"""
Validation utilities for figure quality control
"""

import os
import numpy as np
from scipy import stats

def validate_figure_output(fig_num, output_files, computed_metrics, validation_flags, config):
    """
    Validate figure output against quality thresholds
    
    Args:
        fig_num: Figure number (1-15)
        output_files: List of generated file paths
        computed_metrics: Dictionary of computed metrics
        validation_flags: Dictionary of boolean validation flags
        config: Configuration dictionary with validation thresholds
        
    Returns:
        dict: Validation result with pass/fail status
    """
    
    result = {
        "figure": f"Fig{fig_num:02d}",
        "output_files": output_files,
        "metrics": computed_metrics,
        "flags": validation_flags,
        "pass": True,
        "errors": []
    }
    
    # Check file existence
    for filepath in output_files:
        if not os.path.exists(filepath):
            result["pass"] = False
            result["errors"].append(f"File not found: {filepath}")
        elif os.path.getsize(filepath) == 0:
            result["pass"] = False
            result["errors"].append(f"Empty file: {filepath}")
    
    # Figure-specific validation
    validation_thresholds = config.get('validation', {})
    
    if fig_num == 3:  # OCV fitting
        r2 = computed_metrics.get('r2', 0)
        r2_min = validation_thresholds.get('fig03_r2_min', 0.99)
        if r2 < r2_min:
            result["pass"] = False
            result["errors"].append(f"R² too low: {r2:.4f} < {r2_min}")
    
    elif fig_num == 7:  # Baseline validation
        v_i_corr = computed_metrics.get('v_i_correlation', 0)
        corr_max = validation_thresholds.get('fig07_v_i_corr_max', -0.5)
        if v_i_corr > corr_max:
            result["pass"] = False
            result["errors"].append(f"V-I correlation not negative enough: {v_i_corr:.3f} > {corr_max}")
    
    elif fig_num == 9:  # Scenario comparison
        if 'delta_tte_match' in validation_flags:
            if not validation_flags['delta_tte_match']:
                result["pass"] = False
                result["errors"].append("ΔTTE annotation mismatch")
    
    elif fig_num == 13:  # Survival curve
        if 'survival_monotonic' in validation_flags:
            if not validation_flags['survival_monotonic']:
                result["pass"] = False
                result["errors"].append("Survival curve not monotonic")
    
    return result

def compute_r2(y_true, y_pred):
    """Compute R-squared coefficient of determination"""
    ss_res = np.sum((y_true - y_pred) ** 2)
    ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)
    return 1 - (ss_res / ss_tot)

def check_monotonic_decreasing(arr):
    """Check if array is monotonically decreasing"""
    return np.all(np.diff(arr) <= 0)