Implementation of Empirical Mode Decomposition (EMD) and its variations
Project description
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
Probably in most cases default settings are enough. In such case simply import EMD and pass your signal to emd() method.
from PyEMD import EMD
s = np.random.random(100)
IMFs = EMD().emd(s)The Figure below was produced with input: \(S(t) = cos(22 \pi t^2) + 6t^2\)
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.
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