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183 lines
7.2 KiB
Python
183 lines
7.2 KiB
Python
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# Drop in replace for python 3.2's collections.lru_cache, from...
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# http://code.activestate.com/recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/
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#
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# ... which is under the MIT license. Stem users should *not* rely upon this
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# module. It will be removed when we drop support for python 3.2 and below.
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"""
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Memoization decorator that caches a function's return value. If later called
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with the same arguments then the cached value is returned rather than
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reevaluated.
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This is a a python 2.x port of `functools.lru_cache
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<http://docs.python.org/3/library/functools.html#functools.lru_cache>`_. If
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using python 3.2 or later you should use that instead.
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"""
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from collections import namedtuple
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from functools import update_wrapper
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from threading import RLock
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_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
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class _HashedSeq(list):
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__slots__ = 'hashvalue'
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def __init__(self, tup, hash=hash):
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self[:] = tup
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self.hashvalue = hash(tup)
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def __hash__(self):
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return self.hashvalue
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def _make_key(args, kwds, typed,
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kwd_mark = (object(),),
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fasttypes = set([int, str, frozenset, type(None)]),
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sorted=sorted, tuple=tuple, type=type, len=len):
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'Make a cache key from optionally typed positional and keyword arguments'
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key = args
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if kwds:
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sorted_items = sorted(kwds.items())
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key += kwd_mark
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for item in sorted_items:
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key += item
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if typed:
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key += tuple(type(v) for v in args)
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if kwds:
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key += tuple(type(v) for k, v in sorted_items)
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elif len(key) == 1 and type(key[0]) in fasttypes:
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return key[0]
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return _HashedSeq(key)
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def lru_cache(maxsize=100, typed=False):
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"""Least-recently-used cache decorator.
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If *maxsize* is set to None, the LRU features are disabled and the cache
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can grow without bound.
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If *typed* is True, arguments of different types will be cached separately.
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For example, f(3.0) and f(3) will be treated as distinct calls with
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distinct results.
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Arguments to the cached function must be hashable.
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View the cache statistics named tuple (hits, misses, maxsize, currsize) with
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f.cache_info(). Clear the cache and statistics with f.cache_clear().
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Access the underlying function with f.__wrapped__.
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See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
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"""
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# Users should only access the lru_cache through its public API:
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# cache_info, cache_clear, and f.__wrapped__
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# The internals of the lru_cache are encapsulated for thread safety and
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# to allow the implementation to change (including a possible C version).
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def decorating_function(user_function):
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cache = dict()
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stats = [0, 0] # make statistics updateable non-locally
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HITS, MISSES = 0, 1 # names for the stats fields
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make_key = _make_key
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cache_get = cache.get # bound method to lookup key or return None
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_len = len # localize the global len() function
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lock = RLock() # because linkedlist updates aren't threadsafe
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root = [] # root of the circular doubly linked list
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root[:] = [root, root, None, None] # initialize by pointing to self
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nonlocal_root = [root] # make updateable non-locally
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PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
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if maxsize == 0:
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def wrapper(*args, **kwds):
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# no caching, just do a statistics update after a successful call
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result = user_function(*args, **kwds)
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stats[MISSES] += 1
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return result
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elif maxsize is None:
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def wrapper(*args, **kwds):
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# simple caching without ordering or size limit
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key = make_key(args, kwds, typed)
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result = cache_get(key, root) # root used here as a unique not-found sentinel
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if result is not root:
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stats[HITS] += 1
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return result
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result = user_function(*args, **kwds)
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cache[key] = result
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stats[MISSES] += 1
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return result
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else:
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def wrapper(*args, **kwds):
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# size limited caching that tracks accesses by recency
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key = make_key(args, kwds, typed) if kwds or typed else args
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with lock:
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link = cache_get(key)
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if link is not None:
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# record recent use of the key by moving it to the front of the list
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root, = nonlocal_root
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link_prev, link_next, key, result = link
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link_prev[NEXT] = link_next
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link_next[PREV] = link_prev
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last = root[PREV]
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last[NEXT] = root[PREV] = link
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link[PREV] = last
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link[NEXT] = root
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stats[HITS] += 1
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return result
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result = user_function(*args, **kwds)
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with lock:
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root, = nonlocal_root
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if key in cache:
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# getting here means that this same key was added to the
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# cache while the lock was released. since the link
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# update is already done, we need only return the
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# computed result and update the count of misses.
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pass
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elif _len(cache) >= maxsize:
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# use the old root to store the new key and result
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oldroot = root
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oldroot[KEY] = key
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oldroot[RESULT] = result
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# empty the oldest link and make it the new root
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root = nonlocal_root[0] = oldroot[NEXT]
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oldkey = root[KEY]
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root[KEY] = root[RESULT] = None
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# now update the cache dictionary for the new links
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del cache[oldkey]
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cache[key] = oldroot
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else:
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# put result in a new link at the front of the list
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last = root[PREV]
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link = [last, root, key, result]
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last[NEXT] = root[PREV] = cache[key] = link
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stats[MISSES] += 1
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return result
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def cache_info():
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"""Report cache statistics"""
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with lock:
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return _CacheInfo(stats[HITS], stats[MISSES], maxsize, len(cache))
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def cache_clear():
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"""Clear the cache and cache statistics"""
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with lock:
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cache.clear()
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root = nonlocal_root[0]
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root[:] = [root, root, None, None]
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stats[:] = [0, 0]
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wrapper.__wrapped__ = user_function
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wrapper.cache_info = cache_info
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wrapper.cache_clear = cache_clear
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return update_wrapper(wrapper, user_function)
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return decorating_function
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