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        "author": "Dimitri Vorona",
        "author_email": "vorona@in.tum.de",
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            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "UCRDTW-cffi: Dynamic Time Warping with UCR optimizations\n=========================================================\n\n[![Build Status](https://travis-ci.org/alendit/ucrdtw_cffi.svg?branch=master)](https://travis-ci.org/alendit/ucrdtw_cffi)\n\nBased on [Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping](http://www.cs.ucr.edu/~eamonn/SIGKDD_trillion.pdf) .\n\nC implementation from [libdtw](https://github.com/b/libdtw).\n\nInterface and tests from https://github.com/klon/ucrdtw/.\n\n### Requirements\n\nPython 2.7+ or 3.3+, numpy 1.8+\n\n### Usage\n\n```\nfrom ucrdtw_cffi import dtw_query\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = np.cumsum(np.random.uniform(-0.5, 0.5, 1000000))\nquery = np.cumsum(np.random.uniform(-0.5, 0.5, 100))\nloc, dist = _ucrdtw.ucrdtw(data, query, 0.05, True)\nquery = np.concatenate((np.linspace(0.0, 0.0, loc), query)) + (data[loc] - query[0])\n\nplt.figure()\nplt.plot(data)\nplt.plot(query)\nplt.show()\n```",
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