updated git ignore and new files

.gitignore

@@ -1,8 +1,16 @@
-# Ignore all contents of these directories
+# 1. Broad Ignores
-
+/Data/*
-/Data/
+/attach/*
-/attach/
+/results/*
-/results/
+/enarcelona/*
-/enarcelona/
 .env
+__pycache__/
+*.pyc
+
+# 2. Ignore virtual environments COMPLETELY
+# This must come BEFORE the unignore rule
+env*/
+
+# 3. The "Unignore" rule (Whitelisting)
+# We only unignore .py files that aren't already blocked by the rules above
 !**/*.py

Data/show_plots.py

@@ -0,0 +1,594 @@
+# %% Scatter
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# Create scatter plot
+plt.figure(figsize=(8, 6))
+plt.scatter(df_clean['GT_EDSS'], df_clean['LLM_Results'], alpha=0.7, color='blue')
+
+# Add labels and title
+plt.xlabel('GT_EDSS')
+plt.ylabel('LLM_Results')
+plt.title('Comparison of GT_EDSS vs LLM_Results')
+
+# Optional: Add a diagonal line for reference (perfect prediction)
+plt.plot([0, max(df_clean['GT_EDSS'])], [0, max(df_clean['GT_EDSS'])], color='red', linestyle='--', label='Perfect Prediction')
+plt.legend()
+
+# Show plot
+plt.grid(True)
+plt.tight_layout()
+plt.show()
+
+##
+
+
+# %% Bland0-altman
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import statsmodels.api as sm
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# Create Bland-Altman plot
+f, ax = plt.subplots(1, figsize=(8, 5))
+sm.graphics.mean_diff_plot(df_clean['GT_EDSS'], df_clean['LLM_Results'], ax=ax)
+
+# Add labels and title
+ax.set_title('Bland-Altman Plot: GT_EDSS vs LLM_Results')
+ax.set_xlabel('Mean of GT_EDSS and LLM_Results')
+ax.set_ylabel('Difference between GT_EDSS and LLM_Results')
+
+# Display Bland-Altman plot
+plt.tight_layout()
+plt.show()
+
+# Print some statistics
+mean_diff = np.mean(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
+std_diff = np.std(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
+print(f"Mean difference: {mean_diff:.3f}")
+print(f"Standard deviation of differences: {std_diff:.3f}")
+print(f"95% Limits of Agreement: [{mean_diff - 1.96*std_diff:.3f}, {mean_diff + 1.96*std_diff:.3f}]")
+
+##
+
+
+
+# %%  Confusion matrix
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import confusion_matrix, classification_report
+import seaborn as sns
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_results_unique.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# For confusion matrix, we need to categorize the values
+# Let's create categories up to 10 (0-1, 1-2, 2-3, ..., 9-10)
+def categorize_edss(value):
+    if pd.isna(value):
+        return np.nan
+    elif value <= 1.0:
+        return '0-1'
+    elif value <= 2.0:
+        return '1-2'
+    elif value <= 3.0:
+        return '2-3'
+    elif value <= 4.0:
+        return '3-4'
+    elif value <= 5.0:
+        return '4-5'
+    elif value <= 6.0:
+        return '5-6'
+    elif value <= 7.0:
+        return '6-7'
+    elif value <= 8.0:
+        return '7-8'
+    elif value <= 9.0:
+        return '8-9'
+    elif value <= 10.0:
+        return '9-10'
+    else:
+        return '10+'
+
+# Create categorical versions
+df_clean['GT_EDSS_cat'] = df_clean['GT_EDSS'].apply(categorize_edss)
+df_clean['LLM_Results_cat'] = df_clean['LLM_Results'].apply(categorize_edss)
+
+# Remove any NaN categories
+df_clean = df_clean.dropna(subset=['GT_EDSS_cat', 'LLM_Results_cat'])
+
+# Create confusion matrix
+cm = confusion_matrix(df_clean['GT_EDSS_cat'], df_clean['LLM_Results_cat'],
+                     labels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+
+# Plot confusion matrix
+plt.figure(figsize=(10, 8))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'],
+            yticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
