perf(lab): stream sealed spatial playback tracks

src/k1link/perception/geometry.py

@@ -344,6 +344,27 @@ class RecordedGeometryStore:
         points.setflags(write=False)
         return points
 
+    def playback_points_map(self) -> FloatArray:
+        """Expose the sealed contiguous map-point track for binary LAB playback.
+
+        The returned array is the exact source-pack point index space.  It is
+        read-only and deliberately excludes any UI projection or resampling so
+        the browser can retain it once and derive the current increment by the
+        verified offsets below.
+        """
+
+        points = np.asarray(self._source["cloud_points_map"], dtype=np.dtype("<f4"))
+        if not points.flags.c_contiguous:
+            raise GeometryProviderError("source playback point track is not contiguous")
+        points.setflags(write=False)
+        return points
+
+    def playback_point_offsets(self) -> tuple[int, ...]:
+        """Return immutable offsets into :meth:`playback_points_map`."""
+
+        offsets = np.asarray(self._source["cloud_offsets"], dtype=np.int64)
+        return tuple(int(value) for value in offsets)
+
     def point_step_candidates_for_frame(self, frame_index: int) -> UInt8Array | None:
         """Expose the sealed low-step diagnostic in the source point index space.
 

src/k1link/perception/threat_timeline.py

@@ -150,14 +150,23 @@ class RecordedThreatTimeline:
             "access": "read-only-bounded-recorded-replay",
         }
 
-    def chunk(self, *, start_sequence: int, frame_count: int) -> dict[str, object]:
+    def chunk(
+        self,
+        *,
+        start_sequence: int,
+        frame_count: int,
+        include_points: bool = True,
+    ) -> dict[str, object]:
         if not 0 <= start_sequence < len(self.index.offsets):
             raise RecordedThreatTimelineError("recorded timeline chunk start is invalid")
         if not 1 <= frame_count <= RECORDED_SPATIAL_MAX_CHUNK_FRAMES:
             raise RecordedThreatTimelineError("recorded timeline chunk size is invalid")
         stop = min(len(self.index.offsets), start_sequence + frame_count)
         with self._lock:
-            frames = [self._project_frame(sequence) for sequence in range(start_sequence, stop)]
+            frames = [
+                self._project_frame(sequence, include_points=include_points)
+                for sequence in range(start_sequence, stop)
+            ]
         return {
             "schema_version": RECORDED_SPATIAL_CHUNK_SCHEMA,
             "result_id": self.result.result_id,
@@ -170,7 +179,12 @@ class RecordedThreatTimeline:
             "access": "read-only-bounded-recorded-replay",
         }
 
-    def _project_frame(self, sequence: int) -> dict[str, object]:
+    def _project_frame(
+        self,
+        sequence: int,
+        *,
+        include_points: bool,
+    ) -> dict[str, object]:
         row = _read_frame_at(self.frames_path, self.index, sequence)
         frame_id = row.get("frame_id")
         if not isinstance(frame_id, str) or not frame_id:
@@ -193,11 +207,13 @@ class RecordedThreatTimeline:
                 raise RecordedThreatTimelineError(
                     "recorded timeline current increment binding changed"
                 )
-            point_cloud, point_source_count = sample_points_in_body_frame(
-                points,
-                body_frame,
-                point_limit=RECORDED_SPATIAL_POINT_LIMIT,
-            )
+            point_source_count = int(points.shape[0])
+            if include_points:
+                point_cloud, point_source_count = sample_points_in_body_frame(
+                    points,
+                    body_frame,
+                    point_limit=RECORDED_SPATIAL_POINT_LIMIT,
+                )
             metric_visuals = project_metric_obstacles_to_body(
                 _mapping_array(row.get("metric_obstacles"), "metric obstacles"),
                 body_frame,
@@ -219,13 +235,13 @@ class RecordedThreatTimeline:
             if body_frame is None
             else {
                 "origin_map_xyz_m": list(body_frame.origin_map_xyz_m),
-                "basis_map_from_body": [
-                    list(row) for row in body_frame.basis_map_from_body
-                ],
+                "basis_map_from_body": [list(row) for row in body_frame.basis_map_from_body],
             },
             "point_cloud_body_xyz_m": point_cloud,
             "point_cloud_source_count": point_source_count,
-            "point_cloud_sample_count": len(point_cloud),
+            "point_cloud_sample_count": point_source_count
+            if not include_points
+            else len(point_cloud),
             "point_cloud_layer": "current-increment",
             "rolling_map_component_count": sum(
                 item.get("state") == "retained" for item in metric_visuals