python-constraint
Introduction
The Python constraint module offers solvers for Constraint Satisfaction Problems (CSPs) over finite domains in simple and pure Python. 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 the module basic operation:
>>> from constraint import *
>>> problem = Problem()
>>> problem.addVariable("a", [1,2,3])
>>> problem.addVariable("b", [4,5,6])
>>> problem.getSolutions()
[{'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(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()
[{'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
>>> solutions
[{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()Features
The following solvers are available:
Backtracking solver
Recursive backtracking solver
Minimum conflicts solver
Predefined constraint types currently available:
FunctionConstraintAllDifferentConstraintAllEqualConstraintExactSumConstraintMaxSumConstraintMinSumConstraintInSetConstraintNotInSetConstraintSomeInSetConstraintSomeNotInSetConstraint
API documentation
Documentation for the module is available at: http://labix.org/doc/constraint/
Download and install
$ pip install python-constraintRoadmap
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
Create some unit tests - DONE
Enable continuous integration - DONE
Port to Python 3 (Python 2 being also supported) - DONE
Respect Style Guide for Python Code (PEP8) - DONE
Improve code coverage writting more unit tests - ToDo
Move doc to Sphinx or MkDocs - https://readthedocs.org/ - ToDo
Contact
But it’s probably better to open an issue.
Release files for python-constraint 1.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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Release files / python-constraint-1.4.0.tar.bz2
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|---|---|
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