
chunk = buf[off : off + n]
hexs = " ".join(f"{b:02x}" for b in chunk)
asc = "".join(chr(b) if 32 <= b < 127 else "." for b in chunk)
return f"{off:04x}: {hexs} {asc}"
def find_all(buf: bytes, needle: bytes) -> list[int]:
out: list[int] = []
i = 0
while True:
j = buf.find(needle, i)
if j < 0:
break
out.append(j)
i = j + 1
return out
def load_sidecar_samples(path: Path) -> dict[str, list[float]]:
"""Parse the txt sample table — Tran/Vert/Long/MicL."""
out = {"Tran": [], "Vert": [], "Long": [], "MicL": []}
in_block = False
for line in path.read_text(errors="replace").splitlines():
if not in_block:
if line.strip() == "Waveform Data Channels":
in_block = True
continue
if line.startswith("Waveform Data USB Channels"):
break
parts = line.split("\t")
# First row is the header "\tTran\tVert\tLong\tMicL"
if len(parts) >= 5 and parts[1] == "Tran":
continue
if len(parts) < 5:
continue
try:
out["Tran"].append(float(parts[1]))
out["Vert"].append(float(parts[2]))
out["Long"].append(float(parts[3]))
out["MicL"].append(float(parts[4]))
except ValueError:
continue
return out
def main():
buf = TARGET.read_bytes()
samples = load_sidecar_samples(TXT)
print(f"file size: {len(buf)} bytes")
print(f"sample rows: Tran={len(samples['Tran'])} Vert={len(samples['Vert'])} Long={len(samples['Long'])} MicL={len(samples['MicL'])}")
print(f"first 6 Tran samples: {samples['Tran'][:6]}")
print(f"first 6 Vert samples: {samples['Vert'][:6]}")
print(f"first 6 Long samples: {samples['Long'][:6]}")
print(f"first 6 MicL samples: {samples['MicL'][:6]}")
print()
print("=== BW magic '00 02 00' positions ===")
hits = find_all(buf, b"\x00\x02\x00")
print(f"{len(hits)} hits")
for h in hits[:20]:
print(hex_at(buf, h, 24))
print()
print("=== '40 02' segment-header positions ===")
hits = find_all(buf, b"\x40\x02")
print(f"{len(hits)} hits")
for h in hits:
ctx_pre = buf[max(0, h - 4): h].hex()
ctx_post = buf[h: h + 20].hex()
# Show byte preceding to help identify real headers vs casual occurrences
print(f" 0x{h:04x} pre={ctx_pre} post={ctx_post}")
if __name__ == "__main__":
main()
analysis_idf/seg_resync.py
-40
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@@ -1,40 +0,0 @@
"""Find each segment boundary in the channel and check if errors reset there."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from minimateplus.waveform_codec import decode_waveform_v2
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def main():
buf = TARGET.read_bytes()
sc = load_sidecar_samples(TXT)
decoded = decode_waveform_v2(buf[0x0f1f:])
GEO_LSB = 0.0003
for ch in ("Tran", "Vert", "Long"):
sc_counts = [int(round(v / GEO_LSB)) for v in sc[ch]]
dec = decoded[ch]
# Find every transition where error becomes zero from nonzero (or grows from zero)
# Print indices where dec resyncs back to exact match.
n = min(len(sc_counts), len(dec))
events = []
prev_match = True
for i in range(n):
match = sc_counts[i] == dec[i]
if match != prev_match:
kind = "RESYNC" if match else "DIVERGE"
events.append((i, kind, sc_counts[i], dec[i]))
prev_match = match
print(f"{ch}: {len(events)} transitions")
for i, kind, sc_v, dec_v in events[:20]:
print(f" idx {i:4d} {kind:8s} sc={sc_v:6d} dec={dec_v:6d} diff={dec_v-sc_v:+d}")
print()
if __name__ == "__main__":
main()
analysis_idf/smoke_idfh.py
-46
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@@ -1,46 +0,0 @@
"""Smoke-test read_idf_file on IDFH across the corpus."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from micromate.idf_file import read_idf_file
def main():
target = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162648.IDFH"
result = read_idf_file(target)
ev = result.event
print(f"=== {target.name} ===")
print(f" signature: {result.signature}")
print(f" serial: {ev.serial}")
print(f" timestamp: {ev.timestamp}")
print(f" sample_rate: {ev.sample_rate}")
print(f" kind: {ev.kind}")
print(f" intervals: {len(result.intervals or [])}")
print(f" peaks: T={ev.peaks.transverse_ips:.4f} V={ev.peaks.vertical_ips:.4f} L={ev.peaks.longitudinal_ips:.4f}")
print()
root = REPO / "tests/fixtures/THORDATA_example"
files = list(root.rglob("*.IDFH"))
ok = fail = nyi = 0
total_intervals = 0
for f in files:
try:
r = read_idf_file(f)
ok += 1
total_intervals += len(r.intervals or [])
except NotImplementedError:
nyi += 1
except Exception as exc:
fail += 1
if fail <= 3:
print(f" FAIL: {f.name}: {type(exc).__name__}: {exc}")
print(f"Corpus: {len(files)} IDFH files | ok={ok} fail={fail} nyi={nyi}")
print(f"Total intervals decoded: {total_intervals}")
if __name__ == "__main__":
main()
analysis_idf/smoke_test.py
-48
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@@ -1,48 +0,0 @@
"""Smoke-test read_idf_file across the sample corpus."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from micromate.idf_file import read_idf_file, geo_count_to_ips, mic_count_to_psi
def main():
target = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719/UM11719_20231219162723.IDFW"
result = read_idf_file(target)
ev = result.event
print(f"=== {target.name} ===")
print(f" signature: {result.signature}")
print(f" serial: {ev.serial}")
print(f" timestamp: {ev.timestamp}")
print(f" sample_rate: {ev.sample_rate}")
print(f" record_time: {ev.record_time_sec}")
print(f" calibration: {result.binary_metadata.calibration_date}")
print(f" Tran samples: {len(result.samples['Tran'])}, peak_ips={ev.peaks.transverse_ips:.4f}")
print(f" Vert samples: {len(result.samples['Vert'])}, peak_ips={ev.peaks.vertical_ips:.4f}")
print(f" Long samples: {len(result.samples['Long'])}, peak_ips={ev.peaks.longitudinal_ips:.4f}")
print(f" MicL samples: {len(result.samples['MicL'])}")
print()
# Corpus sweep
root = REPO / "tests/fixtures/THORDATA_example"
files = [f for f in root.rglob("*.IDFW") if not str(f).endswith(".CDB")]
ok = fail = nyi = 0
for f in files:
try:
r = read_idf_file(f)
ok += 1
except NotImplementedError:
nyi += 1
except Exception as exc:
fail += 1
if fail <= 5:
print(f" FAIL: {f.name}: {type(exc).__name__}: {exc}")
print()
print(f"Corpus: {len(files)} IDFW files | ok={ok} fail={fail} not-implemented={nyi}")
if __name__ == "__main__":
main()
analysis_idf/test_adapter.py
-47
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@@ -1,47 +0,0 @@
"""Verify build_bw_report_from_idf against a known sidecar."""
from __future__ import annotations
import json
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from micromate.idf_ascii_report import parse_idf_report
from micromate.idf_to_bw_report import build_bw_report_from_idf
from micromate.idf_file import read_idf_file
def show(prefix: str, d: dict, indent: int = 0):
for k, v in d.items():
if isinstance(v, dict):
print(f"{' '*indent}{prefix}{k}:")
show("", v, indent + 1)
else:
print(f"{' '*indent}{prefix}{k}: {v!r}")
def main():
base = REPO / "tests/fixtures/THORDATA_example/THORDATA_example/UPMC Presby/UM11719"
idfw = base / "UM11719_20231219162723.IDFW"
txt = base / "TXT" / f"{idfw.name}.txt"
report_dict = parse_idf_report(txt.read_text(errors="replace"))
res = read_idf_file(idfw)
bw = build_bw_report_from_idf(report_dict, binary_md=res.binary_metadata)
print("=== IDFW → bw_report ===")
show("", bw)
print()
print("=== IDFH (single trigger row) ===")
idfh = base / "UM11719_20231219162648.IDFH"
txt_h = base / "TXT" / f"{idfh.name}.txt"
rh = parse_idf_report(txt_h.read_text(errors="replace"))
res_h = read_idf_file(idfh)
bw_h = build_bw_report_from_idf(rh, binary_md=res_h.binary_metadata, intervals=res_h.intervals)
show("", bw_h)
if __name__ == "__main__":
main()
analysis_idf/thor_report.pdf
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analysis_idf/thor_report_idfh.pdf
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analysis_idf/trace_path.py
-73
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@@ -1,73 +0,0 @@
"""Trace Tran sample-by-sample to find exactly where the codec drifts."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def s4(n: int) -> int:
return n if n < 8 else n - 16
def i8(b: int) -> int:
return b if b < 128 else b - 256
def main():
buf = TARGET.read_bytes()
sc = load_sidecar_samples(TXT)
GEO_LSB = 0.0003
sc_tran = [int(round(v / GEO_LSB)) for v in sc["Tran"]]
body = buf[0x0f1f:]
# Tran[0], Tran[1] from preamble
t0 = int.from_bytes(body[3:5], "big", signed=True)
t1 = int.from_bytes(body[5:7], "big", signed=True)
print(f"preamble Tran[0]={t0} Tran[1]={t1} (sidecar: {sc_tran[0]}, {sc_tran[1]})")
# Block 0: 10 f8 at body[7:9]
print(f"block 0: tag {body[7]:02x} {body[8]:02x}")
print(f" block 0 first 10 data bytes: {body[9:19].hex()}")
# Walk block 0 manually, comparing each sample
cur = t1
samples = [t0, t1]
block_off = 7
nn = body[8]
print(f" NN = {nn}")
data = body[9 : 9 + nn // 2]
for byi, byte in enumerate(data):
for nib_idx, nib in enumerate(((byte >> 4) & 0xF, byte & 0xF)):
cur += s4(nib)
samples.append(cur)
idx = len(samples) - 1
if 0 <= idx < len(sc_tran):
sc_v = sc_tran[idx]
match = "✓" if sc_v == cur else "✗"
if idx < 12 or 240 <= idx <= 260:
print(f" idx {idx:3d}: nibble byte={byte:02x} nib={nib:x} delta={s4(nib):+d} cur={cur:+d} sc={sc_v:+d} {match}")
print(f"end of block 0: cur={cur}, len(samples)={len(samples)}, decoder expected 250 here")
# Block 1: 20 28 starts at offset 9 + 124 = 133 from block_off=7
block1_off = 9 + nn // 2
print(f"block 1: tag {body[block1_off]:02x} {body[block1_off+1]:02x} (expecting 20 28)")
nn1 = body[block1_off + 1]
print(f" block 1 NN = {nn1}")
data1 = body[block1_off + 2 : block1_off + 2 + nn1]
for byi, byte in enumerate(data1):
cur += i8(byte)
samples.append(cur)
idx = len(samples) - 1
if idx < len(sc_tran):
sc_v = sc_tran[idx]
match = "✓" if sc_v == cur else "✗"
if 248 <= idx <= 295:
print(f" idx {idx:3d}: int8 byte={byte:02x} delta={i8(byte):+d} cur={cur:+d} sc={sc_v:+d} {match}")
if __name__ == "__main__":
main()
analysis_idf/try_codec.py
-42
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@@ -1,42 +0,0 @@
"""Feed candidate body offsets to the BW codec and compare with sidecar."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from minimateplus.waveform_codec import decode_waveform_v2, walk_body, find_data_start
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def main():
buf = TARGET.read_bytes()
sc = load_sidecar_samples(TXT)
# Sidecar samples in 0.0003 counts (Thor geo LSB).
sc_tran = [int(round(v / 0.0003)) for v in sc["Tran"][:30]]
sc_vert = [int(round(v / 0.0003)) for v in sc["Vert"][:30]]
sc_long = [int(round(v / 0.0003)) for v in sc["Long"][:30]]
sc_micl = [int(round(v / 1e-6)) for v in sc["MicL"][:30]] # 1 µ unit for mic? Will iterate.
print(f"sidecar Tran (counts): {sc_tran}")
print(f"sidecar Vert (counts): {sc_vert}")
print(f"sidecar Long (counts): {sc_long}")
print(f"sidecar MicL (×1e-6): {sc_micl}")
print()
# Try candidate body start offsets.
for off in (0x0f1f, 0x1057, 0x11f1, 0x1333, 0x1bde, 0x0d30):
print(f"=== body @ 0x{off:04x} ===")
body = buf[off:]
decoded = decode_waveform_v2(body)
if not decoded:
print(" decode_waveform_v2 returned None")
continue
for ch in ("Tran", "Vert", "Long", "MicL"):
arr = decoded.get(ch, [])
print(f" {ch}[{len(arr)}]: {arr[:20]}")
print()
if __name__ == "__main__":
main()
analysis_idf/verify_full.py
-51
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@@ -1,51 +0,0 @@
"""Verify decode_waveform_v2 against sidecar across all 2304 samples per channel."""
from __future__ import annotations
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO))
from minimateplus.waveform_codec import decode_waveform_v2
from analysis_idf.recon import TARGET, TXT, load_sidecar_samples
def main():
buf = TARGET.read_bytes()
sc = load_sidecar_samples(TXT)
body = buf[0x0f1f:]
decoded = decode_waveform_v2(body)
print(f"Sidecar lengths: Tran={len(sc['Tran'])} Vert={len(sc['Vert'])} Long={len(sc['Long'])} MicL={len(sc['MicL'])}")
print(f"Decoded lengths: Tran={len(decoded['Tran'])} Vert={len(decoded['Vert'])} Long={len(decoded['Long'])} MicL={len(decoded['MicL'])}")
print()
GEO_LSB = 0.0003 # in/s per count
for ch in ("Tran", "Vert", "Long"):
sc_counts = [int(round(v / GEO_LSB)) for v in sc[ch]]
dec = decoded[ch]
n = min(len(sc_counts), len(dec))
matches = sum(1 for i in range(n) if sc_counts[i] == dec[i])
first_mismatch = next((i for i in range(n) if sc_counts[i] != dec[i]), None)
print(f"{ch}: compared {n}, exact matches {matches} ({100*matches/n:.2f}%)")
if first_mismatch is not None:
i = first_mismatch
print(f" first mismatch at idx {i}: sidecar={sc_counts[i]} ({sc[ch][i]}), decoded={dec[i]}")
print(f" context sidecar[{i-2}..{i+5}]: {sc_counts[max(0,i-2):i+5]}")
print(f" context decoded[{i-2}..{i+5}]: {dec[max(0,i-2):i+5]}")
# MicL: find the multiplicative factor that fits
print()
print("=== MicL scale analysis ===")
sc_micl = sc["MicL"]
dec_micl = decoded["MicL"]
# Skip zero values when computing ratio
ratios = [sc_micl[i] / dec_micl[i] for i in range(min(50, len(sc_micl), len(dec_micl))) if dec_micl[i] != 0]
if ratios:
avg = sum(ratios) / len(ratios)
print(f" avg ratio sidecar/decoded over first 50 nonzero: {avg:.4e} (n={len(ratios)})")
print(f" ratios sample: {[f'{r:.4e}' for r in ratios[:6]]}")
if __name__ == "__main__":
main()
bridges/ach_server.py
-76
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@@ -177,8 +177,6 @@ class AchSession:
store: "WaveformStore",
clear_after_download: bool = False,
restart_monitoring: bool = False,
rescue_stop_monitoring: bool = False,
rescue_disable_ach: bool = False,
force_redownload: bool = False,
) -> None:
self.sock = sock
@@ -192,9 +190,6 @@ class AchSession:
self.store = store
self.clear_after_download = clear_after_download
self.restart_monitoring = restart_monitoring
# Rescue actions for a runaway unit — fired before the event walk.
self.rescue_stop_monitoring = rescue_stop_monitoring
self.rescue_disable_ach = rescue_disable_ach
# `force_redownload` tells this session to ignore ach_state and
# re-download every event currently on the device, regardless of any
# (key, timestamp) match. Useful as a manual override when state has
@@ -295,41 +290,6 @@ class AchSession:
root_logger.addHandler(fh)
try:
# ── Step 1.5: rescue actions ──────────────────────────────────────
# Fired BEFORE the event walk so a runaway unit is quieted as early
# in the session as possible. A unit whose geophone sits above the
# trigger threshold records back-to-back and, with ACH set to "after
# event recorded", re-dials every time — saturating its own firmware
# so it never services inbound requests. See
# docs/runbooks/wedged_unit_recovery.md.
#
# Each action is independently guarded: a failure here must not
# abort the download that follows.
if self.rescue_stop_monitoring or self.rescue_disable_ach:
rescue: dict = {"peer": self.peer, "ts": ts}
if self.rescue_stop_monitoring:
log.info("Step 1.5: RESCUE — stop monitoring (SUB 0x97)")
try:
client.stop_monitoring()
rescue["stop_monitoring"] = "ok"
log.info(" stop monitoring OK — device should stop recording")
except Exception as exc:
rescue["stop_monitoring"] = f"failed: {exc}"
log.error(" stop monitoring FAILED: %s", exc)
if self.rescue_disable_ach:
log.info("Step 1.5: RESCUE — disable auto call home (SUB 0x2C/0x7E/0x7F)")
try:
client.set_call_home_config(auto_call_home_enabled=False)
rescue["disable_ach"] = "ok"
log.info(" disable ACH OK — unit should stop calling home")
except Exception as exc:
rescue["disable_ach"] = f"failed: {exc}"
log.error(" disable ACH FAILED: %s", exc)
_save_json(session_dir / "rescue.json", rescue)
# ── Step 2: device info ───────────────────────────────────────────
device_info = None
if not self.events_only:
@@ -556,7 +516,6 @@ class AchSession:
serial=serial or self.peer,
session_id=None,
waveform_records=waveform_records,
device_family="series3",
)
_ml_ins, _ml_skip = self.db.insert_monitor_log(
new_monitor_entries, session_id=None
@@ -787,13 +746,6 @@ def serve(args: argparse.Namespace) -> None:
print(f" Max events per session: {max_ev if max_ev else 'unlimited'}")
print(f" Clear device after download: {'YES' if args.clear_after_download else 'no'}")
print(f" Restart monitoring after download: {'YES' if args.restart_monitoring else 'no'}")
_stop_mon = args.stop_monitoring or args.rescue
_dis_ach = args.disable_ach or args.rescue
print(f" RESCUE stop monitoring on connect: {'YES' if _stop_mon else 'no'}")
print(f" RESCUE disable auto call home: {'YES' if _dis_ach else 'no'}")
if _stop_mon and args.restart_monitoring:
print(" !! --restart-monitoring will re-start the unit after download,")
print(" undoing --stop-monitoring. Drop one of them.")
print(f" Force re-download all (ignore state): {'YES' if args.force_redownload_all else 'no'}")
print(f"{'='*60}")
print(f"\n Point your test unit's ACEmanager call-home settings to:")
@@ -835,8 +787,6 @@ def serve(args: argparse.Namespace) -> None:
store=store,
clear_after_download=args.clear_after_download,
restart_monitoring=args.restart_monitoring,
rescue_stop_monitoring=args.stop_monitoring or args.rescue,
rescue_disable_ach=args.disable_ach or args.rescue,
force_redownload=args.force_redownload_all,
)
t = threading.Thread(target=session.run, daemon=True, name=f"ach-{peer}")
@@ -911,32 +861,6 @@ def parse_args() -> argparse.Namespace:
"DCD on disconnect — without this the unit stays idle after a call-home."
),
)
p.add_argument(
"--stop-monitoring",
action="store_true",
default=False,
help=(
"RESCUE: send SUB 0x97 (stop monitoring) immediately after the "
"handshake, before any event download. Use on a unit that is "
"recording back-to-back because of a stuck-triggered geophone."
),
)
p.add_argument(
"--disable-ach",
action="store_true",
default=False,
help=(
"RESCUE: disable Auto Call Home on the device (SUB 0x2C read → "
"0x7E write → 0x7F confirm) immediately after the handshake. The "
"unit stops dialing out until ACH is explicitly re-enabled."
),
)
p.add_argument(
"--rescue",
action="store_true",
default=False,
help="Shorthand for --stop-monitoring --disable-ach.",
)
p.add_argument(
"--clear-after-download",
action="store_true",
bridges/mm_link.py
-338
View File
@@ -1,338 +0,0 @@
#!/usr/bin/env python3
"""
mm_link.py — a "perfect modem" between THOR and a Micromate, with a readable
log and deliberate fault injection.
Why
---
THOR gives almost no visibility into a connection: a refresh button, two poll
intervals, and no way to see whether a check succeeded, timed out, or was never
sent. When a unit "won't stay connected" there is nothing to look at.
This sits where the cellular modem would sit and answers the question directly:
* **What is THOR actually doing?** Every frame is decoded and timestamped —
`POLL`, `MONITOR_STATUS`, `SETUP_NAME_READ` — not a hex dump.
* **Is it even trying?** Silence is visible: the log shows gaps.
* **How does it behave when the link misbehaves?** Faults can be injected on
demand, which a real cell link will not do on cue.
Point THOR at this host and port exactly as if it were a modem (Communication:
TCP, IP: <this host>, Port: <--listen>).
Fault injection
---------------
Write a mode into the control file (default `mm_link.ctl`) and it takes effect
on the next byte:
echo pass > mm_link.ctl # normal relay
echo blackhole > mm_link.ctl # TCP stays up, bytes are swallowed
echo drop > mm_link.ctl # close the connection abruptly (RST-ish)
echo delay:2.0 > mm_link.ctl # forward, but 2 s late in both directions
echo onewaydev > mm_link.ctl # THOR->unit passes, unit->THOR is swallowed
**`blackhole` is the one that matters.** It reproduces the classic cellular
failure: the socket is still open as far as both ends are concerned, but nothing
crosses. A client that relies on TCP to tell it the peer is gone will sit there
until the OS keepalive fires — which by default is about two hours.
Usage
-----
python3 bridges/mm_link.py --serial /dev/ttyACM0 --baud 115200 \\
--listen 12345 --logdir ~/mm-captures
Writes, per session:
<logdir>/mmlink_<ts>/session.log decoded, timestamped, human-readable
<logdir>/mmlink_<ts>/raw_bw.bin THOR -> unit, raw
<logdir>/mmlink_<ts>/raw_s3.bin unit -> THOR, raw
The raw pair loads straight into `scratch/mm_frame_parse.py`.
"""
from __future__ import annotations
import argparse
import datetime
import os
import socket
import sys
import threading
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scratch"))
try:
from mm_frame_parse import SUBNAME, destuff # noqa: F401
except Exception: # pragma: no cover
SUBNAME = {}
import errno
import select
import termios
DLE, STX, ETX, ACK = 0x10, 0x02, 0x03, 0x41
_BAUD = {9600: termios.B9600, 19200: termios.B19200, 38400: termios.B38400,
57600: termios.B57600, 115200: termios.B115200}
class SerialPort:
"""Minimal raw serial port on stdlib termios — no pyserial dependency.
The bench hosts are whatever is to hand; requiring a pip install on someone
else's machine is a poor trade for the ~30 lines this saves.
"""
def __init__(self, path: str, baud: int):
if baud not in _BAUD:
raise ValueError(f"unsupported baud {baud}; pick one of {sorted(_BAUD)}")
self.fd = os.open(path, os.O_RDWR | os.O_NOCTTY | os.O_NONBLOCK)
a = termios.tcgetattr(self.fd)
a[0] = 0 # iflag: no translation
a[1] = 0 # oflag: raw
a[2] = termios.CS8 | termios.CREAD | termios.CLOCAL # cflag: 8N1, ignore modem lines
a[3] = 0 # lflag: non-canonical, no echo
a[4] = a[5] = _BAUD[baud]
a[6] = list(a[6])
a[6][termios.VMIN] = 0
a[6][termios.VTIME] = 0
termios.tcsetattr(self.fd, termios.TCSANOW, a)
termios.tcflush(self.fd, termios.TCIOFLUSH)
def read(self, n: int) -> bytes:
r, _, _ = select.select([self.fd], [], [], 0.2)
if not r:
return b""
try:
return os.read(self.fd, n)
except OSError as e:
if e.errno in (errno.EAGAIN, errno.EWOULDBLOCK):
return b""
raise
def write(self, data: bytes) -> None:
while data:
try:
data = data[os.write(self.fd, data):]
except OSError as e:
if e.errno in (errno.EAGAIN, errno.EWOULDBLOCK):
select.select([], [self.fd], [], 0.2)
continue
raise
def close(self) -> None:
try:
os.close(self.fd)
except OSError:
pass
def name_of(sub: int, is_request: bool) -> str:
if is_request:
return SUBNAME.get(sub, f"SUB_{sub:02X}")
return "rsp " + SUBNAME.get(0xFF - sub, f"SUB_{0xFF - sub:02X}")
class FrameSniffer:
"""Accumulate bytes and report complete frames, without altering the stream."""
def __init__(self, is_request: bool):
self.is_request = is_request
self.buf = bytearray()
def feed(self, data: bytes):
"""Yield (sub, payload_len) for each complete frame seen."""
self.buf.extend(data)
while True:
start = -1
for i, b in enumerate(self.buf):
if self.is_request and b == ACK and i + 1 < len(self.buf) and self.buf[i + 1] == STX:
start = i
break
if not self.is_request and b == STX:
start = i
break
if start < 0:
if len(self.buf) > 8192:
del self.buf[:-16]
return
j = start + (2 if self.is_request else 1)
end = -1
while j < len(self.buf):
if self.buf[j] == DLE and j + 1 < len(self.buf):
j += 2
continue
if self.buf[j] == ETX:
end = j
break
j += 1
if end < 0:
return # wait for more bytes
body = self.buf[start:end + 1]
del self.buf[:end + 1]
# SUB sits at a fixed spot past the leading framing -- but it is
# DLE-escaped when its own value is 0x02/0x03/0x04/0x10, so a raw
# read reports 0x10 for those. SUB 0x02 was being logged as
# "SUB_10" until this was handled.
off = 5 if self.is_request else 3
if len(body) > off:
sub = body[off]
if sub == DLE and len(body) > off + 1:
sub = body[off + 1]
yield sub, len(body)
class Link:
def __init__(self, args):
self.args = args
self.mode = "pass"
self.delay = 0.0
self.ctl = Path(args.control)
self.session: Path | None = None
self.log_fh = None
self.raw = {}
self.t0 = time.time()
self.counts = {}
# ── logging ────────────────────────────────────────────────────────────
def open_session(self):
ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
self.session = Path(self.args.logdir) / f"mmlink_{ts}"
self.session.mkdir(parents=True, exist_ok=True)
self.log_fh = open(self.session / "session.log", "a", buffering=1)
self.raw = {
"bw": open(self.session / "raw_bw.bin", "ab"),
"s3": open(self.session / "raw_s3.bin", "ab"),
}
self.say(f"=== session {ts} — serial {self.args.serial} @ {self.args.baud} ===")
def say(self, text: str):
line = f"{datetime.datetime.now().strftime('%H:%M:%S.%f')[:-3]} {text}"
print(line, flush=True)
if self.log_fh:
self.log_fh.write(line + "\n")
# ── control file ───────────────────────────────────────────────────────
def poll_control(self):
while True:
try:
if self.ctl.exists():
want = self.ctl.read_text().strip().lower()
if want.startswith("delay:"):
d = float(want.split(":", 1)[1])
if ("delay", d) != (self.mode, self.delay):
self.mode, self.delay = "delay", d
self.say(f"*** MODE -> delay {d}s ***")
elif want and want != self.mode:
self.mode, self.delay = want, 0.0
self.say(f"*** MODE -> {want} ***")
except Exception:
pass
time.sleep(0.25)
# ── the relay ──────────────────────────────────────────────────────────
def pump(self, src, dst, tag: str, is_request: bool, stop: threading.Event):
sniff = FrameSniffer(is_request)
arrow = "THOR->unit" if is_request else "unit->THOR"
last = time.time()
while not stop.is_set():
timed_out = False
try:
data = src.recv(4096) if isinstance(src, socket.socket) else src.read(4096)
except TimeoutError:
timed_out = True
# socket.timeout subclasses OSError, so it MUST be caught first.
# Treating it as a dead socket closes the connection after 200 ms
# of quiet -- which is exactly what `blackhole` produces, so the
# relay killed the link it was supposed to be faking a fault on.
data = b""
except OSError:
break
if isinstance(src, socket.socket) and data == b"" and not timed_out:
self.say(f"{arrow}: peer closed the connection")
break
if not data:
if time.time() - last > self.args.quiet_after and self.counts:
self.say(f"--- {self.args.quiet_after:.0f}s with no traffic ---")
last = time.time()
continue
last = time.time()
self.raw[tag].write(data)
self.raw[tag].flush()
for sub, ln in sniff.feed(data):
label = name_of(sub, is_request)
self.counts[label] = self.counts.get(label, 0) + 1
self.say(f"{arrow} {label:<20} ({ln} B)"
+ ("" if self.mode == "pass" else f" [mode={self.mode}]"))
mode = self.mode
if mode == "drop":
self.say(f"{arrow}: DROPPING the connection (fault injection)")
stop.set()
break
if mode == "blackhole":
continue # swallow, keep the socket open
if mode == "onewaydev" and not is_request:
continue # unit's replies never reach THOR
if mode == "delay" and self.delay:
time.sleep(self.delay)
try:
if isinstance(dst, socket.socket):
dst.sendall(data)
else:
dst.write(data)
except OSError:
break
stop.set()
def serve(self):
srv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
srv.bind(("0.0.0.0", self.args.listen))
srv.listen(5)
self.open_session()
self.say(f"listening on 0.0.0.0:{self.args.listen} control file: {self.ctl}")
self.say("point THOR at this host/port as Communication=TCP")
threading.Thread(target=self.poll_control, daemon=True).start()
while True:
conn, addr = srv.accept()
conn.settimeout(0.2)
self.say(f"+++ THOR connected from {addr[0]}:{addr[1]} +++")
try:
ser = SerialPort(self.args.serial, self.args.baud)
except OSError as e:
self.say(f"!!! cannot open {self.args.serial}: {e}")
conn.close()
continue
stop = threading.Event()
ts = [
threading.Thread(target=self.pump, args=(conn, ser, "bw", True, stop), daemon=True),
threading.Thread(target=self.pump, args=(ser, conn, "s3", False, stop), daemon=True),
]
for t in ts:
t.start()
for t in ts:
t.join()
conn.close()
ser.close()
summary = ", ".join(f"{k}x{v}" for k, v in sorted(self.counts.items()))
self.say(f"--- connection closed. frames this session: {summary or 'none'} ---")
def main():
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--serial", default="/dev/ttyACM0")
ap.add_argument("--baud", type=int, default=115200)
ap.add_argument("--listen", type=int, default=12345)
ap.add_argument("--logdir", default=os.path.expanduser("~/mm-captures"))
ap.add_argument("--control", default="mm_link.ctl")
ap.add_argument("--quiet-after", type=float, default=30.0,
help="log a marker after this many seconds of silence")
Link(ap.parse_args()).serve()
if __name__ == "__main__":
main()
bridges/mm_probe.py
-239
View File
@@ -1,239 +0,0 @@
#!/usr/bin/env python3
"""
mm_probe.py — answer "why can't we reach this unit?" in one command.
THOR reports a failed connection as "disconnected" and nothing else. That single
word covers at least four completely different faults with four different fixes,
and telling them apart is the difference between a modem reboot and a site visit:
* **connection refused** something answered and said no — wrong port, or the
modem is refusing a further session
* **connect timed out** nothing answered at all — trusted-IP whitelist,
firewall, or the modem is off the network
* **connected, no reply** the MODEM answered but the unit did not. The TCP
path is fine; the modem is not forwarding to serial.
This is the signature of a wedged transparent-TCP
session, and it is the one THOR cannot distinguish
from any of the others
* **replied** the unit is alive; the problem is upstream software
Read-only. It sends `POLL`, then optionally `SERIAL` and the state read — the
same three commands THOR's own connection check uses — and never writes.
Usage
-----
python3 bridges/mm_probe.py 63.45.161.30:9034
python3 bridges/mm_probe.py 10.0.0.8:12345 --timeout 5
python3 bridges/mm_probe.py <host:port> --slots 3
`--slots N` opens N connections at once and reports how many the far end accepts.
A transparent-TCP modem typically serves **one** session; if the first succeeds
and the rest are refused or hang, that confirms the single-slot behaviour and
explains why a leaked session takes a unit offline until the slot frees.
Works for both series: a Series III reply opens `DLE STX`, a Micromate reply
opens with a bare `STX`, so the probe also tells you which one answered.
"""
from __future__ import annotations
import argparse
import socket
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from minimateplus.framing import build_bw_frame # noqa: E402
DLE, STX, ETX = 0x10, 0x02, 0x03
def destuff(raw: bytes) -> bytes:
"""Strip framing and DLE escapes; return the payload without its checksum."""
i = 1 if raw and raw[0] == STX else (2 if len(raw) > 1 and raw[1] == STX else 0)
out = bytearray()
while i < len(raw):
b = raw[i]
if b == DLE and i + 1 < len(raw):
out.append(raw[i + 1])
i += 2
continue
if b == ETX:
break
out.append(b)
i += 1
return bytes(out[:-1]) if len(out) > 1 else b""
# Reads are two-step on Series III: a probe at offset 0, then a data read at the
# block's length. THOR sends these offsets, and they also work on a Micromate.
OFFSETS = {0x5B: 0x0030, 0x15: 0x000A, 0x49: 0xFFFF}
def exchange(sock: socket.socket, sub: int, timeout: float) -> tuple[bytes, float]:
sock.sendall(build_bw_frame(sub, OFFSETS.get(sub, 0)))
t0 = time.time()
buf, deadline = b"", t0 + timeout
sock.settimeout(0.3)
while time.time() < deadline:
try:
chunk = sock.recv(4096)
if not chunk:
break
buf += chunk
if buf.endswith(bytes([ETX])) and len(buf) > 8:
break
except TimeoutError:
continue
except OSError:
break
return buf, time.time() - t0
def step(n: int, label: str, result: str) -> None:
print(f" [{n}] {label:.<28} {result}")
def probe(host: str, port: int, timeout: float) -> int:
print(f"\ntarget {host}:{port} (read-only: POLL, SERIAL, state)\n")
# ── 1. TCP ────────────────────────────────────────────────────────────
t0 = time.time()
try:
sock = socket.create_connection((host, port), timeout=timeout)
except ConnectionRefusedError:
step(1, "TCP connect", f"REFUSED after {1000*(time.time()-t0):.0f} ms")
print("\nverdict: something answered and actively refused.")
print(" Not a silent firewall drop — the host is reachable.")
print(" Wrong port, the service is down, or the modem is refusing")
print(" an additional session because its one slot is in use.")
return 2
except (TimeoutError, socket.timeout):
step(1, "TCP connect", f"TIMED OUT after {time.time()-t0:.1f} s")
print("\nverdict: nothing answered at all.")
print(" A silent drop, which is what a trusted-IP whitelist looks")
print(" like — it discards rather than refuses. Check the modem's")
print(" Trusted IPs (and note a VPN changes the IP you arrive from),")
print(" the firewall, and whether the modem is on the network.")
return 3
except OSError as e:
step(1, "TCP connect", f"FAILED: {e}")
return 4
step(1, "TCP connect", f"ok ({1000*(time.time()-t0):.0f} ms)")
# ── 2. POLL ───────────────────────────────────────────────────────────
try:
raw, dt = exchange(sock, 0x5B, timeout)
except OSError as e:
step(2, "POLL", f"send failed: {e}")
sock.close()
return 4
if not raw:
step(2, "POLL", f"NO REPLY in {timeout:.1f} s")
print("\nverdict: the MODEM answered but the unit did not.")
print(" TCP is fine end to end — something accepted the connection.")
print(" What is missing is the serial side. Two quite different")
print(" causes produce this, and they are NOT distinguishable from")
print(" here:")
print("\n 1. SOMEONE ELSE HOLDS THE SESSION. These modems bridge ONE")
print(" TCP session to serial at a time. A second connection is")
print(" accepted and then simply not forwarded. Confirmed 2026-09-26:")
print(" with THOR connected this probe saw exactly this; the moment")
print(" THOR disconnected the same probe returned the serial number.")
print(" ** Check whether THOR (or anything else) has the unit first. **")
print("\n 2. The serial path is genuinely broken — a stale session the")
print(" modem never released, a cable the unit cannot enumerate, or")
print(" a unit that is off.")
print("\n Try, in order:")
print(" 1. Disconnect any other client and re-probe. If it answers,")
print(" it was contention, not a fault.")
print(" 2. The cable's chipset. A Micromate drives FTDI and CDC-ACM")
print(" only — a Prolific PL2303 gives it no serial port at all.")
print(" lsusb: FTDI is 0403, Prolific 067b.")
print(" 3. Power-cycle the UNIT with the cable attached (hold power")
print(" 5 s, through the two-stage prompt). Its USB host rescans")
print(" on cold boot; it may not on hot-swap.")
print(" 4. AirLink OS -> TCP Idle Timeout. If 0/disabled, a stale")
print(" session holds the slot indefinitely. 2 minutes is the")
print(" value this project standardised on.")
sock.close()
return 5
series = "Series III (DLE STX)" if raw[0] == DLE else "Micromate (bare STX)"
step(2, "POLL", f"reply {len(raw)} B in {1000*dt:.0f} ms")
p = destuff(raw)
ok = len(p) > 3 and p[2] == 0xFF - 0x5B
step(3, "frame", f"{'valid' if ok else 'MALFORMED'}, {series}")
if not ok:
print("\nverdict: something replied, but not a seismograph.")
print(" Another service is on this port, or the modem is in a mode")
print(" that injects its own text (check Quiet Mode / AT echo).")
print(f" first bytes: {raw[:16].hex(' ')}")
sock.close()
return 6
# ── 3. identity + state ───────────────────────────────────────────────
for n, (sub, label) in enumerate(((0x15, "serial"), (0x49, "state")), start=4):
try:
r, dt = exchange(sock, sub, timeout)
d = destuff(r)[5:]
if sub == 0x15:
# serial is a null-terminated run; a further field follows it
serial = bytes(d[11:]).split(b"\x00")[0]
step(n, label, serial.decode("ascii", "replace") or "(empty)")
else:
step(n, label, "MONITORING" if len(d) > 11 and d[11] else "idle")
except OSError:
step(n, label, "no reply")
sock.close()
print("\nverdict: the unit is alive and answering.")
print(" If THOR still shows it disconnected, the fault is in THOR, not")
print(" the network or the device.")
return 0
def slots(host: str, port: int, n: int, timeout: float) -> None:
print(f"\nopening {n} simultaneous connections to {host}:{port}\n")
held = []
for i in range(n):
try:
s = socket.create_connection((host, port), timeout=timeout)
held.append(s)
step(i + 1, f"connection {i+1}", "accepted")
except ConnectionRefusedError:
step(i + 1, f"connection {i+1}", "REFUSED")
except (TimeoutError, socket.timeout):
step(i + 1, f"connection {i+1}", "timed out")
except OSError as e:
step(i + 1, f"connection {i+1}", f"failed: {e}")
print(f"\n{len(held)} of {n} accepted.")
if len(held) == 1:
print(" Single-slot behaviour confirmed — this far end serves ONE")
print(" session at a time. A connection that is never closed takes")
print(" the unit offline until the idle timeout frees the slot.")
for s in held:
s.close()
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("target", help="host:port, e.g. 63.45.161.30:9034")
ap.add_argument("--timeout", type=float, default=10.0)
ap.add_argument("--slots", type=int, metavar="N",
help="open N simultaneous connections to test single-slot behaviour")
a = ap.parse_args()
host, _, port = a.target.rpartition(":")
if not host:
ap.error("target must be host:port")
if a.slots:
slots(host, int(port), a.slots, a.timeout)
return 0
return probe(host, int(port), a.timeout)
if __name__ == "__main__":
raise SystemExit(main())
docs/histogram_codec_re_status.md
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> ## SUPERSEDED 2026-08-25 — the block is uniformly BIG-ENDIAN
>
> The `uint8` peak / `annotation` byte model described below is wrong,
> though it decoded quiet data correctly. The real layout:
>
> - **Every per-channel field is `uint16` big-endian.** `T_peak` is
> `[5:7]`, `T_halfperiod` `[7:9]`, `V_peak` `[9:11]`, and so on.
> Only `block_ctr` at `[2:4]` is little-endian.
> - The **marker is `block[4]` alone**, not a `uint16 LE` at `[4:6]`.
> Testing `[4:6] == 10` forced `block[5] == 0`, which is exactly what
> capped every geo peak at one byte (255 counts = 1.275 in/s).
> - The **"annotation" byte was never an annotation** — it is the high
> byte of the big-endian half-period. That is why it was non-zero
> precisely on the sub-Hz intervals Blastware renders as `<1.0`.
> - The **final block of the stream carries tail `9c 06 00 42`** instead
> of `1e 0a 00 00`, and arbitrary bytes at `[21:23]`. Rejecting it
> dropped the last interval of nearly every histogram — often the one
> holding the event peak, so the file's PPV read low.
>
> Verified against 1211 production histograms paired with their Blastware
> ASCII exports: **1211/1211 decode exactly** (interval count plus every
> per-interval peak), and 842,442 per-interval frequency comparisons match
> with zero mismatches. The uint8 model scored 1204/1211 — the seven
> failures are exactly the files containing a peak above 1.275 in/s.
>
> The section below is retained as the reasoning trail.
# Histogram body codec — FULLY DECODED (2026-05-20)
Clean working status doc for the MiniMate Plus histogram-mode event
body codec. Companion to `waveform_codec_re_status.md`. The deep
historical record (with retractions and dated analyses) lives in
`docs/instantel_protocol_reference.md §7.6.2`; the authoritative
implementation lives in `minimateplus/histogram_codec.py`.
## TL;DR
**The codec is fully decoded.** Every field of every block in the
in-repo histogram fixture corpus decodes byte-exact against BW's
ASCII export.
26 regression tests pass against ~3,500 blocks across 5 in-repo
fixtures, plus a synthetic regression block taken from a real
BE9558 prod event to lock in the uint8-peak interpretation.
**Important correction (2026-05-21):** the per-channel peak count
is `uint8` at byte[6]/[10]/[14]/[18], NOT `uint16 LE` at byte[6:8]
etc. The N844 fixture corpus the original RE was done against has
zero values in bytes [7]/[11]/[15]/[19] for every block, so the
two interpretations happened to be equivalent. Cross-correlating
non-N844 events (BE9558 Tran-drift, BE18003 Histogram+Continuous)
against BW's per-interval ASCII export — 4 channels × ~1400 blocks
per event × multiple events = 100% byte-exact only when the peak
is read as uint8. Reading as uint16 LE produced peaks up to 268
in/s per channel and 35× inflated PVS sums when first deployed to
prod (rolled back, root-caused, and fixed in commit 7183b95+1).
## Body format
```
body = [stream of 32-byte data blocks] + [small trailing remnant]
```
Each block represents one histogram interval. Block layout:
```
[0] 0x00 always-zero tag
[1] segment_id (uint8) 0x00..0x03 — 256 blocks per segment
[2:4] block_ctr (uint16 LE) resets each segment (0x0100, 0x0101, …)
[4:6] 0x000a (uint16 LE) constant marker (= 10)
[6] T_peak_count uint8 Tran peak (count × 0.005 → in/s at Normal,
max 1.275 in/s — fits in uint8)
[7] T_annotation uint8 empirically non-zero on intervals with sub-Hz
or unmeasurable freq; meaning not fully RE'd
[8:10] T_halfperiod uint16 LE Tran half-period in samples
(freq_Hz = 512 / halfp; ≤ 5 means ">100 Hz")
[10] V_peak_count uint8 Vert peak
[11] V_annotation uint8
[12:14] V_halfperiod uint16 LE Vert freq half-period
[14] L_peak_count uint8 Long peak
[15] L_annotation uint8
[16:18] L_halfperiod uint16 LE Long freq half-period
[18] M_peak_count uint8 MicL peak count
(dB via waveform_codec.mic_count_to_db)
[19] M_annotation uint8
[20:22] M_halfperiod uint16 LE MicL freq half-period
[22:24] 0x00 0x00 constant
[24:28] 4-byte variable purpose unknown — possibly CRC,
timestamp delta, or psi(L) numeric;
not needed for waveform reconstruction
[28:32] 0x1e 0x0a 0x00 0x00 constant block-end signature
```
Reliable block-identification anchor:
```python
block[22:24] == b"\x00\x00" and block[28:32] == b"\x1e\x0a\x00\x00"
```
(The `1e 0a 00 00` constant tail is the most distinctive signature.)
## Per-channel encoding
| Channel | Peak encoding | Frequency encoding |
|---|---|---|
| Tran | count × 0.005 = in/s at Normal range | `freq_Hz = 512 / halfperiod` |
| Vert | same | same |
| Long | same | same |
| MicL | count → dB via `mic_count_to_db(count)` (same formula as waveform codec) | same |
**`>100 Hz` sentinel**: when halfperiod ≤ 5 (giving ≥100 Hz from the
512/halfp formula), BW displays `>100 Hz`. Codec's `half_period_to_hz`
returns `None` in this range.
## Verified facts (cross-checked against fixture corpus)
Example: N844L6Z8.ZR0H block 130 → all 8 decoded fields byte-exact:
```
binary samples [10, 6, 24, 4, 18, 5, 21, 5, 9]
TXT row [0.030, 21, 0.020, 28, 0.025, 24, 0.040, 0.000, 95.92, 57]
slot[0] = 10 marker
slot[1] = 6 × 0.005 = 0.030 in/s ✓ T_peak
slot[2] = 24 → 512/24 = 21.3 → 21 Hz ✓ T_freq
slot[3] = 4 × 0.005 = 0.020 in/s ✓ V_peak
slot[4] = 18 → 512/18 = 28.4 → 28 Hz ✓ V_freq
slot[5] = 5 × 0.005 = 0.025 in/s ✓ L_peak
slot[6] = 21 → 512/21 = 24.4 → 24 Hz ✓ L_freq
slot[7] = 5 → 81.94 + 20·log10(5) = 95.92 dB ✓ M_peak
slot[8] = 9 → 512/9 = 56.9 → 57 Hz ✓ M_freq
```
## Verified test coverage
`tests/test_histogram_codec.py` (24 tests):
- Block walking: yields one record per `.TXT` interval ± 1 (off-by-one
at the tail when recording was stopped mid-write). Segment-ID
groups of 256 blocks confirmed.
- Geo peaks: every block of N844L20G, N844L6Z8, N844L6XE, N844L23B
matches `.TXT` within the 0.0005 in/s quantization step.
- Geo freqs: every block of N844L6Z8 and N844L6XE matches `.TXT`
within 1 Hz (BW display rounds). `>100 Hz` sentinel handled correctly.
- Mic dB: every block of N844L6XE, N844L23B, N844L6Z8 matches `.TXT`
within 0.1 dB (BW display precision).
- Mic freq: matches `.TXT` within 1 Hz across active blocks.
## What's NOT yet decoded
- **Annotation bytes (`block[7]/[11]/[15]/[19]`)**. Empirically
non-zero on intervals where the per-channel ZC frequency comes
out as `N/A` or sub-Hz (`<1.0`, `1.X`). Hypothesis tested in the
RE session: byte != 0 ↔ sub-Hz freq. Only ~50% correlation
across the K558 corpus, so the relationship is more complex.
Possibilities: time-of-peak-within-interval, halfp extension for
very-long-period signals, or a debug/diagnostic field the firmware
writes opportunistically. Doesn't affect peak amplitudes or
waveform reconstruction. Captured as `record["annotations"]` for
future RE.
- **4-byte variable metadata field (bytes 24:28)**. Not needed for
waveform reconstruction. Speculation: per-block CRC, sub-second
timestamp offset, or a Mic psi(L) count not in the 9 samples.
Punt until something needs it.
- **Geo PVS (TXT col 7, e.g. "0.040 in/s")**. Not stored in the
block; can be approximated as `sqrt(T_peak² + V_peak² + L_peak²)`
but BW's value sometimes differs slightly (probably computed from
waveform-instant samples, not from per-channel peaks). Punt — the
`.h5` consumers don't need PVS as a sample channel.
- **Mic psi(L) value (TXT col 8)**. TXT shows it as a small psi value
derived from the dB measurement. Not in the 9 samples. Could be
derived from `M_peak_count` via the inverse of the dB formula plus
a psi calibration constant. Defer.
## Output shape
`decode_histogram_body` returns the standard 4-channel dict that
mirrors `waveform_codec.decode_waveform_v2`'s output:
```python
{
"Tran": [peak_count_per_interval, ...], # 16-count units (LSB = 0.005 in/s)
"Vert": [..., ...],
"Long": [..., ...],
"MicL": [..., ...], # raw ADC counts
}
```
Run through `waveform_codec.decoded_to_adc_counts` to get 1-count ADC
units (geo ×16, mic passthrough) for the standard `.h5` writer.
For the full per-interval record with frequencies + metadata, use
`decode_histogram_body_full()`.
## Where it's wired
- `minimateplus/event_file_io.py:read_blastware_file()` — first tries
the waveform codec, falls back to the histogram codec when the
waveform preamble isn't present. Same output shape, same
downstream pipeline.
- `scripts/backfill_sidecars.py` — the `has_samples` short-circuit
added during the histogram-codec-pending era still serves as a
defensive guard against truly undecodable files, but no longer
fires for valid histograms.
## Companion reference
- `docs/waveform_codec_re_status.md` — sibling status doc for the
much-more-complex waveform-mode codec.
- `docs/instantel_protocol_reference.md §7.6.2` — historical
protocol-reference entry. Structural framing matches what we
found; per-sample semantics were less documented than the `✅
CONFIRMED` badge suggested. This doc supersedes §7.6.2 where they
conflict on confidence level.
docs/idf_protocol_reference.md
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# IDF Protocol Reference — Thor / Micromate Series IV
Starting-point reference for reverse-engineering Instantel's Micromate
Series IV event-file format. Sibling to
[instantel_protocol_reference.md](instantel_protocol_reference.md) (the
Series III "Rosetta Stone") — this doc holds what we know so far and
the open questions still to crack.
> ⚠ **The "Status (2026-05-28)" block below is SUPERSEDED.** Its geo LSB
> (0.0003), its IDFH scale (`/32768 × 10`), its fixed body offset (`0x0f1f`)
> and its "87–99% byte-exact / loud events truncate" caveat were all wrong or
> incomplete. See **[Verified against Thor's own exports
> (2026-09-10)](#verified-against-thors-own-exports-2026-09-10)** — the
> decoder is now per-sample exact on 1,057,536/1,057,536 samples. The block
> is kept only for the reverse-engineering trail.
**Status (2026-05-28, SUPERSEDED):** ASCII text sidecar fully decoded (1,014
sample files round-trip). **Thor IDFW** binary now decodes via
`micromate.idf_file.read_idf_file()` — reuses the BW segment-rotated
block codec verbatim at fixed body offset `0x0f1f`; metadata (serial,
timestamp, sample_rate, record_time, calibration_date) extracted from
the binary header. Sample fidelity is 87–99% byte-exact on quiet
events; loud events hit the BW codec's known walker-stops-early
limitation. Residual ~3% drift on per-sample deltas (likely a
Thor-specific 12-bit delta refinement not yet modelled).
**Thor IDFH histograms also decoded.** Body has one or more segments;
each 12-byte segment header `[length_be 2B][0a 00 00 00][00 NN][05 3f]`
introduces `N = (length - 10) // 72` interval records of 72 bytes
each. Each interval = 4 × 16-byte per-channel records:
`[int16 min][int16 max][int16 ??][uint16 halfp][2B 00][uint16 ??][2B 00][uint16 ??]`.
Geo peak `= max(|min|, |max|) / 32768 × 10` in/s (matches sidecar
~1.8%); freq `= 512 / halfp` Hz (None for halfp ≤ 5 → ">100"
sentinel). Corpus: **all 859 Thor IDFH files decode, 181,071
intervals**. Wired through `read_idf_file()` →
`save_imported_idf()` → sidecar's `extensions.idf_intervals`.
**Note on the BE9439 outliers in the example corpus:** Two files
(`BE9439_20200713131747.IDFW` and `BE9439_20200713124251.IDFH`) are
**Series III Blastware** binaries, not Thor. Provenance: TMI tried
to use Thor to manage auto-call-homes for Series III units; the
experiment didn't work out, but it did leave a few BW event files
in Thor's per-serial directory structure with `.IDFW`/`.IDFH`
extensions — Thor's forwarder applied its own naming convention to
the BW bodies it was relaying. Their header `10 00 01 80 00 00
Instantel STRT ff fe <end_key> <start_key>` is the BW SUB 5A STRT
record, not a Thor body preamble. The reader detects them by
signature and raises `NotImplementedError` pointing callers at
`read_blastware_file()`, which extracts BW-format peaks from them.
**Still NYI for Thor IDFH:** per-channel `int16 field4` (possibly
time-of-peak); the two uint16 fields (probably PVS contributions);
8-byte interval tail (PVS data); mic dB(L) exact conversion constant.
## Verified against Thor's own exports (2026-09-10)
**The series-4 decoder is now per-sample exact.** 1,057,536 / 1,057,536
geophone samples across all 153 genuine Thor waveform files reproduce Thor's
own CSV export exactly; histogram peaks land within 2% on 858/858 files
(median error −0.004%).
### Ground truth — it was there all along
Thor writes `TXT/`, `CSV/`, `XML/` and `PDF/` exports beside every binary:
```
<serial dir>/UM13981_20220207084555.IDFW
<serial dir>/CSV/UM13981_20220207084555.IDFW.csv
```
The **CSV carries a per-sample block** — four columns (Tran, Vert, Long, Mic)
in in/s and psi, after the 2-column report header. That is the series-4
equivalent of Blastware's `_ASCII.TXT` exports, and it gives 1,012 paired
files (152 IDFW + 860 IDFH). Earlier notes in this file and in
`micromate/idf_file.py` asserted "Thor has no ASCII ground truth in the
corpus"; that was wrong, and it is why the decoder sat pinned to a
superseded walker with a scaling constant nobody could check.
Harness: `scratch/verify_thor_against_csv.py`.
### Geo LSB = 0.000310308 in/s per count (NOT 0.0003)
The old 0.0003 was read off the smallest non-zero sample in the exports —
but that is Thor's **4-decimal display rounding of the LSB, not the LSB**.
It read every series-4 geophone sample **3.3% low**. The quantisation
ladder gives it away: counts 1..6 export as 0.0003, 0.0006, 0.0009, 0.0012,
0.0016, 0.0019 — an LSB of exactly 0.0003 would end 0.0015, 0.0018.
Each exported sample constrains the LSB to the window that rounds to its
printed value. Intersecting 991,415 such constraints gives
```
LSB ∈ [0.000310307933, 0.000310308057] width 1.2e-10
```
so `_GEO_LSB_IPS = 0.000310308`, i.e. full scale 10.0 in/s = **32226.05
counts**. Corroboration: an IDFH interval that never recorded keeps its
min/max accumulator at its ±full-scale seed, and that seed is
`(min=+32226, max=-32226)`. ⚠ The tempting closed form `10.0/32226` is
very slightly wrong — it lands 4.5e-10 above the feasible window and loses
78 boundary samples while never winning one. **Series III uses 32000 counts
for the same 10.0 in/s, so the two generations do not share a scale.**
Independently confirmed on 8 production units (UM6047, UM11402, UM11719,
UM12947, UM13981, UM14133, UM20146, UM20147): every unit's median PPV error
against its device-reported peak moved from −3.3% to within ±0.03%. It is a
global constant, not a per-unit calibration.
### IDFH segment header: the counter is a uint16, and it is cumulative
```
[length_be 2B][0a 00 00 00][counter_be 2B][05 3f]
```
`counter` is the **0-based cumulative index of the last interval in the
segment** — 9, 19, 29, ... for the usual 10-intervals-per-segment layout
(`length` = 730).
The validator used to require `counter`'s high byte to be `0x00`. That
silently **capped every histogram at 250 intervals**: once the cumulative
counter passed 255 the high byte went non-zero and every later segment was
rejected. Any run longer than ~4 hours lost its tail — frequently the part
holding the event peak, so the file's PPV read low. **540 of 858 corpus
files were affected**; fixing it moved histogram peaks from 48.3% to 93.8%
within 0.5% of Thor's reported PPV.
### Unwritten interval slots carry a ±full-scale seed
An interval the device reserved but never wrote keeps `min = +32226`,
`max = -32226` on all four channels — `min > max`, impossible for real data.
Decoded naively it yields a 10.0 in/s peak on every channel and, being a
max-over-intervals, poisons the whole file's PPV. Rare but real: exactly 1
of 497,611 corpus intervals, and it inflated that file's Long PPV from
0.0081 to 10.0 in/s. The inversion is all-or-nothing across channels (0
partial cases), so requiring every channel to be inverted is a safe test.
### Record mode `00 00` — raw int16 absolute (MODE_RAW16)
The record chain's mode field at `off+8` takes a fourth value:
| mode | meaning | header |
|---|---|---|
| `02 00` | deltas + two int16 anchors | 14 B |
| `01 00` | absolute, tagged blocks | 10 B |
| `00 03` | raw 12-bit absolute, untagged | 10 B |
| **`00 00`** | **raw int16 BE absolute, untagged** | **10 B** |
A `MODE_RAW16` record with `length = 1032` carries exactly
`(1032 - 8) / 2 = 512` samples and reproduced Thor's export **512/512
exactly** on first test. Thor uses it for segment 0 (the pre-trigger
window) on some events. Before this mode existed the record fell through
the dispatch unhandled, so the channel silently lost its first 512 samples —
which is what produced the "loud events truncate" symptom.
`MODE_ABSOLUTE` is also valid as a **preamble** (the implicit segment-0 Tran
record); its tagged blocks start at `body[3]`, not `body[7]`, because its
header is 10 bytes rather than 14.
### Body offset is not fixed at 0x0f1f — and 0x0f1f is really a record + 7
A "body offset" is `<record start> + 7`, so that `body[0]` is the segment
index and `body[1:3]` is the mode. The canonical `0x0f1f` is simply the
record at `0x0f18`.
Searching for the literal preamble `00 02 00` finds only MODE_DELTA bodies,
and worse, it **matches the `[seg][mode]` bytes inside any record header**,
so the scan could pick a candidate part-way down the chain. That decodes a
plausible-looking but rotation-shifted body which drops each channel's
segment 0 — the real cause of the remaining truncations.
`_find_waveform_body_offset()` now anchors on record headers (the
`<channel_id> 00 00` signature at `+4`, validated with `is_record()`),
takes the **chain head** — a record no other record's length field points at
— and trial-decodes `head + 7`, preferring the candidate where all four
channels come out the same length.
⚠ Do **not** scan for candidate preambles instead: `MODE_RAW16` is
`00 00`, so every run of three zero bytes looks like a body start and each
costs a full trial decode (~0.5 s/file measured, vs 6 ms/file now).
### `40 NN` is not capped at NN=8 (2026-09-11)
`data_block_len()` rejected any `40 NN` int16 block with `NN > 0x08`. The cap
had no evidence behind it — every corpus available when it was written used
only NN ∈ {1, 2, 3, 4, 8}, so it was never exercised. Loud events use much
wider blocks:
| corpus | `40 NN` values | walker stops |
|---|---|---|
| first + 3-channel corpora | 1, 2, 3, 4, 8 | none |
| UM12947 2025-07..09 | 2, 4, 8, **12, 16, 20 … 196** | every value > 8 |
Because `walk_body`/`run` stop at the first unrecognised tag rather than
raising, this surfaced as **silently short channels** — e.g. Tran 1812 /
Vert 2132 / Long 2324 on a file whose export has 2324 for all three. The
real bound is the buffer (and the caller's record end), not a magic constant.
Verified against Thor's exports for UM12947 (2025-07-14 … 2025-09-25, 167
waveforms): length mismatches **22 → 0**, and **1,476,242 / 1,476,249**
samples exact.
⚠ These events are **not** truncated recordings, which was the competing
hypothesis — the exports carry the full sample count.
**The 7 residual samples are Thor's rounding, not ours.** Each differs by
exactly one 4th-decimal tick (e.g. decoded 3.3551 vs export 3.3550).
Intersecting the per-sample rounding constraints over this corpus is
**infeasible** — the binding pair (count 2013 → 0.6247, count 4351 → 1.3501)
contradict by 2.3e-11, i.e. 7e-5 relative. No single linear LSB can
reproduce every printed value, so Thor is not doing plain round-half-up on
`count × LSB`. Do not retune `_GEO_LSB_IPS` to chase these; it is already
pinned to ~1e-11.
### Mic-disabled units are a distinct shape (2026-09-10, second corpus)
Some units run with the microphone disabled — **3 channels, not 4** — and that
changes two structural things. Confirmed on the `9-10-26-csv-req` corpus
(UM11402, UM12947, UM20147): 139/139 waveforms and 877/877 histograms.
**Waveform: the body starts earlier.** A 3-channel unit has a shorter fixed
header and puts its record chain head at **`0x0dba`**, below the old
`_BODY_SCAN_FLOOR` of `0x0E00`. The head was therefore invisible to the scan,
which fell through to the *Vert* segment-0 record and decoded a body shifted
one position around the channel rotation. The signature is unmistakable:
```
Tran 3072 / Vert 2560 / Long 3072 / MicL 0 <- Vert exactly 512 short
```
46 of 139 files in that corpus were affected; all 46 became per-sample exact
once the floor dropped to `0x0C00`. Note the body-offset scoring also had to
stop requiring four channels — `len(lengths) >= 3`, not `== 4`, or `equal` is
permanently False for these events and the pick falls back to raw sample count.
**Histogram: the interval record is 56 bytes, not 72.**
```
interval_size = 16 × n_channels + 8 (72 for 4 channels, 56 for 3)
```
It is **not a constant**, and it cannot be inferred from `length` alone.
Derive the interval count from the segment counter — it is cumulative, so
`n = counter - previous_counter` — and then `stride = (length - 10) / n`.
`n_channels` follows from `(stride - 8) / 16`.
Assuming 72 read 7 intervals out of each 10-interval segment and then walked
off alignment into garbage that decoded as ~10 in/s peaks — inflating those
files' PPV by up to 191,000%. Fixing it moved the second corpus from 56.6% to
**100.0%** of histograms within 2% of Thor's reported PPV, and recovered 4
files that previously decoded no intervals at all.
### What is still open
- ~~23 of 575 production IDFW files~~ — **RESOLVED 2026-09-11.** Production
IDFW is now **575/575** with zero truncations and zero decode failures
(median PPV error −0.0007%). See "`40 NN` is not capped at NN=8" above.
- Mic → psi scale is still the rough `2.14e-6` regression, not derived.
- Per-channel `int16 field4` in the IDFH interval record (possibly
time-of-peak) and the 8-byte tail (PVS data) remain undecoded.
⚠ **Thor's histogram PPV has a display floor of 0.0050 in/s.** In the
production store 6,080 sidecar PPV values are exactly 0.0050 (next most
common value: 275 occurrences), and **41.4% of IDFH sidecars report a
component PPV larger than their own vector sum** — geometrically impossible.
On those quiet files the decoder's ~0.0025 in/s is *more* accurate than the
reference; do not "fix" the decoder to match it.
### Codec breakthroughs (2026-05-28)
- **Body offset is a fixed `0x0f1f`** across 151/154 corpus IDFW
files. Preceded by a 4-byte record-type marker (`46 00 00 00`)
+ magic preamble `00 02 00 [Tran[0] BE] [Tran[1] BE]`.
- **Sample stream is BW's segment-rotated block codec verbatim.**
Thor reuses `10 NN` (nibble), `20 NN` (int8), `00 NN` (RLE),
`30 NN` (packed12), `40 02` (segment header) tags with the same
semantics. Channel rotation Tran→Vert→Long→MicL.
- **Geo LSB = 0.0003 in/s** (not BW's 0.005), because Thor's 16-bit
ADC range maps to 10 in/s without the 16-count BW quantization step.
- **Mic ≈ 2.14×10⁻⁶ psi/count** (rough scale; refine after channel
block calibration constants are decoded).
- **BW compliance anchor `\xbe\x80\x00\x00\x00\x00` reappears at
IDFW offset 0x952** — sample_rate at anchor−6 (uint16 BE),
record_time at anchor+6 (float32 BE), same layout as BW.
- **Event timestamp at offset 0x97A** — 8 bytes `[day][month]
[year_be][unk][hour][min][sec]`. Stop-time mirrors at 0x982.
- **Serial as null-terminated ASCII at 0x14E**.
- **Calibration date** at 0x194–0x197 (day, month, year_be).
- Per-sample residual drift of ~3% suggests Thor encodes int8/nibble
deltas with an extra refinement bit that BW doesn't carry —
unsolved; errors resync within a few samples so cumulative impact
is small.
---
## File model
### Filename convention
```
<SERIAL>_<YYYYMMDDHHMMSS>.<KIND>
```
- **SERIAL** — literal device serial, two-letter prefix + numeric
suffix. Examples seen: `UM11719`, `UM13981`, `UM20147`, `BE9439`.
Unlike Series III BW filenames (`M529LK44.AB0`, base-36 stem),
Series IV filenames carry the serial in plain text.
- **YYYYMMDDHHMMSS** — 14-char ASCII timestamp in **device local
time** (no timezone marker).
- **KIND** — `IDFH` for histograms, `IDFW` for waveforms.
The `.IDFH.txt` / `.IDFW.txt` ASCII sidecar lives in a `TXT/`
**subfolder** of the unit's directory, not alongside the binary.
This pairing convention is encoded in
`event_forwarder.idf_report_path()`.
### Directory layout
```
C:\THORDATA\
└── <Project>\
└── <UM####>\ ← unit serial dir
├── UM12345_20260520100000.MLG ← monitor log (not events)
├── UM12345_20260520100000.IDFH ← histogram event (binary)
├── UM12345_20260520100000.IDFW ← waveform event (binary)
├── UM12345_20260520100000.IDFW.CDB ← cache-DB variant (skip)
├── TXT\
│ ├── UM12345_20260520100000.IDFH.txt ← histogram ASCII sidecar
│ └── UM12345_20260520100000.IDFW.txt ← waveform ASCII sidecar
├── CSV\, HTML\, PDF\, XML\ ← operator-facing derived exports
└── ...
```
The `.IDFW.CDB` files share the binary's basename but appear to be a
separate cache/database variant. Their first 8 bytes match the
**old**-firmware Thor signature (see below) regardless of which
signature the paired `.IDFW` uses. Purpose unknown; sizes vary
wildly (observed 123 B → 40,491 B). Thor-watcher's forwarder
deliberately skips them.
### Sample corpus
The `thor-watcher/example-data/THORDATA_example/` tree carries
**1,014 paired .IDFW / .IDFH + .txt files** spanning 2020–2023
across nine units (UM11719, UM13981, UM20147, …, plus BE9439 from
2020). This is the reverse-engineering ground truth.
---
## ASCII sidecar (`.IDFW.txt` / `.IDFH.txt`) — fully decoded
Shape: plain text, one `"Key : Value"` line per metadata field,
followed for waveforms by a tab-separated sample table headed by
the literal line `Waveform Data Channels`. Parsed by
[`micromate/idf_ascii_report.py`](../micromate/idf_ascii_report.py).
See [`micromate/models.py`](../micromate/models.py) for the typed
`IdfReport` shape.
### Notable conventions
- **Units are native to Thor** — geophone in **in/s**, microphone in
**dB(L)** (not psi like Series III BW reports), frequency in Hz,
acceleration in g, displacement in in.
- **Below-threshold readings** appear as the literal string
`<0.005 in/s` (155 occurrences in the sample corpus) — the parser
strips the `<` and treats the numeric remainder as the value.
- **Out-of-range / not-measured** values appear as `N/A` — parser
drops the field rather than letting the string leak into a numeric
column.
- **Firmware string** observed: `Micromate ISEE 11.0AK`.
- **TitleString1..4** are operator-defined free-text slots; Thor's
default labels map them to Location / Client / Company / Notes,
which the parser surfaces as `project` / `client` / `operator` /
`notes`.
- **Histogram sidecars** use `HistogramStartDate` / `HistogramStartTime`
in place of waveform's `EventDate` / `EventTime`. Parser falls
through to either.
- **Histogram tabular block** lacks the `Waveform Data Channels`
marker; instead it's a multi-line column header followed by
per-interval rows (`<date> <time> <tran-ppv> <freq> ...`). Parser
silently ignores lines after the metadata block since they lack a
colon-separated `key : value` shape (the timestamps DO contain
colons but produce garbage keys that don't collide with any
recognised field).
---
## Binary header signatures (observed)
Hex dump of the first 32 bytes across 1,014 sample files reveals
**two distinct file signatures**, both anchored by the literal
ASCII string `"\x00Instantel\x00"` at offset 6–16:
### Signature A — newer firmware (1,012 files, 99.8% of corpus)
```
00000000: 0012 0100 0000 496e 7374 616e 7465 6c00 ......Instantel.
00000010: 0000 a695 002e b500 4f70 6572 6174 6f72 ........Operator
^^^^^^^^^^^^^^^^
operator/title string starts at 0x18
```
Header bytes 0–5: `00 12 01 00 00 00`. Followed immediately by the
8-byte ASCII tag, then 6 unknown bytes, then ASCII operator-supplied
strings (Operator name, etc.) and on through the project / client /
title strings. No `STRT` record observed in this layout.
### Signature B — older firmware (2 files: BE9439 from 2020)
```
00000000: 1000 0180 0000 496e 7374 616e 7465 6c00 ......Instantel.
00000010: 072c 0012 0300 5354 5254 fffe 0111 2340 .,....STRT....#@
^^^^^^^^^ ^^^^^^^^^
STRT magic 4-byte end_key
00000020: 0111 0000 2e5f 00ac 4600 0000 0200 0000 ....._..F.......
^^^^^^^^^ ^^^
4-byte start_key 0x46 (BW WAVEHDR record-type marker)
```
Header bytes 0–5: `10 00 01 80 00 00`. The structure after the
`Instantel` magic is **byte-for-byte identical to a BW SUB 5A
probe-response STRT record** as documented in
[instantel_protocol_reference.md → "SUB 5A — STRT record encodes
end_offset"](instantel_protocol_reference.md). Specifically:
| Offset | Bytes | Meaning (per BW reference) |
|--------|---------------------|--------------------------------------|
| 0x14 | `53 54 52 54` | `STRT` magic |
| 0x18 | `ff fe` | STRT sentinel |
| 0x1A | `01 11 23 40` | `end_key` (4 bytes) |
| 0x1E | `01 11 00 00` | `start_key` (4 bytes) |
| 0x26 | `46` | `0x46` waveform-record type marker |
**Hypothesis:** Older Micromate firmware writes a wrapped BW-format
event into the `.IDFW` file — essentially the same on-disk shape as
a Series III device, with the new filename convention applied at
export time. Newer firmware (signature A) abandoned the
BW-compatible layout for an Instantel-specific format.
If that hypothesis holds, the 2 signature-B files can already be
parsed via `minimateplus/event_file_io.read_blastware_file()` — worth
testing. The 1,012 signature-A files are the real reverse-engineering
target.
### `.IDFW.CDB` cache files
Always carry signature B (`10 00 01 80 ...`), even when the paired
`.IDFW` carries signature A. Plausible explanation: the CDB is an
internal Thor cache-database export that retains the legacy BW-style
record layout regardless of the user-facing `.IDFW` format version.
Not currently consumed by the forwarder.
---
## File-size patterns (Signature A, the main target)
Survey of 1,012 signature-A files:
| Event type | Typical size | Source of variance |
|--------------|-------------------|----------------------------------------------|
| `.IDFW` 2-sec | 9,200 – 10,500 B | Operator-supplied strings (TitleString1..4) of varying length |
| `.IDFH` | 2,944 – 4,076 B | Histogram interval count (record duration / interval) |
**Naive arithmetic for 2-sec waveform:**
- 4 channels × 2 sec × 1024 sps = 8,192 samples
- At 2 bytes/sample (int16) = 16,384 sample bytes → file would be > 16 KB
- Observed: ~9–10 KB
- → samples are likely **1 byte each** (int8 quantised), **or** stored
with bit-packing / delta encoding, **or** only one channel's
full-rate samples are stored with the others reconstructed
arithmetically. Verifying this is the **first RE milestone**.
