added subproxy

server/core/elevation.py

@@ -307,3 +307,129 @@ def build_elevation_profile(
     if not elev_vals:
         result["api_error"] = "elevation API returned no values"
     return result
+
+
+def _offset_m(lat: float, lon: float, north_m: float, east_m: float) -> tuple[float, float]:
+    dlat = north_m / 111_320.0
+    dlon = east_m / (111_320.0 * max(math.cos(math.radians(lat)), 1e-6))
+    return lat + dlat, lon + dlon
+
+
+def find_nearest_hill(
+    lat: float,
+    lon: float,
+    radius_m: float = 5000.0,
+    step_m: float = 300.0,
+    min_prominence_m: float = 8.0,
+) -> dict[str, Any]:
+    """Find nearest local elevation maximum around a point."""
+    probe = probe_elevation_api()
+    if not probe["ok"]:
+        return {
+            "ok": False,
+            "error": f"elevation API unreachable: {probe['error']}",
+            "elevation_url": ELEVATION_API_URL,
+        }
+
+    radius_m = max(500.0, min(float(radius_m), 15_000.0))
+    step_m = max(100.0, min(float(step_m), 500.0))
+    min_prominence_m = max(3.0, min(float(min_prominence_m), 100.0))
+
+    center_elev = fetch_elevation_m(lat, lon)
+    if center_elev is None:
+        return {"ok": False, "error": "no elevation at center"}
+
+    steps = int(radius_m / step_m)
+    grid_cells: list[tuple[int, int, float, float, float]] = []
+    for i in range(-steps, steps + 1):
+        for j in range(-steps, steps + 1):
+            north = i * step_m
+            east = j * step_m
+            dist = math.hypot(north, east)
+            if dist > radius_m:
+                continue
+            la, lo = _offset_m(lat, lon, north, east)
+            grid_cells.append((i, j, la, lo, dist))
+
+    if not grid_cells:
+        return {"ok": False, "error": "empty search grid"}
+
+    lats = [c[2] for c in grid_cells]
+    lons = [c[3] for c in grid_cells]
+    elevations = fetch_elevations_batch(lats, lons)
+
+    grid: dict[tuple[int, int], dict[str, Any]] = {}
+    for (i, j, la, lo, dist), elev in zip(grid_cells, elevations):
+        grid[(i, j)] = {
+            "lat": round(la, 6),
+            "lon": round(lo, 6),
+            "dist_m": round(dist, 1),
+            "elevation_m": elev,
+        }
+
+    def is_local_max(i: int, j: int, elev: float) -> bool:
+        for di in (-1, 0, 1):
+            for dj in (-1, 0, 1):
+                if di == 0 and dj == 0:
+                    continue
+                n = grid.get((i + di, j + dj))
+                if n and n["elevation_m"] is not None and n["elevation_m"] >= elev:
+                    return False
+        return True
+
+    candidates: list[dict[str, Any]] = []
+    for (i, j), cell in grid.items():
+        elev = cell.get("elevation_m")
+        if elev is None:
+            continue
+        prominence = float(elev) - center_elev
+        if prominence < min_prominence_m:
+            continue
+        if is_local_max(i, j, float(elev)):
+            candidates.append({**cell, "prominence_m": round(prominence, 1)})
+
+    if not candidates:
+        best = None
+        for cell in grid.values():
+            elev = cell.get("elevation_m")
+            if elev is None:
+                continue
+            prominence = float(elev) - center_elev
+            if prominence < min_prominence_m * 0.5:
+                continue
+            if best is None or cell["dist_m"] < best["dist_m"]:
+                best = {
+                    **cell,
+                    "prominence_m": round(prominence, 1),
+                    "is_local_max": False,
+                }
+        if best is None:
+            return {
+                "ok": False,
+                "error": "no hill found in radius",
+                "center": {
+                    "lat": round(lat, 6),
+                    "lon": round(lon, 6),
+                    "elevation_m": center_elev,
+                },
+                "radius_m": radius_m,
+            }
+        hill = best
+    else:
+        candidates.sort(key=lambda c: c["dist_m"])
+        hill = {**candidates[0], "is_local_max": True}
+
+    return {
+        "ok": True,
+        "center": {
+            "lat": round(lat, 6),
+            "lon": round(lon, 6),
+            "elevation_m": center_elev,
+        },
+        "hill": hill,
+        "candidates": len(candidates),
+        "radius_m": radius_m,
+        "step_m": step_m,
+        "api_source": "elevation",
+        "elevation_url": ELEVATION_API_URL,
+    }