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C Extensions

Warning

The modules documented before are compiled C extensions and are subject to change. It is recommended that users use the standard modules detailed in the previous sections over directly using these modules.

Go-Fast! Readers

Go Fast! (TM) - TBW, TBN, DRX, DR Spectrometer, and VDIF readers written in C

lsl.reader._gofast.readCOR()

Function to read in a single COR frame (header+data) and store the contents as a Frame object.

Changed in version 1.2.1: Updated readCOR for the switch over to 72 channels, complex64 data, and no data weights

New in version 1.2.0.

lsl.reader._gofast.readDRSpec()

Function to read in a DR spectrometer header structure and data and return a drspec.Frame instance.

Note

This function normalizes the spectra by the number of relevant fills. For products that are a function of more than one primary input, i.e., XY* or I, the minimum fill of X and Y are used for normalization.

lsl.reader._gofast.readDRX()

Function to read in a single DRX frame (header+data) and store the contents as a Frame object. This function serves as a replacement for the pure python reader lsl.reader.drx.readFrame.

In order to use this reader in place of lsl.reader.drx.readFrame change:

>>> import lsl.reader.tbn as drx
>>> fh = open('some-drx-file.dat', 'rb')
>>> frame = drx.readFrame(fh)

to:

>>> import lsl.reader.drx as drx
>>> from lsl.reader._gofast import ReadDRX, syncError, eofError
>>> fh = open('some-drx-file.dat', 'rb')
>>> frame = readDRX(fh, tbn.Frame())

In addition, the exceptions checked for in the try...except blocks wrapping the frame reader need to be changed to ‘IOError’ since syncError and eofError are are sub-classes of IOError.

Changed in version 0.4.0: The Go Fast! readers are the default readers used by the lsl.reader.drx module.

lsl.reader._gofast.readTBF()

Function to read in a single TBW frame (header+data) and store the contents as a Frame object.

New in version 1.2.0.

lsl.reader._gofast.readTBN()

Function to read in a single TBN frame (header+data) and store the contents as a Frame object. This function serves as a replacement for the pure python reader lsl.reader.tbn.readFrame.

In order to use this reader in place of lsl.reader.tbn.readFrame change:

>>> import lsl.reader.tbn as tbn
>>> fh = open('some-tbn-file.dat', 'rb')
>>> frame = tbn.readFrame(fh)

to:

>>> import lsl.reader.tbn as tbn
>>> from lsl.reader._gofast import ReadTBN, syncError, eofError
>>> fh = open('some-tbn-file.dat', 'rb')
>> frame = readTBN(fh, tbn.Frame())

In addition, the exceptions checked for in the try...except blocks wrapping the frame reader need to be changed to ‘IOError’ since syncError and eofError are are sub-classes of IOError.

Changed in version 0.4.0: The Go Fast! readers are the default readers used by the lsl.reader.tbn module.

lsl.reader._gofast.readTBW()

Function to read in a single TBW frame (header+data) and store the contents as a Frame object. This function serves as a replacement for the pure python reader lsl.reader.tbw.readFrame.

In order to use this reader in place of lsl.reader.tbw.readFrame change:

>>> import lsl.reader.tbw as tbw
>>> fh = open('some-tbw-file.dat', 'rb')
>>> frame = tbw.readFrame(fh)

to:

>>> import lsl.reader.tbw as tbw
>>> from lsl.reader._gofast import ReadTBW, syncError, eofError
>>> fh = open('some-tbw-file.dat', 'rb')
>>> frame = readTBW(fh, tbw.Frame())

In addition, the exceptions checked for in the try...except blocks wrapping the frame reader need to be changed to ‘IOError’ since syncError and eofError are are sub-classes of IOError.

Changed in version 0.4.0: The Go Fast! readers are the default readers used by the lsl.reader.tbw module.

lsl.reader._gofast.readVDIF()

Function to read in a single VDIF frame (header+data) and store the contents as a Frame object. This function serves as a replacement for the pure python reader lsl.reader.vdif.readFrame.

