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Implementation of Empirical Mode Decomposition (EMD) and its variations

Project description

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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

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

$ python 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.

.. code:: python

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:
:math:`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.

.. code:: python

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``.

.. code:: python

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.

.. code:: python

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**.

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