Sudoku generator and solver with a step-by-step guidance

## Project description

Sudoku generator and solver with a step-by-step guidance

## Installation

pip install dokusan

## Quickstart

### Sudoku Solvers

#### 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))

### Sudoku Generator

Generator algorithm is mainly based on article by Daniel Beer. The average time to generate Sudoku with rank of 150 is 700ms.

To generate a new sudoku:

from dokusan import generators, renderers

sudoku = generators.random_sudoku(avg_rank=150)
print(renderers.colorful(sudoku))

#### 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

## Project details

Uploaded source
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