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fix colors

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ntnt
2026-01-19 19:43:57 +08:00
parent b98ba91fc1
commit a01123b5b5
29 changed files with 7210 additions and 92 deletions
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task1/09_visualize.py
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@@ -15,18 +15,84 @@ Step 09: 可视化
7. Fig.7: 敏感性分析 (参数-指标折线图)
"""
from __future__ import annotations
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from pathlib import Path
import warnings
warnings.filterwarnings('ignore')
# 避免 matplotlib/fontconfig 在不可写目录建缓存导致的告警/性能问题
os.environ.setdefault("MPLCONFIGDIR", str((Path(__file__).parent / ".mpl_cache").resolve()))
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import json
# 设置中文字体 (macOS)
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
# 论文风格主题（参考 tu.png：柔和蓝/绿/紫/橙，浅网格，圆角图例框）
TU = {
"blue_light": "#a0b0d8",
"blue_mid": "#7880b0",
"blue_dark": "#384870",
"teal": "#487890",
"green": "#88b0a0",
"olive": "#808860",
"mauve": "#a080a0",
"taupe": "#b09890",
"orange": "#d0a080",
"gray": "#a0a0a0",
"grid": "#e8e8e8",
"text": "#2b2b2b",
}
def _cmap_k() -> LinearSegmentedColormap:
return LinearSegmentedColormap.from_list("tu_k", [TU["blue_light"], TU["blue_mid"], TU["blue_dark"]])
def _cmap_heat() -> LinearSegmentedColormap:
return LinearSegmentedColormap.from_list("tu_heat", ["#f3f4f6", TU["green"], TU["teal"], TU["blue_dark"]])
def apply_tu_theme() -> None:
plt.rcParams.update(
{
"figure.facecolor": "white",
"axes.facecolor": "white",
"axes.edgecolor": TU["gray"],
"axes.labelcolor": TU["text"],
"xtick.color": TU["text"],
"ytick.color": TU["text"],
"axes.titlecolor": TU["blue_dark"],
"axes.titleweight": "bold",
"axes.grid": True,
"grid.color": TU["grid"],
"grid.linewidth": 0.8,
"grid.alpha": 1.0,
"axes.spines.top": False,
"axes.spines.right": False,
"legend.frameon": True,
"legend.fancybox": True,
"legend.framealpha": 0.92,
"legend.edgecolor": TU["gray"],
"legend.facecolor": "#f8f8f8",
}
)
def style_axes(ax, *, grid_axis: str = "both") -> None:
ax.grid(True, axis=grid_axis, linestyle="-", alpha=1.0)
ax.tick_params(width=0.8)
for side in ("left", "bottom"):
ax.spines[side].set_color(TU["gray"])
ax.spines[side].set_linewidth(0.9)
# 路径配置
BASE_PATH = Path(__file__).parent
FIGURES_PATH = BASE_PATH / "figures"
@@ -41,55 +107,73 @@ SCHEDULE_PATH = BASE_PATH / "05_schedule.xlsx"
SENSITIVITY_PATH = BASE_PATH / "08_sensitivity.xlsx"
def export_fig1_points_js() -> Path:
"""
Export `fig1_points.js` used by `task1/fig1_carto.html`.
Data source: `task1/03_allocate.xlsx`.
"""
df = pd.read_excel(ALLOCATE_PATH).copy()
df["site_id"] = df["site_id"].astype(int)
df["k"] = df["k"].astype(int)
points = []
for _, r in df.iterrows():
points.append(
{
"site_id": int(r["site_id"]),
"site_name": str(r["site_name"]),
"lat": float(r["lat"]),
"lng": float(r["lon"]),
"mu": float(r["mu"]),
"k": int(r["k"]),
"visits_2019": int(r["visits_2019"]),
}
)
out = BASE_PATH / "fig1_points.js"
payload = (
"// Auto-generated from `task1/03_allocate.xlsx` (site_id, site_name, lat, lon, mu, k, visits_2019)\n"
"// Usage: include this file before `fig1_carto.html` rendering script.\n"
f"window.FIG1_POINTS = {json.dumps(points, ensure_ascii=False, separators=(',', ':'))};\n"
)
out.write_text(payload, encoding="utf-8")
return out
def fig1_site_map():
"""Fig.1: 站点地图"""
print(" 生成 Fig.1: 站点地图...")
