pysdkit.faemd#

Created on 2025/02/01 22:30:27 @author: Whenxuan Wang @email: wwhenxuan@gmail.com

`pysdkit._faemd.faemd`	Created on 2025/02/01 22:30:40 @author: Whenxuan Wang @email: wwhenxuan@gmail.com
`pysdkit._faemd.faemd2d`	Created on 2025/02/01 22:33:51 @author: Whenxuan Wang @email: wwhenxuan@gmail.com
`pysdkit._faemd.faemd3d`	Created on 2025/02/01 22:33:42 @author: Whenxuan Wang @email: wwhenxuan@gmail.com

faemd.faemd#

Created on 2025/02/01 22:30:40 @author: Whenxuan Wang @email: wwhenxuan@gmail.com

class pysdkit._faemd.faemd.FAEMD(max_imfs: int | None, tol: float | None = None, window_type: int | None = 0)[source]#

Bases: object

Fast and Adaptive Empirical Mode Decomposition

Thirumalaisamy, Mruthun R., and Phillip J. Ansell. “Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals.” IEEE Signal Processing Letters, vol. 25, no. 10, Institute of Electrical and Electronics Engineers (IEEE), Oct. 2018, pp. 1550-54, doi:10.1109/lsp.2018.2867335.

MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/71270-fast-and-adaptive-multivariate-and-multidimensional-emd

also see: EMD, EEMD, REMD and CEEMDAN.

OSF(H: ndarray, w_sz: float | ndarray) → Tuple[ndarray, ndarray][source]#: Used to generate upper and lower envelope spectra of a signal

__call__(signal: ndarray, return_all: bool = False, max_imfs: int | None = None) → Tuple[ndarray, ndarray, ndarray, ndarray] | ndarray[source]#: allow instances to be called like functions

__init__(max_imfs: int | None, tol: float | None = None, window_type: int | None = 0) → None[source]#

Compared to the EMD algorithm, FAEMD3D requires simpler parameters to be specified and is faster

:param max_imfs:The number of IMFs to be extracted :param tol: The threshold for loop stopping in an iterative decomposition :param window_type: Sliding window type using smoothing algorithm

__module__ = 'pysdkit._faemd.faemd'#

__str__() → str[source]#: Get the full name and abbreviation of the algorithm

__weakref__#: list of weak references to the object (if defined)

_get_tol(signal: ndarray) → float[source]#: Get the tolerance parameter for FAEMD3D

filter_size1D(imax: ndarray, imin: ndarray)[source]#: To determine the window size for order statistics filtering of a signal. The determination of the window size is based on the work of Bhuiyan et al

fit_transform(signal: ndarray, return_all: bool = False, max_imfs: int | None = None) → Tuple[ndarray, ndarray, ndarray, ndarray] | ndarray[source]#

Execute the signal decomposition algorithm

Parameters:

signal – The input 1D NumPy signal
return_all – whether to return all results or just the IMFs
max_imfs – The number of IMFs to be extracted

Returns:

The IMFs of input signal

get_imfs_and_residue() → Tuple[ndarray, ndarray][source]#

Provides access to separated imfs and residue from recently analysed signal

Returns:: obtained IMFs and residue through EMD

get_imfs_and_trend() → Tuple[ndarray, ndarray][source]#

Provides access to separated imfs and trend from recently analysed signal.

Note that this may differ from the get_imfs_and_residue as the trend isn’t necessarily the residue. Residue is a point-wise difference between input signal and all obtained components, whereas trend is the slowest component (can be zero).

Returns:: obtained IMFs and main trend through EMD

static ord_filt1(signal, order, window_size) → ndarray[source]#: 1-D Rank order filter function

sift(H: ndarray, w_sz: float | ndarray) → ndarray[source]#: Perform an iteration of the EMD algorithm

faemd.faemd2d#

Created on 2025/02/01 22:33:51 @author: Whenxuan Wang @email: wwhenxuan@gmail.com

class pysdkit._faemd.faemd2d.FAEMD2D[source]#

Bases: object

Multidimensional Fast and Adaptive Empirical Mode Decomposition

Thirumalaisamy, Mruthun R., and Phillip J. Ansell. “Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals.” IEEE Signal Processing Letters, vol. 25, no. 10, Institute of Electrical and Electronics Engineers (IEEE), Oct. 2018, pp. 1550–54, doi:10.1109/lsp.2018.2867335.

MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/71270-fast-and-adaptive-multivariate-and-multidimensional-emd

__call__(*args, **kwargs)[source]#: Call self as a function.

__init__()[source]#

__module__ = 'pysdkit._faemd.faemd2d'#

__str__() → str[source]#: Get the full name and abbreviation of the algorithm

__weakref__#: list of weak references to the object (if defined)

fit_transform()[source]#

faemd.faemd3d#

Created on 2025/02/01 22:33:42 @author: Whenxuan Wang @email: wwhenxuan@gmail.com

class pysdkit._faemd.faemd3d.FAEMD3D[source]#

Bases: object

Multivariate Fast and Adaptive Empirical Mode Decomposition

Thirumalaisamy, Mruthun R., and Phillip J. Ansell. “Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals.” IEEE Signal Processing Letters, vol. 25, no. 10, Institute of Electrical and Electronics Engineers (IEEE), Oct. 2018, pp. 1550–54, doi:10.1109/lsp.2018.2867335. MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/71270-fast-and-adaptive-multivariate-and-multidimensional-emd

__call__(*args, **kwargs)[source]#: Call self as a function.

__init__()[source]#

__module__ = 'pysdkit._faemd.faemd3d'#

__str__() → str[source]#: Get the full name and abbreviation of the algorithm

__weakref__#: list of weak references to the object (if defined)

fit_transform()[source]#