<｜begin▁of▁sentence｜>### Instruction:
As a biologist, you might be familiar with the concept of interaction networks within ecological systems—think of it like mapping the symbiotic relationships between species in an ecosystem. Imagine we have such a network with species as nodes and the strength of their interactions as weighted edges. For instance, we documented interactions with the following data: a mutualistic interaction between species 1 and 2 with a weight of 0.5, a stronger interaction between species 2 and 3 with a weight of 1.5, and another interaction between species 1 and 3 with a weight of 1.0.

Now, when we translate this ecological network into a mathematical structure for analysis, we often use graphs. If we want to adjust our mathematical representation to take into account the overall interaction each species has within the network, one approach is by enhancing the self-interaction term, which can be akin to self-regulation or self-support, for each species in our adjacency matrix—essentially tweaking the diagonal values.

In this theoretical scenario, could you employ the `augment_diagonal` function available in the graspologic toolkit—a resource that is particularly handy for network analysis—to incorporate either the average degree of interaction or the aggregate weight of interactions into the diagonal of our adjacency matrix? After making this adjustment, let's have a look at what our new adjacency matrix would be like. This step is crucial for ensuring our network's representation reflects the true complexity of species interactions within the ecosystem.
### Response:
import numpy as np
from graspologic.utils import augment_diagonal
import networkx as nx

# Creating an undirected graph and adding weighted edges
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 0.5), (2, 3, 1.5), (1, 3, 1.0)])

# Get the adjacency matrix (in numpy array format) of the graph G
adj_matrix = nx.to_numpy_array(G)

# Augment the diagonal of the adjacency matrix
new_adj_matrix = augment_diagonal(adj_matrix)

# Print the new adjacency matrix
print(new_adj_matrix)
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