python-constraint
problem.addConstraint(["50 <= x * y < 100"]) is parsed to [MinProdConstraint(50, ["x", "y"]), MaxProdConstraint(100, ["x", "y"])].problem.addConstraint(["x / y == z"]) is parsed to [ExactProdConstraint("x", ["z", "y"])].Introduction
The python-constraint module offers efficient solvers for Constraint Satisfaction Problems (CSPs) 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:
>>> 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:
>>> 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:
>>> 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: +SKIPFeatures
The following solvers are available:
OptimizedBacktrackingSolver(default)BacktrackingSolverRecursiveBacktrackingSolverMinConflictsSolverParallelSolver
Predefined constraint types currently available (use the parsing for automatic conversion to these types):
FunctionConstraintAllDifferentConstraintAllEqualConstraintExactSumConstraintMinSumConstraintMaxSumConstraintMinProdConstraintExactProdConstraintMaxProdConstraintVariableExactSumConstraintVariableMinSumConstraintVariableMaxSumConstraintVariableMinProdConstraintVariableExactProdConstraintVariableMaxProdConstraintInSetConstraintNotInSetConstraintSomeInSetConstraintSomeNotInSetConstraint
API documentation
Documentation for the module is available at: http://python-constraint.github.io/python-constraint/.
It can be built locally by running make clean html from the docs folder.
For viewing RST files locally, restview is recommended.
Download and install
$ pip install python-constraint2Conda / Mamba:
$ conda config --add channels conda-forge
$ conda config --set channel_priority strict
$ conda install python-constraint2
$ mamba install python-constraint2Testing
Run 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 pytest (optionally add --no-cov if you have the C-extensions enabled).
Contributing
Feel free to contribute by submitting pull requests or opening issues. Please refer to the contribution guidelines before doing so.
Roadmap
This GitHub organization and repository is a global effort to help to maintain 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.
Planned development:
Support constant modifiers on parsed (variable) constraints instead defaulting to FunctionConstraint, e.g.
problem.addConstraint("a+2 == b")orproblem.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 <fjwillemsen97@gmail.com> (current maintainer)
Sébastien Celles <s.celles@gmail.com> (former maintainer)
Gustavo Niemeyer <gustavo@niemeyer.net> (initial developer)
But it’s probably better to open an issue.
1 maintainer
Metadata
1 maintainer
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