677 lines
31 KiB
Python
677 lines
31 KiB
Python
"""Concrete :class:`StoreApi` implementation backed by NPZ files."""
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from __future__ import annotations
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from contextlib import suppress
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from datetime import datetime, timezone
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import json
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import logging
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import os
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from pathlib import Path
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import shutil
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import tempfile
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from typing import Any
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from zipfile import BadZipFile
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import numpy as np
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from python_app.models.dataset_model import ComboKey, ResultCollection, SweepCollection, TraceData
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from python_app.models.run_config_model import RunConfigModel
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from python_app.storage.npz.paths import radar_key_from_config, sanitize_path_component
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from python_app.storage.npz.serialize import PREPROC_MAGIC, RAW_MAGIC, serialize_trace_collection, validate_trace_collection
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from python_app.storage.npz.snapshot_numpy import (
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save_result_history_binary,
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save_result_history_numpy,
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save_trace_history_binary,
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save_trace_history_numpy,
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select_aligned_histories,
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atomic_snapshot_directory,
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)
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from python_app.storage.npz.vna_history_json import build_vna_history_payload
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from python_app.storage.store_api import StoreApi
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logger = logging.getLogger(__name__)
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_SET_METADATA_KEY = "metadata_json"
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def _compose_snapshot_stem(name: str, name_prefix: str) -> str:
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"""Compose a sanitized snapshot stem from an optional prefix and the operator's name.
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The name falls back to a UTC timestamp when blank, so a save is always uniquely named.
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"""
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base = name.strip() or datetime.now(timezone.utc).strftime("snapshot_%Y%m%d_%H%M%S")
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return sanitize_path_component(f"{name_prefix}_{base}" if name_prefix else base)
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class SnapshotStreamWriter:
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"""Append received raw/preprocessed/result collections to a snapshot dir in chunks.
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A streaming alternative to :meth:`NpzStore.save_runtime_snapshot_numpy` for long
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recordings: the caller flushes small chunks as measurements arrive and frees them,
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so memory stays flat instead of buffering every measurement. The on-disk layout is
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identical (``raw``/``preprocessed``/``results`` subtrees), and per-collection
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directory indices continue across chunks via a running offset per stage.
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"""
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__slots__ = ("_dir", "_raw_written", "_preprocessed_written", "_result_written")
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def __init__(self, snapshot_dir: Path) -> None:
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self._dir = snapshot_dir
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self._raw_written = 0
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self._preprocessed_written = 0
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self._result_written = 0
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@property
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def directory(self) -> Path:
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return self._dir
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@property
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def result_count(self) -> int:
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"""Number of result collections written to disk so far."""
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return self._result_written
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def append(
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self,
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raw: list[SweepCollection],
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preprocessed: list[SweepCollection],
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results: list[ResultCollection],
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) -> None:
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"""Write a chunk without requiring equal stage lengths, continuing each index."""
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# Stage a whole chunk before publishing complete collection directories. A
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# failed write leaves earlier chunks intact and the next retry uses the same indices.
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chunk_dir = Path(tempfile.mkdtemp(prefix=".chunk-", dir=self._dir))
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published: list[Path] = []
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try:
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save_trace_history_numpy(chunk_dir / "raw", raw, index_offset=self._raw_written)
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save_trace_history_numpy(
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chunk_dir / "preprocessed", preprocessed, index_offset=self._preprocessed_written
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)
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save_result_history_numpy(chunk_dir / "results", results, index_offset=self._result_written)
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for stage in ("raw", "preprocessed", "results"):
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stage_dir = self._dir / stage
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stage_dir.mkdir(exist_ok=True)
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for source in sorted((chunk_dir / stage).iterdir()):
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destination = stage_dir / source.name
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if destination.exists():
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raise FileExistsError(f"Collection directory already exists: {destination}")
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source.rename(destination)
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published.append(destination)
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except BaseException:
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for destination in published:
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shutil.rmtree(destination)
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raise
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finally:
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shutil.rmtree(chunk_dir)
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self._raw_written += len(raw)
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self._preprocessed_written += len(preprocessed)
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self._result_written += len(results)
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class NpzStore(StoreApi):
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"""Persist preprocess sets and runtime snapshots using NumPy files."""
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def __init__(self, root_dir: Path) -> None:
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"""Create store rooted at `root_dir`."""