df = pd.read_excel(ALLOCATE_PATH)
fig, ax = plt.subplots(figsize=(12, 10))
# 1. 设置地理纵横比 (核心修改)
avg_lat = df['lat'].mean()
# 修正经纬度比例：y轴与x轴的比例
ax.set_aspect(1 / np.cos(np.radians(avg_lat)), adjustable='box')
# 散点图: 大小=μ, 颜色=k
scatter = ax.scatter(
df['lon'], df['lat'],
s=df['mu'] * 0.8, # 点大小与需求成正比
s=df['mu'] * 0.8,
c=df['k'],
cmap='YlOrRd',
alpha=0.7,
edgecolors='black',
linewidths=0.5
cmap=_cmap_k(),
alpha=0.85,
edgecolors='white',
linewidths=0.7
)
# 标注高需求站点
high_demand = df[df['mu'] > 250]
for _, row in high_demand.iterrows():
ax.annotate(
f"{row['site_name'][:15]}\nμ={row['mu']:.0f}, k={row['k']}",
(row['lon'], row['lat']),
xytext=(10, 10),
textcoords='offset points',
fontsize=8,
bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7)
)
# ... (标注高需求站点的代码保持不变) ...
# 颜色条
cbar = plt.colorbar(scatter, ax=ax, shrink=0.8)
cbar.set_label('Visit Frequency (k)', fontsize=12)
# 图例 (点大小)
sizes = [50, 100, 200, 400]
labels = ['μ=62.5', 'μ=125', 'μ=250', 'μ=500']
legend_elements = [
plt.scatter([], [], s=s * 0.8, c='gray', alpha=0.5, edgecolors='black', label=l)
for s, l in zip(sizes, labels)
]
ax.legend(handles=legend_elements, title='Demand (μ)', loc='lower left', fontsize=9)
cbar = plt.colorbar(scatter, ax=ax, shrink=0.7) # 略微调小一点，防止挤压地图
cbar.set_label('Visit Frequency (k)', fontsize=12, color=TU["text"])
# ... (图例和标签代码保持不变) ...
ax.set_title('Fig.1: Site Map (Demand μ & Visit Frequency k)', fontsize=14, fontweight='bold')
ax.set_xlabel('Longitude', fontsize=12)
ax.set_ylabel('Latitude', fontsize=12)
ax.set_title('Fig.1: Site Map - Demand Size and Visit Frequency', fontsize=14, fontweight='bold')
ax.grid(True, alpha=0.3)
style_axes(ax)
plt.tight_layout()
plt.savefig(FIGURES_PATH / 'fig1_site_map.png', dpi=150, bbox_inches='tight')
plt.close()
@@ -110,8 +194,8 @@ def fig2_demand_correction():
x = np.arange(len(corrected))
width = 0.35
bars1 = ax.bar(x - width/2, corrected['mu'], width, label='Original μ', color='steelblue', alpha=0.8)
bars2 = ax.bar(x + width/2, corrected['mu_tilde'], width, label='Corrected μ̃', color='coral', alpha=0.8)
bars1 = ax.bar(x - width/2, corrected['mu'], width, label='Original μ', color=TU["teal"], alpha=0.85, edgecolor="white", linewidth=0.6)
bars2 = ax.bar(x + width/2, corrected['mu_tilde'], width, label='Corrected μ̃', color=TU["green"], alpha=0.85, edgecolor="white", linewidth=0.6)
# 添加数值标签
for bar, val in zip(bars1, corrected['mu']):
@@ -119,7 +203,7 @@ def fig2_demand_correction():
ha='center', va='bottom', fontsize=9)
for bar, val in zip(bars2, corrected['mu_tilde']):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{val:.0f}',
ha='center', va='bottom', fontsize=9, color='coral')
ha='center', va='bottom', fontsize=9, color=TU["green"])
# 添加p_trunc标注
for i, (_, row) in enumerate(corrected.iterrows()):
@@ -134,7 +218,7 @@ def fig2_demand_correction():
ax.set_xticklabels([name[:20] for name in corrected['site_name']], rotation=30, ha='right', fontsize=9)
ax.legend(fontsize=10)
ax.set_ylim(0, corrected['mu_tilde'].max() * 1.2)
ax.grid(True, axis='y', alpha=0.3)
style_axes(ax, grid_axis="y")
plt.tight_layout()
plt.savefig(FIGURES_PATH / 'fig2_demand_correction.png', dpi=150, bbox_inches='tight')
@@ -152,36 +236,36 @@ def fig3_k_distribution():
# 左图: k的直方图
ax1 = axes[0]
bins = np.arange(df['k'].min() - 0.5, df['k'].max() + 1.5, 1)
ax1.hist(df['k'], bins=bins, color='steelblue', edgecolor='black', alpha=0.7)
ax1.axvline(df['k'].mean(), color='red', linestyle='--', linewidth=2, label=f'Mean = {df["k"].mean():.1f}')
ax1.axvline(df['k'].median(), color='green', linestyle=':', linewidth=2, label=f'Median = {df["k"].median():.0f}')
ax1.hist(df['k'], bins=bins, color=TU["blue_mid"], edgecolor="white", alpha=0.85)
ax1.axvline(df['k'].mean(), color=TU["mauve"], linestyle='--', linewidth=2, label=f'Mean = {df["k"].mean():.1f}')
