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        "author": "Lee A.D. Cooper",
        "author_email": "cooperle@gmail.com",
        "bugtrack_url": null,
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            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: Apache Software License",
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            "Programming Language :: Python :: 2",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
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        "description": "This package implements adaptive kernel density estimation algorithms for 1-dimensional \nsignals developed by Hideaki Shimazaki. This enables the generation of smoothed histograms\nthat preserve important density features at multiple scales, as opposed to naive\nsingle-bandwidth kernel density methods that can either over or under smooth density\nestimates. These methods are described in Shimazaki's paper:\n\n H. Shimazaki and S. Shinomoto, \"Kernel Bandwidth Optimization in Spike Rate Estimation,\" \n in Journal of Computational Neuroscience 29(1-2): 171\u2013182, 2010 \n http://dx.doi.org/10.1007/s10827-009-0180-4.\n \nLicense:\nAll software in this package is licensed under the Apache License 2.0.\nSee LICENSE.txt for more details.\n \nAuthors:\nHideaki Shimazaki (shimazaki@jhu.edu) shimazaki on Github\nLee A.D. Cooper (cooperle@gmail.com) cooperlab on GitHub\nSubhasis Ray (ray.subhasis@gmail.com)\n \nThree methods are implemented in this package:\n1. sshist - can be used to determine the optimal number of histogram bins for independent \nidentically distributed samples from an underlying one-dimensional distribution. The\nprincipal here is to minimize the L2 norm of the difference between the histogram and the\nunderlying distribution.\n\n2. sskernel - implements kernel density estimation with a single globally-optimized \nbandwidth.\n\n3. ssvkernel - implements kernel density estimation with a locally variable bandwidth.\n \nDependencies: These functions in this package depend on NumPy for various operations \nincluding fast-fourier transforms and histogram generation.",
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