IIabm/analysis_interaction_heatmap.ipynb

In [2]:

import pandas as pd
data = pd.read_csv("analysis\\20260128experiment_result.csv")
data = data[["n_max_trial","cap_limit_level","mean_end_ts"]]
data

Out [2]:

	n_max_trial	cap_limit_level	mean_end_ts
0	7	5.0	1.8726
1	7	10.0	2.2829
2	7	15.0	2.5124
3	5	5.0	1.8726
4	5	10.0	2.2840
5	5	15.0	2.5189
6	3	5.0	1.8754
7	3	10.0	2.3349
8	3	15.0	2.5918

In [8]:

import matplotlib.pyplot as plt

# 设置中文字体为宋体
plt.rcParams['font.sans-serif'] = ['SimSun']  # 宋体
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

# 1. 画交互作用图
plt.figure(figsize=(4, 3.5), dpi=300)

# 为每个 n_max_trial 水平画一条线
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
for i, n_trial in enumerate(sorted(data['n_max_trial'].unique())):
  subset = data[data['n_max_trial'] == n_trial].sort_values('cap_limit_level')
  plt.plot(
      subset['cap_limit_level'],
      subset['mean_end_ts'],
      marker='o',
      linewidth=2,
      markersize=6,
      alpha=0.85,
      color=colors[i % len(colors)],   # <- key line
      label=f'{int(n_trial)}'
  )

plt.xlabel("额外产能分布均值P6", fontsize=11)
plt.ylabel("产业链供应链恢复时间", fontsize=11)
# plt.title("交互作用图（系统恢复用时R1）\n数据均值", fontsize=12)
plt.legend(title='最大尝试次数P3', loc='upper left')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

In [10]:

import seaborn as sns
import matplotlib.pyplot as plt

# 设置中文字体为宋体
plt.rcParams['font.sans-serif'] = ['SimSun']  # 宋体
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

# 2. 透视成二维网格
pivot = data.pivot(index="n_max_trial",
                  columns="cap_limit_level",
                  values="mean_end_ts")

# 3. 画热力图
plt.figure(figsize=(4, 3), dpi=300)  # 正方形，提高分辨率
sns.heatmap(
    pivot,
    annot=True,
    fmt=".3f",
    cmap="Blues",  # 使用蓝色配色方案
    # square=True    # 确保每个单元格也是正方形
)

plt.xlabel("额外产能分布均值P6")
plt.ylabel("最大尝试次数P3")
# plt.title("产业链供应链恢复时间")
plt.tight_layout()
plt.show()

In [7]:

from mpl_toolkits.mplot3d import Axes3D
# 网格
Y = data["cap_limit_level"].values.reshape(3,3)
X = data["n_max_trial"].values.reshape(3,3)
Z = data["mean_end_ts"].values.reshape(3,3)

# 画图
fig = plt.figure(figsize=(6,4))
ax = fig.add_subplot(111, projection="3d")

ax.plot_surface(X, Y, Z)

ax.set_xlabel("cap_limit_level")
ax.set_ylabel("n_max_trial")
ax.set_zlabel("mean_end_ts")
ax.set_title("Response Surface")

plt.tight_layout()
plt.show()

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