Express constraint programming problem with python and solve it with minizinc

# zython

Express constraint programming problem with python and solve it with minizinc

Constraint programming (CP) is a paradigm for solving combinatorial problems. Minizinc is used for model and optimization problems solving using CP. You can express a model as a number of parameter, variables and constraints as minizinc will solve it (or said it if there isn't any solution).

If you are wonder which digit should be assigned to letters, so the expression `SEND+MORE=MONEY` will be hold, or how many color you should have to brush map of Australia and two states with the same border won't have any common color, or try to understand which units you should hire in your favourite strategy game, so you will have the strongest army for that amount of money you can use CP.

Zython lets you express such model with pure python, so there is no need to learning new language and you can easily integrate CP into your python programs.

## Getting Started

### Prerequisites

• You should have minizinc >=2.5 install and have it executable in `\$PATH`. You can download it from official site.
• Python 3.6+

### Installation

The project will be added to pypi.

### Usage

Our first example will be quadratic equation solving.

It can be expressed in minizinc as:

``````var -100..100: x;
int: a; int: b; int: c;
constraint a*(x*x) + b*x = c;
solve satisfy;
``````

or using minizinc-python package as

``````import minizinc

# Create a MiniZinc model
model = minizinc.Model()
var -100..100: x;
int: a; int: b; int: c;
constraint a*(x*x) + b*x = c;
solve satisfy;
""")

# Transform Model into a instance
gecode = minizinc.Solver.lookup("gecode")
inst = minizinc.Instance(gecode, model)
inst["a"] = 1
inst["b"] = 4
inst["c"] = 0

# Solve the instance
result = inst.solve(all_solutions=True)
for i in range(len(result)):
print("x = {}".format(result[i, "x"]))
``````

While zython makes it possible to describe this model using python only:

``````class MyModel(zython.Model):
def __init__(self, a: int, b: int, c: int):
self.a = var(a)
self.b = var(b)
self.c = var(c)
self.x = var(range(-100, 101))
self.constraints = [self.a * self.x ** 2 + self.b * self.x + self.c == 0]

model = MyModel(1, 4, 0)
result = model.solve_satisfy(all_solutions=True)
``````

## Collaboration

Zython uses the following libraries:

• Test is created with pytest library
• nox for test execution
• flake8 for coding style checking
• sphinx for documentation

Requirements necessary for zython run specified in requirements.txt file, while testing, development requirements are specified in requirements_dev.txt, and documentation requirements are in requirements_doc.txt. For example, if you decided to fix bug, and you need no documentation fixes, you shouldn't install requirements_doc.txt. Project can be cloned from github and all dependencies can be installed via pip.

``````git clone git@github.com:ArtyomKaltovich/zython.git
python -m venv /path/to/new/venv if needed
pip install -r requirements.txt
pip install -r requirements_dev.txt
``````

Note: flake8 isn't specified as dependency in any file, this is made for avoiding it installation for every python version tested in CI. You should install it manually if you want to check your code before submitting PR. You can do it with the following commands:

``````pip install flake8
nox -s lint
``````

You can also run all checks locally:

``````nox --reuse-existing-virtualenvs
``````

It is recommended to open new issue and describe a bug or feature request before submitting PR. While implementing new feature or fixing bug it is necessary to add tests to cover it.

Good Luck and thank you for improvements. :)