p2: fig

p1/richards_curve_2010.py

@@ -1,160 +1,160 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Richards S-Curve Fit for 2010-2025 Data with K=4298
-
-Fits Richards model to 2010-2025 launch data with carrying capacity
-constrained to K=4298 (close to physical limit of 3650).
-"""
-
-import pandas as pd
-import numpy as np
-from scipy.optimize import curve_fit
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings('ignore')
-
-plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
-plt.rcParams['axes.unicode_minus'] = False
-
-
-def richards(t, K, r, t0, v):
-    """Richards curve (generalized logistic model)"""
-    exp_term = np.exp(-r * (t - t0))
-    exp_term = np.clip(exp_term, 1e-10, 1e10)
-    return K / np.power(1 + exp_term, 1/v)
-
-
-def richards_fixed_K(t, r, t0, v):
-    """Richards curve with fixed K=4298"""
-    K = 4298
-    exp_term = np.exp(-r * (t - t0))
-    exp_term = np.clip(exp_term, 1e-10, 1e10)
-    return K / np.power(1 + exp_term, 1/v)
-
-
-def load_data(filepath="rocket_launch_counts.csv"):
-    """Load rocket launch data"""
-    df = pd.read_csv(filepath)
-    df = df.rename(columns={"YDate": "year", "Total": "launches"})
-    df["year"] = pd.to_numeric(df["year"], errors="coerce")
-    df["launches"] = pd.to_numeric(df["launches"], errors="coerce")
-    df = df.dropna(subset=["year", "launches"])
-    df = df[(df["year"] >= 1957) & (df["year"] <= 2025)]
-    df = df.astype({"year": int, "launches": int})
-    df = df.sort_values("year").reset_index(drop=True)
-    return df
-
-
-def main():
-    print("=" * 60)
-    print("Richards S-Curve Fit (2010-2025 Data, K=4298)")
-    print("=" * 60)
-    
-    # Load data
-    df = load_data()
-    
-    # Filter 2010-2025
-    start_year = 2010
-    end_year = 2025
-    data = df[(df["year"] >= start_year) & (df["year"] <= end_year)].copy()
-    years = data["year"].values
-    launches = data["launches"].values
-    
-    print(f"Data range: {start_year} - {end_year}")
-    print(f"Data points: {len(data)}")
-    print(f"\nHistorical data:")
-    for y, l in zip(years, launches):
-        print(f"  {y}: {l} launches")
-    
-    # Fit with fixed K=4298
-    K_fixed = 4298
-    t = (years - start_year).astype(float)
-    
-    p0 = [0.2, 20.0, 2.0]  # r, t0, v
-    bounds = ([0.01, 5, 0.5], [1.0, 100, 10.0])
-    
-    try:
-        popt, pcov = curve_fit(richards_fixed_K, t, launches, p0=p0, bounds=bounds, maxfev=100000)
-        r, t0, v = popt
-        
-        # Calculate R²
-        y_pred = richards_fixed_K(t, *popt)
-        ss_res = np.sum((launches - y_pred) ** 2)
-        ss_tot = np.sum((launches - np.mean(launches)) ** 2)
-        r_squared = 1 - (ss_res / ss_tot)
-        
-        print(f"\nFitted Parameters (K fixed at {K_fixed}):")
-        print(f"  K (carrying capacity) = {K_fixed} launches/year (FIXED)")
