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A题/ZJ_v2/fig05_radio_tail.py

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+"""
+Fig 5: Radio Tail Energy Illustration
+Shows the tail effect in network power consumption
+"""
+
+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 5: Radio Tail Energy Illustration"""
+    
+    set_oprice_style()
+    
+    # Time axis
+    t = np.linspace(0, 10, 1000)
+    
+    # Data burst events (brief pulses)
+    burst_times = [1.0, 4.5, 7.0]
+    burst_duration = 0.2
+    data_activity = np.zeros_like(t)
+    
+    for bt in burst_times:
+        mask = (t >= bt) & (t < bt + burst_duration)
+        data_activity[mask] = 1.0
+    
+    # Power state with tail effect
+    # After each burst, power decays exponentially
+    power_state = np.zeros_like(t)
+    tau_tail = 2.0  # Tail decay time constant
+    
+    for i, ti in enumerate(t):
+        # Find most recent burst
+        recent_bursts = [bt for bt in burst_times if bt <= ti]
+        if recent_bursts:
+            t_since_burst = ti - max(recent_bursts)
+            if t_since_burst < burst_duration:
+                # During burst: high power
+                power_state[i] = 1.0
+            else:
+                # After burst: exponential decay (tail)
+                power_state[i] = 1.0 * np.exp(-(t_since_burst - burst_duration) / tau_tail)
+    
+    # Create figure with two subplots
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 7), sharex=True)
+    
+    # Top: Data activity
+    ax1.fill_between(t, 0, data_activity, alpha=0.6, color='#2ca02c', label='Data Transmission')
+    ax1.set_ylabel('Data Activity', fontsize=11)
+    ax1.set_ylim(-0.1, 1.2)
+    ax1.set_yticks([0, 1])
+    ax1.set_yticklabels(['Idle', 'Active'])
+    ax1.grid(True, alpha=0.3, axis='x')
+    ax1.legend(loc='upper right')
+    ax1.set_title('Network Radio Tail Effect Illustration', fontsize=12, fontweight='bold')
+    
+    # Annotate burst durations
+    for bt in burst_times:
+        ax1.annotate('', xy=(bt + burst_duration, 1.1), xytext=(bt, 1.1),
+                    arrowprops=dict(arrowstyle='<->', color='black', lw=1))
+        ax1.text(bt + burst_duration/2, 1.15, f'{int(burst_duration*1000)}ms',
+                ha='center', fontsize=8)
+    
+    # Bottom: Power state
+    ax2.fill_between(t, 0, power_state, alpha=0.6, color='#ff7f0e', label='Radio Power State')
+    ax2.plot(t, power_state, 'r-', linewidth=1.5)
+    ax2.set_xlabel('Time (seconds)', fontsize=11)
+    ax2.set_ylabel('Power State', fontsize=11)
+    ax2.set_ylim(-0.1, 1.2)
+    ax2.set_yticks([0, 0.5, 1])
+    ax2.set_yticklabels(['Idle', 'Mid', 'High'])
+    ax2.grid(True, alpha=0.3)
+    ax2.legend(loc='upper right')
+    
+    # Highlight tail regions
+    for bt in burst_times:
+        tail_start = bt + burst_duration
+        tail_end = tail_start + 3 * tau_tail
+        ax2.axvspan(tail_start, min(tail_end, 10), alpha=0.2, color='yellow')
+    
+    # Add annotation explaining the tail
+    ax2.text(0.98, 0.95, 
+             'Tail Effect:\nPower remains elevated\nafter data transmission\n' +
+             r'$P(t) = P_{high} \cdot e^{-t/\tau}$',
+             transform=ax2.transAxes,
+             fontsize=9, verticalalignment='top', horizontalalignment='right',
+             bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
+    
+    # Add decay time constant annotation
+    bt = burst_times[0]
+    t_tau = bt + burst_duration + tau_tail
+    idx_tau = np.argmin(np.abs(t - t_tau))
+    ax2.plot([bt + burst_duration, t_tau], [1.0, power_state[idx_tau]], 
+             'k--', linewidth=1, alpha=0.5)
+    ax2.text(t_tau, power_state[idx_tau] + 0.05, r'$\tau$ = 2s', 
+             fontsize=9, ha='left')
+    
+    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, 'Fig05_Radio_Tail')
+    save_figure(fig, output_base, dpi=config.get('global', {}).get('dpi', 300))
+    plt.close()
+    
+    # Compute tail energy waste
+    total_burst_energy = np.sum(data_activity) * (t[1] - t[0])
+    total_power_energy = np.sum(power_state) * (t[1] - t[0])
+    tail_waste_ratio = (total_power_energy - total_burst_energy) / total_power_energy
+    
+    return {
+        "output_files": [f"{output_base}.pdf", f"{output_base}.png"],
+        "computed_metrics": {
+            "tail_waste_ratio": float(tail_waste_ratio),
+            "tau_seconds": tau_tail
+        },
+        "validation_flags": {},
+        "pass": True
+    }