p2: fig
This commit is contained in:
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Richards S-Curve Fit for 2010-2025 Data with K=4298
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Fits Richards model to 2010-2025 launch data with carrying capacity
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constrained to K=4298 (close to physical limit of 3650).
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"""
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import pandas as pd
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import numpy as np
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from scipy.optimize import curve_fit
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings('ignore')
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def richards(t, K, r, t0, v):
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"""Richards curve (generalized logistic model)"""
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exp_term = np.exp(-r * (t - t0))
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exp_term = np.clip(exp_term, 1e-10, 1e10)
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return K / np.power(1 + exp_term, 1/v)
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def richards_fixed_K(t, r, t0, v):
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"""Richards curve with fixed K=4298"""
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K = 4298
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exp_term = np.exp(-r * (t - t0))
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exp_term = np.clip(exp_term, 1e-10, 1e10)
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return K / np.power(1 + exp_term, 1/v)
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def load_data(filepath="rocket_launch_counts.csv"):
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"""Load rocket launch data"""
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df = pd.read_csv(filepath)
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df = df.rename(columns={"YDate": "year", "Total": "launches"})
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df["year"] = pd.to_numeric(df["year"], errors="coerce")
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df["launches"] = pd.to_numeric(df["launches"], errors="coerce")
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df = df.dropna(subset=["year", "launches"])
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df = df[(df["year"] >= 1957) & (df["year"] <= 2025)]
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df = df.astype({"year": int, "launches": int})
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df = df.sort_values("year").reset_index(drop=True)
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return df
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def main():
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print("=" * 60)
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print("Richards S-Curve Fit (2010-2025 Data, K=4298)")
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print("=" * 60)
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# Load data
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df = load_data()
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# Filter 2010-2025
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start_year = 2010
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end_year = 2025
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data = df[(df["year"] >= start_year) & (df["year"] <= end_year)].copy()
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years = data["year"].values
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launches = data["launches"].values
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print(f"Data range: {start_year} - {end_year}")
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print(f"Data points: {len(data)}")
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print(f"\nHistorical data:")
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for y, l in zip(years, launches):
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print(f" {y}: {l} launches")
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# Fit with fixed K=4298
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K_fixed = 4298
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t = (years - start_year).astype(float)
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p0 = [0.2, 20.0, 2.0] # r, t0, v
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bounds = ([0.01, 5, 0.5], [1.0, 100, 10.0])
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try:
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popt, pcov = curve_fit(richards_fixed_K, t, launches, p0=p0, bounds=bounds, maxfev=100000)
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r, t0, v = popt
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# Calculate R²
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y_pred = richards_fixed_K(t, *popt)
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ss_res = np.sum((launches - y_pred) ** 2)
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ss_tot = np.sum((launches - np.mean(launches)) ** 2)
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r_squared = 1 - (ss_res / ss_tot)
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print(f"\nFitted Parameters (K fixed at {K_fixed}):")
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print(f" K (carrying capacity) = {K_fixed} launches/year (FIXED)")
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print(f" r (growth rate) = {r:.4f}")
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print(f" t0 (inflection point) = {start_year + t0:.1f}")
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print(f" v (shape parameter) = {v:.3f}")
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print(f" R² = {r_squared:.4f}")
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except Exception as e:
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print(f"Fitting error: {e}")
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return
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# Physical limit
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physical_max = 3650
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print(f"\nPhysical limit: {physical_max} (10 sites × 365 days)")
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print(f"K / Physical limit = {K_fixed/physical_max:.2f}x")
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# ========== Create Visualization ==========
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# 缩小图片尺寸（比例不变），使字体相对更大
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fig, ax = plt.subplots(figsize=(8, 4.67))
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# 中低饱和度配色
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color_data = '#5D6D7E' # 灰蓝色 - 数据点
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color_curve = '#52796F' # 暗绿色 - S曲线
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color_target = '#7B9EA8' # 灰蓝绿色 - 2050标记
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# Historical data points
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ax.scatter(years, launches, color=color_data, s=80, alpha=0.85,
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label='Historical Data (2010-2025)', zorder=4, edgecolor='white', linewidth=1)
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# Generate smooth S-curve
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years_smooth = np.linspace(start_year, 2055, 500)
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t_smooth = years_smooth - start_year
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pred_smooth = richards(t_smooth, K_fixed, r, t0, v)
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# S-curve prediction
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ax.plot(years_smooth, pred_smooth, color=color_curve, lw=2.5,
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label=f'Richards Model (K={K_fixed}, R²={r_squared:.3f})', zorder=2)
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# K=4298 saturation line
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ax.axhline(K_fixed, color=color_curve, ls=':', lw=1.5, alpha=0.6,
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label=f'K = {K_fixed}')
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# Mark 2050 line only
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ax.axvline(2050, color=color_target, ls=':', lw=1.5, alpha=0.7)
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ax.text(2050.5, K_fixed*0.83, '2050\n(Target)', fontsize=9, color=color_target)
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# Only show 2050 prediction point
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t_2050 = 2050 - start_year
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pred_2050 = richards(t_2050, K_fixed, r, t0, v)
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ax.scatter([2050], [pred_2050], color=color_target, s=60, marker='D', zorder=4)
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ax.annotate(f'{pred_2050:.0f}', xy=(2050, pred_2050),
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xytext=(2050.5, pred_2050+180),
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fontsize=9, color=color_target, fontweight='bold')
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# Formatting
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ax.set_xlabel('Year', fontsize=11)
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ax.set_ylabel('Annual Launches', fontsize=11)
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ax.legend(loc='upper left', fontsize=9)
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ax.grid(True, alpha=0.25)
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ax.set_xlim(2010, 2055)
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ax.set_ylim(0, K_fixed * 1.15)
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plt.tight_layout()
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plt.savefig('richards_curve_2010_K4298.png', dpi=150, bbox_inches='tight')
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plt.close()
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print("\nPlot saved: richards_curve_2010_K4298.png")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Richards S-Curve Fit for 2010-2025 Data with K=4298
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Fits Richards model to 2010-2025 launch data with carrying capacity
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constrained to K=4298 (close to physical limit of 3650).
