Update app.py

app.py

@@ -670,8 +670,8 @@ def calculate_statistics(df_filtered, lens_selected):
     return stats
 
 def update_manifold_visualization(species_selection, emotion_selection, lens_selection, 
-                                alpha_range, srl_range, feature_range, point_size,
-                                show_boundary, show_trajectories, color_scheme):
+                                alpha_min, alpha_max, srl_min, srl_max, feature_min, feature_max, 
+                                point_size, show_boundary, show_trajectories, color_scheme):
     """Main update function for the manifold visualization."""
     
     # Filter data based on selections
@@ -691,14 +691,14 @@ def update_manifold_visualization(species_selection, emotion_selection, lens_sel
     
     if alpha_col in df_filtered.columns:
         df_filtered = df_filtered[
-            (df_filtered[alpha_col] >= alpha_range[0]) & 
-            (df_filtered[alpha_col] <= alpha_range[1])
+            (df_filtered[alpha_col] >= alpha_min) & 
+            (df_filtered[alpha_col] <= alpha_max)
         ]
     
     if srl_col in df_filtered.columns:
         df_filtered = df_filtered[
-            (df_filtered[srl_col] >= srl_range[0]) & 
-            (df_filtered[srl_col] <= srl_range[1])
+            (df_filtered[srl_col] >= srl_min) & 
+            (df_filtered[srl_col] <= srl_max)
         ]
     
     # Feature magnitude filter (using first few feature columns if they exist)
@@ -706,8 +706,8 @@ def update_manifold_visualization(species_selection, emotion_selection, lens_sel
     if feature_cols:
         feature_magnitudes = np.sqrt(df_filtered[feature_cols[:3]].pow(2).sum(axis=1))
         df_filtered = df_filtered[
-            (feature_magnitudes >= feature_range[0]) & 
-            (feature_magnitudes <= feature_range[1])
+            (feature_magnitudes >= feature_min) & 
+            (feature_magnitudes <= feature_max)
         ]
     
     # Create visualizations
@@ -1194,23 +1194,32 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) a
                     
                     # Advanced filtering sliders
                     with gr.Accordion("🎛️ Advanced CMT Filters", open=False):
-                        alpha_range = gr.RangeSlider(
-                            label="CMT Alpha Range (Geometric Consistency)",
-                            minimum=0, maximum=1, value=[0, 1], step=0.01,
-                            info="Filter by geometric consistency measure"
-                        )
+                        gr.Markdown("**CMT Alpha Range (Geometric Consistency)**")
+                        with gr.Row():
+                            alpha_min = gr.Slider(
+                                label="Alpha Min", minimum=0, maximum=1, value=0, step=0.01
+                            )
+                            alpha_max = gr.Slider(
+                                label="Alpha Max", minimum=0, maximum=1, value=1, step=0.01
+                            )
                         
-                        srl_range = gr.RangeSlider(
-                            label="SRL Range (Complexity Level)",
-                            minimum=0, maximum=100, value=[0, 100], step=1,
-                            info="Filter by spike response level (complexity)"
-                        )
+                        gr.Markdown("**SRL Range (Complexity Level)**")
+                        with gr.Row():
+                            srl_min = gr.Slider(
+                                label="SRL Min", minimum=0, maximum=100, value=0, step=1
+                            )
+                            srl_max = gr.Slider(
+                                label="SRL Max", minimum=0, maximum=100, value=100, step=1
+                            )
                         
-                        feature_magnitude = gr.RangeSlider(
-                            label="Feature Magnitude Range",
-                            minimum=-3, maximum=3, value=[-3, 3], step=0.1,
-                            info="Filter by overall feature strength"
-                        )
+                        gr.Markdown("**Feature Magnitude Range**")
+                        with gr.Row():
+                            feature_min = gr.Slider(
+                                label="Feature Min", minimum=-3, maximum=3, value=-3, step=0.1
+                            )
+                            feature_max = gr.Slider(
+                                label="Feature Max", minimum=-3, maximum=3, value=3, step=0.1
+                            )
                     
                     # Visualization options
                     with gr.Accordion("🎨 Visualization Options", open=True):
@@ -1309,8 +1318,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) a
             # Wire up all the interactive components
             manifold_inputs = [
                 species_filter, emotion_filter, lens_selector,
-                alpha_range, srl_range, feature_magnitude, point_size,
-                show_species_boundary, show_trajectories, color_scheme
+                alpha_min, alpha_max, srl_min, srl_max, feature_min, feature_max, 
+                point_size, show_species_boundary, show_trajectories, color_scheme
             ]
             
             manifold_outputs = [
@@ -1333,9 +1342,12 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) a
                     ["Human", "Dog"],  # species_selection
                     list(df_combined['label'].unique()),  # emotion_selection
                     "gamma",  # lens_selection
-                    [0, 1],   # alpha_range
-                    [0, 100], # srl_range
-                    [-3, 3],  # feature_range
+                    0,        # alpha_min
+                    1,        # alpha_max
+                    0,        # srl_min
+                    100,      # srl_max
+                    -3,       # feature_min
+                    3,        # feature_max
                     6,        # point_size
                     True,     # show_boundary
                     False,    # show_trajectories