utils.py

import numpy as np
from scipy.spatial import distance
from scipy.spatial.distance import pdist


def haversine_distance(lat1, lon1, lat2, lon2):
    """
    Calculate the great circle distance between two points on the earth (specified in decimal degrees). 
    Harvesine sklearn distance was not working as expected, that is why this function was added maily extracted from:
    https://stackoverflow.com/questions/29545704/fast-haversine-approximation-python-pandas 
    Input is two lat-lon observations.

    Args:
        lat1 : Latitude 1 of the observation.
        lon1 : Longitude 1 of the observation.
        lat2 : Latitude 2 of the observation.
        lon2 : Longitude 2 of the observation.
    
    Returns:
        Harvesine distance in KM.
    """
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2
    
    c = 2 * np.arcsin(np.sqrt(a))
    km = (6371000/1000) * c
    return km


def manhattan_distance(lat1, lon1, lat2, lon2):
    """
    Calculates the manhattan distance between two coordinates (remember is the sum of the absolute difference of the coordinates).
    Args:
        lat1 : Latitude 1 of the observation.
        lon1 : Longitude 1 of the observation.
        lat2 : Latitude 2 of the observation.
        lon2 : Longitude 2 of the observation.
    Returns:
        Manhattan distance.
    """
    manhattan_dist = np.abs(lat1 - lat2) + np.abs(lon1 - lon2)
    return(manhattan_dist)