Sat scan improvements

src/stream_web/app.py

@@ -405,7 +405,7 @@ def api_packets():
     """Poll-and-drain: return all decodes since last call as JSONL, then clear.
 
     Each line is a JSON object with: device_id, seq_num, device_type,
-    timestamp, rssi_dB, channel_num, freq_offset_hz.
+    timestamp, rssi_dB, channel_num, freq_offset_hz, pdu_n_corr, header_n_corr.
     """
     with state.lock:
         entries = list(state.packet_feed)
@@ -430,6 +430,8 @@ def api_packets():
             "channel_num": e.get("channel_num"),
             "freq_offset_hz": e.get("freq_delta_hz"),
             "payload_b64": payload_b64,
+            "pdu_n_corr": e.get("pdu_n_corr"),
+            "header_n_corr": e.get("header_n_corr"),
         }))
     payload = "\n".join(lines) + ("\n" if lines else "")
     return Response(payload, mimetype="application/x-ndjson")

src/stream_web/processor.py

@@ -236,7 +236,7 @@ def _extract_last(n):
                     if not decode_info.get("energy_dB"):
                         decode_info["energy_dB"] = td_hit.get("total_energy_dB")
                     try:
-                        td_img = render_td_plot(td_seg, decode_info=decode_info)
+                        td_img, td_stats = render_td_plot(td_seg, decode_info=decode_info)
                         status = f"t={td_hit['time_s']:.3f}s"
                         if decode_info["decoded"]:
                             seq = decode_info.get("seq_num")
@@ -248,6 +248,7 @@ def _extract_last(n):
                             k: v for k, v in decode_info.items()
                             if isinstance(v, (str, int, float, bool, list, type(None)))
                         }
+                        td_decode_info_out.update(td_stats)
                         td_iq_seg_out = td_seg.copy()
                     except Exception as e:
                         print(f"[TD] Plot error: {e}")

src/stream_web/spectrogram.py

@@ -175,8 +175,16 @@ def _draw_decoder_overlay(ax, decode_info: dict):
                 fontfamily="monospace", va="bottom", ha="left", alpha=1.0)
 
 
-def render_td_plot(iq_segment: np.ndarray, decode_info: dict | None = None) -> bytes:
-    """Render a time-domain magnitude plot + spectrogram with annotations."""
+def render_td_plot(
+    iq_segment: np.ndarray, decode_info: dict | None = None
+) -> tuple[bytes, dict]:
+    """Render a time-domain magnitude plot + spectrogram with annotations.
+
+    Returns ``(image_bytes, stats)`` where *stats* contains symbol and gap
+    duration statistics derived from the envelope-based edge detection:
+    ``sym_count``, ``sym_mean_ms``, ``sym_std_ms``,
+    ``gap_count``, ``gap_mean_ms``, ``gap_std_ms``.
+    """
     n = len(iq_segment)
     t_ms = np.arange(n) / config.SAMPLE_RATE * 1e3
     mag = np.abs(iq_segment)
@@ -397,4 +405,13 @@ def render_td_plot(iq_segment: np.ndarray, decode_info: dict | None = None) -> b
     buf = io.BytesIO()
     canvas.print_png(buf)
     buf.seek(0)
-    return buf.read()
+
+    stats: dict = {
+        "sym_count": int(len(sym_dur_ms)),
+        "sym_mean_ms": float(round(np.mean(sym_dur_ms), 4)) if len(sym_dur_ms) else None,
+        "sym_std_ms": float(round(np.std(sym_dur_ms), 4)) if len(sym_dur_ms) else None,
+        "gap_count": int(len(gap_dur_ms)),
+        "gap_mean_ms": float(round(np.mean(gap_dur_ms), 4)) if len(gap_dur_ms) else None,
+        "gap_std_ms": float(round(np.std(gap_dur_ms), 4)) if len(gap_dur_ms) else None,
+    }
+    return buf.read(), stats