Vectorize haversine distance computation along path given by list of coordinates

Question

I have a list of coordinates and can calculate a distance matrix among all points using the haversine distance metric.

Coordinates come a as numpy.array

Answer 1

Here's one way you can vectorize that calculation without creating a big matrix. coslat is the array of cosines of the latitudes, and coslat[:-1]*coslat[1:] is the vectorized version of the expression cos(ϕ₁)cos(ϕ₂) in the Haversine formula.

from __future__ import division, print_function

import numpy as np


def hav(theta):
    return np.sin(theta/2)**2


coords = [[  16.34576887, -107.90942116],
          [  12.49474931, -107.76030036],
          [  27.79461514, -107.98607881],
          [  12.90258404, -107.96786569],
          [  -6.29109889, -107.88681145],
          [  -2.68531605, -107.72796034]]
r = 6371

coordinates = np.deg2rad(coords)
lat = coordinates[:, 0]
lng = coordinates[:, 1]
coslat = np.cos(lat)
t = hav(np.diff(lat)) + coslat[:-1]*coslat[1:]*hav(np.diff(lng))
d = 2*r*np.arcsin(np.sqrt(t))

print(d)

Output:

[  428.51472353  1701.42935412  1655.91938575  2134.25895299   401.33113696]