<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at a community support network where each point of aid (which we'll call nodes) is connected by paths that represent the flow of assistance. Now, there are three key points of aid: the community center (0), the local shelter (1), and the food bank (2). These points are connected by directed paths that carry different levels of support, indicated by varying weights - a path from the community center to the local shelter carries a support level of 0.5, the path from the shelter to the food bank carries a support level of 1.5, and one that leads back from the food bank to the community center has a support level of 1.0.

We're tasked with developing a deeper understanding of this support flow by calculating the directed Laplacian matrix of this network, using a method similar to how one would measure resource allocation in a PageRank model. How might we employ NetworkX as a tool to represent this network and carry out such a specialized calculation, in order to better inform our strategies for community support?
### Response:
import networkx as nx
import numpy as np

# Create a directed graph
DG = nx.DiGraph()
# Add edges with weights
DG.add_weighted_edges_from([(0, 1, 0.5), (1, 2, 1.5), (2, 0, 1.0)])

# Calculate the directed Laplacian matrix
# Here, `nodelist` is optional and `weight` is the default 'weight'
# `walk_type` is 'pagerank' and `alpha` is set to 0.85 by default for pagerank
laplacian_matrix = nx.directed_laplacian_matrix(DG, walk_type='pagerank')

# Convert the NumPy matrix to a readable array for display
laplacian_array = np.array(laplacian_matrix)
print('Directed Laplacian Matrix:\n', laplacian_array)
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