In order to use this reader in place of lsl.reader.vdif.readFrame change:

>>> import lsl.reader.vdif as vdif
>>> fh = open('some-vdif-file.dat', 'rb')
>>> frame = vdif.readFrame(fh)

to:

>>> import lsl.reader.vdif as vdif
>>> from lsl.reader._vdif import readVDIF, syncError, eofError
>>> fh = open('some-vdif-file.dat', 'rb')
>> frame = readVDIF(fh, vdif.Frame())

In addition, the exceptions checked for in the try...except blocks wrapping the frame reader need to be changed to ‘IOError’ since syncError and eofError are are sub-classes of IOError.

exception lsl.reader._gofast.eofError

Exception raised when a reader encounters the end-of-file while reading.

exception lsl.reader._gofast.syncError

Exception raised when a reader encounters an error with one or more of the four sync. words.

Power Spectral Density Calculation

Linear Polarization

Extension to take timeseries data and convert it to the frequency domain.

The functions defined in this module are:
  • FPSDR2 - FFT and integrate function for computing a series of overlapped Fourier transforms for a real-valued (TBW) signal from a collection of stands all at once.
  • FPSDR3 - Similar to FPSDR2, but allows for a window function to be applied to the data.
  • PFBPSDR2 - Similar to FPSDR2, but using a 4-tap + Hanning windowed polyphase filter bank.
  • FPSDC2 - FFT and integrate function for computing a series of overlapped Fourier transforms for a complex-valued (TBN and DRX) signal from a collection of stands/beams all at once.
  • FPSDC3 - Similar to FPSDC2, but allows for a window function to be applied to the data.
  • PFBPSDC3 - Similar to FPSDC2, but using a 4-tap + Hanning windowed polyphase filter bank.

See the inidividual functions for more details.

Changed in version 1.0.1: Removed the polyphase filterbank versions of the four core functions.

lsl.correlator._spec.FPSDC2()

Perform a series of Fourier transforms on complex-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data
lsl.correlator._spec.FPSDC3()

Perform a series of Fourier transforms with windows on complex-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data
lsl.correlator._spec.FPSDR2()

Perform a series of Fourier transforms on real-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data
lsl.correlator._spec.FPSDR3()

Perform a series of Fourier transforms with windows on real-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data
lsl.correlator._spec.PFBPSDC2()

Perform a series of polyphase filter bank transforms (4-tap plus a Hanning window) on complex-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data
lsl.correlator._spec.PFBPSDR2()

Perform a series of polyphase filter bank transforms (4-tap plus a Hanning window) on real-valued data to get the PSD.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 2-D numpy.double (stands by channels) of PSD data

Stokes Parameters

Extension to take X and Y timeseries data and create the four Stokes parameters.

The functions defined in this module are:
  • FPSDR2 - FFT and integrate function for computing a series of overlapped Fourier transforms for a real-valued (TBW) signal from a collection of stands all at once.
  • FPSDR3 - Similar to FPSDR2, but allows for a window function to be applied to the data.
  • PFBPSDR2 - Similar to FPSDR2, but using a 4-tap + Hanning windowed polyphase filter bank.
  • FPSDC2 - FFT and integrate function for computing a series of overlapped Fourier transforms for a complex-valued (TBN and DRX) signal from a collection of stands/beams all at once.
  • FPSDC3 - Similar to FPSDC2, but allows for a window function to be applied to the data.
  • PFBPSDC3 - Similar to FPSDC2, but using a 4-tap + Hanning windowed polyphase filter bank.

Also included is an X-Engine for use with the lsl.correlator._core module to perform cross-correlations for the stokes parameters.