Project-string–length variance (~1 KB across the corpus) is consistent
with the file carrying a single copy of each TitleString1..4 plus
operator + setup-name as null-padded ASCII regions.
---
## Open questions
The reverse-engineering targets, roughly in dependency order:
1. **Sample encoding (signature A)** — int8? int16 LE/BE? Bit-packed?
Delta-coded? Per-channel interleaved or sequential blocks?
2. **Header field layout (signature A)** — where do sample_rate,
record_time, channel count, and per-channel peaks live in the
binary? The ASCII sidecar gives the device-authoritative values,
so binary fields can be confirmed by diff.
3. **Operator-string offsets** — `Operator` at 0x18 is the first
visible string in signature-A files; the rest (project, client,
notes, setup) follow. Need to map exact offsets and null-padding
conventions.
4. **Signature-B → BW codec compatibility** — does
`minimateplus/event_file_io.read_blastware_file()` actually parse
the 2 BE9439 signature-B files as-is? If yes, the OLD-format
ingest is free.
5. **`.IDFW.CDB` purpose** — is it an internal Thor cache, a
ring-buffer dump, or something else? Worth a single small effort
to characterise so we know what we're skipping.
6. **Footer / checksum** — every BW event file has a footer; does
IDF? Where does the per-channel sample block end?
---
## Reverse-engineering playbook (when we start)
The Series III BW codec took ~2 months of MITM wire captures
because we didn't have ground-truth metadata. Thor's situation is
**substantially better**:
- **Ground truth is on disk.** Every binary in `example-data/`
has a paired `.IDFW.txt` carrying the full decoded sample table
(`Waveform Data Channels` block — see any sample file in
`thor-watcher/example-data/.../TXT/`). Aligning binary bytes
to the table's float-per-row values gives an immediate per-byte
hypothesis test.
- **Cross-event diffing.** 1,012 signature-A samples from 9 units
spanning 4 years means any field that varies between events is
immediately localisable. Fields that are constant across all
files (firmware ID, channel labels, format-version word) are also
immediately localisable by complementary search.
- **No protocol surface.** Files at rest, not a wire dialect. No
DLE stuffing, no inner-frame parsing, no probe/data two-step.
Suggested first session (2-4 hours): hand-decode `UM11719_20231219162723.IDFW`
(10,290 bytes) against its `TXT/UM11719_20231219162723.IDFW.txt`
sample table (the 2-sec waveform at 1024 sps × 4 channels = 8,192
sample rows). Find the first per-channel sample value (`0.0003` in
the Tran column at t=0) in the binary. Confirms sample encoding.
Everything else flows from there.
---
## Code seams ready to receive the codec
When the codec lands, it goes into
[`micromate/idf_file.py`](../micromate/idf_file.py) (currently a
stub raising `NotImplementedError`). Public API:
```python
from micromate import IdfEvent
from micromate.idf_file import read_idf_file
event: IdfEvent = read_idf_file(Path("UM11719_20231219163444.IDFW"))
# event.peaks.transverse_ips, event.timestamp, event.raw_samples, ...
```
The ingest pipeline (`WaveformStore.save_imported_idf`) currently
builds the `IdfEvent` from the `.txt` parser only. Once
`read_idf_file()` works, the binary becomes authoritative; the
`.txt` parser drops to fast-path metadata cross-check. Operators
who don't enable Thor's TXT exporter still get fully populated
events.
---
## See also
- [instantel_protocol_reference.md](instantel_protocol_reference.md) — Series III BW protocol reference (the Rosetta Stone). STRT record format, DLE framing, BW filename encoding.
- [`micromate/idf_ascii_report.py`](../micromate/idf_ascii_report.py) — `.txt` sidecar parser.
- [`micromate/models.py`](../micromate/models.py) — `IdfEvent`, `IdfReport` typed dataclasses.
- [`micromate/idf_file.py`](../micromate/idf_file.py) — placeholder for the binary codec.
- [`thor-watcher/example-data/THORDATA_example/`](../../thor-watcher/example-data/) — 1,014 paired binary + .txt files for codec validation.
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# Spec — a live client for Series IV (Micromate)
Drafted 2026-09-26, ahead of implementation. The protocol work is finished; this
is the plan for turning `docs/micromate_protocol_reference.md` into code SFM can
run.
**Read that document first.** Everything here assumes it, and every constant
below is sourced from it rather than restated with justification.
---
## Goal and scope
`micromate/` is codec-only today — `idf_file.py`, `models.py`, the report
writers. There is no way to talk to a unit. This adds the live half, mirroring
`minimateplus/`.
**In scope, first pass:**
- connect over TCP (a field modem) or serial/USB (a bench unit)
- identify a unit, read its state, clock, memory and setups
- walk the event chain and download events
- return `Event` objects the existing codec already understands
**Explicitly out of scope, first pass:**
- ⚠ **Any write.** Setups, schedules, call-home config, monitoring start/stop,
and per-event delete are all mapped, and none of them will be implemented
here. **No command has ever been originated against a unit by this project**
— every write observed was performed by THOR while we recorded. Keeping that
true through the read client is deliberate: it means the first thing we ever
send to a customer's instrument is a decision someone made on purpose, not a
side effect of a client that happened to grow a method.
- the inbound call-home session — still the one protocol unknown
---
## Layout
```
micromate/
framing.py NEW frame building, response parsing, checksum
protocol.py NEW one method per wire command, returns raw payloads
client.py NEW high-level API, returns models
idf_file.py (existing — decodes what 0x5A returns, unchanged)
models.py (existing — extend, do not fork)
```
**Transport is reused, not rewritten.** `minimateplus/transport.py` is
byte-level and protocol-agnostic — `BaseTransport`, `SerialTransport`,
`TcpTransport`, plus `read_until_idle()` which already handles the RV50/RV55
habit of emitting `\r\nRING\r\n\r\nCONNECT\r\n` to a caller. Import it.
⚠ Do **not** import `minimateplus.framing`. The two framings differ in ways
that look small and are not, and a shared module would accumulate `if series ==`
branches until neither case is readable.
---
## `micromate/framing.py`
### Requests
Series IV accepts Series III request frames unmodified. The simplest correct
implementation re-exports the builder rather than duplicating it:
```python
from minimateplus.framing import build_bw_frame # requests are identical
```
⚠ One open question, flagged in the protocol reference and **not** settled:
whether `0x10` bytes inside request *params* need stuffing. No probe we sent
carried one. Until it is settled, assert on it rather than guessing:
```python
def build_request(sub: int, offset: int = 0, params: bytes = bytes(10)) -> bytes:
if 0x10 in params:
raise NotImplementedError(
"params containing 0x10 — stuffing rule unconfirmed; see "
"micromate_protocol_reference.md, 'Untested and unsafe-until-agreed'"
)
return build_bw_frame(sub, offset, params)
```
That turns an unknown into a loud failure instead of a corrupt frame.
### Responses — where Series III's parser cannot follow
| | Series III | Micromate |
|---|---|---|
| frame start | `DLE STX` | **bare `STX`** |
| `payload[1]` | `0x10` | `0xC5` (Blastware fw) / `0x03` (Thor fw) |
| destuffing | `DLE+ETX` kept as literal inner-frame data | **`10 XX` → `XX`, uniformly** |
The first row is why `S3FrameParser` returns nothing at all on Series IV traffic:
it scans for `DLE+STX`, which never appears.
The third is a genuine **simplification** — no inner-frame carve-out. Validated
by checksum across every capture in `bridges/captures/9-24-26 - micromate2/`:
four candidate destuffing rules were tried, and only this one makes all frames
validate.
### Checksum
```python
def checksum(payload: bytes) -> int:
return sum(b for b in payload if b != 0x10) & 0xFF
```
The DLE-aware variant, same as Series III's `5A` and write frames — not the plain
SUM8 of ordinary Series III reads.
### ⚠ The SUB byte can be escaped
When a SUB's value is `0x02`, `0x03`, `0x04` or `0x10` it arrives as `10 XX`.
Reading it positionally without destuffing reports `0x10`. This bit once
already — `SUB 0x02` was logged as `SUB_10` for an afternoon. Destuff first,
then index.
### Response shape
```python
@dataclass
class MicromateFrame:
sub: int # response SUB; request = 0xFF - sub
flags: int # 0xC5 Blastware line, 0x03 Thor line
page_hi: int
page_lo: int
data: bytes # payload[5:], checksum stripped
checksum_valid: bool
@property
def firmware_line(self) -> str: # "blastware" | "thor" | "unknown"
@property
def declared_length(self) -> int: # uint16 BE at data[3:5] (= payload[8:10])
```
⚠ **`declared_length` is a uint16 BE.** Read as a single byte it under-reads
`SUB 0x1A` by 47x — 44 against a true 2092. This is the single most expensive
mistake available in this protocol and it has already been made once.
`MicromateFrameParser` mirrors `S3FrameParser`: `feed(bytes) -> list[frame]`,
accumulates in `.frames`, `reset()`, and keeps the `bytes_fed` counter (it is
what distinguishes "no bytes at all" from "bytes but no complete frame" on a
timeout, and that distinction earned its keep during the Series III work).
---
## `micromate/protocol.py`
One method per command, returning raw payload bytes. No interpretation — that
belongs in `client.py`.
**Reads use `offset = 0xFFFF`** and return the whole block in one response;
Series III's two-step probe/data dance is unnecessary. `POLL` is the exception,
taking its data length. Per-command offsets, all observed:
| command | SUB | rsp | offset | returns |
|---|---|---|---|---|
| poll | `0x5B` | `0xA4` | `0x0030` | device string, model |
| serial | `0x15` | `0xEA` | `0x000A` | `UM12947` |
| device info | `0x01` | `0xFE` | `0xFFFF` | firmware, calibration |
| state | `0x49` | `0xB6` | `0xFFFF` | `data[11]`: non-zero = monitoring |
| monitor status | `0x1C` | `0xE3` | `0xFFFF` | flag, **device clock**, battery, memory |
| storage range | `0x06` | `0xF9` | `0xFFFF` | event storage extent |
| active setup name | `0x41` | `0xBE` | `0xFFFF` | `TEST1.mmb` |
| first setup | `0x3F` | `0xC0` | `0xFFFF` | setup-list walk head |
| next setup | `0x40` | `0xBF` | `0xFFFF` | …until an empty name |
| compliance config | `0x1A` | `0xE5` | `0xFFFF` | ~2103 B setup block |
| call-home config | `0x2C` | `0xD3` | `0xFFFF` | 137 B |
| arm event | `0x93` | `0x6C` | — | before every event |
| first event | `0x1E` | `0xE1` | `0xFFFF` | key + size |
| next event | `0x1F` | `0xE0` | `0xFFFF` | key + size |
| event record | `0x0C` | `0xF3` | `0xFFFF` | 210 B — project, location, peaks |
| event header | `0x0A` | `0xF5` | `0xFFFF` | 30 B list record |
| bulk download | `0x5A` | `0xA5` | computed | **the `.IDFW` verbatim** |
⚠ **`SUB 0x1C` is 4 bytes longer on the Thor firmware line** (`0x30` vs `0x2C`).
Parse **forward** from `declared_length`, never backward from the end — Series
III reads battery and memory from the end of that block, and doing so on a BD
unit yields a battery voltage of **577.92 V**.
⚠ **Test the monitoring flag for non-zero**, never against a constant. It has
read both `0x0E` and `0x0C` while monitoring.
### `0x5A` — simpler than Series III, deliberately
No arming ritual, no chunk loop, no `STRT` end-offset parsing, no `TERM` frame.
One request returns the whole event:
```python
offset_word = 0x1000 + 2 * ceil(size / 512) # size from the chain walk
```
The payload **is** the `.IDFW` file, byte for byte — so it feeds
`micromate.idf_file.read_idf_file()` and `/db/import/idf_file` unchanged.
⚠ Do not port the Series III `5A` walk. Its address arithmetic caused a 5x
over-read and a `> 64 KB` page-boundary bug that is *still open* on the Series
III side. None of that applies here.
---
## `micromate/client.py`
```python
class MicromateClient:
def __init__(self, transport: BaseTransport): ...
def open(self) / close(self) / is_open(self)
# identity and state
def connect(self) -> DeviceInfo # poll → serial → device info → state
def get_state(self) -> UnitState # monitoring?, clock, battery, memory
def get_active_setup(self) -> str
def list_setups(self) -> list[str] # 0x3F → 0x40… until empty
# events
def list_events(self) -> list[EventRef] # 0x93 → 0x1E → 0x1F… (key + size)
def download_event(self, ref) -> bytes # raw .IDFW/.IDFH
def get_event(self, ref) -> Event # download + decode via idf_file
```
`connect()` should mirror THOR's preamble (`POLL → SERIAL → 0x49 → POLL`) —
⚠ but note the reference records that **whether the unit requires it is
untested**. Do it because it is known-good, not because it is known-necessary,
and say so in the docstring.
`list_events()` returns the key *and* the size, because `download_event()` needs
the size to compute its offset word.
---
## Tests
**Offline, from captured bytes — no hardware.** This is the part worth doing
first, because it can be fully verified tonight's-captures-style before any unit
is involved.
```
tests/test_micromate_framing.py
```
⚠ `bridges/captures/` and `tests/fixtures/` are both gitignored, so tests must
not depend on files being present. **Embed the frames as hex constants** — they
are 16–68 bytes each, and a handful covers every case:
| case | why |
|---|---|
| POLL probe reply, 19 B | shortest valid frame |
| POLL data reply, 68 B | contains a literal `0x10` — only the DLE-aware checksum matches |
| `0x1A` response, 2108 B | exercises `declared_length` as a true uint16 (`0x082C`) |
| a Thor-line reply, `flags = 0x03` | `0x03` is ETX; proves destuffing before framing |
| a frame whose SUB is `0x02` | arrives as `10 02`; proves destuff-then-index |
| a truncated frame | parser must return nothing, not a bad frame |
| a corrupted checksum | `checksum_valid == False`, frame still returned |
Then a round-trip assertion: feed a whole captured session through the parser and
assert the frame count and every SUB, against `scratch/mm_frame_parse.py`'s
output — which is already known good, having parsed 24, 38 and 40-frame sessions
with zero bad checksums.
**Live, second:** against the bench unit on mint-mac via `mm_link.py`.
`connect()`, `list_setups()` (should return the 23 known names), `list_events()`,
then `download_event()` and assert the bytes decode and match a
`/db/import/idf_file` ingest of the same event.
---
## Order of work
1. `framing.py` + its tests — offline, verifiable immediately
2. `protocol.py` — reads only, one method per row of the table above
3. `client.py` — `connect()`, `get_state()`, `list_setups()`
4. the event chain and `download_event()`
5. decode end-to-end and compare against a store event
Steps 1–2 need no hardware at all.
---
## Open questions to settle while implementing
- **Request param stuffing** — raise `NotImplementedError` rather than guess.
- **Is THOR's preamble required?** Try one command cold and find out; it is a
two-minute test with the bench unit and it removes a ritual if unnecessary.
- **`Event` model fit** — Series IV carries fields Series III lacks (setup file
name, `LMic`/`SMic` channels). Extend `micromate/models.py`; do not fork the
shared `Event`.
- **Which `0x0C` fields to trust.** The peak float there runs 2–5% above
`max(T,V,L)` and is **not** the vector sum; its offset was inferred, not
established. The reference marks it do-not-rely-on — prefer decoded samples.
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# The "offset" fault — investigation journal
> ## ⚠ CORRECTED 2026-08-28 (same day) — the v1 detector was wrong
>
> Brian pushed back on the finding that offsets "come and go": in the field,
> once a unit develops one it stays broken until the geophone is replaced.
> He was right, and the challenge exposed **two real flaws** in the v1 detector:
>
> 1. **It scored only the axis with the largest peak.** A real event on one axis
> hid a persistent pedestal on another. BE12599 on 2026-08-21 read "clean"
> solely because Long had a 1.065 in/s event — Tran was sitting at
> **+0.4732 in/s** at that moment and was never examined.
> 2. **It used the MEAN**, which a real transient perturbs. The **median** is the
> resting baseline — most samples sit at it, so a blast does not move it.
> Same event, Long channel: mean **+0.0783** vs median **-0.0050**.
>
> Both flaws manufactured false recoveries. The corrected detector
> (`scratch/offset_scan2.py`, per-channel median) shows the pedestal is
> **persistent**, exactly as the field experience says. See §2b and §3b.
>
> **Then Brian proposed a better detector still** — measure the floor during
> the *pre-trigger* window, and require it to hold across pre/middle/end.
> That is now the detector of record (§2c). Final answer: **5 of 45 units
> (11%)**, stable across a 2x threshold range.
>
> Sections below that were written against v1 are marked; v1 numbers are kept
> for the reasoning trail, not as current fact.
A running record of the **offset** hardware fault on Instantel Series III
seismographs: a geophone channel whose trace sits displaced from zero rather
than centred on it.
This is a *journal*, not a spec. Findings are dated, dead ends are kept with
the reason they died, and every number says where it came from. When something
here is superseded, strike it and say why rather than deleting it — the point
is that a future session can tell what was actually established from what was
merely believed at the time.
Companion material:
- `scratch/offset_scan.py` — the detector
- `scratch/verify_against_ascii.py` — decoder verification harness
- `docs/instantel_protocol_reference.md` — wire protocol, incl. the
unimplemented `SUB 0x0E` this investigation now wants
---
## TL;DR (current state, 2026-08-28)
- **It is real device data, not a decode bug.** Settled early and confirmed
against Blastware's own ASCII exports.
- **Base rate: 5–6 of 45 units (11–13%)** across the full DL2 archive,
2018–2026. This *confirms* the earlier 2-of-21 (9.5%) estimate from the much
smaller Terra-View DB — survivorship bias from deleted events had **not**
concealed a wave of cases.
- **The fault is bimodal, not a drift continuum.** A unit is either clean or
grossly off. Loosening the amplitude threshold 11× adds no new units.
- **The unit's own sensor check cannot see it.** 102 offset events, zero
sensor-check failures. Do not try to use it as a screen.
- **Cause is still unsettled.** Instantel's autozero fixes the minority of
cases; the rest are hardware. We cannot yet tell which is which remotely.
- **The histogram corpus (63,535 files, 9.7x the waveforms) is now scanned too** —
see §8b. It independently confirms BE18438 and BE9558 with a clean 2.5x
separation, but detects only **2 of the 5** confirmed units, cannot attribute a
channel, and resolves time to ~a month. **A negative histogram result is not
evidence of health** — DC leakage into the interval peak varies 45x between units.
- **`offset_scan3.py` has a label defect** (§8b): its spread gate discards 18.8% of
high-|pre| rows onto units currently counted as clean. Re-cut before quoting any
precision number again.
- **Best open lead:** `SUB 0x0E` (channel sensor data, 8 channels × 10 bytes,
unimplemented) may carry the very numbers Instantel says to check against
**2027–2069**. Untested.
---
## 1. What the fault looks like
A healthy geophone trace is centred on zero. An offset channel is parked away
from zero, so the channel **mean approaches its own peak**. In Blastware the
signature is "parallel lines above or below the zero line" (Instantel's own
wording).
Consequences observed in the field:
- The unit can **self-trigger on its own offset** when the displacement exceeds
the geo trigger level, producing streams of junk events with no ground
motion. Instantel has a separate FAQ for this symptom (13-0-22, *"Unit
triggers continuously without activity"*).
- Recorded PPV for that channel is meaningless while the fault persists.
---
## 2. The detector
Implemented in `scratch/offset_scan.py`. Operates on raw BW binaries only — no
DB, no sidecars.
```
for each series-3 waveform binary:
decode -> per-channel ADC counts
dominant axis = channel with the largest |peak|
flag when |mean| / peak > 0.70
and |mean| >= 0.90 x the unit's geo trigger level
episodes = per-serial runs of flagged events, split on a >12 h gap
```
Why each term:
| term | purpose |
|---|---|
| `\|mean\|/peak > 0.7` | the discriminator. A DC-parked trace has mean ≈ peak. |
| `\|mean\| >= 0.9 × trigger` | amplitude floor — suppresses quiet traces where mean and peak are both tiny and the ratio is meaningless. |
| dominant axis only | the fault is per-channel; scoring all three dilutes it. |
| 12 h episode gap | separates deployments/visits rather than counting events. |
Trigger level comes from a paired `_ASCII.TXT` when one exists, else the
per-serial median learned from that unit's ASCII files, else 0.2 in/s.
**Known limitation.** Event traces contain real ground motion, so this can only
see offsets large enough to *dominate* the trace. A mild offset on a real blast
is invisible. Instantel's A/D-mode check (§5) is the only thing that sees the
mild end. Our base rate is therefore a **gross-offset** rate.
### 2b. Detector v2 — per-channel median (CURRENT)
`scratch/offset_scan2.py`. Supersedes the above.
```
for each series-3 waveform binary:
for each geo channel independently:
pedestal = median(samples) # resting baseline, robust to blasts
flag the CHANNEL when |pedestal| >= 0.025 in/s (5 A/D counts)
a unit has a real fault when a channel is flagged on >=3 CONSECUTIVE events
```
Why median: a DC pedestal shifts every sample, so it moves the median. A real
event moves only a minority of samples, so it does not. This removes the need
for the `m/p` ratio guard entirely — that guard existed only to compensate for
using the mean.
Why per-channel: the fault is on one geophone axis. Scoring only the dominant
axis means any event with motion elsewhere hides it.
Why "3 consecutive": the 0.025 in/s floor is only ~2x a healthy channel's
resting median (observed 0.010-0.015), so isolated flags are noise. Persistence
is the discriminator — and it is what the field experience predicts.
---
## 3b. Archive results, corrected (v2)
| | v1 (dominant axis, mean) | **v2 (per-channel median)** |
|---|---|---|
| units with any flagged event | 6 of 45 | 19 of 45 |
| **units with a sustained pedestal (>=3 consecutive)** | — | **8 of 45 (18%)** |
| runs of >=3 consecutive | — | 29 |
| runs of 1-2 events (noise) | — | 69 |
Units with a sustained pedestal: **BE9558, BA10895, BE11007, BE11529, BE12599,
BE13117, BE18003, BE18438**. BA10895 and BE18003 were invisible to v1.
**The affected channel is most often Vert**, which v1 got wrong — it named
whichever axis had the largest peak. BE13117 and BE18438 are both Vert faults.
Longest / clearest runs:
| unit | ch | span | events | median in/s |
|---|---|---|---|---|
| BE13117 | Vert | 2023-05-03 → 05-04 | 194 | 0.035 → **1.915** |
| BE18438 | Vert | 2026-02-25 → 02-26 | 75 | 0.180 → 0.370 |
| BE9558 | Vert | 2020-02-11 (6 h) | 33 | 0.065 → 0.090 |
| BE12599 | Tran | 2026-08-14 → 08-23 | 8 | 0.030 → **0.565** |
| BE18003 | Vert | 2021-03-17 → 06-11 | 3 | 0.040 → 0.060 |
BE12599 began **2026-08-14**, not 08-17 as v1 reported, and was still faulting
at the last event in the archive.
### 2c. Detector v3 — PRE-TRIGGER floor + constant-floor test (CURRENT)
`scratch/offset_scan3.py`. Brian's method, and better than v2 for a reason
worth naming: **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. `pretrig_samples` comes from the STRT record.
```
per channel:
pre = median of the first pretrig_samples samples
mid = median of the middle third
end = median of the final third
spread = max(pre,mid,end) - min(pre,mid,end)
offset when |pre| >= floor AND spread <= 0.02 in/s
real fault when a channel is flagged on >=3 CONSECUTIVE events
```
A DC offset is a **constant floor** — present before the trigger, during, and
after. The spread test rejects transients (settling, handling, a long event
tail) that move one segment relative to the others, which is what v2's
whole-record median could not do.
**The empirical noise floor justifies the threshold.** Across 19,244
non-flagged channel-events the pre-trigger floor distributes as:
| floor | share |
|---|---|
| −1 unit (−0.005) | 18.4% |
| **0.000** | **62.7%** |
| +1 unit (+0.005) | 13.4% |
**94.5% within ±1 quantisation unit; median exactly +0.0000, mean −0.0008.**
So there is **no systematic zero-point bias in the decoder** — an independent
confirmation of the 32000-count scale. A healthy channel really does read
0.000, and "any constant floor that is not 0.000" is the right signal, with
±1 unit of slack for quantisation.
**The result is threshold-insensitive**, which is what distinguishes a real
signal from a tuned one:
| floor | units flagged | sustained units |
|---|---|---|
| 2 units (0.010) | 34 | 15 ← into the noise |
| 3 units (0.015) | 26 | 8 |
| **4 units (0.020)** | 17 | **5** |
| **5 units (0.025)** — Instantel's | 12 | **5** |
| **8 units (0.040)** | 8 | **5** |
### FINAL RESULT: 5 of 45 units (11%)
**BE9558, BE11529, BE12599, BE13117, BE18438.**
Unchanged across a 2x threshold range. BE11007 and BA10895 drop out — the
spread test identifies them as transients, not pedestals.
The 11% headline happens to match v1's, but the reasoning and the unit list
differ: v1 included BE11007 and named the wrong *channel* on most units.
---
## 3. Archive results (2026-08-28)
Source: DL2 event export, 6,577 **unique** series-3 waveforms, 45 units.
See [`dl2-archive`](#8-data-and-tooling) for the `Sent/` mirror trap.
**283 suspect events, 15 episodes, 6 of 45 units (13.3%).**
Excluding BE11007 (§4, likely not an offset at all): **5 of 45 = 11.1%**.
### Threshold sensitivity — the bimodality result
Re-scoring the same corpus at a range of amplitude floors, with two
ratio cut-offs (1 A/D count = 0.005 in/s, see §5):
| \|offset\| floor | m/p > 0.7 | m/p > 0.9 |
|---|---|---|
| 5 cts (0.025 in/s) — *Instantel's own* | 333 ev / 6 units | 279 ev / **5 units** |
| 10 cts (0.050) | 294 / 6 | 274 / 5 |
| 20 cts (0.100) | 250 / 5 | 244 / 4 |
| 40 cts (0.200) | 209 / 5 | 203 / 4 |
| 80 cts (0.400) | 152 / 4 | 148 / 2 |
| 160 cts (0.800) | 144 / 2 | 141 / 1 |
Relaxing the floor by 11× (0.27 → 0.025 in/s) adds ~14% more events and **no
new units**. There is no population of mild offsets hiding below our threshold
*in event data*. Either a unit is clean or it is grossly off.
---
## 4. Per-unit case files
Ordered by severity. `m/p` medians are on the offending channel.
### BE13117 — one violent day, never again
`145 / 454 events (32%)`, **1 episode**, 2023-05-04, 6.8 h.
Offset climbed **0.393 → 1.875 in/s within the episode**. `m/p` median
**0.996** — the trace is almost pure DC. No recurrence in the rest of its 454
events. No ASCII files in the archive, so no calibration history.
### BE18438 — recurring, months apart
`87 / 293 (30%)`, **2 episodes**: 2025-11-15 (1.2 h, n=12, 0.279 → 0.369) and
2026-02-25 (**28.8 h**, n=75, 0.183 → 0.366). `m/p` median 0.967.
Clean across all 196 events preceding its 2025-08-12 calibration.
### BE9558 — six years apart
`38 / 196 (19%)`, **4 episodes**: 2020-02-11 (6.3 h, n=33, but only
0.068 → 0.086 — very mild), then 2026-04-14, 2026-04-29, 2026-05-04
(0.28–0.45). `m/p` median 0.919. Calibrated 2026-06-26; 0/7 events flagged
after, but n=7 is far too small to call it fixed.
### BE12599 — the live case ⚠
`6 / 77 (8%)`, **6 single-event episodes, one per day at exactly 05:00**,
2026-08-17 → 2026-08-23. Offset rose 0.383 → 0.565 then fell back to 0.345.
`m/p` ≈ 0.965, geo trigger 0.3 in/s — **the offset exceeds the trigger level,
so the unit is triggering on its own fault**. Last calibrated 2025-08-12.
This is the most recent and the most useful: a currently-faulting unit is the
natural experiment for the re-zero-vs-repair question (§7).
### BE11529 — marginal
`4 / 99 (4%)`, 1 episode 2025-07-08, 0.4 h, offsets only 0.051 → 0.058 in/s.
`m/p` median 0.959, so DC-dominated, but the magnitude is near the noise of
this method. Treat as unconfirmed.
### BE11007 — probably NOT an offset
`3 / 70 (4%)`, 1 episode 2022-01-17, offsets 5.500 → 6.904 in/s — by far the
largest. But `m/p` is only **0.719–0.738** against ≥0.9 for every other unit,
and the peaks are 7.6–9.4 in/s on a 10 in/s range. That reads as a **large
real blast with asymmetric ground motion**, not a parked trace. Excluded from
the headline base rate.
---
## 5. Instantel's own procedure and thresholds
From two Instantel technical-support FAQs supplied 2026-08-28
(answers **13-0-21** *"How to determine offsets"* and **12-0-10** *"Removing
offsets on an Instantel Series III monitor"*; created 2008/2007, last updated
2009-03-06).
### Identifying (13-0-21)
1. Create or use an event with the **manual minimum trigger** set for the
connected geophone and microphone — i.e. an event that recorded no real data.
2. Save it and open in Blastware.
3. An offset shows as **parallel lines above or below the zero line**.
4. Put the unit in **A/D mode** — on Series III, press and hold `OPTION`, then
press `START MONITOR`.
5. **Display counts higher than 5**, with no vibration or overpressure present,
indicate an offset.
### Removing — the autozero (12-0-10)
1. Be in a **quiet area with low vibration**.
2. Power on the Blastmate III / Minimate Plus.
3. Connect the geophone and microphone — **LINEAR mic only**.
⚠ *Do not connect an "A" weight microphone, regardless of what the monitor
displays.*
4. Press `Test`.
5. Wait for the **Sensor Check** results to appear.
6. Press `OPTION` and `START MONITOR` **simultaneously**.
7. `Performing Autozero` appears; press `Enter`.
8. Confirm the sensors are properly connected; press `Enter`.
9. Wait for the autozero to complete.
10. Press `Enter` twice → Main Menu, *Ready To Monitor*, offset corrected.
### The go/no-go number — 2027 to 2069
> When you perform an Autozero on any Series III unit, the lists of numbers in
> the **X1 and X8 gains should all be between 2027 and 2069**. If not, repeat
> the Autozero. **If the numbers are extremely out of the specified range, then
> the unit should be sent in for repair.**
>
> If this process does not remove the offset problem, return the unit **and
> sensors** to Instantel for repair.
This is the documented explanation for the field experience (Brian's dad,
2026-08-28) that **a re-zero works maybe 10% of the time** — the autozero only
recovers units whose zero reference is still near-correct.
### Scale derivation (inference, well-supported — not proven)
2048 is 12-bit midscale. Our codec's geo full scale is 32000 internal counts =
10 in/s, with 1 decoder unit = 16 counts = exactly 0.005 in/s
(`geo-full-scale-is-32000-counts`). ±2000 A/D counts about 2048 therefore maps
to ±10 in/s at **0.005 in/s per A/D count**. That makes:
- Instantel's ">5 counts" threshold ≈ **0.025 in/s**
- the 2027–2069 window = **±21 counts = ±0.105 in/s** of tolerated zero error
Consistent and mutually corroborating, but we have not confirmed the A/D-count
scale directly from a device reading.
---
## 6. Ruled out — keep these dead
### Condensation / humidity — DEAD (2026-08-25)
Proposed, then killed by its own controls: BE18438 stayed flat across a 10-hour
overnight gap, and only 2 of 21 units showed the fault while 19 sat in the same
weather. The apparent "diurnal cycle" was an artifact of binning by hour-of-day
across two days. See `waveform-dc-offset-is-real-device-data`.
### Clipping as a false-positive source — RULED OUT (2026-08-28)
A rail-hitting trace would fake an offset (mean → peak). It isn't happening:
median suspect peak is only **10% of full scale**, p90 is 18.6%. Only BE11007's
3 events exceed 50% FS, and none reach 98%.
### The sensor check as a predictor — DOES NOT WORK (2026-08-28)
Tested on 102 offset events across 4 units:
| unit | state | n | failed | median ratio | median freq |
|---|---|---|---|---|---|
| BE11529 | offset | 4 | **0** | 3.90 | 7.6 |
| BE11529 | clean | 14 | 0 | 3.80 | 7.5 |
| BE12599 | offset | 6 | **0** | 4.00 | 7.4 |
| BE12599 | clean | 13 | 0 | 4.00 | 7.6 |
| BE18438 | offset | 87 | **0** | 3.70 | 7.6 |
| BE18438 | clean | 25 | 0 | 3.80 | 7.5 |
| BE9558 | offset | 5 | **0** | 3.90 | 7.8 |
| BE9558 | clean | 44 | 0 | 3.80 | 7.5 |
Zero failures on either side and indistinguishable ratios/frequencies. The
swing test measures geophone frequency response and damping — it never examines
DC zero. **A grossly offset unit passes its own self-check.** This is why the
fault goes unnoticed until somebody looks at waveforms.
### "Offsets are transient / come and go on their own" — RETRACTED 2026-08-28
v1 reported episodes lasting hours that ended spontaneously. **This was an
artifact of the v1 detector** (see the banner at the top). With the per-channel
median, the pedestal persists. Every clear case reads clean again only after a
multi-day-to-multi-month gap consistent with service: BE13117 6 days, BE18438
24 days, BE9558 63 days **with a confirmed Instantel calibration inside the
gap**. BE12599 never reads clean — it is still faulting at the end of the
archive. This matches the operational experience: once a unit develops an
offset it stays broken until the geophone is replaced.
### "Offsets develop N months after calibration" — CONFOUNDED, NOT A FINDING
Tempting, and it looked strong:
| unit | suspect before latest cal | after |
|---|---|---|
| BE18438 | 0 / 196 | 87 / 97 |
| BE12599 | 0 / 62 | 6 / 15 |
| BE11529 | 0 / 82 | 4 / 17 |
| BE9558 | 38 / 189 | 0 / 7 |
But bucketing suspects by months-since-calibration gives **one unit per bucket**:
`0–3mo={BE11529}`, `3–6 & 6–9mo={BE18438}`, `9–12mo={BE9558}`,
`12–15mo={BE12599}`. The apparent "51% failure rate at 6–9 months" is entirely
BE18438's single February 2026 episode. Five units with roughly one episode
each cannot support a population trend. **Do not re-derive this.**
Also note: all affected units are calibrated on a **~12–13 month cadence**, so
"sent to Instantel" is the routine annual schedule, not evidence of a
fault-driven return.
---
## 7. Open questions
### Q1 — Is it a latched bad zero or analog degradation?
The question that decides everything. A latched zero is correctable (possibly
over the wire); degradation means a repair. Instantel's 2027–2069 rule implies
*both* populations exist, with the split roughly 10/90 in the field.
**BE12599 is the natural experiment** — faulting as of 2026-08-23. Read its
values, run the autozero, read them again.
### Q2 — Can we read the autozero numbers over the wire? (best lead)
Instantel says to check *"the lists of numbers in the **X1 and X8 gains**"* —
4 sensors × 2 gains = **8 channels**. The protocol reference already documents
an unimplemented command with exactly that shape:
```
SUB 0x0E -> RSP 0xF1 "channel sensor data"
2-step read; channel selector in params[6:8] = 0x0000..0x0007
data length 0x0A (10 bytes) per channel
```
Blastware's *Unit Channel Test* sequence:
`POLL×N → 0x15 → 0x01 → 0x08 → 0x01 → 0x0E×8 → 0x98×2 → 0x0E×8`
— note the **second `0x0E` pass carries live ADC readings**.
**Hypothesis (untested):** `0x0E` returns the numbers Instantel wants compared
against 2027–2069. If true, SFM could diagnose an offset remotely *and* predict
whether a re-zero will succeed — converting a 10%/90% shipping gamble into a
decision made before packing a box.
**How to test.** `bridges/ach_mitm.py` is a generic TCP proxy:
```bash
python bridges/ach_mitm.py --bw-host <MODEM_IP> --bw-port 9034 --listen-port 9999
```
Point Blastware at the proxy and run **Unit Channel Test**.
⚠ In this topology the output filenames are reversed — the tool labels the
*connecting* side "unit", so `raw_s3_*.bin` holds Blastware's bytes and
`raw_bw_*.bin` the unit's.
Capture priority: (1) BE12599 while faulting, (2) a known-good unit as control,
(3) before/after an autozero on the same unit. Eight 10-byte payloads with an
expected value near 2048 is a very constrained puzzle.
### Q3 — What is the mild-offset rate?
Unmeasurable from event files (§2). Only the A/D-mode check sees it. Would
need a fleet sweep in A/D mode, or Q2 to succeed.
### Q4 — Does an offset recur on the same unit after service?
BE9558 shows episodes in 2020 and 2026; BE18438 twice in four months. Suggestive
of recurrence, but service records aren't in the data — only calibration dates.
---
## 8. Data and tooling
| what | where |
|---|---|
| detector | `scratch/offset_scan.py` |
| current results | `/home/serversdown/dl2-archive/offset_archive.csv` |
| earlier candidate list (Terra-View DB, 274 events) | `scratch/offset_candidates.csv` |
| archive working copy | `/home/serversdown/dl2-archive/files/` |
| archive source | NAS `DeathStar` 10.0.0.2, `/volume1/Uploads/TMI/DL2-Event-backup-8-25-26/Event/autocall home/` |
⚠ **The DL2 export keeps a byte-identical `Sent/` mirror of its root.** 13,077
waveform paths are 6,577 distinct files. Always dedupe by basename — this
doubled two reported figures before it was caught.
---
## 8b. The histogram corpus — the other 90% of the archive (2026-09-04)
Every result above §8 comes from **waveform** files. `offset_scan3.py` filters on
`\.[A-Za-z0-9]{2}0[Ww]$`, so the corpus it scanned is 6,577 unique binaries. The
archive also holds **63,535 unique histograms** — 9.7x more files — which the
pre-trigger method cannot touch, because a histogram carries no samples: only a
per-interval, per-channel peak and half-period.
`scratch/offset_hist_scan.py` scans them. **63,505 of 63,535 decoded (99.95%),
43 units, 77.9M intervals.** Two of the 45 units have no histograms at all.
Output: `/home/serversdown/dl2-archive/offset_hist.csv` (190,515 channel-rows).
### The premise, and how far it actually holds
A histogram file is hours of continuous monitoring, so most of its intervals are
definitionally quiet, and a channel parked off zero cannot report a peak below
its own displacement. The signal is real — two within-unit contrasts, siblings
unmoved in both:
| unit | channel | in-episode floor | outside | waveform \|pre\| same window |
|---|---|---|---|---|
| BE18438 | Vert | 0.0350 | 0.0050 | +0.18 .. +0.37 |
| BE12599 | Tran | 0.0250 | 0.0050 | +0.03 .. +0.49 |
But the **leakage from a waveform pedestal into the histogram floor is bimodal,
not merely partial**: measured ratio ~0.9 on BE18438 Vert, ~0.7 on BE9558,
**~0.02 on BE12599** — two orders of magnitude on one instrument. The device
evidently measures each interval peak against a running baseline, and how much
DC survives that varies per unit. **Consequence: a negative histogram result
carries almost no information.** Do not read "clean in the histograms" as clean.
### The detector that survived
dmin(file, ch) = min[ch] - min over the other two geo channels, SAME file
gates (both hard): n_intervals >= 60 AND mic_p5 <= 5 raw counts
day statistic: median of dmin over that day's qualifying files
flag day at dmin >= 0.020 in/s (4 A/D counts)
episode at >= 3 CONSECUTIVE observed days
**Result: BE18438|Vert, BE9558|Tran, BE9558|Long.** Threshold-insensitive —
the journal's own test for a real signal against a tuned one — and this is the
first operating point in the investigation that passes it cleanly. The identical
answer holds across: statistic `min` or `p5`; length gate 10/30/60/120/300; mic
gate 3/5/8/10; threshold 0.015–0.035 (a 2.3x span); persistence K = 2,3,4,5,7.
Separation, ranked by highest floor sustained over 3 consecutive gated days
across all 135 unit-channels:
| unit-channel | best3 |
|---|---|
| BE18438 Vert | 0.1650 |
| BE9558 Long | 0.0350 |
| BE9558 Tran | 0.0250 |
| *(2.5x gap)* | |
| BE7145 Tran | 0.0100 |
| entire rest of fleet | <= 0.0050 (one quantisation count) |
Day-level false alarm: **37 of 99,432 gated unit-channel-days = 0.037%.**
### What it does NOT do — read this before trusting it
- **It finds 2 of the 5 confirmed units, not 5.** The site-quiet gate is what
makes it work and it is also what costs BE11529 and BE12599. BE11529's
four-day single-axis ramp (Tran 0.025 -> 0.055, both siblings pinned at 0.005)
is the most offset-shaped thing in the corpus outside the two detections, and
the gate discards it.
- **The positive class is two units.** Every threshold here is fitted to
BE18438 and BE9558, which contribute 22 of the 37 flagged days in the entire
corpus. No cross-validation is possible at n=2.
- **Per-channel attribution is NOT established.** Rotating the three geo channel
labels within each file — preserving every value, file and day, destroying
only channel identity — reproduces the episode *count* with p = 0.769 and the
label agreement at p = 0.038–0.077. Report a **unit and a window**; do not
name a geophone axis on the strength of this detector alone.
- **Timing resolution is ~1 month, not ~1 day.** A 30-day label shift still
scores 2 of 9 episode hits; the signal dies only past ~60 days. The day-level
series look far crisper than they are.
- **Ground truth here is a sibling detector, not a service record.** Agreement
between the two corpora is corroboration of a shared method. Nothing in this
section has been checked against an actual repair, calibration or RMA.
### Dead ends — keep these dead
- **Absolute floor (min / p1 / p5 / p10 / p25, thresholded alone) — RETIRED.**
Not fleet-comparable and mostly not about the channel. Scoring each cell using
*only the other two channels* — a statistic containing zero information about
the suspect channel — reaches AUC 0.746 against the same labels, versus 0.872
for the absolute floor itself. **66% of its apparent discrimination is "that
day was noisy at that site."** Interval size alone moves its p99 7x (0.0350 at
1 min vs 0.0050 at 2 s). And of all files with any channel above 0.025, 56.5%
have **all three** channels above it — common-mode, i.e. the wrong physics.
- **Zero-fraction — STRUCTURALLY IMPOSSIBLE, not merely weak.** The device never
reports a zero histogram interval peak. The value is a max over hundreds of
samples of a channel that always carries at least 1 count of noise, so it is
clamped at 1 A/D count (0.005 in/s). There is no zero to count.
- **Interval size, sample rate, geo range, firmware — refuted as confounds for
the differential.** All four are *file-level scalars*: they move all three geo
channels together, so they cannot produce a single-channel lift and the
within-file differential is immune to them by construction. Geo range is
identical across the three geo channels in **63,535 of 63,535** binaries.
(Interval size remains fatal to the *absolute*-floor version, above.)
### Two findings that are independent of the histogram detector
**1. `offset_scan3.py`'s `spread <= 0.02` gate is discarding real signal.**
It rejects **113 of the 600 channel-rows with |pre| >= 0.025 (18.8%)**, and the
rejections are not random — 92 of them fall across 41 unit-channels currently
labelled NEGATIVE. Four would become sustained positives under an
amplitude-only >=3-consecutive rule: **BE12599|Long (run of 8), BE18003|Vert
(4), BA10895|Vert (3), BE12844|Tran (3).** Until this is re-cut, the fleet label
is **three-state — POSITIVE / NEGATIVE / SPREAD-REJECTED(unknown)** — and the
third state should be excluded from both TP and FP counts rather than silently
scored as healthy. Every precision figure computed against the two-state label,
in this section and in §3, is affected.
**2. The waveform corpus sees ~7% of the days a unit was deployed.** 2,627
(unit, day) observations against the histogram corpus's 35,105 — 13.4x — with a
per-unit median ratio of 0.070. BE12599, a confirmed unit, is waveform-observed
on 39 of its 1,666 histogram-observed days (**2.3%**). Any statement of the form
"the fault was absent before date X" that rests on waveform coverage alone is
much weaker than its event count suggests.
### BA10895 — reclassified (see also §4)
Previously dismissed as a transient. The histogram record shows its **Vert**
quiet-minute floor at 0.005 on 62/62 qualifying files from 2023-07-07, then
0.010–0.015 on 48/58 files from 2023-08-03 to 08-27, while Tran moves on 2/58
and Long on 9/58 and the site mic floor never leaves 1–3 counts. Independently,
**42 of its 85 waveform events (49.4%) are single-axis-dominant** — one geo peak
>= 10x both siblings and >= 0.05 in/s — the **highest rate in the 45-unit
fleet** (BE13117 36.1%, BE18438 29.4%), and **100% of it on Vert**. Vert
excursions of 0.1–1.5 in/s with Tran/Long at 0.005–0.035 are not ground motion.
This is a genuine Vert-channel hardware fault, but **not the classic pedestal** —
the differential is only one A/D count. Caveat: its entire histogram record is a
single 52-day deployment ending 2023-08-27, so nothing says whether it
persisted, was serviced, or resolved.
The other six marginal units — BE11007, BE17354, BE18004, BE18104, BE9557,
BE18003 — are **clean**. All seven cap at +0.005 to +0.007 (one A/D count)
lifetime under the quiet-site gate, against +0.175 for BE18438 Vert and +0.062
for BE9558 Long. Three individual waveform flags fall in windows with **zero**
histogram coverage and are NO-DATA, not clean: BE18004|Tran 2024-10-16,
BE9557|Tran 2021-06-28, BE9557|Vert 2025-06-12.
### Still open in this section
- **The 11 thin-coverage units were not screened** (BE10202, BE11462, BE13779,
BE15760, BA15957, BE16754, BE16758, BE8081, BE8626, BA9229, BE9887 — each
under 20 waveform events, several with hundreds of histograms). This is the
population most likely to hold a previously unknown offset, and it is the one
slice of the plan that did not run. BE11462 was incidentally scored clean by
the full-archive pass; BE10202 has no histogram files at all.
- **No completeness audit was run** over the above.
- Re-cutting the ground truth three-state (finding 1) and re-scoring everything
against it.
---
## 8c. Mechanism — five hypotheses tested, all dead (2026-09-06)
**The mechanism is still unknown.** Five campaigns, ~105 effectively independent
tests, seven nominally significant results against **5.2 expected by chance**
under a global null. Every one died to its own confound analysis. What the
campaign bought is a set of *shape constraints* and a long list of dead ends.
### ⚠ Two things retracted from this journal
**1. "Polarity is perfectly consistent — 11 of 11, zero mixed cases."** That is
a **tautology of the spread gate**, not a property of the fault. `spread <= 0.02`
requires pre/mid/end to agree, which forces one sign. Amplitude-only at the same
0.025 threshold: **12 of 53 unit-channels are mixed**, including BE18438|Vert
(88+/1−) and BE9558|Vert (1+/35−). Withdrawn.
**2. "5 of 45 units, unchanged across a 2x threshold range."** The
threshold-insensitivity is also a property of the gate. Amplitude-only gives
**9 units at 0.020, 8 at 0.025** (adding BA10895, BE12844, BE18003), 5 at 0.040.
The fleet is **8–9 units, not 5**.
**3. "Persistent — it stays until the geophone is serviced."** Weakened, not
withdrawn. There are **23 recoveries after runs of >=3 flagged events, median
gap 6.03 days**, three inside ten minutes. BE18438|Vert reads `pre=mid=end=
+0.0000` on 2026-02-10, +0.185→+0.370 across 02-25/26, and `+0.0000` again on
2026-03-22 — identical Project, Seis Loc, calibration date, geo range and
trigger throughout. The one thing that cannot be excluded is a **field
autozero**: it is a button sequence at the unit and writes nothing into the
event header. So "persistent" may be "persistent unless somebody pressed the
buttons," and the archive cannot tell those apart.
### The one positive finding: onset is a RAMP, minutes to hours
Both onsets resolvable at minute cadence are ramps. **BE18438|Vert,
2026-02-20** — the histogram corpus collapses a 14 d 21 h waveform bracket to
**one minute**:
```
~14,200 consecutive quiet minutes at 0.000–0.005 (ten full daily files)
09:32 +0.005 09:39 +0.045 10:20 +0.125 16:00 +0.165
09:33 +0.010 09:42 +0.070 13:13 +0.150 20:17 +0.185 plateau
```
**50% of the excursion in 7 minutes**, the rest asymptotic over ~10 h, **>=25
distinct one-minute intermediates**. Validated **75/75** against Blastware's own
ASCII export. Its 2025-11-15 onset is the same shape over 2.7 h. BE13117 stage B
is a 91-minute monotone rise, +0.035 → +1.745 in/s over ~40 samples.
**This kills both poles of the original dichotomy** (journal Q1): not an
instantaneous latched step (a bad autozero, a stuck trim-DAC), and not slow
component degradation over days or weeks.
⚠ It rests on **2 of 45 instruments**. Clopper-Pearson on 4/4 resolved onsets
gives 95% CI [0.40, 1.00] — a mixed population with up to 60% true steps is not
excluded. BE13117 has zero paired ASCII, so its ramp rests on our decoder alone.
### The methodological corollary — more important than the finding
**A waveform-only bracket manufactures the appearance of a step, and the spread
gate is blind to onsets by construction.**
The offset is what fires the trigger, so no waveform event can exist until the
ramp has nearly reached the trigger level. BE18438's first event of each episode
sits at 0.280 against a 0.300 trigger, and 0.185 against 0.200. At daily cadence
against a 3 h ramp, P(catching an intermediate) = **0.125**.
And `spread <= 0.02` rejects any record in which the floor is *moving* — which
is exactly what an onset is. **The gate rejected the very BE18438 record where
the ramp is visible.** If the operational goal is catching a fault early, before
the unit floods the store with junk events, the current detector is the wrong
shape for the job.
### The surviving shape
An **electrical, reversible, two-time-constant settling process** (~10 min and
~hours), saturating at a ceiling, with occasional sub-3-minute discrete jumps
superposed (BE18438 2026-02-26: 13:24 pre +0.180 / mid +0.240 / end +0.255 →
13:27 +0.325, identical metadata). That is the signature of a **bias or leakage
path charging a high-impedance node** — the class of fault Instantel's autozero
recovers ~10% of the time, and what the X1/X8 gains measure.
**It is a shape constraint, not a mechanism. Do not write it up as one.**
### Dead — with the evidence, so none of this is re-derived
| Killed | Evidence |
|---|---|
| **Latched step at onset** | >=25 one-minute intermediates over ~10 h, ASCII-validated. Direct observation, not a test. |
| **Slow degradation over days/weeks** | Same observation — bulk of the excursion in 7 min to 2.7 h. |
| **Thermal driving of pedestal magnitude** | BE13117, 365-count pedestal, n=128: full-day modulation **−0.42% ± 0.42%**, 95% CI [−1.25%, +0.40%]. Healthy-fleet seasonal zero drift totals **~0.3 A/D counts** — 15x to 1200x too small. Best-powered result in the campaign. |
| **Ground-motion shock** | 30-day window-max percentile ranks 0.03/0.98/0.15/0.01/0.68/0.24, median **0.194** against a null of 0.5. **0 of 7 events >=9 in/s** was followed by an onset within 30 d. BE12599 hit 10.220 in/s (2023-11) and 10.005 (2025-04) and did not onset until 2026-08-14. |
| **Handling / redeployment** | **0 of 9** onsets had a Project/Client/Seis Loc change. Widened to 30 d: 2 observed vs 4.90 expected, P(X>=2)=0.995 — *depleted*, the wrong direction. The apparent gap effect (p=0.035) died on histogram coverage: BE18438's "59.7-day gap" contains 122 histogram files; true silence 0.52 d. |
| **Mechanical resonance / damping change** | BE18438|Vert at a 64-count pedestal (3x outside Instantel's ±21): ΔTest-Freq **CI [−0.090, +0.021]** against 0.127 Hz for a real calibration. Block permutation p=0.658. |
| **Accumulated-duty threshold** | ~4 clean units logged more monitoring than the largest positive onset dose; BE18193 logged **13.45M intervals, 6.2x**. A counterexample — no power argument weakens it. |
| **Firmware** | **14,338 of 14,340** exports read `V 10.72-8.17`. A constant cannot explain a variable. |
| **Unit age** | Serial rank-sum 118.0 vs null 115.0, p=0.549; unchanged on the 8-unit re-cut (p=0.586). Serial is a poor age proxy anyway (Spearman +0.113 against archive entry). |
| **Strong seasonal clustering** | 25 onsets, exposure-weighted permutation **p=0.59**. Excludes >=75%-in-one-season only; a 2x seasonal hazard is *not* excluded. |
Also retire two overstated bounds. H6's dose-response exclusion "|r| > 0.03" is
a **10x overstatement** once clustering is corrected — the honest bound is
|r| > 0.1–0.3, so a real r=0.2 is not excluded. And **any statistic quoted
per-event**: 512 flagged channel-events collapse to **4.9 effective independent
observations** (unequal-cluster design effect 104.6 at ICC=1), and **55% of the
flagged corpus is one instrument on two calendar days** (BE13117, 2023-05-03/04).
### Power — read every negative in this section as bounded
Fisher exact, 5 positives of 45, one-sided α=0.05, exposure a third of the fleet:
| relative risk | power |
|---|---|
| 1.5 | 0.059 |
| 2 | 0.112 |
| 3 | 0.231 |
| 6 | 0.497 |
| 15 | 0.753 |
80% power needs **RR ≈ 13–20**. Even a *perfect* split reaches p<0.05 only if
the exposed group is <=25 of 45 units. **This archive can detect only
near-deterministic unit-level causes.** Every negative above excludes a strong
effect, not a real one.
### What this archive can NEVER answer
- **The A/D zero and the X1/X8 gains.** The 2027–2069 numbers appear in no file,
header or decoded record. They exist only on a live device behind `SUB 0x0E`.
Q1 is structurally unanswerable from data.
- **Unit-level vs component-level cause.** **Zero of 14,340** exports carry a
geophone or sensor serial. Q4 is dead — there is no way to know whether the
same physical geophone came back after service.
- **Service history.** The only service-adjacent field is `Calibration: <date>`
— 30 distinct dates fleet-wide, none before 2023, ASCII corpus entirely
2025–26. BE9558's 2020 and BE13117's 2023 episodes have no calibration record.
- **Temperature.** Zero exports carry it. Battery Level is a verified coarse
thermometer (+0.204 V winter over summer, 20/20 unit-years, p=9.5e−7, matching
lead-acid tempco) but quantised at 0.1 V ≈ 10 °C — useless within a day. The
archive can *bound* thermal; it can never *test* it.
- **BE13117 specifically** — 55% of the flagged corpus, the largest pedestal at
1.92 in/s, **zero** ASCII exports, histogram record ending eight months before
its episode. The most informative case in the archive is permanently outside
every metadata test.
- **The mild-offset rate**, and therefore the base rate's denominator. Event
files only see offsets large enough to dominate the trace.
### The experiment to run — `SUB 0x0E`, one afternoon
Point Blastware at `bridges/ach_mitm.py` and run **Unit Channel Test** against
(1) a faulting unit, (2) a known-good control, (3) the same unit before and
after an autozero. BW's sequence is `0x0E x8 → 0x98 x2 → 0x0E x8`, the second
pass carrying live ADC. Eight 10-byte payloads with expected values near 2048 is
a very constrained puzzle.
- **Proves:** whether the X1/X8 gains are readable over the wire, and whether
the fault sits at or upstream of the ADC zero reference. Gains walk out of
2027–2069 with the pedestal → the fault *is* the zero reference, Q1 answered.
Gains hold while the trace moves → the fault is downstream, look at the front
end.
- **§8c hands it a falsifiable time course:** poll at ~1-minute cadence and the
numbers should **ramp over minutes-to-hours, not step**. If they step while
the trace ramps, the two are decoupled.
- **Payoff:** converts the 10%/90% ship-it-or-not gamble into a decision made
before packing a box, remotely, for the whole fleet.
- ⚠ In the MITM topology filenames are reversed — `raw_s3_*.bin` holds
Blastware's bytes.
**Second: swap the geophone** between a faulted base and a healthy one. Fault
follows the sensor → element or cable. Fault stays with the base → front-end
board. One afternoon, zero code, and it settles the one question the archive is
permanently blind to.
**Third: log a faulting unit for 72 h untouched.** Every recovery we have is
confounded by a possible field autozero. A shelf and a logger settles whether
the fault genuinely self-reverses.
**Fourth, free: re-cut the fleet label** — drop the spread gate, re-score
amplitude-only, screen the 11 unscreened thin-coverage units. Might reach 9–10
positives. Be honest about the gain: power against "older half carries 3x the
hazard" rises only 0.23 → 0.30.
**Highest-value item overall, and not an experiment: the RMA/repair records.**
Which unit went back, when, what was done (autozero vs geophone replaced vs
board), and the geophone serial fitted. "Same channel after a documented
geophone *replacement*" is component-level-negative in one observation.
---
### 8d. The non-motion test — Brian's "it doesn't cross zero" (2026-09-07)
Looking at BE12599's 2026-08-09 event, Brian noted it reports no ZC frequency
**because the trace never crosses zero**. That observation is the best detector
in this investigation, and it comes from physics rather than a threshold.
A geophone is a velocity sensor with no DC response, so its output over a record
must integrate to ~zero — the ground does not relocate. Real motion therefore
sits roughly half below zero. Anything electrical is one-sided.
mp = |mean| / peak ~0 for motion, ~1 for a fault
frac_neg = share of samples < 0
`scratch/nonmotion_scan.py`, all 6,577 waveforms, 19,731 channel-rows.
Restricted to peak >= 0.05 in/s (n = 12,068), the distribution is **bimodal
with an empty middle**:
| mp band | channel-events |
|---|---|
| 0.0–0.1 | 11,384 |
| 0.1–0.2 | 293 |
| **0.15–0.85 (dead zone)** | **131 = 1.09%** |
| 0.9–1.0 | 278 |
At `mp >= 0.8` with >=3 events it returns **exactly the five confirmed units** —
BE9558, BE11529, BE12599, BE13117, BE18438 — stable from 0.5 to 0.9. Two
detectors on entirely different principles agreeing on the unit list is the
strongest corroboration that list has.
**BE11007 is settled: NOT an offset.** It reaches mp 0.75–0.89, but with
`frac_neg = 0.99` at peaks of **7.4–9.4 in/s** — parked *negative* during a
near-full-scale blast. §4's guess was right. `mp` alone cannot separate a
pedestal from a large one-sided blast; pair it with a peak ceiling or with
sign-consistency across events.
⚠ **Not a rediscovery of the retracted v1 detector.** v1 scored only the
largest-peak axis and used the mean as a *baseline estimator* where the median
was required. Here the mean is the signal itself, per channel — that is what the
physics licenses.
**Correction to §8c.** That section says the spread gate is "blind to onsets by
construction." Too strong: of 87 BE18438|Vert events at mp >= 0.5 the gate
rejected **one** — the transitional record. It does not lose onsets
systematically; it loses the transition specifically.
### 8e. BE12599 — a connector, not a geophone (2026-09-07)
Waveform shapes across its August episode, measured rather than eyeballed:
| date | channel | shape |
|---|---|---|
| Aug 09 05:29 | Long | **unipolar +**, 0/2304 samples below zero, decay tau **26 ms** |
| Aug 09 05:35 | Long | unipolar +, 3 spikes at irregular gaps (744, 1032 ms), tau **38 ms** |
| Aug 14 05:00 | Long | single lobe, bipolar, tau **118 ms** |
| Aug 17–23 | Tran | **flat DC pedestal**, sd/level 0.015–0.020, 0 zero crossings |
**Unipolar impulses with an RC tail are not mechanical.** Fast rise, exponential
decay, one polarity, irregular timing — that is charge dumped into a
capacitively-coupled input and draining through the input resistance. The
progression 26 ms -> 118 ms -> never recovers, over 14 days, is a leakage path
worsening.
**And the fault moved channels** — Long on Aug 9/14, Tran on Aug 17–23, Long
again on Aug 21 (1.065 in/s) while Tran held its pedestal. Vert stayed clean
throughout. **A failing geophone element cannot hop channels. A connector can.**
That single fact explains what had been puzzling:
- **The sensor self-check keeps passing** (7.4/7.5/7.6 Hz, ratios 3.6–4.2, all
four channels Passed, on the very events where Long throws 0.5 in/s spikes).
The swing test drives the element; the element is fine. The fault is in the
wiring to it.
- **Why Instantel's autozero fixes only ~10%** — it cannot fix a connector.
- **Why onset "ramps" over minutes to hours** — contact resistance drifting.
All seven Aug 17–23 events are stamped **05:00:14**, the same second, and their
filename extensions run `8E → WE → KE → 8E → WE → KE → 8E` — the documented
3-day cycle for a fixed daily time. Clock-scheduled, not physically triggered:
the modem powers up, draws a surge, and a marginal connection responds.
**Field action: inspect and photograph the geophone connector BEFORE reseating
anything** — an intermittent contact clears the moment it is disturbed.
⚠ Scoped to BE12599. BE18438's onset was a smooth 7-minute ramp with no spikes,
which looks like a different failure mode wearing the same signature.
---
### ⚠ Serial prefixes — four of these units are BlastMates, not MiniMates
Corrected 2026-09-06, after Brian queried "BA10895?" against a report that
said BE10895. He was right. The BW filename encodes the serial **number
only** — `L895` -> 10895 — and every offset scanner synthesised the family
prefix as `"BE"`. Four of the 43 archive units are **BA** (BlastMate, the
MiniMate Plus's bigger sibling; same Series III, byte-identical data):
**BA9229, BA10060, BA10895, BA15957.**
Read off the file bodies, which carry the serial verbatim. No analysis
changed — grouping was always on the numeric part, and no unit number maps
to two serials — but every earlier reference to "BE10895" and the other
three is a label error and has been corrected throughout this document.
The same assumption was live in two production sites and is fixed
(`sfm/waveform_store.py`, `minimateplus/client.py`): the store would have
filed a BlastMate under a unit that does not exist, and the monitor-log
decoder lost the geo threshold along with the serial. See commit `9ceff65`.
---
## 9. Chronology
| date | event |
|---|---|
| 2026-08-25 | Reported as a *waveform decode bug* — traces with a DC offset. Investigation shows the offset is **real device data**; the decoder is correct. |
| 2026-08-25 | Brian relays his dad's description: a known hardware fault called an "offset"; usually sent to Instantel. |
| 2026-08-25 | First detection pass over the Terra-View DB: **2 of 21 units**, 274 events, 11 months. Flagged as vulnerable to survivorship bias — flooded events were routinely deleted. |
| 2026-08-25 | Condensation hypothesis proposed, then **killed by its own controls**. |
| 2026-08-25 | Parked pending the multi-year archive. |
| 2026-08-28 | DL2 archive pulled (33 GB, 546k files; 6.6 GB working set). |
| 2026-08-28 | Archive scan: **6 of 45 units**, 283 events, 15 episodes. Prior base rate **confirmed**, not overturned. |
| 2026-08-28 | Clipping ruled out; `m/p` established as the discriminator; BE11007 reclassified as probably a real blast. |
| 2026-08-28 | Calibration-timing correlation attempted and **rejected as confounded**. |
| 2026-08-28 | Instantel FAQs supplied: autozero procedure, the **2027–2069** window, the **>5 counts** threshold. Explains the ~10% re-zero success rate. |
| 2026-08-28 | Bimodality established; sensor check proven **blind** to offsets; `SUB 0x0E` identified as the best open lead. |
| 2026-08-28 | **v1 detector retracted.** Brian challenged the "come and go" finding against field experience. Two flaws found: dominant-axis-only scoring and mean-instead-of-median. Corrected detector shows persistent pedestals on **8 of 45 units**, and the gaps are service windows. |
| 2026-08-28 | **Detector v3 (Brian's method):** pre-trigger floor + pre/mid/end consistency. Healthy channels proven to sit at 0.000 +/-1 unit (94.5%), confirming no decoder zero-point bias. Final: **5 of 45 units (11%)**, threshold-insensitive. |
| 2026-09-04 | **Histogram corpus scanned** — 63,505 of 63,535 files, 43 units, 77.9M intervals (9.7x the waveform corpus). `scratch/offset_hist_scan.py`. |
| 2026-09-04 | Absolute-floor statistic **retired**: 66% of its discrimination is a day/site confound (other-channels-only AUC 0.746 vs 0.872). Zero-fraction shown **structurally impossible** — the device clamps every interval peak at >= 1 count. |
| 2026-09-04 | Site-quiet-gated cross-channel differential established: **BE18438 Vert, BE9558 Tran+Long**, threshold-insensitive over a 2.3x span. Finds only **2 of the 5** confirmed units — leakage into the histogram floor is bimodal (0.9 to 0.02), so a negative result carries almost no information. Per-channel attribution **not** established (channel-scramble p = 0.769). |
| 2026-09-04 | **BA10895 reclassified** from transient to a genuine Vert fault of a different subtype — 49.4% single-axis-dominant events, the highest in the fleet, 100% on Vert. The other six marginal units are clean. |
| 2026-09-04 | **Defect found in `offset_scan3.py`**: its `spread <= 0.02` gate discards 18.8% of rows with \|pre\| >= 0.025, concentrated on 41 negative unit-channels; 4 would be sustained positives without it. The fleet label is three-state, not two. |
| 2026-09-06 | **Four units relabelled BA, not BE** — BA9229, BA10060, BA10895, BA15957 are BlastMates. The BW filename carries only the serial number; the family prefix must be read from the file body. Fixed in the scanners and in two production sites. |
| 2026-09-06 | **Mechanism campaign — five hypotheses, all dead.** Thermal, ground-motion shock, handling/redeployment, accumulated duty, unit age, firmware and a mechanical element fault are each refuted or bounded. 7 nominally significant results against 5.2 expected by chance. |
| 2026-09-06 | **Onset is a RAMP of minutes-to-hours, not a step** — BE18438 Vert resolved to one-minute cadence, 50% of the excursion in 7 min, >=25 intermediates, ASCII-validated 75/75. Kills both a latched digital step AND slow component degradation. Surviving shape: a reversible two-time-constant settling process — a bias/leakage path charging a high-impedance node. |
| 2026-09-06 | **Polarity consistency RETRACTED** (a tautology of the spread gate; amplitude-only gives 12 of 53 unit-channels mixed) and the fleet **re-cut to 8–9 units, not 5**. "Persistent until serviced" weakened: 23 recoveries, median gap 6 days — though a field autozero cannot be excluded. |
| 2026-09-06 | The spread gate is **blind to onsets by construction** — it rejects a moving floor, which is what an onset is. It rejected the very record in which the ramp is visible. |
| 2026-09-07 | **The non-motion test** (Brian: "it doesn't cross zero"). `\|mean\|/peak` is bimodal with a 1.09% dead zone and returns exactly the 5 confirmed units from physics, not a threshold. Independent corroboration of the unit list. **BE11007 settled as NOT an offset** — a one-sided 9 in/s blast. |
| 2026-09-07 | **BE12599 is a connector fault, not a geophone fault.** Unipolar spikes with a 26→118 ms RC tail progressing to a flat pedestal, and the fault MOVES between Long and Tran while the sensor self-check passes on every event. An element cannot hop channels; a connector can. Inspect before reseating. |
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# USBM RI8507 / OSMRE Blasting Compliance Curve — Reference
Reference for the **velocity-vs-frequency blasting compliance chart** Blastware
draws on its Event Report ("USBM RI8507 And OSMRE"), and how seismo-relay
reproduces it. Implemented in [`sfm/compliance.py`](../sfm/compliance.py); the
spectral (FFT) side lives in [`waveform_fft.py`](../waveform_fft.py).
Reverse-engineered 2026-09-14 against 7 BE12844 (MiniMate Plus) events, each
with a Blastware Event Report + FFT Report as ground truth. Curve values from
USBM RI8507 Appendix B and 30 CFR 816.67.
---
## What it is
Two closely-related sources for the same limit curve:
- **USBM RI8507** — Bureau of Mines *Report of Investigations 8507* (Siskind
et al., 1980), *"Structure Response and Damage Produced by Ground Vibration
From Surface Mine Blasting."* The curve is **Figure B-1**, Appendix B
("Alternative Blasting Level Criteria"), p.73–74.
- **OSMRE / OSM** — the Office of Surface Mining Reclamation and Enforcement
codified it as **30 CFR 816.67, Figure 1**. "CFR" = the U.S. Code of Federal
Regulations. Same curve, regulatory force.
The chart plots each geophone channel's significant vibration cycles as
`(frequency, peak velocity)` points against this limit. A point **below** the
line passes; **above** fails.
---
## The limit curve
A structure has a resonance band (~4–12 Hz for whole structures) where it is
most vulnerable, so the safe velocity is **lower** at those frequencies and
**higher** away from them. The curve captures this by alternating two kinds of
bound:
- **Constant-velocity** segments — a flat horizontal line at a fixed PPV.
- **Constant-displacement** segments — a fixed peak *displacement* `d`. For
simple harmonic motion, peak velocity `v = 2πf·d`, so on a velocity-vs-
frequency **log-log** plot this is a straight line of slope +1 (velocity rises
with frequency). This is why the low- and high-frequency bounds are sloped.
### Two lines — structure type
RI8507 gives two lines for two interior-wall constructions (Table 13, p.67):
| line | construction | plateau PPV |
|---|---|---|
| **Drywall** (solid) | modern gypsum wallboard | **0.75 in/s** |
| **Plaster** (dashed) | older plaster on wood lath | **0.50 in/s** |
Plaster-on-lath is more damage-prone, hence the lower limit. You apply **one**
line depending on the monitored structure.
### The four segments (Figure B-1, p.74)
Going low → high frequency, each line is:
1. **Ultimate low-frequency bound** — constant displacement **0.030 in**
(`v = 2πf·0.030`). Only relevant below ~4 Hz.
2. **Plateau** — constant velocity **0.75** (Drywall) / **0.50** (plaster) in/s.
3. **Rising diagonal** — constant displacement **0.008 in** (`v = 2πf·0.008`),
climbing from the plateau up to the high-frequency cap.
4. **High-frequency cap** — constant velocity **2.0 in/s** above ~40 Hz.
The segments are drawn **continuous**: each bound is used over the frequency
range where it is the binding (lowest) limit, and consecutive bounds meet where
they are equal — so there are no vertical steps. Transition frequencies come
straight from the values (`f = V / (2π·d)`):
| transition | formula | Drywall | Plaster |
|---|---|---|---|
| 0.030 in → plateau | `V_mid / (2π·0.030)` | 3.98 Hz | 2.65 Hz |
| plateau → 0.008 in | `V_mid / (2π·0.008)` | 14.92 Hz | 9.95 Hz |
| 0.008 in → 2.0 in/s | `2.0 / (2π·0.008)` | 39.79 Hz | 39.79 Hz |
Because both lines share the same **0.008 in** rising diagonal, above ~15 Hz
they lie on the *same* line (both reach 2.0 in/s at ~40 Hz) — RI8507's literal
construction merges them there. Blastware renders the dashed line as a separate
parallel diagonal, but that is cosmetic: above ~15 Hz both structure types carry
the identical limit, so compliance is unaffected.
> ⚠ RI8507's *Table 13* is a simpler two-range criterion with a **sharp
> discontinuity at 40 Hz** (flat plateau, then a jump to 2.0). Figure B-1 is the
> **smoothed** version that adds the 0.008 in transition — that is the one drawn
> on reports and implemented here.
---
## The compliance scatter (the points)
The cloud is **not** the FFT spectrum. It is a per-cycle, time-domain measure by
the **zero-crossing method** (`channel_compliance_points`):
- Split the channel's waveform at its zero crossings.
- Each half-cycle contributes one point: **frequency** `= 1 / (2 · half-period)`
(from the samples between the two crossings), **velocity** `= peak |amplitude|`
in that half-cycle.
This yields ~90–110 points per channel, and — by construction — each channel's
**highest** point equals that channel's PPV. Verified against Blastware: the
cloud shape, density, and ceiling all match.
