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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, and DRX readers written in C

lsl.reader._gofast.readDRSpec()

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

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.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.

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.

DRSU Direct Access Module

Module to provide the information necessary for direct access to files stored on a data recorder storage unit (DRSU).

Warning

This module is currently in an experimental phase.

lsl.reader._drsu.getDeviceChunkSize()

Function to return a long integer of the chunk size used on the specified DRSU device.

lsl.reader._drsu.getFileSize()

Function to return a long integer of the file size for a particular file on the specified DRSU device. An IOError is raised if the file cannot be found on the device.

lsl.reader._drsu.getFileStart()

Function to return a long integer of the file start position in bytes (corrected for the file start pattern) for a particular file on the specified DRSU device. An IOError is raised if the file cannot be found on the device.

lsl.reader._drsu.getFileTime()

Function to return a long integer of the file modification time for a particular file on the specified DRSU device. An IOError is raised if the file cannot be found on the device.

lsl.reader._drsu.getFileType()

Function to return a string of the file meta-data type (DEFAULT_DRX, etc.) for a particular file on the specified DRSU device. An IOError is raised if the file cannot be found on the device.

lsl.reader._drsu.listDevice()

Function to return a list of all files on a DRSU specified by device and return them as a list of filenames.

lsl.reader._drsu.listFiles()

Function to list all of the files on a DRSU specified by device and return them as a list of ‘File’ instances. These instances can then be used to provide direct access to the files stored on the DRSU.

The use of this function is preferred over listDevice/getFileSize/etc. because it stores all of the information returned in the other function in the File structure along with additional information. It also makes it ‘easier’ to open the specified file for reading.

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.
  • 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.
  • PPSDR2 - Polyphase filterband (PFB) and integration function for computing a series of overlapped PFB transforms for a real-valued (TBW) signal from a collection of stands all at once.
  • PPSDR3 - Similar to PPSDR2, but allows for a window function to be applied to the data.
  • PPSDC2 - PFB and integrate function for computing a series of overlapped PFB transforms for a complex-valued (TBN and DRX) signal from a collection of stands/beams all at once.
  • PPSDC3 - Similar to PPSDC2, but allows for a window function to be applied to the data.

See the inidividual functions for more details.

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
  • 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
  • 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.PPSDC2()

Perform a series of filter bank 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.PPSDC3()

Perform a series of filter bank 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
  • 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.PPSDR2()

Perform a series of filter bank 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.PPSDR3()

Perform a series of filter bank 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
  • 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.
  • 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.

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
  • 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
  • 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.
  • 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.
  • 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
  • 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
  • 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._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.

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