diff --git a/Data/show_plots.py b/Data/show_plots.py
index e699989..ab94934 100644
--- a/Data/show_plots.py
+++ b/Data/show_plots.py
@@ -1397,7 +1397,448 @@ os.makedirs(os.path.dirname(figure_save_path), exist_ok=True)
 plt.savefig(figure_save_path, format='svg', bbox_inches='tight')
 plt.show()
 
-#
+##
 
 
 
+
+
+
+
+
+
+
+# %% Difference Plot Functional system
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import json
+import os
+import numpy as np
+
+# --- Configuration ---
+# Set the font to Arial for all text in the plot, as per the guidelines
+plt.rcParams['font.family'] = 'Arial'
+
+# Define the path to your data file
+data_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/comparison.tsv'
+
+# Define the path to save the color mapping JSON file
+color_json_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/functional_system_colors.json'
+
+# Define the path to save the final figure
+figure_save_path = 'project/visuals/edss_functional_systems_comparison.svg'
+
+# --- 1. Load the Dataset ---
+try:
+    # Load the TSV file
+    df = pd.read_csv(data_path, sep='\t')
+    print(f"Successfully loaded data from {data_path}")
+    print(f"Data shape: {df.shape}")
+except FileNotFoundError:
+    print(f"Error: The file at {data_path} was not found.")
+    # Exit or handle the error appropriately
+    raise
+
+# --- 2. Define Functional Systems and Create Color Mapping ---
+# List of tuples containing (ground_truth_column, result_column)
+functional_systems_to_plot = [
+    ('GT.VISUAL_OPTIC_FUNCTIONS', 'result.VISUAL OPTIC FUNCTIONS'),
+    ('GT.CEREBELLAR_FUNCTIONS', 'result.CEREBELLAR FUNCTIONS'),
+    ('GT.BRAINSTEM_FUNCTIONS', 'result.BRAINSTEM FUNCTIONS'),
+    ('GT.SENSORY_FUNCTIONS', 'result.SENSORY FUNCTIONS'),
+    ('GT.PYRAMIDAL_FUNCTIONS', 'result.PYRAMIDAL FUNCTIONS'),
+    ('GT.AMBULATION', 'result.AMBULATION'),
+    ('GT.CEREBRAL_FUNCTIONS', 'result.CEREBRAL FUNCTIONS'),
+    ('GT.BOWEL_AND_BLADDER_FUNCTIONS', 'result.BOWEL AND BLADDER FUNCTIONS')
+]
+
+# Extract system names for color mapping and legend
+system_names = [name.split('.')[1] for name, _ in functional_systems_to_plot]
+
+# Define a professional color palette (dark blue theme)
+# This is a qualitative palette with distinct, accessible colors
+colors = [
+    '#003366', # Dark Blue
+    '#336699', # Medium Blue
+    '#6699CC', # Light Blue
+    '#99CCFF', # Very Light Blue
+    '#FF9966', # Coral
+    '#FF6666', # Light Red
+    '#CC6699', # Magenta
+    '#9966CC'  # Purple
+]
+
+# Create a dictionary mapping system names to colors
+color_map = dict(zip(system_names, colors))
+
+# Ensure the directory for the JSON file exists
+os.makedirs(os.path.dirname(color_json_path), exist_ok=True)
+
+# Save the color map to a JSON file
+with open(color_json_path, 'w') as f:
+    json.dump(color_map, f, indent=4)
+
+print(f"Color mapping saved to {color_json_path}")
+
+# --- 3. Calculate Agreement Percentages and Format Legend Labels ---
+agreement_percentages = {}
+legend_labels = {}
+
+for gt_col, res_col in functional_systems_to_plot:
+    system_name = gt_col.split('.')[1]
+
+    # Convert columns to numeric, setting errors to NaN
+    gt_numeric = pd.to_numeric(df[gt_col], errors='coerce')
+    res_numeric = pd.to_numeric(df[res_col], errors='coerce')
+
+    # Ensure we are comparing the same rows
+    common_index = gt_numeric.dropna().index.intersection(res_numeric.dropna().index)
+    gt_data = gt_numeric.loc[common_index]
+    res_data = res_numeric.loc[common_index]
+
+    # Calculate agreement percentage
+    if len(gt_data) > 0:
+        agreement = (gt_data == res_data).mean() * 100
+    else:
