Metadata-Version: 2.4
Name: python-constraint2
Version: 2.5.0
Summary: python-constraint is a module for efficiently solving CSPs (Constraint Solving Problems) over finite domains.
License-Expression: BSD-2-Clause
License-File: LICENSE
Keywords: CSP,constraint solving problems,problem solver,SMT,satisfiability modulo theory,SAT
Author: Floris-Jan Willemsen
Author-email: fjwillemsen97@gmail.com
Maintainer: Floris-Jan Willemsen
Maintainer-email: fjwillemsen97@gmail.com
Requires-Python: >= 3.9
Classifier: Environment :: Console
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Natural Language :: English
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Project-URL: Changelog, https://github.com/python-constraint/python-constraint/blob/main/CHANGELOG.md
Project-URL: Documentation, http://python-constraint.github.io/python-constraint/
Project-URL: Homepage, http://python-constraint.github.io/python-constraint/
Project-URL: Issues, https://github.com/python-constraint/python-constraint/issues
Project-URL: Repository, https://github.com/python-constraint/python-constraint
Description-Content-Type: text/x-rst

|License| |Build Status| |Docs| |Python Versions| |Downloads| |Status| |Code Coverage|

.. image:: https://github.com/python-constraint/python-constraint/raw/main/docs/assets/logo/N-Queens_problem_Python.svg
    :align: center
    :width: 50%

python-constraint
=================

| This software is now back to active development / maintainance status.
| IMPORTANT: the new version can be installed with `pip install python-constraint2`, as the original pip release will not be updated.
| For an overview of recent changes, visit the `Changelog <https://github.com/python-constraint/python-constraint/blob/main/CHANGELOG.md>`_.
| The complete documentation can be found `here <http://python-constraint.github.io/python-constraint/>`_.

| New: writing constraints in the new string format is preferable over functions and lambdas. 
| These strings, even as compound statements, are automatically parsed to faster built-in constraints, are more concise, and do not require constraint solving familiarity by the user to be efficient.
| For example, :code:`problem.addConstraint(["50 <= x * y < 100"])` is parsed to :code:`[MinProdConstraint(50, ["x", "y"]), MaxProdConstraint(100, ["x", "y"])]`. 
| Similarly, :code:`problem.addConstraint(["x / y == z"])` is parsed to :code:`[ExactProdConstraint("x", ["z", "y"])]`.
| This feature is in beta and subject to possible change, please provide feedback.

.. contents::
    :local:
    :depth: 1

Introduction
------------
The :code:`python-constraint` module offers efficient solvers for `Constraint Satisfaction Problems (CSPs) <https://en.wikipedia.org/wiki/Constraint_satisfaction_problem>`_ over finite domains in an accessible Python package.
CSP is class of problems which may be represented in terms of variables (a, b, ...), domains (a in [1, 2, 3], ...), and constraints (a < b, ...).

Examples
--------

Basics
~~~~~~

This interactive Python session demonstrates basic operations:

.. code-block:: python

    >>> from constraint import *
    >>> problem = Problem()
    >>> problem.addVariable("a", [1,2,3])
    >>> problem.addVariable("b", [4,5,6])
    >>> problem.getSolutions()  # doctest: +NORMALIZE_WHITESPACE
    [{'a': 3, 'b': 6}, {'a': 3, 'b': 5}, {'a': 3, 'b': 4},
     {'a': 2, 'b': 6}, {'a': 2, 'b': 5}, {'a': 2, 'b': 4},
     {'a': 1, 'b': 6}, {'a': 1, 'b': 5}, {'a': 1, 'b': 4}]
    
    >>> problem.addConstraint("a*2 == b") # string constraints are preferable over the black-box problem.addConstraint(lambda a, b: a*2 == b, ("a", "b"))
    >>> problem.getSolutions()
    [{'a': 3, 'b': 6}, {'a': 2, 'b': 4}]

    >>> problem = Problem()
    >>> problem.addVariables(["a", "b"], [1, 2, 3])
    >>> problem.addConstraint(AllDifferentConstraint())
    >>> problem.getSolutions()  # doctest: +NORMALIZE_WHITESPACE
    [{'a': 3, 'b': 2}, {'a': 3, 'b': 1}, {'a': 2, 'b': 3},
     {'a': 2, 'b': 1}, {'a': 1, 'b': 2}, {'a': 1, 'b': 3}]

Rooks problem
~~~~~~~~~~~~~

The following example solves the classical Eight Rooks problem:

.. code-block:: python

    >>> problem = Problem()
    >>> numpieces = 8
    >>> cols = range(numpieces)
    >>> rows = range(numpieces)
    >>> problem.addVariables(cols, rows)
    >>> for col1 in cols:
    ...     for col2 in cols:
    ...         if col1 < col2:
    ...             problem.addConstraint(lambda row1, row2: row1 != row2, (col1, col2))
    >>> solutions = problem.getSolutions()
    >>> solutions   # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    [{0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1, 7: 0},
     {0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 0, 7: 1},
     {0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 1, 6: 2, 7: 0},
     {0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 1, 6: 0, 7: 2},
     ...
     {0: 7, 1: 5, 2: 3, 3: 6, 4: 2, 5: 1, 6: 4, 7: 0},
     {0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 2, 6: 0, 7: 4},
     {0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 2, 6: 4, 7: 0},
     {0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 4, 6: 2, 7: 0},
     {0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 4, 6: 0, 7: 2},
     ...]