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self._root_dir = root_dir
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self._root_dir.mkdir(parents=True, exist_ok=True)
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logger.debug("NpzStore rooted at %s", self._root_dir)
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@staticmethod
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def _vna_json_output_dir(output_root_dir: Path, output_stem: str) -> Path:
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"""Return per-export directory for VNA history JSON files."""
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return output_root_dir / output_stem
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def save_set(self, kind: str, radar_key: str, set_name: str, collection: SweepCollection) -> None:
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"""Persist named preprocess set as NPZ and metadata JSON."""
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self._validate_set_name(set_name)
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validate_trace_collection(collection, require_finite=True)
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set_dir = self._set_dir(kind, radar_key)
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set_dir.mkdir(parents=True, exist_ok=True)
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npz_path = set_dir / f"{set_name}.npz"
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meta_path = set_dir / f"{set_name}.json"
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payload: dict[str, Any] = {}
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combo_records: list[dict[str, str | int]] = []
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for trace in collection.traces:
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suffix = f"i{trace.combo.input}_o{trace.combo.output}"
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freq_key = f"freq_{suffix}"
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s11_key = f"s11_{suffix}"
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s21_key = f"s21_{suffix}"
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payload[freq_key] = np.asarray(trace.frequency_hz, dtype=np.float32)
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payload[s11_key] = np.asarray(trace.s11, dtype=np.complex64)
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payload[s21_key] = np.asarray(trace.s21, dtype=np.complex64)
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combo_records.append(
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{
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"input": trace.combo.input,
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"output": trace.combo.output,
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"capture_start_ns": int(trace.capture_start_ns),
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"capture_end_ns": int(trace.capture_end_ns),
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"freq_key": freq_key,
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"s11_key": s11_key,
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"s21_key": s21_key,
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}
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)
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meta = {
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"collection_id": int(collection.collection_id),
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"monotonic_ns": int(collection.monotonic_ns),
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"capture_start_ns": int(collection.capture_start_ns),
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"capture_end_ns": int(collection.capture_end_ns),
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"combos": combo_records,
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}
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# Metadata in the NPZ makes a single atomic replacement authoritative. The
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# JSON remains a readable sidecar and legacy NPZ/JSON pairs remain loadable.
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metadata_json = json.dumps(meta, indent=2)
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payload[_SET_METADATA_KEY] = np.asarray(metadata_json)
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npz_tmp = meta_tmp = backup_path = None
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archive_replaced = False
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try:
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npz_tmp = self._temporary_file(npz_path)
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meta_tmp = self._temporary_file(meta_path)
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backup_path = self._temporary_file(npz_path) if npz_path.exists() else None
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if backup_path is not None:
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backup_path.unlink()
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os.link(npz_path, backup_path)
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with npz_tmp.open("wb") as npz_file:
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np.savez(npz_file, **payload)
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meta_tmp.write_text(metadata_json, encoding="utf-8")
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npz_tmp.replace(npz_path)
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archive_replaced = True
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meta_tmp.replace(meta_path)
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except BaseException:
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logger.exception("Failed to save set %s/%s/%s; rolling back temp files", kind, radar_key, set_name)
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if archive_replaced:
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if backup_path is None:
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npz_path.unlink(missing_ok=True)
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else:
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try:
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backup_path.replace(npz_path)
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except OSError:
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logger.exception("Set rollback failed; previous archive retained at %s", backup_path)
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backup_path = None # retain the recovery copy rather than deleting it below
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raise
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raise
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finally:
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for tmp_path in (npz_tmp, meta_tmp, backup_path):
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if tmp_path is None:
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continue
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with suppress(OSError):
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tmp_path.unlink(missing_ok=True)
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logger.info(
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"Saved set %s/%s/%s (%d traces) to %s", kind, radar_key, set_name, len(combo_records), npz_path
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)
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def save_sets(self, radar_key: str, set_name: str, collections: dict[str, SweepCollection]) -> None:
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"""Save related sets together, restoring their previous files if any save fails."""
