p4: fig

p4/environmental_impact.py

@@ -490,99 +490,326 @@ def optimize_for_environment(
 # ============== 可视化 ==============
 
 def plot_environmental_comparison(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
-    """绘制环境影响对比图"""
+    """绘制环境影响对比图（2张子图版本，马卡龙色系论文风格）"""
     df = analyze_all_scenarios()
     
-    fig, axes = plt.subplots(2, 2, figsize=(14, 12))
+    # 马卡龙色系
+    MACARON = {
+        'pink': '#F8B4C0',       # 樱花粉
+        'mint': '#A8E6CF',       # 薄荷绿
+        'lavender': '#C5A3FF',   # 薰衣草紫
+        'peach': '#FFCBA4',      # 蜜桃橙
+        'sky': '#A8D8EA',        # 天空蓝
+        'cream': '#FFEAA7',      # 奶油黄
+        'rose': '#DDA0DD',       # 玫瑰粉
+        'sage': '#B5EAD7',       # 鼠尾草绿
+    }
+    
+    # 论文风格设置
+    plt.rcParams.update({
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'axes.titlesize': 13,
+        'xtick.labelsize': 10,
+        'ytick.labelsize': 10,
+        'legend.fontsize': 9,
+        'axes.linewidth': 0.8,
+        'axes.edgecolor': '#666666',
+    })
+    
+    fig, axes = plt.subplots(1, 2, figsize=(10, 4.5))
+    fig.patch.set_facecolor('white')
     
     scenarios = df['Scenario'].tolist()
     x = np.arange(len(scenarios))
-    width = 0.6
+    width = 0.35
     
-    # ========== 图1: CO2排放分解 ==========
+    # ========== 左图: CO2排放分解 + H2O排放 ==========
-    ax1 = axes[0, 0]
+    ax1 = axes[0]
+    ax1.set_facecolor('white')
     
+    # CO2数据（左侧柱子）
     co2_combustion = df['CO2 Combustion (Mt)'].values
     co2_production = df['CO2 Production (Mt)'].values
     co2_electricity = df['CO2 Electricity (Mt)'].values
     
-    bars1 = ax1.bar(x, co2_combustion, width, label='Combustion', color='#FF6B6B')
+    bars1 = ax1.bar(x - width/2, co2_combustion, width, label='Combustion', 
-    bars2 = ax1.bar(x, co2_production, width, bottom=co2_combustion, label='Fuel Production', color='#4ECDC4')
+                    color=MACARON['pink'], edgecolor='#666666', linewidth=0.5)
-    bars3 = ax1.bar(x, co2_electricity, width, bottom=co2_combustion+co2_production, 
+    bars2 = ax1.bar(x - width/2, co2_production, width, bottom=co2_combustion, 
-                    label='Electricity', color='#45B7D1')
+                    label='Fuel Prod.', color=MACARON['mint'], edgecolor='#666666', linewidth=0.5)
+    bars3 = ax1.bar(x - width/2, co2_electricity, width, bottom=co2_combustion+co2_production, 
+                    label='Electricity', color=MACARON['sky'], edgecolor='#666666', linewidth=0.5)
     
-    ax1.set_ylabel('CO₂ Emissions (Mt)', fontsize=12)
+    ax1.set_ylabel('CO₂ Emissions (Mt)', color='#333333')
-    ax1.set_title('CO₂ Emissions Breakdown by Scenario', fontsize=14)
+    ax1.tick_params(axis='y', labelcolor='#333333')
-    ax1.set_xticks(x)
-    ax1.set_xticklabels(scenarios, rotation=15, ha='right')
-    ax1.legend()
-    ax1.grid(True, alpha=0.3, axis='y')
     
-    # 添加总量标签
+    # 添加CO2总量标签
     totals = df['CO2 Total (Mt)'].values
     for i, total in enumerate(totals):
-        ax1.text(i, total + 50, f'{total:.0f}', ha='center', fontsize=10, fontweight='bold')
+        ax1.text(i - width/2, total + 300, f'{total:.0f}', ha='center', fontsize=9, fontweight='bold')
     
-    # ========== 图2: 平流层水蒸气 ==========
+    # H2O数据（右侧柱子）
-    ax2 = axes[0, 1]
+    ax1_twin = ax1.twinx()
     
     h2o_surface = df['H2O Total (Mt)'].values - df['H2O Stratosphere (Mt)'].values
     h2o_strat = df['H2O Stratosphere (Mt)'].values
     