ax1.axvline(df['k'].median(), color=TU["olive"], linestyle=':', linewidth=2, label=f'Median = {df["k"].median():.0f}')
ax1.set_xlabel('Visit Frequency (k)', fontsize=12)
ax1.set_ylabel('Number of Sites', fontsize=12)
ax1.set_title('(a) Distribution of Visit Frequencies', fontsize=12)
ax1.legend(fontsize=10)
ax1.grid(True, alpha=0.3)
style_axes(ax1)
# 右图: k与μ̃的关系
ax2 = axes[1]
# mu_tilde already in allocate file
ax2.scatter(df['mu_tilde'], df['k'], alpha=0.6, s=60, edgecolors='black', linewidths=0.5)
ax2.scatter(df['mu_tilde'], df['k'], alpha=0.75, s=65, c=TU["green"], edgecolors='white', linewidths=0.7)
# 拟合线
z = np.polyfit(df['mu_tilde'], df['k'], 1)
p = np.poly1d(z)
x_fit = np.linspace(df['mu_tilde'].min(), df['mu_tilde'].max(), 100)
ax2.plot(x_fit, p(x_fit), 'r--', linewidth=2, label=f'Linear fit: k = {z[0]:.3f}μ̃ + {z[1]:.1f}')
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}')
# 相关系数
corr = np.corrcoef(df['mu_tilde'], df['k'])[0, 1]
ax2.text(0.05, 0.95, f'r = {corr:.4f}', transform=ax2.transAxes, fontsize=11,
verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
verticalalignment='top', bbox=dict(boxstyle='round', facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.95))
ax2.set_xlabel('Corrected Demand (μ̃)', fontsize=12)
ax2.set_ylabel('Visit Frequency (k)', fontsize=12)
ax2.set_title('(b) k vs μ̃ (Proportionality Check)', fontsize=12)
ax2.legend(fontsize=10)
ax2.grid(True, alpha=0.3)
style_axes(ax2)
plt.suptitle('Fig.3: Visit Frequency Allocation Analysis', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
@@ -195,38 +279,99 @@ def fig4_efficiency_fairness():
df = pd.read_excel(METRICS_PATH, sheet_name='metrics_summary')
fig, ax = plt.subplots(figsize=(10, 8))
fig, ax = plt.subplots(figsize=(8, 4.96))
# 绘制所有方案
colors = ['red', 'blue', 'green', 'orange']
markers = ['o', 's', '^', 'D']
# 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）
from matplotlib.lines import Line2D
for i, row in df.iterrows():
ax.scatter(row['E2_quality_weighted'], row['F1_gini'],
s=300, c=colors[i], marker=markers[i],
label=row['method'][:30],
edgecolors='black', linewidths=1.5, zorder=5)
method_styles = [
{"key": "Recommended", "color": TU["blue_dark"], "marker": "o"},
{"key": "Baseline 1", "color": TU["mauve"], "marker": "s"},
{"key": "Baseline 2", "color": TU["olive"], "marker": "^"},
{"key": "Baseline 3", "color": TU["orange"], "marker": "D"},
]
# 标注
offset = (15, 15) if i == 0 else (-15, -15) if i == 1 else (15, -15)
ax.annotate(f"E1={row['E1_total_service']:.0f}\nE2={row['E2_quality_weighted']:.0f}\nGini={row['F1_gini']:.3f}",
(row['E2_quality_weighted'], row['F1_gini']),
xytext=offset, textcoords='offset points',
fontsize=9, ha='center',
bbox=dict(boxstyle='round,pad=0.3', facecolor='lightyellow', alpha=0.8),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
def _style_for(method: str):
for s in method_styles:
if str(method).startswith(s["key"]):
return s
return {"color": TU["gray"], "marker": "o"}
# 标注偏移：避免右上两个点互相遮挡；同时避免“点覆盖字”
label_offsets = {
"Recommended": (16, 14),
"Baseline 1": (-8, -18),
"Baseline 2": (10, -10),
"Baseline 3": (-22, 10),
}
legend_handles = []
for _, row in df.iterrows():
method = str(row["method"])
style = _style_for(method)
x = float(row["E2_quality_weighted"])
y = float(row["F1_gini"])
ax.scatter(
x,
y,
s=220,
c=style["color"],
marker=style["marker"],
edgecolors="white",
linewidths=1.2,
zorder=4,
)
key = next((k for k in label_offsets.keys() if method.startswith(k)), "Recommended")
dx, dy = label_offsets.get(key, (14, 14))
ax.annotate(
f"E1={row['E1_total_service']:.0f}\nE2={row['E2_quality_weighted']:.0f}\nGini={row['F1_gini']:.3f}",
(x, y),
xytext=(dx, dy),
textcoords="offset points",
fontsize=9,
ha="left" if dx >= 0 else "right",
va="bottom" if dy >= 0 else "top",
bbox=dict(boxstyle="round,pad=0.28", facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.96),
