dokusan 0.1.0

Step-by-step solver

This solver tries to solve sudoku using human-like strategies. Currently following techniques are supported:

• Naked/Hidden singles

• Naked Pairs/Triplets

• Locked Candidate

• XY-Wing

• Unique Rectangle

For example to see all techniques that sudoku has:

from dokusan import solvers
from dokusan.boards import BoxSize, Sudoku


sudoku = Sudoku.from_list(
    [
        [0, 0, 0, 0, 9, 0, 1, 0, 0],
        [0, 0, 0, 0, 0, 2, 3, 0, 0],
        [0, 0, 7, 0, 0, 1, 8, 2, 5],
        [6, 0, 4, 0, 3, 8, 9, 0, 0],
        [8, 1, 0, 0, 0, 0, 0, 0, 0],
        [0, 0, 9, 0, 0, 0, 0, 0, 8],
        [1, 7, 0, 0, 0, 0, 6, 0, 0],
        [9, 0, 0, 0, 1, 0, 7, 4, 3],
        [4, 0, 3, 0, 6, 0, 0, 0, 1],
    ],
    box_size=BoxSize(3, 3),
)

{step.combination.name for step in solvers.steps(sudoku)}

Backtracking-based solver

This solver is based on backtracking algorithm, however slightly modified to work fast

from dokusan import solvers, renderers
from dokusan.boards import BoxSize, Sudoku


sudoku = Sudoku.from_list(
    [
        [0, 0, 0, 0, 0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0, 3, 0, 8, 5],
        [0, 0, 1, 0, 2, 0, 0, 0, 0],
        [0, 0, 0, 5, 0, 7, 0, 0, 0],
        [0, 0, 4, 0, 0, 0, 1, 0, 0],
        [0, 9, 0, 0, 0, 0, 0, 0, 0],
        [5, 0, 0, 0, 0, 0, 0, 7, 3],
        [0, 0, 2, 0, 1, 0, 0, 0, 0],
        [0, 0, 0, 0, 4, 0, 0, 0, 9],
    ],
    box_size=BoxSize(3, 3),
)

solution = solvers.backtrack(sudoku)
print(renderers.colorful(solution))

Ranking and Sudoku difficulty

avg_rank option roughly defines the difficulty of the sudoku. Sudoku with rank lower than 100 contains only naked/hidden singles. Sudoku with rank greater than 150 contains Naked Subsets/Locked Candidate/XY Wing/etc…, however this is not always guaranteed.

For higher ranks it is also not guaranteed that generated Sudoku rank will be higher than provided avg_rank, so to ensure sudoku has desired rank one can do the following:

from dokusan import generators, stats


avg_rank = 450
while stats.rank(sudoku := generators.random_sudoku(avg_rank)) < avg_rank:
    continue