变分模态分解python代码
时间: 2023-02-26 10:39:12 浏览: 92
变分模态分解 (Variational Mode Decomposition, VMD) 是一种信号分析方法,用于将一个复杂的信号分解成其独立的模态。
下面是一个简单的 Python 实现 VMD 的代码示例:
```
import numpy as np
from scipy import linalg
def vmd(x, dt, num_modes=None, tau=None):
# Calculate the covariance matrix
X = np.cov(x)
# Compute eigenvalues and eigenvectors
eigen_vals, eigen_vecs = linalg.eig(X)
# Sort eigenvalues in descending order
idx = np.argsort(eigen_vals)[::-1]
eigen_vals = eigen_vals[idx]
eigen_vecs = eigen_vecs[:,idx]
# Select the number of modes based on the eigenvalue threshold
if num_modes is None:
num_modes = np.sum(eigen_vals >= np.max(eigen_vals)*1e-6)
elif num_modes > len(eigen_vals):
raise ValueError("The number of modes is greater than the number of data points")
# Select the decay constant based on the eigenvalue threshold
if tau is None:
tau = -dt / np.log(eigen_vals[num_modes:] / eigen_vals[num_modes-1])
# Compute the intrinsic mode functions
modes = np.zeros((len(x), num_modes), dtype=complex)
for i in range(num_modes):
b = np.exp(-dt / tau[i])
c = np.dot(x, eigen_vecs[:,i])
modes[:,i] = c * np.exp(b * np.arange(len(x)) * dt)
return modes
```
该代码实现了基本的 VMD 算法,通过计算输入信号的协方差矩阵,并计算其特征值和特征向量,来分解出独立的模态。在代码中,还包括了选择模态数量和衰减常数的过程。