eemdpython实现

eemd是一种基于本地平均分解的信号处理方法，主要用于非线性和非平稳信号的分解和分析。下面是一个使用Python实现eemd的示例代码：

import numpy as np
from scipy.interpolate import interp1d

def emd(x):
    max_imfs = int(np.floor(np.log2(x.size))) - 1
    imfs = np.zeros((max_imfs, x.size))

    for i in range(max_imfs):
        imf = x.copy()
        for j in range(i):
            imf -= imfs[j]
        sd = np.inf
        while sd > 0.1:
            max_min = np.diff(np.sign(np.diff(imf))).nonzero()[0] + 1
            if max_min.size < 3:
                break
            pmax, pmin = max_min[::2], max_min[1::2]
            max_env = interp1d(pmax, imf[pmax], kind='cubic')(np.arange(x.size))
            min_env = interp1d(pmin, imf[pmin], kind='cubic')(np.arange(x.size))
            mean_env = (max_env + min_env) / 2
            imf_old, imf = imf, imf - mean_env
            sd = np.sum((imf_old - imf) ** 2) / np.sum(imf_old ** 2)
        imfs[i] = imf

    return imfs[:i+1]


def eemd(x, num_ensembles=100):
    emd_ensemble = np.zeros((num_ensembles, x.size))
    for i in range(num_ensembles):
        noise = np.random.randn(x.size)
        imfs = emd(x + noise)
        emd_ensemble[i] = imfs[-1]
    return emd_ensemble.mean(axis=0)

其中，emd函数用于计算x的本征模态函数(imf)，eemd函数则是对x进行多次重构，得到多个emf，并返回它们的平均值。