0. last version geo-squizzy-0.2.2(stable)
1. last version geo-squizzy-0.2.2.dev0(unstable)
2. no external dependencies

1.pip install geo-squizzy

See the DEMO page.

===================

Import

from geosquizzy.geosquizzy import GeoSquizzy

Initialization(Currently support only for GeoJSON docs type FeatureCollection)

geojson_options = {'mode': 'static', 'geojson_type': 'FeatureCollection'}
outcome_options = {}
optim = {'batch': 1, 'loss': -5.0}
socket_options = {'HOST': 'localhost',
                  'PORT': 8030,
                  'FAMILY': AF_INET,
                  'TYPE': SOCK_STREAM}

geojson_options = {'geojson_options': geojson_options},
                   'outcome_options': outcome_options,
                   'optim': optim,
                   'socket_options': socket_options}

geo_squizzy = GeoSquizzy(**geojson_options)

Fetch data(keep in mind that data passed to start() method has to be in utf-8 format) and execute start() method

data = get_geojson(url="https://raw.githubusercontent.com/LowerSilesians/geo-squizzy/
                  master/build_big_data/test_data/ExampleDataPoint.json")
geo_squizzy.start(geojson=data)

Consume

geo_squizzy.get_results()

===================

GeoSquizzy(kwargs)
@geojson_options: only possible and required option for now {'mode': 'static', 'geojson_type': 'FeatureCollection'}
@outcome_options: no kwargs for this options
@optim: required
    @batch is the size of a item group on which optimization will be made range(1, +oo)
speed of algorithm grows with decrease of batch size
    @loss factor of possible data loss range(-oo, +oo)
speed of algorithm grows when loss head to minus infinity
@socket_options: no required
    If added, together with GsDemon allow for interactive communication with algorithm

GeoSquizzy.get_results()
@return python list() object which consist of dict() elements where each has this structure
{'values': ['-168.8205037850924', ' 131.69420530060995'], 'keys': ['coordinates', 'geometry', 'features']}
@values example values of @keys[0]
@keys a list of founded keys, presented in descending order(leaf -> root)

from demon.gs_demon import GsDemon
  
from socket import AF_INET, SOCK_STREAM  

import sys  

GsDemon example run script

if __name__ == "__main__":

demon = GsDemon(pid_file='PATH/logs/pid.txt',
                std_in='PATH/logs/in.txt',
                std_out='PATH/logs/out.txt',
                std_err='PATH/logs/err.txt',
                HOST='localhost',
                PORT=PORT,
                FAMILY=AF_INET,
                TYPE=SOCK_STREAM,
                CONNECTIONS=INT)
if len(sys.argv) == 2:
    if 'start' == sys.argv[1]:
        demon.start()
    elif 'stop' == sys.argv[1]:
        demon.stop()
    elif 'restart' == sys.argv[1]:
        demon.restart()
    else:
        print("Unknown command")
        sys.exit(2)
    sys.exit(0)
else:
    print("usage: %s start|stop|restart" % sys.argv[0])
    sys.exit(2)
    

python demon_script.py start || stop || restart

After initializing demon, we can provide socket_options for geosquizzy client
which will communicate with demon(for now sending new keys), demon will then broadcast all new messages to other clients

Example will be provided with geogeschenk demo app
Server https://github.com/LowerSilesians/geo-geschenk-server
Client
(If You would create a client socket and listen(read data) on demon port then all the time during algorithm work You would be informed about new keys