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self._validate_set_name(set_name)
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for collection in collections.values():
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validate_trace_collection(collection, require_finite=True)
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backup_dir = Path(tempfile.mkdtemp(prefix=".sets-", dir=self._root_dir))
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backups: dict[Path, Path | None] = {}
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retain_backups = False
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try:
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for kind in collections:
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set_dir = self._set_dir(kind, radar_key)
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for extension in ("npz", "json"):
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destination = set_dir / f"{set_name}.{extension}"
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backup = backup_dir / f"{kind}.{extension}" if destination.exists() else None
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if backup is not None:
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os.link(destination, backup)
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backups[destination] = backup
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try:
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for kind, collection in collections.items():
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self.save_set(kind, radar_key, set_name, collection)
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except BaseException as save_error:
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retain_backups = True # preserve recovery files if rollback itself is interrupted
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rollback_failed = False
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for destination, backup in backups.items():
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try:
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if backup is None:
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destination.unlink(missing_ok=True)
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else:
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backup.replace(destination)
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except OSError:
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rollback_failed = True
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logger.exception("Failed to restore previous set file %s", destination)
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retain_backups = rollback_failed
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if retain_backups:
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raise RuntimeError(
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f"Saving set group '{set_name}' failed and rollback was incomplete; "
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f"previous files are retained at {backup_dir}"
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) from save_error
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raise
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finally:
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if not retain_backups:
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with suppress(OSError):
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shutil.rmtree(backup_dir)
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def load_set(self, kind: str, radar_key: str, set_name: str) -> SweepCollection:
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"""Load named preprocess set from NPZ representation."""
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self._validate_set_name(set_name)
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set_dir = self._set_dir(kind, radar_key)
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npz_path = set_dir / f"{set_name}.npz"
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meta_path = set_dir / f"{set_name}.json"
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if not npz_path.exists():
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logger.error("Missing set files for %s/%s/%s", kind, radar_key, set_name)
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raise FileNotFoundError(f"Missing set files for {kind}/{radar_key}/{set_name}")
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set_label = f"{kind}/{radar_key}/{set_name}"
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try:
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with np.load(npz_path, allow_pickle=False) as arrays:
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meta = json.loads(
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str(arrays[_SET_METADATA_KEY].item())
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if _SET_METADATA_KEY in arrays
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else meta_path.read_text(encoding="utf-8")
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)
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if not isinstance(meta, dict):
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raise ValueError("Set metadata must be a JSON object")
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traces: list[TraceData] = []
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for combo in meta["combos"]:
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traces.append(
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TraceData(
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combo=ComboKey(input=combo["input"], output=combo["output"]),
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frequency_hz=np.asarray(arrays[combo["freq_key"]], dtype=np.float32),
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s11=np.asarray(arrays[combo["s11_key"]], dtype=np.complex64),
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s21=np.asarray(arrays[combo["s21_key"]], dtype=np.complex64),
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capture_start_ns=combo.get("capture_start_ns", 0),
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capture_end_ns=combo.get("capture_end_ns", 0),
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)
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)
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collection = SweepCollection(
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collection_id=meta["collection_id"],
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monotonic_ns=meta["monotonic_ns"],
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traces=traces,
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capture_start_ns=meta.get("capture_start_ns", 0),
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capture_end_ns=meta.get("capture_end_ns", 0),
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)
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validate_trace_collection(collection, require_finite=True)
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logger.debug("Loaded set %s (%d traces)", set_label, len(traces))
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return collection
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except (OSError, BadZipFile, EOFError, KeyError, TypeError, ValueError) as exc:
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logger.exception("Corrupted preprocess set %s", set_label)
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raise RuntimeError(f"Corrupted preprocess set {set_label}: {exc}") from exc
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def list_sets(self, kind: str, radar_key: str) -> list[str]:
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"""List available set names for `(kind, radar_key)`."""
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set_dir = self._set_dir(kind, radar_key)
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if not set_dir.exists():
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return []
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if kind == "s21_object_calibration":
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return sorted(path.name for path in set_dir.iterdir()
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if not path.name.startswith(".") and (path / "cal_meta.json").is_file())
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return sorted(path.stem for path in set_dir.glob("*.npz"))
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def save_object_calibration(self, radar_key: str, set_name: str, calibration: dict[str, Any]) -> None:
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"""Save an object calibration in its shared archive format without overwriting a set."""
