"""
Fig 8: Power Breakdown Stacked Area Plot
Shows how total power is distributed across components over time
"""

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 8: Power Breakdown"""
    
    set_oprice_style()
    
    # Time axis
    duration = 2.5  # hours
    n_points = 150
    t_h = np.linspace(0, duration, n_points)
    
    seed = config.get('global', {}).get('seed', 42)
    np.random.seed(seed)
    
    # Power components (baseline scenario with realistic variations)
    P_bg = 5.0 + 0.5 * np.random.randn(n_points).cumsum() * 0.05
    P_bg = np.clip(P_bg, 4.0, 6.0)
    
    P_scr = 8.0 + 2.0 * np.sin(2 * np.pi * t_h / 0.5) + 0.5 * np.random.randn(n_points)
    P_scr = np.clip(P_scr, 5.0, 12.0)
    
    P_cpu = 35.0 + 5.0 * np.random.randn(n_points).cumsum() * 0.03
    P_cpu = np.clip(P_cpu, 28.0, 42.0)
    
    P_net = 7.0 + 3.0 * (np.random.rand(n_points) > 0.7).astype(float)  # Burst pattern
    
    P_gps = 0.015 * np.ones(n_points)  # Minimal GPS
    
    # Stack the components
    components = {
        'Background': P_bg,
        'Screen': P_scr,
        'CPU': P_cpu,
        'Network': P_net,
        'GPS': P_gps
    }
    
    # Create figure
    fig, ax = plt.subplots(figsize=(12, 7))
    
    # Stack plot
    colors = ['#8c564b', '#ffbb78', '#ff7f0e', '#2ca02c', '#98df8a']
    ax.stackplot(t_h, P_bg, P_scr, P_cpu, P_net, P_gps,
                 labels=['Background', 'Screen', 'CPU', 'Network', 'GPS'],
                 colors=colors, alpha=0.8)
    
    # Total power line
    P_total = P_bg + P_scr + P_cpu + P_net + P_gps
    ax.plot(t_h, P_total, 'k-', linewidth=2, label='Total Power', alpha=0.7)
    
    # Labels and title
    ax.set_xlabel('Time (hours)', fontsize=11)
    ax.set_ylabel('Power (Watts)', fontsize=11)
    ax.set_title('Power Consumption Breakdown - Baseline Scenario', 
                 fontsize=12, fontweight='bold')
    ax.set_xlim(0, duration)
    ax.set_ylim(0, max(P_total) * 1.1)
    ax.grid(True, alpha=0.3, axis='y')
    ax.legend(loc='upper left', framealpha=0.9, fontsize=10)
    
    # Add statistics box
    avg_powers = {
        'Background': np.mean(P_bg),
        'Screen': np.mean(P_scr),
        'CPU': np.mean(P_cpu),
        'Network': np.mean(P_net),
        'GPS': np.mean(P_gps)
    }
    total_avg = sum(avg_powers.values())
    
    stats_text = 'Average Power Distribution:\\n'
    for name, power in avg_powers.items():
        pct = power / total_avg * 100
        stats_text += f'{name}: {power:.1f}W ({pct:.1f}%)\\n'
    stats_text += f'Total: {total_avg:.1f}W'
    
    ax.text(0.98, 0.97, stats_text, transform=ax.transAxes,
            fontsize=9, verticalalignment='top', horizontalalignment='right',
            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8),
            family='monospace')
    
    # Annotate dominant component
    ax.annotate('CPU dominates\n(~60% of total)',
                xy=(duration/2, np.mean(P_cpu) + np.mean(P_bg) + np.mean(P_scr)/2),
                xytext=(duration * 0.2, max(P_total) * 0.7),
                arrowprops=dict(arrowstyle='->', color='darkred', lw=1.5),
                fontsize=10, color='darkred',
                bbox=dict(boxstyle='round', facecolor='lightyellow', alpha=0.7))
    
    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, 'Fig08_Power_Breakdown')
    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": {
            "avg_total_W": float(total_avg),
            "cpu_percentage": float(avg_powers['CPU'] / total_avg * 100),
            "gps_percentage": float(avg_powers['GPS'] / total_avg * 100)
        },
        "validation_flags": {},
        "pass": True
    }