xoutil.functools - Higher-order functions and operations on callable objects

Extensions to the functools module from the Python’s standard library.

You may use this module as drop-in replacement of functools.

xoutil.functools.lru_cache(maxsize=128, typed=False)

Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments.

Since a dictionary is used to cache results, the positional and keyword arguments to the function must be hashable.

If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound. The LRU feature performs best when maxsize is a power-of-two.

If typed is set to True, function arguments of different types will be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with distinct results.

To help measure the effectiveness of the cache and tune the maxsize parameter, the wrapped function is instrumented with a cache_info() function that returns a named tuple showing hits, misses, maxsize and currsize. In a multi-threaded environment, the hits and misses are approximate.

The decorator also provides a cache_clear() function for clearing or invalidating the cache.

The original underlying function is accessible through the __wrapped__ attribute. This is useful for introspection, for bypassing the cache, or for rewrapping the function with a different cache.

An LRU (least recently used) cache works best when the most recent calls are the best predictors of upcoming calls (for example, the most popular articles on a news server tend to change each day). The cache’s size limit assures that the cache does not grow without bound on long-running processes such as web servers.

xoutil.functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function. The default values for these arguments are the module level constants WRAPPER_ASSIGNMENTS (which assigns to the wrapper function’s __name__, __module__, __annotations__ and __doc__, the documentation string) and WRAPPER_UPDATES (which updates the wrapper function’s __dict__, i.e. the instance dictionary).

To allow access to the original function for introspection and other purposes (e.g. bypassing a caching decorator such as lru_cache()), this function automatically adds a __wrapped__ attribute to the wrapper that refers to the original function.

The main intended use for this function is in decorator functions which wrap the decorated function and return the wrapper. If the wrapper function is not updated, the metadata of the returned function will reflect the wrapper definition rather than the original function definition, which is typically less than helpful.

update_wrapper() may be used with callables other than functions. Any attributes named in assigned or updated that are missing from the object being wrapped are ignored (i.e. this function will not attempt to set them on the wrapper function). AttributeError is still raised if the wrapper function itself is missing any attributes named in updated.

xoutil.functools.compose(*funcs, math=True)

Returns a function that is the composition of several callables.

By default compose behaves like mathematical function composition: this is to say that compose(f1, ... fn) is equivalent to lambda _x: fn(...(f1(_x))...).

If any “intermediate” function returns a ctuple it is expanded as several positional arguments to the next function.

Changed in version 1.5.5: At least a callable must be passed, otherwise a TypeError is raised. If a single callable is passed it is returned without change.

Parameters:math – Indicates if compose should behave like mathematical function composition: last function in funcs is applied last. If False, then the last function in func is applied first.

xoutil.functools.power(*funcs, times)

Returns the “power” composition of several functions.

Examples:

>>> import operator
>>> f = power(partial(operator.mul, 3), 3)
>>> f(23) == 3*(3*(3*23))
True

>>> power(operator.neg)
Traceback (most recent call last):
...
TypeError: Function `power` requires at least two arguments

class xoutil.functools.ctuple

Simple tuple marker for compose().

Since is a callable you may use it directly in compose instead of changing your functions to returns ctuples instead of tuples:

>>> def compat_print(*args):
...     for arg in args:
...         print(arg)

>>> compose(compat_print, ctuple, list, range, math=False)(3)
0
1
2

# Without ctuple prints the list
>>> compose(compat_print, list, range, math=False)(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

class xoutil.functools.lwraps(f, n, *, name=None, doc=None, wrapped=None)

Lambda wrapper.

Useful for decorate lambda functions with name and documentation.

As positional arguments could be passed the function to be decorated and the name in any order. So the next two identity definitions are equivalents:

>>> from xoutil.functools import lwraps as lw

>>> identity = lw('identity', lambda arg: arg)

>>> identity = lw(lambda arg: arg, 'identity')

As keyword arguments could be passed some special values, and any number of literal values to be assigned:

• name: The name of the function (__name__); only valid if not given as positional argument.
• doc: The documentation (__doc__ field).
• wrapped: An object to extract all values not yet assigned. These values are (‘__module__’, ‘__name__’ and ‘__doc__’) to be assigned, and ‘__dict__’ to be updated.

If the function to decorate is present in the positional arguments, this same argument function is directly returned after decorated; if not a decorator is returned similar to standard wraps().

For example:

>>> from xoutil.validators.connote import lwraps as lw

>>> is_valid_age = lw('is-valid-human-age', lambda age: 0 < age <= 120,
...                   doc=('A predicate to evaluate if an age is '
...                        'valid for a human being.')

>>> @lw(wrapped=is_valid_age)
... def is_valid_working_age(age):
...     return 18 < age <= 70

>>> is_valid_age(16)
True

>>> is_valid_age(200)
False

>>> is_valid_working_age(16)
False

New in version 1.7.0.