Acoular 24.07 documentation


«  BeamformerCleansc   ::   fbeamform   ::   BeamformerSODIX  »


class acoular.fbeamform.BeamformerCMF

Bases: BeamformerBase

Covariance Matrix Fitting algorithm.

This is not really a beamformer, but an inverse method. See [12] for details.

method = Trait(

Type of fit method to be used (‘LassoLars’, ‘LassoLarsBIC’, ‘OMPCV’ or ‘NNLS’, defaults to ‘LassoLars’). These methods are implemented in the scikit-learn module.

alpha = Range(0.0, 1.0, 0.0, desc='Lasso weight factor')

Weight factor for LassoLars method, defaults to 0.0. (Use values in the order of 10^⁻9 for good results.)

max_iter = Int(500, desc='maximum number of iterations')

Maximum number of iterations, tradeoff between speed and precision; defaults to 500

unit_mult = Float(1e9, desc='unit multiplier')

Unit multiplier for evaluating, e.g., nPa instead of Pa. Values are converted back before returning. Temporary conversion may be necessary to not reach machine epsilon within fitting method algorithms. Defaults to 1e9.

show = Bool(False, desc='show output of PyLops solvers')

If True, shows the status of the PyLops solver. Only relevant in case of FISTA or Split_Bregman

r_diag_norm = Enum(

Energy normalization in case of diagonal removal not implemented for inverse methods.

«  BeamformerCleansc   ::   fbeamform   ::   BeamformerSODIX  »