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 1to1, Nto1, or the elementwise NtoN 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 elementwise 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 pointlike 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
counterclockwise 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