Make function of numpy array cacheable

npCache.md

caching functions and methods with a numpy array as first argument

from copy import copy
from time import sleep

array = np.random.rand(900,600)
param = int(np.random.rand()*1000)

print('\nwith @npCache:')
@vocalTimeit()
@npCache()
def doSomething(array, param):
    print("Calculating...")
    sleep(1)
    return True

for i in range(5):
    res = copy(doSomething(array, param))

class DummyClass():
    def __init__(self):
        for i in range(5):
            res = copy(self.doSomethingMethod(array, param))
    @vocalTimeit()
    @npCacheMethod()
    def doSomethingMethod(self, array, param):
        print("Calculating...")
        sleep(1)
        return True
    
print('\nwith @npCacheMethod')
dummyClass = DummyClass()

with @npCache:
Calculating...
doSomething execution time: 1.073955507017672 s
doSomething execution time: 0.0814172513782978 s
doSomething execution time: 0.08347152546048164 s
doSomething execution time: 0.08153043873608112 s
doSomething execution time: 0.08101261034607887 s

with @npCacheMethod
Calculating...
doSomethingMethod execution time: 1.075349354185164 s
doSomethingMethod execution time: 0.08380787633359432 s
doSomethingMethod execution time: 0.08389369770884514 s
doSomethingMethod execution time: 0.0843976391479373 s
doSomethingMethod execution time: 0.0834763403981924 s

npCache.py

import numpy as np
from functools import lru_cache, wraps
from timeit import default_timer

def vocalTimeit(*args, **kwargs):
    ''' provides the decorator @vocalTime which will print the name of the function as well as the
        execution time in seconds '''

    def decorator(function):
        @wraps(function)
        def wrapper(*args, **kwargs):
            start = default_timer()
            results = function(*args, **kwargs)
            end = default_timer()
            print('{} execution time: {} s'.format(function.__name__, end-start))
            return results
        return wrapper
    return decorator


def npCache(*args, **kwargs):
    ''' LRU cache implementation for functions whose FIRST parameter is a numpy array
        forked from: https://gist.github.com/Susensio/61f4fee01150caaac1e10fc5f005eb75 '''

    def decorator(function):
        @wraps(function)
        def wrapper(np_array, *args, **kwargs):
            hashable_array = tuple(map(tuple, np_array))
            return cached_wrapper(hashable_array, *args, **kwargs)

        @lru_cache(*args, **kwargs)
        def cached_wrapper(hashable_array, *args, **kwargs):
            array = np.array(hashable_array)
            return function(array, *args, **kwargs)

        # copy lru_cache attributes over too
        wrapper.cache_info = cached_wrapper.cache_info
        wrapper.cache_clear = cached_wrapper.cache_clear
        return wrapper
    return decorator


def npCacheMethod(*args, **kwargs):
    ''' LRU cache implementation for methods whose FIRST parameter is a numpy array
        modified from: https://gist.github.com/Susensio/61f4fee01150caaac1e10fc5f005eb75 '''

    def decorator(function):
        @wraps(function)
        def wrapper(s, np_array, *args, **kwargs):
            hashable_array = tuple(map(tuple, np_array))
            return cached_wrapper(s, hashable_array, *args, **kwargs)

        @lru_cache(*args, **kwargs)
        def cached_wrapper(s, hashable_array, *args, **kwargs):
            array = np.array(hashable_array)
            return function(s, array, *args, **kwargs)

        # copy lru_cache attributes over too
        wrapper.cache_info = cached_wrapper.cache_info
        wrapper.cache_clear = cached_wrapper.cache_clear
        return wrapper
    return decorator


if __name__ == "__main__":
    from copy import copy
    from time import sleep

    array = np.random.rand(900,600)
    param = int(np.random.rand()*1000)

    print('\nwith @npCache:')
    @vocalTimeit()
    @npCache()
    def doSomething(array, param):
        print("Calculating...")
        sleep(1)
        return True

    for i in range(5):
        res = copy(doSomething(array, param))

    print('\nwith @npCacheMethod')
    class DummyClass():
        def __init__(self):
            for i in range(5):
                res = copy(self.doSomethingMethod(array, param))

        @vocalTimeit()
        @npCacheMethod()
        def doSomethingMethod(self, array, param):
            print("Calculating...")
            sleep(1)
            return True
    dummyClass = DummyClass()