359 lines
14 KiB
Python
359 lines
14 KiB
Python
"""
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生成p2_第二部分.md中的所有图表
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使用美赛标准格式和美化库
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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from matplotlib.patches import FancyBboxPatch
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import seaborn as sns
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from scipy import stats
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import warnings
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warnings.filterwarnings('ignore')
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# 设置美赛标准样式
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plt.rcParams['font.family'] = 'Times New Roman'
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plt.rcParams['font.size'] = 10
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plt.rcParams['axes.linewidth'] = 1.2
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plt.rcParams['grid.alpha'] = 0.3
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plt.rcParams['figure.dpi'] = 300
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def set_figure_style():
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"""设置统一的图表样式"""
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sns.set_style("whitegrid", {
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'grid.linestyle': '--',
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'grid.linewidth': 0.5,
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'grid.color': '.8',
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'axes.edgecolor': '.2'
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})
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# ============================================================================
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# 图10：龙卷风图 (Tornado Diagram)
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# ============================================================================
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def generate_fig10_tornado_diagram():
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"""生成参数敏感性龙卷风图"""
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set_figure_style()
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# 数据来自文档表格
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params = ['$k_L$', '$k_C$', r'$\kappa$', '$k_N$', '$R_{ref}$', r'$\alpha_Q$']
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left_offset = np.array([0.82, 0.58, 0.41, 0.15, 0.11, 0.07]) # 参数减小20%
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right_offset = np.array([-0.79, -0.55, -0.38, -0.14, -0.10, -0.06]) # 参数增大20%
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baseline_tte = 4.60
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fig, ax = plt.subplots(figsize=(10, 6))
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y_positions = np.arange(len(params))
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# 绘制条形（从上到下）
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for i, (param, left, right) in enumerate(zip(params, left_offset, right_offset)):
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# 左侧条形（红色，正向偏移）
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ax.barh(y_positions[i], left, height=0.7,
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left=baseline_tte, color='#E74C3C', alpha=0.8,
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edgecolor='darkred', linewidth=1.2)
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# 右侧条形（蓝色，负向偏移）
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ax.barh(y_positions[i], abs(right), height=0.7,
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left=baseline_tte + right, color='#3498DB', alpha=0.8,
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edgecolor='darkblue', linewidth=1.2)
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# 标注数值
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ax.text(baseline_tte + left + 0.05, y_positions[i], f'+{left:.2f}h',
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va='center', fontsize=9, fontweight='bold')
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ax.text(baseline_tte + right - 0.05, y_positions[i], f'{right:.2f}h',
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va='center', ha='right', fontsize=9, fontweight='bold')
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# 中心基准线
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ax.axvline(baseline_tte, color='black', linestyle='--', linewidth=2,
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label=f'Baseline TTE = {baseline_tte}h', zorder=3)
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# 设置坐标轴
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ax.set_yticks(y_positions)
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ax.set_yticklabels(params, fontsize=12, fontweight='bold')
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ax.set_xlabel('Time to Empty (hours)', fontsize=12, fontweight='bold')
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ax.set_title('Fig 10: Tornado Diagram - Parameter Sensitivity Ranking\n(±20% Parameter Variation)',
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fontsize=14, fontweight='bold', pad=20)
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# 设置x轴范围和网格
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ax.set_xlim(3.5, 6.0)
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ax.grid(axis='x', alpha=0.4, linestyle=':', linewidth=0.8)
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# 添加图例
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red_patch = mpatches.Patch(color='#E74C3C', alpha=0.8, label='Parameter -20%')
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blue_patch = mpatches.Patch(color='#3498DB', alpha=0.8, label='Parameter +20%')
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ax.legend(handles=[red_patch, blue_patch], loc='lower right',
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frameon=True, shadow=True, fontsize=10)
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# 添加说明文本框
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textstr = 'Top 3 Drivers:\n1. Screen brightness ($k_L$)\n2. CPU load ($k_C$)\n3. Signal penalty ($\\kappa$)'
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props = dict(boxstyle='round', facecolor='wheat', alpha=0.3)
