<｜begin▁of▁sentence｜>### Instruction:
In pursuing an advanced understanding of the functionalities inherent in the learning and memory regions of the Drosophila larva, specifically the mushroom body of the right hemisphere, we must look to employ a comprehensive approach. This involves the meticulous assembly of the structural connectome of said region, which necessitates access to both a precise adjacency matrix for graph theoretical analysis and detailed cell-type classifications for each neuron within the connectome.

To facilitate this endeavor, could you advise on the protocols for procuring such critical data? Moreover, in the interest of examining the connective efficiency and potential information-processing capabilities within this network, it would be essential to determine the graph density of the constructed neuronal network. Would you have insights into how one could achieve this using available resources or datasets?
### Response:
import numpy as np
from graspologic.datasets import load_drosophila_right

# Load the Drosophila right hemisphere connectome adjacency matrix
# and also retrieve the corresponding cell type labels for each node
adjacency_matrix, labels = load_drosophila_right(return_labels=True)

# Do some analysis on the graph, like computing its density
num_nodes = len(adjacency_matrix)
num_edges = np.sum(adjacency_matrix > 0)
graph_density = num_edges / (num_nodes * (num_nodes - 1))

# Print out the density of the graph
print(f'The density of the Drosophila right hemisphere connectome is: {graph_density:.2f}')
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