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from python_app.workflows.mimo_calibration import save_calibration
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self._validate_set_name(set_name)
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destination = self._set_dir("s21_object_calibration", radar_key) / set_name
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if destination.exists():
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raise FileExistsError(f"Object calibration already exists: {set_name}")
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save_calibration(calibration, destination)
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def load_object_calibration(self, radar_key: str, set_name: str) -> dict[str, Any]:
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"""Load and validate a saved object calibration, retaining all estimation metadata."""
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from python_app.workflows.mimo_calibration import load_calibration
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self._validate_set_name(set_name)
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return load_calibration(self._set_dir("s21_object_calibration", radar_key) / set_name)
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def has_combo_coverage(self, kind: str, radar_key: str, set_name: str, combos: list[ComboKey]) -> bool:
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"""Validate that named set covers all required switch combinations."""
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if kind == "s21_object_calibration":
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calibration = self.load_object_calibration(radar_key, set_name)
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return all(combo.input in calibration["rx_input_pos"] and combo.output in calibration["tx_output_pos"]
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for combo in combos)
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collection = self.load_set(kind, radar_key, set_name)
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existing = {(trace.combo.input, trace.combo.output) for trace in collection.traces}
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required = {(combo.input, combo.output) for combo in combos}
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return required.issubset(existing)
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def export_set_bundle(self, kind: str, radar_key: str, set_name: str, output_path: Path) -> Path:
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"""Export named set as binary collection bundle for C++ preprocessing stage."""
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collection = self.load_set(kind, radar_key, set_name)
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return self._export_collection_bundle(collection, output_path)
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def export_object_calibration_bundle(
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self, radar_key: str, set_name: str, output_path: Path, config: RunConfigModel,
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) -> Path:
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"""Resample pair multipliers on the through-calibration grids used by the acquisition."""
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from python_app.workflows.mimo_calibration import calibration_on_grid, validate_for_config
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calibration = self.load_object_calibration(radar_key, set_name)
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validate_for_config(calibration, config.to_dict())
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tx_indices = {int(port): index for index, port in enumerate(calibration["tx_output_pos"])}
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rx_indices = {int(port): index for index, port in enumerate(calibration["rx_input_pos"])}
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source = self.load_set("s21_calibration", radar_key, config.preprocess.s21.calibration.set_name)
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by_combo = {trace.combo: trace for trace in source.traces}
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grids: dict[bytes, np.ndarray] = {}
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traces: list[TraceData] = []
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for combo in config.combos:
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key = ComboKey(input=combo.input, output=combo.output)
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trace = by_combo[key]
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frequency = trace.frequency_hz
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grid_key = frequency.tobytes()
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if grid_key not in grids:
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grids[grid_key] = calibration_on_grid(calibration, frequency)
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correction = grids[grid_key][tx_indices[key.output], rx_indices[key.input]]
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traces.append(TraceData(key, frequency, np.zeros(frequency.size, np.complex64), correction))
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return self._export_collection_bundle(SweepCollection(0, 0, traces), output_path)
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def _export_collection_bundle(self, collection: SweepCollection, output_path: Path) -> Path:
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"""Publish a complete binary trace bundle using one atomic replacement."""
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validate_trace_collection(collection, require_finite=True)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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temporary = self._temporary_file(output_path)
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try:
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temporary.write_bytes(serialize_trace_collection(collection, RAW_MAGIC))
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temporary.replace(output_path)
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finally:
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temporary.unlink(missing_ok=True)
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logger.info("Exported %d traces to bundle %s", len(collection.traces), output_path)
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return output_path
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def save_runtime_snapshot(
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self,
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output_dir: Path,
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raw_history: list[SweepCollection],
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preprocessed_history: list[SweepCollection],
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result_history: list[ResultCollection],
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last_n: int,
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) -> Path:
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"""Save historical runtime collections using binary on-disk format."""
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if last_n <= 0:
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raise ValueError("last_n must be > 0")
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output_dir.mkdir(parents=True, exist_ok=True)
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timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
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snapshot_dir = output_dir / f"snapshot_{timestamp}"
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with atomic_snapshot_directory(snapshot_dir) as staging:
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save_trace_history_binary(staging / "raw", raw_history[-last_n:], RAW_MAGIC)
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save_trace_history_binary(staging / "preprocessed", preprocessed_history[-last_n:], PREPROC_MAGIC)
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save_result_history_binary(staging / "results", result_history[-last_n:])
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logger.info("Saved binary runtime snapshot (last_n=%d) to %s", last_n, snapshot_dir)
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return snapshot_dir
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def snapshot_directory(
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self, output_root_dir: Path, snapshot_name: str, *, name_prefix: str = ""
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) -> Path:
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"""Return the directory a snapshot save would create for these inputs.
