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    "info": {
        "author": "Brian Skinn",
        "author_email": "bskinn@alum.mit.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Utilities"
        ],
        "description": "pent Extracts Numerical Text\n============================\n\n*Mini-language driven parser for structured numerical (or other) data\nin free text*\n\n**Current Development Version:**\n\n.. image::  https://img.shields.io/travis/bskinn/pent?label=travis-ci&logo=travis\n    :target: https://travis-ci.org/bskinn/pent\n\n.. image:: https://codecov.io/gh/bskinn/pent/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/bskinn/pent\n\n**Most Recent Stable Release:**\n\n.. image:: https://img.shields.io/pypi/v/pent.svg?logo=pypi\n    :target: https://pypi.org/project/pent\n\n.. image:: https://img.shields.io/pypi/pyversions/pent.svg?logo=python\n\n**Info:**\n\n.. image:: https://img.shields.io/readthedocs/pent/latest\n    :target: http://pent.readthedocs.io/en/v0.2/\n\n.. image:: https://img.shields.io/github/license/mashape/apistatus.svg\n    :target: https://github.com/bskinn/pent/blob/stable/LICENSE.txt\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/psf/black\n\n----\n\n**Do you have structured numerical data stored as text?**\n\n**Does the idea of writing regex to parse it fill you with loathing?**\n\n``pent`` *can help!*\n\nSay you have data in a text file that looks like this:\n\n.. code::\n\n    $vibrational_frequencies\n    18\n        0        0.000000\n        1        0.000000\n        2        0.000000\n        3        0.000000\n        4        0.000000\n        5        0.000000\n        6      194.490162\n        7      198.587114\n        8      389.931897\n        9      402.713910\n       10      538.244274\n       11      542.017838\n       12      548.246738\n       13      800.613516\n       14     1203.096114\n       15     1342.200360\n       16     1349.543713\n       17     1885.157022\n\nWhat's the most efficient way to get that list of floats\nextracted into a ``numpy`` array?\nThere's clearly structure here, but how to exploit it?\n\nIt would work to import the text into a spreadsheet, split columns appropriately,\n`re-export just the one column to CSV <https://github.com/bskinn/excel-csvexporter>`__,\nand import to Python from there,\nbut that's just exhausting drudgery if there are dozens of files involved.\n\nAutomating the parsing via a line-by-line string search would work fine\n(this is how |cclib|_ implements its data imports), but a new line-by-line\nmethod is needed for every new kind of dataset,\nand any time the formatting of a given dataset changes.\n\nIt's not *too* hard to\n`write regex <https://github.com/bskinn/opan/blob/12c8e98de2a81bbd570c821644063d975e2ab03e/opan/hess.py#L688-L701>`__\nthat will parse it, but because of the mechanics of regex group captures\nyou have to write two patterns: one to capture the entire block, including the header\n(to ensure other, similarly-formatted data isn't also captured); and then one to\n`iterate line-by-line <https://github.com/bskinn/opan/blob/12c8e98de2a81bbd570c821644063d975e2ab03e/opan/hess.py#L1192-L1207>`__\nover just the data block to extract the individual values. And, of course, one has to actually *write*\n(and proofread, and maintain) the regex.\n\n``pent`` **provides a better way.**\n\nThe data above comes from `this file <https://github.com/bskinn/pent/blob/cbb3c9b24c773b51b4988485b838537043ec8299/pent/test/C2F4_01.hess>`__,\n``C2F4_01.hess``. With ``pent``, the data can be pulled into ``numpy`` in just a couple\nof lines, without writing **any** regex at all:\n\n.. code:: python\n\n    >>> data = pathlib.Path(\"pent\", \"test\", \"C2F4_01.hess\").read_text()\n    >>> prs = pent.Parser(\n    ...     