258 lines
11 KiB
Python
258 lines
11 KiB
Python
"""Raw producer that replays a recorded snapshot dataset (no hardware).
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Selected by the supervisor instead of the model's normal acquisition producer
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when ``radar.replay.dataset_dir`` is non-empty. The dataset is a directory of
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snapshot folders named ``NNNN_idK_ns<timestamp>`` (the VNA_AlGo
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preprocessed-capture layout), each holding ``i{input}_o{output}_s21.npy`` and
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``i{input}_o{output}_freq.npy`` files for every run combo.
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The recorded data is already background-subtracted, so the profile must pair
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this producer with NEUTRAL preprocess sets (calibration = 1, reference = 0,
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no notch) — then the C++ preprocessor passes the traces through bit-exactly
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and the processors (e.g. sar3d) see the recorded scene.
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Timeline: snapshot timestamps are re-anchored to this process's monotonic
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clock, preserving the recorded inter-frame gaps. On loop, the seam advances by
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the median inter-frame gap, so motion-model consumers (sar3d aperture offsets)
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never see time jump backwards. ``collection_id`` increases forever.
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"""
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from __future__ import annotations
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from contextlib import ExitStack
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import json
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import logging
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from pathlib import Path
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import re
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import statistics
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import time
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import numpy as np
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from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
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from python_app.orchestration.shm.decoder import RAW_MAGIC
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from python_app.scripts.producer_common import RawPublisher, load_producer_config
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from python_app.storage.npz.serialize import serialize_trace_collection
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logger = logging.getLogger(__name__)
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_SNAPSHOT_NAME = re.compile(r"^(\d+)_id\d+_ns(\d+)$")
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class ReplayDataset:
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"""Recorded snapshots preloaded into memory, with their recorded timeline."""
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def __init__(self, snapshots: list[list[TraceData]], timestamps_ns: list[int]) -> None:
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self.snapshots = snapshots
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self.timestamps_ns = timestamps_ns
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gaps = [b - a for a, b in zip(timestamps_ns, timestamps_ns[1:])]
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# Median gap doubles as the loop-seam step and the capture-window span;
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# a single-snapshot dataset falls back to an arbitrary sane cadence.
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self.median_gap_ns = int(statistics.median(gaps)) if gaps else 160_000_000
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def __len__(self) -> int:
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return len(self.snapshots)
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def _load_dataset(dataset_dir: Path, combos: list[ComboKey], expected_points: int) -> ReplayDataset:
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"""Preload every snapshot's traces; fail loudly on a malformed dataset."""
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if not dataset_dir.is_dir():
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raise RuntimeError(f"radar.replay.dataset_dir is not a directory: {dataset_dir}")
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named: list[tuple[int, str, int]] = []
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for entry in dataset_dir.iterdir():
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match = _SNAPSHOT_NAME.match(entry.name)
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if entry.is_dir() and match:
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named.append((int(match.group(1)), entry.name, int(match.group(2))))
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if not named:
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raise RuntimeError(
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f"No snapshot folders (NNNN_idK_ns<ts>) found in {dataset_dir}"
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)
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snapshots: list[list[TraceData]] = []
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timestamps_ns: list[int] = []
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for _, name, timestamp_ns in sorted(named):
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folder = dataset_dir / name
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# Recorded per-trace capture windows from the snapshot's meta.json: the
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# sar3d segmented timing model reconstructs the acquisition segments
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# from them. Missing/partial meta leaves the zero "unmeasured" sentinel
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# and the consumer falls back to its uniform-segment model.
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windows: dict[tuple[int, int], tuple[int, int]] = {}
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meta_path = folder / "meta.json"
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if meta_path.is_file():
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try:
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meta = json.loads(meta_path.read_text())
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for entry in meta.get("traces", []):
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windows[(int(entry["input"]), int(entry["output"]))] = (
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int(entry["capture_start_ns"]),
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int(entry["capture_end_ns"]),
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)
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except (OSError, ValueError, KeyError, TypeError):
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windows = {}
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traces: list[TraceData] = []
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for combo in combos:
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s21_path = folder / f"i{combo.input}_o{combo.output}_s21.npy"
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freq_path = folder / f"i{combo.input}_o{combo.output}_freq.npy"
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if not s21_path.is_file() or not freq_path.is_file():
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raise RuntimeError(
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f"Snapshot {name} is missing files for combo "
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f"input={combo.input} output={combo.output}: expected "
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f"{s21_path.name} and {freq_path.name}. Check run.combos vs the dataset."
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)
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s21 = np.load(s21_path, allow_pickle=False).astype(np.complex64, copy=False)
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freq = np.load(freq_path, allow_pickle=False).astype(np.float32, copy=False)
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if freq.ndim != 1 or s21.shape != freq.shape:
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raise RuntimeError(f"Snapshot {name}: frequency and S21 must be equally sized 1D arrays")
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if expected_points and s21.size != expected_points:
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raise RuntimeError(
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f"{s21_path}: {s21.size} points, but radar.sweep.points = "
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f"{expected_points}. Align the profile's sweep with the dataset."
