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PyContracts is a Python package that allows to declare constraints on function parameters and return values. Contracts can be specified using Python3 annotations, in a decorator, or inside a docstring :type: and :rtype: tags. PyContracts supports a basic type system, variables binding, arithmetic constraints, and has several specialized contracts (notably for Numpy arrays), as well as an extension API.

Project description

PyContracts is a Python package that allows to declare constraints on function parameters and return values. It supports a basic type system, variables binding, arithmetic constraints, and has several specialized contracts (notably for Numpy arrays).

A brief summary follows. See the full documentation at: <http://andreacensi.github.com/contracts/>

Why: The purpose of PyContracts is not to turn Python into a statically-typed language (albeit you can be as strict as you wish), but, rather, to avoid the time-consuming and obfuscating checking of various preconditions. In fact, more than the type constraints, I found useful the ability to impose value and size constraints. For example, “I need a list of at least 3 positive numbers” can be expressed as list[>=3](number, >0)). If you find that PyContracts is overkill for you, you might want to try a simpler alternative, such as typecheck. If you find that PyContracts is not enough for you, you probably want to be using Haskell instead of Python.

Specifying contracts: Contracts can be specified in three ways:

  1. Using the ``@contract`` decorator:

    @contract(a='int,>0', b='list[N],N>0', returns='list[N]')
    def my_function(a, b):
        ...
    
  2. Using annotations (for Python 3):

    @contract
    def my_function(a : 'int,>0', b : 'list[N],N>0') -> 'list[N]':
         # Requires b to be a nonempty list, and the return
         # value to have the same length.
         ...
    
  3. Using docstrings, with the :type: and :rtype: tags:

    @contract
    def my_function(a, b):
        """ Function description.
            :type a: int,>0
            :type b: list[N],N>0
            :rtype: list[N]
        """
        ...
    

Deployment: In production, all checks can be disabled using the function contracts.disable_all(), so the performance hit is 0.

Extensions: You can extend PyContracts with new contracts types:

new_contract('valid_name', lambda s: isinstance(s, str) and len(s)>0)
@contract(names='dict(int: (valid_name, int))')
def process_accounting(records):
    ...

Any Python type is a contract:

@contract(a=int, # simple contract
          b='int,>0' # more complicated
          )
def f(a, b):
    ...

Enforcing interfaces: ContractsMeta is a metaclass like ABCMeta that propagates contracts to the subclasses:

from contracts import contract, ContractsMeta

class Base(object):
    __metaclass__ = ContractsMeta

    @abstractmethod
    @contract(probability='float,>=0,<=1')
    def sample(probability):
        pass

class Derived(Base):
    # The contract above is automatically enforced,
    # without this class having to know about PyContracts at all!
    def sample(probability):
        ....

Numpy: There is special support for Numpy:

@contract(image='array[HxWx3](uint8),H>10,W>10')
def recolor(image):
    ...

Status: PyContracts is very well tested and documented. The syntax is stable and it won’t be changed.

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