Python library to look up timezone from lat / long offline. Improved version of "pytzwhere".
Project description
This is a fast and lightweight python project to lookup the corresponding timezone for a given lat/lng on earth entirely offline.
This project is derived from and has been successfully tested against pytzwhere (github), but aims to provide improved performance and usability.
It is also similar to django-geo-timezones
The underlying timezone data is based on work done by Eric Muller.
Timezones at sea and Antarctica are not yet supported (because somewhat special rules apply there).
Dependencies
(python, math, struct, os)
numpy
maybe also numba and its Requirements
This is only for precompiling the time critical algorithms. If you want to use this, just uncomment all the @jit(...) annotations and the import ... line in timezonefinder.py. When you only look up a few points once in a while, the compilation time is probably outweighing the benefits. When using certain_timezone_at() and especially closest_timezone_at() however, I highly recommend using numba (see speed comparison below)! The amount of shortcuts used in the .bin are also only optimized for the use with numba.
Installation
(install the dependencies)
in your terminal simply:
pip install timezonefinder
Usage
Basics:
from timezonefinder import TimezoneFinder tf = TimezoneFinder()
fast algorithm:
# point = (longitude, latitude) point = (13.358, 52.5061) print( tf.timezone_at(*point) ) # = Europe/Berlin
To make sure a point is really inside a timezone (slower):
print( tf.certain_timezone_at(*point) ) # = Europe/Berlin
To find the closest timezone (slow):
# only use this when the point is not inside a polygon! # this only checks the polygons in the surrounding shortcuts (not all polygons) point = (12.773955, 55.578595) print( tf.closest_timezone_at(*point) ) # = Europe/Copenhagens
To increase search radius even more (very slow, use ``numba``!):
# this checks all the polygons within +-3 degree lng and +-3 degree lat # I recommend only slowly increasing the search radius # keep in mind that x degrees lat are not the same distance apart than x degree lng! print( tf.closest_timezone_at(lng=point[0],lat=point[1],delta_degree=3) ) # = Europe/Copenhagens
(to make sure you really got the closest timezone increase the search radius until you get a result. then increase the radius once more and take this result.)
Further application:
To maximize the chances of getting a result in a Django view it might look like:
def find_timezone(request, lat, lng):
lat = float(lat)
lng = float(lng)
try:
timezone_name = tf.timezone_at(lng, lat)
if timezone_name is None:
timezone_name = tf.closest_timezone_at(lng, lat)
# maybe even increase the search radius when it is still None
except ValueError:
# the coordinates were out of bounds
# {handle error}
# ... do something with timezone_name ...
To get an aware datetime object from the timezone name:
# first pip install pytz
from pytz import timezone, utc
from pytz.exceptions import UnknownTimeZoneError
# tzinfo has to be None (means naive)
naive_datetime = YOUR_NAIVE_DATETIME
try:
tz = timezone(timezone_name)
aware_datetime = naive_datetime.replace(tzinfo=tz)
aware_datetime_in_utc = aware_datetime.astimezone(utc)
naive_datetime_as_utc_converted_to_tz = tz.localize(naive_datetime)
except UnknownTimeZoneError:
# ... handle the error ...
also see the pytz Doc.
Using the conversion tool:
Place the file_converter.py in one folder with the tz_world.csv from tzwhere and run it as a script. It converts the .csv in a new .csv and transforms this file into the needed .bin
Place this .bin in your timezonfinder folder (overwriting the old file) to make it being used.
Please note: Neither the tests nor the file_converter.py are optimized or really beautiful. Sorry for that.
Comparison to pytzwhere
In comparison to pytzwhere I managed to speed up the queries by more than 100 times (s. test results below). Initialisation time and memory usage are also significanlty reduced, while my algorithm yields the same results. In some cases pytzwhere even does not find anything and timezonefinder does, for example when only one timezone is close to the point.
Similarities:
results
data being used
Differences:
the data is now stored in a memory friendly 35MB .bin and needed data is directly being read on the fly (instead of reading and converting the 76MB .csv (mostly floats stored as strings!) into memory every time a class is created).
precomputed shortcuts are stored in the .bin to quickly look up which polygons have to be checked (instead of creating the shortcuts on every startup)
optimized algorithms
introduced proximity algorithm
use of numba for speeding things up much further.
Excerpt from my test results*:
testing 1000 realistic points MISMATCHES**: / testing 10000 random points MISMATCHES**: / in 11000 tries 0 mismatches were made fail percentage is: 0.0 TIMES for 1000 realistic queries***: pytzwhere: 0:00:18.184299 timezonefinder: 0:00:00.126715 143.51 times faster TIMES for 10000 random queries****: pytzwhere: 0:01:36.431927 timezonefinder: 0:00:00.626145 154.01 times faster Startup times: pytzwhere: 0:00:09.531322 timezonefinder: 0:00:00.000361 26402.55 times faster
*timezone_at() with numba active
**mismatch: pytzwhere finds something and then timezonefinder finds something else
***realistic queries: just points within a timezone (= pytzwhere yields result)
****random queries: random points on earth
Speed Impact of Numba
TIMES for 1000 realistic queries***: timezone_at(): wo/ numa: 0:00:01.017575 w/ numa: 0:00:00.289854 3.51 times faster certain_timezone_at(): wo/ numa: 0:00:05.445209 w/ numa: 0:00:00.290441 14.92 times faster closest_timezone_at(): (delta_degree=1) wo/ numa: 0:02:32.666238 w/ numa: 0:00:02.688353 40.2 times faster
(this is not inlcuded in my tests because one cannot automatically enable and disable Numba)
Contact
If you notice that the tz data is outdated, encounter any bugs, have suggestions, criticism, etc. feel free to open an Issue on Git or contact me: python at michelfe dot it
License
timezonefinder is distributed under the terms of the MIT license (see LICENSE.txt).
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