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        "author_email": "petrinchor@gmail.com",
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            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# python-memoization\n\n[![Version][aucsvg]][auc] [![Supports Python][pythonsvg]][python] [![Build Status][travismaster]][travis]\n[![Coverage Status][coverallssvg]][coveralls] [![Repository][repositorysvg]][repository] [![Downloads][downloadssvg]][repository]\n[![PRs welcome][prsvg]][pr] [![License][licensesvg]][license]\n\n[aucsvg]: https://img.shields.io/badge/memoization-v0.1.4-brightgreen.svg\n[auc]: https://github.com/lonelyenvoy/python-memoization\n\n[pythonsvg]: https://img.shields.io/pypi/pyversions/memoization.svg\n[python]: https://www.python.org\n\n[travismaster]: https://travis-ci.org/lonelyenvoy/python-memoization.svg?branch=master\n[travis]: https://travis-ci.org/lonelyenvoy/python-memoization\n\n[coverallssvg]: https://coveralls.io/repos/github/lonelyenvoy/python-memoization/badge.svg?branch=master\n[coveralls]: https://coveralls.io/github/lonelyenvoy/python-memoization?branch=master\n\n[repositorysvg]: https://img.shields.io/pypi/v/memoization.svg\n[repository]: https://pypi.org/project/memoization\n\n[downloadssvg]: https://img.shields.io/pypi/dm/memoization.svg\n\n[prsvg]: https://img.shields.io/badge/PRs-welcome-blue.svg\n[pr]: https://github.com/lonelyenvoy/python-memoization#contributing\n\n[licensesvg]: https://img.shields.io/badge/License-MIT-blue.svg\n[license]: https://github.com/lonelyenvoy/python-memoization/blob/master/LICENSE\n\nA powerful caching library for Python, with TTL support and multiple algorithm options.\n\n\n## Why choose this library?\n\nPerhaps you know about [```functools.lru_cache```](https://docs.python.org/3/library/functools.html#functools.lru_cache)\nin Python 3, and you may be wondering why I am reinventing the wheel.\n\nWell, actually not. This lib is based on ```functools```. Please find below the comparison with ```lru_cache```.\n\n|Features|```functools.lru_cache```|```memoization```|\n|--------|-------------------|-----------|\n|Configurable max size|\u2714\ufe0f|\u2714\ufe0f|\n|Thread safety|\u2714\ufe0f|\u2714\ufe0f|\n|Flexible argument typing (typed & untyped)|\u2714\ufe0f|Always typed|\n|Cache statistics|\u2714\ufe0f|\u2714\ufe0f|\n|LRU (Least Recently Used) as caching algorithm|\u2714\ufe0f|\u2714\ufe0f|\n|LFU (Least Frequently Used) as caching algorithm|No support|\u2714\ufe0f|\n|FIFO (First In First Out) as caching algorithm|No support|\u2714\ufe0f|\n|TTL (Time-To-Live) support|No support|\u2714\ufe0f|\n|Support for unhashable arguments (dict, list, etc.)|No support|\u2714\ufe0f|\n|Partial cache clearing|No support|Pending implementation in v0.2.x|\n|Python version|3.2+|2.6, 2.7, 3.4+|\n\n```memoization``` solves some drawbacks of ```functools.lru_cache```:\n\n1. ```lru_cache``` does not support __unhashable types__, which means function arguments cannot contain dict or list.\n\n```python\n>>> from functools import lru_cache\n>>> @lru_cache()\n... def f(x): return x\n... \n>>> f([1, 2])  # unsupported\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nTypeError: unhashable type: 'list'\n```\n\n2. ```lru_cache``` is vulnerable to [__hash collision attack__](https://learncryptography.com/hash-functions/hash-collision-attack)\n   and can be hacked or compromised. In ```memoization```, caching is always typed, which means ```f(3)``` and \n   ```f(3.0)``` will be treated as different calls and cached separately. This prevents the attack from happening\n   (or at least makes it a lot harder).\n\n```python\n>>> hash((1,))\n3430019387558\n>>> hash(3430019387558.0)  # two different arguments have an identical hash value\n3430019387558\n```\n\n\n## Installation\n\n```bash\npip install memoization\n```\n\n## Usage in 2 lines\n\n```python\nfrom memoization import cached\n\n@cached\ndef func(arg):\n    ...  # do something slow\n```\n\nSimple enough - the results of ```func()``` are cached. \nRepetitive calls to ```func()``` with the same arguments run ```func()``` only once, enhancing performance.\n\n\n## Advanced features\n\nConfigurable options include `ttl`, `max_size`, `algorithm` and `thread_safe`.\n\n### TTL (Time-To-Live)\n\n```python\n@cached(ttl=5)  # the cache expires after 5 seconds\ndef expensive_db_query(user_id):\n    ...\n```\n\nFor impure functions, TTL (in second) will be a solution. This will be useful when the function returns resources that is valid only for a short time, e.g. fetching something from databases.\n\n### Limited cache capacity\n\n```python\n@cached(max_size=128)  # the cache holds no more than 128 items\ndef get_a_very_large_object(filename):\n    ...\n```\n\nBy default, if you don't specify ```max_size```, the cache can hold unlimited number of items.\nWhen the cache is fully occupied, the former data will be overwritten by a certain algorithm described below.\n\n### Choosing your caching algorithm\n\n```python\nfrom memoization import cached, CachingAlgorithmFlag\n\n@cached(max_size=128, algorithm=CachingAlgorithmFlag.LFU)  # the cache overwrites items using the LFU algorithm\ndef func(arg):\n    ...\n```\n\nPossible values for ```algorithm``` are:\n\n- `CachingAlgorithmFlag.LRU`: _Least Recently Used_  (default)\n- `CachingAlgorithmFlag.LFU`: _Least Frequently Used_ \n- `CachingAlgorithmFlag.FIFO`: _First In First Out_ \n\nThis option is valid only when a ```max_size``` is explicitly specified.\n\n### Thread safe?\n\n```python\n@cached(thread_safe=False)\ndef func(arg):\n    ...\n```\n\n```thread_safe``` is ```True``` by default. Setting it to ```False``` enhances performance.\n\n### Knowing how well the cache is behaving\n\n```python\n>>> @cached\n... def f(x): return x\n... \n>>> f.cache_info()\nCacheInfo(hits=0, misses=0, current_size=0, max_size=None, algorithm=<CachingAlgorithmFlag.LRU: 2>, ttl=None, thread_safe=True)\n```\n\nWith ```cache_info```, you can retrieve the number of ```hits``` and ```misses``` of the cache, and other information indicating the caching status.\n\n- `hits`: the number of cache hits\n- `misses`: the number of cache misses\n- `current_size`: the number of items that were cached\n- `max_size`: the maximum number of items that can be cached (user-specified)\n- `algorithm`: caching algorithm (user-specified)\n- `ttl`: Time-To-Live value (user-specified)\n- `thread_safe`: whether the cache is thread safe (user-specified)\n\n### Other APIs\n\n- Access the original function ```f``` by ```f.__wrapped__```.\n- Clear the cache by ```f.cache_clear()```.\n\n## Contributing\n\nThis project welcomes contributions from anyone.\n- [Submit bugs](https://github.com/lonelyenvoy/python-memoization/issues) and help me verify fixes.\n- [Submit pull requests](https://github.com/lonelyenvoy/python-memoization/pulls) for bug fixes and features and discuss existing proposals. Please make sure that your PR passes the tests in ```test/```.\n\n## License\n\n[The MIT License](https://github.com/lonelyenvoy/python-memoization/blob/master/LICENSE)\n\n\n",
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