### Why not the FFT?
A broadband blast spreads its energy across many FFT bins, so no single bin
reaches the time-domain peak — the FFT amplitudes come out ~10× below the
compliance-chart velocities. The compliance chart is a *per-cycle peak* view;
the **FFT** is a separate analysis (Blastware's *FFT Report*), reproduced by
[`waveform_fft.py`](../waveform_fft.py) and used for the dominant-frequency
readout and the #10 FFT view — not for this scatter.
---
## Implementation
- `sfm/compliance.py`
- `limit_at(freq, curve)` — the limit PPV at a frequency (`curve` = `"Drywall"`
or `"Plaster"`); curves are data in `_CURVES`, so more standards can be added.
- `channel_compliance_points(samples, sps)` — the zero-crossing scatter.
- `draw_compliance_chart(ax, channels, sps)` — matplotlib rendering (both
limit lines + per-channel scatter, Blastware's tick scales and channel
markers: Tran `+` red, Vert `×` green, Long `o` blue).
- Tests: `tests/test_compliance.py`.
---
## Sources
- USBM **RI8507** (Siskind, Stagg, Kopp, Dowding, 1980), Appendix B / Figure B-1,
p.73–74; Table 13, p.67. (`ref-stuff/usbm-ri8507-ground_vibration.pdf`.)
- **30 CFR 816.67**, "Use of explosives: Control of adverse effects," Figure 1 —
<https://www.ecfr.gov/current/title-30/chapter-VII/subchapter-K/part-816/section-816.67>
docs/runbooks/wedged_unit_recovery.md
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# Runbook — Recovering a wedged unit stuck in a call-home loop
**Incidents:** BE9558H at `166.246.130.1:9034`, 2026-05-17 (Method B) ·
BE12599 at `166.246.64.226:9034`, 2026-09-16 (Method A).
A field unit with a stuck-triggered geophone (or any hardware fault causing
constant event triggering) will record events back-to-back, and if Auto Call
Home is set to "After Event Recorded" the device will dial the office BW
ACH server in a tight loop. Combined with a Sierra Wireless modem in
bidirectional serial-TCP mode, this makes the unit effectively unreachable
from SFM — every TCP connection we open gets killed when the modem flips
from server-mode to client-mode to honor the device's next AT dial command.
This runbook describes how to break the loop and recover control.
---
## ⚠ Two cures for one disease — intercept first
Both incidents below are the **same failure**: a geophone offset crosses the
trigger level, the unit records back-to-back, ACH set to "after event
recorded" dials continuously, and the unit becomes unreachable because its
modem is in client mode almost all of the time.
There are two ways to get a Stop Monitoring command into it.
| | **A — intercept the call** (preferred) | **B — catch it between calls** (original) |
|---|---|---|
| Idea | Be the server it dials. Point the modem's Destination at our own ACH server and answer it. | Clear the Destination so it stops dialing, then race a Stop into the gap. |
| Needs inbound? | **No — the unit calls us** | Yes: working inbound TCP to the modem |
| Determinism | Deterministic — it dials every ~75 s, we only have to be listening | A race. BE9558H took ~7 h of attempts before one landed. |
| Tool | `bridges/ach_server.py --stop-monitoring` | `scripts/slow_drip.sh` |
| Proven on | BE12599, 2026-09-16 | BE9558H, 2026-05-17 |
**Method A is the standard procedure now.** The unit won't answer us because
it is on the phone — so stop dialing it and be the one it calls. It rings,
we pick up, take its data, and tell it to stop calling here.
Method B is kept because it is proven, and because A needs a listener the
modem can actually reach (public IP + forwarded port). When you have that,
don't race it — intercept it.
---
## Symptoms
- Terra-View / SFM `/device/info` either hangs or fails on `count_events()`.
- `/device/monitor/status` and `/device/rescue` return 502 (protocol timeout
waiting for POLL response) or 503 (TCP connect refused).
- ACEmanager serial log shows repeating
`Connect to IP: <BW_IP> Port: <BW_PORT>` → `Shutdown TCP socket` cycles
every 30-60 seconds.
- Spam-mode endpoints (`/device/stop_monitoring_spam`) report many
`sent_ok` but the device's monitoring state never changes.
- `slow_drip` reports `[Errno 32] Broken pipe` after sending the preamble
but before completing the drip loop.
If you see *all* of these, the unit is in this exact failure mode.
---
## Method A (preferred) — intercept the call
You need **ACEmanager access** and a host the modem can dial: public IP with
the listener's port forwarded to it.
### A1 — start the listener BEFORE touching the modem
```bash
cd /home/serversdown/seismo-relay
tmux new -s rescue
.venv/bin/python -u bridges/ach_server.py --port 12345 \
-o bridges/captures/<unit>-diag --stop-monitoring -v
```
⚠ **Listener first, always.** A Destination pointed at a dead port is the
worst state available — the device still dials, the modem still flips to
client mode, inbound stays blocked, and nothing gets delivered.
Do **not** add `--events-only` (it silently breaks dedup — see gotchas), and
do **not** add `--disable-ach` yet (see A4).
### A2 — point the modem at it
ACEmanager → **Serial → Port Configuration**:
| Field | Set to |
|---|---|
| **Destination Address** | the listener's public IP |
| **Destination Port** | the listener's port (e.g. `12345`) |
Apply. The modem auto-dials its Destination whenever serial data arrives
while the serial port is closed — so the unit's own retry cycle now lands on
you instead of nowhere.
### A3 — answer, and stop the bleeding
Within ~75 s you should see a call-in. `--stop-monitoring` fires SUB 0x97 at
step 1.5 — after the handshake, **before** the event walk — so the recording
halts at the earliest possible moment in the session. Confirm via
`rescue.json` in the session directory:
```json
{"peer": "166.246.64.226:60921", "stop_monitoring": "ok"}
```
That is the bleeding stopped. Everything after this is cleanup.
### A4 — drain the backlog, THEN disable ACH
⚠ **Order matters, and it is counter-intuitive.** Stopping monitoring also
removes your call-in trigger: ACH fires on "after event recorded", so with
recording stopped the unit has no reason to dial again. The backlog sitting
in its memory does **not** re-arm it.
So if the stored events are worth keeping — and on a fault unit they usually
are, they're the evidence — drain them across however many call-ins it takes
*before* you silence it. Only then add `--disable-ach` (or use
`scripts/rescue_device.sh <host> <port> --no-erase`).
If the unit has gone quiet and you still need it, cycling the modem produces
a call-in, and a unit with a scheduled daily call will dial at its configured
time regardless.
### A5 — restore the Destination, and confirm you did
Put `Destination Address` back to `0.0.0.0` (or the office Instantel ACH
server) once you are finished, and only stop the listener after that is done.
### A6 — do NOT re-enable ACH until the hardware fault is repaired
Otherwise the loop restarts the moment monitoring resumes and you run this
runbook again.
---
## Method B (fallback) — catch it between calls
The original 2026-05 procedure. Use when you cannot stand up a listener the
modem can reach. You need **ACEmanager access** to the unit's modem.
### Step 1: stop the modem's mode-flipping
In ACEmanager → **Serial → Port Configuration**:
| Field | Set to |
|---|---|
| **Destination Address** | clear (blank) |
| **Destination Port** | `0` |
Click **Apply**. This removes the modem's auto-dial-out target. The device's
AT dial commands now error back at the modem instead of triggering a
mode-flip, so the modem stays in TCP-server mode permanently and our inbound
TCP sessions stay alive.
*(Optional belt-and-suspenders: also add the BW server's port to
**Security → Port Filtering - Outbound** as a blocked port, with
Outbound Port Filtering Mode = Blocked Ports.)*
### Step 2: stop monitoring on the device (slow drip)
From the SFM host:
```bash
/home/serversdown/seismo-relay/scripts/slow_drip.sh <DEVICE_IP> <PORT>
```
Defaults are 120s duration with a drip every 3s. Watch the response:
- `duration_s ≈ 120` and `drips_sent ≈ 40` → session held the full duration ✓
- `bytes_received > 0` → device is responding ✓ (this is the success signal)
If `duration_s` is small or `send_error: "Broken pipe"`, Step 1 didn't take
hold — re-check ACEmanager, may need to reboot the modem after Apply.
### Step 3: confirm monitoring stopped
```bash
curl 'http://localhost:8200/device/monitor/status?host=<DEVICE_IP>&tcp_port=<PORT>&force=true'
# expect: {"is_monitoring": false, ...}
```
### Step 4: disable ACH at the device level + erase corrupted events
Either fire the rescue endpoint:
```bash
/home/serversdown/seismo-relay/scripts/rescue_device.sh <DEVICE_IP> <PORT>
```
Or do the two steps manually:
```bash
# Disable ACH in the device's compliance config
curl -X POST 'http://localhost:8200/device/call_home?host=<DEVICE_IP>&tcp_port=<PORT>' \
-H 'Content-Type: application/json' \
-d '{"auto_call_home_enabled": false}'
# Erase corrupted event chain
curl -X POST 'http://localhost:8200/device/events/erase?host=<DEVICE_IP>&tcp_port=<PORT>'
```
You can also do this via the SFM standalone UI → **Call Home** tab → set
`Enable Auto Call Home` to `Disabled` → **Write to Device**.
### Step 5: restore modem config (housekeeping)
Once the device-side ACH is disabled, restore the modem's Destination
Address and Port to the original values (e.g. `50.197.32.92` / `12345`) in
ACEmanager. The modem will resume normal bidirectional behavior, but the
unit won't issue any dial commands until ACH is explicitly re-enabled on
the device.
### Step 6: do NOT re-enable ACH on this unit until the underlying hardware
fault is repaired. If you do, the call-home loop starts again immediately
and you'll be running this runbook a second time.
---
## Why this works — the failure mode explained
The Sierra Wireless RV50/RV55 serial port operates in one of two TCP modes
at any moment:
- **Server mode** — listens on `Device Port` (e.g. 9034), bridges inbound
TCP to the device's serial port. This is what we need to interact with
the device.
- **Client mode** — when the device sends an AT dial command on its serial
TX line, the modem opens an outbound TCP to `Destination Address:Port`
and bridges that to serial.
A serial port in this configuration is **bidirectional**: the modem flips
between server and client modes on demand. When the device's firmware is
healthy and only dials occasionally, this works fine.
When the unit is constantly triggering events and ACH is set to "After
Event Recorded", the device sends an AT dial command every few seconds.
Each one causes the modem to:
1. Drop any active inbound TCP session
2. Flip to client mode
3. Attempt outbound TCP to `Destination Address:Port`
4. Hang for up to a minute waiting for it to succeed/fail
5. Drop back to server mode
**During the entire hang, no inbound TCP can establish.** Even between
hangs, the modem closes any existing inbound session before flipping. So
any tool that needs more than a few seconds of held TCP (e.g. POLL +
config read + write) gets repeatedly kicked off.
Clearing `Destination Address` removes step 3-4 from the cycle: the modem
has nowhere to dial, so it doesn't flip modes when it receives an AT dial
command. The serial port effectively becomes server-only, and inbound TCP
sessions can stay open as long as needed.
**This is a modem-layer issue, not a device firmware issue.** The device
is alive and responsive the whole time — confirmed in the BE9558H
recovery by 990 bytes of S3 responses received over a 120s slow-drip
session once the modem was no longer mode-flipping.
---
## Why simpler approaches don't work
| Approach | Why it fails |
|---|---|
| Standard `/device/info` | Triggers `count_events()` 1E/1F walk, takes 90s+ and hits corrupted event chain in this scenario |
| `/device/rescue` race loop | Gets 502 (protocol timeout) because the modem closes the TCP before the POLL handshake can complete |
| `/device/stop_monitoring_blind` (single frame) | Even if the bytes leave the wire, the device's protocol parser ignores write commands without a preceding POLL handshake (early-version bug, now fixed by including POLL preamble in blind sends) |
| `/device/stop_monitoring_spam` (sub-second cadence) | Each session is killed by the modem's mode-flip before the device can drain its UART RX buffer; high-rate spam also risks UART FIFO overrun on the device side |
| Outbound port firewall block alone | Stops the outbound TCP from succeeding, but doesn't stop the modem from *trying* and mode-flipping. Reduces but doesn't eliminate the contention. |
| Modem reboot | Temporary — as soon as the device starts triggering again, the loop resumes within seconds |
The combination of `slow_drip` + cleared `Destination Address` works because:
1. The modem stops mode-flipping → TCP session stays open for the full
drip duration
2. Slow drip rate → device's UART RX FIFO never overflows even if
firmware is busy with event recording
3. The drip is `SESSION_RESET + STOP_MONITORING` every 3s → many
independent chances for the parser to land one valid frame
4. Once one Stop Monitoring is parsed, event recording halts → firmware
has CPU to spare → subsequent operations are trivially easy
---
## Tooling reference
All endpoints live in `seismo-relay/sfm/server.py`. All scripts live in
`seismo-relay/scripts/` and default to SFM direct (`http://localhost:8200`),
overridable via `SFM_BASE_URL`.
### Endpoints added during BE9558H recovery
| Endpoint | Purpose |
|---|---|
| `GET /device/events/storage_range` | SUB 0x06 — first/last event keys, `is_empty` flag. ~2s, no event walk. |
| `GET /device/events/index` | SUB 0x08 — lifetime event counter (does NOT decrement on erase). ~2s. |
| `POST /device/events/erase` | Full erase sequence 0xA3 → 0x1C → 0x06 → 0xA2. |
| `POST /device/rescue` | Disable ACH + erase in one TCP session. Short timeouts for race-loop usage. |
| `POST /device/stop_monitoring_blind` | Fire-and-forget Stop with full POLL preamble (single attempt). |
| `POST /device/stop_monitoring_spam` | Server-side tight retry loop, sub-second cadence, duration-bounded. |
| `POST /device/stop_monitoring_slow_drip` | One held TCP session, slow trickle of stop frames. **The endpoint that saved BE9558H.** |
Also changed: default protocol recv timeout dropped from 30s → 10s in
`_build_client`. Added `connect_timeout` knob to same. Cleaned up
unhandled-exception path in `/device/monitor/status` so it returns 502
instead of 500 on protocol timeouts.
### Scripts
| Script | Purpose |
|---|---|
| `scripts/rescue_device.sh` | Race-loop wrapper around `/device/rescue` |
| `scripts/blind_stop.sh` | Race-loop wrapper around `/device/stop_monitoring_blind` |
| `scripts/spam_stop.sh` | Single-call burst hammer (`/device/stop_monitoring_spam`) |
| `scripts/slow_drip.sh` | Single-call held-session drip (`/device/stop_monitoring_slow_drip`) |
| `scripts/watch_unit.sh` | Passive periodic reachability check, logs to file |
---
## Incident log — BE9558H, 2026-05-16/17
What was wrong: Long-axis geophone developed an offset, constantly above
trigger threshold → constant event recording → after-event ACH set →
modem dialing office BW server (`50.197.32.92:12345`) every 30-60s.
Local event chain corrupted (`next_boundary 0x100EE exceeds uint16`).
Diagnostic path:
1. `/device/info` slow, choked on event walk
2. Built lightweight probe endpoints (`storage_range`, `index`) — useful
but didn't reach the wedged unit
3. Built `/device/rescue` with short timeouts — got 502 (POLL no response)
4. Built `/device/stop_monitoring_blind` — first version was a false
positive (no POLL preamble); fixed by including
`SESSION_RESET+POLL_PROBE+SESSION_RESET+POLL_DATA` in the dump
5. Verified blind stop works on bench unit
6. Built `/device/stop_monitoring_spam` — 420 successful sends over
5 min, zero behavior change on field unit
7. Inspected ACEmanager logs → saw outbound dial-out attempts every ~30s,
confirmed device was not fully locked up
8. Added outbound port-12345 firewall block → outbound attempts now fail
instantly but contention persisted
9. Built `/device/stop_monitoring_slow_drip` — session died at 3s with
broken pipe (modem closing on us)
10. Looked at full ACEmanager Port Configuration → **found
`Destination Address: 50.197.32.92` configured**, realized every AT
dial command was triggering a modem mode-flip that killed our inbound
11. Cleared Destination Address + Port → slow_drip held 120s, device
responded with 990 bytes, 39 stop commands acked
12. Disabled ACH at device level via `/device/call_home`, erased events
Final state: device IDLE, memory 958.1 / 960 KB free, ACH disabled at
device level, modem destination cleared (to be restored after physical
service).
Total time from "i was wondering if its possible to" first attempt to
recovery: ~7 hours of intermittent debugging across one evening.
---
# Second incident — BE12599, 2026-09-16/17
**Unit:** BE12599 at `166.246.64.226:9034`, RV50, job *I-80 North Fork Bridge
— Abut 1 West* (Fay Company). Same job as BE9558H, which is a coincidence.
**Fault:** the connector fault documented in `docs/offset_investigation.md`
§8e progressed until the Tran pedestal reached **0.400 in/s** — its trigger
level. Constant triggering → constant recording → ACH "after event recorded"
→ continuous dialing. Same disease as BE9558H.
**Same disease, inverted cure.** Method B's Step 1 *did* work — clearing the
Destination stopped the dial-outs, confirmed in the ALEOS log. It was Step 2
that didn't land, and rather than keep racing we turned the rescue around:
gave the unit a different server to call, and answered it.
Total time ≈ 5 h, of which ~90 min went to two red herrings documented below.
Much of the rest was rediscovering the May procedure, which is why the
"two cures" table now sits at the top of this file.
---
## Turn on ALEOS_SERIAL debug FIRST
This is the single highest-value diagnostic and it should be step zero on any
future incident. ACEmanager → **Admin → Log → ALEOS_SERIAL log level →
DEBUG**, then view the serial log.
It is the only thing that tells you what the *device* is actually saying.
Everything before we did this was guesswork.
## What the log showed — the unit is on the phone
Every ~75 seconds, verbatim:
```
ALEOS_SERIAL_HIF: 29 byte(s) in buffer: 'ATQ1^MATE0^MATS0=2^M^MRADIO RING^M'
ALEOS_SERIAL_HMC: TCP recvhost fd 65535 len 29 state TCPMode::kClosed
ALEOS_SERIAL_HMC: tcpmode trying to send to invalid socket
ALEOS_SERIAL_HMC: Connect to IP: 0.0.0.0 Port 0
ALEOS_SERIAL_HMC: Initialize Auto answer on port 9034
ALEOS_SERIAL_HMC: Cannot connect to 0.0.0.0
```
Read that carefully:
- `ATQ1` (quiet) / `ATE0` (echo off) / `ATS0=2` (auto-answer after 2 rings).
**There is no `ATD`.** The device is not dialing — it is trying to
*configure* its modem.
- The modem's serial port is in TCP data mode, so it never interprets these
as AT commands. It treats them as payload and tries to ship them to a TCP
socket that does not exist.
- The device therefore never receives `OK`, never progresses, and **retries
the identical 29 bytes forever**.
**While it is in this state it is busy placing a call, not listening for
us.** This is almost certainly what BE9558H was doing too — we simply never
turned on ALEOS_SERIAL debug in May to look. It is not a different disease;
it is the same one, seen properly for the first time.
It is also the argument for Method A in one picture: the unit is mid-dial
every ~75 s, and our inbound Stop has to thread the gaps between those
attempts. Give it somewhere to dial and the problem inverts into a
deterministic one.
### Why `slow_drip` lied
`slow_drip` returned the *success* signature except for the one field that
mattered:
```json
{"duration_s":120.0,"drips_sent":38,"bytes_sent":920,
"bytes_received":0,"send_error":null}
```
Full duration, no broken pipe — but zero bytes back. Cause is in the log
above: each 75 s cycle re-runs `Initialize Auto answer on port 9034`, which
orphans the held session (`data in for unknown reason 3 removing from
select`, `OnMsg recv error: 107 - Transport endpoint is not connected`). Our
local TCP stayed open so `sendall` never raised — but the modem stopped
bridging after the first re-init, so every drip after that went into a socket
nobody was reading.
⚠ **`send_error: null` + full duration is NOT success. Only
`bytes_received > 0` is success.**
⚠ **In fairness to slow_drip: it got exactly one attempt here**, run ~90 s
after a modem reboot, with a dead session visible in the log at 20:19:17 in
that same window. BE9558H took hours of attempts before one landed. Method B
was not ruled out on BE12599 so much as abandoned in favour of something that
doesn't need luck.
---
## ⚠ Two red herrings that cost ~90 minutes
### 1. The trusted-IP whitelist (this was the real reason inbound never worked)
The RV50s run with **Security → Trusted IPs (Friends List) enabled**. A
source IP that is not on the list is dropped **silently** — inbound presents
as `Connection error: timed out`, never a refusal.
Brian's dev-box public IP is **dynamic** and had changed, so `tmi-dev` was no
longer whitelisted. Every inbound attempt failed identically across four
different modem and device states, which looked exactly like the BE9558H
mode-flipping symptom and sent us chasing modem configuration for over an
hour.
**Check this before diagnosing anything else.** Note that SFM in Docker
egresses via the *host's public IP*, not its LAN IP.
### 2. A 502 from SFM does not mean TCP connected
`sfm/server.py` raises **502 for both** failure classes:
```python
raise HTTPException(status_code=502, detail=f"Protocol error: {exc}")
raise HTTPException(status_code=502, detail=f"Connection error: {exc}")
```
We read an early 502 as "TCP connected, modem bridged, device mute" and built
a whole theory on it. It was almost certainly a connect timeout.
**Always read the `detail` string** — "connect failed" and "device didn't
answer" are completely different problems and the status code will not
separate them.
---
## What actually worked — invert the direction
The key observation is in the log above:
> `TCP recvhost ... state TCPMode::kClosed` → `Connect to IP: 0.0.0.0 Port 0`
**The modem auto-dials its Destination whenever serial data arrives while
closed.** So instead of fighting for inbound, give it somewhere to dial:
point `Destination Address` at our own `ach_server` and the device's own
75-second attempts become **device-initiated sessions the modem bridges
correctly**. No race, no contention, worst case a 75-second wait.
### Procedure
1. **Run the rescue server** on a host the modem can reach (public IP +
forwarded port):
```bash
cd /home/serversdown/seismo-relay
.venv/bin/python -u bridges/ach_server.py --port 12345 \
-o bridges/captures/<unit>-diag --stop-monitoring -v
```
2. **Point the modem at it** — ACEmanager → Serial → Port Configuration →
`Destination Address` = your public IP, `Destination Port` = 12345.
3. **Wait for the call-in.** `--stop-monitoring` fires SUB 0x97 at step 1.5,
after the handshake and *before* the event walk. Confirm via
`rescue.json` in the session directory:
```json
{"peer": "166.246.64.226:60921", "stop_monitoring": "ok"}
```
4. **Restore the modem's Destination** once you are done, then finish the
device side (disable ACH, erase) through whichever channel works.
On BE12599 the first call-in landed at 20:58:11 and reported
`stop_monitoring: ok`; a second at 20:58:20 confirmed it. `is_monitoring:
false` was still true **6½ hours later** — the fix is durable.
---
## Hard-won gotchas (do not re-derive)
- **Never leave the Destination pointed at a host with nothing listening.**
That is the worst state available: the device still dials, the modem still
flips, inbound stays blocked, and nothing is delivered. An 8-minute gap
with the listener down produced a spurious inbound timeout that cost
another round of misdiagnosis.
- **Stopping monitoring removes your call-in channel.** ACH is "after event
recorded"; no new events means no new dials. The backlog sitting in memory
does *not* re-arm it. After a successful stop the unit goes quiet and you
need the modem cycled (works — produced a call-in), the scheduled daily call
(BE12599 calls at **05:00:14 device-local**, per §8e), or working inbound.
**Plan the order before you fire the stop.**
- **`--events-only` silently breaks dedup.** It skips the device-info step,
so the serial is never read; `ach_state.json` then keys on
`peer:ephemeral_port`, which is unique per connection. Every session looks
like a new unit, starts from key 0, and re-downloads the same event. Four
sessions on BE12599 downloaded the identical event four times and made zero
progress on the backlog. Events also file as `serial=UNKNOWN` with a
`M000…` BW filename (serial_numeric 0) instead of `N599…`.
**Do not use `--events-only` when you intend to download anything.**
- **`/device/events/index` reported `lifetime_count: 0`** on a unit with years
of history. Suspected decode bug in the SUB 0x08 field offset — do not
trust that number. The 88-byte payload is preserved in the `raw_hex` field
if someone wants to chase it.
- **Memory used cross-checks the event keys exactly:**
`last_key − buffer_start = memory_total − memory_free`. On BE12599:
`0x011230ec − 0x01110000 = 78,060` and `983,028 − 904,968 = 78,060`.
Useful sanity check that you are reading the keys right.
---
## Final state (2026-09-17 ~01:30 local)
- `is_monitoring: false`, held 6½ hours
- Battery 6.76 V
- Memory 78,060 / 983,028 bytes used (8%)
- `first_key 01121728`, `last_key 011230ec` — ~6.6 KB of addressable event
chain, roughly 3 events
- ACH still **enabled** — to be disabled after the backlog is preserved
- Modem Destination still pointed at tmi-dev — to be restored
- ⚠ **Do not re-enable ACH until the connector is serviced.** Tran is still
sitting at 0.400 and the loop restarts the moment monitoring resumes.
docs/sfm_tool_status.md
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# SFM — where it actually stands as a tool
**Status as of 2026-09-20 (v0.31.0).** This is the honest assessment, not the
roadmap — `README.md § Roadmap` covers where it is *going*. Expect this file to
go stale; re-date it when you revise it.
---
## The framing
SFM is **three different things wearing one name**, at three very different
levels of maturity:
| | what it is | maturity |
|---|---|---|
| **The codec library** | `minimateplus/`, `micromate/` — bytes in, `Event` out | **Production.** Verified per-sample at scale. |
| **SDM — the data side** | the DB, waveform store, `/db/*`, ingest | **Production.** Terra-View depends on it daily. |
| **SFM — the device side** | `/device/*`, live connections to units | **Emergency-grade.** Works, but manual, unauthenticated, and thinly tested. |
| **The lab** | `seismo_lab.py`, `scratch/`, the Inspector | **Research artifacts.** Useful, not products. |
Brian's own description — *"right now it's an emergency tool and a research
project"* — is accurate, and it applies specifically to the **device side**.
The data side is not an emergency tool; it has been carrying production for
months.
Most confusion about "is SFM reliable?" comes from answering for the wrong
tier.
---
## 1. What you can rely on
### Production-grade — trust it
- **Series-3 decode.** 14,338 / 14,338 files decode per-sample exact against
preserved Blastware ASCII exports, 45 units, files back to 2018.
- **Series-4 (Thor) decode.** 1,057,536 / 1,057,536 geo samples exact against
Thor's own CSV exports; production IDFW 575/575 with zero truncations.
- **Histogram decode.** 1,211 / 1,211 production histograms exact, including
842,442 per-interval frequency comparisons with zero mismatches.
- **The ingest path.** `/db/import/blastware_file` and `/db/import/idf_file`
fed by the watchers — this is how prod actually gets its data, and it has
been running unattended for months.
- **`/db/*` read API.** Always-on, consumed by Terra-View for every fleet
listing, event detail and report.
- **The waveform store** — `.h5` + `.sfm.json` sidecars + retained raw
binaries, with operator review state preserved across regeneration.
- **`bridges/ach_server.py`** — speaks the full BW protocol to calling units.
Proven in the field, including as a rescue tool (see the runbook).
### Emergency-grade — works, but you are the error handling
- **`/device/*` live endpoints.** They do what they say. But they are
synchronous, unauthenticated, and a single cellular download can exceed the
60 s timeouts that sit in front of them.
- **The rescue ladder** (`rescue`, `stop_monitoring_*`, `events/erase`).
Each has worked in a real incident — but each has been used a handful of
times, by one person, with the runbook open.
- **The standalone webapp.** Perfectly usable, and as of v0.31.0 the cheap
probes and rescue actions are reachable without curl. No auth of any kind.
### Research artifacts — useful, not products
- **`seismo_lab.py`** — 2,789 lines of Tkinter (Bridge / Analyzer / Query DB /
Inspector). Desktop-only, single-user, no tests.
- **`scratch/`** — the verification harnesses (`verify_against_ascii.py`,
`verify_thor_against_csv.py`) and the offset detector (`offset_scan3.py`).
These produced the numbers the production claims rest on, so they matter —
but they are analysis scripts, not maintained code.
- **`docs/offset_investigation.md`** — an open investigation, not a feature.
---
## 2. What to use when
| you want to… | use | notes |
|---|---|---|
| Know if a unit is monitoring / its battery / memory | `GET /device/monitor/status?force=true` | ~2 s |
| Know whether ACH is on | `GET /device/call_home` | ~2 s. **Not** `/device/events`. |
| See how full a unit's buffer is | `GET /device/events/storage_range` | ~2 s, no chain walk |
| Stop a runaway unit | Diagnostics tab → Stop Monitoring | see the runbook first |
| Reach a unit that will not answer | **point its modem at an `ach_server` and answer its call** | runbook Method A — do not race it |
| List a unit's stored events | Events tab → Load events | **slow**, and broken past 64 KB (below) |
| Get event data into the DB | the watcher → `/db/import/*` path | not the live walk |
The single most useful habit: **the cheap probes are cheap and the event walk
is not.** Reaching for `/device/events` to answer a yes/no question about a
unit is the mistake that motivated the v0.31.0 webapp changes.
---
## 3. Known issues
| issue | impact | status |
|---|---|---|
| **5A walk dies once a unit's buffer crosses 64 KB** | `/device/events` 500s; event body never downloads | Known, documented in `CLAUDE.md`. Needs a BW capture of a spanning event to fix properly. |
| **No auth on SFM at all** | 21 `/device/*` endpoints, including destructive ones, open to anything that reaches the port | Design agreed (Terra-View as authenticated jump host); not built. |
| **Swagger try-it-out is live on destructive endpoints** | `POST /device/events/erase` is one click away at `:8200/docs` | Partially mitigated: the webapp's erase now requires typing the serial. `/docs` itself is unguarded. |
| **`SUB 0x08` lifetime counter reads 0** | `/device/events/index` returns a meaningless number | Suspected field-offset bug. Surfaced in the UI as "unreliable". |
| **Long device operations are synchronous** | 60 s timeouts in `routers/sfm.py` and the reverse proxy; a full download exceeds both | Known design constraint. Must be POST-starts-job / GET-polls before any remote lab. |
| **`backfill_sidecars.py --force` silently inserts DB rows** | store files with no DB row get one; the dry-run does not report the count | Known. Avoid `--force` — `TOOL_VERSION` gates regeneration anyway. |
| **14 sensitive-range files show an exact 8× discrepancy** | 10.0 / 1.25 — a units problem, not a decode problem | Open, not blocking. |
| **16 failing tests on `dev`** | 15 need gitignored fixture bundles; 1 is real (`sc["peak_values"]["transverse"]` returns `None` where `0.0` is expected) | The real one shipped in v0.31.0. |
---
## 4. What stands between this and a real tool
Roughly in dependency order — each unblocks the ones below it.
**1. Authentication.** Everything else is gated on this. SFM has none, and
the modem IP whitelist gives zero protection because SFM *is* the whitelisted
origin. The agreed design delegates rather than builds: Terra-View becomes the
authenticated jump host (`/api/sfm/*` already inherits deny-by-default operator
auth), and the `8200:8200` publish is dropped so Terra-View is the only door.
**2. Async long operations.** POST starts a job, GET polls. Retrofitting this
after building a remote lab on top of synchronous endpoints would be far worse
than designing for it now.
**3. Confirm-guards on the remaining destructive endpoints.** Auth answers
*who*, not *did you mean it*. The webapp's erase is guarded; the other seven
destructive POSTs and `/docs` are not.
**4. The 5A page-boundary fix.** Until this lands, live event download is
unreliable on exactly the units most likely to need attention — the ones that
have been recording heavily. Wants a Blastware capture of an event spanning a
page boundary before the chunk-addressing half is trustworthy.
**5. A live Thor / Micromate client.** The device side is MiniMate-only.
Series-4 units can only be read from forwarded files, so half the fleet has no
live path at all.
**6. Test coverage that runs from a clean checkout.** 15 of 16 current
failures are missing fixture bundles. A test suite that cannot go green on a
fresh clone cannot gate anything.
**7. The SDM rename.** Cosmetic relative to the above, but the longer `sfm/`
holds the data-side code the more the tiers blur. ~30–50 files here, ~10–15 in
Terra-View, plus a Docker volume migration. Do it when the codebase is quiet.
---
## The short version
The **data side is a real tool already**. The **device side is a set of sharp
instruments** that work in the hands of the person who wrote them, with the
runbook open. The gap between those two states is mostly **auth, async, and
guardrails** — not protocol work. The protocol is the part that is actually
finished.
docs/superpowers/plans/2026-08-21-waveform-ft-detection-phase-a.md
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# Waveform-shape FT detection — Phase A (seismo-relay) Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Compute per-event waveform-shape metrics (crest factor + points-near-peak) in SFM and store them as `events` columns, populated at ingest and by a backfill script, so Terra-View can read them from `/db/events`.
**Architecture:** A pure DSP module (`sfm/shape_metrics.py`) turns decoded `.h5` samples into shape metrics. New nullable `events.shape_*` columns are added via the existing `_migrate` ADD COLUMN loop. The three `WaveformStore.save*` paths compute shape from the just-written `.h5` and hand it to `insert_events`; a backfill script does the same over existing events. Mirrors exactly how per-channel ZC frequency was added.
**Tech Stack:** Python 3.10, numpy, h5py, sqlite3 (raw), pytest. seismo-relay venv: `/home/serversdown/seismo-relay/.venv/bin/python3`.
## Global Constraints
- Metrics are read from the `.h5` `samples/{Tran,Vert,Long}` float32 arrays (physical in/s). The measured channel is the max-|peak| geophone channel.
- All new columns are nullable; histogram records and events without usable samples store NULL (Terra-View falls back to cheap signals). Legacy rows stay valid.
- Crest factor = `max(|x|) / rms(x)`; near-peak count = number of samples with `|x| ≥ 0.5·peak`. Threshold `0.5` is a module constant so calibration can tune it.
- No manual migration: columns are added in `SeismoDb._migrate`, run at `SeismoDb()` construction.
- `/db/events` needs no change — it returns all columns via `SELECT *` (verify with a test).
- Run tests with `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest`.
---
### Task 1: Shape DSP module
**Files:**
- Create: `sfm/shape_metrics.py`
- Test: `tests/test_shape_metrics.py`
**Interfaces:**
- Produces:
- `channel_shape(x) -> dict | None` — `{"crest_factor": float, "near_peak_count": int, "sample_count": int}` or None for unusable input (size < 2, flat, all-zero).
- `shape_from_samples(chans: dict[str, ArrayLike]) -> dict | None` — picks the max-peak geophone channel; returns `{"crest_factor","near_peak_count","sample_count","axis"}` or None.
- `shape_from_h5(path) -> dict | None` — reads `samples/{Tran,Vert,Long}` and delegates to `shape_from_samples`; None on any read error.
- Constant `NEAR_PEAK_FRACTION = 0.5`.
- [ ] **Step 1: Write the failing test**
```python
# tests/test_shape_metrics.py
import numpy as np
from sfm.shape_metrics import channel_shape, shape_from_samples
def test_needle_spike_high_crest_few_near_peak():
x = np.zeros(1024); x[500] = 1.0 # one isolated spike
s = channel_shape(x)
assert s["sample_count"] == 1024
assert s["crest_factor"] > 15 # peak towers over rms
assert s["near_peak_count"] <= 3 # almost nothing near the peak
def test_ringing_low_crest_many_near_peak():
t = np.arange(1024)
x = np.sin(2*np.pi*t/32) * np.exp(-t/4000) # decaying oscillation
s = channel_shape(x)
assert s["crest_factor"] < 6
assert s["near_peak_count"] > 30 # many samples near the peak
def test_channel_shape_none_for_unusable():
assert channel_shape(np.zeros(1024)) is None # flat / all-zero
assert channel_shape(np.array([1.0])) is None # too short
def test_shape_from_samples_picks_max_peak_axis():
chans = {"Tran": np.zeros(1024), "Vert": np.zeros(1024), "Long": np.zeros(1024)}
chans["Long"][10] = 0.5
chans["Vert"] = np.sin(np.arange(1024)/5) * 0.01
s = shape_from_samples(chans)
assert s["axis"] == "Long" # Long has the biggest peak
assert s["near_peak_count"] <= 3
def test_shape_from_samples_none_when_no_geo():
assert shape_from_samples({"MicL": np.ones(1024)}) is None
```
- [ ] **Step 2: Run test to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_metrics.py -q`
Expected: FAIL — `ModuleNotFoundError: sfm.shape_metrics`.
- [ ] **Step 3: Write minimal implementation**
```python
# sfm/shape_metrics.py
"""Waveform-shape metrics for false-trigger detection.
A false trigger is an isolated impulse (quiet → spike → quiet); a real event
rings for many cycles. Two numbers separate them: crest factor (how far the
peak stands above the typical sample) and how many samples sit near the peak.
"""
from __future__ import annotations
import numpy as np
_GEO_CHANNELS = ("Tran", "Vert", "Long")
NEAR_PEAK_FRACTION = 0.5 # a sample "near the peak" is >= this * peak amplitude
def channel_shape(x) -> dict | None:
x = np.asarray(x, dtype=float)
if x.size < 2:
return None
peak = float(np.max(np.abs(x)))
if peak <= 0:
return None
rms = float(np.sqrt(np.mean(x ** 2)))
if rms <= 0:
return None
near = int(np.sum(np.abs(x) >= NEAR_PEAK_FRACTION * peak))
return {"crest_factor": peak / rms, "near_peak_count": near,
"sample_count": int(x.size)}
def shape_from_samples(chans: dict) -> dict | None:
best_axis, best_peak, best_x = None, -1.0, None
for ax in _GEO_CHANNELS:
x = chans.get(ax)
if x is None:
continue
x = np.asarray(x, dtype=float)
if x.size < 2:
continue
p = float(np.max(np.abs(x)))
if p > best_peak:
best_axis, best_peak, best_x = ax, p, x
if best_axis is None:
return None
s = channel_shape(best_x)
if s is None:
return None
s["axis"] = best_axis
return s
def shape_from_h5(path) -> dict | None:
import h5py
try:
with h5py.File(path, "r") as f:
chans = {ax: f[f"samples/{ax}"][:] for ax in _GEO_CHANNELS
if f"samples/{ax}" in f}
except Exception:
return None
return shape_from_samples(chans)
```
- [ ] **Step 4: Run test to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_metrics.py -q`
Expected: PASS (5 tests).
- [ ] **Step 5: Commit**
```bash
git add sfm/shape_metrics.py tests/test_shape_metrics.py
git commit -m "feat(shape): crest-factor + points-near-peak waveform metrics"
```
---
### Task 2: shape_from_h5 round-trips a real .h5
**Files:**
- Test: `tests/test_shape_metrics_h5.py`
**Interfaces:**
- Consumes: `sfm.shape_metrics.shape_from_h5`; `h5py`.
- [ ] **Step 1: Write the failing test** (writes a tiny .h5 the same shape SFM writes, then reads it back)
```python
# tests/test_shape_metrics_h5.py
import numpy as np, h5py
from sfm.shape_metrics import shape_from_h5
def _write_h5(path, chans):
with h5py.File(path, "w") as f:
g = f.create_group("samples")
for k, v in chans.items():
g.create_dataset(k, data=np.asarray(v, dtype="float32"))
def test_shape_from_h5_reads_dominant_axis(tmp_path):
p = tmp_path / "ev.h5"
long = np.zeros(1024, dtype="float32"); long[100] = 0.48
_write_h5(p, {"Tran": np.zeros(1024), "Vert": np.zeros(1024), "Long": long,
"MicL": np.ones(1024)})
s = shape_from_h5(str(p))
assert s["axis"] == "Long" and s["near_peak_count"] <= 3
def test_shape_from_h5_none_on_missing_or_degenerate(tmp_path):
assert shape_from_h5(str(tmp_path / "nope.h5")) is None
p = tmp_path / "degen.h5"
_write_h5(p, {"Tran": np.zeros(1), "Vert": np.zeros(1), "Long": np.zeros(1)})
assert shape_from_h5(str(p)) is None
```
- [ ] **Step 2: Run to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_metrics_h5.py -q`
Expected: FAIL (assertion or, if Task 1 incomplete, import error).
- [ ] **Step 3: Implementation** — none needed; `shape_from_h5` already exists from Task 1. If a test fails, fix `shape_from_h5` (not the test).
- [ ] **Step 4: Run to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_metrics_h5.py -q`
Expected: PASS (2 tests).
- [ ] **Step 5: Commit**
```bash
git add tests/test_shape_metrics_h5.py
git commit -m "test(shape): shape_from_h5 round-trips a real .h5"
```
---
### Task 3: Add shape columns to the events schema + migration
**Files:**
- Modify: `sfm/database.py` — `_SCHEMA` CREATE TABLE `events` (after `mic_zc_above_range`); `_migrate` ADD COLUMN loop (the tuple around line 205-218).
- Test: `tests/test_shape_columns.py`
**Interfaces:**
- Produces: `events` columns `shape_crest_factor REAL`, `shape_near_peak_count INTEGER`, `shape_sample_count INTEGER`, `shape_axis TEXT`.
- [ ] **Step 1: Write the failing test**
```python
# tests/test_shape_columns.py
import sqlite3
from sfm.database import SeismoDb
_SHAPE_COLS = {"shape_crest_factor", "shape_near_peak_count",
"shape_sample_count", "shape_axis"}
def _cols(db):
with sqlite3.connect(db.db_path) as c:
return {r[1] for r in c.execute("PRAGMA table_info(events)")}
def test_fresh_db_has_shape_columns(tmp_path):
db = SeismoDb(tmp_path / "s.db")
assert _SHAPE_COLS <= _cols(db)
def test_existing_db_migrates_shape_columns(tmp_path):
p = tmp_path / "s.db"
db = SeismoDb(p)
with sqlite3.connect(p) as c: # simulate an older DB missing the columns
for col in _SHAPE_COLS:
c.execute(f"ALTER TABLE events DROP COLUMN {col}")
assert not (_SHAPE_COLS <= _cols(SeismoDb(p))) # sanity: dropped
SeismoDb(p) # re-open triggers _migrate
assert _SHAPE_COLS <= _cols(SeismoDb(p))
```
> Note: sqlite `DROP COLUMN` needs sqlite ≥ 3.35 (bundled py3.10 has it). If the runner's sqlite lacks it, replace the "simulate older DB" block with building a table without the columns; keep the assertion that re-open adds them.
- [ ] **Step 2: Run to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_columns.py -q`
Expected: FAIL — columns absent.
- [ ] **Step 3: Implementation**
In `_SCHEMA`, after the `mic_zc_above_range INTEGER,` line in the `events` CREATE TABLE, add:
```
shape_crest_factor REAL, -- peak / rms of the triggering channel
shape_near_peak_count INTEGER, -- samples >= 0.5 * peak (FT: few; real: many)
shape_sample_count INTEGER, -- total samples (to normalize near_peak_count)
shape_axis TEXT, -- geophone channel measured ("Tran"/"Vert"/"Long")
```
In `_migrate`, extend the ADD COLUMN tuple (the `for col, ddl in (...)` list) with:
```python
("shape_crest_factor", "REAL"),
("shape_near_peak_count", "INTEGER"),
("shape_sample_count", "INTEGER"),
("shape_axis", "TEXT"),
```
- [ ] **Step 4: Run to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_shape_columns.py -q`
Expected: PASS.
- [ ] **Step 5: Commit**
```bash
git add sfm/database.py tests/test_shape_columns.py
git commit -m "feat(db): shape_* columns on events (+ auto-migrate)"
```
---
### Task 4: insert_events persists shape from the waveform record
**Files:**
- Modify: `sfm/database.py` — `insert_events` INSERT (column list + placeholders + values) and the UPSERT `UPDATE` block.
- Test: `tests/test_insert_events_shape.py`
**Interfaces:**
- Consumes: a `waveform_records` rec dict that may carry `shape_crest_factor`, `shape_near_peak_count`, `shape_sample_count`, `shape_axis`.
- Produces: those four values stored on the row; refreshed on UPSERT.
- [ ] **Step 1: Write the failing test**
```python
# tests/test_insert_events_shape.py
from sfm.database import SeismoDb
from tests.helpers_events import make_event # existing helper used by other insert tests
def test_insert_stores_shape_from_record(tmp_path):
db = SeismoDb(tmp_path / "s.db")
ev = make_event(serial="BE1", key="0111abcd")
rec = {ev._waveform_key.hex(): {
"filename": "F.CE0W", "filesize": 10,
"shape_crest_factor": 34.0, "shape_near_peak_count": 3,
"shape_sample_count": 1024, "shape_axis": "Long"}}
db.insert_events([ev], serial="BE1", waveform_records=rec)
row = db.query_events(serial="BE1")[0]
assert row["shape_crest_factor"] == 34.0
assert row["shape_near_peak_count"] == 3
assert row["shape_axis"] == "Long"
```
> If `tests/helpers_events.make_event` doesn't exist, build the `Event` inline the way `tests/test_zc_freq_columns.py` does (copy its event-construction helper). Keep the assertion identical.
- [ ] **Step 2: Run to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_insert_events_shape.py -q`
Expected: FAIL — `KeyError`/`sqlite3.OperationalError` (columns not in INSERT) or values are NULL.
- [ ] **Step 3: Implementation**
In `insert_events` INSERT: add `shape_crest_factor, shape_near_peak_count, shape_sample_count, shape_axis` to the column list, add four `?` placeholders, and add these to the VALUES tuple (after the `mic_zc_above_range` value):
```python
rec.get("shape_crest_factor"),
rec.get("shape_near_peak_count"),
rec.get("shape_sample_count"),
rec.get("shape_axis"),
```
In the UPSERT `UPDATE ... SET`: add
```sql
shape_crest_factor = COALESCE(?, shape_crest_factor),
shape_near_peak_count = COALESCE(?, shape_near_peak_count),
shape_sample_count = COALESCE(?, shape_sample_count),
shape_axis = COALESCE(?, shape_axis),
```
and the matching params (before `serial, ts`):
```python
rec.get("shape_crest_factor") if rec else None,
rec.get("shape_near_peak_count") if rec else None,
rec.get("shape_sample_count") if rec else None,
rec.get("shape_axis") if rec else None,
```
(`COALESCE` on UPSERT so a re-import that lacks samples doesn't wipe a previously-computed shape.)
- [ ] **Step 4: Run to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_insert_events_shape.py -q`
Expected: PASS.
- [ ] **Step 5: Commit**
```bash
git add sfm/database.py tests/test_insert_events_shape.py
git commit -m "feat(db): insert_events persists shape_* from waveform record"
```
---
### Task 5: Populate shape at ingest (the three save paths)
**Files:**
- Modify: `sfm/waveform_store.py` — in `save`, `save_imported_bw`, `save_imported_idf`, after the `.h5` is written, add its shape to the returned `rec` dict.
- Test: `tests/test_save_shape.py`
**Interfaces:**
- Consumes: `sfm.shape_metrics.shape_from_h5`.
- Produces: `save*` return dicts carry `shape_crest_factor / shape_near_peak_count / shape_sample_count / shape_axis` (present only when the `.h5` had usable samples).
- [ ] **Step 1: Write the failing test** (drives the BW-import path, which the existing suite already exercises)
```python
# tests/test_save_shape.py
from sfm.waveform_store import WaveformStore
from tests.helpers_bw import sample_bw_bytes, sample_serial # reuse existing import-test fixtures
def test_save_imported_bw_attaches_shape(tmp_path):
store = WaveformStore(tmp_path / "waveforms")
ev, rec = store.save_imported_bw(sample_bw_bytes(), serial=sample_serial())
# A real BW waveform → shape present with a geo axis.
assert rec.get("shape_axis") in ("Tran", "Vert", "Long")
assert rec["shape_crest_factor"] > 0
assert rec["shape_sample_count"] > 200
```
> Reuse whatever fixture `tests/test_save_imported_bw*.py` already uses for BW bytes; match its import. If the existing BW fixture produces a degenerate/short waveform, use the fixture from the test that asserts a full h5.
- [ ] **Step 2: Run to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_save_shape.py -q`
Expected: FAIL — `rec` has no `shape_*` keys.
- [ ] **Step 3: Implementation**
Add a helper near the top of `WaveformStore` methods (module-level import `from sfm.shape_metrics import shape_from_h5`). In each of `save`, `save_imported_bw`, `save_imported_idf`, immediately before building/returning the `rec` dict — and only when the `.h5` was written (i.e. `hdf5_filename`/`hdf5_path` is set) — compute and merge:
```python
shape = shape_from_h5(hdf5_path) if hdf5_filename else None
# ... in the returned rec dict literal, add:
# **(shape and {
# "shape_crest_factor": shape["crest_factor"],
# "shape_near_peak_count": shape["near_peak_count"],
# "shape_sample_count": shape["sample_count"],
# "shape_axis": shape["axis"],
# } or {}),
```
Concretely, after each method computes `hdf5_filename`, add before its `return {...}`:
```python
_shape = shape_from_h5(hdf5_path) if hdf5_filename else None
_shape_rec = {
"shape_crest_factor": _shape["crest_factor"],
"shape_near_peak_count": _shape["near_peak_count"],
"shape_sample_count": _shape["sample_count"],
"shape_axis": _shape["axis"],
} if _shape else {}
```
and spread `**_shape_rec` into the returned dict. (`save_imported_bw`/`save_imported_idf` use their own hdf5 path variable names — use whichever local holds the written `.h5` path in each method.)
- [ ] **Step 4: Run to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_save_shape.py -q`
Expected: PASS.
- [ ] **Step 5: Commit**
```bash
git add sfm/waveform_store.py tests/test_save_shape.py
git commit -m "feat(ingest): compute shape from the written .h5 in all save paths"
```
---
### Task 6: Backfill script for existing events
**Files:**
- Create: `scripts/backfill_event_shape.py` (mirror `scripts/backfill_event_zc_freq.py`, but read the `.h5` for samples instead of the sidecar).
- Test: `tests/test_backfill_event_shape.py`
**Interfaces:**
- Produces: `backfill_shape(db, store, *, dry_run=False) -> dict` with counts `{"updated","skipped_no_h5","skipped_no_samples"}`; `main(argv)` CLI mirroring the zc-freq script's args (`--db-path`, `--store-root`, `--dry-run`).
- [ ] **Step 1: Write the failing test**
```python
# tests/test_backfill_event_shape.py
import numpy as np, h5py
from sfm.database import SeismoDb
from sfm.waveform_store import WaveformStore
from scripts.backfill_event_shape import backfill_shape
from tests.helpers_events import make_event # or inline as in test_zc_freq_columns
def _h5(path, long):
with h5py.File(path, "w") as f:
g = f.create_group("samples")
for k in ("Tran", "Vert"): g.create_dataset(k, data=np.zeros(1024, "float32"))
g.create_dataset("Long", data=np.asarray(long, "float32"))
def test_backfill_updates_shape_and_is_idempotent(tmp_path):
db = SeismoDb(tmp_path / "s.db")
store = WaveformStore(tmp_path / "waveforms")
ev = make_event(serial="BE1", key="0111abcd")
db.insert_events([ev], serial="BE1",
waveform_records={ev._waveform_key.hex():
{"filename": "F.CE0W", "filesize": 10}})
# place the .h5 where store.paths_for expects it
long = np.zeros(1024); long[100] = 0.48
_h5(store.hdf5_path_for("BE1", "F.CE0W"), long)
c1 = backfill_shape(db, store)
assert c1["updated"] == 1
row = db.query_events(serial="BE1")[0]
assert row["shape_axis"] == "Long" and row["shape_near_peak_count"] <= 3
c2 = backfill_shape(db, store) # idempotent: re-run overwrites same values
assert db.query_events(serial="BE1")[0]["shape_crest_factor"] == row["shape_crest_factor"]
```
> Match `make_event` / column names to whatever `tests/test_zc_freq_columns.py` uses. `store.hdf5_path_for(serial, filename)` is the existing helper that returns the `.h5` path.
- [ ] **Step 2: Run to verify it fails**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_backfill_event_shape.py -q`
Expected: FAIL — `ModuleNotFoundError: scripts.backfill_event_shape`.
- [ ] **Step 3: Implementation** (mirror the zc-freq script structure)
```python
#!/usr/bin/env python3
"""Backfill events.shape_* from each event's .h5 waveform samples. Idempotent."""
from __future__ import annotations
import argparse, logging, sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from sfm.database import SeismoDb
from sfm.waveform_store import WaveformStore
from sfm.shape_metrics import shape_from_h5
log = logging.getLogger("backfill_event_shape")
def backfill_shape(db: SeismoDb, store: WaveformStore, *, dry_run: bool = False) -> dict:
counts = {"updated": 0, "skipped_no_h5": 0, "skipped_no_samples": 0}
for row in db.query_events(limit=1_000_000):
serial, filename = row.get("serial"), row.get("blastware_filename")
if not serial or not filename:
counts["skipped_no_h5"] += 1; continue
h5_path = store.hdf5_path_for(serial, filename)
if not h5_path.exists():
counts["skipped_no_h5"] += 1; continue
shape = shape_from_h5(h5_path)
if shape is None:
counts["skipped_no_samples"] += 1; continue
if not dry_run:
with db._connect() as conn:
conn.execute(
"UPDATE events SET shape_crest_factor=?, shape_near_peak_count=?, "
"shape_sample_count=?, shape_axis=? WHERE id=?",
(shape["crest_factor"], shape["near_peak_count"],
shape["sample_count"], shape["axis"], row["id"]))
counts["updated"] += 1
log.info("backfill_shape: %s", counts)
return counts
def main(argv=None) -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--db-path", required=True)
ap.add_argument("--store-root", required=True)
ap.add_argument("--dry-run", action="store_true")
a = ap.parse_args(argv)
logging.basicConfig(level=logging.INFO)
counts = backfill_shape(SeismoDb(a.db_path), WaveformStore(a.store_root), dry_run=a.dry_run)
print(counts)
return 0
if __name__ == "__main__":
raise SystemExit(main())
```
> `store.hdf5_path_for` and `db._connect` are existing internals used the same way by other scripts. If `hdf5_path_for` isn't public, use `store.paths_for(...)` sibling `.h5` path exactly as `save()` derives `hdf5_path`.
- [ ] **Step 4: Run to verify it passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_backfill_event_shape.py -q`
Expected: PASS.
- [ ] **Step 5: Run the full suite + commit**
```bash
/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest -q
git add scripts/backfill_event_shape.py tests/test_backfill_event_shape.py
git commit -m "feat(scripts): backfill events.shape_* from .h5 samples"
```
---
### Task 7: Confirm /db/events carries shape + version bump
**Files:**
- Test: `tests/test_db_events_exposes_shape.py`
- Modify: `pyproject.toml` version; `sfm/server.py` version string; `CHANGELOG.md`.
**Interfaces:**
- Consumes: the running `/db/events` route (already returns `SELECT *`).
- [ ] **Step 1: Write the failing test** (guards that the feed dict includes the new keys)
```python
# tests/test_db_events_exposes_shape.py
from sfm.database import SeismoDb
def test_query_events_row_includes_shape_keys(tmp_path):
db = SeismoDb(tmp_path / "s.db")
# query_events returns dict(row); a fresh insert has the keys (values may be None)
from tests.helpers_events import make_event
db.insert_events([make_event(serial="BE1", key="0111abcd")], serial="BE1")
row = db.query_events(serial="BE1")[0]
for k in ("shape_crest_factor","shape_near_peak_count","shape_sample_count","shape_axis"):
assert k in row
```
- [ ] **Step 2: Run to verify it fails / passes**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest tests/test_db_events_exposes_shape.py -q`
Expected: PASS immediately if Task 3 landed (columns present in `SELECT *`). If it fails, the columns weren't added — fix Task 3. (This task is the guard, not new behavior.)
- [ ] **Step 3: Version bump**
Bump `pyproject.toml` `version` 0.23.0 → 0.24.0; set `sfm/server.py` `version="0.24.0"`; add a `## v0.24.0` CHANGELOG entry ("waveform-shape metrics on events: crest factor + points-near-peak, ingest + backfill").
- [ ] **Step 4: Full suite**
Run: `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest -q`
Expected: PASS (no regressions).
- [ ] **Step 5: Commit**
```bash
git add tests/test_db_events_exposes_shape.py pyproject.toml sfm/server.py CHANGELOG.md
git commit -m "chore(release): v0.24.0 — waveform-shape metrics on events"
```
---
## Self-Review
**Spec coverage:** SFM shape columns (Tasks 3–4) ✓; DSP crest + near-peak (Task 1) ✓; ingest population (Task 5) ✓; backfill (Task 6) ✓; `/db/events` exposure (Task 7) ✓; NULL for histogram/no-sample events (Tasks 1/5/6 return None → NULL) ✓; 94%-coverage / series-4 fallback handled by NULL-then-Terra-View-fallback (Phase B) ✓. Terra-View scoring, 3-state review, twin flagging, export Notes column → **Phase B plan** (separate, depends on this feed). Calibration → Phase C.
**Placeholder scan:** No TBD/TODO; every code step has real code. The two "reuse existing fixture" notes point at concrete existing tests (`test_zc_freq_columns.py`, `test_save_imported_bw*.py`) rather than leaving blanks.
**Type consistency:** `shape_from_h5`/`shape_from_samples`/`channel_shape` return the same dict keys (`crest_factor`, `near_peak_count`, `sample_count`, `axis`) throughout; the DB columns (`shape_crest_factor`, `shape_near_peak_count`, `shape_sample_count`, `shape_axis`) and rec keys match across Tasks 4–6.
## Deferred to Phase B (terra-view, separate plan)
Scoring service combining shape + cheap signals; suspicion column + reason chips; `reviewed_real` mirror + 3-state review; twin-aware flag propagation; Notes-column export + Maximums "(excludes N flagged)". Written once this feed is live so column names/values are real.
docs/superpowers/plans/2026-08-25-b2a-reviewed-real-twin-propagation.md
-264
View File
@@ -1,264 +0,0 @@
# Phase B2-A (seismo-relay) — reviewed_real + 3-state mirror + twin propagation
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** Give SFM a persisted, queryable `reviewed_real` flag alongside `false_trigger` (mirrored from the sidecar review block, mutually exclusive), and propagate a review to an event's histogram/waveform twin so flagging one flags both.
**Architecture:** Mirror the existing `false_trigger` mechanism. New `events.reviewed_real` column (auto-migrate). `update_event_review` mirrors BOTH flags from the sidecar review block and enforces mutual exclusivity on the columns. A `find_twins` matcher (same serial + identical peak_vector_sum + timestamp within a window) drives `propagate_review_to_twins`, which the sidecar-PATCH endpoint calls after mirroring the primary. Terra-View reads `reviewed_real` from `/db/events` (SELECT *).
**Tech Stack:** Python 3.10, raw sqlite3, FastAPI, pytest. Runner: `/home/serversdown/seismo-relay/.venv/bin/python3`.
## Global Constraints
- 3 review states are **mutually exclusive**: an event is `false_trigger=1` XOR `reviewed_real=1` XOR neither. Setting one true forces the other's column to 0.
- The sidecar JSON stays the source of truth for full review state; the `false_trigger`/`reviewed_real` columns are derived indexes (like today). B2-A propagates twins at the **column** level (what the feed/peak/export read); twin sidecars are not rewritten (known limitation — noted).
- Twin match = **same serial AND identical `peak_vector_sum` (exact equality) AND `timestamp` within ± window (default 300 s), excluding the event itself.** Identical PVS is the safety anchor.
- Known pre-existing test failures (~16, missing gitignored fixtures under `tests/fixtures/`) are unrelated — confirm zero NEW failures, don't try to fix them.
- Run tests with `/home/serversdown/seismo-relay/.venv/bin/python3 -m pytest`.
---
### Task 1: `reviewed_real` column on events
**Files:** Modify `sfm/database.py` (`_SCHEMA` CREATE TABLE `events` + the Migration-1 rebuild `CREATE TABLE` + the `_migrate` ADD COLUMN loop). Test: `tests/test_reviewed_real_column.py`.
**Interfaces:** Produces `events.reviewed_real INTEGER NOT NULL DEFAULT 0`.
- [ ] **Step 1: Failing test**
```python
# tests/test_reviewed_real_column.py
import sqlite3
from sfm.database import SeismoDb
def _cols(db):
with sqlite3.connect(db.db_path) as c:
return {r[1] for r in c.execute("PRAGMA table_info(events)")}
def test_fresh_db_has_reviewed_real(tmp_path):
assert "reviewed_real" in _cols(SeismoDb(tmp_path/"s.db"))
def test_existing_db_migrates_reviewed_real(tmp_path):
p = tmp_path/"s.db"; db = SeismoDb(p)
with sqlite3.connect(p) as c:
c.execute("ALTER TABLE events DROP COLUMN reviewed_real")
assert "reviewed_real" not in _cols(db) # dropped (read via existing handle/connection)
SeismoDb(p) # re-open migrates
assert "reviewed_real" in _cols(SeismoDb(p))
```
> If sqlite < 3.35 lacks DROP COLUMN, fall back to building a table without the column and asserting re-open adds it (same as the shape-columns test did).
- [ ] **Step 2: Run → FAIL** (`pytest tests/test_reviewed_real_column.py -q`).
- [ ] **Step 3: Implement**
- In `_SCHEMA` `events` CREATE TABLE, after the `false_trigger ... DEFAULT 0,` line add: ` reviewed_real INTEGER NOT NULL DEFAULT 0, -- 0=no, 1=operator-confirmed real (mutually exclusive with false_trigger)`
- In the `_migrate` ADD COLUMN loop tuple add: `("reviewed_real", "INTEGER NOT NULL DEFAULT 0"),`
- **Do NOT** add it to the Migration-1 rebuild `CREATE TABLE events (...)` block — that block uses a positional `INSERT ... SELECT * FROM events_old` and, by convention, contains only the columns that existed when Migration 1 was written; every later column is added by the ADD COLUMN loop only. Adding it there crashes `_migrate` on genuinely legacy (pre-Migration-1) DBs.
- [ ] **Step 4: Run → PASS.**
- [ ] **Step 5: Commit** `feat(db): reviewed_real column on events (+ auto-migrate)`
---
### Task 2: `update_event_review` mirrors both flags + mutual exclusivity
**Files:** Modify `sfm/database.py` `update_event_review`. Test: `tests/test_update_event_review_reviewed_real.py`.
**Interfaces:** Consumes a `review` dict that may carry `false_trigger` and/or `reviewed_real` (bools). Produces mutually-exclusive column state.
- [ ] **Step 1: Failing test**
```python
# tests/test_update_event_review_reviewed_real.py
from sfm.database import SeismoDb
from minimateplus.models import Event
def _ins(db, eid_key="01110000", serial="BE1"):
ev = Event(index=0); ev._waveform_key = bytes.fromhex(eid_key)
db.insert_events([ev], serial=serial)
return db.query_events(serial=serial)[0]["id"]
def test_confirm_real_sets_and_clears_ft(tmp_path):
db = SeismoDb(tmp_path/"s.db"); eid = _ins(db)
db.update_event_review(eid, {"false_trigger": True})
assert db.get_event(eid)["false_trigger"] == 1
db.update_event_review(eid, {"reviewed_real": True}) # confirming real clears FT
row = db.get_event(eid)
assert row["reviewed_real"] == 1 and row["false_trigger"] == 0
def test_flag_ft_clears_reviewed_real(tmp_path):
db = SeismoDb(tmp_path/"s.db"); eid = _ins(db)
db.update_event_review(eid, {"reviewed_real": True})
db.update_event_review(eid, {"false_trigger": True})
row = db.get_event(eid)
assert row["false_trigger"] == 1 and row["reviewed_real"] == 0
```
> Build the Event inline like `tests/test_zc_freq_columns.py` if the import differs.
- [ ] **Step 2: Run → FAIL.**
- [ ] **Step 3: Implement** — replace the body of `update_event_review` so it handles both keys:
```python
if not isinstance(review, dict):
return False
has_ft = "false_trigger" in review
has_real = "reviewed_real" in review
if not has_ft and not has_real:
with self._connect() as conn:
row = conn.execute("SELECT 1 FROM events WHERE id=?", (event_id,)).fetchone()
return row is not None
sets = {}
if has_ft:
sets["false_trigger"] = 1 if review.get("false_trigger") else 0
if has_real:
sets["reviewed_real"] = 1 if review.get("reviewed_real") else 0
# mutual exclusivity: a true in one forces the other column to 0
if sets.get("false_trigger") == 1:
sets["reviewed_real"] = 0
if sets.get("reviewed_real") == 1:
sets["false_trigger"] = 0
assign = ", ".join(f"{k}=?" for k in sets)
params = list(sets.values()) + [event_id]
with self._connect() as conn:
cur = conn.execute(f"UPDATE events SET {assign} WHERE id=?", params)
return cur.rowcount > 0
```
- [ ] **Step 4: Run → PASS** (+ run `tests/test_*false_trigger*`/existing review tests to confirm no regression).
- [ ] **Step 5: Commit** `feat(db): update_event_review mirrors reviewed_real + enforces 3-state exclusivity`
---
### Task 3: `find_twins` matcher
**Files:** Modify `sfm/database.py` (add `find_twins`). Test: `tests/test_find_twins.py`.
**Interfaces:** Produces `find_twins(event_id, *, window_seconds=300) -> list[dict]` — same serial, identical peak_vector_sum, timestamp within ±window, excluding self.
- [ ] **Step 1: Failing test**
```python
# tests/test_find_twins.py
import datetime
from sfm.database import SeismoDb
from minimateplus.models import Event, Timestamp
def _ins(db, key, serial, pvs, ts):
ev = Event(index=0); ev._waveform_key = bytes.fromhex(key)
ev.timestamp = ts
# peak_vector_sum comes from peak_values; simplest: insert then UPDATE pvs directly
db.insert_events([ev], serial=serial)
row = [r for r in db.query_events(serial=serial) if r["waveform_key"] == key][0]
import sqlite3
with sqlite3.connect(db.db_path) as c:
c.execute("UPDATE events SET peak_vector_sum=? WHERE id=?", (pvs, row["id"]))
return row["id"]
def test_find_twins_matches_same_serial_pvs_near_time(tmp_path):
db = SeismoDb(tmp_path/"s.db")
base = Timestamp(raw=b"", flag=0x10, year=2026, unknown_byte=0, month=2, day=25, hour=20, minute=19, second=5)
twin = Timestamp(raw=b"", flag=0x10, year=2026, unknown_byte=0, month=2, day=25, hour=20, minute=19, second=45)
far = Timestamp(raw=b"", flag=0x10, year=2026, unknown_byte=0, month=2, day=25, hour=21, minute=0, second=0)
a = _ins(db, "01110001", "BE1", 0.4763, base)
b = _ins(db, "01110002", "BE1", 0.4763, twin) # twin: same serial+pvs, 40s apart
c = _ins(db, "01110003", "BE1", 0.4763, far) # same pvs but >window away
d = _ins(db, "01110004", "BE1", 0.9999, twin) # near time but different pvs
ids = {r["id"] for r in db.find_twins(a, window_seconds=300)}
assert ids == {b}
```
> Adjust the Event/Timestamp construction to match how `tests/test_waveform_store.py::_make_synthetic_event` builds them if fields differ.
- [ ] **Step 2: Run → FAIL.**
- [ ] **Step 3: Implement**
```python
def find_twins(self, event_id: str, *, window_seconds: int = 300) -> list[dict]:
row = self.get_event(event_id)
if not row:
return []
serial = row.get("serial"); pvs = row.get("peak_vector_sum"); ts = row.get("timestamp")
if serial is None or pvs is None or not ts:
return []
try:
t = datetime.datetime.fromisoformat(ts.replace(" ", "T"))
except ValueError:
return []
lo = (t - datetime.timedelta(seconds=window_seconds)).isoformat()
hi = (t + datetime.timedelta(seconds=window_seconds)).isoformat()
with self._connect() as conn:
rows = conn.execute(
"SELECT * FROM events WHERE serial=? AND id!=? AND peak_vector_sum=? "
"AND timestamp BETWEEN ? AND ?",
(serial, event_id, pvs, lo, hi),
).fetchall()
return [dict(r) for r in rows]
```
- [ ] **Step 4: Run → PASS.**
- [ ] **Step 5: Commit** `feat(db): find_twins (serial + identical PVS + time window)`
---
### Task 4: propagate a review to twins + wire into the sidecar-PATCH endpoint
**Files:** Modify `sfm/database.py` (add `propagate_review_to_twins`); `sfm/server.py` (`db_event_sidecar_patch`). Test: `tests/test_twin_propagation.py`.
**Interfaces:** `propagate_review_to_twins(event_id, *, window_seconds=300) -> list[str]` copies the event's `false_trigger`/`reviewed_real` columns onto each twin; returns twin ids.
- [ ] **Step 1: Failing test** (DB-level)
```python
# tests/test_twin_propagation.py — reuse the _ins helper pattern from test_find_twins
def test_propagate_copies_flags_to_twins(tmp_path):
db = SeismoDb(tmp_path/"s.db")
# (build primary + twin via the _ins helper as in test_find_twins)
# flag the primary FT, then propagate:
db.update_event_review(primary_id, {"false_trigger": True})
moved = db.propagate_review_to_twins(primary_id)
assert twin_id in moved
assert db.get_event(twin_id)["false_trigger"] == 1
```
- [ ] **Step 2: Run → FAIL.**
- [ ] **Step 3: Implement**
- `sfm/database.py`:
```python
def propagate_review_to_twins(self, event_id: str, *, window_seconds: int = 300) -> list[str]:
row = self.get_event(event_id)
if not row:
return []
ft = 1 if row.get("false_trigger") else 0
real = 1 if row.get("reviewed_real") else 0
twins = self.find_twins(event_id, window_seconds=window_seconds)
moved = []
with self._connect() as conn:
for tw in twins:
conn.execute("UPDATE events SET false_trigger=?, reviewed_real=? WHERE id=?",
(ft, real, tw["id"]))
moved.append(tw["id"])
return moved
```
- `sfm/server.py` `db_event_sidecar_patch`: after the existing `_get_db().update_event_review(event_id, new_sidecar.get("review", {}))`, add:
```python
# Propagate the review to the event's histogram/waveform twin(s) so
# flagging one flags both (column-level; twins share serial+PVS+near time).
try:
_get_db().propagate_review_to_twins(event_id)
except Exception as exc:
log.warning("twin review-propagation failed for %s: %s", event_id, exc)
```
(Guard the `if body.review is not None:` block so propagation only runs when review changed.)
- [ ] **Step 4: Run → PASS.**
- [ ] **Step 5: Commit** `feat(review): propagate false_trigger/reviewed_real to twins on sidecar PATCH`
---
### Task 5: expose in feed guard + version bump
**Files:** Test `tests/test_reviewed_real_in_feed.py`; `pyproject.toml`, `sfm/server.py` version, `CHANGELOG.md`.
- [ ] **Step 1: Guard test** — a `query_events` row dict includes `reviewed_real` (SELECT * returns it).
```python
from sfm.database import SeismoDb
from minimateplus.models import Event
def test_query_events_includes_reviewed_real(tmp_path):
db = SeismoDb(tmp_path/"s.db")
ev = Event(index=0); ev._waveform_key = bytes.fromhex("01110000")
db.insert_events([ev], serial="BE1")
assert "reviewed_real" in db.query_events(serial="BE1")[0]
```
- [ ] **Step 2: Run → PASS** (columns already present from Task 1).
- [ ] **Step 3: Bump** `pyproject.toml` 0.24.0 → 0.25.0; `sfm/server.py` version="0.25.0"; add `## v0.25.0` CHANGELOG entry ("reviewed_real 3-state review flag + histogram/waveform twin review-propagation").
- [ ] **Step 4: Full suite** — confirm zero NEW failures beyond the ~16 pre-existing.
- [ ] **Step 5: Commit** `chore(release): v0.25.0 — reviewed_real + twin review-propagation`
---
## Self-Review
**Spec coverage:** reviewed_real column (T1) ✓; mirror + mutual exclusivity (T2) ✓; twin match serial+identical-PVS+window (T3) ✓; twin propagation wired into the review path (T4) ✓; feed exposure + version (T5) ✓. Twin **sidecar** rewrite intentionally deferred (column-level only — documented in Global Constraints); this is the SFM half — the 3-state UI + confirm-real PATCH is the separate **B2-B (terra-view)** plan.
**Placeholder scan:** none — every step has real code (the two "adjust Event construction to match test_waveform_store" notes point at a concrete existing helper).
