hpyhex 0.2.0

Simplified implementations of the HappyHex game components and hexagonal system in Python.

hpyhex

Simplified implementations of the HappyHex game components and hexagonal system.

Installation

pip install hpyhex

Features

• Hexagonal grid representation
• Basic game mechanics for HappyHex
• Utility functions for hexagonal calculations

Author

Developed by William Wu.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Usage

from hpyhex.hex import Hex, Piece, HexEngine
from hpyhex.game import Game, PieceFactory

# Create a hexagonal coordinate
coo = Hex(0, 1)

# Create a piece by name
piece = PieceFactory.get_piece("triangle_3_a")

# Create a game engine with radius 3
engine = HexEngine(radius=3)

# Add a piece to the engine
engine.add_piece(piece, coo)

# Eliminate lines and get score
score = len(engine.eliminate()) * 5

# Create a game with engine radius and queue size
game = Game(engine=3, queue=5)
print(game)

# Make a move using a custom algorithm
def simple_algorithm(engine, queue):
	# Always place the first piece at the center
	return 0, Hex(0, 0)
game.make_move(simple_algorithm)

Main Classes

• Hex: Represents a hexagonal grid coordinate using a custom line-based system. Supports arithmetic, hashing, and tuple compatibility.
• Piece: Represents a shape made of 7 blocks, optimized for memory and performance. Use PieceFactory to create pieces by name or byte value.
• HexEngine: Manages the hexagonal grid, supports adding pieces, eliminating lines, and computing entropy.
• PieceFactory: Utility for creating pieces by name, byte, or randomly. Provides access to all predefined pieces.
• Game: Manages the game state, piece queue, score, and turn. Supports adding pieces and making moves with algorithms.

Quickstart

1. Install the package:

   pip install hpyhex
   

2. Import and use the main classes as shown above.
3. Create custom algorithms to interact with the game environment.

Benchmarking Algorithms

The package includes benchmarking tools to evaluate and compare algorithms:

from hpyhex.benchmark import benchmark, compare

# Benchmark a single algorithm
avg_score, avg_turn = benchmark(simple_algorithm, engine_radius=3, queue_size=5, eval_times=10)

# Compare two algorithms
similarity = compare(alg1, alg2, engine_radius=3, queue_size=5, eval_times=100)