added linear slider

server/core/elevation.py

@@ -18,6 +18,7 @@ from .config import (
 logger = logging.getLogger(__name__)
 
 _BATCH_SIZE = 100
+_MAX_PROFILE_POINTS = 500
 _CACHE: dict[tuple[float, float], Optional[float]] = {}
 _probe_checked_at = 0.0
 _probe_ok = False
@@ -251,12 +252,66 @@ def resample_track_path(
     return samples
 
 
+def resample_track_path_count(
+    points: list[dict[str, Any]], count: int
+) -> list[dict[str, float]]:
+    """Sample exactly `count` points evenly spaced along polyline."""
+    if not points or count < 2:
+        return []
+    cleaned: list[tuple[float, float]] = []
+    for p in points:
+        lat = p.get("lat")
+        lon = p.get("lon")
+        if lat is None or lon is None:
+            continue
+        lat_f, lon_f = float(lat), float(lon)
+        if not cleaned or haversine_m(cleaned[-1][0], cleaned[-1][1], lat_f, lon_f) > 0.5:
+            cleaned.append((lat_f, lon_f))
+    if not cleaned:
+        return []
+    if len(cleaned) == 1:
+        return [{"lat": cleaned[0][0], "lon": cleaned[0][1], "dist_m": 0.0}]
+
+    cum = [0.0]
+    for i in range(1, len(cleaned)):
+        cum.append(
+            cum[-1]
+            + haversine_m(
+                cleaned[i - 1][0], cleaned[i - 1][1], cleaned[i][0], cleaned[i][1]
+            )
+        )
+    total = cum[-1]
+    if total < 1e-6:
+        return [{"lat": cleaned[0][0], "lon": cleaned[0][1], "dist_m": 0.0}]
+
+    n = max(2, min(_MAX_PROFILE_POINTS, int(count)))
+    samples: list[dict[str, float]] = []
+    for i in range(n):
+        dist = (total * i) / (n - 1)
+        lat, lon = _interp_at_dist(cleaned, cum, dist)
+        samples.append({"lat": lat, "lon": lon, "dist_m": round(dist, 1)})
+    return samples
+
+
 def build_elevation_profile(
-    points: list[dict[str, Any]], step_m: float = 10.0
+    points: list[dict[str, Any]],
+    step_m: float = 10.0,
+    target_points: int | None = None,
 ) -> dict[str, Any]:
     """Resample track and fetch terrain elevations."""
-    step_m = max(5.0, min(10.0, float(step_m)))
-    samples = resample_track_path(points, step_m)
+    if target_points is not None:
+        n = max(2, min(_MAX_PROFILE_POINTS, int(target_points)))
+        samples = resample_track_path_count(points, n)
+        if len(samples) > 1:
+            step_m = round(
+                (samples[-1]["dist_m"] - samples[0]["dist_m"]) / (len(samples) - 1),
+                2,
+            )
+        else:
+            step_m = 0.0
+    else:
+        step_m = max(5.0, min(10.0, float(step_m)))
+        samples = resample_track_path(points, step_m)
     if not samples:
         return {
             "step_m": step_m,