nxsim is a Python package for simulating agents in a network.
Project description
nxsim
Nxsim is a Python package for simulating agents connected by any type of network using SimPy and Networkx in Python 3.4.
Install
pip3 install nxsim # from PyPI pip3 install git+git://github.com/kentwait/nxsim.git # from GitHub
Quickstart
Nxsim provides a framework for doing forward-time simulations of events occurring in a network. It uses Networkx to create a network and SimPy 3 to create agents over each node in the network.
To create a simulation, nxsim requires a graph generated by Networkx and an “agent” class to populate each node of the network.
First, create a graph using Networkx.
import networkx as nx number_of_nodes = 10 G = nx.complete_graph(number_of_nodes)
Then, subclass BaseNetworkAgent to create your own agent based on your needs.
from nxsim import BaseNetworkAgent # Just like subclassing a process in SimPy class MyAgent(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments super().__init__(environment=environment, agent_id=agent_id, state=state) # Add your own attributes here def run(self): # Add your behaviors here
Notice that “agents” in nxsim use the same concepts as “processes” in SimPy 3 except that their interactions can be limited by the graph in the simulation environment. For more information about SimPy, they have a great introduction posted on their website.
Here is a graph-based example:
import random from nxsim import BaseNetworkAgent class ZombieOutbreak(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.bite_prob = 0.05 def run(self): while True: if self.state['id'] == 1: self.zombify() yield self.env.timeout(1) else: yield self.env.event() def zombify(self): normal_neighbors = self.get_neighboring_agents(state_id=0) for neighbor in normal_neighbors: if random.random() < self.bite_prob: neighbor.state['id'] = 1 # zombie print(self.env.now, self.id, neighbor.id, sep='\t') break
You can now set-up your simulation by creating a NetworkSimulation instance.
from nxsim import NetworkSimulation # Initialize agent states. Let's assume everyone is normal. # Add keys as as necessary, but "id" must always refer to that state category init_states = [{'id': 0, } for _ in range(number_of_nodes)] # Seed a zombie init_states[5] = {'id': 1} sim = NetworkSimulation(topology=G, states=init_states, agent_type=ZombieOutbreak, max_time=30, dir_path='sim_01', num_trials=1, logging_interval=1.0)
And finally, start it up.
sim.run_simulation()
Running the simulation saves pickled dictionaries into the dir_path folder, in this case to “sim_01”. Now, let’s retrieve the history and states of the trial
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
And plot the number of zombies per time interval using matplotlib:
from matplotlib import pyplot as plt zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()] plt.plot(zombie_census)
And that’s it!
Note
This package is still under development. If you encounter a bug, please file an issue at https://github.com/kentwait/nxsim/issues to get it resolved.
Acknowledgment
Thanks to Joé Schaul for bringing ComplexNetworkSim to the world. This project is a SimPy 3- and Python 3.4-compatible fork of ComplexNetworkSim.
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