gstlal 1.13.0
Loading...
Searching...
No Matches
python.kernels.PSDFirKernel Class Reference
Inheritance diagram for python.kernels.PSDFirKernel:
Collaboration diagram for python.kernels.PSDFirKernel:

Public Member Functions

 __init__ (self)
None set_phase (self, lal.REAL8FrequencySeries psd, float f_low=10.0, float m1=1.4, float m2=1.4)
Tuple[numpy.ndarray, int, int] psd_to_linear_phase_whitening_fir_kernel (self, lal.REAL8FrequencySeries psd, Optional[bool] invert=True, Optional[float] nyquist=None)
Tuple[numpy.ndarray, numpy.ndarray] linear_phase_fir_kernel_to_minimum_phase_whitening_fir_kernel (self, numpy.ndarray linear_phase_kernel, int sample_rate)

Public Attributes

 revplan = None
 fwdplan = None
 target_phase = None
 target_phase_mask = None

Detailed Description

Definition at line 31 of file kernels.py.

Constructor & Destructor Documentation

◆ __init__()

python.kernels.PSDFirKernel.__init__ ( self)

Definition at line 32 of file kernels.py.

Member Function Documentation

◆ linear_phase_fir_kernel_to_minimum_phase_whitening_fir_kernel()

Tuple[numpy.ndarray, numpy.ndarray] python.kernels.PSDFirKernel.linear_phase_fir_kernel_to_minimum_phase_whitening_fir_kernel ( self,
numpy.ndarray linear_phase_kernel,
int sample_rate )
Compute the minimum-phase response filter (zero latency)
associated with a linear-phase response filter (latency
equal to half the filter length).

From "Design of Optimal Minimum-Phase Digital FIR Filters
Using Discrete Hilbert Transforms", IEEE Trans. Signal
Processing, vol. 48, pp. 1491-1495, May 2000.

Args:
    linear_phase_kernel:
        numpy.ndarray, the kernel to compute the minimum-phase kernel with
    sample_rate:
        int, the sample rate

Returns:
    Tuple[numpy.ndarray. numpy.ndarray], the kernel and the phase response.
    The kernel is a numpy array containing the filter kernel. The kernel
    can be used, for example, with gstreamer's stock audiofirfilter element.

Definition at line 211 of file kernels.py.

◆ psd_to_linear_phase_whitening_fir_kernel()

Tuple[numpy.ndarray, int, int] python.kernels.PSDFirKernel.psd_to_linear_phase_whitening_fir_kernel ( self,
lal.REAL8FrequencySeries psd,
Optional[bool] invert = True,
Optional[float] nyquist = None )
Compute an acausal finite impulse-response filter kernel
from a power spectral density conforming to the LAL
normalization convention, such that if colored Gaussian
random noise with the given PSD is fed into an FIR filter
using the kernel the filter's output will be zero-mean
unit-variance Gaussian random noise.  The PSD must be
provided as a lal.REAL8FrequencySeries object.

The phase response of this filter is 0, just like whitening
done in the frequency domain.

Args:
    psd:
        lal.REAL8FrequencySeries, the reference PSD
    invert:
        bool, default true, whether to invert the kernel
    nyquist:
        float, disabled by default, whether to change
        the Nyquist frequency.

Returns:
    Tuple[numpy.ndarray, int, int], the kernel, latency,
    sample rate pair.  The kernel is a numpy array containing
    the filter kernel, the latency is the filter latency in
    samples and the sample rate is in Hz.  The kernel and
    latency can be used, for example, with gstreamer's stock
    audiofirfilter element.

Definition at line 81 of file kernels.py.

◆ set_phase()

None python.kernels.PSDFirKernel.set_phase ( self,
lal.REAL8FrequencySeries psd,
float f_low = 10.0,
float m1 = 1.4,
float m2 = 1.4 )
Compute the phase response of zero-latency whitening filter
given a reference PSD.

Definition at line 38 of file kernels.py.

Member Data Documentation

◆ fwdplan

python.kernels.PSDFirKernel.fwdplan = None

Definition at line 34 of file kernels.py.

◆ revplan

python.kernels.PSDFirKernel.revplan = None

Definition at line 33 of file kernels.py.

◆ target_phase

python.kernels.PSDFirKernel.target_phase = None

Definition at line 35 of file kernels.py.

◆ target_phase_mask

python.kernels.PSDFirKernel.target_phase_mask = None

Definition at line 36 of file kernels.py.


The documentation for this class was generated from the following file: