Metadata-Version: 1.1
Name: P4J
Version: 0.13
Summary: Periodic light curve analysis tools based on Information Theory
Home-page: https://github.com/phuijse/P4J
Author: Pablo Huijse
Author-email: pablo.huijse@gmail.com
License: MIT
Download-URL: https://github.com/phuijse/P4J/tarball/stable
Description: P4J
        ===
        
        **Description**
        
        P4J is a python package for periodicity analysis of irregularly sampled
        time series based on Information Theoretic objective functions. P4J was
        developed for astronomical light curves, irregularly sampled time series
        of stellar magnitude or flux. These routines are build on the information 
        theoretic concepts such as entropy and **correntropy** [1]. Correntropy is
        a generalized correlation function that incorporates higher order 
        statistics of the process, lifting the assumption of Gaussianity. 
        Correntropy has been used in astronomical time series problems in [2, 4].
        To compute entropy we adopt the Renyi's quadratic entropy definition and
        estimate it via Parzen windows [1]. Minimizing the entropy of the error 
        between observations and model yields a robust regression criterion. Using
        entropy and correntropy based regression on harmonic function robust 
        periodograms are obtained.
        
        **Contents**
        
        -  Regression using the Weighted Maximum Correntropy Criterion (WMCC)
        -  Regression using the Weighted Minimum Error Entropy (WMEE) criterion
        -  Robust periodogram based on WMCC and WMEE
        -  False alarm probability for periodogram peaks based on extreme value
           statistics
        -  Basic synthetic light curve generator
        
        **Instalation**
        
        ::
        
            pip install P4J
        
        **Example**
        
        https://github.com/phuijse/P4J/blob/master/examples/periodogram\_demo.ipynb
        
        **TODO**
        
        -  Cython backend for WMCC/WMEE
        -  Multidimensional time series support
        
        **Authors**
        
        -  Pablo Huijse pablo.huijse@gmail.com (Millennium Institute of
           Astrophysics and Universidad de Chile)
        -  Pavlos Protopapas (Harvard Institute of Applied Computational
           Sciences)
        -  Pablo A. Estévez (Millennium Institute of Astrophysics and
           Universidad de Chile)
        -  Pablo Zegers (Universidad de los Andes, Chile)
        -  José C. Príncipe (University of Florida)
        
        (P4J = Four Pablos and one Jose)
        
        **References**
        
        1. José C. Príncipe, "Information Theoretic Learning: Renyi's Entropy
           and Kernel Perspectives", Springer, 2010
        2. Pavlos Protopapas et al., "A Novel, Fully Automated Pipeline for
           Period Estimation in the EROS 2 Data Set", The Astrophysical Journal
           Supplement, 216 (2), 2015
        3. Pablo Huijse et al., "Computational Intelligence Challenges and
           Applications on Large-Scale Astronomical Time Series Databases", IEEE
           Mag. Computational Intelligence, 2014
        4. Pablo Huijse et al., "An Information Theoretic Algorithm for Finding
           Periodicities in Stellar Light Curves", IEEE Trans. Signal Processing
           60(10), pp. 5135-5145, 2012
        
Keywords: astronomy periodic time series correntropy
Platform: UNKNOWN
Classifier: Natural Language :: English
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
