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    "info": {
        "author": "Dmitry Kislov",
        "author_email": "kislov@easydan.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development"
        ],
        "description": "Caching results of functions in Python\n======================================\n\n.. image:: https://travis-ci.com/scidam/cachepy.svg?branch=dev\n    :target: https://travis-ci.com/scidam/cachepy\n\n\nA caching toolset for Python. It is tested for both\nPython 2.7.x and 3.4+ (<3.8).\n\nFeatures\n--------\n\n    * Memory-based and file-based caches;\n    * Ability to set the TTL (time-to-live) and NOC (the number of calls) on caches;\n    * Encryption of the cached data (symmetric encryption algorithm (RSA) is used);\n    * LFU (least frequently used) and MFU (most frequently used) cache-clearing strategies;\n    * caches of limited size;\n\nNotes\n-----\n\n    - Encryption functionality requires the `PyCryptodome` package to be installed;\n    - File-based caches save the cached data in a file; files with cached data should be\n      cleaned up manually, if needed.\n\nExamples\n--------\n\n.. code-block:: python\n\n    from cachepy import *\n\n    mycache = Cache()\n    # save the cached data to memory without encryption\n\n    @mycache\n    def my_heavy_function(x):\n        '''Performs heavy computations'''\n\n        print('Hi, I am called...')\n        return x**2\n\n    my_heavy_function(2)\n    # \"Hi, I am called...\" will be printed to stdout only once\n    # return 4\n\n    my_heavy_function(2)\n    # return 4\n\n\nTo store data to a file, one needs to initialize a decorator, as follows:\n\n.. code-block:: python\n\n    # create cache-to-file decorator\n    filecache = FileCache('mycache')  # mycache.dat file will be created;\n    # `.dat` extension is appended automatically to the filename\n    # (depends on the shelve module implementation);\n\nIts behavior is the same as a memory-based one, but all cached data is stored in\nthe specified file.\n\nOne can set up time-to-live (TTL) and/or maximum number of calls (NOC)\nfor the cached data when the decorator is initialized:\n\n.. code-block:: python\n\n    import time\n    from cachepy import *\n\n    cache_with_ttl = Cache(ttl=2)  # ttl given in seconds\n\n    @cache_with_ttl\n    def my_heavy_function(x):\n        '''Performs heavy computations'''\n\n        print('Hi, I am called...')\n        return x**2\n\n    my_heavy_function(3)\n    # Hi, I am called... will be printed\n    # return 9\n    my_heavy_function(3)\n    # 'Hi, I am called ...' will not be printed\n    # return 9\n    time.sleep(2)\n    my_heavy_function(3)\n    # 'Hi, I am called ...' will be printed again\n    # return 9\n\n\n.. code-block:: python\n\n    cache_with_noc = Cache(noc=2)  # the number-of-calls: noc = 2\n\n    @cache_with_noc\n    def my_heavy_function(x):\n        '''Performs heavy computations'''\n\n        print('Hi, I am called...')\n        return x**2\n\n    my_heavy_function(3)\n    my_heavy_function(3) # 'Hi, I am called ...' will not be printed\n    my_heavy_function(3) # 'Hi, I am called ...' will be printed again\n\n\nIt is easy to use both `NOC` and `TTL` arguments when defining\na caching decorator:\n\n.. code-block:: python\n\n    cache_with_noc_ttl = Cache(noc=2, ttl=1)\n\n    @cache_with_noc_ttl\n    def my_heavy_function(x):\n        '''Performs heavy computations'''\n\n        print('Hi, I am called...')\n        return x**2\n\n    my_heavy_function(3)\n    my_heavy_function(3)  # 'Hi, I am called ...' will not be printed\n    my_heavy_function(3)  # 'Hi, I am called ...' will be printed (noc is\n    # reached, recompute the func value)\n    time.sleep(2)  # get ttl expired\n    my_heavy_function(3) # 'Hi, I am called ...' will be printed again\n\nOne can encrypt the cached data by providing a non-empty `key` argument as\na password (RSA encryption algorithm is used):\n\n.. code-block:: python\n\n    cache_to_file_ttl_noc = FileCache('mycache',\n                                      noc=2, ttl = 2,\n                                      key='mypassword')\n\n    @cache_to_file_ttl_noc\n    def my_heavy_function(x):\n        '''Performs heavy computations'''\n\n        print('Hi, I am called...')\n        return x**2\n\n    my_heavy_function(2) # 'Hi, I am called...' will be printed\n    my_heavy_function(2) # 'Hi, I am called...' will not be printed\n\nWhen `my_heavy_function` is decorated by `cache_to_file_ttl_noc`, as shown\nin the example above, the value `2**2 = 4` will be computed and the result of\nthe computation will be stored in the file named `mycache.dat`. Along\nwith the result of the computation,  additional information will be stored\nin the file `mycache.dat`. The additional information includes:\n1) the result's expiration time (computed from the TTL), \n2) NOC and 3) the number of already performed calls of the function being\ndecorated (`my_heavy_function`).\n\nEncryption is available only if `PyCryptodome` package is installed and the\n`key` parameter (a non-empty string representing the password) is passed to the\ncache constructor. It also could work with the old PyCrypto package.\n\nIf you passed the non-empty `key` parameter to the cache constructor\nbut `PyCryptodome` was not found, a special warning would be raised in this case\n(\"PyCryptodome not installed. Data will not be encrypted\") and\nthe cache would work as usual but without encryption functionality.\n\n\nCaching with limitations\n------------------------\n\nStandard cache constructors are used to initialize caches of unlimited capacity.\nThere are also caches of limited capacity.\nSuch caches are initialized by constructors named `LimitedCache` and `LimitedFileCache`.\nThese constructors have additional\nparameters `cache_size` (the maximum number of items stored in the cache) and\n`algorithm` (cache-clearing algorithm). Available `algorithm` values are\n`lfu` (default, which stands for least frequently used) and `mfu` (most frequently used).\nWhen `algorithm='lfu'`, then the least frequently used item is removed from the cache,\nif it is exhausted. In case of `algorithm='mfu'`, everything behaves the same way,\nwith the only difference being that the most frequently used item is removed.\n\n\nTesting\n-------\n\n.. code-block:: bash\n\n         python -m  cachepy.test\n\n\nTODO\n----\n\n    * Writing backend for redis server\n\n\nLog list\n--------\n\n    * Version 1.1\n    * Version 1.0 (broken installation via pip/pipenv)\n    * Version 0.1\n        - initial release\n\n\nAuthor\n------\n\n    Dmitry Kislov <kislov@easydan.com>\n\n\n",
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