head=(\"@.$vibrational_frequencies\", \"#.+i\"),\n    ...     body=(\"#.+i #!..f\")\n    ... )\n    >>> arr = np.array(prs.capture_body(data), dtype=float)\n    >>> print(arr)\n    [[[   0.      ]\n      [   0.      ]\n      [   0.      ]\n      [   0.      ]\n      [   0.      ]\n      [   0.      ]\n      [ 194.490162]\n      [ 198.587114]\n      [ 389.931897]\n      [ 402.71391 ]\n      [ 538.244274]\n      [ 542.017838]\n      [ 548.246738]\n      [ 800.613516]\n      [1203.096114]\n      [1342.20036 ]\n      [1349.543713]\n      [1885.157022]]]\n\nThe result comes out as a length-one list of 2-D matrices, since the search pattern\noccurs only once in the data file. The single 2-D matrix is laid out as a\ncolumn vector, because the data runs down the column in the file.\n\n``pent`` can handle larger, more deeply nested data as well.\nTake `this 18x18 matrix <https://github.com/bskinn/pent/blob/cbb3c9b24c773b51b4988485b838537043ec8299/pent/test/C2F4_01.hess#L13-L71>`__\nwithin ``C2F4_01.hess``, for example.\nHere, it's necessary to pass a ``Parser`` as the *body* of another ``Parser``:\n\n.. code:: python\n\n    >>> prs_hess = pent.Parser(\n    ...     head=(\"@.$hessian\", \"#.+i\"),\n    ...     body=pent.Parser(\n    ...         head=\"#++i\",\n    ...         body=\"#.+i #!+.f\"\n    ...     )\n    ... )\n    >>> result = prs_hess.capture_body(data)\n    >>> arr = np.column_stack([np.array(_, dtype=float) for _ in result[0]])\n    >>> print(arr[:3, :7])\n    [[ 0.468819 -0.006771  0.020586 -0.38269   0.017874 -0.05449  -0.044552]\n     [-0.006719  0.022602 -0.016183  0.010997 -0.033397  0.014422 -0.01501 ]\n     [ 0.020559 -0.016184  0.066859 -0.033601  0.014417 -0.072836  0.045825]]\n\nThe need for the generator expression, the ``[0]`` index into ``result``,\nand the composition via ``np.column_stack`` arises\ndue to the manner in which ``pent`` returns data from a nested match like this.\nSee the `documentation <https://pent.readthedocs.io/en/v0.2>`__,\nin particular `this example <https://pent.readthedocs.io/en/v0.2/tutorial/examples/nested_parsers.html>`__,\nfor more information.\n\nThe grammar of the ``pent`` mini-language is designed to be flexible enough that\nit should handle essentially all well-formed structured data, and even some data\nthat's not especially well formed. Some datasets will require post-processing of the\ndata structures generated by ``pent`` before they can be pulled into\n``numpy`` (see, e.g., `this test <https://github.com/bskinn/pent/blob/eaa79a09af88d3836deff4f4efaff26ea085786b/pent/test/pent_livedata.py#L329-L345>`__,\nparsing `this data block <https://github.com/bskinn/pent/blob/eaa79a09af88d3836deff4f4efaff26ea085786b/pent/test/mwfn_li_di_elfbasin.txt#L520-L526>`__).\n\n-----\n\nBeta releases available on `PyPI <https://pypi.org/project/pent>`__: ``pip install pent``\n\nFull documentation is hosted at\n`Read The Docs <http://pent.readthedocs.io/en/v0.2/>`__.\n\nSource on `GitHub <https://github.com/bskinn/pent>`__.  Bug reports,\nfeature requests, and ``Parser`` construction help requests\nare welcomed at the\n`Issues <https://github.com/bskinn/pent/issues>`__ page there.\n\nCopyright (c) Brian Skinn 2018-2019\n\nLicense: The MIT License. See `LICENSE.txt <https://github.com/bskinn/pent/blob/master/LICENSE.txt>`__\nfor full license terms.\n\n\n.. |cclib| replace:: ``cclib``\n\n.. _cclib: https://github.com/cclib/cclib\n\n\n",
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