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    "info": {
        "author": "Janos Brezniczky",
        "author_email": "brezniczky@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 1 - Planning",
            "License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "DrDigit\n=======\n\nDrDigit is a digit doctoring detection package at an early stage.\nInterested in contributing? Please feel free to contact me, e.g. by\ncommenting on the issue \"Contributors welcome!\" at \nhttps://github.com/brezniczky/drdigit/issues/1.\n\nRequirements\n------------\n\nDrDigit requires Python 3.5 or later.\n\nConcept\n-------\n\nThe tests are based on the statistics of digits which are assumed to have a\nuniform distribution. Near-uniform distributions can be obtained by looking\nat the last digits of sufficiently large values - such as vote counts\n(possibly above 100).\n\nOn a smaller scale, you can query for the probablity of a digit sequence using\nprobability mass functions represented by Python functions.\n\nThere are larger scale tests for a sequence of digit groups. This is so to\nsupport situations where different groups are expected to be doctored by\ndifferent people - testing for an overarching, consistent anomaly could be too\nstrict in such cases.\n\nBased on the current features (entropy, digit repetition, coincident digits in\nparallel sequences), it is possible to sort a data frame containing digit groups\nby probability, so then it is possible to inspect if there is any apparent\nsanity behind the doctoring.\n\nA couple of hints\n-----------------\n\n* Handle results with care, **there is always some uncertainity**\n* Try to **focus** on interesting groups, this should yield much sharper results\n* When committing **Kaggle** scripts, switch off the on-disk caching of tests \n  before committing, e.g. via\n\n    ```\n    import drdigit as drd\n    drd.set_option(physical_cache_path=\"\")\n    ```\n\n  You can find more about it via `help(drd.set_option)`.\n\nQuick start\n-----------\n\nDrDigit can be installed using pip:\n\n    $ pip install drdigit-brezniczky\n    $ ipython\n\nDigit entropy behaves a little weirdly when different digit sequence lengths are \nconsidered - isn't the sequence 1, 2 as diverse as possible?\n\n    Python 3.5.2 (default, Nov 12 2018, 13:43:14)\n    Type 'copyright', 'credits' or 'license' for more information\n    IPython 7.7.0 -- An enhanced Interactive Python. Type '?' for help.\n\n    In [1]: import drdigit as drd\n\n    In [2]: help(drd)\n\n    In [3]: print(drd.get_entropy([1, 2]))                                                                                                       \n    0.6931471805599453\n\n    In [4]: print(drd.get_entropy([1, 1, 2, 2]))                                                                                                 \n    0.6931471805599453\n\nProbabilities are often more suited for a comparison:\n\n    In [6]: drd.prob_of_entr(2, drd.get_entropy([1, 2]))                                                                                   \n    cdf for 2 was generated\n    Out[6]: 1.0\n\n    In [7]: drd.prob_of_entr(4, drd.get_entropy([1, 1, 2, 2]))                                                                                   \n    cdf for 4 was generated\n    Out[7]: 0.0624\n\nIndeed, the latter sequence is unusually repetitive.\n\nMore examples to follow, for now you can have a look at the Kaggle notebook at \nhttps://www.kaggle.com/brezniczky/poland-2019-ep-elections-doctoring-quick-check\nor around\nhttps://github.com/brezniczky/ep_elections_2019_hun/blob/master/PL/\nfor instance in the `process_data.py` file.\n\nSome complicated (and - sorry, sometimes unreliabe/slightly outdated) details \nabout the considerations/methodology and future ideas can be found in the\n[Hungarian elections document](\nhttps://nbviewer.jupyter.org/github/brezniczky/ep_elections_2019_hun/blob/master/report.ipynb\n)\n\n\nTests\n-----\n\nThe few tests that there are can be run by `pytest`. \n\nFor this, I would just use `virtualenvwrapper` and do something akin to\n\n    $ mkvirtualenv drdigit_test\n    $ pip install -r requirements/requirements_test.txt\n    $ pytest\n\nfrom the directory of the `drdigit` clone.\n\n\n",
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        "name": "drdigit-brezniczky",
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