graphworks 0.9.0

Graph theoretic classes and algorithm helper functions.

Graphworks

A Python module for efficient graph theoretic programming

Documentation | Wiki

Quick Start

pip install graphworks

import json
from graphworks.graph import Graph

json_graph = {"label": "my graph", "graph": {"A": ["B"], "B": []}}
graph = Graph(input_graph=json.dumps(json_graph))
print(graph)

Optional extras:

pip install graphworks[matrix]   # numpy adjacency matrix support
pip install graphworks[viz]      # graphviz export
pip install graphworks[docs]     # generate documentation

Development

Requirements

• Python 3.13+
• uv (>= 0.10.12)

Setup

uv sync --extra all

Running Tests

# Run all tests (includes coverage; fails under 90%)
uv run pytest

# Run a single test file
uv run pytest tests/test_graph.py

# Run a single test by name
uv run pytest tests/test_graph.py -k "test_method_name"

Linting and Formatting

# Lint
uv run ruff check --fix src/ tests/

# Format
uv run black src/ tests/
uv run isort src/ tests/

# Type checking
uv run ty check

# Code complexity
uv run xenon --max-average=A --max-modules=B --max-absolute=B src/

# Run all pre-commit hooks
prek run --all-files

Publishing

Version is managed automatically via git tags using hatchling-vcs.

• Tag a commit: git tag -a X.Y.Z -m 'release message'
• Push the tag: git push --tags
• The GitHub Actions workflow will build and publish to PyPI automatically.

Roadmap

Tier 1: Data model

• Vertex class with name, optional label, and immutable attribute mapping
• Edge dataclass with source, target, directed flag, optional weight, label, and attributes
• Both classes are frozen (immutable) with identity-based equality and hashing

Tier 2: Graph refactor

• Internal storage uses dict[str, Vertex] for vertex lookup
• Adjacency structure uses dict[str, dict[str, Edge]] for O(1) edge access
• Edge weights, labels, and attributes survive all operations
• add_edge supports weight and label keyword arguments
• edge() lookup method for direct O(1) edge retrieval

Tier 3: Lossless conversions

• to_adjacency_matrix() / from_adjacency_matrix() round-trips that preserve vertex names via an index mapping
• Fix get_complement to preserve original vertex names instead of generating UUIDs
• to_edge_list() / from_edge_list() conversions
• Parse weighted JSON format (e.g. g4.json dict-as-neighbor style)

Tier 4: Algorithms

• Dijkstra's shortest path (weighted)
• Prim's / Kruskal's minimum spanning tree
• Strongly connected components (Tarjan or Kosaraju)
• Improved shortest-path implementations leveraging weighted edges

Tier 5: Export and CLI

• JSON export (save_to_json)
• Graphviz DOT export (save_to_dot)
• Rich-based demo script (uv run demo)
• CLI application for common graph operations
• Rich rendering integration for interactive graph display

Tier 6: Cross-cutting quality

• Thread safety (immutable graph views or locking around mutations)
• Input validation hardening
• Performance benchmarks