mySweetCache 1.0.0

Package to fast and easy cache

my Sweet Cache

This project let make fast and simply cache

autors

Bartłomiej Chwiłkowski (github: chwilko)

Structure

mySweetCache: mySweetCache.py: main functions utils.py: other function usefull to

Functions

cache

def cache(
    MSC_name: Optional[str] = None,
    *,
    header: Optional[str] = None,
    sep_in_data: str = "",
) -> Callable:

Wrapper add possibility caching function result to wrapped function

If file MSC_name exist in _CACHE_FILES wraped function return cache from right cache. else make new cache Wrapper add optional argument 'use_cache'. If use_cache == False cache will overwrite.

Args: MSC_name (Optional[str], optional): cache key. Defaults to None. header (Optional[str], optional): data description. Defaults to None. sep_in_data (str, optional): Separator between array items for text output. If "" (empty), a binary file is written, equivalent to file.write(a.tobytes()). Defaults to "".

Returns: callable: Function with cache functionality.

read_cache

def read_cache(MSC_name: str) -> Any:

Function to fast read MSC.

Args: MSC_name (str): cache key to read.

Raises: NameError: If this cache don't exist.

Returns: np.array: Previously saved data.

save_cache

save_cache(
    MSC_name: str,
    data: ndarray,
    *,
    header: Optional[str] = None,
    sep_in_data: str = "",
)

Function to fast save MSC.

Args: MSC_name (str): cache key to save. data (ndarray): data to save sep_in_data (Optional[str], optional): Separator between array items for text output. If "" (empty), a binary file is written, equivalent to file.write(a.tobytes()).

use_par

Decorator set self argument as first function argument.

def use_par(par):

use_pars

Decorator set self arguments as first function arguments.

def use_pars(*pars):

How to use

example use:
  @cache("key")
  def long_working_function() -> np.ndarray:
      # long code to work
      return ...

example use 2:
  @cache("key")
  @use_parms(11, 21)
  def long_working_function(arg1, arg2) -> np.ndarray:
      # long code to work
      return ...

After first call this function result is saved in .MScache_files/key In next call instead recount value will be read from cache. To recount call long_counting_function(use_cache=False) then cache will be overwrited or delete .MScache_files/try file.

WARNING! If you define two function with the same key, there two functions will share the same cache file.

Licence

MIT