# PyChoco
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[](https://pypi.org/project/pychoco/)
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Python bindings for the Choco Constraint programming solver (https://choco-solver.org/).
Choco-solver is an open-source Java library for Constraint Programming (see https://choco-solver.org/).
It comes with many features such as various types of variables, various state-of-the-art constraint,
various search strategies, etc.
The PyChoco library uses a *native-build* of the original Java Choco-solver library, in the form
of a shared library, which means that it can be used without any JVM. This native-build is created
with [GraalVM](https://www.graalvm.org/) native-image tool.
We heavily relied on [JGraphT Python bindings](https://python-jgrapht.readthedocs.io/) source code to
understand how such a thing could be achieved, so many thanks to JGraphT authors!
## Installation
We automatically build 64-bit wheels for Python versions 3.6, 3.7, 3.8, 3.9, and 3.10 on Linux, Windows and
MacOSX. They can be directly downloaded from PyPI (https://pypi.org/project/pychoco/) or using pip:
pip install pychoco
## Documentation
If you do not have any knowledge about Constraint Programming (CP) and Choco-solver, you can have a look at
https://choco-solver.org/tutos/ for a quick introduction to CP and to Choco-solver features. For this Python API,
we also provide an API documentation which is available online at https://pychoco.readthedocs.io/ .
## Quickstart
Pychoco's API is quite close to Choco's Java API. The first thing to do is to import the
library and create a model object:
```python
from pychoco import Model
model = Model("My Choco Model")
```
Then, you can use this model object to create variables:
```python
intvars = model.intvars(10, 0, 10)
sum_var = model.intvar(0, 100)
```
You can also create views from this Model object:
```python
b6 = model.int_ge_view(intvars[6], 6)
```
Create and post (or reify) constraints:
```python
model.all_different(intvars).post()
model.sum(intvars, "=", sum_var).post()
b7 = model.arithm(intvars[7], ">=", 7).reify()
```
Solve your problem:
```python
model.get_solver().solve()
```
And retrieve the solution:
```python
print("intvars = {}".format([i.get_value() for i in intvars]))
print("sum = {}".format(sum_var.get_value()))
print("intvar[6] >= 6 ? {}".format(b6.get_value()))
print("intvar[7] >= 7 ? {}".format(b7.get_value()))
```
```
> intvars = [3, 5, 9, 6, 7, 2, 0, 1, 4, 8]
> sum = 45
> intvar[6] >= 6 ? False
> intvar[7] >= 7 ? False
```
## Build from source
The following system dependencies are required to build PyChco from sources:
- GraalVM >= 22 (see https://www.graalvm.org/)
- Native Image component for GraalVM (see https://www.graalvm.org/22.1/reference-manual/native-image/)
- Apache Maven (see https://maven.apache.org/)
- Python >= 3.6 (see https://www.python.org/)
- SWIG >= 3 (see https://www.swig.org/)
Once these dependencies are satisfied, clone the current repository:
git clone --recurse-submodules https://github.com/chocoteam/pychoco.git
The `--recurse-submodules` is necessary as the `choco-solver-capi` is a separate git project included
as a submodule (see https://github.com/chocoteam/choco-solver-capi). It contains all the necessary
to compile Choco-solver as a shared native library using GraalVM native-image.
Ensure that the `$JAVA_HOME` environment variable is pointing to GraalVM, and from the cloned repository
execute the following command:
sh build.sh
This command will compile Choco-solver into a shared native library and compile the Python bindings
to this native API using SWIG.
Finally, run:
pip install .
And voilà !
## Citation
Coming soon.
## Getting help or contribute
If you have any questions about using the library, suggestions for improvements, or if you
detect a bug, please [open an issue](https://github.com/chocoteam/pychoco/issues/new/choose).
