scripts/correlation_plot.py

#!/usr/bin/env python3
"""
Create correlation plots for muscle fat vs Cobb angles
"""

import pandas as pd # type: ignore
import numpy as np # type: ignore
import matplotlib.pyplot as plt # type: ignore
import seaborn as sns # type: ignore
from pathlib import Path
from scipy.stats import pearsonr  # type: ignore
import argparse

plt.style.use('seaborn-v0_8')
sns.set_palette("husl")

dev_correlation_csv = Path("../pearson_correlation/dev_cobb_corr/fatty_atrophy_thoracic_correlations.csv")
test_correlation_csv = Path("../pearson_correlation/test_cobb_corr/fatty_atrophy_thoracic_correlations.csv")

dev_fatty_csv = Path("../fatty_data/dev_fat.csv")
test_fatty_csv = Path("../fatty_data/test_fat.csv")

dev_cobb_csv = Path("../cobb_angles/dev_cobb.csv")
test_cobb_csv = Path("../cobb_angles/test_cobb.csv")

def load_correlation_data(dataset="dev"):
    """Load the correlation data from the CSV file."""
    if dataset == "dev":
        csv_path = dev_correlation_csv
    else:
        csv_path = test_correlation_csv
    
    if not csv_path.exists():
        print(f"Error: Correlation file not found at {csv_path}")
        return None
    
    df = pd.read_csv(csv_path)
    print(f"Loaded correlation data: {len(df)} muscles")
    return df

def create_dev_correlation_scatter(df):
    """Create a scatter plot for development dataset (100-120)."""

    fatty_df = pd.read_csv(dev_fatty_csv)
    manual_cobb_df = pd.read_csv(dev_cobb_csv, sep='\t', header=None)  # type: ignore

    thor_avg = np.round(manual_cobb_df.mean(axis=1)).astype(int)

    fatty_manual = fatty_df[fatty_df['case_id'].str.isdigit()].copy()
    fatty_manual['case_id'] = pd.to_numeric(fatty_manual['case_id'])  # type: ignore

    fig, ax = plt.subplots(figsize=(14, 8))  # type: ignore
    fig.patch.set_facecolor('#f8f9fa')
    ax.set_facecolor('#ffffff')

    muscle_cols = [col for col in fatty_manual.columns if col.endswith('_fat_pct')]

    trapezius_col = None
    for col in muscle_cols:
        if 'trapezius' in col.lower():
            trapezius_col = col
            break
    
    if trapezius_col is None:
        print("Trapezius muscle not found in the data")
        return None, None

    colors = ['#1f77b4']
    col = trapezius_col

    muscle_data = pd.to_numeric(fatty_manual[col], errors='coerce').values  # type: ignore
    cobb_data = thor_avg[:len(muscle_data)]

    valid_mask = ~(np.isnan(muscle_data) | np.isnan(cobb_data))  # type: ignore
    muscle_clean = muscle_data[valid_mask]
    cobb_clean = cobb_data[valid_mask]
    
    if len(muscle_clean) > 1:
        muscle_name = col.replace('_fat_pct', '').replace('_', ' ').title()
        ax.scatter(muscle_clean, cobb_clean, color=colors[0],   # type: ignore
                  label=muscle_name, s=60, alpha=0.7, edgecolors='black', linewidth=0.5)

        z = np.polyfit(muscle_clean, cobb_clean, 1)
        p = np.poly1d(z)
        ax.plot(muscle_clean, p(muscle_clean), color=colors[0],   # type: ignore
               linestyle='-', alpha=0.8, linewidth=2)

        muscle_name = col.replace('_fat_pct', '')
        correlation_row = df[df['Muscle'] == muscle_name]
        if not correlation_row.empty:
            r = correlation_row['Correlation'].iloc[0]
            p_val = correlation_row['P_Value'].iloc[0]
        else:
            r, p_val = pearsonr(muscle_clean, cobb_clean)  # type: ignore

    ax.set_xlabel('Fat Percentage (%)', fontsize=12, fontweight='bold')
    ax.set_ylabel('Thoracic Cobb Angle (deg)', fontsize=12, fontweight='bold')
    ax.set_title('Trapezius Muscle Fat Percentage vs Thoracic Cobb Angle\n(n = 21 cases)', 
                 fontsize=14, fontweight='bold', pad=20)

    ax.grid(True, alpha=0.3, color='gray', linestyle='-', linewidth=0.5)

    for spine in ax.spines.values():
        spine.set_edgecolor('#333333')
        spine.set_linewidth(1.5)

    plt.tight_layout()

    output_path = "../pearson_correlation/dev_cobb_corr/muscle_correlation_scatter.png"
    plt.savefig(output_path, dpi=300, bbox_inches='tight', 
                facecolor='white', edgecolor='none')
    print(f"Saved correlation scatter plot to: {output_path}")
    
    return fig, ax

def create_test_correlation_scatter(df):
    """Create a scatter plot for test dataset (251-500)."""

