codec-re: channel rotation CONFIRMED — full multi-channel decoder works
The segment-channel scoring analyzer (from scratch/next_experiment_skeleton.py) ran and immediately confirmed the rotation hypothesis: SP0 seg 0: best fit Vert 508/508 ✓ SP0 seg 1: best fit Long 508/508 ✓ SP0 seg 3: best fit Tran 508/508 ✓ (Tran continuation) SP0 seg 5: best fit Long 508/508 ✓ SP0 seg 9: best fit Long 508/508 ✓ V70 seg 0: best fit Vert 508/508 ✓ V70 seg 1: best fit Long 508/508 ✓ Channels rotate Tran → Vert → Long → MicL per 40 02 segment header. Also discovered the segment header has DOUBLE duty: bytes [14:18] anchor the NEW segment's channel (2 samples as int16 BE in 16-count units), AND bytes [0:4] extend the PREVIOUS channel by 2 more samples (2 deltas as int16 BE). This is the same "2 anchors + delta stream" structure as the body preamble for Tran. decode_waveform_v2 now returns full per-channel sample dicts. Byte-exact verified ranges: V70: Tran 512, Vert 512, Long 512 (all first segments) JQ0: Tran 512, Vert 258 SP0: Long 1536 (all 3 L segments) Still open: the 30 NN block format (high-amplitude packed deltas) — appears mid-segment when single-byte deltas can't carry the magnitude. 6 new tests bring the count to 46. All passing.
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Claude
@@ -263,29 +263,62 @@ def score_against_truth(
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def score_segment_against_all_channels(
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event: FixtureEvent,
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segment_index: int,
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) -> List[Tuple[str, str, int, int, int]]:
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"""For segment *segment_index* of *event*, try decoding it as each channel
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with each candidate anchor source.
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) -> List[Tuple[str, int, int, int]]:
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"""For segment *segment_index* of *event*, find the best (channel, start_sample)
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fit.
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Returns rows of (channel_name, anchor_source_label, anchor_value, n_matches, n_compared)
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sorted by match count descending.
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For each candidate channel C and each candidate starting truth-sample index s,
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we pick the anchor that makes the FIRST decoded value match truth[C][s], then
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score the remaining decoded values against truth[C][s+1 : s+N].
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Anchor source candidates to try:
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- "header[0:2]" int16 BE from segment header bytes [0:2]
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- "header[2:4]" int16 BE from segment header bytes [2:4]
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- "header[4:6]" int16 BE from segment header bytes [4:6]
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- "header[14:16]" int16 BE from segment header bytes [14:16]
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- "header[16:18]" int16 BE from segment header bytes [16:18]
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- "channel[0]" truth[channel][0] (= "this segment starts at sample 0 of this channel")
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- "channel[prev]" truth[channel][segment_sample_starts[segment_index] - 1]
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(= "this segment continues from sample N-1 of this channel")
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For each combination of (channel, anchor source, "starts at sample X of channel"),
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decode the segment and score against truth.
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TODO: implement this — it's the heart of the experiment.
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Returns rows of (channel_name, start_sample, n_matches, n_compared)
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sorted by match-count descending.
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"""
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raise NotImplementedError("This is the next experiment to run.")
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# Block range of this segment: from the segment header (inclusive) up to
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# the next segment header (exclusive), or end-of-blocks.
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seg_header_idx = event.segment_starts[segment_index]
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next_header_idx = (
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event.segment_starts[segment_index + 1]
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if segment_index + 1 < len(event.segment_starts)
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else len(event.blocks)
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)
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# Decode the segment's data blocks (skip the segment-header block itself).
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# Use anchor=0 — we'll re-anchor when scoring against each channel.
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deltas_trajectory = decode_segment_as_channel(
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event.blocks, seg_header_idx + 1, next_header_idx, anchor=0
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)
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if not deltas_trajectory:
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return []
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n = len(deltas_trajectory)
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results = []
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for ch in ("Tran", "Vert", "Long"):
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truth = event.truth.get(ch)
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if not truth or len(truth) < n + 1:
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continue
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# For each candidate starting sample s in truth, check if applying
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# the deltas starting from truth[s] reproduces truth[s+1:s+n+1].
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best = (0, -1)
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for s in range(len(truth) - n):
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anchor = truth[s]
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offset = anchor - deltas_trajectory[0] + truth[s + 1] - anchor
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# Recompute: trajectory[i] = anchor + cumulative_delta_through_i
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# but we already have deltas_trajectory computed from anchor=0,
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# so trajectory_relative[i] = anchor + deltas_trajectory[i].
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matches = 0
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for i in range(n):
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if truth[s + i + 1] == anchor + deltas_trajectory[i]:
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matches += 1
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# Note: we could break early on first mismatch for "matches start",
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# but counting total matches gives a more robust score.
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if matches > best[0]:
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best = (matches, s)
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results.append((ch, best[1], best[0], n))
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results.sort(key=lambda r: -r[2])
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return results
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# ── Driver ──────────────────────────────────────────────────────────────────
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@@ -310,11 +343,17 @@ def main():
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for si, sample_start in enumerate(event.segment_sample_starts):
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print(f" seg {si}: sample {sample_start}")
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# When score_segment_against_all_channels is implemented:
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# for si in range(len(event.segment_starts)):
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# results = score_segment_against_all_channels(event, si)
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# best = results[0]
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# print(f" seg {si}: best fit = {best}")
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for si in range(len(event.segment_starts)):
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results = score_segment_against_all_channels(event, si)
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if not results:
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print(f" seg {si}: (no scorable data)")
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continue
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tag = "✓" if results[0][2] / max(results[0][3], 1) > 0.9 else " "
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top = results[0]
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print(f" seg {si}: best fit {tag} = {top[0]:<5} "
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f"starting at sample {top[1]:>5}, {top[2]:>4}/{top[3]:<4} match"
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+ (f" (next: {results[1][0]} @{results[1][1]} {results[1][2]}/{results[1][3]})"
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if len(results) > 1 else ""))
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if __name__ == "__main__":
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