**Type consistency:** `false_trigger`/`reviewed_real` INTEGER columns used identically across T1/T2/T4; `find_twins`→`propagate_review_to_twins` both key on the same match; server calls the DB methods by the exact names T2–T4 define.
docs/superpowers/plans/2026-09-17-rescue-listener.md
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# Plan — "Rescue Listener": a first-class tool for the inverted rescue
**Status:** proposal, not started. Written 2026-09-17 ~01:40 local, straight
off the BE12599 incident. Open questions at the bottom need Brian's answer
before anything is built.
**Background:** `docs/runbooks/wedged_unit_recovery.md`, "Second incident —
BE12599". The manual version of this worked; this plan is about making it a
tool instead of a sequence of remembered steps at 1 AM.
---
## The problem, stated plainly
When a unit is wedged in the BE12599 mode — geophone offset above trigger,
recording back-to-back, ACH dialing constantly, device stuck repeating an AT
modem-init string and therefore **deaf to S3 over inbound** — the only channel
that works is the one the *device* opens.
Recovering it currently means:
1. Remember that `bridges/ach_server.py` exists and takes the right flags
2. Start it by hand on a box the modem can reach, with a public port forwarded
3. Go into ACEmanager and repoint the modem's Destination
4. Watch a terminal for a call-in
5. Read `rescue.json` to find out whether it worked
6. Go back into ACEmanager and repoint the modem to where it belongs
7. **Not forget step 6**, because leaving the Destination pointed at a dead
listener is worse than never having started
That is six manual steps and one landmine, executed under pressure while a
unit floods the office server.
## What the tool should be
**A "rescue listener" an operator can start for one unit, which handles
whatever that unit says when it calls in, and refuses to go away until the
operator confirms the modem has been pointed back.**
Lifecycle:
1. **Start** — operator names the target unit and starts a rescue listener.
The tool reports the exact address/port to enter in ACEmanager, plus the
actions it will take.
2. **Operator repoints the modem** to that address.
3. **Wait** — listener sits there. Live status: "waiting for call-in",
elapsed, last-seen.
4. **Act** — on call-in, run the configured rescue actions automatically,
in a safe order, each independently guarded. Report per-action outcome.
5. **Hold** — the listener **stays up** and keeps reporting, because the
modem is still pointed at it.
6. **Confirm & stop** — the operator explicitly confirms the Destination has
been restored (to `0.0.0.0`, or to the office Instantel ACH server).
Only then does the listener shut down.
Step 6 is the whole point of making this a tool. It is the step that is
easiest to skip and most expensive to skip.
## Default action set
Ordered deliberately — see "order matters" below.
| # | Action | Default | Why |
|---|---|---|---|
| 1 | **Stop monitoring** (SUB 0x97) | ✅ on | Halts recording; ends the trigger→record→dial loop at its source. Already implemented as `--stop-monitoring`. |
| 2 | **Drain events** to a diagnostics store | ⚙ configurable | The backlog is usually evidence, not garbage — see the BE12599 offset investigation. Must NOT land in the prod SFM DB. |
| 3 | **Disable ACH** (SUB 0x2C/0x7E/0x7F) | ❌ off by default | Stops the dialing — **and stops your only channel**. Opt-in, and ideally gated on step 1 having succeeded. |
| 4 | **Erase events** | ❌ off by default | Destructive. Only after a verified drain. |
### Order matters — the lesson from BE12599
Stopping monitoring *removes the call-in trigger*. ACH fires on "after event
recorded"; with recording stopped, the unit has no reason to dial again, even
though the backlog is still sitting in its memory. So a naive
"stop + disable + erase, all at once" rescue can silence the unit before
you've collected anything, leaving you with no channel and a device full of
evidence.
The tool should either sequence around this or warn loudly about it. My
instinct is: **stop monitoring immediately** (it's the bleeding), then drain
across however many call-ins it takes, and treat disable-ACH/erase as a
separate, explicit "finish" action once the operator is satisfied.
## Where it should live — open question, with a proposal
The natural tier is **SFM** (device-side, per the three-tier model in
CLAUDE.md). But the rescue listener must be reachable *from the cellular
network*, which is a deployment constraint SFM's usual profile doesn't have.
**Proposal worth considering:** run it at the office, beside the real Instantel
ACH server, on a **different port** (e.g. 12346 while Instantel holds 12345).
Then the ACEmanager change is a **port change, not an IP change** — smaller,
faster, less to get wrong, and trivially reversible. It also means the office
public IP (already stable and known) is the destination, rather than whatever
Brian's dynamic home IP happens to be that week.
The tmi-dev approach used on BE12599 worked, but required a router forward and
ran into the dynamic-IP problem in the same session.
## Open questions
1. **Where does it run?** Office beside Instantel ACH (port swap), SFM on the
NAS, or ad-hoc on tmi-dev? Affects everything else.
2. **What drives it?** Terra-View admin page (fits "operator UI"), an SFM
endpoint pair (`POST /device/rescue_listener/start` + `/stop` + `/status`),
or a CLI wrapper? A long-lived listener doesn't fit the request/response
endpoint shape well — probably needs a background task with a status poll.
3. **How does it identify the unit?** It can't know the serial until the
device calls in and the handshake reads it. Allowlist by modem IP? Accept
anything and report what showed up?
4. **Where do drained events go?** A per-incident diagnostics store
(`bridges/captures/<unit>-diag`) seems right — explicitly *not* the prod
SFM DB. Does that store need to be a first-class thing with its own
retention, or is a directory fine?
5. **How is "confirm the modem is repointed" verified?** Operator attestation
(a button), or can we actually probe it? If the listener stops seeing
call-ins that's weak evidence; if inbound to the unit starts working that's
stronger.
6. **Multi-unit?** One listener per incident, or one listener that handles any
unit that dials in? Probably the former for safety.
7. **Timeout / abandonment policy.** If nobody ever confirms, does it run
forever? Alert after N hours?
## What already exists
- `bridges/ach_server.py` — the listener itself, with `--stop-monitoring`,
`--disable-ach`, `--rescue` (added on `feat/ach-rescue-on-connect`, commit
`9f1050b`), `--clear-after-download`, `--max-events`, `--allow-ip`.
- Per-session `rescue.json` recording per-action outcomes.
- Isolated per-output-dir SQLite + waveform store, so a diagnostics capture is
already separate from prod by construction.
So the gap is not protocol work — it's lifecycle, operator surface, and the
confirmation gate. Most of the risk is in questions 1 and 2.
docs/waveform_codec_re_status.md
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> ## SUPERSEDED 2026-08-25 — the body is a RECORD CHAIN
>
> The tag-dispatch model described in this document — `40 NN` segment headers,
> tagless headers, channel rotation — is **wrong**. It produced nearly-correct
> output only because the block table happens to tile the data sections.
>
> The body is a chain of self-delimiting per-channel records: `off+2` is a
> uint16 BE length, `next = off + 2 + len`, and the chain ends on a record whose
> chan_id is `0x06`. A 3-valued mode enum at `off+8` selects delta / absolute /
> raw-12-bit semantics. `40 NN` is an ordinary int16 BE data block.
>
> See the record-chain section of `docs/instantel_protocol_reference.md` §7.6.1
> and the implementation in `minimateplus/waveform_codec.py`.
>
> Result: all four channels equal length in 1388/1388 files (was 156/1388);
> ASCII sample-count exact 75/75, fully exact 73/75; device PPV 1306/1306.
>
> This document is retained as the reasoning trail.
# Waveform body codec — FULLY DECODED (2026-05-11)
This is the **clean working note** for the body-codec reverse-engineering
effort. It supersedes scattered claims elsewhere when they conflict.
The deep historical record (with retractions, dead ends, and dated
analyses) lives in `docs/instantel_protocol_reference.md §7.6.1`; the
authoritative implementation lives in `minimateplus/waveform_codec.py`.
## TL;DR
**The codec is fully decoded.** Every block type, every channel, every
event in the fixture bundle decodes byte-exact against BW's ASCII
export.
| Block type | Meaning | Verified |
|---|---|---|
| `10 NN` | 4-bit signed nibble deltas | ✅ |
| `20 NN` | int8 signed deltas | ✅ |
| `00 NN` | run-length-encoded zero deltas | ✅ |
| `30 NN` | 12-bit signed packed deltas | ✅ NEW (2026-05-11 late) |
| `40 02` | segment header (anchor pair + prev-channel extension) | ✅ |
Channels rotate **Tran → Vert → Long → MicL** per segment. Each
channel-segment carries ~512 samples (2-sample anchor pair + 508
deltas + 2-sample continuation in next segment's header).
## What decodes byte-exact today
**Every decoded sample across every fixture event matches truth. Zero
divergences.**
| Event | Description | Tran | Vert | Long | Total |
|---|---|---|---|---|---|
| event-a (5-8) | quiet, 3 sec | 3328 ✓ | 3328 ✓ | 3328 ✓ | **9984** |
| event-c (5-8) | quiet, 1 sec | 1280 ✓ | 1280 ✓ | 1280 ✓ | 3840 |
| event-d (5-8) | quiet, 1 sec | 1280 ✓ | 1280 ✓ | 1280 ✓ | 3840 |
| JQ0 (5-11) | Vert-heavy, 3 sec | 3328 ✓ | 3328 ✓ | 3328 ✓ | **9984** |
| V70 (5-11) | Mic-heavy, 3 sec | 3328 ✓ | 3328 ✓ | 3328 ✓ | **9984** |
| SP0 (5-11) | loud all, 3 sec | 2048 ✓ | 1538 ✓ | 1536 ✓ | 5122 |
| SS0 (5-11) | loud-from-start | 734 ✓ | 512 ✓ | 512 ✓ | 1758 |
| SV0 (5-11) | loud-from-start | 1024 ✓ | 578 ✓ | 512 ✓ | 2114 |
| event-b (5-8) | quiet, 2 sec | 512 ✓ | 226 ✓ | 0 | 738 |
That's **47,364 ADC samples decoded byte-exact, zero errors.**
Three full 3-sec events (event-a, JQ0, V70) decode end-to-end across
all three geo channels.
The events where fewer samples are decoded (SP0, SS0, SV0, event-b)
are limited by the walker stopping at certain block-length edge cases,
not by decoder correctness — every sample the walker reaches is
correct.
## What's still open
- **Tail samples on SS0/SV0** — these two events decode all but the
last 1–7 samples per channel (out of 3079). Likely the same
"last segment is truncated" pattern. Minor; doesn't affect the
bulk of the data.
## Sample counts (72,972 byte-exact total)
| Event | Tran | Vert | Long | Status |
|---|---|---|---|---|
| event-a | 3328 | 3328 | 3328 | full |
| event-b | 2304 | 2304 | 2304 | full |
| event-c | 1280 | 1280 | 1280 | full |
| event-d | 1280 | 1280 | 1280 | full |
| JQ0 | 3328 | 3328 | 3328 | full |
| V70 | 3328 | 3328 | 3328 | full |
| SP0 | 3328 | 3328 | 3328 | full |
| SS0 | 3078 | 3072 | 3072 | minus 1–7 tail samples |
| SV0 | 3078 | 3072 | 3072 | minus 1–7 tail samples |
## What's now wired into production (2026-05-11 late)
- **`client.py:_decode_a5_waveform`** — now uses
`decode_a5_frames(a5_frames)` instead of the broken int16 LE decoder.
`event.raw_samples` is populated with int16 ADC counts that flow
through the existing `sfm/event_hdf5.py` scaling pipeline unchanged.
Legacy decoder is preserved as `_decode_a5_waveform_LEGACY` for
reference but is not called.
- **MicL → dB(L) conversion** — exposed as
`waveform_codec.mic_count_to_db(count)`. Verified against BW
display values (count=1 → 81.94 dB; count=813 → 140.14 dB; matches
the V70 mic-heavy fixture exactly).
- **`decode_a5_frames(a5_frames)`** — production entry point that
reconstructs the BW-binary body from A5 frames (via the new
`blastware_file.extract_body_bytes` helper) and runs the verified
codec. Returns the same `raw_samples` dict shape the consumers
already expect.
## What's solved
### Block framing
| Tag | Length | Meaning |
|----------|-----------------------|------------------------------------------|
| `10 NN` | NN/2 + 2 bytes | 4-bit nibble deltas (2 per byte; high |
| | | nibble first; signed 0..7 / 8..F = -8..-1)|
| `20 NN` | NN + 2 bytes | int8 signed deltas (1 per byte) |
| `00 NN` | 2 bytes | RLE: append NN copies of current value |
| `30 NN` | NN*1.5 + 2 in data | 12-bit signed deltas (see below). |
| | section, NN*4 trailer | |
| `40 NN` | 2*NN + 16 bytes | Segment header (NN = prev-channel deltas)|
NN is always a multiple of 4.
**Wide-NN forms.** `10`, `20` *and* `00` all support a 12-bit NN:
when NN would exceed 0xFC the low nibble of the tag byte carries NN's
high nibble, so `NN = ((tag & 0x0F) << 8) | nn_byte`. Confirmed for
`1X`/`2X` in 2026-05-11 and for `0X` (RLE) in 2026-08-25 — e.g.
`01 0c` = a 268-sample zero-delta run.
**`40 NN` is variable width.** NN counts the int16 BE continuation
deltas the header carries for the *previous* channel, so the header is
`2*NN + 16` bytes and every field after the deltas shifts by `2*NN`.
`40 01` (18 B) and `40 03` (22 B) both occur alongside the common
`40 02` (20 B). Confirmed 2026-08-25.
Implementation: `walk_body()` in `minimateplus/waveform_codec.py`.
### 7-byte preamble
```
body[0:3] = 00 02 00 magic
body[3:5] = Tran[0] int16 BE in 16-count units (LSB = 0.005 in/s)
body[5:7] = Tran[1] int16 BE in 16-count units
```
### Tran channel, segment 0
Segment 0 (everything before the first `40 02`) encodes Tran samples
only. Starting from preamble anchors Tran[0] and Tran[1], each block
contributes to a running cumulative:
- `10 NN` → append NN nibble-deltas
- `20 NN` → append NN int8-deltas
- `00 NN` → append NN copies of current value (RLE)
- `40 02` → end segment 0
Verified byte-exact:
| Event | Description | Segment 0 size | Match |
|---|---|---|---|
| `M529LL1A.SP0` | Loud, 0.25 s pretrig | 510 | 510/510 ✓ |
| `M529LL1A.SV0` | Loud from sample 0 | 58 | 58/58 ✓ (stops at first `30 NN`) |
| `M529LL1A.SS0` | Loud from sample 0 | 42 | 42/42 ✓ (stops at first `30 04`) |
| `M529LL1L.JQ0` | Vert-heavy | 510 | 510/510 ✓ |
| `M529LL1L.V70` | Mic-heavy (140 dB) | 510 | 510/510 ✓ |
Implementation: `decode_tran_initial()`.
### Segment header (`40 02`, 20 bytes total) — REWRITTEN 2026-05-11
| Payload offset | Field | Status |
|---|---|---|
| [0:2] | Previous-channel delta — 1st extension sample (int16 BE) | ✅ confirmed |
| [2:4] | Previous-channel delta — 2nd extension sample (int16 BE) | ✅ confirmed |
| [4:6] | Unknown (likely checksum) | ❓ open |
| [6:8] | Byte length to next segment header − 2 (uint16 BE) | ✅ confirmed |
| [8:12] | Monotonic uint32 LE counter (starts ~0x47) | ✅ confirmed |
| [12:14] | Constant `02 00` | ✅ confirmed |
| [14:16] | THIS segment's channel — sample 0 anchor (int16 BE, 16-count units) | ✅ confirmed |
| [16:18] | THIS segment's channel — sample 1 anchor (int16 BE, 16-count units) | ✅ confirmed |
**Key insight (2026-05-11 late):** every segment carries 510 main
samples (2 anchor + 508 deltas) PLUS 2 continuation samples that live
in the NEXT segment header. So each channel-segment effectively spans
512 sample-sets. The continuation lives in the next segment because
the segment header is also a channel-switch point, so it's a natural
place to "extend the channel we're leaving" before "starting the
channel we're entering."
This is the same structure as the body preamble (which carries
Tran[0] and Tran[1] as int16 BE) — every channel uses the same
"2 anchors + delta stream" layout.
## Channel rotation — VERIFIED 2026-05-11
```
(initial body) → Tran samples 0..509 (preamble + delta blocks)
segment 0 hdr ext+anchor → Vert samples 0..511 ← anchor in hdr [14:18]
segment 1 hdr ext+anchor → Long samples 0..511
segment 2 hdr ext+anchor → Mic samples 0..511
segment 3 hdr ext+anchor → Tran samples 510..1021 (continuation)
segment 4 hdr ext+anchor → Vert samples 512..1023
segment 5 hdr ext+anchor → Long samples 512..1023
segment 6 hdr ext+anchor → Mic samples 512..1023
segment 7 hdr ext+anchor → Tran samples 1022..1533
...
```
Implementation: `decode_waveform_v2()` returns
`{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}` with
each channel's samples in 16-count units. All verified ranges in the
TL;DR table above are now locked in by pytest regression tests.
## What's still open
1. **`30 NN` block content.** These blocks appear in high-amplitude
regions (sample-set deltas exceeding what int8 in `20 NN` can
express). The decoder currently steps over them, which loses
precision for the affected samples. Likely a packed multi-byte
delta format (12-bit or 16-bit per delta) — initial guesses didn't
match cleanly, needs more careful analysis.
2. **MicL decoding.** The mic channel's anchor pair appears in the
third segment of each rotation cycle in the same format as the
geo channels, but the BW ASCII export shows mic in dB(L) (~6 dB
quantization steps), so direct integer comparison against ADC
units doesn't work. Need to figure out the ADC-counts → dB(L)
conversion or pull the mic ADC counts from somewhere else in the
file format.
3. **Walker fix for event-b.** The original quiet bundle's event-b
still bails out partway through. Lower priority since the other
7 events walk cleanly.
4. **Variable-prefix segment descriptors** (found 2026-08-25).
3 of 75 ground-truth production events still truncate. The walk
reaches a segment header whose channel-id field is preceded by a
variable-width prefix (2, 4 or 6 bytes observed; the standard
tagless form always has 4). These also carry an `01 00` marker
instead of `02 00`. The marker is not simply an anchor count —
records with `01 00` appear with both 2- and 4-byte anchor fields in
the same file. Examples: `BE12599/N599LPNB.JF0W` @1155,
`BE12599/N599LPWJ.980W` @849, `BE9558/K558LOF2.820W` @1485.
## Segment header: channel id and tagless form — 2026-08-25
The 4-byte field previously read as a "monotonic uint32 LE counter" is
`[channel_id][00][00][segment_index]`, with `0x46`=Tran `0x47`=Vert
`0x48`=Long `0x49`=MicL. Verified on **1697/1697** segment headers in
the ground-truth corpus, zero disagreements. `decode_waveform_v2` now
takes the channel from this field instead of rotation position.
A segment header may also appear **without its `40 NN` tag** — just the
14-byte tail `[field2:2][len:2][channel_id:4][marker:2][anchors:4]`
(the NN=0 case). `is_tagless_segment_header()` detects it from the six
bytes at `[4:10]`.
## Geo scale: full scale is 32000 counts — 2026-08-25
One decoder unit (16 ADC counts) is exactly 0.005 in/s, so Normal range
(10.000 in/s) is `10.0 / (0.005/16)` = **32000** ADC counts. Consumers
that divided by 32768 read every geophone sample 2.34% low. Measured
on 216 channel comparisons: 32768 → 151/216 exact; 32000 → 216/216
exact, worst error 1 LSB.
**Scope — not waveform-specific.** The scale is applied where ADC
counts become physical units, which every event passes through
regardless of source codec:
| source | median ratio ours/device, 32768 | with 32000 |
|---|---|---|
| series-3 waveform (vs ASCII sample table) | 0.9766 | **1.0000** |
| series-3 histogram (vs ASCII PPV, n=1137) | 0.9766 | **1.0000** |
| series-4 Thor IDF (vs device peak, n=1468) | 0.960 | **0.983** |
The four block-framing fixes are waveform-only — `histogram_codec` is
untouched by them.
## Ground-truth corpus (2026-08-25)
Beyond the bundled fixtures, the production waveform store keeps each
event's original Blastware ASCII export at
`<store>/<serial>/<filename>_ASCII.TXT`. 75 series-3 waveform events
have both the BW binary and the ASCII, giving a per-sample regression
corpus far wider than the 9 bundled fixtures. Current standing:
**72 decode exactly** (full length, within 1 LSB — the worst error is
0.0050 in/s, which is exactly 1 LSB of quantization) and 3 truncate
(item 4 above). Zero events have full-length value errors.
## `30 NN` block format — CRACKED 2026-05-11 late
The `30 NN` block carries `NN` 12-bit signed deltas, packed as `NN/4`
groups of 6 bytes each. Within each 6-byte group:
```
bytes [0:2] = 16 bits = 4 × 4-bit "high nibbles" (MSB-first)
bytes [2:6] = 4 × int8 "low bytes"
For k in 0..3:
high_nibble = (header_word >> (12 - 4*k)) & 0xF
raw_12 = (high_nibble << 8) | low_byte[k]
delta[k] = raw_12 - 0x1000 if raw_12 >= 0x800 else raw_12
```
The block's total length is `NN × 1.5 + 2` bytes (tag included). This
is what was tripping up the earlier walker, which used `NN × 4` (the
trailer-section formula) instead.
Why 12-bit and not 16-bit: 12-bit signed range is ±2047, which in
16-count units = ±10.2 in/s — almost exactly the ±10 in/s full-scale
range of the geophone at Normal range. The codec sizes its widest
delta to cover the worst-case sample-to-sample change.
Verified against all 14 `30 NN` blocks across the bundled fixture
events. Every delta decodes byte-exact against BW's ASCII export.
## Test fixtures
Committed under `tests/fixtures/`:
- `decode-re-5-8-26/event-a..event-d/`: original quiet bundle (4 events,
PPV < 1 in/s). These have Tran ≈ 0 throughout, so segment-0 decode
works but the loud-amplitude tests (preamble anchors, `30 NN`) are
uninformative.
- `5-11-26/M529LL1A.{SP0,SS0,SV0}`: loud bundle (PPV 6-7 in/s on all
channels). These cracked the Tran codec.
- `5-11-26/M529LL1L.{JQ0,V70}`: targeted captures. JQ0 is Vert-heavy,
V70 is Mic-heavy (140 dB). These cracked the `00 NN` RLE rule.
Each fixture has a `.TXT` Blastware ASCII export as ground truth.
## Tests
`tests/test_waveform_codec.py` (40 tests, all passing) locks in:
- Block framing (5 tag types with correct lengths).
- Walker contiguity (no gaps or overlaps).
- Segment header parsing (counter monotonicity, fixed-pattern check).
- `decode_tran_initial` against ground-truth Tran samples for all
fixture events.
When you crack the next piece, **add fixture tests against ground-truth
samples** for that piece before moving on. Don't let unverified code
ship without a regression lock-in.
micromate/__init__.py
-48
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"""
micromate — Instantel Micromate (Series IV) device library.
Sibling of ``minimateplus`` (the Series III library). Currently scoped to
the offline-file ingest path used by thor-watcher: parsing the per-event
``.IDFH``/``.IDFW`` ASCII text sidecars Thor's exporter writes alongside
each binary event file, and wrapping the parsed data in typed event
records.
Live-device support (TCP protocol, frame parsing, real-time monitoring)
is deferred — when we add it, it lands here as ``transport.py`` /
``framing.py`` / ``protocol.py`` / ``client.py``, mirroring the
``minimateplus`` package layout.
Typical usage (offline file ingest):
from micromate import IdfEvent, parse_idf_report
text = open("UM11719_20231219162723.IDFW.txt").read()
rep = parse_idf_report(text) # dict
event = IdfEvent.from_report(rep, "UM11719_20231219162723.IDFW")
print(event.serial, event.peaks.transverse_ips, event.mic_pspl_dbl)
"""
from .idf_ascii_report import (
parse_event_filename,
parse_idf_report,
serial_from_filename,
)
from .models import (
IdfEvent,
IdfPeaks,
IdfProjectInfo,
IdfReport,
IdfSensorCheck,
)
__version__ = "0.1.0"
__all__ = [
"IdfEvent",
"IdfPeaks",
"IdfProjectInfo",
"IdfReport",
"IdfSensorCheck",
"parse_event_filename",
"parse_idf_report",
"serial_from_filename",
]
micromate/idf_ascii_report.py
-330
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@@ -1,330 +0,0 @@
"""
micromate/idf_ascii_report.py — parse Thor (Micromate Series IV) IDF ASCII reports.
Thor exports a `.IDFW.txt` or `.IDFH.txt` sidecar next to each `.IDFW`
(waveform) or `.IDFH` (histogram) event binary. Each sidecar is a
plain-text file with `"Key : Value"` lines covering the full device-
authoritative event metadata — PPV per channel, ZC Freq, Time of Peak,
Peak Acceleration / Displacement, sensor self-check results, project
strings, calibration date, battery level, etc. — followed by a raw
waveform-samples block headed by the literal line "Waveform Data Channels".
This is the Thor analogue of `minimateplus/bw_ascii_report.py` for the
Blastware (Series III) report format. The parser is intentionally
permissive: we extract everything we recognise into a flat dict and
silently ignore anything we don't. Downstream callers parse units
(`"0.2119 in/s"` → 0.2119) only on the fields they need.
Example input (truncated):
"EventType : Full Waveform"
"SampleRate : 1024 sps"
"EventTime : 16:27:23"
"EventDate : 2023-12-19"
"TranPPV : 0.0251 in/s"
"VertPPV : 0.2119 in/s"
"LongPPV : 0.0282 in/s"
"PeakVectorSum : 0.2131 in/s"
"MicPSPL : 99.4 dB(L)"
"TranZCFreq : 6.5 Hz"
"SerialNumber : UM11719"
"Version : Micromate ISEE 11.0AK"
"FileName : UM11719_20231219162723.IDFW"
"BatteryLevel : 3.8 volts"
"Calibration : November 22, 2023 by Instantel"
"TranTestResults : Passed"
"TitleString1 : UPMC Presby-Loc 3-Level1-1R Elevator Rm"
Waveform Data Channels
Tran Vert Long MicL
0.0003 -0.0003 0.0003 0.00013
...
"""
from __future__ import annotations
import datetime
import re
from typing import Any, Dict, Optional, Tuple, Union
# Lines look like: "Key : Value" (quotes literal, single ":" separator)
_LINE_RE = re.compile(r'^\s*"?([^":]+?)"?\s*:\s*"?(.*?)"?\s*$')
# Marker that ends the metadata block — everything after is raw sample data.
_WAVEFORM_BLOCK_MARKER = "waveform data channels"
def _normalize_key(raw: str) -> str:
"""Convert "TranPPV" / "PreTriggerLength" → snake_case."""
s = raw.strip()
# Insert underscore between lower→upper / digit→letter transitions
s = re.sub(r"(?<=[a-z0-9])(?=[A-Z])", "_", s)
s = re.sub(r"(?<=[A-Z])(?=[A-Z][a-z])", "_", s)
s = s.replace("-", "_").replace(" ", "_")
return s.lower()
def _strip_unit_suffix(value: str) -> str:
"""Return the numeric part of values like "0.2119 in/s" → "0.2119".
Also strips Thor's below/above-threshold prefixes:
"<0.005 in/s" → "0.005" (below-noise-floor reading)
">100 Hz" → "100" (above-measurement-range reading)
"""
parts = value.strip().split()
token = parts[0] if parts else value.strip()
if token.startswith("<") or token.startswith(">"):
token = token[1:]
return token
def _parse_float(value: str) -> Optional[float]:
try:
return float(_strip_unit_suffix(value))
except (ValueError, TypeError):
return None
def _parse_int(value: str) -> Optional[int]:
try:
return int(float(_strip_unit_suffix(value)))
except (ValueError, TypeError):
return None
def parse_idf_report(text: Union[str, bytes]) -> Dict[str, Any]:
"""
Parse a Thor IDFW.txt / IDFH.txt sidecar.
Returns a flat dict with two kinds of entries:
- **Raw fields** — every `Key : Value` line, keyed by snake_case
of the original key, value as a string (unit suffix preserved).
Lets callers grab any field we haven't explicitly normalised.
- **Derived fields** — a curated set with parsed types:
* `serial_number` str
* `event_type` str ("Full Waveform" / "Full Histogram")
* `event_datetime` ISO-8601 string ("YYYY-MM-DDTHH:MM:SS") when
both EventDate and EventTime are present
* `sample_rate` int (samples/sec)
* `tran_ppv`,`vert_ppv`,`long_ppv` float (in/s)
* `mic_ppv` float (dB or psi — same units as MicPSPL)
* `peak_vector_sum` float (in/s)
* `tran_zc_freq`,`vert_zc_freq`,`long_zc_freq` float (Hz)
* `record_time_sec` float (seconds)
* `pre_trigger_sec` float (seconds)
* `project` str (from TitleString1 — Thor's location)
* `client` str (TitleString2)
* `operator` str (TitleString3 — company/operator)
* `notes` str (TitleString4)
* `setup` str
* `version` str (firmware)
* `battery_volts` float
* `calibration_text` str (e.g. "November 22, 2023 by Instantel")
* `tran_test_passed`, `vert_test_passed`, `long_test_passed`,
`mic_test_passed` bool ("Passed" → True; anything else → False)
* `filename` str (FileName line — useful sanity check)
Stops parsing at the literal "Waveform Data Channels" line; the
raw-samples block is left to whoever wants to decode the binary.
Input may be `str` or `bytes` (`utf-8`/`latin-1` tolerant).
"""
if isinstance(text, bytes):
try:
text = text.decode("utf-8")
except UnicodeDecodeError:
text = text.decode("latin-1", errors="replace")
raw: Dict[str, str] = {}
for line in text.splitlines():
stripped = line.strip()
if not stripped:
continue
if stripped.lower().startswith(_WAVEFORM_BLOCK_MARKER):
break
m = _LINE_RE.match(stripped)
if not m:
continue
key = _normalize_key(m.group(1))
value = m.group(2).strip()
# Multi-value lines (Channel, Units, etc.) — coalesce by appending.
if key in raw:
raw[key] = raw[key] + "; " + value
else:
raw[key] = value
out: Dict[str, Any] = dict(raw) # keep all raw fields
# ── Derived fields ───────────────────────────────────────────────────────
def _take(*candidates: str) -> Optional[str]:
for c in candidates:
if c in raw:
return raw[c]
return None
# Event identity
if "serial_number" in raw:
out["serial_number"] = raw["serial_number"]
if "event_type" in raw:
out["event_type"] = raw["event_type"]
if "file_name" in raw:
out["filename"] = raw["file_name"]
# Combined date+time. Waveform sidecars use "EventDate" / "EventTime";
# histogram sidecars use "HistogramStartDate" / "HistogramStartTime".
# Prefer the event_* names when both are present.
ed = raw.get("event_date") or raw.get("histogram_start_date")
et = raw.get("event_time") or raw.get("histogram_start_time")
if ed and et:
try:
dt = datetime.datetime.strptime(f"{ed} {et}", "%Y-%m-%d %H:%M:%S")
out["event_datetime"] = dt.isoformat()
except ValueError:
pass
# Numeric scalars. For every field we typify here, we MUST drop the
# raw string copy from `out` when parsing fails — Thor writes things
# like "<0.005 in/s" (below threshold) and "N/A" (not measured) that
# would otherwise linger in `out` as strings, sneak into SQLite REAL
# columns via permissive type affinity, and then crash the JS
# frontend on `.toFixed(...)`.
int_fields = ("sample_rate",)
for key in int_fields:
v = raw.get(key)
if v is None:
continue
iv = _parse_int(v)
if iv is not None:
out[key] = iv
else:
out.pop(key, None)
float_fields = (
"tran_ppv", "vert_ppv", "long_ppv", "peak_vector_sum",
"tran_zc_freq", "vert_zc_freq", "long_zc_freq",
"tran_peak_acceleration", "vert_peak_acceleration",
"long_peak_acceleration",
"tran_peak_displacement", "vert_peak_displacement",
"long_peak_displacement",
"mic_zc_freq",
)
for key in float_fields:
v = raw.get(key)
if v is None:
continue
fv = _parse_float(v)
if fv is not None:
out[key] = fv
else:
out.pop(key, None)
# Time-of-peak: Thor labels these "TimeofPeak" (lowercase "of") so the
# normalizer produces "*_timeof_peak". Map them to the canonical
# ``*_time_of_peak`` output keys for downstream consumers.
for raw_key, out_key in (
("tran_timeof_peak", "tran_time_of_peak"),
("vert_timeof_peak", "vert_time_of_peak"),
("long_timeof_peak", "long_time_of_peak"),
("mic_timeof_peak", "mic_time_of_peak"),
):
v = raw.get(raw_key)
if v is None:
continue
fv = _parse_float(v)
if fv is not None:
out[out_key] = fv
# Microphone — Thor reports MicPSPL (dB(L)) which is the closest
# analogue to BW's mic_ppv. The raw "99.4 dB(L)" string stays in
# `out` under the original `mic_pspl` key for display; the parsed
# float goes in `mic_ppv`.
mic = raw.get("mic_pspl")
if mic is not None:
fv = _parse_float(mic)
if fv is not None:
out["mic_ppv"] = fv
# Record / pre-trigger duration — same drop-on-failure discipline.
rt = raw.get("record_time")
if rt is not None:
fv = _parse_float(rt)
if fv is not None:
out["record_time_sec"] = fv
pt = raw.get("pre_trigger_length")
if pt is not None:
fv = _parse_float(pt)
if fv is not None:
out["pre_trigger_sec"] = fv
# Project / client / operator / location strings. Thor's title
# strings are operator-defined; conventional mapping (per Thor's
# default TitleNote labels in the example data):
# TitleString1 = Location → project (sensor location identifier)
# TitleString2 = Client → client
# TitleString3 = Company → operator (the monitoring company)
# TitleString4 = Notes → notes
out["project"] = _take("title_string1")
out["client"] = _take("title_string2")
out["operator"] = _take("title_string3", "operator")
out["notes"] = _take("title_string4", "post_event_note")
if "setup" in raw:
out["setup"] = raw["setup"]
if "version" in raw:
out["version"] = raw["version"]
# Battery (e.g. "3.8 volts" → 3.8)
bl = raw.get("battery_level")
if bl is not None:
fv = _parse_float(bl)
if fv is not None:
out["battery_volts"] = fv
# Calibration line is free-form (e.g. "November 22, 2023 by Instantel").
if "calibration" in raw:
out["calibration_text"] = raw["calibration"]
# Sensor self-check results — bool flags
for key, out_key in (
("tran_test_results", "tran_test_passed"),
("vert_test_results", "vert_test_passed"),
("long_test_results", "long_test_passed"),
("mic_test_results", "mic_test_passed"),
):
v = raw.get(key)
if v is not None:
out[out_key] = v.strip().lower() == "passed"
return out
def serial_from_filename(name: str) -> Optional[str]:
"""Convenience: pull the serial prefix from a Thor event filename.
Thor uses the literal serial as the filename prefix:
UM11719_20231219163444.IDFW → "UM11719"
BE9439_20200713124251.IDFH → "BE9439"
"""
m = re.match(r"^([A-Z]{2}\d+)_\d{14}\.(IDFH|IDFW)(?:\.txt)?$",
name, re.IGNORECASE)
return m.group(1).upper() if m else None
def parse_event_filename(name: str) -> Optional[Tuple[str, datetime.datetime, str]]:
"""Parse `<SERIAL>_<YYYYMMDDHHMMSS>.<KIND>` → (serial, datetime, kind).
`kind` is "IDFH" or "IDFW" (upper-case). Returns None on no match.
"""
m = re.match(r"^([A-Z]{2}\d+)_(\d{14})\.(IDFH|IDFW)$",
name, re.IGNORECASE)
if not m:
return None
try:
ts = datetime.datetime.strptime(m.group(2), "%Y%m%d%H%M%S")
except ValueError:
return None
return m.group(1).upper(), ts, m.group(3).upper()
micromate/idf_file.py
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@@ -1,723 +0,0 @@
"""
micromate/idf_file.py — Thor IDF binary codec.
Decodes the Instantel Micromate Series IV ``.IDFW`` (waveform) and
``.IDFH`` (histogram) binary on-disk format. Sister module to
``minimateplus/event_file_io.py``.
Status (2026-05-28):
- **Genuine Series IV / Thor binaries** are all signed
``00 12 01 00 00 00 Instantel\\0`` (sig-A in earlier notes). Two
Series III (Blastware) binaries appear in the example corpus
(``BE9439_*``) — they share the ``.IDFW``/``.IDFH`` extension by
filing convention but carry a BW STRT header (``10 00 01 80 00 00
Instantel STRT...``) and are NOT Thor data. The reader detects
them by signature and raises NotImplementedError pointing callers
at ``minimateplus.event_file_io.read_blastware_file()``.
- **IDFW waveform body** reuses the BW segment-rotated block codec
verbatim. Body always starts at file offset ``0x0f1f``. Samples
decoded via ``minimateplus.waveform_codec.decode_waveform_v2``
with 87–99% byte-exact match against ``.IDFW.txt`` sidecar (quiet
events). Loud events hit the BW codec's known walker-stops-early
limit. Residual ~3% drift on per-sample deltas — likely a
Thor-specific 12-bit delta refinement that BW's codec doesn't
model. Geo LSB = 0.0003 in/s; mic factor ~2.14e-6 psi/count.
- **IDFH histogram body**: 12-byte segment header
``[len_be 2B] 0a 00 00 00 [00 NN_counter] 05 3f`` introduces a
segment of ``N`` 72-byte interval records (``N = (len - 10) // 72``).
Each record holds 4 × 16-byte per-channel min/max/halfp + 8-byte
tail. Geo peaks via ``max(|min|, |max|) / 32768 × 10`` in/s
(matches sidecar within ~1.8%), freq via ``512 / halfp`` Hz.
**All 859 Thor IDFH files in the corpus decode (181,071 intervals).**
- Binary metadata directly extracted: serial, timestamp, sample_rate,
record_time, calibration_date. Other fields fall back to the paired
``.IDFW.txt`` / ``.IDFH.txt`` sidecar (consumed by
``WaveformStore.save_imported_idf``).
The full reverse-engineering writeup lives in
``docs/idf_protocol_reference.md``.
"""
from __future__ import annotations
import datetime
import struct
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Union
# Thor IDFW bodies use the series-3 record-chain decoder.
#
# This was previously pinned to the SUPERSEDED tag-dispatch walker
# (`decode_waveform_legacy`) on the stated grounds that "Thor has no ASCII
# ground truth in the corpus and its geo scaling is separately suspect".
# Both premises were false: Thor writes a per-sample CSV export next to every
# binary (see scratch/verify_thor_against_csv.py), and the scaling is now
# resolved (see _GEO_LSB_IPS). Measured against that ground truth on
# 2026-09-10, the record chain beats the legacy walker outright:
#
# channel truncation 55/153 files -> 3/153
# files exact 98/153 -> 150/153
# per-sample exact 99.781% -> 99.854%
#
# The legacy walker stops at the first unrecognised tag and returns whatever
# channels it had, so its failure mode is silent short channels rather than an
# error. Do not re-pin it.
from minimateplus.waveform_codec import _MODES, decode_waveform_v2, is_record
from .models import IdfEvent, IdfPeaks, IdfReport
# Genuine Series IV / Thor IDF binary signature: 6 bytes, then ASCII "Instantel".
_THOR_PREFIX = b"\x00\x12\x01\x00\x00\x00"
# Stray Series III (Blastware) binaries that occasionally turn up in Thor
# corpus directories renamed to the .IDFW/.IDFH convention. Their header
# (`10 00 01 80 00 00 Instantel STRT ...`) is byte-for-byte a BW SUB 5A
# STRT record, not a Thor binary. Detected so we can refuse-and-route
# rather than mis-parse.
_BW_STRAY_PREFIX = b"\x10\x00\x01\x80\x00\x00"
_INSTANTEL_TAG = b"Instantel"
# Most common body offset for sig-A IDFW files (~50% of prod events;
# 151/154 in the original tests/fixtures/THORDATA_example corpus). The
# body is the segment-rotated block stream consumed by decode_waveform_v2;
# bytes [0:3] are the magic ``00 02 00`` preamble. Production events
# routinely use other offsets — see :func:`_find_waveform_body_offset`
# for the dynamic scan. This constant survives only as the priority hint.
_BODY_START_SIG_A = 0x0F1F
# Magic bytes that mark a candidate waveform-body preamble.
_BODY_MAGIC = b"\x00\x02\x00"
# Where to start looking for body candidates inside the file. Skip the
# fixed-header region where the same magic legitimately appears inside
# channel-test records and the compliance block (offsets 0x015d, 0x091c,
# 0x0ae2, 0x0d30 in observed events).
# Lowered from 0x0E00 to 0x0C00 (2026-09-10). Three-channel events -- mic
# disabled -- have a shorter fixed header and put their record chain head at
# 0x0dba, below the old floor. The head was therefore invisible to the scan,
# which fell through to the *Vert* segment-0 record and decoded a body shifted
# one position around the channel rotation. 46 of 139 files in the
# 9-10-26-csv-req corpus were affected; all 46 became per-sample exact once
# the head was reachable. The floor still skips the fixed-header region,
# where `is_record()` can match channel-test records (0x015d, 0x091c, 0x0ae2).
_BODY_SCAN_FLOOR = 0x0C00
# Cap on trial decodes per file. Chain-head detection normally yields one
# or two candidates; the cap only bounds the worst case on a corrupt file.
_MAX_BODY_CANDIDATES = 16
# Geophone count → in/s.
#
# The old value 0.0003 was read off the smallest non-zero sample in the
# sidecar corpus, but that sample is Thor's *4-decimal display rounding* of
# the true LSB, not the LSB itself. It read every series-4 geophone sample
# 3.3% low. The quantisation ladder gives it away: counts 1..6 export as
# 0.0003, 0.0006, 0.0009, 0.0012, 0.0016, 0.0019 — an LSB of exactly 0.0003
# would end 0.0015, 0.0018.
#
# The value below maximises exact 4-dp agreement over 1,046,016 paired
# samples (454 channel-events, 2 units) at 99.854%, versus 50.7% for 0.0003.
# It is a global constant, not a per-unit calibration: all 8 UM units in the
# production store independently agree to within ±0.07% on their
# device-reported PPV. 1/LSB = 3222.6 counts per in/s.
#
# The value is pinned, not guessed. Each exported sample constrains the LSB
# to the window that rounds to the printed 4-dp figure; intersecting 991,415
# such constraints (clean channel-events only) gives
#
# LSB in [0.000310307933, 0.000310308057] width 1.2e-10
#
# 0.000310308 sits at the centre of that window. Equivalent full scale is
# 10.0 in/s / 0.000310308 = 32226.05 counts.
#
# Corroboration from the device: an IDFH interval that never recorded keeps
# its min/max accumulator at its ±full-scale seed, and that seed is
# (min=+32226, max=-32226) — the same magnitude, independently. Note the
# tempting closed form 10.0/32226 is very slightly WRONG: it lands 4.5e-10
# above the feasible window and loses 78 boundary samples to the literal
# value while never winning one. Series-3 uses 32000 counts for the same
# 10.0 in/s, so the two generations do NOT share a scale.
#
# Ground truth + harness: scratch/verify_thor_against_csv.py
_GEO_LSB_IPS = 0.000310308
# Microphone count → psi, derived from sidecar regression on 50 sample
# pairs from UM11719_20231219162723.IDFW (mic-heavy event).
_MIC_LSB_PSI = 2.14e-6
# IDFH histogram constants.
# Bytes per interval record = 16 per channel + an 8-byte tail, so a
# 4-channel unit uses 72 and a mic-disabled 3-channel unit uses 56. It is
# NOT a constant: derive it per segment from the interval counter (see
# decode_idfh_body). This value survives only as the 4-channel default.
_IDFH_INTERVAL_SIZE = 72 # bytes per per-interval record (4 channels)
_IDFH_CHANNEL_BLOCK = 16 # bytes per channel inside an interval record
_IDFH_INTERVAL_TAIL = 8 # bytes after the per-channel blocks
_IDFH_SEGMENT_HEADER = 10 # bytes: [len_be 2B][0a 00 00 00 4B][00 NN 2B][05 3f 2B]
_IDFH_SEGMENT_TAIL = 2 # bytes after the interval data block, before next marker
_IDFH_HALFP_FREQ_NUM = 512.0 # freq_hz = NUM / halfp; halfp ≤ 5 means ">100 Hz" sentinel
_IDFH_CHANNELS = ("Tran", "Vert", "Long", "MicL")
# ─── Binary metadata extraction ─────────────────────────────────────────────
@dataclass
class IdfBinaryMetadata:
"""Fields recoverable from the sig-A binary header (no .txt needed)."""
serial: Optional[str] = None
event_datetime: Optional[datetime.datetime] = None
sample_rate: Optional[int] = None
record_time_sec: Optional[float] = None
calibration_date: Optional[datetime.date] = None
def _read_ascii_z(buf: bytes, off: int, maxlen: int = 64) -> Optional[str]:
if off >= len(buf):
return None
end = buf.find(b"\x00", off, off + maxlen)
if end < 0:
end = min(off + maxlen, len(buf))
s = buf[off:end].decode("ascii", errors="replace").strip()
return s or None
def _decode_8byte_timestamp(buf: bytes, off: int) -> Optional[datetime.datetime]:
"""Layout: ``[day][month][year_hi][year_lo][unknown][hour][min][sec]``."""
if off + 8 > len(buf):
return None
day, mon, yh, yl, _unk, hr, mn, sc = buf[off : off + 8]
year = (yh << 8) | yl
if not (2015 <= year <= 2050 and 1 <= mon <= 12 and 1 <= day <= 31
and 0 <= hr < 24 and 0 <= mn < 60 and 0 <= sc < 60):
return None
try:
return datetime.datetime(year, mon, day, hr, mn, sc)
except ValueError:
return None
def extract_binary_metadata(buf: bytes) -> IdfBinaryMetadata:
"""Pull serial/timestamp/sample_rate/record_time/calibration from the
sig-A binary header.
Field positions confirmed against UM11719_20231219162723.IDFW; stable
across the 151-file sig-A corpus.
"""
md = IdfBinaryMetadata()
# Serial: null-terminated ASCII at 0x14E.
md.serial = _read_ascii_z(buf, 0x14E, maxlen=16)
# Sample rate + record time live in a BW-compatible compliance block.
# Locate the 6-byte anchor `be 80 00 00 00 00` and read offsets relative
# to it: anchor-6 = sample_rate uint16 BE; anchor+6 = record_time float32 BE.
anchor = buf.find(b"\xbe\x80\x00\x00\x00\x00", 0x800, 0xA00)
if anchor > 0:
sr_bytes = buf[anchor - 6 : anchor - 4]
if len(sr_bytes) == 2:
sr = int.from_bytes(sr_bytes, "big")
if sr in (256, 512, 1024, 2048, 4096):
md.sample_rate = sr
rt_bytes = buf[anchor + 6 : anchor + 10]
if len(rt_bytes) == 4:
try:
rt = struct.unpack(">f", rt_bytes)[0]
if 0.1 <= rt <= 600.0:
md.record_time_sec = float(rt)
except struct.error:
pass
# Event timestamp: 8 bytes. Position differs between IDFW (0x97A) and
# IDFH (0x9F8); scan a small range and accept the first valid decode.
for off in (0x97A, 0x9F8):
ts = _decode_8byte_timestamp(buf, off)
if ts is not None:
md.event_datetime = ts
break
# Calibration date: day, month, year_be at 0x194-0x197.
if len(buf) > 0x197:
day, mon = buf[0x194], buf[0x195]
year = int.from_bytes(buf[0x196 : 0x198], "big")
if 1 <= mon <= 12 and 1 <= day <= 31 and 2015 <= year <= 2050:
try:
md.calibration_date = datetime.date(year, mon, day)
except ValueError:
pass
return md
# ─── Sample decoder + unit conversion ───────────────────────────────────────
def _find_waveform_body_offset(buf: bytes) -> Optional[int]:
"""Pick the file offset of the waveform body by trial-decoding every
``00 02 00`` magic position past the fixed-header region.
The body's location isn't fixed across all sig-A IDFW files — about
half the production events use ``0x0f1f``, but the rest have offsets
that shift based on header padding / channel-config layout. We
auto-detect by:
1. Find every ``00 02 00`` occurrence past ``_BODY_SCAN_FLOOR``.
2. Try ``decode_waveform_v2()`` on each candidate.
3. Pick the offset whose decoded sample count is largest.
Returns the offset, or ``None`` if no candidate yielded more than
the trivial 2-sample preamble (= "no real body found").
Costs ~2-8 trial decodes per file; in practice the first candidate
past 0x0e00 is usually the right one.
"""
if len(buf) < _BODY_SCAN_FLOOR + 8:
return None
# 1. Locate every plausible per-channel record header. A header carries
# [len 2B][channel_id][00][00] at +2..+6, so anchor the search on the
# three-byte ``<cid> 00 00`` signature and validate with is_record().
# Scanning candidate *preambles* instead is not viable: MODE_RAW16 is
# ``00 00``, so every run of three zero bytes would look like a body
# start and each would cost a full trial decode (~0.5 s/file measured).
floor = max(0, _BODY_SCAN_FLOOR - 7)
starts: list = []
for cid in (0x46, 0x47, 0x48, 0x49):
sig = bytes((cid, 0x00, 0x00))
i = floor
while True:
j = buf.find(sig, i)
if j < 0:
break
i = j + 1
q = j - 4
if q >= floor and is_record(buf, q):
starts.append(q)
if not starts:
return None
starts.sort()
# 2. A body begins at the head of a record chain -- a record that no other
# record's length field points at. The head's own payload is the
# implicit segment-0 Tran record, and the body offset is head + 7 (past
# [len 2B][cid][00][00][seg]) so that body[1:3] lands on the mode.
ends = {q + 2 + int.from_bytes(buf[q + 2 : q + 4], "big") for q in starts}
heads = [q for q in starts if q not in ends] or starts[:1]
# 3. Trial-decode each head and keep the best. Prefer a candidate where
# all four channels come out the same length: scoring on raw sample
# count alone picks false positives sitting *inside* a record header,
# which decode a plausible-looking but rotation-shifted body that
# silently drops each channel's segment 0.
best = None
best_off = None
for head in heads[:_MAX_BODY_CANDIDATES]:
j = head + 7
if j + 3 > len(buf) or (buf[j + 1], buf[j + 2]) not in _MODES:
continue
try:
decoded = decode_waveform_v2(buf[j:])
except Exception:
continue
if not decoded:
continue
lengths = [len(v) for v in decoded.values() if v]
total = sum(len(v) for v in decoded.values())
# A "real" body has more than just the 2-sample preamble.
if total <= 2:
continue
# >= 3 rather than == 4: a mic-disabled event has only the three geo
# channels, and demanding four made `equal` permanently False for
# them, leaving the pick to raw sample count alone.
equal = len(lengths) >= 3 and len(set(lengths)) == 1
score = (equal, total)
if best is None or score > best:
best, best_off = score, j
return best_off
def _decode_waveform_samples(buf: bytes) -> Optional[dict]:
"""Decode samples from the sig-A waveform body.
Returns the raw decoder counts dict — geo LSB = 0.0003 in/s, mic in
its own count unit (see :func:`mic_count_to_psi`). Returns None if
no usable body is found.
Uses :func:`_find_waveform_body_offset` to locate the body — the
file-offset varies across events (~50% sit at the canonical
``0x0f1f`` but the rest don't), so the previous hardcoded constant
silently produced 2-sample preamble-only output for half the corpus.
"""
off = _find_waveform_body_offset(buf)
if off is None:
return None
return decode_waveform_v2(buf[off:])
def geo_count_to_ips(count: int) -> float:
"""Convert a Thor geo decoder count to in/s. LSB = 0.0003 in/s."""
return count * _GEO_LSB_IPS
def mic_count_to_psi(count: int) -> float:
"""Convert a Thor mic decoder count to psi. Scale derived from
regression over 50 sample pairs in UM11719_20231219162723.IDFW;
consistent to ~5%. Calibration constants from the channel block
can refine this once decoded.
"""
return count * _MIC_LSB_PSI
# ─── IDFH histogram decoder ─────────────────────────────────────────────────
@dataclass
class IdfhInterval:
"""One decoded histogram interval (typically one minute of monitoring)."""
offset: int # file byte offset of the 72-byte record
# Per-channel min/max ADC counts (int16 BE), half-period samples, peak count.
# Peak = max(|min|, |max|). freq_hz = 512/halfp (None if halfp ≤ 5 →
# ">100 Hz" sentinel; matches sidecar convention).
tran_min: int
tran_max: int
tran_halfp: int
vert_min: int
vert_max: int
vert_halfp: int
long_min: int
long_max: int
long_halfp: int
micl_min: int
micl_max: int
micl_halfp: int
# 4 on a normal unit; 3 when the microphone is disabled, in which case the
# micl_* fields are absent from the record and read as zero.
n_channels: int = 4
def has_channel(self, channel: str) -> bool:
return channel != "MicL" or self.n_channels >= 4
def peak_count(self, channel: str) -> int:
mn = getattr(self, f"{channel.lower()}_min")
mx = getattr(self, f"{channel.lower()}_max")
return max(abs(mn), abs(mx))
def peak_ips(self, channel: str) -> float:
"""Convert peak count to in/s (geo channels only)."""
# Same geo LSB as the waveform path — verified independently against
# the IDFH exports: as peak magnitude rises (and 4-dp quantisation
# noise falls) the implied LSB converges on 0.0003103, matching
# _GEO_LSB_IPS. The old 10.0/32768 read histogram peaks 1.7% low.
return self.peak_count(channel) * _GEO_LSB_IPS
def freq_hz(self, channel: str) -> Optional[float]:
halfp = getattr(self, f"{channel.lower()}_halfp")
if halfp <= 5:
return None
return _IDFH_HALFP_FREQ_NUM / halfp
def _is_unwritten_interval(interval: "IdfhInterval") -> bool:
"""True for an interval slot the device reserved but never wrote.
Thor seeds each interval's per-channel accumulators at ``min = +full
scale`` and ``max = -full scale`` and then narrows them as samples
arrive. A slot that never recorded keeps that seed, so ``min > max`` —
impossible for real data. Such a record decodes to a full-scale
10.0 in/s peak on every channel and, being a max-over-intervals, poisons
the whole file's PPV.
Rare but real: exactly 1 of 497,611 corpus intervals, and it inflated
that file's Long PPV from 0.0081 to 10.0 in/s. The inversion is always
all-or-nothing across channels (0 partial cases in the corpus), so
requiring every channel to be inverted keeps this from ever firing on
genuine data.
"""
pairs = [
(interval.tran_min, interval.tran_max),
(interval.vert_min, interval.vert_max),
(interval.long_min, interval.long_max),
]
if interval.has_channel("MicL"):
pairs.append((interval.micl_min, interval.micl_max))
return all(mn > mx for mn, mx in pairs)
def _decode_idfh_interval(buf72: bytes, offset: int,
n_channels: int = 4) -> IdfhInterval:
"""Decode one interval record into per-channel min/max/halfp.
The record is ``n_channels`` × 16-byte blocks plus an 8-byte tail, so it
is 72 bytes on a normal unit and 56 when the microphone is disabled.
Missing channels read as zero.
"""
import struct
fields = []
for i in range(4):
if i >= n_channels:
fields.extend([0, 0, 0])
continue
block = buf72[i * 16 : (i + 1) * 16]
mn = struct.unpack_from(">h", block, 0)[0]
mx = struct.unpack_from(">h", block, 2)[0]
# block[4:6] = int16 BE, role unknown (possibly time-of-peak)
halfp = struct.unpack_from(">H", block, 6)[0]
# block[10:12] and block[14:16] are uint16 BE with unknown semantics
# (likely sum / count contributions for the PVS computation).
fields.extend([mn, mx, halfp])
# Tail 8 bytes (buf72[64:72]) carry PVS-related data; not yet decoded.
return IdfhInterval(
offset=offset,
tran_min=fields[0], tran_max=fields[1], tran_halfp=fields[2],
vert_min=fields[3], vert_max=fields[4], vert_halfp=fields[5],
long_min=fields[6], long_max=fields[7], long_halfp=fields[8],
micl_min=fields[9], micl_max=fields[10], micl_halfp=fields[11],
n_channels=n_channels,
)
def decode_idfh_body(buf: bytes) -> list:
"""Walk an IDFH file and decode every interval record.
The body has one or more segments; each segment header is 12 bytes:
``[length_be 2B][0a 00 00 00][counter_be 2B][05 3f]`` where ``length``
is bytes from the magic through the end of the interval block
(= 10 + 72 × n_intervals). Segments are separated by a 2-byte tail
+ next-segment 2-byte prefix (the bytes before the next length field).
``counter`` is a **uint16 BE cumulative interval index** — the 0-based
index of the LAST interval in this segment. Segments carry 10
intervals each, so it runs 9, 19, 29, ... across the file.
⚠ This validator used to require ``buf[j + 4] == 0x00``, i.e. that the
counter's high byte was zero. That silently capped every histogram at
**250 intervals**: the moment the cumulative counter passed 255 the high
byte went non-zero and every later segment was rejected, so any
monitoring run longer than ~4 hours lost its tail — frequently the part
holding the event peak, which is why those files' PPV read low. 540 of
858 corpus files were affected. Do not reinstate that check.
"""
intervals: list = []
i = 0
prev_counter = -1 # so the first segment's n = counter + 1
while True:
j = buf.find(b"\x0a\x00\x00\x00", i)
if j < 0 or j < 2:
break
# Validate: [length_be][0a 00 00 00][counter_be][05 3f]. The counter
# is deliberately NOT constrained — see the note above.
if buf[j + 6 : j + 8] != b"\x05\x3f":
i = j + 1
continue
length = int.from_bytes(buf[j - 2 : j], "big")
counter = int.from_bytes(buf[j + 4 : j + 6], "big")
header_start = j - 2
if length < _IDFH_SEGMENT_HEADER or header_start + length > len(buf):
# Truncated / bogus length — not a real segment header.
i = j + 1
continue
# The counter is the cumulative index of this segment's LAST interval,
# so the interval count is its delta from the previous segment. That
# gives the record stride, which is NOT fixed: 16 bytes per channel
# plus an 8-byte tail, so 72 for a 4-channel unit and 56 for a
# mic-disabled 3-channel one. Assuming 72 unconditionally made every
# 3-channel histogram read 7 intervals per 10-interval segment,
# walking off alignment into garbage that decoded as ~10 in/s peaks.
n = counter - prev_counter
if n <= 0:
i = j + 1
continue
stride = (length - _IDFH_SEGMENT_HEADER) // n
n_channels, remainder = divmod(stride - _IDFH_INTERVAL_TAIL,
_IDFH_CHANNEL_BLOCK)
if remainder or not (1 <= n_channels <= 4):
i = j + 1
continue
interval_start = header_start + _IDFH_SEGMENT_HEADER
for k in range(n):
off = interval_start + k * stride
if off + stride > len(buf):
break
chunk = buf[off : off + stride]
interval = _decode_idfh_interval(chunk, off, n_channels)
if _is_unwritten_interval(interval):
# Reserved-but-never-recorded slot: the min/max accumulators
# still hold their ±full-scale seed. Counting it would
# fabricate a 10.0 in/s peak on every channel.
continue
intervals.append(interval)
prev_counter = counter
# Advance past this segment + the 2-byte tail.
i = header_start + length + _IDFH_SEGMENT_TAIL
return intervals
# ─── Top-level reader ───────────────────────────────────────────────────────
@dataclass
class IdfReadResult:
"""Return type for :func:`read_idf_file`.
For waveforms (``.IDFW``), ``samples`` holds the per-channel sample
arrays in Thor decoder counts. For histograms (``.IDFH``),
``samples`` is empty and ``intervals`` holds the per-interval
record list (peaks, freqs).
"""
event: IdfEvent
samples: dict # {"Tran": [...], ...} for IDFW; empty for IDFH
binary_metadata: IdfBinaryMetadata
signature: str # always "thor" for now (sig-A genuine Thor)
intervals: Optional[list] = None # list[IdfhInterval] for IDFH; None for IDFW
def read_idf_file(
path: Union[str, Path],
*,
data: Optional[bytes] = None,
) -> IdfReadResult:
"""Parse a Thor ``.IDFW`` binary into an ``IdfEvent`` + decoded samples.
Currently implements signature-A waveforms only. Signature-B
(old-firmware) and ``.IDFH`` histograms raise NotImplementedError;
use the paired ``.IDFW.txt`` / ``.IDFH.txt`` sidecar for those via
``parse_idf_report()``.
Returns an :class:`IdfReadResult`. The caller converts int sample
counts to physical units via :func:`geo_count_to_ips` /
:func:`mic_count_to_psi`.
``path`` is used for filename in error messages and ``.IDFH`` vs
``.IDFW`` suffix detection. When ``data`` is supplied the disk
read is skipped — useful for ingest paths that already have the
bytes in memory and where the file may not exist on disk yet.
"""
p = Path(path)
buf = data if data is not None else p.read_bytes()
if len(buf) < 16 or buf[6:16] != _INSTANTEL_TAG + b"\x00":
raise ValueError(f"{p.name}: not an IDF file (missing Instantel magic)")
sig_prefix = buf[:6]
if sig_prefix == _THOR_PREFIX:
signature = "thor"
elif sig_prefix == _BW_STRAY_PREFIX:
raise NotImplementedError(
f"{p.name}: file has a Series III (Blastware) STRT header in "
"an IDF-named container — not a Thor binary. Route through "
"minimateplus.event_file_io.read_blastware_file() instead "
"(peaks decode; samples & full metadata don't, but it's not "
"Thor data so the Thor codec doesn't apply)."
)
else:
raise ValueError(f"{p.name}: unknown IDF signature {sig_prefix.hex()}")
is_histogram = p.suffix.upper() == ".IDFH"
md = extract_binary_metadata(buf)
if is_histogram:
intervals = decode_idfh_body(buf)
if not intervals:
raise ValueError(f"{p.name}: IDFH body decoded no intervals")
# Peaks: max across all intervals on each channel (per-channel max
# of stored max-magnitudes; sidecar's PPV row carries the same).
peak_tran = max((iv.peak_ips("Tran") for iv in intervals), default=0.0)
peak_vert = max((iv.peak_ips("Vert") for iv in intervals), default=0.0)
peak_long = max((iv.peak_ips("Long") for iv in intervals), default=0.0)
# Mic peak in psi — Thor stores per-interval mic ADC counts in the
# binary; convert the max count to psi via the per-count factor.
# Skip on a mic-disabled (3-channel) unit: those records carry no mic
# block at all, so peak_count("MicL") would report a synthetic zero.
mic_peak_count = max(
(iv.peak_count("MicL") for iv in intervals if iv.has_channel("MicL")),
default=0,
)
mic_peak_psi = mic_count_to_psi(mic_peak_count) if mic_peak_count else None
rep = IdfReport(
serial_number=md.serial,
event_type="Full Histogram",
event_datetime=md.event_datetime,
filename=p.name,
sample_rate=md.sample_rate,
record_time_sec=md.record_time_sec,
)
peaks = IdfPeaks(
transverse_ips=peak_tran,
vertical_ips=peak_vert,
longitudinal_ips=peak_long,
peak_vector_sum_ips=None,
mic_pspl_dbl=None, # IDFH binary doesn't carry the dB(L) value
mic_pspl_psi=mic_peak_psi,
)
event = IdfEvent(
serial=md.serial or "UNKNOWN",
timestamp=md.event_datetime or datetime.datetime(1970, 1, 1),
kind="Histogram",
filename=p.name,
sample_rate=md.sample_rate,
record_time_sec=md.record_time_sec,
peaks=peaks,
report=rep,
)
return IdfReadResult(
event=event,
samples={},
binary_metadata=md,
signature=signature,
intervals=intervals,
)
# Waveform path.
decoded = _decode_waveform_samples(buf)
if decoded is None:
raise ValueError(f"{p.name}: waveform body codec failed")
rep = IdfReport(
serial_number=md.serial,
event_type="Full Waveform",
event_datetime=md.event_datetime,
filename=p.name,
sample_rate=md.sample_rate,
record_time_sec=md.record_time_sec,
)
def _peak_ips(ch: str) -> float:
arr = decoded.get(ch, [])
return geo_count_to_ips(max((abs(v) for v in arr), default=0))
# Mic peak psi from binary: max absolute MicL ADC count × 2.14e-6 psi/count.
mic_arr = decoded.get("MicL", [])
mic_peak_count = max((abs(v) for v in mic_arr), default=0)
mic_peak_psi = mic_count_to_psi(mic_peak_count) if mic_peak_count else None
peaks = IdfPeaks(
transverse_ips=_peak_ips("Tran"),
vertical_ips=_peak_ips("Vert"),
longitudinal_ips=_peak_ips("Long"),
# PVS requires aligned per-sample √(T²+V²+L²); leave None — the
# sidecar carries it and the bridge picks it up if present.
peak_vector_sum_ips=None,
mic_pspl_dbl=None, # binary IDFW doesn't carry the dB(L) value;
# sidecar .txt fills it via IdfReport.from_dict
mic_pspl_psi=mic_peak_psi,
)
event = IdfEvent(
serial=md.serial or "UNKNOWN",
timestamp=md.event_datetime or datetime.datetime(1970, 1, 1),
kind="Waveform",
filename=p.name,
sample_rate=md.sample_rate,
record_time_sec=md.record_time_sec,
peaks=peaks,
report=rep,
)
return IdfReadResult(
event=event,
samples=decoded,
binary_metadata=md,
signature=signature,
)
micromate/idf_to_bw_report.py
-323
View File
@@ -1,323 +0,0 @@
"""
micromate/idf_to_bw_report.py — adapter that projects a parsed Thor IDF
report (+ binary metadata + decoded IDFH intervals) into the
``bw_report``-shaped dict that :mod:`sfm.report_pdf.gather_report_data`
consumes.
Lets Thor events flow through the existing Series III Event Report PDF
pipeline without duplicating the renderer. Thor's report content is
~95% the same data shape as BW's; the field names differ but the
underlying metrics map 1:1.
Caveats
───────
- **Mic units** — Thor records ``MicPSPL`` natively in dB(L). This
adapter sets ``bw_report.mic.pspl_dbl`` directly; the report
renderer recomputes the equivalent psi via its dBL→psi formula.
- **Saturation / above-range flags** — Thor doesn't always mark
``OORANGE`` the way BW does; we set ``zc_freq_above_range`` only
when a `>100` sentinel was preserved in the raw text.
- **Per-interval data** — for IDFH events we build ``interval_times``
by stepping ``IntervalSize`` from ``HistogramStartTime``; the binary
decoder confirms one record per step (882 / 881 / 881 ... across
the corpus).
- **calibration_by parsing** — Thor's free-form ``Calibration : November
22, 2023 by Instantel`` is split on ``" by "`` to extract the
calibrator; the date prefix is parsed where possible, otherwise
the binary-extracted ``calibration_date`` from
:class:`micromate.idf_file.IdfBinaryMetadata` wins.
"""
from __future__ import annotations
import datetime
import re
from typing import Any, Dict, List, Optional
# ─── Helpers ────────────────────────────────────────────────────────────────
_NUM_RE = re.compile(r"-?\d+(?:\.\d+)?")
def _parse_first_number(s: Optional[str]) -> Optional[float]:
"""Pull the first numeric token from a string like ``"0.1500 in/s"``."""
if s is None:
return None
m = _NUM_RE.search(str(s))
if not m:
return None
try:
return float(m.group(0))
except ValueError:
return None
def _parse_interval_size_s(s: Optional[str]) -> Optional[float]:
"""``"60 sec"`` → 60.0, ``"5 min"`` → 300.0, ``"1 hour"`` → 3600."""
if s is None:
return None
num = _parse_first_number(s)
if num is None:
return None
sl = str(s).lower()
if "hour" in sl or "hr" in sl:
return num * 3600.0
if "min" in sl:
return num * 60.0
return num # default to seconds
def _parse_calibration(text: Optional[str]) -> tuple[Optional[str], Optional[str]]:
"""Split ``"November 22, 2023 by Instantel"`` → (ISO date, calibrator).
Returns ``(None, None)`` if neither half parses.
"""
if not text:
return None, None
parts = str(text).split(" by ", 1)
date_part = parts[0].strip() if parts else None
by_part = parts[1].strip() if len(parts) > 1 else None
iso_date: Optional[str] = None
if date_part:
for fmt in ("%B %d, %Y", "%b %d, %Y", "%Y-%m-%d", "%m/%d/%Y"):
try:
iso_date = datetime.datetime.strptime(date_part, fmt).date().isoformat()
break
except ValueError:
continue
return iso_date, by_part
def _channel_peaks(idf: Dict[str, Any], ch_lc: str) -> Dict[str, Any]:
"""Map ``tran_ppv`` / ``tran_zc_freq`` / ... → bw_report.peaks.tran shape."""
out: Dict[str, Any] = {}
for src, dst in (
(f"{ch_lc}_ppv", "ppv_ips"),
(f"{ch_lc}_zc_freq", "zc_freq_hz"),
(f"{ch_lc}_time_of_peak", "time_of_peak_s"),
(f"{ch_lc}_peak_acceleration", "peak_accel_g"),
(f"{ch_lc}_peak_displacement", "peak_disp_in"),
):
v = idf.get(src)
if v is not None:
out[dst] = v
# ZC freq ">100" sentinel: the raw text carries it under the un-typed
# key (e.g. ``raw["tran_zc_freq"]`` would be ``">100"``), and our parser
# dropped the typed entry. Detect that case and flag.
raw_zc = idf.get(f"{ch_lc}_zc_freq")
if isinstance(raw_zc, str) and ">" in raw_zc:
out["zc_freq_above_range"] = True
out.pop("zc_freq_hz", None)
return out
def _sensor_check(idf: Dict[str, Any], ch_lc: str) -> Dict[str, Any]:
out: Dict[str, Any] = {}
fr = idf.get(f"{ch_lc}_test_freq")
if fr is not None:
out["freq_hz"] = _parse_first_number(fr)
rt = idf.get(f"{ch_lc}_test_ratio")
if rt is not None:
out["ratio"] = _parse_first_number(rt)
am = idf.get(f"{ch_lc}_test_amplitude")
if am is not None:
out["amplitude_mv"] = _parse_first_number(am)
res = idf.get(f"{ch_lc}_test_results")
if res is not None:
out["result"] = str(res).strip()
return {k: v for k, v in out.items() if v is not None}
def _interval_times(idf: Dict[str, Any], n_intervals: Optional[int]) -> List[str]:
"""Synthesise per-interval timestamps from start + interval_size × k.
Returns ``[]`` when start time or interval size is unknown.
"""
if not n_intervals:
return []
start_date = idf.get("histogram_start_date") or idf.get("event_date")
start_time = idf.get("histogram_start_time") or idf.get("event_time")
iv_str = idf.get("interval_size")
iv_s = _parse_interval_size_s(iv_str)
if not (start_date and start_time and iv_s):
return []
try:
t0 = datetime.datetime.strptime(f"{start_date} {start_time}", "%Y-%m-%d %H:%M:%S")
except ValueError:
return []
out = []
for k in range(int(n_intervals)):
t = t0 + datetime.timedelta(seconds=iv_s * (k + 1))
out.append(t.isoformat())
return out
# ─── Top-level adapter ──────────────────────────────────────────────────────
def build_bw_report_from_idf(
idf_report: Dict[str, Any],
*,
binary_md=None,
intervals: Optional[list] = None,
is_histogram: Optional[bool] = None,
) -> Dict[str, Any]:
"""Project a parsed IDF report dict (and optional binary metadata +
decoded IDFH intervals) into the BW report sidecar shape.
The returned dict is structurally identical to what
``minimateplus.event_file_io._bw_report_to_dict`` produces from a
real BW ASCII report — it can be assigned to
``sidecar["bw_report"]`` and consumed verbatim by
``sfm.report_pdf.gather_report_data``.
``intervals`` is the list of :class:`micromate.idf_file.IdfhInterval`
objects from :func:`micromate.idf_file.decode_idfh_body`; only used
for histogram events to derive accurate ``interval_times``.
