psycopgr 1.0.6

Description:

psycopgr 1.0.6

____ _______ ___________ ____ ____ ______
/ __ \/ ___/ / / / ___/ __ \/ __ \/ __ `/ ___/
/ /_/ (__ ) /_/ / /__/ /_/ / /_/ / /_/ / /
/ .___/____/\__, /\___/\____/ .___/\__, /_/
/_/ /____/ /_/ /____/




psycopgr is a Python wrapper of pgRouting with one purpose:
Computing routes on real map for humans.
Tested with

Python 3.6.5
PostgreSQL 11.2
PostGIS 2.5.2
pgRouting 2.6.2
osm2pgrouting 2.3.6

Preparation

Install PostgreSQL, PostGIS, and pgRouting
Create database to store map data
Import OpenStreet map data into database

A step by step note can be found here.
Installation
pip install psycopgr

or
pipenv install psycopgr

Routing with Python!
First,
from psycopgr import PgrNode, PGRouting

Create an PGRouting instance with database connection:
pgr = PGRouting(database='mydb', user='user')

Adjust 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.:
pgr.set_meta_data(cost='cost_s', reverse_cost='reverse_cost_s', directed=true)

This is the default meta data:
{
'table': 'ways',
'id': 'gid',
'source': 'source',
'target': 'target',
'cost': 'cost_s', # driving time in second
'reverse_cost': 'reverse_cost_s', # reverse driving time in second
'x1': 'x1',
'y1': 'y1',
'x2': 'x2',
'y2': 'y2',
'geometry': 'the_geom',
'has_reverse_cost': True,
'directed': True,
'srid': 4326
}

Nodes 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:
PgrNode = namedtuple('PgrNode', ['id', 'lon', 'lat'])

in which id could be None or self-defined value, and lon and lat are double precision values.
For example:
nodes = [PgrNode(None, 116.30150, 40.05500),
PgrNode(None, 116.36577, 40.00253),
PgrNode(None, 116.30560, 39.95458),
PgrNode(None, 116.46806, 39.99857)]

Now we can do routings! This is really straightforward:
# many-to-many
routings = pgr.get_routes(nodes, nodes, end_speed=5.0, pgx_file='r.pgx')
# one-to-one
routings = pgr.get_routes(nodes[0], nodes[1])
# one-to-many
routings = pgr.get_routes(nodes[0], nodes)
# many-to-one
routings = pgr.get_routes(nodes, node[2])


end_speed: speed from node to nearest vertices on ways in unit km/h.
gpx_file: set it to output paths to a gpx file.

The returned is a dict of dict: {(start_node, end_node): {'path': [PgrNode], 'cost': cost}
By 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.
We can also get only costs without detailed paths returned:
costs = pgr.get_costs(nodes, nodes)

The returned is also a dict: {(start_node, end_node): cost}
Low-level wrapper of pgRouting functions



psycopgr function
pgRouting function




dijkstra
pgr_dijkstra


dijkstra_cost
pgr_dijkstraCost


astar
pgr_astar



These are direct wrappings of pgRouting functions. For example, dijkstra takes vertex ids as input. This list may be extended in the future.
Tutorial
Here is a tutorial.