-        print(f"  r (growth rate)       = {r:.4f}")
-        print(f"  t0 (inflection point) = {start_year + t0:.1f}")
-        print(f"  v (shape parameter)   = {v:.3f}")
-        print(f"  R²                    = {r_squared:.4f}")
-        
-    except Exception as e:
-        print(f"Fitting error: {e}")
-        return
-    
-    # Physical limit
-    physical_max = 3650
-    print(f"\nPhysical limit: {physical_max} (10 sites × 365 days)")
-    print(f"K / Physical limit = {K_fixed/physical_max:.2f}x")
-    
-    # ========== Create Visualization ==========
-    # 缩小图片尺寸（比例不变），使字体相对更大
-    fig, ax = plt.subplots(figsize=(8, 4.67))
-    
-    # 中低饱和度配色
-    color_data = '#5D6D7E'       # 灰蓝色 - 数据点
-    color_curve = '#52796F'      # 暗绿色 - S曲线
-    color_target = '#7B9EA8'     # 灰蓝绿色 - 2050标记
-    
-    # Historical data points
-    ax.scatter(years, launches, color=color_data, s=80, alpha=0.85, 
-               label='Historical Data (2010-2025)', zorder=4, edgecolor='white', linewidth=1)
-    
-    # Generate smooth S-curve
-    years_smooth = np.linspace(start_year, 2055, 500)
-    t_smooth = years_smooth - start_year
-    pred_smooth = richards(t_smooth, K_fixed, r, t0, v)
-    
-    # S-curve prediction
-    ax.plot(years_smooth, pred_smooth, color=color_curve, lw=2.5, 
-            label=f'Richards Model (K={K_fixed}, R²={r_squared:.3f})', zorder=2)
-    
-    # K=4298 saturation line
-    ax.axhline(K_fixed, color=color_curve, ls=':', lw=1.5, alpha=0.6,
-               label=f'K = {K_fixed}')
-    
-    # Mark 2050 line only
-    ax.axvline(2050, color=color_target, ls=':', lw=1.5, alpha=0.7)
-    ax.text(2050.5, K_fixed*0.83, '2050\n(Target)', fontsize=9, color=color_target)
-    
-    # Only show 2050 prediction point
-    t_2050 = 2050 - start_year
-    pred_2050 = richards(t_2050, K_fixed, r, t0, v)
-    ax.scatter([2050], [pred_2050], color=color_target, s=60, marker='D', zorder=4)
-    ax.annotate(f'{pred_2050:.0f}', xy=(2050, pred_2050), 
-                xytext=(2050.5, pred_2050+180),
-                fontsize=9, color=color_target, fontweight='bold')
-    
-    # Formatting
-    ax.set_xlabel('Year', fontsize=11)
-    ax.set_ylabel('Annual Launches', fontsize=11)
-    ax.legend(loc='upper left', fontsize=9)
-    ax.grid(True, alpha=0.25)
-    ax.set_xlim(2010, 2055)
-    ax.set_ylim(0, K_fixed * 1.15)
-    
-    plt.tight_layout()
-    plt.savefig('richards_curve_2010_K4298.png', dpi=150, bbox_inches='tight')
-    plt.close()
-    
-    print("\nPlot saved: richards_curve_2010_K4298.png")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Richards S-Curve Fit for 2010-2025 Data with K=4298
+
+Fits Richards model to 2010-2025 launch data with carrying capacity
+constrained to K=4298 (close to physical limit of 3650).
