Audit: corrections de bugs identifiés

- rendering.py: colorbar cassée quand NaN mask actif — créer un
  ScalarMappable avec le cmap sauvegardé au lieu de rely sur
  l'image RGBA qui n'a plus de cmap
- rendering.py: nettoyage du PNG temporaire avec try/finally et
  missing_ok=True pour éviter les fichiers orphelins
- gpu.py: to_gpu() convertit en float32 au lieu de float64 pour
  réduire la consommation mémoire GPU
- dtm.py: utiliser _file_basename() de pipeline.py au lieu de
  dupliquer la logique d'extraction du basename
- pipeline.py: docstring corrigé (18 visualisations, pas 19)
- cli.py: --file supporte aussi les noms sans .copc
  (recherche .copc.laz et .copc.las en plus de .laz et .las)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

lidar_pipeline/rendering.py

@@ -345,13 +345,19 @@ def tif_to_png(tif_file, vis_dir, resolution):
             has_nan_mask = nan_mask is not None and not is_rgb_result
             if has_nan_mask:
                 # data is normalized 0-1 from _apply_colormap; apply cmap to get RGBA
-                cmap_obj = plt.get_cmap(cmap) if isinstance(cmap, str) else cmap
-                rgba = cmap_obj(data)  # (H, W, 4) float RGBA
+                # Save the colormap for colorbar before converting to RGBA
+                saved_cmap = plt.get_cmap(cmap) if isinstance(cmap, str) else cmap
+                saved_vmin = float(np.nanmin(data)) if not nan_mask.all() else 0
+                saved_vmax = float(np.nanmax(data)) if not nan_mask.all() else 1
+                rgba = saved_cmap(data)  # (H, W, 4) float RGBA
                 rgba[nan_mask, 3] = 0.0  # transparent where no data
                 data = rgba
                 is_rgba = True
             else:
                 is_rgba = False
+                saved_cmap = None
+                saved_vmin = None
+                saved_vmax = None
 
             # Create figure — adapt width to data resolution for sharp rendering
             # At high res (5000+px wide), we need a larger figure to avoid downsampling artifacts
@@ -375,7 +381,14 @@ def tif_to_png(tif_file, vis_dir, resolution):
             ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
 
             if not is_rgb:
-                cbar = plt.colorbar(im, ax=ax, pad=0.02, shrink=0.85, aspect=30)
+                if is_rgba and saved_cmap is not None:
+                    # Create a ScalarMappable for the colorbar from the saved colormap
+                    sm = plt.cm.ScalarMappable(cmap=saved_cmap,
+                                               norm=plt.Normalize(vmin=saved_vmin, vmax=saved_vmax))
+                    sm.set_array([])
+                    cbar = plt.colorbar(sm, ax=ax, pad=0.02, shrink=0.85, aspect=30)
+                else:
+                    cbar = plt.colorbar(im, ax=ax, pad=0.02, shrink=0.85, aspect=30)
                 cbar.ax.tick_params(labelsize=9, width=1.5)
                 cbar.outline.set_linewidth(1.5)
                 cbar.set_label(legend_label, fontsize=10, fontweight='bold')
@@ -486,16 +499,18 @@ def tif_to_png(tif_file, vis_dir, resolution):
             # Save as PNG then convert to WebP — use higher DPI for large data
             save_dpi = 200 if width > 3000 else 150
             png_temp = vis_dir / f"{tif_file.stem}_temp.png"
-            plt.savefig(png_temp, dpi=save_dpi, bbox_inches='tight', pad_inches=0.15,
-                       facecolor='white', format='png')
-            plt.close()
+            try:
+                plt.savefig(png_temp, dpi=save_dpi, bbox_inches='tight', pad_inches=0.15,
+                           facecolor='white', format='png')
+            finally:
+                plt.close()
 
             img = PILImage.open(str(png_temp))
             img.save(str(webp_file), format='WEBP', lossless=True)
-            png_temp.unlink()
+            png_temp.unlink(missing_ok=True)
 
             # Delete source TIFF
-            tif_file.unlink()
+            tif_file.unlink(missing_ok=True)
 
             return webp_file