updated git ignore and new files

Data/styled_tables.py

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+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
+
+
+