591 lines
21 KiB
Python
591 lines
21 KiB
Python
"""
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Step 09: 可视化
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输入: 01_clean.xlsx, 02_demand.xlsx, 03_allocate.xlsx, 04_metrics.xlsx,
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05_schedule.xlsx, 08_sensitivity.xlsx
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输出: figures/*.png
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功能:
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1. Fig.1: 站点地图 (需求大小 + 访问频次)
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2. Fig.2: 需求修正对比 (修正前后μ)
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3. Fig.3: 频次分配分布 (k直方图)
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4. Fig.4: 有效性-公平性权衡 (E-F散点图)
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5. Fig.5: 日历热力图 (全年排程)
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6. Fig.6: 访问间隔箱线图
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7. Fig.7: 敏感性分析 (参数-指标折线图)
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"""
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from __future__ import annotations
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import os
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import pandas as pd
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import numpy as np
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from pathlib import Path
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import warnings
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warnings.filterwarnings('ignore')
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# 避免 matplotlib/fontconfig 在不可写目录建缓存导致的告警/性能问题
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os.environ.setdefault("MPLCONFIGDIR", str((Path(__file__).parent / ".mpl_cache").resolve()))
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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import json
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# 设置中文字体 (macOS)
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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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# 论文风格主题（参考 tu.png：柔和蓝/绿/紫/橙，浅网格，圆角图例框）
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TU = {
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"blue_light": "#a0b0d8",
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"blue_mid": "#7880b0",
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"blue_dark": "#384870",
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"teal": "#487890",
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"green": "#88b0a0",
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"olive": "#808860",
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"mauve": "#a080a0",
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"taupe": "#b09890",
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"orange": "#d0a080",
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"gray": "#a0a0a0",
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"grid": "#e8e8e8",
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"text": "#2b2b2b",
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}
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def _cmap_k() -> LinearSegmentedColormap:
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return LinearSegmentedColormap.from_list("tu_k", [TU["blue_light"], TU["blue_mid"], TU["blue_dark"]])
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def _cmap_heat() -> LinearSegmentedColormap:
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return LinearSegmentedColormap.from_list("tu_heat", ["#f3f4f6", TU["green"], TU["teal"], TU["blue_dark"]])
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def apply_tu_theme() -> None:
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plt.rcParams.update(
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{
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"figure.facecolor": "white",
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"axes.facecolor": "white",
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"axes.edgecolor": TU["gray"],
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"axes.labelcolor": TU["text"],
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"xtick.color": TU["text"],
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"ytick.color": TU["text"],
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"axes.titlecolor": TU["blue_dark"],
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"axes.titleweight": "bold",
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"axes.grid": True,
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"grid.color": TU["grid"],
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"grid.linewidth": 0.8,
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"grid.alpha": 1.0,
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"axes.spines.top": False,
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"axes.spines.right": False,
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"legend.frameon": True,
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"legend.fancybox": True,
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"legend.framealpha": 0.92,
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"legend.edgecolor": TU["gray"],
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"legend.facecolor": "#f8f8f8",
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}
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)
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def style_axes(ax, *, grid_axis: str = "both") -> None:
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ax.grid(True, axis=grid_axis, linestyle="-", alpha=1.0)
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ax.tick_params(width=0.8)
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for side in ("left", "bottom"):
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ax.spines[side].set_color(TU["gray"])
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ax.spines[side].set_linewidth(0.9)
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# 路径配置
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BASE_PATH = Path(__file__).parent
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FIGURES_PATH = BASE_PATH / "figures"
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FIGURES_PATH.mkdir(exist_ok=True)
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# 输入文件
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CLEAN_PATH = BASE_PATH / "01_clean.xlsx"
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DEMAND_PATH = BASE_PATH / "02_demand.xlsx"
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ALLOCATE_PATH = BASE_PATH / "03_allocate.xlsx"
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METRICS_PATH = BASE_PATH / "04_metrics.xlsx"
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SCHEDULE_PATH = BASE_PATH / "05_schedule.xlsx"
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SENSITIVITY_PATH = BASE_PATH / "08_sensitivity.xlsx"
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def export_fig1_points_js() -> Path:
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"""
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Export `fig1_points.js` used by `task1/fig1_carto.html`.
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Data source: `task1/03_allocate.xlsx`.
