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    "info": {
        "author": "Lucas Shen YS",
        "author_email": "shen1ys@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "===============\nLexicalRichness\n===============\n\n\n.. image:: https://img.shields.io/pypi/v/lexicalrichness.svg\n        :target: https://pypi.python.org/pypi/lexicalrichness\n\n.. image:: https://readthedocs.org/projects/lexicalrichness/badge/?version=latest\n        :target: https://lexicalrichness.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\nA small python module to compute textual lexical richness measures\n\nInstallation\n------------\n\t\n.. code-block:: bash\n\n\t$ pip install lexicalrichness\n\nQuickstart\n----------\n\n.. code-block:: python\n\n\t>>> from lexicalrichness import LexicalRichness\n\t\n\t# Generate object of readability statistics.\n\t>>> text = \"\"\"Measure of textual lexical diversity, computed as the mean length of sequential words in\n            \t\ta text that maintains a minimum threshold TTR score.\n\t\t\n            \t\tIterates over words until TTR scores falls below a threshold, then increase factor\n            \t\tcounter by 1 and start over. McCarthy and Jarvis (2010, pg. 385) recommends a factor\n            \t\tthreshold in the range of [0.660, 0.750].\n            \t\t(McCarthy 2005, McCarthy and Jarvis 2010)\"\"\"\n\t\n\t# instantiate new text object (use use_TextBlob=True argument to use the textblob tokenizer)\n\t>>> lex = lexicalrichness(text)\n\t\n\t# Return word count.\n\t>>> lex.words\n\t57\n\t\n\t# Return (unique) term count.\n\t>>> lex.terms\n\t39\n\t\n\t# Return type-token ratio (TTR) of text.\n\t>>> lex.ttr\n\t0.6842105263157895\n\t\n\t# Return root type-token ratio (RTTR) of text.\n\t>>> lex.rttr\n\t5.165676192553671\n\t\n\t# Return corrected type-token ratio (CTTR) of text.\n\t>>> lex.cttr\n\t3.6526846651686067\n\n\t# Return mean segmental type-token ratio (MSTTR).\n\t>>> lex.msttr(segment_window=25)\n\t0.88\n\t\n\t# Return moving average type-token ratio (MATTR).\n\t>>> lex.mattr(window_size=25)\n\t0.8351515151515151\n\t\n\t# Return Measure of Textual Lexical Diversity (MTLD).\n\t>>> lex.mtld(threshold=0.72)\n\t46.79226361031519\n\t\n\t# Return hypergeometric distribution diversity (HD-D) measure.\n\t>>> lex.hdd(draws=42)\n\t0.7468703323966486\n..\t\n\t# Return Herdan's lexical diversity measure.\n\t>>> lex.Herdan\n\t0.9061378160786574\n\t\n\t# Return Summer's lexical diversity measure.\n\t>>> lex.Summer\n\t0.9294460323356605\n\t\n\t# Return Dugast's lexical diversity measure.\n\t>>> lex.Dugast\n\t43.074336212149774\n\t\n\t# Return Maas's lexical diversity measure.\n\t>>> lex.Maas\n\t0.023215679867353005\n\nAttributes and properties\n+++++++++++++++++++++++++\n\n+-------------------------+-----------------------------------------------------------------------------------+ \n| ``wordlist``            | list of words                                                   \t\t      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``words``  \t\t  | number of words (w) \t\t\t\t   \t\t\t      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``terms``\t\t  | number of unique terms (t)\t\t\t                                      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``tokenizer``           | tokenizer used\t\t                                                      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``ttr``\t\t  | type-token ratio computed as t / w (Chotlos 1944, Templin 1957)         \t      |\n+-------------------------+-----------------------------------------------------------------------------------+\n| ``rttr``\t          | root TTR computed as t / sqrt(w) (Guiraud 1954, 1960)                             | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``cttr``\t          | corrected TTR computed as t / sqrt(2w) (Carrol 1964)\t\t              |\t \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``Herdan`` \t          | log(t) / log(w) (Herdan 1960, 1964)                                               | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``Summer``    \t  | log(log(t)) / log(log(w)) Summer (1966)                                           | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``Dugast``          \t  | (log(w) ** 2) / (log(w) - log(t) Dugast (1978)\t\t\t\t      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``Maas`` \t          | (log(w) - log(t)) / (log(w) ** 2) Maas (1972)                                     | \n+-------------------------+-----------------------------------------------------------------------------------+\n\nMethods\n+++++++\n\n+-------------------------+-----------------------------------------------------------------------------------+ \n| ``msttr``            \t  | Mean segmental TTR (Johnson 1944)\t\t\t\t\t\t      |  \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``mattr``  \t\t  | Moving average TTR (Covington 2007, Covington and McFall 2010)\t\t      | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``mtld``\t\t  | Measure of Lexical Diversity (McCarthy 2005, McCarthy and Jarvis 2010)            | \n+-------------------------+-----------------------------------------------------------------------------------+\n| ``hdd``                 | HD-D (McCarthy and Jarvis 2007)                                                   | \n+-------------------------+-----------------------------------------------------------------------------------+\n\nAssessing method docstrings\n---------------------------\n.. code-block:: python\n\n\t>>> import inspect\n\t\n\t# docstring for hdd (HD-D)\n\t>>> print(inspect.getdoc(LexicalRichness.hdd))\n\t\n\tHypergeometric distribution diversity (HD-D) score.\n\n\tFor each term (t) in the text, compute the probabiltiy (p) of getting at least one appearance\n\tof t with a random draw of size n < N (text size). The contribution of t to the final HD-D\n\tscore is p * (1/n). The final HD-D score thus sums over p * (1/n) with p computed for\n\teach term t. Described in McCarthy and Javis 2007, p.g. 465-466.\n\t(McCarthy and Jarvis 2007)\n\n\tParameters\n\t__________\n\tdraws: int\n\t    Number of random draws in the hypergeometric distribution (default=42).\n\n\tReturns\n\t_______\n\tfloat\n\n\n\n=======\nHistory\n=======\n\n0.1.2 (2018-05-09)\n------------------\n\n* First release on PyPI.\n\n0.1.3 (2018-05-27)\n------------------\n\n* Minor fix for compatibility issue with hyphens (ascii) in python 2.\n",
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