hodoku-py 0.2.1

Python port of HoDoKu's sudoku solver, hint engine, and generator

hodoku-py

A pure Python port of HoDoKu — Sudoku solver, hint engine, puzzle generator, and difficulty rater minus the GUI.

hodoku-py has full fidelity with HoDoKu 2.2.0: exact same solution path and score across all tested puzzles, in pure python (well, a couple small bits in c).

Nightly parity test results — head-to-head comparison against HoDoKu across thousands of puzzles.

Status

Core solver complete through all techniques. Public API (Solver.solve, get_hint, rate) implemented. Puzzle generator implemented with symmetric and pattern-based generation.

Layer	Techniques	Status
Singles	Full House, Naked Single, Hidden Single	✅
Intersections + Subsets	Locked Candidates 1&2, Naked/Hidden Pair/Triple/Quad	✅
Single-digit patterns	Skyscraper, 2-String Kite, Empty Rectangle	✅
Wings	XY-Wing, XYZ-Wing, W-Wing	✅
Coloring	Simple Colors, Multi-Colors 1&2	✅
Uniqueness	UT1–6, Hidden Rectangle, BUG+1	✅
Fish	X-Wing through Whale; Finned/Sashimi, Franken, Mutant variants	✅
Chains	X-Chain, XY-Chain, Remote Pair, DNL, CNL, AIC, Grouped Nice Loops/AIC	✅
ALS	ALS-XZ, ALS-XY-Wing, ALS-XY-Chain, Death Blossom	✅
Forcing chains/nets	Contradiction + Verity	✅
Misc	Sue de Coq	✅
Templates	Template Set/Delete	✅
Public API	Solver.solve, get_hint, rate, find_all_steps, SolverConfig	✅
Generator	Puzzle generation	✅

See docs/ROADMAP.md for full details and known gaps.

Requirements

• Python 3.11+
• (optional) C build tools (build-essential, xcode, or MSVC) to build native extensions that accelerate large Mutant fish searches and the backtracking solver used for generation/uniqueness checking. Both are optional with pure Python fallbacks. To build: python setup.py build_ext --inplace (add --compiler=mingw32 on Windows with MinGW). See Implementation notes for details.

Installation

pip (recommended)

pip install hodoku-py

from source

pip install -e .

Testing

Install test dependencies

pip install -e ".[test]"

Unit tests

pytest -m unit -v

reglib — HoDoKu's built-in regression suite (~2 min, ~1100 tests)

Each test reconstructs a fixed pencilmark board and asserts that one specific technique fires with the expected eliminations.

pytest tests/reglib/ -q

# Single technique by code
pytest tests/reglib/ -k "0901" -v

# Technique family
pytest tests/reglib/ --reglib-section 09 -v

parity — head-to-head HoDoKu comparison (requires Java JRE installation)

Comparison of full solution solve path against HoDoKu.jar via Py4J.

pip install .[test-parity]

pytest tests/parity/ --puzzle-file exemplars-1.0 -v
pytest tests/parity/ --puzzle-file top1465 --puzzle-count 50 --puzzle-seed 7 -v

Puzzle files are plain text (one puzzle per line) sourced from tests/testdata/.

The full parity suite runs nightly. Latest results.

Project structure

src/hodoku/
├── core/          # Grid, CellSet, SolutionStep, enums, scoring
├── solver/        # One file per technique family + central dispatcher
│   ├── step_finder.py   # SudokuStepFinder — routes get_step() calls
│   ├── solver.py        # SudokuSolver — solve loop and difficulty rating
│   ├── simple.py        # Singles, locked candidates, subsets
│   ├── fish.py          # Basic, finned/sashimi, Franken, Mutant fish
│   ├── _fish_accel.c    # optional C accelerator for Mutant fish
│   ├── single_digit.py  # Skyscraper, 2-String Kite, Empty Rectangle
│   ├── uniqueness.py    # Uniqueness Tests 1–6, BUG+1
│   ├── wings.py         # XY-Wing, XYZ-Wing, W-Wing
│   ├── coloring.py      # Simple Colors, Multi-Colors
│   ├── chains.py        # X-Chain, XY-Chain, Nice Loop / AIC families
│   ├── tabling.py       # Forcing Chains/Nets
│   ├── als.py           # ALS-XZ, ALS-XY-Wing, ALS-XY-Chain, Death Blossom
│   ├── misc.py          # Sue de Coq
│   └── templates.py     # Template Set/Delete
└── generator/     # Backtracking solver, uniqueness checker, puzzle generation
    ├── generator.py     # SudokuGenerator — backtracking solver + puzzle generation
    ├── _gen_accel.c      # optional C accelerator for backtracking solver
    └── pattern.py       # GeneratorPattern for pattern-constrained generation

hodoku/            # Bundled HoDoKu 2.2.0 JAR (for validation)
docs/              # Architecture, roadmap, specs
tests/
├── reglib/        # Technique-isolation regression suite (pure Python)
├── parity/        # Head-to-head parity suite (requires Java + py4j)
├── generator/     # Generator unit, integration, and parity tests
└── testdata/      # Puzzle files for parity testing

Validation approach

Every technique is validated by solving the puzzle and comparing the solution with HoDoKu's in detail — same list of techniques, eliminations, and placements, in the same order. See docs/ARCHITECTURE.md for internals and porting notes.

Implementation notes

C accelerators

CPython is ~50-100x slower than Java JIT for tight bitwise loops, so two optional C extensions accelerate the most performance-sensitive code paths:

• Fish cover search (solver/_fish_accel.c): The Mutant and Franken variants of the largest Fish (Squirmbag, Whale, Leviathan) have enormous search spaces. The cover DFS is implemented in C.
• Backtracking solver (generator/_gen_accel.c): The backtracking solver used for uniqueness checking and puzzle generation runs ~10x faster with the C accelerator.

Both are built via python setup.py build_ext --inplace and have pure Python fallbacks.

Quick demo

from hodoku import Solver, Generator, DifficultyType, SolverConfig

# Solve a puzzle
solver = Solver()
result = solver.solve(
    "530070000600195000098000060800060003400803001700020006060000280000419005000080079"
)
print(f"Difficulty: {result.level.name} (score {result.score})")
print(f"Solved in {len(result.steps)} steps")
# Difficulty: EASY (score 204)
# Solved in 51 steps

# Generate a puzzle
gen = Generator()
puzzle = gen.generate(difficulty=DifficultyType.HARD)
print(puzzle)  # 81-char string, 0s for empty cells

# Custom solver configuration
from hodoku.config import StepSearchConfig, FishSearchConfig, FishType
cfg = SolverConfig(
    solve_search=StepSearchConfig(
        fish=FishSearchConfig(fish_type=FishType.BASIC_FRANKEN_MUTANT),
    ),
)
solver = Solver(config=cfg)

Why?

Good question. I was curious whether one could use Claude Code to port a complex code base from java to python. Turns out one could.

License

HoDoKu is GPL-3.0. This port carries the same license.