`BeamformerGridlessOrth`#

class acoular.fbeamform.BeamformerGridlessOrth

Bases: BeamformerAdaptiveGrid

Orthogonal beamforming without predefined grid.

See [15] for details.

eva_list = CArray(dtype=int, value=np.array([-1])): List of components to consider, use this to directly set the eigenvalues used in the beamformer. Alternatively, set n.

n = Int(1): Number of components to consider, defaults to 1. If set, eva_list will contain the indices of the n largest eigenvalues. Setting eva_list afterwards will override this value.

bounds = List(Tuple(Float, Float), minlen=3, maxlen=3, value=[(-1.0, 1.0), (-1.0, 1.0), (0.01, 1.0)]): Geometrical bounds of the search domain to consider. bound is a list that contains exactly three tuple of (min,max) for each of the coordinates x, y, z. Defaults to [(-1.,1.),(-1.,1.),(0.01,1.)]

shgo = Dict: options dictionary for the SHGO solver, see scipy docs. Default is Sobol sampling Nelder-Mead local minimizer, 256 initial sampling points and 1 iteration

r_diag_norm = Enum(1.0): If diagonal of the csm is removed, some signal energy is lost. This is handled via this normalization factor. For this class, normalization is not implemented. Defaults to 1.0.

digest = Property( …: A unique identifier for the beamformer, based on its properties. (read-only)