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Flexible, concise preconditions.

Project description

preconditions - A precondition decorator utility which relies on parameter-name equivalence for conciseness and consistency.

Examples

First let’s take a tour of examples. All examples assume the preconditions decorator has been imported:

from preconditions import preconditions

Basic type checking

The double application function requires that the i parameter is an int, which is verified by a single predicate (the lambda expression):

@preconditions(lambda i: isinstance(i, int))
def double(i):
    return 2*i

Multiple predicates

Multiple predicates may be specified:

@preconditions(
    lambda i: isinstance(i, int),
    lambda i: i > 0,
    )
def double(i):
    return 2*i

Note that this is functionally equivalent to this single predicate version:

@preconditions(
    lambda i: isinstance(i, int) and i > 0,
    )
def double(i):
    return 2*i

The multi-predicate version should (eventually) have more specific error reporting for a failure, while the single predicate version may be more efficient.

Multiple arguments

Multiple predicates can express preconditions for multiple arguments:

@preconditions(
    lambda s: isinstance(s, unicode),
    lambda n: isinstance(n, int) and n >= 0,
    )
def repeat(s, n):
    return s*n

However, a single predicate can express preconditions for multiple arguments. This allows relational preconditions:

@preconditions(
    lambda a, b: a <= b
    )
def strict_range(a, b):
    return range(a, b)

Method preconditions

Predicates can be expressed for methods, including relations to self. For example, a Monotonic instance ensures that each call to .set must pass a value larger than any previous call:

class Monotonic (object):
    def __init__(self):
        self.v = 0

    @preconditions(lambda self, v: v > self.v)
    def set(self, v):
        self.v = v

Preconditions can be applied to special methods, such as __new__, __init__, __call__, etc…

class LinearRange (tuple):
    @preconditions(
           lambda a: isinstance(a, float),
           lambda b: isinstance(b, float),
           lambda a, b: a < b,
           )
    def __new__(cls, a, b):
        return super(OrderedTuple, cls).__new__(cls, (a, b))

    @preconditions(lambda w: 0 <= w < 1.0)
    def __call__(self, w):
        lo, hi = self
        return w * (hi - lo) + lo

    @preconditions(lambda x: self[0] <= x < self[1])
    def invert(self, x):
        lo, hi = self
        return (x - lo) / (hi - lo)

Concepts

An application function may be guarded with precondition predicates. These predicates are callables passed to the preconditions decorator. Consider this code:

@preconditions(
    lambda a: isinstance(a, float) and a >= 0,
    lambda b: isinstance(b, float) and b >= 0,
    )
def area(a, b):
    return a*b

The application function is area, and it has two predicates defined with lambda, each of which ensures one of the arguments is a non-negative float.

Parameter Name Equivalence

The parameter names in a predicate must match parameter names in the application function. This is known as parameter name equivalence [1].

[1]This is a bit magical, relying on function introspection. The design assumes the conciseness and consistency benefits outweigh the potential confusion of “magic”.

One exception to this rule is for default parameters within predicates. Default parameters may be used to associate some state at predicate definition time. For example:

scores = {}

@preconditions(
    lambda color, _colors=['RED', 'GREEN', 'BLUE']: color in _colors
    )
def get_color_score(color):
    return scores[color]

This feature may be most convenient when there’s a need to remember a local loop variable.

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