Graph Fundamentals#

The Graph class is the foundation for all graph-based analysis in volmdlr_tools. It wraps networkx graphs with dessia_common serialization support.

Overview#

All graph types in volmdlr_tools inherit from the base Graph class:

AttributedAdjacencyGraph (AAG): Face adjacency graphs for BRep models
GraphAssembly: Assembly structure graphs

The base class provides common operations for node/edge manipulation, attribute management, and filtering.

Key Concepts#

Nodes and Edges#

Graphs consist of nodes (vertices) and edges (connections):

from volmdlr_tools.graph import Graph

# Create a graph
graph = Graph()

# Add nodes with attributes
graph.add_node(0, name="part_a", color="red")
graph.add_node(1, name="part_b", color="blue")

# Add edge between nodes
graph.add_edge(0, 1, distance=10.5)

Attribute Management#

Set and get attributes on nodes and edges:

# Get node attribute
name = graph.get_node_attribute(0, "name")

# Set node attribute
graph.set_node_attribute(0, "mass", 2.5)

# Get edge attribute
dist = graph.get_edge_attribute((0, 1), "distance")

# Set edge attribute with computation function
graph.set_edges_attribute("weight", lambda e: compute_weight(e))

Filtering#

Filter nodes and edges using callable functions:

# Get nodes matching a condition
red_nodes = graph.get_nodes(filter_function=lambda n: graph[n].get("color") == "red")

# Get edges matching a condition
short_edges = graph.get_edges(filter_function=lambda e: graph[e].get("distance") < 5)

Subgraphs#

Extract subgraphs from filtered nodes or edges:

# Get subgraph containing only specific nodes
subgraph = graph.subgraph([0, 1, 2])

# Get edge-induced subgraph
edge_subgraph = graph.edge_subgraph([(0, 1), (1, 2)])