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Lets callers check ``.exists()`` *before* acquiring data (e.g. the disk
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recorder arms a run only once the destination is free), so a name clash is
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reported up front instead of after the measurements are collected. Note a
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blank ``snapshot_name`` resolves to a fresh timestamp each call, so this is
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meaningful only for explicit names — exactly the case that can collide.
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"""
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return output_root_dir / _compose_snapshot_stem(snapshot_name, name_prefix)
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def create_snapshot_stream(
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self, output_root_dir: Path, snapshot_name: str, *, name_prefix: str = ""
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) -> SnapshotStreamWriter:
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"""Create an empty snapshot directory and return a chunked stream writer for it.
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Raises ``FileExistsError`` if the directory already exists (same guard as
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:meth:`save_runtime_snapshot_numpy`), so a name clash is reported at arm time.
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"""
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snapshot_dir = self.snapshot_directory(output_root_dir, snapshot_name, name_prefix=name_prefix)
|
|
output_root_dir.mkdir(parents=True, exist_ok=True)
|
|
if snapshot_dir.exists():
|
|
raise FileExistsError(f"Snapshot directory already exists: {snapshot_dir}")
|
|
snapshot_dir.mkdir(parents=True, exist_ok=False)
|
|
logger.info("Opened streaming snapshot directory %s", snapshot_dir)
|
|
return SnapshotStreamWriter(snapshot_dir)
|
|
|
|
def save_runtime_snapshot_numpy(
|
|
self,
|
|
output_root_dir: Path,
|
|
snapshot_name: str,
|
|
raw_history: list[SweepCollection],
|
|
preprocessed_history: list[SweepCollection],
|
|
result_history: list[ResultCollection],
|
|
last_n: int,
|
|
*,
|
|
name_prefix: str = "",
|
|
include_unaligned: bool = False,
|
|
) -> tuple[Path, dict[str, Any]]:
|
|
"""Save historical runtime collections in NumPy tree format."""
|
|
if last_n <= 0:
|
|
raise ValueError("last_n must be > 0")
|
|
|
|
snapshot_stem = _compose_snapshot_stem(snapshot_name, name_prefix)
|
|
|
|
output_root_dir.mkdir(parents=True, exist_ok=True)
|
|
snapshot_dir = output_root_dir / snapshot_stem
|
|
if snapshot_dir.exists():
|
|
raise FileExistsError(f"Snapshot directory already exists: {snapshot_dir}")
|
|
|
|
selected_raw, selected_preprocessed, selected_results, selection_summary = select_aligned_histories(
|
|
raw_history,
|
|
preprocessed_history,
|
|
result_history,
|
|
last_n,
|
|
include_unaligned=include_unaligned,
|
|
)
|
|
|
|
if selection_summary.get("anchor_stage") == "results":
|
|
expected = min(int(last_n), len(result_history))
|
|
if len(selected_results) != expected:
|
|
raise RuntimeError(
|
|
"Snapshot results selection invariant failed: "
|
|
f"expected={expected}, selected={len(selected_results)}"
|
|
)
|
|
|
|
with atomic_snapshot_directory(snapshot_dir) as staging:
|
|
save_trace_history_numpy(staging / "raw", selected_raw)
|
|
save_trace_history_numpy(staging / "preprocessed", selected_preprocessed)
|
|
save_result_history_numpy(staging / "results", selected_results)
|
|
|
|
selection_summary["raw_count"] = len(selected_raw)
|
|
selection_summary["preprocessed_count"] = len(selected_preprocessed)
|
|
selection_summary["result_count"] = len(selected_results)
|
|
selection_summary["snapshot_stem"] = snapshot_stem
|
|
selection_summary["snapshot_dir"] = str(snapshot_dir)
|
|
logger.info(
|
|
"Saved NumPy runtime snapshot to %s (raw=%d preprocessed=%d results=%d, anchor=%s)",
|
|
snapshot_dir,
|
|
len(selected_raw),
|
|
len(selected_preprocessed),
|
|
len(selected_results),
|
|
selection_summary.get("anchor_stage"),
|
|
)
|
|
return snapshot_dir, selection_summary
|
|
|
|
def save_runtime_vna_history_json(
|
|
self,
|
|
output_root_dir: Path,
|
|
output_name: str,
|
|
raw_history: list[SweepCollection],
|
|
preprocessed_history: list[SweepCollection],
|
|
result_history: list[ResultCollection],
|
|
last_n: int,
|
|
*,
|
|
input_index: int = 0,
|
|
output_index: int = 0,
|
|
channel: str = "s21",
|
|
primary_stage: str = "preprocessed",
|
|
include_unaligned: bool = False,
|
|
) -> tuple[Path, dict[str, Any]]:
|
|
"""Save runtime history as vna_system-compatible JSON file."""