    fatty_df = pd.read_csv(test_fatty_csv)
    cobb_df = pd.read_csv(test_cobb_csv, header=None, names=['cobb_angle'])  # type: ignore

    fatty_df = fatty_df[fatty_df['case_id'] != 'Mean ± SD'].copy()
    fatty_df = fatty_df[pd.to_numeric(fatty_df['case_id'], errors='coerce').notna()]  # type: ignore
    fatty_df['case_id'] = fatty_df['case_id'].astype(int)

    n_cases = min(len(cobb_df), len(fatty_df))
    print(f"Using {n_cases} cases for test correlation analysis")

    cobb_values = cobb_df.iloc[:n_cases, 0].values  # Get the first column as numpy array
    fat_values = fatty_df.iloc[:n_cases]

    trapezius_col = None
    for col in fat_values.columns:
        if 'trapezius' in col.lower() and col.endswith('_fat_pct'):
            trapezius_col = col
            break
    
    if trapezius_col is None:
        print("Trapezius muscle not found in the test data")
        return None, None

    trapezius_data = pd.to_numeric(fat_values[trapezius_col], errors='coerce').values  # type: ignore
    cobb_data = cobb_values

    valid_mask = ~(np.isnan(trapezius_data) | np.isnan(cobb_data))  # type: ignore
    trapezius_clean = trapezius_data[valid_mask]
    cobb_clean = cobb_data[valid_mask]
    
    print(f"Valid data points: {len(trapezius_clean)}")
    print(f"Trapezius range: {trapezius_clean.min():.2f} to {trapezius_clean.max():.2f}")
    print(f"Cobb range: {cobb_clean.min():.1f} to {cobb_clean.max():.1f}")

    fig, ax = plt.subplots(figsize=(14, 8))  # type: ignore
    fig.patch.set_facecolor('#f8f9fa')
    ax.set_facecolor('#ffffff')

    ax.scatter(trapezius_clean, cobb_clean, color='#1f77b4', s=60, alpha=0.7,   # type: ignore
              edgecolors='black', linewidth=0.5)

    z = np.polyfit(trapezius_clean, cobb_clean, 1)
    p = np.poly1d(z)
    ax.plot(trapezius_clean, p(trapezius_clean), color='#1f77b4',   # type: ignore
           linestyle='-', alpha=0.8, linewidth=2)

    muscle_name = trapezius_col.replace('_fat_pct', '')
    correlation_row = df[df['Muscle'] == muscle_name]
    if not correlation_row.empty:
        r = correlation_row['Correlation'].iloc[0]
        p_val = correlation_row['P_Value'].iloc[0]
    else:
        r, p_val = pearsonr(trapezius_clean, cobb_clean)  # type: ignore

    ax.set_xlabel('Trapezius Fat Percentage (%)', fontsize=12, fontweight='bold')
    ax.set_ylabel('Thoracic Cobb Angle (deg)', fontsize=12, fontweight='bold')
    ax.set_title(f'Trapezius Muscle Fat Percentage vs Thoracic Cobb Angle\n(n = {len(trapezius_clean)} cases)', 
                 fontsize=14, fontweight='bold', pad=20)

    ax.grid(True, alpha=0.3, color='gray', linestyle='-', linewidth=0.5)

    for spine in ax.spines.values():
        spine.set_edgecolor('#333333')
        spine.set_linewidth(1.5)

    plt.tight_layout()

    output_path = "../pearson_correlation/test_cobb_corr/trapezius_fat_vs_thoracic_cobb_250_cases.png"
    plt.savefig(output_path, dpi=300, bbox_inches='tight', 
                facecolor='white', edgecolor='none')
    print(f"Saved test correlation plot to: {output_path}")
    
    return fig, ax

def create_aggregate_plot(df, dataset="dev"):
    """Create a 3x3 aggregate plot showing all 9 muscles."""

    if dataset == "dev":
        fatty_df = pd.read_csv(dev_fatty_csv)
        cobb_df = pd.read_csv(dev_cobb_csv, sep='\t', header=None)  # type: ignore

        cobb_data = np.round(cobb_df.mean(axis=1)).astype(int)
        n_cases = 21
    else:
        fatty_df = pd.read_csv(test_fatty_csv)
        cobb_df = pd.read_csv(test_cobb_csv, header=None, names=['cobb_angle'])  # type: ignore
        cobb_data = cobb_df.iloc[:, 0].values
        n_cases = 250

    fatty_df = fatty_df[fatty_df['case_id'] != 'Mean ± SD'].copy()
    fatty_df = fatty_df[pd.to_numeric(fatty_df['case_id'], errors='coerce').notna()]  # type: ignore
    fatty_df['case_id'] = fatty_df['case_id'].astype(int)

    muscle_cols = [col for col in fatty_df.columns if col.endswith('_fat_pct')]

    fig, axes = plt.subplots(3, 3, figsize=(18, 15))  # type: ignore
    fig.patch.set_facecolor('#f8f9fa')

    axes_flat = axes.flatten()

