Source code for pycalphad.core.cache

"""
The cache module handles caching of expensive function calls.

Based on Raymond Hettinger's backport of lru_cache
The difference is _HashedSeq has been modified to use a custom hash function.
http://code.activestate.com/recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/
"""

from collections import Iterable, Mapping, namedtuple
from threading import RLock
from functools import update_wrapper


[docs]def fhash(thing): "Compute a hash of an object. Works on dicts and lists." try: return hash(thing) except TypeError: if isinstance(thing, Mapping): return fhash(sorted(thing.items())) elif isinstance(thing, Iterable): try: return hash(tuple(fhash(t) for t in thing)) except TypeError: # iteration over 0-d array return hash(type(thing)) raise
_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"]) class _HashedSeq(list): __slots__ = 'hashvalue' def __init__(self, tup, hash=fhash): self[:] = tup self.hashvalue = hash(tup) def __hash__(self): return self.hashvalue 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 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) 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
cacheit = lru_cache()