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
+# 5. Create a "Beautiful" Table using Seaborn Heatmap
-if len(match_df) == 0:
+def create_luxury_table(df, output_file="edss_agreement.png"):
-    print("No valid column pairs found for comparison")
+    # Set the aesthetic style
-    exit()
+    sns.set_theme(style="white", font="sans-serif")
 
-# 5. Create the Plot
+    # Prepare data for heatmap
-plt.figure(figsize=(10, 8))
+    plot_data = df.set_index('GT')[['Match %']]
-sns.set_theme(style="white")
 
-# Create heatmap
+    # Initialize the figure
-ax = sns.heatmap(
+    # Height is dynamic based on number of rows
-    match_df.set_index('GT')[['Match %']],  # Just the percentage column
+    fig, ax = plt.subplots(figsize=(8, len(df) * 0.6))
-    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)
+    # Create a custom diverging color map (Deep Red -> Mustard -> Emerald)
-plt.tight_layout()
+    # 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")
 

Data/style2.py

@@ -0,0 +1,135 @@
+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')
+
+# 1. Identify all 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.')]
+
+print("GT Columns found:", gt_columns)
+print("Result Columns found:", result_columns)
+
+# 2. Create proper mapping between GT and result columns
+# Handle various naming conventions (spaces, underscores, etc.)
+column_mapping = {}
+
+for gt_col in gt_columns:
+    base_name = gt_col.replace('GT.', '')
+
+    # Clean the base name for matching - remove spaces, underscores, etc.
+    # Try different matching approaches
+    candidates = [
+        f'result.{base_name}',  # Exact match
+        f'result.{base_name.replace(" ", "_")}',  # With underscores
+        f'result.{base_name.replace("_", " ")}',  # With spaces
+        f'result.{base_name.replace(" ", "")}',   # No spaces
+        f'result.{base_name.replace("_", "")}'    # No underscores
+    ]
+
+    # Also try case-insensitive matching
+    candidates.append(f'result.{base_name.lower()}')
+    candidates.append(f'result.{base_name.upper()}')
+
+    # Try to find matching result column
+    matched = False
+    for candidate in candidates:
+        if candidate in result_columns:
+            column_mapping[gt_col] = candidate
+            matched = True
+            break
+
+    # If no exact match found, try partial matching
+    if not matched:
+        # Try to match by removing special characters and comparing
+        base_clean = ''.join(e for e in base_name if e.isalnum() or e in ['_', ' '])
+        for result_col in result_columns:
+            result_base = result_col.replace('result.', '')
+            result_clean = ''.join(e for e in result_base if e.isalnum() or e in ['_', ' '])
+            if base_clean.lower() == result_clean.lower():
+                column_mapping[gt_col] = result_col
+                matched = True
+                break
+
+print("Column mapping:", column_mapping)
+
+# 3. Faster, vectorized computation using the corrected mapping
+data_list = []
+
+for gt_col, result_col in column_mapping.items():
+    print(f"Processing {gt_col} vs {result_col}")
+
+    # Convert to numeric, forcing errors to NaN
+    s1 = pd.to_numeric(df[gt_col], errors='coerce').astype(float)
+    s2 = pd.to_numeric(df[result_col], errors='coerce').astype(float)
+
+    # Calculate matches (abs difference <= 0.5)
+    diff = np.abs(s1 - s2)
+    matches = (diff <= 0.5).sum()
+
+    # Determine the denominator (total valid comparisons)
+    valid_count = diff.notna().sum()
+
+    if valid_count > 0:
+        percentage = (matches / valid_count) * 100
+    else:
+        percentage = 0
+
+    # Extract clean base name for display
+    base_name = gt_col.replace('GT.', '')
+
+    data_list.append({
+        'GT': base_name,
+        'Match %': round(percentage, 1)
+    })
+
+
+
+# 4. Prepare Data for Plotting
+match_df = pd.DataFrame(data_list)
+match_df = match_df.sort_values('Match %', ascending=False) # Sort for better visual flow
+
+# 5. Create the Styled Gradient Table
+def style_agreement_table(df):
+    return (df.style
+        .format({'Match %': '{:.1f}%'}) # Add % sign
+        .background_gradient(cmap='RdYlGn', subset=['Match %'], vmin=50, vmax=100) # Red to Green gradient
+        .set_properties(**{
+            'text-align': 'center',
+            'font-size': '12pt',
+            'border-collapse': 'collapse',
+            'border': '1px solid #D3D3D3'
+        })
+        .set_table_styles([
+            # Style the header
+            {'selector': 'th', 'props': [
+                ('background-color', '#404040'), 
+                ('color', 'white'),
+                ('font-weight', 'bold'),
+                ('text-transform', 'uppercase'),
+                ('padding', '10px')
+            ]},
+            # Add hover effect
+            {'selector': 'tr:hover', 'props': [('background-color', '#f5f5f5')]}
+        ])
+        .set_caption("EDSS Agreement Analysis: Ground Truth vs. Results (Tolerance ±0.5)")
+    )
+
+# To display in a Jupyter Notebook:
+styled_table = style_agreement_table(match_df)
+styled_table
+
+dfi.export(styled_table, "styled_table.png")
+#styled_table.to_html("agreement_report.html")
+# 6. Save as SVG
+
+#plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
+#print("Successfully saved agreement_table.svg")
+
+# Show plot if running in a GUI environment
+plt.show()
+
+