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        "author": "Peter Brooks",
        "author_email": "peter@sealevelresearch.com",
        "bugtrack_url": null,
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            "Development Status :: 3 - Alpha",
            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering"
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        "description": ".. image:: https://travis-ci.org/sealevelresearch/timeseries.svg?branch=master\n    :target: https://travis-ci.org/sealevelresearch/timeseries\n.. image:: https://coveralls.io/repos/github/sealevelresearch/timeseries/badge.svg?branch=master\n    :target: https://coveralls.io/github/sealevelresearch/timeseries?branch=master\n.. image:: https://requires.io/github/sealevelresearch/timeseries/requirements.svg?branch=master\n     :target: https://requires.io/github/sealevelresearch/timeseries/requirements/?branch=master\n     :alt: Requirements Status\n\n\nTime Series\n==================\n\nA time series built upon `pandas <http://pandas.pydata.org>`_ for dealing with window/point data sources, which has interpolation mindful of gap's.\n\n\nDesign\n######\n\nEach window is represented by `valid_from`, `valid_to`, `value`.\n\nDuring interpolation, the window time range is transformed into a center point `datetime`.\n\nA constraint on the data model is a predefined length of a window, this length is used to query all suitable data and compute gaps.\n\nGaps are determined and a mask is applied to the original data frame.\n\nWhen performing a query on a data frame, missing data at the tail and head are filled in.\n\nSample data\n###########\nBelow are a visual representation of data within the tests.\n\n.. image:: design/ExampleA0.png\n    :alt: Example A0 - Single data day\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleA1.png\n    :alt: Example A1 - Non-numeric content\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleA2.png\n    :alt: Example A2 - Multiple with non-numeric content\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleB0.png\n    :alt: Example B0 - Missing window at the start\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleB1.png\n    :alt: Example B1 - Missing window in the middle\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleB2.png\n    :alt: Example B2 - Missing window at the end \n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleC.png\n    :alt: Example C - Gaps between windows\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleD.png\n    :alt: Example D - No data\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleE.png\n    :alt: Example E - Multiple columns\n    :width: 100% \n    :align: center\n\n.. image:: design/ExampleF.png\n    :alt: Example F - Multiple with non-numeric content\n    :width: 100% \n    :align: center\n\n\nCompatibility\n*************\nThis project is compatible with Python 3.5+, Pandas 0.19.\n\nDevelopment state\n*****************\nThis library is in alpha state and is subject to revision. \n",
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