<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine we're producing a sci-fi thriller where characters are located in a futuristic networked city. Each character is represented by a node, and we have 50 individuals in total. We want to depict who can communicate directly with whom based on their proximity and the combined strength of their communication devices.

The script calls for a scene where two characters can only establish a direct connection if they're close enough—let's say a maximum Minkowski distance of 0.2 units apart. Additionally, their communication devices must have a combined signal power of at least 0.1 units for the connection to be viable.

So for the upcoming production meeting, we need a visual representation of this networked city: a schematic showing all the points (characters) and which pairs have established communication links based on our distance and signal power thresholds.

I'd like you to create this schematic for our VFX team to potentially model in 3D, and report back on how many direct connections we're looking at here. This will help us budget both our visual effects and understand how complex the communication network is. If you can capture this in a graphic, it'll be a great visual aid for our discussion. 

In film terms, we're crafting a "scene blueprint" here—no need for any technical jargon, just the essence we need to construct our cinematic world. Can you create this visual storyline for us?
### Response:
import networkx as nx

# Number of nodes
n = 50
# Distance threshold
radius = 0.2
# Weight threshold
theta = 0.1
# Optional: Specify the dimension of the space
# If not specified, the default is 2
# Optional: Specify the p-norm (Minkowski distance)
# If not specified, the default is 2 (Euclidean distance)

# Use the thresholded random geometric graph generator
g = nx.thresholded_random_geometric_graph(n, radius, theta)

# Print the number of edges in the generated graph
print('Number of edges in the graph:', g.number_of_edges())

# Optional: Visualize the graph
# Requires matplotlib
import matplotlib.pyplot as plt
nx.draw(g, with_labels=True)
plt.show()
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