vmdpy: Variational mode decomposition in Python

Function for decomposing a signal according to the Variational Mode Decomposition (Dragomiretskiy and Zosso, 2014) method.

This package is a Python translation of the original VMD MATLAB toolbox

Installation

1. pip install vmdpy

OR

2. Dowload the project from https://github.com/vrcarva/vmdpy, then run "python setup.py install" from the project folder

Citation and Contact

Paper available at: https://doi.org/10.1016/j.bspc.2020.102073

If you find this package useful, we kindly ask you to cite it in your work:
Vinícius R. Carvalho, Márcio F.D. Moraes, Antônio P. Braga, Eduardo M.A.M. Mendes, Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification, Biomedical Signal Processing and Control, Volume 62, 2020, 102073, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2020.102073.

If you developed a new funcionality or fixed anything in the code, just provide me the corresponding files and which credit should I include in this readme file.

For suggestions, questions, comments, etc: vrcarva@ufmg.br
Vinícius Rezende Carvalho
Programa de Pós-Graduação em Engenharia Elétrica – Universidade Federal de Minas Gerais, Belo Horizonte, Brasil
Núcleo de Neurociências - Universidade Federal de Minas Gerais

Example script

#%% Simple example: generate signal with 3 components + noise  
import numpy as np  
import matplotlib.pyplot as plt  
from vmdpy import VMD  

#. Time Domain 0 to T  
T = 1000  
fs = 1/T  
t = np.arange(1,T+1)/T  
freqs = 2*np.pi*(t-0.5-fs)/(fs)  

#. center frequencies of components  
f_1 = 2  
f_2 = 24  
f_3 = 288  

#. modes  
v_1 = (np.cos(2*np.pi*f_1*t))  
v_2 = 1/4*(np.cos(2*np.pi*f_2*t))  
v_3 = 1/16*(np.cos(2*np.pi*f_3*t))  

f = v_1 + v_2 + v_3 + 0.1*np.random.randn(v_1.size)  

#. some sample parameters for VMD  
alpha = 2000       # moderate bandwidth constraint  
tau = 0.            # noise-tolerance (no strict fidelity enforcement)  
K = 3              # 3 modes  
DC = 0             # no DC part imposed  
init = 1           # initialize omegas uniformly  
tol = 1e-7  


#. Run VMD 
u, u_hat, omega = VMD(f, alpha, tau, K, DC, init, tol)  

#. Visualize decomposed modes
plt.figure()
plt.subplot(2,1,1)
plt.plot(f)
plt.title('Original signal')
plt.xlabel('time (s)')
plt.subplot(2,1,2)
plt.plot(u.T)
plt.title('Decomposed modes')
plt.xlabel('time (s)')
plt.legend(['Mode %d'%m_i for m_i in range(u.shape[0])])
plt.tight_layout()