+plt.title('Confusion Matrix: GT_EDSS vs interferred EDSS (Categorized 0-10)')
+plt.xlabel('LLM_Results Category')
+plt.ylabel('GT_EDSS Category')
+plt.tight_layout()
+plt.show()
+
+# Print classification report
+print("Classification Report:")
+print(classification_report(df_clean['GT_EDSS_cat'], df_clean['LLM_Results_cat']))
+
+# Print raw counts
+print("\nConfusion Matrix (Raw Counts):")
+print(cm)
+
+##
+
+
+
+# %% Classification 
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+import numpy as np
+
+# Load your data from TSV file
+file_path ='/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_results_unique.tsv'
+
+df = pd.read_csv(file_path, sep='\t')
+
+# Check data structure
+print("Data shape:", df.shape)
+print("First few rows:")
+print(df.head())
+print("\nColumn names:")
+for col in df.columns:
+    print(f"  {col}")
+
+# Function to safely convert to boolean
+def safe_bool_convert(series):
+    '''Safely convert series to boolean, handling various input formats'''
+    # Convert to string first, then to boolean
+    series_str = series.astype(str).str.strip().str.lower()
+
+    # Handle different true/false representations
+    bool_map = {
+        'true': True, '1': True, 'yes': True, 'y': True,
+        'false': False, '0': False, 'no': False, 'n': False
+    }
+
+    converted = series_str.map(bool_map)
+
+    # Handle remaining NaN values
+    converted = converted.fillna(False)  # or True, depending on your preference
+
+    return converted
+
+# Convert columns safely
+if 'LLM_klassifizierbar' in df.columns:
+    print("\nLLM_klassifizierbar column info:")
+    print(df['LLM_klassifizierbar'].head(10))
+    print("Unique values:", df['LLM_klassifizierbar'].unique())
+
+    df['LLM_klassifizierbar'] = safe_bool_convert(df['LLM_klassifizierbar'])
+    print("After conversion:")
+    print(df['LLM_klassifizierbar'].value_counts())
+
+if 'GT_klassifizierbar' in df.columns:
+    print("\nGT_klassifizierbar column info:")
+    print(df['GT_klassifizierbar'].head(10))
+    print("Unique values:", df['GT_klassifizierbar'].unique())
+
+    df['GT_klassifizierbar'] = safe_bool_convert(df['GT_klassifizierbar'])
+    print("After conversion:")
+    print(df['GT_klassifizierbar'].value_counts())
+
+# Create bar chart showing only True values for klassifizierbar
+if 'LLM_klassifizierbar' in df.columns and 'GT_klassifizierbar' in df.columns:
+    # Get counts for True values only
+    llm_true_count = df['LLM_klassifizierbar'].sum()
+    gt_true_count = df['GT_klassifizierbar'].sum()
+
+    # Plot using matplotlib directly
+    fig, ax = plt.subplots(figsize=(8, 6))
+
+    x = np.arange(2)
+    width = 0.35
+
+    bars1 = ax.bar(x[0] - width/2, llm_true_count, width, label='LLM', color='skyblue', alpha=0.8)
+    bars2 = ax.bar(x[1] + width/2, gt_true_count, width, label='GT', color='lightcoral', alpha=0.8)
+
+    # Add value labels on bars
+    ax.annotate(f'{llm_true_count}',
+                xy=(x[0], llm_true_count),
+                xytext=(0, 3),
+                textcoords="offset points",
+                ha='center', va='bottom')
+
+    ax.annotate(f'{gt_true_count}',
+                xy=(x[1], gt_true_count),
+                xytext=(0, 3),
+                textcoords="offset points",
+                ha='center', va='bottom')
+
+    ax.set_xlabel('Classification Status (klassifizierbar)')
+    ax.set_ylabel('Count')
+    ax.set_title('True Values Comparison: LLM vs GT for "klassifizierbar"')
+    ax.set_xticks(x)
+    ax.set_xticklabels(['LLM', 'GT'])
+    ax.legend()
+
+    plt.tight_layout()
+    plt.show()
+
+# Create confusion matrix if both columns exist
+if 'LLM_klassifizierbar' in df.columns and 'GT_klassifizierbar' in df.columns:
+    try:
+        # Ensure both columns are boolean
+        llm_bool = df['LLM_klassifizierbar'].fillna(False).astype(bool)
+        gt_bool = df['GT_klassifizierbar'].fillna(False).astype(bool)
+
+        cm = confusion_matrix(gt_bool, llm_bool)
+
+        # Plot confusion matrix
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+                   xticklabels=['False ', 'True '],
+                   yticklabels=['False', 'True '],
+                   ax=ax)
+        ax.set_xlabel('LLM Predictions ')
+        ax.set_ylabel('GT Labels ')
+        ax.set_title('Confusion Matrix: LLM vs GT for "klassifizierbar"')
+
+        plt.tight_layout()
+        plt.show()
+
+        print("Confusion Matrix:")
+        print(cm)
+
+    except Exception as e:
+        print(f"Error creating confusion matrix: {e}")
+
+# Show final data info
+print("\nFinal DataFrame info:")
+print(df[['LLM_klassifizierbar', 'GT_klassifizierbar']].info())
+
+##
+
+
+
+
+# %% Boxplot
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+# Load your data from TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_results_unique.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
+df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
+df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
+
+# Convert to float (handle invalid entries gracefully)
+df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
+df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
+
+# Drop rows where either column is NaN
+df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
+
+# 1. DEFINE CATEGORY ORDER
+# This ensures the X-axis is numerically logical (0-1 comes before 1-2)
+category_order = ['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10', '10+']
+
+# Convert the column to a Categorical type with the specific order
+df_clean['GT_EDSS_cat'] = pd.Categorical(df_clean['GT_EDSS'].apply(categorize_edss), 
+                                         categories=category_order, 
+                                         ordered=True)
+
+plt.figure(figsize=(14, 8))
+
+# 2. ADD HUE FOR LEGEND
+# Assigning x to 'hue' allows Seaborn to generate a legend automatically
+box_plot = sns.boxplot(
+    data=df_clean, 
+    x='GT_EDSS_cat', 
+    y='LLM_Results',
+    hue='GT_EDSS_cat',  # Added hue
+    palette='viridis', 
+    linewidth=1.5,
+    legend=True         # Ensure legend is enabled
+)
+
+# 3. CUSTOMIZE PLOT
+plt.title('Distribution of LLM_Results by GT_EDSS Category', fontsize=18, pad=20)
+plt.xlabel('Ground Truth EDSS Category', fontsize=14)
+plt.ylabel('LLM Predicted EDSS', fontsize=14)
+
+# Move legend to the side or top
+plt.legend(title="EDSS Categories", bbox_to_anchor=(1.05, 1), loc='upper left')
+
+plt.xticks(rotation=45, ha='right', fontsize=10)
+plt.grid(True, axis='y', alpha=0.3)
+plt.tight_layout()
+
+plt.show()
+##
+
+
+# %% Postproccessing Column names
+
+import pandas as pd
+
+# Read the TSV file
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Create a mapping dictionary for German to English column names
+column_mapping = {
+    'EDSS':'GT.EDSS',
+    'klassifizierbar': 'GT.klassifizierbar',
+    'Sehvermögen': 'GT.VISUAL_OPTIC_FUNCTIONS',
+    'Cerebellum': 'GT.CEREBELLAR_FUNCTIONS',
+    'Hirnstamm': 'GT.BRAINSTEM_FUNCTIONS',
+    'Sensibiliät': 'GT.SENSORY_FUNCTIONS',
+    'Pyramidalmotorik': 'GT.PYRAMIDAL_FUNCTIONS',
+    'Ambulation': 'GT.AMBULATION',
+    'Cerebrale_Funktion': 'GT.CEREBRAL_FUNCTIONS',
+    'Blasen-_und_Mastdarmfunktion': 'GT.BOWEL_AND_BLADDER_FUNCTIONS'
+}
+
+# Rename columns
+df = df.rename(columns=column_mapping)
+
+# Save the modified dataframe back to TSV file
+df.to_csv(file_path, sep='\t', index=False)
+
+print("Columns have been successfully renamed!")