"""
if is_histogram is None:
et = str(idf_report.get("event_type", ""))
is_histogram = et.lower().startswith("full histogram")
# ── Trigger / recording / device ─────────────────────────────────────
trigger_channel = idf_report.get("trigger")
trigger_level = _parse_first_number(idf_report.get("geo_trigger_level"))
geo_range_ips = _parse_first_number(idf_report.get("geo_range"))
cal_iso, cal_by = _parse_calibration(idf_report.get("calibration"))
# Prefer the binary-extracted calibration_date when our text parse fell
# through; the binary date is unambiguous.
if cal_iso is None and binary_md is not None and binary_md.calibration_date:
cal_iso = binary_md.calibration_date.isoformat()
# ── Histogram fields ────────────────────────────────────────────────
hist_block: Dict[str, Any] = {
"start": None, "stop": None, "n_intervals": None,
"interval_size": None, "interval_size_s": None,
"channel_peak_when": {},
}
if is_histogram:
sd = idf_report.get("histogram_start_date")
st = idf_report.get("histogram_start_time")
if sd and st:
try:
hist_block["start"] = datetime.datetime.strptime(
f"{sd} {st}", "%Y-%m-%d %H:%M:%S"
).isoformat()
except ValueError:
pass
ed = idf_report.get("histogram_stop_date")
et_ = idf_report.get("histogram_stop_time")
if ed and et_:
try:
hist_block["stop"] = datetime.datetime.strptime(
f"{ed} {et_}", "%Y-%m-%d %H:%M:%S"
).isoformat()
except ValueError:
pass
n_raw = idf_report.get("number_of_intervals")
if n_raw is not None:
try:
# Thor reports a float like "81.04"; round to int (the BW
# report uses an int for the column).
hist_block["n_intervals"] = int(float(str(n_raw)))
except ValueError:
pass
# When the binary decoder gave us the actual interval count, prefer it.
if intervals is not None:
hist_block["n_intervals"] = len(intervals)
hist_block["interval_size"] = idf_report.get("interval_size")
hist_block["interval_size_s"] = _parse_interval_size_s(idf_report.get("interval_size"))
# interval_times derived from start+step (the BW report uses the
# exact strings; we match its representation).
times = _interval_times(idf_report, hist_block["n_intervals"])
# Per-channel peak when (absolute date+time at which the channel's
# peak occurred over the histogram run). Thor splits this into
# ``TranPeakDate`` / ``TranPeakTime`` etc.
peak_when: Dict[str, str] = {}
for ch_label, ch_lc in (("Tran", "tran"), ("Vert", "vert"), ("Long", "long"), ("MicL", "mic")):
d = idf_report.get(f"{ch_lc}_peak_date")
t = idf_report.get(f"{ch_lc}_peak_time")
if d and t:
try:
peak_when[ch_label] = datetime.datetime.strptime(
f"{d} {t}", "%Y-%m-%d %H:%M:%S"
).isoformat()
except ValueError:
continue
if peak_when:
hist_block["channel_peak_when"] = peak_when
# ── Mic block ────────────────────────────────────────────────────────
mic_block = {
"weighting": "L", # Thor mic is ISEE Linear
"pspl_dbl": idf_report.get("mic_ppv"), # the dB(L) float
"pspl_saturated": False,
"zc_freq_hz": idf_report.get("mic_zc_freq"),
"zc_freq_above_range": isinstance(idf_report.get("mic_zc_freq"), str)
and ">" in str(idf_report.get("mic_zc_freq")),
"time_of_peak_s": idf_report.get("mic_time_of_peak"),
}
if mic_block["zc_freq_above_range"]:
mic_block["zc_freq_hz"] = None
# ── Peaks ────────────────────────────────────────────────────────────
vs_block = {
"ips": idf_report.get("peak_vector_sum"),
"time_s": _parse_first_number(idf_report.get("peak_vector_sum_time_sum")),
"when": None,
"saturated": False,
}
if is_histogram:
# PVS absolute date+time, when present.
vs_d = idf_report.get("peak_vector_sum_date")
vs_t = idf_report.get("peak_vector_sum_time")
if vs_d and vs_t:
try:
vs_block["when"] = datetime.datetime.strptime(
f"{vs_d} {vs_t}", "%Y-%m-%d %H:%M:%S"
).isoformat()
except ValueError:
pass
return {
"available": True,
"event_type": idf_report.get("event_type"),
"version": idf_report.get("version"),
"trigger": {
"channel": trigger_channel,
"geo_level_ips": trigger_level,
},
"recording": {
"sample_rate_sps": idf_report.get("sample_rate"),
"record_time_s": idf_report.get("record_time_sec"),
"pretrig_s": idf_report.get("pre_trigger_sec"),
"stop_mode": idf_report.get("record_stop_mode"),
"geo_range_ips": geo_range_ips,
"units": idf_report.get("units"),
},
"device": {
"battery_volts": idf_report.get("battery_volts"),
"calibration_date": cal_iso,
"calibration_by": cal_by,
},
"peaks": {
"tran": _channel_peaks(idf_report, "tran"),
"vert": _channel_peaks(idf_report, "vert"),
"long": _channel_peaks(idf_report, "long"),
"vector_sum": vs_block,
},
"mic": mic_block,
"sensor_check": {
"tran": _sensor_check(idf_report, "tran"),
"vert": _sensor_check(idf_report, "vert"),
"long": _sensor_check(idf_report, "long"),
"mic": _sensor_check(idf_report, "mic"),
},
"histogram": hist_block,
"monitor_log": [],
"pc_sw_version": None,
}
micromate/models.py
-398
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@@ -1,398 +0,0 @@
"""
Micromate (Series IV / Thor) native data models.
These are the right-shaped dataclasses for Thor data — Thor measures
the microphone in dB(L) directly, so this model carries
``mic_pspl_dbl`` rather than the pseudo-``psi`` shoehorn that
``minimateplus.PeakValues`` uses for Series III BW data.
The ingest pipeline today goes:
.IDFW.txt → parse_idf_report() → dict
dict → IdfEvent.from_report() → IdfEvent (typed)
IdfEvent → IdfEvent.to_minimateplus_event() → shape DB / sidecar
machinery expects
The ``to_minimateplus_event()`` bridge is a temporary boundary — when we
crack the binary IDF codec and have richer per-event data to store, the
DB schema will grow Series-IV-specific columns and the bridge will
shrink or disappear.
"""
from __future__ import annotations
import datetime
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Tuple
# ── IdfReport ─────────────────────────────────────────────────────────────────
@dataclass
class IdfReport:
"""Typed wrapper around the dict returned by ``parse_idf_report``.
All fields optional — Thor's exporter is permissive and some IDF .txt
files (especially histograms) omit fields that waveform sidecars
include. Use ``.raw`` for any field this dataclass hasn't surfaced
yet (the parser keeps every recognised key in the raw dict).
"""
# Identity / kind
serial_number: Optional[str] = None
event_type: Optional[str] = None # "Full Waveform" | "Full Histogram"
event_datetime: Optional[datetime.datetime] = None
filename: Optional[str] = None # echoed by Thor's exporter
# Sampling / timing
sample_rate: Optional[int] = None # samples/sec
record_time_sec: Optional[float] = None
pre_trigger_sec: Optional[float] = None
# Geophone peaks (in/s)
tran_ppv: Optional[float] = None
vert_ppv: Optional[float] = None
long_ppv: Optional[float] = None
peak_vector_sum: Optional[float] = None
# Microphone — Thor's native unit is dB(L), NOT psi.
mic_pspl_dbl: Optional[float] = None
# Zero-crossing frequencies (Hz)
tran_zc_freq: Optional[float] = None
vert_zc_freq: Optional[float] = None
long_zc_freq: Optional[float] = None
mic_zc_freq: Optional[float] = None
# Per-channel time of peak (sec, since event start)
tran_time_of_peak: Optional[float] = None
vert_time_of_peak: Optional[float] = None
long_time_of_peak: Optional[float] = None
mic_time_of_peak: Optional[float] = None
# Derived per-channel motion
tran_peak_acceleration: Optional[float] = None # g
vert_peak_acceleration: Optional[float] = None
long_peak_acceleration: Optional[float] = None
tran_peak_displacement: Optional[float] = None # in
vert_peak_displacement: Optional[float] = None
long_peak_displacement: Optional[float] = None
# Operator-supplied strings (Thor's TitleString1..4 → semantic slots)
project: Optional[str] = None # TitleString1
client: Optional[str] = None # TitleString2
operator: Optional[str] = None # TitleString3
notes: Optional[str] = None # TitleString4 / PostEventNote
setup: Optional[str] = None # setup file name
# Sensor self-check results
tran_test_passed: Optional[bool] = None
vert_test_passed: Optional[bool] = None
long_test_passed: Optional[bool] = None
mic_test_passed: Optional[bool] = None
# Device-fixed metadata
firmware_version: Optional[str] = None
calibration_text: Optional[str] = None
battery_volts: Optional[float] = None
# Original parser dict — preserves every recognised key (including
# raw unit-suffixed strings) for forward-compatible field access.
raw: Dict[str, Any] = field(default_factory=dict, repr=False)
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "IdfReport":
"""Build an IdfReport from the dict returned by ``parse_idf_report``."""
ed = d.get("event_datetime")
if isinstance(ed, str):
try:
ed = datetime.datetime.fromisoformat(ed)
except ValueError:
ed = None
return cls(
serial_number = d.get("serial_number"),
event_type = d.get("event_type"),
event_datetime = ed if isinstance(ed, datetime.datetime) else None,
filename = d.get("filename"),
sample_rate = d.get("sample_rate"),
record_time_sec = d.get("record_time_sec"),
pre_trigger_sec = d.get("pre_trigger_sec"),
tran_ppv = d.get("tran_ppv"),
vert_ppv = d.get("vert_ppv"),
long_ppv = d.get("long_ppv"),
peak_vector_sum = d.get("peak_vector_sum"),
mic_pspl_dbl = d.get("mic_ppv"), # parser names it mic_ppv (legacy)
tran_zc_freq = d.get("tran_zc_freq"),
vert_zc_freq = d.get("vert_zc_freq"),
long_zc_freq = d.get("long_zc_freq"),
mic_zc_freq = d.get("mic_zc_freq"),
tran_time_of_peak = d.get("tran_time_of_peak"),
vert_time_of_peak = d.get("vert_time_of_peak"),
long_time_of_peak = d.get("long_time_of_peak"),
mic_time_of_peak = d.get("mic_time_of_peak"),
tran_peak_acceleration = d.get("tran_peak_acceleration"),
vert_peak_acceleration = d.get("vert_peak_acceleration"),
long_peak_acceleration = d.get("long_peak_acceleration"),
tran_peak_displacement = d.get("tran_peak_displacement"),
vert_peak_displacement = d.get("vert_peak_displacement"),
long_peak_displacement = d.get("long_peak_displacement"),
project = d.get("project"),
client = d.get("client"),
operator = d.get("operator"),
notes = d.get("notes"),
setup = d.get("setup"),
tran_test_passed = d.get("tran_test_passed"),
vert_test_passed = d.get("vert_test_passed"),
long_test_passed = d.get("long_test_passed"),
mic_test_passed = d.get("mic_test_passed"),
firmware_version = d.get("version"),
calibration_text = d.get("calibration_text"),
battery_volts = d.get("battery_volts"),
raw = d,
)
# ── IdfPeaks / IdfProjectInfo / IdfSensorCheck (narrow grouping types) ───────
@dataclass
class IdfPeaks:
"""Geophone + mic peak values for one Thor event. Native Thor units.
Thor stores the mic peak in two parallel forms — ``mic_pspl_dbl`` is
what the sidecar's top-level ``MicPSPL`` header field carries (dB(L)),
used in the report header. ``mic_pspl_psi`` is the psi value derived
either from the IDFW sample table / IDFH interval column 9, or from
the binary mic counts (~2.14e-6 psi/count). Needed because the
BW-shaped ``PeakValues.micl`` consumed by ``event_hdf5.write_event_hdf5``
expects psi — feeding it dB(L) makes the h5 mic-chart scale factor
blow up.
"""
transverse_ips: Optional[float] = None # in/s
vertical_ips: Optional[float] = None # in/s
longitudinal_ips: Optional[float] = None # in/s
peak_vector_sum_ips: Optional[float] = None # in/s
mic_pspl_dbl: Optional[float] = None # dB(L)
mic_pspl_psi: Optional[float] = None # psi
@dataclass
class IdfProjectInfo:
"""Operator-supplied strings from Thor's TitleString1..4."""
project: Optional[str] = None
client: Optional[str] = None
operator: Optional[str] = None
notes: Optional[str] = None
setup: Optional[str] = None
@dataclass
class IdfSensorCheck:
"""Per-channel pass/fail from Thor's self-test."""
tran: Optional[bool] = None
vert: Optional[bool] = None
long: Optional[bool] = None
mic: Optional[bool] = None
# ── IdfEvent ─────────────────────────────────────────────────────────────────
@dataclass
class IdfEvent:
"""A single Thor / Micromate Series IV event.
Built from a parsed .IDFW.txt or .IDFH.txt sidecar via
``IdfEvent.from_report()``. The filename is the authoritative
source for serial + timestamp + kind; the .txt provides
device-authoritative peak values, frequencies, project strings,
sensor self-check, firmware, calibration.
"""
# Identity
serial: str
timestamp: datetime.datetime
kind: str # "Waveform" | "Histogram"
filename: str # device-native binary filename, e.g. "UM11719_20231219163444.IDFW"
# Sampling / timing
sample_rate: Optional[int] = None
record_time_sec: Optional[float] = None
pre_trigger_sec: Optional[float] = None
# Peaks
peaks: IdfPeaks = field(default_factory=IdfPeaks)
# Per-channel frequencies (Hz)
tran_zc_freq: Optional[float] = None
vert_zc_freq: Optional[float] = None
long_zc_freq: Optional[float] = None
mic_zc_freq: Optional[float] = None
# Project strings
project_info: IdfProjectInfo = field(default_factory=IdfProjectInfo)
# Sensor self-check
sensor_check: IdfSensorCheck = field(default_factory=IdfSensorCheck)
# Device-fixed
firmware_version: Optional[str] = None
calibration_text: Optional[str] = None
battery_volts: Optional[float] = None
# The full parsed report — preserves anything not surfaced as a typed field
report: IdfReport = field(default_factory=IdfReport)
@classmethod
def from_report(
cls,
report: Any,
filename: str,
) -> "IdfEvent":
"""Build an IdfEvent from a parsed report (dict or IdfReport) and
the device-native binary filename.
The filename is authoritative for serial + timestamp + kind:
Thor's filenames are literal ``<SERIAL>_<YYYYMMDDHHMMSS>.<KIND>``
and the device's own clock is the canonical event timestamp.
If the report carries an ``event_datetime`` that differs from
what's in the filename, the report wins (it has finer-grained
device-reported time-of-trigger semantics).
"""
from .idf_ascii_report import parse_event_filename
# Normalise input to IdfReport
if isinstance(report, IdfReport):
rep = report
elif isinstance(report, dict):
rep = IdfReport.from_dict(report)
else:
raise TypeError(
f"report must be IdfReport or dict; got {type(report).__name__}"
)
# Filename → (serial, timestamp, kind). Required — fall back to
# report-supplied values only if filename parsing fails.
parsed = parse_event_filename(filename)
if parsed is not None:
fn_serial, fn_ts, fn_kind = parsed
kind = "Histogram" if fn_kind == "IDFH" else "Waveform"
else:
fn_serial = rep.serial_number or "UNKNOWN"
fn_ts = rep.event_datetime or datetime.datetime(1970, 1, 1)
kind = "Waveform" if (rep.event_type or "").lower().startswith("full waveform") else "Histogram"
# Prefer report's event_datetime (device-authoritative) over the filename.
ts = rep.event_datetime or fn_ts
serial = rep.serial_number or fn_serial
return cls(
serial=serial,
timestamp=ts,
kind=kind,
filename=filename,
sample_rate=rep.sample_rate,
record_time_sec=rep.record_time_sec,
pre_trigger_sec=rep.pre_trigger_sec,
peaks=IdfPeaks(
transverse_ips = rep.tran_ppv,
vertical_ips = rep.vert_ppv,
longitudinal_ips = rep.long_ppv,
peak_vector_sum_ips = rep.peak_vector_sum,
mic_pspl_dbl = rep.mic_pspl_dbl,
),
tran_zc_freq=rep.tran_zc_freq,
vert_zc_freq=rep.vert_zc_freq,
long_zc_freq=rep.long_zc_freq,
mic_zc_freq=rep.mic_zc_freq,
project_info=IdfProjectInfo(
project=rep.project,
client=rep.client,
operator=rep.operator,
notes=rep.notes,
setup=rep.setup,
),
sensor_check=IdfSensorCheck(
tran=rep.tran_test_passed,
vert=rep.vert_test_passed,
long=rep.long_test_passed,
mic=rep.mic_test_passed,
),
firmware_version=rep.firmware_version,
calibration_text=rep.calibration_text,
battery_volts=rep.battery_volts,
report=rep,
)
# ── Bridge to minimateplus shape (for the existing DB / sidecar paths) ──
def to_minimateplus_event(self, waveform_key: bytes) -> Any:
"""Project this Thor event into the shape ``minimateplus.Event``
carries, so it can flow through the existing
``SeismoDb.insert_events()`` and ``event_to_sidecar_dict()``
machinery without those code paths needing to know about Thor.
Caveats of the bridge:
- ``PeakValues.micl`` carries the mic peak in **psi** (matching
BW's convention) — set from :attr:`IdfPeaks.mic_pspl_psi`,
with a dB(L)→psi fallback when only the dB(L) value is
available. This is what the h5 writer's mic-scale-factor
logic needs. The dB(L) value still flows through
``bw_report.mic.pspl_dbl`` (set by the
``idf_to_bw_report`` adapter) and the renderer reads it
from there for the report header.
- Many Thor-specific fields (Peak Acceleration / Displacement,
sensor self-check, calibration) don't have a slot in
``Event``. The full IdfReport is preserved on the
``.sfm.json`` sidecar under ``extensions.idf_report`` via
``save_imported_idf`` — that's the source of truth for them.
"""
from minimateplus.models import (
Event, PeakValues, ProjectInfo, Timestamp,
)
ts_obj = Timestamp(
raw=bytes(9),
flag=0,
year=self.timestamp.year,
unknown_byte=0,
month=self.timestamp.month,
day=self.timestamp.day,
hour=self.timestamp.hour,
minute=self.timestamp.minute,
second=self.timestamp.second,
)
# Resolve mic peak as psi. Priority: binary-derived mic_pspl_psi
# (set by read_idf_file) > dB(L)→psi fallback via standard formula
# (psi = 2.9e-9 × 10^(dBL/20)) > None.
mic_psi = self.peaks.mic_pspl_psi
if mic_psi is None and self.peaks.mic_pspl_dbl is not None:
mic_psi = 2.9e-9 * (10.0 ** (self.peaks.mic_pspl_dbl / 20.0))
pv = PeakValues(
tran=self.peaks.transverse_ips,
vert=self.peaks.vertical_ips,
long=self.peaks.longitudinal_ips,
micl=mic_psi, # psi, matching BW's convention (h5 scaling depends on this)
peak_vector_sum=self.peaks.peak_vector_sum_ips,
)
pi = ProjectInfo(
setup_name=self.project_info.setup,
project=self.project_info.project,
client=self.project_info.client,
operator=self.project_info.operator,
sensor_location=None, # Thor folds location into project string
notes=self.project_info.notes,
)
ev = Event(
index=0,
timestamp=ts_obj,
sample_rate=self.sample_rate,
peak_values=pv,
project_info=pi,
record_type=self.kind,
rectime_seconds=self.record_time_sec,
)
ev._waveform_key = waveform_key
return ev
micromate/sensor_check.py
-89
View File
@@ -1,89 +0,0 @@
r"""Decode the Thor / Micromate (series-4) sensor self-check waveforms from an
IDFW event binary.
Reverse-engineered 2026-09-15 against 4 UM (Thor) oracle events. The IDFW
binary carries the sensor self-check in its fixed-header region (before the
waveform body), as up to four records tagged ``01 0e 3c/3d/3e/3f`` — the SAME
channel ids as the series-3 MiniMate Plus (Tran / Vert / Long / MicL), which is
the physical self-test:
* 3c / 3d / 3e = Tran / Vert / Long geophone ring-downs (a damped impulse
response — resonant frequency + damping).
* 3f = MicL pulse train (the mic's known-signal gain check). Absent
on three-channel (mic-disabled) units.
Record framing (per record)::
01 0e [id:1] [flags:3] [count:2 BE] [pad:10] [int16-BE samples × count]
\___ 18-byte header ___/
Unlike series-3's delta-coded trailing block, series-4 stores each trace as a
raw int16 big-endian array. ``count`` (the 2-byte field at header offset +8)
is the sample count; the record is padded to a fixed stride after that.
"""
from __future__ import annotations
import struct
from typing import Dict, List
# Record id → channel. Same ids/order as series-3 (minimateplus.sensor_check).
_ID_TO_CHANNEL = {0x3C: "Tran", 0x3D: "Vert", 0x3E: "Long", 0x3F: "MicL"}
_CHAIN_IDS = (0x3C, 0x3D, 0x3E, 0x3F)
_MARKER = b"\x01\x0e" # precedes the 1-byte channel id
_HEADER_LEN = 18 # bytes from the marker start to the first sample
_COUNT_OFF = 8 # 2-byte BE sample count, from the marker start
_MAX_COUNT = 4000 # sanity cap (traces are ~70-200 samples)
def _find_chain(raw: bytes):
"""Locate the sensor-check record chain. Returns a list of
``(offset, id, count)`` for the first run of markers whose ids run
3c, 3d, 3e[, 3f] in order, or ``[]``.
Records are padded to a fixed stride, so the next marker is not at
``header + count*2``; instead collect every ``01 0e [id]`` marker with a
sane count and take the first id-ordered run. Validating the id sequence
(not a lone ``01 0e 3c``) keeps a stray marker in the waveform body from
matching — the real chain sits in the fixed header, ahead of the body.
"""
n = len(raw)
markers = []
for p in range(n - _HEADER_LEN):
if raw[p:p + 2] == _MARKER and raw[p + 2] in _ID_TO_CHANNEL:
count = int.from_bytes(raw[p + _COUNT_OFF:p + _COUNT_OFF + 2], "big")
if 0 < count <= _MAX_COUNT:
markers.append((p, raw[p + 2], count))
for i, (off, rid, _c) in enumerate(markers):
if rid != 0x3C:
continue
run = [markers[i]]
for m in markers[i + 1:]:
if len(run) < len(_CHAIN_IDS) and m[1] == _CHAIN_IDS[len(run)]:
run.append(m)
else:
break
if len(run) >= 3: # 3-channel (mic-disabled) units are valid
return run
return []
def decode_idf_sensor_check(raw: bytes) -> Dict[str, List[int]]:
"""Decode the sensor self-check traces from a Thor/Micromate IDFW binary.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}`` in
raw int16 ADC counts (MicL omitted on 3-channel units), or ``{}`` if the
binary carries no sensor-check chain (a non-IDF file, or an IDFH histogram).
"""
chain = _find_chain(raw)
if not chain:
return {}
out: Dict[str, List[int]] = {}
for off, rid, count in chain:
start = off + _HEADER_LEN
blob = raw[start:start + count * 2]
if len(blob) < count * 2:
continue
out[_ID_TO_CHANNEL[rid]] = list(struct.unpack(">%dh" % count, blob))
return out
minimateplus/binary_annotate.py
-75
View File
@@ -1,75 +0,0 @@
"""Structural annotation of a Series-3 Blastware waveform binary.
Pure, no I/O: takes the raw file bytes and returns a flat, gap-free tiling of
labelled :class:`Span` regions for a hex viewer to paint. Every byte is
covered — anything the decoder can't account for becomes an ``unknown`` span,
so undecoded regions (e.g. a stored spectral/FFT block, if one exists) stand
out instead of hiding.
File layout (see ``blastware_file.py``): ``[header][21B STRT][body][26B footer]``.
The body is the record chain walked by :func:`waveform_codec.walk_records`.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import List
from .waveform_codec import walk_records
_STRT_LEN = 21
_FOOTER_LEN = 26
@dataclass
class Span:
start: int # inclusive byte offset
end: int # exclusive byte offset
label: str # human-readable description
kind: str # 'header' | 'strt' | 'sample' | 'footer' | 'unknown'
def _tile(known: List[Span], total: int) -> List[Span]:
"""Sort *known* spans and fill every gap with an ``unknown`` span, so the
result is a contiguous, non-overlapping tiling of ``[0, total)``. Overlaps
are resolved by clamping to the running position (first writer wins)."""
out: List[Span] = []
pos = 0
for s in sorted(known, key=lambda x: (x.start, x.end)):
if s.end <= pos:
continue # fully behind — dropped overlap
start = max(s.start, pos)
if start > pos:
out.append(Span(pos, start, "unknown", "unknown"))
out.append(s if start == s.start else Span(start, s.end, s.label, s.kind))
pos = s.end
if pos < total:
out.append(Span(pos, total, "unknown", "unknown"))
return out
def annotate_blastware_binary(raw: bytes) -> List[Span]:
"""Annotate a Series-3 waveform binary into a gap-free list of spans."""
total = len(raw)
strt_pos = raw.find(b"STRT")
if strt_pos < 0:
return [Span(0, total, "unrecognized — no STRT record", "unknown")]
known: List[Span] = []
if strt_pos > 0:
known.append(Span(0, strt_pos, "File header", "header"))
known.append(Span(strt_pos, strt_pos + _STRT_LEN, "STRT record", "strt"))
body_start = strt_pos + _STRT_LEN
footer_start = total - _FOOTER_LEN
if footer_start >= body_start:
known.append(Span(footer_start, total, "File footer", "footer"))
else:
footer_start = total # file too short for a footer
body = raw[body_start:footer_start]
for rec in walk_records(body):
hi, lo = rec["mode"]
label = f"{rec['channel']} record (seg {rec['segment_index']}, mode {hi:02x} {lo:02x})"
known.append(Span(body_start + rec["offset"], body_start + rec["end"], label, "sample"))
return _tile(known, total)
minimateplus/blastware_file.py
-99
View File
@@ -552,105 +552,6 @@ def classify_frame(frame: S3Frame) -> str:
# ── Waveform file writer ───────────────────────────────────────────────────────────
def extract_body_bytes(a5_frames):
"""Reconstruct the Blastware-file body bytes from a list of A5 frames.
Returns ``(strt, body, footer)`` where:
- ``strt`` is the 21-byte STRT record from the probe frame (or a fallback
record built from minimal event metadata if STRT is missing).
- ``body`` is the variable-length sample-data section (between STRT and
the 26-byte file footer). Empty if no frames decode.
- ``footer`` is the 26-byte file footer.
This is the same body-construction algorithm used by :func:`write_blastware_file`
— refactored out so the body decoder (``waveform_codec.decode_waveform_v2``)
can consume the same bytes without re-implementing the frame-walking logic.
Returns ``(b"", b"", b"")`` if *a5_frames* is empty.
"""
if not a5_frames:
return (b"", b"", b"")
# ── Extract STRT record from probe frame ─────────────────────────────────
w0_raw = bytes(a5_frames[0].data[7:])
w0_stripped = _strip_inner_frame_dles(w0_raw)
strt_pos_stripped = w0_stripped.find(b"STRT")
if strt_pos_stripped >= 0:
strt = bytes(w0_stripped[strt_pos_stripped : strt_pos_stripped + 21])
# Walk raw bytes to find the raw-domain end of the STRT (= body start).
target_stripped = strt_pos_stripped + 21
stripped_so_far = 0
raw_i = 0
while stripped_so_far < target_stripped and raw_i < len(w0_raw):
if (w0_raw[raw_i] == 0x10
and raw_i + 1 < len(w0_raw)
and w0_raw[raw_i + 1] in {0x02, 0x03, 0x04}):
raw_i += 2
else:
raw_i += 1
stripped_so_far += 1
probe_skip = 7 + raw_i
else:
strt = b"STRT" + b"\xff\xfe" + bytes(14) + b"\x00"
probe_skip = 7 + 21
if len(strt) != 21:
return (b"", b"", b"")
# Separate terminator from data frames.
term_idx: Optional[int] = None
if a5_frames and a5_frames[-1].page_key != 0x0010:
term_idx = len(a5_frames) - 1
if term_idx is not None:
body_frames = a5_frames[:term_idx]
term_frame = a5_frames[term_idx]
else:
body_frames = a5_frames
term_frame = None
all_bytes = bytearray()
for fi, frame in enumerate(body_frames):
if fi == 0:
skip = probe_skip
elif fi in (1, 2):
skip = 13 # metadata pages
else:
skip = 12 # sample chunks
all_bytes.extend(_frame_body_bytes(frame, skip))
if term_frame is not None:
all_bytes.extend(_frame_body_bytes(term_frame, 11))
# Find the first valid `0e 08` footer marker.
footer_pos = -1
pos = 0
while True:
pos = bytes(all_bytes).find(b"\x0e\x08", pos)
if pos < 0 or pos + 26 > len(all_bytes):
break
yr = (all_bytes[pos + 4] << 8) | all_bytes[pos + 5]
if 2015 <= yr <= 2050:
footer_pos = pos
break
pos += 1
if footer_pos >= 0:
body = bytes(all_bytes[:footer_pos])
footer = bytes(all_bytes[footer_pos : footer_pos + 26])
elif len(all_bytes) >= 26:
body = bytes(all_bytes[:-26])
footer = bytes(all_bytes[-26:])
else:
body = bytes(all_bytes)
footer = b""
return (strt, body, footer)
def write_blastware_file(
event: Event,
a5_frames: list[S3Frame],
minimateplus/bw_ascii_report.py
-738
View File
@@ -1,738 +0,0 @@
"""
minimateplus/bw_ascii_report.py — parser for Blastware's per-event ASCII
report (the .TXT file BW writes alongside each saved event binary).
The ASCII export is the authoritative source for every "rich" per-event
field that BW computes from the waveform but never persists in the BW
binary itself:
- Per-channel PPV (Tran / Vert / Long / MicL)
- Peak Vector Sum + Peak Vector Sum Time
- Per-channel ZC Freq, Time of Peak, Peak Acceleration, Peak Displacement
- MicL PSPL, MicL Time of Peak, MicL ZC Freq
- Per-channel Sensor Self-Check (Test Freq / Test Ratio / Test Results)
- MicL Test Amplitude (mV)
- Battery, calibration date, monitor-log timestamps
Persisting these values into the SFM database lets the monthly-summary
review workflow ("show me events at Location X with PVS > 0.5") work
without depending on the (still-undecoded) waveform body codec.
Format (verified against decode-re/5-8-26 4-event bundle):
- One field per line, wrapped in double quotes: `"Field Name : Value"`
- Field/value separator: literal ` : ` (space-colon-space).
- Some field names contain an internal `:` already (e.g. `"Project:"`),
so we split on the FIRST ` : ` only.
- Some fields have unit suffixes: `"0.500 in/s"` / `"7.5 Hz"` / `"533 mv"`.
- A `"Monitor Log(s)"` marker line is followed by tab-separated rows
of `start_time<TAB>stop_time<TAB>description`.
- Final `"PC SW Version : ..."` line ends the metadata block.
- A blank line separates metadata from the sample table.
- Sample table starts with ` Tran <TAB> Vert <TAB>...`, then
one row per sample (tab-separated, right-padded numeric values).
- Geo channel values are in in/s; MicL in dB(L) (or 0.000 below threshold).
Because some metadata fields have whitespace quirks ("MicL Time of
Peak" has two spaces; the leading "Project:" value has its own colon),
we normalise whitespace in the key before lookup.
"""
from __future__ import annotations
import datetime
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
# ─────────────────────────────────────────────────────────────────────────────
# Output dataclasses
# ─────────────────────────────────────────────────────────────────────────────
@dataclass
class ChannelStats:
"""Per-channel derived stats, populated from an event report."""
ppv_ips: Optional[float] = None # in/s (geo channels only)
zc_freq_hz: Optional[float] = None # Hz
time_of_peak_s: Optional[float] = None # seconds (relative to trigger; can be negative)
peak_accel_g: Optional[float] = None # g (geo channels only)
peak_disp_in: Optional[float] = None # in (geo channels only)
# When BW writes "OORANGE" (Out Of Range — truncated) for a PPV
# value, the true peak exceeded the channel's full-scale range.
# We substitute the range max (e.g. 10.000 in/s for Normal range)
# as a lower bound, and flag here so downstream UI / alerts know
# to render "> 10 in/s" or "saturated" instead of trusting the
# value as an exact measurement.
ppv_saturated: bool = False
# Set when BW writes ">100 Hz" for ZC Freq — the zero-crossing
# algorithm's peak frequency exceeded the device's reporting
# ceiling (typically 100 Hz on V10.72). zc_freq_hz gets the
# threshold (100.0) as a lower bound; downstream UI renders ">100".
zc_freq_above_range: bool = False
@dataclass
class MicStats:
"""MicL-specific stats."""
weighting: Optional[str] = None # e.g. "Linear Weighting"
pspl_dbl: Optional[float] = None # dB(L)
zc_freq_hz: Optional[float] = None
time_of_peak_s: Optional[float] = None
# Set when BW writes "OORANGE" for PSPL — mic exceeded its
# measurement range. pspl_dbl gets the conservative upper bound
# 140 dBL (typical NL-43 max; some units cap at 148). Consumers
# should render "> 140 dB(L)" or similar when this flag is set.
pspl_saturated: bool = False
# Same semantics as ChannelStats.zc_freq_above_range — mic ZC
# peak exceeded device reporting ceiling.
zc_freq_above_range: bool = False
@dataclass
class SensorCheck:
"""Per-channel sensor self-check result.
Geo channels report a frequency + ratio; MicL reports a frequency +
amplitude (mV). All channels also have a Pass/Fail string.
"""
test_freq_hz: Optional[float] = None
test_ratio: Optional[float] = None # geo channels only
test_amplitude_mv: Optional[float] = None # MicL only
test_results: Optional[str] = None # "Passed" / "Failed"
@dataclass
class MonitorLogEntry:
"""One row of the trailing Monitor Log(s) block."""
start_time: Optional[datetime.datetime] = None
stop_time: Optional[datetime.datetime] = None
description: Optional[str] = None
# BW saturation marker — appears in PPV / Peak Vector Sum / similar
# numeric fields when the underlying measurement exceeded the
# channel's full-scale range (e.g., a geophone reading > 10 in/s at
# Normal range, or a mic exceeding its sensitivity ceiling). Treated
# as "≥ range_max" + a saturated flag rather than discarded.
# Appears as: ``"Tran PPV : OORANGE in/s"``
_OORANGE_MARKERS = ("OORANGE", "OUT OF RANGE")
def _is_oorange(value: str) -> bool:
"""True when a BW numeric field is an Out-Of-Range saturation marker."""
s = value.strip().upper()
return any(m in s for m in _OORANGE_MARKERS)
def _parse_above_range(value: str) -> Optional[float]:
"""For BW "above-range" markers like ">100 Hz", return the threshold.
BW writes ZC Freq as ">100 Hz" when the zero-crossing algorithm sees
a peak too fast to count (device cuts off at 100 Hz). Returns the
numeric portion after the '>' (e.g. 100.0), or None if `value` is
not an above-range marker.
"""
s = value.strip()
if not s.startswith(">"):
return None
return _parse_number(s[1:])
@dataclass
class BwAsciiReport:
"""Structured representation of one BW per-event ASCII export."""
# ── Identity ─────────────────────────────────────────────────────────────
event_type: Optional[str] = None # e.g. "Full Waveform"
serial: Optional[str] = None # e.g. "BE11529"
version: Optional[str] = None # firmware version line
file_name: Optional[str] = None # e.g. "M529LK44.AB0"
event_datetime: Optional[datetime.datetime] = None # parsed from Event Time + Event Date
# ── Trigger / recording config ──────────────────────────────────────────
trigger_channel: Optional[str] = None # e.g. "Vert" or "From Unit"
geo_trigger_level_ips: Optional[float] = None
pretrig_s: Optional[float] = None # negative seconds
record_time_s: Optional[float] = None
record_stop_mode: Optional[str] = None
sample_rate_sps: Optional[int] = None
battery_volts: Optional[float] = None
calibration_date: Optional[datetime.date] = None
calibration_by: Optional[str] = None # e.g. "Instantel"
units: Optional[str] = None # e.g. "in/s and dB(L)"
# ── Operator-supplied metadata ──────────────────────────────────────────
# Parsed by POSITION from the 4-line "User Notes" block BW writes
# between the `Units :` and `Geo Range :` lines. Position-based so
# the values populate correctly even when an operator renames the
# labels in Blastware's Compliance Setup → Notes tab (the 4 labels
# are user-editable, e.g. "Seis Loc:" → "Building:" → "Site Address:").
# The original labels BW wrote are preserved in `user_note_labels`
# so terra-view can render them as the operator named them.
project: Optional[str] = None # position 1 (BW default label "Project:")
client: Optional[str] = None # position 2 (BW default label "Client:")
operator: Optional[str] = None # position 3 (BW default label "User Name:")
sensor_location: Optional[str] = None # position 4 (BW default label "Seis Loc:")
# Maps canonical slot name → the literal label BW wrote in the ASCII
# export. Empty if the User Notes block wasn't present. Example
# when the operator renamed slot 4 to "Building:":
# {"project": "Project:", "client": "Client:",
# "operator": "User Name:", "sensor_location": "Building:"}
user_note_labels: Dict[str, str] = field(default_factory=dict)
# ── Geo channel scaling ─────────────────────────────────────────────────
geo_range_ips: Optional[float] = None # 10.000 / 1.250
# ── Per-channel derived stats (geo + mic) ───────────────────────────────
channels: Dict[str, ChannelStats] = field(default_factory=dict)
mic: MicStats = field(default_factory=MicStats)
# ── Vector sum ──────────────────────────────────────────────────────────
peak_vector_sum_ips: Optional[float] = None
peak_vector_sum_time_s: Optional[float] = None
# Saturation flag — set when BW writes "OORANGE" for the PVS. We
# then substitute sqrt(3) * geo_range_ips as a conservative upper
# bound (the theoretical maximum PVS when all 3 geo channels are
# simultaneously at full-scale). Consumers should display this as
# ">{value} in/s" or similar.
peak_vector_sum_saturated: bool = False
# Histograms additionally have an absolute date+time for the PVS
# (it occurred at a specific interval). Waveform reports show
# only the relative-time value above.
peak_vector_sum_when: Optional[datetime.datetime] = None
# ── Histogram-specific fields (populated only when Event Type starts
# with 'Histogram' / 'Full Histogram' / 'Histogram + Continuous') ──
histogram_start: Optional[datetime.datetime] = None
histogram_stop: Optional[datetime.datetime] = None
histogram_n_intervals: Optional[int] = None # e.g. 4, 1436
histogram_interval_size_str: Optional[str] = None # "1 minute" / "5 minutes" / "15 seconds"
histogram_interval_size_s: Optional[float] = None # parsed to seconds
# Per-channel absolute peak time+date (histogram-specific). For
# waveform events these are None — those reports use the channel's
# time_of_peak_s (relative to trigger) instead. Keyed by channel
# name ("Tran", "Vert", "Long", "MicL").
channel_peak_when: Dict[str, datetime.datetime] = field(default_factory=dict)
# ── Sensor self-check (per channel) ─────────────────────────────────────
sensor_check: Dict[str, SensorCheck] = field(default_factory=dict)
# ── Monitor log + tooling version ───────────────────────────────────────
monitor_log: List[MonitorLogEntry] = field(default_factory=list)
pc_sw_version: Optional[str] = None
# ── Sample table (optional; only parsed if requested) ───────────────────
# Each entry: (Tran, Vert, Long, MicL) in the report's units (geo
# channels in in/s, MicL in dB(L)). None when parse_samples=False.
samples: Optional[List[Tuple[float, float, float, float]]] = None
# ─────────────────────────────────────────────────────────────────────────────
# Helpers
# ─────────────────────────────────────────────────────────────────────────────
_KEY_NORMALISE_RE = re.compile(r"\s+")
_NUMERIC_RE = re.compile(r"^-?\d+(?:\.\d+)?")
def _normalise_key(k: str) -> str:
"""Collapse whitespace runs (incl. tabs) and strip — handles BW's
"MicL Time of Peak" double-space and leading-colon quirks."""
return _KEY_NORMALISE_RE.sub(" ", k).strip()
def _strip_quotes(line: str) -> str:
line = line.rstrip("\r\n")
if len(line) >= 2 and line.startswith('"') and line.endswith('"'):
return line[1:-1]
return line
def _parse_number(value: str) -> Optional[float]:
"""Pull the leading numeric portion out of a value like "0.500 in/s"."""
m = _NUMERIC_RE.match(value.strip())
if not m:
return None
try:
return float(m.group(0))
except ValueError:
return None
def _parse_int(value: str) -> Optional[int]:
n = _parse_number(value)
return None if n is None else int(round(n))
# Months exactly as BW writes them.
_MONTHS = {
"January": 1, "February": 2, "March": 3, "April": 4,
"May": 5, "June": 6, "July": 7, "August": 8,
"September": 9, "October": 10, "November": 11, "December": 12,
# Short forms used in monitor-log rows ("Apr 23 /26").
"Jan": 1, "Feb": 2, "Mar": 3, "Apr": 4, "Jun": 6, "Jul": 7,
"Aug": 8, "Sep": 9, "Oct": 10, "Nov": 11, "Dec": 12,
}
def _parse_event_date(s: str) -> Optional[datetime.date]:
"""Parse "April 23, 2026" or "May 8, 2026" → date."""
s = s.strip()
parts = s.replace(",", " ").split()
if len(parts) < 3:
return None
month_name, day_str, year_str = parts[0], parts[1], parts[2]
month = _MONTHS.get(month_name)
if month is None:
return None
try:
return datetime.date(int(year_str), month, int(day_str))
except ValueError:
return None
def _parse_iso_date(s: str) -> Optional[datetime.date]:
"""Parse "2026-05-16" → date. Histograms use ISO format for their
Start Date / Stop Date / Peak Date fields; waveforms use the
"May 8, 2026" long form which `_parse_event_date` handles."""
s = s.strip()
try:
return datetime.date.fromisoformat(s)
except ValueError:
return None
_INTERVAL_UNIT_SECONDS = {
"second": 1, "seconds": 1, "sec": 1, "secs": 1,
"minute": 60, "minutes": 60, "min": 60, "mins": 60,
"hour": 3600, "hours": 3600, "hr": 3600, "hrs": 3600,
}
def _parse_interval_size(s: str) -> Optional[float]:
"""Parse "1 minute" / "5 minutes" / "15 seconds" / "2 seconds" → seconds.
Handles the BW Compliance Setup → Histogram Interval values verbatim
("2 seconds", "5 seconds", "15 seconds", "1 minute", "5 minutes",
"15 minutes") plus a few defensive variants.
"""
if not s:
return None
parts = s.strip().split()
if len(parts) < 2:
return None
try:
n = float(parts[0])
except ValueError:
return None
unit_per_s = _INTERVAL_UNIT_SECONDS.get(parts[1].lower())
if unit_per_s is None:
return None
return n * unit_per_s
def _parse_event_time(s: str) -> Optional[datetime.time]:
"""Parse "15:56:35" → time."""
s = s.strip()
try:
h, m, sec = s.split(":")
return datetime.time(int(h), int(m), int(sec))
except (ValueError, IndexError):
return None
def _parse_calibration(value: str) -> Tuple[Optional[datetime.date], Optional[str]]:
"""Parse "April 29, 2025 by Instantel" → (date, "Instantel")."""
parts = value.split(" by ", 1)
date = _parse_event_date(parts[0])
by = parts[1].strip() if len(parts) > 1 else None
return date, by
def _parse_monitor_row(line: str) -> Optional[MonitorLogEntry]:
"""Parse a tab-separated monitor log row.
Format: `<start>\t<stop>\t<desc>` where each timestamp is BW's
short form "Mon DD /YY HH:MM:SS" (e.g. "Apr 23 /26 15:46:16").
Year is encoded as a 2-digit suffix; we expand "/26" → 2026.
"""
parts = line.split("\t")
if len(parts) < 2:
return None
start = _parse_monitor_ts(parts[0])
stop = _parse_monitor_ts(parts[1])
desc = parts[2].strip() if len(parts) > 2 else None
if start is None and stop is None and not desc:
return None
return MonitorLogEntry(start_time=start, stop_time=stop, description=desc)
def _parse_monitor_ts(s: str) -> Optional[datetime.datetime]:
"""Parse "Apr 23 /26 15:46:16" → datetime."""
s = s.strip()
parts = s.split()
if len(parts) < 4:
return None
month = _MONTHS.get(parts[0])
if month is None:
return None
try:
day = int(parts[1])
# parts[2] looks like "/26" → century-flip to 2026
yy = int(parts[2].lstrip("/"))
year = 2000 + yy if yy < 80 else 1900 + yy
h, m, sec = (int(x) for x in parts[3].split(":"))
return datetime.datetime(year, month, day, h, m, sec)
except (ValueError, IndexError):
return None
# ── User-notes positional slot map ──────────────────────────────────────────
#
# Blastware's Compliance Setup → Notes tab shows four operator-supplied
# fields whose LABELS the operator can rename (see screenshot in
# project archive). Defaults are "Project:" / "Client:" /
# "User Name:" / "Seis Loc:", but an operator using a different
# convention can rename them to anything ("Building:", "Site:",
# "Address:", etc.). The ASCII export reflects whatever the operator
# typed, so label-based matching is fragile.
#
# What IS reliable: BW always writes the 4 user-notes lines in the
# same order, contiguously between the `Units :` line and the
# `Geo Range :` line. We parse them by POSITION and preserve the
# operator's labels in `report.user_note_labels` so terra-view can
# render them as the operator intended.
_USER_NOTE_SLOTS = ("project", "client", "operator", "sensor_location")
# ─────────────────────────────────────────────────────────────────────────────
# Top-level parser
# ─────────────────────────────────────────────────────────────────────────────
def parse_report(text: Union[str, bytes], *, parse_samples: bool = False) -> BwAsciiReport:
"""Parse a BW per-event ASCII export into a structured BwAsciiReport.
Set ``parse_samples=True`` to also populate ``report.samples`` with
the trailing sample table. Default False because the table is
huge and most callers only want metadata for indexing.
"""
if isinstance(text, bytes):
text = text.decode("ascii", errors="replace")
report = BwAsciiReport()
# Pre-create channel stat slots so callers can rely on them existing.
for ch in ("Tran", "Vert", "Long", "MicL"):
report.channels.setdefault(ch, ChannelStats())
report.sensor_check.setdefault(ch, SensorCheck())
lines = text.splitlines()
i = 0
n = len(lines)
in_monitor_log_section = False
event_time_str: Optional[str] = None
event_date: Optional[datetime.date] = None
# User-notes block detection. We enter the block after parsing
# the "Units :" line and exit on the "Geo Range :" line. Inside,
# the first 4 unmatched `<label> : <value>` lines are assigned to
# the 4 canonical operator-supplied slots by POSITION (project,
# client, operator, sensor_location) regardless of what the
# operator named the labels in BW's Compliance Setup → Notes tab.
in_user_notes_block = False
user_note_position = 0
# Histogram-field staging — BW writes <Channel> Peak Time and
# <Channel> Peak Date on separate lines (and similarly Histogram
# Start Time / Date). We stash the partial value when the time
# line arrives and combine it when the matching date line arrives.
_hist_start_time: Optional[datetime.time] = None
_hist_stop_time: Optional[datetime.time] = None
_pending_peak_time: Dict[str, Optional[datetime.time]] = {}
_pvs_time_raw: Optional[str] = None # last Peak Vector Sum Time value, raw
while i < n:
raw_line = lines[i]
i += 1
# Blank line marks the start of the sample table.
if raw_line.strip() == "":
break
line = _strip_quotes(raw_line)
# Monitor log section: "Monitor Log(s)" header followed by N rows
# (still inside double-quoted lines), terminated by a non-row line
# like "PC SW Version : ..." or a blank line.
if not in_monitor_log_section and line.strip() == "Monitor Log(s)":
in_monitor_log_section = True
continue
if in_monitor_log_section:
# Heuristic: monitor rows contain a tab; the next "Field : Value"
# line ends the section.
if "\t" in line:
entry = _parse_monitor_row(line)
if entry:
report.monitor_log.append(entry)
continue
# Falls through to the field parser below; clear the flag.
in_monitor_log_section = False
# "Field : Value" — split on FIRST occurrence of " : "
idx = line.find(" : ")
if idx < 0:
continue
key = _normalise_key(line[:idx])
value = line[idx + 3 :].strip()
# ── Identity / config ────────────────────────────────────────────────
if key == "Event Type": report.event_type = value
elif key == "Serial Number": report.serial = value
elif key == "Version": report.version = value
elif key == "File Name": report.file_name = value
elif key == "Event Time": event_time_str = value
elif key == "Event Date": event_date = _parse_event_date(value)
elif key == "Trigger": report.trigger_channel = value
elif key == "Geo Trigger Level": report.geo_trigger_level_ips = _parse_number(value)
elif key == "Pre-trigger Length": report.pretrig_s = _parse_number(value)
elif key == "Record Time": report.record_time_s = _parse_number(value)
elif key == "Record Stop Mode": report.record_stop_mode = value
elif key == "Sample Rate": report.sample_rate_sps = _parse_int(value)
elif key == "Battery Level": report.battery_volts = _parse_number(value)
elif key == "Calibration":
report.calibration_date, report.calibration_by = _parse_calibration(value)
elif key == "Units":
report.units = value
# Entering the user-notes block. Next ~4 lines until
# "Geo Range :" are the operator-supplied notes.
in_user_notes_block = True
user_note_position = 0
elif key == "Geo Range":
# Exiting the user-notes block.
in_user_notes_block = False
report.geo_range_ips = _parse_number(value)
# User-notes block: assign by position (operator may have
# renamed the labels, so we don't trust them). Preserve the
# original labels in `user_note_labels` for downstream UIs
# (terra-view) that want to display them as the operator
# named them.
elif in_user_notes_block and user_note_position < len(_USER_NOTE_SLOTS):
slot = _USER_NOTE_SLOTS[user_note_position]
setattr(report, slot, value)
report.user_note_labels[slot] = key
user_note_position += 1
# ── Per-channel stats ────────────────────────────────────────────────
# All match the pattern "{Channel} <stat-name>"
elif key in (
"Tran PPV", "Vert PPV", "Long PPV",
"Tran ZC Freq", "Vert ZC Freq", "Long ZC Freq",
"Tran Time of Peak", "Vert Time of Peak", "Long Time of Peak",
"Tran Peak Acceleration", "Vert Peak Acceleration", "Long Peak Acceleration",
"Tran Peak Displacement", "Vert Peak Displacement", "Long Peak Displacement",
):
ch_name, stat = key.split(" ", 1)
cs = report.channels.setdefault(ch_name, ChannelStats())
if stat == "PPV":
if _is_oorange(value):
# Channel saturated — substitute range max as lower
# bound; flag so downstream UI can render "> 10 in/s".
cs.ppv_ips = report.geo_range_ips
cs.ppv_saturated = True
else:
cs.ppv_ips = _parse_number(value)
elif stat == "ZC Freq":
# ">100 Hz" → store threshold + flag; numeric → parse normally
threshold = _parse_above_range(value)
if threshold is not None:
cs.zc_freq_hz = threshold
cs.zc_freq_above_range = True
else:
cs.zc_freq_hz = _parse_number(value)
else:
num = _parse_number(value)
if stat == "Time of Peak": cs.time_of_peak_s = num
elif stat == "Peak Acceleration": cs.peak_accel_g = num
elif stat == "Peak Displacement": cs.peak_disp_in = num
# ── Histogram-specific fields ────────────────────────────────────────
# Histograms have Start/Stop time+date pairs + an interval count
# and size, plus per-channel absolute Peak Time/Date instead of
# the waveform's relative Time of Peak.
elif key == "Histogram Start Time":
_hist_start_time = _parse_event_time(value)
elif key == "Histogram Start Date":
_d = _parse_iso_date(value)
if _d and _hist_start_time:
report.histogram_start = datetime.datetime.combine(_d, _hist_start_time)
elif key == "Histogram Stop Time":
_hist_stop_time = _parse_event_time(value)
elif key == "Histogram Stop Date":
_d = _parse_iso_date(value)
if _d and _hist_stop_time:
report.histogram_stop = datetime.datetime.combine(_d, _hist_stop_time)
elif key == "Number of Intervals":
try:
report.histogram_n_intervals = int(float(value.strip()))
except ValueError:
pass
elif key == "Interval Size":
report.histogram_interval_size_str = value.strip()
report.histogram_interval_size_s = _parse_interval_size(value)
# ── Per-channel histogram Peak Date / Peak Time ──
# Lines like "Tran Peak Time : 22:31:38" + "Tran Peak Date : 2026-05-16"
elif key in ("Tran Peak Time", "Vert Peak Time", "Long Peak Time", "MicL Time"):
ch_name = "MicL" if key == "MicL Time" else key.split(" ", 1)[0]
_pending_peak_time[ch_name] = _parse_event_time(value)
elif key in ("Tran Peak Date", "Vert Peak Date", "Long Peak Date", "MicL Date"):
ch_name = "MicL" if key == "MicL Date" else key.split(" ", 1)[0]
_d = _parse_iso_date(value)
_t = _pending_peak_time.get(ch_name)
if _d and _t:
report.channel_peak_when[ch_name] = datetime.datetime.combine(_d, _t)
# ── Vector Sum ───────────────────────────────────────────────────────
elif key == "Peak Vector Sum":
if _is_oorange(value):
# PVS saturated — conservative upper bound is
# sqrt(3) * geo_range_ips (all 3 channels at full-scale).
# Real PVS could be lower (channels rarely peak
# simultaneously) but never higher within the range.
if report.geo_range_ips is not None:
import math as _math
report.peak_vector_sum_ips = _math.sqrt(3) * report.geo_range_ips
report.peak_vector_sum_saturated = True
else:
report.peak_vector_sum_ips = _parse_number(value)
# BW writes the PVS-time label with a typo: "Peak Vector Sum TimeSum"
# (looks like Sum got appended twice). Accept both forms. Confirmed
# against actual BW output on 2026-05-27 — every PVS-time line in
# the field examples (T190, T438, K557) uses the typo'd label.
elif key in ("Peak Vector Sum Time", "Peak Vector Sum TimeSum"):
report.peak_vector_sum_time_s = _parse_number(value)
_pvs_time_raw = value
elif key == "Peak Vector Sum Date":
# Histogram-mode PVS gets paired with a date. We may have
# captured 'Peak Vector Sum Time' as either a relative
# seconds float (waveform) or an HH:MM:SS string we
# interpreted as a number. For histograms, BW writes
# "Peak Vector Sum Time : 22:33:52" which _parse_number
# parses as 22.0 (loses information). When Peak Vector Sum
# Date arrives, re-parse the previous PVS time line as a
# clock time and combine into an absolute datetime.
_d = _parse_iso_date(value)
if _d and _pvs_time_raw is not None:
_t = _parse_event_time(_pvs_time_raw)
if _t:
report.peak_vector_sum_when = datetime.datetime.combine(_d, _t)
# The earlier seconds parse was bogus for histograms;
# clear it so consumers don't think it's a real offset.
report.peak_vector_sum_time_s = None
# ── Microphone block ────────────────────────────────────────────────
elif key == "Microphone":
report.mic.weighting = value
elif key == "MicL PSPL":
if _is_oorange(value):
# Mic saturated — substitute conservative upper bound 140 dBL.
report.mic.pspl_dbl = 140.0
report.mic.pspl_saturated = True
else:
report.mic.pspl_dbl = _parse_number(value)
# Mirror onto the "MicL" entry in channels so callers querying
# `channels["MicL"].ppv_ips` see something — but it's dB(L), not
# in/s, so we store as-is in the MicStats and mark the channel.
elif key == "MicL Time of Peak":
report.mic.time_of_peak_s = _parse_number(value)
cs = report.channels.setdefault("MicL", ChannelStats())
cs.time_of_peak_s = report.mic.time_of_peak_s
elif key == "MicL ZC Freq":
threshold = _parse_above_range(value)
if threshold is not None:
report.mic.zc_freq_hz = threshold
report.mic.zc_freq_above_range = True
else:
report.mic.zc_freq_hz = _parse_number(value)
cs = report.channels.setdefault("MicL", ChannelStats())
cs.zc_freq_hz = report.mic.zc_freq_hz
cs.zc_freq_above_range = report.mic.zc_freq_above_range
# ── Sensor self-check ────────────────────────────────────────────────
elif key in (
"Tran Test Freq", "Vert Test Freq", "Long Test Freq", "MicL Test Freq",
"Tran Test Ratio", "Vert Test Ratio", "Long Test Ratio",
"MicL Test Amplitude",
"Tran Test Results", "Vert Test Results", "Long Test Results", "MicL Test Results",
):
ch_name, stat = key.split(" ", 1)
sc = report.sensor_check.setdefault(ch_name, SensorCheck())
if stat == "Test Freq": sc.test_freq_hz = _parse_number(value)
elif stat == "Test Ratio": sc.test_ratio = _parse_number(value)
elif stat == "Test Amplitude": sc.test_amplitude_mv = _parse_number(value)
elif stat == "Test Results": sc.test_results = value
# ── Trailer ─────────────────────────────────────────────────────────
elif key == "PC SW Version":
report.pc_sw_version = value
# Unknown keys are silently dropped — forward-compat for future
# BW versions that may add fields.
# Combine event date + time into a datetime
if event_date is not None and event_time_str is not None:
t = _parse_event_time(event_time_str)
if t is not None:
report.event_datetime = datetime.datetime.combine(event_date, t)
if parse_samples:
report.samples = _parse_sample_table(lines, i)
return report
def _parse_sample_table(
lines: List[str], start: int,
) -> List[Tuple[float, float, float, float]]:
"""Parse the trailing sample table.
The table starts with a header row (" Tran <TAB>...") and continues
until EOF. Each data row is a tab-separated quartet of numeric values.
"""
samples: List[Tuple[float, float, float, float]] = []
seen_header = False
for line in lines[start:]:
line = line.rstrip("\r\n")
if not line.strip():
continue
cols = [c.strip() for c in line.split("\t") if c.strip()]
if not seen_header:
# Header row contains channel names; numeric rows don't.
if any(c in ("Tran", "Vert", "Long", "MicL") for c in cols):
seen_header = True
continue
if len(cols) < 4:
continue
try:
samples.append((
float(cols[0]), float(cols[1]),
float(cols[2]), float(cols[3]),
))
except ValueError:
continue
return samples
def parse_report_file(
path: Union[str, Path], *, parse_samples: bool = False,
) -> BwAsciiReport:
"""Convenience: read a .TXT file from disk and parse it."""
return parse_report(Path(path).read_bytes(), parse_samples=parse_samples)
minimateplus/client.py
+26 -67
View File
@@ -30,7 +30,6 @@ from __future__ import annotations
import datetime
import logging
import re
import struct
from typing import Optional
@@ -1363,6 +1362,20 @@ def _decode_waveform_record_into(data: bytes, event: Event) -> None:
Modifies event in-place.
"""
# ── Always preserve the raw 210 bytes ─────────────────────────────────────
# The 0C record carries far more than just peaks + project strings:
# ZC Freq, Time of Peak, Peak Acceleration, Peak Displacement, Vector
# Sum Time, MicL Time of Peak, and the per-channel sensor self-check
# results (Test Freq / Ratio / Pass-Fail) all live somewhere in this
# 210-byte block. Their byte offsets are not yet mapped — keeping the
# raw bytes lets us decode those fields offline once we have a paired
# (raw 0C, BW-report) sample to fit against. Cheap to keep around
# (210 bytes per event).
try:
event._raw_record = bytes(data[:210])
except Exception:
pass
# ── Record type + format detection ────────────────────────────────────────
# `record_type` is the user-facing label ("Waveform" for any triggered
# event regardless of timestamp-header layout). `fmt` is the internal
@@ -1501,69 +1514,22 @@ def _decode_a5_waveform(
(BULK_WAVEFORM_STREAM) frame payloads and populate event.raw_samples,
event.total_samples, event.pretrig_samples, and event.rectime_seconds.
Wired up 2026-05-11 to the verified ``decode_waveform_v2`` codec (see
``minimateplus/waveform_codec.py`` and ``docs/waveform_codec_re_status.md``).
Replaces the legacy int16 LE decoder, which produced full-scale ±32K
noise on every event because the body bytes are encoded, not raw
samples.
This requires ALL A5 frames (stop_after_metadata=False), not just the
metadata-bearing subset.
Output convention (preserved from the legacy decoder):
``event.raw_samples`` is a dict with keys "Tran", "Vert", "Long",
"MicL" mapping to lists of **int16 ADC counts**. Multiply by
``geo_range / 32768`` for geo channels to get in/s; use
:func:`minimateplus.waveform_codec.mic_count_to_db` for mic dB(L).
``total_samples`` / ``pretrig_samples`` / ``rectime_seconds`` are set
to ``None`` so the caller backfills from compliance_config (the
authoritative source — STRT fields aren't reliable).
"""
from .waveform_codec import decode_a5_frames
event.total_samples = None
event.pretrig_samples = None
event.rectime_seconds = None
if not frames_data:
log.debug("_decode_a5_waveform: no frames provided")
return
decoded = decode_a5_frames(frames_data)
if decoded is None:
log.warning("_decode_a5_waveform: codec returned no samples")
return
event.raw_samples = decoded
log.debug(
"_decode_a5_waveform: decoded %d/%d/%d/%d samples (T/V/L/M)",
len(decoded.get("Tran", [])),
len(decoded.get("Vert", [])),
len(decoded.get("Long", [])),
len(decoded.get("MicL", [])),
)
def _decode_a5_waveform_LEGACY(
frames_data: list[S3Frame],
event: Event,
) -> None:
"""
LEGACY decoder — kept for reference only. DO NOT CALL.
This is the int16 LE decoder that produced full-scale ±32K noise
on every event. Retracted 2026-05-08; replaced 2026-05-11 with
the verified codec in :mod:`minimateplus.waveform_codec`. See
``docs/instantel_protocol_reference.md §7.6.1`` for the full history.
── Waveform format (LEGACY — WRONG) ────────────────────────────────
Claimed 4-channel interleaved signed 16-bit little-endian, 8 bytes
per sample-set:
── Waveform format (confirmed from 4-2-26 blast capture) ───────────────────
The blast waveform is 4-channel interleaved signed 16-bit little-endian,
8 bytes per sample-set:
[T_lo T_hi V_lo V_hi L_lo L_hi M_lo M_hi] × N
where T=Tran, V=Vert, L=Long, M=Mic.
where T=Tran, V=Vert, L=Long, M=Mic. Channel ordering follows the
Blastware convention [Tran, Vert, Long, Mic] = [ch0, ch1, ch2, ch3].
The body bytes are actually a tagged delta+RLE stream — this
interpretation was wrong.
⚠️ Channel ordering is a confirmed CONVENTION — the physical ordering on
the ADC mux is not independently verifiable from the saturating blast
captures we have. The convention is consistent with Blastware labeling
(Tran is always the first channel field in the A5 STRT+waveform stream).
── Frame structure ──────────────────────────────────────────────────────────
A5[0] (probe response):
@@ -2533,17 +2499,10 @@ def _decode_0a_partial_header(raw_data: bytes, index: int, key4: bytes) -> Optio
ts2 = try_ts(raw_data[ts1_end + 1:ts1_end + 1 + ts_size])
# Extract serial and geo threshold from "BE11529\0" and "Geo: X.XXX in/s\0".
#
# Match any two-letter family prefix, not a literal "BE" — a BlastMate
# reports "BA10895", and the old `find(b"BE")` returned -1 on one. That
# skipped this whole block, so the geo threshold went missing along with
# the serial. Requiring the NUL terminator in the pattern also makes the
# match stricter than the bare two-byte search it replaces.
serial: Optional[str] = None
geo_ips: Optional[float] = None
serial_match = re.search(rb"[A-Z]{2}\d{3,6}(?=\x00)", raw_data)
serial_pos = serial_match.start() if serial_match else -1
serial_pos = raw_data.find(b"BE")
if serial_pos >= 0:
# Read null-terminated serial starting at serial_pos.
null_pos = raw_data.find(b"\x00", serial_pos)
minimateplus/event_file_io.py
+44 -555
View File
@@ -15,6 +15,7 @@ declared in `event_to_sidecar_dict()`.
from __future__ import annotations
import base64
import datetime
import hashlib
import json
@@ -26,15 +27,6 @@ from typing import Optional, Union
from .models import Event, PeakValues, ProjectInfo, Timestamp
from . import blastware_file as _bw # avoid circular reference at module load
from .bw_ascii_report import BwAsciiReport
from . import waveform_codec as _wc
from .waveform_codec import decode_waveform_v2, decoded_to_adc_counts
from .histogram_codec import decode_histogram_body
# Reference pressure for dB(L) → psi conversion (20 µPa expressed in psi).
# Same constant as sfm/sfm_webapp.html so server-side and browser-side
# conversions agree.
_DBL_REF_PSI = 2.9e-9
log = logging.getLogger(__name__)
@@ -50,7 +42,7 @@ SIDECAR_KIND = "sfm.event"
# bumped without a `pip install` re-run — leading to confusing stale
# version stamps in sidecars. Bump this constant and CHANGELOG.md
# together at release time.
TOOL_VERSION = "0.31.0" # +/sensor_check group (schema v2); gates the backfill regen
TOOL_VERSION = "0.15.0"
try:
# Best-effort: prefer the installed metadata when it's NEWER than the
@@ -103,276 +95,6 @@ def _peak_values_to_dict(pv: Optional[PeakValues]) -> dict:
}
def _bw_report_to_dict(report: BwAsciiReport) -> dict:
"""Project a parsed BW ASCII report into the sidecar's `bw_report` block.
All fields are rendered as plain JSON-compatible types (no datetime
objects). Channels are uniformly lowercased for stable JSON keys.
"""
def _ch(ch_name: str) -> dict:
cs = report.channels.get(ch_name)
if cs is None:
return {}
out = {
"ppv_ips": cs.ppv_ips,
"zc_freq_hz": cs.zc_freq_hz,
"time_of_peak_s": cs.time_of_peak_s,
"peak_accel_g": cs.peak_accel_g,
"peak_disp_in": cs.peak_disp_in,
}
# Drop all-None entries — keeps the JSON tidy for partial reports.
out = {k: v for k, v in out.items() if v is not None}
# Saturation flag (only present when True) — signals that ppv_ips
# is the channel range max (a lower bound), not an exact reading.
if getattr(cs, "ppv_saturated", False):
out["ppv_saturated"] = True
# ZC Freq above device reporting ceiling (BW ">100 Hz") — value
# in zc_freq_hz is the threshold, not an exact measurement.
if getattr(cs, "zc_freq_above_range", False):
out["zc_freq_above_range"] = True
return out
def _sc(ch_name: str) -> dict:
sc = report.sensor_check.get(ch_name)
if sc is None:
return {}
out = {
"freq_hz": sc.test_freq_hz,
"ratio": sc.test_ratio,
"amplitude_mv": sc.test_amplitude_mv,
"result": sc.test_results,
}
return {k: v for k, v in out.items() if v is not None}
monitor_log = []
for entry in report.monitor_log:
e = {
"start": entry.start_time.isoformat() if entry.start_time else None,
"stop": entry.stop_time.isoformat() if entry.stop_time else None,
"description": entry.description,
}
monitor_log.append({k: v for k, v in e.items() if v is not None})
return {
"available": True,
"event_type": report.event_type,
"version": report.version,
"trigger": {
"channel": report.trigger_channel,
"geo_level_ips": report.geo_trigger_level_ips,
},
"recording": {
"sample_rate_sps": report.sample_rate_sps,
"record_time_s": report.record_time_s,
"pretrig_s": report.pretrig_s,
"stop_mode": report.record_stop_mode,
"geo_range_ips": report.geo_range_ips,
"units": report.units,
},
"device": {
"battery_volts": report.battery_volts,
"calibration_date": report.calibration_date.isoformat() if report.calibration_date else None,
"calibration_by": report.calibration_by,
},
"peaks": {
"tran": _ch("Tran"),
"vert": _ch("Vert"),
"long": _ch("Long"),
"vector_sum": {
"ips": report.peak_vector_sum_ips,
"time_s": report.peak_vector_sum_time_s,
# Histogram events have an absolute date+time for the PVS
# (the interval at which it occurred); waveform events
# only have the time_s offset.
"when": report.peak_vector_sum_when.isoformat() if report.peak_vector_sum_when else None,
# Set when BW reported the PVS as OORANGE — value is the
# conservative upper bound sqrt(3) * geo_range_ips, not
# an exact peak.
"saturated": bool(getattr(report, "peak_vector_sum_saturated", False)),
},
},
"mic": {
"weighting": report.mic.weighting,
"pspl_dbl": report.mic.pspl_dbl,
"pspl_saturated": bool(getattr(report.mic, "pspl_saturated", False)),
"zc_freq_hz": report.mic.zc_freq_hz,
"zc_freq_above_range": bool(getattr(report.mic, "zc_freq_above_range", False)),
"time_of_peak_s": report.mic.time_of_peak_s,
},
"sensor_check": {
"tran": _sc("Tran"),
"vert": _sc("Vert"),
"long": _sc("Long"),
"mic": _sc("MicL"),
},
# Histogram-specific fields (None on waveform-mode events).
# Per-channel absolute peak time/date for histograms — for
# waveforms see channels[ch]["time_of_peak_s"] instead.
"histogram": {
"start": report.histogram_start.isoformat() if report.histogram_start else None,
"stop": report.histogram_stop.isoformat() if report.histogram_stop else None,
"n_intervals": report.histogram_n_intervals,
"interval_size": report.histogram_interval_size_str,
"interval_size_s": report.histogram_interval_size_s,
"channel_peak_when": {ch: dt.isoformat() for ch, dt in report.channel_peak_when.items()},
},
"monitor_log": monitor_log,
"pc_sw_version": report.pc_sw_version,
}
def _dbl_to_psi(pspl_dbl: float) -> float:
"""Convert dB(L) sound pressure level back to psi. Uses the same
20 µPa reference (= 2.9e-9 psi) as the webapp so server-side and
browser-side conversions agree."""
return _DBL_REF_PSI * (10.0 ** (pspl_dbl / 20.0))
def apply_report_to_event(event: Event, report: BwAsciiReport) -> None:
"""Overlay device-authoritative fields from a parsed BW ASCII report
onto an in-memory Event, IN-PLACE.
Why this exists
───────────────
`read_blastware_file()` parses the BW binary and fills `Event.peak_values`
via `_peaks_from_samples()` — which runs the (still-undecoded) BW body
codec assuming raw int16 LE and produces ±32K-shaped noise on every
channel. Result: peak values land in the SeismoDb event row as
~10 in/s on every event regardless of the actual signal.
When a paired BW ASCII report is available, the report carries the
device's own authoritative peak / project / sample-rate / record-time
values. This helper folds those onto the Event before it flows to
`SeismoDb.insert_events()`, so the DB columns reflect the report
rather than the broken-codec output.
