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        "author": "JuanBC",
        "author_email": "jbc.develop@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering"
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        "description": "feets: feATURE eXTRACTOR FOR tIME sERIES.\n\nIn time-domain astronomy, data gathered from the telescopes is usually\nrepresented in the form of light-curves. These are time series that show the\nbrightness variation of an object through a period of time\n(for a visual representation see video below). Based on the variability\ncharacteristics of the light-curves, celestial objects can be classified into\ndifferent groups (quasars, long period variables, eclipsing binaries, etc.)\nand consequently be studied in depth independentely.\n\nIn order to characterize this variability, some of the existing methods use\nmachine learning algorithms that build their decision on the light-curves\nfeatures. Features, the topic of the following work, are numerical descriptors\nthat aim to characterize and distinguish the different variability classes.\nThey can go from basic statistical measures such as the mean or the standard\ndeviation, to complex time-series characteristics such as the autocorrelation\nfunction.\n\nIn this package we present a library with a compilation of some of the\nexisting light-curve features. The main goal is to create a collaborative and\nopen tool where every user can characterize or analyze an astronomical\nphotometric database while also contributing to the library by adding new\nfeatures. However, it is important to highlight that **this library is not**\n**restricted to the astronomical field** and could also be applied to any kind\nof time series.\n\nOur vision is to be capable of analyzing and comparing light-curves from all\nthe available astronomical catalogs in a standard and universal way. This\nwould facilitate and make more efficient tasks as modelling, classification,\ndata cleaning, outlier detection and data analysis in general. Consequently,\nwhen studying light-curves, astronomers and data analysts would be on the same\nwavelength and would not have the necessity to find a way of comparing or\nmatching different features. In order to achieve this goal, the library should\nbe run in every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, etc)\nand future surveys (LSST) and the results should be ideally shared in the same\nopen way as this library.",
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        "keywords": "machine-learning,feature-extraction,timeseries,astronomy",
        "license": "MIT",
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        "name": "feets",
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