Representing a location

Given some location with a latitude and longitude, for example: -35.6498, 150.2935 you can easily create a hash as either a simple string or a numeric value:

>>> latitude = -35.6498
>>> longitude = 150.2935
>>> import geogrids
>>> geogrids.gdgg.latitude_longitude_to_readable_hash(latitude=latitude, longitude=longitude)
'702020210311'
>>> geogrids.gdgg.latitude_longitude_to_numeric_hash(latitude=latitude, longitude=longitude)
12108871

Of course you can go the other direction as well:

>>> geogrids.gdgg.numeric_hash_to_latitude_longitude(12108871)
(-35.65283203125, 150.2789682218808)
>>> geogrids.gdgg.readable_hash_to_latitude_longitude('702020210311')
(-35.65283203125, 150.2789682218808)

Notice that the hashes are location approximations depending on a level of precision - the higher the precision the better the accuracy:

>>> numeric_hash = geogrids.gdgg.latitude_longitude_to_numeric_hash(latitude=latitude, longitude=longitude, precision=55)
>>> geogrids.gdgg.numeric_hash_to_latitude_longitude(numeric_hash, precision=55)
(-35.64979965984821, 150.2934998246466)

Effectively these hashes define a location within a triangular region, which you can retrieve from either the numeric_hash_to_area or the readable_hash_to_area functions, which return a collection of Location objects (usually the three vertices of a triangular region, but close to the poles for simplification the default is to return a box):

>>> vertices = geogrids.gdgg.numeric_hash_to_area(numeric_hash)
>>> vertices
[<Location [702020210311]>, <Location [702020210311]>, <Location [702020210311]>]
>>> vertices[0].latitude, vertices[0].longitude
(-35.63964843750004, 150.24252223120465)

In general it’s advisable to just stick to the hashes and the latitudes and longitudes, but the Location object does implement a __geo_feature__ interface which means you can use it with other libraries that work with this interface for more complicated geometric operations, for example via the Shapely library:

>>> from shapely import geometry
>>> points = [geometry.shape(vertex) for vertex in vertices]
>>> line = geometry.LineString(points)
>>> line.length
0.11570586750499379
>>> polygon = geometry.Polygon(line)
>>> polygon.area
0.0015986572857657128

Encoding and decoding a hash

The encoders allow you to transform a hash to an easily memorisable string and back again. Out of the box this comes with a number of encoders:

• geogrids.encoders.fucks as featured in http://www.what3fucks.com

• geogrids.encoders.goshdarnits as featured in http://www.what3goshdarnits.com/

• geogrids.encoders.pokes as featured in http://www.what3pokemon.com/

• geogrids.encoders.cheeses which doesn’t yet feature anywhere (AFAIK)

• geogrids.encoders.ducks as featured in

  http://www.what3ducks.com

Given a numeric hash of a location (see above) these are easy to use:

>>> geogrids.encoders.cheeses.hash_to_string(numeric_hash, precision=25)
'Dubliner Requeijão Provolone Telemea'
>>> geogrids.encoders.cheeses.hash_to_string(numeric_hash, precision=55)
'Dubliner Requeijão Provolone Telemea Danablu Coulommiers Chevrotin'

Or given the readable encoding it’s simple to go back the other way:

>>> numeric_hash, precision = geogrids.encoders.cheeses.string_to_hash('Dubliner Requeijão Provolone Telemea')
>>> numeric_hash, precision
(3870868551, 32)
>>> geogrids.gdgg.numeric_hash_to_latitude_longitude(numeric_hash, precision)
(-35.647064208984375, 150.2948563112389)

If you don’t want to use one of the builtin encoders, you can generate your own easily:

>>> wordlist = list('😀😎🤬😱😈👍🖖⚽🐶🐍🐡🦜🍀🌞🌚🔥')

Note the wordlist should be length that is a power of two - the level used for calculating precisions is rounded down to the closest power of two - any words after that number will be skipped.

>>> emoji_encoder = geogrids.encoders.Encoder(wordlist, separator='')
>>> emoji_encoder.hash_to_string(numeric_hash, precision)
'⚽😈😈🍀🐶🦜🖖🌚'

Warning One key consideration with the encoders: if you create an encoding and share it with someone else the wordlist must be in exactly the same order! Otherwise when decoding you’ll get completely different results!

>>> numeric_hash, precision = emoji_encoder.string_to_hash('⚽😈😈🍀🐶🦜🖖🌚')
>>> geogrids.gdgg.numeric_hash_to_latitude_longitude(numeric_hash, precision)
(-35.647064208984375, 150.2948563112389)

Requirements

geogrids doesn’t have any third party library requirements