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app.py

@@ -113,6 +113,13 @@ def plot_tsne(tech_filter, snr_filter, mod_filter, mob_filter, representation, c
     
     filtered_df['x'] = projections[:, 0]
     filtered_df['y'] = projections[:, 1]
+    # If t-SNE collapses to a line/point, add tiny jitter so points are visible.
+    x_span = filtered_df['x'].max() - filtered_df['x'].min()
+    y_span = filtered_df['y'].max() - filtered_df['y'].min()
+    if x_span < 1e-6:
+        filtered_df['x'] += np.random.normal(scale=1e-3, size=len(filtered_df))
+    if y_span < 1e-6:
+        filtered_df['y'] += np.random.normal(scale=1e-3, size=len(filtered_df))
     x_min, x_max = filtered_df['x'].min(), filtered_df['x'].max()
     y_min, y_max = filtered_df['y'].min(), filtered_df['y'].max()
     x_pad = max(1e-3, (x_max - x_min) * 0.05)
@@ -133,7 +140,8 @@ def plot_tsne(tech_filter, snr_filter, mod_filter, mob_filter, representation, c
     fig.update_xaxes(range=[x_min - x_pad, x_max + x_pad])
     fig.update_yaxes(range=[y_min - y_pad, y_max + y_pad])
     
-    return fig, f"{status_msg} | filtered samples: {len(filtered_df)}"
+    coord_info = f"x[{x_min:.3f},{x_max:.3f}] y[{y_min:.3f},{y_max:.3f}]"
+    return fig, f"{status_msg} | filtered samples: {len(filtered_df)} | {coord_info}"
 
 # Convenience preset: single-tech, modulation-colored embedding view
 def quick_modulation_plot():