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A题/ZJ_v2/fig13_survival_curve.py

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+"""
+Fig 13: Survival/Reliability Curve
+Shows probability of battery lasting beyond time t
+"""
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from plot_style import set_oprice_style, save_figure
+from validation import check_monotonic_decreasing
+
+def make_figure(config):
+    """Generate Fig 13: Survival Curve"""
+    
+    set_oprice_style()
+    
+    seed = config.get('global', {}).get('seed', 42)
+    np.random.seed(seed)
+    
+    # Generate TTE distribution (log-normal)
+    n_samples = 300
+    tte_mean = 2.5  # hours
+    tte_std = 0.4
+    
+    # Log-normal parameters
+    mu = np.log(tte_mean**2 / np.sqrt(tte_mean**2 + tte_std**2))
+    sigma = np.sqrt(np.log(1 + tte_std**2 / tte_mean**2))
+    
+    tte_samples = np.random.lognormal(mu, sigma, n_samples)
+    tte_samples = np.clip(tte_samples, 1.0, 5.0)
+    
+    # Sort for survival function
+    tte_sorted = np.sort(tte_samples)
+    
+    # Compute survival function: S(t) = P(TTE > t)
+    t_values = np.linspace(0, 4.5, 200)
+    survival_prob = np.zeros(len(t_values))
+    
+    for i, t in enumerate(t_values):
+        survival_prob[i] = np.sum(tte_sorted > t) / n_samples
+    
+    # Find confidence levels
+    tte_50 = np.percentile(tte_sorted, 50)  # Median
+    tte_95 = np.percentile(tte_sorted, 5)   # 95% confidence (5th percentile)
+    
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+    
+    # Plot survival curve
+    ax.plot(t_values, survival_prob * 100, 'b-', linewidth=3, label='Survival Function')
+    
+    # Fill area under curve
+    ax.fill_between(t_values, 0, survival_prob * 100, alpha=0.2, color='lightblue')
+    
+    # Mark key percentiles
+    ax.axhline(50, color='orange', linestyle='--', linewidth=1.5, alpha=0.7, label='50% Survival')
+    ax.axvline(tte_50, color='orange', linestyle=':', linewidth=1.5, alpha=0.7)
+    ax.plot(tte_50, 50, 'o', markersize=10, color='orange', markeredgecolor='white', 
+            markeredgewidth=2, zorder=5)
+    ax.text(tte_50 + 0.1, 50, f'Median TTE = {tte_50:.2f}h', fontsize=10, 
+            verticalalignment='center')
+    
+    ax.axhline(95, color='green', linestyle='--', linewidth=1.5, alpha=0.7, label='95% Confidence')
+    ax.axvline(tte_95, color='green', linestyle=':', linewidth=1.5, alpha=0.7)
+    ax.plot(tte_95, 95, 's', markersize=10, color='green', markeredgecolor='white', 
+            markeredgewidth=2, zorder=5)
+    ax.text(tte_95 + 0.1, 95, f'95% Confident TTE = {tte_95:.2f}h', fontsize=10, 
+            verticalalignment='center')
+    
+    # Annotate interpretation
+    ax.annotate('95% of devices will\nlast at least this long',
+                xy=(tte_95, 95), xytext=(tte_95 - 0.5, 75),
+                arrowprops=dict(arrowstyle='->', color='green', lw=2),
+                fontsize=10, color='darkgreen',
+                bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.7))
+    
+    # Labels and styling
+    ax.set_xlabel('Time (hours)', fontsize=11)
+    ax.set_ylabel('Survival Probability (%)', fontsize=11)
+    ax.set_title('Battery Reliability: Survival Function Analysis', 
+                 fontsize=12, fontweight='bold')
+    ax.set_xlim(0, 4.5)
+    ax.set_ylim(0, 105)
+    ax.grid(True, alpha=0.3)
+    ax.legend(loc='upper right', framealpha=0.9, fontsize=10)
+    
+    # Add distribution statistics
+    stats_text = f'TTE Distribution:\\n'
+    stats_text += f'Mean: {tte_mean:.2f}h\\n'
+    stats_text += f'Std: {tte_std:.2f}h\\n'
+    stats_text += f'Median: {tte_50:.2f}h\\n'
+    stats_text += f'95% CI: {tte_95:.2f}h\\n'
+    stats_text += f'Sample Size: {n_samples}'
+    
+    ax.text(0.02, 0.35, stats_text, transform=ax.transAxes,
+            fontsize=9, verticalalignment='top',
+            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8),
+            family='monospace')
+    
+    # Add interpretation guide
+    guide_text = 'Interpretation:\\n' + \
+                 '• S(t) = P(TTE > t)\\n' + \
+                 '• Steep drop = high variability\\n' + \
+                 '• Use 95% value for conservative estimates'
+    
+    ax.text(0.98, 0.02, guide_text, transform=ax.transAxes,
+            fontsize=9, verticalalignment='bottom', horizontalalignment='right',
+            bbox=dict(boxstyle='round', facecolor='lightblue', 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, 'Fig13_Survival_Curve')
+    save_figure(fig, output_base, dpi=config.get('global', {}).get('dpi', 300))
+    plt.close()
+    
+    # Validation
+    is_monotonic = check_monotonic_decreasing(survival_prob)
+    
+    return {
+        "output_files": [f"{output_base}.pdf", f"{output_base}.png"],
+        "computed_metrics": {
+            "median_tte_h": float(tte_50),
+            "tte_95_confidence_h": float(tte_95),
+            "n_samples": n_samples
+        },
+        "validation_flags": {"survival_monotonic": is_monotonic},
+        "pass": is_monotonic
+    }