Calculation¶
- @memoize¶
Memoizes decorated function results, trading memory for performance. Can skip memoization for failed calculation attempts:
@memoize 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 # return None, but don't memoize it
Use raise memoize.skip(some_value) to make function return some_value on fail instead of None.
- @make_lookuper¶
As memoize(), but with prefilled memory. Decorated function should return fully filled memory, which should be a dict or a sequence of pairs. Resulting function will raise LookupError 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 returns None on memory miss.
- @cache(timeout)¶
Same as memoize(), but doesn’t use cached results older than timeout. It can be either number of seconds or datetime.timedelta. Also, doesn’t support skipping.