time_lag#

sunkit_image.time_lag.time_lag(signal_a, signal_b, time: Unit('s'), lag_bounds:
astropy.units.Quantity
 None = None, **kwargs)[source]# Compute the time lag that maximizes the crosscorrelation between
signal_a
andsignal_b
.For a pair of signals \(A,B\), e.g. time series from two EUV channels on AIA, the time lag is the lag which maximizes the crosscorrelation,
\[\tau_{AB} = \mathop{\mathrm{arg\,max}}_{\tau}\mathcal{C}_{AB},\]where \(\mathcal{C}_{AB}\) is the crosscorrelation as a function of lag (computed in
cross_correlation()
). Qualitatively, this can be thought of as how muchsignal_a
needs to be shifted in time to best “match”signal_b
. Note that the sign of \(\tau_{AB}`\) is determined by the ordering of the two signals such that,\[\tau_{AB} = \tau_{BA}.\] Parameters:
signal_a (arraylike) – The first dimension must be the same length as
time
.signal_b (arraylike) – Must have the same dimensions as
signal_a
.time (
Quantity
) – Time array corresponding to the intensity time seriessignal_a
andsignal_b
.lag_bounds (
Quantity
, optional) – Minimum and maximum lag to consider when finding the time lag that maximizes the crosscorrelation. This is useful for minimizing boundary effects.array_check_hook (function) – Function to apply to the resulting time lag result. This should take in the
lags
array and the indices that specify the location of the maximum of the crosscorrelation and return an array that has used those indices to select thelags
which maximize the crosscorrelation. As an example, iflags
andindices
are bothndarray
objects, this would just returnlags[indices]
. It is probably only necessary to specify this if you are working with arrays that are something other than andarray
orArray
object.
 Returns:
arraylike – Lag which maximizes the crosscorrelation. The dimensions will be consistent with those of
signal_a
andsignal_b
, i.e. if the input arrays are of dimension(K,M,N)
, the resulting array will have dimensions(M,N)
. Similarly, if the input signals are onedimensional time series(K,)
, the result will have dimension(1,)
.
References
Viall, N.M. and Klimchuk, J.A. Evidence for Widespread Cooling in an Active Region Observed with the SDO Atmospheric Imaging Assembly ApJ, 753, 35, 2012 (https://doi.org/10.1088/0004637X/753/1/35)