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        "author": "Daniel Roberson",
        "author_email": "daniel@planethacker.net",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# dmfrbloom\nBloom filters with Python standard library.\n\n## Examples\nNormal bloom filter. Expects 10,000 elements with 99.99% accuracy:\n```\n>>> from dmfrbloom.bloomfilter import BloomFilter\n>>> bf = BloomFilter(10000, 0.01)\n>>> bf.add(\"test\")\n>>> bf.lookup(\"test\")\nTrue\n>>> bf.lookup(\"not in filter\")\nFalse\n>>> bf.save(\"/home/daniel/filter\")\n>>> bf2 = BloomFilter(1, 0.1)\n>>> bf2.load(\"/home/daniel/filter\")\n>>> bf2.lookup(\"test\")\nTrue\n>>> bf2.lookup(\"also not in filter\")\nFalse\n```\n\nTime-based filter. 10k elements, 99.99% accuracy, results decay after\n60 seconds:\n```\n>>> from dmfrbloom.timefilter import TimeFilter\n>>> tf = TimeFilter(10000, 0.01, 60)\n>>> tf.add(\"asdf\")\n>>> tf.lookup(\"asdf\")\nTrue\n>>> import time\n>>> time.sleep(60)\n>>> tf.lookup(\"asdf\")\nFalse\n```\n\n\n",
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