import streamlit as st import matplotlib.pyplot as plt import networkx as nx # Add a sidebar with options sidebar_option = st.sidebar.radio("Select an option", ["Select an option", "Basic: Properties"]) # Function to display properties and graph def display_graph_properties(G): # Initialize a list for path lengths pathlengths = [] # Display the source-target shortest path lengths st.write("### Source vertex {target:length, }") for v in G.nodes(): spl = dict(nx.single_source_shortest_path_length(G, v)) st.write(f"Vertex {v}: {spl}") for p in spl: pathlengths.append(spl[p]) # Calculate and display the average shortest path length avg_path_length = sum(pathlengths) / len(pathlengths) st.write(f"### Average shortest path length: {avg_path_length}") # Calculate and display the distribution of path lengths dist = {} for p in pathlengths: if p in dist: dist[p] += 1 else: dist[p] = 1 st.write("### Length #paths") for d in sorted(dist.keys()): st.write(f"Length {d}: {dist[d]} paths") # Display the graph metrics with a "Properties" heading st.write("### Properties") st.write(f"Radius: {nx.radius(G)}") st.write(f"Diameter: {nx.diameter(G)}") st.write(f"Eccentricity: {nx.eccentricity(G)}") st.write(f"Center: {nx.center(G)}") st.write(f"Periphery: {nx.periphery(G)}") st.write(f"Density: {nx.density(G)}") # Visualize the graph st.write("### Graph Visualization") pos = nx.spring_layout(G, seed=3068) # Seed layout for reproducibility plt.figure(figsize=(8, 6)) nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500, font_size=10, font_weight='bold') st.pyplot(plt) # Only display Basic: Properties when it is selected from the sidebar if sidebar_option == "Basic: Properties": # Set the title for the page st.title("Basic: Properties") # Add a radio button for selecting the graph type option = st.radio("Choose a graph type:", ("Default Example", "Create your own")) # Default example if option == "Default Example": G = nx.lollipop_graph(4, 6) display_graph_properties(G) # Create your own graph elif option == "Create your own": # Let the user input number of nodes and edges num_nodes = st.number_input("Number of nodes:", min_value=2, max_value=50, value=5) num_edges = st.number_input("Number of edges per group (for lollipop graph):", min_value=1, max_value=10, value=3) if num_nodes >= 2 and num_edges >= 1: G = nx.lollipop_graph(num_nodes, num_edges) display_graph_properties(G)