seaborn styled table

Data/show_plots.py

@@ -592,3 +592,65 @@ print("\nraw comparison data:")
 print(comprehensive_matrix.head())
 
 ##
+
+# %% name
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load data
+df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
+
+# 1. Faster, vectorized computation
+gt_columns = [col for col in df.columns if col.startswith('GT.')]
+data_list = []
+
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+    result_col = f'result.{base_name}'
+
+    if result_col in df.columns:
+        # Convert to numeric, force invalid to NaN
+        s1 = pd.to_numeric(df[gt_col], errors='coerce')
+        s2 = pd.to_numeric(df[result_col], errors='coerce')
+
+        # Calculate matches (abs difference <= 0.5)
+        # Note: .sum() treats True as 1
+        matches = (np.abs(s1 - s2) <= 0.5).sum()
+        percentage = (matches / len(df)) * 100
+
+        data_list.append({
+            'GT': gt_col.replace('GT.', ''),
+            'Match %': round(percentage, 1)
+        })
+
+# 2. Prepare Data for Plotting
+match_df = pd.DataFrame(data_list)
+# We pivot to get a format suitable for a heatmap
+plot_data = match_df.set_index('GT')
+
+# 3. Create the Plot
+plt.figure(figsize=(10, 8))
+sns.set_theme(style="white")
+
+# Create heatmap
+ax = sns.heatmap(
+    plot_data,
+    annot=True,          # Show the numbers in the boxes
+    fmt=".1f",           # Format to 1 decimal place
+    cmap="YlGnBu",       # Yellow-Green-Blue color palette
+    cbar_kws={'label': 'Agreement (%)'},
+    linewidths=.5
+)
+
+plt.title('Agreement Percentage (Tolerance ±0.5)', pad=20)
+plt.tight_layout()
+
+# 4. Save as SVG
+plt.savefig("agreement_table.svg", format='svg')
+print("Successfully saved agreement_table.svg")
+
+# Show plot if running in a GUI environment
+plt.show()
+##