+print("Renamed columns:")
+for old_name, new_name in column_mapping.items():
+    if old_name in df.columns:
+        print(f"  {old_name} -> {new_name}")
+
+
+##
+
+
+# %% name
+import pandas as pd
+import numpy as np
+import seaborn as sns
+
+# Sample data (replace with your actual df)
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# Identify GT and Result columns
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+# Create mapping
+column_mapping = {}
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+    result_col = f'result.{base_name}'
+    if result_col in result_columns:
+        column_mapping[gt_col] = result_col
+
+# Function to compute match percentage for each GT-Result pair
+def compute_match_percentages(df, column_mapping):
+    percentages = []
+    for gt_col, result_col in column_mapping.items():
+        count = 0
+        total = len(df)
+
+        for _, row in df.iterrows():
+            gt_val = row[gt_col]
+            result_val = row[result_col]
+
+            # Handle NaN values
+            if pd.isna(gt_val) or pd.isna(result_val):
+                continue
+
+            # Handle non-numeric values
+            try:
+                gt_float = float(gt_val)
+                result_float = float(result_val)
+            except (ValueError, TypeError):
+                # Skip rows with non-numeric values
+                continue
+
+            # Check if values are within 0.5 tolerance
+            if abs(gt_float - result_float) <= 0.5:
+                count += 1
+
+        percentage = (count / total) * 100
+        percentages.append({
+            'GT_Column': gt_col,
+            'Result_Column': result_col,
+            'Match_Percentage': round(percentage, 1)
+        })
+
+    return pd.DataFrame(percentages)
+
+# Compute match percentages
+match_df = compute_match_percentages(df, column_mapping)
+
+# Create a pivot table for gradient display (optional but helpful)
+pivot_table = match_df.set_index(['GT_Column', 'Result_Column'])['Match_Percentage'].unstack(fill_value=0)
+
+# Apply gradient background
+cm = sns.light_palette("green", as_cmap=True)
+styled_table = pivot_table.style.background_gradient(cmap=cm, axis=None)
+
+# Display result
+print("Agreement Percentage Table (with gradient):")
+styled_table
+
+
+##
+
+
+
+
+# %% name
+import pandas as pd
+import numpy as np
+import seaborn as sns
+
+# first, let's identify the gt and result columns
+gt_columns = [col for col in df.columns if col.startswith('gt.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+# create a mapping dictionary for matching columns
+column_mapping = {}
+for gt_col in gt_columns:
+    # extract the base name (remove 'gt.' prefix)
+    base_name = gt_col.replace('gt.', '')
+
+    # find matching result column
+    result_col = f'result.{base_name}'
+    if result_col in result_columns:
+        column_mapping[gt_col] = result_col
+
+# create comparison dataframe with error handling
+def safe_float_convert(value):
+    '''safely convert value to float, returning nan for non-numeric values'''
+    if pd.isna(value):
+        return np.nan
+    try:
+        return float(value)
+    except (valueerror, typeerror):
+        return np.nan
+
+def create_comparison_table(df, column_mapping):
+    # create a table showing match status for all comparisons
+    comparison_matrix = pd.dataframe(index=df.index, columns=[f"{gt_col}_vs_{result_col}" for gt_col, result_col in column_mapping.items()])
+
+    for idx, row in df.iterrows():
+        for gt_col, result_col in column_mapping.items():
+            gt_val = row[gt_col]
+            result_val = row[result_col]
+
+            # handle nan values
+            if pd.isna(gt_val) or pd.isna(result_val):
+                comparison_matrix.loc[idx, f"{gt_col}_vs_{result_col}"] = 0
+            else:
+                # safely convert to float
+                gt_float = safe_float_convert(gt_val)
+                result_float = safe_float_convert(result_val)
+
+                # if either conversion failed, mark as no match
+                if pd.isna(gt_float) or pd.isna(result_float):
+                    comparison_matrix.loc[idx, f"{gt_col}_vs_{result_col}"] = 0
+                else:
+                    # check if values are within 0.5 tolerance
+                    if abs(gt_float - result_float) <= 0.5:
