Extensible memoizing collections and decorators

## Project description

This module provides various memoizing collections and decorators, including variants of the Python Standard Library’s @lru_cache function decorator.

from cachetools import cached, LRUCache, TTLCache

# speed up calculating Fibonacci numbers with dynamic programming
@cached(cache={})
def fib(n):
return n if n < 2 else fib(n - 1) + fib(n - 2)

# cache least recently used Python Enhancement Proposals
@cached(cache=LRUCache(maxsize=32))
def get_pep(num):
url = 'http://www.python.org/dev/peps/pep-%04d/' % num
with urllib.request.urlopen(url) as s:

# cache weather data for no longer than ten minutes
@cached(cache=TTLCache(maxsize=1024, ttl=600))
def get_weather(place):
return owm.weather_at_place(place).get_weather()

For the purpose of this module, a cache is a mutable mapping of a fixed maximum size. When the cache is full, i.e. by adding another item the cache would exceed its maximum size, the cache must choose which item(s) to discard based on a suitable cache algorithm. In general, a cache’s size is the total size of its items, and an item’s size is a property or function of its value, e.g. the result of sys.getsizeof(value). For the trivial but common case that each item counts as 1, a cache’s size is equal to the number of its items, or len(cache).

Multiple cache classes based on different caching algorithms are implemented, and decorators for easily memoizing function and method calls are provided, too.

## Installation

cachetools is available from PyPI and can be installed by running:

pip install cachetools

Typing stubs for this package are provided by typeshed and can be installed by running:

pip install types-cachetools

## Project details

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