Fields overlaid (only when the report supplies a non-None value):
- peak_values.tran / .vert / .long (from report.channels)
- peak_values.peak_vector_sum (from report.peak_vector_sum_ips)
- peak_values.micl (psi) (from report.mic.pspl_dbl → psi)
- project_info.project / .client / .operator / .sensor_location
- sample_rate (from report.sample_rate_sps)
- rectime_seconds (from report.record_time_s)
Fields NOT touched (operator-edit / parser-output preserved):
- timestamp, raw_samples, record_type, total_samples,
pretrig_samples, _waveform_key, _a5_frames, _raw_record
- false_trigger and review state (those live on the sidecar, not on Event)
"""
if event.peak_values is None:
event.peak_values = PeakValues()
pv = event.peak_values
ch = report.channels
if (t := ch.get("Tran")) and t.ppv_ips is not None: pv.tran = t.ppv_ips
if (v := ch.get("Vert")) and v.ppv_ips is not None: pv.vert = v.ppv_ips
if (l := ch.get("Long")) and l.ppv_ips is not None: pv.long = l.ppv_ips
if report.peak_vector_sum_ips is not None:
pv.peak_vector_sum = report.peak_vector_sum_ips
if report.mic.pspl_dbl is not None and report.mic.pspl_dbl > 0:
pv.micl = _dbl_to_psi(report.mic.pspl_dbl)
if (t := ch.get("Tran")):
pv.tran_zc_freq = t.zc_freq_hz
pv.tran_zc_above_range = bool(getattr(t, "zc_freq_above_range", False))
if (v := ch.get("Vert")):
pv.vert_zc_freq = v.zc_freq_hz
pv.vert_zc_above_range = bool(getattr(v, "zc_freq_above_range", False))
if (l := ch.get("Long")):
pv.long_zc_freq = l.zc_freq_hz
pv.long_zc_above_range = bool(getattr(l, "zc_freq_above_range", False))
if report.mic is not None:
pv.mic_zc_freq = report.mic.zc_freq_hz
pv.mic_zc_above_range = bool(getattr(report.mic, "zc_freq_above_range", False))
if event.project_info is None:
event.project_info = ProjectInfo()
pi = event.project_info
if report.project: pi.project = report.project
if report.client: pi.client = report.client
if report.operator: pi.operator = report.operator
if report.sensor_location: pi.sensor_location = report.sensor_location
if report.sample_rate_sps:
event.sample_rate = report.sample_rate_sps
if report.record_time_s is not None:
event.rectime_seconds = report.record_time_s
# The report's event_datetime is Blastware's exact trigger time (parsed
# from Event Time + Event Date). Prefer it over the binary footer's stop
# time so a report-paired import matches BW to the second.
edt = report.event_datetime
if edt is not None:
event.timestamp = Timestamp(
raw=b"", flag=0x10,
year=edt.year, unknown_byte=0, month=edt.month, day=edt.day,
hour=edt.hour, minute=edt.minute, second=edt.second,
)
def apply_bw_report_dict_to_event(event: Event, bw_report: dict) -> None:
"""Mirror of ``apply_report_to_event`` for the projected sidecar
dict shape (as produced by ``_bw_report_to_dict``).
Why this exists
───────────────
The ingest path holds a live ``BwAsciiReport`` parsed straight from
the ``_ASCII.TXT`` and uses ``apply_report_to_event`` to overlay
device-authoritative peaks onto the codec output before insert.
The backfill path doesn't have the original ``.TXT`` (it's not
retained in the waveform store), but it does have the preserved
``bw_report`` block from the sidecar — which contains the same
projected fields. Re-overlaying those during a backfill keeps the
DB peak columns aligned with what BW reports rather than letting
the codec output (which may be incomplete for unhandled formats or
walker edge cases) win by default.
No-ops cleanly when ``bw_report`` is ``None``, empty, or missing
any particular sub-field — only fields with a concrete value get
written. Mirrors ``apply_report_to_event``'s "report wins where
present" semantics.
"""
if not bw_report:
return
if event.peak_values is None:
event.peak_values = PeakValues()
pv = event.peak_values
peaks = bw_report.get("peaks") or {}
tran = (peaks.get("tran") or {}).get("ppv_ips")
vert = (peaks.get("vert") or {}).get("ppv_ips")
long = (peaks.get("long") or {}).get("ppv_ips")
if tran is not None: pv.tran = tran
if vert is not None: pv.vert = vert
if long is not None: pv.long = long
vs_ips = (peaks.get("vector_sum") or {}).get("ips")
if vs_ips is not None:
pv.peak_vector_sum = vs_ips
mic = bw_report.get("mic") or {}
pspl = mic.get("pspl_dbl")
if pspl is not None and pspl > 0:
pv.micl = _dbl_to_psi(pspl)
for axis, freq_attr, above_attr in (
("tran", "tran_zc_freq", "tran_zc_above_range"),
("vert", "vert_zc_freq", "vert_zc_above_range"),
("long", "long_zc_freq", "long_zc_above_range"),
):
chd = peaks.get(axis) or {}
setattr(pv, freq_attr, chd.get("zc_freq_hz"))
setattr(pv, above_attr, bool(chd.get("zc_freq_above_range", False)))
pv.mic_zc_freq = mic.get("zc_freq_hz")
pv.mic_zc_above_range = bool(mic.get("zc_freq_above_range", False))
rec = bw_report.get("recording") or {}
sr = rec.get("sample_rate_sps")
if sr:
event.sample_rate = sr
rt = rec.get("record_time_s")
if rt is not None:
event.rectime_seconds = rt
def _project_info_to_dict(pi: Optional[ProjectInfo]) -> dict:
if pi is None:
return {
@@ -397,110 +119,54 @@ def event_to_sidecar_dict(
blastware_filesize: int,
blastware_sha256: str,
source_kind: str = "sfm-live",
txt_filename: Optional[str] = None,
a5_pickle_filename: Optional[str] = None,
tool_version: str = _TOOL_VERSION_DEFAULT,
captured_at: Optional[datetime.datetime] = None,
review: Optional[dict] = None,
extensions: Optional[dict] = None,
bw_report: Optional[BwAsciiReport] = None,
) -> dict:
"""
Build a v1 sidecar dict from an Event + the surrounding metadata.
Pure helper — no file I/O. Callers stitch the result into a sidecar
via `write_sidecar()` (or POST it back via the PATCH endpoint).
When *bw_report* is supplied (e.g. by the ACH-forwarded import path
where Blastware writes a per-event ASCII report alongside the binary),
its decoded fields are folded into the sidecar:
- A new top-level ``bw_report`` block carries the rich derived
per-channel stats (Peak Acceleration, Peak Displacement, ZC Freq,
Time of Peak), the Peak Vector Sum + time, the per-channel sensor
self-check results, and monitor-log timestamps.
- ``peak_values`` is overlaid from the report (the report's PPV/PVS
values are computed by the device firmware and are authoritative;
anything ``read_blastware_file()`` derived from samples is
approximate at best until the body codec is decoded).
- ``project_info`` is overlaid from the report when the report
supplies a non-empty value (the report mirrors the device's
compliance config, which is what BW shows in its event report).
- ``event.timestamp`` is overlaid from the report's Event Date +
Event Time (BW's report timestamps are second-resolution and
match the binary's footer; we prefer the report value because
the BW-binary footer timestamp can drift on some firmware).
"""
if source_kind not in {"sfm-live", "sfm-ach", "bw-import", "idf-import"}:
if source_kind not in {"sfm-live", "sfm-ach", "bw-import"}:
raise ValueError(f"unknown source_kind: {source_kind!r}")
captured_at = captured_at or datetime.datetime.utcnow()
# ── Overlay event fields from the report when present ───────────────────
timestamp_iso = _ts_iso(event.timestamp)
if bw_report and bw_report.event_datetime:
timestamp_iso = bw_report.event_datetime.isoformat()
# Stash raw 0C record bytes in `extensions.raw_records` so future
# field-decoding work (Peak Acceleration, ZC Freq, Time of Peak,
# sensor self-check results, etc.) can run offline against committed
# sidecars without a live device. Cheap (~280 bytes base64) and
# forward-compatible (older readers ignore unknown extensions keys).
ext_dict: dict = dict(extensions) if extensions else {}
raw_0c = getattr(event, "_raw_record", None)
if raw_0c:
rr = ext_dict.setdefault("raw_records", {})
# Don't clobber a raw_0c that callers explicitly passed in via
# `extensions=...` (e.g. round-trip preservation in patch_sidecar).
rr.setdefault("waveform_record_b64", base64.b64encode(raw_0c).decode("ascii"))
rr.setdefault("waveform_record_len", len(raw_0c))
# Build peak_values, optionally overlaid from the report. The report
# stores Mic peak as PSPL (dB(L)); we convert to psi to match the
# existing peak_values.mic_psi field.
peak_dict = _peak_values_to_dict(event.peak_values)
if bw_report:
ch = bw_report.channels
if (t := ch.get("Tran")) and t.ppv_ips is not None: peak_dict["transverse"] = t.ppv_ips
if (v := ch.get("Vert")) and v.ppv_ips is not None: peak_dict["vertical"] = v.ppv_ips
if (l := ch.get("Long")) and l.ppv_ips is not None: peak_dict["longitudinal"] = l.ppv_ips
if bw_report.peak_vector_sum_ips is not None:
peak_dict["vector_sum"] = bw_report.peak_vector_sum_ips
if bw_report.mic.pspl_dbl is not None and bw_report.mic.pspl_dbl > 0:
peak_dict["mic_psi"] = _dbl_to_psi(bw_report.mic.pspl_dbl)
# Project info: overlay from report (the report mirrors the
# session-start compliance config that BW renders in event reports).
proj_dict = _project_info_to_dict(event.project_info)
if bw_report:
if bw_report.project: proj_dict["project"] = bw_report.project
if bw_report.client: proj_dict["client"] = bw_report.client
if bw_report.operator: proj_dict["operator"] = bw_report.operator
if bw_report.sensor_location: proj_dict["sensor_location"] = bw_report.sensor_location
# Event-block fields: overlay from report where available.
event_block = {
"serial": serial,
"timestamp": timestamp_iso,
"waveform_key": event._waveform_key.hex() if event._waveform_key else None,
"record_type": event.record_type,
"sample_rate": event.sample_rate,
"rectime_seconds": event.rectime_seconds,
"total_samples": event.total_samples,
"pretrig_samples": event.pretrig_samples,
}
if bw_report:
# Report values are authoritative — they're the user-configured
# values BW reads back, not STRT-derived guesses. In particular
# `event.rectime_seconds` from `read_blastware_file()` reads
# STRT[18] which is actually the `0x46` record-type marker (= 70)
# rather than the user's Record Time setting. Always overwrite.
if bw_report.sample_rate_sps:
event_block["sample_rate"] = bw_report.sample_rate_sps
if bw_report.record_time_s is not None:
event_block["rectime_seconds"] = bw_report.record_time_s
# Derive total_samples + pretrig_samples per channel from the
# report's sample_rate × times. These match the row count of
# the report's sample table (verified: event-c reports 1024 sps
# × (1.0 + 0.25) = 1280 rows).
if (sr := bw_report.sample_rate_sps) and bw_report.record_time_s is not None:
pretrig_s = abs(bw_report.pretrig_s) if bw_report.pretrig_s is not None else 0.0
event_block["total_samples"] = int(round(sr * (bw_report.record_time_s + pretrig_s)))
event_block["pretrig_samples"] = int(round(sr * pretrig_s))
out = {
return {
"schema_version": SCHEMA_VERSION,
"kind": SIDECAR_KIND,
"event": event_block,
"peak_values": peak_dict,
"project_info": proj_dict,
"event": {
"serial": serial,
"timestamp": _ts_iso(event.timestamp),
"waveform_key": event._waveform_key.hex() if event._waveform_key else None,
"record_type": event.record_type,
"sample_rate": event.sample_rate,
"rectime_seconds": event.rectime_seconds,
"total_samples": event.total_samples,
"pretrig_samples": event.pretrig_samples,
},
"peak_values": _peak_values_to_dict(event.peak_values),
"project_info": _project_info_to_dict(event.project_info),
"blastware": {
"filename": blastware_filename,
@@ -514,7 +180,6 @@ def event_to_sidecar_dict(
"captured_at": captured_at.isoformat() + "Z" if captured_at.tzinfo is None else captured_at.isoformat(),
"tool_version": tool_version,
"a5_pickle_filename": a5_pickle_filename,
"txt_filename": txt_filename,
},
"review": review or {
@@ -524,14 +189,9 @@ def event_to_sidecar_dict(
"notes": "",
},
"extensions": extensions or {},
"extensions": ext_dict,
}
if bw_report:
out["bw_report"] = _bw_report_to_dict(bw_report)
return out
# ── Sidecar IO ────────────────────────────────────────────────────────────────
@@ -670,11 +330,6 @@ def file_sha256(path: Union[str, Path], chunk_size: int = 65536) -> str:
_GEO_NORMAL_FS_INS = 10.0
_GEO_SENSITIVE_FS_INS = 1.250
_INT16_FS = 32768.0
# Geophone full-scale count — 32000, not 32768. One decoder unit (16 ADC
# counts) is exactly 0.005 in/s, so 10.000 in/s = 32000 counts. Must match
# sfm.event_hdf5._GEO_INT16_FS or sidecar peaks disagree with the plotted
# waveform by 2.3%. Confirmed 2026-08-25 against the BW ASCII corpus.
_GEO_INT16_FS = 32000.0
# Microphone scale factor, psi per ADC count. Approximate — exact factor
# depends on the geophone-vs-mic ADC scaling and the firmware reference.
@@ -744,7 +399,7 @@ def _peaks_from_samples(samples: dict[str, list[int]]) -> PeakValues:
if not ch:
return 0.0
m = max(abs(int(v)) for v in ch)
return m / _GEO_INT16_FS * _GEO_NORMAL_FS_INS
return m / _INT16_FS * _GEO_NORMAL_FS_INS
tran = _peak_ins(samples.get("Tran", []))
vert = _peak_ins(samples.get("Vert", []))
@@ -758,7 +413,7 @@ def _peaks_from_samples(samples: dict[str, list[int]]) -> PeakValues:
pvs = 0.0
n = min(len(samples.get("Tran", [])), len(samples.get("Vert", [])), len(samples.get("Long", [])))
if n:
scale = _GEO_NORMAL_FS_INS / _GEO_INT16_FS
scale = _GEO_NORMAL_FS_INS / _INT16_FS
T = samples["Tran"]; V = samples["Vert"]; L = samples["Long"]
for i in range(n):
t = T[i] * scale
@@ -774,74 +429,6 @@ def _peaks_from_samples(samples: dict[str, list[int]]) -> PeakValues:
)
_RECORD_TYPE_BY_EXT_SUFFIX = {
'H': 'Histogram',
'W': 'Waveform',
'M': 'Manual',
'E': 'Event',
'C': 'Combo',
}
def derive_record_type_from_filename(filename, default: str = "Waveform") -> str:
"""Derive a BW Event's record_type from its filename's extension suffix.
V10.72+ MiniMate Plus firmware encodes the event type as the LAST
character of the extension (the `T` in BW's `AB0T` scheme):
``M529LKIQ.G10H`` → H → ``"Histogram"``
``T350L385.VY0W`` → W → ``"Waveform"``
``...M`` → M → ``"Manual"``
``...E`` → E → ``"Event"``
``...C`` → C → ``"Combo"``
Old S338 firmware uses 3-char extensions ending in ``0`` whose
encoding is not yet known — those fall through to ``default``.
Micromate Series 4 uses a different scheme entirely (observed:
``IDFH``, ``IDFW``) but the LAST-char convention (H / W) still holds
for the type code, so it works for both families.
Returns ``default`` if filename is empty, has no extension, or the
suffix char isn't a recognized type code.
"""
if not filename:
return default
try:
name = Path(filename).name
except (TypeError, ValueError):
return default
if '.' not in name:
return default
ext = name.rsplit('.', 1)[1]
if not ext:
return default
return _RECORD_TYPE_BY_EXT_SUFFIX.get(ext[-1].upper(), default)
# Marker for the recording-setup config block, and the offset of the record-time
# float32 within it. The configured post-trigger record time (seconds) is a
# big-endian float32 exactly 30 bytes before the "Standard Recording Setup"
# label. Verified across the corpus reading 1.0 / 2.0 / 3.0 s on different
# setups — and ts2 - record_time reproduces Blastware's trigger to the second
# (N844LQHB: stop 10:33:32 - 3.0 = 10:33:29).
_RECSETUP_MARKER = b"Standard Recording Setup"
_RECTIME_OFFSET_BEFORE_MARKER = 30
def _parse_record_time_seconds(raw: bytes) -> Optional[float]:
"""The configured post-trigger record time in seconds, from the recording-
setup config block, or None when absent / implausible."""
a = raw.find(_RECSETUP_MARKER)
if a < _RECTIME_OFFSET_BEFORE_MARKER:
return None
off = a - _RECTIME_OFFSET_BEFORE_MARKER
try:
rt = struct.unpack(">f", raw[off:off + 4])[0]
except struct.error:
return None
return rt if 0.05 <= rt <= 600.0 else None
def read_blastware_file(path: Union[str, Path]) -> Event:
"""
Parse a Blastware waveform file into an Event.
@@ -878,13 +465,7 @@ def read_blastware_file(path: Union[str, Path]) -> Event:
# Footer: locate the 0e 08 marker, validating the year is in a sane range.
body_start = _bw._WAVEFORM_HEADER_SIZE + 21
# The 0e 08 + plausible-year footer signature can occur inside the sample
# stream. Collect every candidate and prefer the first whose body yields a
# waveform record chain terminating on the 0x06 marker; fall back to the
# first candidate otherwise. Blast radius measured 2026-08-25: changes the
# chosen footer on exactly 1 of 1,388 series-3 waveform files
# (BE17353/S353L4O5.OX0W, false positive at 3800, real footer at 8576).
footer_candidates = []
footer_pos = -1
pos = body_start
while True:
pos = raw.find(b"\x0e\x08", pos)
@@ -892,24 +473,10 @@ def read_blastware_file(path: Union[str, Path]) -> Event:
break
yr = (raw[pos + 4] << 8) | raw[pos + 5]
if 2015 <= yr <= 2050:
footer_candidates.append(pos)
footer_pos = pos
break
pos += 1
footer_pos = -1
for cand in footer_candidates:
cand_body = raw[body_start:cand]
try:
recs = _wc.walk_records(cand_body)
except Exception:
recs = []
if recs:
tail = recs[-1]["end"]
if tail + 5 <= len(cand_body) and cand_body[tail + 4] == _wc.STREAM_END_ID:
footer_pos = cand
break
if footer_pos < 0 and footer_candidates:
footer_pos = footer_candidates[0]
if footer_pos < 0 and len(raw) >= 26:
footer_pos = len(raw) - 26
if footer_pos < body_start:
@@ -927,44 +494,11 @@ def read_blastware_file(path: Union[str, Path]) -> Event:
ts1 = _bw._decode_ts_be(footer[2:10])
ts2 = _bw._decode_ts_be(footer[10:18])
# Body: decode via the verified body codecs. Two formats coexist:
#
# 1. Waveform-mode (.AB0W) — starts with 7-byte preamble
# ``00 02 00 [Tran[0] BE] [Tran[1] BE]`` followed by the
# tagged-block delta stream documented in
# ``docs/waveform_codec_re_status.md`` and §7.6.1 of the
# protocol reference. Decoded by ``waveform_codec.decode_waveform_v2``.
#
# 2. Histogram-mode (.AB0H) — a sequence of 32-byte blocks, one
# per histogram interval, each carrying per-channel peak +
# half-period values. Decoded by
# ``histogram_codec.decode_histogram_body``. Both codecs
# return the same channel-grouped output shape, so consumers
# don't need to special-case mode.
#
# The historical ``_decode_samples_4ch_int16_le`` int16-LE
# interpretation was retracted 2026-05-08 (see protocol-ref §7.6.1
# retraction box) — it produced ±32K noise on every event.
#
# If both codecs fail (malformed file, truncated body, unrecognised
# mode, synthetic test input), fall back to empty channels — the
# rest of the event (timestamp, waveform_key, project strings) is
# still recoverable and useful.
decoded = decode_waveform_v2(body)
# Discriminator for the timestamp logic below: a waveform (trigger) event
# vs a histogram window. Keyed on the codec, not the filename — the
# save_imported_bw path passes a tmp ".bw" name whose extension lies.
is_waveform_body = decoded is not None
if decoded is None:
decoded = decode_histogram_body(body)
if decoded is None:
log.warning(
"%s: body codec failed to decode (body starts %s) — "
"raw_samples will be empty", path, body[:8].hex(" "),
)
samples = {"Tran": [], "Vert": [], "Long": [], "MicL": []}
else:
samples = decoded_to_adc_counts(decoded)
# Body: first 6 bytes are the preamble (00 00 ff ff ff ff). Strip
# them before decoding samples. Any trailing tail past the last
# full sample-set is silently truncated by _decode_samples_4ch.
sample_bytes = body[6:] if body[:6].hex() in ("0000ffffffff", "0000FFFFFFFF") else body
samples = _decode_samples_4ch_int16_le(sample_bytes)
# Metadata strings (label-anchored search across the body).
project = _find_first_string(body, b"Project:")
@@ -976,41 +510,12 @@ def read_blastware_file(path: Union[str, Path]) -> Event:
ev = Event(index=-1)
if strt_fields.get("waveform_key"):
ev._waveform_key = bytes.fromhex(strt_fields["waveform_key"])
# Derive record_type from the filename's extension suffix (H/W/M/E/C).
# When called from save_imported_bw the path here is a tmp file with a
# ".bw" suffix, so the derivation falls back to "Waveform" and the
# caller overrides ev.record_type using the original filename — see
# waveform_store.save_imported_bw.
ev.record_type = derive_record_type_from_filename(path.name)
ev.record_type = "Waveform"
ev.rectime_seconds = strt_fields.get("rectime_seconds")
ev.total_samples = strt_fields.get("total_samples")
ev.pretrig_samples = strt_fields.get("pretrig_samples")
# Event timestamp. The footer's two timestamps mean different things by
# record type:
# * Waveform: ts1 = the monitoring-SESSION start (shared across every
# event that day — a unit arming at 06:00 stamps 06:00 on all of them),
# ts2 = THIS event's recording STOP. Blastware's Date/Time is the
# TRIGGER = ts2 - record time, and the record time is a float32 in the
# recording-setup config block (see _parse_record_time_seconds), so the
# exact trigger is recoverable from the binary alone. Falls back to ts2
# (the stop, within the record duration) if the config block is absent.
# (Stamping ts1 showed the session start, hours off.)
# * Histogram / undecodable: ts1 = the window start, which IS the event
# time — keep it.
# Discriminate by ``is_waveform_body`` (the codec), not the filename.
if is_waveform_body and ts2 is not None:
_stop = datetime.datetime(ts2.year, ts2.month, ts2.day,
ts2.hour, ts2.minute, ts2.second)
_rt = _parse_record_time_seconds(raw)
_trig = _stop - datetime.timedelta(seconds=_rt) if _rt is not None else _stop
ev.timestamp = Timestamp(
raw=footer[10:18],
flag=0x10,
year=_trig.year, unknown_byte=0, month=_trig.month, day=_trig.day,
hour=_trig.hour, minute=_trig.minute, second=_trig.second,
)
elif ts1 is not None:
if ts1 is not None:
ev.timestamp = Timestamp(
raw=footer[2:10],
flag=0x10,
@@ -1022,23 +527,7 @@ def read_blastware_file(path: Union[str, Path]) -> Event:
project=project, client=client, operator=user, sensor_location=seisloc,
)
ev.raw_samples = samples
# Sensor self-check traces from the binary's trailing block (waveform
# events only; returns {} for histograms / when absent). Carried on the
# Event so the .h5 writer persists them device-agnostically.
from minimateplus.sensor_check import decode_sensor_check
ev.sensor_check = decode_sensor_check(raw) or None
# Only compute peaks from samples when we actually have samples.
# For events the codec couldn't decode (histogram-mode bodies, until
# the §7.6.2 histogram codec is wired in), samples is an empty dict
# and ``_peaks_from_samples`` would return PeakValues(0, 0, 0, 0, 0).
# That would then OVERWRITE existing good DB peak values (e.g. from
# paired BW ASCII reports) during the backfill UPSERT path.
# Leaving peak_values=None signals "we don't know" to downstream
# consumers; the backfill script seeds from the DB row when it sees
# None, and ``apply_report_to_event`` overlays from a paired ASCII
# report when one is supplied.
has_samples = any(samples.get(ch) for ch in ("Tran", "Vert", "Long", "MicL"))
ev.peak_values = _peaks_from_samples(samples) if has_samples else None
ev.peak_values = _peaks_from_samples(samples)
ev._a5_frames = None # not recoverable from BW file
return ev
minimateplus/histogram_codec.py
-487
View File
@@ -1,487 +0,0 @@
"""
histogram_codec.py — decoder for MiniMate Plus histogram-mode event bodies.
FULLY DECODED 2026-05-20. Every field in every block, verified
byte-exact against BW's ASCII export across multiple histogram
fixtures.
The histogram-mode body is a stream of 32-byte fixed-length blocks,
one block per histogram interval. Each block carries the per-interval
peak amplitude + zero-crossing frequency for all four channels (Tran,
Vert, Long, MicL).
────────────────────────────────────────────────────────────────────────────
Body layout (CONFIRMED 2026-05-20)
────────────────────────────────────────────────────────────────────────────
[stream of 32-byte blocks]
Body length is approximately ``n_intervals * 32`` bytes plus a small
trailing remnant (1-9 bytes typically) at the very end. Walker should
iterate 32-stride and stop before the tail.
────────────────────────────────────────────────────────────────────────────
32-byte block layout
────────────────────────────────────────────────────────────────────────────
[0] 0x00 always-zero tag
[1] segment_id (uint8) 0x00..0x03 - 256 blocks per segment
[2:4] block_ctr (uint16 LE) resets each segment (0x0100, 0x0101, ...)
[4] 0x0a (uint8) constant marker (= 10)
[5:7] T_peak_count uint16 BE Tran peak (count x 0.005 -> in/s)
[7:9] T_halfperiod uint16 BE Tran half-period in samples (freq = 512 / halfp)
[9:11] V_peak_count uint16 BE
[11:13] V_halfperiod uint16 BE
[13:15] L_peak_count uint16 BE
[15:17] L_halfperiod uint16 BE
[17:19] M_peak_count uint16 BE MicL peak (count -> dB via mic_count_to_db)
[19:21] M_halfperiod uint16 BE MicL half-period in samples
[21:23] 0x00 0x00 constant on standard blocks
[24:28] 4-byte variable purpose unknown (possibly CRC or timestamp delta)
[28:32] block-end signature see "Two block tails" below
**Every per-channel field is uint16 BIG-endian** (confirmed 2026-08-25).
Only ``block_ctr`` at [2:4] is little-endian.
HISTORY - two earlier readings of this block were wrong in ways that
cancelled out on quiet data:
1. *peak as uint16 LE at [6:8]* - produced 268 in/s peaks on any
interval whose next byte was non-zero.
2. *peak as uint8 at [6] with an "annotation" byte at [7]* - correct
for every peak below 256 counts (1.275 in/s), but it silently
**clipped larger peaks**: the final interval of
BE18193/T193LQ9K.OE0H reads 8.270 in/s in BW's export
(1654 counts = 0x0676) and decoded as 0x76 = 118 = 0.590 in/s.
The "annotation" byte was never an annotation - it is the high
byte of the big-endian half-period, which is why it was non-zero
exactly on the sub-Hz intervals BW renders as "<1.0".
Both readings also forced ``block[5] == 0`` via a bogus ``uint16 LE``
marker check at [4:6], which is what capped the peak at one byte.
The marker is ``block[4]`` alone.
Verified 2026-08-25 against 1211 production histograms paired with
their Blastware ASCII exports: **1211/1211 decode exactly** (interval
count plus every per-interval peak), and 842,442 per-interval
frequency comparisons match with **zero** mismatches.
Two block tails
---------------
Standard blocks end with ``1e 0a 00 00``. The **final block of the
stream** ends with ``9c 06 00 42`` instead, and carries arbitrary bytes
at [21:23]. Rejecting it dropped the last interval of nearly every
histogram - and the last interval is frequently the one holding the
event peak, so the file's reported PPV came out low. Observed in 1206
of 1211 production histograms, always positioned after every
standard-tail block.
Block-identification anchor: ``block[0] == 0x00`` AND
``block[4] == 0x0A`` AND the tail is one of the two signatures above;
standard-tail blocks additionally require ``block[22] == 0x00``.
────────────────────────────────────────────────────────────────────────────
Per-channel encoding
────────────────────────────────────────────────────────────────────────────
Geophone channels (Tran, Vert, Long):
- peak_count × 0.005 = peak amplitude in in/s at Normal range
- half-period in samples → freq_Hz = 512 / half-period
Microphone channel (MicL):
- peak_count → dB via the same formula used by the waveform codec:
dB = sign(c) × (81.94 + 20·log10(|c|)) for |c| ≥ 1
dB = 0 for c == 0
- half-period → freq_Hz = 512 / half-period (same as geo)
Frequency `>100 Hz` sentinel: the device emits half-period ≤ 5 when the
measured zero-crossing rate exceeds the geophone's measurement range
(since 512/5 = 102 Hz; the BW display rounds anything > 100 to ">100").
────────────────────────────────────────────────────────────────────────────
Output shape
────────────────────────────────────────────────────────────────────────────
``decode_histogram_body`` returns a per-channel dict matching the
waveform codec's shape so the rest of the pipeline (.h5 writer,
sidecar, viewer) consumes it without special-casing:
{"Tran": [peak_count_i for each interval i],
"Vert": [peak_count_i ...],
"Long": [peak_count_i ...],
"MicL": [peak_count_i ...]}
Values are in **16-count units for geo** (LSB = 0.005 in/s, matching
``decode_waveform_v2``) and **1-count units for mic** (matching the
waveform codec's mic convention). Run through
``waveform_codec.decoded_to_adc_counts`` to scale geo to 1-count ADC.
Per-interval frequencies are NOT returned — they're auxiliary data,
not waveform samples. Consumers needing frequencies can call
``decode_histogram_body_full()`` for the structured per-interval
record list.
"""
from __future__ import annotations
import struct
from typing import List, Optional, Tuple
# Block-end signature: constant `1e 0a 00 00` in bytes [28:32] of every
# real data block. More distinctive than the byte-22 `00 00` (which
# matches many false positives), so we anchor on this.
_BLOCK_TAIL = b"\x1e\x0a\x00\x00"
# The final block of a histogram stream ends with this instead. It is a
# real data block - same layout - and holds the last interval. See the
# module docstring, "Two block tails".
_BLOCK_TAIL_TERMINAL = b"\x9c\x06\x00\x42"
_BLOCK_SIZE = 32
# Marker byte at block[4:6] of every histogram data block. Used as
# additional validation that we're looking at a real block.
_BLOCK_MARKER = 10
# Geo peak scaling: stored as "count × 0.005 in/s" where 1 count = one
# 0.005 in/s display quantum. Equivalent to the waveform codec's
# 16-count-unit output (1 unit = 0.005 in/s = 16 ADC counts).
_GEO_LSB_INS = 0.005
# Frequency formula: freq_Hz = _FREQ_NUMERATOR / half_period_samples.
# Empirically determined to be 512 (= sample_rate / 2, where sample rate
# is 1024 sps for the standard MiniMate Plus configuration).
_FREQ_NUMERATOR = 512
def _is_data_block(block: bytes) -> bool:
"""Tight identification of a histogram data block.
Accepts both tail signatures. ``block[4]`` alone is the marker -
``block[5]`` is the high byte of the Tran peak and is non-zero on any
interval above 1.275 in/s, so it must not be part of the marker test.
The ``block[22] == 0`` constraint is what keeps trailer content out,
but it applies only to standard-tail blocks: terminal blocks carry
arbitrary bytes there.
"""
if len(block) < _BLOCK_SIZE:
return False
if block[0] != 0x00:
return False
if block[4] != _BLOCK_MARKER:
return False
# The 4-byte tail plus block[0]==0 and block[4]==0x0A is already six bytes
# of constraint — enough to keep trailer content out. There is NO extra
# test on block[22]: it was documented as a constant 0x00 but carries data
# on loud blocks, and rejecting those threw away the interval holding the
# event peak. BE18350/T350L7HR.NL0H is the proof: its block 92 has
# block[22]=0x26 and a Tran peak of 0x0563 = 1379 counts = 6.895 in/s,
# exactly the device-reported PPV, while the file decoded to 0.015 in/s.
return block[28:32] in (_BLOCK_TAIL, _BLOCK_TAIL_TERMINAL)
def _decode_block(block: bytes) -> Optional[dict]:
"""Decode one 32-byte histogram block. Caller must have validated
with ``_is_data_block`` first.
Returns a record with per-channel peak counts (uint8) and
half-periods (uint16 LE).
"""
# Every per-channel field is uint16 BIG-endian; only block_ctr is LE.
# See the module docstring for the two superseded readings and why
# each looked correct on quiet data.
def _be16(i: int) -> int:
return (block[i] << 8) | block[i + 1]
t_peak = _be16(5)
t_halfp = _be16(7)
v_peak = _be16(9)
v_halfp = _be16(11)
l_peak = _be16(13)
l_halfp = _be16(15)
m_peak = _be16(17)
m_halfp = _be16(19)
segment_id = block[1]
block_ctr = block[2] | (block[3] << 8)
var_meta = bytes(block[24:28])
return {
"segment_id": segment_id,
"block_ctr": block_ctr,
"t_peak": t_peak,
"t_halfp": t_halfp,
"v_peak": v_peak,
"v_halfp": v_halfp,
"l_peak": l_peak,
"l_halfp": l_halfp,
"m_peak": m_peak,
"m_halfp": m_halfp,
"meta_var": var_meta,
"is_terminal": block[28:32] == _BLOCK_TAIL_TERMINAL,
}
def walk_body(body: bytes) -> List[dict]:
"""Walk the body and return one dict per histogram interval.
Iterates 32-byte strides from offset 0. Yields a decoded record
for every block that passes ``_is_data_block`` validation. Stops
when the remaining bytes are too short to form a complete block.
In Histogram+Continuous mode the body interleaves data blocks with
other 32-byte content (likely continuous-mode waveform blocks) that
fail the data-block validation; the walker naturally skips them
without losing 32-byte alignment. Use ``block_ctr`` from each
returned record to map back to the original interval index — the
record list is sparse when other block types are interleaved.
"""
records: List[dict] = []
for off in range(0, len(body) - _BLOCK_SIZE + 1, _BLOCK_SIZE):
blk = body[off:off + _BLOCK_SIZE]
if not _is_data_block(blk):
# Hit non-block content (likely a sync or stream marker).
# Continue walking — block alignment is fixed at 32-stride
# from offset 0, so we don't lose alignment by skipping.
continue
decoded = _decode_block(blk)
if decoded is None:
# Block validated as a histogram block but had peak fields
# outside the plausible range — undocumented extension.
# Skip rather than propagating bogus PVS contributions.
continue
records.append(decoded)
return records
def _walk_auto(body: bytes) -> List[dict]:
"""Pick the block model by signature strength, not by which returns first.
The multi-interval variant announces itself with consecutive block headers
at an exact ``12 + 20*n`` stride — far stronger evidence than a handful of
scattered standard-tail blocks, which a multi-interval body will also yield
by coincidence. Dispatching on "whichever decoder returns something"
handed 193 BE18193 files to the standard walker and produced peaks of
149 in/s against a 10 in/s full scale.
"""
if detect_multi_interval_stride(body):
recs = walk_multi_interval_blocks(body)
if recs:
return recs
return walk_body(body)
def decode_histogram_body(body: bytes) -> Optional[dict]:
"""Decode a histogram-mode body into per-channel peak-sample arrays.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}``
where each channel's list contains one peak value per histogram
interval (in the same units the waveform codec uses: 16-count units
for geo, 1-count ADC units for mic). Returns ``None`` if the body
doesn't contain any valid histogram blocks.
To convert to physical units:
- Geo channels: ``count * 0.005`` = peak in in/s at Normal range
(or run through ``waveform_codec.decoded_to_adc_counts`` first
to get 1-count ADC values, then ``count / 32767 * 10.0`` for in/s)
- Mic channel: use ``waveform_codec.mic_count_to_db(count)``
"""
records = _walk_auto(body)
if not records:
return None
return {
"Tran": [r["t_peak"] for r in records],
"Vert": [r["v_peak"] for r in records],
"Long": [r["l_peak"] for r in records],
"MicL": [r["m_peak"] for r in records],
}
def decode_histogram_body_full(body: bytes) -> Optional[List[dict]]:
"""Decode a histogram-mode body into the full per-interval record list.
Same data as ``decode_histogram_body`` but in a structured form that
preserves the half-period (frequency) data for each channel + the
per-block segment_id, block_ctr, and 4-byte variable metadata.
Useful for diagnostic tools, sidecar enrichment, and future-codec
work.
Returns ``None`` if the body has no valid blocks.
"""
records = _walk_auto(body)
return records if records else None
def half_period_to_hz(halfp: int) -> Optional[float]:
"""Convert a half-period in samples to frequency in Hz.
Returns ``None`` for half-period ≤ 5 — the device emits values in
that range when the measured zero-crossing rate exceeds 100 Hz
(the BW display reports `>100 Hz` for such cases). Callers can
treat ``None`` as the `>100 Hz` sentinel.
"""
if halfp <= 5:
return None
return _FREQ_NUMERATOR / halfp
def geo_count_to_ins(count: int) -> float:
"""Convert a histogram geo peak count to in/s at Normal range."""
return count * _GEO_LSB_INS
# ── Multi-interval block variant (CONFIRMED 2026-08-26) ─────────────────────
#
# When the histogram interval is SHORTER than one minute, the device packs
# several intervals into a single block so that every block still covers
# exactly one minute of data:
#
# interval size intervals/block stride
# 1 minute 1 32 <- the standard block above
# 15 seconds 4 92
# 2 seconds 30 612
#
# stride = 12 + n_intervals * 20
#
# Block layout:
# [0] 0x00
# [1] segment_id (256 blocks per segment, same as the standard block)
# [2:4] block_ctr uint16 LE (0x0100.., resets each segment)
# [4] 0x0a marker
# [5] 0x00
# [6 ...] n x 20-byte interval records, each carrying 8 x uint16
# LITTLE-endian values:
# T_peak, T_halfperiod, V_peak, V_halfperiod,
# L_peak, L_halfperiod, M_peak, M_halfperiod
# then 2 more words; the first is 0x0000 on every real interval.
# [-6:] 6-byte block trailer
#
# ⚠ ENDIANNESS: the standard 32-byte block is BIG-endian. This variant is
# LITTLE-endian. Do not share the accessor.
#
# These files previously decoded to nothing at all — 415 of them in the
# production snapshot, 216 on BE18193 (2 s intervals) and 199 on BE9440
# (15 s). Before that they were being accepted by the WAVEFORM codec, which
# returned garbage peaking up to 400x the device-reported PPV.
#
# Ground truth: BE9440/K440L3AQ.T70H (15 s intervals, 5,710 of them) decodes
# against its Blastware ASCII export with 17,130/17,130 geo peak counts,
# 22,840/22,840 frequencies and 5,710/5,710 mic dB(L) values matching exactly.
_MULTI_HEADER_LEN = 6
_MULTI_RECORD_LEN = 20
_MULTI_TRAILER_LEN = 6
# At least 2 records: a 1-record block would have stride 12 + 20 = 32, which
# collides with the standard big-endian block and mis-decodes it.
_MULTI_MIN_RECORDS = 2
_MULTI_MAX_RECORDS = 64
# Geo full scale in 16-count units: 10.000 in/s / 0.005 = 2000. A peak above
# this is physically impossible and marks buffer garbage in a partial block.
_GEO_MAX_COUNTS = 2000
def _is_multi_header(body: bytes, off: int) -> bool:
return (off + _MULTI_HEADER_LEN <= len(body)
and body[off] == 0x00
and body[off + 4] == 0x0A
and body[off + 5] == 0x00)
def detect_multi_interval_stride(body: bytes) -> Optional[int]:
"""Block stride of a multi-interval histogram body, or None.
Found by locating the second block header; validated against
``stride = 12 + n * 20`` and confirmed on a third block where present.
"""
if not _is_multi_header(body, 0):
return None
lo = _MULTI_HEADER_LEN + _MULTI_TRAILER_LEN + _MULTI_RECORD_LEN * _MULTI_MIN_RECORDS
hi = _MULTI_HEADER_LEN + _MULTI_TRAILER_LEN + _MULTI_RECORD_LEN * _MULTI_MAX_RECORDS
for stride in range(lo, min(hi, len(body)) + 1, 2):
if (stride - 12) % _MULTI_RECORD_LEN:
continue
if not _is_multi_header(body, stride):
continue
# DECISIVE CHECK: consecutive blocks differ by exactly 1 in block_ctr.
# Without it this false-positives on ordinary standard-block bodies:
# those carry a header every 32 bytes, and 192 = 12 + 20*9 and
# 512 = 12 + 20*25 are both multiples of 32, so a stride "fits" while
# actually skipping 6 or 16 real blocks. Sampling a standard body at
# stride 192 handed 9,082 files to the wrong decoder and produced peaks
# of 149 in/s against a 10 in/s full scale.
def _ctr(o: int) -> int:
return body[o + 2] | (body[o + 3] << 8)
if (_ctr(stride) - _ctr(0)) & 0xFFFF != 1:
continue
# Confirm on a third block WHEN ONE IS ACTUALLY PRESENT. A body can
# be longer than two strides and still hold only two real blocks: a
# final *partial* block leaves trailing padding. E.g. 51 intervals at
# 2 s = one full 30-interval block + a 21-interval remainder, in a
# 2787-byte body — long enough to demand a third header at 1224 that
# does not exist. Requiring it unconditionally threw away the correct
# stride and the file decoded to nothing (BE18193 T193L0XM.CI0H).
# The block-counter check above is the decisive anti-false-positive
# test; this one is corroboration, so a missing third header means
# end-of-stream, not disqualification.
if (2 * stride + _MULTI_HEADER_LEN <= len(body)
and _is_multi_header(body, 2 * stride)):
if (_ctr(2 * stride) - _ctr(stride)) & 0xFFFF != 1:
continue
return stride
return None
def walk_multi_interval_blocks(body: bytes,
stride: Optional[int] = None) -> List[dict]:
"""Decode a multi-interval histogram body into per-interval records."""
if stride is None:
stride = detect_multi_interval_stride(body)
if not stride:
return []
n_per_block = (stride - _MULTI_HEADER_LEN - _MULTI_TRAILER_LEN) // _MULTI_RECORD_LEN
if n_per_block < 1:
return []
def u16le(p: int) -> int:
return body[p] | (body[p + 1] << 8)
out: List[dict] = []
for off in range(0, len(body) - stride + 1, stride):
if not _is_multi_header(body, off):
break # end of the block run; trailer follows
for k in range(n_per_block):
q = off + _MULTI_HEADER_LEN + _MULTI_RECORD_LEN * k
out.append({
"_tail0": u16le(q + 16),
"segment_id": body[off + 1],
"block_ctr": u16le(off + 2),
"t_peak": u16le(q), "t_halfp": u16le(q + 2),
"v_peak": u16le(q + 4), "v_halfp": u16le(q + 6),
"l_peak": u16le(q + 8), "l_halfp": u16le(q + 10),
"m_peak": u16le(q + 12), "m_halfp": u16le(q + 14),
"meta_var": bytes(body[q + 16:q + 20]),
"is_terminal": False,
})
# A session ending mid-block leaves the remaining slots of the FINAL block
# filled with whatever was in the buffer. Those decoded as peaks thousands
# of times the device-reported PPV, so they have to go — but only from the
# final block: a non-zero tail word occurs mid-file on real intervals, and
# trimming on that alone truncated four BE9440 files by up to 2,800
# intervals, while trimming purely from the end left garbage stranded
# behind one slot that happened to have a zero tail word.
#
# Within the final block, stop at the first slot that is not plausibly
# real: a non-zero tail word, or a geo peak above full scale. 16-count
# units put Normal-range full scale (10.000 in/s) at 2000 counts, so
# anything beyond that is physically impossible.
if out:
last_block_start = ((len(out) - 1) // n_per_block) * n_per_block
for i in range(last_block_start, len(out)):
r = out[i]
if (r["_tail0"] != 0
or max(r["t_peak"], r["v_peak"], r["l_peak"]) > _GEO_MAX_COUNTS):
del out[i:]
break
for r in out:
r.pop("_tail0", None)
return out
minimateplus/models.py
-17
View File
@@ -352,14 +352,6 @@ class PeakValues:
long: Optional[float] = None # Longitudinal PPV (in/s) ✅
micl: Optional[float] = None # Air overpressure (psi) 🔶 (units uncertain)
peak_vector_sum: Optional[float] = None # Scalar geo PVS (in/s) ✅
tran_zc_freq: Optional[float] = None
vert_zc_freq: Optional[float] = None
long_zc_freq: Optional[float] = None
mic_zc_freq: Optional[float] = None
tran_zc_above_range: bool = False
vert_zc_above_range: bool = False
long_zc_above_range: bool = False
mic_zc_above_range: bool = False
# ── Project / operator metadata ───────────────────────────────────────────────
@@ -544,15 +536,6 @@ class Event:
pretrig_samples: Optional[int] = None # from STRT record: pre-trigger sample count
rectime_seconds: Optional[int] = None # from STRT record: record duration (seconds)
# Sensor self-check traces keyed by channel label — the short diagnostic
# waveforms the unit records when it pulses each sensor before monitoring
# (geophone ring-downs + a mic pulse train). Decoded from the binary by
# the per-series decoder (minimateplus.sensor_check / micromate.sensor_check)
# and carried here so the .h5 writer can persist them device-agnostically.
# Raw ADC counts; the source series' scale differs but the trace is a
# shape diagnostic (rendered fit-to-box). None when absent.
sensor_check: Optional[dict] = None # {"Tran": [...], ..., "MicL": [...]}
# ── Debug / introspection ─────────────────────────────────────────────────
# Raw 210-byte waveform record bytes, set when debug mode is active.
# Exposed by the SFM server via ?debug=true so field layouts can be verified.
minimateplus/sensor_check.py
-146
View File
@@ -1,146 +0,0 @@
r"""Decode the Blastware sensor self-check waveforms from a series-3 event binary.
Reverse-engineered 2026-09-15 against 7 BE12844 (MiniMate Plus) oracle events.
After the main waveform record-chain and the trailing metadata / per-channel
calibration records, the binary carries four length-prefixed records tagged
0x3c-0x3f: the sensor self-check traces the unit records when it pulses each
sensor before monitoring. Blastware draws these as the little waveforms in the
"Sensor Check" strip on the right of the Event Report.
* 0x3c / 0x3d / 0x3e = Tran / Vert / Long geophone ring-downs (a damped
oscillation at the geophone's resonance, ~7-8 Hz at 1024 sps).
* 0x3f = MicL, a pulse train at the mic self-test frequency
(~20 Hz), whose zero-crossing frequency is BW's mic "Channel Test" freq.
Record framing (per record, all four chained by their length prefix)::
[len:2 BE][id:1][00 00][Nchan:1][12-byte header][delta stream][40 02][6B]
\_________________ payload (len bytes) _______________________________/
The delta stream is ``payload[20 : len-8]`` (the ``40 02`` terminator sits at
``len-8``, followed by 6 trailing bytes). It uses the exact same 10/20/30/00
delta-block tags as the main waveform codec
(:mod:`minimateplus.waveform_codec`), decoded here from an implicit anchor of 0
— so the traces come out in the same 16-count raw units as the main waveform
(LSB = 0.005 in/s at Normal range for the geophones).
"""
from __future__ import annotations
from typing import Dict, List
from minimateplus.waveform_codec import walk_body
# Record id → channel. Order mirrors the trailing per-channel calibration
# records (Tran / Vert / Long / MicL), confirmed against BW's sensor-check
# frequencies on all 7 oracle events.
_ID_TO_CHANNEL = {0x3C: "Tran", 0x3D: "Vert", 0x3E: "Long", 0x3F: "MicL"}
_CHAIN_IDS = (0x3C, 0x3D, 0x3E, 0x3F)
_HEADER_LEN = 20 # payload bytes before the delta stream
_TRAILER_LEN = 8 # 40 02 terminator + 6 trailing bytes after the stream
def _s4(nib: int) -> int:
"""Sign-extend a 4-bit nibble delta."""
return nib - 16 if nib >= 8 else nib
def _i8(byte: int) -> int:
"""Sign-extend an 8-bit int delta."""
return byte - 256 if byte >= 128 else byte
def _decode_delta_stream(buf: bytes) -> List[int]:
"""Accumulate a 10/20/30/00 delta-block stream from an anchor of 0,
stopping at the 0x40 terminator.
Mirrors the block semantics in
:func:`minimateplus.waveform_codec.decode_waveform_v2` (fully decoded &
byte-exact as of 2026-05-11); see that module for the format details.
"""
out: List[int] = []
cur = 0
for blk in walk_body(buf, 0):
fam = blk.tag_hi & 0xF0
if fam == 0x10:
# nibble deltas, high nibble first
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += _s4(nib)
out.append(cur)
elif fam == 0x20:
# int8 deltas
for byte in blk.data:
cur += _i8(byte)
out.append(cur)
elif fam == 0x30:
# 12-bit signed deltas, packed as tag_lo/4 groups of 6 bytes
for g in range(blk.tag_lo // 4):
grp = blk.data[g * 6:(g + 1) * 6]
if len(grp) < 6:
break
high_word = (grp[0] << 8) | grp[1]
for k in range(4):
nib = (high_word >> (12 - 4 * k)) & 0xF
v = (nib << 8) | grp[2 + k]
if v >= 0x800:
v -= 0x1000
cur += v
out.append(cur)
elif fam == 0x00:
# RLE zero-delta run (wide form carries the high nibble in the tag)
run = ((blk.tag_hi & 0x0F) << 8) | blk.tag_lo
out.extend([cur] * run)
elif fam == 0x40:
# segment / record terminator
break
return out
def _find_chain(body: bytes):
"""Locate the four length-prefixed sensor-check records.
Returns a list of ``(offset, id, length)`` or ``None``. The chain is
validated by walking the ids 0x3c → 0x3d → 0x3e → 0x3f via their own length
prefixes, so a stray 0x3c byte in the waveform data cannot match.
"""
for p in range(len(body) - 6):
if body[p + 2] == 0x3C and body[p + 3] == 0 and body[p + 4] == 0:
q = p
recs = []
ok = True
for expect in _CHAIN_IDS:
if q + 3 > len(body) or body[q + 2] != expect:
ok = False
break
length = int.from_bytes(body[q:q + 2], "big")
recs.append((q, expect, length))
q = q + 2 + length
if ok and len(recs) == 4:
return recs
return None
def decode_sensor_check(raw: bytes) -> Dict[str, List[int]]:
"""Decode the four sensor self-check traces from a series-3 event binary.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}`` in
raw decode units (same 16-count LSB as the main waveform), or ``{}`` if the
binary carries no sensor-check block (a histogram event, a non-series-3
file, or a unit/firmware that doesn't store it).
"""
strt = raw.find(b"STRT")
if strt < 0 or len(raw) < strt + 21 + 26:
return {}
body = raw[strt + 21: len(raw) - 26]
chain = _find_chain(body)
if not chain:
return {}
out: Dict[str, List[int]] = {}
for off, rid, length in chain:
payload = body[off + 2: off + 2 + length]
if len(payload) < _HEADER_LEN + _TRAILER_LEN:
continue
stream = payload[_HEADER_LEN: length - _TRAILER_LEN]
out[_ID_TO_CHANNEL[rid]] = _decode_delta_stream(stream)
return out
minimateplus/waveform_codec.py
-948
View File
@@ -1,948 +0,0 @@
"""
waveform_codec.py — block-walker and verified decoder for the MiniMate Plus
waveform-file body.
FULLY DECODED 2026-05-11. Every block type, every channel, and the
channel-rotation rule are verified byte-exact against BW's ASCII export
across the 9-event fixture bundle (47,364 ADC samples, zero errors).
The Blastware waveform-file body — the bytes between the 21-byte STRT
record and the 26-byte file footer — is a tagged variable-length block
stream with a custom delta + RLE codec. (Not raw int16 LE, which was
the historical wrong assumption that produced ±32K noise on every event.)
Current status:
- Block framing: ✅ solved (5 block types and lengths all confirmed)
- Per-channel decode: ✅ solved (Tran / Vert / Long / MicL all byte-exact)
- Channel rotation: ✅ Tran → Vert → Long → MicL per segment
- Segment header: ✅ fully decoded (anchor pair + prev-channel extension)
- 30 NN packed-delta block: ✅ NN × 12-bit signed deltas in NN/4 groups
- MicL → dB(L) conversion: ✅ ``mic_count_to_db`` matches BW display
- Production wiring: ✅ ``client.py:_decode_a5_waveform`` uses the new
codec (via ``decode_a5_frames``). ``.h5`` sidecars now render
correctly.
Known limitations:
- Walker stops early on the loudest events (SP0, SS0, SV0, event-b) at
some mid-segment edge cases not yet fully characterized. Every
sample reached IS correct; the walker just doesn't reach all of
them yet. The cleanly-decoded subset is still ~5000–15000 samples
per loud event.
────────────────────────────────────────────────────────────────────────────
Body layout (CONFIRMED 2026-05-11 against 8 fixture events)
────────────────────────────────────────────────────────────────────────────
[7-byte preamble] [stream of tagged blocks] [trailer]
The preamble is always exactly 7 bytes:
body[0:3] = 00 02 00 magic
body[3:5] = Tran[0] int16 BE in 16-count units (LSB = 0.005 in/s)
body[5:7] = Tran[1] int16 BE in 16-count units
(Earlier drafts of this module described a "7-or-9-byte preamble";
that was wrong — single-shot and continuous events both use 7 bytes.
The "extra 2 bytes" on continuous events were the first ``00 NN`` RLE
marker, not part of the preamble.)
Block types and lengths (all confirmed):
| Tag | Length | Meaning |
|----------|-----------------------|----------------------------------------|
| ``10 NN``| NN/2 + 2 bytes | 4-bit nibble deltas (2 per byte; high |
| | | nibble first; signed 0..7 / 8..F = -8..-1)|
| ``20 NN``| NN + 2 bytes | int8 signed deltas (1 per byte) |
| ``00 NN``| 2 bytes | RLE: append NN copies of current value |
| ``30 NN``| NN*2 in data, NN*4 | Unknown content. Only in loud events. |
| | in trailer | |
| ``40 02``| 20 bytes (fixed) | Segment header |
NN is always a multiple of 4.
────────────────────────────────────────────────────────────────────────────
Tran channel, segment 0 (CONFIRMED 2026-05-11)
────────────────────────────────────────────────────────────────────────────
Segment 0 — everything before the first ``40 02`` segment header — encodes
Tran samples only. Starting from preamble anchors Tran[0] and Tran[1],
each subsequent block contributes to the running Tran value:
10 NN → append NN deltas (4-bit signed nibbles)
20 NN → append NN deltas (int8 signed bytes)
00 NN → append NN copies of the current value (RLE zeros)
40 02 → segment 0 ends; multi-segment continuation is open
This decodes the first 482–510 samples of Tran for each event with zero
errors against BW's ASCII export. The exact segment-0 sample count
varies per event (it's bounded by a fixed device-flash byte budget, not
a fixed sample count — quiet events fit more samples because zero
deltas pack into ``00 NN`` markers compactly).
Implementation: :func:`decode_tran_initial`.
────────────────────────────────────────────────────────────────────────────
Segment header (40 02, 20 bytes total)
────────────────────────────────────────────────────────────────────────────
The 18-byte payload of the ``40 02`` block:
| Offset | Field | Status |
|-----------|---------------------------------------------|-------------|
| [0:2] | T_delta at first sample of new segment | ✅ confirmed|
| | (int16 BE, in 16-count units) | |
| [2:4] | Likely T_delta at sample seg_start+1 | 🟡 likely |
| [4:6] | Unknown (varies; possibly checksum) | ❓ open |
| [6:8] | Byte length to next segment header − 2 | ✅ confirmed|
| | (uint16 BE; useful for walker pre-scan) | |
| [8:12] | Monotonic uint32 LE counter | ✅ confirmed|
| | (starts ~0x47, increments by 1 per segment) | |
| [12:14] | Constant ``02 00`` | ✅ confirmed|
| [14:18] | Unknown 4-byte field | ❓ open |
────────────────────────────────────────────────────────────────────────────
What breaks the multi-segment decoder (the main open question)
────────────────────────────────────────────────────────────────────────────
After segment 0 ends and the segment header T_delta is consumed,
applying segment 1's blocks as Tran continuation produces values that
diverge from truth by sample ~512. The block structure inside segment
1 is IDENTICAL to segment 0 (same alternating 10 NN / 00 NN pattern),
and the delta budget matches the segment size exactly (V70 segment 1
has 264 nibble-deltas + 244 RLE zeros = 508 = the segment's sample
count). But the cumulative is wrong.
The strongest unverified hypothesis is that segments rotate channels:
segment 0 → Tran samples 0..509
segment 1 → Vert samples 0..507
segment 2 → Long samples 0..507
segment 3 → Mic samples 0..507
segment 4 → Tran samples 510..N (continuation)
...
This is consistent with the segment-1 block sums net-to-near-zero in
V70 (where all 4 channels are near zero) and with the per-segment delta
budget matching the segment size for a single channel. It is NOT yet
verified because the per-segment channel anchor isn't pinned down in
the segment header — bytes [4:6] and [14:18] of the header are still
open and probably encode V/L/M anchors.
See ``docs/waveform_codec_re_status.md`` for the current working notes
and the suggested next experiment ("segment-channel scoring analyzer").
"""
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple
@dataclass
class WaveformBlock:
"""One tagged block parsed out of a Blastware waveform-file body."""
offset: int # byte offset into body
tag_hi: int # first tag byte (0x10 / 0x20 / 0x00 / 0x30 / 0x40)
tag_lo: int # second tag byte (NN)
data: bytes # block payload (excludes the 2-byte tag)
length: int # total block length on the wire (includes the tag)
@property
def kind(self) -> str:
return f"{self.tag_hi:02x} {self.tag_lo:02x}"
def find_data_start(body: bytes) -> int:
"""Auto-detect the offset of the first data block.
The body starts with a 7-byte preamble (magic ``00 02 00`` + two int16 BE
Tran anchors). After that, the data section starts with a tag — usually
``10 NN`` or ``20 NN``, but quiet events may begin with a ``00 NN`` RLE
marker. We return the offset of the first recognized tag.
"""
# Try fixed offset 7 first (canonical preamble length).
if len(body) >= 9:
b, nn = body[7], body[8]
# Accept the same tag vocabulary ``walk_body`` accepts, including the
# wide-NN forms (``0X``/``1X``/``2X``) and the variable-width ``40 NN``
# segment header.
if ((b & 0xF0) in (0x00, 0x10, 0x20) and nn % 4 == 0
and ((b & 0x0F) != 0 or 0 < nn <= 0xFC)) \
or (b == 0x30 and nn % 4 == 0 and 0 < nn <= 0xFC) \
or (b == 0x40 and 0 < nn <= 0x08) \
or is_tagless_segment_header(body, 7):
return 7
# Fall back to scanning the first 20 bytes.
for i in range(min(20, len(body) - 1)):
b = body[i]
nn = body[i + 1]
if b in (0x10, 0x20) and nn % 4 == 0 and 0 < nn <= 0xFC:
return i
return -1
# Channel-id byte carried in every segment header. Previously mis-read as a
# "monotonic uint32 LE counter"; it is really ``[channel][00][00][segment]``.
# Verified 2026-08-25 on 1697/1697 segment headers across the ground-truth
# corpus with zero disagreements against the decoded channel rotation.
SEGMENT_CHANNEL_IDS = {0x46: "Tran", 0x47: "Vert", 0x48: "Long", 0x49: "MicL"}
# A tagless segment header: the 14-byte tail of a ``40 NN`` header with no tag
# and no previous-channel continuation deltas (the NN=0 case).
_TAGLESS_HEADER_LEN = 14
def is_tagless_segment_header(body: bytes, i: int) -> bool:
"""True if a bare 14-byte segment header starts at *i*.
Layout ``[field2:2][len_to_next:2][channel_id:4][marker:2][anchors:4]``.
The discriminator is the 6 bytes at ``[4:10]``: a known channel id, two
zero bytes, a small segment index, and the ``01 00`` / ``02 00`` marker.
"""
if i + _TAGLESS_HEADER_LEN > len(body):
return False
return (body[i + 4] in SEGMENT_CHANNEL_IDS
and body[i + 5] == 0x00 and body[i + 6] == 0x00
and body[i + 8] in (0x01, 0x02) and body[i + 9] == 0x00)
def walk_body(body: bytes, start: Optional[int] = None) -> List[WaveformBlock]:
"""Walk the tagged-block sequence starting at *start* (auto-detected by default).
Stops when an unrecognized tag is encountered or end of body is reached.
Returned blocks are in stream order.
"""
if start is None:
start = find_data_start(body)
if start < 0:
return []
blocks: List[WaveformBlock] = []
i = start
while i + 1 < len(body):
t0 = body[i]
t1 = body[i + 1]
if t0 == 0x10 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 // 2 + 2
elif (t0 & 0xF0) == 0x10 and (t0 & 0x0F) != 0 and t1 % 4 == 0:
# Wide-NN nibble block: ``1X NN`` where X is the high nibble of a
# 12-bit NN value. NN = ((t0 & 0x0F) << 8) | t1. Block length
# = NN/2 + 2 bytes (NN nibble deltas, same as ``10 NN`` semantics
# but with NN > 0xFC). Confirmed 2026-05-11 in SP0 segment 12
# where V continuation uses ``11 90`` = NN=0x190=400.
wide_nn = ((t0 & 0x0F) << 8) | t1
length = wide_nn // 2 + 2
elif t0 == 0x20 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
length = t1 + 2
elif (t0 & 0xF0) == 0x20 and (t0 & 0x0F) != 0 and t1 % 4 == 0:
# Wide-NN int8 block: ``2X NN`` extends NN to 12 bits the same way.
wide_nn = ((t0 & 0x0F) << 8) | t1
length = wide_nn + 2
elif (t0 & 0xF0) == 0x00 and t1 % 4 == 0:
# ``00 NN`` RLE zero-delta run, plus its wide form ``0X NN``
# (X != 0) which extends NN to 12 bits exactly like ``1X``/``2X``:
# NN = ((t0 & 0x0F) << 8) | t1. A narrow run maxes out at
# NN=0xFC, so quiet stretches longer than 252 samples must use
# the wide form. Confirmed 2026-08-25 against six production
# events (e.g. ``01 0c`` = 268 repeats in K558LKOF.460W).
length = 2
elif t0 == 0x30 and t1 % 4 == 0 and 0 < t1 <= 0xFC:
# Data-section ``30 NN`` blocks carry NN 12-bit signed deltas packed
# as NN/4 groups of (2-byte high-nibble field + 4 × int8 low byte).
# Length = NN/4 × 6 + 2 = NN × 1.5 + 2 (= 8 for NN=4, 14 for NN=8,
# 20 for NN=12, etc.). Confirmed 2026-05-11 by full-decoder
# verification against BW ASCII export.
#
# Trailer-section ``30 NN`` blocks have a different length formula
# (NN × 4 = 32 for NN=8 in trailers). We try the data-section
# length first and fall back to the trailer length if needed.
cand_data = t1 * 3 // 2 + 2
cand_trailer = t1 * 4
if (i + cand_data < len(body) - 1
and body[i + cand_data] in (0x10, 0x20, 0x00, 0x30, 0x40)):
length = cand_data
else:
length = cand_trailer
elif t0 == 0x40 and 0 < t1 <= 0x08:
# ``40 NN`` segment header. NN is the number of int16 BE
# continuation deltas the header carries for the PREVIOUS
# channel, so the header grows with NN:
# length = 2 (tag) + 2*NN (deltas) + 14 (fixed tail)
# ``40 02`` (20 bytes) dominates, but ``40 01`` (18) and
# ``40 03`` (22) both occur in production files. Confirmed
# 2026-08-25; the constant ``02 00`` marker moves with NN too
# (see :func:`parse_segment_header`).
length = 2 * t1 + 16
elif is_tagless_segment_header(body, i):
# Segment header with no ``40 NN`` tag (NN=0 — the previous channel
# needed no continuation deltas). Emit it as a synthetic ``40 00``
# block whose ``data`` is the whole 14-byte record, so the nd=0
# offsets in :func:`decode_waveform_v2` line up unchanged.
blocks.append(WaveformBlock(
offset=i, tag_hi=0x40, tag_lo=0x00,
data=bytes(body[i : i + _TAGLESS_HEADER_LEN]),
length=_TAGLESS_HEADER_LEN,
))
i += _TAGLESS_HEADER_LEN
continue
else:
# Unknown tag; stop. Caller can inspect ``i`` to see where.
break
if i + length > len(body):
break
data = bytes(body[i + 2 : i + length])
blocks.append(WaveformBlock(offset=i, tag_hi=t0, tag_lo=t1, data=data, length=length))
i += length
return blocks
def split_segments(blocks: List[WaveformBlock]) -> List[List[WaveformBlock]]:
"""Group consecutive blocks into segments separated by ``40 02`` headers.
The first segment is whatever runs before the first ``40 02`` header
(typically the "segment 0" preamble data after the body preamble).
Subsequent segments start with a ``40 02`` block, then have their
own data blocks until the next ``40 02``.
"""
segments: List[List[WaveformBlock]] = []
current: List[WaveformBlock] = []
for b in blocks:
if b.tag_hi == 0x40:
if current:
segments.append(current)
current = [b]
else:
current.append(b)
if current:
segments.append(current)
return segments
def parse_segment_header(block: WaveformBlock) -> Optional[dict]:
"""Decode the payload of a ``40 NN`` segment header.
NN (the tag's low byte) is the number of int16 BE continuation deltas
the header carries for the PREVIOUS channel, so every field after
those deltas shifts by ``2 * NN``. The payload is ``2 * NN + 14``
bytes. ``40 02`` is the common case; ``40 01`` and ``40 03`` also
occur in production files (confirmed 2026-08-25).
Returns a dict with the labelled fields, or None if *block* is not a
segment header or is too short.
"""
if block.tag_hi != 0x40 or block.tag_lo > 0x08:
return None
nd = block.tag_lo
if len(block.data) < 2 * nd + 14:
return None
p = block.data
counter = int.from_bytes(p[2 * nd + 4 : 2 * nd + 8], "little", signed=False)
return {
"n_prev_deltas": nd,
# ``nd`` int16 BE deltas extending the previous channel.
"prev_deltas": [
int.from_bytes(p[2 * k : 2 * k + 2], "big", signed=True)
for k in range(nd)
],
"field2": p[2 * nd : 2 * nd + 4], # 4-byte field, role unconfirmed
"counter": counter, # legacy: raw uint32 LE of the id field
"channel": SEGMENT_CHANNEL_IDS.get(p[2 * nd + 4]),
"segment_index": p[2 * nd + 7],
"marker": p[2 * nd + 8 : 2 * nd + 10], # always b"\x02\x00"
"anchors": [
int.from_bytes(p[2 * nd + 10 : 2 * nd + 12], "big", signed=True),
int.from_bytes(p[2 * nd + 12 : 2 * nd + 14], "big", signed=True),
],
}
def _s4(n: int) -> int:
"""Sign-extend a 4-bit value to signed int (0..7 → 0..7; 8..F → -8..-1)."""
return n if n < 8 else n - 16
def _i8(b: int) -> int:
"""Reinterpret an unsigned byte as signed int8."""
return b if b < 128 else b - 256
def decode_tran_initial(body: bytes) -> Optional[List[int]]:
"""
Decode the initial Tran-channel samples — VERIFIED 2026-05-11.
Returns Tran samples in **16-count units** (LSB = 0.005 in/s at Normal
range — the same quantization BW uses for its ASCII export). Returns
``None`` if the body cannot be parsed.
The decoded list extends from sample 0 through the end of segment 0
(= just before the first ``40 02`` segment header; ~510 sample-sets
for the events tested). Multi-segment decoding requires continuing
past the segment header — that's done by :func:`decode_tran_full`
when the per-segment rules are pinned down for all signal types.
Codec for segment 0 (CONFIRMED 2026-05-11 against 7 fixture events):
- Body bytes [0:3] are the magic ``00 02 00``.
- Body bytes [3:5] = ``Tran[0]`` as int16 BE in 16-count units.
- Body bytes [5:7] = ``Tran[1]`` as int16 BE in 16-count units.
- Data blocks (``10 NN`` or ``20 NN``) carry Tran deltas starting
at sample 2:
* ``10 NN``: NN nibbles = NN/2 bytes; each nibble is a 4-bit
signed delta (0..7 → 0..+7; 8..F → -8..-1). High nibble of
each byte comes first.
* ``20 NN``: NN int8 signed deltas (one delta per byte).
- ``00 NN`` blocks are run-length-encoded zero deltas: append NN
copies of the current cumulative Tran value (no change).
- ``30 NN`` blocks have not yet been decoded for content — they
appear in segment 0 of loud-from-start events (SS0, SV0) and
seem to signal a transition or special-case interpretation.
The walker steps over them but their data is ignored.
The walk stops at the first ``40 02`` segment header.
"""
if len(body) < 7 or body[0:3] != b"\x00\x02\x00":
return None
t0 = int.from_bytes(body[3:5], "big", signed=True)
t1 = int.from_bytes(body[5:7], "big", signed=True)
start = find_data_start(body)
if start < 0:
return [t0, t1]
out = [t0, t1]
cur = t1
for blk in walk_body(body, start):
if blk.tag_hi == 0x40:
# Segment boundary — stop. Multi-segment decode is decode_tran_full.
break
if blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += _s4(nib)
out.append(cur)
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += _i8(byte)
out.append(cur)
elif blk.tag_hi == 0x00:
# RLE zero deltas: append NN copies of current Tran value.
for _ in range(blk.tag_lo):
out.append(cur)
# 30 NN: unknown content; skip.
return out
def decode_waveform_legacy(body: bytes) -> Optional[dict]:
"""
SUPERSEDED 2026-08-25 — the tag-dispatch / segment-header model.
Retained because ``micromate/idf_file.py`` trial-decodes Thor IDFW bodies
at many candidate offsets and keeps whichever yields the most samples;
the record-chain decoder returns None where this one returned garbage,
which shifts that heuristic's winner. Thor is pinned here until its own
body-offset search is reworked. Do not use for series-3.
Decode the body into per-channel sample arrays.
Status (2026-05-11 evening — channel-rotation hypothesis CONFIRMED):
segments rotate channels in fixed order **Tran → Vert → Long → MicL**.
Each channel-segment carries a 2-sample anchor pair in segment-header
bytes [14:18] (or in the body preamble for the initial Tran segment)
plus a stream of delta blocks for samples 2 onward.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}``
with each channel's decoded samples in 16-count units (LSB = 0.005
in/s at Normal range). Returns ``None`` if the body cannot be
parsed.
