求测试信号多尺度排列熵的python代码

以下是一个使用Python计算多尺度排列熵的示例代码：

import numpy as np
from scipy.spatial.distance import pdist, squareform
from itertools import permutations

def multiscale_permutation_entropy(signal, scale, delay):
    n = len(signal)
    permutations = np.array(list(permutations(range(delay))))
    num_permutations = len(permutations)
    
    # Split the signal into non-overlapping segments of length scale
    num_subseries = int(np.floor((n - scale) / scale)) + 1
    split_indices = np.arange(num_subseries) * scale
    
    # Calculate permutation entropy for each subseries
    pe_subseries = np.zeros(num_subseries)
    for i in range(num_subseries):
        subseries = signal[split_indices[i]:split_indices[i] + scale]
        pe_subseries[i] = permutation_entropy(subseries, delay, permutations)
    
    # Calculate multiscale permutation entropy
    mpe = np.mean(pe_subseries)
    return mpe

def permutation_entropy(signal, delay, permutations):
    n = len(signal)
    d = squareform(pdist(signal.reshape(-1,1)))
    count = 0
    for i in range(n - delay):
        for j in range(i + delay, n):
            if d[i, j] < d[i + delay, j]:
                count += 1
    pe = np.log(count / len(permutations))
    return pe

# Example usage
signal = np.random.rand(1000)
scale = 10
delay = 1
mpe = multiscale_permutation_entropy(signal, scale, delay)
print("Multiscale permutation entropy:", mpe)

该代码使用了Scipy中的`pdist`函数来计算信号中各数据点之间的距离，并使用了`itertools`模块来生成所有可能的排列。函数`multiscale_permutation_entropy`通过将信号分割成多个子序列，并计算每个子序列的排列熵来计算多尺度排列熵。函数`permutation_entropy`计算给定延迟和排列的排列熵。