update gitignore

.gitignore

@@ -7,6 +7,10 @@
 __pycache__/
 *.pyc
 
+=======
+/reference/
+*.svg
+>>>>>>> Stashed changes
 # 2. Ignore virtual environments COMPLETELY
 # This must come BEFORE the unignore rule
 env*/

Data/show_plots.py

@@ -662,7 +662,7 @@ print("\nFirst few rows:")
 print(df.head())
 
 # Hardcode specific patient names
-patient_names = ['113c1470']
+patient_names = ['6b56865d']
 
 # Define the functional systems (columns to plot) - adjust based on actual column names
 functional_systems = ['EDSS', 'Visual', 'Sensory', 'Motor', 'Brainstem', 'Cerebellar', 'Autonomic', 'Bladder', 'Intellectual']
@@ -1183,7 +1183,6 @@ import matplotlib.colors as mcolors
 # --- Configuration ---
 plt.rcParams['font.family'] = 'Arial'
 data_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/comparison.tsv'
-color_json_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/functional_system_colors.json'
 figure_save_path = 'project/visuals/edss_combined_confusion_matrix_mixed.svg'
 
 # --- 1. Load the Dataset ---
@@ -1212,19 +1211,19 @@ n_categories = len(categories)
 
 def categorize_edss(value):
     if pd.isna(value): return np.nan
-    idx = int(min(max(value, 0), 10) - 0.001) if value > 0 else 0
+    # Ensure value is float to avoid TypeError
+    val = float(value)
+    idx = int(min(max(val, 0), 10) - 0.001) if val > 0 else 0
     return categories[min(idx, len(categories)-1)]
 
-# --- 4. Prepare Mixed Color Matrix ---
+# --- 4. Prepare Mixed Color Matrix with Saturation ---
 cell_system_counts = np.zeros((n_categories, n_categories, len(system_names)))
 
 for s_idx, (gt_col, res_col) in enumerate(functional_systems_to_plot):
-    # CRITICAL FIX: Convert to numeric and drop NaNs in one go
+    # Fix: Ensure numeric conversion to avoid string comparison errors
-    # 'coerce' turns non-numeric strings into NaN so they don't crash the script
     temp_df = df[[gt_col, res_col]].copy()
     temp_df[gt_col] = pd.to_numeric(temp_df[gt_col], errors='coerce')
     temp_df[res_col] = pd.to_numeric(temp_df[res_col], errors='coerce')
-
     valid_df = temp_df.dropna()
 
     for _, row in valid_df.iterrows():
@@ -1233,10 +1232,9 @@ for s_idx, (gt_col, res_col) in enumerate(functional_systems_to_plot):
         if gt_cat in category_to_index and res_cat in category_to_index:
             cell_system_counts[category_to_index[gt_cat], category_to_index[res_cat], s_idx] += 1
 
-# Create an RGB image matrix (initially white/empty)
+# Create an RGBA image matrix (10x10x4)
-rgb_matrix = np.ones((n_categories, n_categories, 3)) 
+rgba_matrix = np.zeros((n_categories, n_categories, 4))
 
-# Create an Alpha matrix for the "Total Count" intensity
 total_counts = np.sum(cell_system_counts, axis=2)
 max_count = np.max(total_counts) if np.max(total_counts) > 0 else 1
 
@@ -1244,52 +1242,69 @@ for i in range(n_categories):
     for j in range(n_categories):
         count_sum = total_counts[i, j]
         if count_sum > 0:
-            # Calculate weighted average color
             mixed_rgb = np.zeros(3)
             for s_idx, s_name in enumerate(system_names):
                 weight = cell_system_counts[i, j, s_idx] / count_sum
                 system_rgb = mcolors.to_rgb(color_map[s_name])
                 mixed_rgb += np.array(system_rgb) * weight
-            rgb_matrix[i, j] = mixed_rgb
+
+            # Set RGB channels
+            rgba_matrix[i, j, :3] = mixed_rgb
+
+            # Set Alpha channel (Saturation Effect)
+            # Using a square root scale to make lower counts more visible but still "lighter"
+            alpha = np.sqrt(count_sum / max_count)
+            # Ensure alpha is at least 0.1 so it's not invisible
+            rgba_matrix[i, j, 3] = max(0.1, alpha)
+        else:
+            # Empty cells are white
+            rgba_matrix[i, j] = [1, 1, 1, 0]
 
 # --- 5. Plotting ---
 fig, ax = plt.subplots(figsize=(12, 10))
 
-# Display the mixed color matrix
+# Show the matrix
-# We use alpha based on count to show density (optional, but recommended)
+# Note: we use origin='lower' if you want 0-1 at the bottom,
-im = ax.imshow(rgb_matrix, interpolation='nearest', origin='upper')
+# but confusion matrices usually have 0-1 at the top (origin='upper')
+im = ax.imshow(rgba_matrix, interpolation='nearest', origin='upper')
 
-# Add text labels for total counts in each cell
+# Add count labels
 for i in range(n_categories):
     for j in range(n_categories):
         if total_counts[i, j] > 0:
-            # Determine text color based on brightness of background
+            # Background brightness for text contrast
-            lum = 0.2126 * rgb_matrix[i,j,0] + 0.7152 * rgb_matrix[i,j,1] + 0.0722 * rgb_matrix[i,j,2]
+            bg_color = rgba_matrix[i, j, :3]
-            text_col = "white" if lum < 0.5 else "black"
+            lum = 0.2126 * bg_color[0] + 0.7152 * bg_color[1] + 0.0722 * bg_color[2]
-            ax.text(j, i, int(total_counts[i, j]), ha="center", va="center", color=text_col, fontsize=9)
+            # If alpha is low, background is effectively white, so use black text
+            text_col = "white" if (lum < 0.5 and rgba_matrix[i,j,3] > 0.5) else "black"
+            ax.text(j, i, int(total_counts[i, j]), ha="center", va="center",
+                    color=text_col, fontsize=10, fontweight='bold')
 
 # --- 6. Styling ---
-ax.set_xlabel('LLM Inference (EDSS Category)', fontsize=12)
+ax.set_xlabel('LLM Inference (EDSS Category)', fontsize=12, labelpad=10)
-ax.set_ylabel('Ground Truth (EDSS Category)', fontsize=12)
+ax.set_ylabel('Ground Truth (EDSS Category)', fontsize=12, labelpad=10)
-ax.set_title('Blended Confusion Matrix (Color = Weighted System Mixture)', fontsize=14, pad=20)
+ax.set_title('Saturated Confusion Matrix\nColor = System Mixture | Opacity = Density', fontsize=14, pad=20)
 
 ax.set_xticks(np.arange(n_categories))
 ax.set_xticklabels(categories)
 ax.set_yticks(np.arange(n_categories))
 ax.set_yticklabels(categories)
 
+# Remove the frame/spines for a cleaner look
+for spine in ax.spines.values():
+    spine.set_visible(False)
+
 # Custom Legend
 handles = [plt.Rectangle((0,0),1,1, color=color_map[name]) for name in system_names]
 labels = [name.replace('_', ' ').capitalize() for name in system_names]
-ax.legend(handles, labels, title='Functional Systems', loc='upper left', bbox_to_anchor=(1.05, 1), frameon=False)
+ax.legend(handles, labels, title='Functional Systems', loc='upper left',
+          bbox_to_anchor=(1.05, 1), frameon=False)
 
 plt.tight_layout()
 os.makedirs(os.path.dirname(figure_save_path), exist_ok=True)
 plt.savefig(figure_save_path, format='svg', bbox_inches='tight')
 plt.show()
 
-
-
 #