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    "info": {
        "author": "Tim Cera, P.E.",
        "author_email": "tim@cerazone.net",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": ".. image:: https://travis-ci.org/timcera/tstoolbox.svg?branch=master\n    :target: https://travis-ci.org/timcera/tstoolbox\n    :height: 20\n\n.. image:: https://coveralls.io/repos/timcera/tstoolbox/badge.png?branch=master\n    :target: https://coveralls.io/r/timcera/tstoolbox?branch=master\n    :height: 20\n\n.. image:: https://img.shields.io/pypi/v/tstoolbox.svg\n    :alt: Latest release\n    :target: https://pypi.python.org/pypi/tstoolbox\n\n.. image:: http://img.shields.io/badge/license-BSD-lightgrey.svg\n    :alt: tstoolbox license\n    :target: https://pypi.python.org/pypi/tstoolbox/\n\nTSToolbox - Quick Guide\n=======================\nThe tstoolbox is a Python script to manipulate time-series on the command line\nor by function calls within Python.  Uses pandas (http://pandas.pydata.org/)\nor numpy (http://numpy.scipy.org) for any heavy lifting.\n\nRequirements\n------------\n* pandas - on Windows this is part scientific Python distributions like\n  Python(x,y), Anaconda, or Enthought.\n\n* mando - command line parser\n\nInstallation\n------------\nShould be as easy as running ``pip install tstoolbox`` or ``easy_install\ntstoolbox`` at any command line.  Not sure on Windows whether this will bring\nin pandas, but as mentioned above, if you start with scientific Python\ndistribution then you shouldn't have a problem.\n\nUsage - Command Line\n--------------------\nJust run 'tstoolbox --help' to get a list of subcommands::\n\n\n    usage: tstoolbox [-h]\n                     {accumulate, add_trend, aggregate, calculate_fdc,\n                     calculate_kde, clip, convert, convert_index,\n                     convert_index_to_julian, converttz, lag, correlation,\n                     createts, date_offset, date_slice, describe, dtw,\n                     equation, ewm_window, expanding_window, fill, filter, gof,\n                     normalization, pca, pct_change, peak_detection, pick,\n                     plot, rank, read, remove_trend, replace, rolling_window,\n                     stack, stdtozrxp, tstopickle, unstack, about} ...\n    \n    positional arguments:\n      {accumulate, add_trend, aggregate, calculate_fdc, calculate_kde, clip,\n      convert, convert_index, convert_index_to_julian, converttz, lag,\n      correlation, createts, date_offset, date_slice, describe, dtw, equation,\n      ewm_window, expanding_window, fill, filter, gof, normalization, pca,\n      pct_change, peak_detection, pick, plot, rank, read, remove_trend,\n      replace, rolling_window, stack, stdtozrxp, tstopickle, unstack, about}\n\n    accumulate          \n        Calculate accumulating statistics.\n    add_trend           \n        Add a trend.\n    aggregate           \n        Take a time series and aggregate to specified frequency.\n    calculate_fdc       \n        Return the frequency distribution curve.\n    calculate_kde       \n        Return the kernel density estimation (KDE) curve.\n    clip                \n        Return a time-series with values limited to [a_min, a_max].\n    convert             \n        Convert values of a time series by applying a factor and offset.\n    convert_index       \n        Convert datetime to/from Julian dates from different epochs.\n    convert_index_to_julian\n        DEPRECATED: Use convert_index instead.\n    converttz           \n        Convert the time zone of the index.\n    lag                 \n        Create a series of lagged time-series.\n    correlation         \n        Develop a correlation between time-series and potentially lags.\n    createts            \n        Create empty time series, optionally fill with a value.\n    date_offset         \n        Apply an offset to a time-series.\n    date_slice          \n        Print out data to the screen between start_date and end_date.\n    describe            \n        Print out statistics for the time-series.\n    dtw                 \n        Dynamic Time Warping.\n    equation            \n        Apply <equation_str> to the time series data.\n    ewm_window          \n        Calculate exponential weighted functions.\n    expanding_window    \n        Calculate an expanding window statistic.\n    fill                \n        Fill missing values (NaN) with different methods.\n    filter              \n        Apply different filters to the time-series.\n    gof                 \n        Will calculate goodness of fit statistics between two time-series.\n    normalization       \n        Return the normalization of the time series.\n    pca                 \n        Return the principal components analysis of the time series.\n    pct_change          \n        Return the percent change between times.\n    peak_detection      \n        Peak and valley detection.\n    pick                \n        Will pick a column or list of columns from input.\n    plot                \n        Plot data.\n    rank                \n        Compute numerical data ranks (1 through n) along axis.\n    read                \n        Collect time series from a list of pickle or csv files.\n    remove_trend        \n        Remove a 'trend'.\n    replace             \n        Return a time-series replacing values with others.\n    rolling_window      \n        Calculate a rolling window statistic.\n    stack               \n        Return the stack of the input table.\n    stdtozrxp           \n        Print out data to the screen in a WISKI ZRXP format.\n    tstopickle          \n        Pickle the data into a Python pickled file.\n    unstack             \n        Return the unstack of the input table.\n    about               \n        Display version number and system information.\n\n    optional arguments:\n        -h, --help            show this help message and exit\n\nThe default for all of the subcommands is to accept data from stdin (typically\na pipe).  If a subcommand accepts an input file for an argument, you can use\n\"--input_ts=input_file_name.csv\", or to explicitly specify from stdin (the\ndefault) \"--input_ts='-'\".\n\nFor the subcommands that output data it is printed to the screen and you can\nthen redirect to a file.\n\nUsage - API\n-----------\nYou can use all of the command line subcommands as functions.  The function\nsignature is identical to the command line subcommands.  The return is always\na PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS\nDataFrame and is supplied to the function via the 'input_ts' keyword.\n\nSimply import tstoolbox::\n\n    from tstoolbox import tstoolbox\n\n    # Then you could call the functions\n    ntsd = tstoolbox.fill(method='linear', input_ts='tests/test_fill_01.csv')\n\n    # Once you have a PANDAS DataFrame you can use that as input to other \n    # tstoolbox functions.\n    ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd)",
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