Overview
The IdealFlow module provides tools for creating and managing Ideal Flow Networks (IFN). It includes functionalities for managing data, manipulating network structures, and querying network information. This module is designed to be flexible and scalable for various network analysis applications.
Main Classes
The IdealFlow module includes the following primary class:
- IFN: The main class representing an Ideal Flow Network, which provides various methods for analyzing and manipulating nodes, links, adjacency lists, matrices,
and performing network analysis and metric, including path finding, cycles analysis, analyzing connectivity, signature, and more. This class is designed to handle flow networks and their properties through mathematical operations and context.
Example Usage
Here is a basic example of how to use the IdealFlow module:
from IdealFlow.Network import IFN
# Create a new Ideal Flow Network instance
n = IFN()
# Set up the adjacency list
adj_list = {'a': {'b': 2}, 'b': {'c': 2}, 'c': {'a': 1, 'd': 1}, 'd': {'e': 1}, 'e': {'a': 1}}
n.set_data(adj_list)
# Add a link between nodes 'a' and 'b'
n.add_link('a', 'b', 5)
# Query the neighbors of node 'a'
in_neighbors = n.in_neighbors('a')
print("In-Neighbors of 'a':", in_neighbors)
out_neighbors = n.out_neighbors('a')
print("Out-Neighbors of 'a':", out_neighbors)
# Display the flows
n.show();
The above example demonstrates how to create a network, add links, and retrieve network information.
Categories
For more detailed information, see the following sections: