liang_causality (pyleoclim.utils.causality.liang_causality)¶

pyleoclim.utils.causality.liang_causality(y1, y2, npt=1, signif_test='isospec', nsim=1000, qs=[0.005, 0.025, 0.05, 0.95, 0.975, 0.995])[source]¶

Estimate the Liang information transfer from series y2 to series y1 with significance estimates using either an AR(1) test with series with the same persistence or surrogates with randomized phases.

Parameters

y1 (array) – vectors of (real) numbers with identical length, no NaNs allowed
y2 (array) – vectors of (real) numbers with identical length, no NaNs allowed
npt (int >=1) – time advance in performing Euler forward differencing, e.g., 1, 2. Unless the series are generated with a highly chaotic deterministic system, npt=1 should be used
signif_test ({'isopersist', 'isospec'}) – the method for significance test see signif_isospec and signif_isopersist for details.
nsim (int) – the number of AR(1) surrogates for significance test
qs (list) – the quantiles for significance test

Returns

res –

A dictionary of results including:

T21float: information flow from y2 to y1 (Note: not y1 -> y2!)
tau21float: the standardized information flow from y2 to y1
Zfloat: the total information flow from y2 to y1
T21_noise_qslist: the quantiles of the information flow from noise2 to noise1 for significance testing
tau21_noise_qslist: the quantiles of the standardized information flow from noise2 to noise1 for significance testing

Return type

dict