# Acoular 21.05 documentation

class acoular.fbeamform.PointSpreadFunction

Bases: traits.has_traits.HasPrivateTraits

This class provides tools to calculate the PSF depending on the used microphone geometry, focus grid, flow environment, etc. The PSF is needed by several deconvolution algorithms to correct the aberrations when using simple delay-and-sum beamforming.

steer = Property(desc="steering vector object")

SteeringVector or derived object. Defaults to SteeringVector object.

grid_indices = CArray( dtype=int, value=array([]), …

Indices of grid points to calculate the PSF for.

calcmode = Trait('single', 'block', 'full', 'readonly', …

Flag that defines how to calculate and store the point spread function defaults to ‘single’.

• ‘full’: Calculate the full PSF (for all grid points) in one go (should be used if the PSF at all grid points is needed, as with DAMAS)

• ‘single’: Calculate the PSF for the grid points defined by grid_indices, one by one (useful if not all PSFs are needed, as with CLEAN)

• ‘block’: Calculate the PSF for the grid points defined by grid_indices, in one go (useful if not all PSFs are needed, as with CLEAN)

• ‘readonly’: Do not attempt to calculate the PSF since it should already be cached (useful if multiple processes have to access the cache file)

precision = Trait('float64', 'float32', …

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

psf = Property(desc="point spread function")

freq = Float(1.0, desc="frequency")
calc_psf(ac, gp)