Skip to content

Latest commit

 

History

History
executable file
·
130 lines (90 loc) · 3.77 KB

README.md

File metadata and controls

executable file
·
130 lines (90 loc) · 3.77 KB
Raw

Haversine

Calculate the distance (in various units) between two points on Earth using their latitude and longitude.

Installation

pip install haversine

Usage

Calculate the distance between Lyon and Paris

from haversine import haversine, Unit

lyon = (45.7597, 4.8422) # (lat, lon)
paris = (48.8567, 2.3508)

haversine(lyon, paris)
>> 392.2172595594006  # in kilometers

haversine(lyon, paris, unit=Unit.MILES)
>> 243.71201856934454  # in miles

# you can also use the string abbreviation for units:
haversine(lyon, paris, unit='mi')
>> 243.71201856934454  # in miles

haversine(lyon, paris, unit=Unit.NAUTICAL_MILES)
>> 211.78037755311516  # in nautical miles

The haversine.Unit enum contains all supported units:

import haversine

print(tuple(haversine.Unit))

outputs

(<Unit.KILOMETERS: 'km'>, <Unit.METERS: 'm'>, <Unit.MILES: 'mi'>,
 <Unit.NAUTICAL_MILES: 'nmi'>, <Unit.FEET: 'ft'>, <Unit.INCHES: 'in'>,
 <Unit.RADIANS: 'rad'>, <Unit.DEGREES: 'deg'>)

Inverse Haversine Formula

Calculates a point from a given vector (distance and direction) and start point. Currently explicitly supports both cardinal (north, east, south, west) and intercardinal (northeast, southeast, southwest, northwest) directions. But also allows for explicit angles expressed in Radians.

Example: Finding arbitary point from Paris

from haversine import inverse_haversine, Direction
from math import pi
paris = (48.8567, 2.3508) # (lat, lon)
# Finding 32 km west of Paris
inverse_haversine(paris, 32, Direction.WEST)
# returns tuple (49.1444, 2.3508)
# Finding 32 km southwest of Paris
inverse_haversine(paris, 32, pi * 1.25)
# returns tuple (48.5377, 1.8705)
# Finding 50 miles north of Paris
inverse_haversine(paris, 50, Direction.NORTH, unit=Unit.MILES)
# returns tuple (49.5803, 2.3508)
# Finding 10 nautical miles south of Paris
inverse_haversine(paris, 10, Direction.SOUTH, unit=Unit.NAUTICAL_MILES)
# returns tuple (48.6901, 2.3508)

Performance optimisation for distances between all points in two vectors

You will need to add numpy in order to gain performance with vectors.

You can then do this:

from haversine import haversine_vector, Unit

lyon = (45.7597, 4.8422) # (lat, lon)
paris = (48.8567, 2.3508)
new_york = (40.7033962, -74.2351462)

haversine_vector([lyon, lyon], [paris, new_york], Unit.KILOMETERS)

>> array([ 392.21725956, 6163.43638211])

It is generally slower to use haversine_vector to get distance between two points, but can be really fast to compare distances between two vectors.

Combine matrix

You can generate a matrix of all combinations between coordinates in different vectors by setting comb parameter as True.

from haversine import haversine_vector, Unit

lyon = (45.7597, 4.8422) # (lat, lon)
london = (51.509865, -0.118092)
paris = (48.8567, 2.3508)
new_york = (40.7033962, -74.2351462)

haversine_vector([lyon, london], [paris, new_york], Unit.KILOMETERS, comb=True)

>> array([[ 392.21725956,  343.37455271],
 	  [6163.43638211, 5586.48447423]])

The output array from the example above returns the following table:

Paris New York
Lyon Lyon <-> Paris Lyon <-> New York
London London <-> Paris London <-> New York

By definition, if you have a vector a with n elements, and a vector b with m elements. The result matrix M would be $n x m$ and a element M[i,j] from the matrix would be the distance between the ith coordinate from vector a and jth coordinate with vector b.

Contributing

Clone the project.

Install pipenv.

Run pipenv install --dev

Launch test with pipenv run pytest