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"""
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import pandas as pd
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import numpy as np
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from scipy.optimize import curve_fit
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings('ignore')
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plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def richards(t, K, r, t0, v):
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"""Richards curve (generalized logistic model)"""
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exp_term = np.exp(-r * (t - t0))
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exp_term = np.clip(exp_term, 1e-10, 1e10)
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return K / np.power(1 + exp_term, 1/v)
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def richards_fixed_K(t, r, t0, v):
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"""Richards curve with fixed K=4298"""
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K = 4298
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exp_term = np.exp(-r * (t - t0))
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exp_term = np.clip(exp_term, 1e-10, 1e10)
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return K / np.power(1 + exp_term, 1/v)
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def load_data(filepath="rocket_launch_counts.csv"):
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"""Load rocket launch data"""
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df = pd.read_csv(filepath)
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df = df.rename(columns={"YDate": "year", "Total": "launches"})
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df["year"] = pd.to_numeric(df["year"], errors="coerce")
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df["launches"] = pd.to_numeric(df["launches"], errors="coerce")
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df = df.dropna(subset=["year", "launches"])
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df = df[(df["year"] >= 1957) & (df["year"] <= 2025)]
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df = df.astype({"year": int, "launches": int})
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df = df.sort_values("year").reset_index(drop=True)
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return df
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def main():
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print("=" * 60)
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print("Richards S-Curve Fit (2010-2025 Data, K=4298)")
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print("=" * 60)
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# Load data
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df = load_data()
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# Filter 2010-2025
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start_year = 2010
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end_year = 2025
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data = df[(df["year"] >= start_year) & (df["year"] <= end_year)].copy()
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years = data["year"].values
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launches = data["launches"].values
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print(f"Data range: {start_year} - {end_year}")
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print(f"Data points: {len(data)}")
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print(f"\nHistorical data:")
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for y, l in zip(years, launches):
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print(f" {y}: {l} launches")
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# Fit with fixed K=4298
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K_fixed = 4298
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t = (years - start_year).astype(float)
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p0 = [0.2, 20.0, 2.0] # r, t0, v
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bounds = ([0.01, 5, 0.5], [1.0, 100, 10.0])
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try:
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popt, pcov = curve_fit(richards_fixed_K, t, launches, p0=p0, bounds=bounds, maxfev=100000)
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r, t0, v = popt
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# Calculate R²
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y_pred = richards_fixed_K(t, *popt)
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ss_res = np.sum((launches - y_pred) ** 2)
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ss_tot = np.sum((launches - np.mean(launches)) ** 2)
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r_squared = 1 - (ss_res / ss_tot)
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print(f"\nFitted Parameters (K fixed at {K_fixed}):")
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print(f" K (carrying capacity) = {K_fixed} launches/year (FIXED)")
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print(f" r (growth rate) = {r:.4f}")
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print(f" t0 (inflection point) = {start_year + t0:.1f}")
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print(f" v (shape parameter) = {v:.3f}")
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print(f" R² = {r_squared:.4f}")
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except Exception as e:
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print(f"Fitting error: {e}")
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return
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# Physical limit
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physical_max = 3650
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print(f"\nPhysical limit: {physical_max} (10 sites × 365 days)")
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print(f"K / Physical limit = {K_fixed/physical_max:.2f}x")
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# ========== Create Visualization ==========
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# 缩小图片尺寸（比例不变），使字体相对更大
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fig, ax = plt.subplots(figsize=(8, 4.67))
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# 中低饱和度配色
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color_data = '#5D6D7E' # 灰蓝色 - 数据点
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color_curve = '#52796F' # 暗绿色 - S曲线
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color_target = '#7B9EA8' # 灰蓝绿色 - 2050标记
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# Historical data points
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ax.scatter(years, launches, color=color_data, s=80, alpha=0.85,
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label='Historical Data (2010-2025)', zorder=4, edgecolor='white', linewidth=1)
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# Generate smooth S-curve
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years_smooth = np.linspace(start_year, 2055, 500)
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t_smooth = years_smooth - start_year
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pred_smooth = richards(t_smooth, K_fixed, r, t0, v)
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# S-curve prediction
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ax.plot(years_smooth, pred_smooth, color=color_curve, lw=2.5,
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label=f'Richards Model (K={K_fixed}, R²={r_squared:.3f})', zorder=2)