See the inidividual functions for more details.

lsl.correlator._stokes.FPSDR2()

Perform a series of Fourier transforms on real-valued data to get the PSD for the four Stokes parameters: I, Q, U, and V.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 3-D numpy.double (Stokes parameter (I,Q,U,V) by stands by channels) of PSD data
lsl.correlator._stokes.FPSDR3()

Perform a series of Fourier transforms with windows on real-valued data to get the PSD for the four Stokes parameters: I, Q, U, and V.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 3-D numpy.double (Stokes parameter (I,Q,U,V) by stands by channels) of PSD data
lsl.correlator._stokes.FPSDC2()

Perform a series of Fourier transforms on complex-valued data to get the PSD for the four Stokes parameters: I, Q, U, and V.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 3-D numpy.double (Stokes parameter (I,Q,U,V) by stands by channels) of PSD data
lsl.correlator._stokes.FPSDC3()

Perform a series of Fourier transforms with windows on complex-valued data to get the PSD for the four Stokes parameters: I, Q, U, and V.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • psd: 3-D numpy.double (Stokes parameter (I,Q,U,V) by stands by channels) of PSD data

FX Correlator Core

C-based F and X engines for the LWA software FX correlator. These function are meant to provide an alternative to the lsl.correlator.fx.correlate function and provide a much-needed speed boost to cross-correlation.

The function defined in this module are:
  • FEngineR2 -F-engine for computing a series of overlapped Fourier transforms with delay corrections for a real-valued (TBW) signal from a collection of stands all at once.
  • FEngineR3 - Similar to FEngineR2, but allows for a window function to be applied to the data.
  • PFBEngineR2 - Similar to FEngineR2, but using a 4-tap + Hanning windowed polyphase filter bank.
  • FEngineC2 - F-engine for computing a series of overlapped Fourier transforms with delay corrections for a complex-valued (TBN) signal from a collection of stands all at once.
  • FEngineC3 - Similar to FEngineC2, but allows for a window function to be applied to the data.
  • PFBEngineC2 - Similar to FEngineC2, but using a 4-tap + Hanning windowed polyphase filter bank.
  • XEngine2 - Similar to XEngine, but works with a collection of stands all at once.

See the inidividual functions for more details.

lsl.correlator._core.FEngineC2()

Perform a series of overlapped Fourier transforms on complex-valued data using OpenMP.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=100e3)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.FEngineC3()

Perform a series of overlapped Fourier transforms on complex-valued data using OpenMP and allow for windowing of the data.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=100e3)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.FEngineR2()

Perform a series of overlapped Fourier transforms on real-valued data using OpenMP.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=196e6)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.FEngineR3()

Perform a series of overlapped Fourier transforms on real-valued data using OpenMP and windows.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=196e6)
  • window: Callable Python function for generating the window or None for no window
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.PFBEngineC2()

Perform a series of overlapped polyphase filter bank transforms (4-tap plus a Hanning window) on complex-valued data using OpenMP.

Input arguments are:
  • signals: 2-D numpy.complex64 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=100e3)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.PFBEngineR2()

Perform a series of overlapped polyphase filter bank transforms (4-tap plus a Hanning window) on real-valued data using OpenMP.

Input arguments are:
  • signals: 2-D numpy.int16 (stands by samples) array of data to FFT
  • frequency: 1-D numpy.double array of frequency values in Hz for the FFT channels
  • delays: 1-D numpy.double array of delays to apply to each stand
Input keywords are:
  • LFFT: number of FFT channels to make (default=64)
  • Overlap: number of overlapped FFTs to use (default=1)
  • SampleRate: sample rate of the data (default=196e6)
  • ClipLevel: count value of ‘bad’ data. FFT windows with instantaneous powers greater than or equal to this value greater are zeroed. Setting the ClipLevel to zero disables time-domain blanking
Outputs:
  • fsignals: 3-D numpy.complex64 (stands by channels by FFT_set) of FFTd data
  • valid: 2-D numpy.uint8 (stands by FFT_set) of whether or not the FFT set is valid (1) or not (0)
lsl.correlator._core.XEngine2()

Perform all XMACs for a data stream out of the F engine using OpenMP.

Changed in version 0.5: The second signal is not longer input as a conjugated array. Rather the conjucation is performed as part of the cross-correlation.

Input arguments are:
  • fsignals1: 3-D numpy.complex64 (stand by channels by FFT_set) array of FFTd data from an F engine.
  • fsignals2: 3-D numpy.complex64 (stand by channels by FFT_set) array of FFTd data from an F engine.
  • sigValid1: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the first signal.
  • sigValid2: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the second signal.
Ouputs:
  • visibility: 2-D numpy.complex64 (baseline by channel) array of cross- correlated and averaged visibility data.
lsl.correlator._core.XEngine3()

Perform all XMACs for a data stream out of the F engine using OpenMP that creates the four linear polarization products

New in version 1.1.2.