|
|
if last_n <= 0:
|
|
raise ValueError("last_n must be > 0")
|
|
|
|
output_stem = output_name.strip() or datetime.now(timezone.utc).strftime("snapshot_%Y%m%d_%H%M%S")
|
|
output_stem = sanitize_path_component(output_stem)
|
|
|
|
output_root_dir.mkdir(parents=True, exist_ok=True)
|
|
output_dir = self._vna_json_output_dir(output_root_dir, output_stem)
|
|
output_path = output_dir / f"{output_stem}_vna_bscan_history.json"
|
|
|
|
selected_raw, selected_preprocessed, selected_results, selection_summary = select_aligned_histories(
|
|
raw_history,
|
|
preprocessed_history,
|
|
result_history,
|
|
last_n,
|
|
include_unaligned=include_unaligned,
|
|
)
|
|
payload = build_vna_history_payload(
|
|
selected_raw,
|
|
selected_preprocessed,
|
|
selected_results,
|
|
input_index=input_index,
|
|
output_index=output_index,
|
|
channel=channel,
|
|
primary_stage=primary_stage,
|
|
)
|
|
with atomic_snapshot_directory(output_dir) as staging:
|
|
(staging / output_path.name).write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
|
|
|
|
summary: dict[str, Any] = dict(selection_summary)
|
|
summary["raw_count"] = len(selected_raw)
|
|
summary["preprocessed_count"] = len(selected_preprocessed)
|
|
summary["result_count"] = len(selected_results)
|
|
summary["raw_record_count"] = int(payload.get("raw_record_count", 0))
|
|
summary["preprocessed_record_count"] = int(payload.get("preprocessed_record_count", 0))
|
|
summary["sweep_count"] = len(payload.get("sweep_history", []))
|
|
summary["input_index"] = int(input_index)
|
|
summary["output_index"] = int(output_index)
|
|
summary["channel"] = str(channel)
|
|
summary["primary_stage"] = str(primary_stage)
|
|
summary["output_stem"] = output_stem
|
|
summary["output_dir"] = str(output_dir)
|
|
summary["output_path"] = str(output_path)
|
|
logger.info(
|
|
"Saved VNA history JSON to %s (input=%d output=%d channel=%s sweeps=%d)",
|
|
output_path,
|
|
int(input_index),
|
|
int(output_index),
|
|
channel,
|
|
summary["sweep_count"],
|
|
)
|
|
return output_path, summary
|
|
|
|
def save_runtime_vna_history_json_batch(
|
|
self,
|
|
output_root_dir: Path,
|
|
output_name: str,
|
|
raw_history: list[SweepCollection],
|
|
preprocessed_history: list[SweepCollection],
|
|
result_history: list[ResultCollection],
|
|
last_n: int,
|
|
*,
|
|
channel: str = "s21",
|
|
primary_stage: str = "preprocessed",
|
|
name_prefix: str = "",
|
|
include_unaligned: bool = False,
|
|
) -> tuple[list[Path], dict[str, Any]]:
|
|
"""Save one runtime VNA-history JSON per available combo."""