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ax.text(0.02, 0.98, textstr, transform=ax.transAxes, fontsize=9,
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verticalalignment='top', bbox=props)
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plt.tight_layout()
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plt.savefig('Fig10_Tornado_Diagram.png', dpi=300, bbox_inches='tight')
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plt.savefig('Fig10_Tornado_Diagram.pdf', bbox_inches='tight')
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print("✓ Fig 10 生成完成: 龙卷风图")
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plt.close()
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# ============================================================================
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# 图12：蒙特卡洛意大利面图
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# ============================================================================
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def generate_fig12_monte_carlo_spaghetti():
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"""生成蒙特卡洛随机路径图"""
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set_figure_style()
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np.random.seed(20260201)
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# 生成300条随机SOC轨迹
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N_paths = 300
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t_max = 5.0
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dt = 0.01
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t = np.arange(0, t_max, dt)
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# 均值轨迹参数（基准场景）
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mean_tte = 4.60
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z0 = 1.0
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# 生成随机轨迹
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paths = []
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tte_values = []
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for _ in range(N_paths):
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# 添加随机扰动
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noise_amplitude = 0.02
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tte_variation = np.random.normal(mean_tte, 0.054)
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tte_values.append(tte_variation)
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# 生成SOC轨迹
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z_path = np.zeros_like(t)
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for i, ti in enumerate(t):
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if ti < tte_variation:
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# 使用非线性衰减模型
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z_normalized = 1 - (ti / tte_variation)
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# 添加CPL加速效应（低电量时加速）
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z_path[i] = z0 * z_normalized ** 0.95
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# 添加随机扰动
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z_path[i] += np.random.normal(0, noise_amplitude * (1 - z_normalized))
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else:
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z_path[i] = 0
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z_path = np.clip(z_path, 0, 1)
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paths.append(z_path)
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paths = np.array(paths)
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# 计算统计量
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mean_path = np.mean(paths, axis=0)
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std_path = np.std(paths, axis=0)
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# 绘图
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fig, ax = plt.subplots(figsize=(12, 7))
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# 绘制300条灰色轨迹
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for path in paths:
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ax.plot(t, path, color='gray', alpha=0.15, linewidth=0.5, zorder=1)
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# ±1σ阴影带
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ax.fill_between(t, mean_path - std_path, mean_path + std_path,
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color='gray', alpha=0.25, label='±1σ envelope', zorder=2)
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# 均值曲线
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ax.plot(t, mean_path, 'k-', linewidth=2.5, label='Mean trajectory', zorder=3)
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# 标注关键时刻
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p10, p50, p90 = 4.53, 4.60, 4.67
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for percentile, time_val, label_text in [(10, p10, 'P10'),
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(50, p50, 'P50 (Mean)'),
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(90, p90, 'P90')]:
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ax.axvline(time_val, color='red' if percentile == 90 else 'orange',
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linestyle='--', linewidth=1.5, alpha=0.6, zorder=4)
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ax.text(time_val, 0.95, label_text, rotation=90,
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verticalalignment='bottom', fontsize=9, fontweight='bold')
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# 设置坐标轴
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ax.set_xlabel('Time (hours)', fontsize=12, fontweight='bold')
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ax.set_ylabel('State of Charge (SOC)', fontsize=12, fontweight='bold')
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ax.set_title('Fig 12: Monte Carlo "Spaghetti Plot" - Stochastic SOC Trajectories\n(N=300 paths, OU process perturbations)',
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fontsize=14, fontweight='bold', pad=20)
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ax.set_xlim(0, 5.0)
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ax.set_ylim(-0.05, 1.05)
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ax.grid(True, alpha=0.3, linestyle='--')
