""" Aviation Cost Structure Analysis 证明：在成熟运输体系中，能源成本与总成本具有强相关性 Data Source: MIT Airline Data Project / US DOT Form 41 via BTS """ import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib import rcParams import matplotlib.patches as mpatches # 设置字体和样式 rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Helvetica'] rcParams['axes.unicode_minus'] = False plt.style.use('seaborn-v0_8-whitegrid') # ============== 数据加载 ============== def load_data(): """加载并清洗所有Excel数据""" # 1. 燃油费用/ASM (美元) df_fuel = pd.read_excel('Fuel Expense per ASM.xls', engine='xlrd') # 2. 总费用/ASM (美分) df_total = pd.read_excel('System Total Expense per Available Seat Mile (CASM ex Transport Related).xls', engine='xlrd') # 3. 非人工费用/ASM (美分) - 不含燃油 df_nonlabor = pd.read_excel('System Non-Labor Expense per Available Seat Mile (CASM ex fuel, Transport Related and Labor).xls', engine='xlrd') # 4. 总维护费用 (百万美元) df_maint = pd.read_excel('Total Flight Equipment Maintenance Expense.xls', engine='xlrd') # 提取年份 (第3行，从第3列开始) years_raw = df_fuel.iloc[2, 2:].dropna().values years = np.array([int(y) for y in years_raw]) n_years = len(years) # 提取 "Total All Sectors" 行数据 (第28行，索引27) fuel_per_asm = df_fuel.iloc[27, 2:2+n_years].values.astype(float) # 美元 total_per_asm = df_total.iloc[27, 2:2+n_years].values.astype(float) # 美分 nonlabor_per_asm = df_nonlabor.iloc[27, 2:2+n_years].values.astype(float) # 美分 maint_total = df_maint.iloc[27, 2:2+n_years].values.astype(float) # 百万美元 # 统一单位为美分/ASM fuel_per_asm_cents = fuel_per_asm * 100 # 美元转美分 return { 'years': years, 'fuel_cents': fuel_per_asm_cents, 'total_cents': total_per_asm, 'nonlabor_cents': nonlabor_per_asm, 'maint_millions': maint_total, } # ============== 图表1: 燃油占比时间序列 ============== def plot_fuel_share_trend(data, save_path='fuel_share_trend.png'): """绘制燃油成本占总成本的比例趋势""" 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 fig, ax1 = plt.subplots(figsize=(14, 7)) # 绘制燃油占比（左轴） color1 = '#E74C3C' ax1.fill_between(years, 0, fuel_share, alpha=0.3, color=color1) line1, = ax1.plot(years, fuel_share, 'o-', color=color1, linewidth=2.5, markersize=8, label='Fuel Share (%)') ax1.set_xlabel('Year', fontsize=13) ax1.set_ylabel('Fuel as % of Total Operating Cost', fontsize=13, 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=1.5, alpha=0.7) ax1.text(years[-1]+0.5, avg_share+1, f'Avg: {avg_share:.1f}%', fontsize=11, color=color1, fontweight='bold') # 右轴：绝对成本 ax2 = ax1.twinx() color2 = '#3498DB' line2, = ax2.plot(years, total_cents, 's--', color=color2, linewidth=2, markersize=6, alpha=0.8, label='Total CASM (cents)') line3, = ax2.plot(years, fuel_cents, '^--', color='#27AE60', linewidth=2, markersize=6, alpha=0.8, label='Fuel CASM (cents)') ax2.set_ylabel('Cost per ASM (cents)', fontsize=13, 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=10, ha='center', arrowprops=dict(arrowstyle='->', color='gray'), bbox=dict(boxstyle='round,pad=0.3', facecolor='lightyellow', 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=10, ha='center', arrowprops=dict(arrowstyle='->', color='gray'), bbox=dict(boxstyle='round,pad=0.3', facecolor='lightcyan', 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=11) plt.title('Aviation Industry: Fuel Cost as Percentage of Total Operating Cost\n' '(US Domestic Airlines, 1995-2018)', fontsize=15, fontweight='bold') # 添加数据来源 fig.text(0.99, 0.01, 'Data Source: MIT Airline Data Project / US DOT Form 41', fontsize=9, ha='right', style='italic', alpha=0.7) 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2: 成本收敛曲线 ============== def plot_cost_convergence(data, save_path='cost_convergence.png'): """ 绘制成本收敛曲线：证明当复用次数增加时，总成本收敛于能源成本 模型假设 (Boeing 737-800): - 飞机购置价: $80M - 设计寿命: 50,000 cycles - 平均航程: ~1000 miles/cycle """ # 飞机参数 AIRCRAFT_PRICE = 80_000_000 # $80M DESIGN_LIFE_CYCLES = 50_000 AVG_MILES_PER_CYCLE = 1000 AVG_SEATS = 160 # B737-800 座位数 # 从实际数据中提取 2018 年数据作为"成熟状态"参考 idx_2018 = np.where(data['years'] == 2018)[0][0] fuel_per_asm_cents = data['fuel_cents'][idx_2018] total_per_asm_cents = data['total_cents'][idx_2018] nonlabor_per_asm_cents = data['nonlabor_cents'][idx_2018] # 计算单次飞行（cycle）的成本 asm_per_cycle = AVG_MILES_PER_CYCLE * AVG_SEATS fuel_per_cycle = fuel_per_asm_cents / 100 * asm_per_cycle # 美元 ops_per_cycle = (total_per_asm_cents - fuel_per_asm_cents) / 100 * asm_per_cycle # 其他运营成本 print(f"\n=== 2018年单次飞行成本估算 (B737-800) ===") print(f"ASM per cycle: {asm_per_cycle:,}") print(f"Fuel cost per cycle: ${fuel_per_cycle:,.0f}") print(f"Other ops per cycle: ${ops_per_cycle:,.0f}") print(f"Total ops per cycle: ${fuel_per_cycle + ops_per_cycle:,.0f}") # 复用次数范围 N = np.logspace(0, np.log10(DESIGN_LIFE_CYCLES * 2), 500) # 1 到 100,000 # 成本模型 amortized_hardware = AIRCRAFT_PRICE / N energy_cost = np.full_like(N, fuel_per_cycle) other_ops_cost = np.full_like(N, ops_per_cycle) total_cost = amortized_hardware + energy_cost + other_ops_cost # 创建图表 fig, ax = plt.subplots(figsize=(14, 8)) # 填充区域 ax.fill_between(N, 0, energy_cost, alpha=0.4, color='#E74C3C', label='Energy (Fuel) Cost') ax.fill_between(N, energy_cost, energy_cost + other_ops_cost, alpha=0.3, color='#3498DB', label='Other Operating Cost') ax.fill_between(N, energy_cost + other_ops_cost, total_cost, alpha=0.3, color='#95A5A6', label='Amortized Hardware') # 总成本线 ax.plot(N, total_cost, 'k-', linewidth=3, label='Total Cost per Flight') # 渐进线（能量底限） asymptote = fuel_per_cycle + ops_per_cycle ax.axhline(y=asymptote, color='#E74C3C', linestyle='--', linewidth=2, alpha=0.8) ax.text(1.5, asymptote * 1.05, f'Asymptote: ${asymptote:,.0f}', fontsize=11, color='#E74C3C', fontweight='bold') # 标记关键点 # N=1 (一次性) ax.plot(1, total_cost[0], 'ro', markersize=12, zorder=5) ax.annotate(f'N=1 (Expendable)\n${total_cost[0]/1e6:.1f}M', xy=(1, total_cost[0]), xytext=(3, total_cost[0]*0.7), fontsize=10, ha='left', arrowprops=dict(arrowstyle='->', color='red'), bbox=dict(boxstyle='round', facecolor='mistyrose', alpha=0.9)) # N=20 (Falcon 9 现状) idx_20 = np.argmin(np.abs(N - 20)) ax.plot(20, total_cost[idx_20], 'o', color='orange', markersize=10, zorder=5) ax.annotate(f'N=20 (Falcon 9)\n${total_cost[idx_20]/1e6:.2f}M', xy=(20, total_cost[idx_20]), xytext=(50, total_cost[idx_20]*1.5), fontsize=10, ha='left', arrowprops=dict(arrowstyle='->', color='orange'), bbox=dict(boxstyle='round', facecolor='moccasin', alpha=0.9)) # N=50,000 (航空业) idx_50k = np.argmin(np.abs(N - 50000)) ax.plot(50000, total_cost[idx_50k], 'go', markersize=12, zorder=5) ax.annotate(f'N=50,000 (Aviation)\n${total_cost[idx_50k]:,.0f}', xy=(50000, total_cost[idx_50k]), xytext=(10000, total_cost[idx_50k]*3), fontsize=10, ha='center', arrowprops=dict(arrowstyle='->', color='green'), bbox=dict(boxstyle='round', facecolor='honeydew', alpha=0.9)) # 标记"能量主导区" ax.axvspan(5000, N[-1], alpha=0.1, color='green') ax.text(20000, ax.get_ylim()[1]*0.15, 'Energy Dominance Zone', fontsize=12, color='green', fontweight='bold', ha='center', style='italic') ax.set_xscale('log') ax.set_yscale('log') ax.set_xlabel('Number of Reuses (N)', fontsize=13) ax.set_ylabel('Cost per Flight ($)', fontsize=13) ax.set_title('Cost Convergence: Hardware Amortization → Energy Dominance\n' '(Boeing 737-800 Model, 2018 US Airline Data)', fontsize=15, fontweight='bold') ax.legend(loc='upper right', fontsize=11) ax.set_xlim(0.8, N[-1]*1.5) ax.grid(True, which='both', alpha=0.3) # 添加公式 formula_text = r'$Cost(N) = \frac{C_{hardware}}{N} + C_{fuel} + C_{ops}$' ax.text(0.02, 0.02, formula_text, transform=ax.transAxes, fontsize=14, verticalalignment='bottom', bbox=dict(boxstyle='round', facecolor='white', alpha=0.9)) 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3: 成本结构对比 (饼图) ============== def plot_cost_structure_comparison(data, save_path='cost_structure_comparison.png'): """ 对比三种场景的成本结构： 1. 一次性火箭 (N=1) 2. 可复用火箭 (N=20, Falcon 9) 3. 成熟航空业 (N=50,000) """ # 参数 AIRCRAFT_PRICE = 80_000_000 ROCKET_PRICE = 60_000_000 # Falcon 9 估算 # 从 2018 数据估算单次成本 idx_2018 = np.where(data['years'] == 2018)[0][0] fuel_per_asm = data['fuel_cents'][idx_2018] / 100 # 美元 total_per_asm = data['total_cents'][idx_2018] / 100 # 飞机参数 asm_per_flight = 160_000 # 160 seats * 1000 miles fuel_cost = fuel_per_asm * asm_per_flight other_ops = (total_per_asm - fuel_per_asm) * asm_per_flight # 火箭参数 (估算) rocket_fuel = 500_000 # $500K 燃料 rocket_ops = 1_000_000 # $1M 地面运营 # 三种场景 scenarios = { 'Expendable Rocket\n(N=1)': { 'Hardware': ROCKET_PRICE / 1, 'Fuel/Energy': rocket_fuel, 'Operations': rocket_ops, }, 'Reusable Rocket\n(N=20, e.g. Falcon 9)': { 'Hardware': ROCKET_PRICE / 20, 'Fuel/Energy': rocket_fuel, 'Operations': rocket_ops, }, 'Commercial Aviation\n(N=50,000)': { 'Hardware': AIRCRAFT_PRICE / 50000, 'Fuel/Energy': fuel_cost, 'Operations': other_ops, }, } # 创建图表 fig, axes = plt.subplots(1, 3, figsize=(16, 6)) colors = ['#95A5A6', '#E74C3C', '#3498DB'] # 灰色(硬件), 红色(能源), 蓝色(运营) for idx, (scenario_name, costs) in enumerate(scenarios.items()): ax = axes[idx] values = list(costs.values()) labels = list(costs.keys()) total = sum(values) # 计算百分比 percentages = [v/total*100 for v in values] # 绘制饼图 wedges, texts, autotexts = ax.pie( values, labels=None, autopct=lambda pct: f'{pct:.1f}%' if pct > 3 else '', colors=colors, explode=(0.02, 0.05, 0.02), startangle=90, pctdistance=0.6, textprops={'fontsize': 11, 'fontweight': 'bold'} ) ax.set_title(f'{scenario_name}\nTotal: ${total:,.0f}', fontsize=12, fontweight='bold') # 添加能源占比高亮 fuel_pct = percentages[1] if fuel_pct < 5: highlight_color = 'lightcoral' note = 'Energy: Negligible!' elif fuel_pct < 20: highlight_color = 'lightyellow' note = f'Energy: {fuel_pct:.1f}%' else: highlight_color = 'lightgreen' note = f'Energy: {fuel_pct:.1f}%' ax.text(0, -1.4, note, ha='center', fontsize=11, fontweight='bold', bbox=dict(boxstyle='round', facecolor=highlight_color, alpha=0.8)) # 添加统一图例 legend_patches = [ mpatches.Patch(color=colors[0], label='Hardware (Amortized)'), mpatches.Patch(color=colors[1], label='Fuel / Energy'), mpatches.Patch(color=colors[2], label='Other Operations'), ] fig.legend(handles=legend_patches, loc='lower center', ncol=3, fontsize=12, bbox_to_anchor=(0.5, -0.02)) plt.suptitle('Cost Structure Evolution: From Hardware-Dominant to Energy-Dominant\n' '(Transition as Reusability Increases)', fontsize=15, fontweight='bold', y=1.02) 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4: 相关性分析 ============== def plot_correlation_analysis(data, save_path='energy_cost_correlation.png'): """ 分析燃油成本与总成本的相关性 证明：在成熟系统中，能源是成本的主要驱动因素 """ 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 fig, axes = plt.subplots(1, 2, figsize=(14, 6)) # 左图：散点图 + 回归线 ax1 = axes[0] scatter = ax1.scatter(fuel, total, c=years, cmap='viridis', s=100, alpha=0.8, edgecolors='white', linewidth=1.5) ax1.plot(fuel_range, poly(fuel_range), 'r--', linewidth=2, label=f'Linear Fit (R²={correlation**2:.3f})') # 颜色条 cbar = plt.colorbar(scatter, ax=ax1) cbar.set_label('Year', fontsize=11) ax1.set_xlabel('Fuel Cost per ASM (cents)', fontsize=12) ax1.set_ylabel('Total Cost per ASM (cents)', fontsize=12) ax1.set_title(f'Fuel Cost vs Total Cost Correlation\n' f'Pearson r = {correlation:.3f}', fontsize=13, fontweight='bold') ax1.legend(fontsize=11) ax1.grid(True, alpha=0.3) # 添加相关性解释 ax1.text(0.05, 0.95, f'Strong Positive Correlation\nEnergy drives ~{correlation**2*100:.0f}% of cost variance', transform=ax1.transAxes, fontsize=10, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.8)) # 右图：时间序列对比（归一化） ax2 = axes[1] # 归一化到基准年 (1995) fuel_norm = fuel / fuel[0] * 100 total_norm = total / total[0] * 100 ax2.plot(years, fuel_norm, 'o-', color='#E74C3C', linewidth=2.5, markersize=8, label='Fuel Cost (Indexed)') ax2.plot(years, total_norm, 's-', color='#3498DB', linewidth=2.5, markersize=8, label='Total