|
|
if last_n <= 0:
|
|
raise ValueError("last_n must be > 0")
|
|
|
|
output_stem = _compose_snapshot_stem(output_name, name_prefix)
|
|
output_root_dir.mkdir(parents=True, exist_ok=True)
|
|
output_dir = self._vna_json_output_dir(output_root_dir, output_stem)
|
|
|
|
selected_raw, selected_preprocessed, selected_results, selection_summary = select_aligned_histories(
|
|
raw_history,
|
|
preprocessed_history,
|
|
result_history,
|
|
last_n,
|
|
include_unaligned=include_unaligned,
|
|
)
|
|
|
|
combos = sorted(
|
|
{
|
|
(int(trace.combo.input), int(trace.combo.output))
|
|
for collection in [*selected_raw, *selected_preprocessed]
|
|
for trace in collection.traces
|
|
}
|
|
)
|
|
if not combos:
|
|
logger.warning("No raw/preprocessed combos found in runtime history for VNA JSON batch export")
|
|
raise ValueError("No matching raw/preprocessed traces were found in runtime history for any combo.")
|
|
|
|
output_paths: list[Path] = []
|
|
payloads: list[dict[str, Any]] = []
|
|
for input_index, output_index in combos:
|
|
output_path = output_dir / (
|
|
f"{output_stem}_i{input_index}_o{output_index}_{channel}_vna_bscan_history.json"
|
|
)
|
|
if output_path.exists():
|
|
raise FileExistsError(f"Output JSON file already exists: {output_path}")
|
|
payloads.append(
|
|
build_vna_history_payload(
|
|
selected_raw,
|
|
selected_preprocessed,
|
|
selected_results,
|
|
input_index=input_index,
|
|
output_index=output_index,
|
|
channel=channel,
|
|
primary_stage=primary_stage,
|
|
)
|
|
)
|
|
output_paths.append(output_path)
|
|
|
|
with atomic_snapshot_directory(output_dir) as staging:
|
|
for output_path, payload in zip(output_paths, payloads, strict=True):
|
|
(staging / output_path.name).write_text(
|
|
json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8"
|
|
)
|
|
|
|
summary: dict[str, Any] = dict(selection_summary)
|
|
summary["raw_count"] = len(selected_raw)
|
|
summary["preprocessed_count"] = len(selected_preprocessed)
|
|
summary["result_count"] = len(selected_results)
|
|
summary["preprocessed_record_count"] = int(sum(int(payload.get("preprocessed_record_count", 0)) for payload in payloads))
|
|
summary["combo_count"] = len(combos)
|
|
summary["combos"] = [list(combo) for combo in combos]
|
|
summary["channel"] = str(channel)
|
|
summary["primary_stage"] = str(primary_stage)
|
|
summary["output_stem"] = output_stem
|
|
summary["output_dir"] = str(output_dir)
|
|
summary["output_paths"] = [str(path) for path in output_paths]
|
|
logger.info(
|
|
"Saved %d VNA history JSON file(s) to %s (channel=%s)", len(output_paths), output_dir, channel
|
|
)
|
|
return output_paths, summary
|
|
|
|
def _set_dir(self, kind: str, radar_key: str) -> Path:
|
|
"""Return directory for set kind and radar key."""
|
|
self._validate_set_name(kind)
|
|
self._validate_set_name(radar_key)
|
|
return self._root_dir / kind / radar_key
|
|
|
|
@staticmethod
|
|
def _validate_set_name(value: str) -> None:
|
|
"""Keep caller-provided set components inside their storage directory."""
|
|
if not value or value in {".", ".."} or Path(value).name != value or "\\" in value:
|
|
raise ValueError(f"Invalid set path component: {value!r}")
|
|
|
|
@staticmethod
|
|
def _temporary_file(destination: Path) -> Path:
|
|
"""Create a unique temporary file beside its atomic-replacement destination."""
|
|
with tempfile.NamedTemporaryFile(
|
|
dir=destination.parent, prefix=f".{destination.name}.", suffix=".tmp", delete=False
|
|
) as handle:
|
|
path = Path(handle.name)
|
|
return path
|
|
|
|
def preview_png_dir(self, kind: str, set_name: str, radar_key: str) -> Path:
|
|
"""Return directory for preview PNGs of one set/radar variant.
|
|
|
|
Lives under ``preview_png/`` inside the store so saved graphs sit next to
|
|
the data they describe, grouped by set name then radar variant. The set
|
|
name is operator-supplied, so it is sanitized; ``radar_key`` is already
|
|
filesystem-safe by construction.
|
|
"""
|
|
return self._root_dir / "preview_png" / kind / sanitize_path_component(set_name) / radar_key
|
|
|
|
|
|
__all__ = ["NpzStore", "radar_key_from_config"]
|