<｜begin▁of▁sentence｜>### Instruction:
In the context of planning an underwater exploration, imagine we're charting a route through a network of sunken vessels and submerged cultural relics, with each path between points having a distinct effort cost associated with the weight and difficulty of traversal. Specifically, we have a map with various routes: ('s', 'u', 10) represents a 10 unit effort from the starting point 's' to 'u'; ('s', 'x', 5) indicates a 5 unit effort to 'x'; and so on for the other paths [('u', 'v', 1), ('u', 'x', 2), ('v', 'y', 1), ('x', 'u', 3), ('x', 'v', 5), ('x', 'y', 2), ('y', 's', 7), ('y', 'v', 6)].

As a marine archaeologist planning the logistics of a comprehensive survey of each site, it's crucial to determine the most efficient sequence of dives. How can we employ Floyd's algorithm within the NetworkX environment to ascertain the least labor-intensive paths and corresponding effort values between all pairs of submerged sites, thus ensuring an optimal itinerary for the field team's underwater excursions?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add weighted edges
G.add_weighted_edges_from([
    ('s', 'u', 10),
    ('s', 'x', 5),
    ('u', 'v', 1),
    ('u', 'x', 2),
    ('v', 'y', 1),
    ('x', 'u', 3),
    ('x', 'v', 5),
    ('x', 'y', 2),
    ('y', 's', 7),
    ('y', 'v', 6)
])

# Apply the Floyd-Warshall algorithm to find all-pairs shortest path
predecessor, distance = nx.floyd_warshall_predecessor_and_distance(G)

# Print the shortest path from node 's' to node 'v'
path = nx.reconstruct_path('s', 'v', predecessor)
print('Shortest path from s to v:', path)

# Print the shortest distance from node 's' to node 'v'
dist = distance['s']['v']
print('Shortest distance from s to v:', dist)
<|EOT|>
