EMD-signal 0.1.12

Implementation of Empirical Mode Decomposition (EMD) and its variations

PyEMD

The project is ongoing. This is very limited part of my private collection, but before I upload everything I want to make sure it works as it should. If there is something you wish to have, do email me as there is high chance that I have already done it, but it just sits around and waits until I’ll have more time. Don’t hesitate contacting me for anything.

This is yet another Python implementation of Empirical Mode Decomposition (EMD). The package contains many EMD variations, like Ensemble EMD (EEMD), and different settings.

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

Available splines:

• Natural cubic [default]

• Pointwise cubic

• Akima

• Linear

Available stopping criteria:

• Cauchy convergence [default]

• Fixed number of iterations

• Number of consecutive proto-imfs

Extrema detection:

• Discrete extrema [default]

• Parabolic interpolation

Installation

Recommended

Simply download this directory either directly from GitHub, or via command line:

$ git clone https://github.com/laszukdawid/PyEMD

Then go into the downloaded project and run from command line:

$ python setup.py install

PyPi

Packaged obtained from PyPi is/will be slightly behind this project, so some features might not be the same. However, it seems to be the easiest/nicest way of installing any Python packages, so why not this one?

$ pip install EMD-signal

Example

EMD

In most cases default settings are enough. Simply import EMD and pass your signal to emd() method.

from PyEMD.EMD import EMD

s = np.random.random(100)
emd = EMD()
IMFs = emd.emd(s)

The Figure below was produced with input: \(S(t) = cos(22 \pi t^2) + 6t^2\)

EEMD

Simplest case of using Esnembld EMD (EEMD) is by importing EEMD and passing your signal to eemd() method.

from PyEMD.EEMD import EEMD

s = np.random.random(100)
eemd = EEMD()
eIMFs = eemd.eemd(s)

Contact

Feel free to contact me with any questions, requests or simply saying hi. It’s always nice to know that I might have contributed to saving someone’s time or that I might improve my skills/projects.

Contact me either through gmail ({my_username}@gmail) or search me favourite web search.