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        "author": "Andre van Delft",
        "author_email": "andrevandelft@outlook.com",
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        "description": "# Smart Reading\n\n## About\n\n`smart_reading` is a Python module designed for increasing the understanding of various textforms by using natural language processing. It is heavily based on tools available from the [Natural Language Toolkit](https://www.nltk.org) (NLTK), which are used in various applications and provided with extensions.\n\n### Installation\n\nThe module is available for Python 2.7+, but recommended to run on Python 3+ for a more thorough unicode support and prettier graphs. Install via pip (or any other desired client):\n\n```\n$ pip install smart_reading\n```\n\nor by downloading the source code on [PyPI](https://pypi.org/project/smart-reading/) or [GitHub](https://github.com/andredelft/smart_reading) and running the following command in the root folder:\n\n```\n$ python setup.py install\n```\n\n## Importing texts\n\nThe basic functionality of `smart_reading` is to import a given textfile into a `smart_reading.book.Book` object, which can be used to perform several developed analyses. Textfiles can be imported via the function `smart_reading.book.load(filename)`. This function utilizes the functionality of the module `textract` to extract textual information of almost any given data structure, including .txt, .pdf, .epub and .docx. See its [online documentation](https://textract.readthedocs.io/en/stable/) for more details on the inner workings of this module. When this module is not found on the system, the program continues with a limited functionality, in which only .txt files can be read. This limited functionality is added because it is experienced that the installation of `textract` does not always succeed. The user that is not able to install `textract` but does want to import other text formats is encouraged to build alternative pipelines to extract text into a .txt file, which in turn can be imported in the `smart_reading.book.load` function.\n\nAdditionally, a given string can be imported as an e-book via `smart_reading.book.fromstring(text)`.\n\nThree sample texts are included with different file structures, callable via the function `smart_reading.book.sample`:\n\n```pycon\n>>> import smart_reading as sr\n>>> sr.book.sample() # or sr.book.sample('txt')\nSuccesfully loaded 'Benn_Ch_II_The_Metaphysicians.txt' as an e-book\nTotal n.o. tokens: 10420\n<smart_reading.book.Book instance at 0x105a546c8>\n>>> sr.book.sample('pdf')\nSuccesfully loaded 'PhysRev.47.777.pdf' as an e-book\nTotal n.o. tokens: 3192\n<smart_reading.book.Book instance at 0x110c0ebd8>\n>>> sr.book.sample('epub') # this one takes a while to load\nSuccesfully loaded 'Galileo_The_Sidereal_Messenger.epub' as an e-book\nTotal n.o. tokens: 40372\n<smart_reading.book.Book instance at 0x105a46e60>\n```\n\n## Functionality\n\nAs mentioned, the given text are imported into a `smart_reading.book.Book` type object. The different tools that this object provides are listed below.\n\n### Concordance\n\nA concordance is developed as an extension of the `nltk.text.Text.concordance` function that incorporates [example 3.6](http://www.nltk.org/book/ch03.html#code-stemmer-indexing) of the NLTK manual, such that it not only matches with exact copies of a given word, but also inflections:\n\n```pycon\n>>> import smart_reading as sr\n>>> bk = sr.book.sample()\nSuccesfully loaded 'Benn_Ch_II_The_Metaphysicians.txt' as an e-book\nTotal n.o. tokens: 10420\n>>> bk.concordance('philosopher')\nDisplaying 17 of 17 matches:\nnce of an independent income enabled the philosopher to live where he liked ; and\nby our opinion of his metaphysics . As a philosopher Descartes has , to begin wit\nr dazzle ; they could not convince . The philosophers professed to teach truth ; \ninctly are all true . In his other great philosophical work , the _Meditations_ ,\no his postulate of universal doubt , our philosopher argues from this to an imper\nHere he agrees with another mathematical philosopher , Plato , who says the same \nat least one astronomer , who was also a philosopher , declared that the ultimate\n personality of God . SPINOZA . With the philosopher whom I have just named we co\nsion of 500 florins on Spinoza , but the philosopher would accept no more than 30\nl . To appreciate the work of the Hebrew philosopher , of the lonely muser , bred\n divine substance . In fact , the Hebrew philosopher does this , declaring boldly\npeppers his pages . Yet , like the Greek philosophers , he is much more modern , \n name of his great work that for him the philosophical problem is essentially a p\n . But he parts company with the English philosopher in his theory of what it mea\n alone , however , does not make a great philosopher ; character also is required\nrity than any one utterance of any other philosopher ; but that fame is due to th\n work . On _\u00e0 priori_ grounds the German philosopher seems to have an incontrover\n```\n\nIn order to deal with inflections, the stemmer `nltk.PorterStemmer` is used by default. Other stemmers can be sent through the keyword *stemmer* when importing a textfile.\n\n### `nltk.text.Text` attribute\n\nA `smart_reading.book.Book` object contains an attribute `Text`, which is an `nltk.text.Text` type object and as such includes all its attributes as developed by NLTK. These include finding collocations, similar words, and creating lexical disperion plots. See the [NLTK API](https://www.nltk.org/api/nltk.html#nltk.text.Text) for its full documentation\n\n```pycon\n>>> bk.Text.collocations()\nfullest extent; infinite Power; material world; Princess Elizabeth;\nexternal world; paramount object; supernatural revelation; two\nattributes; necessarily exist; Queen Christina; early age; whole\nuniverse; final causes; mathematical demonstration; metaphysical\nsystem; perfection involves; best possible; many distinct;\nmathematical method; divine substance\n>>> bk.Text.dispersion_plot(['Descartes','Malebranche','Spinoza','Leibniz'])\n```\n![alt text](https://i.imgur.com/TgGu656.png \"Lexical Dispersion Plot\")\n\n### The `smart_reading.stats` submodule\n\nThe `smart_reading` module comes with a `stats` submodule, which uses `matplotlib.pyplot` to create the several types of graps out of a given `smart_reading.book.Book` object, callable via the following functions:\n\n#### smart_reading.stats.plot_noun_hist(book, no_nouns = 20, named_entities = True, exceptions = [], **kwargs)\n\nCreate a histogram of the most common nouns appearing in given text.\n* *no_nouns*: Number of nouns that will be included in the graph (i.e. number of bars).\n* *named_entities*: If false, this will exclude named entities like people and places that are recognized by the `nltk.chunk.ne_chunk` routine. This option is not supported in versions of Python 2.\n* *exceptions*: An iterable of nouns that will be excluded from the analysis\n* Further keyword arguments are passed to `matplotlib.pyplot.fig`.\n\n```pycon\n>>> bk2 = sr.book.sample('pdf')\nSuccesfully loaded 'PhysRev.47.777.pdf' as an e-book\nTotal n.o. tokens: 3192\n>>> sr.stats.plot_noun_hist(bk2)\n```\n![alt text](https://imgur.com/fRlb1aw.png \"Frequency Plot\")\n\n#### smart_reading.stats.plot_network_graph(book, no_nouns = 10, treshold = 3, exclude_empty = True, named_entities = True, exceptions = [], **kwargs)\n\nCreate a network graph using the module `networkx` which depicts the relationship between frequently appearing nouns. The nouns appear as nodes, and edges are drawn between nouns if they appear frequently in the same sentences. A [temperature color scheme](https://en.wikipedia.org/wiki/Color_temperature) is used on the edges to depict the frequency in which nouns appear together (red = very often, blue = a few times).\n* *no_nouns*: Number of nouns that will be included in the graph (i.e. number of nodes). Can become less in the final result if *exclude_empty* is true, see below.\n* *treshold*: The minimal number of sentences in which two given nouns have to appear in order for an edge to be drawn. Can be used to simplify graphs with a lot of edges.\n* *exclude_empty*: if True, this will exclude the nodes from the graph that do not have edges. Note that this will reduce the number of nouns depicted, as declared in *no_nouns* above\n* *exceptions*: an iterable of nouns that will be excluded from the analysis.\n* Further keyword arguments are passed to `matplotlib.pyplot.fig`.\n\n```pycon\n>>> sr.stats.plot_network_graph(bk2)\n```\n![alt text](https://imgur.com/pRA0yy2.png \"Network Graph\")\n```pycon\n>>> bk3 = sr.book.sample('epub')\nSuccesfully loaded 'Galileo_The_Sidereal_Messenger.epub' as an e-book\nTotal n.o. tokens: 40372\n>>> sr.stats.plot_network_graph(bk3, exceptions = ['Galileo'], treshold = 10)\n```\n![alt text](https://imgur.com/NdG4vIs.png \"Network Graph\")\n\n",
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