Quick Start Guide#

This guide will help you get started with the basics of using the pyseldonlib package.

Installation#

Usage#

import pyseldonlib

pyseldonlib.run_simulation_from_config_file(
    config_file_path="path/to/config.toml",
    agent_file_path="path/to/agent.csv",
    network_file_path="path/to/network.csv",
    output_dir_path="path/to/output"
)

This will run the simulation as per the configuration file and save the output files in the specified directory.

Configuration TOML file Format#

[simulation]
model = "DeGroot"
rng_seed = 120

[io]
n_output_network = 20
n_output_agents = 1
print_progress = false
output_initial = true
start_output = 1
start_numbering_from = 0

[model]
max_iterations = 20

[DeGroot]
convergence = 1e-3

[network]
number_of_agents = 300
connections_per_agent = 10

Specifications#

[simulation] - model: The model to run. Options are DeGroot, Deffuant, ActivityDriven, ActivityDrivenInertial. - rng_seed: Seed for random number generation. If left empty, a random seed is used.
[io] - n_output_network: Number of iterations between writing network files. - n_output_agents: Number of iterations between writing agent opinion files. - print_progress: Whether to print the iteration time. Default is false. - output_initial: Whether to print the initial opinions and network file from step 0. Default is true. - start_output: Iteration number from which to start writing outputs. - start_numbering_from: Initial step number before the simulation starts. Default is 0.
[model] - max_iterations: Maximum number of iterations. If not set, the maximum is infinite.
[DeGroot] - convergence: Convergence threshold for the DeGroot model.
[network] - number_of_agents: Number of agents in the network. - connections_per_agent: Number of connections each agent has.