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Empirical Wavelet Transofrm (EWT) algorithm

Project description

Empirical Wavelet Transform Python package

Original paper: Gilles, J., 2013. Empirical Wavelet Transform. IEEE Transactions on Signal Processing, 61(16), pp.3999–4010. Available at:
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

If you find this package useful, we kindly ask you to cite it in your work.
Vinicius Carvalho (2019-), Empirical Wavelet Transform in Python

A paper will soon be submitted and linked here.

@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

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

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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