"""
if len(body) < 7 or body[0:3] != b"\x00\x02\x00":
return None
channels = ["Tran", "Vert", "Long", "MicL"]
out: dict = {ch: [] for ch in channels}
# Initial Tran segment: preamble anchor pair + delta blocks before first 40 02.
t0 = int.from_bytes(body[3:5], "big", signed=True)
t1 = int.from_bytes(body[5:7], "big", signed=True)
out["Tran"].extend([t0, t1])
start = find_data_start(body)
if start < 0:
return out
blocks = walk_body(body, start)
seg_idx = [i for i, b in enumerate(blocks) if b.tag_hi == 0x40]
def apply_blocks(channel: str, anchor: int,
block_start: int, block_end: int) -> int:
"""Apply delta blocks [block_start, block_end) to *channel*'s sample
list, starting from *anchor*. Returns the final cumulative value."""
cur = anchor
for bi in range(block_start, block_end):
blk = blocks[bi]
if (blk.tag_hi & 0xF0) == 0x10:
# Both ``10 NN`` (NN ≤ 0xFC) and wide-NN ``1X NN`` (X != 0)
# are nibble-delta streams. The walker has already used the
# right length; here we just iterate the payload bytes.
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += _s4(nib)
out[channel].append(cur)
elif (blk.tag_hi & 0xF0) == 0x20:
# ``20 NN`` and wide ``2X NN`` both carry int8 deltas.
for byte in blk.data:
cur += _i8(byte)
out[channel].append(cur)
elif (blk.tag_hi & 0xF0) == 0x00:
# RLE zero-delta run. Wide form ``0X NN`` carries the high
# nibble of a 12-bit NN in the tag byte, same as ``1X``/``2X``.
run = ((blk.tag_hi & 0x0F) << 8) | blk.tag_lo
for _ in range(run):
out[channel].append(cur)
elif blk.tag_hi == 0x30:
# 12-bit signed deltas, packed as NN/4 groups of 6 bytes each:
# bytes [0:2] = 16 bits = 4 × 4-bit high nibbles (MSB first)
# bytes [2:6] = 4 × int8 low bytes
# Each delta = sign_extend_12((high_nibble << 8) | low_byte).
# Confirmed 2026-05-11 against all 14 ``30 NN`` blocks in the
# bundled fixtures.
n_groups = blk.tag_lo // 4
for g in range(n_groups):
grp = blk.data[g * 6 : (g + 1) * 6]
if len(grp) < 6:
break
high_word = (grp[0] << 8) | grp[1]
for k in range(4):
nib = (high_word >> (12 - 4 * k)) & 0xF
v = (nib << 8) | grp[2 + k]
if v >= 0x800:
v -= 0x1000
cur += v
out[channel].append(cur)
# 40 02: should not occur in segment data.
return cur
# Initial Tran segment: deltas from start of body up to first 40 02 (or end).
first_seg = seg_idx[0] if seg_idx else len(blocks)
last_tran_value = apply_blocks("Tran", t1, 0, first_seg)
# Subsequent segments rotate channels. Each segment header carries:
# bytes [0:2] and [2:4] = 2 deltas extending the PREVIOUS channel
# bytes [14:16] and [16:18] = anchor pair for THIS segment's channel
#
# Rotation: V, L, M, T, V, L, M, T, ... (initial Tran segment is the
# implicit T in the cycle.)
rotation = ["Vert", "Long", "MicL", "Tran"]
# Track each channel's "running cumulative value" so we can apply the
# previous-channel extension deltas at every segment boundary.
last_value = {"Tran": last_tran_value, "Vert": None, "Long": None, "MicL": None}
prev_channel = "Tran"
for k, hi in enumerate(seg_idx):
header = blocks[hi]
# Channel comes from the header's own id byte, which is authoritative.
# The old rotation-by-position fallback is kept for headers whose id
# byte isn't one of the four known values — but a single missed or
# extra header would desync rotation and corrupt every later channel,
# which is exactly what tagless headers used to cause.
_nd = header.tag_lo
channel = None
if len(header.data) >= 2 * _nd + 8:
channel = SEGMENT_CHANNEL_IDS.get(header.data[2 * _nd + 4])
if channel is None:
channel = rotation[k % 4]
# ``40 NN``: NN int16 BE continuation deltas for the previous channel
# come first, so every later field shifts by 2*NN. NN is usually 2
# but 1 and 3 both occur (confirmed 2026-08-25).
nd = header.tag_lo
if len(header.data) < 2 * nd + 14:
continue
# Validate: real segment headers have the constant `02 00` marker
# right after the counter. Trailer/footer "40 NN" markers contain
# ASCII serial bytes or other non-header data there and would
# otherwise be mis-read as segment headers, adding spurious tail
# samples.
if header.data[2 * nd + 8 : 2 * nd + 10] != b"\x02\x00":
break
# Extend the PREVIOUS channel by NN more samples.
if last_value[prev_channel] is not None:
v = last_value[prev_channel]
for d in range(nd): # NB: not `k` — that's the segment index
v += int.from_bytes(
header.data[2 * d : 2 * d + 2], "big", signed=True
)
out[prev_channel].append(v)
last_value[prev_channel] = v
# Anchor pair for THIS segment's channel.
c0 = int.from_bytes(
header.data[2 * nd + 10 : 2 * nd + 12], "big", signed=True
)
c1 = int.from_bytes(
header.data[2 * nd + 12 : 2 * nd + 14], "big", signed=True
)
out[channel].extend([c0, c1])
# Apply delta blocks for this segment.
next_hi = seg_idx[k + 1] if k + 1 < len(seg_idx) else len(blocks)
last_value[channel] = apply_blocks(channel, c1, hi + 1, next_hi)
prev_channel = channel
return out
# ── ADC-scale conversion helpers ────────────────────────────────────────────
# Scaling factor: decode_waveform_v2 produces geo-channel samples in the BW
# display quantization (16-count units, LSB = 0.005 in/s at Normal range).
# The legacy consumer pipeline (sfm/event_hdf5.py) expects raw_samples in
# 1-count ADC units (× full_scale / 32768 → physical). To plug the new
# decoder in without rewriting consumers, multiply geo values by 16.
#
# Mic samples are already in raw ADC counts (decoded value 1 = 1 mic ADC count
# = -81.94 dB on the BW display). Mic values pass through unchanged.
_GEO_DECODER_TO_ADC = 16
def decoded_to_adc_counts(decoded: dict) -> dict:
"""Convert :func:`decode_waveform_v2` output to int16 ADC counts.
Geo channels are scaled by ×16 (decoder produces 16-count units,
consumer expects 1-count ADC). Mic is passed through as raw counts.
"""
if not decoded:
return {}
return {
"Tran": [v * _GEO_DECODER_TO_ADC for v in decoded.get("Tran", [])],
"Vert": [v * _GEO_DECODER_TO_ADC for v in decoded.get("Vert", [])],
"Long": [v * _GEO_DECODER_TO_ADC for v in decoded.get("Long", [])],
"MicL": list(decoded.get("MicL", [])),
}
def mic_count_to_db(count: int) -> float:
"""Convert a MicL ADC count to dB(L) for BW-display-compatible output.
Empirical formula (confirmed 2026-05-11 against V70 fixture: count=813
→ 140.1 dB; count=±1 → ±81.94 dB; count=±24 → ±109.5 dB):
dB = sign(count) × (81.94 + 20 × log10(|count|)) for |count| ≥ 1
dB = 0.0 for count == 0
The constant 81.94 corresponds to 10^(81.94/20) ≈ 12490 mic ADC counts
being the dB(L) reference level — almost certainly a calibration
constant from the device's mic.
"""
if count == 0:
return 0.0
sign = 1.0 if count > 0 else -1.0
return sign * (81.94 + 20.0 * math.log10(abs(count)))
# ── A5-frame entry point ────────────────────────────────────────────────────
def decode_a5_frames(a5_frames) -> Optional[dict]:
"""Decode a list of A5 (BULK_WAVEFORM_STREAM) frames into per-channel
int16 ADC samples.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}``
with each channel's samples in **1-count ADC units** (the legacy
``event.raw_samples`` convention — multiply by ``full_scale / 32768``
to convert to physical units; for mic, use :func:`mic_count_to_db` or
a per-count psi factor).
Returns ``None`` if the frames cannot be parsed.
This is the wired-up production entry point. It:
1. Reconstructs the BW-binary body bytes from the A5 frames
(``blastware_file.extract_body_bytes``).
2. Runs the verified codec (``decode_waveform_v2``) on the body.
3. Converts to int16 ADC counts via :func:`decoded_to_adc_counts`.
"""
# Local import to avoid a cycle: blastware_file imports models and
# ultimately client.py imports waveform_codec.
from .blastware_file import extract_body_bytes
if not a5_frames:
return None
_strt, body, _footer = extract_body_bytes(a5_frames)
if not body:
return None
decoded = decode_waveform_v2(body)
if decoded is None:
return None
return decoded_to_adc_counts(decoded)
# ── Record-chain body model (CONFIRMED 2026-08-25) ──────────────────────────
#
# The body is NOT a flat tag-dispatch stream with ``40 NN`` segment headers.
# It is a chain of self-delimiting per-channel RECORDS:
#
# off+0 field2 uint16 purpose unknown (not a length, not a checksum)
# off+2 len uint16 BE next_record = off + 2 + len <- authoritative
# off+4 chan_id 0x46 Tran / 0x47 Vert / 0x48 Long / 0x49 MicL
# 0x06 = end of waveform stream
# off+5 0x00
# off+6 0x00
# off+7 segment index
# off+8 mode 2 bytes, a 3-valued enum (see below)
# off+10 anchors 2 x int16 BE, ABSOLUTE — present only when mode is 02 00
#
# Mode semantics, all ground-truth verified:
# 02 00 14-byte header; emit the 2 anchors, then blocks are CUMULATIVE deltas
# 01 00 10-byte header; no anchors; blocks carry ABSOLUTE sample values
# 00 03 10-byte header; NO TAGS AT ALL — the data section is raw 12-bit
# packed ABSOLUTE samples (6 bytes -> 4 samples)
#
# ``40 NN`` is an ordinary int16 BE DATA block (length 2*NN + 2), never a header.
# The previous model read it as a variable-width segment header of length
# 2*NN + 16, which is why walks drifted and channels came out unequal.
#
# Verified over the 1,388 series-3 waveform binaries in the production
# snapshot: the length chain terminates on a 0x06 record in 1,387 of them (the
# exception has an ambiguous footer, handled by the caller), and all four
# channels come out at identical length in 1,388/1,388 — against 156/1,388
# under the superseded model. Against the 75 events with a preserved
# Blastware ASCII export: sample-count exact 72/75 -> 75/75, fully exact
# 70/75 -> 73/75.
CHANNEL_IDS = {0x46: "Tran", 0x47: "Vert", 0x48: "Long", 0x49: "MicL"}
STREAM_END_ID = 0x06
MODE_DELTA = (0x02, 0x00)
MODE_ABSOLUTE = (0x01, 0x00)
MODE_RAW12 = (0x00, 0x03)
# Raw int16 BE absolute samples, 10-byte header, no tags — the same shape as
# MODE_RAW12 but two bytes per sample instead of 1.5. Found on Thor/Micromate
# segment-0 records (2026-09-10): a `len=1032` record carries exactly
# (1032 - 8) / 2 = 512 samples and reproduces Thor's own export 512/512
# exactly. Before this mode existed the record fell through the dispatch
# unhandled, so the channel silently lost its first 512 samples.
MODE_RAW16 = (0x00, 0x00)
_MODES = (MODE_DELTA, MODE_ABSOLUTE, MODE_RAW12, MODE_RAW16)
# Preambles whose leading data is untagged and therefore cannot be
# block-walked; find_first_record() must scan for the next record instead.
_UNTAGGED_MODES = (MODE_RAW12, MODE_RAW16)
def _u16(b: bytes, p: int) -> int:
return (b[p] << 8) | b[p + 1]
def _i16(b: bytes, p: int) -> int:
v = _u16(b, p)
return v - 0x10000 if v >= 0x8000 else v
def data_block_len(body: bytes, p: int) -> Tuple[Optional[int], Optional[int]]:
"""``(byte_length, n_samples)`` of the data block at *p*, or ``(None, None)``.
Data-section blocks only — there is no segment-header tag in this model.
``30 NN`` has no trailer-length fallback here; that fallback corrupted
records whose ``30 NN`` sat near a record boundary.
"""
if p + 2 > len(body):
return None, None
t0, t1 = body[p], body[p + 1]
hi = t0 & 0xF0
nn = ((t0 & 0x0F) << 8) | t1
if hi == 0x40: # int16 BE data block
# NN was capped at 0x08 until 2026-09-11. That cap had no basis: the
# two corpora available at the time only ever used NN in {1,2,3,4,8},
# so it was never exercised. Loud UM12947 events use NN of 12, 16,
# 20 ... up to 196, and every value above 8 halted the walk, which
# surfaced as silently short channels (walk_body/run stop at the first
# unrecognised tag rather than raising). Verified against Thor's own
# exports: 22 length-mismatched files -> 0, and the affected corpus
# went to 1,476,242/1,476,249 samples exact. The real bound is the
# buffer; the caller additionally clamps to the record end.
if nn == 0 or p + 2 * nn + 2 > len(body):
return None, None
return 2 * nn + 2, nn
if nn == 0 or nn % 4:
return None, None
if hi == 0x00:
return 2, nn # RLE hold
if hi == 0x10:
return nn // 2 + 2, nn # 4-bit nibble
if hi == 0x20:
return nn + 2, nn # int8
if hi == 0x30:
return nn * 3 // 2 + 2, nn # 12-bit packed
return None, None
def unpack16(data: bytes) -> List[int]:
"""Raw int16 BE absolute samples (MODE_RAW16)."""
return [_i16(data, 2 * k) for k in range(len(data) // 2)]
def unpack12(data: bytes) -> List[int]:
"""Raw 12-bit packed samples: 6 bytes -> 4 signed values."""
out: List[int] = []
for g in range(len(data) // 6):
hi = (data[6 * g] << 8) | data[6 * g + 1]
for k in range(4):
x = (((hi >> (12 - 4 * k)) & 0xF) << 8) | data[6 * g + 2 + k]
out.append(x - 0x1000 if x >= 0x800 else x)
return out
def is_record(body: bytes, p: int) -> bool:
"""True if a per-channel record header starts at *p*."""
return (p + 10 <= len(body)
and body[p + 4] in CHANNEL_IDS
and body[p + 5] == 0x00 and body[p + 6] == 0x00
and 8 <= _u16(body, p + 2) <= len(body) - p
and (body[p + 8], body[p + 9]) in _MODES)
def find_first_record(body: bytes) -> Optional[int]:
"""Offset of the first record, or None.
Under the normal ``00 02 00`` preamble the leading bytes are segment-0's
Tran blocks, so walk them. Under the untagged preambles (``00 00 03``
raw-12 and ``00 00 00`` raw-16) that data has no tags at all and cannot
be block-walked — scan for the next record header instead.
"""
if len(body) >= 3 and (body[1], body[2]) in _UNTAGGED_MODES:
scan_from = 3
else:
# Tagged preamble. MODE_DELTA carries a 14-byte record header (two
# int16 anchors), so its blocks start at body[7]; MODE_ABSOLUTE has a
# 10-byte header and starts at body[3].
i = 3 if (len(body) >= 3 and (body[1], body[2]) == MODE_ABSOLUTE) else 7
while i < len(body):
if is_record(body, i):
nxt = i + 2 + _u16(body, i + 2)
if nxt + 5 <= len(body) and (is_record(body, nxt)
or body[nxt + 4] == STREAM_END_ID):
return i
length, _ = data_block_len(body, i)
if length is None:
return None
i += length
return None
for i in range(scan_from, max(scan_from, len(body) - 10)):
if is_record(body, i):
nxt = i + 2 + _u16(body, i + 2)
if nxt + 5 <= len(body) and (is_record(body, nxt)
or body[nxt + 4] == STREAM_END_ID):
return i
return None
def walk_records(body: bytes, first: Optional[int] = None) -> List[dict]:
"""Follow the length chain from *first* to the ``0x06`` terminator."""
if first is None:
first = find_first_record(body)
out: List[dict] = []
if first is None:
return out
p, seen = first, set()
while p is not None and p + 10 <= len(body):
if p in seen:
break
seen.add(p)
cid = body[p + 4]
if cid == STREAM_END_ID or cid not in CHANNEL_IDS:
break
length = _u16(body, p + 2)
if length < 8 or p + 2 + length > len(body):
break
out.append({"offset": p, "channel": CHANNEL_IDS[cid],
"segment_index": body[p + 7],
"mode": (body[p + 8], body[p + 9]),
"end": p + 2 + length})
p += 2 + length
return out
def decode_waveform_v2(body: bytes) -> Optional[dict]:
"""Decode a Blastware waveform body into per-channel sample arrays.
Returns ``{"Tran": [...], "Vert": [...], "Long": [...], "MicL": [...]}``
in 16-count units (LSB = 0.005 in/s at Normal range), or None if *body*
is not a decodable waveform body.
Implements the record-chain model documented above.
"""
if len(body) < 8 or body[0] != 0x00:
return None
preamble = (body[1], body[2])
if preamble not in (MODE_DELTA, MODE_ABSOLUTE, MODE_RAW12, MODE_RAW16):
return None
first = find_first_record(body)
if first is None:
return None
out: dict = {c: [] for c in ("Tran", "Vert", "Long", "MicL")}
def run(channel: str, start: int, end: int, absolute: bool) -> None:
cur = out[channel][-1] if out[channel] else 0
i = start
while i < end:
length, nn = data_block_len(body, i)
if length is None or i + length > end:
return # stop this record; the chain resyncs at end
hi = body[i] & 0xF0
if hi == 0x00:
vals = [None] * nn
elif hi == 0x10:
vals = []
for k in range(nn):
byte = body[i + 2 + k // 2]
v = (byte >> 4) if k % 2 == 0 else (byte & 0xF)
vals.append(v - 16 if v >= 8 else v)
elif hi == 0x20:
vals = [v - 256 if v >= 128 else v
for v in body[i + 2:i + 2 + nn]]
elif hi == 0x30:
vals = unpack12(body[i + 2:i + length])
else:
vals = [_i16(body, i + 2 + 2 * k) for k in range(nn)]
for v in vals:
if v is None:
pass # RLE hold, in delta AND absolute modes
elif absolute:
cur = v
else:
cur += v
out[channel].append(cur)
i += length
# Segment 0 is an implicit Tran record carried in the preamble.
if preamble == MODE_DELTA:
out["Tran"].extend([_i16(body, 3), _i16(body, 5)])
run("Tran", 7, first, absolute=False)
elif preamble == MODE_ABSOLUTE:
run("Tran", 3, first, absolute=True)
elif preamble == MODE_RAW16:
out["Tran"].extend(unpack16(body[3:first]))
else:
out["Tran"].extend(unpack12(body[3:first]))
for rec in walk_records(body, first):
ch, off, mode, end = (rec["channel"], rec["offset"],
rec["mode"], rec["end"])
if mode == MODE_DELTA:
out[ch].extend([_i16(body, off + 10), _i16(body, off + 12)])
run(ch, off + 14, end, absolute=False)
elif mode == MODE_ABSOLUTE:
run(ch, off + 10, end, absolute=True)
elif mode == MODE_RAW12:
out[ch].extend(unpack12(body[off + 10:end]))
elif mode == MODE_RAW16:
out[ch].extend(unpack16(body[off + 10:end]))
return out
pyproject.toml
+3 -4
View File
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "seismo-relay"
version = "0.31.0"
version = "0.15.0"
description = "Python client and REST server for MiniMate Plus seismographs"
requires-python = ">=3.10"
dependencies = [
@@ -15,10 +15,9 @@ dependencies = [
"python-multipart>=0.0.7",
"h5py>=3.10",
"numpy>=1.24",
"matplotlib>=3.8",
]
[tool.setuptools.packages.find]
# Auto-discovers minimateplus/, micromate/, sfm/, bridges/ as packages
# Auto-discovers minimateplus/, sfm/, bridges/ as packages
where = ["."]
include = ["minimateplus*", "micromate*", "sfm*", "bridges*"]
include = ["minimateplus*", "sfm*", "bridges*"]
requirements.txt
-1
View File
@@ -5,4 +5,3 @@ pyserial
python-multipart
h5py
numpy
matplotlib
scratch/fake_unit.py
-33
View File
@@ -1,33 +0,0 @@
"""Pretend to be a Micromate on a serial port: log what arrives, reply to POLL.
Proves the modem's return path (serial -> TCP) independently of the real unit.
"""
import os, select, sys, termios, time
path, baud = sys.argv[1], int(sys.argv[2]) if len(sys.argv) > 2 else 115200
B = {9600: termios.B9600, 38400: termios.B38400, 115200: termios.B115200}[baud]
fd = os.open(path, os.O_RDWR | os.O_NOCTTY | os.O_NONBLOCK)
a = termios.tcgetattr(fd)
a[0] = a[1] = a[3] = 0
a[2] = termios.CS8 | termios.CREAD | termios.CLOCAL
a[4] = a[5] = B
a[6] = list(a[6]); a[6][termios.VMIN] = 0; a[6][termios.VTIME] = 0
termios.tcsetattr(fd, termios.TCSANOW, a)
termios.tcflush(fd, termios.TCIOFLUSH)
# A real POLL probe reply, captured from UM12947 on 2026-09-24.
REPLY = bytes.fromhex("0200c5a4000000000000300000000000000099") + b"\x03"
print(f"fake unit on {path} @ {baud}; will answer any inbound frame", flush=True)
while True:
r, _, _ = select.select([fd], [], [], 1.0)
if not r:
continue
data = os.read(fd, 4096)
if not data:
continue
ts = time.strftime("%H:%M:%S")
print(f"{ts} IN {len(data):3} B {data.hex(' ')}", flush=True)
time.sleep(0.02)
os.write(fd, REPLY)
print(f"{ts} OUT {len(REPLY):3} B {REPLY.hex(' ')} <- canned POLL reply", flush=True)
scratch/mm_frame_parse.py
-202
View File
@@ -1,202 +0,0 @@
#!/usr/bin/env python3
"""
mm_frame_parse.py — parse Micromate (Series IV) frames out of a seismo_lab
raw capture pair.
Why this exists
---------------
`minimateplus.framing.S3FrameParser` cannot see Micromate traffic. It locates
frames by scanning for `DLE STX`, and a Micromate response has **no leading
DLE** — it starts at a bare `STX`. It also expects `payload[1] == 0x10`, where
the Micromate sends `0xC5` (Blastware firmware) or `0x03` (Thor firmware).
The practical consequence, seen on the 9-24-26 setup-push capture: the
Blastware-side requests parse fine (Thor emits Series III request frames), but
**every device response is silently dropped or mis-framed** — so a capture that
actually contains 12 acked writes looks like 12 unanswered requests.
Destuffing
----------
One rule covers both directions: after the leading doubled `BW_CMD`, every
`10 XX` pair on the wire destuffs to `XX`. That includes `10 03` — Thor
escapes literal `0x03` bytes in write data so they are not mistaken for ETX,
exactly as Blastware does.
That rule was chosen by evidence, not assumption: of the four candidates tried
against the 9-24-26 capture's four data-carrying write frames, it is the only
one under which all four checksums validate. See
`docs/micromate_protocol_reference.md` → *The write path*.
Usage
-----
python scratch/mm_frame_parse.py <capture-dir>
python scratch/mm_frame_parse.py <raw_bw.bin> <raw_s3.bin>
python scratch/mm_frame_parse.py <capture-dir> --dump 0x71
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
DLE, STX, ETX, ACK = 0x10, 0x02, 0x03, 0x41
# Request SUB -> short name. Series III names where they carry over; the
# Series IV additions are marked.
SUBNAME = {
0x01: "DEVICE_INFO",
0x06: "STORAGE_RANGE",
0x08: "EVENT_INDEX",
0x0A: "WAVEFORM_HDR",
0x0C: "WAVEFORM_REC",
0x15: "SERIAL",
0x1A: "COMPLIANCE_CFG",
0x1C: "MONITOR_STATUS",
0x1E: "EVENT_HDR",
0x2C: "CALLHOME_CFG",
0x2E: "TRIGGER_CFG_READ", # Series IV
0x3E: "OPERATOR",
0x41: "SETUP_NAME_READ", # Series IV
0x5A: "BULK_DOWNLOAD",
0x5B: "POLL",
0x68: "EVENT_INDEX_WRITE",
0x69: "WAVEFORM_WRITE",
0x71: "COMPLIANCE_WRITE",
0x72: "CONFIRM_A",
0x73: "CONFIRM_B",
0x74: "CONFIRM_C",
0x82: "TRIGGER_WRITE",
0x83: "TRIGGER_CONFIRM",
0xDA: "SETUP_FILE_DECL", # Series IV — names the target .MMB
0xFE: "FULL_CFG",
}
def destuff(blob: bytes, start: int, *, is_request: bool) -> tuple[bytes, int, int]:
"""Destuff one frame starting at `start`.
Returns (payload, checksum, index_of_terminating_ETX). `payload` excludes
the trailing checksum byte. A request frame opens `ACK STX 10 10`; a
response opens with a bare `STX`.
"""
i = start + (2 if is_request else 1)
out = bytearray()
if is_request:
# The doubled BW_CMD is the one guaranteed stuffed byte.
if blob[i : i + 2] != bytes([DLE, DLE]):
raise ValueError(f"@0x{start:04x}: request does not open with 10 10")
out.append(DLE)
i += 2
while i < len(blob):
b = blob[i]
if b == DLE and i + 1 < len(blob):
out.append(blob[i + 1])
i += 2
continue
if b == ETX:
break
out.append(b)
i += 1
if len(out) < 2:
raise ValueError(f"@0x{start:04x}: frame too short")
return bytes(out[:-1]), out[-1], i
def frames(blob: bytes, *, is_request: bool):
"""Yield (offset, payload, chk, checksum_kind)."""
i, n = 0, len(blob)
while i < n:
if is_request:
if not (blob[i] == ACK and i + 1 < n and blob[i + 1] == STX):
i += 1
continue
elif blob[i] != STX:
i += 1
continue
try:
payload, chk, end = destuff(blob, i, is_request=is_request)
except ValueError:
i += 1
continue
sum8 = sum(payload) & 0xFF
dle_aware = (sum(b for b in payload if b != DLE) & 0xFF)
if sum8 == chk:
kind = "SUM8"
elif dle_aware == chk:
kind = "DLE-aware"
else:
kind = "BAD"
yield i, payload, chk, kind
i = end + 1
def describe(payload: bytes, is_request: bool) -> str:
if len(payload) < 3:
return "??"
sub = payload[2]
if is_request:
return SUBNAME.get(sub, f"SUB_{sub:02X}")
req = 0xFF - sub
return "rsp<-" + SUBNAME.get(req, f"SUB_{req:02X}")
def report(path: Path, *, is_request: bool, dump_sub: int | None) -> None:
blob = path.read_bytes()
side = "Thor" if is_request else "unit"
print(f"== {side:4} {path.name} ({len(blob)} bytes)")
n_bad = 0
for idx, (off, p, chk, kind) in enumerate(frames(blob, is_request=is_request)):
if kind == "BAD":
n_bad += 1
sub = p[2] if len(p) > 2 else -1
flags = p[1] if len(p) > 1 else -1
# Requests carry offset at payload[4:6]; responses page at [3:5].
word = int.from_bytes(p[4:6] if is_request else p[3:5], "big")
data = len(p) - 16 if is_request else max(len(p) - 5, 0)
print(
f" [{idx:2}] @0x{off:04x} payload={len(p):5} data={data:5} "
f"flags=0x{flags:02x} SUB=0x{sub:02x} {describe(p, is_request):18} "
f"{'offset' if is_request else 'page'}=0x{word:04x} chk={kind}"
)
if dump_sub is not None and sub == dump_sub:
body = p[16:] if is_request else p[5:]
print(f" ---- data ({len(body)} bytes) ----")
for o in range(0, len(body), 16):
chunk = body[o : o + 16]
txt = "".join(chr(c) if 32 <= c < 127 else "." for c in chunk)
print(f" {o:06x} {chunk.hex(' '):<47} |{txt}|")
print(f" -- {idx + 1} frames, {n_bad} bad checksum\n")
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("paths", nargs="+",
help="a capture directory, or raw_bw.bin and raw_s3.bin")
ap.add_argument("--dump", default=None,
help="hex-dump the data section of this SUB (e.g. 0x71)")
args = ap.parse_args()
dump_sub = int(args.dump, 0) if args.dump else None
if len(args.paths) == 1 and Path(args.paths[0]).is_dir():
d = Path(args.paths[0])
bw = sorted(d.glob("raw_bw_*.bin"))
s3 = sorted(d.glob("raw_s3_*.bin"))
if not bw or not s3:
print(f"{d}: need one raw_bw_*.bin and one raw_s3_*.bin", file=sys.stderr)
return 2
pairs = [(bw[0], True), (s3[0], False)]
elif len(args.paths) == 2:
pairs = [(Path(args.paths[0]), True), (Path(args.paths[1]), False)]
else:
ap.error("pass a capture directory, or exactly two .bin files")
for path, is_request in pairs:
report(path, is_request=is_request, dump_sub=dump_sub)
return 0
if __name__ == "__main__":
sys.exit(main())
scratch/next_experiment_skeleton.py
-360
View File
@@ -1,360 +0,0 @@
"""
scratch/next_experiment_skeleton.py — segment-channel scoring analyzer.
This is the suggested NEXT EXPERIMENT for cracking the waveform body codec.
The goal is to figure out what segments 1+ contain, since segment 0 = Tran
is solved but multi-segment continuation diverges from truth at sample ~512.
────────────────────────────────────────────────────────────────────────────
The hypothesis to test
────────────────────────────────────────────────────────────────────────────
Segments rotate through channels:
segment 0 → Tran samples 0..509
segment 1 → Vert samples 0..507
segment 2 → Long samples 0..507
segment 3 → Mic samples 0..507
segment 4 → Tran samples 510..N (continuation)
...
This would explain why segment 0 works perfectly (it's pure Tran) and why
applying segment 1's blocks as Tran continuation gives wrong values
(it's actually Vert).
────────────────────────────────────────────────────────────────────────────
What the analyzer should do
────────────────────────────────────────────────────────────────────────────
For each segment in each fixture event:
1. Run the segment-0 block-walker + RLE decode (the same algorithm that
``decode_tran_initial`` uses) over the segment's blocks. Start from
some anchor value and produce a cumulative trajectory of length =
number-of-deltas-in-segment.
2. For each candidate channel C ∈ {Tran, Vert, Long, MicL}:
For each candidate anchor location in the segment-header payload
(try [0:2], [2:4], [4:6], [14:16], [16:18] as int16 BE):
Compare the decoded trajectory against truth[C] starting from
the segment's first sample index.
Score = number of matches (or sum of squared errors).
3. Report the best (channel, anchor-location) combination per segment.
If the rotation hypothesis is correct, you'll see:
segment 0 → best score for (Tran, preamble bytes [3:5]) ✓ already known
segment 1 → best score for (Vert, <some-header-byte>)
segment 2 → best score for (Long, <some-header-byte>)
segment 3 → best score for (MicL, <some-header-byte>)
segment 4 → best score for (Tran, continuing from segment 0's end)
If the rotation hypothesis is NOT correct, the scorer will at least narrow
down what segment 1 actually carries. Maybe channels interleave at finer
granularity, or maybe segments alternate by something other than channel.
────────────────────────────────────────────────────────────────────────────
Why this is a scoring analyzer, not a hand-written decoder
────────────────────────────────────────────────────────────────────────────
Direct hand-coding ("assume segment 1 is Vert with anchor at byte X") gets
stuck when the assumption is wrong because the failure mode is silent —
you get plausible-looking-but-wrong samples and have to manually diff
against truth to debug.
The scorer is brute-force but cheap: every fixture event × every segment ×
4 channels × 5 anchor-byte candidates is only ~hundreds of comparisons.
The winning combination jumps out by score.
────────────────────────────────────────────────────────────────────────────
Skeleton
────────────────────────────────────────────────────────────────────────────
"""
from __future__ import annotations
import os
import re
import sys
from dataclasses import dataclass
from typing import List, Optional, Tuple
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
from minimateplus.waveform_codec import walk_body, find_data_start, WaveformBlock
# ── Reusable pieces ──────────────────────────────────────────────────────────
CHANNELS = ("Tran", "Vert", "Long", "MicL")
LSB_INV = 200 # 1 in/s / 0.005 in/s/LSB; multiply BW-export floats by this
# to get 16-count units (the body's native quantization).
@dataclass
class FixtureEvent:
name: str # e.g. "M529LL1A.SP0"
bin_path: str
txt_path: str
body: bytes
truth: dict # {channel: list of int16-quantized samples}
blocks: List[WaveformBlock]
segment_starts: List[int] # block indices of each 40 02 segment header
segment_sample_starts: List[int] # for each segment, the truth sample index it starts at
def s4(n: int) -> int:
"""4-bit signed nibble decode."""
return n if n < 8 else n - 16
def i8(b: int) -> int:
"""int8 reinterpret of unsigned byte."""
return b if b < 128 else b - 256
def load_fixture(name: str) -> FixtureEvent:
"""Load a fixture event with its truth values and parsed block stream."""
# Find the fixture (search both subdirs of tests/fixtures/).
base = os.path.join(os.path.dirname(__file__), "..", "tests", "fixtures")
candidates = [
os.path.join(base, "5-11-26", name),
os.path.join(base, "decode-re-5-8-26", "event-a", name), # not used directly
]
bin_path = next((c for c in candidates if os.path.exists(c)), None)
if bin_path is None:
# Try a glob walk for the 5-8 fixtures (they're in subdirs).
for root, _, files in os.walk(base):
if name in files:
bin_path = os.path.join(root, name)
break
if bin_path is None:
raise FileNotFoundError(name)
txt_path = bin_path + ".TXT"
with open(bin_path, "rb") as f:
raw = f.read()
body = raw[43:-26]
truth = _parse_txt(txt_path)
blocks = walk_body(body, find_data_start(body))
seg_idx = [i for i, b in enumerate(blocks) if b.tag_hi == 0x40]
# Segment 0 starts at sample 0; subsequent segments start at the
# cumulative sample count from previous segment(s). Tran's segment 0
# is N samples; if rotation hypothesis is correct, segment 1's data
# starts at sample 0 for a *different* channel. The analyzer should
# try both "continues from previous segment" and "starts at sample 0
# of a different channel."
seg_sample_starts = _compute_segment_sample_starts(blocks, seg_idx)
return FixtureEvent(
name=name, bin_path=bin_path, txt_path=txt_path,
body=body, truth=truth, blocks=blocks,
segment_starts=seg_idx, segment_sample_starts=seg_sample_starts,
)
def _parse_txt(path: str) -> dict:
"""Parse BW ASCII TXT export into {channel: [int_samples_in_16_count_units]}."""
with open(path, "r", encoding="utf-8", errors="replace") as f:
lines = f.read().splitlines()
header_idx = next(
(i for i, l in enumerate(lines)
if all(c in l for c in CHANNELS)),
None,
)
if header_idx is None:
return {ch: [] for ch in CHANNELS}
out = {ch: [] for ch in CHANNELS}
for line in lines[header_idx + 1:]:
parts = re.split(r"\s+", line.strip())
if len(parts) < 4:
continue
try:
vals = [float(p) for p in parts[:4]]
except ValueError:
continue
for ch, v in zip(CHANNELS, vals):
# Multiply by LSB_INV; geo channels are in in/s, MicL is in dB(L)
# (which doesn't quantize the same way — leaving raw for MicL is fine,
# the scorer should treat MicL specially).
out[ch].append(round(v * LSB_INV) if ch != "MicL" else v)
return out
def _compute_segment_sample_starts(
blocks: List[WaveformBlock], seg_idx: List[int]
) -> List[int]:
"""Cumulative sample-count up to each segment header (if all blocks treated
as Tran continuation). Useful as one candidate for segment-1-Tran tests.
The scorer should ALSO try "segment 1 starts at sample 0 of a new channel"
as the rotation hypothesis predicts.
"""
starts = []
cum = 2 # T[0] + T[1] from preamble
for i, b in enumerate(blocks):
if i in seg_idx:
starts.append(cum)
if b.tag_hi == 0x10:
cum += b.tag_lo
elif b.tag_hi == 0x20:
cum += b.tag_lo
elif b.tag_hi == 0x00:
cum += b.tag_lo
# 30 NN and 40 02 don't contribute samples (for this hypothesis)
return starts
# ── The core algorithm: decode a segment's blocks as deltas ─────────────────
def decode_segment_as_channel(
blocks: List[WaveformBlock],
seg_start_block_idx: int,
seg_end_block_idx: int,
anchor: int,
) -> List[int]:
"""Apply the segment-0 codec rules to a range of blocks, starting from *anchor*.
Returns a list of cumulative sample values (one per delta). Does NOT include
the anchor itself in the output — the first returned value is anchor + first_delta.
"""
out = []
cur = anchor
for bi in range(seg_start_block_idx, seg_end_block_idx):
blk = blocks[bi]
if blk.tag_hi == 0x10:
for byte in blk.data:
for nib in ((byte >> 4) & 0xF, byte & 0xF):
cur += s4(nib)
out.append(cur)
elif blk.tag_hi == 0x20:
for byte in blk.data:
cur += i8(byte)
out.append(cur)
elif blk.tag_hi == 0x00:
for _ in range(blk.tag_lo):
out.append(cur)
# 30 NN: skip (content unknown)
# 40 02: shouldn't appear in segment data (it's the segment header)
return out
def score_against_truth(
decoded: List[int],
truth: List[int],
truth_start: int,
) -> Tuple[int, int]:
"""Compare *decoded* to truth[truth_start : truth_start + len(decoded)].
Returns (n_matches, n_compared).
"""
n = min(len(decoded), len(truth) - truth_start)
if n <= 0:
return (0, 0)
matches = sum(1 for i in range(n) if decoded[i] == truth[truth_start + i])
return (matches, n)
# ── TODO for the next pass ──────────────────────────────────────────────────
def score_segment_against_all_channels(
event: FixtureEvent,
segment_index: int,
) -> List[Tuple[str, int, int, int]]:
"""For segment *segment_index* of *event*, find the best (channel, start_sample)
fit.
For each candidate channel C and each candidate starting truth-sample index s,
we pick the anchor that makes the FIRST decoded value match truth[C][s], then
score the remaining decoded values against truth[C][s+1 : s+N].
Returns rows of (channel_name, start_sample, n_matches, n_compared)
sorted by match-count descending.
"""
# Block range of this segment: from the segment header (inclusive) up to
# the next segment header (exclusive), or end-of-blocks.
seg_header_idx = event.segment_starts[segment_index]
next_header_idx = (
event.segment_starts[segment_index + 1]
if segment_index + 1 < len(event.segment_starts)
else len(event.blocks)
)
# Decode the segment's data blocks (skip the segment-header block itself).
# Use anchor=0 — we'll re-anchor when scoring against each channel.
deltas_trajectory = decode_segment_as_channel(
event.blocks, seg_header_idx + 1, next_header_idx, anchor=0
)
if not deltas_trajectory:
return []
n = len(deltas_trajectory)
results = []
for ch in ("Tran", "Vert", "Long"):
truth = event.truth.get(ch)
if not truth or len(truth) < n + 1:
continue
# For each candidate starting sample s in truth, check if applying
# the deltas starting from truth[s] reproduces truth[s+1:s+n+1].
best = (0, -1)
for s in range(len(truth) - n):
anchor = truth[s]
offset = anchor - deltas_trajectory[0] + truth[s + 1] - anchor
# Recompute: trajectory[i] = anchor + cumulative_delta_through_i
# but we already have deltas_trajectory computed from anchor=0,
# so trajectory_relative[i] = anchor + deltas_trajectory[i].
matches = 0
for i in range(n):
if truth[s + i + 1] == anchor + deltas_trajectory[i]:
matches += 1
# Note: we could break early on first mismatch for "matches start",
# but counting total matches gives a more robust score.
if matches > best[0]:
best = (matches, s)
results.append((ch, best[1], best[0], n))
results.sort(key=lambda r: -r[2])
return results
# ── Driver ──────────────────────────────────────────────────────────────────
def main():
"""Run the analyzer on all loud-bundle events and print best scores."""
events = ["M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0",
"M529LL1L.JQ0", "M529LL1L.V70"]
for name in events:
try:
event = load_fixture(name)
except FileNotFoundError:
print(f"{name}: fixture not found")
continue
print(f"\n=== {name} ===")
print(f" body bytes: {len(event.body)}")
print(f" blocks: {len(event.blocks)}")
print(f" segments: {len(event.segment_starts)}")
print(f" segment sample-starts (if all blocks are 1 channel):")
for si, sample_start in enumerate(event.segment_sample_starts):
print(f" seg {si}: sample {sample_start}")
for si in range(len(event.segment_starts)):
results = score_segment_against_all_channels(event, si)
if not results:
print(f" seg {si}: (no scorable data)")
continue
tag = "✓" if results[0][2] / max(results[0][3], 1) > 0.9 else " "
top = results[0]
print(f" seg {si}: best fit {tag} = {top[0]:<5} "
f"starting at sample {top[1]:>5}, {top[2]:>4}/{top[3]:<4} match"
+ (f" (next: {results[1][0]} @{results[1][1]} {results[1][2]}/{results[1][3]})"
if len(results) > 1 else ""))
if __name__ == "__main__":
main()
scratch/nonmotion_scan.py
-91
View File
@@ -1,91 +0,0 @@
#!/usr/bin/env python3
"""Detect NON-MOTION on a geophone channel: |mean| / peak.
A geophone is a velocity sensor with no DC response, so its output over a
record must integrate to ~zero — the ground does not relocate. Real motion
therefore sits roughly half above and half below zero. Anything electrical —
a charge-injection spike, a step, a parked pedestal — is one-sided.
mp = |mean| / peak ~0 for motion, ~1 for a pedestal
frac_neg = share of samples < 0 ~0.3-0.5 for motion, ~0 for a fault
Why this beats the pre-trigger floor (`offset_scan3.py`): that detector's
`spread <= 0.02` gate rejects any record whose floor is MOVING, which is
exactly what an onset is — it discarded the one BE18438 record in which the
ramp was visible. This test is indifferent to whether the fault is a spike,
a ramp or a flat pedestal; none of them cross zero.
⚠ Not a rediscovery of the retracted v1 detector. v1 scored only the
largest-peak axis and used the mean as a BASELINE estimator, where the median
was required. Here the mean is the signal itself, per channel, and that is
what the physics licenses.
"""
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})")
_SER=re.compile(rb"[A-Z]{2}\d{3,6}")
def serial_of(name, path=None):
m=_STEM.match(name)
if not m: return "?"
num=(ord(m.group(1))-ord("B"))*1000+int(m.group(2))
if path is not None:
try:
for s in _SER.findall(Path(path).read_bytes()):
s=s.decode()
if s[2:].lstrip("0")==str(num): return s
except Exception: pass
return f"BE{num}"
def scan(ps):
import logging; logging.disable(logging.WARNING)
p=Path(ps)
try: ev=read_blastware_file(p)
except Exception: return None
s=ev.raw_samples or {}
if not all(s.get(c) for c in GEO): return None
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 ""
ser=serial_of(p.name,p); out=[]
for ch in GEO:
a=[x*K for x in s[ch]]
pk=max(abs(x) for x in a)
if pk<=0: continue
out.append({"serial":ser,"timestamp":stamp,"filename":p.name,"channel":ch,
"peak":round(pk,4),
"mean":round(statistics.fmean(a),4),
"mp":round(abs(statistics.fmean(a))/pk,4),
"frac_neg":round(sum(1 for x in a if x<0)/len(a),4),
"n":len(a)})
return out
COLS=["serial","timestamp","filename","channel","peak","mean","mp","frac_neg","n"]
def main():
ap=argparse.ArgumentParser()
ap.add_argument("--dir",required=True); ap.add_argument("--out",required=True)
ap.add_argument("--jobs",type=int,default=4)
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%1000==0: print(f" {i}/{len(files)}",flush=True)
with open(a.out,"w",newline="") as fh:
w=csv.DictWriter(fh,fieldnames=COLS); w.writeheader(); w.writerows(rows)
print(f"\nwrote {a.out} ({len(rows)} channel-rows)")
if __name__=="__main__": main()
scratch/offset_candidates.csv
-275
View File
@@ -1,275 +0,0 @@
event_id,serial,timestamp,filename,channel,offset_ips,peak_ips,mean_over_peak,trigger_level_ips,already_flagged_ft
1e7a7808-034c-423b-80e9-d6788da80993,BE18438,2025-11-15T08:57:40,T438LBX4.W40W,Vert,0.2722,0.2930,0.929,0.2,0
346e383f-c5a1-41d1-b972-3d652e599d39,BE18438,2025-11-15T09:13:15,T438LBX5.M30W,Vert,0.2747,0.2930,0.938,0.2,0
45ef3023-22c5-44a8-a606-a0900bc17861,BE18438,2025-11-15T09:16:13,T438LBX5.R10W,Vert,0.2929,0.3027,0.967,0.2,0
1cbcd2aa-ec55-4e2f-af42-7178f26cc6a1,BE18438,2025-11-15T09:19:29,T438LBX5.WH0W,Vert,0.2816,0.2979,0.945,0.2,0
caf43634-4528-4fd5-9674-5f4f563661c0,BE18438,2025-11-15T09:22:32,T438LBX6.1K0W,Vert,0.2883,0.2930,0.984,0.2,0
456ae646-c834-4e5c-82f1-7df18b3440a1,BE18438,2025-11-15T09:25:31,T438LBX6.6J0W,Vert,0.3189,0.3223,0.989,0.2,0
afb91ba2-351b-42a0-b33f-acebd5f1829d,BE18438,2025-11-15T09:28:29,T438LBX6.BH0W,Vert,0.3057,0.3076,0.994,0.2,0
39847146-3537-4300-943d-01c0c771e9cd,BE18438,2025-11-15T09:31:26,T438LBX6.GE0W,Vert,0.3225,0.3271,0.986,0.2,0
e47335c1-fc3f-4c90-88c6-8edd0f296ce4,BE18438,2025-11-15T09:34:24,T438LBX6.LC0W,Vert,0.3275,0.3320,0.986,0.2,0
25b6eda6-ba98-422f-8834-94b6cbe34971,BE18438,2025-11-15T09:37:22,T438LBX6.QA0W,Vert,0.3398,0.3418,0.994,0.2,0
7af99377-e47f-4914-8e91-ac7f7f50b8c6,BE18438,2025-11-15T09:40:20,T438LBX6.V80W,Vert,0.3515,0.3564,0.986,0.2,0
c4330605-c257-49ef-aede-20deb641e7d7,BE18438,2025-11-15T10:09:48,T438LBX8.8C0W,Vert,0.3605,0.3955,0.912,0.2,0
b665caf9-64ee-4352-a391-60c1ddeebc1e,BE18438,2026-02-25T10:58:04,T438LH66.GS0W,Vert,0.1803,0.1953,0.923,0.2,0
3b0c32c3-fd16-4b83-9917-942a56dea168,BE18438,2026-02-25T18:12:04,T438LH6Q.K40W,Vert,0.1869,0.1953,0.957,0.2,0
ca66e601-d10f-4424-9c26-08a688e7b08c,BE18438,2026-02-25T18:17:16,T438LH6Q.SS0W,Vert,0.1870,0.1953,0.958,0.2,0
31aa30ba-eb0a-49a9-98ca-c77d2cc31275,BE18438,2026-02-25T18:21:11,T438LH6Q.ZB0W,Vert,0.1868,0.1953,0.956,0.2,0
fdb55a93-fc91-4fdf-a7f4-fdd1a6b48f59,BE18438,2026-02-25T18:26:27,T438LH6R.830W,Vert,0.1893,0.1953,0.969,0.2,0
cbd718d7-2378-4d74-8656-842f229d19a8,BE18438,2026-02-25T18:33:25,T438LH6R.JP0W,Vert,0.1887,0.1953,0.966,0.2,0
bfaf58ec-6163-47eb-9d2a-70da880bd948,BE18438,2026-02-25T18:37:48,T438LH6R.R00W,Vert,0.1872,0.1953,0.959,0.2,0
ddaa530b-886e-4415-b93a-f4ebf4c4a882,BE18438,2026-02-25T18:59:54,T438LH6S.RU0W,Vert,0.1874,0.1953,0.959,0.2,0
57f15284-ab69-48fc-9660-8c347c80c681,BE18438,2026-02-25T19:15:58,T438LH6T.IM0W,Vert,0.1873,0.1953,0.959,0.2,0
03715718-7aa8-4e1d-9b0b-c2ab75fc1975,BE18438,2026-02-25T19:19:58,T438LH6T.PA0W,Vert,0.1891,0.1953,0.968,0.2,0
9f921cd5-1bf9-4713-aef6-41e7f780eff1,BE18438,2026-02-25T19:24:02,T438LH6T.W20W,Vert,0.1871,0.1953,0.958,0.2,0
44994949-58d4-44fa-bd78-ed75b8acc667,BE18438,2026-02-25T19:44:57,T438LH6U.UX0W,Vert,0.1877,0.1953,0.961,0.2,0
c4be01c3-1001-4e17-aecd-d6d26dbb09f3,BE18438,2026-02-25T19:49:19,T438LH6V.270W,Vert,0.1884,0.1953,0.964,0.2,0
3db004e6-ee34-43c5-ae8e-f22894ead5d0,BE18438,2026-02-25T20:08:22,T438LH6V.XY0W,Vert,0.1878,0.1953,0.962,0.2,0
d276ae48-0434-41e5-af43-e0fb366acf11,BE18438,2026-02-25T20:11:33,T438LH6W.390W,Vert,0.1880,0.1953,0.963,0.2,0
c027dc12-88fd-4a45-872b-a6a945f2008e,BE18438,2026-02-25T20:14:35,T438LH6W.8B0W,Vert,0.1874,0.1953,0.959,0.2,0
9665bc3a-3d08-4852-b008-37ef8bfd0023,BE18438,2026-02-25T20:18:51,T438LH6W.FF0W,Vert,0.1884,0.1953,0.965,0.2,0
5fb5a05e-d7e2-4268-9b85-cfe8d82aa6c9,BE18438,2026-02-25T20:29:50,T438LH6W.XQ0W,Vert,0.1890,0.1953,0.968,0.2,0
57ec0b10-9324-4459-a07e-0c8532b8928f,BE18438,2026-02-25T20:35:39,T438LH6X.7F0W,Vert,0.1889,0.1953,0.967,0.2,0
ac86f73f-1174-4fae-a206-ecdcb73e1ffc,BE18438,2026-02-25T20:39:12,T438LH6X.DC0W,Vert,0.1887,0.1953,0.966,0.2,0
dcf25ddd-9cf0-41c1-b109-f3ef2e490ce5,BE18438,2026-02-25T20:43:41,T438LH6X.KT0W,Vert,0.1871,0.1953,0.958,0.2,0
aa79bbdd-2392-4ef6-8a5c-873a9f72746d,BE18438,2026-02-25T20:46:51,T438LH6X.Q30W,Vert,0.1877,0.1953,0.961,0.2,0
ad46d1ce-b417-4c8c-b029-bb3b008ba809,BE18438,2026-02-25T20:53:33,T438LH6Y.190W,Vert,0.1882,0.1953,0.964,0.2,0
3d3a8f34-86a7-4d70-a7e8-fa768a723325,BE18438,2026-02-26T07:03:39,T438LH7Q.A30W,Vert,0.1809,0.1953,0.926,0.2,0
7817525f-dc3d-4f5e-b2cb-5af9ca12ede8,BE18438,2026-02-26T07:09:42,T438LH7Q.K60W,Vert,0.1814,0.1953,0.929,0.2,0
a2091302-0b34-46c1-ab83-174be2b59bcf,BE18438,2026-02-26T13:12:35,T438LH87.CZ0W,Vert,0.1896,0.1953,0.971,0.2,0
e1c0c9de-a900-479f-9572-9675b13296a9,BE18438,2026-02-26T13:24:46,T438LH87.XA0W,Vert,0.2237,0.2588,0.864,0.2,0
76a6dc6b-88e1-434b-a137-f5e9b63f248e,BE18438,2026-02-26T13:27:43,T438LH88.270W,Vert,0.3185,0.3223,0.988,0.2,0
281b508b-7982-4668-86af-9b0b28be608a,BE18438,2026-02-26T13:30:36,T438LH88.700W,Vert,0.3263,0.3320,0.983,0.2,0
68bbaf77-bb23-40fd-94f9-f928728d90dc,BE18438,2026-02-26T13:33:28,T438LH88.BS0W,Vert,0.3207,0.3271,0.980,0.2,0
5c975640-6ff1-441a-aa11-41642f6a6b31,BE18438,2026-02-26T13:36:21,T438LH88.GL0W,Vert,0.3261,0.3320,0.982,0.2,0
dea248ac-1124-4bba-b054-953f86c852b2,BE18438,2026-02-26T13:40:34,T438LH88.NM0W,Vert,0.3271,0.3320,0.985,0.2,0
4e463201-f008-41ad-9c52-11fcc3d93095,BE18438,2026-02-26T13:43:29,T438LH88.SH0W,Vert,0.3357,0.3418,0.982,0.2,0
507262c5-d58f-4a05-a6a3-a6cbb5b4de20,BE18438,2026-02-26T13:46:24,T438LH88.XC0W,Vert,0.3372,0.3418,0.987,0.2,0
4c2d8ee8-842f-4e44-9e03-ff34d26e4fdb,BE18438,2026-02-26T13:49:17,T438LH89.250W,Vert,0.3253,0.3320,0.980,0.2,0
b184b112-603f-44f4-ae97-087493fbd8ce,BE18438,2026-02-26T13:52:10,T438LH89.6Y0W,Vert,0.3321,0.3369,0.986,0.2,0
2ff1a552-5a2e-4396-9083-b9c8d334eed7,BE18438,2026-02-26T13:55:04,T438LH89.BS0W,Vert,0.3378,0.3418,0.988,0.2,0
928e21a7-1990-4792-803e-58014d8140ce,BE18438,2026-02-26T13:57:57,T438LH89.GL0W,Vert,0.3341,0.3369,0.992,0.2,0
f521b5db-6100-41e6-954e-0abdbf9660c1,BE18438,2026-02-26T14:00:51,T438LH89.LF0W,Vert,0.3421,0.3467,0.987,0.2,0
efba962f-e511-41ef-9fc5-313f202a9d96,BE18438,2026-02-26T14:03:46,T438LH89.QA0W,Vert,0.3506,0.3564,0.984,0.2,0
0b51e273-8d10-4494-bda6-8c75eaeec66f,BE18438,2026-02-26T14:06:41,T438LH89.V50W,Vert,0.3448,0.3516,0.981,0.2,0
6d46e4ee-1647-4169-b6b2-33da32240eb9,BE18438,2026-02-26T14:09:35,T438LH89.ZZ0W,Vert,0.3463,0.3516,0.985,0.2,0
1ca294cd-4dc2-41fb-9374-e277d4550761,BE18438,2026-02-26T14:12:28,T438LH8A.4S0W,Vert,0.3442,0.3467,0.993,0.2,0
7b683dcf-1aed-4d9b-be1e-b10f92374398,BE18438,2026-02-26T14:15:23,T438LH8A.9N0W,Vert,0.3486,0.3516,0.992,0.2,0
2b5cb8e7-c947-4914-bcb5-504f74603547,BE18438,2026-02-26T14:18:21,T438LH8A.EL0W,Vert,0.3519,0.3613,0.974,0.2,0
5ea3a6bf-10de-4911-aedc-66daecf86b76,BE18438,2026-02-26T14:21:16,T438LH8A.JG0W,Vert,0.3460,0.3564,0.971,0.2,0
d93be7f5-39ec-45fd-9949-fa531f2f9d34,BE18438,2026-02-26T14:24:13,T438LH8A.OD0W,Vert,0.3457,0.3516,0.983,0.2,0
cfa113f2-8cee-4a1d-b1e7-0e936fa771e2,BE18438,2026-02-26T14:28:21,T438LH8A.V90W,Vert,0.3485,0.3564,0.978,0.2,0
46d322ac-38cb-4c99-a4c2-9e9d7ad06171,BE18438,2026-02-26T14:31:17,T438LH8B.050W,Vert,0.3367,0.3467,0.971,0.2,0
42500a83-bd67-4deb-8bb2-963ee2b8a7ce,BE18438,2026-02-26T14:34:11,T438LH8B.4Z0W,Vert,0.3365,0.3516,0.957,0.2,0
3ccf6bda-2e88-473c-951d-9ea86d0d9363,BE18438,2026-02-26T14:37:05,T438LH8B.9T0W,Vert,0.3472,0.3564,0.974,0.2,0
9050487d-d238-4631-b8e1-5a693a026542,BE18438,2026-02-26T14:39:58,T438LH8B.EM0W,Vert,0.3420,0.3564,0.959,0.2,0
e62989e4-a326-4bae-8d47-a8becee96462,BE18438,2026-02-26T14:42:52,T438LH8B.JG0W,Vert,0.3430,0.3564,0.962,0.2,0
f0e30fab-dc57-4189-8439-48df17007a2c,BE18438,2026-02-26T14:45:46,T438LH8B.OA0W,Vert,0.3389,0.3516,0.964,0.2,0
03e1ca09-78df-4c6f-9915-0b6e4f2141d2,BE18438,2026-02-26T14:48:43,T438LH8B.T70W,Vert,0.3449,0.3564,0.968,0.2,0
945d39f7-1b98-4a1f-80a8-6a18fd9df25f,BE18438,2026-02-26T14:51:37,T438LH8B.Y10W,Vert,0.3522,0.3613,0.975,0.2,0
d27eb2e3-e313-4125-98f4-f9d6d3737d28,BE18438,2026-02-26T14:54:32,T438LH8C.2W0W,Vert,0.3502,0.3613,0.969,0.2,0
916901cb-ebea-441e-a2a1-40f4e49f32f4,BE18438,2026-02-26T14:57:25,T438LH8C.7P0W,Vert,0.3524,0.3662,0.962,0.2,0
5d8d7e29-fdbb-4fa3-bd25-506c9352a354,BE18438,2026-02-26T15:01:35,T438LH8C.EN0W,Vert,0.3530,0.3613,0.977,0.2,0
c6c7a3af-b03b-43dc-97de-024f8e09b62f,BE18438,2026-02-26T15:04:27,T438LH8C.JF0W,Vert,0.3514,0.3613,0.973,0.2,0
b6856898-3d10-4967-9577-7f17902f77ea,BE18438,2026-02-26T15:11:34,T438LH8C.VA0W,Vert,0.3566,0.3613,0.987,0.2,0
7a4802f6-621e-4230-8d8c-d7f0c9a65bf2,BE18438,2026-02-26T15:14:24,T438LH8D.000W,Vert,0.3594,0.3711,0.968,0.2,0
79f38b7e-ec56-417e-943a-8c944252c2b8,BE18438,2026-02-26T15:18:32,T438LH8D.6W0W,Vert,0.3550,0.3613,0.983,0.2,0
1b4190c4-0d54-48ba-8ff5-8ddb0eb91e50,BE18438,2026-02-26T15:22:22,T438LH8D.DA0W,Vert,0.3571,0.3613,0.988,0.2,0
de57b5ae-a3a1-4ede-9d2b-3e87bfb3fd19,BE9558,2026-04-14T11:16:32,K558LJN3.BK0W,Tran,0.3448,0.3662,0.942,0.2,0
43afeaf5-02ec-41f6-9e23-0c9772821ed4,BE9558,2026-04-14T11:27:15,K558LJN3.TF0W,Tran,0.3094,0.3223,0.960,0.2,0
8123c0ef-84c9-4f6c-8d82-9dc32e2e470d,BE9558,2026-04-14T14:45:30,K558LJNC.ZU0W,Tran,0.2721,0.3564,0.763,0.2,0
8c787af3-596e-411b-9e10-29485fa5114f,BE9558,2026-04-29T16:18:47,K558LKF9.BB0W,Tran,0.2943,0.3027,0.972,0.2,0
431928ff-b4c4-4caa-b933-acc917f3717c,BE9558,2026-05-04T15:02:30,K558LKOF.460W,Tran,0.4364,0.5225,0.835,0.2,0
6c3c07f8-c36a-4493-acce-7441c9d22cec,BE9558,2026-05-15T08:50:06,K558LL8B.7I0W,Long,0.2892,0.2930,0.987,0.2,0
13c268df-b652-422e-a642-6453f6a314aa,BE9558,2026-05-15T10:18:34,K558LL8F.AY0W,Long,0.2907,0.2979,0.976,0.2,0
9b0d0871-8810-467d-806a-5bbfa4e667fe,BE9558,2026-05-15T15:52:00,K558LL8U.QO0W,Long,0.2659,0.2979,0.893,0.2,0
092640f9-a944-48b7-b872-364d75c2c5e7,BE9558,2026-05-15T16:13:12,K558LL8V.Q00W,Long,0.2428,0.2979,0.815,0.2,0
0573741a-96ab-4b36-af7a-b5bc11a79009,BE9558,2026-05-16T03:23:26,K558LL9Q.R20W,Long,0.2861,0.2930,0.976,0.2,0
321f03ea-6696-47de-ad46-2be43d4d6cae,BE9558,2026-05-16T03:30:49,K558LL9R.3D0W,Long,0.2886,0.2930,0.985,0.2,0
cc423a6a-3e3c-466a-b39c-6146ca7f34c8,BE9558,2026-05-16T03:33:55,K558LL9R.8J0W,Long,0.2880,0.2881,1.000,0.2,0
efa52a14-cc68-4bea-aab4-4ee3a3cbff3a,BE9558,2026-05-16T03:36:55,K558LL9R.DJ0W,Long,0.2890,0.2930,0.986,0.2,0
92895bfe-122f-4cdc-bd3d-40609633d278,BE9558,2026-05-16T03:43:41,K558LL9R.OT0W,Long,0.2880,0.2881,1.000,0.2,0
2a4c81db-2307-44e5-9b27-d5154d98e38d,BE9558,2026-05-16T03:46:36,K558LL9R.TO0W,Long,0.2921,0.2979,0.981,0.2,0
656b0fdc-e275-4c9c-8936-9eee267ea04e,BE9558,2026-05-16T03:49:28,K558LL9R.YG0W,Long,0.2966,0.2979,0.996,0.2,0
3b9989ae-e0f2-4652-9724-c145603543d2,BE9558,2026-05-16T03:52:29,K558LL9S.3H0W,Long,0.2880,0.2930,0.983,0.2,0
837d0d84-d609-4f4a-b542-b7e2b064ea22,BE9558,2026-05-16T03:56:32,K558LL9S.A80W,Long,0.2880,0.2930,0.983,0.2,0
affcb008-ab99-4bc5-8f35-83dbe620499d,BE9558,2026-05-16T04:02:50,K558LL9S.KQ0W,Long,0.2896,0.2930,0.989,0.2,0
e1f1c6d2-545c-451f-9018-714d52e20a05,BE9558,2026-05-16T04:05:47,K558LL9S.PN0W,Long,0.3024,0.3076,0.983,0.2,0
93ef80f1-7e83-4703-a0f9-89bbfeeae6c3,BE9558,2026-05-16T04:08:41,K558LL9S.UH0W,Long,0.2976,0.2979,0.999,0.2,0
5c118ca9-fc37-4080-876d-b3f57ce0b121,BE9558,2026-05-16T04:11:42,K558LL9S.ZI0W,Long,0.2881,0.2930,0.983,0.2,0
d98b2eaa-7763-4c78-88fb-c21195cb9978,BE9558,2026-05-16T04:14:38,K558LL9T.4E0W,Long,0.2898,0.2930,0.989,0.2,0
6a146a01-0d02-4413-b231-ecb4a6fde776,BE9558,2026-05-16T04:17:35,K558LL9T.9B0W,Long,0.2880,0.2930,0.983,0.2,0
f6587a55-27e3-454f-997e-57e7516a296c,BE9558,2026-05-16T04:20:32,K558LL9T.E80W,Long,0.3046,0.3076,0.990,0.2,0
1fb48250-2214-41e6-92cb-362aff1ba62b,BE9558,2026-05-16T04:26:05,K558LL9T.NH0W,Long,0.2876,0.2930,0.982,0.2,0
2f4f3d06-1189-4e28-a1ac-5454ea586a0c,BE9558,2026-05-16T04:59:47,K558LL9V.7N0W,Long,0.2961,0.3027,0.978,0.2,0
62b7bdab-b744-41e4-8d0e-c9141b9d4821,BE9558,2026-05-16T05:02:46,K558LL9V.CM0W,Long,0.2903,0.2930,0.991,0.2,0
cadcb6dc-74c0-48b5-974c-87bb7a57816c,BE9558,2026-05-16T05:05:43,K558LL9V.HJ0W,Long,0.3090,0.3125,0.989,0.2,0
a0235177-be5a-4f11-b741-c65744698e5b,BE9558,2026-05-16T05:08:35,K558LL9V.MB0W,Long,0.3167,0.3223,0.983,0.2,0
1968c2f6-f52c-4544-a0ed-f79b91ce0da0,BE9558,2026-05-16T05:11:28,K558LL9V.R40W,Long,0.3446,0.3516,0.980,0.2,0
caa06b94-8813-4949-b47b-61ffb16e61c4,BE9558,2026-05-16T05:14:22,K558LL9V.VY0W,Long,0.3318,0.3320,0.999,0.2,0
fdaa8101-f033-47e6-9c65-abd76dea1870,BE9558,2026-05-16T05:17:16,K558LL9W.0S0W,Long,0.3407,0.3418,0.997,0.2,0
29bd16bd-81b9-4ccd-a981-f1a80858536c,BE9558,2026-05-16T05:23:05,K558LL9W.AH0W,Long,0.3450,0.3516,0.981,0.2,0
1f8bc862-d5fd-4cce-abc0-e2bd8c2f11fa,BE9558,2026-05-16T05:26:00,K558LL9W.FC0W,Long,0.3417,0.3467,0.986,0.2,0
20220068-d498-4fca-9a2a-a7fd2ee52ab1,BE9558,2026-05-16T05:28:53,K558LL9W.K50W,Long,0.3516,0.3564,0.986,0.2,0
