bscan added s11

python_app/storage/npz/store.py

@@ -225,6 +225,7 @@ class NpzStore(StoreApi):
         *,
         input_index: int = 0,
         output_index: int = 0,
+        channel: str = "s21",
         primary_stage: str = "preprocessed",
     ) -> tuple[Path, dict[str, Any]]:
         """Save runtime history as vna_system-compatible JSON file."""
@@ -251,6 +252,7 @@ class NpzStore(StoreApi):
             selected_results,
             input_index=input_index,
             output_index=output_index,
+            channel=channel,
             primary_stage=primary_stage,
         )
         output_path.write_text(
@@ -267,6 +269,7 @@ class NpzStore(StoreApi):
         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_path"] = str(output_path)
         return output_path, summary

python_app/storage/npz/vna_history_json.py

@@ -21,7 +21,20 @@ class TraceRecord:
     monotonic_ns: int
     stage_index: int
     frequency_hz: np.ndarray
-    s21: np.ndarray
+    samples: np.ndarray
+
+
+def _normalize_channel(channel: str) -> str:
+    normalized = str(channel).strip().lower()
+    if normalized not in {"s21", "s11"}:
+        raise ValueError("channel must be either 's21' or 's11'")
+    return normalized
+
+
+def _select_trace_samples(trace: TraceData, channel: str) -> np.ndarray:
+    if channel == "s11":
+        return np.asarray(trace.s11, dtype=np.complex128).reshape(-1)
+    return np.asarray(trace.s21, dtype=np.complex128).reshape(-1)
 
 
 def _pick_trace(collection: SweepCollection, input_index: int, output_index: int) -> TraceData | None:
@@ -37,6 +50,7 @@ def _build_stage_records(
     *,
     input_index: int,
     output_index: int,
+    channel: str,
 ) -> list[TraceRecord]:
     records: list[TraceRecord] = []
     for stage_index, collection in enumerate(history):
@@ -45,18 +59,18 @@ def _build_stage_records(
             continue
 
         frequency_hz = np.asarray(trace.frequency_hz, dtype=np.float64).reshape(-1)
-        s21 = np.asarray(trace.s21, dtype=np.complex128).reshape(-1)
-        if frequency_hz.shape != s21.shape:
+        samples = _select_trace_samples(trace, channel)
+        if frequency_hz.shape != samples.shape:
             raise ValueError(
                 f"Shape mismatch in {stage} stage for collection_id={collection.collection_id}: "
-                f"freq{frequency_hz.shape} vs s21{s21.shape}"
+                f"freq{frequency_hz.shape} vs {channel}{samples.shape}"
             )
         if frequency_hz.size == 0:
             continue
         if not (
             np.isfinite(frequency_hz).all()
-            and np.isfinite(np.real(s21)).all()
-            and np.isfinite(np.imag(s21)).all()
+            and np.isfinite(np.real(samples)).all()
+            and np.isfinite(np.imag(samples)).all()
         ):
             raise ValueError(f"Non-finite values in {stage} stage for collection_id={collection.collection_id}")
 
@@ -67,7 +81,7 @@ def _build_stage_records(
                 monotonic_ns=int(collection.monotonic_ns),
                 stage_index=int(stage_index),
                 frequency_hz=frequency_hz,
-                s21=s21,
+                samples=samples,
             )
         )
     return records
@@ -94,6 +108,7 @@ def _build_sweep_history(
     raw_records: list[TraceRecord],
     preprocessed_records: list[TraceRecord],
     *,
+    channel: str,
     primary_stage: str,
 ) -> list[dict[str, Any]]:
     raw_map, raw_order = _index_by_collection_occurrence(raw_records)
@@ -124,11 +139,11 @@ def _build_sweep_history(
         history.append(
             {
                 "timestamp": timestamp_sec,
-                "sweep_points": _complex_to_points(sweep_source.s21),
-                "calibrated_points": _complex_to_points(calibrated_source.s21),
+                "sweep_points": _complex_to_points(sweep_source.samples),
+                "calibrated_points": _complex_to_points(calibrated_source.samples),
                 "reference_points": [],
                 "vna_config": {
-                    "mode": "s11",
+                    "mode": channel,
                     "start_freq": start_freq_hz,
                     "stop_freq": stop_freq_hz,
                     "points": int(base.frequency_hz.size),
@@ -178,9 +193,11 @@ def build_vna_history_payload(
     *,
     input_index: int,
     output_index: int,
+    channel: str = "s21",
     primary_stage: str = "preprocessed",
 ) -> dict[str, Any]:
     """Build vna_system-compatible history JSON payload from runtime histories."""
+    channel = _normalize_channel(channel)
     if primary_stage not in {"preprocessed", "raw"}:
         raise ValueError("primary_stage must be either 'preprocessed' or 'raw'")
 
@@ -189,12 +206,14 @@ def build_vna_history_payload(
         raw_history,
         input_index=input_index,
         output_index=output_index,
+        channel=channel,
     )
     preprocessed_records = _build_stage_records(
         "preprocessed",
         preprocessed_history,
         input_index=input_index,
         output_index=output_index,
+        channel=channel,
     )
     if not raw_records and not preprocessed_records:
         raise ValueError(
@@ -205,6 +224,7 @@ def build_vna_history_payload(
     sweep_history = _build_sweep_history(
         raw_records,
         preprocessed_records,
+        channel=channel,
         primary_stage=primary_stage,
     )
     if not sweep_history:
@@ -217,6 +237,7 @@ def build_vna_history_payload(
         "source_snapshot_dir": "<runtime_history>",
         "input_index": int(input_index),
         "output_index": int(output_index),
+        "channel": channel,
         "primary_stage": str(primary_stage),
         "raw_record_count": len(raw_records),
         "preprocessed_record_count": len(preprocessed_records),
@@ -228,4 +249,3 @@ def build_vna_history_payload(
         payload["alignment_warning"] = alignment_warning
 
     return payload
-