Metadata-Version: 1.1
Name: feets
Version: 0.4
Summary: feets: feATURE eXTRACTOR FOR tIME sERIES.
Home-page: https://github.com/carpyncho/feets
Author: JuanBC
Author-email: jbc.develop@gmail.com
License: MIT
Description: feets: feATURE eXTRACTOR FOR tIME sERIES.
        
        In time-domain astronomy, data gathered from the telescopes is usually
        represented in the form of light-curves. These are time series that show the
        brightness variation of an object through a period of time
        (for a visual representation see video below). Based on the variability
        characteristics of the light-curves, celestial objects can be classified into
        different groups (quasars, long period variables, eclipsing binaries, etc.)
        and consequently be studied in depth independentely.
        
        In order to characterize this variability, some of the existing methods use
        machine learning algorithms that build their decision on the light-curves
        features. Features, the topic of the following work, are numerical descriptors
        that aim to characterize and distinguish the different variability classes.
        They can go from basic statistical measures such as the mean or the standard
        deviation, to complex time-series characteristics such as the autocorrelation
        function.
        
        In this package we present a library with a compilation of some of the
        existing light-curve features. The main goal is to create a collaborative and
        open tool where every user can characterize or analyze an astronomical
        photometric database while also contributing to the library by adding new
        features. However, it is important to highlight that **this library is not**
        **restricted to the astronomical field** and could also be applied to any kind
        of time series.
        
        Our vision is to be capable of analyzing and comparing light-curves from all
        the available astronomical catalogs in a standard and universal way. This
        would facilitate and make more efficient tasks as modelling, classification,
        data cleaning, outlier detection and data analysis in general. Consequently,
        when studying light-curves, astronomers and data analysts would be on the same
        wavelength and would not have the necessity to find a way of comparing or
        matching different features. In order to achieve this goal, the library should
        be run in every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, etc)
        and future surveys (LSST) and the results should be ideally shared in the same
        open way as this library.
        
        
Keywords: machine-learning,feature-extraction,timeseries,astronomy
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
