Metadata-Version: 1.1
Name: P4J
Version: 0.27
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
Description: P4J
        ===
        
        **Description**
        
        P4J is a python package for period detection on irregularly sampled and
        heteroscedastic time series based on Information Theoretic objective
        functions. P4J was developed for astronomical light curves, irregularly
        sampled time series of stellar magnitude or flux. The core of this
        package is a class called periodogram that sweeps an array of
        periods/frequencies looking for the one that maximizes a given criteria.
        The main contribution of this work is a criterion for period detection
        based on the maximization of Cauchy-Schwarz Quadratic Mutual Information
        [1]. Information theoretic criteria incorporate information on the whole
        probability density function of the process and are more robust than
        classical second-order statistics based criteria [2, 3, 4]. For
        comparison P4J also incorporates other period detection methods used in
        astronomy such as the Phase Dispersion Minimization periodogram [5],
        Lafler-Kinman's string length [6] and the Orthogonal multiharmonic AoV
        periodogram [7]
        
        **Contents**
        
        -  Quadratic Mutual Information periodogram for light curves
        -  Phase Dispersion Minimization, Analysis of Variance and String Length
           methods
        -  Basic synthetic light curve generator
        
        **Instalation**
        
        Dependencies
        
        ::
        
                Numpy
                GCC 
                Cython (optional)
        
        If you have a UNIX system the GCC compiler is most likely already
        installed. If you have a Windows system you may want to install the
        Microsoft Visual C++ (MSVC) compiler. You can find relevant information
        at: https://wiki.python.org/moin/WindowsCompilers.
        
        Note on Cython: If Cython is found in your system, pyx are compiled to c
        sources. If not the provided c sources are used.
        
        Install from PyPI using
        
        ::
        
                pip install P4J
        
        or clone the github repository and do
        
        ::
        
                python setup.py install --user
        
        **Example**
        
        https://github.com/phuijse/P4J/blob/master/examples/periodogram\_demo.ipynb
        
        **TODO**
        
        -  Multidimensional time series support
        -  More period detection criteria (Conditional Entropy)
        
        **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, vol. 216, n. 2, 2015
        3. Pablo Huijse et al., "Computational Intelligence Challenges and
           Applications on Large-Scale Astronomical Time Series Databases", IEEE
           Mag. Computational Intelligence, vol. 9, n. 3, pp. 27-39, 2014
        4. Pablo Huijse et al., "An Information Theoretic Algorithm for Finding
           Periodicities in Stellar Light Curves", IEEE Trans. Signal Processing
           vol. 60, n. 10, pp. 5135-5145, 2012
        5. R. F. Stellingwerf, "Period determination using phase dispersion
           minimization", The Astrophysical Journal, vol. 224, pp. 953-960, 1978
        6. D. Clarke, "String/Rope length methods using the Lafler-Kinman
           statistic", Astronomy & Astrophysics, vol. 386, n. 2, pp. 763-774,
           2002
        7. A. Schwarzenberg-Czerny "Fast and Statistically Optimal Period Search
           in Uneven Sampled Observations", Astrophysical Journal Letters, vol.
           460, pp. 107, 1996
        
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: Programming Language :: C
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 :: 3.6
