<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in the pre-production phase of our latest film, "The Network Nexus," where the intricate relationships between characters are as pivotal to the plot as the dialogue itself. Our main characters, let's call them Alice, Bryan, Charlie, and Dana, are intertwined in a complex dance of interactions—some harmonious, others discordant.

To visually map out these character dynamics, our screenwriter has scripted a series of encounters: Alice meets Bryan, a scene charged with a 4.7 level of tension; Bryan and Charlie's interaction has a subtler intensity at 2.3; Alice and Charlie share a brief but critical 1.2 moment; and Charlie's encounter with Dana reaches a 3.4 on our dramatic scale.

Now, imagine these narrative threads as literal pathways in a graph, with our nodelist of protagonists - ['Alice', 'Bryan', 'Charlie', 'Dana'] - at the vertices of our story's geometry. I need you to translate this web of interactions into an adjacency matrix that's just as precise and considerate of details as our storyboards, ensuring each weighted relationship is captured accurately within the matrix.

We'll be using this as a tool to study character engagements and visually trace the narrative beats. Treat the emotional charge between characters as numerical values and ensure to use float data-type to maintain subtlety. Could you craft this adjacency matrix as a distilled representation of our story's relational choreography?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix
G = nx.Graph()
G.add_edge('A', 'B', weight=4.7)
G.add_edge('B', 'C', weight=2.3)
G.add_edge('A', 'C', weight=1.2)
G.add_edge('C', 'D', weight=3.4)
nodelist = ['A', 'B', 'C', 'D']
adj_matrix = nx.adjacency_matrix(G, nodelist=nodelist, dtype=float)
print(adj_matrix.todense())
dense_adj_matrix = adj_matrix.todense()
print(dense_adj_matrix)
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