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"""
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df = pd.read_excel(ALLOCATE_PATH).copy()
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df["site_id"] = df["site_id"].astype(int)
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df["k"] = df["k"].astype(int)
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points = []
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for _, r in df.iterrows():
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points.append(
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{
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"site_id": int(r["site_id"]),
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"site_name": str(r["site_name"]),
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"lat": float(r["lat"]),
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"lng": float(r["lon"]),
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"mu": float(r["mu"]),
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"k": int(r["k"]),
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"visits_2019": int(r["visits_2019"]),
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}
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)
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out = BASE_PATH / "fig1_points.js"
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payload = (
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"// Auto-generated from `task1/03_allocate.xlsx` (site_id, site_name, lat, lon, mu, k, visits_2019)\n"
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"// Usage: include this file before `fig1_carto.html` rendering script.\n"
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f"window.FIG1_POINTS = {json.dumps(points, ensure_ascii=False, separators=(',', ':'))};\n"
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)
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out.write_text(payload, encoding="utf-8")
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return out
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def fig1_site_map():
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"""Fig.1: 站点地图"""
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print(" 生成 Fig.1: 站点地图...")
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df = pd.read_excel(ALLOCATE_PATH)
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fig, ax = plt.subplots(figsize=(12, 10))
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# 1. 设置地理纵横比 (核心修改)
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avg_lat = df['lat'].mean()
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# 修正经纬度比例：y轴与x轴的比例
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ax.set_aspect(1 / np.cos(np.radians(avg_lat)), adjustable='box')
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# 散点图: 大小=μ, 颜色=k
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scatter = ax.scatter(
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df['lon'], df['lat'],
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s=df['mu'] * 0.8,
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c=df['k'],
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cmap=_cmap_k(),
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alpha=0.85,
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edgecolors='white',
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linewidths=0.7
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)
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# ... (标注高需求站点的代码保持不变) ...
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# 颜色条
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cbar = plt.colorbar(scatter, ax=ax, shrink=0.7) # 略微调小一点，防止挤压地图
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cbar.set_label('Visit Frequency (k)', fontsize=12, color=TU["text"])
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# ... (图例和标签代码保持不变) ...
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ax.set_title('Fig.1: Site Map (Demand μ & Visit Frequency k)', fontsize=14, fontweight='bold')
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ax.set_xlabel('Longitude', fontsize=12)
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ax.set_ylabel('Latitude', fontsize=12)
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style_axes(ax)
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plt.tight_layout()
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plt.savefig(FIGURES_PATH / 'fig1_site_map.png', dpi=150, bbox_inches='tight')
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plt.close()
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def fig2_demand_correction():
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"""Fig.2: 需求修正对比"""
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print(" 生成 Fig.2: 需求修正对比...")
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df = pd.read_excel(DEMAND_PATH)
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# 只显示被修正的站点
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corrected = df[df['is_corrected']].copy()
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corrected = corrected.sort_values('mu', ascending=False)
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fig, ax = plt.subplots(figsize=(10, 6))
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x = np.arange(len(corrected))
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width = 0.35
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bars1 = ax.bar(x - width/2, corrected['mu'], width, label='Original μ', color=TU["teal"], alpha=0.85, edgecolor="white", linewidth=0.6)
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bars2 = ax.bar(x + width/2, corrected['mu_tilde'], width, label='Corrected μ̃', color=TU["green"], alpha=0.85, edgecolor="white", linewidth=0.6)
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# 添加数值标签
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for bar, val in zip(bars1, corrected['mu']):
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ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{val:.0f}',
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ha='center', va='bottom', fontsize=9)
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for bar, val in zip(bars2, corrected['mu_tilde']):
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ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{val:.0f}',
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ha='center', va='bottom', fontsize=9, color=TU["green"])
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# 添加p_trunc标注
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for i, (_, row) in enumerate(corrected.iterrows()):
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ax.text(i, max(row['mu'], row['mu_tilde']) + 25,
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f"p={row['p_trunc']:.2%}",
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ha='center', fontsize=8, style='italic')
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ax.set_xlabel('Site', fontsize=12)
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ax.set_ylabel('Demand per Visit', fontsize=12)
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ax.set_title('Fig.2: Truncation Correction for High-Demand Sites', fontsize=14, fontweight='bold')
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ax.set_xticks(x)
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ax.set_xticklabels([name[:20] for name in corrected['site_name']], rotation=30, ha='right', fontsize=9)
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ax.legend(fontsize=10)
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ax.set_ylim(0, corrected['mu_tilde'].max() * 1.2)
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style_axes(ax, grid_axis="y")
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plt.tight_layout()
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plt.savefig(FIGURES_PATH / 'fig2_demand_correction.png', dpi=150, bbox_inches='tight')
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plt.close()
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def fig3_k_distribution():
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"""Fig.3: 频次分配分布"""
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print(" 生成 Fig.3: 频次分配分布...")
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df = pd.read_excel(ALLOCATE_PATH)
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fig, axes = plt.subplots(1, 2, figsize=(14, 5))
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# 左图: k的直方图
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ax1 = axes[0]
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bins = np.arange(df['k'].min() - 0.5, df['k'].max() + 1.5, 1)
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ax1.hist(df['k'], bins=bins, color=TU["blue_mid"], edgecolor="white", alpha=0.85)
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ax1.axvline(df['k'].mean(), color=TU["mauve"], linestyle='--', linewidth=2, label=f'Mean = {df["k"].mean():.1f}')
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ax1.axvline(df['k'].median(), color=TU["olive"], linestyle=':', linewidth=2, label=f'Median = {df["k"].median():.0f}')
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ax1.set_xlabel('Visit Frequency (k)', fontsize=12)
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ax1.set_ylabel('Number of Sites', fontsize=12)
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ax1.set_title('(a) Distribution of Visit Frequencies', fontsize=12)
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ax1.legend(fontsize=10)
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style_axes(ax1)
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# 右图: k与μ̃的关系
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ax2 = axes[1]
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# mu_tilde already in allocate file
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ax2.scatter(df['mu_tilde'], df['k'], alpha=0.75, s=65, c=TU["green"], edgecolors='white', linewidths=0.7)
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# 拟合线
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z = np.polyfit(df['mu_tilde'], df['k'], 1)
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p = np.poly1d(z)
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x_fit = np.linspace(df['mu_tilde'].min(), df['mu_tilde'].max(), 100)
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ax2.plot(x_fit, p(x_fit), linestyle='--', color=TU["blue_dark"], linewidth=2, label=f'Linear fit: k = {z[0]:.3f}μ̃ + {z[1]:.1f}')
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# 相关系数
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corr = np.corrcoef(df['mu_tilde'], df['k'])[0, 1]
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ax2.text(0.05, 0.95, f'r = {corr:.4f}', transform=ax2.transAxes, fontsize=11,
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verticalalignment='top', bbox=dict(boxstyle='round', facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.95))
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ax2.set_xlabel('Corrected Demand (μ̃)', fontsize=12)
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ax2.set_ylabel('Visit Frequency (k)', fontsize=12)
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ax2.set_title('(b) k vs μ̃ (Proportionality Check)', fontsize=12)
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ax2.legend(fontsize=10)
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style_axes(ax2)
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plt.suptitle('Fig.3: Visit Frequency Allocation Analysis', fontsize=14, fontweight='bold', y=1.02)
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plt.tight_layout()
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plt.savefig(FIGURES_PATH / 'fig3_k_distribution.png', dpi=150, bbox_inches='tight')
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plt.close()
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def fig4_efficiency_fairness():
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"""Fig.4: 有效性-公平性权衡"""
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print(" 生成 Fig.4: 有效性-公平性权衡...")
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df = pd.read_excel(METRICS_PATH, sheet_name='metrics_summary')
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fig, ax = plt.subplots(figsize=(8, 4.96))
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# 绘制所有方案（固定4个点，采用显式样式，便于控制图例与标注）
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from matplotlib.lines import Line2D
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method_styles = [
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{"key": "Recommended", "color": TU["blue_dark"], "marker": "o"},
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{"key": "Baseline 1", "color": TU["mauve"], "marker": "s"},
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{"key": "Baseline 2", "color": TU["olive"], "marker": "^"},
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{"key": "Baseline 3", "color": TU["orange"], "marker": "D"},
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]
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def _style_for(method: str):
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for s in method_styles:
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if str(method).startswith(s["key"]):
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return s
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return {"color": TU["gray"], "marker": "o"}
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# 标注偏移：避免右上两个点互相遮挡；同时避免“点覆盖字”
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label_offsets = {
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"Recommended": (16, 14),
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"Baseline 1": (-8, -18),
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"Baseline 2": (10, -10),
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"Baseline 3": (-22, 10),
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}
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legend_handles = []
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for _, row in df.iterrows():
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method = str(row["method"])
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style = _style_for(method)
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x = float(row["E2_quality_weighted"])
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y = float(row["F1_gini"])
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ax.scatter(
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x,
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y,
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s=220,
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c=style["color"],
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marker=style["marker"],
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edgecolors="white",
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linewidths=1.2,
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zorder=4,
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)
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key = next((k for k in label_offsets.keys() if method.startswith(k)), "Recommended")
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dx, dy = label_offsets.get(key, (14, 14))
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ax.annotate(
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f"E1={row['E1_total_service']:.0f}\nE2={row['E2_quality_weighted']:.0f}\nGini={row['F1_gini']:.3f}",
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(x, y),
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xytext=(dx, dy),
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textcoords="offset points",
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fontsize=9,
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ha="left" if dx >= 0 else "right",
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va="bottom" if dy >= 0 else "top",
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bbox=dict(boxstyle="round,pad=0.28", facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.96),
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arrowprops=dict(arrowstyle="->", color=TU["gray"], lw=1.0, shrinkA=6, shrinkB=6),
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zorder=6,
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)
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legend_handles.append(
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Line2D(
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[0],
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[0],
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marker=style["marker"],
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color="none",
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markerfacecolor=style["color"],
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markeredgecolor=TU["gray"],
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markeredgewidth=1.0,
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markersize=11,
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label=method,
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)
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)
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# 添加权衡箭头
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ax.annotate('', xy=(135000, 0.05), xytext=(105000, 0.30),
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arrowprops=dict(arrowstyle='<->', color=TU["mauve"], lw=2))
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ax.text(115000, 0.20, 'Efficiency-Fairness\nTradeoff', fontsize=10, ha='center',
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color=TU["mauve"], style='italic', bbox=dict(facecolor='white', edgecolor='none', alpha=0.8, pad=2), zorder=10)
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ax.set_xlabel('E2 (Quality-Weighted Service Volume)', fontsize=12)
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ax.set_ylabel('F1 (Gini Coefficient, lower = fairer)', fontsize=12)
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ax.set_title('Fig.4: Efficiency-Fairness Tradeoff Analysis', fontsize=14, fontweight='bold')
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ax.legend(
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handles=legend_handles,
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loc="upper left",
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fontsize=9.5,
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labelspacing=0.6,
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borderpad=0.6,
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handletextpad=0.6,
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framealpha=0.92,
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)
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style_axes(ax)
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# 设置轴范围
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ax.set_xlim(95000, 140000)
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ax.set_ylim(0, 0.40)
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plt.tight_layout()
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plt.savefig(FIGURES_PATH / 'fig4_efficiency_fairness.png', dpi=150, bbox_inches='tight')
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plt.close()
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def fig5_calendar_heatmap():
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"""Fig.5: 日历热力图"""
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print(" 生成 Fig.5: 日历热力图...")
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df_calendar = pd.read_excel(SCHEDULE_PATH, sheet_name='calendar')
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df_allocate = pd.read_excel(ALLOCATE_PATH)
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# 创建站点μ映射
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mu_map = dict(zip(df_allocate['site_id'], df_allocate['mu']))
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# 计算每天的总需求
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daily_demand = []
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for _, row in df_calendar.iterrows():
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demand = 0
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if pd.notna(row['site_1_id']):
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demand += mu_map.get(int(row['site_1_id']), 0)
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if pd.notna(row['site_2_id']):
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demand += mu_map.get(int(row['site_2_id']), 0)
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daily_demand.append(demand)
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df_calendar['total_demand'] = daily_demand
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# 创建12x31的热力图矩阵
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heatmap_data = np.full((12, 31), np.nan)
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for _, row in df_calendar.iterrows():
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day = row['day']
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# 简单映射: 假设每月30/31天
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month = (day - 1) // 31
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day_of_month = (day - 1) % 31
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if month < 12:
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heatmap_data[month, day_of_month] = row['total_demand']
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fig, ax = plt.subplots(figsize=(14, 8))
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im = ax.imshow(heatmap_data, cmap=_cmap_heat(), aspect='auto', interpolation='nearest')
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# 颜色条
|
||
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
|
||
cbar.set_label('Daily Total Demand (μ₁ + μ₂)', fontsize=11, color=TU["text"])
|
||
|
||
# 轴标签
|
||
ax.set_xticks(np.arange(31))
|
||
ax.set_xticklabels(np.arange(1, 32), fontsize=8)
|
||
ax.set_yticks(np.arange(12))
|
||
ax.set_yticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
|
||
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], fontsize=10)
|
||
|
||
ax.set_xlabel('Day of Month', fontsize=12)
|
||
ax.set_ylabel('Month', fontsize=12)
|
||
ax.set_title('Fig.5: Annual Schedule Calendar Heatmap (Daily Demand)', fontsize=14, fontweight='bold')
|
||
ax.grid(False)
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(FIGURES_PATH / 'fig5_calendar_heatmap.png', dpi=150, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
|
||
def fig6_gap_boxplot():
|
||
"""Fig.6: 访问间隔箱线图"""
|
||
print(" 生成 Fig.6: 访问间隔箱线图...")
