timezonefinder 1.5.6

Python library to look up timezone from lat / long offline. Improved version of "pytzwhere".

This is a fast and lightweight python project for looking up 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 at providing improved performance and usability.

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).

timezone_finder is a ruby port of this package.

Dependencies

(python, math, struct, os)

numpy

Optional:

Numba and its Requirements

This is only for precompiling the time critical algorithms. 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 is also only optimized for the use with numba.

Installation

(install the dependencies)

in your terminal simply:

pip install timezonefinder

(you might need to run this command as administrator)

Usage

Basics:

from timezonefinder import TimezoneFinder

tf = TimezoneFinder()

for testing if numba is being used: (if the import of the optimized algorithms worked)

print(TimezoneFinder.using_numba())
# this is a static method returning True or False

fast algorithm:

This approach is fast, but might not be what you are looking for: For example when there is only one possible timezone in proximity, this timezone would be returned (without checking if the point is included first).

# 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 checks all the polygons within +-1 degree lng and +-1 degree lat

#
point = (12.773955, 55.578595)
print( tf.closest_timezone_at(*point) )
# = Europe/Copenhagens

Other options:

To increase search radius even more, use the delta_degree-option:

print( tf.closest_timezone_at(point[0],point[1],delta_degree=3))
# = Europe/Copenhagens

This checks all the polygons within +-3 degree lng and +-3 degree lat. I recommend only slowly increasing the search radius, since computation time increases quite quickly (with the amount of polygons which need to be evaluated). When you want to use this feature a lot, consider using Numba to save computing time.

Also keep in mind that x degrees lat are not the same distance apart than x degree lng! So to really make sure you got the closest timezone increase the search radius until you get a result, then increase the radius once more and take this result. This should only make a difference in really rare cases however.

With exact_computation=True the distance to every polygon edge is computed (way more complicated) , instead of just evaluating the distances to all the vertices. This only makes a real difference when polygons are very close.

With return_distances=True the output looks like this:

( ‘tz_name_of_the_closest_polygon’,[ distances to all polygons in km], [tz_names of all polygons])

Note that some polygons might not be tested (for example when a zone is found to be the closest already). To prevent this use force_evaluation=True.

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:

Make sure you installed the GDAL framework (thats for converting .shp shapefiles into .json) Change to the directory of the timezonefinder package (location of file_converter.py) in your terminal and then:

wget http://efele.net/maps/tz/world/tz_world.zip
# on mac: curl "http://efele.net/maps/tz/world/tz_world.zip" -o "tz_world.zip"
unzip tz_world
ogr2ogr -f GeoJSON -t_srs crs:84 tz_world.json ./world/tz_world.shp
rm ./world/ -r
rm tz_world.zip

There should be a tz_world.json (of approx. 100MB) in the folder together with the file_converter.py now. Then run the converter by:

python file_converter.py

This converts the .json into the needed .bin (overwriting the old version!) and also updates the timezone_names.py.

Please note: Neither tests nor the file_converter.py are optimized or really beautiful. Sorry for that. If you have questions just write me (s. section ‘Contact’ below)

Comparison to pytzwhere

In comparison to pytzwhere most notably initialisation time and memory usage are significantly reduced, while the algorithms yield the same results and are as fast or even faster (depending on the dependencies used, s. test results below). 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:

• highly decreased memory usage

• highly reduced start up time

• the data is now stored in a memory friendly 18MB .bin and needed data is directly being read on the fly (instead of reading, converting and KEEPING 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 computing and storing the shortcuts on every startup)

• introduced proximity algorithm

• use of numba for precompilation (almost reaching the speed of tzwhere with shapely on and keeping the hole data in the memory)

test results*:

