GraphModels 0.1.0

GraphModels: A package containing algorithms for pathfinding, scheduling, and maximum flow problems

GraphModels

GraphModels is a Python package providing implementations of optimization algorithms for networks, including pathfinding problem and scheduling problem like Project management.

• Why GraphModels?
• Installation
• Basic Usage
• GraphModels In The Wild
• Thanks

Why GraphModels?

GraphModels is designed to offer efficient and easy-to-use implementations of essential optimization algorithms for network-related problems. Whether you need to find the shortest path in a graph or manage project schedules effectively, GraphModels provides robust solutions.

GraphModels offers several compelling reasons to use it:

• Optimization Algorithms: Provides implementations of optimization algorithms for networks, including pathfinding algorithms like Dijkstra's algorithm.

• Scheduling Solutions: Includes algorithms for scheduling problems such as Pert algorithm, useful in project management.

• Ease of Use: Designed for simplicity with "batteries included," while still allowing easy customization of key methods.

• JSON Serialization: Models can be serialized to JSON, enabling caching and efficient storage of results.

• Pure Python Implementation: Relies only on pure-Python libraries, minimizing external dependencies.

• Python Compatibility: Tested on Python versions 3.7, 3.8, 3.9, and 3.10, ensuring compatibility across recent Python releases.

By leveraging these features, GraphModels provides a robust framework for solving network optimization and scheduling problems with ease and efficiency.

Installation

To install GraphModels, simply use pip:

pip install GraphModels

## Basic Usage
### Dijkstra algorithm

```python
from GraphModels import DijkstraAlgorithm

# Create an instance of Dijkstra's algorithm
dijkstra = DijkstraAlgorithm()

# Example usage: find the shortest path in a graph
shortest_path = dijkstra.final_solution()
print("Shortest path:", shortest_path)
# For the visualization of the solution 
print(dijkstra.visualization())

Bellman algorithm

from GraphModels import BellmanAlgorithm

# Create an instance of Dijkstra's algorithm
bellman = BellmanAlgorithm()

# Example usage: find the shortest path in a graph
shortest_path = bellman.final_solution()
print("Shortest path:", shortest_path)
# For the visualization of the solution 
print(bellman.visualization())

Pert algorithm

from GraphModels import PertAlgorithm

# Create an instance of Pert algorithm
pert = PertAlgorithm()

# Example usage: manage project schedules
earliest_dates = pert.date_au_plutot()
latest_dates = pert.date_au_plus_tard()
critical_path = pert.chemin_critique()
print("Earliest dates:", earliest_dates)
print("Critical path:", critical_path)