Metadata-Version: 2.1
Name: shops
Version: 0.0.3
Summary: Provides data about shops in a given location, based on OpenStreetMap data.
Home-page: https://codeberg.org/matkoniecz/shop_listing
Author: Mateusz Konieczny
Author-email: matkoniecz@tutanota.com
License: UNKNOWN
Description: # Shops Package
        
        Provides data about shoplike objecs in a given location, based on OpenStreetMap data.
        
        Remember about license, see [https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright)
        
        ```
        import shops
        import os
        
        # data from https://download.geofabrik.de/europe/andorra.html
        # you can try north-america/us code standing for
        # https://download.geofabrik.de/north-america/us.html
        # but in such case you should expect much longer processing
        location_code = "europe/andorra"
        # pernament location is better, this is used in example as it likely to exist on almost any Linux
        path_processing_directory = "/tmp/ATP"
        if os.path.isdir(path_processing_directory) == False:
            os.mkdir(path_processing_directory)
        for entry in shops.osm.list_shops(location_code, path_processing_directory):
            print(entry)
        ```
        
        It will provide filtered OSM data, in a form of yielded dicts, each with `tags`, `center` and `osm_link` fields, `center` being a dict with `lat` and `lon` fields:
        
        ```
        ...
        {'tags': {'amenity': 'restaurant', 'building': 'yes', 'name': "Vermutería Arrosseria del Poble D'Auvinya"}, 'center': {'lat': 42.45450165, 'lon': 1.4926907}, 'osm_link': 'https://www.openstreetmap.org/way/1236442460'}
        {'tags': {'access': 'yes', 'amenity': 'charging_station', 'capacity': '8', 'fee': 'yes', 'name': '313 Aparcament telecabina del Tarter', 'operator': "Forces Elèctriques d'Andorra", 'payment:app': 'yes', 'payment:electromaps': 'yes'}, 'center': {'lat': 42.57838705, 'lon': 1.6470569}, 'osm_link': 'https://www.openstreetmap.org/way/1270600406'}
        {'tags': {'building': 'commercial', 'shop': 'car_repair'}, 'center': {'lat': 42.5531873, 'lon': 1.50726665}, 'osm_link': 'https://www.openstreetmap.org/way/1276422261'}
        {'tags': {'addr:city': 'Incles', 'addr:postcode': 'AD100', 'addr:street': "Camí Pont D'Incles", 'amenity': 'restaurant', 'building': 'yes', 'name': 'Lamont'}, 'center': {'lat': 42.5831009, 'lon': 1.6640846}, 'osm_link': 'https://www.openstreetmap.org/way/1286995487'}
        {'tags': {'addr:city': 'El Tarter', 'addr:postcode': 'AD100', 'addr:street': 'CG-2', 'amenity': 'restaurant', 'building': 'yes', 'cuisine': 'bar&grill', 'name': 'The Boss', 'name:ca': 'The Boss'}, 'center': {'lat': 42.57929245, 'lon': 1.64093665}, 'osm_link': 'https://www.openstreetmap.org/way/1288987664'}
        {'tags': {'building': 'yes', 'office': 'government', 'type': 'multipolygon'}, 'center': {'lat': 42.50669325, 'lon': 1.5222641000000001}, 'osm_link': 'https://www.openstreetmap.org/relation/14084224'}
        {'tags': {'amenity': 'bank', 'building': 'yes', 'name': 'Andbank', 'name:ca': 'Andbank', 'type': 'multipolygon'}, 'center': {'lat': 42.51118895, 'lon': 1.5346714750000001}, 'osm_link': 'https://www.openstreetmap.org/relation/14119424'}
        ```
        
        # Installation
        
        `pip install shops`
        
        It is uploaded to [pypi.org](https://pypi.org/project/shops/)
        
        # Behind scenes
        
        Data is downloaded, preprocessed and output cached within specified folder.
        
        First run will take long time especially for longer datasets.
        
        # Run tests
        
        ```
        python3 -m unittest
        ```
        
        # Contributions
        
        Bug reports, benchmarks, ideas, pull requests, suggestions and maybe thanks are welcome on the issue tracker!
        
        I am especially looking for ways to make this code faster.
        
        Note that for larger code changes opening issue first, before sending patch, may be a good idea.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
