beautiful plot

Data/style2.py

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+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()
+
+