<｜begin▁of▁sentence｜>### Instruction:
Imagine we are analyzing the geological network of fault lines and rock formations, represented in a graph format within a 'geonet.gml' file. In this network, each node embodies a distinct geological formation and is characterized by a 'color' attribute indicating its type—let's say 'red' for igneous and 'blue' for sedimentary rocks. Furthermore, the edges connecting these nodes have an 'etype' attribute specifying the nature of the boundary—'Strong' for major fault lines and 'Weak' for minor cracks.

Could you clarify how we might employ the method of snap_aggregation in NetworkX to construct a condensed geological summary graph that amalgamates the nodes based on their color and provides an aggregate view of the edge types? This would help us in visualizing the overarching structure and relationships between the various rock types and fault lines in a simplified manner.
### Response:
import networkx as nx

# Define node and edge attributes
nodes = {
    "A": {"color": "Red"}, "B": {"color": "Red"},
    "C": {"color": "Red"}, "D": {"color": "Red"},
    "E": {"color": "Blue"}, "F": {"color": "Blue"}
}
edges = [
    ("A", "E", "Strong"), ("B", "F", "Strong"),
    ("C", "E", "Weak"), ("D", "F", "Weak")
]

# Create a graph and add nodes and edges with attributes
G = nx.Graph()
for node in nodes:
    G.add_node(node, **nodes[node])
for source, target, etype in edges:
    G.add_edge(source, target, etype=etype)

# Define attributes for snap aggregation
node_attributes = ('color', )
edge_attributes = ('etype', )

# Use SNAP aggregation
summary_graph = nx.snap_aggregation(
    G, node_attributes=node_attributes, edge_attributes=edge_attributes)

# Print summary graph nodes and edges
print(summary_graph.nodes(data=True))
print(summary_graph.edges(data=True))
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