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.
