250 lines
9.8 KiB
Python
250 lines
9.8 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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import argparse
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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 signal
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import statistics
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import threading
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import time
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import numpy as np
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from python_app.logging_setup import coerce_level
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from python_app.models.dataset_model import ComboKey, SweepCollection, TraceData
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from python_app.models.run_config_model import RunConfigModel
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from python_app.orchestration.shm import ShmRingWriter
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from python_app.storage.npz.serialize import RAW_MAGIC, 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[str, int]] = []
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for entry in sorted(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((entry.name, int(match.group(1))))
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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 named:
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folder = dataset_dir / name
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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).astype(np.complex64, copy=False)
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freq = np.load(freq_path).astype(np.float32, copy=False)
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if s21.size != freq.size:
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raise RuntimeError(f"{s21_path}: s21 size {s21.size} != freq size {freq.size}")
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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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traces.append(
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TraceData(
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combo=combo,
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frequency_hz=freq,
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s11=np.zeros_like(s21),
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s21=s21,
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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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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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traces = []
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for source in dataset.snapshots[index]:
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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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s11=source.s11,
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s21=source.s21,
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capture_start_ns=start_ns,
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capture_end_ns=end_ns,
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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=start_ns,
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capture_end_ns=end_ns,
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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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parser = argparse.ArgumentParser(description="Replay a recorded dataset to raw SHM rings")
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parser.add_argument("--config", required=True, type=Path, help="Path to run_config.json")
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s")
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stop_requested = threading.Event()
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def request_stop(_signum: int, _frame: object) -> None:
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stop_requested.set()
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signal.signal(signal.SIGINT, request_stop)
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signal.signal(signal.SIGTERM, request_stop)
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config = RunConfigModel.load_from_path(args.config)
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config.apply_device_model_constraints()
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logging.getLogger("python_app").setLevel(coerce_level(config.logging.level))
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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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raw_writer = ShmRingWriter(
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config.rings.raw.name,
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config.rings.raw.capacity,
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config.rings.raw.slot_size_bytes,
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)
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raw_tap_writer = ShmRingWriter(
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config.rings.raw_tap.name,
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config.rings.raw_tap.capacity,
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config.rings.raw_tap.slot_size_bytes,
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)
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try:
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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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publish_failures = 0
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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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payload = serialize_trace_collection(collection, RAW_MAGIC)
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if raw_writer.push(payload):
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raw_tap_writer.push(payload) # best-effort GUI tap; never fatal
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else:
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publish_failures += 1
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if publish_failures == 1 or publish_failures % 100 == 0:
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logger.error(
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"Replay payload %d B exceeds ring slot %d B; dropping collection %d (drops=%d)",
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len(payload), raw_writer.slot_size_bytes, collection_id, publish_failures,
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)
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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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raw_tap_writer.close()
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raw_writer.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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