<｜begin▁of▁sentence｜>### Instruction:
Suppose a data scientist has a 10x10 matrix representing some form of connectivity between 10 units, along with metadata for each unit indicating its group and, for columns, additional cluster classification. How could they use graspologic's matrixplot function to visualize this matrix while sorting and coloring the rows and columns according to the provided metadata, and using a heatmap representation?
### Response:
import numpy as np
import pandas as pd
import seaborn as sns
from graspologic.plot import matrixplot

# Example data
np.random.seed(42)
data = np.random.rand(10, 10)

# Create metadata for rows and columns
row_meta = pd.DataFrame(index=range(10))
row_meta['group'] = ['A', 'B'] * 5

col_meta = pd.DataFrame(index=range(10))
col_meta['group'] = ['C', 'D'] * 5

col_meta['cluster'] = ['X'] * 5 + ['Y'] * 5

# Create matrix plot with sorted rows and columns based on metadata
ax = matrixplot(data,
               row_meta=row_meta,
               col_meta=col_meta,
               row_group='group',
               col_group='group',
               col_item_order='cluster',
               col_color='cluster',
               plot_type='heatmap',
               cmap='viridis')

# Display the plot
sns.despine()
sns.set_context('notebook')
ax.figure.show()
<|EOT|>
