| Name | geog JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | https://github.com/jwass/geog |
| Summary | Numpy-based vectorized geospatial functions |
| upload_time | 2016-02-04 14:59:37 |
| maintainer | None |
| docs_url | None |
| author | Jacob Wasserman |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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No coveralls.
|
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](http://toblerity.org/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](https://www.turfjs.org)
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
```
See also
--------
* `geog` is partly inspired by [TurfJS](https://www.turfjs.org)
* [PostGIS](http://postgis.net/docs/manual-1.5/ch04.html#Geography_Basics) geography type
* [Shapely](https://github.com/toblerity/shapely)
* [Proj.4](https://trac.osgeo.org/proj/)
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"description": "geog\n====\n\nA pure numpy implementation for geodesic functions. The interfaces are\nvectorized according to numpy broadcasting rules compatible with a variety of\ninputs including lists, numpy arrays, and\n[Shapely](http://toblerity.org/shapely/) geometries - allowing for 1-to-1,\nN-to-1, or the element-wise N-to-N calculations in a single call.\n\n`geog` uses a spherical Earth model (subject to change) with radius 6371.0 km.\n\n`geog` draws inspiration from [TurfJS](https://www.turfjs.org)\n\n\nOperations\n---------\n* `distance` - Compute the distance in meters between any number of longitude,latitude points\n* `course` - Compute the forward azimuth between points\n* `propagate` - Starting from some points and pointing azimuths, move some\ndistance and compute the final points.\n\n\nGetting Started\n---------------\n\nCompute the distance in meters between two locations on the surface of the\nEarth.\n```\n>>> import geog\n\n>>> boston = [-71.0589, 42.3601]\n>>> la = [-118.2500, 34.0500]\n\n>>> geog.distance(boston, la)\n4179393.4717019284\n\n\n>>> geog.course(boston, la)\n176.76437002826202\n\n```\n\n`geog` allows different sizes of inputs conforming to numpy broadcasting\nrules\n\nCompute the distances from several points to one point.\n```\n>>> dc = [-77.0164, 38.9047]\n>>> paris = [2.3508, 48.8567]\n>>> geog.distance([boston, la, dc], paris)\narray([ 5531131.56144631, 9085960.07227854, 6163490.48394848])\n\n```\n\nCompute the element-wise distance of several points to several points\n```\n>>> sydney = [151.2094, -33.865]\n>>> barcelona = [2.1833, 41.3833]\n>>> geog.distance([boston, la, dc], [paris, sydney, barcelona])\narray([ 5531131.56144631, 12072666.9425518 , 6489222.58111716])\n\n```\n\n`geog` functions can take numpy arrays as inputs\n```\n>>> import numpy as np\n>>> points = np.array([boston, la, dc])\n>>> points\narray([[ -71.0589, 42.3601],\n [-118.25 , 34.05 ],\n [ -77.0164, 38.9047]])\n>>> geog.distance(points, sydney)\narray([ 16239763.03982447, 12072666.9425518 , 15711932.63508411])\n```\n\n\n`geog` functions can also take Shapely geometries as inputs\n```\n>>> import shapely.geometry\n>>> p = shapely.geometry.Point([-90.0667, 29.9500])\n>>> geog.distance(points, p)\narray([ 2185738.94680724, 2687705.07260978, 1554066.84579387])\n\n```\n\n\nOther Uses\n----------------\nUse `propagate` to buffer a single point by passing in multiple angles.\n\n```\n>>> n_points = 6\n>>> d = 100 # meters\n>>> angles = np.linspace(0, 360, n_points)\n>>> polygon = geog.propagate(p, angles, d)\n\n```\n\nCompute the length of a line over the surface.\n```\n>>> np.sum(geog.distance(line[:-1,:], line[1:,:]))\n```\n\n\nQuick Documentation\n-------------\n`distance(p0, p1, deg=True)`\n\n`course(p0, p1, deg=True, bearing=False)`\n\n`propagate(p0, angle, d, deg=True, bearing=False)`\n\nFor all of the above, `p0` or `p1` can be:\n- single list, tuple, or Shapely Point of [lon, lat] coordinates\n- list of [lon, lat] coordinates or Shapely Points\n- N x 2 numpy array of (lon, lat) coordinates\n\nIf argument `deg` is False, then all angle arguments, coordinates and\nazimuths, will be used as radians. If `deg` is False in `course()`, then it's\noutput will also be radians.\n\nConsult the documentation on each function for more detailed descriptions of\nthe arguments.\n\n\nConventions\n-----------\n* All points, or point-like objects assume a longitude, latitude ordering.\n* Arrays of points have shape `N x 2`.\n* Azimuth/course is measured with 0 degrees as due East, increasing\n counter-clockwise so that 90 degrees is due North. The functions that\noperate on azimuth accept a `bearing=True` argument to use the more\ntraditional definition where 0 degrees is due North increasing clockwise such\nthat that 90 degrees is due East.\n\n\nInstallation\n-----------\ngeog is hosted on PyPI.\n\n```\npip install geog\n```\n\n\nSee also\n--------\n* `geog` is partly inspired by [TurfJS](https://www.turfjs.org)\n\n* [PostGIS](http://postgis.net/docs/manual-1.5/ch04.html#Geography_Basics) geography type\n* [Shapely](https://github.com/toblerity/shapely)\n* [Proj.4](https://trac.osgeo.org/proj/)",
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