test correctness:
Results:
LOCATION             | EXPECTED             | COMPUTED             | Status
====================================================================
Arlington, TN        | America/Chicago      | America/Chicago      | OK
Memphis, TN          | America/Chicago      | America/Chicago      | OK
Anchorage, AK        | America/Anchorage    | America/Anchorage    | OK
Eugene, OR           | America/Los_Angeles  | America/Los_Angeles  | OK
Albany, NY           | America/New_York     | America/New_York     | OK
Moscow               | Europe/Moscow        | Europe/Moscow        | OK
Los Angeles          | America/Los_Angeles  | America/Los_Angeles  | OK
Moscow               | Europe/Moscow        | Europe/Moscow        | OK
Aspen, Colorado      | America/Denver       | America/Denver       | OK
Kiev                 | Europe/Kiev          | Europe/Kiev          | OK
Jogupalya            | Asia/Kolkata         | Asia/Kolkata         | OK
Washington DC        | America/New_York     | America/New_York     | OK
St Petersburg        | Europe/Moscow        | Europe/Moscow        | OK
Blagoveshchensk      | Asia/Yakutsk         | Asia/Yakutsk         | OK
Boston               | America/New_York     | America/New_York     | OK
Chicago              | America/Chicago      | America/Chicago      | OK
Orlando              | America/New_York     | America/New_York     | OK
Seattle              | America/Los_Angeles  | America/Los_Angeles  | OK
London               | Europe/London        | Europe/London        | OK
Church Crookham      | Europe/London        | Europe/London        | OK
Fleet                | Europe/London        | Europe/London        | OK
Paris                | Europe/Paris         | Europe/Paris         | OK
Macau                | Asia/Macau           | Asia/Macau           | OK
Russia               | Asia/Yekaterinburg   | Asia/Yekaterinburg   | OK
Salo                 | Europe/Helsinki      | Europe/Helsinki      | OK
Staffordshire        | Europe/London        | Europe/London        | OK
Muara                | Asia/Brunei          | Asia/Brunei          | OK
Puerto Montt seaport | America/Santiago     | America/Santiago     | OK
Akrotiri seaport     | Asia/Nicosia         | Asia/Nicosia         | OK
Inchon seaport       | Asia/Seoul           | Asia/Seoul           | OK
Nakhodka seaport     | Asia/Vladivostok     | Asia/Vladivostok     | OK
Truro                | Europe/London        | Europe/London        | OK
Aserbaid. Enklave    | Asia/Baku            | Asia/Baku            | OK
Tajikistani Enklave  | Asia/Dushanbe        | Asia/Dushanbe        | OK
Busingen Ger         | Europe/Busingen      | Europe/Busingen      | OK
Genf                 | Europe/Zurich        | Europe/Zurich        | OK
Lesotho              | Africa/Maseru        | Africa/Maseru        | OK
usbekish enclave     | Asia/Tashkent        | Asia/Tashkent        | OK
usbekish enclave     | Asia/Tashkent        | Asia/Tashkent        | OK
Arizona Desert 1     | America/Denver       | America/Denver       | OK
Arizona Desert 2     | America/Phoenix      | America/Phoenix      | OK
Arizona Desert 3     | America/Phoenix      | America/Phoenix      | OK
Far off Cornwall     | None                 | None                 | OK

closest_timezone_at():
LOCATION             | EXPECTED             | COMPUTED             | Status
====================================================================
Arlington, TN        | America/Chicago      | America/Chicago      | OK
Memphis, TN          | America/Chicago      | America/Chicago      | OK
Anchorage, AK        | America/Anchorage    | America/Anchorage    | OK
Shore Lake Michigan  | America/New_York     | America/New_York     | OK
English Channel1     | Europe/London        | Europe/London        | OK
English Channel2     | Europe/Paris         | Europe/Paris         | OK
Oresund Bridge1      | Europe/Stockholm     | Europe/Stockholm     | OK
Oresund Bridge2      | Europe/Copenhagen    | Europe/Copenhagen    | OK

testing 10000 realistic points
[These tests dont make sense at the moment because tzwhere is still using old data]
testing 1000 realistic points
MISMATCHES:
Point                                    | timezone_at()        | certain_timezone_at() | tzwhere
=========================================================================

in 1000 tries 0 mismatches were made

testing 1000 random points
MISMATCHES:
Point                                    | timezone_at()        | certain_timezone_at() | tzwhere
=========================================================================
(57.71985093778474, 50.93465824884237)   | Europe/Kirov         | Europe/Kirov          | Europe/Volgograd
(56.993217193375955, -123.66721983141636) | America/Dawson_Creek | America/Dawson_Creek  | America/Vancouver


shapely: OFF (tzwhere)
Numba: OFF (timezonefinder)

