<｜begin▁of▁sentence｜>### Instruction:
Imagine that as a Volunteer Coordinator, you've created a comprehensive diagram to represent the different roles within your organization and how they collaborate. This diagram is carefully organized in a digital format where each role is a node, and their collaborative activities are the connections between nodes, with varying intensities represented by weights.

Your diagram's data is structured as a sophisticated dictionary, with each key acting as a node (a specific volunteer role), and for each node, there is another dictionary which details the connected roles and the intensity of their collaboration through numerical weights.

You've now stumbled upon NetworkX, a powerful tool that enables the creation and manipulation of complex network graphs. The challenge lies in translating your meticulously crafted dictionary of dictionaries into a numpy array using NetworkX, allowing for a new realm of analysis and visualization possibilities of the interconnection amongst your volunteers.

Could you enlighten me on how one might adeptly convert the existing network, encapsulated in the dictionary of dictionaries format, into a numpy array using the capabilities of NetworkX? This way, one could leverage the array to streamline the process of analyzing these connections, ultimately striving for optimal organization and coordination within the volunteer teams.
### Response:
import networkx as nx
import numpy as np

# Create a dictionary of dictionaries
dict_of_dicts = {
    'a': {'a': 1, 'b': 2, 'c': 3},
    'b': {'a': 2, 'b': 1, 'c': 4},
    'c': {'a': 3, 'b': 4, 'c': 1}
}

# Convert this dictionary of dictionaries to a numpy array
array = nx.convert_matrix.dict_to_numpy_array(dict_of_dicts)

print(array)
<|EOT|>
