<｜begin▁of▁sentence｜>### Instruction:
Imagine you're coordinating a political campaign and you have mapped out the relationships between different regional campaign offices. Each office sends out a certain number of representatives to other offices, and these numbers are represented by weights in a directed graph. Specifically, the headquarters (0) sends 3 representatives to the first regional office (1), which in turn sends 2 representatives to the second regional office (2), and so on, with the final office (4) being the most local level in this chain of command.

To optimize our outreach strategy, we need to understand how well-connected our offices are based on the flow of representatives between them. This can be measured by examining the normalized mixing patterns of the 'in' and 'out' degrees of our network. 

Could you provide an analysis that includes a normalized mixing dictionary revealing the connection patterns between offices based on the incoming and outgoing representatives? This would be invaluable in assessing the efficiency of our resource distribution and collaboration between the campaign offices.
### Response:
import networkx as nx

# Create a directed graph
DG = nx.DiGraph()
DG.add_weighted_edges_from([(0, 1, 3), (1, 2, 2), (2, 3, 1), (3, 4, 5)])

# Get the degree mixing dictionary for the 'in' and 'out' degrees
mixing_dict = nx.degree_mixing_dict(DG, x='out', y='in', normalized=True)

print(mixing_dict)
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