pygwb.util

The util module combines miscellaneous functions used in several parts of the pygwb package. These functions mainly perform small computations, necessary at multiple stages of the analysis.

Functions

StatKS(DKS)	Compute the Kolgomorov-Smirnov test.
calc_bias(segmentDuration, deltaF, deltaT[, ...])	Calculate the bias factor introduced by Welch averaging.
calc_rho(N, j[, window_tuple, overlap_factor])	Calculate the normalised correlation of a window with itself shifted j times.
calc_rho1(N[, window_fftgram_dict, ...])	Calculate the combined window factor rho.
effective_welch_averages(nSamples, N[, ...])	Calculate the "effective" number of averages used in Welch's PSD estimate after taking into account windowing and overlap.
get_window_tuple([window_fftgram_dict])	Unpack the window_fft_dict dictionary into a tuple that can be read by scipy.signal.get_window.
interpolate_frequency_series(fSeries, ...)	Interpolate a frequency series, given a new set of frequencies.
omega_to_power(omega_GWB, frequencies)	Compute the GW power spectrum starting from the \(\Omega\)GWB spectrum.
parse_window_dict(window_dict)	Parse the window dictionary properly for scipy compatibility.
window_factors(N[, window_fftgram_dict, ...])	Calculate window factors.