python tips: calculate distance between N points and M points using numpy array

This is memory consuming way of calculate distance between N points and M points. Let l_lat and l_lon are vectors for N points and v_lat and v_lon are for M points. Using numpy array, distance calculation can be written as follows.

#!/usr/bin/env python                                                                                                                                                                                        
#coding:utf-8                                                                                                                                                                                                

import numpy

M = 3
N = 5

# points1                                                                                                                                                                                                    
l_lat = numpy.arange(N)
l_lon = numpy.arange(N)
m_l_lat = numpy.tile(l_lat, (M, 1))
m_l_lon = numpy.tile(l_lon, (M, 1))

# points2                                                                                                                                                                                                    
v_lat = numpy.arange(M)
v_lon = numpy.arange(M)
m_v_lat_t = numpy.tile(v_lat, (N, 1)).T
m_v_lon_t = numpy.tile(v_lon, (N, 1)).T

distance = numpy.sqrt((m_v_lat_t - m_l_lat) ** 2 + (m_v_lon_t - m_l_lat) ** 2)
print distance

Although code generates lat and lon with numpy.arange(), in real case you should fill each vector with appropriate lat and lon values. Below is an output of previous code.

~$ python test_calc_distance.py 
[[ 0.          1.41421356  2.82842712  4.24264069  5.65685425]
 [ 1.41421356  0.          1.41421356  2.82842712  4.24264069]
 [ 2.82842712  1.41421356  0.          1.41421356  2.82842712]]