+"""
+
+import pandas as pd
+import numpy as np
+from scipy.optimize import curve_fit
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+
+plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+
+
+def richards(t, K, r, t0, v):
+    """Richards curve (generalized logistic model)"""
+    exp_term = np.exp(-r * (t - t0))
+    exp_term = np.clip(exp_term, 1e-10, 1e10)
+    return K / np.power(1 + exp_term, 1/v)
+
+
+def richards_fixed_K(t, r, t0, v):
+    """Richards curve with fixed K=4298"""
+    K = 4298
+    exp_term = np.exp(-r * (t - t0))
+    exp_term = np.clip(exp_term, 1e-10, 1e10)
+    return K / np.power(1 + exp_term, 1/v)
+
+
+def load_data(filepath="rocket_launch_counts.csv"):
+    """Load rocket launch data"""
+    df = pd.read_csv(filepath)
+    df = df.rename(columns={"YDate": "year", "Total": "launches"})
+    df["year"] = pd.to_numeric(df["year"], errors="coerce")
+    df["launches"] = pd.to_numeric(df["launches"], errors="coerce")
+    df = df.dropna(subset=["year", "launches"])
+    df = df[(df["year"] >= 1957) & (df["year"] <= 2025)]
+    df = df.astype({"year": int, "launches": int})
+    df = df.sort_values("year").reset_index(drop=True)
+    return df
+
+
+def main():
+    print("=" * 60)
+    print("Richards S-Curve Fit (2010-2025 Data, K=4298)")
+    print("=" * 60)
+    
+    # Load data
+    df = load_data()
+    
+    # Filter 2010-2025
+    start_year = 2010
+    end_year = 2025
+    data = df[(df["year"] >= start_year) & (df["year"] <= end_year)].copy()
+    years = data["year"].values
+    launches = data["launches"].values
+    
+    print(f"Data range: {start_year} - {end_year}")
+    print(f"Data points: {len(data)}")
+    print(f"\nHistorical data:")
+    for y, l in zip(years, launches):
+        print(f"  {y}: {l} launches")
+    
+    # Fit with fixed K=4298
+    K_fixed = 4298
+    t = (years - start_year).astype(float)
+    
+    p0 = [0.2, 20.0, 2.0]  # r, t0, v
+    bounds = ([0.01, 5, 0.5], [1.0, 100, 10.0])
+    
+    try:
+        popt, pcov = curve_fit(richards_fixed_K, t, launches, p0=p0, bounds=bounds, maxfev=100000)
+        r, t0, v = popt
+        
+        # Calculate R²
+        y_pred = richards_fixed_K(t, *popt)
+        ss_res = np.sum((launches - y_pred) ** 2)
+        ss_tot = np.sum((launches - np.mean(launches)) ** 2)
+        r_squared = 1 - (ss_res / ss_tot)
+        
+        print(f"\nFitted Parameters (K fixed at {K_fixed}):")
+        print(f"  K (carrying capacity) = {K_fixed} launches/year (FIXED)")
+        print(f"  r (growth rate)       = {r:.4f}")
+        print(f"  t0 (inflection point) = {start_year + t0:.1f}")
+        print(f"  v (shape parameter)   = {v:.3f}")
+        print(f"  R²                    = {r_squared:.4f}")
+        
+    except Exception as e:
+        print(f"Fitting error: {e}")
+        return
+    
+    # Physical limit
+    physical_max = 3650
+    print(f"\nPhysical limit: {physical_max} (10 sites × 365 days)")
+    print(f"K / Physical limit = {K_fixed/physical_max:.2f}x")
+    
+    # ========== Create Visualization ==========
+    # 缩小图片尺寸（比例不变），使字体相对更大
+    fig, ax = plt.subplots(figsize=(8, 4.67))
+    
+    # 中低饱和度配色
+    color_data = '#5D6D7E'       # 灰蓝色 - 数据点
+    color_curve = '#52796F'      # 暗绿色 - S曲线
+    color_target = '#7B9EA8'     # 灰蓝绿色 - 2050标记
+    
+    # Historical data points
+    ax.scatter(years, launches, color=color_data, s=80, alpha=0.85, 
