2014-11-17 10:21:37 +00:00
|
|
|
These3Words
|
2014-11-16 21:31:11 +00:00
|
|
|
=============
|
|
|
|
|
2014-11-17 06:01:42 +00:00
|
|
|
Address any 3meter x 3meter square on earth with a unique three word name.
|
2014-11-16 21:31:50 +00:00
|
|
|
|
|
|
|
Inspired by http://what3words.com/
|
2014-11-17 00:03:10 +00:00
|
|
|
|
|
|
|
|
|
|
|
example
|
|
|
|
=======
|
|
|
|
|
|
|
|
>>> import thesethreewords as these
|
|
|
|
|
|
|
|
# the home of particle physics
|
|
|
|
>>> CERN = (46.232355, 6.055419)
|
|
|
|
|
|
|
|
>>> three = these.three_words(CERN)
|
|
|
|
>>> print three
|
|
|
|
'engirt-aleutic-canun'
|
|
|
|
>>> these.decode(three)
|
|
|
|
(46.232335567474365, 6.055419445037842)
|
|
|
|
|
|
|
|
Check out where this is on [google maps][cernmap].
|
|
|
|
|
|
|
|
|
|
|
|
requirements
|
|
|
|
============
|
|
|
|
|
|
|
|
You need to install the [geohash][geohash] library:
|
|
|
|
|
|
|
|
$ pip install geohash
|
|
|
|
|
|
|
|
|
|
|
|
six words
|
|
|
|
=========
|
|
|
|
|
|
|
|
There are a lot of 3x3m squares on the earth's surface. To encode
|
|
|
|
them in only three words requires a long wordlist, as a result
|
|
|
|
some fairly obscure words get on it. If you can live with
|
|
|
|
having to remember six words the wordlist is much shorter.
|
|
|
|
The six word wordlist comes from the amazing [humanhash][humanhash]
|
|
|
|
library. Words were chosen to maximise clarity in human
|
2014-11-17 00:06:19 +00:00
|
|
|
communication, they should be more familiar than the words
|
|
|
|
on the three wordlist:
|
2014-11-17 00:03:10 +00:00
|
|
|
|
|
|
|
>>> six = these.six_words(CERN)
|
|
|
|
>>> print six
|
|
|
|
'spaghetti-carolina-kentucky-oscar-iowa-table'
|
|
|
|
>>> these.decode(six)
|
|
|
|
(46.232335567474365, 6.055419445037842)
|
|
|
|
|
|
|
|
|
|
|
|
how it works
|
|
|
|
============
|
|
|
|
|
|
|
|
Each latitude/longitude pair is converted to a nine
|
|
|
|
character geohash. This provides about 3meter
|
|
|
|
resolution at all latitudes. The geohash is then
|
|
|
|
converted to an integer which is encoded as a string
|
|
|
|
of words.
|
|
|
|
|
|
|
|
The wordlist used to encode the `geohash` into just
|
|
|
|
three words uses your local computers dictionary. Some
|
|
|
|
attempts are made to remove really obscure words but
|
|
|
|
it could be better. You need to use the same wordlist
|
|
|
|
when encoding and decoding a `these-3-words` hash.
|
|
|
|
|
|
|
|
The `these-3-words` hash shares the
|
|
|
|
property of a `geohash` that nearby locations share
|
|
|
|
have similar `these-3-words` hashes
|
|
|
|
|
|
|
|
>>> other_CERN_site = (46.256811, 6.056792)
|
|
|
|
>>> six = these.six_words(other_CERN_site)
|
|
|
|
>>> print six
|
|
|
|
''spaghetti-carolina-kentucky-utah-seventeen-neptune'
|
|
|
|
>>> these.decode(six)
|
|
|
|
(46.256797313690186, 6.056792736053467)
|
|
|
|
|
|
|
|
The other CERN site is [here][othercernmap].
|
|
|
|
|
|
|
|
this is a [@betatim][betatim] kind of idea
|
|
|
|
|
|
|
|
[humanhash]: https://github.com/zacharyvoase/humanhash
|
|
|
|
[geohash]: https://code.google.com/p/python-geohash/
|
|
|
|
[cernmap]: https://www.google.ch/maps/place/46%C2%B013'56.4%22N+6%C2%B003'19.5%22E/@46.2323356,6.0554194,17z/data=!3m1!4b1!4m2!3m1!1s0x0:0x0
|
|
|
|
[othercernmap]: https://www.google.ch/maps/place/46%C2%B015'24.5%22N+6%C2%B003'24.4%22E/@46.256811,6.056792,14z/data=!4m2!3m1!1s0x0:0x0
|
2014-11-17 00:06:19 +00:00
|
|
|
[betatim]: https://twitter.com/betatim
|