+                        comparison_matrix.loc[idx, f"{gt_col}_vs_{result_col}"] = 1
+                    else:
+                        comparison_matrix.loc[idx, f"{gt_col}_vs_{result_col}"] = 0
+
+    return comparison_matrix
+
+# generate the comparison matrix
+comprehensive_matrix = create_comparison_table(df, column_mapping)
+
+# create summary statistics
+summary_data = []
+for gt_col, result_col in column_mapping.items():
+    match_count = 0
+    total_count = len(df)
+
+    for idx, row in df.iterrows():
+        gt_val = row[gt_col]
+        result_val = row[result_col]
+
+        # handle nan values
+        if pd.isna(gt_val) or pd.isna(result_val):
+            continue
+        else:
+            # safely convert to float
+            gt_float = safe_float_convert(gt_val)
+            result_float = safe_float_convert(result_val)
+
+            # if both conversions succeeded, check tolerance
+            if not pd.isna(gt_float) and not pd.isna(result_float):
+                if abs(gt_float - result_float) <= 0.5:
+                    match_count += 1
+
+    summary_data.append({
+        'gt_column': gt_col,
+        'result_column': result_col,
+        'match_count': match_count,
+        'total_records': total_count,
+        'match_percentage': f"{(match_count/total_count*100):.1f}%" if total_count > 0 else "0.0%"
+    })
+
+summary_df = pd.dataframe(summary_data)
+
+# display the summary
+print("comparison summary:")
+print("="*80)
+for _, row in summary_df.iterrows():
+    print(f"{row['gt_column']} vs {row['result_column']}:")
+    print(f"  matches: {row['match_count']}/{row['total_records']} ({row['match_percentage']})")
+    print()
+
+# create gradient styled table
+cm = sns.light_palette("green", as_cmap=true)
+print("comparison results with gradient:")
+comprehensive_gradient = comprehensive_matrix.style.background_gradient(cmap=cm, axis=0)
+
+# display the gradient table
+comprehensive_gradient
+
+# if you want to see the actual comparison data
+print("\nraw comparison data:")
+print(comprehensive_matrix.head())
+
+##

Data/styled_tables.py

@@ -0,0 +1,71 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+
+# Sample data (replace with your actual df)
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# Identify GT and Result columns
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+result_columns = [col for col in df.columns if col.startswith('result.')]
+
+# Create mapping
+column_mapping = {}
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+    result_col = f'result.{base_name}'
+    if result_col in result_columns:
+        column_mapping[gt_col] = result_col
+
+# Function to compute match percentage for each GT-Result pair
+def compute_match_percentages(df, column_mapping):
+    percentages = []
+    for gt_col, result_col in column_mapping.items():
+        count = 0
+        total = len(df)
+
+        for _, row in df.iterrows():
+            gt_val = row[gt_col]
+            result_val = row[result_col]
+
+            # Handle NaN values
+            if pd.isna(gt_val) or pd.isna(result_val):
+                continue
+
+            # Handle non-numeric values
+            try:
+                gt_float = float(gt_val)
+                result_float = float(result_val)
+            except (ValueError, TypeError):
+                # Skip rows with non-numeric values
+                continue
+
+            # Check if values are within 0.5 tolerance
+            if abs(gt_float - result_float) <= 0.5:
+                count += 1
+
+        percentage = (count / total) * 100
+        percentages.append({
+            'GT_Column': gt_col,
+            'Result_Column': result_col,
+            'Match_Percentage': round(percentage, 1)
+        })
+
+    return pd.DataFrame(percentages)
+
+# Compute match percentages
+match_df = compute_match_percentages(df, column_mapping)
+
+# Create a pivot table for gradient display (optional but helpful)
+pivot_table = match_df.set_index(['GT_Column', 'Result_Column'])['Match_Percentage'].unstack(fill_value=0)
+
+# Apply gradient background
+cm = sns.light_palette("green", as_cmap=True)
+styled_table = pivot_table.style.background_gradient(cmap=cm, axis=None)
+
+# Display result
+print("Agreement Percentage Table (with gradient):")
+styled_table
+
+
+