Package: haversine
HaversineCalculate the distance (in various units) between two points on Earth using their latitude and longitude.InstallationpipinstallhaversineUsageCalculate the distance between Lyon and Parisfromhaversineimporthaversine,Unitlyon=(45.7597,4.8422)# (lat, lon)paris=(48.8567,2.3508)haversine(lyon,paris)>>392.2172595594006# in kilometershaversine(lyon,paris,unit=Unit.MILES)>>243.71250609539814# in miles# you can also use the string abbreviation for units:haversine(lyon,paris,unit='mi')>>243.71250609539814# in mileshaversine(lyon,paris,unit=Unit.NAUTICAL_MILES)>>211.78037755311516# in nautical milesThe lat/lon values need to be provided in degrees of the ranges [-90,90] (lat) and [-180,180] (lon).
If values are outside their ranges, an error will be raised. This can be avoided by automatic normalization via thenormalizeparameter.Thehaversine.Unitenum contains all supported units:importhaversineprint(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'>)Note for radians and degreesThe radian and degrees returns thegreat circle distancebetween two points on a sphere.Notes:on a unit-sphere the angular distance in radians equals the distance between the two points on the sphere (definition of radians)When using "degree", this angle is just converted from radians to degreesInverse Haversine FormulaCalculates 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 Parisfromhaversineimportinverse_haversine,Directionfrommathimportpiparis=(48.8567,2.3508)# (lat, lon)# Finding 32 km west of Parisinverse_haversine(paris,32,Direction.WEST)# returns tuple (48.85587279023947, 1.9134085092836945)# Finding 32 km southwest of Parisinverse_haversine(paris,32,pi*1.25)# returns tuple (48.65279552300661, 2.0427666779658806)# Finding 50 miles north of Parisinverse_haversine(paris,50,Direction.NORTH,unit=Unit.MILES)# returns tuple (49.58035791599536, 2.3508)# Finding 10 nautical miles south of Parisinverse_haversine(paris,10,Direction.SOUTH,unit=Unit.NAUTICAL_MILES)# returns tuple (48.690145868497645, 2.3508)Performance optimisation for distances between all points in two vectorsYou will need to installnumpyin order to gain performance with vectors.
For optimal performance, you can turn off coordinate checking by addingcheck=Falseand install the optional packagesnumbaandicc_rt.You can then do this:fromhaversineimporthaversine_vector,Unitlyon=(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 usehaversine_vectorto get distance between two points, but can be really fast to compare distances between two vectors.Combine matrixYou can generate a matrix of all combinations between coordinates in different vectors by settingcombparameter as True.fromhaversineimporthaversine_vector,Unitlyon=(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:ParisNew YorkLyonLyon <-> ParisLyon <-> New YorkLondonLondon <-> ParisLondon <-> New YorkBy definition, if you have a vectorawithnelements, and a vectorbwithmelements. The result matrixMwould be $n x m$ and a element M[i,j] from the matrix would be the distance between the ith coordinate from vectoraand jth coordinate with vectorb.ContributingClone the project.Installpipenv.Runpipenv install --devLaunch test withpipenv run pytest