Added Python Script for NetworkX Operations

2023-round-2/axif0/nx_tutorial_script.py

@@ -0,0 +1,38 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+
+# Create a directed graph and add nodes and edges
+def create_graph():
+    G = nx.DiGraph()
+    nodes = ['Outreachy', 1, (1, 2), 1.1, True, 'networkx', (2, 3), 2.2, False, 'asif']
+    G.add_nodes_from((node, {'color': 'blue'}) for node in nodes)
+    edges = [('Outreachy', 1), (1, (1, 2)), ((1, 2), 1.1), (1.1, True), (True, 'networkx'), ('networkx', (2, 3)), ((2, 3), 2.2), (2.2, False), (False, 'asif'), ('asif', 'Outreachy'), ('Outreachy', 2.2), (1, (2, 3)), ((1, 2), True), (1.1, False)]
+    G.add_edges_from((edge[0], edge[1], {'color': 'green'}) for edge in edges)
+    return G
+
+#  Calculate the shortest path between all pairs of nodes
+def calculate_shortest_path(G):
+    paths = nx.floyd_warshall(G)
+    for source, targets in paths.items():
+        for target, path in targets.items():
+            print(f'Shortest path from {source} to {target}: {path}')
+
+# Calculate the degree centrality of the nodes
+def calculate_centrality(G):
+    centrality = nx.degree_centrality(G)
+    print('Degree centrality:', centrality)
+
+# Draw and show the graph
+def plot_graph(G):
+    pos = nx.spring_layout(G, seed=42)
+    nx.draw(G, pos, with_labels=True, node_color=[data['color'] for node, data in G.nodes(data=True)], edge_color=[data['color'] for _, _, data in G.edges(data=True)])
+    plt.show()
+
+def main():
+    G = create_graph()
+    calculate_shortest_path(G)
+    calculate_centrality(G)
+    plot_graph(G)
+
+if __name__ == "__main__":
+    main()