Performance optimizations and rendering improvements

GPU multi-processing fix:
- gpu.py: revert to CUDA_VISIBLE_DEVICES approach with lazy CuPy init
  (Device.use() caused CUDA_ERROR_NO_BINARY_FOR_GPU on GPU 1)
- CuPy is imported lazily on first to_gpu() call, allowing
  CUDA_VISIBLE_DEVICES to be set before CUDA context creation
- nvidia-smi used for GPU count detection (no CUDA import needed)
- pipeline.py: add tip message suggesting -w N when multiple GPUs detected

Rendering improvements:
- Title: split into bold title (14pt) + italic description (10pt)
- North arrow: moved inside data area (top-right) with transparent
  background — no longer overlaps title
- Colorbar: full height (compass gap removed), ScalarFormatter with
  useOffset=False to prevent scientific notation on small values

Performance:
- rendering.py: save matplotlib figure to BytesIO instead of temp PNG
  file — eliminates disk I/O between matplotlib and PIL
- visualizations.py: cap max_dist at 300 for ray-tracing (SVF,
  openness, aniso_open) — avoids 500+ iterations at 0.2m resolution
- pipeline.py: deduplicate n_gpus calculation in parallel path

lidar_pipeline/visualizations.py

@@ -480,7 +480,9 @@ def generate_svf(dem_file, basename, vis_dir, resolution, shared=None):
         angles = np.linspace(0, 2 * np.pi, n_dirs, endpoint=False)
         dx_dir = np.cos(angles)
         dy_dir = np.sin(angles)
-        max_dist = int(100 / res)
+        # Cap max_dist to avoid excessive computation at high resolution
+        # 100m radius is sufficient; at 0.2m that's 500 steps which is very slow
+        max_dist = min(int(100 / res), 300)
 
         padded = xp.pad(dem, max_dist, mode='constant', constant_values=xp.nan)
         svf = xp.zeros_like(dem)
@@ -556,7 +558,7 @@ def generate_openness(dem_file, basename, vis_dir, resolution, positive=True, sh
         angles = np.linspace(0, 2 * np.pi, n_dirs, endpoint=False)
         dx_dir = np.cos(angles)
         dy_dir = np.sin(angles)
-        max_dist = int(100 / res)
+        max_dist = min(int(100 / res), 300)
 
         padded = xp.pad(dem, max_dist, mode='constant', constant_values=xp.nan)
         openness_sum = xp.zeros_like(dem)
@@ -1320,7 +1322,7 @@ def generate_svf(dem_file, basename, vis_dir, resolution, shared=None):
         angles = np.linspace(0, 2 * np.pi, n_dirs, endpoint=False)
         dx_dir = np.cos(angles)
         dy_dir = np.sin(angles)
-        max_dist = int(100 / res)
+        max_dist = min(int(100 / res), 300)
 
         padded = xp.pad(dem, max_dist, mode='constant', constant_values=xp.nan)
         svf_sum = xp.zeros_like(dem)
@@ -1405,7 +1407,7 @@ def generate_aniso_open(dem_file, basename, vis_dir, resolution, shared=None):
         # aligned with Roman and medieval settlement patterns in France
         weights = np.array([1.0, 1.5, 1.0, 1.5, 1.0, 1.5, 1.0, 1.5])
 
-        max_dist = int(100 / res)
+        max_dist = min(int(100 / res), 300)
         padded = xp.pad(dem, max_dist, mode='constant', constant_values=xp.nan)
 
         pos_sum = xp.zeros_like(dem)