sigpyproc.fourierseries¶

class sigpyproc.fourierseries.PowerSpectrum(input_array, header)¶

Bases: numpy.ndarray

An array class to handle pulsar power spectrum.

Parameters

input_array (ArrayLike) – 1 dimensional array of shape (nsamples)
header (Header) – observational metadata

Returns

1 dimensional power spectra with header

Return type

ndarray

Notes

Data is converted to 32 bits regardless of original type.

bin2freq(bin_num)¶

Return centre frequency of a given bin.

Parameters: bin_num (int) – Bin number
Returns: frequency of the given bin
Return type: float

bin2period(bin_num)¶

Return centre period of a given bin.

Parameters: bin_num (int) – Bin number
Returns: period of bin
Return type: float

freq2bin(freq)¶

Return nearest bin to a given frequency.

Parameters: freq (float) – frequency
Returns: nearest bin to frequency
Return type: int

period2bin(period)¶

Return nearest bin to a given periodicity.

Parameters: period (float) – periodicity
Returns: nearest bin to period
Return type: int

harmonic_fold(nfolds=1)¶

Perform Lyne-Ashworth harmonic folding of the power spectrum.

Parameters: nfolds (int, optional) – number of harmonic folds to perform, by default 1
Returns: A list of folded spectra where the i ^th element is the spectrum folded i times.
Return type: list of PowerSpectrum

class sigpyproc.fourierseries.FourierSeries(input_array, header)¶

Bases: numpy.ndarray

An array class to handle Fourier series with headers.

Parameters

input_array (ArrayLike) – 1 dimensional array of shape (nsamples)
header (Header) – observational metadata

Returns

1 dimensional fourier series with header

Return type

ndarray

ifft()¶

Perform 1-D complex to real inverse FFT.

Returns: a time series
Return type: TimeSeries

form_spec(interpolated=True)¶

Form power spectrum.

Parameters: interpolated (bool, optional) – flag to set nearest bin interpolation, by default True
Returns: a power spectrum
Return type: PowerSpectrum

remove_rednoise(startwidth=6, endwidth=100, endfreq=1.0)¶

Perform rednoise removal via Presto style method.

Parameters

startwidth (int, optional) – size of initial array for median calculation, by default 6
endwidth (int, optional) – size of largest array for median calculation, by default 100
endfreq (float, optional) – remove rednoise up to this frequency, by default 1.0

Returns

whitened fourier series

Return type

FourierSeries

recon_prof(freq, nharms=32)¶

Reconstruct the time domain pulse profile from a signal and its harmonics.

Parameters

freq (float) – frequency of signal to reconstruct
nharms (int, optional) – number of harmonics to use in reconstruction, by default 32

Returns

a pulse profile

Return type

Profile

to_file(filename=None)¶

Write Fourier series to file in sigproc format.

Parameters: filename (str, optional) – name of file to write to, by default basename.spec
Returns: output file name
Return type: str

to_fftfile(basename=None)¶

Write spectrum to file in presto .fft format.

Parameters: basename (str, optional) – basename of .fft and .inf file to be written, by default None
Returns: name of files written to
Return type: tuple of str

classmethod read_fft(fftfile, inffile=None)¶

Read a presto format .fft file.

Parameters

fftfile (str) – the name of the .fft file to read
inffile (str, optional) – the name of the corresponding .inf file, by default None

Returns

an array containing the whole file contents

Return type

FourierSeries

Raises

IOError – If no .inf file found in the same directory of .fft file.

Notes

If inf is None, then the associated .inf file must be in the same directory.

classmethod read_spec(filename)¶

Read a sigpyproc format .spec file.

Parameters: filename (str) – the name of the .spec file to read
Returns: an array containing the whole file contents
Return type: FourierSeries

Notes

This is not setup to handle .spec files such as are created by Sigprocs seek module. To do this would require a new header parser for that file format.

sigpyproc3

sigpyproc.fourierseries

sigpyproc.fourierseries¶