-    bars1 = ax2.bar(x, h2o_surface, width, label='Troposphere', color='#96CEB4')
+    bars4 = ax1_twin.bar(x + width/2, h2o_surface, width, label='Troposphere', 
-    bars2 = ax2.bar(x, h2o_strat, width, bottom=h2o_surface, label='Stratosphere', color='#D4A5A5')
+                         color=MACARON['sage'], edgecolor='#666666', linewidth=0.5)
+    bars5 = ax1_twin.bar(x + width/2, h2o_strat, width, bottom=h2o_surface, 
+                         label='Stratosphere', color=MACARON['rose'], edgecolor='#666666', linewidth=0.5)
     
-    ax2.set_ylabel('H₂O Emissions (Mt)', fontsize=12)
+    ax1_twin.set_ylabel('H₂O Emissions (Mt)', color='#7B68A0')
-    ax2.set_title('Water Vapor Emissions\n(Stratospheric emissions have climate impact)', fontsize=14)
+    ax1_twin.tick_params(axis='y', labelcolor='#7B68A0')
-    ax2.set_xticks(x)
-    ax2.set_xticklabels(scenarios, rotation=15, ha='right')
-    ax2.legend()
-    ax2.grid(True, alpha=0.3, axis='y')
     
-    # ========== 图3: 年均CO2排放 ==========
+    ax1.set_title('(a) CO₂ & H₂O Emissions', fontweight='bold', pad=10)
-    ax3 = axes[1, 0]
+    ax1.set_xticks(x)
+    ax1.set_xticklabels(scenarios, rotation=20, ha='right')
+    ax1.grid(True, alpha=0.2, axis='y', linestyle='--')
+    ax1.set_axisbelow(True)
+    
+    # 合并图例
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax1_twin.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right', 
+               framealpha=0.95, edgecolor='#cccccc', ncol=2)
+    
+    # ========== 右图: 年均CO2排放 + 碳强度 ==========
+    ax2 = axes[1]
+    ax2.set_facecolor('white')
     
     annual_co2 = df['Annual CO2 (Mt/yr)'].values
-    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4']
-    
-    bars = ax3.bar(x, annual_co2, width, color=colors)
-    
-    # 添加参考线：全球年排放量的比例
-    global_annual_co2 = 37000  # Mt/yr (2023年全球CO2排放)
-    for i, val in enumerate(annual_co2):
-        pct = val / global_annual_co2 * 100
-        ax3.text(i, val + 1, f'{val:.1f}\n({pct:.2f}%)', ha='center', fontsize=9)
-    
-    ax3.set_ylabel('Annual CO₂ (Mt/yr)', fontsize=12)
-    ax3.set_title('Annual CO₂ Emissions\n(% of global annual emissions shown)', fontsize=14)
-    ax3.set_xticks(x)
-    ax3.set_xticklabels(scenarios, rotation=15, ha='right')
-    ax3.grid(True, alpha=0.3, axis='y')
-    
-    # ========== 图4: 能效对比 (CO2/ton) ==========
-    ax4 = axes[1, 1]
-    
     co2_per_ton = df['CO2/ton (kg)'].values
     
-    bars = ax4.bar(x, co2_per_ton, width, color=colors)
+    # 年均CO2（左侧柱子）- 使用渐变马卡龙色
+    colors_annual = [MACARON['pink'], MACARON['mint'], MACARON['sky'], MACARON['sage']]
+    bars_annual = ax2.bar(x - width/2, annual_co2, width, color=colors_annual, 
+                          edgecolor='#666666', linewidth=0.5)
     
-    ax4.set_ylabel('CO₂ per ton payload (kg)', fontsize=12)
+    ax2.set_ylabel('Annual CO₂ (Mt/yr)', color='#333333')
-    ax4.set_title('Carbon Intensity: CO₂ Emissions per Ton Payload', fontsize=14)
+    ax2.tick_params(axis='y', labelcolor='#333333')
-    ax4.set_xticks(x)
-    ax4.set_xticklabels(scenarios, rotation=15, ha='right')
-    ax4.grid(True, alpha=0.3, axis='y')
     