    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', 
              '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22']
    
    for i, col in enumerate(muscle_cols):
        if i >= 9:
            break
            
        ax = axes_flat[i]
        ax.set_facecolor('#ffffff')

        muscle_data = pd.to_numeric(fatty_df[col], errors='coerce').values  # type: ignore
        cobb_clean = cobb_data[:len(muscle_data)]

        valid_mask = ~(np.isnan(muscle_data) | np.isnan(cobb_clean))  # type: ignore
        muscle_clean = muscle_data[valid_mask]
        cobb_clean = cobb_clean[valid_mask]
        
        if len(muscle_clean) > 1:

            muscle_name = col.replace('_fat_pct', '').replace('_', ' ').title()

            ax.scatter(muscle_clean, cobb_clean, color=colors[i],   # type: ignore
                     s=40, alpha=0.7, edgecolors='black', linewidth=0.3)

            if len(muscle_clean) > 1:
                z = np.polyfit(muscle_clean, cobb_clean, 1)
                p = np.poly1d(z)
                ax.plot(muscle_clean, p(muscle_clean), color=colors[i],   # type: ignore
                       linestyle='-', alpha=0.8, linewidth=1.5)

            muscle_name_csv = col.replace('_fat_pct', '')
            correlation_row = df[df['Muscle'] == muscle_name_csv]
            if not correlation_row.empty:
                r = correlation_row['Correlation'].iloc[0]
                p_val = correlation_row['P_Value'].iloc[0]
            else:
                r, p_val = pearsonr(muscle_clean, cobb_clean)  # type: ignore

            ax.set_title(f'{muscle_name}\nr = {r:.3f}', fontsize=10, fontweight='bold')

        ax.set_xlabel('Fat %', fontsize=8)
        ax.set_ylabel('Cobb Angle (deg)', fontsize=8)
        ax.grid(True, alpha=0.3, linewidth=0.5)

        for spine in ax.spines.values():
            spine.set_edgecolor('#333333')
            spine.set_linewidth(0.8)

    for i in range(len(muscle_cols), 9):
        axes_flat[i].set_visible(False)

    plt.tight_layout()

    output_path = f"../pearson_correlation/{dataset}_cobb_corr/aggregate_muscle_correlations.png"
    plt.savefig(output_path, dpi=300, bbox_inches='tight', 
                facecolor='white', edgecolor='none')
    print(f"Saved aggregate plot to: {output_path}")
    
    return fig, axes

def main():
    """Main function to create correlation plots."""
    parser = argparse.ArgumentParser(description='Create muscle correlation plots')
    parser.add_argument('--dataset', choices=['dev', 'test', 'both'], default='both',
                       help='Which dataset to plot (dev, test, or both)')
    parser.add_argument('--plot-type', choices=['trapezius', 'aggregate', 'both'], default='both',
                       help='Which plot type to create (trapezius, aggregate, or both)')
    args = parser.parse_args()
    
    print("=== MUSCLE CORRELATION VISUALIZATION ===")
    
    if args.dataset in ['dev', 'both']:
        print("\n=== DEVELOPMENT DATASET (100-120) ===")
        df_dev = load_correlation_data("dev")
        if df_dev is not None:
            if args.plot_type in ['trapezius', 'both']:
                fig1, ax1 = create_dev_correlation_scatter(df_dev)
                if fig1 is not None:
                    print("Development trapezius plot created successfully")
                plt.show()
            
            if args.plot_type in ['aggregate', 'both']:
                fig2, ax2 = create_aggregate_plot(df_dev, "dev")
                if fig2 is not None:
                    print("Development aggregate plot created successfully")
                plt.show()
    
    if args.dataset in ['test', 'both']:
        print("\n=== TEST DATASET (251-500) ===")
        df_test = load_correlation_data("test")
        if df_test is not None:
            if args.plot_type in ['trapezius', 'both']:
                fig3, ax3 = create_test_correlation_scatter(df_test)
                if fig3 is not None:
                    print("Test trapezius plot created successfully")
                plt.show()
            
            if args.plot_type in ['aggregate', 'both']:
                fig4, ax4 = create_aggregate_plot(df_test, "test")
                if fig4 is not None:
                    print("Test aggregate plot created successfully")
                plt.show()
    
    print("\n=== VISUALIZATION COMPLETE ===")
    print("Generated Pearson correlation scatter plots for muscle fat percentages vs thoracic Cobb angles")

if __name__ == "__main__":
    main()