Metadata-Version: 2.1
Name: geograpy3
Version: 0.1.1
Summary: Extract countries, regions and cities from a URL or text
Home-page: https://github.com/somnathrakshit/geograpy3
Author: Somnath Rakshit
Author-email: somnath52@gmail.com
License: MIT
Download-URL: https://github.com/somnathrakshit/geograpy3
Description: 
        Geograpy3 is a fork of [Geograpy2](https://github.com/Corollarium/geograpy2), which is itself a fork of [geograpy](https://github.com/ushahidi/geograpy) and inherits
        most of it, but solves several problems (such as support for utf8, places names 
        with multiple words, confusion over homonyms etc). Also, Geograpy3 is compatible with Python 3, unlike Geography2.
        
        Geograpy3
        ========
        
        Extract place names from a URL or text, and add context to those names -- for 
        example distinguishing between a country, region or city. 
        
        ## Install & Setup
        
        Grab the package using `pip` (this will take a few minutes)
        
            pip install geograpy3
        
        Geograpy3 uses [NLTK](http://www.nltk.org/) for entity recognition, so you'll also need 
        to download the models we're using. Fortunately there's a command that'll take 
        care of this for you. 
        
            geograpy-nltk
        
        ## Basic Usage
        
        Import the module, give some text or a URL, and presto.
        
            import geograpy
            url = 'http://www.bbc.com/news/world-europe-26919928'
            places = geograpy.get_place_context(url=url)
        
        Now you have access to information about all the places mentioned in the linked 
        article. 
        
        * `places.countries` _contains a list of country names_
        * `places.regions` _contains a list of region names_
        * `places.cities` _contains a list of city names_
        * `places.other` _lists everything that wasn't clearly a country, region or city_
        
        Note that the `other` list might be useful for shorter texts, to pull out 
        information like street names, points of interest, etc, but at the moment is 
        a bit messy when scanning longer texts that contain possessive forms of proper 
        nouns (like "Russian" instead of "Russia").
        
        ## But Wait, There's More
        
        In addition to listing the names of discovered places, you'll also get some 
        information about the relationships between places.
        
        * `places.country_regions` _regions broken down by country_
        * `places.country_cities` _cities broken down by country_
        * `places.address_strings` _city, region, country strings useful for geocoding_
        
        ## Last But Not Least
        
        While a text might mention many places, it's probably focused on one or two, so 
        Geograpy also breaks down countries, regions and cities by number of mentions.
        
        * `places.country_mentions`
        * `places.region_mentions`
        * `places.city_mentions`
        
        Each of these returns a list of tuples. The first item in the tuple is the place 
        name and the second item is the number of mentions. For example:
        
            [('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)]  
        
        ## If You're Really Serious
        
        You can of course use each of Geograpy's modules on their own. For example:
        
            from geograpy import extraction
        
            e = extraction.Extractor(url='http://www.bbc.com/news/world-europe-26919928')
            e.find_entities()
        
            # You can now access all of the places found by the Extractor
            print e.places
        
        Place context is handled in the `places` module. For example:
        
            from geograpy import places
        
            pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States'])
            
            pc.set_countries()
            print pc.countries #['United States']
        
            pc.set_regions()
            print pc.regions #['Ohio']
        
            pc.set_cities()
            print pc.cities #['Cleveland']
        
            print pc.address_strings #['Cleveland, Ohio, United States']
        
        And of course all of the other information shown above (`country_regions` etc) 
        is available after the corresponding `set_` method is called.
        
        
        ## Credits
        
        Geograpy uses the following excellent libraries:
        
        * [NLTK](http://www.nltk.org/) for entity recognition
        * [newspaper](https://github.com/codelucas/newspaper) for text extraction from HTML
        * [jellyfish](https://github.com/sunlightlabs/jellyfish) for fuzzy text match
        * [pycountry](https://pypi.python.org/pypi/pycountry) for country/region lookups
        
        Geograpy uses the following data sources:
        
        * [GeoLite2](http://dev.maxmind.com/geoip/geoip2/geolite2/) for city lookups
        * [ISO3166ErrorDictionary](https://github.com/bodacea/countryname/blob/master/countryname/databases/ISO3166ErrorDictionary.csv) for common country mispellings _via [Sara-Jayne Terp](https://github.com/bodacea)_
        
        Hat tip to [Chris Albon](https://github.com/chrisalbon) for the name.
        
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