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        "author": "Timid Robot Zehta",
        "author_email": "tim@clockwork.net",
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            "Environment :: Console",
            "License :: OSI Approved :: MIT License",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "python-iqm\n==========\n\nInterquartile Mean pure-Python module. It contains two classes:\n\n1. DictIQM\n2. MovingIQM\n\n\nDictIQM\n=======\n\nThis class is efficient for datasets in which many numbers are repeated. It\nshould not be used for large datasets with a uniform distribution. The\ntrade-off between accuracy and memory usage can be manged with its\n`round_digits` argument.\n\nUsage\n-----\n\n.. code:: python\n\n    from iqm import DictIQM\n    import sys\n\n    diqm = DictIQM(round_digits=-1, tenth_precise=True)\n    for line in open(\"source1_numbers_list.txt\", \"r\"):\n        diqm(\"source1\", line)\n\n    print \"# {:12,.2f}    Dict IQM\".format(diqm.report(\"source1\"))\n\n\nMovingIQM\n=========\n\nThis class sacrifices accuracy for speed and low memory usage.\n\nUsage\n-----\n\n.. code:: python\n\n    from iqm import MovingIQM\n    import sys\n\n    miqm = MovingIQM(1000)\n    for line in open(\"source1_numbers_list.txt\", \"r\"):\n        miqm(\"source1\", line)\n\n    print \"# {:12,.2f}    Moving IQM\".format(miqm.report(\"source1\"))\n\n\nLicense\n=======\n\nSee `LICENSE.txt`_ (`MIT License`_).\n\n.. _`LICENSE.txt`:\n   https://github.com/ClockworkNet/python-iqm/blob/master/LICENSE.txt\n.. _`MIT License`: http://www.opensource.org/licenses/MIT",
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