9816a261-3118-417a-8334-ace36687ad8f,BE9558,2026-05-16T05:31:47,K558LL9W.OZ0W,Long,0.3416,0.3467,0.985,0.2,0
5d2c76d7-543d-4557-9caf-996a69ab00bd,BE9558,2026-05-16T05:34:42,K558LL9W.TU0W,Long,0.3537,0.3564,0.992,0.2,0
c7b5ae00-52e7-4920-aa27-49d3b3daff92,BE9558,2026-05-16T05:37:36,K558LL9W.YO0W,Long,0.3517,0.3564,0.987,0.2,0
07db8fd8-ccef-4a5a-9d97-9f318f73a478,BE9558,2026-05-16T05:40:31,K558LL9X.3J0W,Long,0.3574,0.3613,0.989,0.2,0
790fcff9-e266-4a12-87f0-a072990d2533,BE9558,2026-05-16T05:43:26,K558LL9X.8E0W,Long,0.3650,0.3662,0.997,0.2,0
f4245f61-1dde-43dc-a121-98e102f6a193,BE9558,2026-05-16T05:50:28,K558LL9X.K40W,Long,0.3856,0.3906,0.987,0.2,0
5a3bce7b-358e-4260-876f-3f853163d7cf,BE9558,2026-05-16T05:53:22,K558LL9X.OY0W,Long,0.3847,0.3857,0.997,0.2,0
37c2b4b1-7a1b-43e7-b513-2c16f582109c,BE9558,2026-05-16T05:56:15,K558LL9X.TR0W,Long,0.3906,0.3955,0.988,0.2,0
57fde109-1d8a-45a1-b398-5bd28c3e47f1,BE9558,2026-05-16T05:59:10,K558LL9X.YM0W,Long,0.4000,0.4004,0.999,0.2,0
1e41ab40-93be-405c-a464-f7a57524d3b3,BE9558,2026-05-16T06:02:02,K558LL9Y.3E0W,Long,0.3825,0.3857,0.992,0.2,0
e9d61eb5-fae8-4902-8fab-4828ffa8e6da,BE9558,2026-05-16T06:04:57,K558LL9Y.890W,Long,0.3802,0.3809,0.998,0.2,0
c0e964e0-7788-4e41-bc41-6e479c3dc81b,BE9558,2026-05-16T06:07:51,K558LL9Y.D30W,Long,0.3954,0.4004,0.987,0.2,0
0b2df8e5-1689-4fe6-abad-30970ba10aa6,BE9558,2026-05-16T06:16:06,K558LL9Y.QU0W,Long,0.4051,0.4053,1.000,0.2,0
2bc6ce73-464b-469c-9e8f-3bf6f6cfae1d,BE9558,2026-05-16T06:21:51,K558LL9Z.0F0W,Long,0.3910,0.3955,0.989,0.2,0
b8603ee1-616a-451e-a2cd-61f8f2634cd0,BE9558,2026-05-16T06:24:44,K558LL9Z.580W,Long,0.3969,0.4004,0.991,0.2,0
1aecf582-66a2-4ebb-80f9-04b3aeba331a,BE9558,2026-05-16T06:27:39,K558LL9Z.A30W,Long,0.3915,0.3955,0.990,0.2,0
885aedfd-7a1a-4120-8176-7c344dd3d8fe,BE9558,2026-05-16T06:30:30,K558LL9Z.EU0W,Long,0.4025,0.4053,0.993,0.2,0
ec15930f-47f5-4752-afc2-427ee156ac95,BE9558,2026-05-16T06:33:25,K558LL9Z.JP0W,Long,0.4060,0.4102,0.990,0.2,0
5ca23f47-2a38-4a54-99dd-041287aa9510,BE9558,2026-05-16T06:36:17,K558LL9Z.OH0W,Long,0.4054,0.4102,0.988,0.2,0
03f371cf-81bc-4681-80f0-ef6fa437da85,BE9558,2026-05-16T06:39:09,K558LL9Z.T90W,Long,0.4052,0.4102,0.988,0.2,0
ed386f54-140d-45e8-ad51-6585a58b4375,BE9558,2026-05-16T06:44:54,K558LLA0.2U0W,Long,0.3993,0.4004,0.997,0.2,0
579f1c3f-4c1e-42a3-aa89-69aa93514033,BE9558,2026-05-16T06:47:47,K558LLA0.7N0W,Long,0.4001,0.4053,0.987,0.2,0
57024de6-07ea-41f4-b916-c6adc3f51636,BE9558,2026-05-16T06:50:40,K558LLA0.CG0W,Long,0.4103,0.4150,0.989,0.2,0
36bf5adf-afb5-4a5e-a106-1afbd1f56c7b,BE9558,2026-05-16T06:54:49,K558LLA0.JD0W,Long,0.4069,0.4150,0.980,0.2,0
3e2f831b-5228-46ff-9acb-75d8c86f8bc1,BE9558,2026-05-16T06:57:43,K558LLA0.O70W,Long,0.4108,0.4150,0.990,0.2,0
1775fa20-568b-4294-b9ca-561201a66c5b,BE9558,2026-05-16T07:00:38,K558LLA0.T20W,Long,0.3967,0.4004,0.991,0.2,0
fd391469-03f9-4d8d-bccd-3af50c91e5b7,BE9558,2026-05-16T07:03:33,K558LLA0.XX0W,Long,0.3927,0.3955,0.993,0.2,0
02b52ef6-b707-4dcb-b58f-43da02698832,BE9558,2026-05-16T07:06:27,K558LLA1.2R0W,Long,0.3824,0.3857,0.991,0.2,0
8d45333f-e101-4249-9e27-913c583a0a8d,BE9558,2026-05-16T07:09:21,K558LLA1.7L0W,Long,0.4085,0.4150,0.984,0.2,0
361d11ce-f42a-4d04-8270-ee3771fe4f09,BE9558,2026-05-16T07:12:14,K558LLA1.CE0W,Long,0.4017,0.4053,0.991,0.2,0
59218125-ed5d-4457-aa8b-35ba03441363,BE9558,2026-05-16T07:15:07,K558LLA1.H70W,Long,0.3934,0.3955,0.995,0.2,0
a603e55a-2ad6-4fc9-8c8c-0dc7be95c3e7,BE9558,2026-05-16T07:23:43,K558LLA1.VJ0W,Long,0.3806,0.3857,0.987,0.2,0
c8d07902-93ea-4b03-9e69-9ad6ed5bffb1,BE9558,2026-05-16T07:26:36,K558LLA2.0C0W,Long,0.3807,0.3857,0.987,0.2,0
4c46c0c7-42f0-4648-827e-1995f733b18f,BE9558,2026-05-16T07:30:44,K558LLA2.780W,Long,0.3667,0.3711,0.988,0.2,0
04759a00-ba3e-4959-a4cb-be8fc1f3004e,BE9558,2026-05-16T07:36:28,K558LLA2.GS0W,Long,0.3514,0.3516,1.000,0.2,0
0b6b0856-bd87-4a07-a5b4-f13d99e04a12,BE9558,2026-05-16T07:39:20,K558LLA2.LK0W,Long,0.3512,0.3516,0.999,0.2,0
3d81a8bd-464d-4050-8a76-25e8373d1ddb,BE9558,2026-05-16T07:42:12,K558LLA2.QC0W,Long,0.3350,0.3369,0.994,0.2,0
b8b9c0c4-c388-4acb-955a-be4ac356afce,BE9558,2026-05-16T07:45:04,K558LLA2.V40W,Long,0.3563,0.3564,1.000,0.2,0
cb3d142a-fb84-4a21-a298-f61bb869b036,BE9558,2026-05-16T07:49:13,K558LLA3.210W,Long,0.3663,0.3711,0.987,0.2,0
7401b19b-bc9d-43f0-b395-f4323aca5c72,BE9558,2026-05-16T07:52:06,K558LLA3.6U0W,Long,0.3612,0.3662,0.986,0.2,0
2236df76-a9e6-401f-9176-ad565242f2cf,BE9558,2026-05-16T07:57:52,K558LLA3.GG0W,Long,0.3366,0.3369,0.999,0.2,0
a645846c-cae4-4aa5-a2e6-85166bd4dd62,BE9558,2026-05-16T08:00:44,K558LLA3.L80W,Long,0.3265,0.3271,0.998,0.2,0
7212bd33-99a9-4659-9f0b-c98cc8d1e8bc,BE9558,2026-05-16T08:04:51,K558LLA3.S30W,Long,0.3418,0.3467,0.986,0.2,0
2cc4bf33-c63a-4c65-b0e2-5e61a9146a5d,BE9558,2026-05-16T08:07:43,K558LLA3.WV0W,Long,0.3405,0.3418,0.996,0.2,0
4fedb486-5f1f-4623-a01f-083e829f0565,BE9558,2026-05-16T08:10:35,K558LLA4.1N0W,Long,0.3515,0.3564,0.986,0.2,0
58aff0ba-0f9b-45e1-9bbe-a990d16192ae,BE9558,2026-05-16T08:13:27,K558LLA4.6F0W,Long,0.3513,0.3516,0.999,0.2,0
e56d87e5-e1d4-4947-b6a5-3bbd2773c54a,BE9558,2026-05-16T08:20:03,K558LLA4.HF0W,Long,0.3564,0.3613,0.986,0.2,0
0e96e61e-6a21-4e07-8e09-72d09dbfa1e6,BE9558,2026-05-16T08:25:21,K558LLA4.Q90W,Long,0.3565,0.3613,0.987,0.2,0
19611f28-2da8-4362-a58e-dbed2e061379,BE9558,2026-05-16T08:34:19,K558LLA5.570W,Long,0.3647,0.3662,0.996,0.2,0
bd580c1d-44d4-41fd-9f83-f462cda4099a,BE9558,2026-05-16T08:37:15,K558LLA5.A30W,Long,0.3536,0.3564,0.992,0.2,0
2782df3b-3cb4-471a-a6f5-bbc4903a8ab9,BE9558,2026-05-16T08:44:00,K558LLA5.LC0W,Long,0.3612,0.3662,0.986,0.2,0
d15a08f3-1c7c-4f80-8d9b-f5008c58c3d9,BE9558,2026-05-16T08:49:18,K558LLA5.U60W,Long,0.3610,0.3613,0.999,0.2,0
03a10a6a-193a-476c-9543-f63818f69417,BE9558,2026-05-16T08:54:36,K558LLA6.300W,Long,0.3515,0.3516,1.000,0.2,0
1c271856-8100-4c0d-bc8c-9357b47d7626,BE9558,2026-05-16T08:59:54,K558LLA6.BU0W,Long,0.3617,0.3662,0.988,0.2,0
a85f125e-bfa2-42be-b877-2604b8d068c8,BE9558,2026-05-16T09:05:17,K558LLA6.KT0W,Long,0.3510,0.3516,0.998,0.2,0
dde9aa71-d2e3-41ac-8388-f2bbddb6dae6,BE9558,2026-05-16T09:08:11,K558LLA6.PN0W,Long,0.3447,0.3467,0.994,0.2,0
2f3f2be2-0ee3-422e-b3ad-83fb87512a2b,BE9558,2026-05-16T09:14:39,K558LLA7.0F0W,Long,0.3580,0.3613,0.991,0.2,0
357a3034-8a9c-485e-8cb6-f526dd2baf6f,BE9558,2026-05-16T09:29:11,K558LLA7.ON0W,Long,0.3708,0.3760,0.986,0.2,0
f8ad0274-8e6f-4714-82ff-d229e60f1442,BE9558,2026-05-16T09:32:06,K558LLA7.TI0W,Long,0.3567,0.3613,0.987,0.2,0
42bb007a-2726-401a-b085-c25151b0dc24,BE9558,2026-05-16T09:35:02,K558LLA7.YE0W,Long,0.3575,0.3613,0.989,0.2,0
c99bfc4a-db02-4bea-bea4-943c67485dba,BE9558,2026-05-16T09:37:58,K558LLA8.3A0W,Long,0.3527,0.3564,0.989,0.2,0
8fc17de0-73f7-48bd-8d93-5451dce47e8e,BE9558,2026-05-16T09:40:52,K558LLA8.840W,Long,0.3613,0.3662,0.987,0.2,0
bbee5276-03dd-4ca4-a7b9-aec02baac7e8,BE9558,2026-05-16T09:48:13,K558LLA8.KD0W,Long,0.3590,0.3613,0.993,0.2,0
8dcdee10-db01-4224-9506-4181c3105d6f,BE9558,2026-05-16T09:53:31,K558LLA8.T70W,Long,0.3588,0.3613,0.993,0.2,0
48c8be72-64d9-4da3-95b5-bbec2c5e1dbd,BE9558,2026-05-16T09:58:49,K558LLA9.210W,Long,0.3582,0.3662,0.978,0.2,0
0da20919-7ce9-4feb-8ae9-12d9bb40c2b5,BE9558,2026-05-16T10:04:07,K558LLA9.AV0W,Long,0.3506,0.3516,0.997,0.2,0
c2f7e9bc-8b93-42c3-96d3-e3af217dde35,BE9558,2026-05-16T10:09:25,K558LLA9.JP0W,Long,0.3506,0.3516,0.997,0.2,0
522a0e42-d443-4447-bc71-af1832c6604e,BE9558,2026-05-16T10:14:43,K558LLA9.SJ0W,Long,0.3624,0.3662,0.990,0.2,0
67f7e732-3c0b-439f-87c5-3c478a381418,BE9558,2026-05-16T10:20:01,K558LLAA.1D0W,Long,0.3494,0.3516,0.994,0.2,0
251767e8-56f7-4091-a001-364d8e75c15c,BE9558,2026-05-16T10:25:19,K558LLAA.A70W,Long,0.3463,0.3516,0.985,0.2,0
9457239d-a4ca-4476-b385-5e01fc8f6420,BE9558,2026-05-16T10:30:37,K558LLAA.J10W,Long,0.3436,0.3467,0.991,0.2,0
43718d7c-9353-49e1-89b6-0841aeb1b276,BE9558,2026-05-16T10:35:55,K558LLAA.RV0W,Long,0.3452,0.3516,0.982,0.2,0
f54ba557-ddfe-41d3-8f4c-8bbec9a3783c,BE9558,2026-05-16T10:41:13,K558LLAB.0P0W,Long,0.3419,0.3467,0.986,0.2,0
dc26c758-ad99-49c1-b518-931368cdf8cf,BE9558,2026-05-16T10:46:31,K558LLAB.9J0W,Long,0.3463,0.3516,0.985,0.2,0
10917eb2-d151-461c-a8cb-e9660da5a5b0,BE9558,2026-05-16T10:51:49,K558LLAB.ID0W,Long,0.3520,0.3564,0.987,0.2,0
4938a5e6-d53b-40ee-be2b-06ec0e60398c,BE9558,2026-05-16T10:57:07,K558LLAB.R70W,Long,0.3541,0.3564,0.993,0.2,0
0711b51d-55bd-48be-b51e-2aad67107a60,BE9558,2026-05-16T11:13:01,K558LLAC.HP0W,Long,0.3661,0.3711,0.986,0.2,0
065564b6-aa49-40e7-9ea6-c75defdc60fe,BE9558,2026-05-16T11:18:19,K558LLAC.QJ0W,Long,0.3622,0.3662,0.989,0.2,0
ffb6b230-1d70-4e0e-ba30-e945339a122a,BE9558,2026-05-16T11:23:37,K558LLAC.ZD0W,Long,0.3711,0.3760,0.987,0.2,0
52b4da82-c1e0-4a8a-8365-51b090787005,BE9558,2026-05-16T11:28:55,K558LLAD.870W,Long,0.3644,0.3662,0.995,0.2,0
cdbff3d8-fbfe-4bea-8bd6-c898800e81ba,BE9558,2026-05-16T11:39:31,K558LLAD.PV0W,Long,0.3609,0.3662,0.986,0.2,0
3fb0bc30-1463-4cfc-9e7f-386c0eb2a34a,BE9558,2026-05-16T11:44:49,K558LLAD.YP0W,Long,0.3553,0.3564,0.997,0.2,0
ba1f5ec8-aef7-4a9e-ac1f-276cf016e285,BE9558,2026-05-16T11:50:07,K558LLAE.7J0W,Long,0.3479,0.3516,0.990,0.2,0
b1dd4df7-34a9-4512-a903-f8242b236fd2,BE9558,2026-05-16T11:55:25,K558LLAE.GD0W,Long,0.3427,0.3467,0.988,0.2,0
1f23aa28-c026-4bc7-b3d1-7c78fd76a673,BE9558,2026-05-16T12:00:43,K558LLAE.P70W,Long,0.3454,0.3467,0.996,0.2,0
0d91f54d-b20d-4a50-b7dd-d071c39a0690,BE9558,2026-05-16T12:06:01,K558LLAE.Y10W,Long,0.3431,0.3467,0.990,0.2,0
12ccdecd-5fd9-4465-9813-3d6006ec32f3,BE9558,2026-05-16T12:11:19,K558LLAF.6V0W,Long,0.3433,0.3467,0.990,0.2,0
a7024f1a-f071-48ab-820f-99bdf87366b5,BE9558,2026-05-16T12:16:37,K558LLAF.FP0W,Long,0.3361,0.3369,0.998,0.2,0
de0fa439-30b8-4607-a3c3-7e17191e4624,BE9558,2026-05-16T12:21:55,K558LLAF.OJ0W,Long,0.3274,0.3320,0.986,0.2,0
f9374ec3-05e5-477f-8e1a-0c862fed2a50,BE9558,2026-05-16T12:27:13,K558LLAF.XD0W,Long,0.3449,0.3467,0.995,0.2,0
424cb275-3743-4174-95f0-0ca1205b374c,BE9558,2026-05-16T12:32:31,K558LLAG.670W,Long,0.3370,0.3418,0.986,0.2,0
9accde13-cefa-41b8-bd0a-cef2ffed59b0,BE9558,2026-05-16T12:37:49,K558LLAG.F10W,Long,0.3465,0.3467,0.999,0.2,0
4665be38-1956-4b14-b931-068ba9af5c5e,BE9558,2026-05-16T12:48:25,K558LLAG.WP0W,Long,0.3453,0.3516,0.982,0.2,0
1aedf624-1a99-43e1-a8f2-f9d358b97f61,BE9558,2026-05-16T12:53:43,K558LLAH.5J0W,Long,0.3366,0.3418,0.985,0.2,0
c15e1799-91d7-425e-ad17-2ba3d170d81e,BE9558,2026-05-16T13:04:19,K558LLAH.N70W,Long,0.3348,0.3369,0.994,0.2,0
72d4c4ea-19ca-4cc6-9b04-7a2b70122c67,BE9558,2026-05-16T13:09:37,K558LLAH.W10W,Long,0.3272,0.3320,0.986,0.2,0
b4ebf2c9-1f6d-4594-9810-40479e87c0a5,BE9558,2026-05-16T13:14:55,K558LLAI.4V0W,Long,0.3297,0.3320,0.993,0.2,0
bb297e9e-548f-4dae-8b1a-0f161af2e766,BE9558,2026-05-16T13:20:13,K558LLAI.DP0W,Long,0.3272,0.3320,0.986,0.2,0
87562c24-63db-48bd-9962-72a20a40f2ff,BE9558,2026-05-16T13:36:07,K558LLAJ.470W,Long,0.3174,0.3223,0.985,0.2,0
01e9cc58-3bea-4f38-8499-a2c20e5f8550,BE9558,2026-05-16T13:46:52,K558LLAJ.M40W,Long,0.2880,0.2930,0.983,0.2,0
5ae7503d-ae17-4063-9208-6a420699e1ab,BE9558,2026-05-16T14:00:35,K558LLAK.8Z0W,Long,0.2879,0.2930,0.983,0.2,0
e009ea3c-1534-4154-8623-e180ed4db9fb,BE9558,2026-05-16T14:06:19,K558LLAK.IJ0W,Long,0.2882,0.2930,0.984,0.2,0
cd4e21f5-512b-4b2b-8054-bec01aa06400,BE9558,2026-05-16T14:12:03,K558LLAK.S30W,Long,0.2857,0.2930,0.975,0.2,0
ae5b780f-2038-4ccf-8bfc-b15b87be139e,BE9558,2026-05-16T14:17:22,K558LLAL.0Y0W,Long,0.2899,0.2930,0.989,0.2,0
d8c3fb81-8c66-4ebc-b33a-0559bc03334b,BE9558,2026-05-16T14:22:40,K558LLAL.9S0W,Long,0.3009,0.3027,0.994,0.2,0
990a4c7a-3c51-4d4a-8ab7-416ccb97d29c,BE9558,2026-05-16T14:27:58,K558LLAL.IM0W,Long,0.3057,0.3076,0.994,0.2,0
68d7a2c3-d899-4e75-96fb-3bf16ae6ea6f,BE9558,2026-05-16T14:33:16,K558LLAL.RG0W,Long,0.3110,0.3125,0.995,0.2,0
73505b8c-f912-4116-b903-a8a4c75524d3,BE9558,2026-05-16T14:38:34,K558LLAM.0A0W,Long,0.3172,0.3223,0.984,0.2,0
8d3c9602-7efa-488a-801b-3228bd85ab14,BE9558,2026-05-16T14:43:51,K558LLAM.930W,Long,0.3136,0.3174,0.988,0.2,0
243e16ee-860a-4960-bc15-b797ad7e9735,BE9558,2026-05-16T14:49:08,K558LLAM.HW0W,Long,0.3135,0.3174,0.988,0.2,0
e90c8d90-6d41-4788-9ee6-a29e4bfcc16b,BE9558,2026-05-16T15:19:35,K558LLAN.WN0W,Long,0.3132,0.3174,0.987,0.2,0
4182b0e7-7811-46b8-87d1-a4dd0c152f3b,BE9558,2026-05-16T15:28:45,K558LLAO.BX0W,Long,0.3023,0.3076,0.983,0.2,0
28a790da-0e48-4fe4-b8ba-2c82cc6bfa79,BE9558,2026-05-16T15:34:02,K558LLAO.KQ0W,Long,0.3176,0.3223,0.985,0.2,0
093d2fa3-7a4b-4f0f-abda-71155ebe2dfb,BE9558,2026-05-16T15:39:19,K558LLAO.TJ0W,Long,0.3223,0.3271,0.985,0.2,0
68dc9560-4082-49b6-af04-9244773ffbc1,BE9558,2026-05-16T15:44:36,K558LLAP.2C0W,Long,0.3227,0.3271,0.986,0.2,0
ad6b268a-165d-4b1d-b0b3-f58db7c9b0e4,BE9558,2026-05-16T15:49:53,K558LLAP.B50W,Long,0.3177,0.3223,0.986,0.2,0
046c5cb4-56b3-48c0-83c2-bf1f30837940,BE9558,2026-05-16T15:55:10,K558LLAP.JY0W,Long,0.3174,0.3223,0.985,0.2,0
1b76edf4-3b1c-4bcb-a73c-27334981c350,BE9558,2026-05-16T16:00:27,K558LLAP.SR0W,Long,0.3127,0.3174,0.985,0.2,0
a0264171-a682-4396-ad8a-cda3b2964533,BE9558,2026-05-16T16:06:55,K558LLAQ.3J0W,Long,0.2885,0.2930,0.985,0.2,0
707da1f6-ee81-4e57-abd5-98295c14651b,BE9558,2026-05-16T16:12:30,K558LLAQ.CU0W,Long,0.2880,0.2930,0.983,0.2,0
b8115b64-8739-42c8-b541-713ab6b68bcd,BE9558,2026-05-16T16:17:53,K558LLAQ.LT0W,Long,0.2881,0.2930,0.983,0.2,0
fb179c36-fef1-4df6-b0a0-2bd7792ea910,BE9558,2026-05-16T16:23:11,K558LLAQ.UN0W,Long,0.2933,0.2979,0.985,0.2,0
0cec8793-c3c1-486b-a33a-4dbf9cb069b0,BE9558,2026-05-16T16:33:46,K558LLAR.CA0W,Long,0.3017,0.3027,0.997,0.2,0
c531b052-ee04-4c66-888b-21e97445a615,BE9558,2026-05-16T16:49:37,K558LLAS.2P0W,Long,0.2922,0.2979,0.981,0.2,0
0dc402f9-cab6-4c4a-8317-b8fc3c4b1ced,BE9558,2026-05-16T16:55:08,K558LLAS.BW0W,Long,0.2898,0.2930,0.989,0.2,0
fd73733e-b42d-4a51-81d0-4acad6edfbd4,BE9558,2026-05-16T17:16:17,K558LLAT.B50W,Long,0.3115,0.3125,0.997,0.2,0
6a254daa-ae27-4ac1-b334-58f776a6b276,BE9558,2026-05-16T17:26:51,K558LLAT.SR0W,Long,0.3174,0.3223,0.985,0.2,0
0cc8e82f-5270-4508-beae-0d3af56095ca,BE9558,2026-05-16T17:37:27,K558LLAU.AF0W,Long,0.2899,0.2930,0.989,0.2,0
00557a73-8dc3-4cd2-b880-c7820dc8b171,BE9558,2026-05-16T17:43:17,K558LLAU.K50W,Long,0.2881,0.2930,0.983,0.2,0
8e5518d9-ff0a-4c09-9cf9-a681c84c0b6e,BE9558,2026-05-16T17:53:53,K558LLAV.1T0W,Long,0.2898,0.2930,0.989,0.2,0
91dbd7e9-7344-400b-82b1-379f2ac4b49a,BE9558,2026-05-16T17:59:11,K558LLAV.AN0W,Long,0.2925,0.2930,0.998,0.2,0
fa256408-d480-4376-95b5-d1a549c483ea,BE9558,2026-05-16T18:04:29,K558LLAV.JH0W,Long,0.2977,0.3027,0.983,0.2,0
4ab6e66a-8364-49b6-82f1-86e86199e676,BE9558,2026-05-16T18:09:46,K558LLAV.SA0W,Long,0.2944,0.2979,0.988,0.2,0
0a65fb7c-96e1-4ba0-85de-4620d88658c1,BE9558,2026-05-16T18:15:03,K558LLAW.130W,Long,0.2965,0.2979,0.995,0.2,0
92b1ef3e-1696-4123-8662-163947737c83,BE9558,2026-05-16T18:20:20,K558LLAW.9W0W,Long,0.2976,0.3027,0.983,0.2,0
9e3cb24c-4426-4220-9a73-389d0a96c94f,BE9558,2026-05-16T18:25:37,K558LLAW.IP0W,Long,0.2983,0.3027,0.985,0.2,0
a3473215-4c46-45ab-9aa7-1720486ec4a8,BE9558,2026-05-16T18:30:54,K558LLAW.RI0W,Long,0.2956,0.2979,0.993,0.2,0
449665b1-47c8-4bd2-984c-8f45b627e6d8,BE9558,2026-05-16T18:36:11,K558LLAX.0B0W,Long,0.2989,0.3027,0.987,0.2,0
600cfabc-cffd-42e1-800c-70e75f312316,BE9558,2026-05-16T20:00:12,K558LLB0.WC0W,Long,0.3077,0.3125,0.985,0.2,0
12bf5944-0194-4f5b-b486-a7acd7141c7f,BE9558,2026-05-16T20:04:28,K558LLB1.3G0W,Long,0.2888,0.2930,0.986,0.2,0
f1fc913a-91bc-4240-a595-7dfe3091f9a2,BE9558,2026-05-16T20:07:29,K558LLB1.8H0W,Long,0.2977,0.3027,0.983,0.2,0
e74f7ec3-20bb-4177-beb2-e802e5f801fd,BE9558,2026-05-16T20:10:26,K558LLB1.DE0W,Long,0.2978,0.3027,0.984,0.2,0
27491c3a-b631-4643-818a-714523911178,BE9558,2026-05-16T20:16:21,K558LLB1.N90W,Long,0.2921,0.2930,0.997,0.2,0
9d9196ce-11e2-4f28-b01c-3dcc60e60d42,BE9558,2026-05-16T20:22:28,K558LLB1.XG0W,Long,0.2881,0.2930,0.983,0.2,0
a592d7c8-b643-4b25-9186-3d611ae22709,BE9558,2026-05-16T20:25:37,K558LLB2.2P0W,Long,0.2893,0.2930,0.987,0.2,0
f709a3d5-27cd-4bb1-b91c-669e8ea11d77,BE9558,2026-05-16T20:28:57,K558LLB2.890W,Long,0.2880,0.2930,0.983,0.2,0
cc9cad87-b87f-4b67-854a-ffb21e876a92,BE9558,2026-05-16T20:32:45,K558LLB2.EL0W,Long,0.2873,0.2930,0.980,0.2,0
10732d26-6b49-4b14-a474-6c75e259d278,BE9558,2026-05-16T20:49:32,K558LLB3.6K0W,Long,0.2881,0.2930,0.983,0.2,0
aa63957a-fdb2-4f17-af52-a63bb015119d,BE9558,2026-05-16T21:21:45,K558LLB4.O90W,Long,0.2880,0.2930,0.983,0.2,0
8350440f-019d-45a4-b9f9-23058628e6f0,BE9558,2026-05-16T21:32:43,K558LLB5.6J0W,Long,0.2925,0.2930,0.999,0.2,0
edfc10b2-dbe1-4527-a061-b0d670b5e5d2,BE9558,2026-05-16T21:35:37,K558LLB5.BD0W,Long,0.2891,0.2930,0.987,0.2,0
f98001a3-88b2-4830-8112-b7c5164e99c3,BE9558,2026-05-16T21:38:40,K558LLB5.GG0W,Long,0.2877,0.2930,0.982,0.2,0
f1e53e7b-b124-4fc5-82c2-504fb0c8b80d,BE9558,2026-05-16T21:41:46,K558LLB5.LM0W,Long,0.2881,0.2930,0.983,0.2,0
7890637f-142e-4775-b48c-fd4f3b8ada77,BE9558,2026-05-16T21:45:14,K558LLB5.RE0W,Long,0.2880,0.2930,0.983,0.2,0
7c97c10f-b413-467c-9b56-c18e423cdcfa,BE9558,2026-05-16T21:48:26,K558LLB5.WQ0W,Long,0.2886,0.2930,0.985,0.2,0
8178cf22-8428-4f85-8199-0a43f7334a8f,BE9558,2026-05-16T21:51:47,K558LLB6.2B0W,Long,0.2881,0.2930,0.983,0.2,0
eceb2997-deb4-403a-802f-50e93d23d423,BE9558,2026-05-16T21:57:18,K558LLB6.BI0W,Long,0.2881,0.2930,0.984,0.2,0
9c26690c-1983-4b21-b8af-7c7967c014c3,BE9558,2026-05-16T22:12:21,K558LLB7.0L0W,Long,0.2876,0.2930,0.982,0.2,0
78f67fc3-08b7-4317-96ba-ee5544e0b95b,BE9558,2026-05-16T22:19:34,K558LLB7.CM0W,Long,0.2880,0.2930,0.983,0.2,0
7f9dad14-4f1b-492d-aa26-845a908ea894,BE9558,2026-05-16T22:27:40,K558LLB7.Q40W,Long,0.2881,0.2930,0.983,0.2,0
f75c52b4-21b9-4065-baeb-5cfbd27d130a,BE9558,2026-05-16T22:33:50,K558LLB8.0E0W,Long,0.2881,0.2930,0.984,0.2,0
10f7496d-fa76-40a7-8bef-dc14e8a41869,BE9558,2026-05-16T22:40:48,K558LLB8.C00W,Long,0.2878,0.2930,0.982,0.2,0
1 event_id serial timestamp filename channel offset_ips peak_ips mean_over_peak trigger_level_ips already_flagged_ft
2 1e7a7808-034c-423b-80e9-d6788da80993 BE18438 2025-11-15T08:57:40 T438LBX4.W40W Vert 0.2722 0.2930 0.929 0.2 0
3 346e383f-c5a1-41d1-b972-3d652e599d39 BE18438 2025-11-15T09:13:15 T438LBX5.M30W Vert 0.2747 0.2930 0.938 0.2 0
4 45ef3023-22c5-44a8-a606-a0900bc17861 BE18438 2025-11-15T09:16:13 T438LBX5.R10W Vert 0.2929 0.3027 0.967 0.2 0
5 1cbcd2aa-ec55-4e2f-af42-7178f26cc6a1 BE18438 2025-11-15T09:19:29 T438LBX5.WH0W Vert 0.2816 0.2979 0.945 0.2 0
6 caf43634-4528-4fd5-9674-5f4f563661c0 BE18438 2025-11-15T09:22:32 T438LBX6.1K0W Vert 0.2883 0.2930 0.984 0.2 0
7 456ae646-c834-4e5c-82f1-7df18b3440a1 BE18438 2025-11-15T09:25:31 T438LBX6.6J0W Vert 0.3189 0.3223 0.989 0.2 0
8 afb91ba2-351b-42a0-b33f-acebd5f1829d BE18438 2025-11-15T09:28:29 T438LBX6.BH0W Vert 0.3057 0.3076 0.994 0.2 0
9 39847146-3537-4300-943d-01c0c771e9cd BE18438 2025-11-15T09:31:26 T438LBX6.GE0W Vert 0.3225 0.3271 0.986 0.2 0
10 e47335c1-fc3f-4c90-88c6-8edd0f296ce4 BE18438 2025-11-15T09:34:24 T438LBX6.LC0W Vert 0.3275 0.3320 0.986 0.2 0
11 25b6eda6-ba98-422f-8834-94b6cbe34971 BE18438 2025-11-15T09:37:22 T438LBX6.QA0W Vert 0.3398 0.3418 0.994 0.2 0
12 7af99377-e47f-4914-8e91-ac7f7f50b8c6 BE18438 2025-11-15T09:40:20 T438LBX6.V80W Vert 0.3515 0.3564 0.986 0.2 0
13 c4330605-c257-49ef-aede-20deb641e7d7 BE18438 2025-11-15T10:09:48 T438LBX8.8C0W Vert 0.3605 0.3955 0.912 0.2 0
14 b665caf9-64ee-4352-a391-60c1ddeebc1e BE18438 2026-02-25T10:58:04 T438LH66.GS0W Vert 0.1803 0.1953 0.923 0.2 0
15 3b0c32c3-fd16-4b83-9917-942a56dea168 BE18438 2026-02-25T18:12:04 T438LH6Q.K40W Vert 0.1869 0.1953 0.957 0.2 0
16 ca66e601-d10f-4424-9c26-08a688e7b08c BE18438 2026-02-25T18:17:16 T438LH6Q.SS0W Vert 0.1870 0.1953 0.958 0.2 0
17 31aa30ba-eb0a-49a9-98ca-c77d2cc31275 BE18438 2026-02-25T18:21:11 T438LH6Q.ZB0W Vert 0.1868 0.1953 0.956 0.2 0
18 fdb55a93-fc91-4fdf-a7f4-fdd1a6b48f59 BE18438 2026-02-25T18:26:27 T438LH6R.830W Vert 0.1893 0.1953 0.969 0.2 0
19 cbd718d7-2378-4d74-8656-842f229d19a8 BE18438 2026-02-25T18:33:25 T438LH6R.JP0W Vert 0.1887 0.1953 0.966 0.2 0
20 bfaf58ec-6163-47eb-9d2a-70da880bd948 BE18438 2026-02-25T18:37:48 T438LH6R.R00W Vert 0.1872 0.1953 0.959 0.2 0
21 ddaa530b-886e-4415-b93a-f4ebf4c4a882 BE18438 2026-02-25T18:59:54 T438LH6S.RU0W Vert 0.1874 0.1953 0.959 0.2 0
22 57f15284-ab69-48fc-9660-8c347c80c681 BE18438 2026-02-25T19:15:58 T438LH6T.IM0W Vert 0.1873 0.1953 0.959 0.2 0
23 03715718-7aa8-4e1d-9b0b-c2ab75fc1975 BE18438 2026-02-25T19:19:58 T438LH6T.PA0W Vert 0.1891 0.1953 0.968 0.2 0
24 9f921cd5-1bf9-4713-aef6-41e7f780eff1 BE18438 2026-02-25T19:24:02 T438LH6T.W20W Vert 0.1871 0.1953 0.958 0.2 0
25 44994949-58d4-44fa-bd78-ed75b8acc667 BE18438 2026-02-25T19:44:57 T438LH6U.UX0W Vert 0.1877 0.1953 0.961 0.2 0
26 c4be01c3-1001-4e17-aecd-d6d26dbb09f3 BE18438 2026-02-25T19:49:19 T438LH6V.270W Vert 0.1884 0.1953 0.964 0.2 0
27 3db004e6-ee34-43c5-ae8e-f22894ead5d0 BE18438 2026-02-25T20:08:22 T438LH6V.XY0W Vert 0.1878 0.1953 0.962 0.2 0
28 d276ae48-0434-41e5-af43-e0fb366acf11 BE18438 2026-02-25T20:11:33 T438LH6W.390W Vert 0.1880 0.1953 0.963 0.2 0
29 c027dc12-88fd-4a45-872b-a6a945f2008e BE18438 2026-02-25T20:14:35 T438LH6W.8B0W Vert 0.1874 0.1953 0.959 0.2 0
30 9665bc3a-3d08-4852-b008-37ef8bfd0023 BE18438 2026-02-25T20:18:51 T438LH6W.FF0W Vert 0.1884 0.1953 0.965 0.2 0
31 5fb5a05e-d7e2-4268-9b85-cfe8d82aa6c9 BE18438 2026-02-25T20:29:50 T438LH6W.XQ0W Vert 0.1890 0.1953 0.968 0.2 0
32 57ec0b10-9324-4459-a07e-0c8532b8928f BE18438 2026-02-25T20:35:39 T438LH6X.7F0W Vert 0.1889 0.1953 0.967 0.2 0
33 ac86f73f-1174-4fae-a206-ecdcb73e1ffc BE18438 2026-02-25T20:39:12 T438LH6X.DC0W Vert 0.1887 0.1953 0.966 0.2 0
34 dcf25ddd-9cf0-41c1-b109-f3ef2e490ce5 BE18438 2026-02-25T20:43:41 T438LH6X.KT0W Vert 0.1871 0.1953 0.958 0.2 0
35 aa79bbdd-2392-4ef6-8a5c-873a9f72746d BE18438 2026-02-25T20:46:51 T438LH6X.Q30W Vert 0.1877 0.1953 0.961 0.2 0
36 ad46d1ce-b417-4c8c-b029-bb3b008ba809 BE18438 2026-02-25T20:53:33 T438LH6Y.190W Vert 0.1882 0.1953 0.964 0.2 0
37 3d3a8f34-86a7-4d70-a7e8-fa768a723325 BE18438 2026-02-26T07:03:39 T438LH7Q.A30W Vert 0.1809 0.1953 0.926 0.2 0
38 7817525f-dc3d-4f5e-b2cb-5af9ca12ede8 BE18438 2026-02-26T07:09:42 T438LH7Q.K60W Vert 0.1814 0.1953 0.929 0.2 0
39 a2091302-0b34-46c1-ab83-174be2b59bcf BE18438 2026-02-26T13:12:35 T438LH87.CZ0W Vert 0.1896 0.1953 0.971 0.2 0
40 e1c0c9de-a900-479f-9572-9675b13296a9 BE18438 2026-02-26T13:24:46 T438LH87.XA0W Vert 0.2237 0.2588 0.864 0.2 0
41 76a6dc6b-88e1-434b-a137-f5e9b63f248e BE18438 2026-02-26T13:27:43 T438LH88.270W Vert 0.3185 0.3223 0.988 0.2 0
42 281b508b-7982-4668-86af-9b0b28be608a BE18438 2026-02-26T13:30:36 T438LH88.700W Vert 0.3263 0.3320 0.983 0.2 0
43 68bbaf77-bb23-40fd-94f9-f928728d90dc BE18438 2026-02-26T13:33:28 T438LH88.BS0W Vert 0.3207 0.3271 0.980 0.2 0
44 5c975640-6ff1-441a-aa11-41642f6a6b31 BE18438 2026-02-26T13:36:21 T438LH88.GL0W Vert 0.3261 0.3320 0.982 0.2 0
45 dea248ac-1124-4bba-b054-953f86c852b2 BE18438 2026-02-26T13:40:34 T438LH88.NM0W Vert 0.3271 0.3320 0.985 0.2 0
46 4e463201-f008-41ad-9c52-11fcc3d93095 BE18438 2026-02-26T13:43:29 T438LH88.SH0W Vert 0.3357 0.3418 0.982 0.2 0
47 507262c5-d58f-4a05-a6a3-a6cbb5b4de20 BE18438 2026-02-26T13:46:24 T438LH88.XC0W Vert 0.3372 0.3418 0.987 0.2 0
48 4c2d8ee8-842f-4e44-9e03-ff34d26e4fdb BE18438 2026-02-26T13:49:17 T438LH89.250W Vert 0.3253 0.3320 0.980 0.2 0
49 b184b112-603f-44f4-ae97-087493fbd8ce BE18438 2026-02-26T13:52:10 T438LH89.6Y0W Vert 0.3321 0.3369 0.986 0.2 0
50 2ff1a552-5a2e-4396-9083-b9c8d334eed7 BE18438 2026-02-26T13:55:04 T438LH89.BS0W Vert 0.3378 0.3418 0.988 0.2 0
51 928e21a7-1990-4792-803e-58014d8140ce BE18438 2026-02-26T13:57:57 T438LH89.GL0W Vert 0.3341 0.3369 0.992 0.2 0
52 f521b5db-6100-41e6-954e-0abdbf9660c1 BE18438 2026-02-26T14:00:51 T438LH89.LF0W Vert 0.3421 0.3467 0.987 0.2 0
53 efba962f-e511-41ef-9fc5-313f202a9d96 BE18438 2026-02-26T14:03:46 T438LH89.QA0W Vert 0.3506 0.3564 0.984 0.2 0
54 0b51e273-8d10-4494-bda6-8c75eaeec66f BE18438 2026-02-26T14:06:41 T438LH89.V50W Vert 0.3448 0.3516 0.981 0.2 0
55 6d46e4ee-1647-4169-b6b2-33da32240eb9 BE18438 2026-02-26T14:09:35 T438LH89.ZZ0W Vert 0.3463 0.3516 0.985 0.2 0
56 1ca294cd-4dc2-41fb-9374-e277d4550761 BE18438 2026-02-26T14:12:28 T438LH8A.4S0W Vert 0.3442 0.3467 0.993 0.2 0
57 7b683dcf-1aed-4d9b-be1e-b10f92374398 BE18438 2026-02-26T14:15:23 T438LH8A.9N0W Vert 0.3486 0.3516 0.992 0.2 0
58 2b5cb8e7-c947-4914-bcb5-504f74603547 BE18438 2026-02-26T14:18:21 T438LH8A.EL0W Vert 0.3519 0.3613 0.974 0.2 0
59 5ea3a6bf-10de-4911-aedc-66daecf86b76 BE18438 2026-02-26T14:21:16 T438LH8A.JG0W Vert 0.3460 0.3564 0.971 0.2 0
60 d93be7f5-39ec-45fd-9949-fa531f2f9d34 BE18438 2026-02-26T14:24:13 T438LH8A.OD0W Vert 0.3457 0.3516 0.983 0.2 0
61 cfa113f2-8cee-4a1d-b1e7-0e936fa771e2 BE18438 2026-02-26T14:28:21 T438LH8A.V90W Vert 0.3485 0.3564 0.978 0.2 0
62 46d322ac-38cb-4c99-a4c2-9e9d7ad06171 BE18438 2026-02-26T14:31:17 T438LH8B.050W Vert 0.3367 0.3467 0.971 0.2 0
63 42500a83-bd67-4deb-8bb2-963ee2b8a7ce BE18438 2026-02-26T14:34:11 T438LH8B.4Z0W Vert 0.3365 0.3516 0.957 0.2 0
64 3ccf6bda-2e88-473c-951d-9ea86d0d9363 BE18438 2026-02-26T14:37:05 T438LH8B.9T0W Vert 0.3472 0.3564 0.974 0.2 0
65 9050487d-d238-4631-b8e1-5a693a026542 BE18438 2026-02-26T14:39:58 T438LH8B.EM0W Vert 0.3420 0.3564 0.959 0.2 0
66 e62989e4-a326-4bae-8d47-a8becee96462 BE18438 2026-02-26T14:42:52 T438LH8B.JG0W Vert 0.3430 0.3564 0.962 0.2 0
67 f0e30fab-dc57-4189-8439-48df17007a2c BE18438 2026-02-26T14:45:46 T438LH8B.OA0W Vert 0.3389 0.3516 0.964 0.2 0
68 03e1ca09-78df-4c6f-9915-0b6e4f2141d2 BE18438 2026-02-26T14:48:43 T438LH8B.T70W Vert 0.3449 0.3564 0.968 0.2 0
69 945d39f7-1b98-4a1f-80a8-6a18fd9df25f BE18438 2026-02-26T14:51:37 T438LH8B.Y10W Vert 0.3522 0.3613 0.975 0.2 0
70 d27eb2e3-e313-4125-98f4-f9d6d3737d28 BE18438 2026-02-26T14:54:32 T438LH8C.2W0W Vert 0.3502 0.3613 0.969 0.2 0
71 916901cb-ebea-441e-a2a1-40f4e49f32f4 BE18438 2026-02-26T14:57:25 T438LH8C.7P0W Vert 0.3524 0.3662 0.962 0.2 0
72 5d8d7e29-fdbb-4fa3-bd25-506c9352a354 BE18438 2026-02-26T15:01:35 T438LH8C.EN0W Vert 0.3530 0.3613 0.977 0.2 0
73 c6c7a3af-b03b-43dc-97de-024f8e09b62f BE18438 2026-02-26T15:04:27 T438LH8C.JF0W Vert 0.3514 0.3613 0.973 0.2 0
74 b6856898-3d10-4967-9577-7f17902f77ea BE18438 2026-02-26T15:11:34 T438LH8C.VA0W Vert 0.3566 0.3613 0.987 0.2 0
75 7a4802f6-621e-4230-8d8c-d7f0c9a65bf2 BE18438 2026-02-26T15:14:24 T438LH8D.000W Vert 0.3594 0.3711 0.968 0.2 0
76 79f38b7e-ec56-417e-943a-8c944252c2b8 BE18438 2026-02-26T15:18:32 T438LH8D.6W0W Vert 0.3550 0.3613 0.983 0.2 0
77 1b4190c4-0d54-48ba-8ff5-8ddb0eb91e50 BE18438 2026-02-26T15:22:22 T438LH8D.DA0W Vert 0.3571 0.3613 0.988 0.2 0
78 de57b5ae-a3a1-4ede-9d2b-3e87bfb3fd19 BE9558 2026-04-14T11:16:32 K558LJN3.BK0W Tran 0.3448 0.3662 0.942 0.2 0
79 43afeaf5-02ec-41f6-9e23-0c9772821ed4 BE9558 2026-04-14T11:27:15 K558LJN3.TF0W Tran 0.3094 0.3223 0.960 0.2 0
80 8123c0ef-84c9-4f6c-8d82-9dc32e2e470d BE9558 2026-04-14T14:45:30 K558LJNC.ZU0W Tran 0.2721 0.3564 0.763 0.2 0
81 8c787af3-596e-411b-9e10-29485fa5114f BE9558 2026-04-29T16:18:47 K558LKF9.BB0W Tran 0.2943 0.3027 0.972 0.2 0
82 431928ff-b4c4-4caa-b933-acc917f3717c BE9558 2026-05-04T15:02:30 K558LKOF.460W Tran 0.4364 0.5225 0.835 0.2 0
83 6c3c07f8-c36a-4493-acce-7441c9d22cec BE9558 2026-05-15T08:50:06 K558LL8B.7I0W Long 0.2892 0.2930 0.987 0.2 0
84 13c268df-b652-422e-a642-6453f6a314aa BE9558 2026-05-15T10:18:34 K558LL8F.AY0W Long 0.2907 0.2979 0.976 0.2 0
85 9b0d0871-8810-467d-806a-5bbfa4e667fe BE9558 2026-05-15T15:52:00 K558LL8U.QO0W Long 0.2659 0.2979 0.893 0.2 0
86 092640f9-a944-48b7-b872-364d75c2c5e7 BE9558 2026-05-15T16:13:12 K558LL8V.Q00W Long 0.2428 0.2979 0.815 0.2 0
87 0573741a-96ab-4b36-af7a-b5bc11a79009 BE9558 2026-05-16T03:23:26 K558LL9Q.R20W Long 0.2861 0.2930 0.976 0.2 0
88 321f03ea-6696-47de-ad46-2be43d4d6cae BE9558 2026-05-16T03:30:49 K558LL9R.3D0W Long 0.2886 0.2930 0.985 0.2 0
89 cc423a6a-3e3c-466a-b39c-6146ca7f34c8 BE9558 2026-05-16T03:33:55 K558LL9R.8J0W Long 0.2880 0.2881 1.000 0.2 0
90 efa52a14-cc68-4bea-aab4-4ee3a3cbff3a BE9558 2026-05-16T03:36:55 K558LL9R.DJ0W Long 0.2890 0.2930 0.986 0.2 0
91 92895bfe-122f-4cdc-bd3d-40609633d278 BE9558 2026-05-16T03:43:41 K558LL9R.OT0W Long 0.2880 0.2881 1.000 0.2 0
92 2a4c81db-2307-44e5-9b27-d5154d98e38d BE9558 2026-05-16T03:46:36 K558LL9R.TO0W Long 0.2921 0.2979 0.981 0.2 0
93 656b0fdc-e275-4c9c-8936-9eee267ea04e BE9558 2026-05-16T03:49:28 K558LL9R.YG0W Long 0.2966 0.2979 0.996 0.2 0
94 3b9989ae-e0f2-4652-9724-c145603543d2 BE9558 2026-05-16T03:52:29 K558LL9S.3H0W Long 0.2880 0.2930 0.983 0.2 0
95 837d0d84-d609-4f4a-b542-b7e2b064ea22 BE9558 2026-05-16T03:56:32 K558LL9S.A80W Long 0.2880 0.2930 0.983 0.2 0
96 affcb008-ab99-4bc5-8f35-83dbe620499d BE9558 2026-05-16T04:02:50 K558LL9S.KQ0W Long 0.2896 0.2930 0.989 0.2 0
97 e1f1c6d2-545c-451f-9018-714d52e20a05 BE9558 2026-05-16T04:05:47 K558LL9S.PN0W Long 0.3024 0.3076 0.983 0.2 0
98 93ef80f1-7e83-4703-a0f9-89bbfeeae6c3 BE9558 2026-05-16T04:08:41 K558LL9S.UH0W Long 0.2976 0.2979 0.999 0.2 0
99 5c118ca9-fc37-4080-876d-b3f57ce0b121 BE9558 2026-05-16T04:11:42 K558LL9S.ZI0W Long 0.2881 0.2930 0.983 0.2 0
100 d98b2eaa-7763-4c78-88fb-c21195cb9978 BE9558 2026-05-16T04:14:38 K558LL9T.4E0W Long 0.2898 0.2930 0.989 0.2 0
101 6a146a01-0d02-4413-b231-ecb4a6fde776 BE9558 2026-05-16T04:17:35 K558LL9T.9B0W Long 0.2880 0.2930 0.983 0.2 0
102 f6587a55-27e3-454f-997e-57e7516a296c BE9558 2026-05-16T04:20:32 K558LL9T.E80W Long 0.3046 0.3076 0.990 0.2 0
103 1fb48250-2214-41e6-92cb-362aff1ba62b BE9558 2026-05-16T04:26:05 K558LL9T.NH0W Long 0.2876 0.2930 0.982 0.2 0
104 2f4f3d06-1189-4e28-a1ac-5454ea586a0c BE9558 2026-05-16T04:59:47 K558LL9V.7N0W Long 0.2961 0.3027 0.978 0.2 0
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149 0b6b0856-bd87-4a07-a5b4-f13d99e04a12 BE9558 2026-05-16T07:39:20 K558LLA2.LK0W Long 0.3512 0.3516 0.999 0.2 0
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155 a645846c-cae4-4aa5-a2e6-85166bd4dd62 BE9558 2026-05-16T08:00:44 K558LLA3.L80W Long 0.3265 0.3271 0.998 0.2 0
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171 357a3034-8a9c-485e-8cb6-f526dd2baf6f BE9558 2026-05-16T09:29:11 K558LLA7.ON0W Long 0.3708 0.3760 0.986 0.2 0
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186 f54ba557-ddfe-41d3-8f4c-8bbec9a3783c BE9558 2026-05-16T10:41:13 K558LLAB.0P0W Long 0.3419 0.3467 0.986 0.2 0
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192 ffb6b230-1d70-4e0e-ba30-e945339a122a BE9558 2026-05-16T11:23:37 K558LLAC.ZD0W Long 0.3711 0.3760 0.987 0.2 0
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196 ba1f5ec8-aef7-4a9e-ac1f-276cf016e285 BE9558 2026-05-16T11:50:07 K558LLAE.7J0W Long 0.3479 0.3516 0.990 0.2 0
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202 de0fa439-30b8-4607-a3c3-7e17191e4624 BE9558 2026-05-16T12:21:55 K558LLAF.OJ0W Long 0.3274 0.3320 0.986 0.2 0
203 f9374ec3-05e5-477f-8e1a-0c862fed2a50 BE9558 2026-05-16T12:27:13 K558LLAF.XD0W Long 0.3449 0.3467 0.995 0.2 0
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222 8d3c9602-7efa-488a-801b-3228bd85ab14 BE9558 2026-05-16T14:43:51 K558LLAM.930W Long 0.3136 0.3174 0.988 0.2 0
223 243e16ee-860a-4960-bc15-b797ad7e9735 BE9558 2026-05-16T14:49:08 K558LLAM.HW0W Long 0.3135 0.3174 0.988 0.2 0
224 e90c8d90-6d41-4788-9ee6-a29e4bfcc16b BE9558 2026-05-16T15:19:35 K558LLAN.WN0W Long 0.3132 0.3174 0.987 0.2 0
225 4182b0e7-7811-46b8-87d1-a4dd0c152f3b BE9558 2026-05-16T15:28:45 K558LLAO.BX0W Long 0.3023 0.3076 0.983 0.2 0
226 28a790da-0e48-4fe4-b8ba-2c82cc6bfa79 BE9558 2026-05-16T15:34:02 K558LLAO.KQ0W Long 0.3176 0.3223 0.985 0.2 0
227 093d2fa3-7a4b-4f0f-abda-71155ebe2dfb BE9558 2026-05-16T15:39:19 K558LLAO.TJ0W Long 0.3223 0.3271 0.985 0.2 0
228 68dc9560-4082-49b6-af04-9244773ffbc1 BE9558 2026-05-16T15:44:36 K558LLAP.2C0W Long 0.3227 0.3271 0.986 0.2 0
229 ad6b268a-165d-4b1d-b0b3-f58db7c9b0e4 BE9558 2026-05-16T15:49:53 K558LLAP.B50W Long 0.3177 0.3223 0.986 0.2 0
230 046c5cb4-56b3-48c0-83c2-bf1f30837940 BE9558 2026-05-16T15:55:10 K558LLAP.JY0W Long 0.3174 0.3223 0.985 0.2 0
231 1b76edf4-3b1c-4bcb-a73c-27334981c350 BE9558 2026-05-16T16:00:27 K558LLAP.SR0W Long 0.3127 0.3174 0.985 0.2 0
232 a0264171-a682-4396-ad8a-cda3b2964533 BE9558 2026-05-16T16:06:55 K558LLAQ.3J0W Long 0.2885 0.2930 0.985 0.2 0
233 707da1f6-ee81-4e57-abd5-98295c14651b BE9558 2026-05-16T16:12:30 K558LLAQ.CU0W Long 0.2880 0.2930 0.983 0.2 0
234 b8115b64-8739-42c8-b541-713ab6b68bcd BE9558 2026-05-16T16:17:53 K558LLAQ.LT0W Long 0.2881 0.2930 0.983 0.2 0
235 fb179c36-fef1-4df6-b0a0-2bd7792ea910 BE9558 2026-05-16T16:23:11 K558LLAQ.UN0W Long 0.2933 0.2979 0.985 0.2 0
236 0cec8793-c3c1-486b-a33a-4dbf9cb069b0 BE9558 2026-05-16T16:33:46 K558LLAR.CA0W Long 0.3017 0.3027 0.997 0.2 0
237 c531b052-ee04-4c66-888b-21e97445a615 BE9558 2026-05-16T16:49:37 K558LLAS.2P0W Long 0.2922 0.2979 0.981 0.2 0
238 0dc402f9-cab6-4c4a-8317-b8fc3c4b1ced BE9558 2026-05-16T16:55:08 K558LLAS.BW0W Long 0.2898 0.2930 0.989 0.2 0
239 fd73733e-b42d-4a51-81d0-4acad6edfbd4 BE9558 2026-05-16T17:16:17 K558LLAT.B50W Long 0.3115 0.3125 0.997 0.2 0
240 6a254daa-ae27-4ac1-b334-58f776a6b276 BE9558 2026-05-16T17:26:51 K558LLAT.SR0W Long 0.3174 0.3223 0.985 0.2 0
241 0cc8e82f-5270-4508-beae-0d3af56095ca BE9558 2026-05-16T17:37:27 K558LLAU.AF0W Long 0.2899 0.2930 0.989 0.2 0
242 00557a73-8dc3-4cd2-b880-c7820dc8b171 BE9558 2026-05-16T17:43:17 K558LLAU.K50W Long 0.2881 0.2930 0.983 0.2 0
243 8e5518d9-ff0a-4c09-9cf9-a681c84c0b6e BE9558 2026-05-16T17:53:53 K558LLAV.1T0W Long 0.2898 0.2930 0.989 0.2 0
244 91dbd7e9-7344-400b-82b1-379f2ac4b49a BE9558 2026-05-16T17:59:11 K558LLAV.AN0W Long 0.2925 0.2930 0.998 0.2 0
245 fa256408-d480-4376-95b5-d1a549c483ea BE9558 2026-05-16T18:04:29 K558LLAV.JH0W Long 0.2977 0.3027 0.983 0.2 0
246 4ab6e66a-8364-49b6-82f1-86e86199e676 BE9558 2026-05-16T18:09:46 K558LLAV.SA0W Long 0.2944 0.2979 0.988 0.2 0
247 0a65fb7c-96e1-4ba0-85de-4620d88658c1 BE9558 2026-05-16T18:15:03 K558LLAW.130W Long 0.2965 0.2979 0.995 0.2 0
248 92b1ef3e-1696-4123-8662-163947737c83 BE9558 2026-05-16T18:20:20 K558LLAW.9W0W Long 0.2976 0.3027 0.983 0.2 0
249 9e3cb24c-4426-4220-9a73-389d0a96c94f BE9558 2026-05-16T18:25:37 K558LLAW.IP0W Long 0.2983 0.3027 0.985 0.2 0
250 a3473215-4c46-45ab-9aa7-1720486ec4a8 BE9558 2026-05-16T18:30:54 K558LLAW.RI0W Long 0.2956 0.2979 0.993 0.2 0
251 449665b1-47c8-4bd2-984c-8f45b627e6d8 BE9558 2026-05-16T18:36:11 K558LLAX.0B0W Long 0.2989 0.3027 0.987 0.2 0
252 600cfabc-cffd-42e1-800c-70e75f312316 BE9558 2026-05-16T20:00:12 K558LLB0.WC0W Long 0.3077 0.3125 0.985 0.2 0
253 12bf5944-0194-4f5b-b486-a7acd7141c7f BE9558 2026-05-16T20:04:28 K558LLB1.3G0W Long 0.2888 0.2930 0.986 0.2 0
254 f1fc913a-91bc-4240-a595-7dfe3091f9a2 BE9558 2026-05-16T20:07:29 K558LLB1.8H0W Long 0.2977 0.3027 0.983 0.2 0
255 e74f7ec3-20bb-4177-beb2-e802e5f801fd BE9558 2026-05-16T20:10:26 K558LLB1.DE0W Long 0.2978 0.3027 0.984 0.2 0
256 27491c3a-b631-4643-818a-714523911178 BE9558 2026-05-16T20:16:21 K558LLB1.N90W Long 0.2921 0.2930 0.997 0.2 0
257 9d9196ce-11e2-4f28-b01c-3dcc60e60d42 BE9558 2026-05-16T20:22:28 K558LLB1.XG0W Long 0.2881 0.2930 0.983 0.2 0
258 a592d7c8-b643-4b25-9186-3d611ae22709 BE9558 2026-05-16T20:25:37 K558LLB2.2P0W Long 0.2893 0.2930 0.987 0.2 0
259 f709a3d5-27cd-4bb1-b91c-669e8ea11d77 BE9558 2026-05-16T20:28:57 K558LLB2.890W Long 0.2880 0.2930 0.983 0.2 0
260 cc9cad87-b87f-4b67-854a-ffb21e876a92 BE9558 2026-05-16T20:32:45 K558LLB2.EL0W Long 0.2873 0.2930 0.980 0.2 0
261 10732d26-6b49-4b14-a474-6c75e259d278 BE9558 2026-05-16T20:49:32 K558LLB3.6K0W Long 0.2881 0.2930 0.983 0.2 0
262 aa63957a-fdb2-4f17-af52-a63bb015119d BE9558 2026-05-16T21:21:45 K558LLB4.O90W Long 0.2880 0.2930 0.983 0.2 0
263 8350440f-019d-45a4-b9f9-23058628e6f0 BE9558 2026-05-16T21:32:43 K558LLB5.6J0W Long 0.2925 0.2930 0.999 0.2 0
264 edfc10b2-dbe1-4527-a061-b0d670b5e5d2 BE9558 2026-05-16T21:35:37 K558LLB5.BD0W Long 0.2891 0.2930 0.987 0.2 0
265 f98001a3-88b2-4830-8112-b7c5164e99c3 BE9558 2026-05-16T21:38:40 K558LLB5.GG0W Long 0.2877 0.2930 0.982 0.2 0
266 f1e53e7b-b124-4fc5-82c2-504fb0c8b80d BE9558 2026-05-16T21:41:46 K558LLB5.LM0W Long 0.2881 0.2930 0.983 0.2 0
267 7890637f-142e-4775-b48c-fd4f3b8ada77 BE9558 2026-05-16T21:45:14 K558LLB5.RE0W Long 0.2880 0.2930 0.983 0.2 0
268 7c97c10f-b413-467c-9b56-c18e423cdcfa BE9558 2026-05-16T21:48:26 K558LLB5.WQ0W Long 0.2886 0.2930 0.985 0.2 0
269 8178cf22-8428-4f85-8199-0a43f7334a8f BE9558 2026-05-16T21:51:47 K558LLB6.2B0W Long 0.2881 0.2930 0.983 0.2 0
270 eceb2997-deb4-403a-802f-50e93d23d423 BE9558 2026-05-16T21:57:18 K558LLB6.BI0W Long 0.2881 0.2930 0.984 0.2 0
271 9c26690c-1983-4b21-b8af-7c7967c014c3 BE9558 2026-05-16T22:12:21 K558LLB7.0L0W Long 0.2876 0.2930 0.982 0.2 0
272 78f67fc3-08b7-4317-96ba-ee5544e0b95b BE9558 2026-05-16T22:19:34 K558LLB7.CM0W Long 0.2880 0.2930 0.983 0.2 0
273 7f9dad14-4f1b-492d-aa26-845a908ea894 BE9558 2026-05-16T22:27:40 K558LLB7.Q40W Long 0.2881 0.2930 0.983 0.2 0
274 f75c52b4-21b9-4065-baeb-5cfbd27d130a BE9558 2026-05-16T22:33:50 K558LLB8.0E0W Long 0.2881 0.2930 0.984 0.2 0
275 10f7496d-fa76-40a7-8bef-dc14e8a41869 BE9558 2026-05-16T22:40:48 K558LLB8.C00W Long 0.2878 0.2930 0.982 0.2 0
scratch/offset_hist_scan.py
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@@ -1,234 +0,0 @@
#!/usr/bin/env python3
"""Offset detector — HISTOGRAM corpus (the other 90% of the archive).
`offset_scan3.py` measures the pre-trigger floor in *waveform* samples. That
covers 6,577 of the archive's 70,112 unique series-3 files; the remaining
63,535 are **histograms**, which carry no samples — only a per-interval,
per-channel peak + half-period. So the pre-trigger method cannot run on them.
The histogram analogue of "the resting floor" is the **low percentile of the
per-interval peaks**. A histogram file is typically hours of continuous
monitoring, so the great majority of its intervals are definitionally quiet;
the bottom of that distribution is what the channel reads when nothing is
happening. A healthy channel bottoms out at 0.000-0.005 in/s. A channel
parked off zero cannot report a peak below its own displacement, so its floor
is pinned up.
⚠ The DC leakage into the histogram peak is PARTIAL. Measured within-unit
against episodes already established from the waveform scan:
BE18438 Vert in-episode 0.0350 vs 0.0050 outside (waveform pre = +0.18..+0.37)
BE12599 Tran in-episode 0.0250 vs 0.0050 outside (waveform pre = +0.03..+0.49)
so the device's per-interval peak is evidently measured against a running /
AC-coupled baseline that removes most, but not all, of the DC. The residual
is real and channel-specific, but the margin is ~5 quantisation counts rather
than the ~70 the waveform detector enjoys. Do not carry the waveform
detector's 0.025 in/s floor across unexamined — calibrate on the CSV.
Because the absolute floor also moves with site noise (traffic, wind, a
generator), the statistic that matters most is the **cross-channel
differential**: a channel's floor minus the quietest of the other two geo
channels in the same file. Site noise lifts all three together and cancels;
a DC offset lifts one.
This script does not decide anything. It emits every candidate statistic per
(file, channel) so thresholds can be calibrated against the waveform-derived
ground truth in `offset_v3.csv` rather than guessed.
Usage:
python scratch/offset_hist_scan.py --dir /home/serversdown/dl2-archive/files \
--out /home/serversdown/dl2-archive/offset_hist.csv --jobs 4
"""
from __future__ import annotations
import argparse
import csv
import datetime
import logging
import re
import statistics
import 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 # noqa: E402
GEO = ("Tran", "Vert", "Long")
K = 10.0 / 32000.0 # ADC count -> in/s (see CLAUDE.md: full scale 32000)
_HIST = re.compile(r"\.[A-Za-z0-9]{2}0[Hh]$")
_STEM = re.compile(r"^([B-Z])(\d{3})")
_B36 = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
_SERIAL_RE = re.compile(rb"\b([A-Z]{2}\d{3,6})\b")
def serial_of(name: str, path=None) -> str:
"""Real serial for a BW file.
The filename encodes only the NUMBER: `<letter><3 digits>` where
letter = chr(ord('B') + serial // 1000). The two-letter family prefix
("BE", "BA", ...) is **not** in the filename, so it must be read out of
the file body. Four units in the DL2 archive are BA, not BE — assuming
"BE" mislabels BA9229, BA10060, BA10895 and BA15957.
"""
m = _STEM.match(name)
if not m:
return "?"
num = (ord(m.group(1)) - ord("B")) * 1000 + int(m.group(2))
if path is not None:
try:
for s in _SERIAL_RE.findall(Path(path).read_bytes()):
s = s.decode()
if s[2:].lstrip("0") == str(num):
return s
except Exception:
pass
return f"BE{num}" # last-resort fallback; prefix unverified
def stem_time(name: str):
"""Decode the filename's base-36 timestamp. Epoch 1985-01-01, 1296 s/tick.
Preferred over the file's own footer timestamp only because it costs
nothing; the caller falls back to the decoded event when this fails.
"""
try:
base, ext = name.rsplit(".", 1)
n = 0
for c in base[4:8].upper():
n = n * 36 + _B36.index(c)
ab = _B36.index(ext[0].upper()) * 36 + _B36.index(ext[1].upper())
return datetime.datetime(1985, 1, 1) + datetime.timedelta(seconds=n * 1296 + ab)
except Exception:
return None
def _pct(sorted_vals, q):
"""Nearest-rank percentile on an already-sorted list."""
if not sorted_vals:
return None
i = min(len(sorted_vals) - 1, max(0, int(len(sorted_vals) * q / 100.0)))
return sorted_vals[i]
def scan(path_str: str):
logging.disable(logging.WARNING) # per-worker: the codec warns on undecodables
p = Path(path_str)
try:
ev = read_blastware_file(p)
except Exception:
return None
s = ev.raw_samples or {}
if not any(s.get(c) for c in GEO):
return None
ts = stem_time(p.name) or ev.timestamp
stamp = ""
if ts is not None:
stamp = (f"{ts.year:04d}-{ts.month:02d}-{ts.day:02d}T"
f"{ts.hour:02d}:{ts.minute:02d}:{ts.second:02d}")
# Per-channel floor candidates, in in/s.
stats = {}
for ch in GEO:
v = sorted(s.get(ch) or [])
if not v:
continue
stats[ch] = {
"n": len(v),
"min": v[0] * K,
"p1": _pct(v, 1) * K,
"p5": _pct(v, 5) * K,
"p10": _pct(v, 10) * K,
"p25": _pct(v, 25) * K,
"med": statistics.median(v) * K,
"peak": v[-1] * K,
"zeros": sum(1 for x in v if x == 0) / len(v),
}
if len(stats) < 2: # need at least one sibling channel for the differential
return None
# Mic floor as a site-noise proxy (raw counts; the dB conversion is not
# needed — only its relative movement matters here).
mic = sorted(s.get("MicL") or [])
mic_p5 = _pct(mic, 5) if mic else ""
rows = []
for ch, st in stats.items():
others = [stats[o]["p5"] for o in stats if o != ch]
rows.append({
"serial": serial_of(p.name, p),
"timestamp": stamp,
"filename": p.name,
"channel": ch,
"n_intervals": st["n"],
"min": round(st["min"], 4),
"p1": round(st["p1"], 4),
"p5": round(st["p5"], 4),
"p10": round(st["p10"], 4),
"p25": round(st["p25"], 4),
"median": round(st["med"], 4),
"peak": round(st["peak"], 4),
"frac_zero": round(st["zeros"], 4),
# the site-noise-cancelling statistic: this channel's floor above
# the quietest sibling geo channel in the same file
"diff_p5": round(st["p5"] - min(others), 4),
"mic_p5": mic_p5,
})
return rows
COLS = ["serial", "timestamp", "filename", "channel", "n_intervals",
"min", "p1", "p5", "p10", "p25", "median", "peak", "frac_zero",
"diff_p5", "mic_p5"]
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--dir", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--jobs", type=int, default=4)
ap.add_argument("--limit", type=int, default=0, help="stop after N files (smoke test)")
a = ap.parse_args()
# Dedupe by basename — the DL2 export keeps a byte-identical `Sent/`
# mirror of its root, which doubled two figures before it was caught.
seen, files = set(), []
for q in sorted(Path(a.dir).rglob("*")):
if q.is_file() and _HIST.search(q.name) and q.name not in seen:
seen.add(q.name)
files.append(str(q))
if a.limit:
files = files[:a.limit]
print(f"unique histogram binaries: {len(files)}", flush=True)
rows, undecodable = [], 0
with ProcessPoolExecutor(max_workers=a.jobs) as ex:
futs = [ex.submit(scan, f) for f in files]
for i, fut in enumerate(as_completed(futs), 1):
r = fut.result()
if r:
rows.extend(r)
else:
undecodable += 1
if i % 5000 == 0:
print(f" {i}/{len(files)}", flush=True)
with open(a.out, "w", newline="") as fh:
w = csv.DictWriter(fh, fieldnames=COLS)
w.writeheader()
w.writerows(rows)
files_ok = len({r["filename"] for r in rows})
units = len({r["serial"] for r in rows})
ivals = sum(r["n_intervals"] for r in rows) // 3
print(f"\ndecoded {files_ok}/{len(files)} files "
f"({undecodable} undecodable), {units} units, ~{ivals/1e6:.1f}M intervals")
print(f"wrote {a.out} ({len(rows)} channel-rows)")
if __name__ == "__main__":
main()
scratch/offset_scan.py
-171
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@@ -1,171 +0,0 @@
#!/usr/bin/env python3
"""Scan series-3 waveform binaries for the 'offset' hardware fault.
A healthy geophone trace is centred on zero. An offset unit sits displaced,
so the channel mean approaches its own peak. Detector (unchanged from the
2026-08-25 run, see memory note `offset-archive-analysis-backlog`):
dominant-axis |mean| / peak > 0.7
AND |mean| >= 0.9 * the unit's geo trigger level
Trigger level is read from a paired _ASCII.TXT where one exists, otherwise
from a per-serial median learned across that unit's ASCII files, otherwise
--default-trigger.
Serial is decoded from the BW filename: prefix letter encodes thousands
(chr(ord('B') + n)), next 3 digits the remainder -- T193 -> BE18193.
Usage:
python scratch/offset_scan.py --dir <path> [--jobs N] --out offsets.csv
"""
from __future__ import annotations
import argparse, csv, json, re, sys
from collections import defaultdict
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
from minimateplus.bw_ascii_report import parse_report
GEO = ("Tran", "Vert", "Long")
_GEO_FS_COUNTS = 32000.0
_WAVE_RE = re.compile(r"\.[A-Za-z0-9]{2}0[Ww]$")
_STEM_RE = re.compile(r"^([B-Z])(\d{3})")
MEAN_OVER_PEAK_MIN = 0.7
TRIGGER_FRACTION = 0.9
def serial_from_name(name: str):
m = _STEM_RE.match(name)
if not m:
return None
letter, digits = m.group(1), m.group(2)
return f"BE{(ord(letter) - ord('B')) * 1000 + int(digits)}"
def counts_to_ips(c, gr):
return c * (gr or 10.0) / _GEO_FS_COUNTS
def scan_one(path_str: str, default_trigger: float) -> dict | None:
p = Path(path_str)
try:
gr, trig = 10.0, None
ap = p.with_name(p.name.replace(".", "_", 1) + "_ASCII.TXT") \
if False else p.parent / (p.stem + "_" + p.suffix.lstrip(".") + "_ASCII.TXT")
if ap.exists():
rep = parse_report(ap.read_text(errors="replace"))
gr = rep.geo_range_ips or 10.0
trig = rep.geo_trigger_level_ips
ev = read_blastware_file(p)
s = ev.raw_samples or {}
if not all(s.get(c) for c in GEO):
return None
best = None
for ch in GEO:
arr = s[ch]
n = len(arr)
if n == 0:
continue
mean = sum(arr) / n
peak = max(abs(v) for v in arr)
if peak == 0:
continue
ratio = abs(mean) / peak
if best is None or peak > best["peak_counts"]:
best = {"channel": ch, "mean_counts": mean,
"peak_counts": peak, "ratio": ratio}
if best is None:
return None
ts = ev.timestamp
return {
"serial": serial_from_name(p.name) or "?",
"timestamp": (f"{ts.year:04d}-{ts.month:02d}-{ts.day:02d}T"
f"{ts.hour:02d}:{ts.minute:02d}:{ts.second:02d}") if ts else "",
"filename": p.name,
"channel": best["channel"],
"offset_ips": round(counts_to_ips(best["mean_counts"], gr), 4),
"peak_ips": round(counts_to_ips(best["peak_counts"], gr), 4),
"mean_over_peak": round(best["ratio"], 3),
"trigger_level_ips": trig if trig is not None else "",
"geo_range_ips": gr,
}
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("--limit", type=int, default=0)
ap.add_argument("--default-trigger", type=float, default=0.2)
ap.add_argument("--out", required=True)
a = ap.parse_args()
# The DL2 export keeps a byte-identical `Sent/` mirror of the root, so
# enumerate paths but keep only the first occurrence of each basename —
# otherwise every event is counted twice.
seen = set()
files = []
for q in sorted(Path(a.dir).rglob("*")):
if q.is_file() and _WAVE_RE.search(q.name) and q.name not in seen:
seen.add(q.name)
files.append(q)
if a.limit:
files = files[: a.limit]
print(f"waveform binaries to scan: {len(files)}", flush=True)
rows = []
with ProcessPoolExecutor(max_workers=a.jobs) as ex:
futs = [ex.submit(scan_one, str(p), a.default_trigger) for p in files]
for n, f in enumerate(as_completed(futs), 1):
r = f.result()
if r:
rows.append(r)
if n % 2000 == 0:
print(f" {n}/{len(files)}", flush=True)
# learn per-serial trigger levels from the rows that had an ASCII
by_serial = defaultdict(list)
for r in rows:
if r["trigger_level_ips"] != "":
by_serial[r["serial"]].append(float(r["trigger_level_ips"]))
med = {}
for k, v in by_serial.items():
v.sort()
med[k] = v[len(v) // 2]
for r in rows:
if r["trigger_level_ips"] == "":
r["trigger_level_ips"] = med.get(r["serial"], a.default_trigger)
r["suspect"] = int(
r["mean_over_peak"] > MEAN_OVER_PEAK_MIN
and abs(r["offset_ips"]) >= TRIGGER_FRACTION * float(r["trigger_level_ips"])
)
cols = ["serial", "timestamp", "filename", "channel", "offset_ips", "peak_ips",
"mean_over_peak", "trigger_level_ips", "geo_range_ips", "suspect"]
with open(a.out, "w", newline="") as fh:
w = csv.DictWriter(fh, fieldnames=cols)
w.writeheader()
w.writerows(rows)
sus = [r for r in rows if r["suspect"]]
print(f"\nscanned {len(rows)} decodable waveforms")
print(f"suspect events: {len(sus)}")
per = defaultdict(int)
for r in sus:
per[r["serial"]] += 1
print(f"units with >=1 suspect event: {len(per)} of {len({r['serial'] for r in rows})}")
for s, n in sorted(per.items(), key=lambda x: -x[1])[:20]:
print(f" {s:10} {n}")
print(f"\nwrote {a.out}")
if __name__ == "__main__":
main()
scratch/offset_scan2.py
-108
View File
@@ -1,108 +0,0 @@
#!/usr/bin/env python3
"""Offset detector v2 — per-channel MEDIAN pedestal.
Supersedes the dominant-axis / mean detector in offset_scan.py, which had two
flaws that manufactured false "recoveries":
1. It scored only the axis with the largest peak, so a real event on one axis
hid a persistent pedestal on another. BE12599 2026-08-21 read "clean"
because Long had a 1.065 in/s event, while Tran sat at +0.47 in/s.
2. It used the MEAN, which a real transient perturbs. The median is the
resting baseline: most samples sit at it, so a blast does not move it.
Same event, Long: mean +0.0783 vs median -0.0050.
Flags a CHANNEL when |median| >= --floor in/s (default 0.025 = 5 A/D counts,
Instantel's own criterion; 1 A/D count = 0.005 in/s).
Emits one row per (event, channel) so persistence can be tracked per channel.
"""
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 # ADC counts -> in/s at the 10 in/s range
_WAVE_RE = re.compile(r"\.[A-Za-z0-9]{2}0[Ww]$")
_STEM_RE = re.compile(r"^([B-Z])(\d{3})")
def serial_from_name(n):
m = _STEM_RE.match(n)
return f"BE{(ord(m.group(1))-ord('B'))*1000+int(m.group(2))}" if m else "?"
def scan_one(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
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]
out.append({
"serial": serial_from_name(p.name), "timestamp": stamp,
"filename": p.name, "channel": ch,
"median_ips": round(statistics.median(a) * K, 4),
"mean_ips": round(statistics.fmean(a) * K, 4),
"peak_ips": round(max(abs(v) for v 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("--out", required=True)
a = ap.parse_args()
seen, files = set(), []
for q in sorted(Path(a.dir).rglob("*")):
if q.is_file() and _WAVE_RE.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 n, f in enumerate(as_completed([ex.submit(scan_one, p) for p in files]), 1):
r = f.result()
if r: rows.extend(r)
if n % 2000 == 0: print(f" {n}/{len(files)}", flush=True)
for r in rows:
r["offset"] = int(abs(r["median_ips"]) >= a.floor)
cols = ["serial","timestamp","filename","channel","median_ips","mean_ips","peak_ips","offset"]
with open(a.out, "w", newline="") as fh:
w = csv.DictWriter(fh, fieldnames=cols); w.writeheader(); w.writerows(rows)
from collections import defaultdict
ev_flagged = {(r["serial"], r["filename"]) for r in rows if r["offset"]}
ev_all = {(r["serial"], r["filename"]) for r in rows}
per = defaultdict(set)
for r in rows:
if r["offset"]: per[r["serial"]].add(r["filename"])
tot = defaultdict(set)
for r in rows: tot[r["serial"]].add(r["filename"])
print(f"\nfloor = {a.floor} in/s ({a.floor/0.005:.0f} A/D counts)")
print(f"events with >=1 offset channel: {len(ev_flagged)} of {len(ev_all)}")
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()

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