pygwb.postprocessing.postprocess_Y_sigma

pygwb.postprocessing.postprocess_Y_sigma(Y_fs, var_fs, segment_duration, deltaF, new_sample_rate, frequency_mask=True, badtimes_mask=None, window_fftgram_dict={'window_fftgram': 'hann'}, window_fftgram_dict_welch={'window_fftgram': 'hann'}, overlap_factor=0.5, overlap_factor_welch=0.5, N_avg_segs=2)[source]

Run postprocessing of point estimate and sigma spectrograms, combining even and odd segments in the case of overlapping data. For more details see - https://dcc.ligo.org/public/0027/T040089/000/T040089-00.pdf

Parameters:

Y_fs (array-like) – 2D array of point estimates with Ntimes x Nfreqs with overlapping segments.
var_fs (array-like) – 2D array of variances or 2D with dimensions Ntimes x Nfreqs with overlapping time segments.
segment_duration (float) – Duration of each time segment.
deltaF (float) – Frequency resolution.
new_sample_rate (float) – Sample rate of timeseries after resampling.
frequency_mask (array-like, optional) – Boolean mask to apply to frequencies for the calculation. Defaults to True which includes all frequencies in the analysis.
badtimes_mask (array-like, optional) – Boolean mask to apply to GPStimes in the calculation. Defaults to None such that all times are included.
window_fftgram_dict (dictionary, optional) – Dictionary with window characteristics used in PSD estimation. Default is window_fftgram_dict={"window_fftgram": "hann"}
overlap_factor (float, optional) – Overlap factor used in PSD estimation. Default is 0.5.
N_avg_segs (int, optional) – Number of segments over which the average is performed. This is useful for computing the bias, nothing more. Default is 2.

Returns:

Y_f_new: array-like: 1D point estimate spectrum.
sigma_f_few: array-like: 1D sigma spectrum.