KAGraph 2024.2.26.2

Easy and Fast Implementation for Graph Class (AI Universidad Panamericana - Dr Barrera)

KAGraph Package

The KAGraph package offers a powerful and intuitive way to work with directed, weighted graphs in Python. It utilizes adjacency lists to efficiently represent graph structures, providing users with a broad range of functionalities for graph manipulation and analysis.

Installation

To get started with KAGraph, you can easily install it using pip:

pip install KAGraph

Getting Started

Here's a quick example to show you how to create a graph, add edges, and use some of the available methods:

from KAGraph import KAGraph

# Initialize the Graph
print("\nCreating graph...\n")
graph = KAGraph()
print("Graph created\n")

# Add edges to the graph
graph.new_edge('A', 'B', 10)
print("Added edge A-B with cost 10")
graph.new_edge('B', 'C', 20)
print("Added edge B-C with cost 20\n")

# Display the graph
print("Displaying graph")
graph.view_all()
print("------------\nGraph displayed\n")

# Query the cost of a path
print("Querying cost of direct edge A-C\n")
print(graph.getCost('A', 'C'))
print("Edge cost queried\n")

Loading Data Into the Graph

To populate your graph with data from a file, you can follow the pattern shown below. This example assumes you have a text file (data.txt) where the first line indicates the number of nodes and edges, and each subsequent line represents an edge with an origin node, a destiny node, and a weight.

Example data.txt Format

3 3
A B 10
B C 20
A C 15

This file indicates there are 3 nodes and 3 edges in the graph. Each of the following lines describes an edge between two nodes and its weight.

Loading the Data

Here's how you can read data.txt and load its contents into your graph:

from KAGraph import KAGraph

# Initialize your graph
graph = KAGraph()

# Load data from file
with open("data.txt") as file:
    lines = file.readlines()
nodes, edges = lines[0].split() # The first line contains counts of nodes and edges (unused here, but could be useful for validation)
for i in range(1, len(lines)): # Read each edge and add it to the graph, skip the first line with counts
    origin, destiny, weight = lines[i].split()
    graph.new_edge(origin, destiny, int(weight))  # Ensure weight is an integer

Now your graph is populated with the data from data.txt

Features

Adding and Removing Elements

• Add Edges: Create a new connection between two nodes with a specified weight using new_edge(origin, destiny, weight).
• Remove Edges: Eliminate a connection between two nodes with remove_edge(origin, destiny).
• Remove Nodes: Remove a node along with all its connected edges using remove_node(node).

Pathfinding and Analysis

• Find Shortest Path: Discover the shortest path between two nodes with find_shortest_path(start, end), which returns the sequence of nodes representing the path.
• Get Cost: Retrieve the cost of traveling from one node to another with getCost(origin, destiny).

Graph Inspection

• View All Edges: Print a comprehensive list of all edges and their respective weights with view_all().
• Get Connections: Output all outgoing connections from a specified node using getConnections(origin).
• Get Incoming Nodes: List all nodes with edges leading to a specified node with getFromNode(node).
• Get Outgoing Nodes: List all nodes directly reachable from a specified node with getToNode(node).

Usage Examples

Creating and Modifying a Graph

from KAGraph import KAGraph

# Initialize a new Graph instance
graph = KAGraph

# Add multiple edges
graph.new_edge('X', 'Y', 7)
graph.new_edge('Y', 'Z', 3)

# Remove an edge
graph.remove_edge('X', 'Y')

# Remove a node and its associated edges
graph.remove_node('Z')

Analyzing Graphs

# Find the shortest path between two nodes
path = graph.find_shortest_path('A', 'D')
print("Shortest Path:", path)

# Determine the cost of a specific path
cost = graph.getCost('A', 'D')
print("Path Cost:", cost)

Contributing

Contributions to KAGraph are welcome! Feel free to fork the repository, make your changes, and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.