TIMES for  1000 realistic queries:
tzwhere: 0:00:17.819268
timezonefinder: 0:00:03.269472
5.45 times faster


TIMES for  1000 random queries:
tzwhere: 0:00:09.189154
timezonefinder: 0:00:01.748470
5.26 times faster


shapely: OFF (tzwhere)
Numba: ON (timezonefinder)


TIMES for  10000 realistic points
tzwhere: 0:03:01.536640
timezonefinder: 0:00:00.930006
195.2 times faster


TIMES for  10000 random points
tzwhere: 0:01:34.495648
timezonefinder: 0:00:00.545236
173.31 times faster

Startup times:
tzwhere: 0:00:07.760545
timezonefinder: 0:00:00.000874
8879.34 times faster


shapely: ON (tzwhere)
Numba: ON (timezonefinder)

TIMES for  10000 realistic points
tzwhere: 0:00:00.787326
timezonefinder: 0:00:00.895679
0.88 times faster

TIMES for  10000 random queries:
tzwhere: 0:00:01.358131
timezonefinder: 0:00:01.042770
1.3 times faster

Startup times:
tzwhere: 0:00:35.286660
timezonefinder: 0:00:00.000281
125575.3 times faster

* System: MacBookPro 2,4GHz i5 (2014) 4GB RAM SSD pytzwhere with numpy 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

Known Issues

I ran tests for approx. 5M points and these are no mistakes I found.

Contact

This is the first public python project I did, so most certainly there is stuff I missed, things I could have optimized even further etc. That’s why I would be really glad to get some feedback on my code.

If you notice that the tz data is outdated, encounter any bugs, have suggestions, criticism, etc. feel free to open an Issue, add a Pull Requests on Git or …

contact me: python at michelfe dot it

Credits

Thanks to: Adam for adding organisational features to the project and for helping me with publishing and testing routines. cstich for the little conversion script (.shp to .json)

License

timezonefinder is distributed under the terms of the MIT license (see LICENSE.txt).

Changelog

1.5.6 (2016-06-16)

• using little endian encoding now

• introduced test for checking the proper functionality of the helper functions

• wrote tests for proximity algorithms

• improved proximity algorithms: introduced exact_computation, return_distances and force_evaluation functionality (s. Readme or documentation for more info)

1.5.5 (2016-06-03)

• using the newest version (2016d, May 2016) of the tz world data

• holes in the polygons which are stored in the tz_world data are now correctly stored and handled

• rewrote the file_converter for storing the holes at the end of the timezone_data.bin

• added specific test cases for hole handling

• made some optimizations in the algorithms

1.5.4 (2016-04-26)

• using the newest version (2016b) of the tz world data

• rewrote the file_converter for parsing a .json created from the tz_worlds .shp

• had to temporarily fix one polygon manually which had the invalid TZID: ‘America/Monterey’ (should be ‘America/Monterrey’)

• had to make tests less strict because tzwhere still used the old data at the time and some results were simply different now

1.5.3 (2016-04-23)

• using 32-bit ints for storing the polygons now (instead of 64-bit): I calculated that the minimum accuracy (at the equator) is 1cm with the encoding being used. Tests passed.

• Benefits: 18MB file instead of 35MB, another 10-30% speed boost (depending on your hardware)

1.5.2 (2016-04-20)

• added python 2.7.6 support: replaced strings in unpack (unsupported by python 2.7.6 or earlier) with byte strings

• timezone names are now loaded from a separate file for better modularity

1.5.1 (2016-04-18)

• added python 2.7.8+ support:

  Therefore I had to change the tests a little bit (some operations were not supported). This only affects output. I also had to replace one part of the algorithms to prevent overflow in Python 2.7

1.5.0 (2016-04-12)

• automatically using optimized algorithms now (when numba is installed)

• added TimezoneFinder.using_numba() function to check if the import worked

1.4.0 (2016-04-07)

• Added the file_converter.py to the repository: It converts the .csv from pytzwhere to another .csv and this one into the used .bin.

  Especially the shortcut computation and the boundary storage in there save a lot of reading and computation time, when deciding which timezone the coordinates are in. It will help to keep the package up to date, even when the timezone data should change in the future.