drag+drop support

flares.py

@@ -1115,10 +1115,10 @@ def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2,
         is_bad = (hbo_ch in raw.info['bads']) or (hbr_ch in raw.info['bads'])
         is_short = pair_distances[i] < short_channels_threshold
 
-        if is_bad:
-            bad_pairs.append(i)
-        elif is_short:
+        if is_short:
             n_short_excluded += 1
+        elif is_bad:
+            bad_pairs.append(i)
         else:
             good_pairs.append(i)
  
@@ -1182,7 +1182,7 @@ def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2,
                                      constrained_layout=True)
     if n_bad == 1: axes = [axes] # Handle single subplot case
     
-    axes_flat = np.asarray(axes).get_data().flatten() if hasattr(axes, 'get_data') else np.asarray(axes).ravel()
+    axes_flat = np.asarray(axes).ravel()
     for j in range(n_bad, len(axes_flat)):
         if j >= n_bad:
             axes_flat[j].axis('off')
@@ -1222,9 +1222,6 @@ def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2,
     print("Bads cleared:", raw.info['bads'])
     raw.info['bads'] = []
 
-    for ch in raw.info['bads']:
-        print(f"Channel {ch} still marked as bad.")
-
     fig_raw_after = raw.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="After interpolation", show=False)
 
     return raw, fig_raw_after, fig_compare