File size: 1,187 Bytes
9af7384 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
"""
A module to have different distance metrics for spatial interpolation
"""
import numpy as np
from scipy.spatial.distance import cdist
def haversine(X1, X2):
"""
Arguments
---------
One test point
Multiple Train Points
Long Lat Order
"""
# Non-vectorized version
# X1 = X1.reshape(1, 2)
# difference = (X1 - X2) * np.pi / 180
# test_point_lat = X1[:, 1] * np.pi / 180
# training_locations_lat = X2[:, 1] * np.pi / 180
# a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\
# np.sin(difference[:, 1] / 2)**2
# radius = 6371
# c = 2 * np.arcsin(np.sqrt(a))
# return radius * c
# Vectorized code
lon1, lat1, lon2, lat2 = map(
np.radians,
[X1[:, 0, None], X1[:, 1, None], X2[:, 0, None], X2[:, 1, None]],
)
dlon = lon2.T - lon1
dlat = lat2.T - lat1
a = (
np.sin(dlat / 2.0) ** 2
+ np.cos(lat1) @ np.cos(lat2.T) * np.sin(dlon / 2.0) ** 2
)
c = 2 * np.arcsin(np.sqrt(a))
km = 6371 * c
return km
def euclidean(X1, X2):
# return np.linalg.norm(X1 - X2, 2, axis=1)
return cdist(X1, X2)
|