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)
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window = windows.get((combo.input, combo.output), (0, 0))
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traces.append(
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TraceData(
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combo=combo,
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frequency_hz=freq,
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s21=s21,
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capture_start_ns=window[0],
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capture_end_ns=window[1],
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)
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)
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snapshots.append(traces)
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timestamps_ns.append(timestamp_ns)
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return ReplayDataset(snapshots, timestamps_ns)
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def _build_collection(
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dataset: ReplayDataset,
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index: int,
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collection_id: int,
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frame_ns: int,
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) -> SweepCollection:
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"""Wrap one preloaded snapshot in a SweepCollection at the given frame time.
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Recorded per-trace capture windows are re-anchored by the same shift as the
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frame time, preserving the real intra-frame segment layout. Traces without
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recorded windows get a synthetic window spanning the median frame gap.
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"""
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span_ns = dataset.median_gap_ns
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start_ns = frame_ns - span_ns // 2
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end_ns = frame_ns + span_ns // 2
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shift_ns = frame_ns - dataset.timestamps_ns[index]
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traces = []
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for source in dataset.snapshots[index]:
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if source.capture_end_ns > source.capture_start_ns:
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capture = (source.capture_start_ns + shift_ns, source.capture_end_ns + shift_ns)
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else:
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capture = (start_ns, end_ns)
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traces.append(
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TraceData(
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combo=source.combo,
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frequency_hz=source.frequency_hz,
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s21=source.s21,
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capture_start_ns=capture[0],
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capture_end_ns=capture[1],
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)
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)
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return SweepCollection(
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collection_id=collection_id,
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monotonic_ns=frame_ns,
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traces=traces,
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capture_start_ns=min(t.capture_start_ns for t in traces),
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capture_end_ns=max(t.capture_end_ns for t in traces),
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)
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def main() -> int:
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"""Replay the configured dataset into the raw SHM rings until stopped."""
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_, config, stop_requested = load_producer_config("Replay a recorded dataset to raw SHM rings")
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replay = config.radar.replay
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if not replay.dataset_dir:
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raise RuntimeError("replay_raw_producer requires a non-empty radar.replay.dataset_dir")
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rate_scale = float(replay.rate_scale) if replay.rate_scale > 0 else 1.0
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combos = [ComboKey(input=combo.input, output=combo.output) for combo in config.combos]
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# expanduser: profiles are copied between machines, so "~/..." dataset paths
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# must resolve against the local home instead of failing as a literal "~".
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dataset = _load_dataset(
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Path(replay.dataset_dir).expanduser(), combos, int(config.radar.sweep.points)
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)
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logger.info(
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"Replay producer starting: dataset=%s, snapshots=%d, combos=%d, "
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"median_gap=%.3f s, loop=%s, rate_scale=%.2f",
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replay.dataset_dir, len(dataset), len(combos),
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dataset.median_gap_ns / 1e9, replay.loop, rate_scale,
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)
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cleanup = ExitStack()
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try:
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publisher = RawPublisher(config.rings, cleanup)
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payload_size = len(serialize_trace_collection(
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_build_collection(dataset, 0, 1, time.monotonic_ns()), RAW_MAGIC, require_capture_window=True,
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))
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for name in ("raw", "raw_tap", "preprocessed", "preprocessed_tap"):
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ring = getattr(config.rings, name)
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if name.endswith("_tap") and not ring.name:
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continue
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if ring.slot_size_bytes < payload_size:
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raise ValueError(f"Replay requires {payload_size} bytes per slot; ring {ring.name} has {ring.slot_size_bytes}")
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base_ns = time.monotonic_ns()
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wall_start_s = time.monotonic()
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first_ts = dataset.timestamps_ns[0]
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cycle_span_ns = dataset.timestamps_ns[-1] - first_ts + dataset.median_gap_ns
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collection_id = 1
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cycle = 0
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while not stop_requested.is_set():
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for index in range(len(dataset)):
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if stop_requested.is_set():
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break
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# Monotonic replay timeline: recorded offsets re-anchored at
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# base_ns, each loop shifted by one full cycle span.
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frame_ns = (
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base_ns
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+ (dataset.timestamps_ns[index] - first_ts)
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+ cycle * cycle_span_ns
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)
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# Publish on the recorded cadence (scaled); waiting via the stop
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# event keeps shutdown prompt.
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deadline_s = wall_start_s + (frame_ns - base_ns) / 1e9 / rate_scale
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delay_s = deadline_s - time.monotonic()
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if delay_s > 0 and stop_requested.wait(delay_s):
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break
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collection = _build_collection(dataset, index, collection_id, frame_ns)
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# Acquisition identity uses real monotonic time, even at accelerated
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# replay rates. Keep the recorded duration for legacy GPR and the
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# virtual per-trace timeline for SAR aperture/motion reconstruction.
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duration_ns = collection.capture_end_ns - collection.capture_start_ns
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collection.capture_start_ns = time.monotonic_ns()
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collection.capture_end_ns = collection.capture_start_ns + duration_ns
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if not publisher.publish(collection):
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raise RuntimeError(f"Replay raw ring rejected collection {collection_id}")
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if collection_id == 1 or collection_id % 50 == 0:
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logger.info(
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"replay collection %d published (snapshot %d/%d, cycle %d)",
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collection_id, index + 1, len(dataset), cycle,
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)
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collection_id += 1
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if not config.runtime.continuous:
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stop_requested.set()
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break
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if not replay.loop:
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break
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cycle += 1
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finally:
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cleanup.close()
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logger.info("replay raw producer stopped")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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