Haversine Formula in Python (Bearing and Distance between two GPS points)

Here's a Python version:

from math import radians, cos, sin, asin, sqrtdef haversine(lon1, lat1, lon2, lat2):    """    Calculate the great circle distance in kilometers between two points     on the earth (specified in decimal degrees)    """    # convert decimal degrees to radians     lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])    # haversine formula     dlon = lon2 - lon1     dlat = lat2 - lat1     a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2    c = 2 * asin(sqrt(a))     r = 6371 # Radius of earth in kilometers. Use 3956 for miles. Determines return value units.    return c * r

Most of these answers are "rounding" the radius of the earth. If you check these against other distance calculators (such as geopy), these functions will be off.

This works well:

from math import radians, cos, sin, asin, sqrtdef haversine(lat1, lon1, lat2, lon2):      R = 3959.87433 # this is in miles.  For Earth radius in kilometers use 6372.8 km      dLat = radians(lat2 - lat1)      dLon = radians(lon2 - lon1)      lat1 = radians(lat1)      lat2 = radians(lat2)      a = sin(dLat/2)**2 + cos(lat1)*cos(lat2)*sin(dLon/2)**2      c = 2*asin(sqrt(a))      return R * c# Usagelon1 = -103.548851lat1 = 32.0004311lon2 = -103.6041946lat2 = 33.374939print(haversine(lat1, lon1, lat2, lon2))

There is also a vectorized implementation, which allows to use 4 numpy arrays instead of scalar values for coordinates:

def distance(s_lat, s_lng, e_lat, e_lng):   # approximate radius of earth in km   R = 6373.0   s_lat = s_lat*np.pi/180.0                         s_lng = np.deg2rad(s_lng)        e_lat = np.deg2rad(e_lat)                          e_lng = np.deg2rad(e_lng)     d = np.sin((e_lat - s_lat)/2)**2 + np.cos(s_lat)*np.cos(e_lat) * np.sin((e_lng - s_lng)/2)**2   return 2 * R * np.arcsin(np.sqrt(d))