sigpyproc.block

Block classes for handling 2D data blocks.

This module contains the base class for handling 2D data blocks and the FilterbankBlock and DMTBlock classes for handling filterbank and DM-time transform data blocks, respectively.

Classes

BaseBlock	Base class for handling data blocks.
DMTBlock	A class to handle a DM-time transform block in time-major order.
FilterbankBlock	A class to handle a block of filterbank data in time-major order.

class sigpyproc.block.BaseBlock(data, header)

Bases: ABC

Base class for handling data blocks.

Parameters:

datandarray

2-D array of shape (nchans, nsamples).

headerHeader

Header object containing metadata.

Attributes:

data

Block data array.

header

Metadata header object.

nsamples

Number of samples.

Methods

normalise([loc_method, scale_method, axis])	Normalise/standardise the data block.
pad_samples(nsamps_final, offset[, pad_mode])	Pad the data block with the given mode.
plot(*args, **kwargs)	Plot the data block.

property data

Block data array.

Returns:

NDArray[float32]

2-D array of shape (nchans, nsamples).

property header

Metadata header object.

Returns:

Header

Header object containing metadata.

property nsamples

Number of samples.

Returns:

int

Number of samples in the data block.

normalise(loc_method='mean', scale_method='std', axis=1)

Normalise/standardise the data block.

Normalisation is performed by subtracting the loc estimate, and dividing by the scale estimate along the given axis.

Parameters:

loc_method{“mean”, “median”}, optional

Method to estimate location to subtract, by default “mean”.

scale_method{“std”, “iqr”, “mad”}, optional

Method to estimate scale to divide by, by default “std”.

axisint, optional

Axis along which to perform normalisation, by default 1.

Returns:

BaseBlock

Normalised data block.

pad_samples(nsamps_final, offset, pad_mode='median')

Pad the data block with the given mode.

Parameters:

nsamps_finalint

Number of time samples in the final padded data block.

offsetint

Offset to start padding.

pad_mode{“mean”, “median”}, optional

Mode for padding the data, by default “median”.

Returns:

BaseBlock

Padded data block.

Raises:

ValueError

If the pad_mode is not “mean” or “median”.

abstractmethod plot(*args, **kwargs)

Plot the data block.

class sigpyproc.block.DMTBlock(data, header, dms)

Bases: BaseBlock

A class to handle a DM-time transform block in time-major order.

Parameters:

datandarray

2-D array of shape (ndms, nsamples).

headerHeader

Header object containing metadata.

dmsndarray

Array of DM values corresponding to each row of data.

Attributes:

dms

Array of DM values corresponding to each row of data.

ndms

Number of DM values.

Methods

plot(*args, **kwargs)

Plot the data block.

property dms

Array of DM values corresponding to each row of data.

Returns:

ndarray

DM array.

property ndms

Number of DM values.

Returns:

int

Number of DMs.

plot(*args, **kwargs)

Plot the data block.

class sigpyproc.block.FilterbankBlock(data, header, dm=0)

Bases: BaseBlock

A class to handle a block of filterbank data in time-major order.

Parameters:

datandarray

2-D array of shape (nchans, nsamples).

headerHeader

Header object containing metadata.

dmfloat, optional

Dispersion measure of the data, by default 0.

Attributes:

dm

Dispersion measure of the data.

nchans

Number of frequency channels.

Methods

dedisperse(dm[, ref_freq, only_valid_samples])	Dedisperse the block.
dmt_transform(dm[, dmsteps, ref_freq, ...])	Compute the DM-time transform.
downsample([ffactor, tfactor, filter_method])	Downsample data block in frequency and/or time.
get_bandpass()	Sum across all time samples for each channel.
get_tim()	Sum across all frequencies for each time sample.
plot(*args, **kwargs)	Plot the data block.
to_file([filename])	Write the data to file.

property dm

Dispersion measure of the data.

Returns:

float

Dispersion measure.

property nchans

Number of frequency channels.

Returns:

int

Number of frequency channels.

plot(*args, **kwargs)

Plot the data block.

downsample(ffactor=1, tfactor=1, filter_method='mean')

Downsample data block in frequency and/or time.

Parameters:

ffactorint, optional

Factor by which to downsample in frequency, by default 1.

tfactorint, optional

Factor by which to downsample in time, by default 1.

filter_method{“mean”, “median”}, optional

Method to downsample, by default ‘mean’.

Returns:

FilterbankBlock

Downsampled data block.

get_tim()

Sum across all frequencies for each time sample.

Returns:

TimeSeries

Sum of all channels as timeseries.

get_bandpass()

Sum across all time samples for each channel.

Returns:

ndarray

Bandpass of the data block.

dedisperse(dm, ref_freq='fch1', *, only_valid_samples=False)

Dedisperse the block.

Frequency dependent delays are applied as rotations to each channel in the block with respect to the reference frequency.

Parameters:

dmfloat

Dispersion measure to dedisperse to.

ref_freqstr, optional

Reference frequency to dedisperse to, by default “fch1”.

only_valid_samplesbool, optional

Return a FilterbankBlock for valid time samples, by default False.

Returns:

FilterbankBlock

Dedispersed data block.

Raises:

ValueError

If there are not enough time samples to dedisperse.

dmt_transform(dm, dmsteps=512, ref_freq='fch1', *, only_valid_samples=False)

Compute the DM-time transform.

The transform is computed by dedispersing data block at adjacent DMs.

Parameters:

dmfloat

Central DM to dedisperse to.

dmstepsint, optional

Number of adjacent DMs to dedisperse to, by default 512.

ref_freqstr | float, optional

Reference frequency to dedisperse to, by default “fch1”.

only_valid_samplesbool, optional

Return a DMTBlock for valid time samples, by default False.

Returns:

DMTBlock

DM-time transform block.

to_file(filename=None)

Write the data to file.

Parameters:

filenamestr, optional

Name of the output file, by default basename_split_start_to_end.fil.

Returns:

str

Name of the output file.