<｜begin▁of▁sentence｜>### Instruction:
Imagine you're an Energy Engineer working to optimize the flow of electricity through a network of substations. You've got two diagrams representing the current network paths: The first diagram, we'll call it 'Flowchart_A', details connections from Substation A to both Substation B and Substation C. The second diagram, 'Flowchart_B', captures a separate path starting from Substation B to Substation C, and then extending from Substation C to Substation D.

To craft an overarching strategy for electricity distribution, you're considering a theoretical model where you can see the combined pathways from both diagrams simultaneously, without the risk of altering the original plans. How could you utilize a digital tool, specifically NetworkX, to view a merged read-only snapshot of the adjacencies in these two flowcharts? The necessity here is to ensure that while working with the synthesized model, the integrity of the original connection diagrams remains untouched. This will enable you to assess potential efficiency improvements in the network without committing to any physical changes just yet.
### Response:
import networkx as nx

# Create two directed graphs
G1 = nx.DiGraph()
G2 = nx.DiGraph()

# Add edges to G1
G1.add_edge('A', 'B')
G1.add_edge('A', 'C')

# Add edges to G2
G2.add_edge('B', 'C')
G2.add_edge('C', 'D')

# Create a view on the union of G1 and G2 adjacencies
union_adjacency = nx.classes.coreviews.UnionAdjacency(G1.succ, G2.succ)

# Access edges from the union adjacency view
for node, nbrdict in union_adjacency.items():
    for nbr, edgedict in nbrdict.items():
        print(f'Edge from {node} to {nbr}')
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