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# K=4298 saturation line
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ax.axhline(K_fixed, color=color_curve, ls=':', lw=1.5, alpha=0.6,
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label=f'K = {K_fixed}')
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# Mark 2050 line only
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ax.axvline(2050, color=color_target, ls=':', lw=1.5, alpha=0.7)
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ax.text(2050.5, K_fixed*0.83, '2050\n(Target)', fontsize=9, color=color_target)
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# Only show 2050 prediction point
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t_2050 = 2050 - start_year
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pred_2050 = richards(t_2050, K_fixed, r, t0, v)
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ax.scatter([2050], [pred_2050], color=color_target, s=60, marker='D', zorder=4)
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ax.annotate(f'{pred_2050:.0f}', xy=(2050, pred_2050),
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xytext=(2050.5, pred_2050+180),
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fontsize=9, color=color_target, fontweight='bold')
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# Formatting
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ax.set_xlabel('Year', fontsize=11)
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ax.set_ylabel('Annual Launches', fontsize=11)
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ax.legend(loc='upper left', fontsize=9)
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ax.grid(True, alpha=0.25)
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ax.set_xlim(2010, 2055)
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ax.set_ylim(0, K_fixed * 1.15)
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plt.tight_layout()
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plt.savefig('richards_curve_2010_K4298.png', dpi=150, bbox_inches='tight')
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plt.close()
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print("\nPlot saved: richards_curve_2010_K4298.png")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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p2/completion_time_distribution_paper.pdf
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p2/completion_time_distribution_paper.pdf
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p2/completion_time_distribution_paper.png
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p2/completion_time_distribution_paper.png
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p2/energy_distribution_paper.pdf
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p2/energy_distribution_paper.pdf
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p2/energy_distribution_paper.png
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89
p2/plot_completion_time_paper.py
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p2/plot_completion_time_paper.py
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@@ -0,0 +1,89 @@
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"""
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生成论文用的完成时间分布图 - 改进版
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"""
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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import pandas as pd
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# 设置字体
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rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Helvetica']
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rcParams['axes.unicode_minus'] = False
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rcParams['font.size'] = 11
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# 读取模拟结果数据
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df = pd.read_csv('/Volumes/Files/code/mm/20260130_b/p2/simulation_results.csv')
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# 中低饱和度配色方案 - 柔和学术风格
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colors = ['#7BAE7F', '#6A9ECF', '#9B8EC2'] # 柔和的绿、蓝、紫
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# 方案名称
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scenario_labels = {
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'Scenario_A': 'Cost Priority',
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'Scenario_B': 'Time Priority',
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'Scenario_C': 'Balanced'
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}
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# 创建图表 - 缩小尺寸以放大字体效果
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fig, axes = plt.subplots(1, 3, figsize=(10, 3.2))
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for idx, (scenario_key, label) in enumerate(scenario_labels.items()):
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ax = axes[idx]
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# 获取该方案的数据
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data = df[df['scenario'] == scenario_key]['completion_years']
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# 计算统计量
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mean_val = np.mean(data)
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p5 = np.percentile(data, 5)
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p95 = np.percentile(data, 95)
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# 绘制柱状图 - 实心柱状图，增粗柱子
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ax.hist(data, bins=15, color=colors[idx], alpha=0.9,
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edgecolor='white', linewidth=0.8, rwidth=0.9)
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# 设置子图标题 - 简洁的 (a) (b) (c) 标签
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ax.set_title(f'({chr(97+idx)}) {label}', fontsize=12, fontweight='normal', pad=8)
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# 设置坐标轴标签
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ax.set_xlabel('Completion Years', fontsize=11)
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if idx == 0:
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ax.set_ylabel('Frequency', fontsize=11)
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# 在图内右上角添加纯文本统计信息（无边框）
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text_str = f'Mean: {mean_val:.1f}\n5%: {p5:.1f}\n95%: {p95:.1f}'
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ax.text(0.97, 0.97, text_str, transform=ax.transAxes, fontsize=9,
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verticalalignment='top', horizontalalignment='right',
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bbox=dict(boxstyle='round,pad=0.3', facecolor='white',
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edgecolor='none', alpha=0.85))
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# 网格线 - 非常淡
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ax.grid(True, alpha=0.15, linestyle='-', linewidth=0.5)
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# 设置刻度字体大小
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ax.tick_params(axis='both', labelsize=10)
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# 中间图 (b) 的 x 轴使用整数刻度
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if idx == 1:
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from matplotlib.ticker import MaxNLocator
|
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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")
|
||||
85
p2/plot_energy_distribution_paper.py
Normal file
85
p2/plot_energy_distribution_paper.py
Normal file
@@ -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")
|
||||
Reference in New Issue
Block a user