beautiful plot

Data/show_plots.py

@@ -407,7 +407,7 @@ import pandas as pd
 import numpy as np
 import seaborn as sns
 import matplotlib.pyplot as plt
-
+import dataframe_image as dfi
 # Load data
 df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
 
@@ -491,32 +491,76 @@ for gt_col, result_col in column_mapping.items():
         'Match %': round(percentage, 1)
     })
 
-# 4. Prepare Data for Plotting
+
+
+
+# 4. Prepare Data
 match_df = pd.DataFrame(data_list)
+# Clean up labels: Replace underscores with spaces and capitalize
+match_df['GT'] = match_df['GT'].str.replace('_', ' ').str.title()
+match_df = match_df.sort_values('Match %', ascending=False)
 
-# Handle case where no matches were found
-if len(match_df) == 0:
-    print("No valid column pairs found for comparison")
-    exit()
+# 5. Create a "Beautiful" Table using Seaborn Heatmap
+def create_luxury_table(df, output_file="edss_agreement.png"):
+    # Set the aesthetic style
+    sns.set_theme(style="white", font="sans-serif")
 
-# 5. Create the Plot
-plt.figure(figsize=(10, 8))
-sns.set_theme(style="white")
+    # Prepare data for heatmap
+    plot_data = df.set_index('GT')[['Match %']]
 
-# Create heatmap
-ax = sns.heatmap(
-    match_df.set_index('GT')[['Match %']],  # Just the percentage column
-    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
-)
+    # Initialize the figure
+    # Height is dynamic based on number of rows
+    fig, ax = plt.subplots(figsize=(8, len(df) * 0.6))
 
-plt.title('Agreement Percentage (Tolerance ±0.5)', pad=20)
-plt.tight_layout()
+    # Create a custom diverging color map (Deep Red -> Mustard -> Emerald)
+    # This looks more professional than standard 'RdYlGn'
+    cmap = sns.diverging_palette(15, 135, s=80, l=55, as_cmap=True)
 
+    # Draw the heatmap
+    sns.heatmap(
+        plot_data,
+        annot=True,
+        fmt=".1f",
+        cmap=cmap,
+        center=85,      # Centers the color transition
+        vmin=50, vmax=100, # Range of the gradient
+        linewidths=2,
+        linecolor='white',
+        cbar=False,     # Remove color bar for a "table" look
+        annot_kws={"size": 14, "weight": "bold", "family": "sans-serif"}
+    )
+
+    # Styling the Axes (Turning the heatmap into a table)
+    ax.set_xlabel("")
+    ax.set_ylabel("")
+    ax.xaxis.tick_top() # Move "Match %" label to top
+    ax.set_xticklabels(['Agreement (%)'], fontsize=14, fontweight='bold', color='#2c3e50')
+    ax.tick_params(axis='y', labelsize=12, labelcolor='#2c3e50', length=0)
+
+    # Add a thin border around the plot
+    for _, spine in ax.spines.items():
+        spine.set_visible(True)
+        spine.set_color('#ecf0f1')
+
+    plt.title('EDSS Subcategory Consistency Analysis', fontsize=16, pad=40, fontweight='bold', color='#2c3e50')
+
+    # Add a subtle footer
+    plt.figtext(0.5, 0.02, "Tolerance: ±0.5 points | N = [Total Samples]",
+                wrap=True, horizontalalignment='center', fontsize=10, color='gray', style='italic')
+
+    # Save with high resolution
+    plt.tight_layout()
+    plt.savefig(output_file, dpi=300, bbox_inches='tight')
+    print(f"Beautiful table saved as {output_file}")
+
+# Execute
+create_luxury_table(match_df)
+
+
+# Run the function
+save_styled_table(match_df)
 # 6. Save as SVG
+
 #plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
 #print("Successfully saved agreement_table.svg")