Metadata-Version: 2.1
Name: road-agent
Version: 1.0.0
Summary: Object-oriented framework for modeling of mobile agents.
Home-page: http://gitlab.com/rgarcia-herrera/road-agent
Author: Rodrigo Garcia
Author-email: rgarcia@iecologia.unam.mx
License: GPLv3
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Dist: LatLon
Requires-Dist: requests
Requires-Dist: sklearn
Requires-Dist: osmnx

==========
Road Agent
==========

A Python framework for modeling mobile agents.

Create simulations of mobile agents, to be run on real-world maps. Like 
the game of tag described in the docs, some of whose frames are shown below.

The Road Agent framework is distributed with two router alternatives:
 1. NXRouter, wich uses shortest paths computed with the NetworkX library.
 2. BRouter wich uses a BRouter server to query routes.

NXRouter is simpler but easier to run. BRouter is very robust but it
requires the installation and running of the BRouter server which is a
java program and maybe a long download, but routes are more realistic.

Read the `full documentation`__.

.. __: https://road-agent.readthedocs.io/


.. image:: https://readthedocs.org/projects/road-agent/badge/?version=latest

.. image:: https://badge.fury.io/py/road-agent.svg
    :target: https://badge.fury.io/py/road-agent

.. image:: https://road-agent.readthedocs.io/en/latest/_images/frames.png


Short example simulation
========================

This is a veery short stub just to show the main feature of the agent
class, which is to navigate a map, in this case using routes computed
using `NetworkX`__'s *shortest_path*.

.. __: https://networkx.github.io/

To create a simulation one must extend the Agent class with the behaviours under
study. This example just does an empty extension.

A more interesting simulation is included in this `Jupyter Notebook`__.

.. __: https://road-agent.readthedocs.io/en/latest/jupyter_tutorial_BRouter.html

::

  from LatLon import LatLon
  from road_agent import Agent
  from road_agent.router import NXRouter
  import osmnx as ox

  G = ox.graph_from_point((19.3838,-99.1758), distance=1000)
  router = NXRouter(G)
  r = router.get_route(points=points, speed=3)

  # extend Agent class to code behaviours specific to your mobile agent
  class Bike(Agent):
      pass

  point = LatLon(19.3881769, -99.1794493)
  dest = LatLon(19.3858363, -99.1767216)

  # create bike instance
  b = Bike(point=point, dest=dest)

  # create route using NetworkX
  b.update_route()

  # traverse agent's route, step by step
  for t in range(len(b.route)):
      b.step()




Installation
============

Install library using pip::

  $ pip install road-agent


Install optional dependency: BRouter
____________________________________

Agents may use `BRouter`__ for routing, which can use several different profiles for
routing and is specially good creating routes for riding bikes. It is a java 
program, which runs in a web server. Here's how to install a local server.

.. __: http://brouter.de/brouter/

BRouters needs data files for its routing algorithm. Download `data
files`__ (rd5) into segments4 dir. You may copy-paste these steps to
download them all, but beware: it's about 5.2 gigabytes:  ::

  mkdir -p ~/opt/brouter/segments4
  cd ~/opt/brouter/segments4
  wget https://gitlab.com/rgarcia-herrera/road-agent/raw/master/get_segments.sh
  wget https://gitlab.com/rgarcia-herrera/road-agent/raw/master/segments.txt
  chmod +x get_segments.sh
  ./get_segments.sh


.. __: http://brouter.de/brouter/segments4/


Download and unzip BRouter (replace ~/opt/ with your preferred install
dir and 1_4_11 with latest version)::

  cd ~/opt/brouter
  wget http://brouter.de/brouter_bin/brouter_1_4_11.zip
  unzip brouter_1_4_11.zip
  chmod +x ./standalone/server.sh


Run server.sh to start BRouter server.




