<｜begin▁of▁sentence｜>### Instruction:
Imagine you're setting up a photo exhibition displaying snapshots of various interconnected cityscapes symbolized by a series of intricate patterns. Each pattern corresponds to a different city network, akin to paintings in a gallery, each with its own tale and characteristics. Now, you're particularly intrigued by the tenth masterpiece in this series, which encapsulates a city's network in its graphical essence. 

You're looking to delve deeper into the story behind this urban fabric by uncovering the number of junctions (nodes), pathways (edges), and the bustling activity at each junction (degree sequence). Just as you would meticulously analyze the elements of a photograph for an exhibition, you wish to examine these details of the network canvas, bringing into focus the underlying structure of this complex city grid. Could you depict how one might efficiently extract and interpret these characteristics from the graph indexed as number 10 in this rich tapestry of the NetworkX Graph Atlas?
### Response:
import networkx as nx

# Get the list of all graphs from Graph Atlas with up to seven nodes
atlas = nx.graph_atlas_g()

# Access a specific graph in the atlas by its index, for example, G[10]
G = atlas[10]

# Print some properties of the graph
print('Graph G[10] information:')
print('Number of nodes:', G.number_of_nodes())
print('Number of edges:', G.number_of_edges())
print('Edges:', G.edges())
print('Degree sequence:', sorted(d for n, d in G.degree()))
<|EOT|>