-    for bar, val in zip(bars, co2_per_ton):
+    # 添加年均CO2标签
-        ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, 
+    global_annual_co2 = 37000
-                f'{val:.0f}', ha='center', fontsize=10, fontweight='bold')
+    for i, val in enumerate(annual_co2):
+        pct = val / global_annual_co2 * 100
+        ax2.text(i - width/2, val + 2, f'{val:.1f}\n({pct:.2f}%)', ha='center', fontsize=8)
+    
+    # 碳强度（右侧柱子）
+    ax2_twin = ax2.twinx()
+    
+    bars_intensity = ax2_twin.bar(x + width/2, co2_per_ton, width, color=MACARON['cream'], 
+                                   edgecolor='#666666', linewidth=0.5)
+    
+    ax2_twin.set_ylabel('CO₂ per ton payload (kg)', color='#B8860B')
+    ax2_twin.tick_params(axis='y', labelcolor='#B8860B')
+    
+    # 添加碳强度标签
+    for i, val in enumerate(co2_per_ton):
+        ax2_twin.text(i + width/2, val + 3000, f'{val:.0f}', ha='center', fontsize=9, fontweight='bold')
+    
+    ax2.set_title('(b) Annual Emissions & Carbon Intensity', fontweight='bold', pad=10)
+    ax2.set_xticks(x)
+    ax2.set_xticklabels(scenarios, rotation=20, ha='right')
+    ax2.grid(True, alpha=0.2, axis='y', linestyle='--')
+    ax2.set_axisbelow(True)
+    
+    # 图例说明
+    from matplotlib.patches import Patch
+    legend_elements = [Patch(facecolor=MACARON['pink'], edgecolor='#666666', label='Annual CO₂'),
+                       Patch(facecolor=MACARON['cream'], edgecolor='#666666', label='Carbon Intensity')]
+    ax2.legend(handles=legend_elements, loc='upper right', framealpha=0.95, edgecolor='#cccccc')
     
     plt.tight_layout()
-    plt.savefig(f'{save_dir}/environmental_comparison.png', dpi=150, bbox_inches='tight')
+    plt.savefig(f'{save_dir}/environmental_comparison.png', dpi=300, bbox_inches='tight', 
+                facecolor='white', edgecolor='none')
     print(f"环境影响对比图已保存至: {save_dir}/environmental_comparison.png")
     
