clouddrift.signal.rotary_to_cartesian#
- clouddrift.signal.rotary_to_cartesian(wp: ndarray | DataArray, wn: ndarray | DataArray, time_axis: int = -1) tuple[ndarray, ndarray][source]#
Return Cartesian analytic signals (ua, va) from rotary signals (wp, wn) as ua = wp + wn and va = -1j * (wp - wn).
This function is the inverse of
cartesian_to_rotary().Parameters#
- wparray_like
Complex-valued positive (counterclockwise) rotary signal.
- wnarray_like
Complex-valued negative (clockwise) rotary signal.
- time_axisint, optional
The axis of the time array. Default is -1, which corresponds to the last axis.
Returns#
- uaarray_like
Complex-valued analytic signal, first Cartesian component (zonal, east-west)
- vaarray_like
Complex-valued analytic signal, second Cartesian component (meridional, north-south)
Examples#
To obtain the Cartesian analytic signals from a pair of rotary signals (wp,wn):
>>> ua, va = rotary_to_cartesian(wp, wn)
To specify that the time axis is along the first axis:
>>> ua, va = rotary_to_cartesian(wp, wn, time_axis=0)
Raises#
- ValueError
If the input arrays do not have the same shape. If the time axis is outside of the valid range ([-1, N-1]).
References#
Lilly JM, Olhede SC. 2010 Bivariate instantaneous frequency and bandwidth. IEEE T. Signal Proces. 58, 591–603. (10.1109/TSP.2009.2031729)
See Also#