Magic squares
~~~~~~~~~~~~~

This example solves a 4x4 magic square:

.. code-block:: python

    >>> problem = Problem()
    >>> problem.addVariables(range(0, 16), range(1, 16 + 1))
    >>> problem.addConstraint(AllDifferentConstraint(), range(0, 16))
    >>> problem.addConstraint(ExactSumConstraint(34), [0, 5, 10, 15])
    >>> problem.addConstraint(ExactSumConstraint(34), [3, 6, 9, 12])
    >>> for row in range(4):
    ...     problem.addConstraint(ExactSumConstraint(34), [row * 4 + i for i in range(4)])
    >>> for col in range(4):
    ...     problem.addConstraint(ExactSumConstraint(34), [col + 4 * i for i in range(4)])
    >>> solutions = problem.getSolutions()  # doctest: +SKIP

Features
--------

The following solvers are available:

- :code:`OptimizedBacktrackingSolver` (default)
- :code:`BacktrackingSolver`
- :code:`RecursiveBacktrackingSolver`
- :code:`MinConflictsSolver`
- :code:`ParallelSolver`

.. role:: python(code)
   :language: python

Predefined constraint types currently available (use the parsing for automatic conversion to these types):

- :code:`FunctionConstraint`
- :code:`AllDifferentConstraint`
- :code:`AllEqualConstraint`
- :code:`ExactSumConstraint`
- :code:`MinSumConstraint`
- :code:`MaxSumConstraint`
- :code:`MinProdConstraint`
- :code:`ExactProdConstraint`
- :code:`MaxProdConstraint`
- :code:`VariableExactSumConstraint`
- :code:`VariableMinSumConstraint`
- :code:`VariableMaxSumConstraint`
- :code:`VariableMinProdConstraint`
- :code:`VariableExactProdConstraint`
- :code:`VariableMaxProdConstraint`
- :code:`InSetConstraint`
- :code:`NotInSetConstraint`
- :code:`SomeInSetConstraint`
- :code:`SomeNotInSetConstraint`

API documentation
-----------------
Documentation for the module is available at: http://python-constraint.github.io/python-constraint/.
It can be built locally by running :code:`make clean html` from the `docs` folder.
For viewing RST files locally, `restview <https://pypi.org/project/restview/>`_ is recommended.

Download and install
--------------------

.. code-block:: shell

    $ pip install python-constraint2

Testing
-------

Run :code:`nox` (tests for all supported Python versions in own virtual environment).

To test against your local Python version: make sure you have the development dependencies installed.
Run :code:`pytest` (optionally add :code:`--no-cov` if you have the C-extensions enabled).

Contributing
------------

Feel free to contribute by `submitting pull requests <https://github.com/python-constraint/python-constraint/pulls>`_ or `opening issues <https://github.com/python-constraint/python-constraint/issues>`_.
Please refer to the `contribution guidelines <https://github.com/python-constraint/python-constraint/contribute>`_ before doing so.

Roadmap
-------

This GitHub organization and repository is a global effort to help to maintain :code:`python-constraint`, which was written by Gustavo Niemeyer and originaly located at https://labix.org/python-constraint.
For an overview of recent changes, visit the `Changelog <https://github.com/python-constraint/python-constraint/blob/main/CHANGELOG.md>`_.

Planned development:

- Support constant modifiers on parsed (variable) constraints instead defaulting to `FunctionConstraint`, e.g. :code:`problem.addConstraint("a+2 == b")` or :code:`problem.addConstraint("x / y == 100")`
- Parse using Abstract Syntax Trees (AST) instead of the current parser to be more robust and support decomposing lambdas
- Rewrite hotspots in C / Pyx instead of pure python mode
- Improvements to make the ParallelSolver competitive (experiments reveal the freethreading mode to be promising)
- Versioned documentation

Contact
-------
- `Floris-Jan Willemsen <https://github.com/fjwillemsen>`_ <fjwillemsen97@gmail.com> (current maintainer)
- `Sébastien Celles <https://github.com/s-celles/>`_ <s.celles@gmail.com> (former maintainer)
- `Gustavo Niemeyer <https://github.com/niemeyer/>`_ <gustavo@niemeyer.net> (initial developer)

But it's probably better to `open an issue <https://github.com/python-constraint/python-constraint/issues>`_.

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