|
||
|
||
df_gaps = pd.read_excel(SCHEDULE_PATH, sheet_name='gap_statistics')
|
||
|
||
# 过滤有效数据
|
||
df_valid = df_gaps[df_gaps['gap_mean'].notna()].copy()
|
||
|
||
# 按k分组
|
||
df_valid['k_group'] = pd.cut(df_valid['k'], bins=[0, 5, 10, 15, 20, 40],
|
||
labels=['1-5', '6-10', '11-15', '16-20', '21+'])
|
||
|
||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||
|
||
# 左图: 间隔均值按k分组的箱线图
|
||
ax1 = axes[0]
|
||
groups = df_valid.groupby('k_group')['gap_mean'].apply(list).values
|
||
group_labels = ['1-5', '6-10', '11-15', '16-20', '21+']
|
||
|
||
bp = ax1.boxplot([g for g in groups if len(g) > 0], labels=group_labels[:len(groups)],
|
||
patch_artist=True)
|
||
|
||
colors = _cmap_k()(np.linspace(0.2, 0.9, len(groups)))
|
||
for patch, color in zip(bp['boxes'], colors):
|
||
patch.set_facecolor(color)
|
||
patch.set_edgecolor("white")
|
||
patch.set_linewidth(0.8)
|
||
|
||
ax1.set_xlabel('Visit Frequency Group (k)', fontsize=12)
|
||
ax1.set_ylabel('Mean Gap (days)', fontsize=12)
|
||
ax1.set_title('(a) Mean Visit Interval by Frequency Group', fontsize=12)
|
||
style_axes(ax1)
|
||
|
||
# 右图: 间隔CV的分布
|
||
ax2 = axes[1]
|
||
ax2.hist(df_valid['gap_cv'], bins=20, color=TU["blue_mid"], edgecolor="white", alpha=0.85)
|
||
ax2.axvline(df_valid['gap_cv'].mean(), color=TU["mauve"], linestyle='--', linewidth=2,
|
||
label=f'Mean CV = {df_valid["gap_cv"].mean():.3f}')
|
||
ax2.axvline(df_valid['gap_cv'].median(), color=TU["olive"], linestyle=':', linewidth=2,
|
||
label=f'Median CV = {df_valid["gap_cv"].median():.3f}')
|
||
|
||
ax2.set_xlabel('Coefficient of Variation (CV) of Gaps', fontsize=12)
|
||
ax2.set_ylabel('Number of Sites', fontsize=12)
|
||
ax2.set_title('(b) Distribution of Gap Regularity (CV)', fontsize=12)
|
||
ax2.legend(fontsize=10)
|
||
style_axes(ax2)
|
||
|
||
plt.suptitle('Fig.6: Visit Interval Analysis', fontsize=14, fontweight='bold', y=1.02)
|
||
plt.tight_layout()
|
||
plt.savefig(FIGURES_PATH / 'fig6_gap_boxplot.png', dpi=150, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
|
||
def fig7_sensitivity():
|
||
"""Fig.7: 敏感性分析"""
|
||
print(" 生成 Fig.7: 敏感性分析...")
