Getting started

This page is an example of how to use the network echos modeling framework.

Importing network-echos

Start by importing the model's classes, and matplotlib for plotting support later on.

>>> import matplotlib.pyplot as plt
>>> from netechos.core import NetworkModel
>>> from netechos.plot import NetworkPlot

Initializing a model

With the modules imported, you can access all of the model's functionality. To run a model, invoke NetworkModel from the netechos.core module to create an instance of the model object. The object will store all the data and serve as the model interface.

>>> nemodel = NetworkModel(number_of_nodes=100, interaction_type="symmetric")

With the model object instantiated as nemodel, you can use its attached methods to perform operations. For example, if you wanted to simulate with an Erdős-Rényi random network as your basis, you could initialize the starting network for that like so:

>>> nemodel.create_network(network_type="erdos_renyi", p=0.3)

Simulations also depend on the individual attitudes of the in the basis network and the connections between them, which you can initialize like so:

>>> nemodel.initialize_connections()
>>> nemodel.initialize_attitudes()

The initialize_connections() and initialize_attitudes() methods each include several optional arguments you can specify to customize how the initial connections and attitudes are assigned.

Running a simulation

With the basis network, connections, and attitudes all initialized on the model object, you can now run a simulation.

>>> nemodel.run_simulation(tmax=1000)

Once the simulation is complete, the model object can be passed to NetworkPlot from the netechos.plot module to see how the simulation went. This module allows quick access to simple aspects of the simulation results.

Reviewing a simulation

>>> neplot = NetworkPlot(nemodel)

You can examine how the mean node attitudes and mean connection strength evolved over time:

>>> means_over_time = neplot.attitude_and_connection_means()
>>> plt.show()

And you can view how each individual node's attitude evolved over time:

>>> attitudes_over_time = neplot.node_attitudes_over_time()
>>> plt.show()

For large networks, e.g., 1000+ nodes, this individual node plot will be slow and unhelpful, but it can be used with a smaller network to give an illustrative example to pair with the plot of the means earlier.