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