Implementation of the Empirical Mode Decomposition (EMD) and its variations

PyEMD

Introduction

Python implementation of the Empirical Mode Decomposition (EMD). The package contains multiple EMD variations and intends to deliver more in time.

EMD variations

• Ensemble EMD (EEMD),
• "Complete Ensemble EMD" (CEEMDAN)
• different settings and configurations of vanilla EMD.
• Image decomposition (EMD2D & BEMD) (experimental, no support)
• Just-in-time compiled EMD (JitEMD)

PyEMD allows you to use different splines for envelopes, stopping criteria and extrema interpolations.

Available splines

• Natural cubic (default)
• Pointwise cubic
• Hermite cubic
• Akima
• PChip
• Linear

Available stopping criteria

• Cauchy convergence (default)
• Fixed number of iterations
• Number of consecutive proto-imfs

Extrema detection

• Discrete extrema (default)
• Parabolic interpolation

Installation

PyPi (recommended)

The quickest way to install package is through pip.

$pip install EMD-signal From source In case, if you only want to use EMD and its variations, the best way to install PyEMD is through pip. However, if you want to modify the code, anyhow you might want to download the code and build package yourself. The source is publicaly available and hosted on GitHub. To download the code you can either go to the source code page and click Code -> Download ZIP, or use git command line$ git clone https://github.com/laszukdawid/PyEMD

Installing package from source is done using command line:

EMD2D/BEMD

Unfortunately, this is Experimental and we can't guarantee that the output is meaningful. The simplest use is to pass image as monochromatic numpy 2D array. Sample as with the other modules one can use the default setting of an instance or, more explicitly, use the emd2d() method.

from PyEMD.EMD2d import EMD2D  #, BEMD
import numpy as np

x, y = np.arange(128), np.arange(128).reshape((-1,1))
img = np.sin(0.1*x)*np.cos(0.2*y)
emd2d = EMD2D()  # BEMD() also works
IMFs_2D = emd2d(img)


F.A.Q

Why is EEMD/CEEMDAN so slow?

Unfortunately, that's their nature. They execute EMD multiple times every time with slightly modified version. Added noise can cause a creation of many extrema which will decrease performance of the natural cubic spline. For some tweaks on how to deal with that please see Speedup tricks in the documentation.

Contact

Feel free to contact me with any questions, requests or simply to say hi. It's always nice to know that I've helped someone or made their work easier. Contributing to the project is also acceptable and warmly welcomed.

Citation

If you found this package useful and would like to cite it in your work please use the following structure:

@misc{pyemd,
author = {Laszuk, Dawid},
title = {Python implementation of Empirical Mode Decomposition algorithm},
year = {2017},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/laszukdawid/PyEMD}},
doi = {10.5281/zenodo.5459184}
}


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