+               label='Historical Data (2010-2025)', zorder=4, edgecolor='white', linewidth=1)
+    
+    # Generate smooth S-curve
+    years_smooth = np.linspace(start_year, 2055, 500)
+    t_smooth = years_smooth - start_year
+    pred_smooth = richards(t_smooth, K_fixed, r, t0, v)
+    
+    # S-curve prediction
+    ax.plot(years_smooth, pred_smooth, color=color_curve, lw=2.5, 
+            label=f'Richards Model (K={K_fixed}, R²={r_squared:.3f})', zorder=2)
+    
+    # K=4298 saturation line
+    ax.axhline(K_fixed, color=color_curve, ls=':', lw=1.5, alpha=0.6,
+               label=f'K = {K_fixed}')
+    
+    # Mark 2050 line only
+    ax.axvline(2050, color=color_target, ls=':', lw=1.5, alpha=0.7)
+    ax.text(2050.5, K_fixed*0.83, '2050\n(Target)', fontsize=9, color=color_target)
+    
+    # Only show 2050 prediction point
+    t_2050 = 2050 - start_year
+    pred_2050 = richards(t_2050, K_fixed, r, t0, v)
+    ax.scatter([2050], [pred_2050], color=color_target, s=60, marker='D', zorder=4)
+    ax.annotate(f'{pred_2050:.0f}', xy=(2050, pred_2050), 
+                xytext=(2050.5, pred_2050+180),
+                fontsize=9, color=color_target, fontweight='bold')
+    
+    # Formatting
+    ax.set_xlabel('Year', fontsize=11)
+    ax.set_ylabel('Annual Launches', fontsize=11)
+    ax.legend(loc='upper left', fontsize=9)
+    ax.grid(True, alpha=0.25)
+    ax.set_xlim(2010, 2055)
+    ax.set_ylim(0, K_fixed * 1.15)
+    
+    plt.tight_layout()
+    plt.savefig('richards_curve_2010_K4298.png', dpi=150, bbox_inches='tight')
+    plt.close()
+    
+    print("\nPlot saved: richards_curve_2010_K4298.png")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

p2/plot_completion_time_paper.py

@@ -0,0 +1,89 @@
+"""
+生成论文用的完成时间分布图 - 改进版
+"""
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+import pandas as pd
+
+# 设置字体
+rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Helvetica']
+rcParams['axes.unicode_minus'] = False
+rcParams['font.size'] = 11
+
+# 读取模拟结果数据
+df = pd.read_csv('/Volumes/Files/code/mm/20260130_b/p2/simulation_results.csv')
+
+# 中低饱和度配色方案 - 柔和学术风格
+colors = ['#7BAE7F', '#6A9ECF', '#9B8EC2']  # 柔和的绿、蓝、紫
+
+# 方案名称
+scenario_labels = {
+    'Scenario_A': 'Cost Priority',
+    'Scenario_B': 'Time Priority', 
+    'Scenario_C': 'Balanced'
+}
+
+# 创建图表 - 缩小尺寸以放大字体效果
+fig, axes = plt.subplots(1, 3, figsize=(10, 3.2))
+
+for idx, (scenario_key, label) in enumerate(scenario_labels.items()):
+    ax = axes[idx]
+    
+    # 获取该方案的数据
+    data = df[df['scenario'] == scenario_key]['completion_years']
+    
+    # 计算统计量
+    mean_val = np.mean(data)
+    p5 = np.percentile(data, 5)
+    p95 = np.percentile(data, 95)
+    
+    # 绘制柱状图 - 实心柱状图，增粗柱子
+    ax.hist(data, bins=15, color=colors[idx], alpha=0.9, 
+            edgecolor='white', linewidth=0.8, rwidth=0.9)
+    
+    # 设置子图标题 - 简洁的 (a) (b) (c) 标签
+    ax.set_title(f'({chr(97+idx)}) {label}', fontsize=12, fontweight='normal', pad=8)
+    
+    # 设置坐标轴标签
+    ax.set_xlabel('Completion Years', fontsize=11)
+    if idx == 0:
+        ax.set_ylabel('Frequency', fontsize=11)
+    
+    # 在图内右上角添加纯文本统计信息（无边框）
+    text_str = f'Mean: {mean_val:.1f}\n5%: {p5:.1f}\n95%: {p95:.1f}'
+    ax.text(0.97, 0.97, text_str, transform=ax.transAxes, fontsize=9,
+            verticalalignment='top', horizontalalignment='right',
+            bbox=dict(boxstyle='round,pad=0.3', facecolor='white', 
