<｜begin▁of▁sentence｜>### Instruction:
Imagine we're designing a visual aid to illustrate the interconnected relationships of various food groups in a person's diet, where the food groups are represented by nodes in a star-like configuration. The central node, '0', signifies a primary food group, and the surrounding nodes, tagged as '1', '2', and '3', represent secondary food groups that are directly linked to the main one. In this scenario, we want to develop a matrix that reflects the interrelation (or 'degree of mixing') of these food groups based on their consumption frequency.

Now, to ensure that our matrix is comprehensive and can adapt to potential dietary adjustments—a scenario where particular food groups might not be consumed at all—we're looking to incorporate into the matrix the instances of degree '0', which implies no consumption. Can we devise a way, using NetworkX, to manifest such a matrix? It's important that our matrix not only includes the actively consumed food groups but also has placeholders for those groups that are currently not part of the diet, indicated by a 'degree of zero'. How could we elegantly integrate these placeholders into our matrix, keeping in mind that it should accommodate changes or expansions in dietary habits?
### Response:
import networkx as nx
import numpy as np

# Create a star graph with 4 nodes (0 is the center node)
G = nx.star_graph(3)

# Compute the degree mixing matrix for the graph (unnormalized)
mix_mat = nx.degree_mixing_matrix(G, normalized=False)

# Access the mixing from node degree 1 to node degree 3
print(mix_mat[0, 1])  # Output: 3.0

# Including degrees without nodes in the graph
max_degree = max(deg for n, deg in G.degree())
mapping = {x: x for x in range(max_degree + 1)}  # identity mapping
mix_mat_with_all_degrees = nx.degree_mixing_matrix(G, mapping=mapping)

# Access the mixing from node degree 3 to node degree 1 (should now be included)
print(mix_mat_with_all_degrees[3, 1])  # Output: 0.0
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