<｜begin▁of▁sentence｜>### Instruction:
As a safety consultant, imagine we are analyzing the potential communication pathways within a large facility consisting of 100 staff members. For this risk assessment, we want to simulate the network of interactions where there's a 5% likelihood of any two individuals having a direct communication line, which could represent the spread of information or, in a broader sense, potential safety-related concerns. We'll be using the 'er_np' function from the 'graspologic' library to generate an 'Erdos Renyi graph' which is a suitable model for this scenario.

Once we've established the theoretical framework of our facility's communication network, we'll convert this model into a 'networkx' graph format. This will enable us to leverage 'networkx' capabilities for a more detailed analysis. Following that, visual representation is crucial for our assessment report. We'll use 'matplotlib' to generate a visual map of this network, which will help us evaluate the connectivity and identify critical points that may need attention from a safety protocol perspective.

To summarize, we have a facility with 100 staff members; I'd like you to model the communication network using the 'er_np' function to simulate a 5% chance of a link between any two members. Then, convert this into a 'networkx' graph and create a visual diagram using 'matplotlib'. This will aid us in visualizing potential points of safety intervention.
### Response:
import numpy as np
from graspologic.simulations import er_np
import networkx as nx

# Parameters for the Erdos Renyi graph
n = 100  # number of nodes
p = 0.05  # probability of edge creation

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

# Generate an Erdos Renyi graph as an adjacency matrix
A = er_np(n, p)

# Convert the adjacency matrix to a networkx graph
G = nx.from_numpy_array(A)

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(G)
plt.show()
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