GraphePython is a Python library for creating and analyzing graphs. It provides an implementation of Dijkstra's algorithm to find the shortest path in an undirected graph, along with tools for graph visualization and random graph generation.
- Create graphs with nodes and weighted edges
- Find the shortest path in an undirected graph
- Visualize graphs with NetworkX and Matplotlib
- Generate random connected graphs
- Python 3.7 or higher
- You must have the following libraries installed :
matplotlib3.0 or highernetworkx2.0 or higher
You can install GraphePython via pip by running the following command :
pip install GraphePythonIf you want to run some tests to check if the module is working correctly, you can use the tests in the tests folder and run the following command :
python -m unittest discover testsTo use GraphePython, you first have to import it and create a new graph :
importGraphePythonasgpgraph=gp.Graph()The next step is to add nodes to your graph. You can either add them one by one or from an array :
graph.add_node("A") # Adding a single nodegraph.add_nodes_from_array(["B", "C", "D", "E", "F", "G", "H", "I", "J", "K"]) # Adding all the nodes from B to K at onceNow that you have multiple nodes you can connect them by adding edges. Unlike nodes, edges can only be added one at a time :
graph.add_edge("A", "B", 18) # Adds an edge from A to B (and from B to A) with a weight (or cost) of 18graph.add_edge("A", "C", 22)
graph.add_edge("B", "C", 31)
graph.add_edge("C", "F", 17)
graph.add_edge("B", "E", 26)
graph.add_edge("B", "D", 12)
graph.add_edge("E", "F", 12)
graph.add_edge("D", "G", 24)
graph.add_edge("H", "G", 12)
graph.add_edge("H", "I", 7)
graph.add_edge("H", "K", 24)
graph.add_edge("K", "J", 18)
graph.add_edge("I", "J", 12)
graph.add_edge("F", "I", 13)
graph.add_edge("G", "E", 9)You can also see your graph by setting the draw input to True or by calling the draw_graph function :
graph.get_path("A", "K", draw=True) # Returns the shortest path and shows it in a Matplotlib windowgraph.draw_graph(path= [], path_text="Graph title") # This creates a new window. You can provide any path you want and it will be highlighted in red (e.g : ['A', 'B', 'E']) You can also provide a text that will be displayed above the graph in the window.This should give you something like this :
You can get the shortest path between two nodes by using the get_path function :
graph.get_path("A", "K", draw=False) # Returns the shortest path between A and K in an array here : ['A', 'C', 'F', 'I', 'J', 'K']You can also generate random graphs with GraphePython. Here's an example:
importGraphePythonasgp# Create a new graph instancegraph=gp.Graph()
# Generate a random graph with the following parameters:# - 10 nodes# - 15 edges# - Edge weights ranging from 1 to 10# - Node naming method: LETTERS (nodes will be named A, B, C, ...)graph.generate_random_graph(
number_of_nodes=10,
number_of_edges=15,
weight_range=(1, 10),
node_naming_method="LETTERS"
)
# Visualize the generated graphgraph.draw_graph(path=[], path_text="Randomly Generated Graph")
# Save the generated graph to a filegraph.save_graph("random_graph.txt", "myGraphs/")
# Load the graph back from the file (to verify saving/loading works)graph.load_graph("random_graph.txt", "myGraphs/")
graph.draw_graph(path=[], path_text="Loaded Random Graph")This will generate a random graph, visualize it, save it to a file, and reload it for further use.
As mentioned above, you can save your graphs into text files :
graph.save_graph("graph.txt", "myGraphs/") # This will save the current graph into the graph.txt file in the myGraphs foldergraph.load_graph("graph.txt", "myGraphs/") # This loads the previously saved graphThis project is licensed under the MIT License - see the LICENSE file for details.