added timing

python_app/orchestration/gpr_locator.py

@@ -1,10 +1,11 @@
-"""Helpers for extracting GPR objects and locator observations from results."""
+"""Helpers for extracting GPR objects from result collections.
+
+Locator TCP delivery now lives in the C++ data_processor. This module retains
+only the inspection helpers that the GUI uses for plotting.
+"""
 
 from __future__ import annotations
 
-from datetime import datetime
-from typing import Any
-
 import numpy as np
 
 from python_app.models.dataset_model import ResultCollection, ResultPayload
@@ -64,64 +65,3 @@ def gpr_object_rows(collection: ResultCollection) -> np.ndarray:
             return centers[:, :3]
 
     return np.zeros((0, 3), dtype=np.float32)
-
-
-def locator_observations_from_collection(
-    collection: ResultCollection,
-    min_score: float,
-    *,
-    visible_bounds: tuple[float, float, float, float] | None = None,
-    object_draw_limits: tuple[int, int] | None = None,
-) -> list[dict[str, float]]:
-    """Build locator observations from GPR rows using score threshold and optional X/Z bounds."""
-    rows = gpr_object_rows(collection)
-    if rows.size == 0:
-        return []
-
-    finite_mask = np.all(np.isfinite(rows[:, :3]), axis=1)
-    visible_mask = finite_mask & (rows[:, 2] >= float(min_score))
-    if visible_bounds is not None:
-        x_min, x_max, z_min, z_max = (float(value) for value in visible_bounds)
-        visible_mask &= (
-            (rows[:, 0] >= x_min)
-            & (rows[:, 0] <= x_max)
-            & (rows[:, 1] >= z_min)
-            & (rows[:, 1] <= z_max)
-        )
-    filtered = rows[visible_mask]
-    if object_draw_limits is not None and filtered.size > 0:
-        max_detected_objects, draw_top_objects = object_draw_limits
-        if filtered.shape[0] > int(max_detected_objects):
-            filtered = np.zeros((0, filtered.shape[1]), dtype=filtered.dtype)
-        else:
-            filtered = filtered[: max(0, int(draw_top_objects))]
-
-    observations: list[dict[str, float]] = []
-    for x_m, z_m, _score in filtered:
-        observations.append(
-            {
-                "dst": round(float(z_m), 2),
-                "crs": round(float(x_m), 2),
-            }
-        )
-    return observations
-
-
-def build_locator_payload(
-    observations: list[dict[str, float]],
-    *,
-    protocol_version: int,
-    status: int = 1,
-) -> dict[str, Any]:
-    """Assemble one outbound locator payload from precomputed observations."""
-    return {
-        "ver": int(protocol_version),
-        "tim": _format_timestamp(),
-        "sts": int(status),
-        "obs": observations,
-    }
-
-
-def _format_timestamp() -> str:
-    """Return wall-clock timestamp with millisecond precision."""
-    return datetime.now().strftime("%H:%M:%S.%f")[:-3]