Skip to main content

Variational Mode Decomposition (VMD) algorithm

Project description

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

  1. 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
Vinicius 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  
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 actual VMD code  
u, u_hat, omega = VMD(f, alpha, tau, K, DC, init, tol)  

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

vmdpy-0.2-py2.py3-none-any.whl (6.5 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page