+        agreement = 0 # Handle case with no valid data
+
+    agreement_percentages[system_name] = agreement
+
+    # Format the system name for the legend (e.g., "VISUAL_OPTIC_FUNCTIONS" -> "Visual Optic Functions")
+    formatted_name = " ".join(word.capitalize() for word in system_name.split('_'))
+    legend_labels[system_name] = f"{formatted_name} ({agreement:.1f}%)"
+
+
+ # --- 4. Robustly Prepare Error Data for Boxplot ---
+
+def safe_parse(s):
+    '''Convert to float, handling comma decimals (e.g., '3,5' → 3.5)'''
+    if pd.isna(s):
+        return np.nan
+    if isinstance(s, (int, float)):
+        return float(s)
+    # Replace comma with dot, then strip whitespace
+    s_clean = str(s).replace(',', '.').strip()
+    try:
+        return float(s_clean)
+    except ValueError:
+        return np.nan
+
+plot_data = []
+for gt_col, res_col in functional_systems_to_plot:
+    system_name = gt_col.split('.')[1]
+
+    # Parse both columns with robust comma handling
+    gt_numeric = df[gt_col].apply(safe_parse)
+    res_numeric = df[res_col].apply(safe_parse)
+
+    # Compute error (only where both are finite)
+    error = res_numeric - gt_numeric
+
+    # Create temp DataFrame
+    temp_df = pd.DataFrame({
+        'system': system_name,
+        'error': error
+    }).dropna()  # drop rows where either was unparseable
+
+    plot_data.append(temp_df)
+
+plot_df = pd.concat(plot_data, ignore_index=True)
+
+if plot_df.empty:
+    print("⚠️   Warning: No valid numeric error data to plot after robust parsing.")
+else:
+    print(f"✅ Prepared error data with {len(plot_df)} data points.")
+    # Diagnostic: show a few samples
+    print("\n📌 Sample errors by system:")
+    for sys, grp in plot_df.groupby('system'):
+        print(f"  {sys:25s}: n={len(grp)}, mean err = {grp['error'].mean():+.2f}, min = {grp['error'].min():+.2f}, max = {grp['error'].max():+.2f}")
+
+# Ensure categorical ordering
+plot_df['system'] = pd.Categorical(
+    plot_df['system'],
+    categories=[name.split('.')[1] for name, _ in functional_systems_to_plot],
+    ordered=True
+)
+
+
+# --- 5. Prepare Data for Diverging Stacked Bar Plot ---
+print("\n📊 Preparing diverging stacked bar plot data...")
+
+# Define bins for error direction
+def categorize_error(err):
+    if pd.isna(err):
+        return 'missing'
+    elif err < 0:
+        return 'underestimate'
+    elif err > 0:
+        return 'overestimate'
+    else:
+        return 'match'
+
+# Add category column (only on finite errors)
+plot_df_clean = plot_df[plot_df['error'].notna()].copy()
+plot_df_clean['category'] = plot_df_clean['error'].apply(categorize_error)
+
+# Count by system + category
+category_counts = (
+    plot_df_clean
+    .groupby(['system', 'category'])
+    .size()
+    .unstack(fill_value=0)
+    .reindex(columns=['underestimate', 'match', 'overestimate'], fill_value=0)
+)
+# Reorder systems
+category_counts = category_counts.reindex(system_names)
+
+# Prepare for diverging plot:
+# - Underestimates: plotted to the *left* (negative x)
+# - Overestimates: plotted to the *right* (positive x)
+# - Matches: centered (no width needed, or as a bar of width 0.2)
+underestimate_counts = category_counts['underestimate']
+match_counts = category_counts['match']
+overestimate_counts = category_counts['overestimate']
+
+# For diverging: left = -underestimate, right = overestimate
+left_counts = underestimate_counts
+right_counts = overestimate_counts
+
+# Compute max absolute bar height (for symmetric x-axis)
+max_bar = max(left_counts.max(), right_counts.max(), 1)
+plot_range = (-max_bar, max_bar)
+
+# X-axis positions: 0 = center, left systems to -1, -2, ..., right systems to +1, +2, ...
