<｜begin▁of▁sentence｜>### Instruction:
Imagine you've just presented a case at an orthodontic conference where you describe a network of five clinics. In your discussed scenario, these clinics are connected by their referral patterns. Specifically, Clinic 0 sends referrals to Clinic 1, Clinic 1 to Clinic 2, and Clinic 3 sends them to Clinic 4, thus forming a referral network graph with two separate referral loops, or in network terms, two connected components.

Now, to analyze the stability and connectivity of this referral network efficiently, you are considering applying a diagnostic tool analogous to the Laplacian matrix used in network analysis. By determining the Laplacian spectrum, you aim to understand better the referral dynamics and how separate clinic clusters interact.

Your question to the attendees is how to compute the Laplacian spectrum of this interconnected system of clinics, given their referral patterns, using the network analysis tool NetworkX. This calculation would provide insights into the robustness of connections between clinics, potentially indicating areas of strength or isolation within the network that could be addressed for optimal patient referral flow.
### Response:
import networkx as nx
import numpy as np

# Create a graph with 5 nodes and 2 connected components
G = nx.Graph()
G.add_nodes_from(range(5))
G.add_edges_from([(0, 1), (1, 2), (3, 4)])

# Compute the Laplacian spectrum
spect = nx.laplacian_spectrum(G)
print('Laplacian spectrum:', np.round(spect, 2))
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