API: graph & analyzer

Graph construction and analysis for STL documents.

Module: stl_parser.graph, stl_parser.analyzer Import: from stl_parser import STLGraph, STLAnalyzer

STLGraph

NetworkX-based directed multigraph built from STL statements.

Constructor

STLGraph(parse_result: Optional[ParseResult] = None)

Factory Method

STLGraph.from_parse_result(parse_result: ParseResult) -> STLGraph

Methods

Method	Parameters	Return Type	Description
`build_graph(parse_result)`	`ParseResult`	`None`	Build graph from statements
`find_paths(source_id, target_id)`	`str, str`	`List[List[str]]`	Find all simple paths between nodes
`find_cycles()`	—	`List[List[str]]`	Find all simple cycles
`get_node_degree(anchor_id)`	`str`	`int`	Total degree (in + out) of a node
`get_node_centrality()`	—	`Dict[str, float]`	Degree centrality for all nodes
`get_subgraph(domain)`	`str`	`STLGraph`	Subgraph filtered by domain modifier
`detect_conflicts(functional_relations)`	`Optional[set]`	`List[Dict]`	Detect functional relation violations
`calculate_tension_metrics()`	—	`Dict[str, Any]`	Graph-wide tension scores

Properties

Property	Type	Description
`summary`	`Dict[str, int]`	Node and edge counts

Example

from stl_parser import parse, STLGraph

result = parse("""
[A] -> [B] ::mod(rule="causal", confidence=0.9)
[B] -> [C] ::mod(rule="causal", confidence=0.8)
[C] -> [A] ::mod(rule="logical", confidence=0.7)
""")

graph = STLGraph(result)
print(graph.summary)            # {"nodes": 3, "edges": 3}
print(graph.find_cycles())      # [["A", "B", "C"]]
print(graph.get_node_centrality())

STLAnalyzer

Comprehensive statistical analysis of STL documents.

Constructor

STLAnalyzer(
    parse_result: ParseResult,
    stl_graph: Optional[STLGraph] = None
)

Auto-constructs an STLGraph if none provided.

Methods

Method	Return Type	Description
`count_elements()`	`Dict[str, int]`	Total statements, unique anchors
`analyze_anchor_types()`	`Dict`	Source/target anchor type distribution
`analyze_path_types()`	`Dict[str, int]`	Path type distribution
`analyze_modifier_usage()`	`Dict`	Modifier frequency, min/max, common values
`analyze_confidence_metrics()`	`Dict`	Confidence/certainty stats (min, max, mean)
`identify_missing_provenance()`	`List[Dict]`	High-confidence statements missing source
`get_graph_metrics()`	`Dict`	Nodes, edges, cycles, centrality
`get_full_analysis_report()`	`Dict`	All metrics combined

Convenience Function

from stl_parser.analyzer import analyze_parse_result

report = analyze_parse_result(result)

Example

from stl_parser import parse, STLAnalyzer

result = parse("""
[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85, strength=0.8)
[Smoking] -> [Lung_Cancer] ::mod(rule="causal", confidence=0.92)
[Water] -> [H2O] ::mod(rule="definitional", confidence=0.99)
""")

analyzer = STLAnalyzer(result)
report = analyzer.get_full_analysis_report()
print(report["counts"])
print(report["confidence_metrics"])