+n_systems = len(system_names)
+positions = np.arange(n_systems)
+left_positions = -positions - 0.5  # left-aligned underestimates
+right_positions = positions + 0.5  # right-aligned overestimates
+
+# --- 6. Create Diverging Stacked Bar Plot ---
+plt.figure(figsize=(12, 7))
+
+# Colors: diverging palette
+colors = {
+    'underestimate': '#E74C3C',  # Red (left)
+    'match': '#2ECC71',          # Green (center)
+    'overestimate': '#F39C12'    # Orange (right)
+}
+
+# Plot underestimates (left side)
+bars_left = plt.barh(
+    left_positions,
+    left_counts.values,
+    height=0.8,
+    left=0,  # starts at 0, extends left (since bars are negative width would be wrong; instead use negative values)
+    color=colors['underestimate'],
+    edgecolor='black',
+    linewidth=0.5,
+    alpha=0.9,
+    label='Underestimate'
+)
+
+# Plot overestimates (right side)
+bars_right = plt.barh(
+    right_positions,
+    right_counts.values,
+    height=0.8,
+    left=0,
+    color=colors['overestimate'],
+    edgecolor='black',
+    linewidth=0.5,
+    alpha=0.9,
+    label='Overestimate'
+)
+
+# Plot matches (center — narrow bar)
+# Use a very narrow width (0.2) centered at 0
+plt.barh(
+    positions,
+    match_counts.values,
+    height=0.2,
+    left=0,  # starts at 0, extends right
+    color=colors['match'],
+    edgecolor='black',
+    linewidth=0.5,
+    alpha=0.9,
+    label='Exact Match'
+)
+
+# ✨ Better: flip match to be centered symmetrically (left=-match/2, width=match)
+# For perfect symmetry:
+for i, count in enumerate(match_counts.values):
+    if count > 0:
+        plt.barh(
+            positions[i],
+            width=count,
+            left=-count/2,
+            height=0.25,
+            color=colors['match'],
+            edgecolor='black',
+            linewidth=0.5,
+            alpha=0.95
+        )
+
+# --- 7. Styling & Labels ---
+# Zero reference line
+plt.axvline(x=0, color='black', linestyle='-', linewidth=1.2, alpha=0.8)
+
+# X-axis: symmetric around 0
+plt.xlim(plot_range[0] - max_bar*0.1, plot_range[1] + max_bar*0.1)
+plt.xticks(rotation=0, fontsize=10)
+plt.xlabel('Count', fontsize=12)
+
+# Y-axis: system names at original positions (centered)
+plt.yticks(positions, [name.replace('_', '\n').replace('and', '&') for name in system_names], fontsize=10)
+plt.ylabel('Functional System', fontsize=12)
+
+# Title & layout
+plt.title('Diverging Error Direction by Functional System\n(Red: Underestimation | Green: Exact | Orange: Overestimation)', fontsize=13, pad=15)
+
+# Clean axes
+ax = plt.gca()
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
+ax.spines['left'].set_visible(False)  # We only need bottom axis
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('none')
+
+# Grid only along x
+ax.xaxis.grid(True, linestyle=':', alpha=0.5)
+
+# Legend
+from matplotlib.patches import Patch
+legend_elements = [
+    Patch(facecolor=colors['underestimate'], edgecolor='black', label='Underestimate'),
+    Patch(facecolor=colors['match'], edgecolor='black', label='Exact Match'),
+    Patch(facecolor=colors['overestimate'], edgecolor='black', label='Overestimate')
+]
+plt.legend(handles=legend_elements, loc='upper right', frameon=False, fontsize=10)
+
+# Optional: Add counts on bars
+for i, (left, right, match) in enumerate(zip(left_counts, right_counts, match_counts)):
+    if left > 0:
+        plt.text(-left - max_bar*0.05, left_positions[i], str(left), va='center', ha='right', fontsize=9, color='white', fontweight='bold')
+    if right > 0:
+        plt.text(right + max_bar*0.05, right_positions[i], str(right), va='center', ha='left', fontsize=9, color='white', fontweight='bold')
+    if match > 0:
