Metadata-Version: 2.1
Name: bloom_filter
Version: 1.3.3
Summary: Pure Python Bloom Filter module
Home-page: https://github.com/hiway/python-bloom-filter
Author: Harshad Sharma
Author-email: harshad@sharma.io
License: MIT
Keywords: probabilistic set datastructure
Platform: Cross platform
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
License-File: AUTHORS.md

Note: This project has gone unmaintained for a while,
please use the more up-to-date project at: 
- https://github.com/remram44/python-bloom-filter
- https://pypi.org/project/bloom-filter2/

A pure python bloom filter (low storage requirement, probabilistic
set datastructure) is provided.  It is known to work on CPython 2.x,
CPython 3.x, Pypy and Jython.

Includes mmap, in-memory and disk-seek backends.

The user specifies the desired maximum number of elements and the
desired maximum false positive probability, and the module
calculates the rest.

Usage:

::

    from bloom_filter import BloomFilter

    # instantiate BloomFilter with custom settings,
    # max_elements is how many elements you expect the filter to hold.
    # error_rate defines accuracy; You can use defaults with
    # `BloomFilter()` without any arguments. Following example
    # is same as defaults:
    bloom = BloomFilter(max_elements=10000, error_rate=0.1)

    # Test whether the bloom-filter has seen a key:
    assert "test-key" in bloom is False

    # Mark the key as seen
    bloom.add("test-key")

    # Now check again
    assert "test-key" in bloom is True
    


