pyOMA.core.PRCE.PRCE#
- class pyOMA.core.PRCE.PRCE(*args, **kwargs)[source]#
Bases:
ModalBaseMethods
__init__(*args, **kwargs)channel definition: channels start at 0
build_corr_tensor(num_corr_samples)Builds a 3D Tensor of cross correlation functions with the following directions: 1 - related to reference channels 2 - all channels 3 - time
compute_modal_params(max_model_order)init_from_config(mod_ID_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
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_corr_tensor(num_corr_samples)[source]#
Builds a 3D Tensor of cross correlation functions with the following directions: 1 - related to reference channels 2 - all channels 3 - time
- classmethod init_from_config(mod_ID_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