`tbeamform`#

Implements beamformers in the time domain.

`BeamformerTime`	Provides a basic time domain beamformer with time signal output.
`BeamformerTimeTraj`	Provides a basic time domain beamformer with time signal output.
`BeamformerTimeSq`	Time domain beamformer with squared output and optional autopower removal.
`BeamformerTimeSqTraj`	Time domain beamformer with squared output for a grid moving along a trajectory.
`BeamformerCleant`	CLEANT deconvolution method.
`BeamformerCleantTraj`	CLEANT deconvolution method.
`BeamformerCleantSq`	CLEANT deconvolution method with optional removal of autocorrelation.
`BeamformerCleantSqTraj`	CLEANT deconvolution method with optional removal of autocorrelation.
`IntegratorSectorTime`	Provides an Integrator in the time domain.

acoular.tbeamform.const_power_weight(bf)#

Internal helper function for BeamformerTime.

Provides microphone weighting to make the power per unit area of the microphone array geometry constant.

Parameters:

bf: :class:`BeamformerTime` object

Returns:

array of floats: The weight factors.

class acoular.tbeamform.BeamformerTime#

Bases: TimeOut

Provides a basic time domain beamformer with time signal output.

for a spatially fixed grid.

source = Instance(SamplesGenerator)#: Data source; SamplesGenerator or derived object.

num_channels = Property()#: Number of channels in output (=number of grid points).

weights = Map(possible_weights, default_value='none', desc='spatial weighting function')#: Spatial weighting function.

result(num=2048)#

Python generator that yields the time-domain beamformer output.

The output time signal starts for source signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerTimeSq#

Bases: BeamformerTime

Time domain beamformer with squared output and optional autopower removal.

Provides a time domain beamformer with time-dependend power signal output and possible autopower removal for a spatially fixed grid.

r_diag = Bool(True, desc='removal of diagonal')#: Boolean flag, if ‘True’ (default), the main diagonal is removed before beamforming.

result(num=2048)#

Python generator that yields the squared time-domain beamformer output.

The squared output time signal starts for source signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerTimeTraj#

Bases: BeamformerTime

Provides a basic time domain beamformer with time signal output.

for a grid moving along a trajectory.

trajectory = Instance(Trajectory, desc='trajectory of the grid center')#: Trajectory or derived object. Start time is assumed to be the same as for the samples.

rvec = CArray(dtype=float, shape=(3,), value=np.array((0, 0, 0)), desc='reference vector')#: Reference vector, perpendicular to the y-axis of moving grid.

conv_amp = Bool(False, desc='determines if convective amplification of source is considered')#: Considering of convective amplification in beamforming formula.

precision = Enum(64, 32, desc='numeric precision')#: Floating point and integer precision

get_r0(tpos)#

Get reference distance for grid positions.

Parameters:

tposnumpy.ndarray: Grid positions.

Returns:

float or numpy.ndarray: Reference distance(s).

result(num=2048)#

Python generator that yields the time-domain beamformer output.

The output time signal starts for source signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerTimeSqTraj#

Bases: BeamformerTimeSq, BeamformerTimeTraj

Time domain beamformer with squared output for a grid moving along a trajectory.

Provides a time domain beamformer with time-dependent power signal output and possible autopower removal for a grid moving along a trajectory.

result(num=2048)#

Python generator that yields the squared time-domain beamformer output.

The squared output time signal starts for source signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerCleant#

Bases: BeamformerTime

CLEANT deconvolution method.

An implementation of the CLEAN method in time domain. This class can only be used for static sources. See [19] for details.

r_diag = Enum(False, desc='False, as we do not remove autopower in this beamformer')#: Boolean flag, always False

damp = Range(0.01, 1.0, 0.6, desc='damping factor (loop gain)')#: iteration damping factor also referred as loop gain in Cousson et al. defaults to 0.6

n_iter = Int(100, desc='maximum number of iterations')#: max number of iterations

result(num=2048)#

Python generator that yields the deconvolved time-domain beamformer output.

The output starts for signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerCleantSq#

Bases: BeamformerCleant

CLEANT deconvolution method with optional removal of autocorrelation.

An implementation of the CLEAN method in time domain. This class can only be used for static sources. See [19] for details on the method and [20] for details on the autocorrelation removal.

r_diag = Bool(True, desc='removal of diagonal')#: Boolean flag, if ‘True’ (default), the main diagonal is removed before beamforming.

result(num=2048)#

Python generator that yields the squared deconvolved time-domain beamformer output.

The output starts for signals that were emitted from the Grid at t=0. Per default, block-wise removal of autocorrelation is performed, which can be turned of by setting r_diag to False.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerCleantTraj#

Bases: BeamformerCleant, BeamformerTimeTraj

CLEANT deconvolution method.

An implementation of the CLEAN method in time domain for moving sources with known trajectory. See [19] for details.

precision = Enum(32, 64, desc='numeric precision')#: Floating point and integer precision

result(num=2048)#

Python generator that yields the deconvolved time-domain beamformer output.

The output starts for signals that were emitted from the Grid at t=0.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.BeamformerCleantSqTraj#

Bases: BeamformerCleantTraj, BeamformerTimeSq

CLEANT deconvolution method with optional removal of autocorrelation.

An implementation of the CLEAN method in time domain for moving sources with known trajectory. See [19] for details on the method and [20] for details on the autocorrelation removal.

r_diag = Bool(True, desc='removal of diagonal')#: Boolean flag, if ‘True’ (default), the main diagonal is removed before beamforming.

result(num=2048)#

Python generator that yields the squared deconvolved time-domain beamformer output.

The output starts for signals that were emitted from the Grid at t=0. Per default, block-wise removal of autocorrelation is performed, which can be turned of by setting r_diag to False.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block). Defaults to 2048.

Yields:

numpy.ndarray

Samples in blocks of shape (num, num_channels).: num_channels is usually very large (number of grid points). The last block returned by the generator may be shorter than num.

class acoular.tbeamform.IntegratorSectorTime#

Bases: TimeOut

Provides an Integrator in the time domain.

source = Instance(SamplesGenerator)#: Data source; SamplesGenerator or derived object.

grid = Instance(RectGrid, desc='beamforming grid')#: RectGrid object that provides the grid locations.

sectors = List()#: List of sectors in grid

clip = Float(-350.0)#: Clipping, in Decibel relative to maximum (negative values)

num_channels = Property(depends_on=['sectors'])#: Number of channels in output (= number of sectors).

result(num=1)#

Python generator that yields the source output integrated over specified grid sectors.

Parameters:

numint: Size of the blocks to be yielded (number of samples per block). Default is 1.

Returns:

numpy.ndarray: Samples in blocks of shape (num, num_channels).
num_channels is the number of sectors.
The last block may be shorter than num.

tbeamform#

`tbeamform`#