PyLMD 1.0.3

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

requires:

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