+        plt.text(match_counts[i]/2, positions[i], str(match), va='center', ha='center', fontsize=8, color='black')
+
+plt.tight_layout()
+
+# --- 8. Save & Show ---
+os.makedirs(os.path.dirname(figure_save_path), exist_ok=True)
+plt.savefig(figure_save_path, format='svg', bbox_inches='tight')
+print(f"✅ Diverging bar plot saved to {figure_save_path}")
+
+plt.show()
+
+##
+
+
+
+# %% Difference Gemini easy
+
+
+# --- 1. Process Error Data ---
+system_names = [name.split('.')[1] for name, _ in functional_systems_to_plot]
+plot_list = []
+
+for gt_col, res_col in functional_systems_to_plot:
+    sys_name = gt_col.split('.')[1]
+
+    # Robust parsing
+    gt = df[gt_col].apply(safe_parse)
+    res = df[res_col].apply(safe_parse)
+    error = res - gt
+
+    # Calculate counts
+    matches = (error == 0).sum()
+    under = (error < 0).sum()
+    over = (error > 0).sum()
+    total = error.dropna().count()
+
+    # Calculate Percentages
+    # Using max(total, 1) to avoid division by zero
+    divisor = max(total, 1)
+    match_pct = (matches / divisor) * 100
+    under_pct = (under / divisor) * 100
+    over_pct = (over / divisor) * 100
+
+    plot_list.append({
+        'System': sys_name.replace('_', ' ').title(),
+        'Matches': matches,
+        'MatchPct': match_pct,
+        'Under': under,
+        'UnderPct': under_pct,
+        'Over': over,
+        'OverPct': over_pct
+    })
+
+stats_df = pd.DataFrame(plot_list)
+
+# --- 2. Plotting ---
+fig, ax = plt.subplots(figsize=(12, 8)) # Slightly taller for multi-line labels
+
+color_under = '#E74C3C'
+color_over = '#3498DB'
+bar_height = 0.6
+
+y_pos = np.arange(len(stats_df))
+
+ax.barh(y_pos, -stats_df['Under'], bar_height, label='Under-scored', color=color_under, edgecolor='white', alpha=0.8)
+ax.barh(y_pos, stats_df['Over'], bar_height, label='Over-scored', color=color_over, edgecolor='white', alpha=0.8)
+
+# --- 3. Aesthetics & Labels ---
+
+for i, row in stats_df.iterrows():
+    # Constructing a detailed label for the left side
+    # Matches (Bold) | Under % | Over %
+    label_text = (
+        f"$\mathbf{{{row['System']}}}$\n"
+        f"Matches: {int(row['Matches'])} ({row['MatchPct']:.1f}%)\n"
+        f"Under: {int(row['Under'])} ({row['UnderPct']:.1f}%) | Over: {int(row['Over'])} ({row['OverPct']:.1f}%)"
+    )
+
+    # Position text to the left of the x=0 line
+    ax.text(ax.get_xlim()[0] - 0.5, i, label_text, va='center', ha='right', fontsize=9, color='#333333', linespacing=1.3)
+
+# Zero line
+ax.axvline(0, color='black', linewidth=1.2, alpha=0.7)
+
+# Clean up axes
+ax.set_yticks([])
+ax.set_xlabel('Number of Patients with Error', fontsize=11, fontweight='bold', labelpad=10)
+#ax.set_title('Directional Error Analysis by Functional System', fontsize=14, pad=30)
+
+# Make X-axis labels absolute
+ax.set_xticklabels([int(abs(tick)) for tick in ax.get_xticks()])
+
+# Remove spines
+for spine in ['top', 'right', 'left']:
+    ax.spines[spine].set_visible(False)
+
+# Legend
+ax.legend(loc='upper right', frameon=False, bbox_to_anchor=(1, 1.1))
+
+# Grid
+ax.xaxis.grid(True, linestyle='--', alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+##
+
+# %% test
+# Diagnose: what are the actual differences?
+print("\n🔍 Raw differences (first 5 rows per system):")
+for gt_col, res_col in functional_systems_to_plot:
+    gt = df[gt_col].apply(safe_parse)
+    res = df[res_col].apply(safe_parse)
+    diff = res - gt
+    non_zero = (diff != 0).sum()
+    # Check if it's due to floating point noise
+    abs_diff = diff.abs()
+    tiny = (abs_diff > 0) & (abs_diff < 1e-10)
+    print(f"{gt_col.split('.')[1]:25s}: non-zero = {non_zero:3d}, tiny = {tiny.sum():3d}, max abs diff = {abs_diff.max():.12f}")
+
+##