p1: figlambda

.gitignore

@@ -1 +1,3 @@
 latex/
+__pycache__/
+

p1/lambda_cost_analysis.py

@@ -840,5 +840,63 @@ def main():
     return df, sensitivity_results
 
 
+def plot_total_cost_standalone(
+    df: pd.DataFrame = None,
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/p1/total_cost_curves.png'
+):
+    """
+    单独绘制总成本曲线图
+    - 长宽比 0.618
+    - 删去标题
+    - 删去 λ=500，添加目标 λ=504
+    - 低偏中饱和度色系
+    """
+    if df is None:
+        df = generate_tradeoff_curve()
+    
+    # 使用0.618黄金比例
+    fig_width = 8
+    fig_height = fig_width * 0.618
+    fig, ax = plt.subplots(figsize=(fig_width, fig_height))
+    
+    years = df['years'].values
+    
+    # 低偏中饱和度配色（删去500，添加504）
+    lambda_colors = [
+        (450, '#6B9B78'),  # 灰绿色
+        (480, '#B87B6B'),  # 灰橙红色
+        (504, '#8B7BA8'),  # 灰紫色（目标λ）
+        (550, '#5B8FA8'),  # 灰蓝色
+    ]
+    
+    for lam, color in lambda_colors:
+        total_cost = calculate_total_cost(df, lam)
+        linewidth = 2.5 if lam == 504 else 2
+        linestyle = '-'
+        label = f'λ={lam} PJ/year' if lam != 504 else f'λ={lam} PJ/year (target)'
+        
+        ax.plot(years, total_cost / 1000, color=color, linestyle=linestyle, 
+                linewidth=linewidth, label=label)
+        
+        # 标记最小值点
+        opt = find_optimal_point(df, lam)
+        markersize = 11 if lam == 504 else 9
+        ax.plot(opt['years'], opt['total_cost'] / 1000, 'o', color=color, 
+                markersize=markersize, markeredgecolor='#333333', markeredgewidth=1.5)
+    
+    ax.set_xlabel('Construction Timeline T (years)', fontsize=11)
+    ax.set_ylabel('Total Cost J = E + λT (×10³ PJ)', fontsize=11)
+    ax.legend(loc='upper right', fontsize=9)
+    ax.grid(True, alpha=0.3)
+    ax.set_xlim(95, 200)
+    ax.set_ylim(95, 130)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"Total cost curves saved to: {save_path}")
+    
+    return fig
+
+
 if __name__ == "__main__":
     df, results = main()