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haversine

Calculate the distance between 2 points on Earth

https://github.com/mapado/haversine

Science Score: 26.0%

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Keywords

distance earth haversine python

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syntax-highlighting notebook closember progress-bar spec-0 repl ipython flexible alignment interactive
Last synced: 6 months ago · JSON representation

Repository

Calculate the distance between 2 points on Earth

Basic Info
  • Host: GitHub
  • Owner: mapado
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 177 KB
Statistics
  • Stars: 346
  • Watchers: 11
  • Forks: 64
  • Open Issues: 5
  • Releases: 0
Topics
distance earth haversine python
Created about 13 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License

README.md

Haversine

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

Installation

sh pip install haversine

Usage

Calculate the distance between Lyon and Paris

```python 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)

243.71250609539814 # in miles

you can also use the string abbreviation for units:

haversine(lyon, paris, unit='mi')

243.71250609539814 # in miles

haversine(lyon, paris, unit=Unit.NAUTICAL_MILES)

211.78037755311516 # in nautical miles ```

The lat/lon values need to be provided in degrees of the ranges -90,90 and -180,180. If values are outside their ranges, an error will be raised. This can be avoided by automatic normalization via the normalize parameter.

The haversine.Unit enum contains all supported units:

```python import haversine

print(tuple(haversine.Unit)) ```

outputs

text (<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 degrees

The radian and degrees returns the great circle distance between 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 degrees

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 arbitrary point from Paris

```python 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 (48.85587279023947, 1.9134085092836945)

Finding 32 km southwest of Paris

inverse_haversine(paris, 32, pi * 1.25)

returns tuple (48.65279552300661, 2.0427666779658806)

Finding 50 miles north of Paris

inverse_haversine(paris, 50, Direction.NORTH, unit=Unit.MILES)

returns tuple (49.58035791599536, 2.3508)

Finding 10 nautical miles south of Paris

inversehaversine(paris, 10, Direction.SOUTH, unit=Unit.NAUTICALMILES)

returns tuple (48.690145868497645, 2.3508)

```

Performance optimisation for distances between all points in two vectors

You will need to install numpy in order to gain performance with vectors. For optimal performance, you can turn off coordinate checking by adding check=False and install the optional packages numba and icc_rt.

You can then do this:

```python from haversine import haversine_vector, Unit

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

haversinevector([lyon, lyon], [paris, newyork], 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.

```python 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)

haversinevector([lyon, london], [paris, newyork], 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

Owner

  • Name: Mapado
  • Login: mapado
  • Kind: organization
  • Email: bonjour@mapado.com
  • Location: France

Light, empowering and still refined ticketing & CRM tools to save time and increase sales of living arts venues, festivals, museums or large event organizers.

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Last synced: 6 months ago

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Packages

  • Total packages: 2
  • Total downloads:
    • pypi 3,147,591 last-month
  • Total docker downloads: 757,198,820
  • Total dependent packages: 70
    (may contain duplicates)
  • Total dependent repositories: 741
    (may contain duplicates)
  • Total versions: 41
  • Total maintainers: 2
pypi.org: haversine

Calculate the distance between 2 points on Earth.

  • Versions: 28
  • Dependent Packages: 68
  • Dependent Repositories: 731
  • Downloads: 3,147,591 Last month
  • Docker Downloads: 757,198,820
Rankings
Docker downloads count: 0.1%
Dependent packages count: 0.3%
Downloads: 0.3%
Dependent repos count: 0.4%
Average: 1.7%
Stargazers count: 3.8%
Forks count: 5.4%
Maintainers (2)
brouberol mapado
Last synced: 6 months ago
conda-forge.org: haversine
  • Versions: 13
  • Dependent Packages: 2
  • Dependent Repositories: 10
Rankings
Dependent repos count: 11.0%
Dependent packages count: 19.6%
Average: 19.7%
Forks count: 24.0%
Stargazers count: 24.0%
Last synced: 6 months ago

Dependencies

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Pipfile pypi
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  • pytest ~=7.1 develop
Pipfile.lock pypi
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  • numpy ==1.22.0 develop
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  • pluggy ==1.0.0 develop
  • py ==1.11.0 develop
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