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
- pip install vmdpy
OR
- Dowload the project from https://github.com/vrcarva/vmdpy, then run "python setup.py install" from the project folder
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: [email protected]
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
#%% 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()
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