kismuz / pyemd Goto Github PK

• HTML documentation: https://pyemd.readthedocs.org
• Issue tracker: https://github.com/laszukdawid/pyemd/issues
• Source code repository: https://github.com/laszukdawid/pyemd

This is yet another Python implementation of Empirical Mode Decomposition (EMD). The package contains many EMD variations and intends to deliver more in time.

EMD variations:

• Ensemble EMD (EEMD),
• Image decomposotion (EMD2D),
• "Complete Ensembl EMD" (CEEMDAN)
• different settings and configurations of vanilla EMD.

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

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

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

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

$ python setup.py install

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

More detailed examples are included in documentation.

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

from PyEMD import EMD
import numpy as np

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

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

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

from PyEMD import EEMD
import numpy as np

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

As with previous methods, there is also simple way to use CEEMDAN.

from PyEMD import CEEMDAN
import numpy as np

s = np.random.random(100)
ceemdan = CEEMDAN()
cIMFs = ceemdan(s)

Simplest case is to pass image as monochromatic numpy 2D array. As with other modules one can use default setting of instance or more expliclity use emd2d() method.

from PyEMD import EMD2D
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()
IMFs_2D = emd2d(img)

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 (laszukdawid @ gmail) or search me favourite web search.

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

Dawid Laszuk (2017-), Python implementation of Empirical Mode Decomposition algorithm. http://www.laszukdawid.com/codes.