{
    "info": {
        "author": "Yang Liu",
        "author_email": "gloolar@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
        ],
        "description": "\n        ____  _______  ___________  ____  ____ ______\n       / __ \\/ ___/ / / / ___/ __ \\/ __ \\/ __ `/ ___/\n      / /_/ (__  ) /_/ / /__/ /_/ / /_/ / /_/ / /\n     / .___/____/\\__, /\\___/\\____/ .___/\\__, /_/\n    /_/         /____/          /_/    /____/\n\n[![PyPI](https://img.shields.io/pypi/v/psycopgr.svg)](https://pypi.org/project/psycopgr/)\n[![PyPI - License](https://img.shields.io/pypi/l/psycopgr.svg)](https://pypi.org/project/psycopgr/)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/psycopgr.svg)\n\n`psycopgr` is a Python wrapper of [pgRouting](http://pgrouting.org/) with one purpose:\n\n**Computing routes on real map for humans.**\n\nTested with\n\n- Python 3.6.5\n- PostgreSQL 11.2\n- PostGIS 2.5.2\n- pgRouting 2.6.2\n- osm2pgrouting 2.3.6\n\n## Preparation \n\n- Install `PostgreSQL`, `PostGIS`, and `pgRouting`\n- Create database to store map data\n- Import OpenStreet map data into database\n\nA step by step note can be found [here](https://herrkaefer.com/2016/08/30/pgrouting-notes/).\n\n## Installation\n\n```sh\npip install psycopgr\n```\n\nor\n\n```sh\npipenv install psycopgr\n```\n\n## Routing with Python!\n\nFirst,\n\n```python\nfrom psycopgr import PgrNode, PGRouting\n```\n\nCreate an PGRouting instance with database connection:\n\n```python\npgr = PGRouting(database='mydb', user='user')\n```\n\nAdjust meta datas of tables including the edge table properies if they are different from the default (only the different properties needs to be set), e.g.:\n\n```python\npgr.set_meta_data(cost='cost_s', reverse_cost='reverse_cost_s', directed=true)\n```\n\nThis is the default meta data:\n\n```python\n{\n    'table': 'ways',\n    'id': 'gid',\n    'source': 'source',\n    'target': 'target',\n    'cost': 'cost_s', # driving time in second\n    'reverse_cost': 'reverse_cost_s', # reverse driving time in second\n    'x1': 'x1',\n    'y1': 'y1',\n    'x2': 'x2',\n    'y2': 'y2',\n    'geometry': 'the_geom',\n    'has_reverse_cost': True,\n    'directed': True,\n    'srid': 4326\n}\n```\n\nNodes are points on map which are represented by `PgrNode` namedtuple with geographic coordinates (longitude and latitude) rather than vague vertex id (vid) in the tables. `PgrNodes` is defined as:\n\n```python\nPgrNode = namedtuple('PgrNode', ['id', 'lon', 'lat'])\n```\n\nin which `id` could be `None` or self-defined value, and `lon` and `lat` are double precision values. \n\nFor example:\n\n```python\nnodes = [PgrNode(None, 116.30150, 40.05500),\n         PgrNode(None, 116.36577, 40.00253),\n         PgrNode(None, 116.30560, 39.95458),\n         PgrNode(None, 116.46806, 39.99857)]\n```\n\nNow we can do routings! This is really straightforward:\n\n```python\n# many-to-many\nroutings = pgr.get_routes(nodes, nodes, end_speed=5.0, pgx_file='r.pgx')\n# one-to-one\nroutings = pgr.get_routes(nodes[0], nodes[1])\n# one-to-many\nroutings = pgr.get_routes(nodes[0], nodes)\n# many-to-one\nroutings = pgr.get_routes(nodes, node[2])\n```\n\n- `end_speed`: speed from node to nearest vertices on ways in unit km/h.\n- `gpx_file`: set it to output paths to a gpx file.\n\nThe returned is a dict of dict: `{(start_node, end_node): {'path': [PgrNode], 'cost': cost}`\n\nBy default, `cost` is traveling time along the path in unit second. It depends on the means of columns of the edge table that you set as `cost` and `reverse_cost`. You can assign the relations by `set_meta_data` function.\n\nWe can also get only costs without detailed paths returned:\n\n```python\ncosts = pgr.get_costs(nodes, nodes)\n```\n\nThe returned is also a dict: `{(start_node, end_node): cost}`\n\n## Low-level wrapper of pgRouting functions\n\n| psycopgr function | pgRouting function |\n| :---------------- | :----------------- |\n| dijkstra          | pgr_dijkstra       |\n| dijkstra_cost     | pgr_dijkstraCost   |\n| astar             | pgr_astar          |\n\nThese are direct wrappings of pgRouting functions. For example, `dijkstra` takes vertex ids as input. This list may be extended in the future.\n\n## Tutorial\n\nHere is a [tutorial](https://herrkaefer.com/2016/09/01/psycopgr-tutorial/).\n\n\n\n",
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