from math import asin, atan, cos, radians, sin, sqrt, tan
AXIS_A = 6378137.0
AXIS_B = 6356752.314245
RADIUS = 6378137
def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
"""
Calculate great circle distance between two points in a sphere,
given longitudes and latitudes https://en.wikipedia.org/wiki/Haversine_formula
We know that the globe is "sort of" spherical, so a path between two points
isn't exactly a straight line. We need to account for the Earth's curvature
when calculating distance from point A to B. This effect is negligible for
small distances but adds up as distance increases. The Haversine method treats
the earth as a sphere which allows us to "project" the two points A and B
onto the surface of that sphere and approximate the spherical distance between
them. Since the Earth is not a perfect sphere, other methods which model the
Earth's ellipsoidal nature are more accurate but a quick and modifiable
computation like Haversine can be handy for shorter range distances.
Args:
lat1, lon1: latitude and longitude of coordinate 1
lat2, lon2: latitude and longitude of coordinate 2
Returns:
geographical distance between two points in metres
>>> from collections import namedtuple
>>> point_2d = namedtuple("point_2d", "lat lon")
>>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)
>>> YOSEMITE = point_2d(37.864742, -119.537521)
>>> f"{haversine_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
'254,352 meters'
"""
flattening = (AXIS_A - AXIS_B) / AXIS_A
phi_1 = atan((1 - flattening) * tan(radians(lat1)))
phi_2 = atan((1 - flattening) * tan(radians(lat2)))
lambda_1 = radians(lon1)
lambda_2 = radians(lon2)
sin_sq_phi = sin((phi_2 - phi_1) / 2)
sin_sq_lambda = sin((lambda_2 - lambda_1) / 2)
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
h_value = sqrt(sin_sq_phi + (cos(phi_1) * cos(phi_2) * sin_sq_lambda))
return 2 * RADIUS * asin(h_value)
if __name__ == "__main__":
import doctest
doctest.testmod()