kadeng
diff --git a/‎geodesy/lamberts_ellipsoidal_distance.py‎
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+from math import atan, cos, radians, sin, tan
+from haversine_distance import haversine_distance
+
+
+def lamberts_ellipsoidal_distance(
+    lat1: float, lon1: float, lat2: float, lon2: float
+) -> float:
+
+    """
+    Calculate the shortest distance along the surface of an ellipsoid between
+    two points on the surface of earth given longitudes and latitudes
+    https://en.wikipedia.org/wiki/Geographical_distance#Lambert's_formula_for_long_lines
+
+    NOTE: This algorithm uses geodesy/haversine_distance.py to compute central angle, sigma
+
+    Representing the earth as an ellipsoid allows us to approximate distances between points
+    on the surface much better than a sphere. Ellipsoidal formulas treat the Earth as an
+    oblate ellipsoid which means accounting for the flattening that happens at the North 
+    and South poles. Lambert's formulae provide accuracy on the order of 10 meteres over 
+    thousands of kilometeres. Other methods can provide millimeter-level accuracy but this
+    is a simpler method to calculate long range distances without increasing computational
+    intensity.
+
+    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)
+    >>> NEW_YORK = point_2d(40.713019, -74.012647)
+    >>> VENICE = point_2d(45.443012, 12.313071)
+    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *YOSEMITE):0,.0f} meters"
+    '254,351 meters'
+    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *NEW_YORK):0,.0f} meters"
+    '4,138,992 meters'
+    >>> f"{lamberts_ellipsoidal_distance(*SAN_FRANCISCO, *VENICE):0,.0f} meters"
+    '9,737,326 meters'
+    """
+
+    # CONSTANTS per WGS84 https://en.wikipedia.org/wiki/World_Geodetic_System
+    # Distance in metres(m)
+    AXIS_A = 6378137.0
+    AXIS_B = 6356752.314245
+    EQUATORIAL_RADIUS = 6378137
+
+    # Equation Parameters
+    # https://en.wikipedia.org/wiki/Geographical_distance#Lambert's_formula_for_long_lines
+    flattening = (AXIS_A - AXIS_B) / AXIS_A
+    # Parametric latitudes https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
+    b_lat1 = atan((1 - flattening) * tan(radians(lat1)))
+    b_lat2 = atan((1 - flattening) * tan(radians(lat2)))
+
+    # Compute central angle between two points
+    # using haversine theta. sigma =  haversine_distance / equatorial radius
+    sigma = haversine_distance(lat1, lon1, lat2, lon2) / EQUATORIAL_RADIUS
+
+    # Intermediate P and Q values
+    P_value = (b_lat1 + b_lat2) / 2
+    Q_value = (b_lat2 - b_lat1) / 2
+
+    # Intermediate X value
+    # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
+    X_numerator = (sin(P_value) ** 2) * (cos(Q_value) ** 2)
+    X_demonimator = cos(sigma / 2) ** 2
+    X_value = (sigma - sin(sigma)) * (X_numerator / X_demonimator)
+
+    # Intermediate Y value
+    # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
+    Y_numerator = (cos(P_value) ** 2) * (sin(Q_value) ** 2)
+    Y_denominator = sin(sigma / 2) ** 2
+    Y_value = (sigma + sin(sigma)) * (Y_numerator / Y_denominator)
+
+    return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (X_value + Y_value)))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()