     return df
 
 
+def plot_radar_chart(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
+    """单独绘制雷达图（马卡龙色系，无标题，论文风格）"""
+    df = analyze_all_scenarios()
+    
+    # 马卡龙色系
+    MACARON = {
+        'pink': '#F8B4C0',       # 樱花粉
+        'mint': '#A8E6CF',       # 薄荷绿
+        'lavender': '#C5A3FF',   # 薰衣草紫
+        'sky': '#A8D8EA',        # 天空蓝
+    }
+    colors = [MACARON['pink'], MACARON['mint'], MACARON['sky'], MACARON['lavender']]
+    
+    # 论文风格设置
+    plt.rcParams.update({
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'xtick.labelsize': 11,
+        'ytick.labelsize': 10,
+        'legend.fontsize': 10,
+    })
+    
+    fig, ax = plt.subplots(figsize=(5, 5), subplot_kw=dict(projection='polar'))
+    fig.patch.set_facecolor('white')
+    
+    categories = ['Time\n(inv)', 'Energy\n(inv)', 'CO₂\n(inv)', 'Launches\n(inv)', 'Strat. H₂O\n(inv)']
+    N = len(categories)
+    
+    # 归一化数据 (取逆以使"更好"在外围)
+    max_vals = df[['Years', 'Energy (PJ)', 'CO2 Total (Mt)', 'Launches', 'H2O Stratosphere (Mt)']].max()
+    
+    angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist()
+    angles += angles[:1]
+    
+    for i, (idx, row) in enumerate(df.iterrows()):
+        values = [
+            1 - row['Years'] / max_vals['Years'],
+            1 - row['Energy (PJ)'] / max_vals['Energy (PJ)'],
+            1 - row['CO2 Total (Mt)'] / max_vals['CO2 Total (Mt)'],
+            1 - row['Launches'] / max_vals['Launches'] if max_vals['Launches'] > 0 else 1,
+            1 - row['H2O Stratosphere (Mt)'] / max_vals['H2O Stratosphere (Mt)'] if max_vals['H2O Stratosphere (Mt)'] > 0 else 1
+        ]
+        values += values[:1]
+        ax.plot(angles, values, 'o-', linewidth=2, label=row['Scenario'], 
+                color=colors[i], markersize=5)
+        ax.fill(angles, values, alpha=0.2, color=colors[i])
+    
+    ax.set_xticks(angles[:-1])
+    ax.set_xticklabels(categories, fontsize=10)
+    ax.set_ylim(0, 1)
+    
+    # 设置径向刻度
+    ax.set_yticks([0.2, 0.4, 0.6, 0.8, 1.0])
+    ax.set_yticklabels(['0.2', '0.4', '0.6', '0.8', '1.0'], fontsize=8, color='#666666')
+    
+    # 图例放在右侧
+    ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), framealpha=0.95, 
+              edgecolor='#cccccc')
+    
+    # 网格样式
+    ax.grid(True, alpha=0.3, linestyle='--')
+    ax.spines['polar'].set_color('#666666')
+    ax.spines['polar'].set_linewidth(0.8)
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_dir}/radar_chart.png', dpi=300, bbox_inches='tight',
+                facecolor='white', edgecolor='none')
+    print(f"雷达图已保存至: {save_dir}/radar_chart.png")
+    
+    return df
+
+
+def plot_marginal_benefit(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
+    """单独绘制边际收益分析图（马卡龙色系，无标题，论文风格）"""
+    
+    # 马卡龙色系
+    MACARON = {
+        'sky': '#A8D8EA',        # 天空蓝
+        'pink': '#F8B4C0',       # 樱花粉
+        'mint': '#A8E6CF',       # 薄荷绿
+    }
+    
+    # 论文风格设置
+    plt.rcParams.update({
+        'font.size': 11,
+        'axes.labelsize': 12,
+        'xtick.labelsize': 10,
+        'ytick.labelsize': 10,
+        'legend.fontsize': 10,
+        'axes.linewidth': 0.8,
+    })
+    
+    # 生成Pareto数据
+    years_range = np.linspace(101, 200, 100)
+    
+    results = []
+    for years in years_range:
+        # 计算电梯运输比例
+        elevator_payload = min(TOTAL_ELEVATOR_CAPACITY * years, TOTAL_PAYLOAD)
+        elevator_frac = elevator_payload / TOTAL_PAYLOAD
+        
+        # 确定使用的发射场
+        rocket_payload = TOTAL_PAYLOAD - elevator_payload
+        if rocket_payload > 0:
+            rocket_capacity_per_site = 365 * years * PAYLOAD_PER_LAUNCH
+            n_sites_needed = int(np.ceil(rocket_payload / rocket_capacity_per_site))
+            n_sites_needed = min(n_sites_needed, 10)
+            sites = LAUNCH_SITES[:n_sites_needed]
+        else:
+            sites = []
+        
+        impact = calculate_scenario_impact(
+            scenario_name=f'{years:.0f}yr',
+            completion_years=years,
+            elevator_fraction=elevator_frac,
+            rocket_sites_used=sites
+        )
+        
+        results.append({
+            'Years': years,
+            'Energy (PJ)': impact.total_energy_PJ,
+            'CO2 (Mt)': impact.co2_total_Mt,
+        })
+    
+    df = pd.DataFrame(results)
+    
+    # 计算边际减少率
+    T = df['Years'].values
+    E = df['Energy (PJ)'].values
+    C = df['CO2 (Mt)'].values
+    
+    dE_dT = np.gradient(E, T)
+    dC_dT = np.gradient(C, T)
+    
+    energy_reduction_rate = -dE_dT  # 每多1年节省的能量
+    co2_reduction_rate = -dC_dT     # 每多1年减少的CO2
+    
+    # 绘图（黄金比例）
+    fig, ax = plt.subplots(figsize=(8, 8*0.618))
+    fig.patch.set_facecolor('white')
+    ax.set_facecolor('white')
+    
+    # 能量减少率
+    ax.plot(T, energy_reduction_rate, color=MACARON['sky'], linewidth=2, 
+            label='Energy reduction rate')
+    ax.fill_between(T, energy_reduction_rate, alpha=0.2, color=MACARON['sky'])
+    
+    # CO2减少率（使用第二Y轴）
+    ax2 = ax.twinx()
+    ax2.plot(T, co2_reduction_rate, color=MACARON['pink'], linewidth=2, 
+             label='CO₂ reduction rate')
+    ax2.fill_between(T, co2_reduction_rate, alpha=0.2, color=MACARON['pink'])
+    
+    # 推荐点
+    recommended_year = 186
+    ax.axvline(x=recommended_year, color=MACARON['mint'], linestyle='--', linewidth=2,
+               label=f'Recommended: {recommended_year}yr')
+    
+    ax.set_xlabel('Completion Time (years)')
+    ax.set_ylabel('Energy Reduction (PJ/yr)', color='#5B9BD5')
+    ax.tick_params(axis='y', labelcolor='#5B9BD5')
+    
+    ax2.set_ylabel('CO₂ Reduction (Mt/yr)', color='#E57373')
+    ax2.tick_params(axis='y', labelcolor='#E57373')
+    
+    ax.grid(True, alpha=0.2, linestyle='--')
+    ax.set_axisbelow(True)
+    
+    # 合并图例
+    lines1, labels1 = ax.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax.legend(lines1 + lines2, labels1 + labels2, loc='upper right', 
+              framealpha=0.95, edgecolor='#cccccc')
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_dir}/marginal_benefit.png', dpi=300, bbox_inches='tight',
+                facecolor='white', edgecolor='none')
+    print(f"边际收益图已保存至: {save_dir}/marginal_benefit.png")
+    
+    return df
+
+
 def plot_pareto_with_environment(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
     """绘制时间-能量-环境三目标Pareto图"""