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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,34 +1,124 @@ | ||
| '''Utility functions for crunch cube, as well as other modules.''' | ||
| import os | ||
| import collections | ||
| import functools | ||
|
|
||
| try: | ||
| from itertools import ifilterfalse | ||
| except ImportError: | ||
| from itertools import filterfalse as ifilterfalse | ||
|
|
||
| import json | ||
| import os | ||
|
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|
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| class Counter(dict): | ||
| """Mapping where default values are zero""" | ||
| def __missing__(self, key): | ||
| return 0 | ||
|
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|
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| def load_fixture(fixtures_directory, filename): | ||
| '''Loads fixtures for CrunchCube integration tests.''' | ||
| """Loads fixtures for CrunchCube integration tests.""" | ||
| with open(os.path.join(fixtures_directory, filename)) as ctx_file: | ||
| fixture = json.load(ctx_file) | ||
| return fixture | ||
|
|
||
| class lazyproperty(property): | ||
| """ | ||
| borrowed from: https://stackoverflow.com/questions/3012421/python-memoising-deferred-lookup-property-decorator | ||
| """ | ||
| def __init__(self, func, name=None, doc=None): | ||
| self.__name__ = name or func.__name__ | ||
| self.__module__ = func.__module__ | ||
| self.__doc__ = doc or func.__doc__ | ||
| self.func = func | ||
|
|
||
| def lazyproperty(func): | ||
| """@lazyproperty decorator | ||
| def __set__(self, obj, value): | ||
| obj.__dict__[self.__name__] = value | ||
|
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| Decorated method will be called only on first access to calculate | ||
| a cached property value. After that, the cached value is returned. Note | ||
| that the cached value is stored in a variable with the same name as the | ||
| decorated property, with the prefix '_cached_' prepended, such that the | ||
| cached value of property `foobar` is stored in `_cached_foobar`. Be aware | ||
| to avoid naming collisions. | ||
| """ | ||
| cache_attr_name = '_cached_%s' % func.__name__ | ||
| docstring = func.__doc__ | ||
|
|
||
| def get_prop_value(obj): | ||
| try: | ||
| return getattr(obj, cache_attr_name) | ||
| except AttributeError: | ||
| value = func(obj) | ||
| setattr(obj, cache_attr_name, value) | ||
| return value | ||
|
|
||
| return property(get_prop_value, doc=docstring) | ||
| def __get__(self, obj, type=None): | ||
| if obj is None: | ||
| return self | ||
| value = obj.__dict__.get(self.__name__, None) | ||
| if value is None: | ||
| value = self.func(obj) | ||
| obj.__dict__[self.__name__] = value | ||
| return value | ||
|
|
||
| def lru_cache(maxsize=100): | ||
| '''Least-recently-used cache decorator. | ||
| Arguments to the cached function must be hashable. | ||
| Cache performance statistics stored in f.hits and f.misses. | ||
| Clear the cache with f.clear(). | ||
| http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used | ||
| ''' | ||
| maxqueue = maxsize * 10 | ||
|
|
||
| def decorating_function(user_function, | ||
| len=len, iter=iter, tuple=tuple, sorted=sorted, KeyError=KeyError): | ||
| cache = {} # mapping of args to results | ||
| queue = collections.deque() # order that keys have been used | ||
| refcount = Counter() # times each key is in the queue | ||
| sentinel = object() # marker for looping around the queue | ||
| kwd_mark = object() # separate positional and keyword args | ||
|
|
||
| # lookup optimizations (ugly but fast) | ||
| queue_append, queue_popleft = queue.append, queue.popleft | ||
| queue_appendleft, queue_pop = queue.appendleft, queue.pop | ||
|
|
||
| @functools.wraps(user_function) | ||
| def wrapper(*args, **kwds): | ||
| # cache key records both positional and keyword args | ||
| key = args | ||
| if kwds: | ||
| key += (kwd_mark,) + tuple(sorted(kwds.items())) | ||
|
|
||
| # record recent use of this key | ||
| queue_append(key) | ||
| refcount[key] += 1 | ||
|
|
||
| # get cache entry or compute if not found | ||
| try: | ||
| result = cache[key] | ||
| wrapper.hits += 1 | ||
| except KeyError: | ||
| result = user_function(*args, **kwds) | ||
| cache[key] = result | ||
| wrapper.misses += 1 | ||
|
|
||
| # purge least recently used cache entry | ||
| if len(cache) > maxsize: | ||
| key = queue_popleft() | ||
| refcount[key] -= 1 | ||
| while refcount[key]: | ||
| key = queue_popleft() | ||
| refcount[key] -= 1 | ||
| del cache[key], refcount[key] | ||
|
|
||
| # periodically compact the queue by eliminating duplicate keys | ||
| # while preserving order of most recent access | ||
| if len(queue) > maxqueue: | ||
| refcount.clear() | ||
| queue_appendleft(sentinel) | ||
| for key in ifilterfalse(refcount.__contains__, | ||
| iter(queue_pop, sentinel)): | ||
| queue_appendleft(key) | ||
| refcount[key] = 1 | ||
|
|
||
| return result | ||
|
|
||
| def clear(): | ||
| cache.clear() | ||
| queue.clear() | ||
| refcount.clear() | ||
| wrapper.hits = wrapper.misses = 0 | ||
|
|
||
| wrapper.hits = wrapper.misses = 0 | ||
| wrapper.clear = clear | ||
| return wrapper | ||
| return decorating_function | ||
|
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|
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| memoize = lru_cache(100) |