Cost (Indexed)') ax2.axhline(y=100, color='gray', linestyle=':', alpha=0.7) ax2.set_xlabel('Year', fontsize=12) ax2.set_ylabel('Index (1995 = 100)', fontsize=12) ax2.set_title('Fuel and Total Cost Co-movement\n(Indexed to 1995)', fontsize=13, fontweight='bold') ax2.legend(fontsize=11) ax2.grid(True, alpha=0.3) # 标注关键时期 ax2.axvspan(2007, 2009, alpha=0.2, color='red', label='Oil Spike') ax2.axvspan(2014, 2016, alpha=0.2, color='blue', label='Oil Crash') plt.suptitle('Evidence: Energy Cost Strongly Correlates with Total Operating Cost\n' '(US Domestic Airlines Industry)', fontsize=14, fontweight='bold', y=1.02) plt.tight_layout() plt.savefig(save_path, dpi=150, bbox_inches='tight') print(f"图表已保存: {save_path}") # 打印统计信息 print(f"\n=== 相关性分析结果 ===") print(f"Pearson相关系数: {correlation:.4f}") print(f"R²决定系数: {correlation**2:.4f}") print(f"回归方程: Total = {coeffs[0]:.3f} × Fuel + {coeffs[1]:.3f}") return fig # ============== 图表5: 综合信息图 ============== def plot_comprehensive_summary(data, save_path='aviation_cost_summary.png'): """ 创建一张综合性的信息图，用于论文 """ fig = plt.figure(figsize=(16, 12)) # 布局 gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.25) years = data['years'] fuel = data['fuel_cents'] total = data['total_cents'] nonlabor = data['nonlabor_cents'] # ========== 左上：燃油占比趋势 ========== ax1 = fig.add_subplot(gs[0, 0]) fuel_share = (fuel / total) * 100 ax1.fill_between(years, 0, fuel_share, alpha=0.4, color='#E74C3C') ax1.plot(years, fuel_share, 'o-', color='#E74C3C', linewidth=2.5, markersize=6) avg_share = np.mean(fuel_share) ax1.axhline(y=avg_share, color='darkred', linestyle='--', linewidth=1.5) ax1.text(2017, avg_share+2, f'Avg: {avg_share:.1f}%', fontsize=10, fontweight='bold', color='darkred') ax1.set_xlabel('Year', fontsize=11) ax1.set_ylabel('Fuel Share (%)', fontsize=11) ax1.set_title('(A) Fuel as % of Total Operating Cost', fontsize=12, fontweight='bold') ax1.set_ylim(0, 45) ax1.grid(True, alpha=0.3) # ========== 右上：成本构成堆叠图 ========== ax2 = fig.add_subplot(gs[0, 1]) labor_cents = total - fuel - nonlabor # 劳动力成本 = 总成本 - 燃油 - 非劳动力 ax2.fill_between(years, 0, fuel, alpha=0.8, color='#E74C3C', label='Fuel') ax2.fill_between(years, fuel, fuel + nonlabor, alpha=0.8, color='#3498DB', label='Non-Labor (ex Fuel)') ax2.fill_between(years, fuel + nonlabor, total, alpha=0.8, color='#2ECC71', label='Labor & Related') ax2.set_xlabel('Year', fontsize=11) ax2.set_ylabel('CASM (cents)', fontsize=11) ax2.set_title('(B) Cost Structure Breakdown (Stacked)', fontsize=12, fontweight='bold') ax2.legend(loc='upper left', fontsize=9) ax2.grid(True, alpha=0.3) # ========== 左下：收敛曲线 ========== ax3 = fig.add_subplot(gs[1, 0]) # 简化的收敛模型 N = np.logspace(0, 5, 200) hardware = 80_000_000 / N fuel_cost = 5000 # 单次飞行燃油 ops_cost = 4000 # 其他运营 total_cost = hardware + fuel_cost + ops_cost ax3.fill_between(N, 0, fuel_cost, alpha=0.5, color='#E74C3C', label='Energy (Fuel)') ax3.fill_between(N, fuel_cost, fuel_cost + ops_cost, alpha=0.4, color='#3498DB', label='Operations') ax3.fill_between(N, fuel_cost + ops_cost, total_cost, alpha=0.3, color='#95A5A6', label='Hardware') ax3.plot(N, total_cost, 'k-', linewidth=2.5) ax3.axhline(y=fuel_cost + ops_cost, color='#E74C3C', linestyle='--', linewidth=1.5) # 标记点 ax3.plot(1, total_cost[0], 'ro', markersize=10) ax3.plot(20, hardware[np.argmin(np.abs(N-20))] + fuel_cost + ops_cost, 'o', color='orange', markersize=10) ax3.plot(50000, total_cost[np.argmin(np.abs(N-50000))], 'go', markersize=10) ax3.text(1.5, total_cost[0]*0.6, 'N=1\nExpendable', fontsize=9, ha='left') ax3.text(25, 5e6, 'N=20\nFalcon 9', fontsize=9, ha='left') ax3.text(30000, 15000, 'N=50K\nAviation', fontsize=9, ha='left') ax3.set_xscale('log') ax3.set_yscale('log') ax3.set_xlabel('Number of Reuses (N)', fontsize=11) ax3.set_ylabel('Cost per Flight ($)', fontsize=11) ax3.set_title('(C) Cost Convergence Model', fontsize=12, fontweight='bold') ax3.legend(loc='upper right', fontsize=9) ax3.grid(True, which='both', alpha=0.3) # ========== 右下：相关性散点图 ========== ax4 = fig.add_subplot(gs[1, 1]) correlation = np.corrcoef(fuel, total)[0, 1] coeffs = np.polyfit(fuel, total, 1) scatter = ax4.scatter(fuel, total, c=years, cmap='plasma', s=80, alpha=0.8, edgecolors='white') fuel_range = np.linspace(fuel.min(), fuel.max(), 100) ax4.plot(fuel_range, np.poly1d(coeffs)(fuel_range), 'r--', linewidth=2) ax4.set_xlabel('Fuel Cost (cents/ASM)', fontsize=11) ax4.set_ylabel('Total Cost (cents/ASM)', fontsize=11) ax4.set_title(f'(D) Fuel-Total Cost Correlation\n(r = {correlation:.3f}, R² = {correlation**2:.3f})', fontsize=12, fontweight='bold') ax4.grid(True, alpha=0.3) cbar = plt.colorbar(scatter, ax=ax4) cbar.set_label('Year', fontsize=10) # 主标题 fig.suptitle('Aviation Industry Cost Analysis: Evidence for Energy-Cost Correlation\n' '(US Domestic Airlines, 1995-2018 | Data: MIT Airline Data Project)', fontsize=16, fontweight='bold', y=1.01) plt.tight_layout() plt.savefig(save_path, dpi=150, bbox_inches='tight') print(f"综合图表已保存: {save_path}") 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__": print("=" * 60) print("Aviation Cost Structure Analysis") print("证明：成熟运输系统中能源与成本的相关性") print("=" * 60) # 加载数据 data = load_data() print(f"\n数据范围: {data['years'][0]} - {data['years'][-1]}") print(f"年份数量: {len(data['years'])}") # 打印关键统计 fuel_share = data['fuel_cents'] / data['total_cents'] * 100 print(f"\n燃油占比统计:") print(f" 平均值: {np.mean(fuel_share):.1f}%") print(f" 最大值: {np.max(fuel_share):.1f}% ({data['years'][np.argmax(fuel_share)]})") print(f" 最小值: {np.min(fuel_share):.1f}% ({data['years'][np.argmin(fuel_share)]})") # 生成图表 print("\n正在生成图表...") plot_fuel_share_trend(data) plot_cost_convergence(data) plot_cost_structure_comparison(data) plot_correlation_analysis(data) plot_comprehensive_summary(data) print("\n" + "=" * 60) print("所有图表生成完成！") print("=" * 60)