Update app.py

app.py

@@ -18,6 +18,59 @@ def draw_graph(G, pos=None, title="Graph Visualization"):
     nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500, font_size=10, font_weight='bold')
     st.pyplot(plt)
 
+# Function to display Eigenvalue analysis for Drawing: Eigenvalues
+def display_eigenvalue_analysis():
+    st.title("Drawing: Eigenvalues")
+
+    option = st.radio("Choose a graph type:", ("Default Example", "Create your own"))
+
+    if option == "Default Example":
+        # Generate random graph with 1000 nodes and 5000 edges
+        n = 1000
+        m = 5000
+        G = nx.gnm_random_graph(n, m, seed=5040)  # Seed for reproducibility
+
+        # Calculate the normalized Laplacian matrix
+        L = nx.normalized_laplacian_matrix(G)
+        eigenvalues = numpy.linalg.eigvals(L.toarray())
+
+        # Print largest and smallest eigenvalues
+        st.write(f"Largest eigenvalue: {max(eigenvalues)}")
+        st.write(f"Smallest eigenvalue: {min(eigenvalues)}")
+
+        # Display the histogram of eigenvalues
+        st.write("### Eigenvalue Histogram")
+        plt.hist(eigenvalues, bins=100)
+        plt.xlim(0, 2)  # Eigenvalues between 0 and 2
+        st.pyplot(plt)
+
+    elif option == "Create your own":
+        # Allow the user to customize the number of nodes and edges
+        n_nodes = st.number_input("Number of nodes:", min_value=2, max_value=1000, value=100)
+        m_edges = st.number_input("Number of edges:", min_value=1, max_value=n_nodes*(n_nodes-1)//2, value=500)
+
+        if st.button("Generate"):
+            # Generate a random graph with the custom number of nodes and edges
+            G_custom = nx.gnm_random_graph(n_nodes, m_edges, seed=5040)  # Seed for reproducibility
+
+            # Calculate the normalized Laplacian matrix
+            L = nx.normalized_laplacian_matrix(G_custom)
+            eigenvalues = numpy.linalg.eigvals(L.toarray())
+
+            # Print largest and smallest eigenvalues
+            st.write(f"Largest eigenvalue: {max(eigenvalues)}")
+            st.write(f"Smallest eigenvalue: {min(eigenvalues)}")
+
+            # Display the histogram of eigenvalues
+            st.write("### Eigenvalue Histogram")
+            plt.hist(eigenvalues, bins=100)
+            plt.xlim(0, 2)  # Eigenvalues between 0 and 2
+            st.pyplot(plt)
+
+# Display Drawing: Eigenvalues if selected
+if sidebar_option == "Drawing: Eigenvalues":
+    display_eigenvalue_analysis()
+
 # Function to display properties and graph for Basic: Properties
 def display_graph_properties(G):
     pathlengths = []