PyLMD 1.0.4

Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5): 443-454

PyLMD

Method of decomposing signal into Product Functions

This project implements the paper:

Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5):443-454

How to install?

pip install PyLMD

requirements:

1. numpy
2. scipy

Examples

>>> import numpy as np
>>> from PyLMD import LMD
>>> x = np.linspace(0, 100, 101)
>>> y = 2 / 3 * np.sin(x * 30) + 2 / 3 * np.sin(x * 17.5) + 4 / 5 * np.cos(x * 2)
>>> lmd = LMD()
>>> PFs, resdue = lmd.lmd(y)
>>> PFs.shape
(6, 101)

Parameters

• INCLUDE_ENDPOINTS : bool, (default: True)

  Whether to treat the endpoint of the signal as a pseudo-extreme point

• max_smooth_iteration : int, (default: 12)

  Maximum number of iterations of moving average algorithm.

• max_envelope_iteration : int, (default: 200)

  Maximum number of iterations when separating local envelope signals.

• envelope_epsilon : float, (default: 0.01)

  Terminate processing when obtaining pure FM signal.

• convergence_epsilon : float, (default: 0.01)

  Terminate processing when modulation signal converges.

• max_num_pf : int, (default: 8)

  The maximum number of PFs generated.

Return

• PFs : numpy array

  The decompose functions arrange is arranged from high frequency to low frequency.

• residue : numpy array

  residual component

Contact

Use GitHub Issues or the mailing list to post your comments or questions.

License

PyLMD is a free Open Source software released under the MIT license.

Lin, Q., 2020. Python Implementation Of Local Mean Decomposition Algorithm. (https://github.com/shownlin/PyLMD)