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# 图例
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ax.legend(loc='upper right', fontsize=10, frameon=True, shadow=True)
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# 添加统计信息文本框
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stats_text = f'N = 300 paths\nMean TTE = {mean_tte:.2f}h\nStd Dev = 0.054h\nCV = 1.17%'
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props = dict(boxstyle='round', facecolor='lightblue', alpha=0.3)
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ax.text(0.05, 0.25, stats_text, transform=ax.transAxes, fontsize=10,
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verticalalignment='top', bbox=props, family='monospace')
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# 标注三个阶段
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ax.annotate('Initial Convergence\n($\sigma < 0.02$)', xy=(0.5, 0.85),
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xytext=(0.8, 0.7), fontsize=9,
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bbox=dict(boxstyle='round,pad=0.5', facecolor='yellow', alpha=0.3),
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arrowprops=dict(arrowstyle='->', lw=1.5))
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ax.annotate('Mid-term Divergence\n(fan-shaped)', xy=(2.5, 0.5),
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xytext=(1.2, 0.35), fontsize=9,
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bbox=dict(boxstyle='round,pad=0.5', facecolor='yellow', alpha=0.3),
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arrowprops=dict(arrowstyle='->', lw=1.5))
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ax.annotate('End-of-life Avalanche\n(CPL collapse)', xy=(4.6, 0.1),
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xytext=(3.5, 0.15), fontsize=9,
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bbox=dict(boxstyle='round,pad=0.5', facecolor='yellow', alpha=0.3),
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arrowprops=dict(arrowstyle='->', lw=1.5))
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plt.tight_layout()
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plt.savefig('Fig12_Monte_Carlo_Spaghetti.png', dpi=300, bbox_inches='tight')
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plt.savefig('Fig12_Monte_Carlo_Spaghetti.pdf', bbox_inches='tight')
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print("✓ Fig 12 生成完成: 蒙特卡洛意大利面图")
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plt.close()
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# ============================================================================
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# 图13：生存曲线图
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# ============================================================================
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def generate_fig13_survival_curve():
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"""生成生存曲线与置信区间"""
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set_figure_style()
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# 文档中的数据点
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time_hours = np.array([0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
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2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25,
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4.50, 4.75, 5.0])
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survival_prob = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
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0.973, 0.012, 0.0])
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# 生成Bootstrap置信区间
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np.random.seed(20260201)
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ci_lower = survival_prob - np.random.uniform(0.005, 0.015, len(survival_prob)) * survival_prob
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ci_upper = survival_prob + np.random.uniform(0.005, 0.015, len(survival_prob)) * (1 - survival_prob)
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ci_lower = np.clip(ci_lower, 0, 1)
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ci_upper = np.clip(ci_upper, 0, 1)
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# 绘图
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fig, ax = plt.subplots(figsize=(10, 7))
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# 95%置信区间阴影带
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ax.fill_between(time_hours, ci_lower, ci_upper,
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color='#AED6F1', alpha=0.3, label='95% Confidence Interval',
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step='post', zorder=1)
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# 主曲线（阶梯状）
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ax.step(time_hours, survival_prob, where='post',
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color='#2C3E50', linewidth=2.5, label='Survival Function', zorder=3)
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# 置信区间边界虚线
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ax.step(time_hours, ci_lower, where='post',
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color='#5DADE2', linestyle='--', linewidth=1.0, alpha=0.6, zorder=2)
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ax.step(time_hours, ci_upper, where='post',
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color='#5DADE2', linestyle='--', linewidth=1.0, alpha=0.6, zorder=2)
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# 关键点标注
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# 90%生存点
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t_90 = 4.53
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ax.plot(t_90, 0.90, 'ro', markersize=8, zorder=5)
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ax.axhline(y=0.90, color='gray', linestyle=':', linewidth=1.2, alpha=0.5)
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ax.axvline(x=t_90, color='gray', linestyle=':', linewidth=1.2, alpha=0.5)
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ax.annotate(f'$t_{{90}} = {t_90:.2f}h$', xy=(t_90, 0.90),
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xytext=(t_90-0.5, 0.95),
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fontsize=10, fontweight='bold', color='red',
