"""
Module containing a backport of Python 3.3's least recently used cache
decorator.
The original module was downloaded from
http://code.activestate.com/recipes/578078/, then modified to support hashing of
counts dicts and annotationmaps.
"""
from collections import namedtuple
from functools import update_wrapper
from threading import RLock
import numbers
import numpy as np
from functools import reduce
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
class _HashedSeq(list):
__slots__ = 'hashvalue'
def __init__(self, tup, hash=hash):
self[:] = tup
self.hashvalue = hash(tup)
def __hash__(self):
return self.hashvalue
def _make_counts_hashable(counts):
return reduce(lambda x, y: (x << 1) ^ y, (hash(counts[k].tostring())
for k in sorted(counts.keys())))
def _make_annotationmap_hashable(annotationmap):
keys = list(sorted(annotationmap.keys()))
if type(annotationmap[keys[0]]) == np.ndarray:
return str(_make_counts_hashable(annotationmap))
return ''.join(keys + [str(annotationmap[k]) for k in keys])
def _make_annotationmaps_hashable(annotationmaps):
keys = list(sorted(annotationmaps.keys()))
return ''.join(keys + [_make_annotationmap_hashable(annotationmaps[k])
for k in keys])
def _typecheck_counts(counts):
return type(counts) == dict and type(list(counts.keys())[0]) == str and \
type(list(counts.values())[0]) == np.ndarray
def _typecheck_annotationmaps(annotationmaps):
return type(annotationmaps) == dict and \
type(list(annotationmaps.keys())[0]) == str and \
type(list(annotationmaps.values())[0]) == dict and \
type(list(list(annotationmaps.values())[0].keys())[0]) == str and \
(isinstance(list(list(annotationmaps.values())[0].values())[0][0],
numbers.Integral) or
isinstance(list(list(annotationmaps.values())[0].values())[0],
np.ndarray))
def _make_key(args, kwds, typed, kwd_mark=(object(),),
fasttypes={int, str, frozenset, type(None)}, sorted=sorted,
tuple=tuple, type=type, len=len):
""""Make a cache key from optionally typed positional and keyword
arguments"""
key = args
if kwds:
sorted_items = sorted(kwds.items())
key += kwd_mark
for item in sorted_items:
key += item
# new section added to handle hashing of custom types
key_list = list(key)
for i in range(len(key_list)):
if _typecheck_counts(key_list[i]):
key_list[i] = _make_counts_hashable(key_list[i])
elif _typecheck_annotationmaps(key_list[i]):
key_list[i] = _make_annotationmaps_hashable(key_list[i])
key = tuple(key_list)
if typed:
key += tuple(type(v) for v in args)
if kwds:
key += tuple(type(v) for k, v in sorted_items)
elif len(key) == 1 and type(key[0]) in fasttypes:
return key[0]
return _HashedSeq(key)
[docs]def lru_cache(maxsize=100, typed=False):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
If *typed* is True, arguments of different types will be cached separately.
For example, f(3.0) and f(3) will be treated as distinct calls with
distinct results.
Arguments to the cached function must be hashable.
View the cache statistics named tuple (hits, misses, maxsize, currsize) with
f.cache_info(). Clear the cache and statistics with f.cache_clear().
Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
# Users should only access the lru_cache through its public API:
# cache_info, cache_clear, and f.__wrapped__
# The internals of the lru_cache are encapsulated for thread safety and
# to allow the implementation to change (including a possible C version).
def decorating_function(user_function):
cache = dict()
stats = [0, 0] # make statistics updateable non-locally
HITS, MISSES = 0, 1 # names for the stats fields
make_key = _make_key
cache_get = cache.get # bound method to lookup key or return None
_len = len # localize the global len() function
lock = RLock() # because linkedlist updates aren't threadsafe
root = [] # root of the circular doubly linked list
root[:] = [root, root, None, None] # initialize by pointing to self
nonlocal_root = [root] # make updateable non-locally
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
if maxsize == 0:
def wrapper(*args, **kwds):
# no caching, just do a statistics update after a successful call
result = user_function(*args, **kwds)
stats[MISSES] += 1
return result
elif maxsize is None:
def wrapper(*args, **kwds):
# simple caching without ordering or size limit
key = make_key(args, kwds, typed)
result = cache_get(key, root) # root used here as a unique not-found sentinel
if result is not root:
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
cache[key] = result
stats[MISSES] += 1
return result
else:
def wrapper(*args, **kwds):
# size limited caching that tracks accesses by recency
key = make_key(args, kwds, typed) if kwds or typed else args
with lock:
link = cache_get(key)
if link is not None:
# record recent use of the key by moving it to the front of the list
root, = nonlocal_root
link_prev, link_next, key, result = link
link_prev[NEXT] = link_next
link_next[PREV] = link_prev
last = root[PREV]
last[NEXT] = root[PREV] = link
link[PREV] = last
link[NEXT] = root
stats[HITS] += 1
return result
result = user_function(*args, **kwds)
with lock:
root, = nonlocal_root
if key in cache:
# getting here means that this same key was added to the
# cache while the lock was released. since the link
# update is already done, we need only return the
# computed result and update the count of misses.
pass
elif _len(cache) >= maxsize:
# use the old root to store the new key and result
oldroot = root
oldroot[KEY] = key
oldroot[RESULT] = result
# empty the oldest link and make it the new root
root = nonlocal_root[0] = oldroot[NEXT]
oldkey = root[KEY]
oldvalue = root[RESULT]
root[KEY] = root[RESULT] = None
# now update the cache dictionary for the new links
del cache[oldkey]
cache[key] = oldroot
else:
# put result in a new link at the front of the list
last = root[PREV]
link = [last, root, key, result]
last[NEXT] = root[PREV] = cache[key] = link
stats[MISSES] += 1
return result
def cache_info():
"""Report cache statistics"""
with lock:
return _CacheInfo(stats[HITS], stats[MISSES], maxsize, len(cache))
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
root = nonlocal_root[0]
root[:] = [root, root, None, None]
stats[:] = [0, 0]
wrapper.__wrapped__ = user_function
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return update_wrapper(wrapper, user_function)
return decorating_function