sigpyproc.FourierSeries¶

class sigpyproc.FourierSeries.FourierSeries(input_array, header)[source]¶

Bases: numpy.ndarray

Class to handle output of FFT’d time series.

Parameters

input_array (numpy.ndarray) – 1 dimensional array of shape (nsamples)
header (Header) – observational metadata

conjugate()[source]¶

Conjugate the Fourier series.

Returns: conjugated Fourier series.
Return type: sigpyproc.FourierSeries.FourierSeries

Note

Function assumes that the Fourier series is the non-conjugated product of a real to complex FFT.

formSpec(interpolated=True)[source]¶

Form power spectrum.

Parameters: interpolated (bool) – flag to set nearest bin interpolation (def=True)
Returns: a power spectrum
Return type: PowerSpectrum

iFFT()[source]¶

Perform 1-D complex to real inverse FFT using FFTW3.

Returns: a time series
Return type: TimeSeries

reconProf(freq, nharms=32)[source]¶

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

Parameters

freq (float) – frequency of signal to reconstruct
nharms (int) – number of harmonics to use in reconstruction (def=32)

Returns

a pulse profile

Return type

sigpyproc.FoldedData.Profile

rednoise(startwidth=6, endwidth=100, endfreq=1.0)[source]¶

Perform rednoise removal via Presto style method.

Parameters

startwidth (int) – size of initial array for median calculation
endwidth (int) – size of largest array for median calculation
endfreq (float) – remove rednoise up to this frequency

Returns

whitened fourier series

Return type

FourierSeries

toFFTFile(basename=None)[source]¶

Write spectrum to file in sigpyproc format.

Parameters: basename – basename of .fft and .inf file to be written
Returns: name of files written to
Return type: tuple() of str()

toFile(filename=None)[source]¶

Write spectrum to file in sigpyproc format.

Parameters: filename (str) – name of file to write to (def=``basename.spec``)
Returns: name of file written to
Return type: str()

class sigpyproc.FourierSeries.PowerSpectrum(input_array, header)[source]¶

Bases: numpy.ndarray

Class to handle power spectra.

Parameters

input_array (numpy.ndarray) – 1 dimensional array of shape (nsamples)
header (Header) – observational metadata

bin2freq(bin_)[source]¶

Return centre frequency of a given bin.

Parameters: bin (int) – bin number
Returns: frequency of bin
Return type: float

bin2period(bin_)[source]¶

Return centre period of a given bin.

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

freq2bin(freq)[source]¶

Return nearest bin to a given frequency.

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

harmonicFold(nfolds=1)[source]¶

Perform Lyne-Ashworth harmonic folding of the power spectrum.

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

period2bin(period)[source]¶

Return nearest bin to a given periodicity.

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