<｜begin▁of▁sentence｜>### Instruction:
As a cardiologist, imagine that you have before you two intricate cardiovascular networks representing the circulatory systems of two different patient groups. Let's call these networks G1 and G2, each comprising a simplified model of 10 major junctions or 'nodes' within the heart's extensive network. You wish to visually analyze these two models side by side to spot any disparities or unique patterns that could be indicative of underlying health issues. The method you want to utilize is akin to graspologic's gridplot function, which will enable you to view both networks simultaneously. Additionally, you hope to apply a logarithmic transformation to the 'edge weights', which, in this scenario, could metaphorically correspond to the blood flow or pressure in the arteries, to amplify subtle differences and ease the identification of abnormalities or variables that are not immediately apparent to the naked eye. How would you reword this request while maintaining its essence, so that it aligns with your medical expertise and vernacular?
### Response:
import numpy as np
import networkx as nx
from graspologic.plot import gridplot

# Create example graphs
G1 = nx.erdos_renyi_graph(n=10, p=0.5)
G2 = nx.erdos_renyi_graph(n=10, p=0.5)

# Convert networkx graphs to adjacency matrices
A1 = nx.to_numpy_array(G1)
A2 = nx.to_numpy_array(G2)

# List of graph matrices to be passed into the gridplot function
graphs = [A1, A2]

# Labels for each graph
labels = ['Graph 1', 'Graph 2']

# Plotting the graphs
gridplot_result = gridplot(
    X=graphs,
    labels=labels,
    transform='log',
    height=10,
    title='Gridplot of Example Graphs',
    context='talk',
    font_scale=1.5,
    palette='viridis',
    alpha=0.8,
    sizes=(10, 200),
    legend_name='Edge Weight',
    inner_hier_labels=None,
    outer_hier_labels=None,
    hier_label_fontsize=10,
    title_pad=None,
    sort_nodes=True
)
<|EOT|>
