Update app.py

app.py

@@ -98,6 +98,7 @@ class PSDAnalyzer:
                             traces[region_name][cond_key]['subjects'].append(subject)
         return traces
 
+
     def create_plot(self, region, method, selected_conditions, selected_subjects, log_scale, show_bands, align_by_delta):
         """Generate Plotly figure with optional Delta-band alignment."""
         fig = go.Figure()
@@ -200,40 +201,20 @@ class PSDAnalyzer:
             )
             return fig
 
-
-     
-
-        # Add frequency band visualization - Colored bands with legend
+        # ==============================
+        # Frequency Band Visualization
+        # ==============================
         if show_bands:
-            # Use paper coordinates for shapes to avoid affecting data scale
-            # We'll draw rectangles in "paper" space, so they don't distort the plot
-
-            # Get current y-axis range in paper coordinates
-            try:
-                y_min_data = min(trace.y.min() for trace in fig.data if hasattr(trace, 'y'))
-                y_max_data = max(trace.y.max() for trace in fig.data if hasattr(trace, 'y'))
-            except Exception:
-                y_min_data, y_max_data = 1e-5, 1.0
-
-            # Map data y-range to paper y-coordinates (0 to 1)
-            # For log scale, we map log(y) to linear paper space
-            y_log_min = np.log10(y_min_data)
-            y_log_max = np.log10(y_max_data)
-            y_log_range = y_log_max - y_log_min
-
-            # Now loop through bands
+            # Add shaded band regions using paper coordinates (so they don't affect scaling)
             for band, (low, high) in FREQ_BANDS.items():
-                # Only show if band overlaps visible freq range
+                # Only show band if it overlaps with visible freq range
                 if high < freq_range[0] or low > freq_range[1]:
                     continue
 
                 band_low = max(low, freq_range[0])
                 band_high = min(high, freq_range[1])
 
-                # Map y-data to paper coordinates (for log scale)
-                # We want the band to cover full y-axis in paper space
-                # So we set y0=0, y1=1 in paper coordinates
-                # But we must use `yref="paper"` and `xref="x"`
+                # Add shaded rectangle in paper space (full height, behind data)
                 fig.add_shape(
                     type="rect",
                     x0=band_low,
@@ -246,14 +227,13 @@ class PSDAnalyzer:
                     opacity=0.15,
                     layer="below",
                     line_width=0,
-                    name=band,
                 )
 
-                # Add label above the plot (outside the plot area)
+                # Add label above the plot (outside the plotting area)
                 center_x = (band_low + band_high) / 2
                 fig.add_annotation(
                     x=center_x,
-                    y=1.02,  # Slightly above the top of the plot
+                    y=1.02,  # Just above the top of the plot
                     text=band,
                     showarrow=False,
                     font=dict(size=9, color="dimgray"),
@@ -261,10 +241,10 @@ class PSDAnalyzer:
                     yanchor="bottom",
                     xref="x",
                     yref="paper",
-                    opacity=0.8,
+                    opacity=0.85,
                 )
 
-            # Optional: Add vertical lines at band edges
+            # Add vertical dotted lines at band boundaries (only once per edge)
             seen_edges = set()
             for low, high in FREQ_BANDS.values():
                 for edge in [low, high]:
@@ -277,61 +257,9 @@ class PSDAnalyzer:
                         )
                         seen_edges.add(edge)
 
-            # 2. Add band labels just above the x-axis (not floating)
-            for band, (low, high) in FREQ_BANDS.items():
-                center = (low + high) / 2
-                if freq_range[0] <= center <= freq_range[1]:
-                    fig.add_annotation(
-                        x=center,
-                        y=0.01,  # Just above bottom of plot
-                        text=band,
-                        showarrow=False,
-                        font=dict(size=8, color="darkgray", family="sans-serif"),
-                        xanchor="center",
-                        yanchor="bottom",
-                        opacity=0.8,
-                        xref="x",
-                        yref="paper"  # Fixed paper coordinate
-                    )
-
-            # 2. Clean vertical lines only at band boundaries (avoid clutter)
-            seen_edges = set()
-            edges = []
-            for low, high in FREQ_BANDS.values():
-                if freq_range[0] < low < freq_range[1] and low not in seen_edges:
-                    edges.append(low)
-                    seen_edges.add(low)
-                if freq_range[0] < high < freq_range[1] and high not in seen_edges:
-                    edges.append(high)
-                    seen_edges.add(high)
-
-            for edge in sorted(edges):
-                fig.add_vline(
-                    x=edge,
-                    line=dict(color="gray", width=1, dash="dashdot"),
-                    opacity=0.4,
-                    layer='below'
-                )
-            # 3. Band labels above the plot
-            try:
-                y_max = max(trace.y.max() for trace in fig.data if hasattr(trace, 'y'))
-                y_pos = y_max * 1.15 if not log_scale else y_max * 3
-            except Exception:
-                y_pos = 1.1
-
-            for band, (low, high) in FREQ_BANDS.items():
-                center = (low + high) / 2
-                if freq_range[0] <= center <= freq_range[1]:
-                    fig.add_annotation(
-                        x=center, y=y_pos,
-                        text=band,
-                        showarrow=False,
-                        font=dict(size=10, color="dimgray"),
-                        xanchor="center", yanchor="bottom",
-                        opacity=0.9
-                    )
-
-        # Final layout
+        # ==============================
+        # ✅ FIXED: Lock axis ranges to prevent distortion
+        # ==============================
         yaxis_title = "Power"
         if align_by_delta:
             yaxis_title = "Power (norm. to Delta)"
@@ -352,17 +280,19 @@ class PSDAnalyzer:
                 bgcolor="rgba(255,255,255,0.8)", font_size=11
             ),
             margin=dict(r=160, t=60, b=80, l=60),
-            hovermode='x unified'
-        )
-
-        fig.update_xaxes(
-            range=freq_range,
-            showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)',
-            showline=True, linewidth=1, linecolor='gray'
-        )
-        fig.update_yaxes(
-            showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)',
-            showline=True, linewidth=1, linecolor='gray'
+            hovermode='x unified',
+            # 🔒 CRITICAL: Lock axis ranges to prevent visual distortion
+            xaxis=dict(
+                range=[freq_range[0], freq_range[1]],
+                fixedrange=True,  # Prevents zoom/pan from UI, but more importantly — stops auto-resize
+                showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)',
+                showline=True, linewidth=1, linecolor='gray'
+            ),
+            yaxis=dict(
+                fixedrange=True,  # Stops Plotly from auto-resizing y-axis when shapes are added
+                showgrid=True, gridwidth=1, gridcolor='rgba(128,128,128,0.2)',
+                showline=True, linewidth=1, linecolor='gray'
+            )
         )
 
         return fig