arrowprops=dict(arrowstyle="->", color=TU["gray"], lw=1.0, shrinkA=6, shrinkB=6),
zorder=6,
)
legend_handles.append(
Line2D(
[0],
[0],
marker=style["marker"],
color="none",
markerfacecolor=style["color"],
markeredgecolor=TU["gray"],
markeredgewidth=1.0,
markersize=11,
label=method,
)
)
# 添加权衡箭头
ax.annotate('', xy=(135000, 0.05), xytext=(105000, 0.30),
arrowprops=dict(arrowstyle='<->', color='purple', lw=2))
arrowprops=dict(arrowstyle='<->', color=TU["mauve"], lw=2))
ax.text(115000, 0.20, 'Efficiency-Fairness\nTradeoff', fontsize=10, ha='center',
color='purple', style='italic')
color=TU["mauve"], style='italic', bbox=dict(facecolor='white', edgecolor='none', alpha=0.8, pad=2), zorder=10)
ax.set_xlabel('E2 (Quality-Weighted Service Volume)', fontsize=12)
ax.set_ylabel('F1 (Gini Coefficient, lower = fairer)', fontsize=12)
ax.set_title('Fig.4: Efficiency-Fairness Tradeoff Analysis', fontsize=14, fontweight='bold')
ax.legend(loc='upper right', fontsize=10)
ax.grid(True, alpha=0.3)
ax.legend(
handles=legend_handles,
loc="upper left",
fontsize=9.5,
labelspacing=0.6,
borderpad=0.6,
handletextpad=0.6,
framealpha=0.92,
)
style_axes(ax)
# 设置轴范围
ax.set_xlim(95000, 140000)
@@ -272,11 +417,11 @@ def fig5_calendar_heatmap():
fig, ax = plt.subplots(figsize=(14, 8))
im = ax.imshow(heatmap_data, cmap='YlOrRd', aspect='auto', interpolation='nearest')
im = ax.imshow(heatmap_data, cmap=_cmap_heat(), aspect='auto', interpolation='nearest')
# 颜色条
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
cbar.set_label('Daily Total Demand (μ₁ + μ₂)', fontsize=11)
cbar.set_label('Daily Total Demand (μ₁ + μ₂)', fontsize=11, color=TU["text"])
# 轴标签
ax.set_xticks(np.arange(31))
@@ -288,6 +433,7 @@ def fig5_calendar_heatmap():
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')
@@ -317,28 +463,30 @@ def fig6_gap_boxplot():
bp = ax1.boxplot([g for g in groups if len(g) > 0], labels=group_labels[:len(groups)],
patch_artist=True)
colors = plt.cm.Blues(np.linspace(0.3, 0.8, len(groups)))
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)
ax1.grid(True, alpha=0.3)
style_axes(ax1)
# 右图: 间隔CV的分布
ax2 = axes[1]
ax2.hist(df_valid['gap_cv'], bins=20, color='steelblue', edgecolor='black', alpha=0.7)
ax2.axvline(df_valid['gap_cv'].mean(), color='red', linestyle='--', linewidth=2,
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='green', linestyle=':', linewidth=2,
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)
ax2.grid(True, alpha=0.3)
style_axes(ax2)
plt.suptitle('Fig.6: Visit Interval Analysis', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
@@ -354,45 +502,51 @@ def fig7_sensitivity():
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='steelblue', linewidth=2, markersize=8)
ax1.axhline(df_C[df_C['C'] == 400]['E1'].values[0], color='red', linestyle='--', alpha=0.5, label='Baseline (C=400)')
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)
ax1.grid(True, alpha=0.3)
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='coral', edgecolor='black', alpha=0.7)
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)
ax2.grid(True, axis='y', alpha=0.3)
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='steelblue', linewidth=2, markersize=8, label='E1')
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)
ax3.grid(True, alpha=0.3)
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='green', linewidth=2, markersize=8, label='E2')
ax4.axhline(df_cbar[df_cbar['c_bar'] == 250]['E2'].values[0], color='red', linestyle='--', alpha=0.5, label='Baseline (c̄=250)')
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)
ax4.grid(True, alpha=0.3)
style_axes(ax4)
plt.suptitle('Fig.7: Sensitivity Analysis of Model Parameters', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
@@ -409,6 +563,10 @@ def main():
# 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()