This is a python wrapper for the C implementation of the [[http://motion.cs.umn.edu/PowerLaw/][PowerLaw]] from the paper [[http://motion.cs.umn.edu/PowerLaw/KSG-PowerLaw.pdf][Universal Power Law Governing Pedestrian Interactions]]. It wraps the most basic functionality of the simulation.

1. Clone the repository as a submodule into your project (git submodule add [email protected]:AlexanderDavid/Powerlaw.git)
2. Navigate to the Powerlaw directory (cd Powerlaw)
3. Execute the setup.py file to install the library (python setup.py install)
4. The powerlaw library is then installed under the alias powerlaw

The following example code simulates a singular agent starting in a hallway that blocks the straight line path to the hallway.

The model revolves around a central engine (PySimulationEngine) that all agents and obstacles are a member of. The parameters of this engine are the x, and y size of the environment, the timestep, and the max number of frames.

from Powerlaw import PySimulationEngine

engine = PySimulationEngine(50, 50, 1 / 60., 50000)

Agents can be added to the model by explicitly specifying all information about the agent and parameters to be used in the underlying powerlaw equations. A more complex agent configuration is shown in example.py.

position = (0, 2.5)
goal = (10, 5)
velocity = (0.5, 0.5)
radius = 0.5
prefSpeed = 1.4
maxAcc = 20
goalRadius = 0.5
neighborDist = 10
k = 1.5
ksi = 0.54
m = 2
t0 = 3

agent = engine.addAgent(position, goal, velocity, radius, prefSpeed, maxAcc,
                goalRadius, neighborDist, k, ksi, m, t0)

Each agent's state can be queried through the simulation object using the agent's id returned by the addAgent function. Right now the getters implemented are radius, preferred velocity, current position, and current velocity.

engine.getAgentPosition(agent) # (0, 2.5)
engine.getAgentPrefVelocity(agent) # (0.5, 0.5)
engine.getAgentRadius(agent) # 0.5
engine.getAgentVelocity(agent)

The simulation also supports static obstacles defined by a line segment of two points. A more complex shape can be defined through stringing these line segments together.

engine.addObstacle((0, 1.75), (5, 1.75))
engine.addObstacle((0, 3.25), (5, 3.25))

The simulation will continue execution so long as the simulation has not exceeded maximum frames and there are still agents who have not reached their goals¹. The updateVisualisation function prints information about the current status to stdout. For brevity only the first and last 2 lines of the simulation are included. As you can see from the simulation the agent progresses along the hallway until the exit and then straight to the goal.

while not engine.endSimulation():
    engine.updateVisualisation()
    engine.updateSimulation()

Time: 0
0: (0,2.5)
Time: 0.005
0: (0.00251792,2.502)
.
.
.
Time: 7.69013
0: (9.55188,4.7643)
Time: 7.69513
0: (9.55809,4.76754)