<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a Meteorological Technician and you've been tasked with analyzing a conceptual weather network where cities are nodes and the roads between them are edges. You have data on the robustness of communication between these cities during extreme weather conditions. The network data is as follows: connections are between City A and City B, City B and City C, City C and City D, and additionally, City B and City D.

The resilience or the ability to communicate, which we can liken to percolation states, for cities A, B, C, and D are rated at 1.0, 0.8, 0.6, and 0.4 respectively. Now, you need to figure out the percolation centrality for each city, which essentially tells us how critical a city is in the network for maintaining communication during such weather disruptions.

How would you calculate the percolation centrality for each city using NetworkX, bearing in mind the connectivity between cities and their individual percolation states as provided? Please note that this scenario does not require any actual meteorological data or involve actions like recording weather patterns.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('B', 'D')])

# Set up the percolation states of the nodes
states = {'A': 1, 'B': 0.8, 'C': 0.6, 'D': 0.4}

# Calculate the percolation centrality
percolation_cent = nx.percolation_centrality(G, states=states)

print('Percolation centrality:', percolation_cent)
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