<｜begin▁of▁sentence｜>### Instruction:
In the realm of network design and analysis, a telecommunications technician is tasked with simulating two different communication network topologies using the graspologic library, a critical software tool in the industry. The first configuration necessitates constructing a straightforward, unweighted digital network grid consisting of 10 nodes. This grid has a predefined connectivity protocol where each pair of nodes has a 20% chance of being linked by a direct communication line.

Subsequently, the technician is to lay out a second, more complex network map. This version also encompasses 10 nodes but introduces variable signal strengths across the channels. In this scenario, every potential connection between the nodes has the same 20% probability of establishment as the first. However, in this intricate mesh, the strength of each active channel is determined by a randomly assigned value, representing the signal quality, which falls within a continuous range from 0 to 1—akin to a spectrum of signal intensities from weak to strong.

The technician needs to adeptly apply the graspologic library's functionalities to generate these two distinct Erdos Renyi graph models, ensuring the second network's connections accurately represent a uniform distribution of signal strengths. How can one utilize the graspologic library's tools to fulfill the technical requirements of this comparative network study?
### Response:
import numpy as np
from graspologic.simulations import er_np

# Seed random number generator for reproducibility
np.random.seed(42)

# Define number of vertices and edge probability
n = 10
p = 0.2

# Generate a binary Erdos Renyi (n, p) graph
adj_matrix = er_np(n, p)

# Generate a weighted Erdos Renyi (n, p) graph with weights sampled from a uniform distribution
wt = np.random.uniform
wtargs = {'low': 0, 'high': 1}
weighted_adj_matrix = er_np(n, p, wt=wt, wtargs=wtargs)

print('Binary adjacency matrix:\n', adj_matrix)
print('\nWeighted adjacency matrix:\n', weighted_adj_matrix)
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