========
Overview
========
.. start-badges
.. image:: https://github.com/unmade/dokusan/workflows/Lint%20and%20tests/badge.svg
:alt: Build Status
:target: https://github.com/unmade/dokusan/blob/master/.github/workflows/lint-and-tests.yml
.. image:: https://codecov.io/gh/unmade/dokusan/branch/master/graph/badge.svg
:alt: Coverage Status
:target: https://codecov.io/gh/unmade/dokusan
.. image:: http://www.mypy-lang.org/static/mypy_badge.svg
:alt: Checked with mypy
:target: http://mypy-lang.org/
.. image:: https://img.shields.io/pypi/v/dokusan.svg
:alt: PyPI Package latest release
:target: https://pypi.org/project/dokusan
.. image:: https://img.shields.io/pypi/wheel/dokusan.svg
:alt: PyPI Wheel
:target: https://pypi.org/project/dokusan
.. image:: https://img.shields.io/pypi/pyversions/dokusan.svg
:alt: Supported versions
:target: https://pypi.org/project/dokusan
.. image:: https://img.shields.io/badge/License-GPLv3-purple.svg
:alt: GPLv3 License
:target: https://github.com/unmade/dokusan/blob/master/LICENSE
.. end-badges
Sudoku generator and solver with a step-by-step guidance
Installation
============
.. code-block:: bash
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:
.. code-block:: python
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
.. code-block:: python
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 <https://dlbeer.co.nz/articles/sudoku.html>`_ by Daniel Beer.
The average time to generate Sudoku with rank of 150 is 700ms.
To generate a new sudoku:
.. code-block:: python
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:
.. code-block:: python
from dokusan import generators, stats
avg_rank = 450
while stats.rank(sudoku := generators.random_sudoku(avg_rank)) < avg_rank:
continue
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"description": "========\nOverview\n========\n\n.. start-badges\n\n.. image:: https://github.com/unmade/dokusan/workflows/Lint%20and%20tests/badge.svg\n :alt: Build Status\n :target: https://github.com/unmade/dokusan/blob/master/.github/workflows/lint-and-tests.yml\n\n.. image:: https://codecov.io/gh/unmade/dokusan/branch/master/graph/badge.svg\n :alt: Coverage Status\n :target: https://codecov.io/gh/unmade/dokusan\n\n.. image:: http://www.mypy-lang.org/static/mypy_badge.svg\n :alt: Checked with mypy\n :target: http://mypy-lang.org/\n\n.. image:: https://img.shields.io/pypi/v/dokusan.svg\n :alt: PyPI Package latest release\n :target: https://pypi.org/project/dokusan\n\n.. image:: https://img.shields.io/pypi/wheel/dokusan.svg\n :alt: PyPI Wheel\n :target: https://pypi.org/project/dokusan\n\n.. image:: https://img.shields.io/pypi/pyversions/dokusan.svg\n :alt: Supported versions\n :target: https://pypi.org/project/dokusan\n\n.. image:: https://img.shields.io/badge/License-GPLv3-purple.svg\n :alt: GPLv3 License\n :target: https://github.com/unmade/dokusan/blob/master/LICENSE\n\n.. end-badges\n\nSudoku generator and solver with a step-by-step guidance\n\nInstallation\n============\n\n.. code-block:: bash\n\n pip install dokusan\n\nQuickstart\n==========\n\nSudoku Solvers\n--------------\n\nStep-by-step solver\n*******************\n\nThis solver tries to solve sudoku using human-like strategies.\nCurrently following techniques are supported:\n\n- Naked/Hidden singles\n- Naked Pairs/Triplets\n- Locked Candidate\n- XY-Wing\n- Unique Rectangle\n\nFor example to see all techniques that sudoku has:\n\n.. code-block:: python\n\n from dokusan import solvers\n from dokusan.boards import BoxSize, Sudoku\n\n\n sudoku = Sudoku.from_list(\n [\n [0, 0, 0, 0, 9, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 2, 3, 0, 0],\n [0, 0, 7, 0, 0, 1, 8, 2, 5],\n [6, 0, 4, 0, 3, 8, 9, 0, 0],\n [8, 1, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 9, 0, 0, 0, 0, 0, 8],\n [1, 7, 0, 0, 0, 0, 6, 0, 0],\n [9, 0, 0, 0, 1, 0, 7, 4, 3],\n [4, 0, 3, 0, 6, 0, 0, 0, 1],\n ],\n box_size=BoxSize(3, 3),\n )\n\n {step.combination.name for step in solvers.steps(sudoku)}\n\nBacktracking-based solver\n*************************\n\nThis solver is based on backtracking algorithm,\nhowever slightly modified to work fast\n\n.. code-block:: python\n\n from dokusan import solvers, renderers\n from dokusan.boards import BoxSize, Sudoku\n\n\n sudoku = Sudoku.from_list(\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 3, 0, 8, 5],\n [0, 0, 1, 0, 2, 0, 0, 0, 0],\n [0, 0, 0, 5, 0, 7, 0, 0, 0],\n [0, 0, 4, 0, 0, 0, 1, 0, 0],\n [0, 9, 0, 0, 0, 0, 0, 0, 0],\n [5, 0, 0, 0, 0, 0, 0, 7, 3],\n [0, 0, 2, 0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 4, 0, 0, 0, 9],\n ],\n box_size=BoxSize(3, 3),\n )\n\n solution = solvers.backtrack(sudoku)\n print(renderers.colorful(solution))\n\nSudoku Generator\n----------------\n\nGenerator algorithm is mainly based on\n`article <https://dlbeer.co.nz/articles/sudoku.html>`_ by Daniel Beer.\nThe average time to generate Sudoku with rank of 150 is 700ms.\n\nTo generate a new sudoku:\n\n.. code-block:: python\n\n from dokusan import generators, renderers\n\n\n sudoku = generators.random_sudoku(avg_rank=150)\n print(renderers.colorful(sudoku))\n\nRanking and Sudoku difficulty\n*****************************\n\n``avg_rank`` option roughly defines the difficulty of the sudoku.\nSudoku with rank lower than 100 contains only naked/hidden singles.\nSudoku with rank greater than 150 contains\nNaked Subsets/Locked Candidate/XY Wing/etc...,\nhowever this is not always guaranteed.\n\nFor higher ranks it is also not guaranteed that generated Sudoku rank\nwill be higher than provided ``avg_rank``,\nso to ensure sudoku has desired rank one can do the following:\n\n.. code-block:: python\n\n from dokusan import generators, stats\n\n\n avg_rank = 450\n while stats.rank(sudoku := generators.random_sudoku(avg_rank)) < avg_rank:\n continue\n",
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