clouddrift.sphere.distance#
- clouddrift.sphere.distance(lon1: float | list | ndarray | DataArray, lat1: float | list | ndarray | DataArray, lon2: float | list | ndarray | DataArray, lat2: float | list | ndarray | DataArray) float | ndarray[source]#
Return elementwise great circle distance in meters between one or more points from arrays of their latitudes and longitudes, using the Haversine formula.
d = 2⋅r⋅asin √[sin²(Δφ/2) + cos φ1 ⋅ cos φ2 ⋅ sin²(Δλ/2)]
where (φ, λ) is (lat, lon) in radians and r is the radius of the sphere in meters.
Parameters#
- lon1np.ndarray
Longitudes of the first set of points, in degrees
- lat1np.ndarray
Latitudes of the first set of points, in degrees
- lon2np.ndarray
Longitudes of the second set of points, in degrees
- lat2np.ndarray
Latitudes of the second set of points, in degrees
Returns#
- outnp.ndarray
Great circle distance
Examples#
Calculate the distance of one degree longitude on the equator:
>>> distance(0, 0, 0, 1) 111318.84502145034
Calculate the distance of one degree longitude at 45-degrees North latitude:
>>> distance(0, 45, 1, 45) 78713.81064540472
You can also pass array-like inputs to calculate an array of distances:
>>> distance([0, 0], [0, 45], [0, 1], [1, 45]) array([111318.84502145, 78713.8106454 ])