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
Project description
PyLMD
Method of decomposing signal into Product Functions
This project implements the paper:
How to install?
pip install PyLMD
requires:
- numpy
- 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
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