Calculation¶
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@
memoize
(key_func=None)¶ Memoizes decorated function results, trading memory for performance. Can skip memoization for failed calculation attempts:
@memoize # Omitting parentheses is ok def ip_to_city(ip): try: return request_city_from_slow_service(ip) except NotFound: return None # return None and memoize it except Timeout: raise memoize.skip(CITY) # return CITY, but don't memoize it
Additionally
@memoize
exposes its memory for you to manipulate:# Prefill memory ip_to_city.memory.update({...}) # Forget everything ip_to_city.memory.clear()
Custom key_func could be used to work with unhashable objects, insignificant arguments, etc:
@memoize(key_func=lambda obj, verbose=None: obj.key) def do_things(obj, verbose=False): # ...
-
@
make_lookuper
¶ As
@memoize
, but with prefilled memory. Decorated function should return all available arg-value pairs, which should be a dict or a sequence of pairs. Resulting function will raiseLookupError
for any argument missing in it:@make_lookuper def city_location(): return {row['city']: row['location'] for row in fetch_city_locations()}
If decorated function has arguments then separate lookuper with its own lookup table is created for each combination of arguments. This can be used to make lookup tables on demand:
@make_lookuper def function_lookup(f): return {x: f(x) for x in range(100)} fast_sin = function_lookup(math.sin) fast_cos = function_lookup(math.cos)
Or load some resources, memoize them and use as a function:
@make_lookuper def translate(lang): return make_list_of_pairs(load_translation_file(lang)) russian_phrases = map(translate('ru'), english_phrases)
-
@
silent_lookuper
¶ Same as
@make_lookuper
, but returnsNone
on memory miss.
-
@
cache
(timeout, key_func=None)¶ Caches decorated function results for
timeout
. It can be either number of seconds ordatetime.timedelta
:@cache(60 * 60) def api_call(query): # ...
Cache can be invalidated before timeout with:
api_call.invalidate(query) # Forget cache for query api_call.invalidate_all() # Forget everything
Custom
key_func
could be used same way as in@memoize
:# Do not use token in cache key @cache(60 * 60, key_func=lambda query, token=None: query) def api_call(query, token=None): # ...