pyOMA.core.VarSSIRef.VarSSIRef#

class pyOMA.core.VarSSIRef.VarSSIRef(prep_signals)[source]#

Bases: ModalBase

__init__(prep_signals)[source]#

channel definition: channels start at 0

Methods

__init__(prep_signals)

channel definition: channels start at 0

build_subspace_mat(num_block_columns[, ...])

Builds a Block-Hankel Matrix of Covariances with varying time lags

compute_modal_params([max_model_order, ...])

compute_state_matrices([max_model_order, ...])

computes the state and output matrix of the state-space-model by applying a singular value decomposition to the block-hankel-matrix of covariances the state space model matrices are obtained by appropriate truncation of the svd matrices at max_model_order the decision whether to take merged covariances is taken automatically

init_from_config(conf_file, prep_signals)

A method for initializing a modal object from configuration data bypassing common operations in explicit code for semi-automated analyses

integrate_quantities(vector, accel_channels, ...)

Rescales mode shapes from modal accelerations / velocities to modal displacements, by multiplication of the relevant modal coordinates (where accelerometers, or velocimeters were used, with $-1 omega^2$ or $i omega$, respectively,

load_state(fname, prep_signals)

Loads the state of the object from a compressed numpy archive file and returns the object This is only a stub for reimplementing the method in a derived class

plot_covariances()

prepare_sensitivities([variance_algo, debug])

remove_conjugates(eigval[, eigvec_r, ...])

This method finds complex conjugate modes, and removes unstable and overdamped poles.

rescale_mode_shape(modeshape[, rotate_only])

Rescales and rotates modeshapes in the complex plane.

save_state(fname)

Saves the state of the object to a compressed numpy archive file This is only a stub for reimplementing the method in a derived class
build_subspace_mat(num_block_columns, num_block_rows=None, num_blocks=None, subspace_method='covariance')[source]#

Builds a Block-Hankel Matrix of Covariances with varying time lags

R_1 R_2 … R_q |
R_2 R_3 … R_q+1 |
… … … … |
R_p+1 … … R_p+q |
compute_state_matrices(max_model_order=None, lsq_method='pinv')[source]#

computes the state and output matrix of the state-space-model by applying a singular value decomposition to the block-hankel-matrix of covariances the state space model matrices are obtained by appropriate truncation of the svd matrices at max_model_order the decision whether to take merged covariances is taken automatically

classmethod init_from_config(conf_file, prep_signals)[source]#

A method for initializing a modal object from configuration data bypassing common operations in explicit code for semi-automated analyses

This is a stub of the method that must be reimplemented by every derived class

classmethod load_state(fname, prep_signals)[source]#

Loads the state of the object from a compressed numpy archive file and returns the object This is only a stub for reimplementing the method in a derived class

save_state(fname)[source]#

Saves the state of the object to a compressed numpy archive file This is only a stub for reimplementing the method in a derived class