b001_12.py

b001_12.py

from typing import Tuple

from math import radians

def haversine(lng1: float, lat1: float, lng2: float, lat2: float) -> Tuple[float, float]:
    """ returns (haversine distance in km, bearing in degrees from point 1 to point 2), vectorised """

    avg_earth_radius_km = 6371.0072
   
    lng1, lat1, lng2, lat2 = map(np.deg2rad, [lng1, lat1, lng2, lat2])
    d_lat, d_lng = lat2 - lat1, lng2 - lng1
    d = np.sin((d_lat)/2)**2 + np.cos(lat1)*np.cos(lat2) * np.sin((d_lng)/2)**2
    hav_dist = 2 * avg_earth_radius_km * np.arcsin(np.sqrt(d))
   
    y = np.sin(d_lng) * np.cos(lat2)
    x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(d_lng)
    bearing = (np.arctan2(y, x) + 2 * np.pi) % (2 * np.pi)
    
    return hav_dist, np.rad2deg(bearing)

def add_bearing_deg_and_distance_km(df: pd.DataFrame) -> pd.DataFrame:
    """bearing between A and B is the angle between the geodesics connecting A and the north pole, and the geodesics connecting A and B.
    Both the bearing and distance are computed on the Spherical model.
    """
    df = df.copy()
    
    lng_work, lat_work = df.workplace_lng.to_numpy(), df.workplace_lat.to_numpy()
    lng_home, lat_home = df.residence_lng.to_numpy(), df.residence_lat.to_numpy()
    
    df["distance_km"], df["bearing_deg"] = haversine(lng_work, lat_work, lng_home, lat_home)
    
    return df


df_commuters_st_luke_office = add_bearing_deg_and_distance_km(df_commuters_st_luke_office)
df_commuters_albert_road = add_bearing_deg_and_distance_km(df_commuters_albert_road)
df_commuters_st_luke_office.head()