sunkit_image.time_lag.max_cross_correlation(signal_a, signal_b, time: Unit('s'), lag_bounds:
astropy.units.Quantity| None = None)#
Compute the maximum value of the cross-correlation between
This is the maximum value of the cross-correlation as a function of lag (computed in
cross_correlation()). This will always be between -1 (perfectly anti-correlated) and +1 (perfectly correlated) though in practice is nearly always between 0 and +1.
signal_a (array-like) – The first dimension must be the same length as
signal_b (array-like) – Must have the same dimensions as
time (array-like) – Time array corresponding to the intensity time series
tuple, optional) – Minimum and maximum lag to consider when finding the time lag that maximizes the cross-correlation. This is useful for minimizing boundary effects.
array-like – Maximum value of the cross-correlation. The dimensions will be consistent with those of
signal_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 one-dimensional time series
(K,), the result will have dimension
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/10.1088/0004-637X/753/1/35)