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.