pyhctsa.operations.symbolic.motif_three¶

pyhctsa.operations.symbolic.motif_three(y, cg_how='quantile', tau=1)¶

Motifs in a coarse-graining of a time series to a 3-letter alphabet.

Parameters:¶

y : array-like¶

Time series to analyze.

cg_how : {'quantile', 'diffquant'}, optional¶

The coarse-graining method to use:

• ’quantile’: equiprobable alphabet by time-series value

• ’diffquant’: equiprobably alphabet by time-series increments

Default is 'quantile'.

tau : int or str, optional¶

The time series is first downsampled by this factor (polyphase anti-alias filter, scipy.signal.resample_poly()), so that the words are formed at this lag: an integer, or a rule that sets it from the series: 'ac' (the first zero-crossing of the autocorrelation function), 'ac1e' (the floor of its first 1/e crossing) or 'mi' (the smaller of the first minimum of the Kraskov automutual information and the ‘ac1e’ delay; see pyhctsa.utils.get_tau()). A delay set by a rule is capped at floor(N/50). Default is 1 (no downsampling). NaN is returned if tau cannot be determined.

Returns:¶

Statistics on words of length 1, 2, 3, and 4.

Return type:¶

dict