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        "author": "Joway Wong",
        "author_email": "joway.w@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Web Environment",
            "Framework :: Django",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Internet :: WWW/HTTP",
            "Topic :: Internet :: WWW/HTTP :: Dynamic Content"
        ],
        "description": "Django-GeoField\n=====\n![](https://api.travis-ci.org/joway/django-geofield.svg?branch=master)\n\nA lightweight Django Geo Field to save and handle Geo Points. It supports to search the nearby points by their geohash.\n\nQuick start\n-----------\n\n0. Install django-geofield\n\n        pip install django-geofield\n\n1. Define GeoPositionField in your Model like this::\n\n        class Point(models.Model):\n            ...\n            position = GeoPositionField(db_index=True)\n\n2. Run `python manage.py makemigrations` and `python manage.py migrate` to make it effect\n\n3. The field will save a geo point with a string into database like this :\n\n        Point.objects.create(\n                        position=GeoPosition(lat, lon, precision=6))\n\n        'wtmm7w,30.49145747305400533377905958332121372222900390625,120.042387425481223317547119222581386566162109375'\n\n4. If you want to query the points whose geohash is matched exactly with the given point , you can :\n\n        pos = Point.objects.get(id=1)\n\n        points_matched = Point.objects.filter(position__geoprecise=pos.position.geohash)\n\n\n    The '__geoprecise' lookup will find all points have the same geohash.\n\n5. If you want to query the points in expand area to eliminate the geohash's marginal error, you can :\n\n        pos = Point.objects.get(id=1)\n\n        points_matched = Point.objects.filter(position__geosearch=pos.position.geohash)\n\n    The '__geosearch' lookup will find all points have one of 9 ( 1 center point and 8 expand point) geohash .\n\n\n6. If you want to query the points within a specific range , you should lookup the geohash table to get the geohash length you want, then just search the cropped length.\n\n        pos = Point.objects.get(id=1)\n\n        points_matched = Point.objects.filter(position__geosearch=pos.position.geohash[0:4])\n\n    ![](http://images.cnitblog.com/blog/522490/201309/09185913-9f6f65fc3d3c40ecb3328970831c625c.png)\n\n#### PS: \n\nIf you want to limit the distance strictly, you should writer your own codes to filter the result .\n\nAbout GeoHash\n------\n\n[Wikipedia](http://en.wikipedia.org/wiki/Geohash)\n\n\n\n\nSupport\n------\n\npython:\n  - \"2.7\"\n  - \"3.2\"\n  - \"3.3\"\n  - \"3.4\"\n  - \"3.5\"\n  - \"3.5-dev\" # 3.5 development branch\n  - \"nightly\" # currently points to 3.6-dev\n\n\nThanks\n------\n\n[python-geohash](https://github.com/hkwi/python-geohash/blob/master/geohash.py)\n\nLicense\n------\n\nMIT",
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        "license": "MIT License",
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