Raw data
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"description": "# PyChoco\n\n[](https://github.com/chocoteam/pychoco/actions)\n[](https://github.com/chocoteam/pychoco/actions)\n[](https://github.com/chocoteam/pychoco/actions)\n[](https://codecov.io/gh/chocoteam/pychoco)\n[](https://pypi.org/project/pychoco/)\n[](https://pychoco.readthedocs.io/en/latest/?badge=latest)\n[](https://directory.fsf.org/wiki/License:BSD-4-Clause)\n\n\nPython bindings for the Choco Constraint programming solver (https://choco-solver.org/).\n\nChoco-solver is an open-source Java library for Constraint Programming (see https://choco-solver.org/).\nIt comes with many features such as various types of variables, various state-of-the-art constraint,\nvarious search strategies, etc.\n\nThe PyChoco library uses a *native-build* of the original Java Choco-solver library, in the form\nof a shared library, which means that it can be used without any JVM. This native-build is created\nwith [GraalVM](https://www.graalvm.org/) native-image tool.\n\nWe heavily relied on [JGraphT Python bindings](https://python-jgrapht.readthedocs.io/) source code to\nunderstand how such a thing could be achieved, so many thanks to JGraphT authors!\n\n## Installation\n\nWe automatically build 64-bit wheels for Python versions 3.6, 3.7, 3.8, 3.9, and 3.10 on Linux, Windows and\nMacOSX. They can be directly downloaded from PyPI (https://pypi.org/project/pychoco/) or using pip:\n\n pip install pychoco\n\n## Documentation\n\nIf you do not have any knowledge about Constraint Programming (CP) and Choco-solver, you can have a look at \nhttps://choco-solver.org/tutos/ for a quick introduction to CP and to Choco-solver features. For this Python API,\nwe also provide an API documentation which is available online at https://pychoco.readthedocs.io/ .\n\n## Quickstart\n\nPychoco's API is quite close to Choco's Java API. The first thing to do is to import the\nlibrary and create a model object:\n\n```python\nfrom pychoco import Model\n\nmodel = Model(\"My Choco Model\")\n```\n\nThen, you can use this model object to create variables:\n\n```python\nintvars = model.intvars(10, 0, 10)\nsum_var = model.intvar(0, 100)\n```\n\nYou can also create views from this Model object:\n\n```python\nb6 = model.int_ge_view(intvars[6], 6)\n```\n\nCreate and post (or reify) constraints:\n\n```python\nmodel.all_different(intvars).post()\nmodel.sum(intvars, \"=\", sum_var).post()\nb7 = model.arithm(intvars[7], \">=\", 7).reify()\n```\n\nSolve your problem:\n\n```python\nmodel.get_solver().solve()\n```\n\nAnd retrieve the solution:\n\n```python\nprint(\"intvars = {}\".format([i.get_value() for i in intvars]))\nprint(\"sum = {}\".format(sum_var.get_value()))\nprint(\"intvar[6] >= 6 ? {}\".format(b6.get_value()))\nprint(\"intvar[7] >= 7 ? {}\".format(b7.get_value()))\n```\n\n```\n> intvars = [3, 5, 9, 6, 7, 2, 0, 1, 4, 8]\n> sum = 45\n> intvar[6] >= 6 ? False\n> intvar[7] >= 7 ? False\n```\n\n## Build from source\n\nThe following system dependencies are required to build PyChco from sources:\n\n- GraalVM >= 22 (see https://www.graalvm.org/)\n- Native Image component for GraalVM (see https://www.graalvm.org/22.1/reference-manual/native-image/)\n- Apache Maven (see https://maven.apache.org/)\n- Python >= 3.6 (see https://www.python.org/)\n- SWIG >= 3 (see https://www.swig.org/)\n\nOnce these dependencies are satisfied, clone the current repository:\n\n git clone --recurse-submodules https://github.com/chocoteam/pychoco.git\n\nThe `--recurse-submodules` is necessary as the `choco-solver-capi` is a separate git project included\nas a submodule (see https://github.com/chocoteam/choco-solver-capi). It contains all the necessary\nto compile Choco-solver as a shared native library using GraalVM native-image.\n\nEnsure that the `$JAVA_HOME` environment variable is pointing to GraalVM, and from the cloned repository\nexecute the following command:\n\n sh build.sh\n\nThis command will compile Choco-solver into a shared native library and compile the Python bindings\nto this native API using SWIG.\n\nFinally, run:\n\n pip install .\n\nAnd voil\u00e0 !\n\n## Citation\n\nComing soon.\n\n## Getting help or contribute\n\nIf you have any questions about using the library, suggestions for improvements, or if you\ndetect a bug, please [open an issue](https://github.com/chocoteam/pychoco/issues/new/choose).\n",
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