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        "author": "Jordan Mackie",
        "author_email": "jmackie@protonmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Medical Science Apps."
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        "description": "==========================================\nactivityio: Exercise data handling library\n==========================================\n\nExercise/activity data has become a prolific resource, but applying any kind of sophisticated analyses is made difficult by the variety of file formats. This ``python`` library is intended to munge a number of these formats and present the data in a predictable and useable form. Moreover, the API is both closely intertwined with, and an extension of, the awesome `Pandas library <https://github.com/pandas-dev/pandas>`_.\n\nStability\n---------\nPlease note this package is still very much an *alpha* release, so **breaking changes** are likely.\n\nInstallation\n------------\n\nThe package is available on PyPI::\n\n\t$ pip install activityio\n\n\nExample Usage\n-------------\n\nThere is a ``read`` function at the top-level of ``activityio`` that dispatches the appropriate reader based on file extension:\n\n>>> import activityio as aio\n>>> data = aio.read('example.srm')\n\n**NOTE** substitute ``'example.srm'`` with a path to your own activity file.\n\nBut you can also call sub-packages directly:\n\n>>> from activityio import srm\n>>> data = srm.read('example.srm')\n\n``data`` in the above example is a subclass of the ``pandas.DataFrame`` and provides some neat additional functionality. Most notably, certain columns are \"magic\" in that they return specific ``pandas.Series`` subclasses. These subclasses make unit-switching easy, and provide other useful methods:\n\n>>> type(data)\n<class 'activityio._types.activitydata.ActivityData'>\n\n>>> data.head(5)\n          temp  lap   dist  alt  cad  pwr  speed  hr\ntime\n00:00:00  26.1    1  1.027   67    0    0  1.027  71\n00:00:01  26.1    1  2.721   67    0    0  1.694  71\n00:00:02  26.2    1  4.415   67    0    0  1.694  71\n00:00:03  26.2    1  6.331   67    0    0  1.916  71\n00:00:04  26.2    1  8.469   67    0    0  2.138  75\n\n>>> data.normpwr()\n249.54104255943844\n\n>>> type(data.speed)\n<class 'activityio._types.columns.Speed'>\n\n>>> data.speed.base_unit\n'm/s'\n>>> data.speed.kph.mean()  # use a different unit\n38.485063801685477\n\n>>> data.dist.base_unit\n'm'\n>>> data.dist.miles[-1]\n134.78580023361226\n\n>>> data.alt.base_unit\n'm'\n>>> data.alt.ascent.sum()\n1898.0\n```\n\nBut **NOTE** you lose this functionality if you go changing column names\n\n>>> data = data.rename(columns={'alt': 'altitude'})\n>>> type(data.altitude)\n<class 'pandas.core.series.Series'>\n\nAPI Notes\n---------\n\nThe main package is composed of sub-packages that contain the reading logic for the file format after which they're named. (e.g. ``activityio.fit`` is for parsing ANT/Garmin FIT files.)\n\nThe ultimate logic is defined in a ``_reading`` module, which provides two functions: ``gen_records`` and ``read_and_format``.\n\n+ ``gen_records`` is a generator function for iterating over the data-points in a file. The rows of the data table if you like. A \"record\" is a dictionary object.\n+ ``read_and_format`` uses the above generator to return an ``ActivityData`` object.\n\n``read_and_format`` is available at the top-level of a sub-package aliased as ``read``; so reading in a file looks like ``srm.read('path_to_file.srm')``. ``gen_records`` is imported under the same name.\n\nThere are also some useful ``tools`` provided in module by the same name.\n",
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