示例05 持久图与条形码可视化

"""ex05_persim_visualize.py — 第 5 章 persim 可视化与瓶颈距离

对应教程：tutorials/05-visualization.md。

演示：

• plot_diagrams：多维持久图同图
• plot_bars：条形码视图
• PersistenceImager：持久图像（向量化）
• bottleneck：两持久图的瓶颈距离

运行：
python ex05_persim_visualize.py
"""

import numpy as np
from ripser import ripser
from persim import plot_diagrams, plot_bars
import matplotlib.pyplot as plt

from common import sample_circle, top_features

def main():
data = sample_circle(n=120, noise=0.05, seed=7)
dgms = ripser(data, maxdim=1)['dgms']

# ── 1. 持久图 + 条形码 ──
fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))

plot_diagrams(dgms, ax=axes[0], labels=['H0', 'H1'], show=False)
axes[0].set_title('持久图（Persistence Diagram）')

# H1 条形码：每条水平线段 = 一个特征的 [birth, death]
plot_bars(dgms[1], ax=axes[1])
axes[1].set_title('H1 条形码（Barcode）')

plt.tight_layout()

# ── 2. top_features：手动找最显著特征 ──
top, pers = top_features(dgms[1], k=3)
print("=== H1 最显著的 3 个特征 ===")
for row, p in zip(top, pers):
    death = row[1] if np.isfinite(row[1]) else float('inf')
    print(f"  birth={row[0]:.3f}  death={death:.3f}  "
          f"persistence={p:.3f}")

# ── 3. 瓶颈距离：同一圆环两次独立采样的 H1 ──
from persim import bottleneck
data2 = sample_circle(n=120, noise=0.05, seed=99)
dgm2 = ripser(data2, maxdim=1)['dgms'][1]
dist, _ = bottleneck(dgms[1], dgm2)
print(f"\n两次独立采样 H1 瓶颈距离 = {dist:.4f}（应较小）")

plt.show()
​

if name == "main":
main()