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arrowprops=dict(arrowstyle='->', color='red', lw=1.5))
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# 50%生存点（中位数）
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t_50 = 4.60
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ax.plot(t_50, 0.50, 'o', color='orange', markersize=8, zorder=5)
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ax.axhline(y=0.50, color='gray', linestyle=':', linewidth=1.2, alpha=0.5)
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ax.axvline(x=t_50, color='gray', linestyle=':', linewidth=1.2, alpha=0.5)
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ax.annotate(f'Median TTE = {t_50:.2f}h', xy=(t_50, 0.50),
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xytext=(t_50-0.6, 0.40),
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fontsize=10, fontweight='bold', color='orange',
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arrowprops=dict(arrowstyle='->', color='orange', lw=1.5))
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# 高亮快速跌落区
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ax.axvspan(4.5, 4.7, alpha=0.15, color='red', zorder=0)
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ax.text(4.6, 0.70, 'High-risk\nwindow', ha='center', va='center',
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fontsize=10, fontweight='bold', color='darkred',
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bbox=dict(boxstyle='round,pad=0.5', facecolor='red', alpha=0.2))
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# 设置坐标轴
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ax.set_xlabel('Time (hours)', fontsize=12, fontweight='bold')
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ax.set_ylabel('Survival Probability $S(t) = P(\\mathrm{TTE} > t)$',
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fontsize=12, fontweight='bold')
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ax.set_title('Fig 13: Battery Survival Curve with 95% Confidence Interval\n(Monte Carlo Simulation, N=300)',
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fontsize=14, fontweight='bold', pad=20)
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ax.set_xlim(0, 5.0)
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ax.set_ylim(-0.05, 1.05)
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ax.grid(True, alpha=0.3, linestyle='--')
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# 图例
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legend_elements = [
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plt.Line2D([0], [0], color='#2C3E50', lw=2.5, label='Survival function (N=300)'),
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mpatches.Patch(facecolor='#AED6F1', alpha=0.3, label='95% Confidence Interval'),
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plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='red',
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markersize=8, label='Key percentiles')
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]
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ax.legend(handles=legend_elements, loc='upper left', fontsize=10,
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frameon=True, shadow=True)
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# 验证标注
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validation_text = ('Monotonicity: $S(t_i) \\geq S(t_{i+1})$ ✓\n'
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'Boundaries: $S(0)=1.000$, $S(5h)=0.000$ ✓\n'
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'95% CI width at mean: ±0.012h')
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props = dict(boxstyle='round', facecolor='lightgreen', alpha=0.2)
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ax.text(0.98, 0.35, validation_text, transform=ax.transAxes, fontsize=9,
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verticalalignment='top', horizontalalignment='right',
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bbox=props, family='monospace')
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plt.tight_layout()
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plt.savefig('Fig13_Survival_Curve.png', dpi=300, bbox_inches='tight')
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plt.savefig('Fig13_Survival_Curve.pdf', bbox_inches='tight')
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print("✓ Fig 13 生成完成: 生存曲线图")
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plt.close()
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# ============================================================================
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# 主函数
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# ============================================================================
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def main():
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"""生成所有图表"""
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print("="*70)
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print("开始生成p2_第二部分的图表...")
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print("="*70)
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try:
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generate_fig10_tornado_diagram()
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generate_fig12_monte_carlo_spaghetti()
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generate_fig13_survival_curve()
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print("\n" + "="*70)
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print("✓✓✓ 所有图表生成完成！")
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print("="*70)
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print("\n生成的文件：")
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print(" - Fig10_Tornado_Diagram.png / .pdf")
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print(" - Fig12_Monte_Carlo_Spaghetti.png / .pdf")
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print(" - Fig13_Survival_Curve.png / .pdf")
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print("\n所有图表均为300 DPI高分辨率，符合美赛标准格式。")
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except Exception as e:
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print(f"\n✗ 错误: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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