Input arguments are:
  • fsignals1: 3-D numpy.cdouble (stand by channels by FFT_set) array of FFTd data from an F engine.
  • fsignals2: 3-D numpy.cdouble (stand by channels by FFT_set) array of FFTd data from an F engine.
  • sigValid1: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the first signal.
  • sigValid2: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the second signal.
Ouputs:
  • visibility: 3-D numpy.cdouble (Stokes parameter (XX,XY,YX,YY) by baseline by

channel) array of cross-correlated and averaged visibility data.

lsl.correlator._stokes.XEngine2()

Perform all XMACs for a data stream out of the F engine using OpenMP that creates the four Stokes parameters: I, Q, U, and V.

Input arguments are:
  • fsignals1: 3-D numpy.cdouble (stand by channels by FFT_set) array of FFTd data from an F engine.
  • fsignals2: 3-D numpy.cdouble (stand by channels by FFT_set) array of FFTd data from an F engine.
  • sigValid1: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the first signal.
  • sigValid2: 1-D numpy.uint8 (FFT_set) array of whether or not the FFT_set is valid (1) or not (0) for the second signal.
Ouputs:
  • visibility: 3-D numpy.cdouble (Stokes parameter (I,Q,U,V by baseline by

channel) array of cross-correlated and averaged visibility data.

DP-Style Signal Processing

This module contains a collection of function to speed up FIR filtering of TBW data (represented as numpy.int16 arrays) and the SoftwareDP class. The funtions provided in this module are:

  • integer16: Apply a FIR filter to numpy.int16 data,
  • integer16Delayed: Apply a FIFO delay and a FIR filter to numpy.int16 data, and
  • integerBeamformer: Software implementation of the DP beamformer.
lsl.common._fir.integer16()

Given a 1-D numpy.int16 array of data values and a numpy.int16 array of FIR coefficients, apply the coefficients to the data.

Inputs arguments are:
  • data: 1-D numpy.int16 array of data
  • coeffs: 1-D numpy.int16 array of FIR coefficients
Outputs:
  • result: 1-D numpy.float32 array of the filtered data
lsl.common._fir.integer16Delayed()

Given a 1-D numpy.int16 array of data values, a numpy.int16 array of FIR coefficients, and a FIFO sample delay, delay the signal and apply the coefficients to the data.

Inputs arguments are:
  • data: 1-D numpy.int16 array of data
  • coeffs: 1-D numpy.int16 array of FIR coefficients
  • sampleDelay: interger number of samples to delay the signal (must be >=0)
Outputs:
  • result: 1-D numpy.float32 array of the delayed and filtered data
lsl.common._fir.integerBeamformer()

Given a 2-D numpy.int16 array (stands by samples) of data values, 3-D array of FIR filter coefficients (stands by filters by taps), a 1-D numpy.int16 array of course (FIFO) delays, a 1-D numpy.int16 array of fine delay FIR filters, and a 2-D array of gains (stands by [XX, XY, YX, YY]), apply the delays and sum the signals.

Inputs arguments are:

  • data: 2-D numpy.int16 array of data (stands by samples)

  • coeffs: 3-D numpy.int16 array of FIR coefficients (stands by filters by taps)

  • course: 1-D numpy.int16 array of FIFO delays in samples

  • fine: 1-D numpy.int16 array of which FIR filter to apply for fine delay

  • gain: 2-D numpy.int16 arry of gains (stands by [XX, XY, YX, YY]), where XX is the X

    contribution to the output X pol., XY is the X contribution to the output Y pol., YX is the Y contribtion to the output X pol., and YY is the Y contribu- tion to the output Y pol.

Outputs:
  • results: two element tuple (output X, outpuY) of the beamformer sum. Each element is

    a 1-D numpy.float32 array.

Note

The structure of data is assumed to be that the polarizations are ordered, e.g., the X polarization of stand 1 is immediately followed by the Y polarization.

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