<｜begin▁of▁sentence｜>### Instruction:
As we're overseeing the energy distribution in a new wind farm project, think of the transmission system as a directed flow network where wind turbines and substations are the nodes, and the cables between them are the edges. Our network diagram includes turbines and substations labeled as 'x', 'a', 'b', 'c', 'd', 'e', 'y', with cables connecting them having certain energy transfer capacities. Specifically, we have cables with the following energy capacities in megawatts (MW): from 'x' to 'a' is 3.0 MW, 'x' to 'b' is 1.0 MW, 'a' to 'c' is 3.0 MW, 'b' to 'c' is 5.0 MW, 'b' to 'd' is 4.0 MW, 'd' to 'e' is 2.0 MW, 'c' to 'y' is 2.0 MW, and 'e' to 'y' is 3.0 MW. 

To optimize the operation and maintenance schedule and to prepare for potential cable repairs, we need to understand the weakest link in this network using the NetworkX library's minimum_cut function. Can we determine the chokepoint of the system between the 'x' hub (source of energy) and the 'y' node (energy sink)? Specifically, we're interested in finding out the smallest energy capacity that, if exceeded, would disrupt the transmission, as well as identifying which turbines and substations would still be operational and which would be cut off from the grid in such a scenario.
### Response:
import networkx as nx

# Create a directed graph and add edges with capacity
G = nx.DiGraph()
G.add_edge('x', 'a', capacity=3.0)
G.add_edge('x', 'b', capacity=1.0)
G.add_edge('a', 'c', capacity=3.0)
G.add_edge('b', 'c', capacity=5.0)
G.add_edge('b', 'd', capacity=4.0)
G.add_edge('d', 'e', capacity=2.0)
G.add_edge('c', 'y', capacity=2.0)
G.add_edge('e', 'y', capacity=3.0)

# Compute the value and the node partition of a minimum (s, t)-cut
cut_value, partition = nx.minimum_cut(G, 'x', 'y')
reachable, non_reachable = partition

# Output the results
print('Cut Value:', cut_value)
print('Reachable:', reachable)
print('Non-reachable:', non_reachable)
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