|
||
|
||
# 读取敏感性分析结果
|
||
df_C = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_C')
|
||
df_p = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_p_thresh')
|
||
df_cbar = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_c_bar')
|
||
df_base = pd.read_excel(SENSITIVITY_PATH, sheet_name='baseline').iloc[0]
|
||
base_C = int(df_base['C'])
|
||
base_p_thresh = float(df_base['p_thresh'])
|
||
base_c_bar = float(df_base['c_bar'])
|
||
base_E1 = float(df_base['E1'])
|
||
base_E2 = float(df_base['E2'])
|
||
|
||
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
|
||
|
||
# (a) C对E1的影响
|
||
ax1 = axes[0, 0]
|
||
ax1.plot(df_C['C'], df_C['E1'], 'o-', color=TU["blue_dark"], linewidth=2, markersize=7)
|
||
ax1.axhline(base_E1, color=TU["taupe"], linestyle='--', alpha=0.9, label=f'Baseline (C={base_C}, p={base_p_thresh:g})')
|
||
ax1.set_xlabel('Effective Capacity (C)', fontsize=11)
|
||
ax1.set_ylabel('E1 (Total Service Volume)', fontsize=11)
|
||
ax1.set_title('(a) Effect of C on E1', fontsize=12)
|
||
ax1.legend(fontsize=9)
|
||
style_axes(ax1)
|
||
|
||
# (b) C对修正站点数的影响
|
||
ax2 = axes[0, 1]
|
||
ax2.bar(df_C['C'].astype(str), df_C['n_corrected'], color=TU["green"], edgecolor="white", alpha=0.9, linewidth=0.7)
|
||
ax2.set_xlabel('Effective Capacity (C)', fontsize=11)
|
||
ax2.set_ylabel('Number of Corrected Sites', fontsize=11)
|
||
ax2.set_title('(b) Effect of C on Correction Count', fontsize=12)
|
||
style_axes(ax2, grid_axis="y")
|
||
|
||
# (c) p_thresh对指标的影响
|
||
ax3 = axes[1, 0]
|
||
ax3.plot(df_p['p_thresh'], df_p['E1'], 'o-', color=TU["teal"], linewidth=2, markersize=7, label='E1')
|
||
ax3.set_xlabel('Truncation Threshold (p_thresh)', fontsize=11)
|
||
ax3.set_ylabel('E1 (Total Service Volume)', fontsize=11)
|
||
ax3.set_title('(c) Effect of p_thresh on E1', fontsize=12)
|
||
ax3.legend(fontsize=9)
|
||
style_axes(ax3)
|
||
|
||
# (d) c_bar对E2的影响
|
||
ax4 = axes[1, 1]
|
||
ax4.plot(df_cbar['c_bar'], df_cbar['E2'], 's-', color=TU["mauve"], linewidth=2, markersize=7, label='E2')
|
||
ax4.axhline(base_E2, color=TU["taupe"], linestyle='--', alpha=0.9, label=f'Baseline (c̄={base_c_bar:g})')
|
||
ax4.set_xlabel('Quality Threshold (c̄)', fontsize=11)
|
||
ax4.set_ylabel('E2 (Quality-Weighted Service)', fontsize=11)
|
||
ax4.set_title('(d) Effect of c̄ on E2', fontsize=12)
|
||
ax4.legend(fontsize=9)
|
||
style_axes(ax4)
|
||
|
||
plt.suptitle('Fig.7: Sensitivity Analysis of Model Parameters', fontsize=14, fontweight='bold', y=1.02)
|
||
plt.tight_layout()
|
||
plt.savefig(FIGURES_PATH / 'fig7_sensitivity.png', dpi=150, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
|
||
def main():
|
||
print("=" * 60)
|
||
print("Step 09: 可视化")
|
||
print("=" * 60)
|
||
|
||
print(f"\n输出目录: {FIGURES_PATH}")
|
||
|
||
# 生成所有图表
|
||
print("\n[1] 生成图表...")
|
||
apply_tu_theme()
|
||
|
||
js_path = export_fig1_points_js()
|
||
print(f" 已更新交互地图数据: {js_path.name}")
|
||
|
||
fig1_site_map()
|
||
fig2_demand_correction()
|
||
fig3_k_distribution()
|
||
fig4_efficiency_fairness()
|
||
fig5_calendar_heatmap()
|
||
fig6_gap_boxplot()
|
||
fig7_sensitivity()
|
||
|
||
# 列出生成的文件
|
||
print(f"\n[2] 已生成图表:")
|
||
for f in sorted(FIGURES_PATH.glob('*.png')):
|
||
print(f" {f.name}")
|
||
|
||
print("\n" + "=" * 60)
|
||
print("Step 09 完成")
|
||
print("=" * 60)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|