plane: fig

plane/cost_analysis.py

@@ -556,6 +556,143 @@ def plot_comprehensive_summary(data, save_path='aviation_cost_summary.png'):
     return fig
 
 
+# ============== 图表6: 燃油占比趋势（简洁版，中低饱和度）==============
+
+def plot_fuel_share_trend_simple(data, save_path='fuel_share_trend.png'):
+    """绘制燃油成本占总成本的比例趋势 - 简洁版，无标题，中低饱和度色调"""
+    
+    years = data['years']
+    fuel_cents = data['fuel_cents']
+    total_cents = data['total_cents']
+    
+    # 计算燃油占比
+    fuel_share = (fuel_cents / total_cents) * 100
+    
+    # 创建图表 - 更小但保持2:1比例
+    fig, ax1 = plt.subplots(figsize=(10, 5))
+    
+    # 中低饱和度色调
+    color1 = '#B87373'  # 柔和的红色/玫瑰色
+    color2 = '#6B8FAF'  # 柔和的蓝色
+    color3 = '#7AAE7A'  # 柔和的绿色
+    
+    # 绘制燃油占比（左轴）
+    ax1.fill_between(years, 0, fuel_share, alpha=0.25, color=color1)
+    line1, = ax1.plot(years, fuel_share, 'o-', color=color1, linewidth=2, 
+                      markersize=6, label='Fuel Share (%)')
+    ax1.set_xlabel('Year', fontsize=11)
+    ax1.set_ylabel('Fuel as % of Total Operating Cost', fontsize=11, color=color1)
+    ax1.tick_params(axis='y', labelcolor=color1)
+    ax1.set_ylim(0, 45)
+    
+    # 添加平均线
+    avg_share = np.mean(fuel_share)
+    ax1.axhline(y=avg_share, color=color1, linestyle='--', linewidth=1.2, alpha=0.6)
+    ax1.text(years[-1]+0.5, avg_share+1, f'Avg: {avg_share:.1f}%', 
+             fontsize=10, color=color1, fontweight='bold')
+    
+    # 右轴：绝对成本
+    ax2 = ax1.twinx()
+    line2, = ax2.plot(years, total_cents, 's--', color=color2, linewidth=1.8, 
+                      markersize=5, alpha=0.75, label='Total CASM (cents)')
+    line3, = ax2.plot(years, fuel_cents, '^--', color=color3, linewidth=1.8, 
+                      markersize=5, alpha=0.75, label='Fuel CASM (cents)')
+    ax2.set_ylabel('Cost per ASM (cents)', fontsize=11, color=color2)
+    ax2.tick_params(axis='y', labelcolor=color2)
+    
+    # 标注关键年份
+    # 2008年油价高峰
+    idx_2008 = np.where(years == 2008)[0][0]
+    ax1.annotate(f'2008 Oil Crisis\n{fuel_share[idx_2008]:.1f}%', 
+                 xy=(2008, fuel_share[idx_2008]), 
+                 xytext=(2005, fuel_share[idx_2008]+8),
+                 fontsize=9, ha='center',
+                 arrowprops=dict(arrowstyle='->', color='#888888'),
+                 bbox=dict(boxstyle='round,pad=0.3', facecolor='#FFF5E6', alpha=0.8))
+    
+    # 2015年油价低谷
+    idx_2015 = np.where(years == 2015)[0][0]
+    ax1.annotate(f'2015 Oil Crash\n{fuel_share[idx_2015]:.1f}%', 
+                 xy=(2015, fuel_share[idx_2015]), 
+                 xytext=(2017, fuel_share[idx_2015]-8),
+                 fontsize=9, ha='center',
+                 arrowprops=dict(arrowstyle='->', color='#888888'),
+                 bbox=dict(boxstyle='round,pad=0.3', facecolor='#E6F3FA', alpha=0.8))
+    
+    # 图例
+    lines = [line1, line2, line3]
+    labels = [l.get_label() for l in lines]
+    ax1.legend(lines, labels, loc='upper left', fontsize=10)
+    
+    # 添加数据来源
+    fig.text(0.99, 0.01, 'Data Source: MIT Airline Data Project / US DOT Form 41', 
+             fontsize=8, ha='right', style='italic', alpha=0.6)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"图表已保存: {save_path}")
+    
+    return fig
+
+
+# ============== 图表7: 燃油-总成本相关性散点图（单独版）==============
+
+def plot_fuel_total_correlation_scatter(data, save_path='fuel_total_correlation.png'):
+    """绘制燃油成本与总成本的相关性散点图 - 单独版，无标题，中低饱和度色调"""
+    
+    years = data['years']
+    fuel = data['fuel_cents']
+    total = data['total_cents']
+    
+    # 计算相关系数
+    correlation = np.corrcoef(fuel, total)[0, 1]
+    
+    # 线性回归
+    coeffs = np.polyfit(fuel, total, 1)
+    poly = np.poly1d(coeffs)
+    fuel_range = np.linspace(fuel.min(), fuel.max(), 100)
+    
+    # 创建图表 - 与fuel_share_trend类似的尺寸比例
+    fig, ax = plt.subplots(figsize=(10, 5))
+    
+    # 中低饱和度色调 - 与fuel_share_trend类似但不完全相同
+    scatter_cmap = 'YlGnBu'  # 柔和的黄-绿-蓝渐变色
+    line_color = '#9B7B6B'    # 柔和的棕红色
+    text_bg_color = '#D4E8D4'  # 柔和的淡绿色
+    
+    # 散点图
+    scatter = ax.scatter(fuel, total, c=years, cmap=scatter_cmap, s=90, alpha=0.85, 
+                         edgecolors='white', linewidth=1.2)
+    
+    # 回归线
+    ax.plot(fuel_range, poly(fuel_range), '--', color=line_color, linewidth=2, 
+            label=f'Linear Fit (R²={correlation**2:.3f})')
+    
+    # 颜色条
+    cbar = plt.colorbar(scatter, ax=ax)
+    cbar.set_label('Year', fontsize=10)
+    
+    ax.set_xlabel('Fuel Cost per ASM (cents)', fontsize=11)
+    ax.set_ylabel('Total Cost per ASM (cents)', fontsize=11)
+    ax.legend(fontsize=10, loc='lower right')
+    ax.grid(True, alpha=0.3)
+    
+    # 添加相关性解释
+    ax.text(0.05, 0.95, f'Pearson r = {correlation:.3f}\nEnergy drives ~{correlation**2*100:.0f}% of cost variance',
+            transform=ax.transAxes, fontsize=10, verticalalignment='top',
+            bbox=dict(boxstyle='round', facecolor=text_bg_color, alpha=0.8))
+    
+    # 添加数据来源
+    fig.text(0.99, 0.01, 'Data Source: MIT Airline Data Project / US DOT Form 41', 
+             fontsize=8, ha='right', style='italic', alpha=0.6)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"图表已保存: {save_path}")
+    
+    return fig
+
+
 # ============== 主程序 ==============
 
 if __name__ == "__main__":