GraphModels is a Python package providing implementations of optimization algorithms for networks, including pathfinding problem and scheduling problem like Project management.
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:
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Optimization Algorithms: Provides implementations of optimization algorithms for networks, including pathfinding algorithms like Dijkstra's algorithm.
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Scheduling Solutions: Includes algorithms for scheduling problems such as Pert algorithm, useful in project management.
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Ease of Use: Designed for simplicity with "batteries included," while still allowing easy customization of key methods.
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JSON Serialization: Models can be serialized to JSON, enabling caching and efficient storage of results.
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Pure Python Implementation: Relies only on pure-Python libraries, minimizing external dependencies.
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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.
To install GraphModels, simply use pip:
pip install GraphModels
## Basic Usage
### Dijkstra algorithm
```python
from GraphModels.DijkstraAlgorithm 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())from GraphModels.BellmanAlgorithm 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())from GraphModels.PertAlgorithm 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)