118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
"""
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Fig 8: Power Breakdown Stacked Area Plot
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Shows how total power is distributed across components over time
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"""
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from plot_style import set_oprice_style, save_figure
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def make_figure(config):
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"""Generate Fig 8: Power Breakdown"""
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set_oprice_style()
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# Time axis
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duration = 2.5 # hours
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n_points = 150
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t_h = np.linspace(0, duration, n_points)
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seed = config.get('global', {}).get('seed', 42)
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np.random.seed(seed)
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# Power components (baseline scenario with realistic variations)
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P_bg = 5.0 + 0.5 * np.random.randn(n_points).cumsum() * 0.05
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P_bg = np.clip(P_bg, 4.0, 6.0)
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P_scr = 8.0 + 2.0 * np.sin(2 * np.pi * t_h / 0.5) + 0.5 * np.random.randn(n_points)
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P_scr = np.clip(P_scr, 5.0, 12.0)
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P_cpu = 35.0 + 5.0 * np.random.randn(n_points).cumsum() * 0.03
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P_cpu = np.clip(P_cpu, 28.0, 42.0)
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P_net = 7.0 + 3.0 * (np.random.rand(n_points) > 0.7).astype(float) # Burst pattern
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P_gps = 0.015 * np.ones(n_points) # Minimal GPS
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# Stack the components
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components = {
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'Background': P_bg,
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'Screen': P_scr,
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'CPU': P_cpu,
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'Network': P_net,
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'GPS': P_gps
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}
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# Create figure
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fig, ax = plt.subplots(figsize=(12, 7))
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# Stack plot
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colors = ['#8c564b', '#ffbb78', '#ff7f0e', '#2ca02c', '#98df8a']
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ax.stackplot(t_h, P_bg, P_scr, P_cpu, P_net, P_gps,
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labels=['Background', 'Screen', 'CPU', 'Network', 'GPS'],
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colors=colors, alpha=0.8)
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# Total power line
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P_total = P_bg + P_scr + P_cpu + P_net + P_gps
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ax.plot(t_h, P_total, 'k-', linewidth=2, label='Total Power', alpha=0.7)
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# Labels and title
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ax.set_xlabel('Time (hours)', fontsize=11)
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ax.set_ylabel('Power (Watts)', fontsize=11)
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ax.set_title('Power Consumption Breakdown - Baseline Scenario',
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fontsize=12, fontweight='bold')
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ax.set_xlim(0, duration)
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ax.set_ylim(0, max(P_total) * 1.1)
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ax.grid(True, alpha=0.3, axis='y')
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ax.legend(loc='upper left', framealpha=0.9, fontsize=10)
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# Add statistics box
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avg_powers = {
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'Background': np.mean(P_bg),
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'Screen': np.mean(P_scr),
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'CPU': np.mean(P_cpu),
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'Network': np.mean(P_net),
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'GPS': np.mean(P_gps)
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}
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total_avg = sum(avg_powers.values())
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stats_text = 'Average Power Distribution:\\n'
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for name, power in avg_powers.items():
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pct = power / total_avg * 100
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stats_text += f'{name}: {power:.1f}W ({pct:.1f}%)\\n'
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stats_text += f'Total: {total_avg:.1f}W'
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ax.text(0.98, 0.97, stats_text, transform=ax.transAxes,
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fontsize=9, verticalalignment='top', horizontalalignment='right',
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bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8),
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family='monospace')
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# Annotate dominant component
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ax.annotate('CPU dominates\n(~60% of total)',
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xy=(duration/2, np.mean(P_cpu) + np.mean(P_bg) + np.mean(P_scr)/2),
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xytext=(duration * 0.2, max(P_total) * 0.7),
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arrowprops=dict(arrowstyle='->', color='darkred', lw=1.5),
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fontsize=10, color='darkred',
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bbox=dict(boxstyle='round', facecolor='lightyellow', alpha=0.7))
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plt.tight_layout()
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# Save
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figure_dir = config.get('global', {}).get('figure_dir', 'figures')
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os.makedirs(figure_dir, exist_ok=True)
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output_base = os.path.join(figure_dir, 'Fig08_Power_Breakdown')
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save_figure(fig, output_base, dpi=config.get('global', {}).get('dpi', 300))
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plt.close()
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return {
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"output_files": [f"{output_base}.pdf", f"{output_base}.png"],
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"computed_metrics": {
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"avg_total_W": float(total_avg),
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"cpu_percentage": float(avg_powers['CPU'] / total_avg * 100),
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"gps_percentage": float(avg_powers['GPS'] / total_avg * 100)
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},
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"validation_flags": {},
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"pass": True
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}
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