The mpe_params module

These classes are were the parameters used to extract the modal parameters are stored.

Classes:

BaseMPEParams

Base class for storing mpe parameters for modal analysis algorithms.

FDDMPEParams

Class for storing Frequency Domain Decomposition (FDD) MPE parameters.

EFDDMPEParams

Class for storing Enhanced Frequency Domain Decomposition (EFDD) MPE parameters.

SSIMPEParams

Class for storing Stochastic Subspace Identification (SSI) MPE parameters.

pLSCFMPEParams

Class for storing poly-reference Least Square Complex Frequency (pLSCF) MPE parameters.

Warning

The module is designed to be used as part of the pyOMA2 package and relies on its internal data structures and algorithms.

This module provides classes for storing mpe parameters for various modal analysis algorithms included in the pyOMA2 module.

class pyoma2.algorithms.data.mpe_params.BaseMPEParams

Bases: BaseModel

Base class for storing mpe parameters for modal analysis algorithms.

class pyoma2.algorithms.data.mpe_params.EFDDMPEParams(*, sel_freq: ndarray[tuple[Any, ...], dtype[float64]] | None = None, DF1: float = 0.1, DF2: float = 1.0, cm: int = 1, MAClim: float = 0.95, sppk: int = 3, npmax: int = 20)

Bases: BaseMPEParams

Class for storing Enhanced Frequency Domain Decomposition (EFDD) MPE parameters.

sel_freq

Array of selected frequencies for modal parameter estimation,.

Type:

numpy.ndarray

DF1

Frequency resolution for estimation, default is 0.1.

Type:

float, optional

DF2

Frequency resolution for the second stage of EFDD, default is 1.0.

Type:

float

cm

Number of closely spaced modes, default is 1.

Type:

int

MAClim

Minimum acceptable Modal Assurance Criterion value, default is 0.85.

Type:

float

sppk

Number of peaks to skip for the fit, default is 3.

Type:

int

npmax

Maximum number of peaks to use in the fit, default is 20.

Type:

int

class pyoma2.algorithms.data.mpe_params.FDDMPEParams(*, sel_freq: ndarray[tuple[Any, ...], dtype[float64]] | None = None, DF: float = 0.1)

Bases: BaseMPEParams

Class for storing Frequency Domain Decomposition (FDD) MPE parameters.

sel_freq

Array of selected frequencies for modal parameter estimation,.

Type:

numpy.ndarray

DF

Frequency resolution for estimation, default is 0.1.

Type:

float, optional

class pyoma2.algorithms.data.mpe_params.SSIMPEParams(*, sel_freq: List[float] | None = None, order_in: int | List[int] | str = 'find_min', rtol: float = 0.05)

Bases: BaseMPEParams

Class for storing Stochastic Subspace Identification (SSI) MPE parameters.

sel_freq

List of selected frequencies for modal parameter extraction. Default is None.

Type:

list of float or None, optional

order_in

Specified model order(s) for which the modal parameters are to be extracted. If ‘find_min’, the function attempts to find the minimum model order that provides stable poles for each mode of interest.

Type:

int, list of int, or str

rtol

Relative tolerance for comparing identified frequencies with the selected ones. Default is 5e-2.

Type:

float, optional

class pyoma2.algorithms.data.mpe_params.pLSCFMPEParams(*, sel_freq: List[float] | None = None, order_in: int | List[int] | str = 'find_min', rtol: float = 0.05)

Bases: BaseMPEParams

Class for storing poly-reference Least Square Complex Frequency (pLSCF) MPE parameters.

sel_freq

List of selected frequencies for modal parameter extraction. Default is None.

Type:

list of float or None, optional

order_in

Specified model order for extraction. Can be an integer or ‘find_min’. Default is ‘find_min’.

Type:

int or str, optional

rtol

Relative tolerance for comparing identified frequencies with the selected ones. Default is 1e-2.

Type:

float, optional