`BeamformerGridlessOrth`#

class acoular.fbeamform.BeamformerGridlessOrth

Bases: BeamformerAdaptiveGrid

Orthogonal beamforming without predefined grid.

See [15] for details.

eva_list: List of components to consider, use this to directly set the eigenvalues used in the beamformer. Alternatively, set n.

n: 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: 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: 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: 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: A unique identifier for the beamformer, based on its properties. (read-only)

export_gpos(filename)

Export the grid positions to an XML file.

This method generates an XML file containing the positions of all grid points. Each point is represented by a <pos> element with Name, x, y, and z attributes. The generated XML is formatted to match the structure required for importing into the ImportGrid class.

Parameters:

filenamestr: The path to the file to which the grid positions will be written. The file extension must be .xml.

Raises:

OSError: If the file cannot be written due to permissions issues or invalid file paths.

Notes

The file will be saved in UTF-8 encoding.
The Name attribute for each point is set as "Point {i+1}", where i is the index of the grid point.
If subgrids are defined, they will be included as the subgrid attribute.

Examples

Export a grid with 100 points to an XML file:

>>> import acoular as ac
>>> import numpy as np
>>> grid = ac.ImportGrid()
>>> # Create some grid points
>>> points = np.arange(9).reshape(3, 3)
>>> grid.pos = points
>>> grid.export_gpos('grid_points.xml')

The generated grid_points.xml file will look like this:

<?xml version="1.1" encoding="utf-8"?><Grid name="grid_points">
  <pos Name="Point 1" x="0" y="1" z="2"/>
  <pos Name="Point 2" x="3" y="4" z="5"/>
  <pos Name="Point 3" x="6" y="7" z="8"/>
</Grid>

integrate(sector)

Integrates result map over a given sector.

Parameters:

sector: :class:`~acoular.grids.Sector` or derived: Gives the sector over which to integrate

Returns:

array of floats: The spectrum (all calculated frequency bands) for the integrated sector.

sig_loss_norm()

If the diagonal of the CSM is removed one has to handle the loss of signal energy.

Done via a normalization factor.

subdomain(sector)

Return the indices for a subdomain in the grid.

Allows arbitrary subdomains of type Sector.

Parameters:

sectorSector object: Sector describing the subdomain.

Returns:

tuple: A 2-tuple of arrays of integers or numpy.s_ objects that can be used to mask or select the specified subdomain from a grid-shaped array.

Notes

The numpy.where() method is used to determine the the indices.

synthetic(f, num=0)

Evaluates the beamforming result for an arbitrary frequency band.

Parameters:

f: float

Band center frequency.

numinteger

Controls the width of the frequency bands considered; defaults to 0 (single frequency line).

num	frequency band width
0	single frequency line
1	octave band
3	third-octave band
n	1/n-octave band

Returns:

array of floats: The synthesized frequency band values of the beamforming result at each grid point . Note that the frequency resolution and therefore the bandwidth represented by a single frequency line depends on the sampling frequency and used FFT block size.

steer: Instance of SteeringVector or its derived classes that contains information about the steering vector. This is a private trait. Do not set this directly, use steer trait instead.

freq_data: PowerSpectra object that provides the cross spectral matrix and eigenvalues

r_diag: Boolean flag, if ‘True’ (default), the main diagonal is removed before beamforming.

precision: Floating point precision of property result. Corresponding to numpy dtypes. Default = 64 Bit.

cached: Boolean flag, if ‘True’ (default), the result is cached in h5 files.

result: The beamforming result as squared sound pressure values at all grid point locations (readonly). Returns a (number of frequencies, number of gridpoints) array-like of floats. Values can only be accessed via the index operator [].

size: The total number of grid points. This property is automatically calculated based on other defining attributes of the grid. (read-only)

shape: The shape of the grid, represented as a tuple. Primarily useful for Cartesian grids. (read-only)

pos: The grid positions represented as a (3, size) array of floats. (read-only) All positions’ coordinates are in meters by default (see here).

BeamformerGridlessOrth#

`BeamformerGridlessOrth`#