"""
Fig 14: Lifecycle Degradation (Aging)
Shows SOH and TTE evolution over multiple charge cycles
"""

import os
import numpy as np
import matplotlib.pyplot as plt
from plot_style import set_oprice_style, save_figure

def make_figure(config):
    """Generate Fig 14: Lifecycle Degradation"""
    
    set_oprice_style()
    
    # Aging parameters
    lambda_fade = config.get('battery_params', {}).get('lambda_fade', 0.0001)
    n_cycles = 1000
    
    # Cycle numbers
    cycles = np.arange(0, n_cycles + 1)
    
    # SOH degradation (exponential decay with square root of cycles term)
    # More realistic aging: fast initial drop, then slower
    SOH = 1.0 - lambda_fade * cycles - 0.00005 * np.sqrt(cycles)
    SOH = np.clip(SOH, 0.7, 1.0)  # SOH doesn't go below 70%
    
    # TTE degradation (proportional to SOH but with additional resistance increase)
    # As battery ages, internal resistance increases, reducing effective capacity
    TTE_fresh = 2.5  # hours at SOH=1.0
    resistance_factor = 1.0 + 0.5 * (1 - SOH)  # Resistance increases as SOH decreases
    TTE = TTE_fresh * SOH / resistance_factor
    
    # Create figure with dual y-axis
    fig, ax1 = plt.subplots(figsize=(12, 8))
    
    # Plot SOH on left axis
    color1 = '#1f77b4'
    ax1.set_xlabel('Charge-Discharge Cycles', fontsize=11)
    ax1.set_ylabel('State of Health (SOH)', fontsize=11, color=color1)
    line1 = ax1.plot(cycles, SOH * 100, color=color1, linewidth=2.5, 
                     label='SOH', marker='o', markevery=100, markersize=6)
    ax1.tick_params(axis='y', labelcolor=color1)
    ax1.set_ylim(65, 105)
    ax1.grid(True, alpha=0.3, axis='x')
    
    # Mark critical SOH thresholds
    ax1.axhline(80, color='orange', linestyle='--', linewidth=1.5, alpha=0.6, 
                label='80% SOH (End of Life)')
    
    # Second y-axis for TTE
    ax2 = ax1.twinx()
    color2 = '#d62728'
    ax2.set_ylabel('Time to Empty (hours)', fontsize=11, color=color2)
    line2 = ax2.plot(cycles, TTE, color=color2, linewidth=2.5, 
                     label='TTE at Full Charge', marker='s', markevery=100, markersize=6)
    ax2.tick_params(axis='y', labelcolor=color2)
    ax2.set_ylim(1.0, 2.8)
    
    # Title
    ax1.set_title('Battery Lifecycle Degradation: SOH and TTE Evolution', 
                  fontsize=12, fontweight='bold')
    
    # Combined legend
    lines = line1 + line2
    labels = [l.get_label() for l in lines]
    ax1.legend(lines, labels, loc='upper right', framealpha=0.9, fontsize=10)
    
    # Find cycle at 80% SOH
    idx_80 = np.where(SOH <= 0.80)[0]
    if len(idx_80) > 0:
        cycle_80 = cycles[idx_80[0]]
        soh_80 = SOH[idx_80[0]]
        tte_80 = TTE[idx_80[0]]
        
        ax1.axvline(cycle_80, color='gray', linestyle=':', linewidth=1.5, alpha=0.5)
        ax1.annotate(f'End of Life\\nCycle {cycle_80}\\nSOH={soh_80*100:.1f}%\\nTTE={tte_80:.2f}h',
                    xy=(cycle_80, 80), xytext=(cycle_80 + 150, 85),
                    arrowprops=dict(arrowstyle='->', color='darkred', lw=1.5),
                    fontsize=9, color='darkred',
                    bbox=dict(boxstyle='round', facecolor='mistyrose', alpha=0.8))
    
    # Add aging mechanism annotation
    mechanism_text = 'Aging Mechanisms:\\n' + \
                    '• Capacity fade\\n' + \
                    '• Resistance increase\\n' + \
                    '• Accelerated at extremes\\n' + \
                    f'  (λ_fade = {lambda_fade:.6f})'
    
    ax1.text(0.02, 0.30, mechanism_text, transform=ax1.transAxes,
            fontsize=9, verticalalignment='top',
            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
    
    # Add degradation statistics
    initial_tte = TTE[0]
    final_tte = TTE[-1]
    tte_loss = initial_tte - final_tte
    tte_loss_pct = tte_loss / initial_tte * 100
    
    stats_text = f'Performance Metrics:\\n'
    stats_text += f'Initial TTE: {initial_tte:.2f}h\\n'
    stats_text += f'After {n_cycles} cycles: {final_tte:.2f}h\\n'
    stats_text += f'Total loss: {tte_loss:.2f}h ({tte_loss_pct:.1f}%)\\n'
    stats_text += f'Avg loss per 100 cycles: {tte_loss/(n_cycles/100):.3f}h'
    
    ax1.text(0.98, 0.97, stats_text, transform=ax1.transAxes,
            fontsize=9, verticalalignment='top', horizontalalignment='right',
            bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.8),
            family='monospace')
    
    plt.tight_layout()
    
    # Save
    figure_dir = config.get('global', {}).get('figure_dir', 'figures')
    os.makedirs(figure_dir, exist_ok=True)
    output_base = os.path.join(figure_dir, 'Fig14_Lifecycle_Degradation')
    save_figure(fig, output_base, dpi=config.get('global', {}).get('dpi', 300))
    plt.close()
    
    return {
        "output_files": [f"{output_base}.pdf", f"{output_base}.png"],
        "computed_metrics": {
            "initial_tte_h": float(initial_tte),
            "final_tte_h": float(final_tte),
            "tte_loss_pct": float(tte_loss_pct),
            "eol_cycle": int(cycle_80) if len(idx_80) > 0 else n_cycles
        },
        "validation_flags": {},
        "pass": True
    }