## geog

A pure numpy implementation for geodesic functions. The interfaces are vectorized according to numpy broadcasting rules compatible with a variety of inputs including lists, numpy arrays, and Shapely geometries - allowing for 1-to-1, N-to-1, or the element-wise N-to-N calculations in a single call.

`geog`

uses a spherical Earth model (subject to change) with radius 6371.0 km.

`geog`

draws inspiration from TurfJS

## Operations

`distance`

- Compute the distance in meters between any number of longitude,latitude points`course`

- Compute the forward azimuth between points`propagate`

- Starting from some points and pointing azimuths, move some distance and compute the final points.

## Getting Started

Compute the distance in meters between two locations on the surface of the Earth.

```
>>> import geog
>>> boston = [-71.0589, 42.3601]
>>> la = [-118.2500, 34.0500]
>>> geog.distance(boston, la)
4179393.4717019284
>>> geog.course(boston, la)
176.76437002826202
```

`geog`

allows different sizes of inputs conforming to numpy broadcasting rules

Compute the distances from several points to one point.

```
>>> dc = [-77.0164, 38.9047]
>>> paris = [2.3508, 48.8567]
>>> geog.distance([boston, la, dc], paris)
array([ 5531131.56144631, 9085960.07227854, 6163490.48394848])
```

Compute the element-wise distance of several points to several points

```
>>> sydney = [151.2094, -33.865]
>>> barcelona = [2.1833, 41.3833]
>>> geog.distance([boston, la, dc], [paris, sydney, barcelona])
array([ 5531131.56144631, 12072666.9425518 , 6489222.58111716])
```

`geog`

functions can take numpy arrays as inputs

```
>>> import numpy as np
>>> points = np.array([boston, la, dc])
>>> points
array([[ -71.0589, 42.3601],
[-118.25 , 34.05 ],
[ -77.0164, 38.9047]])
>>> geog.distance(points, sydney)
array([ 16239763.03982447, 12072666.9425518 , 15711932.63508411])
```

`geog`

functions can also take Shapely geometries as inputs

```
>>> import shapely.geometry
>>> p = shapely.geometry.Point([-90.0667, 29.9500])
>>> geog.distance(points, p)
array([ 2185738.94680724, 2687705.07260978, 1554066.84579387])
```

## Other Uses

Use `propagate`

to buffer a single point by passing in multiple angles.

```
>>> n_points = 6
>>> d = 100 # meters
>>> angles = np.linspace(0, 360, n_points)
>>> polygon = geog.propagate(p, angles, d)
```

Compute the length of a line over the surface.

```
>>> np.sum(geog.distance(line[:-1,:], line[1:,:]))
```

## Quick Documentation

`distance(p0, p1, deg=True)`

`course(p0, p1, deg=True, bearing=False)`

`propagate(p0, angle, d, deg=True, bearing=False)`

For all of the above, `p0`

or `p1`

can be:

- single list, tuple, or Shapely Point of [lon, lat] coordinates
- list of [lon, lat] coordinates or Shapely Points
- N x 2 numpy array of (lon, lat) coordinates

If argument `deg`

is False, then all angle arguments, coordinates and azimuths, will be used as radians. If `deg`

is False in `course()`

, then it's output will also be radians.

Consult the documentation on each function for more detailed descriptions of the arguments.

## Conventions

- All points, or point-like objects assume a longitude, latitude ordering.
- Arrays of points have shape
`N x 2`

. - Azimuth/course is measured with 0 degrees as due East, increasing counter-clockwise so that 90 degrees is due North. The functions that operate on azimuth accept a
`bearing=True`

argument to use the more traditional definition where 0 degrees is due North increasing clockwise such that that 90 degrees is due East.

## Installation

geog is hosted on PyPI.

```
pip install geog
```