+                     edgecolor='none', alpha=0.85))
+    
+    # 网格线 - 非常淡
+    ax.grid(True, alpha=0.15, linestyle='-', linewidth=0.5)
+    
+    # 设置刻度字体大小
+    ax.tick_params(axis='both', labelsize=10)
+    
+    # 中间图 (b) 的 x 轴使用整数刻度
+    if idx == 1:
+        from matplotlib.ticker import MaxNLocator
+        ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+    
+    # 简化边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+# 调整布局
+plt.tight_layout()
+
+# 保存图片
+plt.savefig('/Volumes/Files/code/mm/20260130_b/p2/completion_time_distribution_paper.png', 
+            dpi=200, bbox_inches='tight', facecolor='white')
+plt.savefig('/Volumes/Files/code/mm/20260130_b/p2/completion_time_distribution_paper.pdf', 
+            dpi=200, bbox_inches='tight', facecolor='white')
+
+print("图表已保存:")
+print("  - completion_time_distribution_paper.png")
+print("  - completion_time_distribution_paper.pdf")

p2/plot_energy_distribution_paper.py

@@ -0,0 +1,85 @@
+"""
+生成论文用的能量分布图 - 改进版
+"""
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+from matplotlib.ticker import MaxNLocator
+import pandas as pd
+
+# 设置字体
+rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Helvetica']
+rcParams['axes.unicode_minus'] = False
+rcParams['font.size'] = 11
+
+# 读取模拟结果数据
+df = pd.read_csv('/Volumes/Files/code/mm/20260130_b/p2/simulation_results.csv')
+
+# 中低饱和度配色方案 - 不同于之前的绿蓝紫，使用暖色调
+colors = ['#D4936A', '#6AACAC', '#C48BB8']  # 柔和的橙、青、玫瑰
+
+# 方案名称
+scenario_labels = {
+    'Scenario_A': 'Cost Priority',
+    'Scenario_B': 'Time Priority', 
+    'Scenario_C': 'Balanced'
+}
+
+# 创建图表 - 缩小尺寸以放大字体效果
+fig, axes = plt.subplots(1, 3, figsize=(10, 3.2))
+
+for idx, (scenario_key, label) in enumerate(scenario_labels.items()):
+    ax = axes[idx]
+    
+    # 获取该方案的数据
+    data = df[df['scenario'] == scenario_key]['total_energy_pj']
+    
+    # 计算统计量
+    mean_val = np.mean(data)
+    p5 = np.percentile(data, 5)
+    p95 = np.percentile(data, 95)
+    
+    # 绘制柱状图 - 实心柱状图，增粗柱子
+    ax.hist(data, bins=15, color=colors[idx], alpha=0.9, 
+            edgecolor='white', linewidth=0.8, rwidth=0.9)
+    
+    # 设置子图标题 - 简洁的 (a) (b) (c) 标签
+    ax.set_title(f'({chr(97+idx)}) {label}', fontsize=12, fontweight='normal', pad=8)
+    
+    # 设置坐标轴标签
+    ax.set_xlabel('Total Energy (PJ)', fontsize=11)
+    if idx == 0:
+        ax.set_ylabel('Frequency', fontsize=11)
+    
+    # 在图内右上角添加纯文本统计信息（无边框）
+    text_str = f'Mean: {mean_val:.0f}\n5%: {p5:.0f}\n95%: {p95:.0f}'
+    ax.text(0.97, 0.97, text_str, transform=ax.transAxes, fontsize=9,
+            verticalalignment='top', horizontalalignment='right',
+            bbox=dict(boxstyle='round,pad=0.3', facecolor='white', 
+                     edgecolor='none', alpha=0.85))
+    
+    # 网格线 - 非常淡
+    ax.grid(True, alpha=0.15, linestyle='-', linewidth=0.5)
+    
+    # 设置刻度字体大小
+    ax.tick_params(axis='both', labelsize=10)
+    
+    # 简化边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+# 调整布局
+plt.tight_layout()
+
+# 保存图片
+plt.savefig('/Volumes/Files/code/mm/20260130_b/p2/energy_distribution_paper.png', 
+            dpi=200, bbox_inches='tight', facecolor='white')
+plt.savefig('/Volumes/Files/code/mm/20260130_b/p2/energy_distribution_paper.pdf', 
+            dpi=200, bbox_inches='tight', facecolor='white')
+
+print("图表已保存:")
+print("  - energy_distribution_paper.png")
+print("  - energy_distribution_paper.pdf")