求测试信号多尺度排列熵python代码

以下是一个计算测试信号多尺度排列熵的 Python 代码示例：

import numpy as np
from itertools import permutations

def permutation_entropy(signal, m, delay, scale):
    """Calculate permutation entropy for a given signal and parameters"""
    # create permutation vectors
    perms = np.array(list(permutations(range(m))))
    n_perms = len(perms)
    
    # create multi-scale signal
    ms_signal = np.zeros((scale, len(signal) - (m-1)*delay))
    for i in range(scale):
        ms_signal[i] = signal[i*delay:i*delay+len(ms_signal[i])]
        
    # calculate permutation entropy for each scale
    pe = np.zeros(scale)
    for i in range(scale):
        # create delay vectors
        delay_vectors = np.zeros((n_perms, len(ms_signal[i])-m+1))
        for j in range(n_perms):
            for k in range(m):
                delay_vectors[j] += ms_signal[i][perms[j,k]:perms[j,k]+len(delay_vectors[j]))
        # calculate frequencies of each permutation
        freqs = np.zeros(n_perms)
        for j in range(n_perms):
            freqs[j] = np.sum(np.all(delay_vectors[j]==delay_vectors, axis=1))
        # calculate probabilities and permutation entropy
        probs = freqs / np.sum(freqs)
        pe[i] = -np.sum(probs * np.log2(probs))
    
    return pe

此函数接受四个参数：原始信号 `signal`、时间延迟 `delay`、排列的长度 `m`、以及要计算的尺度数 `scale`。

例如，如果要计算一个长度为 100 的测试信号的双尺度排列熵，可以这样调用函数：

signal = np.random.rand(100)
pe = permutation_entropy(signal, m=3, delay=1, scale=2)
print(pe)  # 输出双尺度排列熵的结果

该函数将返回一个长度为 `scale` 的数组，包含每个尺度上的排列熵结果。