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Empirical Wavelet Transofrm (EWT) algorithm

Project description

ewtpy - Empirical wavelet transform in Python

Adaptive decomposition of a signal with the EWT (Gilles, 2013) method

Python translation from the original Matlab toolbox.

ewtpy performs the Empirical Wavelet Transform of a 1D signal over N scales. Main function is EWT1D:

ewt, mfb ,boundaries = EWT1D(f, N = 5, log = 0,detect = "locmax", completion = 0, reg = 'average', lengthFilter = 10,sigmaFilter = 5)
Other functions include:

Some functionalities from J.Gilles' MATLAB toolbox have not been implemented, such as EWT of 2D inputs, preprocessing, adaptive/ScaleSpace boundaries_detect.

The Example folder contains test signals and scripts


  1. Dowload the project from, then run "python install" from the project folder


  1. pip install ewtpy

Citation and Contact

Paper available at

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,

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.

Any questions, comments, suggestions and/or corrections, please get in contact with

@author: Vinícius Rezende Carvalho Programa de pós graduação em engenharia elétrica - PPGEE UFMG Universidade Federal de Minas Gerais - Belo Horizonte, Brazil Núcleo de Neurociências - NNC

Example script

#%% Example script
import numpy as np
import matplotlib.pyplot as plt
import ewtpy

T = 1000
t = np.arange(1,T+1)/T
f = np.cos(2*np.pi*0.8*t) + 2*np.cos(2*np.pi*10*t)+0.8*np.cos(2*np.pi*100*t)
ewt,  mfb ,boundaries = ewtpy.EWT1D(f, N = 3)

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