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        "author": "Marcos Alfredo N\u00fa\u00f1ez",
        "author_email": "mnunez@fleni.org.ar",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": ".. highlight:: python3\n===============\nsimplemdx\n===============\n\n\n.. image:: https://img.shields.io/pypi/v/simplemdx.svg\n        :target: https://pypi.python.org/pypi/simplemdx\n\n.. image:: https://img.shields.io/travis/marnunez/simplemdx.svg\n        :target: https://travis-ci.org/marnunez/simplemdx\n\n.. image:: https://ci.appveyor.com/api/projects/status/xb07amo9s7stk37r?svg=true\n     :target: https://ci.appveyor.com/project/marnunez/simplemdx\n     :alt: Windows build status\n\n.. image:: https://readthedocs.org/projects/simplemdx/badge/?version=latest\n        :target: https://simplemdx.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n.. image:: https://pyup.io/repos/github/marnunez/simplemdx/shield.svg\n     :target: https://pyup.io/repos/github/marnunez/simplemdx/\n     :alt: Updates\n\n.. image:: https://coveralls.io/repos/github/marnunez/simplemdx/badge.svg?branch=master\n     :target: https://coveralls.io/github/marnunez/simplemdx?branch=master\n     :alt: Coverage\n\n\n\n\n\nA simple BTS MDX file parser and toolkit written in Python based on BeautifulSoup_\n\n\n* Free software: GNU General Public License v3\n* Documentation: https://simplemdx.readthedocs.io.\n\n\nFeatures\n--------\n* Compatible with Python 2.7 and 3.4 onwards\n* Linux, OSX and Windows support\n\nsimplemdx gives access to:\n\n* trial MDXs (marker coordinates, emg channels, etc)\n* session and patient MDXs (antropometric data, subject metadata)\n* normative ENB files (joint angles normatives, emg normatives, etc)\n\nUsage\n-----\n\nTo load the contents of a trial mdx\n\n.. code:: python\n\n    from simplemdx.parser import Parser\n\n    a = Parser('myfile.mdx')\n\nOnce loaded, metadata can be accessed like:\n\n.. code:: python\n\n    label = a.label\n    date = a.date\n\n\nIt also loads all it's streams, and names them according to their contents. The named streams can be:\n\n* markers\n* emg\n* static\n* cycle\n\n\nStreams\n-------\n\nEvery stream has its own metadata(such as frequency, start time and number of frames\n\n.. code:: python\n\n    a = Parser('myfile.mdx')\n    m = a.markers # marker stream\n\n    m.freq\n    m.nFrames\n    m.startTime\n\n\nMarker streams\n--------------\n\nMarkers can be retrieved from the stream by index or label\n\n.. code:: python\n\n    c7 = a.markers['c7']\n    m = a.markers[0] # The first marker on the stream\n\nor iterated\n\n.. code:: python\n\n    for marker in a.markers:\n        print(marker.label)\n\nThis stream can be converted to an OpenSIM .trc file like this\n\n.. code:: python\n\n    a.markers.toTRC()\n\nBy default, it creates a trc file with the same label as the trial mdx and all the included markers.\nIt is important to note that it will output the largest common chunk of data (the largest interval of time for which all markers are visible). This is to avoid None data in the .trc file. One can restrict the output to certain markers and change the output filename\n\n.. code:: python\n\n    a.markers.toTRC(filename='my_trc_output.trc',labels=['c7','rasis','lasis'])\n\nAs a simple way to inspect the stream, one can plot it\n\n.. code:: python\n\n    a.markers.plot()\n\nThis will display a simple matplotlib 3D scatter plot with the markers and the references\n\nData items\n----------\n\nThe data for the streams inner tags are stored in DataItems. BTS follows an Item/Segment approach for storing most of it. For retrieving a segment of a marker, one can call the data attribute\n\n\n.. code:: python\n\n    c7 = a.markers['c7']\n    s = c7.data\n\ndata will return a Segment object, or a list of Segment objects. Each Segment has a list for each coordinate (for a marker example, X, Y and Z) and the Segment's starting frame\n\n.. code:: python\n\n    seg = c7.data\n    if isintance(seg,Segment):\n        print(\"First frame: {}\".format(seg.frame))\n        print(\"X data: {}\".format(seg.X))\n\naddicionally, data can be retrieved as a continuous stream using datac instead of data. This will merge all segments into one, added a None padding. and return a single Segment starting at frame 0.\n\n.. code:: python\n\n    segc = c7.datac\n    print(\"X data: {}\".format(seg.X))\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n.. _BeautifulSoup: https://www.crummy.com/software/BeautifulSoup/bs4/doc/\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-02-27)\n------------------\n\n* First release on PyPI.\n\n\n",
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