pygwb.postprocessing.odd_even_segment_postprocessing

pygwb.postprocessing.odd_even_segment_postprocessing(Y_fs, var_fs, segment_duration, new_sample_rate, frequency_mask=True, window_fftgram_dict={'window_fftgram': 'hann'}, overlap_factor=0.5, N_avg_segs=2)[source]

Perform averaging which combines even and odd segments for overlapping data.

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.
new_sample_rate (float) – Sample rate of timeseries after resampling.
frequency_mask (array-like, optional) – Boolean mask to apply to frequencies for the calculation.
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) – Defines the overlap between consecutive data chunks used in the calculation. Default is 0.5.

Returns:

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