#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function

from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import find_packages
from setuptools import setup


setup(
    name="bloom_filter",
    version="1.3",
    packages=find_packages('src'),
    package_dir={'': 'src'},
    py_modules=[splitext(basename(path))[0] for path in glob('src/*.py')],

    # metadata for upload to PyPI
    author="Harshad Sharma",
    author_email="harshad@sharma.io",
    description='Pure Python Bloom Filter module',
    long_description="""
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
    
""",
    license="MIT",
    keywords="probabilistic set datastructure",
    url='https://github.com/hiway/python-bloom-filter',
    platforms='Cross platform',
    classifiers=[
        "Development Status :: 5 - Production/Stable",
        "Intended Audience :: Developers",
        "Programming Language :: Python :: 2",
        "Programming Language :: Python :: 3",
    ],
)