<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a psychiatrist tending to the cognitive framework of a community, represented by a network where each individual (node) has varying degrees of influence (degree) and connections to others are weighted by the strength of their interactions. You are given a set of interactions as data points: individual 0 connects to 1 with an influence weight of 3, 1 to 2 with a weight of 2, 2 to 3 with a weight of 1, and 3 to 4 with a weight of 5.

In your assessment, you'd like to understand if people with a higher degree of influence tend to associate with others who also have a high degree of influence, which could imply a phenomenon akin to "birds of a feather flock together," or if there's no such correlation. Specifically, you wish to quantify the degree of this assortative (or disassortative) mingling by calculating the Pearson correlation coefficient—but with a keen eye on the directionality of these associations, acknowledging that influence and support might flow distinctively from one individual to another. 

To carry out this analysis as if diving into the complex psyche of this network, how would you compute this Pearson correlation coefficient of degree assortativity while respecting the directional nature of the relationships using NetworkX tools at your disposal?
### Response:
import networkx as nx

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

# Add edges with weights
G.add_weighted_edges_from([(0, 1, 3), (1, 2, 2), (2, 3, 1), (3, 4, 5)])

# Calculate the Pearson correlation coefficient for the graph
r = nx.degree_pearson_correlation_coefficient(G, x='out', y='in', weight='weight')

print(f"Degree Pearson correlation coefficient: {r:.3f}")
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